diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000000000000000000000000000000000000..6f7efde7263935620f4810989e3710afb4686f77
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,200 @@
+# Covers JetBrains IDEs: IntelliJ, RubyMine, PhpStorm, AppCode, PyCharm, CLion, Android Studio and WebStorm
+# Reference: https://intellij-support.jetbrains.com/hc/en-us/articles/206544839
+
+# User-specific stuff
+.idea/**/workspace.xml
+.idea/**/tasks.xml
+.idea/**/usage.statistics.xml
+.idea/**/dictionaries
+.idea/**/shelf
+
+# Generated files
+.idea/**/contentModel.xml
+
+# Sensitive or high-churn files
+.idea/**/dataSources/
+.idea/**/dataSources.ids
+.idea/**/dataSources.local.xml
+.idea/**/sqlDataSources.xml
+.idea/**/dynamic.xml
+.idea/**/uiDesigner.xml
+.idea/**/dbnavigator.xml
+
+# Gradle
+.idea/**/gradle.xml
+.idea/**/libraries
+
+# Secret
+.idea/**/deployment.xml
+.idea/**/remote-mappings.xml
+
+# Gradle and Maven with auto-import
+# When using Gradle or Maven with auto-import, you should exclude module files,
+# since they will be recreated, and may cause churn.  Uncomment if using
+# auto-import.
+# .idea/modules.xml
+# .idea/*.iml
+# .idea/modules
+# *.iml
+# *.ipr
+
+# CMake
+cmake-build-*/
+
+# Mongo Explorer plugin
+.idea/**/mongoSettings.xml
+
+# File-based project format
+*.iws
+
+# IntelliJ
+out/
+
+# mpeltonen/sbt-idea plugin
+.idea_modules/
+
+# JIRA plugin
+atlassian-ide-plugin.xml
+
+# Cursive Clojure plugin
+.idea/replstate.xml
+
+# Crashlytics plugin (for Android Studio and IntelliJ)
+com_crashlytics_export_strings.xml
+crashlytics.properties
+crashlytics-build.properties
+fabric.properties
+
+# Editor-based Rest Client
+.idea/httpRequests
+
+# Android studio 3.1+ serialized cache file
+.idea/caches/build_file_checksums.ser
+
+checkpoint/*
+outputs/*
+data/data_3d_h36m.npz
+data/own2DFiles/*
+
+
+
+__pycache__/
+*.py[cod]
+
+
+*.pyc
+.ipynb_checkpoints/
+*.gif
+*.jpg
+*.png
+*.npz
+*.zip
+*.json
+*.mp4
+*.tar
+*.pth
+*.weights
+*.avi
+*.caffemodel
+*.npy
+
+
+/st_gcn
+/outputs
+/nohub*
+/VideoSave
+/ActionRecognition   
+/work_dir
+
+
+
+
+
+
+__pycache__/
+*.py[cod]
+*$py.class
+*.so
+
+eggs/
+.eggs/
+*.egg-info/
+.installed.cfg
+*.egg
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+.hypothesis/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# pyenv
+.python-version
+
+# celery beat schedule file
+celerybeat-schedule
+
+# SageMath parsed files
+*.sage.py
+
+# dotenv
+.env
+
+# virtualenv
+.venv
+venv/
+ENV/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+
+# Self-defined files
+/local_test/
+/lab_processing/
+
+
+inputs/
+*.miframes
\ No newline at end of file
diff --git a/HPE2keyframes.py b/HPE2keyframes.py
new file mode 100644
index 0000000000000000000000000000000000000000..f681dabc7d8dcad25cd4e69ff72ae4ff22ed6702
--- /dev/null
+++ b/HPE2keyframes.py
@@ -0,0 +1,323 @@
+import numpy as np
+import matplotlib.pyplot as plt
+import pickle
+import torch
+import torch.nn.functional as F
+from scipy.spatial.transform import Rotation   
+from scipy.ndimage import binary_erosion, binary_dilation
+
+import os
+import json
+
+
+
+def euler_angles_smooth(XYZ_euler_angles):
+
+    if XYZ_euler_angles.ndim == 1:
+        XYZ_euler_angles = XYZ_euler_angles[:, np.newaxis]
+
+    for i in range(XYZ_euler_angles.shape[0]-1):
+        for j in range(XYZ_euler_angles.shape[1]):
+            # smooth
+            if XYZ_euler_angles[i+1, j] - XYZ_euler_angles[i, j] > 180:
+                XYZ_euler_angles[i+1:, j] = XYZ_euler_angles[i+1:, j] - 360
+            elif XYZ_euler_angles[i+1, j] - XYZ_euler_angles[i, j] < -180:
+                XYZ_euler_angles[i+1:, j] = XYZ_euler_angles[i+1:, j] + 360
+
+    return np.squeeze(XYZ_euler_angles)
+
+
+
+def xyz2euler_body(xyz, xyz_body_frame, X_dir=1.0, Y_dir=1.0):
+    '''
+        xyz: Coordinates from 3D human pose estimation. Each dimension: (frame, 3, xyz)
+        xyz_body_frame: Coordinates of body frame. Used to calculate the Y direction rotation of body.
+        X_dir: -1.0 for arm and body.
+        Y_dir: -1.0 for body and head.
+    '''
+
+    # swap y and z to align the coordinate in the mine-imator
+    xyz[:, :, [1, 2]] = xyz[:, :, [2, 1]]
+    xyz[:, :, 0] = -xyz[:, :, 0]
+    xyz_body_frame[:, :, [1, 2]] = xyz_body_frame[:, :, [2, 1]]
+    xyz_body_frame[:, :, 0] = -xyz_body_frame[:, :, 0]
+
+    p0, p1, p2 = torch.unbind(xyz, dim=1)
+    p1_, p4_, p14_, p11_ = torch.unbind(xyz_body_frame, dim=1)
+
+    # solve the cosine pose matrix
+    Y = (p0 - p1) * Y_dir
+    arm = p2 - p1
+
+    Y = F.normalize(Y, dim=1)
+    X = F.normalize(p11_ + p4_ - p1_ - p14_, dim=1)
+    # X = F.normalize(torch.cross(X_dir*arm, Y), dim=1)  # TODO smooth
+    Z = F.normalize(torch.cross(X, Y), dim=1)
+
+    cos_pose_matrix = torch.stack([X, Y, Z], dim=2)
+    r =  Rotation.from_matrix(cos_pose_matrix)
+    YXZ_euler_angles = r.as_euler("YXZ", degrees=True)
+
+    # bend
+    bend = -(Y * F.normalize(arm, dim=1)).sum(dim=1) * Y_dir
+    bend = torch.rad2deg(torch.acos(bend))
+
+    # swap xyz
+    YXZ_euler_angles[:, [0, 1, 2]] = YXZ_euler_angles[:, [1, 0, 2]]
+    XYZ_euler_angles = YXZ_euler_angles
+
+    # arm cos_pose_matrix
+    Y_arm = F.normalize(arm, dim=1)
+    X_arm = X
+    Z_arm = F.normalize(torch.cross(X_arm, Y_arm), dim=1)
+    cos_pose_matrix_arm = torch.stack([X_arm, Y_arm, Z_arm], dim=2)
+
+    # avoid abrupt changes in angle
+    XYZ_euler_angles = euler_angles_smooth(XYZ_euler_angles)
+    bend = euler_angles_smooth(bend.numpy())
+
+    return XYZ_euler_angles, bend, cos_pose_matrix_arm
+
+
+def xyz2euler_relative(xyz, cos_body, X_dir=1.0, Y_dir=1.0, head=False, leg=False, euler_body=None):
+    '''
+        xyz: Coordinates from 3D human pose estimation. Each dimension: (frame, 3, xyz)
+        X_dir: -1.0 for arm and body.
+        Y_dir: -1.0 for body and head.
+    '''
+
+    # swap y and z to align the coordinate in the mine-imator
+    xyz[:, :, [1, 2]] = xyz[:, :, [2, 1]]
+    xyz[:, :, 0] = -xyz[:, :, 0]
+    p0, p1, p2 = torch.unbind(xyz, dim=1)
+
+    # solve the cosine pose matrix
+    Y = (p0 - p1) * Y_dir
+    arm = p2 - p1
+
+    Y = F.normalize(Y, dim=1)
+    X = F.normalize(torch.cross(X_dir*arm, Y), dim=1)  # TODO smooth
+    Z = F.normalize(torch.cross(X, Y), dim=1)
+
+    cos_pose_matrix = torch.stack([X, Y, Z], dim=2)
+
+    if head == True:
+        Y_arm = F.normalize(arm, dim=1)
+        X_arm = X
+        Z_arm = F.normalize(torch.cross(X_arm, Y_arm), dim=1)
+        cos_pose_matrix = torch.stack([X_arm, Y_arm, Z_arm], dim=2)
+
+    # relative to the body rotation Y
+    if leg == True:
+        euler_body_Y = euler_body * 0
+        euler_body_Y[:, 0:1] = euler_body[:, 1:2]
+        r_body_Y = Rotation.from_euler("YXZ", euler_body_Y, degrees=True)
+        cos_body_Y = torch.from_numpy(r_body_Y.as_matrix())
+
+    # relative to the body
+    cos_relative = cos_body if leg == False else cos_body_Y.float()
+    cos_pose_matrix = cos_relative.permute(0, 2, 1) @ cos_pose_matrix
+    r =  Rotation.from_matrix(cos_pose_matrix)
+    YXZ_euler_angles = r.as_euler("YXZ", degrees=True)
+
+    # bend
+    bend = -(Y * F.normalize(arm, dim=1)).sum(dim=1) * Y_dir
+    bend = torch.rad2deg(torch.acos(bend))
+    # if head == True:
+    #     bend = bend * 0.5
+
+    # swap xyz
+    YXZ_euler_angles[:, [0, 1, 2]] = YXZ_euler_angles[:, [1, 0, 2]]
+    XYZ_euler_angles = YXZ_euler_angles
+
+    # avoid abrupt changes in angle
+    XYZ_euler_angles = euler_angles_smooth(XYZ_euler_angles)
+    bend = euler_angles_smooth(bend.numpy())
+
+    return XYZ_euler_angles, bend
+
+
+def calculate_body_offset(euler_body, euler_right_leg, bend_right_leg, euler_left_leg, bend_left_leg, length_leg=[6, 6], prior=False):
+    '''
+        Calculate the offset of the body to make the movement more realistic. 
+        First, determine the foot positions of both legs based on the actual 
+        effect of Euler angle rotation in Mine-imator. Then, determine which 
+        leg is currently touching the ground and fix the grounded leg. This 
+        allows the calculation of the body offset.
+
+    '''
+
+    def calculate_leg_coordinates(r_body_Y, euler_leg, bend_leg, length_leg, right=True):
+        YXZ_euler_leg = euler_leg[:, [1, 0, 2]]
+        r1 = Rotation.from_euler("YXZ", YXZ_euler_leg, degrees=True)
+        m1 = r1.as_matrix()
+        X1 = m1[:, :, 0]  # direction
+        Y1 = m1[:, :, 1]  # vector to be rotated
+        r2 = Rotation.from_rotvec(X1*bend_leg[:, np.newaxis], degrees=True)
+        Y2 = r2.apply(Y1)  # reconstruct the arm vector
+        coordinates = -(Y1 * length_leg[0] + Y2 * length_leg[1])
+        coordinates[:, 0] = coordinates[:, 0] - 2 
+        coordinates = r_body_Y.apply(coordinates)
+        return coordinates
+
+    # calculate the endpoint coordinates of two legs
+    euler_body_Y = euler_body * 0
+    euler_body_Y[:, 0:1] = euler_body[:, 1:2]
+    r_body_Y = Rotation.from_euler("YXZ", euler_body_Y, degrees=True)
+    right_coordinates = calculate_leg_coordinates(r_body_Y, euler_right_leg, bend_right_leg, length_leg)
+    left_coordinates = calculate_leg_coordinates(r_body_Y, euler_left_leg, bend_left_leg, length_leg)
+    # stack, 0: right, 1: left
+    coordinates = np.stack([right_coordinates, left_coordinates], axis=1)
+    
+    # determine which leg grounded, 0: right, 1: left
+    grounded_flag = (right_coordinates[:, 1] > left_coordinates[:, 1])*1
+    # prior knowledge: The more bended legs are not grounded
+    if prior == True:
+        grounded_flag_left = (bend_right_leg - bend_left_leg) > 30
+        grounded_flag_right = (bend_left_leg - bend_right_leg) > 30
+        grounded_flag += grounded_flag_left*1
+        grounded_flag *= (1 - grounded_flag_right)*1
+    # smoothing
+    grounded_flag = binary_erosion(grounded_flag, structure=np.ones(7))*1
+    grounded_flag = binary_dilation(grounded_flag, structure=np.ones(7))*1
+
+    body_POS = np.zeros_like(right_coordinates)
+
+    # POS_Y
+    ind = np.array(range(right_coordinates.shape[0]))
+    body_POS[:, 1] = -coordinates[ind, grounded_flag, 1]
+
+    # extract the X, Z coordinates of grounded leg in time t_1 
+    X_t1 = coordinates[ind[:-1], grounded_flag[:-1], 0]
+    Z_t1 = coordinates[ind[:-1], grounded_flag[:-1], 2]
+    # extract the X, Z coordinates of grounded leg in time t_2
+    # note that the split of grounded_flag not changed
+    X_t2 = coordinates[ind[1:], grounded_flag[:-1], 0]
+    Z_t2 = coordinates[ind[1:], grounded_flag[:-1], 2]
+
+    # calculate the relative displacement between two frames
+    X_relative = X_t2 - X_t1
+    Z_relative = Z_t2 - Z_t1
+
+    # calculate the absolute displacement
+    X_abs = np.cumsum(X_relative)
+    Z_abs = np.cumsum(Z_relative)
+
+    body_POS[1:, 0] = -X_abs
+    body_POS[1:, 2] = -Z_abs
+
+    return body_POS
+
+
+def add_keyframes(data, length, part_name, euler, bend, not_body=True, not_head=True, body_steve=False, body_POS=None):
+    for i in range(length):
+        if not_head:
+            keyframes_dict = {
+                "position": i,
+                "part_name": part_name,
+                "values": {
+                    "ROT_X": float(euler[i][0]),
+                    "ROT_Y": float(euler[i][2]),  # Y, Z args in mine-imator miframes is exchanged. Maybe a bug. 
+                    "ROT_Z": float(euler[i][1]*not_body),
+                    "BEND_ANGLE_X": float(bend[i])
+                }
+            }
+        else:  # no bend
+            keyframes_dict = {
+                "position": i,
+                "part_name": part_name,
+                "values": {
+                    "ROT_X": float(euler[i][0]),
+                    "ROT_Y": float(euler[i][2]),
+                    "ROT_Z": float(euler[i][1]),
+                }
+            }
+        if body_steve == True:
+            keyframes_dict = {
+                "position": i,
+                "values": {
+                    "POS_X": float(body_POS[i][0]),
+                    "POS_Y": float(body_POS[i][2]),
+                    "POS_Z": float(body_POS[i][1]),
+                    "ROT_Z": float(euler[i][1])
+			    }
+            }
+        data["keyframes"].append(keyframes_dict)
+
+    print(f"add_key_frames: {part_name}")
+
+
+def hpe2keyframes(HPE_filename, FPS_mine_imator, keyframes_filename, prior=True):
+
+    # read data
+    with open(HPE_filename, 'rb') as file:
+        data = np.load(file)
+    print(f"open file: {HPE_filename}")
+    xyz = data.copy()
+    length = xyz.shape[0]
+
+    # extract data from each body part
+    xyz_right_leg = torch.from_numpy(xyz[:, 1:4, :])
+    xyz_right_arm = torch.from_numpy(xyz[:, 14:17, :])
+    xyz_left_leg = torch.from_numpy(xyz[:, 4:7, :])
+    xyz_left_arm = torch.from_numpy(xyz[:, 11:14, :])
+    xyz_body = torch.from_numpy(xyz[:, [0, 7, 8], :])
+    xyz_body_frame = torch.from_numpy(xyz[:, [1, 4, 14, 11], :])
+    xyz_head = torch.from_numpy(xyz[:, [8, 9, 10], :])
+
+    # calculate the absolute euler angles of body
+    euler_body, bend_body, cos_pos_matrix = xyz2euler_body(xyz_body, xyz_body_frame, X_dir=-1, Y_dir=-1)
+
+    # calculate the relative euler angles of arm and head with respect to the body ROT_Y
+    euler_right_leg, bend_right_leg = xyz2euler_relative(xyz_right_leg, cos_pos_matrix, leg=True, euler_body=euler_body)
+    euler_left_leg, bend_left_leg = xyz2euler_relative(xyz_left_leg, cos_pos_matrix, leg=True, euler_body=euler_body)
+    
+    # calculate the relative euler angles of arm and head with respect to the upper body
+    euler_right_arm, bend_right_arm = xyz2euler_relative(xyz_right_arm, cos_pos_matrix, X_dir=-1)
+    euler_left_arm, bend_left_arm = xyz2euler_relative(xyz_left_arm, cos_pos_matrix, X_dir=-1)
+    euler_head, bend_head = xyz2euler_relative(xyz_head, cos_pos_matrix, Y_dir=-1, head=True)
+    
+    # create json format data
+    data = {
+        "format": 34,
+        "created_in": "2.0.0",  # mine-imator version
+        "is_model": True,
+        "tempo": FPS_mine_imator,  # FPS
+        "length": length,  # keyframes length
+        "keyframes": [
+        ],
+        "templates": [],
+        "timelines": [],
+        "resources": []
+    }
+    
+    # relative offset makes the model more realistic
+    # caculate the relative offset based on Euler angle and bending angle
+    body_POS = calculate_body_offset(euler_body, euler_right_leg, bend_right_leg, euler_left_leg, bend_left_leg, prior=prior)
+
+
+    add_keyframes(data, length, "left_leg", euler_left_leg, bend_left_leg)
+    add_keyframes(data, length, "right_leg", euler_right_leg, bend_right_leg)
+    add_keyframes(data, length, "left_arm", euler_left_arm, bend_left_arm)
+    add_keyframes(data, length, "right_arm", euler_right_arm, bend_right_arm)
+    add_keyframes(data, length, "body", euler_body, bend_body, not_body=False)
+    add_keyframes(data, length, "head", euler_head, bend_head, not_head=False)
+    add_keyframes(data, length, "abc", euler_body, bend_body, body_steve=True, body_POS=body_POS)  # TODO
+
+    # save json
+    with open(keyframes_filename, "w") as file:
+        json.dump(data, file, indent=4)
+
+    print(f"keyframes file saves successfully, file path: {os.path.abspath(keyframes_filename)}")
+    
+
+
+if __name__ == '__main__':
+    # config
+    HPE_filename = "outputs/test_3d_output_malaoshi_2-00_2-30_postprocess.npy"
+    FPS_mine_imator = 30
+    keyframes_filename = "steve_malaoshi2.miframes"
+    prior = True
+    hpe2keyframes(HPE_filename, FPS_mine_imator, keyframes_filename, prior=prior)
+
+print("Done!")
diff --git a/LICENSE b/LICENSE
new file mode 100644
index 0000000000000000000000000000000000000000..f288702d2fa16d3cdf0035b15a9fcbc552cd88e7
--- /dev/null
+++ b/LICENSE
@@ -0,0 +1,674 @@
+                    GNU GENERAL PUBLIC LICENSE
+                       Version 3, 29 June 2007
+
+ Copyright (C) 2007 Free Software Foundation, Inc. <https://fsf.org/>
+ Everyone is permitted to copy and distribute verbatim copies
+ of this license document, but changing it is not allowed.
+
+                            Preamble
+
+  The GNU General Public License is a free, copyleft license for
+software and other kinds of works.
+
+  The licenses for most software and other practical works are designed
+to take away your freedom to share and change the works.  By contrast,
+the GNU General Public License is intended to guarantee your freedom to
+share and change all versions of a program--to make sure it remains free
+software for all its users.  We, the Free Software Foundation, use the
+GNU General Public License for most of our software; it applies also to
+any other work released this way by its authors.  You can apply it to
+your programs, too.
+
+  When we speak of free software, we are referring to freedom, not
+price.  Our General Public Licenses are designed to make sure that you
+have the freedom to distribute copies of free software (and charge for
+them if you wish), that you receive source code or can get it if you
+want it, that you can change the software or use pieces of it in new
+free programs, and that you know you can do these things.
+
+  To protect your rights, we need to prevent others from denying you
+these rights or asking you to surrender the rights.  Therefore, you have
+certain responsibilities if you distribute copies of the software, or if
+you modify it: responsibilities to respect the freedom of others.
+
+  For example, if you distribute copies of such a program, whether
+gratis or for a fee, you must pass on to the recipients the same
+freedoms that you received.  You must make sure that they, too, receive
+or can get the source code.  And you must show them these terms so they
+know their rights.
+
+  Developers that use the GNU GPL protect your rights with two steps:
+(1) assert copyright on the software, and (2) offer you this License
+giving you legal permission to copy, distribute and/or modify it.
+
+  For the developers' and authors' protection, the GPL clearly explains
+that there is no warranty for this free software.  For both users' and
+authors' sake, the GPL requires that modified versions be marked as
+changed, so that their problems will not be attributed erroneously to
+authors of previous versions.
+
+  Some devices are designed to deny users access to install or run
+modified versions of the software inside them, although the manufacturer
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+pattern of such abuse occurs in the area of products for individuals to
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+have designed this version of the GPL to prohibit the practice for those
+products.  If such problems arise substantially in other domains, we
+stand ready to extend this provision to those domains in future versions
+of the GPL, as needed to protect the freedom of users.
+
+  Finally, every program is threatened constantly by software patents.
+States should not allow patents to restrict development and use of
+software on general-purpose computers, but in those that do, we wish to
+avoid the special danger that patents applied to a free program could
+make it effectively proprietary.  To prevent this, the GPL assures that
+patents cannot be used to render the program non-free.
+
+  The precise terms and conditions for copying, distribution and
+modification follow.
+
+                       TERMS AND CONDITIONS
+
+  0. Definitions.
+
+  "This License" refers to version 3 of the GNU General Public License.
+
+  "Copyright" also means copyright-like laws that apply to other kinds of
+works, such as semiconductor masks.
+
+  "The Program" refers to any copyrightable work licensed under this
+License.  Each licensee is addressed as "you".  "Licensees" and
+"recipients" may be individuals or organizations.
+
+  To "modify" a work means to copy from or adapt all or part of the work
+in a fashion requiring copyright permission, other than the making of an
+exact copy.  The resulting work is called a "modified version" of the
+earlier work or a work "based on" the earlier work.
+
+  A "covered work" means either the unmodified Program or a work based
+on the Program.
+
+  To "propagate" a work means to do anything with it that, without
+permission, would make you directly or secondarily liable for
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diff --git a/common/arguments.py b/common/arguments.py
new file mode 100644
index 0000000000000000000000000000000000000000..8481672d8d0dd49a50f8f3b06b7040c62e62529d
--- /dev/null
+++ b/common/arguments.py
@@ -0,0 +1,102 @@
+# Copyright (c) 2018-present, Facebook, Inc.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+#
+
+import argparse
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(description='Training script')
+
+    # General arguments
+    parser.add_argument('-d', '--dataset', default='h36m', type=str, metavar='NAME', help='target dataset')  # h36m or humaneva
+    parser.add_argument('-k', '--keypoints', default='cpn_ft_h36m_dbb', type=str, metavar='NAME', help='2D detections to use')
+    parser.add_argument('-str', '--subjects-train', default='S1,S5,S6,S7,S8', type=str, metavar='LIST',
+                        help='training subjects separated by comma')
+    parser.add_argument('-ste', '--subjects-test', default='S9,S11', type=str, metavar='LIST', help='test subjects separated by comma')
+    parser.add_argument('-sun', '--subjects-unlabeled', default='', type=str, metavar='LIST',
+                        help='unlabeled subjects separated by comma for self-supervision')
+    parser.add_argument('-a', '--actions', default='*', type=str, metavar='LIST',
+                        help='actions to train/test on, separated by comma, or * for all')
+    parser.add_argument('-c', '--checkpoint', default='checkpoint', type=str, metavar='PATH',
+                        help='checkpoint directory')
+    parser.add_argument('--checkpoint-frequency', default=10, type=int, metavar='N',
+                        help='create a checkpoint every N epochs')
+    parser.add_argument('-r', '--resume', default='', type=str, metavar='FILENAME',
+                        help='checkpoint to resume (file name)')
+    parser.add_argument('--evaluate', default='pretrained_h36m_detectron_coco.bin', type=str, metavar='FILENAME', help='checkpoint to evaluate (file name)')
+    parser.add_argument('--render', action='store_true', help='visualize a particular video')
+    parser.add_argument('--by-subject', action='store_true', help='break down error by subject (on evaluation)')
+    parser.add_argument('--export-training-curves', action='store_true', help='save training curves as .png images')
+
+    # Model arguments
+    parser.add_argument('-s', '--stride', default=1, type=int, metavar='N', help='chunk size to use during training')
+    parser.add_argument('-e', '--epochs', default=60, type=int, metavar='N', help='number of training epochs')
+    parser.add_argument('-b', '--batch-size', default=1024, type=int, metavar='N', help='batch size in terms of predicted frames')
+    parser.add_argument('-drop', '--dropout', default=0.25, type=float, metavar='P', help='dropout probability')
+    parser.add_argument('-lr', '--learning-rate', default=0.001, type=float, metavar='LR', help='initial learning rate')
+    parser.add_argument('-lrd', '--lr-decay', default=0.95, type=float, metavar='LR', help='learning rate decay per epoch')
+    parser.add_argument('-no-da', '--no-data-augmentation', dest='data_augmentation', action='store_false',
+                        help='disable train-time flipping')
+    parser.add_argument('-no-tta', '--no-test-time-augmentation', dest='test_time_augmentation', action='store_false',
+                        help='disable test-time flipping')
+    parser.add_argument('-arc', '--architecture', default='3,3,3,3,3', type=str, metavar='LAYERS', help='filter widths separated by comma')
+    parser.add_argument('--causal', action='store_true', help='use causal convolutions for real-time processing')
+    parser.add_argument('-ch', '--channels', default=1024, type=int, metavar='N', help='number of channels in convolution layers')
+
+    # Experimental
+    parser.add_argument('--subset', default=1, type=float, metavar='FRACTION', help='reduce dataset size by fraction')
+    parser.add_argument('--downsample', default=1, type=int, metavar='FACTOR', help='downsample frame rate by factor (semi-supervised)')
+    parser.add_argument('--warmup', default=1, type=int, metavar='N', help='warm-up epochs for semi-supervision')
+    parser.add_argument('--no-eval', action='store_true', help='disable epoch evaluation while training (small speed-up)')
+    parser.add_argument('--dense', action='store_true', help='use dense convolutions instead of dilated convolutions')
+    parser.add_argument('--disable-optimizations', action='store_true', help='disable optimized model for single-frame predictions')
+    parser.add_argument('--linear-projection', action='store_true', help='use only linear coefficients for semi-supervised projection')
+    parser.add_argument('--no-bone-length', action='store_false', dest='bone_length_term',
+                        help='disable bone length term in semi-supervised settings')
+    parser.add_argument('--no-proj', action='store_true', help='disable projection for semi-supervised setting')
+
+    # Visualization
+    parser.add_argument('--viz-subject', type=str, metavar='STR', help='subject to render')
+    parser.add_argument('--viz-action', type=str, metavar='STR', help='action to render')
+    parser.add_argument('--viz-camera', type=int, default=0, metavar='N', help='camera to render')
+    parser.add_argument('--viz-video', type=str, metavar='PATH', help='path to input video')
+    parser.add_argument('--viz-skip', type=int, default=0, metavar='N', help='skip first N frames of input video')
+    parser.add_argument('--viz-output', type=str, metavar='PATH', help='output file name (.gif or .mp4)')
+    parser.add_argument('--viz-bitrate', type=int, default=30000, metavar='N', help='bitrate for mp4 videos')
+    parser.add_argument('--viz-no-ground-truth', action='store_true', help='do not show ground-truth poses')
+    parser.add_argument('--viz-limit', type=int, default=-1, metavar='N', help='only render first N frames')
+    parser.add_argument('--viz-downsample', type=int, default=1, metavar='N', help='downsample FPS by a factor N')
+    parser.add_argument('--viz-size', type=int, default=5, metavar='N', help='image size')
+    # self add
+    parser.add_argument('-in2d','--input_npz', type=str, default='', help='input 2d numpy file')
+    parser.add_argument('--video', dest='input_video', type=str, default='', help='input video name')
+
+    parser.add_argument('--layers', default=3, type=int)
+    parser.add_argument('--channel', default=256, type=int)
+    parser.add_argument('--d_hid', default=512, type=int)
+    parser.add_argument('-f', '--frames', type=int, default=243)
+    parser.add_argument('--n_joints', type=int, default=17)
+    parser.add_argument('--out_joints', type=int, default=17)
+    parser.add_argument('--in_channels', type=int, default=2)
+    parser.add_argument('--out_channels', type=int, default=3)
+    parser.add_argument('--stride_num', type=list, default=[3, 3, 3, 3, 3])
+
+    parser.set_defaults(bone_length_term=True)
+    parser.set_defaults(data_augmentation=True)
+    parser.set_defaults(test_time_augmentation=True)
+
+    args = parser.parse_args()
+    # Check invalid configuration
+    if args.resume and args.evaluate:
+        print('Invalid flags: --resume and --evaluate cannot be set at the same time')
+        exit()
+
+    if args.export_training_curves and args.no_eval:
+        print('Invalid flags: --export-training-curves and --no-eval cannot be set at the same time')
+        exit()
+
+    return args
diff --git a/common/camera.py b/common/camera.py
new file mode 100644
index 0000000000000000000000000000000000000000..d147a36d7024007ef2461883dd6c1907582ee08e
--- /dev/null
+++ b/common/camera.py
@@ -0,0 +1,107 @@
+# Copyright (c) 2018-present, Facebook, Inc.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+#
+
+import numpy as np
+import torch
+
+from common.quaternion import qrot, qinverse
+from common.utils import wrap
+
+
+def normalize_screen_coordinates(X, w, h):
+    assert X.shape[-1] == 2
+
+    # Normalize so that [0, w] is mapped to [-1, 1], while preserving the aspect ratio
+    return X / w * 2 - [1, h / w]
+
+
+def normalize_screen_coordinates_new(X, w, h):
+    assert X.shape[-1] == 2
+
+    return (X - (w / 2, h / 2)) / (w / 2, h / 2)
+
+
+def image_coordinates_new(X, w, h):
+    assert X.shape[-1] == 2
+
+    # Reverse camera frame normalization
+    return (X * (w / 2, h / 2)) + (w / 2, h / 2)
+
+
+def image_coordinates(X, w, h):
+    assert X.shape[-1] == 2
+
+    # Reverse camera frame normalization
+    return (X + [1, h / w]) * w / 2
+
+
+def world_to_camera(X, R, t):
+    Rt = wrap(qinverse, R)  # Invert rotation
+    return wrap(qrot, np.tile(Rt, (*X.shape[:-1], 1)), X - t)  # Rotate and translate
+
+
+def camera_to_world(X, R, t):
+    return wrap(qrot, np.tile(R, (*X.shape[:-1], 1)), X) + t
+
+
+def project_to_2d(X, camera_params):
+    """
+    Project 3D points to 2D using the Human3.6M camera projection function.
+    This is a differentiable and batched reimplementation of the original MATLAB script.
+
+    Arguments:
+    X -- 3D points in *camera space* to transform (N, *, 3)
+    camera_params -- intrinsic parameteres (N, 2+2+3+2=9)
+    focal length / principal point / radial_distortion / tangential_distortion
+    """
+    assert X.shape[-1] == 3
+    assert len(camera_params.shape) == 2
+    assert camera_params.shape[-1] == 9
+    assert X.shape[0] == camera_params.shape[0]
+
+    while len(camera_params.shape) < len(X.shape):
+        camera_params = camera_params.unsqueeze(1)
+
+    f = camera_params[..., :2]  # focal lendgth
+    c = camera_params[..., 2:4]  # center principal point
+    k = camera_params[..., 4:7]
+    p = camera_params[..., 7:]
+
+    XX = torch.clamp(X[..., :2] / X[..., 2:], min=-1, max=1)
+    r2 = torch.sum(XX[..., :2] ** 2, dim=len(XX.shape) - 1, keepdim=True)
+
+    radial = 1 + torch.sum(k * torch.cat((r2, r2 ** 2, r2 ** 3), dim=len(r2.shape) - 1), dim=len(r2.shape) - 1, keepdim=True)
+    tan = torch.sum(p * XX, dim=len(XX.shape) - 1, keepdim=True)
+
+    XXX = XX * (radial + tan) + p * r2
+
+    return f * XXX + c
+
+
+def project_to_2d_linear(X, camera_params):
+    """
+    使用linear parameters is a little difference for use linear and no-linear parameters
+    Project 3D points to 2D using only linear parameters (focal length and principal point).
+
+    Arguments:
+    X -- 3D points in *camera space* to transform (N, *, 3)
+    camera_params -- intrinsic parameteres (N, 2+2+3+2=9)
+    """
+    assert X.shape[-1] == 3
+    assert len(camera_params.shape) == 2
+    assert camera_params.shape[-1] == 9
+    assert X.shape[0] == camera_params.shape[0]
+
+    while len(camera_params.shape) < len(X.shape):
+        camera_params = camera_params.unsqueeze(1)
+
+    f = camera_params[..., :2]
+    c = camera_params[..., 2:4]
+
+    XX = torch.clamp(X[..., :2] / X[..., 2:], min=-1, max=1)
+
+    return f * XX + c
diff --git a/common/generators.py b/common/generators.py
new file mode 100644
index 0000000000000000000000000000000000000000..f41dfb77fecc4f09bb5a4778ab9b6c6657c48de7
--- /dev/null
+++ b/common/generators.py
@@ -0,0 +1,425 @@
+# Copyright (c) 2018-present, Facebook, Inc.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+#
+
+from itertools import zip_longest
+
+import numpy as np
+
+
+class ChunkedGenerator:
+    """
+    Batched data generator, used for training.
+    The sequences are split into equal-length chunks and padded as necessary.
+
+    Arguments:
+    batch_size -- the batch size to use for training
+    cameras -- list of cameras, one element for each video (optional, used for semi-supervised training)
+    poses_3d -- list of ground-truth 3D poses, one element for each video (optional, used for supervised training)
+    poses_2d -- list of input 2D keypoints, one element for each video
+    chunk_length -- number of output frames to predict for each training example (usually 1)
+    pad -- 2D input padding to compensate for valid convolutions, per side (depends on the receptive field)
+    causal_shift -- asymmetric padding offset when causal convolutions are used (usually 0 or "pad")
+    shuffle -- randomly shuffle the dataset before each epoch
+    random_seed -- initial seed to use for the random generator
+    augment -- augment the dataset by flipping poses horizontally
+    kps_left and kps_right -- list of left/right 2D keypoints if flipping is enabled
+    joints_left and joints_right -- list of left/right 3D joints if flipping is enabled
+    """
+
+    def __init__(self, batch_size, cameras, poses_3d, poses_2d,
+                 chunk_length, pad=0, causal_shift=0,
+                 shuffle=True, random_seed=1234,
+                 augment=False, kps_left=None, kps_right=None, joints_left=None, joints_right=None,
+                 endless=False):
+        assert poses_3d is None or len(poses_3d) == len(poses_2d), (len(poses_3d), len(poses_2d))
+        assert cameras is None or len(cameras) == len(poses_2d)
+
+        # Build lineage info
+        pairs = []  # (seq_idx, start_frame, end_frame, flip) tuples
+        for i in range(len(poses_2d)):
+            assert poses_3d is None or poses_3d[i].shape[0] == poses_3d[i].shape[0]
+            n_chunks = (poses_2d[i].shape[0] + chunk_length - 1) // chunk_length
+            offset = (n_chunks * chunk_length - poses_2d[i].shape[0]) // 2
+            bounds = np.arange(n_chunks + 1) * chunk_length - offset
+            augment_vector = np.full(len(bounds - 1), False, dtype=bool)
+            pairs += zip(np.repeat(i, len(bounds - 1)), bounds[:-1], bounds[1:], augment_vector)
+            if augment:
+                pairs += zip(np.repeat(i, len(bounds - 1)), bounds[:-1], bounds[1:], ~augment_vector)
+
+        # Initialize buffers
+        if cameras is not None:
+            self.batch_cam = np.empty((batch_size, cameras[0].shape[-1]))
+        if poses_3d is not None:
+            self.batch_3d = np.empty((batch_size, chunk_length, poses_3d[0].shape[-2], poses_3d[0].shape[-1]))
+        self.batch_2d = np.empty((batch_size, chunk_length + 2 * pad, poses_2d[0].shape[-2], poses_2d[0].shape[-1]))
+
+        self.num_batches = (len(pairs) + batch_size - 1) // batch_size
+        self.batch_size = batch_size
+        self.random = np.random.RandomState(random_seed)
+        self.pairs = pairs
+        self.shuffle = shuffle
+        self.pad = pad
+        self.causal_shift = causal_shift
+        self.endless = endless
+        self.state = None
+
+        self.cameras = cameras
+        self.poses_3d = poses_3d
+        self.poses_2d = poses_2d
+
+        self.augment = augment
+        self.kps_left = kps_left
+        self.kps_right = kps_right
+        self.joints_left = joints_left
+        self.joints_right = joints_right
+
+    def num_frames(self):
+        return self.num_batches * self.batch_size
+
+    def random_state(self):
+        return self.random
+
+    def set_random_state(self, random):
+        self.random = random
+
+    def augment_enabled(self):
+        return self.augment
+
+    def next_pairs(self):
+        if self.state is None:
+            if self.shuffle:
+                pairs = self.random.permutation(self.pairs)
+            else:
+                pairs = self.pairs
+            return 0, pairs
+        else:
+            return self.state
+
+    def next_epoch(self):
+        enabled = True
+        while enabled:
+            start_idx, pairs = self.next_pairs()
+            for b_i in range(start_idx, self.num_batches):
+                chunks = pairs[b_i * self.batch_size: (b_i + 1) * self.batch_size]
+                for i, (seq_i, start_3d, end_3d, flip) in enumerate(chunks):
+                    start_2d = start_3d - self.pad - self.causal_shift
+                    end_2d = end_3d + self.pad - self.causal_shift
+
+                    # 2D poses
+                    seq_2d = self.poses_2d[seq_i]
+                    low_2d = max(start_2d, 0)
+                    high_2d = min(end_2d, seq_2d.shape[0])
+                    pad_left_2d = low_2d - start_2d
+                    pad_right_2d = end_2d - high_2d
+                    if pad_left_2d != 0 or pad_right_2d != 0:
+                        self.batch_2d[i] = np.pad(seq_2d[low_2d:high_2d], ((pad_left_2d, pad_right_2d), (0, 0), (0, 0)), 'edge')
+                    else:
+                        self.batch_2d[i] = seq_2d[low_2d:high_2d]
+
+                    if flip:
+                        # Flip 2D keypoints
+                        self.batch_2d[i, :, :, 0] *= -1
+                        self.batch_2d[i, :, self.kps_left + self.kps_right] = self.batch_2d[i, :, self.kps_right + self.kps_left]
+
+                    # 3D poses
+                    if self.poses_3d is not None:
+                        seq_3d = self.poses_3d[seq_i]
+                        low_3d = max(start_3d, 0)
+                        high_3d = min(end_3d, seq_3d.shape[0])
+                        pad_left_3d = low_3d - start_3d
+                        pad_right_3d = end_3d - high_3d
+                        if pad_left_3d != 0 or pad_right_3d != 0:
+                            self.batch_3d[i] = np.pad(seq_3d[low_3d:high_3d], ((pad_left_3d, pad_right_3d), (0, 0), (0, 0)), 'edge')
+                        else:
+                            self.batch_3d[i] = seq_3d[low_3d:high_3d]
+
+                        if flip:
+                            # Flip 3D joints
+                            self.batch_3d[i, :, :, 0] *= -1
+                            self.batch_3d[i, :, self.joints_left + self.joints_right] = \
+                                self.batch_3d[i, :, self.joints_right + self.joints_left]
+
+                    # Cameras
+                    if self.cameras is not None:
+                        self.batch_cam[i] = self.cameras[seq_i]
+                        if flip:
+                            # Flip horizontal distortion coefficients
+                            self.batch_cam[i, 2] *= -1
+                            self.batch_cam[i, 7] *= -1
+
+                if self.endless:
+                    self.state = (b_i + 1, pairs)
+                if self.poses_3d is None and self.cameras is None:
+                    yield None, None, self.batch_2d[:len(chunks)]
+                elif self.poses_3d is not None and self.cameras is None:
+                    yield None, self.batch_3d[:len(chunks)], self.batch_2d[:len(chunks)]
+                elif self.poses_3d is None:
+                    yield self.batch_cam[:len(chunks)], None, self.batch_2d[:len(chunks)]
+                else:
+                    yield self.batch_cam[:len(chunks)], self.batch_3d[:len(chunks)], self.batch_2d[:len(chunks)]
+
+            if self.endless:
+                self.state = None
+            else:
+                enabled = False
+
+
+class UnchunkedGenerator:
+    """
+    Non-batched data generator, used for testing.
+    Sequences are returned one at a time (i.e. batch size = 1), without chunking.
+
+    If data augmentation is enabled, the batches contain two sequences (i.e. batch size = 2),
+    the second of which is a mirrored version of the first.
+
+    Arguments:
+    cameras -- list of cameras, one element for each video (optional, used for semi-supervised training)
+    poses_3d -- list of ground-truth 3D poses, one element for each video (optional, used for supervised training)
+    poses_2d -- list of input 2D keypoints, one element for each video
+    pad -- 2D input padding to compensate for valid convolutions, per side (depends on the receptive field)
+    causal_shift -- asymmetric padding offset when causal convolutions are used (usually 0 or "pad")
+    augment -- augment the dataset by flipping poses horizontally
+    kps_left and kps_right -- list of left/right 2D keypoints if flipping is enabled
+    joints_left and joints_right -- list of left/right 3D joints if flipping is enabled
+    """
+
+    def __init__(self, cameras, poses_3d, poses_2d, pad=0, causal_shift=0,
+                 augment=False, kps_left=None, kps_right=None, joints_left=None, joints_right=None):
+        assert poses_3d is None or len(poses_3d) == len(poses_2d)
+        assert cameras is None or len(cameras) == len(poses_2d)
+
+        self.augment = augment
+        self.kps_left = kps_left
+        self.kps_right = kps_right
+        self.joints_left = joints_left
+        self.joints_right = joints_right
+
+        self.pad = pad
+        self.causal_shift = causal_shift
+        self.cameras = [] if cameras is None else cameras
+        self.poses_3d = [] if poses_3d is None else poses_3d
+        self.poses_2d = poses_2d
+
+    def num_frames(self):
+        count = 0
+        for p in self.poses_2d:
+            count += p.shape[0]
+        return count
+
+    def augment_enabled(self):
+        return self.augment
+
+    def set_augment(self, augment):
+        self.augment = augment
+
+    def next_epoch(self):
+        for seq_cam, seq_3d, seq_2d in zip_longest(self.cameras, self.poses_3d, self.poses_2d):
+            batch_cam = None if seq_cam is None else np.expand_dims(seq_cam, axis=0)
+            batch_3d = None if seq_3d is None else np.expand_dims(seq_3d, axis=0)
+            # 2D input padding to compensate for valid convolutions, per side (depends on the receptive field)
+            batch_2d = np.expand_dims(np.pad(seq_2d,
+                                             ((self.pad + self.causal_shift, self.pad - self.causal_shift), (0, 0), (0, 0)),
+                                             'edge'), axis=0)
+            if self.augment:
+                # Append flipped version
+                if batch_cam is not None:
+                    batch_cam = np.concatenate((batch_cam, batch_cam), axis=0)
+                    batch_cam[1, 2] *= -1
+                    batch_cam[1, 7] *= -1
+
+                if batch_3d is not None:
+                    batch_3d = np.concatenate((batch_3d, batch_3d), axis=0)
+                    batch_3d[1, :, :, 0] *= -1
+                    batch_3d[1, :, self.joints_left + self.joints_right] = batch_3d[1, :, self.joints_right + self.joints_left]
+
+                batch_2d = np.concatenate((batch_2d, batch_2d), axis=0)
+                batch_2d[1, :, :, 0] *= -1
+                batch_2d[1, :, self.kps_left + self.kps_right] = batch_2d[1, :, self.kps_right + self.kps_left]
+
+            yield batch_cam, batch_3d, batch_2d
+
+class Evaluate_Generator:
+    """
+    Batched data generator, used for training.
+    The sequences are split into equal-length chunks and padded as necessary.
+    Arguments:
+    batch_size -- the batch size to use for training
+    cameras -- list of cameras, one element for each video (optional, used for semi-supervised training)
+    poses_3d -- list of ground-truth 3D poses, one element for each video (optional, used for supervised training)
+    poses_2d -- list of input 2D keypoints, one element for each video
+    chunk_length -- number of output frames to predict for each training example (usually 1)
+    pad -- 2D input padding to compensate for valid convolutions, per side (depends on the receptive field)
+    causal_shift -- asymmetric padding offset when causal convolutions are used (usually 0 or "pad")
+    shuffle -- randomly shuffle the dataset before each epoch
+    random_seed -- initial seed to use for the random generator
+    augment -- augment the dataset by flipping poses horizontally
+    kps_left and kps_right -- list of left/right 2D keypoints if flipping is enabled
+    joints_left and joints_right -- list of left/right 3D joints if flipping is enabled
+    """
+
+    def __init__(self, batch_size, cameras, poses_3d, poses_2d,
+                 chunk_length, pad=0, causal_shift=0,
+                 shuffle=True, random_seed=1234,
+                 augment=False, kps_left=None, kps_right=None, joints_left=None, joints_right=None,
+                 endless=False):
+        assert poses_3d is None or len(poses_3d) == len(poses_2d), (len(poses_3d), len(poses_2d))
+        assert cameras is None or len(cameras) == len(poses_2d)
+
+        # Build lineage info
+        pairs = []  # (seq_idx, start_frame, end_frame, flip) tuples
+        for i in range(len(poses_2d)):
+            assert poses_3d is None or poses_3d[i].shape[0] == poses_3d[i].shape[0]
+            n_chunks = (poses_2d[i].shape[0] + chunk_length - 1) // chunk_length
+            offset = (n_chunks * chunk_length - poses_2d[i].shape[0]) // 2
+            bounds = np.arange(n_chunks + 1) * chunk_length - offset
+            augment_vector = np.full(len(bounds - 1), False, dtype=bool)
+            pairs += zip(np.repeat(i, len(bounds - 1)), bounds[:-1], bounds[1:], augment_vector)
+
+        # Initialize buffers
+        if cameras is not None:
+            self.batch_cam = np.empty((batch_size, cameras[0].shape[-1]))
+        if poses_3d is not None:
+            self.batch_3d = np.empty((batch_size, chunk_length, poses_3d[0].shape[-2], poses_3d[0].shape[-1]))
+
+        if augment:
+            self.batch_2d_flip = np.empty(
+                (batch_size, chunk_length + 2 * pad, poses_2d[0].shape[-2], poses_2d[0].shape[-1]))
+            self.batch_2d = np.empty((batch_size, chunk_length + 2 * pad, poses_2d[0].shape[-2], poses_2d[0].shape[-1]))
+        else:
+            self.batch_2d = np.empty((batch_size, chunk_length + 2 * pad, poses_2d[0].shape[-2], poses_2d[0].shape[-1]))
+
+        self.num_batches = (len(pairs) + batch_size - 1) // batch_size
+        self.batch_size = batch_size
+        self.random = np.random.RandomState(random_seed)
+        self.pairs = pairs
+        self.shuffle = shuffle
+        self.pad = pad
+        self.causal_shift = causal_shift
+        self.endless = endless
+        self.state = None
+
+        self.cameras = cameras
+        self.poses_3d = poses_3d
+        self.poses_2d = poses_2d
+
+        self.augment = augment
+        self.kps_left = kps_left
+        self.kps_right = kps_right
+        self.joints_left = joints_left
+        self.joints_right = joints_right
+
+    def num_frames(self):
+        return self.num_batches * self.batch_size
+
+    def random_state(self):
+        return self.random
+
+    def set_random_state(self, random):
+        self.random = random
+
+    def augment_enabled(self):
+        return self.augment
+
+    def next_pairs(self):
+        if self.state is None:
+            if self.shuffle:
+                pairs = self.random.permutation(self.pairs)
+            else:
+                pairs = self.pairs
+            return 0, pairs
+        else:
+            return self.state
+
+    def next_epoch(self):
+        enabled = True
+        while enabled:
+            start_idx, pairs = self.next_pairs()
+            for b_i in range(start_idx, self.num_batches):
+                chunks = pairs[b_i * self.batch_size: (b_i + 1) * self.batch_size]
+                for i, (seq_i, start_3d, end_3d, flip) in enumerate(chunks):
+                    start_2d = start_3d - self.pad - self.causal_shift
+                    end_2d = end_3d + self.pad - self.causal_shift
+
+                    # 2D poses
+                    seq_2d = self.poses_2d[seq_i]
+                    low_2d = max(start_2d, 0)
+                    high_2d = min(end_2d, seq_2d.shape[0])
+                    pad_left_2d = low_2d - start_2d
+                    pad_right_2d = end_2d - high_2d
+                    if pad_left_2d != 0 or pad_right_2d != 0:
+                        self.batch_2d[i] = np.pad(seq_2d[low_2d:high_2d], ((pad_left_2d, pad_right_2d), (0, 0), (0, 0)),
+                                                  'edge')
+                        if self.augment:
+                            self.batch_2d_flip[i] = np.pad(seq_2d[low_2d:high_2d],
+                                                           ((pad_left_2d, pad_right_2d), (0, 0), (0, 0)),
+                                                           'edge')
+
+                    else:
+                        self.batch_2d[i] = seq_2d[low_2d:high_2d]
+                        if self.augment:
+                            self.batch_2d_flip[i] = seq_2d[low_2d:high_2d]
+
+                    if self.augment:
+                        self.batch_2d_flip[i, :, :, 0] *= -1
+                        self.batch_2d_flip[i, :, self.kps_left + self.kps_right] = self.batch_2d_flip[i, :,
+                                                                                   self.kps_right + self.kps_left]
+
+                    # 3D poses
+                    if self.poses_3d is not None:
+                        seq_3d = self.poses_3d[seq_i]
+                        low_3d = max(start_3d, 0)
+                        high_3d = min(end_3d, seq_3d.shape[0])
+                        pad_left_3d = low_3d - start_3d
+                        pad_right_3d = end_3d - high_3d
+                        if pad_left_3d != 0 or pad_right_3d != 0:
+                            self.batch_3d[i] = np.pad(seq_3d[low_3d:high_3d],
+                                                      ((pad_left_3d, pad_right_3d), (0, 0), (0, 0)), 'edge')
+                        else:
+                            self.batch_3d[i] = seq_3d[low_3d:high_3d]
+
+                        if flip:
+                            self.batch_3d[i, :, :, 0] *= -1
+                            self.batch_3d[i, :, self.joints_left + self.joints_right] = \
+                                self.batch_3d[i, :, self.joints_right + self.joints_left]
+
+                    # Cameras
+                    if self.cameras is not None:
+                        self.batch_cam[i] = self.cameras[seq_i]
+                        if flip:
+                            # Flip horizontal distortion coefficients
+                            self.batch_cam[i, 2] *= -1
+                            self.batch_cam[i, 7] *= -1
+
+                if self.endless:
+                    self.state = (b_i + 1, pairs)
+
+                if self.augment:
+                    if self.poses_3d is None and self.cameras is None:
+                        yield None, None, self.batch_2d[:len(chunks)], self.batch_2d_flip[:len(chunks)]
+                    elif self.poses_3d is not None and self.cameras is None:
+                        yield None, self.batch_3d[:len(chunks)], self.batch_2d[:len(chunks)], self.batch_2d_flip[
+                                                                                              :len(chunks)]
+                    elif self.poses_3d is None:
+                        yield self.batch_cam[:len(chunks)], None, self.batch_2d[:len(chunks)], self.batch_2d_flip[
+                                                                                               :len(chunks)]
+                    else:
+                        yield self.batch_cam[:len(chunks)], self.batch_3d[:len(chunks)], self.batch_2d[:len(
+                            chunks)], self.batch_2d_flip[:len(chunks)]
+                else:
+                    if self.poses_3d is None and self.cameras is None:
+                        yield None, None, self.batch_2d[:len(chunks)]
+                    elif self.poses_3d is not None and self.cameras is None:
+                        yield None, self.batch_3d[:len(chunks)], self.batch_2d[:len(chunks)]
+                    elif self.poses_3d is None:
+                        yield self.batch_cam[:len(chunks)], None, self.batch_2d[:len(chunks)]
+                    else:
+                        yield self.batch_cam[:len(chunks)], self.batch_3d[:len(chunks)], self.batch_2d[:len(chunks)]
+
+            if self.endless:
+                self.state = None
+            else:
+                enabled = False
\ No newline at end of file
diff --git a/common/h36m_dataset.py b/common/h36m_dataset.py
new file mode 100644
index 0000000000000000000000000000000000000000..0d05c760588f7b7fa936dfde239641bf78f2d01b
--- /dev/null
+++ b/common/h36m_dataset.py
@@ -0,0 +1,258 @@
+# Copyright (c) 2018-present, Facebook, Inc.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+#
+
+import copy
+
+import numpy as np
+
+from common.camera import normalize_screen_coordinates
+from common.mocap_dataset import MocapDataset
+from common.skeleton import Skeleton
+
+h36m_skeleton = Skeleton(parents=[-1, 0, 1, 2, 3, 4, 0, 6, 7, 8, 9, 0, 11, 12, 13, 14, 12,
+                                  16, 17, 18, 19, 20, 19, 22, 12, 24, 25, 26, 27, 28, 27, 30],
+                         joints_left=[6, 7, 8, 9, 10, 16, 17, 18, 19, 20, 21, 22, 23],
+                         joints_right=[1, 2, 3, 4, 5, 24, 25, 26, 27, 28, 29, 30, 31])
+
+h36m_cameras_intrinsic_params = [
+    {
+        'id': '54138969',
+        'center': [512.54150390625, 515.4514770507812],
+        'focal_length': [1145.0494384765625, 1143.7811279296875],
+        'radial_distortion': [-0.20709891617298126, 0.24777518212795258, -0.0030751503072679043],
+        'tangential_distortion': [-0.0009756988729350269, -0.00142447161488235],
+        'res_w': 1000,
+        'res_h': 1002,
+        'azimuth': 70,  # Only used for visualization
+    },
+    {
+        'id': '55011271',
+        'center': [508.8486328125, 508.0649108886719],
+        'focal_length': [1149.6756591796875, 1147.5916748046875],
+        'radial_distortion': [-0.1942136287689209, 0.2404085397720337, 0.006819975562393665],
+        'tangential_distortion': [-0.0016190266469493508, -0.0027408944442868233],
+        'res_w': 1000,
+        'res_h': 1000,
+        'azimuth': -70,  # Only used for visualization
+    },
+    {
+        'id': '58860488',
+        'center': [519.8158569335938, 501.40264892578125],
+        'focal_length': [1149.1407470703125, 1148.7989501953125],
+        'radial_distortion': [-0.2083381861448288, 0.25548800826072693, -0.0024604974314570427],
+        'tangential_distortion': [0.0014843869721516967, -0.0007599993259645998],
+        'res_w': 1000,
+        'res_h': 1000,
+        'azimuth': 110,  # Only used for visualization
+    },
+    {
+        'id': '60457274',
+        'center': [514.9682006835938, 501.88201904296875],
+        'focal_length': [1145.5113525390625, 1144.77392578125],
+        'radial_distortion': [-0.198384091258049, 0.21832367777824402, -0.008947807364165783],
+        'tangential_distortion': [-0.0005872055771760643, -0.0018133620033040643],
+        'res_w': 1000,
+        'res_h': 1002,
+        'azimuth': -110,  # Only used for visualization
+    },
+]
+
+h36m_cameras_extrinsic_params = {
+    'S1': [
+        {
+            'orientation': [0.1407056450843811, -0.1500701755285263, -0.755240797996521, 0.6223280429840088],
+            'translation': [1841.1070556640625, 4955.28466796875, 1563.4454345703125],
+        },
+        {
+            'orientation': [0.6157187819480896, -0.764836311340332, -0.14833825826644897, 0.11794740706682205],
+            'translation': [1761.278564453125, -5078.0068359375, 1606.2650146484375],
+        },
+        {
+            'orientation': [0.14651472866535187, -0.14647851884365082, 0.7653023600578308, -0.6094175577163696],
+            'translation': [-1846.7777099609375, 5215.04638671875, 1491.972412109375],
+        },
+        {
+            'orientation': [0.5834008455276489, -0.7853162288665771, 0.14548823237419128, -0.14749594032764435],
+            'translation': [-1794.7896728515625, -3722.698974609375, 1574.8927001953125],
+        },
+    ],
+    'S2': [
+        {},
+        {},
+        {},
+        {},
+    ],
+    'S3': [
+        {},
+        {},
+        {},
+        {},
+    ],
+    'S4': [
+        {},
+        {},
+        {},
+        {},
+    ],
+    'S5': [
+        {
+            'orientation': [0.1467377245426178, -0.162370964884758, -0.7551892995834351, 0.6178938746452332],
+            'translation': [2097.3916015625, 4880.94482421875, 1605.732421875],
+        },
+        {
+            'orientation': [0.6159758567810059, -0.7626792192459106, -0.15728192031383514, 0.1189815029501915],
+            'translation': [2031.7008056640625, -5167.93310546875, 1612.923095703125],
+        },
+        {
+            'orientation': [0.14291371405124664, -0.12907841801643372, 0.7678384780883789, -0.6110143065452576],
+            'translation': [-1620.5948486328125, 5171.65869140625, 1496.43701171875],
+        },
+        {
+            'orientation': [0.5920479893684387, -0.7814217805862427, 0.1274748593568802, -0.15036417543888092],
+            'translation': [-1637.1737060546875, -3867.3173828125, 1547.033203125],
+        },
+    ],
+    'S6': [
+        {
+            'orientation': [0.1337897777557373, -0.15692396461963654, -0.7571090459823608, 0.6198879480361938],
+            'translation': [1935.4517822265625, 4950.24560546875, 1618.0838623046875],
+        },
+        {
+            'orientation': [0.6147197484970093, -0.7628812789916992, -0.16174767911434174, 0.11819244921207428],
+            'translation': [1969.803955078125, -5128.73876953125, 1632.77880859375],
+        },
+        {
+            'orientation': [0.1529948115348816, -0.13529130816459656, 0.7646096348762512, -0.6112781167030334],
+            'translation': [-1769.596435546875, 5185.361328125, 1476.993408203125],
+        },
+        {
+            'orientation': [0.5916101336479187, -0.7804774045944214, 0.12832270562648773, -0.1561593860387802],
+            'translation': [-1721.668701171875, -3884.13134765625, 1540.4879150390625],
+        },
+    ],
+    'S7': [
+        {
+            'orientation': [0.1435241848230362, -0.1631336808204651, -0.7548328638076782, 0.6188824772834778],
+            'translation': [1974.512939453125, 4926.3544921875, 1597.8326416015625],
+        },
+        {
+            'orientation': [0.6141672730445862, -0.7638262510299683, -0.1596645563840866, 0.1177929937839508],
+            'translation': [1937.0584716796875, -5119.7900390625, 1631.5665283203125],
+        },
+        {
+            'orientation': [0.14550060033798218, -0.12874816358089447, 0.7660516500473022, -0.6127139329910278],
+            'translation': [-1741.8111572265625, 5208.24951171875, 1464.8245849609375],
+        },
+        {
+            'orientation': [0.5912848114967346, -0.7821764349937439, 0.12445473670959473, -0.15196487307548523],
+            'translation': [-1734.7105712890625, -3832.42138671875, 1548.5830078125],
+        },
+    ],
+    'S8': [
+        {
+            'orientation': [0.14110587537288666, -0.15589867532253265, -0.7561917304992676, 0.619644045829773],
+            'translation': [2150.65185546875, 4896.1611328125, 1611.9046630859375],
+        },
+        {
+            'orientation': [0.6169601678848267, -0.7647668123245239, -0.14846350252628326, 0.11158157885074615],
+            'translation': [2219.965576171875, -5148.453125, 1613.0440673828125],
+        },
+        {
+            'orientation': [0.1471444070339203, -0.13377119600772858, 0.7670128345489502, -0.6100369691848755],
+            'translation': [-1571.2215576171875, 5137.0185546875, 1498.1761474609375],
+        },
+        {
+            'orientation': [0.5927824378013611, -0.7825870513916016, 0.12147816270589828, -0.14631995558738708],
+            'translation': [-1476.913330078125, -3896.7412109375, 1547.97216796875],
+        },
+    ],
+    'S9': [
+        {
+            'orientation': [0.15540587902069092, -0.15548215806484222, -0.7532095313072205, 0.6199594736099243],
+            'translation': [2044.45849609375, 4935.1171875, 1481.2275390625],
+        },
+        {
+            'orientation': [0.618784487247467, -0.7634735107421875, -0.14132238924503326, 0.11933968216180801],
+            'translation': [1990.959716796875, -5123.810546875, 1568.8048095703125],
+        },
+        {
+            'orientation': [0.13357827067375183, -0.1367100477218628, 0.7689454555511475, -0.6100738644599915],
+            'translation': [-1670.9921875, 5211.98583984375, 1528.387939453125],
+        },
+        {
+            'orientation': [0.5879399180412292, -0.7823407053947449, 0.1427614390850067, -0.14794869720935822],
+            'translation': [-1696.04345703125, -3827.099853515625, 1591.4127197265625],
+        },
+    ],
+    'S11': [
+        {
+            'orientation': [0.15232472121715546, -0.15442320704460144, -0.7547563314437866, 0.6191070079803467],
+            'translation': [2098.440185546875, 4926.5546875, 1500.278564453125],
+        },
+        {
+            'orientation': [0.6189449429512024, -0.7600917220115662, -0.15300633013248444, 0.1255258321762085],
+            'translation': [2083.182373046875, -4912.1728515625, 1561.07861328125],
+        },
+        {
+            'orientation': [0.14943228662014008, -0.15650227665901184, 0.7681233882904053, -0.6026304364204407],
+            'translation': [-1609.8153076171875, 5177.3359375, 1537.896728515625],
+        },
+        {
+            'orientation': [0.5894251465797424, -0.7818877100944519, 0.13991211354732513, -0.14715361595153809],
+            'translation': [-1590.738037109375, -3854.1689453125, 1578.017578125],
+        },
+    ],
+}
+
+
+class Human36mDataset(MocapDataset):
+    def __init__(self, path, remove_static_joints=True):
+        super().__init__(fps=50, skeleton=h36m_skeleton)
+
+        self._cameras = copy.deepcopy(h36m_cameras_extrinsic_params)
+        for cameras in self._cameras.values():
+            for i, cam in enumerate(cameras):
+                cam.update(h36m_cameras_intrinsic_params[i])
+                for k, v in cam.items():
+                    if k not in ['id', 'res_w', 'res_h']:
+                        cam[k] = np.array(v, dtype='float32')
+
+                # Normalize camera frame
+                cam['center'] = normalize_screen_coordinates(cam['center'], w=cam['res_w'], h=cam['res_h']).astype('float32')
+                cam['focal_length'] = cam['focal_length'] / cam['res_w'] * 2
+                if 'translation' in cam:
+                    cam['translation'] = cam['translation'] / 1000  # mm to meters
+
+                # Add intrinsic parameters vector
+                cam['intrinsic'] = np.concatenate((cam['focal_length'],
+                                                   cam['center'],
+                                                   cam['radial_distortion'],
+                                                   cam['tangential_distortion']))
+
+        # Load serialized dataset
+        data = np.load(path)['positions_3d'].item()
+
+        self._data = {}
+        for subject, actions in data.items():
+            self._data[subject] = {}
+            for action_name, positions in actions.items():
+                self._data[subject][action_name] = {
+                    'positions': positions,
+                    'cameras': self._cameras[subject],
+                }
+
+        #  import ipdb;ipdb.set_trace()
+        if remove_static_joints:
+            # Bring the skeleton to 17 joints instead of the original 32
+            self.remove_joints([4, 5, 9, 10, 11, 16, 20, 21, 22, 23, 24, 28, 29, 30, 31])
+
+            # Rewire shoulders to the correct parents
+            self._skeleton._parents[11] = 8
+            self._skeleton._parents[14] = 8
+
+    def supports_semi_supervised(self):
+        return True
diff --git a/common/humaneva_dataset.py b/common/humaneva_dataset.py
new file mode 100644
index 0000000000000000000000000000000000000000..5dbfe023e75af62a2326a4de5af6675776379ed3
--- /dev/null
+++ b/common/humaneva_dataset.py
@@ -0,0 +1,122 @@
+# Copyright (c) 2018-present, Facebook, Inc.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+#
+
+import copy
+
+import numpy as np
+
+from common.mocap_dataset import MocapDataset
+from common.skeleton import Skeleton
+
+humaneva_skeleton = Skeleton(parents=[-1, 0, 1, 2, 3, 1, 5, 6, 0, 8, 9, 0, 11, 12, 1],
+                             joints_left=[2, 3, 4, 8, 9, 10],
+                             joints_right=[5, 6, 7, 11, 12, 13])
+
+humaneva_cameras_intrinsic_params = [
+    {
+        'id': 'C1',
+        'res_w': 640,
+        'res_h': 480,
+        'azimuth': 0,  # Only used for visualization
+    },
+    {
+        'id': 'C2',
+        'res_w': 640,
+        'res_h': 480,
+        'azimuth': -90,  # Only used for visualization
+    },
+    {
+        'id': 'C3',
+        'res_w': 640,
+        'res_h': 480,
+        'azimuth': 90,  # Only used for visualization
+    },
+]
+
+humaneva_cameras_extrinsic_params = {
+    'S1': [
+        {
+            'orientation': [0.424207, -0.4983646, -0.5802981, 0.4847012],
+            'translation': [4062.227, 663.2477, 1528.397],
+        },
+        {
+            'orientation': [0.6503354, -0.7481602, -0.0919284, 0.0941766],
+            'translation': [844.8131, -3805.2092, 1504.9929],
+        },
+        {
+            'orientation': [0.0664734, -0.0690535, 0.7416416, -0.6639132],
+            'translation': [-797.67377, 3916.3174, 1433.6602],
+        },
+    ],
+    'S2': [
+        {
+            'orientation': [0.4214752, -0.4961493, -0.5838273, 0.4851187],
+            'translation': [4112.9121, 626.4929, 1545.2988],
+        },
+        {
+            'orientation': [0.6501393, -0.7476588, -0.0954617, 0.0959808],
+            'translation': [923.5740, -3877.9243, 1504.5518],
+        },
+        {
+            'orientation': [0.0699353, -0.0712403, 0.7421637, -0.662742],
+            'translation': [-781.4915, 3838.8853, 1444.9929],
+        },
+    ],
+    'S3': [
+        {
+            'orientation': [0.424207, -0.4983646, -0.5802981, 0.4847012],
+            'translation': [4062.2271, 663.2477, 1528.3970],
+        },
+        {
+            'orientation': [0.6503354, -0.7481602, -0.0919284, 0.0941766],
+            'translation': [844.8131, -3805.2092, 1504.9929],
+        },
+        {
+            'orientation': [0.0664734, -0.0690535, 0.7416416, -0.6639132],
+            'translation': [-797.6738, 3916.3174, 1433.6602],
+        },
+    ],
+    'S4': [
+        {},
+        {},
+        {},
+    ],
+
+}
+
+
+class HumanEvaDataset(MocapDataset):
+    def __init__(self, path):
+        super().__init__(fps=60, skeleton=humaneva_skeleton)
+
+        self._cameras = copy.deepcopy(humaneva_cameras_extrinsic_params)
+        for cameras in self._cameras.values():
+            for i, cam in enumerate(cameras):
+                cam.update(humaneva_cameras_intrinsic_params[i])
+                for k, v in cam.items():
+                    if k not in ['id', 'res_w', 'res_h']:
+                        cam[k] = np.array(v, dtype='float32')
+                if 'translation' in cam:
+                    cam['translation'] = cam['translation'] / 1000  # mm to meters
+
+        for subject in list(self._cameras.keys()):
+            data = self._cameras[subject]
+            del self._cameras[subject]
+            for prefix in ['Train/', 'Validate/', 'Unlabeled/Train/', 'Unlabeled/Validate/', 'Unlabeled/']:
+                self._cameras[prefix + subject] = data
+
+        # Load serialized dataset
+        data = np.load(path)['positions_3d'].item()
+
+        self._data = {}
+        for subject, actions in data.items():
+            self._data[subject] = {}
+            for action_name, positions in actions.items():
+                self._data[subject][action_name] = {
+                    'positions': positions,
+                    'cameras': self._cameras[subject],
+                }
diff --git a/common/inference_3d.py b/common/inference_3d.py
new file mode 100644
index 0000000000000000000000000000000000000000..a9a8da8e74306322a675b8c3b71e308816c6daee
--- /dev/null
+++ b/common/inference_3d.py
@@ -0,0 +1,107 @@
+# Copyright (c) 2018-present, Facebook, Inc.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+#
+import hashlib
+import os
+import pathlib
+import shutil
+import sys
+import time
+
+import cv2
+import numpy as np
+import torch
+from torch.autograd import Variable
+
+def get_varialbe(target):
+    num = len(target)
+    var = []
+
+    for i in range(num):
+        temp = Variable(target[i]).contiguous().cuda().type(torch.cuda.FloatTensor)
+        var.append(temp)
+
+    return var
+def input_augmentation(input_2D, input_2D_flip, model_trans, joints_left, joints_right):
+    B, T, J, C = input_2D.shape
+
+    input_2D_flip = input_2D_flip.view(B, T, J, C, 1).permute(0, 3, 1, 2, 4)
+    input_2D_non_flip = input_2D.view(B, T, J, C, 1).permute(0, 3, 1, 2, 4)
+
+    output_3D_flip, output_3D_flip_VTE = model_trans(input_2D_flip)
+
+    output_3D_flip_VTE[:, 0] *= -1
+    output_3D_flip[:, 0] *= -1
+
+    output_3D_flip_VTE[:, :, :, joints_left + joints_right] = output_3D_flip_VTE[:, :, :, joints_right + joints_left]
+    output_3D_flip[:, :, :, joints_left + joints_right] = output_3D_flip[:, :, :, joints_right + joints_left]
+
+    output_3D_non_flip, output_3D_non_flip_VTE = model_trans(input_2D_non_flip)
+
+    output_3D_VTE = (output_3D_non_flip_VTE + output_3D_flip_VTE) / 2
+    output_3D = (output_3D_non_flip + output_3D_flip) / 2
+
+    input_2D = input_2D_non_flip
+
+    return input_2D, output_3D, output_3D_VTE
+
+def step(opt, dataLoader, model, optimizer=None, epoch=None):
+    model_trans = model['trans']
+
+    model_trans.eval()
+
+    joints_left = [4, 5, 6, 11, 12, 13]
+    joints_right = [1, 2, 3, 14, 15, 16]
+    epoch_cnt=0
+    out = []
+    for _, batch, batch_2d, batch_2d_flip in dataLoader.next_epoch():
+        #[gt_3D, input_2D] = get_varialbe([batch, batch_2d])
+        #input_2D = Variable(batch_2d).contiguous().cuda().type(torch.cuda.FloatTensor)
+        input_2D = torch.from_numpy(batch_2d.astype('float32'))
+        input_2D_flip = torch.from_numpy(batch_2d_flip.astype('float32'))
+        if torch.cuda.is_available():
+            input_2D = input_2D.cuda()
+            input_2D_flip = input_2D_flip.cuda()
+
+        N = input_2D.size(0)
+
+        # out_target = gt_3D.clone().view(N, -1, opt.out_joints, opt.out_channels)
+        # out_target[:, :, 0] = 0
+        # gt_3D = gt_3D.view(N, -1, opt.out_joints, opt.out_channels).type(torch.cuda.FloatTensor)
+        #
+        # if out_target.size(1) > 1:
+        #     out_target_single = out_target[:, opt.pad].unsqueeze(1)
+        #     gt_3D_single = gt_3D[:, opt.pad].unsqueeze(1)
+        # else:
+        #     out_target_single = out_target
+        #     gt_3D_single = gt_3D
+
+
+        input_2D, output_3D, output_3D_VTE = input_augmentation(input_2D, input_2D_flip, model_trans, joints_left, joints_right)
+
+
+        output_3D_VTE = output_3D_VTE.permute(0, 2, 3, 4, 1).contiguous().view(N, -1, opt.out_joints, opt.out_channels)
+        output_3D = output_3D.permute(0, 2, 3, 4, 1).contiguous().view(N, -1, opt.out_joints, opt.out_channels)
+
+        output_3D_single = output_3D
+
+
+        pred_out = output_3D_single
+
+        input_2D = input_2D.permute(0, 2, 3, 1, 4).view(N, -1, opt.n_joints, 2)
+
+        pred_out[:, :, 0, :] = 0
+
+        if epoch_cnt == 0:
+            out = pred_out.squeeze(1).cpu()
+        else:
+            out = torch.cat((out, pred_out.squeeze(1).cpu()), dim=0)
+        epoch_cnt +=1
+    return out.numpy()
+
+def val(opt, val_loader, model):
+    with torch.no_grad():
+        return step(opt, val_loader, model)
\ No newline at end of file
diff --git a/common/jpt_arguments.py b/common/jpt_arguments.py
new file mode 100644
index 0000000000000000000000000000000000000000..f56b9b3907062693da5835c5047e3cbebb39542a
--- /dev/null
+++ b/common/jpt_arguments.py
@@ -0,0 +1,92 @@
+# Copyright (c) 2018-present, Facebook, Inc.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+#
+
+import argparse
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(description='Training script')
+
+    # General arguments
+    parser.add_argument('-d', '--dataset', default='h36m', type=str, metavar='NAME', help='target dataset')  # h36m or humaneva
+    parser.add_argument('-k', '--keypoints', default='cpn_ft_h36m_dbb', type=str, metavar='NAME', help='2D detections to use')
+    parser.add_argument('-str', '--subjects-train', default='S1,S5,S6,S7,S8', type=str, metavar='LIST',
+                        help='training subjects separated by comma')
+    parser.add_argument('-ste', '--subjects-test', default='S9,S11', type=str, metavar='LIST', help='test subjects separated by comma')
+    parser.add_argument('-sun', '--subjects-unlabeled', default='', type=str, metavar='LIST',
+                        help='unlabeled subjects separated by comma for self-supervision')
+    parser.add_argument('-a', '--actions', default='*', type=str, metavar='LIST',
+                        help='actions to train/test on, separated by comma, or * for all')
+    parser.add_argument('-c', '--checkpoint', default='checkpoint', type=str, metavar='PATH',
+                        help='checkpoint directory')
+    parser.add_argument('--checkpoint-frequency', default=10, type=int, metavar='N',
+                        help='create a checkpoint every N epochs')
+    parser.add_argument('-r', '--resume', default='', type=str, metavar='FILENAME',
+                        help='checkpoint to resume (file name)')
+    parser.add_argument('--evaluate', default='pretrained_h36m_detectron_coco.bin', type=str, metavar='FILENAME', help='checkpoint to evaluate (file name)')
+    parser.add_argument('--render', action='store_true', help='visualize a particular video')
+    parser.add_argument('--by-subject', action='store_true', help='break down error by subject (on evaluation)')
+    parser.add_argument('--export-training-curves', action='store_true', help='save training curves as .png images')
+
+    # Model arguments
+    parser.add_argument('-s', '--stride', default=1, type=int, metavar='N', help='chunk size to use during training')
+    parser.add_argument('-e', '--epochs', default=60, type=int, metavar='N', help='number of training epochs')
+    parser.add_argument('-b', '--batch-size', default=1024, type=int, metavar='N', help='batch size in terms of predicted frames')
+    parser.add_argument('-drop', '--dropout', default=0.25, type=float, metavar='P', help='dropout probability')
+    parser.add_argument('-lr', '--learning-rate', default=0.001, type=float, metavar='LR', help='initial learning rate')
+    parser.add_argument('-lrd', '--lr-decay', default=0.95, type=float, metavar='LR', help='learning rate decay per epoch')
+    parser.add_argument('-no-da', '--no-data-augmentation', dest='data_augmentation', action='store_false',
+                        help='disable train-time flipping')
+    parser.add_argument('-no-tta', '--no-test-time-augmentation', dest='test_time_augmentation', action='store_false',
+                        help='disable test-time flipping')
+    parser.add_argument('-arc', '--architecture', default='3,3,3,3,3', type=str, metavar='LAYERS', help='filter widths separated by comma')
+    parser.add_argument('--causal', action='store_true', help='use causal convolutions for real-time processing')
+    parser.add_argument('-ch', '--channels', default=1024, type=int, metavar='N', help='number of channels in convolution layers')
+
+    # Experimental
+    parser.add_argument('--subset', default=1, type=float, metavar='FRACTION', help='reduce dataset size by fraction')
+    parser.add_argument('--downsample', default=1, type=int, metavar='FACTOR', help='downsample frame rate by factor (semi-supervised)')
+    parser.add_argument('--warmup', default=1, type=int, metavar='N', help='warm-up epochs for semi-supervision')
+    parser.add_argument('--no-eval', action='store_true', help='disable epoch evaluation while training (small speed-up)')
+    parser.add_argument('--dense', action='store_true', help='use dense convolutions instead of dilated convolutions')
+    parser.add_argument('--disable-optimizations', action='store_true', help='disable optimized model for single-frame predictions')
+    parser.add_argument('--linear-projection', action='store_true', help='use only linear coefficients for semi-supervised projection')
+    parser.add_argument('--no-bone-length', action='store_false', dest='bone_length_term',
+                        help='disable bone length term in semi-supervised settings')
+    parser.add_argument('--no-proj', action='store_true', help='disable projection for semi-supervised setting')
+
+    # Visualization
+    parser.add_argument('--viz-subject', type=str, metavar='STR', help='subject to render')
+    parser.add_argument('--viz-action', type=str, metavar='STR', help='action to render')
+    parser.add_argument('--viz-camera', type=int, default=0, metavar='N', help='camera to render')
+    parser.add_argument('--viz-video', type=str, metavar='PATH', help='path to input video')
+    parser.add_argument('--viz-skip', type=int, default=0, metavar='N', help='skip first N frames of input video')
+    parser.add_argument('--viz-output', type=str, metavar='PATH', help='output file name (.gif or .mp4)')
+    parser.add_argument('--viz-bitrate', type=int, default=30000, metavar='N', help='bitrate for mp4 videos')
+    parser.add_argument('--viz-no-ground-truth', action='store_true', help='do not show ground-truth poses')
+    parser.add_argument('--viz-limit', type=int, default=-1, metavar='N', help='only render first N frames')
+    parser.add_argument('--viz-downsample', type=int, default=1, metavar='N', help='downsample FPS by a factor N')
+    parser.add_argument('--viz-size', type=int, default=5, metavar='N', help='image size')
+    # self add
+    parser.add_argument('--input-npz', dest='input_npz', type=str, default='', help='input 2d numpy file')
+
+    parser.set_defaults(bone_length_term=True)
+    parser.set_defaults(data_augmentation=True)
+    parser.set_defaults(test_time_augmentation=True)
+
+    args = parser.parse_args(args=[])
+    # Check invalid configuration
+    if args.resume and args.evaluate:
+        print('Invalid flags: --resume and --evaluate cannot be set at the same time')
+        exit()
+
+    if args.export_training_curves and args.no_eval:
+        print('Invalid flags: --export-training-curves and --no-eval cannot be set at the same time')
+        exit()
+
+    #  opt = parser.parse_args(args=[])
+    return args
diff --git a/common/loss.py b/common/loss.py
new file mode 100644
index 0000000000000000000000000000000000000000..12e5f437f89a137eff7580f12e37677c7caf797d
--- /dev/null
+++ b/common/loss.py
@@ -0,0 +1,94 @@
+# Copyright (c) 2018-present, Facebook, Inc.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+#
+
+import numpy as np
+import torch
+
+
+def mpjpe(predicted, target):
+    """
+    Mean per-joint position error (i.e. mean Euclidean distance),
+    often referred to as "Protocol #1" in many papers.
+    """
+    assert predicted.shape == target.shape
+    return torch.mean(torch.norm(predicted - target, dim=len(target.shape) - 1))
+
+
+def weighted_mpjpe(predicted, target, w):
+    """
+    Weighted mean per-joint position error (i.e. mean Euclidean distance)
+    """
+    assert predicted.shape == target.shape
+    assert w.shape[0] == predicted.shape[0]
+    return torch.mean(w * torch.norm(predicted - target, dim=len(target.shape) - 1))
+
+
+def p_mpjpe(predicted, target):
+    """
+    Pose error: MPJPE after rigid alignment (scale, rotation, and translation),
+    often referred to as "Protocol #2" in many papers.
+    """
+    assert predicted.shape == target.shape
+
+    muX = np.mean(target, axis=1, keepdims=True)
+    muY = np.mean(predicted, axis=1, keepdims=True)
+
+    X0 = target - muX
+    Y0 = predicted - muY
+
+    normX = np.sqrt(np.sum(X0 ** 2, axis=(1, 2), keepdims=True))
+    normY = np.sqrt(np.sum(Y0 ** 2, axis=(1, 2), keepdims=True))
+
+    X0 /= normX
+    Y0 /= normY
+
+    H = np.matmul(X0.transpose(0, 2, 1), Y0)
+    U, s, Vt = np.linalg.svd(H)
+    V = Vt.transpose(0, 2, 1)
+    R = np.matmul(V, U.transpose(0, 2, 1))
+
+    # Avoid improper rotations (reflections), i.e. rotations with det(R) = -1
+    sign_detR = np.sign(np.expand_dims(np.linalg.det(R), axis=1))
+    V[:, :, -1] *= sign_detR
+    s[:, -1] *= sign_detR.flatten()
+    R = np.matmul(V, U.transpose(0, 2, 1))  # Rotation
+
+    tr = np.expand_dims(np.sum(s, axis=1, keepdims=True), axis=2)
+
+    a = tr * normX / normY  # Scale
+    t = muX - a * np.matmul(muY, R)  # Translation
+
+    # Perform rigid transformation on the input
+    predicted_aligned = a * np.matmul(predicted, R) + t
+
+    # Return MPJPE
+    return np.mean(np.linalg.norm(predicted_aligned - target, axis=len(target.shape) - 1))
+
+
+def n_mpjpe(predicted, target):
+    """
+    Normalized MPJPE (scale only), adapted from:
+    https://github.com/hrhodin/UnsupervisedGeometryAwareRepresentationLearning/blob/master/losses/poses.py
+    """
+    assert predicted.shape == target.shape
+
+    norm_predicted = torch.mean(torch.sum(predicted ** 2, dim=3, keepdim=True), dim=2, keepdim=True)
+    norm_target = torch.mean(torch.sum(target * predicted, dim=3, keepdim=True), dim=2, keepdim=True)
+    scale = norm_target / norm_predicted
+    return mpjpe(scale * predicted, target)
+
+
+def mean_velocity_error(predicted, target):
+    """
+    Mean per-joint velocity error (i.e. mean Euclidean distance of the 1st derivative)
+    """
+    assert predicted.shape == target.shape
+
+    velocity_predicted = np.diff(predicted, axis=0)
+    velocity_target = np.diff(target, axis=0)
+
+    return np.mean(np.linalg.norm(velocity_predicted - velocity_target, axis=len(target.shape) - 1))
diff --git a/common/mocap_dataset.py b/common/mocap_dataset.py
new file mode 100644
index 0000000000000000000000000000000000000000..1697e5b9659639b813d58fa4435fe957ead721cc
--- /dev/null
+++ b/common/mocap_dataset.py
@@ -0,0 +1,40 @@
+# Copyright (c) 2018-present, Facebook, Inc.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+#
+
+
+class MocapDataset:
+    def __init__(self, fps, skeleton):
+        self._skeleton = skeleton
+        self._fps = fps
+        self._data = None  # Must be filled by subclass
+        self._cameras = None  # Must be filled by subclass
+
+    def remove_joints(self, joints_to_remove):
+        kept_joints = self._skeleton.remove_joints(joints_to_remove)
+        for subject in self._data.keys():
+            for action in self._data[subject].keys():
+                s = self._data[subject][action]
+                s['positions'] = s['positions'][:, kept_joints]
+
+    def __getitem__(self, key):
+        return self._data[key]
+
+    def subjects(self):
+        return self._data.keys()
+
+    def fps(self):
+        return self._fps
+
+    def skeleton(self):
+        return self._skeleton
+
+    def cameras(self):
+        return self._cameras
+
+    def supports_semi_supervised(self):
+        # This method can be overridden
+        return False
diff --git a/common/model.py b/common/model.py
new file mode 100644
index 0000000000000000000000000000000000000000..8be5a30e9847cdf7a93403c1a5f8a4e497946829
--- /dev/null
+++ b/common/model.py
@@ -0,0 +1,200 @@
+# Copyright (c) 2018-present, Facebook, Inc.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+#
+
+import torch.nn as nn
+
+
+class TemporalModelBase(nn.Module):
+    """
+    Do not instantiate this class.
+    """
+
+    def __init__(self, num_joints_in, in_features, num_joints_out,
+                 filter_widths, causal, dropout, channels):
+        super().__init__()
+
+        # Validate input
+        for fw in filter_widths:
+            assert fw % 2 != 0, 'Only odd filter widths are supported'
+
+        self.num_joints_in = num_joints_in
+        self.in_features = in_features
+        self.num_joints_out = num_joints_out
+        self.filter_widths = filter_widths
+
+        self.drop = nn.Dropout(dropout)
+        self.relu = nn.ReLU(inplace=True)
+
+        self.pad = [filter_widths[0] // 2]
+        self.expand_bn = nn.BatchNorm1d(channels, momentum=0.1)
+        self.shrink = nn.Conv1d(channels, num_joints_out * 3, 1)
+
+    def set_bn_momentum(self, momentum):
+        self.expand_bn.momentum = momentum
+        for bn in self.layers_bn:
+            bn.momentum = momentum
+
+    def receptive_field(self):
+        """
+        Return the total receptive field of this model as # of frames.
+        """
+        frames = 0
+        for f in self.pad:
+            frames += f
+        return 1 + 2 * frames
+
+    def total_causal_shift(self):
+        """
+        Return the asymmetric offset for sequence padding.
+        The returned value is typically 0 if causal convolutions are disabled,
+        otherwise it is half the receptive field.
+        """
+        frames = self.causal_shift[0]
+        next_dilation = self.filter_widths[0]
+        for i in range(1, len(self.filter_widths)):
+            frames += self.causal_shift[i] * next_dilation
+            next_dilation *= self.filter_widths[i]
+        return frames
+
+    def forward(self, x):
+        assert len(x.shape) == 4
+        assert x.shape[-2] == self.num_joints_in
+        assert x.shape[-1] == self.in_features
+
+        sz = x.shape[:3]
+        x = x.view(x.shape[0], x.shape[1], -1)
+        x = x.permute(0, 2, 1)
+
+        x = self._forward_blocks(x)
+
+        x = x.permute(0, 2, 1)
+        x = x.view(sz[0], -1, self.num_joints_out, 3)
+
+        return x
+
+
+class TemporalModel(TemporalModelBase):
+    """
+    Reference 3D pose estimation model with temporal convolutions.
+    This implementation can be used for all use-cases.
+    """
+
+    def __init__(self, num_joints_in, in_features, num_joints_out,
+                 filter_widths, causal=False, dropout=0.25, channels=1024, dense=False):
+        """
+        Initialize this model.
+
+        Arguments:
+        num_joints_in -- number of input joints (e.g. 17 for Human3.6M)
+        in_features -- number of input features for each joint (typically 2 for 2D input)
+        num_joints_out -- number of output joints (can be different than input)
+        filter_widths -- list of convolution widths, which also determines the # of blocks and receptive field
+        causal -- use causal convolutions instead of symmetric convolutions (for real-time applications)
+        dropout -- dropout probability
+        channels -- number of convolution channels
+        dense -- use regular dense convolutions instead of dilated convolutions (ablation experiment)
+        """
+        super().__init__(num_joints_in, in_features, num_joints_out, filter_widths, causal, dropout, channels)
+
+        self.expand_conv = nn.Conv1d(num_joints_in * in_features, channels, filter_widths[0], bias=False)
+
+        layers_conv = []
+        layers_bn = []
+
+        self.causal_shift = [(filter_widths[0]) // 2 if causal else 0]
+        next_dilation = filter_widths[0]
+        for i in range(1, len(filter_widths)):
+            self.pad.append((filter_widths[i] - 1) * next_dilation // 2)
+            self.causal_shift.append((filter_widths[i] // 2 * next_dilation) if causal else 0)
+
+            layers_conv.append(nn.Conv1d(channels, channels,
+                                         filter_widths[i] if not dense else (2 * self.pad[-1] + 1),
+                                         dilation=next_dilation if not dense else 1,
+                                         bias=False))
+            layers_bn.append(nn.BatchNorm1d(channels, momentum=0.1))
+            layers_conv.append(nn.Conv1d(channels, channels, 1, dilation=1, bias=False))
+            layers_bn.append(nn.BatchNorm1d(channels, momentum=0.1))
+
+            next_dilation *= filter_widths[i]
+
+        self.layers_conv = nn.ModuleList(layers_conv)
+        self.layers_bn = nn.ModuleList(layers_bn)
+
+    def _forward_blocks(self, x):
+        x = self.drop(self.relu(self.expand_bn(self.expand_conv(x))))
+
+        for i in range(len(self.pad) - 1):
+            pad = self.pad[i + 1]
+            shift = self.causal_shift[i + 1]
+            # clip
+            res = x[:, :, pad + shift: x.shape[2] - pad + shift]
+
+            x = self.drop(self.relu(self.layers_bn[2 * i](self.layers_conv[2 * i](x))))
+            x = res + self.drop(self.relu(self.layers_bn[2 * i + 1](self.layers_conv[2 * i + 1](x))))
+
+        x = self.shrink(x)
+        return x
+
+
+class TemporalModelOptimized1f(TemporalModelBase):
+    """
+    3D pose estimation model optimized for single-frame batching, i.e.
+    where batches have input length = receptive field, and output length = 1.
+    This scenario is only used for training when stride == 1.
+
+    This implementation replaces dilated convolutions with strided convolutions
+    to avoid generating unused intermediate results. The weights are interchangeable
+    with the reference implementation.
+    """
+
+    def __init__(self, num_joints_in, in_features, num_joints_out,
+                 filter_widths, causal=False, dropout=0.25, channels=1024):
+        """
+        Initialize this model.
+
+        Arguments:
+        num_joints_in -- number of input joints (e.g. 17 for Human3.6M)
+        in_features -- number of input features for each joint (typically 2 for 2D input)
+        num_joints_out -- number of output joints (can be different than input)
+        filter_widths -- list of convolution widths, which also determines the # of blocks and receptive field
+        causal -- use causal convolutions instead of symmetric convolutions (for real-time applications)
+        dropout -- dropout probability
+        channels -- number of convolution channels
+        """
+        super().__init__(num_joints_in, in_features, num_joints_out, filter_widths, causal, dropout, channels)
+
+        self.expand_conv = nn.Conv1d(num_joints_in * in_features, channels, filter_widths[0], stride=filter_widths[0], bias=False)
+
+        layers_conv = []
+        layers_bn = []
+
+        self.causal_shift = [(filter_widths[0] // 2) if causal else 0]
+        next_dilation = filter_widths[0]
+        for i in range(1, len(filter_widths)):
+            self.pad.append((filter_widths[i] - 1) * next_dilation // 2)
+            self.causal_shift.append((filter_widths[i] // 2) if causal else 0)
+
+            layers_conv.append(nn.Conv1d(channels, channels, filter_widths[i], stride=filter_widths[i], bias=False))
+            layers_bn.append(nn.BatchNorm1d(channels, momentum=0.1))
+            layers_conv.append(nn.Conv1d(channels, channels, 1, dilation=1, bias=False))
+            layers_bn.append(nn.BatchNorm1d(channels, momentum=0.1))
+            next_dilation *= filter_widths[i]
+
+        self.layers_conv = nn.ModuleList(layers_conv)
+        self.layers_bn = nn.ModuleList(layers_bn)
+
+    def _forward_blocks(self, x):
+        x = self.drop(self.relu(self.expand_bn(self.expand_conv(x))))
+
+        for i in range(len(self.pad) - 1):
+            res = x[:, :, self.causal_shift[i + 1] + self.filter_widths[i + 1] // 2:: self.filter_widths[i + 1]]
+
+            x = self.drop(self.relu(self.layers_bn[2 * i](self.layers_conv[2 * i](x))))
+            x = res + self.drop(self.relu(self.layers_bn[2 * i + 1](self.layers_conv[2 * i + 1](x))))
+
+        x = self.shrink(x)
+        return x
diff --git a/common/quaternion.py b/common/quaternion.py
new file mode 100644
index 0000000000000000000000000000000000000000..f8d700f358bfaf2d453a2a403a611e88d117595e
--- /dev/null
+++ b/common/quaternion.py
@@ -0,0 +1,36 @@
+# Copyright (c) 2018-present, Facebook, Inc.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+#
+
+import torch
+
+
+def qrot(q, v):
+    """
+    Rotate vector(s) v about the rotation described by 四元数quaternion(s) q.
+    Expects a tensor of shape (*, 4) for q and a tensor of shape (*, 3) for v,
+    where * denotes any number of dimensions.
+    Returns a tensor of shape (*, 3).
+    """
+    assert q.shape[-1] == 4
+    assert v.shape[-1] == 3
+    assert q.shape[:-1] == v.shape[:-1]
+
+    qvec = q[..., 1:]
+    uv = torch.cross(qvec, v, dim=len(q.shape) - 1)
+    uuv = torch.cross(qvec, uv, dim=len(q.shape) - 1)
+    return (v + 2 * (q[..., :1] * uv + uuv))
+
+
+def qinverse(q, inplace=False):
+    # We assume the quaternion to be normalized
+    if inplace:
+        q[..., 1:] *= -1
+        return q
+    else:
+        w = q[..., :1]
+        xyz = q[..., 1:]
+        return torch.cat((w, -xyz), dim=len(q.shape) - 1)
diff --git a/common/skeleton.py b/common/skeleton.py
new file mode 100644
index 0000000000000000000000000000000000000000..f8e33f2dfb3b12a6969b457b410c6e4637c9a4ec
--- /dev/null
+++ b/common/skeleton.py
@@ -0,0 +1,88 @@
+# Copyright (c) 2018-present, Facebook, Inc.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+#
+
+import numpy as np
+
+
+class Skeleton:
+    def __init__(self, parents, joints_left, joints_right):
+        assert len(joints_left) == len(joints_right)
+
+        self._parents = np.array(parents)
+        self._joints_left = joints_left
+        self._joints_right = joints_right
+        self._compute_metadata()
+
+    def num_joints(self):
+        return len(self._parents)
+
+    def parents(self):
+        return self._parents
+
+    def has_children(self):
+        return self._has_children
+
+    def children(self):
+        return self._children
+
+    def remove_joints(self, joints_to_remove):
+        """
+        Remove the joints specified in 'joints_to_remove'.
+        """
+        valid_joints = []
+        for joint in range(len(self._parents)):
+            if joint not in joints_to_remove:
+                valid_joints.append(joint)
+
+        for i in range(len(self._parents)):
+            while self._parents[i] in joints_to_remove:
+                self._parents[i] = self._parents[self._parents[i]]
+
+        index_offsets = np.zeros(len(self._parents), dtype=int)
+        new_parents = []
+        for i, parent in enumerate(self._parents):
+            if i not in joints_to_remove:
+                new_parents.append(parent - index_offsets[parent])
+            else:
+                index_offsets[i:] += 1
+        self._parents = np.array(new_parents)
+
+        if self._joints_left is not None:
+            new_joints_left = []
+            for joint in self._joints_left:
+                if joint in valid_joints:
+                    new_joints_left.append(joint - index_offsets[joint])
+            self._joints_left = new_joints_left
+        if self._joints_right is not None:
+            new_joints_right = []
+            for joint in self._joints_right:
+                if joint in valid_joints:
+                    new_joints_right.append(joint - index_offsets[joint])
+            self._joints_right = new_joints_right
+
+        self._compute_metadata()
+
+        return valid_joints
+
+    def joints_left(self):
+        return self._joints_left
+
+    def joints_right(self):
+        return self._joints_right
+
+    def _compute_metadata(self):
+        self._has_children = np.zeros(len(self._parents)).astype(bool)
+        for i, parent in enumerate(self._parents):
+            if parent != -1:
+                self._has_children[parent] = True
+
+        self._children = []
+        for i, parent in enumerate(self._parents):
+            self._children.append([])
+        for i, parent in enumerate(self._parents):
+            if parent != -1:
+                self._children[parent].append(i)
diff --git a/common/utils.py b/common/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..e32f0669b8127a5632c13d649b856096bf028de3
--- /dev/null
+++ b/common/utils.py
@@ -0,0 +1,202 @@
+# Copyright (c) 2018-present, Facebook, Inc.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+#
+import hashlib
+import os
+import pathlib
+import shutil
+import sys
+import time
+
+import cv2
+import numpy as np
+import torch
+
+
+def add_path():
+    Alphapose_path = os.path.abspath('joints_detectors/Alphapose')
+    hrnet_path = os.path.abspath('joints_detectors/hrnet')
+    trackers_path = os.path.abspath('pose_trackers')
+    paths = filter(lambda p: p not in sys.path, [Alphapose_path, hrnet_path, trackers_path])
+
+    sys.path.extend(paths)
+
+
+def wrap(func, *args, unsqueeze=False):
+    """
+    Wrap a torch function so it can be called with NumPy arrays.
+    Input and return types are seamlessly converted.
+    """
+
+    # Convert input types where applicable
+    args = list(args)
+    for i, arg in enumerate(args):
+        if type(arg) == np.ndarray:
+            args[i] = torch.from_numpy(arg)
+            if unsqueeze:
+                args[i] = args[i].unsqueeze(0)
+
+    result = func(*args)
+
+    # Convert output types where applicable
+    if isinstance(result, tuple):
+        result = list(result)
+        for i, res in enumerate(result):
+            if type(res) == torch.Tensor:
+                if unsqueeze:
+                    res = res.squeeze(0)
+                result[i] = res.numpy()
+        return tuple(result)
+    elif type(result) == torch.Tensor:
+        if unsqueeze:
+            result = result.squeeze(0)
+        return result.numpy()
+    else:
+        return result
+
+
+def deterministic_random(min_value, max_value, data):
+    digest = hashlib.sha256(data.encode()).digest()
+    raw_value = int.from_bytes(digest[:4], byteorder='little', signed=False)
+    return int(raw_value / (2 ** 32 - 1) * (max_value - min_value)) + min_value
+
+
+def alpha_map(prediction):
+    p_min, p_max = prediction.min(), prediction.max()
+
+    k = 1.6 / (p_max - p_min)
+    b = 0.8 - k * p_max
+
+    prediction = k * prediction + b
+
+    return prediction
+
+
+def change_score(prediction, detectron_detection_path):
+    detectron_predictions = np.load(detectron_detection_path, allow_pickle=True)['positions_2d'].item()
+    pose = detectron_predictions['S1']['Directions 1']
+    prediction[..., 2] = pose[..., 2]
+
+    return prediction
+
+
+class Timer:
+    def __init__(self, message, show=True):
+        self.message = message
+        self.elapsed = 0
+        self.show = show
+
+    def __enter__(self):
+        self.start = time.perf_counter()
+
+    def __exit__(self, exc_type, exc_val, exc_tb):
+        if self.show:
+            print(f'{self.message} --- elapsed time: {time.perf_counter() - self.start} s')
+
+
+def calculate_area(data):
+    """
+    Get the rectangle area of keypoints.
+    :param data: AlphaPose json keypoint format([x, y, score, ... , x, y, score]) or AlphaPose result keypoint format([[x, y], ..., [x, y]])
+    :return: area
+    """
+    data = np.array(data)
+
+    if len(data.shape) == 1:
+        data = np.reshape(data, (-1, 3))
+
+    width = min(data[:, 0]) - max(data[:, 0])
+    height = min(data[:, 1]) - max(data[:, 1])
+
+    return np.abs(width * height)
+
+
+def read_video(filename, fps=None, skip=0, limit=-1):
+    stream = cv2.VideoCapture(filename)
+
+    i = 0
+    while True:
+        grabbed, frame = stream.read()
+        # if the `grabbed` boolean is `False`, then we have
+        # reached the end of the video file
+        if not grabbed:
+            print('===========================> This video get ' + str(i) + ' frames in total.')
+            sys.stdout.flush()
+            break
+
+        i += 1
+        if i > skip:
+            frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+            yield np.array(frame)
+        if i == limit:
+            break
+
+
+def split_video(video_path):
+    stream = cv2.VideoCapture(video_path)
+
+    output_dir = os.path.dirname(video_path)
+    video_name = os.path.basename(video_path)
+    video_name = video_name[:video_name.rfind('.')]
+
+    save_folder = pathlib.Path(f'./{output_dir}/alpha_pose_{video_name}/split_image/')
+    shutil.rmtree(str(save_folder), ignore_errors=True)
+    save_folder.mkdir(parents=True, exist_ok=True)
+
+    total_frames = int(stream.get(cv2.CAP_PROP_FRAME_COUNT))
+    length = len(str(total_frames)) + 1
+
+    i = 1
+    while True:
+        grabbed, frame = stream.read()
+
+        if not grabbed:
+            print(f'Split totally {i + 1} images from video.')
+            break
+
+        save_path = f'{save_folder}/output{str(i).zfill(length)}.png'
+        cv2.imwrite(save_path, frame)
+
+        i += 1
+
+    saved_path = os.path.dirname(save_path)
+    print(f'Split images saved in {saved_path}')
+
+    return saved_path
+
+
+def evaluate(test_generator, model_pos, action=None, return_predictions=False):
+    """
+    Inference the 3d positions from 2d position.
+    :type test_generator: UnchunkedGenerator
+    :param test_generator:
+    :param model_pos: 3d pose model
+    :param return_predictions: return predictions if true
+    :return:
+    """
+    joints_left, joints_right = list([4, 5, 6, 11, 12, 13]), list([1, 2, 3, 14, 15, 16])
+    with torch.no_grad():
+        model_pos.eval()
+        N = 0
+        for _, batch, batch_2d in test_generator.next_epoch():
+            inputs_2d = torch.from_numpy(batch_2d.astype('float32'))
+            if torch.cuda.is_available():
+                inputs_2d = inputs_2d.cuda()
+            # Positional model
+            predicted_3d_pos = model_pos(inputs_2d)
+            if test_generator.augment_enabled():
+                # Undo flipping and take average with non-flipped version
+                predicted_3d_pos[1, :, :, 0] *= -1
+                predicted_3d_pos[1, :, joints_left + joints_right] = predicted_3d_pos[1, :, joints_right + joints_left]
+                predicted_3d_pos = torch.mean(predicted_3d_pos, dim=0, keepdim=True)
+            if return_predictions:
+                return predicted_3d_pos.squeeze(0).cpu().numpy()
+
+
+if __name__ == '__main__':
+    os.chdir('..')
+
+    split_video('outputs/kobe.mp4')
diff --git a/common/visualization.py b/common/visualization.py
new file mode 100644
index 0000000000000000000000000000000000000000..301fa090f87167f1ef8c3d4f84f47ba17a4ab2b0
--- /dev/null
+++ b/common/visualization.py
@@ -0,0 +1,251 @@
+# Copyright (c) 2018-present, Facebook, Inc.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+#
+
+import time
+
+import cv2
+import matplotlib.pyplot as plt
+import numpy as np
+from matplotlib.animation import FuncAnimation, writers
+from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas
+from mpl_toolkits.mplot3d import Axes3D
+from tqdm import tqdm
+
+from common.utils import read_video
+
+
+def ckpt_time(ckpt=None, display=0, desc=''):
+    if not ckpt:
+        return time.time()
+    else:
+        if display:
+            print(desc + ' consume time {:0.4f}'.format(time.time() - float(ckpt)))
+        return time.time() - float(ckpt), time.time()
+
+
+def set_equal_aspect(ax, data):
+    """
+    Create white cubic bounding box to make sure that 3d axis is in equal aspect.
+    :param ax: 3D axis
+    :param data: shape of(frames, 3), generated from BVH using convert_bvh2dataset.py
+    """
+    X, Y, Z = data[..., 0], data[..., 1], data[..., 2]
+
+    # Create cubic bounding box to simulate equal aspect ratio
+    max_range = np.array([X.max() - X.min(), Y.max() - Y.min(), Z.max() - Z.min()]).max()
+    Xb = 0.5 * max_range * np.mgrid[-1:2:2, -1:2:2, -1:2:2][0].flatten() + 0.5 * (X.max() + X.min())
+    Yb = 0.5 * max_range * np.mgrid[-1:2:2, -1:2:2, -1:2:2][1].flatten() + 0.5 * (Y.max() + Y.min())
+    Zb = 0.5 * max_range * np.mgrid[-1:2:2, -1:2:2, -1:2:2][2].flatten() + 0.5 * (Z.max() + Z.min())
+
+    for xb, yb, zb in zip(Xb, Yb, Zb):
+        ax.plot([xb], [yb], [zb], 'w')
+
+
+def downsample_tensor(X, factor):
+    length = X.shape[0] // factor * factor
+    return np.mean(X[:length].reshape(-1, factor, *X.shape[1:]), axis=1)
+
+
+def render_animation(keypoints, poses, skeleton, fps, bitrate, azim, output, viewport,
+                     limit=-1, downsample=1, size=6, input_video_path=None, input_video_skip=0):
+    """
+    TODO
+    Render an animation. The supported output modes are:
+     -- 'interactive': display an interactive figure
+                       (also works on notebooks if associated with %matplotlib inline)
+     -- 'html': render the animation as HTML5 video. Can be displayed in a notebook using HTML(...).
+     -- 'filename.mp4': render and export the animation as an h264 video (requires ffmpeg).
+     -- 'filename.gif': render and export the animation a gif file (requires imagemagick).
+    """
+    plt.ioff()
+    fig = plt.figure(figsize=(size * (1 + len(poses)), size))
+    ax_in = fig.add_subplot(1, 1 + len(poses), 1)
+    ax_in.get_xaxis().set_visible(False)
+    ax_in.get_yaxis().set_visible(False)
+    ax_in.set_axis_off()
+    ax_in.set_title('Input')
+
+    # prevent wired error
+    _ = Axes3D.__class__.__name__
+
+    ax_3d = []
+    lines_3d = []
+    trajectories = []
+    radius = 1.7
+    for index, (title, data) in enumerate(poses.items()):
+        ax = fig.add_subplot(1, 1 + len(poses), index + 2, projection='3d')
+        ax.view_init(elev=15., azim=azim)
+        ax.set_xlim3d([-radius / 2, radius / 2])
+        ax.set_zlim3d([0, radius])
+        ax.set_ylim3d([-radius / 2, radius / 2])
+        # ax.set_aspect('equal')
+        ax.set_xticklabels([])
+        ax.set_yticklabels([])
+        ax.set_zticklabels([])
+        ax.dist = 12.5
+        ax.set_title(title)  # , pad=35
+        ax_3d.append(ax)
+        lines_3d.append([])
+        trajectories.append(data[:, 0, [0, 1]])
+    poses = list(poses.values())
+
+    # Decode video
+    if input_video_path is None:
+        # Black background
+        all_frames = np.zeros((keypoints.shape[0], viewport[1], viewport[0]), dtype='uint8')
+    else:
+        # Load video using ffmpeg
+        all_frames = []
+        for f in read_video(input_video_path, fps=None, skip=input_video_skip):
+            all_frames.append(f)
+
+        effective_length = min(keypoints.shape[0], len(all_frames))
+        all_frames = all_frames[:effective_length]
+
+    if downsample > 1:
+        keypoints = downsample_tensor(keypoints, downsample)
+        all_frames = downsample_tensor(np.array(all_frames), downsample).astype('uint8')
+        for idx in range(len(poses)):
+            poses[idx] = downsample_tensor(poses[idx], downsample)
+            trajectories[idx] = downsample_tensor(trajectories[idx], downsample)
+        fps /= downsample
+
+    initialized = False
+    image = None
+    lines = []
+    points = None
+
+    if limit < 1:
+        limit = len(all_frames)
+    else:
+        limit = min(limit, len(all_frames))
+
+    parents = skeleton.parents()
+    pbar = tqdm(total=limit)
+
+    def update_video(i):
+        nonlocal initialized, image, lines, points
+
+        for n, ax in enumerate(ax_3d):
+            ax.set_xlim3d([-radius / 2 + trajectories[n][i, 0], radius / 2 + trajectories[n][i, 0]])
+            ax.set_ylim3d([-radius / 2 + trajectories[n][i, 1], radius / 2 + trajectories[n][i, 1]])
+
+        # Update 2D poses
+        if not initialized:
+            image = ax_in.imshow(all_frames[i], aspect='equal')
+
+            for j, j_parent in enumerate(parents):
+                if j_parent == -1:
+                    continue
+
+                # if len(parents) == keypoints.shape[1] and 1 == 2:
+                #     # Draw skeleton only if keypoints match (otherwise we don't have the parents definition)
+                #     lines.append(ax_in.plot([keypoints[i, j, 0], keypoints[i, j_parent, 0]],
+                #                             [keypoints[i, j, 1], keypoints[i, j_parent, 1]], color='pink'))
+
+                col = 'red' if j in skeleton.joints_right() else 'black'
+                for n, ax in enumerate(ax_3d):
+                    pos = poses[n][i]
+                    lines_3d[n].append(ax.plot([pos[j, 0], pos[j_parent, 0]],
+                                               [pos[j, 1], pos[j_parent, 1]],
+                                               [pos[j, 2], pos[j_parent, 2]], zdir='z', c=col))
+
+            points = ax_in.scatter(*keypoints[i].T, 5, color='red', edgecolors='white', zorder=10)
+
+            initialized = True
+        else:
+            image.set_data(all_frames[i])
+
+            for j, j_parent in enumerate(parents):
+                if j_parent == -1:
+                    continue
+
+                # if len(parents) == keypoints.shape[1] and 1 == 2:
+                #     lines[j - 1][0].set_data([keypoints[i, j, 0], keypoints[i, j_parent, 0]],
+                #                              [keypoints[i, j, 1], keypoints[i, j_parent, 1]])
+
+                for n, ax in enumerate(ax_3d):
+                    pos = poses[n][i]
+                    lines_3d[n][j - 1][0].set_xdata(np.array([pos[j, 0], pos[j_parent, 0]])) # Hotfix matplotlib's bug. https://github.com/matplotlib/matplotlib/pull/20555
+                    lines_3d[n][j - 1][0].set_ydata([pos[j, 1], pos[j_parent, 1]])
+                    lines_3d[n][j - 1][0].set_3d_properties([pos[j, 2], pos[j_parent, 2]], zdir='z')
+
+            points.set_offsets(keypoints[i])
+
+        pbar.update()
+
+    fig.tight_layout()
+
+    anim = FuncAnimation(fig, update_video, frames=limit, interval=1000.0 / fps, repeat=False)
+    if output.endswith('.mp4'):
+        Writer = writers['ffmpeg']
+        writer = Writer(fps=fps, metadata={}, bitrate=bitrate)
+        anim.save(output, writer=writer)
+    elif output.endswith('.gif'):
+        anim.save(output, dpi=60, writer='imagemagick')
+    else:
+        raise ValueError('Unsupported output format (only .mp4 and .gif are supported)')
+    pbar.close()
+    plt.close()
+
+
+def render_animation_test(keypoints, poses, skeleton, fps, bitrate, azim, output, viewport, limit=-1, downsample=1, size=6, input_video_frame=None,
+                          input_video_skip=0, num=None):
+    t0 = ckpt_time()
+    fig = plt.figure(figsize=(12, 6))
+    canvas = FigureCanvas(fig)
+    fig.add_subplot(121)
+    plt.imshow(input_video_frame)
+    # 3D
+    ax = fig.add_subplot(122, projection='3d')
+    ax.view_init(elev=15., azim=azim)
+    # set 长度范围
+    radius = 1.7
+    ax.set_xlim3d([-radius / 2, radius / 2])
+    ax.set_zlim3d([0, radius])
+    ax.set_ylim3d([-radius / 2, radius / 2])
+    ax.set_aspect('equal')
+    # 坐标轴刻度
+    ax.set_xticklabels([])
+    ax.set_yticklabels([])
+    ax.set_zticklabels([])
+    ax.dist = 7.5
+
+    # lxy add
+    ax.set_xlabel('X Label')
+    ax.set_ylabel('Y Label')
+    ax.set_zlabel('Z Label')
+
+    # array([-1,  0,  1,  2,  0,  4,  5,  0,  7,  8,  9,  8, 11, 12,  8, 14, 15])
+    parents = skeleton.parents()
+
+    pos = poses['Reconstruction'][-1]
+    _, t1 = ckpt_time(t0, desc='1 ')
+    for j, j_parent in enumerate(parents):
+        if j_parent == -1:
+            continue
+
+        if len(parents) == keypoints.shape[1]:
+            color_pink = 'pink'
+            if j == 1 or j == 2:
+                color_pink = 'black'
+
+        col = 'red' if j in skeleton.joints_right() else 'black'
+        # 画图3D
+        ax.plot([pos[j, 0], pos[j_parent, 0]],
+                [pos[j, 1], pos[j_parent, 1]],
+                [pos[j, 2], pos[j_parent, 2]], zdir='z', c=col)
+
+    #  plt.savefig('test/3Dimage_{}.png'.format(1000+num))
+    width, height = fig.get_size_inches() * fig.get_dpi()
+    _, t2 = ckpt_time(t1, desc='2 ')
+    canvas.draw()  # draw the canvas, cache the renderer
+    image = np.fromstring(canvas.tostring_rgb(), dtype='uint8').reshape(int(height), int(width), 3)
+    cv2.imshow('im', image)
+    cv2.waitKey(5)
+    _, t3 = ckpt_time(t2, desc='3 ')
+    return image
diff --git a/data/data_utils.py b/data/data_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..24945af2d6ed0d954c48187b67bad20ee5883498
--- /dev/null
+++ b/data/data_utils.py
@@ -0,0 +1,110 @@
+# Copyright (c) 2018-present, Facebook, Inc.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+#
+
+import h5py
+import numpy as np
+
+mpii_metadata = {
+    'layout_name': 'mpii',
+    'num_joints': 16,
+    'keypoints_symmetry': [
+        [3, 4, 5, 13, 14, 15],
+        [0, 1, 2, 10, 11, 12],
+    ]
+}
+
+coco_metadata = {
+    'layout_name': 'coco',
+    'num_joints': 17,
+    'keypoints_symmetry': [
+        [1, 3, 5, 7, 9, 11, 13, 15],
+        [2, 4, 6, 8, 10, 12, 14, 16],
+    ]
+}
+
+h36m_metadata = {
+    'layout_name': 'h36m',
+    'num_joints': 17,
+    'keypoints_symmetry': [
+        [4, 5, 6, 11, 12, 13],
+        [1, 2, 3, 14, 15, 16],
+    ]
+}
+
+humaneva15_metadata = {
+    'layout_name': 'humaneva15',
+    'num_joints': 15,
+    'keypoints_symmetry': [
+        [2, 3, 4, 8, 9, 10],
+        [5, 6, 7, 11, 12, 13]
+    ]
+}
+
+humaneva20_metadata = {
+    'layout_name': 'humaneva20',
+    'num_joints': 20,
+    'keypoints_symmetry': [
+        [3, 4, 5, 6, 11, 12, 13, 14],
+        [7, 8, 9, 10, 15, 16, 17, 18]
+    ]
+}
+
+
+def suggest_metadata(name):
+    names = []
+    for metadata in [mpii_metadata, coco_metadata, h36m_metadata, humaneva15_metadata, humaneva20_metadata]:
+        if metadata['layout_name'] in name:
+            return metadata
+        names.append(metadata['layout_name'])
+    raise KeyError('Cannot infer keypoint layout from name "{}". Tried {}.'.format(name, names))
+
+
+def import_detectron_poses(path):
+    # Latin1 encoding because Detectron runs on Python 2.7
+    data = np.load(path, encoding='latin1')
+    kp = data['keypoints']
+    bb = data['boxes']
+    results = []
+    for i in range(len(bb)):
+        if len(bb[i][1]) == 0:
+            assert i > 0
+            # Use last pose in case of detection failure
+            results.append(results[-1])
+            continue
+        best_match = np.argmax(bb[i][1][:, 4])
+        #  import ipdb;ipdb.set_trace()
+        keypoints = kp[i][1][best_match].T.copy()
+        results.append(keypoints)
+    results = np.array(results)
+    #  return results[:, :, 4:6] # Soft-argmax
+    return results[:, :, [0, 1, 3]]  # Argmax + score
+
+
+def my_pose(path):
+    data = np.load(path, encoding='latin1')
+
+
+def import_cpn_poses(path):
+    data = np.load(path)
+    kp = data['keypoints']
+    return kp[:, :, :2]
+
+
+def import_sh_poses(path):
+    with h5py.File(path) as hf:
+        positions = hf['poses'].value
+    return positions.astype('float32')
+
+
+def suggest_pose_importer(name):
+    if 'detectron' in name:
+        return import_detectron_poses
+    if 'cpn' in name:
+        return import_cpn_poses
+    if 'sh' in name:
+        return import_sh_poses
+    raise KeyError('Cannot infer keypoint format from name "{}". Tried detectron, cpn, sh.'.format(name))
diff --git a/data/prepare_2d_kpt.py b/data/prepare_2d_kpt.py
new file mode 100644
index 0000000000000000000000000000000000000000..d6a46947c5cc4399049ac3a69d57fe92f7b1fdf5
--- /dev/null
+++ b/data/prepare_2d_kpt.py
@@ -0,0 +1,45 @@
+import argparse
+import os
+import sys
+
+import numpy as np
+from data_utils import suggest_metadata, suggest_pose_importer
+
+sys.path.append('../')
+
+output_prefix_2d = 'data_2d_h36m_'
+cam_map = {
+    '54138969': 0,
+    '55011271': 1,
+    '58860488': 2,
+    '60457274': 3,
+}
+
+if __name__ == '__main__':
+    if os.path.basename(os.getcwd()) != 'data':
+        print('This script must be launched from the "data" directory')
+        exit(0)
+
+    parser = argparse.ArgumentParser(description='Human3.6M dataset converter')
+
+    parser.add_argument('-i', '--input', default='', type=str, metavar='PATH', help='input path to 2D detections')
+    parser.add_argument('-o', '--output', default='detectron_pt_coco', type=str, metavar='PATH',
+                        help='output suffix for 2D detections (e.g. detectron_pt_coco)')
+
+    args = parser.parse_args()
+
+    if not args.input:
+        print('Please specify the input directory')
+        exit(0)
+
+    # according to output name,generate some format. we use detectron
+    import_func = suggest_pose_importer('detectron_pt_coco')
+    metadata = suggest_metadata('detectron_pt_coco')
+
+    print('Parsing 2D detections from', args.input)
+    keypoints = import_func(args.input)
+
+    output = keypoints.astype(np.float32)
+    # 生成的数据用于后面的3D检测
+    np.savez_compressed(output_prefix_2d + 'test' + args.output, positions_2d=output, metadata=metadata)
+    print('npz name is ', output_prefix_2d + 'test' + args.output)
diff --git a/data/prepare_data_2d_h36m_generic.py b/data/prepare_data_2d_h36m_generic.py
new file mode 100644
index 0000000000000000000000000000000000000000..4e766d31dfaf5857ca03a4cbd993a5b7dce50c45
--- /dev/null
+++ b/data/prepare_data_2d_h36m_generic.py
@@ -0,0 +1,108 @@
+# Copyright (c) 2018-present, Facebook, Inc.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+#
+
+import argparse
+import os
+import re
+import sys
+from glob import glob
+
+import ipdb
+import numpy as np
+from data_utils import suggest_metadata, suggest_pose_importer
+
+sys.path.append('../')
+
+output_prefix_2d = 'data_2d_h36m_'
+cam_map = {
+    '54138969': 0,
+    '55011271': 1,
+    '58860488': 2,
+    '60457274': 3,
+}
+
+if __name__ == '__main__':
+    if os.path.basename(os.getcwd()) != 'data':
+        print('This script must be launched from the "data" directory')
+        exit(0)
+
+    parser = argparse.ArgumentParser(description='Human3.6M dataset converter')
+
+    parser.add_argument('-i', '--input', default='', type=str, metavar='PATH', help='input path to 2D detections')
+    parser.add_argument('-o', '--output', default='', type=str, metavar='PATH', help='output suffix for 2D detections (e.g. detectron_pt_coco)')
+
+    args = parser.parse_args()
+
+    if not args.input:
+        print('Please specify the input directory')
+        exit(0)
+
+    if not args.output:
+        print('Please specify an output suffix (e.g. detectron_pt_coco)')
+        exit(0)
+
+    import_func = suggest_pose_importer(args.output)
+    metadata = suggest_metadata(args.output)
+
+    print('Parsing 2D detections from', args.input)
+
+    output = {}
+
+    #  lxy add
+    keypoints = import_func(args.input)
+    output['S1'] = {}
+    output['S1']['Walking'] = [None, None, None, None]
+    output['S1']['Walking'][0] = keypoints.astype(np.float32)
+    np.savez_compressed(output_prefix_2d + '00' + args.output, positions_2d=output, metadata=metadata)
+    data = np.load('data_2d_h36m_detectron_pt_coco.npz')
+    data1 = np.load('data_2d_h36m_00detectron_pt_coco.npz')
+    actions = data['positions_2d'].item()
+    actions1 = data1['positions_2d'].item()
+    meta = data['metadata']
+
+    actions['S1']['Walking'][0] = actions1['S1']['Walking'][0][:, :, :]
+    np.savez_compressed('data_2d_h36m_lxy_cpn_ft_h36m_dbb.npz', positions_2d=actions, metadata=meta)
+
+    os.exit()
+    ipdb.set_trace()
+
+    # match all file with the format
+    file_list = glob(args.input + '/S*/*.mp4.npz')
+    for f in file_list:
+        path, fname = os.path.split(f)
+        subject = os.path.basename(path)
+        assert subject.startswith('S'), subject + ' does not look like a subject directory'
+
+        if '_ALL' in fname:
+            continue
+
+        m = re.search('(.*)\\.([0-9]+)\\.mp4\\.npz', fname)
+        # first parentheses
+        action = m.group(1)
+        # second parentheses
+        camera = m.group(2)
+        camera_idx = cam_map[camera]
+
+        if subject == 'S11' and action == 'Directions':
+            continue  # Discard corrupted video
+
+        # Use consistent naming convention
+        canonical_name = action.replace('TakingPhoto', 'Photo') \
+            .replace('WalkingDog', 'WalkDog')
+
+        keypoints = import_func(f)
+        assert keypoints.shape[1] == metadata['num_joints']
+
+        if subject not in output:
+            output[subject] = {}
+        if canonical_name not in output[subject]:
+            output[subject][canonical_name] = [None, None, None, None]
+        output[subject][canonical_name][camera_idx] = keypoints.astype('float32')
+
+    print('Saving...')
+    np.savez_compressed(output_prefix_2d + args.output, positions_2d=output, metadata=metadata)
+    print('Done.')
diff --git a/data/prepare_data_2d_h36m_sh.py b/data/prepare_data_2d_h36m_sh.py
new file mode 100644
index 0000000000000000000000000000000000000000..a0fa4ea3d6aa3a7489e2a724212a40ab1cd2b3ba
--- /dev/null
+++ b/data/prepare_data_2d_h36m_sh.py
@@ -0,0 +1,112 @@
+# Copyright (c) 2018-present, Facebook, Inc.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+#
+
+import argparse
+import os
+import sys
+import tarfile
+import zipfile
+from glob import glob
+from shutil import rmtree
+
+import h5py
+import numpy as np
+
+sys.path.append('../')
+
+output_filename_pt = 'data_2d_h36m_sh_pt_mpii'
+output_filename_ft = 'data_2d_h36m_sh_ft_h36m'
+subjects = ['S1', 'S5', 'S6', 'S7', 'S8', 'S9', 'S11']
+cam_map = {
+    '54138969': 0,
+    '55011271': 1,
+    '58860488': 2,
+    '60457274': 3,
+}
+
+metadata = {
+    'num_joints': 16,
+    'keypoints_symmetry': [
+        [3, 4, 5, 13, 14, 15],
+        [0, 1, 2, 10, 11, 12],
+    ]
+}
+
+
+def process_subject(subject, file_list, output):
+    if subject == 'S11':
+        assert len(file_list) == 119, "Expected 119 files for subject " + subject + ", got " + str(len(file_list))
+    else:
+        assert len(file_list) == 120, "Expected 120 files for subject " + subject + ", got " + str(len(file_list))
+
+    for f in file_list:
+        action, cam = os.path.splitext(os.path.basename(f))[0].replace('_', ' ').split('.')
+
+        if subject == 'S11' and action == 'Directions':
+            continue  # Discard corrupted video
+
+        if action not in output[subject]:
+            output[subject][action] = [None, None, None, None]
+
+        with h5py.File(f) as hf:
+            positions = hf['poses'].value
+            output[subject][action][cam_map[cam]] = positions.astype('float32')
+
+
+if __name__ == '__main__':
+    if os.path.basename(os.getcwd()) != 'data':
+        print('This script must be launched from the "data" directory')
+        exit(0)
+
+    parser = argparse.ArgumentParser(description='Human3.6M dataset downloader/converter')
+
+    parser.add_argument('-pt', '--pretrained', default='', type=str, metavar='PATH', help='convert pretrained dataset')
+    parser.add_argument('-ft', '--fine-tuned', default='', type=str, metavar='PATH', help='convert fine-tuned dataset')
+
+    args = parser.parse_args()
+
+    if args.pretrained:
+        print('Converting pretrained dataset from', args.pretrained)
+        print('Extracting...')
+        with zipfile.ZipFile(args.pretrained, 'r') as archive:
+            archive.extractall('sh_pt')
+
+        print('Converting...')
+        output = {}
+        for subject in subjects:
+            output[subject] = {}
+            file_list = glob('sh_pt/h36m/' + subject + '/StackedHourglass/*.h5')
+            process_subject(subject, file_list, output)
+
+        print('Saving...')
+        np.savez_compressed(output_filename_pt, positions_2d=output, metadata=metadata)
+
+        print('Cleaning up...')
+        rmtree('sh_pt')
+
+        print('Done.')
+
+    if args.fine_tuned:
+        print('Converting fine-tuned dataset from', args.fine_tuned)
+        print('Extracting...')
+        with tarfile.open(args.fine_tuned, 'r:gz') as archive:
+            archive.extractall('sh_ft')
+
+        print('Converting...')
+        output = {}
+        for subject in subjects:
+            output[subject] = {}
+            file_list = glob('sh_ft/' + subject + '/StackedHourglassFineTuned240/*.h5')
+            process_subject(subject, file_list, output)
+
+        print('Saving...')
+        np.savez_compressed(output_filename_ft, positions_2d=output, metadata=metadata)
+
+        print('Cleaning up...')
+        rmtree('sh_ft')
+
+        print('Done.')
diff --git a/data/prepare_data_h36m.py b/data/prepare_data_h36m.py
new file mode 100644
index 0000000000000000000000000000000000000000..bb58cb911abdc8ffd7dda1acf45d3068be228a08
--- /dev/null
+++ b/data/prepare_data_h36m.py
@@ -0,0 +1,142 @@
+# Copyright (c) 2018-present, Facebook, Inc.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+#
+
+import argparse
+import os
+import sys
+import zipfile
+from glob import glob
+from shutil import rmtree
+
+import h5py
+import numpy as np
+
+sys.path.append('../')
+from common.h36m_dataset import Human36mDataset
+from common.camera import world_to_camera, project_to_2d, image_coordinates
+from common.utils import wrap
+
+output_filename = 'data_3d_h36m'
+output_filename_2d = 'data_2d_h36m_gt'
+subjects = ['S1', 'S5', 'S6', 'S7', 'S8', 'S9', 'S11']
+
+if __name__ == '__main__':
+    if os.path.basename(os.getcwd()) != 'data':
+        print('This script must be launched from the "data" directory')
+        exit(0)
+
+    parser = argparse.ArgumentParser(description='Human3.6M dataset downloader/converter')
+
+    # Default: convert dataset preprocessed by Martinez et al. in https://github.com/una-dinosauria/3d-pose-baseline
+    parser.add_argument('--from-archive', default='', type=str, metavar='PATH', help='convert preprocessed dataset')
+
+    # Alternatively, convert dataset from original source (the Human3.6M dataset path must be specified manually)
+    parser.add_argument('--from-source', default='', type=str, metavar='PATH', help='convert original dataset')
+
+    args = parser.parse_args()
+
+    if args.from_archive and args.from_source:
+        print('Please specify only one argument')
+        exit(0)
+
+    if os.path.exists(output_filename + '.npz'):
+        print('The dataset already exists at', output_filename + '.npz')
+        exit(0)
+
+    if args.from_archive:
+        print('Extracting Human3.6M dataset from', args.from_archive)
+        with zipfile.ZipFile(args.from_archive, 'r') as archive:
+            archive.extractall()
+
+        print('Converting...')
+        output = {}
+        for subject in subjects:
+            output[subject] = {}
+            file_list = glob('h36m/' + subject + '/MyPoses/3D_positions/*.h5')
+            assert len(file_list) == 30, "Expected 30 files for subject " + subject + ", got " + str(len(file_list))
+            for f in file_list:
+                action = os.path.splitext(os.path.basename(f))[0]
+
+                if subject == 'S11' and action == 'Directions':
+                    continue  # Discard corrupted video
+
+                with h5py.File(f) as hf:
+                    positions = hf['3D_positions'].value.reshape(32, 3, -1).transpose(2, 0, 1)
+                    positions /= 1000  # Meters instead of millimeters
+                    output[subject][action] = positions.astype('float32')
+
+        print('Saving...')
+        np.savez_compressed(output_filename, positions_3d=output)
+
+        print('Cleaning up...')
+        rmtree('h36m')
+
+        print('Done.')
+
+    elif args.from_source:
+        print('Converting original Human3.6M dataset from', args.from_source)
+        output = {}
+
+        from scipy.io import loadmat
+
+        import ipdb;
+
+        ipdb.set_trace()
+        for subject in subjects:
+            output[subject] = {}
+            file_list = glob(args.from_source + '/' + subject + '/MyPoseFeatures/D3_Positions/*.cdf.mat')
+            assert len(file_list) == 30, "Expected 30 files for subject " + subject + ", got " + str(len(file_list))
+            for f in file_list:
+                action = os.path.splitext(os.path.splitext(os.path.basename(f))[0])[0]
+
+                if subject == 'S11' and action == 'Directions':
+                    continue  # Discard corrupted video
+
+                # Use consistent naming convention
+                canonical_name = action.replace('TakingPhoto', 'Photo') \
+                    .replace('WalkingDog', 'WalkDog')
+
+                hf = loadmat(f)
+                positions = hf['data'][0, 0].reshape(-1, 32, 3)
+                positions /= 1000  # Meters instead of millimeters
+                output[subject][canonical_name] = positions.astype('float32')
+
+        print('Saving...')
+        np.savez_compressed(output_filename, positions_3d=output)
+
+        print('Done.')
+
+    else:
+        print('Please specify the dataset source')
+        exit(0)
+
+    # Create 2D pose file
+    print('')
+    print('Computing ground-truth 2D poses...')
+    dataset = Human36mDataset(output_filename + '.npz')
+    output_2d_poses = {}
+    for subject in dataset.subjects():
+        output_2d_poses[subject] = {}
+        for action in dataset[subject].keys():
+            anim = dataset[subject][action]
+
+            positions_2d = []
+            for cam in anim['cameras']:
+                pos_3d = world_to_camera(anim['positions'], R=cam['orientation'], t=cam['translation'])
+                pos_2d = wrap(project_to_2d, pos_3d, cam['intrinsic'], unsqueeze=True)
+                pos_2d_pixel_space = image_coordinates(pos_2d, w=cam['res_w'], h=cam['res_h'])
+                positions_2d.append(pos_2d_pixel_space.astype('float32'))
+            output_2d_poses[subject][action] = positions_2d
+
+    print('Saving...')
+    metadata = {
+        'num_joints': dataset.skeleton().num_joints(),
+        'keypoints_symmetry': [dataset.skeleton().joints_left(), dataset.skeleton().joints_right()]
+    }
+    np.savez_compressed(output_filename_2d, positions_2d=output_2d_poses, metadata=metadata)
+
+    print('Done.')
diff --git a/data/prepare_data_humaneva.py b/data/prepare_data_humaneva.py
new file mode 100644
index 0000000000000000000000000000000000000000..2ed83c2b3e3d40a64faef1d2e35ba9ea83249848
--- /dev/null
+++ b/data/prepare_data_humaneva.py
@@ -0,0 +1,242 @@
+# Copyright (c) 2018-present, Facebook, Inc.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+#
+
+import argparse
+import os
+import re
+import sys
+from glob import glob
+
+import numpy as np
+from data_utils import suggest_metadata, suggest_pose_importer
+
+sys.path.append('../')
+from itertools import groupby
+
+subjects = ['Train/S1', 'Train/S2', 'Train/S3', 'Validate/S1', 'Validate/S2', 'Validate/S3']
+
+cam_map = {
+    'C1': 0,
+    'C2': 1,
+    'C3': 2,
+}
+
+# Frame numbers for train/test split
+# format: [start_frame, end_frame[ (inclusive, exclusive)
+index = {
+    'Train/S1': {
+        'Walking 1': (590, 1203),
+        'Jog 1': (367, 740),
+        'ThrowCatch 1': (473, 945),
+        'Gestures 1': (395, 801),
+        'Box 1': (385, 789),
+    },
+    'Train/S2': {
+        'Walking 1': (438, 876),
+        'Jog 1': (398, 795),
+        'ThrowCatch 1': (550, 1128),
+        'Gestures 1': (500, 901),
+        'Box 1': (382, 734),
+    },
+    'Train/S3': {
+        'Walking 1': (448, 939),
+        'Jog 1': (401, 842),
+        'ThrowCatch 1': (493, 1027),
+        'Gestures 1': (533, 1102),
+        'Box 1': (512, 1021),
+    },
+    'Validate/S1': {
+        'Walking 1': (5, 590),
+        'Jog 1': (5, 367),
+        'ThrowCatch 1': (5, 473),
+        'Gestures 1': (5, 395),
+        'Box 1': (5, 385),
+    },
+    'Validate/S2': {
+        'Walking 1': (5, 438),
+        'Jog 1': (5, 398),
+        'ThrowCatch 1': (5, 550),
+        'Gestures 1': (5, 500),
+        'Box 1': (5, 382),
+    },
+    'Validate/S3': {
+        'Walking 1': (5, 448),
+        'Jog 1': (5, 401),
+        'ThrowCatch 1': (5, 493),
+        'Gestures 1': (5, 533),
+        'Box 1': (5, 512),
+    },
+}
+
+# Frames to skip for each video (synchronization)
+sync_data = {
+    'S1': {
+        'Walking 1': (82, 81, 82),
+        'Jog 1': (51, 51, 50),
+        'ThrowCatch 1': (61, 61, 60),
+        'Gestures 1': (45, 45, 44),
+        'Box 1': (57, 57, 56),
+    },
+    'S2': {
+        'Walking 1': (115, 115, 114),
+        'Jog 1': (100, 100, 99),
+        'ThrowCatch 1': (127, 127, 127),
+        'Gestures 1': (122, 122, 121),
+        'Box 1': (119, 119, 117),
+    },
+    'S3': {
+        'Walking 1': (80, 80, 80),
+        'Jog 1': (65, 65, 65),
+        'ThrowCatch 1': (79, 79, 79),
+        'Gestures 1': (83, 83, 82),
+        'Box 1': (1, 1, 1),
+    },
+    'S4': {}
+}
+
+if __name__ == '__main__':
+    if os.path.basename(os.getcwd()) != 'data':
+        print('This script must be launched from the "data" directory')
+        exit(0)
+
+    parser = argparse.ArgumentParser(description='HumanEva dataset converter')
+
+    parser.add_argument('-p', '--path', default='', type=str, metavar='PATH', help='path to the processed HumanEva dataset')
+    parser.add_argument('--convert-3d', action='store_true', help='convert 3D mocap data')
+    parser.add_argument('--convert-2d', default='', type=str, metavar='PATH', help='convert user-supplied 2D detections')
+    parser.add_argument('-o', '--output', default='', type=str, metavar='PATH', help='output suffix for 2D detections (e.g. detectron_pt_coco)')
+
+    args = parser.parse_args()
+
+    if not args.convert_2d and not args.convert_3d:
+        print('Please specify one conversion mode')
+        exit(0)
+
+    if args.path:
+        print('Parsing HumanEva dataset from', args.path)
+        output = {}
+        output_2d = {}
+        frame_mapping = {}
+
+        from scipy.io import loadmat
+
+        num_joints = None
+
+        for subject in subjects:
+            output[subject] = {}
+            output_2d[subject] = {}
+            split, subject_name = subject.split('/')
+            if subject_name not in frame_mapping:
+                frame_mapping[subject_name] = {}
+
+            file_list = glob(args.path + '/' + subject + '/*.mat')
+            for f in file_list:
+                action = os.path.splitext(os.path.basename(f))[0]
+
+                # Use consistent naming convention
+                canonical_name = action.replace('_', ' ')
+
+                hf = loadmat(f)
+                positions = hf['poses_3d']
+                positions_2d = hf['poses_2d'].transpose(1, 0, 2, 3)  # Ground-truth 2D poses
+                assert positions.shape[0] == positions_2d.shape[0] and positions.shape[1] == positions_2d.shape[2]
+                assert num_joints is None or num_joints == positions.shape[1], "Joint number inconsistency among files"
+                num_joints = positions.shape[1]
+
+                # Sanity check for the sequence length
+                assert positions.shape[0] == index[subject][canonical_name][1] - index[subject][canonical_name][0]
+
+                # Split corrupted motion capture streams into contiguous chunks
+                # e.g. 012XX567X9 is split into "012", "567", and "9".
+                all_chunks = [list(v) for k, v in groupby(positions, lambda x: np.isfinite(x).all())]
+                all_chunks_2d = [list(v) for k, v in groupby(positions_2d, lambda x: np.isfinite(x).all())]
+                assert len(all_chunks) == len(all_chunks_2d)
+                current_index = index[subject][canonical_name][0]
+                chunk_indices = []
+                for i, chunk in enumerate(all_chunks):
+                    next_index = current_index + len(chunk)
+                    name = canonical_name + ' chunk' + str(i)
+                    if np.isfinite(chunk).all():
+                        output[subject][name] = np.array(chunk, dtype='float32') / 1000
+                        output_2d[subject][name] = list(np.array(all_chunks_2d[i], dtype='float32').transpose(1, 0, 2, 3))
+                    chunk_indices.append((current_index, next_index, np.isfinite(chunk).all(), split, name))
+                    current_index = next_index
+                assert current_index == index[subject][canonical_name][1]
+                if canonical_name not in frame_mapping[subject_name]:
+                    frame_mapping[subject_name][canonical_name] = []
+                frame_mapping[subject_name][canonical_name] += chunk_indices
+
+        metadata = suggest_metadata('humaneva' + str(num_joints))
+        output_filename = 'data_3d_' + metadata['layout_name']
+        output_prefix_2d = 'data_2d_' + metadata['layout_name'] + '_'
+
+        if args.convert_3d:
+            print('Saving...')
+            np.savez_compressed(output_filename, positions_3d=output)
+            np.savez_compressed(output_prefix_2d + 'gt', positions_2d=output_2d, metadata=metadata)
+            print('Done.')
+
+    else:
+        print('Please specify the dataset source')
+        exit(0)
+
+    if args.convert_2d:
+        if not args.output:
+            print('Please specify an output suffix (e.g. detectron_pt_coco)')
+            exit(0)
+
+        import_func = suggest_pose_importer(args.output)
+        metadata = suggest_metadata(args.output)
+
+        print('Parsing 2D detections from', args.convert_2d)
+
+        output = {}
+        file_list = glob(args.convert_2d + '/S*/*.avi.npz')
+        for f in file_list:
+            path, fname = os.path.split(f)
+            subject = os.path.basename(path)
+            assert subject.startswith('S'), subject + ' does not look like a subject directory'
+
+            m = re.search('(.*) \\((.*)\\)', fname.replace('_', ' '))
+            action = m.group(1)
+            camera = m.group(2)
+            camera_idx = cam_map[camera]
+
+            keypoints = import_func(f)
+            assert keypoints.shape[1] == metadata['num_joints']
+
+            if action in sync_data[subject]:
+                sync_offset = sync_data[subject][action][camera_idx] - 1
+            else:
+                sync_offset = 0
+
+            if subject in frame_mapping and action in frame_mapping[subject]:
+                chunks = frame_mapping[subject][action]
+                for (start_idx, end_idx, labeled, split, name) in chunks:
+                    canonical_subject = split + '/' + subject
+                    if not labeled:
+                        canonical_subject = 'Unlabeled/' + canonical_subject
+                    if canonical_subject not in output:
+                        output[canonical_subject] = {}
+                    kps = keypoints[start_idx + sync_offset:end_idx + sync_offset]
+                    assert len(kps) == end_idx - start_idx, "Got len {}, expected {}".format(len(kps), end_idx - start_idx)
+
+                    if name not in output[canonical_subject]:
+                        output[canonical_subject][name] = [None, None, None]
+
+                    output[canonical_subject][name][camera_idx] = kps.astype('float32')
+            else:
+                canonical_subject = 'Unlabeled/' + subject
+                if canonical_subject not in output:
+                    output[canonical_subject] = {}
+                if action not in output[canonical_subject]:
+                    output[canonical_subject][action] = [None, None, None]
+                output[canonical_subject][action][camera_idx] = keypoints.astype('float32')
+
+        print('Saving...')
+        np.savez_compressed(output_prefix_2d + args.output, positions_2d=output, metadata=metadata)
+        print('Done.')
diff --git a/joints_detectors/Alphapose/.gitignore b/joints_detectors/Alphapose/.gitignore
new file mode 100644
index 0000000000000000000000000000000000000000..1f50207d6d02402f8d659415ee77cae16068759d
--- /dev/null
+++ b/joints_detectors/Alphapose/.gitignore
@@ -0,0 +1,29 @@
+human_detection/output
+examples/results
+examples/res
+PoseFlow/__pycache__
+PoseFlow/*.npy
+PoseFlow/alpha-pose-results-test.json
+PoseFlow/alpha-pose-results-val.json
+PoseFlow/test-predict
+PoseFlow/val-predict
+train_sppe/coco-minival500_images.txt
+train_sppe/person_keypoints_val2014.json
+
+ssd/examples
+images
+
+*.npy
+*.so
+*.pyc
+.ipynb_checkpoints
+*/.ipynb_checkpoints/
+*/.tensorboard/*
+*/exp
+
+*.pth
+*.h5
+*.zip
+*.weights
+
+coco-minival/
diff --git a/joints_detectors/Alphapose/LICENSE b/joints_detectors/Alphapose/LICENSE
new file mode 100644
index 0000000000000000000000000000000000000000..c038374200bf87e2e3c5d636b1e01dff536a474b
--- /dev/null
+++ b/joints_detectors/Alphapose/LICENSE
@@ -0,0 +1,515 @@
+ALPHAPOSE: MULTIPERSON KEYPOINT DETECTION
+SOFTWARE LICENSE AGREEMENT
+ACADEMIC OR NON-PROFIT ORGANIZATION NONCOMMERCIAL RESEARCH USE ONLY
+
+BY USING OR DOWNLOADING THE SOFTWARE, YOU ARE AGREEING TO THE TERMS OF THIS LICENSE AGREEMENT.  IF YOU DO NOT AGREE WITH THESE TERMS, YOU MAY NOT USE OR DOWNLOAD THE SOFTWARE.
+
+This is a license agreement ("Agreement") between your academic institution or non-profit organization or self (called "Licensee" or "You" in this Agreement) and Shanghai Jiao Tong University (called "Licensor" in this Agreement).  All rights not specifically granted to you in this Agreement are reserved for Licensor. 
+
+RESERVATION OF OWNERSHIP AND GRANT OF LICENSE: 
+Licensor retains exclusive ownership of any copy of the Software (as defined below) licensed under this Agreement and hereby grants to Licensee a personal, non-exclusive, 
+non-transferable license to use the Software for noncommercial research purposes, without the right to sublicense, pursuant to the terms and conditions of this Agreement.  As used in this Agreement, the term "Software" means (i) the actual copy of all or any portion of code for program routines made accessible to Licensee by Licensor pursuant to this Agreement, inclusive of backups, updates, and/or merged copies permitted hereunder or subsequently supplied by Licensor,  including all or any file structures, programming instructions, user interfaces and screen formats and sequences as well as any and all documentation and instructions related to it, and (ii) all or any derivatives and/or modifications created or made by You to any of the items specified in (i).
+
+CONFIDENTIALITY: Licensee acknowledges that the Software is proprietary to Licensor, and as such, Licensee agrees to receive all such materials in confidence and use the Software only in accordance with the terms of this Agreement.  Licensee agrees to use reasonable effort to protect the Software from unauthorized use, reproduction, distribution, or publication.
+
+PERMITTED USES:  The Software may be used for your own noncommercial internal research purposes. You understand and agree that Licensor is not obligated to implement any suggestions and/or feedback you might provide regarding the Software, but to the extent Licensor does so, you are not entitled to any compensation related thereto.
+
+DERIVATIVES: You may create derivatives of or make modifications to the Software, however, You agree that all and any such derivatives and modifications will be owned by Licensor and become a part of the Software licensed to You under this Agreement.  You may only use such derivatives and modifications for your own noncommercial internal research purposes, and you may not otherwise use, distribute or copy such derivatives and modifications in violation of this Agreement.
+
+BACKUPS:  If Licensee is an organization, it may make that number of copies of the Software necessary for internal noncommercial use at a single site within its organization provided that all information appearing in or on the original labels, including the copyright and trademark notices are copied onto the labels of the copies.
+
+USES NOT PERMITTED:  You may not distribute, copy or use the Software except as explicitly permitted herein. Licensee has not been granted any trademark license as part of this Agreement and may not use the name or mark “AlphaPose", "Shanghai Jiao Tong" or any renditions thereof without the prior written permission of Licensor.
+
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+
+TERM: The term of the license granted by this Agreement is from Licensee's acceptance of this Agreement by downloading the Software or by using the Software until terminated as provided below.
+
+The Agreement automatically terminates without notice if you fail to comply with any provision of this Agreement.  Licensee may terminate this Agreement by ceasing using the Software.  Upon any termination of this Agreement, Licensee will delete any and all copies of the Software. You agree that all provisions which operate to protect the proprietary rights of Licensor shall remain in force should breach occur and that the obligation of confidentiality described in this Agreement is binding in perpetuity and, as such, survives the term of the Agreement.
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+
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+U.S. export control laws, regulations, and/or other laws related to embargoes and sanction programs administered by the Office of Foreign Assets Control.
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+************************************************************************
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+This project incorporates material from the project(s) listed below (collectively, "Third Party Code").  This Third Party Code is licensed to you under their original license terms set forth below.  We reserves all other rights not expressly granted, whether by implication, estoppel or otherwise.
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+1.	Torch, (https://github.com/torch/distro)
+
+Copyright (c) 2016, Soumith Chintala, Ronan Collobert, Koray Kavukcuoglu, Clement Farabet All rights reserved.
+
+Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:
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+2. TensorFlow (https://github.com/tensorflow/tensorflow)
+Copyright 2018 The TensorFlow Authors.  All rights reserved.
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+
+      To apply the Apache License to your work, attach the following
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+      file or class name and description of purpose be included on the
+      same "printed page" as the copyright notice for easier
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+   Copyright {yyyy} {name of copyright owner}
+
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+   See the License for the specific language governing permissions and
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+5. pose-hg-demo (https://github.com/umich-vl/pose-hg-demo)
+Copyright (c) 2016, University of Michigan
+All rights reserved.
+
+Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:
+
+1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.
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+************END OF THIRD-PARTY SOFTWARE NOTICES AND INFORMATION**********
\ No newline at end of file
diff --git a/joints_detectors/Alphapose/README.md b/joints_detectors/Alphapose/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..1fe8075797d362f2c868b8b67202f4d9a559e671
--- /dev/null
+++ b/joints_detectors/Alphapose/README.md
@@ -0,0 +1,112 @@
+
+<div align="center">
+    <img src="doc/logo.jpg", width="400">
+</div>
+
+
+## News!
+- Apr 2019: [**MXNet** version](https://github.com/MVIG-SJTU/AlphaPose/tree/mxnet) of AlphaPose is released! It runs at **23 fps** on COCO validation set.
+- Feb 2019: [CrowdPose](https://github.com/MVIG-SJTU/AlphaPose/blob/pytorch/doc/CrowdPose.md) is integrated into AlphaPose Now!
+- Dec 2018: [General version](https://github.com/MVIG-SJTU/AlphaPose/tree/pytorch/PoseFlow) of PoseFlow is released! 3X Faster and support pose tracking results visualization!
+- Sep 2018: [**PyTorch** version](https://github.com/MVIG-SJTU/AlphaPose/tree/pytorch) of AlphaPose is released! It runs at **20 fps** on COCO validation set (4.6 people per image on average) and achieves 71 mAP!
+
+## AlphaPose
+[Alpha Pose](http://www.mvig.org/research/alphapose.html) is an accurate multi-person pose estimator, which is the **first open-source system that achieves 70+ mAP (72.3 mAP) on COCO dataset and 80+ mAP (82.1 mAP) on MPII dataset.** 
+To match poses that correspond to the same person across frames, we also provide an efficient online pose tracker called Pose Flow. It is the **first open-source online pose tracker that achieves both 60+ mAP (66.5 mAP) and 50+ MOTA (58.3 MOTA) on PoseTrack Challenge dataset.**
+
+AlphaPose supports both Linux and **Windows!**
+
+<div align="center">
+    <img src="doc/alphapose.gif", width="400">
+</div>
+
+
+## Installation
+**Windows Version** please check out [doc/win_install.md](doc/win_install.md)
+
+1. Get the code.
+  ```Shell
+  git clone -b pytorch https://github.com/MVIG-SJTU/AlphaPose.git
+  ```
+
+2. Install [pytorch 0.4.0](https://github.com/pytorch/pytorch) and other dependencies.
+  ```Shell
+  pip install -r requirements.txt
+  ```
+
+3. Download the models manually: **duc_se.pth** (2018/08/30) ([Google Drive]( https://drive.google.com/open?id=1OPORTWB2cwd5YTVBX-NE8fsauZJWsrtW) | [Baidu pan](https://pan.baidu.com/s/15jbRNKuslzm5wRSgUVytrA)), **yolov3-spp.weights**([Google Drive](https://drive.google.com/open?id=1D47msNOOiJKvPOXlnpyzdKA3k6E97NTC) | [Baidu pan](https://pan.baidu.com/s/1Zb2REEIk8tcahDa8KacPNA)). Place them into `./models/sppe` and `./models/yolo` respectively.
+
+
+## Quick Start
+- **Input dir**:  Run AlphaPose for all images in a folder with:
+```
+python3 demo.py --indir ${img_directory} --outdir examples/res 
+```
+- **Video**:  Run AlphaPose for a video and save the rendered video with:
+```
+python3 video_demo.py --video ${path to video} --outdir examples/res --save_video
+```
+- **Webcam**:  Run AlphaPose using webcam and visualize the results with:
+```
+python3 webcam_demo.py --webcam 0 --outdir examples/res --vis
+```
+- **Input list**:  Run AlphaPose for images in a list and save the rendered images with:
+```
+python3 demo.py --list examples/list-coco-demo.txt --indir ${img_directory} --outdir examples/res --save_img
+```
+- **Note**:  If you meet OOM(out of memory) problem, decreasing the pose estimation batch until the program can run on your computer:
+```
+python3 demo.py --indir ${img_directory} --outdir examples/res --posebatch 30
+```
+- **Getting more accurate**: You can enable flip testing to get more accurate results by disable fast_inference, e.g.:
+```
+python3 demo.py --indir ${img_directory} --outdir examples/res --fast_inference False
+```
+- **Speeding up**:  Checkout the [speed_up.md](doc/speed_up.md) for more details.
+- **Output format**: Checkout the [output.md](doc/output.md) for more details.
+- **For more**:  Checkout the [run.md](doc/run.md) for more options
+
+## Pose Tracking
+
+<p align='center'>
+    <img src="doc/posetrack.gif", width="360">
+    <img src="doc/posetrack2.gif", width="344">
+</p>
+
+Please read [PoseFlow/README.md](PoseFlow/) for details.
+
+### CrowdPose
+<p align='center'>
+    <img src="doc/crowdpose.gif", width="360">
+</p>
+
+Please read [doc/CrowdPose.md](doc/CrowdPose.md) for details.
+
+
+## FAQ
+Check out [faq.md](doc/faq.md) for faq.
+
+## Contributors
+Pytorch version of AlphaPose is developed and maintained by [Jiefeng Li](http://jeff-leaf.site/), [Hao-Shu Fang](https://fang-haoshu.github.io/), [Yuliang Xiu](http://xiuyuliang.cn) and [Cewu Lu](http://www.mvig.org/). 
+
+## Citation
+Please cite these papers in your publications if it helps your research:
+
+    @inproceedings{fang2017rmpe,
+      title={{RMPE}: Regional Multi-person Pose Estimation},
+      author={Fang, Hao-Shu and Xie, Shuqin and Tai, Yu-Wing and Lu, Cewu},
+      booktitle={ICCV},
+      year={2017}
+    }
+
+    @inproceedings{xiu2018poseflow,
+      author = {Xiu, Yuliang and Li, Jiefeng and Wang, Haoyu and Fang, Yinghong and Lu, Cewu},
+      title = {{Pose Flow}: Efficient Online Pose Tracking},
+      booktitle={BMVC},
+      year = {2018}
+    }
+
+
+
+## License
+AlphaPose is freely available for free non-commercial use, and may be redistributed under these conditions. For commercial queries, please drop an e-mail at mvig.alphapose[at]gmail[dot]com and cc lucewu[[at]sjtu[dot]edu[dot]cn. We will send the detail agreement to you.
diff --git a/joints_detectors/Alphapose/SPPE/.gitattributes b/joints_detectors/Alphapose/SPPE/.gitattributes
new file mode 100644
index 0000000000000000000000000000000000000000..dfe0770424b2a19faf507a501ebfc23be8f54e7b
--- /dev/null
+++ b/joints_detectors/Alphapose/SPPE/.gitattributes
@@ -0,0 +1,2 @@
+# Auto detect text files and perform LF normalization
+* text=auto
diff --git a/joints_detectors/Alphapose/SPPE/.gitignore b/joints_detectors/Alphapose/SPPE/.gitignore
new file mode 100644
index 0000000000000000000000000000000000000000..8c72637c230fa6602bea9f5185ee430299436702
--- /dev/null
+++ b/joints_detectors/Alphapose/SPPE/.gitignore
@@ -0,0 +1,114 @@
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+.hypothesis/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# pyenv
+.python-version
+
+# celery beat schedule file
+celerybeat-schedule
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+
+.vscode/
+*.pkl
+exp
+exp/*
+data
+data/*
+model
+model/*
+*/images
+*/images/*
+
+*.h5
+*.pth
+
diff --git a/joints_detectors/Alphapose/SPPE/LICENSE b/joints_detectors/Alphapose/SPPE/LICENSE
new file mode 100644
index 0000000000000000000000000000000000000000..fedbdfd08ffbfa2c2bc457bc7e3988a474304c78
--- /dev/null
+++ b/joints_detectors/Alphapose/SPPE/LICENSE
@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2018 Jeff-sjtu
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
\ No newline at end of file
diff --git a/joints_detectors/Alphapose/SPPE/README.md b/joints_detectors/Alphapose/SPPE/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..2b54b7312219ea6a186a2731891ec0c9a7016962
--- /dev/null
+++ b/joints_detectors/Alphapose/SPPE/README.md
@@ -0,0 +1 @@
+# pytorch-AlphaPose
diff --git a/joints_detectors/Alphapose/SPPE/__init__.py b/joints_detectors/Alphapose/SPPE/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/joints_detectors/Alphapose/SPPE/src/__init__.py b/joints_detectors/Alphapose/SPPE/src/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/joints_detectors/Alphapose/SPPE/src/main_fast_inference.py b/joints_detectors/Alphapose/SPPE/src/main_fast_inference.py
new file mode 100644
index 0000000000000000000000000000000000000000..ff782469885215e83fa820d0bd9d0bbe886050a2
--- /dev/null
+++ b/joints_detectors/Alphapose/SPPE/src/main_fast_inference.py
@@ -0,0 +1,67 @@
+import sys
+
+import torch
+import torch._utils
+import torch.nn as nn
+import torch.utils.data
+import torch.utils.data.distributed
+
+from SPPE.src.models.FastPose import createModel
+from SPPE.src.utils.img import flip, shuffleLR
+
+try:
+    torch._utils._rebuild_tensor_v2
+except AttributeError:
+    def _rebuild_tensor_v2(storage, storage_offset, size, stride, requires_grad, backward_hooks):
+        tensor = torch._utils._rebuild_tensor(storage, storage_offset, size, stride)
+        tensor.requires_grad = requires_grad
+        tensor._backward_hooks = backward_hooks
+        return tensor
+    torch._utils._rebuild_tensor_v2 = _rebuild_tensor_v2
+
+
+class InferenNet(nn.Module):
+    def __init__(self, kernel_size, dataset):
+        super(InferenNet, self).__init__()
+
+        model = createModel().cuda()
+        print('Loading pose model from {}'.format('joints_detectors/Alphapose/models/sppe/duc_se.pth'))
+        sys.stdout.flush()
+        model.load_state_dict(torch.load('joints_detectors/Alphapose/models/sppe/duc_se.pth'))
+        model.eval()
+        self.pyranet = model
+
+        self.dataset = dataset
+
+    def forward(self, x):
+        out = self.pyranet(x)
+        out = out.narrow(1, 0, 17)
+
+        flip_out = self.pyranet(flip(x))
+        flip_out = flip_out.narrow(1, 0, 17)
+
+        flip_out = flip(shuffleLR(
+            flip_out, self.dataset))
+
+        out = (flip_out + out) / 2
+
+        return out
+
+
+class InferenNet_fast(nn.Module):
+    def __init__(self, kernel_size, dataset):
+        super(InferenNet_fast, self).__init__()
+
+        model = createModel().cuda()
+        print('Loading pose model from {}'.format('models/sppe/duc_se.pth'))
+        model.load_state_dict(torch.load('models/sppe/duc_se.pth'))
+        model.eval()
+        self.pyranet = model
+
+        self.dataset = dataset
+
+    def forward(self, x):
+        out = self.pyranet(x)
+        out = out.narrow(1, 0, 17)
+
+        return out
diff --git a/joints_detectors/Alphapose/SPPE/src/models/FastPose.py b/joints_detectors/Alphapose/SPPE/src/models/FastPose.py
new file mode 100644
index 0000000000000000000000000000000000000000..d9e660819e0cd872930f44d81af09cedc92d4f07
--- /dev/null
+++ b/joints_detectors/Alphapose/SPPE/src/models/FastPose.py
@@ -0,0 +1,35 @@
+import torch.nn as nn
+from torch.autograd import Variable
+
+from .layers.SE_Resnet import SEResnet
+from .layers.DUC import DUC
+from opt import opt
+
+
+def createModel():
+    return FastPose()
+
+
+class FastPose(nn.Module):
+    DIM = 128
+
+    def __init__(self):
+        super(FastPose, self).__init__()
+
+        self.preact = SEResnet('resnet101')
+
+        self.suffle1 = nn.PixelShuffle(2)
+        self.duc1 = DUC(512, 1024, upscale_factor=2)
+        self.duc2 = DUC(256, 512, upscale_factor=2)
+
+        self.conv_out = nn.Conv2d(
+            self.DIM, opt.nClasses, kernel_size=3, stride=1, padding=1)
+
+    def forward(self, x: Variable):
+        out = self.preact(x)
+        out = self.suffle1(out)
+        out = self.duc1(out)
+        out = self.duc2(out)
+
+        out = self.conv_out(out)
+        return out
diff --git a/joints_detectors/Alphapose/SPPE/src/models/__init__.py b/joints_detectors/Alphapose/SPPE/src/models/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..b9742821a6f164200bc145e7a847382f08778303
--- /dev/null
+++ b/joints_detectors/Alphapose/SPPE/src/models/__init__.py
@@ -0,0 +1 @@
+from . import *
\ No newline at end of file
diff --git a/joints_detectors/Alphapose/SPPE/src/models/hg-prm.py b/joints_detectors/Alphapose/SPPE/src/models/hg-prm.py
new file mode 100644
index 0000000000000000000000000000000000000000..6de6a345292a5a21dce863905f193373dbe32416
--- /dev/null
+++ b/joints_detectors/Alphapose/SPPE/src/models/hg-prm.py
@@ -0,0 +1,126 @@
+import torch.nn as nn
+from .layers.PRM import Residual as ResidualPyramid
+from .layers.Residual import Residual as Residual
+from torch.autograd import Variable
+from opt import opt
+from collections import defaultdict
+
+
+class Hourglass(nn.Module):
+    def __init__(self, n, nFeats, nModules, inputResH, inputResW, net_type, B, C):
+        super(Hourglass, self).__init__()
+
+        self.ResidualUp = ResidualPyramid if n >= 2 else Residual
+        self.ResidualDown = ResidualPyramid if n >= 3 else Residual
+        
+        self.depth = n
+        self.nModules = nModules
+        self.nFeats = nFeats
+        self.net_type = net_type
+        self.B = B
+        self.C = C
+        self.inputResH = inputResH
+        self.inputResW = inputResW
+
+        self.up1 = self._make_residual(self.ResidualUp, False, inputResH, inputResW)
+        self.low1 = nn.Sequential(
+            nn.MaxPool2d(2),
+            self._make_residual(self.ResidualDown, False, inputResH / 2, inputResW / 2)
+        )
+        if n > 1:
+            self.low2 = Hourglass(n - 1, nFeats, nModules, inputResH / 2, inputResW / 2, net_type, B, C)
+        else:
+            self.low2 = self._make_residual(self.ResidualDown, False, inputResH / 2, inputResW / 2)
+        
+        self.low3 = self._make_residual(self.ResidualDown, True, inputResH / 2, inputResW / 2)
+        self.up2 = nn.UpsamplingNearest2d(scale_factor=2)
+
+        self.upperBranch = self.up1
+        self.lowerBranch = nn.Sequential(
+            self.low1,
+            self.low2,
+            self.low3,
+            self.up2
+        )
+
+    def _make_residual(self, resBlock, useConv, inputResH, inputResW):
+        layer_list = []
+        for i in range(self.nModules):
+            layer_list.append(resBlock(self.nFeats, self.nFeats, inputResH, inputResW,
+                                       stride=1, net_type=self.net_type, useConv=useConv,
+                                       baseWidth=self.B, cardinality=self.C))
+        return nn.Sequential(*layer_list)
+
+    def forward(self, x: Variable):
+        up1 = self.upperBranch(x)
+        up2 = self.lowerBranch(x)
+        out = up1 + up2
+        return out
+
+
+class PyraNet(nn.Module):
+    def __init__(self):
+        super(PyraNet, self).__init__()
+
+        B, C = opt.baseWidth, opt.cardinality
+        self.inputResH = opt.inputResH / 4
+        self.inputResW = opt.inputResW / 4
+        self.nStack = opt.nStack
+
+        self.cnv1 = nn.Sequential(
+            nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3),
+            nn.BatchNorm2d(64),
+            nn.ReLU(True)
+        )
+        self.r1 = nn.Sequential(
+            ResidualPyramid(64, 128, opt.inputResH / 2, opt.inputResW / 2,
+                            stride=1, net_type='no_preact', useConv=False, baseWidth=B, cardinality=C),
+            nn.MaxPool2d(2)
+        )
+        self.r4 = ResidualPyramid(128, 128, self.inputResH, self.inputResW,
+                                  stride=1, net_type='preact', useConv=False, baseWidth=B, cardinality=C)
+        self.r5 = ResidualPyramid(128, opt.nFeats, self.inputResH, self.inputResW,
+                                  stride=1, net_type='preact', useConv=False, baseWidth=B, cardinality=C)
+        self.preact = nn.Sequential(
+            self.cnv1,
+            self.r1,
+            self.r4,
+            self.r5
+        )
+        self.stack_layers = defaultdict(list)
+        for i in range(self.nStack):
+            hg = Hourglass(4, opt.nFeats, opt.nResidual, self.inputResH, self.inputResW, 'preact', B, C)
+            lin = nn.Sequential(
+                hg,
+                nn.BatchNorm2d(opt.nFeats),
+                nn.ReLU(True),
+                nn.Conv2d(opt.nFeats, opt.nFeats, kernel_size=1, stride=1, padding=0),
+                nn.BatchNorm2d(opt.nFeats),
+                nn.ReLU(True)
+            )
+            tmpOut = nn.Conv2d(opt.nFeats, opt.nClasses, kernel_size=1, stride=1, padding=0)
+            self.stack_layers['lin'].append(lin)
+            self.stack_layers['out'].append(tmpOut)
+            if i < self.nStack - 1:
+                lin_ = nn.Conv2d(opt.nFeats, opt.nFeats, kernel_size=1, stride=1, padding=0)
+                tmpOut_ = nn.Conv2d(opt.nClasses, opt.nFeats, kernel_size=1, stride=1, padding=0)
+                self.stack_layers['lin_'].append(lin_)
+                self.stack_layers['out_'].append(tmpOut_)
+
+    def forward(self, x: Variable):
+        out = []
+        inter = self.preact(x)
+        for i in range(self.nStack):
+            lin = self.stack_layers['lin'][i](inter)
+            tmpOut = self.stack_layers['out'][i](lin)
+            out.append(tmpOut)
+            if i < self.nStack - 1:
+                lin_ = self.stack_layers['lin_'][i](lin)
+                tmpOut_ = self.stack_layers['out_'][i](tmpOut)
+                inter = inter + lin_ + tmpOut_
+        return out
+
+
+def createModel(**kw):
+    model = PyraNet()
+    return model
diff --git a/joints_detectors/Alphapose/SPPE/src/models/hgPRM.py b/joints_detectors/Alphapose/SPPE/src/models/hgPRM.py
new file mode 100644
index 0000000000000000000000000000000000000000..d115071735f81cfd3bbdc34d43e1b475b0b8fc8d
--- /dev/null
+++ b/joints_detectors/Alphapose/SPPE/src/models/hgPRM.py
@@ -0,0 +1,236 @@
+import torch.nn as nn
+from .layers.PRM import Residual as ResidualPyramid
+from .layers.Residual import Residual as Residual
+from torch.autograd import Variable
+import torch
+from opt import opt
+import math
+
+
+class Hourglass(nn.Module):
+    def __init__(self, n, nFeats, nModules, inputResH, inputResW, net_type, B, C):
+        super(Hourglass, self).__init__()
+
+        self.ResidualUp = ResidualPyramid if n >= 2 else Residual
+        self.ResidualDown = ResidualPyramid if n >= 3 else Residual
+
+        self.depth = n
+        self.nModules = nModules
+        self.nFeats = nFeats
+        self.net_type = net_type
+        self.B = B
+        self.C = C
+        self.inputResH = inputResH
+        self.inputResW = inputResW
+
+        up1 = self._make_residual(self.ResidualUp, False, inputResH, inputResW)
+        low1 = nn.Sequential(
+            nn.MaxPool2d(2),
+            self._make_residual(self.ResidualDown, False, inputResH / 2, inputResW / 2)
+        )
+        if n > 1:
+            low2 = Hourglass(n - 1, nFeats, nModules, inputResH / 2, inputResW / 2, net_type, B, C)
+        else:
+            low2 = self._make_residual(self.ResidualDown, False, inputResH / 2, inputResW / 2)
+
+        low3 = self._make_residual(self.ResidualDown, True, inputResH / 2, inputResW / 2)
+        up2 = nn.UpsamplingNearest2d(scale_factor=2)
+
+        self.upperBranch = up1
+        self.lowerBranch = nn.Sequential(
+            low1,
+            low2,
+            low3,
+            up2
+        )
+
+    def _make_residual(self, resBlock, useConv, inputResH, inputResW):
+        layer_list = []
+        for i in range(self.nModules):
+            layer_list.append(resBlock(self.nFeats, self.nFeats, inputResH, inputResW,
+                                       stride=1, net_type=self.net_type, useConv=useConv,
+                                       baseWidth=self.B, cardinality=self.C))
+        return nn.Sequential(*layer_list)
+
+    def forward(self, x: Variable):
+        up1 = self.upperBranch(x)
+        up2 = self.lowerBranch(x)
+        # out = up1 + up2
+        out = torch.add(up1, up2)
+        return out
+
+
+class PyraNet(nn.Module):
+    def __init__(self):
+        super(PyraNet, self).__init__()
+
+        B, C = opt.baseWidth, opt.cardinality
+        self.inputResH = opt.inputResH / 4
+        self.inputResW = opt.inputResW / 4
+        self.nStack = opt.nStack
+
+        conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3)
+        if opt.init:
+            nn.init.xavier_normal(conv1.weight, gain=math.sqrt(1 / 3))
+
+        cnv1 = nn.Sequential(
+            conv1,
+            nn.BatchNorm2d(64),
+            nn.ReLU(True)
+        )
+
+        r1 = nn.Sequential(
+            ResidualPyramid(64, 128, opt.inputResH / 2, opt.inputResW / 2,
+                            stride=1, net_type='no_preact', useConv=False, baseWidth=B, cardinality=C),
+            nn.MaxPool2d(2)
+        )
+        r4 = ResidualPyramid(128, 128, self.inputResH, self.inputResW,
+                             stride=1, net_type='preact', useConv=False, baseWidth=B, cardinality=C)
+        r5 = ResidualPyramid(128, opt.nFeats, self.inputResH, self.inputResW,
+                             stride=1, net_type='preact', useConv=False, baseWidth=B, cardinality=C)
+        self.preact = nn.Sequential(
+            cnv1,
+            r1,
+            r4,
+            r5
+        )
+
+        self.stack_lin = nn.ModuleList()
+        self.stack_out = nn.ModuleList()
+        self.stack_lin_ = nn.ModuleList()
+        self.stack_out_ = nn.ModuleList()
+
+        for i in range(self.nStack):
+            hg = Hourglass(4, opt.nFeats, opt.nResidual, self.inputResH, self.inputResW, 'preact', B, C)
+            conv1 = nn.Conv2d(opt.nFeats, opt.nFeats, kernel_size=1, stride=1, padding=0)
+            if opt.init:
+                nn.init.xavier_normal(conv1.weight, gain=math.sqrt(1 / 2))
+            lin = nn.Sequential(
+                hg,
+                nn.BatchNorm2d(opt.nFeats),
+                nn.ReLU(True),
+                conv1,
+                nn.BatchNorm2d(opt.nFeats),
+                nn.ReLU(True)
+            )
+            tmpOut = nn.Conv2d(opt.nFeats, opt.nClasses, kernel_size=1, stride=1, padding=0)
+            if opt.init:
+                nn.init.xavier_normal(tmpOut.weight)
+            self.stack_lin.append(lin)
+            self.stack_out.append(tmpOut)
+            if i < self.nStack - 1:
+                lin_ = nn.Conv2d(opt.nFeats, opt.nFeats, kernel_size=1, stride=1, padding=0)
+                tmpOut_ = nn.Conv2d(opt.nClasses, opt.nFeats, kernel_size=1, stride=1, padding=0)
+                if opt.init:
+                    nn.init.xavier_normal(lin_.weight)
+                    nn.init.xavier_normal(tmpOut_.weight)
+                self.stack_lin_.append(lin_)
+                self.stack_out_.append(tmpOut_)
+
+    def forward(self, x: Variable):
+        out = []
+        inter = self.preact(x)
+        for i in range(self.nStack):
+            lin = self.stack_lin[i](inter)
+            tmpOut = self.stack_out[i](lin)
+            out.append(tmpOut)
+            if i < self.nStack - 1:
+                lin_ = self.stack_lin_[i](lin)
+                tmpOut_ = self.stack_out_[i](tmpOut)
+                inter = inter + lin_ + tmpOut_
+        return out
+
+
+class PyraNet_Inference(nn.Module):
+    def __init__(self):
+        super(PyraNet_Inference, self).__init__()
+
+        B, C = opt.baseWidth, opt.cardinality
+        self.inputResH = opt.inputResH / 4
+        self.inputResW = opt.inputResW / 4
+        self.nStack = opt.nStack
+
+        conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3)
+        if opt.init:
+            nn.init.xavier_normal(conv1.weight, gain=math.sqrt(1 / 3))
+
+        cnv1 = nn.Sequential(
+            conv1,
+            nn.BatchNorm2d(64),
+            nn.ReLU(True)
+        )
+
+        r1 = nn.Sequential(
+            ResidualPyramid(64, 128, opt.inputResH / 2, opt.inputResW / 2,
+                            stride=1, net_type='no_preact', useConv=False, baseWidth=B, cardinality=C),
+            nn.MaxPool2d(2)
+        )
+        r4 = ResidualPyramid(128, 128, self.inputResH, self.inputResW,
+                             stride=1, net_type='preact', useConv=False, baseWidth=B, cardinality=C)
+        r5 = ResidualPyramid(128, opt.nFeats, self.inputResH, self.inputResW,
+                             stride=1, net_type='preact', useConv=False, baseWidth=B, cardinality=C)
+        self.preact = nn.Sequential(
+            cnv1,
+            r1,
+            r4,
+            r5
+        )
+
+        self.stack_lin = nn.ModuleList()
+        self.stack_out = nn.ModuleList()
+        self.stack_lin_ = nn.ModuleList()
+        self.stack_out_ = nn.ModuleList()
+
+        for i in range(self.nStack):
+            hg = Hourglass(4, opt.nFeats, opt.nResidual,
+                           self.inputResH, self.inputResW, 'preact', B, C)
+            conv1 = nn.Conv2d(opt.nFeats, opt.nFeats,
+                              kernel_size=1, stride=1, padding=0)
+            if opt.init:
+                nn.init.xavier_normal(conv1.weight, gain=math.sqrt(1 / 2))
+            lin = nn.Sequential(
+                hg,
+                nn.BatchNorm2d(opt.nFeats),
+                nn.ReLU(True),
+                conv1,
+                nn.BatchNorm2d(opt.nFeats),
+                nn.ReLU(True)
+            )
+            tmpOut = nn.Conv2d(opt.nFeats, opt.nClasses,
+                               kernel_size=1, stride=1, padding=0)
+            if opt.init:
+                nn.init.xavier_normal(tmpOut.weight)
+            self.stack_lin.append(lin)
+            self.stack_out.append(tmpOut)
+            if i < self.nStack - 1:
+                lin_ = nn.Conv2d(opt.nFeats, opt.nFeats,
+                                 kernel_size=1, stride=1, padding=0)
+                tmpOut_ = nn.Conv2d(opt.nClasses, opt.nFeats,
+                                    kernel_size=1, stride=1, padding=0)
+                if opt.init:
+                    nn.init.xavier_normal(lin_.weight)
+                    nn.init.xavier_normal(tmpOut_.weight)
+                self.stack_lin_.append(lin_)
+                self.stack_out_.append(tmpOut_)
+
+    def forward(self, x: Variable):
+        inter = self.preact(x)
+        for i in range(self.nStack):
+            lin = self.stack_lin[i](inter)
+            tmpOut = self.stack_out[i](lin)
+            out = tmpOut
+            if i < self.nStack - 1:
+                lin_ = self.stack_lin_[i](lin)
+                tmpOut_ = self.stack_out_[i](tmpOut)
+                inter = inter + lin_ + tmpOut_
+        return out
+
+
+def createModel(**kw):
+    model = PyraNet()
+    return model
+
+
+def createModel_Inference(**kw):
+    model = PyraNet_Inference()
+    return model
diff --git a/joints_detectors/Alphapose/SPPE/src/models/layers/DUC.py b/joints_detectors/Alphapose/SPPE/src/models/layers/DUC.py
new file mode 100644
index 0000000000000000000000000000000000000000..3592661fc875dbaa7fa70e01ef49befa89569ebd
--- /dev/null
+++ b/joints_detectors/Alphapose/SPPE/src/models/layers/DUC.py
@@ -0,0 +1,23 @@
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+class DUC(nn.Module):
+    '''
+    INPUT: inplanes, planes, upscale_factor
+    OUTPUT: (planes // 4)* ht * wd
+    '''
+    def __init__(self, inplanes, planes, upscale_factor=2):
+        super(DUC, self).__init__()
+        self.conv = nn.Conv2d(inplanes, planes, kernel_size=3, padding=1, bias=False)
+        self.bn = nn.BatchNorm2d(planes)
+        self.relu = nn.ReLU()
+
+        self.pixel_shuffle = nn.PixelShuffle(upscale_factor)
+
+    def forward(self, x):
+        x = self.conv(x)
+        x = self.bn(x)
+        x = self.relu(x)
+        x = self.pixel_shuffle(x)
+        return x
diff --git a/joints_detectors/Alphapose/SPPE/src/models/layers/PRM.py b/joints_detectors/Alphapose/SPPE/src/models/layers/PRM.py
new file mode 100644
index 0000000000000000000000000000000000000000..375bea4e45362ee240632c94ab6bfbf72f324e26
--- /dev/null
+++ b/joints_detectors/Alphapose/SPPE/src/models/layers/PRM.py
@@ -0,0 +1,135 @@
+import torch.nn as nn
+from .util_models import ConcatTable, CaddTable, Identity
+import math
+from opt import opt
+
+
+class Residual(nn.Module):
+    def __init__(self, numIn, numOut, inputResH, inputResW, stride=1,
+                 net_type='preact', useConv=False, baseWidth=9, cardinality=4):
+        super(Residual, self).__init__()
+
+        self.con = ConcatTable([convBlock(numIn, numOut, inputResH,
+                                          inputResW, net_type, baseWidth, cardinality, stride),
+                                skipLayer(numIn, numOut, stride, useConv)])
+        self.cadd = CaddTable(True)
+
+    def forward(self, x):
+        out = self.con(x)
+        out = self.cadd(out)
+        return out
+
+
+def convBlock(numIn, numOut, inputResH, inputResW, net_type, baseWidth, cardinality, stride):
+    numIn = int(numIn)
+    numOut = int(numOut)
+
+    addTable = ConcatTable()
+    s_list = []
+    if net_type != 'no_preact':
+        s_list.append(nn.BatchNorm2d(numIn))
+        s_list.append(nn.ReLU(True))
+
+    conv1 = nn.Conv2d(numIn, numOut // 2, kernel_size=1)
+    if opt.init:
+        nn.init.xavier_normal(conv1.weight, gain=math.sqrt(1 / 2))
+    s_list.append(conv1)
+
+    s_list.append(nn.BatchNorm2d(numOut // 2))
+    s_list.append(nn.ReLU(True))
+
+    conv2 = nn.Conv2d(numOut // 2, numOut // 2,
+                      kernel_size=3, stride=stride, padding=1)
+    if opt.init:
+        nn.init.xavier_normal(conv2.weight)
+    s_list.append(conv2)
+
+    s = nn.Sequential(*s_list)
+    addTable.add(s)
+
+    D = math.floor(numOut // baseWidth)
+    C = cardinality
+    s_list = []
+
+    if net_type != 'no_preact':
+        s_list.append(nn.BatchNorm2d(numIn))
+        s_list.append(nn.ReLU(True))
+
+    conv1 = nn.Conv2d(numIn, D, kernel_size=1, stride=stride)
+    if opt.init:
+        nn.init.xavier_normal(conv1.weight, gain=math.sqrt(1 / C))
+
+    s_list.append(conv1)
+    s_list.append(nn.BatchNorm2d(D))
+    s_list.append(nn.ReLU(True))
+    s_list.append(pyramid(D, C, inputResH, inputResW))
+    s_list.append(nn.BatchNorm2d(D))
+    s_list.append(nn.ReLU(True))
+
+    a = nn.Conv2d(D, numOut // 2, kernel_size=1)
+    a.nBranchIn = C
+    if opt.init:
+        nn.init.xavier_normal(a.weight, gain=math.sqrt(1 / C))
+    s_list.append(a)
+
+    s = nn.Sequential(*s_list)
+    addTable.add(s)
+
+    elewiswAdd = nn.Sequential(
+        addTable,
+        CaddTable(False)
+    )
+    conv2 = nn.Conv2d(numOut // 2, numOut, kernel_size=1)
+    if opt.init:
+        nn.init.xavier_normal(conv2.weight, gain=math.sqrt(1 / 2))
+    model = nn.Sequential(
+        elewiswAdd,
+        nn.BatchNorm2d(numOut // 2),
+        nn.ReLU(True),
+        conv2
+    )
+    return model
+
+
+def pyramid(D, C, inputResH, inputResW):
+    pyraTable = ConcatTable()
+    sc = math.pow(2, 1 / C)
+    for i in range(C):
+        scaled = 1 / math.pow(sc, i + 1)
+        conv1 = nn.Conv2d(D, D, kernel_size=3, stride=1, padding=1)
+        if opt.init:
+            nn.init.xavier_normal(conv1.weight)
+        s = nn.Sequential(
+            nn.FractionalMaxPool2d(2, output_ratio=(scaled, scaled)),
+            conv1,
+            nn.UpsamplingBilinear2d(size=(int(inputResH), int(inputResW))))
+        pyraTable.add(s)
+    pyra = nn.Sequential(
+        pyraTable,
+        CaddTable(False)
+    )
+    return pyra
+
+
+class skipLayer(nn.Module):
+    def __init__(self, numIn, numOut, stride, useConv):
+        super(skipLayer, self).__init__()
+        self.identity = False
+
+        if numIn == numOut and stride == 1 and not useConv:
+            self.identity = True
+        else:
+            conv1 = nn.Conv2d(numIn, numOut, kernel_size=1, stride=stride)
+            if opt.init:
+                nn.init.xavier_normal(conv1.weight, gain=math.sqrt(1 / 2))
+            self.m = nn.Sequential(
+                nn.BatchNorm2d(numIn),
+                nn.ReLU(True),
+                conv1
+            )
+
+    def forward(self, x):
+        if self.identity:
+            return x
+        else:
+            return self.m(x)
diff --git a/joints_detectors/Alphapose/SPPE/src/models/layers/Residual.py b/joints_detectors/Alphapose/SPPE/src/models/layers/Residual.py
new file mode 100644
index 0000000000000000000000000000000000000000..1449a41aa42da847d85b5851bc9c2ff68cb20cb0
--- /dev/null
+++ b/joints_detectors/Alphapose/SPPE/src/models/layers/Residual.py
@@ -0,0 +1,54 @@
+import torch.nn as nn
+import math
+from .util_models import ConcatTable, CaddTable, Identity
+from opt import opt
+
+
+def Residual(numIn, numOut, *arg, stride=1, net_type='preact', useConv=False, **kw):
+    con = ConcatTable([convBlock(numIn, numOut, stride, net_type),
+                       skipLayer(numIn, numOut, stride, useConv)])
+    cadd = CaddTable(True)
+    return nn.Sequential(con, cadd)
+
+
+def convBlock(numIn, numOut, stride, net_type):
+    s_list = []
+    if net_type != 'no_preact':
+        s_list.append(nn.BatchNorm2d(numIn))
+        s_list.append(nn.ReLU(True))
+
+    conv1 = nn.Conv2d(numIn, numOut // 2, kernel_size=1)
+    if opt.init:
+        nn.init.xavier_normal(conv1.weight, gain=math.sqrt(1 / 2))
+    s_list.append(conv1)
+
+    s_list.append(nn.BatchNorm2d(numOut // 2))
+    s_list.append(nn.ReLU(True))
+
+    conv2 = nn.Conv2d(numOut // 2, numOut // 2, kernel_size=3, stride=stride, padding=1)
+    if opt.init:
+        nn.init.xavier_normal(conv2.weight)
+    s_list.append(conv2)
+    s_list.append(nn.BatchNorm2d(numOut // 2))
+    s_list.append(nn.ReLU(True))
+
+    conv3 = nn.Conv2d(numOut // 2, numOut, kernel_size=1)
+    if opt.init:
+        nn.init.xavier_normal(conv3.weight)
+    s_list.append(conv3)
+
+    return nn.Sequential(*s_list)
+
+
+def skipLayer(numIn, numOut, stride, useConv):
+    if numIn == numOut and stride == 1 and not useConv:
+        return Identity()
+    else:
+        conv1 = nn.Conv2d(numIn, numOut, kernel_size=1, stride=stride)
+        if opt.init:
+            nn.init.xavier_normal(conv1.weight, gain=math.sqrt(1 / 2))
+        return nn.Sequential(
+            nn.BatchNorm2d(numIn),
+            nn.ReLU(True),
+            conv1
+        )
diff --git a/joints_detectors/Alphapose/SPPE/src/models/layers/Resnet.py b/joints_detectors/Alphapose/SPPE/src/models/layers/Resnet.py
new file mode 100644
index 0000000000000000000000000000000000000000..72f07db4a7b8d9395e2ac7a8ad51d7607ee21959
--- /dev/null
+++ b/joints_detectors/Alphapose/SPPE/src/models/layers/Resnet.py
@@ -0,0 +1,82 @@
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+class Bottleneck(nn.Module):
+    expansion = 4
+
+    def __init__(self, inplanes, planes, stride=1, downsample=None):
+        super(Bottleneck, self).__init__()
+        self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, stride=1, bias=False)
+        self.bn1 = nn.BatchNorm2d(planes)
+        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
+        self.bn2 = nn.BatchNorm2d(planes)
+        self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, stride=1, bias=False)
+        self.bn3 = nn.BatchNorm2d(planes * 4)
+        self.downsample = downsample
+        self.stride = stride
+
+    def forward(self, x):
+        residual = x
+
+        out = F.relu(self.bn1(self.conv1(x)), inplace=True)
+        out = F.relu(self.bn2(self.conv2(out)), inplace=True)
+        out = self.bn3(self.conv3(out))
+
+        if self.downsample is not None:
+            residual = self.downsample(x)
+
+        out += residual
+        out = F.relu(out, inplace=True)
+
+        return out
+
+
+class ResNet(nn.Module):
+    """ Resnet """
+    def __init__(self, architecture):
+        super(ResNet, self).__init__()
+        assert architecture in ["resnet50", "resnet101"]
+        self.inplanes = 64
+        self.layers = [3, 4, {"resnet50": 6, "resnet101": 23}[architecture], 3]
+        self.block = Bottleneck
+
+        self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False)
+        self.bn1 = nn.BatchNorm2d(64, eps=1e-5, momentum=0.01, affine=True)
+        self.relu = nn.ReLU(inplace=True)
+        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2)
+
+        self.layer1 = self.make_layer(self.block, 64, self.layers[0])
+        self.layer2 = self.make_layer(self.block, 128, self.layers[1], stride=2)
+        self.layer3 = self.make_layer(self.block, 256, self.layers[2], stride=2)
+
+        self.layer4 = self.make_layer(
+            self.block, 512, self.layers[3], stride=2)
+
+    def forward(self, x):
+        x = self.maxpool(self.relu(self.bn1(self.conv1(x))))
+        x = self.layer1(x)
+        x = self.layer2(x)
+        x = self.layer3(x)
+        x = self.layer4(x)
+        return x
+
+    def stages(self):
+        return [self.layer1, self.layer2, self.layer3, self.layer4]
+
+    def make_layer(self, block, planes, blocks, stride=1):
+        downsample = None
+        if stride != 1 or self.inplanes != planes * block.expansion:
+            downsample = nn.Sequential(
+                nn.Conv2d(self.inplanes, planes * block.expansion,
+                          kernel_size=1, stride=stride, bias=False),
+                nn.BatchNorm2d(planes * block.expansion),
+            )
+
+        layers = []
+        layers.append(block(self.inplanes, planes, stride, downsample))
+        self.inplanes = planes * block.expansion
+        for i in range(1, blocks):
+            layers.append(block(self.inplanes, planes))
+
+        return nn.Sequential(*layers)
diff --git a/joints_detectors/Alphapose/SPPE/src/models/layers/SE_Resnet.py b/joints_detectors/Alphapose/SPPE/src/models/layers/SE_Resnet.py
new file mode 100644
index 0000000000000000000000000000000000000000..caecaa36dc09998153eed2ff74ebfbd775bfe828
--- /dev/null
+++ b/joints_detectors/Alphapose/SPPE/src/models/layers/SE_Resnet.py
@@ -0,0 +1,99 @@
+import torch.nn as nn
+from .SE_module import SELayer
+import torch.nn.functional as F
+
+
+class Bottleneck(nn.Module):
+    expansion = 4
+
+    def __init__(self, inplanes, planes, stride=1, downsample=None, reduction=False):
+        super(Bottleneck, self).__init__()
+        self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
+        self.bn1 = nn.BatchNorm2d(planes)
+        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride,
+                               padding=1, bias=False)
+        self.bn2 = nn.BatchNorm2d(planes)
+        self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)
+        self.bn3 = nn.BatchNorm2d(planes * 4)
+        if reduction:
+            self.se = SELayer(planes * 4)
+
+        self.reduc = reduction
+        self.downsample = downsample
+        self.stride = stride
+
+    def forward(self, x):
+        residual = x
+
+        out = F.relu(self.bn1(self.conv1(x)), inplace=True)
+        out = F.relu(self.bn2(self.conv2(out)), inplace=True)
+
+        out = self.conv3(out)
+        out = self.bn3(out)
+        if self.reduc:
+            out = self.se(out)
+
+        if self.downsample is not None:
+            residual = self.downsample(x)
+
+        out += residual
+        out = F.relu(out)
+
+        return out
+
+
+class SEResnet(nn.Module):
+    """ SEResnet """
+
+    def __init__(self, architecture):
+        super(SEResnet, self).__init__()
+        assert architecture in ["resnet50", "resnet101"]
+        self.inplanes = 64
+        self.layers = [3, 4, {"resnet50": 6, "resnet101": 23}[architecture], 3]
+        self.block = Bottleneck
+
+        self.conv1 = nn.Conv2d(3, 64, kernel_size=7,
+                               stride=2, padding=3, bias=False)
+        self.bn1 = nn.BatchNorm2d(64, eps=1e-5, momentum=0.01, affine=True)
+        self.relu = nn.ReLU(inplace=True)
+        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
+
+        self.layer1 = self.make_layer(self.block, 64, self.layers[0])
+        self.layer2 = self.make_layer(
+            self.block, 128, self.layers[1], stride=2)
+        self.layer3 = self.make_layer(
+            self.block, 256, self.layers[2], stride=2)
+
+        self.layer4 = self.make_layer(
+            self.block, 512, self.layers[3], stride=2)
+
+    def forward(self, x):
+        x = self.maxpool(self.relu(self.bn1(self.conv1(x))))  # 64 * h/4 * w/4
+        x = self.layer1(x)  # 256 * h/4 * w/4
+        x = self.layer2(x)  # 512 * h/8 * w/8
+        x = self.layer3(x)  # 1024 * h/16 * w/16
+        x = self.layer4(x)  # 2048 * h/32 * w/32
+        return x
+
+    def stages(self):
+        return [self.layer1, self.layer2, self.layer3, self.layer4]
+
+    def make_layer(self, block, planes, blocks, stride=1):
+        downsample = None
+        if stride != 1 or self.inplanes != planes * block.expansion:
+            downsample = nn.Sequential(
+                nn.Conv2d(self.inplanes, planes * block.expansion,
+                          kernel_size=1, stride=stride, bias=False),
+                nn.BatchNorm2d(planes * block.expansion),
+            )
+
+        layers = []
+        if downsample is not None:
+            layers.append(block(self.inplanes, planes, stride, downsample, reduction=True))
+        else:
+            layers.append(block(self.inplanes, planes, stride, downsample))
+        self.inplanes = planes * block.expansion
+        for i in range(1, blocks):
+            layers.append(block(self.inplanes, planes))
+
+        return nn.Sequential(*layers)
diff --git a/joints_detectors/Alphapose/SPPE/src/models/layers/SE_module.py b/joints_detectors/Alphapose/SPPE/src/models/layers/SE_module.py
new file mode 100644
index 0000000000000000000000000000000000000000..ab8aefe7c23a5b3fed6780350b78773a16df3e5a
--- /dev/null
+++ b/joints_detectors/Alphapose/SPPE/src/models/layers/SE_module.py
@@ -0,0 +1,19 @@
+from torch import nn
+
+
+class SELayer(nn.Module):
+    def __init__(self, channel, reduction=1):
+        super(SELayer, self).__init__()
+        self.avg_pool = nn.AdaptiveAvgPool2d(1)
+        self.fc = nn.Sequential(
+            nn.Linear(channel, channel // reduction),
+            nn.ReLU(inplace=True),
+            nn.Linear(channel // reduction, channel),
+            nn.Sigmoid()
+        )
+
+    def forward(self, x):
+        b, c, _, _ = x.size()
+        y = self.avg_pool(x).view(b, c)
+        y = self.fc(y).view(b, c, 1, 1)
+        return x * y
diff --git a/joints_detectors/Alphapose/SPPE/src/models/layers/__init__.py b/joints_detectors/Alphapose/SPPE/src/models/layers/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..b6e690fd59145ce8900fd9ab8d8a996ee7d33834
--- /dev/null
+++ b/joints_detectors/Alphapose/SPPE/src/models/layers/__init__.py
@@ -0,0 +1 @@
+from . import *
diff --git a/joints_detectors/Alphapose/SPPE/src/models/layers/util_models.py b/joints_detectors/Alphapose/SPPE/src/models/layers/util_models.py
new file mode 100644
index 0000000000000000000000000000000000000000..52d60d8c2535cceb3d348c79c95a47750c5d2d8a
--- /dev/null
+++ b/joints_detectors/Alphapose/SPPE/src/models/layers/util_models.py
@@ -0,0 +1,37 @@
+import torch
+import torch.nn as nn
+from torch.autograd import Variable
+
+
+class ConcatTable(nn.Module):
+    def __init__(self, module_list=None):
+        super(ConcatTable, self).__init__()
+
+        self.modules_list = nn.ModuleList(module_list)
+
+    def forward(self, x: Variable):
+        y = []
+        for i in range(len(self.modules_list)):
+            y.append(self.modules_list[i](x))
+        return y
+
+    def add(self, module):
+        self.modules_list.append(module)
+
+
+class CaddTable(nn.Module):
+    def __init__(self, inplace=False):
+        super(CaddTable, self).__init__()
+        self.inplace = inplace
+
+    def forward(self, x: Variable or list):
+        return torch.stack(x, 0).sum(0)
+
+
+class Identity(nn.Module):
+    def __init__(self, params=None):
+        super(Identity, self).__init__()
+        self.params = nn.ParameterList(params)
+
+    def forward(self, x: Variable or list):
+        return x
diff --git a/joints_detectors/Alphapose/SPPE/src/opt.py b/joints_detectors/Alphapose/SPPE/src/opt.py
new file mode 100644
index 0000000000000000000000000000000000000000..15152207dd476fd2a31dafa248b244c220aa4f66
--- /dev/null
+++ b/joints_detectors/Alphapose/SPPE/src/opt.py
@@ -0,0 +1,102 @@
+import argparse
+import torch
+
+parser = argparse.ArgumentParser(description='PyTorch AlphaPose Training')
+
+"----------------------------- General options -----------------------------"
+parser.add_argument('--expID', default='default', type=str,
+                    help='Experiment ID')
+parser.add_argument('--dataset', default='coco', type=str,
+                    help='Dataset choice: mpii | coco')
+parser.add_argument('--nThreads', default=30, type=int,
+                    help='Number of data loading threads')
+parser.add_argument('--debug', default=False, type=bool,
+                    help='Print the debug information')
+parser.add_argument('--snapshot', default=1, type=int,
+                    help='How often to take a snapshot of the model (0 = never)')
+
+"----------------------------- AlphaPose options -----------------------------"
+parser.add_argument('--addDPG', default=False, type=bool,
+                    help='Train with data augmentation')
+
+"----------------------------- Model options -----------------------------"
+parser.add_argument('--netType', default='hgPRM', type=str,
+                    help='Options: hgPRM | resnext')
+parser.add_argument('--loadModel', default=None, type=str,
+                    help='Provide full path to a previously trained model')
+parser.add_argument('--Continue', default=False, type=bool,
+                    help='Pick up where an experiment left off')
+parser.add_argument('--nFeats', default=256, type=int,
+                    help='Number of features in the hourglass')
+parser.add_argument('--nClasses', default=17, type=int,
+                    help='Number of output channel')
+parser.add_argument('--nStack', default=8, type=int,
+                    help='Number of hourglasses to stack')
+
+"----------------------------- Hyperparameter options -----------------------------"
+parser.add_argument('--LR', default=2.5e-4, type=float,
+                    help='Learning rate')
+parser.add_argument('--momentum', default=0, type=float,
+                    help='Momentum')
+parser.add_argument('--weightDecay', default=0, type=float,
+                    help='Weight decay')
+parser.add_argument('--crit', default='MSE', type=str,
+                    help='Criterion type')
+parser.add_argument('--optMethod', default='rmsprop', type=str,
+                    help='Optimization method: rmsprop | sgd | nag | adadelta')
+
+
+"----------------------------- Training options -----------------------------"
+parser.add_argument('--nEpochs', default=50, type=int,
+                    help='Number of hourglasses to stack')
+parser.add_argument('--epoch', default=0, type=int,
+                    help='Current epoch')
+parser.add_argument('--trainBatch', default=40, type=int,
+                    help='Train-batch size')
+parser.add_argument('--validBatch', default=20, type=int,
+                    help='Valid-batch size')
+parser.add_argument('--trainIters', default=0, type=int,
+                    help='Total train iters')
+parser.add_argument('--valIters', default=0, type=int,
+                    help='Total valid iters')
+parser.add_argument('--init', default=None, type=str,
+                    help='Initialization')
+"----------------------------- Data options -----------------------------"
+parser.add_argument('--inputResH', default=384, type=int,
+                    help='Input image height')
+parser.add_argument('--inputResW', default=320, type=int,
+                    help='Input image width')
+parser.add_argument('--outputResH', default=96, type=int,
+                    help='Output heatmap height')
+parser.add_argument('--outputResW', default=80, type=int,
+                    help='Output heatmap width')
+parser.add_argument('--scale', default=0.25, type=float,
+                    help='Degree of scale augmentation')
+parser.add_argument('--rotate', default=30, type=float,
+                    help='Degree of rotation augmentation')
+parser.add_argument('--hmGauss', default=1, type=int,
+                    help='Heatmap gaussian size')
+
+"----------------------------- PyraNet options -----------------------------"
+parser.add_argument('--baseWidth', default=9, type=int,
+                    help='Heatmap gaussian size')
+parser.add_argument('--cardinality', default=5, type=int,
+                    help='Heatmap gaussian size')
+parser.add_argument('--nResidual', default=1, type=int,
+                    help='Number of residual modules at each location in the pyranet')
+
+"----------------------------- Distribution options -----------------------------"
+parser.add_argument('--dist', dest='dist', type=int, default=1,
+                    help='distributed training or not')
+parser.add_argument('--backend', dest='backend', type=str, default='gloo',
+                    help='backend for distributed training')
+parser.add_argument('--port', dest='port',
+                    help='port of server')
+
+
+opt = parser.parse_args()
+if opt.Continue:
+    opt = torch.load("../exp/{}/{}/option.pkl".format(opt.dataset, opt.expID))
+    opt.Continue = True
+    opt.nEpochs = 50
+    print("--- Continue ---")
diff --git a/joints_detectors/Alphapose/SPPE/src/utils/__init__.py b/joints_detectors/Alphapose/SPPE/src/utils/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..b6e690fd59145ce8900fd9ab8d8a996ee7d33834
--- /dev/null
+++ b/joints_detectors/Alphapose/SPPE/src/utils/__init__.py
@@ -0,0 +1 @@
+from . import *
diff --git a/joints_detectors/Alphapose/SPPE/src/utils/dataset/.coco.py.swp b/joints_detectors/Alphapose/SPPE/src/utils/dataset/.coco.py.swp
new file mode 100644
index 0000000000000000000000000000000000000000..ca05847bb38d6349466f505cd98f962b71deaee0
Binary files /dev/null and b/joints_detectors/Alphapose/SPPE/src/utils/dataset/.coco.py.swp differ
diff --git a/joints_detectors/Alphapose/SPPE/src/utils/dataset/__init__.py b/joints_detectors/Alphapose/SPPE/src/utils/dataset/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/joints_detectors/Alphapose/SPPE/src/utils/dataset/coco.py b/joints_detectors/Alphapose/SPPE/src/utils/dataset/coco.py
new file mode 100644
index 0000000000000000000000000000000000000000..e1f264648b5bac3090d75021b8245251b866b519
--- /dev/null
+++ b/joints_detectors/Alphapose/SPPE/src/utils/dataset/coco.py
@@ -0,0 +1,85 @@
+import os
+import h5py
+from functools import reduce
+
+import torch.utils.data as data
+from ..pose import generateSampleBox
+from opt import opt
+
+
+class Mscoco(data.Dataset):
+    def __init__(self, train=True, sigma=1,
+                 scale_factor=(0.2, 0.3), rot_factor=40, label_type='Gaussian'):
+        self.img_folder = '../data/coco/images'    # root image folders
+        self.is_train = train           # training set or test set
+        self.inputResH = opt.inputResH
+        self.inputResW = opt.inputResW
+        self.outputResH = opt.outputResH
+        self.outputResW = opt.outputResW
+        self.sigma = sigma
+        self.scale_factor = scale_factor
+        self.rot_factor = rot_factor
+        self.label_type = label_type
+
+        self.nJoints_coco = 17
+        self.nJoints_mpii = 16
+        self.nJoints = 33
+
+        self.accIdxs = (1, 2, 3, 4, 5, 6, 7, 8,
+                        9, 10, 11, 12, 13, 14, 15, 16, 17)
+        self.flipRef = ((2, 3), (4, 5), (6, 7),
+                        (8, 9), (10, 11), (12, 13),
+                        (14, 15), (16, 17))
+
+        # create train/val split
+        with h5py.File('../data/coco/annot_clean.h5', 'r') as annot:
+            # train
+            self.imgname_coco_train = annot['imgname'][:-5887]
+            self.bndbox_coco_train = annot['bndbox'][:-5887]
+            self.part_coco_train = annot['part'][:-5887]
+            # val
+            self.imgname_coco_val = annot['imgname'][-5887:]
+            self.bndbox_coco_val = annot['bndbox'][-5887:]
+            self.part_coco_val = annot['part'][-5887:]
+
+        self.size_train = self.imgname_coco_train.shape[0]
+        self.size_val = self.imgname_coco_val.shape[0]
+
+    def __getitem__(self, index):
+        sf = self.scale_factor
+
+        if self.is_train:
+            part = self.part_coco_train[index]
+            bndbox = self.bndbox_coco_train[index]
+            imgname = self.imgname_coco_train[index]
+        else:
+            part = self.part_coco_val[index]
+            bndbox = self.bndbox_coco_val[index]
+            imgname = self.imgname_coco_val[index]
+
+        imgname = reduce(lambda x, y: x + y, map(lambda x: chr(int(x)), imgname))
+        img_path = os.path.join(self.img_folder, imgname)
+
+        metaData = generateSampleBox(img_path, bndbox, part, self.nJoints,
+                                     'coco', sf, self, train=self.is_train)
+
+        inp, out_bigcircle, out_smallcircle, out, setMask = metaData
+
+        label = []
+        for i in range(opt.nStack):
+            if i < 2:
+                # label.append(out_bigcircle.clone())
+                label.append(out.clone())
+            elif i < 4:
+                # label.append(out_smallcircle.clone())
+                label.append(out.clone())
+            else:
+                label.append(out.clone())
+
+        return inp, label, setMask, 'coco'
+
+    def __len__(self):
+        if self.is_train:
+            return self.size_train
+        else:
+            return self.size_val
diff --git a/joints_detectors/Alphapose/SPPE/src/utils/dataset/fuse.py b/joints_detectors/Alphapose/SPPE/src/utils/dataset/fuse.py
new file mode 100644
index 0000000000000000000000000000000000000000..db3e04a20c0b21186402c861c65de0cb7d0940b4
--- /dev/null
+++ b/joints_detectors/Alphapose/SPPE/src/utils/dataset/fuse.py
@@ -0,0 +1,122 @@
+import os
+import h5py
+from functools import reduce
+
+import torch.utils.data as data
+from ..pose import generateSampleBox
+from opt import opt
+
+
+class Mscoco(data.Dataset):
+    def __init__(self, train=True, sigma=1,
+                 scale_factor=0.25, rot_factor=30, label_type='Gaussian'):
+        self.img_folder = '../data/'    # root image folders
+        self.is_train = train           # training set or test set
+        self.inputResH = 320
+        self.inputResW = 256
+        self.outputResH = 80
+        self.outputResW = 64
+        self.sigma = sigma
+        self.scale_factor = (0.2, 0.3)
+        self.rot_factor = rot_factor
+        self.label_type = label_type
+
+        self.nJoints_coco = 17
+        self.nJoints_mpii = 16
+        self.nJoints = 33
+
+        self.accIdxs = (1, 2, 3, 4, 5, 6, 7, 8,             # COCO
+                        9, 10, 11, 12, 13, 14, 15, 16, 17,
+                        18, 19, 20, 21, 22, 23,             # MPII
+                        28, 29, 32, 33)
+
+        self.flipRef = ((2, 3), (4, 5), (6, 7),             # COCO
+                        (8, 9), (10, 11), (12, 13),
+                        (14, 15), (16, 17),
+                        (18, 23), (19, 22), (20, 21),       # MPII
+                        (28, 33), (29, 32), (30, 31))
+
+        '''
+        Create train/val split
+        '''
+        # COCO
+        with h5py.File('../data/coco/annot_clean.h5', 'r') as annot:
+            # train
+            self.imgname_coco_train = annot['imgname'][:-5887]
+            self.bndbox_coco_train = annot['bndbox'][:-5887]
+            self.part_coco_train = annot['part'][:-5887]
+            # val
+            self.imgname_coco_val = annot['imgname'][-5887:]
+            self.bndbox_coco_val = annot['bndbox'][-5887:]
+            self.part_coco_val = annot['part'][-5887:]
+        # MPII
+        with h5py.File('../data/mpii/annot_mpii.h5', 'r') as annot:
+            # train
+            self.imgname_mpii_train = annot['imgname'][:-1358]
+            self.bndbox_mpii_train = annot['bndbox'][:-1358]
+            self.part_mpii_train = annot['part'][:-1358]
+            # val
+            self.imgname_mpii_val = annot['imgname'][-1358:]
+            self.bndbox_mpii_val = annot['bndbox'][-1358:]
+            self.part_mpii_val = annot['part'][-1358:]
+
+        self.size_coco_train = self.imgname_coco_train.shape[0]
+        self.size_coco_val = self.imgname_coco_val.shape[0]
+        self.size_train = self.imgname_coco_train.shape[0] + self.imgname_mpii_train.shape[0]
+        self.size_val = self.imgname_coco_val.shape[0] + self.imgname_mpii_val.shape[0]
+        self.train, self.valid = [], []
+
+    def __getitem__(self, index):
+        sf = self.scale_factor
+
+        if self.is_train and index < self.size_coco_train:  # COCO
+            part = self.part_coco_train[index]
+            bndbox = self.bndbox_coco_train[index]
+            imgname = self.imgname_coco_train[index]
+            imgset = 'coco'
+        elif self.is_train:  # MPII
+            part = self.part_mpii_train[index - self.size_coco_train]
+            bndbox = self.bndbox_mpii_train[index - self.size_coco_train]
+            imgname = self.imgname_mpii_train[index - self.size_coco_train]
+            imgset = 'mpii'
+        elif index < self.size_coco_val:
+            part = self.part_coco_val[index]
+            bndbox = self.bndbox_coco_val[index]
+            imgname = self.imgname_coco_val[index]
+            imgset = 'coco'
+        else:
+            part = self.part_mpii_val[index - self.size_coco_val]
+            bndbox = self.bndbox_mpii_val[index - self.size_coco_val]
+            imgname = self.imgname_mpii_val[index - self.size_coco_val]
+            imgset = 'mpii'
+
+        if imgset == 'coco':
+            imgname = reduce(lambda x, y: x + y, map(lambda x: chr(int(x)), imgname))
+        else:
+            imgname = reduce(lambda x, y: x + y, map(lambda x: chr(int(x)), imgname))[:13]
+
+        img_path = os.path.join(self.img_folder, imgset, 'images', imgname)
+
+        metaData = generateSampleBox(img_path, bndbox, part, self.nJoints,
+                                     imgset, sf, self, train=self.is_train)
+
+        inp, out_bigcircle, out_smallcircle, out, setMask = metaData
+
+        label = []
+        for i in range(opt.nStack):
+            if i < 2:
+                # label.append(out_bigcircle.clone())
+                label.append(out.clone())
+            elif i < 4:
+                # label.append(out_smallcircle.clone())
+                label.append(out.clone())
+            else:
+                label.append(out.clone())
+
+        return inp, label, setMask, imgset
+
+    def __len__(self):
+        if self.is_train:
+            return self.size_train
+        else:
+            return self.size_val
diff --git a/joints_detectors/Alphapose/SPPE/src/utils/dataset/mpii.py b/joints_detectors/Alphapose/SPPE/src/utils/dataset/mpii.py
new file mode 100644
index 0000000000000000000000000000000000000000..eae0dd884ae1c9f02fd46b2924cb0132e8275c74
--- /dev/null
+++ b/joints_detectors/Alphapose/SPPE/src/utils/dataset/mpii.py
@@ -0,0 +1,84 @@
+import os
+import h5py
+from functools import reduce
+
+import torch.utils.data as data
+from ..pose import generateSampleBox
+from opt import opt
+
+
+class Mpii(data.Dataset):
+    def __init__(self, train=True, sigma=1,
+                 scale_factor=0.25, rot_factor=30, label_type='Gaussian'):
+        self.img_folder = '../data/mpii/images'    # root image folders
+        self.is_train = train           # training set or test set
+        self.inputResH = 320
+        self.inputResW = 256
+        self.outputResH = 80
+        self.outputResW = 64
+        self.sigma = sigma
+        self.scale_factor = (0.2, 0.3)
+        self.rot_factor = rot_factor
+        self.label_type = label_type
+
+        self.nJoints_mpii = 16
+        self.nJoints = 16
+
+        self.accIdxs = (1, 2, 3, 4, 5, 6,
+                        11, 12, 15, 16)
+        self.flipRef = ((1, 6), (2, 5), (3, 4),
+                        (11, 16), (12, 15), (13, 14))
+
+        # create train/val split
+        with h5py.File('../data/mpii/annot_mpii.h5', 'r') as annot:
+            # train
+            self.imgname_mpii_train = annot['imgname'][:-1358]
+            self.bndbox_mpii_train = annot['bndbox'][:-1358]
+            self.part_mpii_train = annot['part'][:-1358]
+            # val
+            self.imgname_mpii_val = annot['imgname'][-1358:]
+            self.bndbox_mpii_val = annot['bndbox'][-1358:]
+            self.part_mpii_val = annot['part'][-1358:]
+
+        self.size_train = self.imgname_mpii_train.shape[0]
+        self.size_val = self.imgname_mpii_val.shape[0]
+        self.train, self.valid = [], []
+
+    def __getitem__(self, index):
+        sf = self.scale_factor
+
+        if self.is_train:
+            part = self.part_mpii_train[index]
+            bndbox = self.bndbox_mpii_train[index]
+            imgname = self.imgname_mpii_train[index]
+        else:
+            part = self.part_mpii_val[index]
+            bndbox = self.bndbox_mpii_val[index]
+            imgname = self.imgname_mpii_val[index]
+
+        imgname = reduce(lambda x, y: x + y, map(lambda x: chr(int(x)), imgname))[:13]
+        img_path = os.path.join(self.img_folder, imgname)
+
+        metaData = generateSampleBox(img_path, bndbox, part, self.nJoints,
+                                     'mpii', sf, self, train=self.is_train)
+
+        inp, out_bigcircle, out_smallcircle, out, setMask = metaData
+
+        label = []
+        for i in range(opt.nStack):
+            if i < 2:
+                #label.append(out_bigcircle.clone())
+                label.append(out.clone())
+            elif i < 4:
+                #label.append(out_smallcircle.clone())
+                label.append(out.clone())
+            else:
+                label.append(out.clone())
+
+        return inp, label, setMask
+
+    def __len__(self):
+        if self.is_train:
+            return self.size_train
+        else:
+            return self.size_val
diff --git a/joints_detectors/Alphapose/SPPE/src/utils/eval.py b/joints_detectors/Alphapose/SPPE/src/utils/eval.py
new file mode 100644
index 0000000000000000000000000000000000000000..babff62966de3a47b53ae3aa2c442da39f4b6a7d
--- /dev/null
+++ b/joints_detectors/Alphapose/SPPE/src/utils/eval.py
@@ -0,0 +1,219 @@
+from opt import opt
+try:
+    from utils.img import transformBoxInvert, transformBoxInvert_batch, findPeak, processPeaks
+except ImportError:
+    from SPPE.src.utils.img import transformBoxInvert, transformBoxInvert_batch, findPeak, processPeaks
+import torch
+
+
+class DataLogger(object):
+    def __init__(self):
+        self.clear()
+
+    def clear(self):
+        self.value = 0
+        self.sum = 0
+        self.cnt = 0
+        self.avg = 0
+
+    def update(self, value, n=1):
+        self.value = value
+        self.sum += value * n
+        self.cnt += n
+        self._cal_avg()
+
+    def _cal_avg(self):
+        self.avg = self.sum / self.cnt
+
+
+def accuracy(output, label, dataset):
+    if type(output) == list:
+        return accuracy(output[opt.nStack - 1], label[opt.nStack - 1], dataset)
+    else:
+        return heatmapAccuracy(output.cpu().data, label.cpu().data, dataset.accIdxs)
+
+
+def heatmapAccuracy(output, label, idxs):
+    preds = getPreds(output)
+    gt = getPreds(label)
+
+    norm = torch.ones(preds.size(0)) * opt.outputResH / 10
+    dists = calc_dists(preds, gt, norm)
+    #print(dists)
+    acc = torch.zeros(len(idxs) + 1)
+    avg_acc = 0
+    cnt = 0
+    for i in range(len(idxs)):
+        acc[i + 1] = dist_acc(dists[idxs[i] - 1])
+        if acc[i + 1] >= 0:
+            avg_acc = avg_acc + acc[i + 1]
+            cnt += 1
+    if cnt != 0:
+        acc[0] = avg_acc / cnt
+    return acc
+
+
+def getPreds(hm):
+    ''' get predictions from score maps in torch Tensor
+        return type: torch.LongTensor
+    '''
+    assert hm.dim() == 4, 'Score maps should be 4-dim'
+    maxval, idx = torch.max(hm.view(hm.size(0), hm.size(1), -1), 2)
+
+    maxval = maxval.view(hm.size(0), hm.size(1), 1)
+    idx = idx.view(hm.size(0), hm.size(1), 1) + 1
+
+    preds = idx.repeat(1, 1, 2).float()
+
+    preds[:, :, 0] = (preds[:, :, 0] - 1) % hm.size(3)
+    preds[:, :, 1] = torch.floor((preds[:, :, 1] - 1) / hm.size(3))
+
+    # pred_mask = maxval.gt(0).repeat(1, 1, 2).float()
+    # preds *= pred_mask
+    return preds
+
+
+def calc_dists(preds, target, normalize):
+    preds = preds.float().clone()
+    target = target.float().clone()
+    dists = torch.zeros(preds.size(1), preds.size(0))
+    for n in range(preds.size(0)):
+        for c in range(preds.size(1)):
+            if target[n, c, 0] > 0 and target[n, c, 1] > 0:
+                dists[c, n] = torch.dist(
+                    preds[n, c, :], target[n, c, :]) / normalize[n]
+            else:
+                dists[c, n] = -1
+    return dists
+
+
+def dist_acc(dists, thr=0.5):
+    ''' Return percentage below threshold while ignoring values with a -1 '''
+    if dists.ne(-1).sum() > 0:
+        return dists.le(thr).eq(dists.ne(-1)).float().sum() * 1.0 / dists.ne(-1).float().sum()
+    else:
+        return - 1
+
+
+def postprocess(output):
+    p = getPreds(output)
+
+    for i in range(p.size(0)):
+        for j in range(p.size(1)):
+            hm = output[i][j]
+            pX, pY = int(round(p[i][j][0])), int(round(p[i][j][1]))
+            if 0 < pX < opt.outputResW - 1 and 0 < pY < opt.outputResH - 1:
+                diff = torch.Tensor((hm[pY][pX + 1] - hm[pY][pX - 1], hm[pY + 1][pX] - hm[pY - 1][pX]))
+                p[i][j] += diff.sign() * 0.25
+    p -= 0.5
+
+    return p
+
+
+def getPrediction(hms, pt1, pt2, inpH, inpW, resH, resW):
+    '''
+    Get keypoint location from heatmaps
+    '''
+
+    assert hms.dim() == 4, 'Score maps should be 4-dim'
+    maxval, idx = torch.max(hms.view(hms.size(0), hms.size(1), -1), 2)
+
+    maxval = maxval.view(hms.size(0), hms.size(1), 1)
+    idx = idx.view(hms.size(0), hms.size(1), 1) + 1
+
+    preds = idx.repeat(1, 1, 2).float()
+
+    preds[:, :, 0] = (preds[:, :, 0] - 1) % hms.size(3)
+    preds[:, :, 1] = torch.floor((preds[:, :, 1] - 1) / hms.size(3))
+
+    pred_mask = maxval.gt(0).repeat(1, 1, 2).float()
+    preds *= pred_mask
+
+    # Very simple post-processing step to improve performance at tight PCK thresholds
+    for i in range(preds.size(0)):
+        for j in range(preds.size(1)):
+            hm = hms[i][j]
+            pX, pY = int(round(float(preds[i][j][0]))), int(round(float(preds[i][j][1])))
+            if 0 < pX < opt.outputResW - 1 and 0 < pY < opt.outputResH - 1:
+                diff = torch.Tensor(
+                    (hm[pY][pX + 1] - hm[pY][pX - 1], hm[pY + 1][pX] - hm[pY - 1][pX]))
+                preds[i][j] += diff.sign() * 0.25
+    preds += 0.2
+
+    preds_tf = torch.zeros(preds.size())
+
+    preds_tf = transformBoxInvert_batch(preds, pt1, pt2, inpH, inpW, resH, resW)
+
+    return preds, preds_tf, maxval
+
+
+def getMultiPeakPrediction(hms, pt1, pt2, inpH, inpW, resH, resW):
+
+    assert hms.dim() == 4, 'Score maps should be 4-dim'
+
+    preds_img = {}
+    hms = hms.numpy()
+    for n in range(hms.shape[0]):        # Number of samples
+        preds_img[n] = {}           # Result of sample: n
+        for k in range(hms.shape[1]):    # Number of keypoints
+            preds_img[n][k] = []    # Result of keypoint: k
+            hm = hms[n][k]
+
+            candidate_points = findPeak(hm)
+
+            res_pt = processPeaks(candidate_points, hm,
+                                  pt1[n], pt2[n], inpH, inpW, resH, resW)
+
+            preds_img[n][k] = res_pt
+
+    return preds_img
+
+
+def getPrediction_batch(hms, pt1, pt2, inpH, inpW, resH, resW):
+    '''
+    Get keypoint location from heatmaps
+    pt1, pt2:   [n, 2]
+    OUTPUT:
+        preds:  [n, 17, 2]
+    '''
+
+    assert hms.dim() == 4, 'Score maps should be 4-dim'
+    flat_hms = hms.view(hms.size(0), hms.size(1), -1)
+    maxval, idx = torch.max(flat_hms, 2)
+
+    maxval = maxval.view(hms.size(0), hms.size(1), 1)
+    idx = idx.view(hms.size(0), hms.size(1), 1) + 1
+
+    preds = idx.repeat(1, 1, 2).float()
+
+    preds[:, :, 0] = (preds[:, :, 0] - 1) % hms.size(3)
+    preds[:, :, 1] = torch.floor((preds[:, :, 1] - 1) / hms.size(3))
+
+    pred_mask = maxval.gt(0).repeat(1, 1, 2).float()
+    preds *= pred_mask
+
+    # Very simple post-processing step to improve performance at tight PCK thresholds
+    idx_up = (idx - hms.size(3)).clamp(0, flat_hms.size(2) - 1)
+    idx_down = (idx + hms.size(3)).clamp(0, flat_hms.size(2) - 1)
+    idx_left = (idx - 1).clamp(0, flat_hms.size(2) - 1)
+    idx_right = (idx + 1).clamp(0, flat_hms.size(2) - 1)
+
+    maxval_up = flat_hms.gather(2, idx_up)
+    maxval_down = flat_hms.gather(2, idx_down)
+    maxval_left = flat_hms.gather(2, idx_left)
+    maxval_right = flat_hms.gather(2, idx_right)
+
+    diff1 = (maxval_right - maxval_left).sign() * 0.25
+    diff2 = (maxval_down - maxval_up).sign() * 0.25
+    diff1[idx_up <= hms.size(3)] = 0
+    diff1[idx_down / hms.size(3) >= (hms.size(3) - 1)] = 0
+    diff2[(idx_left % hms.size(3)) == 0] = 0
+    diff2[(idx_left % hms.size(3)) == (hms.size(3) - 1)] = 0
+
+    preds[:, :, 0] += diff1.squeeze(-1)
+    preds[:, :, 1] += diff2.squeeze(-1)
+
+    preds_tf = torch.zeros(preds.size())
+    preds_tf = transformBoxInvert_batch(preds, pt1, pt2, inpH, inpW, resH, resW)
+
+    return preds, preds_tf, maxval
diff --git a/joints_detectors/Alphapose/SPPE/src/utils/img.py b/joints_detectors/Alphapose/SPPE/src/utils/img.py
new file mode 100644
index 0000000000000000000000000000000000000000..18517513843d7f00b06e49d0294d09e5b893dbf8
--- /dev/null
+++ b/joints_detectors/Alphapose/SPPE/src/utils/img.py
@@ -0,0 +1,497 @@
+import numpy as np
+import cv2
+import torch
+import scipy.misc
+from torchvision import transforms
+import torch.nn.functional as F
+from scipy.ndimage import maximum_filter
+
+from PIL import Image
+from copy import deepcopy
+import matplotlib
+matplotlib.use('agg')
+import matplotlib.pyplot as plt
+
+
+def im_to_torch(img):
+    img = np.transpose(img, (2, 0, 1))  # C*H*W
+    img = to_torch(img).float()
+    if img.max() > 1:
+        img /= 255
+    return img
+
+
+def torch_to_im(img):
+    img = to_numpy(img)
+    img = np.transpose(img, (1, 2, 0))  # C*H*W
+    return img
+
+
+def load_image(img_path):
+    # H x W x C => C x H x W
+    return im_to_torch(scipy.misc.imread(img_path, mode='RGB'))
+
+
+def to_numpy(tensor):
+    if torch.is_tensor(tensor):
+        return tensor.cpu().numpy()
+    elif type(tensor).__module__ != 'numpy':
+        raise ValueError("Cannot convert {} to numpy array"
+                         .format(type(tensor)))
+    return tensor
+
+
+def to_torch(ndarray):
+    if type(ndarray).__module__ == 'numpy':
+        return torch.from_numpy(ndarray)
+    elif not torch.is_tensor(ndarray):
+        raise ValueError("Cannot convert {} to torch tensor"
+                         .format(type(ndarray)))
+    return ndarray
+
+
+def drawCircle(img, pt, sigma):
+    img = to_numpy(img)
+    tmpSize = 3 * sigma
+    # Check that any part of the gaussian is in-bounds
+    ul = [int(pt[0] - tmpSize), int(pt[1] - tmpSize)]
+    br = [int(pt[0] + tmpSize + 1), int(pt[1] + tmpSize + 1)]
+
+    if (ul[0] >= img.shape[1] or ul[1] >= img.shape[0] or
+            br[0] < 0 or br[1] < 0):
+        # If not, just return the image as is
+        return to_torch(img)
+
+    # Generate gaussian
+    size = 2 * tmpSize + 1
+    x = np.arange(0, size, 1, float)
+    y = x[:, np.newaxis]
+    x0 = y0 = size // 2
+    sigma = size / 4.0
+    # The gaussian is not normalized, we want the center value to equal 1
+    g = np.exp(- ((x - x0) ** 2 + (y - y0) ** 2) / (2 * sigma ** 2))
+    g[g > 0] = 1
+    # Usable gaussian range
+    g_x = max(0, -ul[0]), min(br[0], img.shape[1]) - ul[0]
+    g_y = max(0, -ul[1]), min(br[1], img.shape[0]) - ul[1]
+    # Image range
+    img_x = max(0, ul[0]), min(br[0], img.shape[1])
+    img_y = max(0, ul[1]), min(br[1], img.shape[0])
+
+    img[img_y[0]:img_y[1], img_x[0]:img_x[1]] = g[g_y[0]:g_y[1], g_x[0]:g_x[1]]
+    return to_torch(img)
+
+
+def drawGaussian(img, pt, sigma):
+    img = to_numpy(img)
+    tmpSize = 3 * sigma
+    # Check that any part of the gaussian is in-bounds
+    ul = [int(pt[0] - tmpSize), int(pt[1] - tmpSize)]
+    br = [int(pt[0] + tmpSize + 1), int(pt[1] + tmpSize + 1)]
+
+    if (ul[0] >= img.shape[1] or ul[1] >= img.shape[0] or
+            br[0] < 0 or br[1] < 0):
+        # If not, just return the image as is
+        return to_torch(img)
+
+    # Generate gaussian
+    size = 2 * tmpSize + 1
+    x = np.arange(0, size, 1, float)
+    y = x[:, np.newaxis]
+    x0 = y0 = size // 2
+    sigma = size / 4.0    
+    # The gaussian is not normalized, we want the center value to equal 1
+    g = np.exp(- ((x - x0) ** 2 + (y - y0) ** 2) / (2 * sigma ** 2))
+
+    # Usable gaussian range
+    g_x = max(0, -ul[0]), min(br[0], img.shape[1]) - ul[0]
+    g_y = max(0, -ul[1]), min(br[1], img.shape[0]) - ul[1]
+    # Image range
+    img_x = max(0, ul[0]), min(br[0], img.shape[1])
+    img_y = max(0, ul[1]), min(br[1], img.shape[0])
+
+    img[img_y[0]:img_y[1], img_x[0]:img_x[1]] = g[g_y[0]:g_y[1], g_x[0]:g_x[1]]
+    return to_torch(img)
+
+
+def drawBigCircle(img, pt, sigma):
+    img = to_numpy(img)
+    tmpSize = 3 * sigma
+    # Check that any part of the gaussian is in-bounds
+    ul = [int(pt[0] - tmpSize), int(pt[1] - tmpSize)]
+    br = [int(pt[0] + tmpSize + 1), int(pt[1] + tmpSize + 1)]
+
+    if (ul[0] >= img.shape[1] or ul[1] >= img.shape[0] or
+            br[0] < 0 or br[1] < 0):
+        # If not, just return the image as is
+        return to_torch(img)
+
+    # Generate gaussian
+    size = 2 * tmpSize + 1
+    x = np.arange(0, size, 1, float)
+    y = x[:, np.newaxis]
+    x0 = y0 = size // 2
+    sigma = size / 4.0
+    # The gaussian is not normalized, we want the center value to equal 1
+    g = np.exp(- ((x - x0) ** 2 + (y - y0) ** 2) / (2 * sigma ** 2))
+    g[g > 0.4] = 1
+    # Usable gaussian range
+    g_x = max(0, -ul[0]), min(br[0], img.shape[1]) - ul[0]
+    g_y = max(0, -ul[1]), min(br[1], img.shape[0]) - ul[1]
+    # Image range
+    img_x = max(0, ul[0]), min(br[0], img.shape[1])
+    img_y = max(0, ul[1]), min(br[1], img.shape[0])
+
+    img[img_y[0]:img_y[1], img_x[0]:img_x[1]] = g[g_y[0]:g_y[1], g_x[0]:g_x[1]]
+    return to_torch(img)
+
+
+def drawSmallCircle(img, pt, sigma):
+    img = to_numpy(img)
+    tmpSize = 3 * sigma
+    # Check that any part of the gaussian is in-bounds
+    ul = [int(pt[0] - tmpSize), int(pt[1] - tmpSize)]
+    br = [int(pt[0] + tmpSize + 1), int(pt[1] + tmpSize + 1)]
+
+    if (ul[0] >= img.shape[1] or ul[1] >= img.shape[0] or
+            br[0] < 0 or br[1] < 0):
+        # If not, just return the image as is
+        return to_torch(img)
+
+    # Generate gaussian
+    size = 2 * tmpSize + 1
+    x = np.arange(0, size, 1, float)
+    y = x[:, np.newaxis]
+    x0 = y0 = size // 2
+    sigma = size / 4.0
+    # The gaussian is not normalized, we want the center value to equal 1
+    g = np.exp(- ((x - x0) ** 2 + (y - y0) ** 2) / (2 * sigma ** 2))
+    g[g > 0.5] = 1
+    # Usable gaussian range
+    g_x = max(0, -ul[0]), min(br[0], img.shape[1]) - ul[0]
+    g_y = max(0, -ul[1]), min(br[1], img.shape[0]) - ul[1]
+    # Image range
+    img_x = max(0, ul[0]), min(br[0], img.shape[1])
+    img_y = max(0, ul[1]), min(br[1], img.shape[0])
+
+    img[img_y[0]:img_y[1], img_x[0]:img_x[1]] = g[g_y[0]:g_y[1], g_x[0]:g_x[1]]
+    return to_torch(img)
+
+
+def transformBox(pt, ul, br, inpH, inpW, resH, resW):
+    center = torch.zeros(2)
+    center[0] = (br[0] - 1 - ul[0]) / 2
+    center[1] = (br[1] - 1 - ul[1]) / 2
+
+    lenH = max(br[1] - ul[1], (br[0] - ul[0]) * inpH / inpW)
+    lenW = lenH * inpW / inpH
+
+    _pt = torch.zeros(2)
+    _pt[0] = pt[0] - ul[0]
+    _pt[1] = pt[1] - ul[1]
+    # Move to center
+    _pt[0] = _pt[0] + max(0, (lenW - 1) / 2 - center[0])
+    _pt[1] = _pt[1] + max(0, (lenH - 1) / 2 - center[1])
+    pt = (_pt * resH) / lenH
+    pt[0] = round(float(pt[0]))
+    pt[1] = round(float(pt[1]))
+    return pt.int()
+
+
+def transformBoxInvert(pt, ul, br, inpH, inpW, resH, resW):
+    center = np.zeros(2)
+    center[0] = (br[0] - 1 - ul[0]) / 2
+    center[1] = (br[1] - 1 - ul[1]) / 2
+
+    lenH = max(br[1] - ul[1], (br[0] - ul[0]) * inpH / inpW)
+    lenW = lenH * inpW / inpH
+
+    _pt = (pt * lenH) / resH
+    _pt[0] = _pt[0] - max(0, (lenW - 1) / 2 - center[0])
+    _pt[1] = _pt[1] - max(0, (lenH - 1) / 2 - center[1])
+
+    new_point = np.zeros(2)
+    new_point[0] = _pt[0] + ul[0]
+    new_point[1] = _pt[1] + ul[1]
+    return new_point
+
+
+def transformBoxInvert_batch(pt, ul, br, inpH, inpW, resH, resW):
+    '''
+    pt:     [n, 17, 2]
+    ul:     [n, 2]
+    br:     [n, 2]
+    '''
+    center = (br - 1 - ul) / 2
+
+    size = br - ul
+    size[:, 0] *= (inpH / inpW)
+
+    lenH, _ = torch.max(size, dim=1)   # [n,]
+    lenW = lenH * (inpW / inpH)
+
+    _pt = (pt * lenH[:, np.newaxis, np.newaxis]) / resH
+    _pt[:, :, 0] = _pt[:, :, 0] - ((lenW[:, np.newaxis].repeat(1, 17) - 1) /
+                                   2 - center[:, 0].unsqueeze(-1).repeat(1, 17)).clamp(min=0)
+    _pt[:, :, 1] = _pt[:, :, 1] - ((lenH[:, np.newaxis].repeat(1, 17) - 1) /
+                                   2 - center[:, 1].unsqueeze(-1).repeat(1, 17)).clamp(min=0)
+
+    new_point = torch.zeros(pt.size())
+    new_point[:, :, 0] = _pt[:, :, 0] + ul[:, 0].unsqueeze(-1).repeat(1, 17)
+    new_point[:, :, 1] = _pt[:, :, 1] + ul[:, 1].unsqueeze(-1).repeat(1, 17)
+    return new_point
+
+
+def cropBox(img, ul, br, resH, resW):
+    ul = ul.int()
+    br = (br - 1).int()
+    # br = br.int()
+    lenH = max((br[1] - ul[1]).item(), (br[0] - ul[0]).item() * resH / resW)
+    lenW = lenH * resW / resH
+    if img.dim() == 2:
+        img = img[np.newaxis, :]
+
+    box_shape = [(br[1] - ul[1]).item(), (br[0] - ul[0]).item()]
+    pad_size = [(lenH - box_shape[0]) // 2, (lenW - box_shape[1]) // 2]
+    # Padding Zeros
+    if ul[1] > 0:
+        img[:, :ul[1], :] = 0
+    if ul[0] > 0:
+        img[:, :, :ul[0]] = 0
+    if br[1] < img.shape[1] - 1:
+        img[:, br[1] + 1:, :] = 0
+    if br[0] < img.shape[2] - 1:
+        img[:, :, br[0] + 1:] = 0
+
+    src = np.zeros((3, 2), dtype=np.float32)
+    dst = np.zeros((3, 2), dtype=np.float32)
+
+    src[0, :] = np.array(
+        [ul[0] - pad_size[1], ul[1] - pad_size[0]], np.float32)
+    src[1, :] = np.array(
+        [br[0] + pad_size[1], br[1] + pad_size[0]], np.float32)
+    dst[0, :] = 0
+    dst[1, :] = np.array([resW - 1, resH - 1], np.float32)
+
+    src[2:, :] = get_3rd_point(src[0, :], src[1, :])
+    dst[2:, :] = get_3rd_point(dst[0, :], dst[1, :])
+
+    trans = cv2.getAffineTransform(np.float32(src), np.float32(dst))
+
+    dst_img = cv2.warpAffine(torch_to_im(img), trans,
+                             (resW, resH), flags=cv2.INTER_LINEAR)
+
+    return im_to_torch(torch.Tensor(dst_img))
+
+
+def cv_rotate(img, rot, resW, resH):
+    center = np.array((resW - 1, resH - 1)) / 2
+    rot_rad = np.pi * rot / 180
+
+    src_dir = get_dir([0, (resH - 1) * -0.5], rot_rad)
+    dst_dir = np.array([0, (resH - 1) * -0.5], np.float32)
+
+    src = np.zeros((3, 2), dtype=np.float32)
+    dst = np.zeros((3, 2), dtype=np.float32)
+
+    src[0, :] = center
+    src[1, :] = center + src_dir
+    dst[0, :] = [(resW - 1) * 0.5, (resH - 1) * 0.5]
+    dst[1, :] = np.array([(resW - 1) * 0.5, (resH - 1) * 0.5]) + dst_dir
+
+    src[2:, :] = get_3rd_point(src[0, :], src[1, :])
+    dst[2:, :] = get_3rd_point(dst[0, :], dst[1, :])
+
+    trans = cv2.getAffineTransform(np.float32(src), np.float32(dst))
+
+    dst_img = cv2.warpAffine(torch_to_im(img), trans,
+                             (resW, resH), flags=cv2.INTER_LINEAR)
+
+    return im_to_torch(torch.Tensor(dst_img))
+
+
+def flip(x):
+    assert (x.dim() == 3 or x.dim() == 4)
+    dim = x.dim() - 1
+    if '0.4.1' in torch.__version__ or '1.0' in torch.__version__:
+        return x.flip(dims=(dim,))
+    else:
+        is_cuda = False
+        if x.is_cuda:
+            is_cuda = True
+            x = x.cpu()
+        x = x.numpy().copy()
+        if x.ndim == 3:
+            x = np.transpose(np.fliplr(np.transpose(x, (0, 2, 1))), (0, 2, 1))
+        elif x.ndim == 4:
+            for i in range(x.shape[0]):
+                x[i] = np.transpose(
+                    np.fliplr(np.transpose(x[i], (0, 2, 1))), (0, 2, 1))
+        # x = x.swapaxes(dim, 0)
+        # x = x[::-1, ...]
+        # x = x.swapaxes(0, dim)
+
+        x = torch.from_numpy(x.copy())
+        if is_cuda:
+            x = x.cuda()
+        return x
+
+
+def shuffleLR(x, dataset):
+    flipRef = dataset.flipRef
+    assert (x.dim() == 3 or x.dim() == 4)
+    for pair in flipRef:
+        dim0, dim1 = pair
+        dim0 -= 1
+        dim1 -= 1
+        if x.dim() == 4:
+            tmp = x[:, dim1].clone()
+            x[:, dim1] = x[:, dim0].clone()
+            x[:, dim0] = tmp.clone()
+            #x[:, dim0], x[:, dim1] = deepcopy((x[:, dim1], x[:, dim0]))
+        else:
+            tmp = x[dim1].clone()
+            x[dim1] = x[dim0].clone()
+            x[dim0] = tmp.clone()
+            #x[dim0], x[dim1] = deepcopy((x[dim1], x[dim0]))
+    return x
+
+
+def drawMPII(inps, preds):
+    assert inps.dim() == 4
+    p_color = ['g', 'b', 'purple', 'b', 'purple',
+               'y', 'o', 'y', 'o', 'y', 'o',
+               'pink', 'r', 'pink', 'r', 'pink', 'r']
+    p_color = ['r', 'r', 'r', 'b', 'b', 'b',
+               'black', 'black', 'black', 'black',
+               'y', 'y', 'white', 'white', 'g', 'g']
+
+    nImg = inps.size(0)
+    imgs = []
+    for n in range(nImg):
+        img = to_numpy(inps[n])
+        img = np.transpose(img, (1, 2, 0))
+        imgs.append(img)
+
+    fig = plt.figure()
+    plt.imshow(imgs[0])
+    ax = fig.add_subplot(1, 1, 1)
+    #print(preds.shape)
+    for p in range(16):
+        x, y = preds[0][p]
+        cor = (round(x), round(y)), 10
+        ax.add_patch(plt.Circle(*cor, color=p_color[p]))
+    plt.axis('off')
+
+    plt.show()
+
+    return imgs
+
+
+def drawCOCO(inps, preds, scores):
+    assert inps.dim() == 4
+    p_color = ['g', 'b', 'purple', 'b', 'purple',
+               'y', 'orange', 'y', 'orange', 'y', 'orange',
+               'pink', 'r', 'pink', 'r', 'pink', 'r']
+
+    nImg = inps.size(0)
+    imgs = []
+    for n in range(nImg):
+        img = to_numpy(inps[n])
+        img = np.transpose(img, (1, 2, 0))
+        imgs.append(img)
+
+    fig = plt.figure()
+    plt.imshow(imgs[0])
+    ax = fig.add_subplot(1, 1, 1)
+    #print(preds.shape)
+    for p in range(17):
+        if scores[0][p][0] < 0.2:
+            continue
+        x, y = preds[0][p]
+        cor = (round(x), round(y)), 3
+        ax.add_patch(plt.Circle(*cor, color=p_color[p]))
+    plt.axis('off')
+
+    plt.show()
+
+    return imgs
+
+
+def get_3rd_point(a, b):
+    direct = a - b
+    return b + np.array([-direct[1], direct[0]], dtype=np.float32)
+
+
+def get_dir(src_point, rot_rad):
+    sn, cs = np.sin(rot_rad), np.cos(rot_rad)
+
+    src_result = [0, 0]
+    src_result[0] = src_point[0] * cs - src_point[1] * sn
+    src_result[1] = src_point[0] * sn + src_point[1] * cs
+
+    return src_result
+
+
+def findPeak(hm):
+    mx = maximum_filter(hm, size=5)
+    idx = zip(*np.where((mx == hm) * (hm > 0.1)))
+    candidate_points = []
+    for (y, x) in idx:
+        candidate_points.append([x, y, hm[y][x]])
+    if len(candidate_points) == 0:
+        return torch.zeros(0)
+    candidate_points = np.array(candidate_points)
+    candidate_points = candidate_points[np.lexsort(-candidate_points.T)]
+    return torch.Tensor(candidate_points)
+
+
+def processPeaks(candidate_points, hm, pt1, pt2, inpH, inpW, resH, resW):
+    # type: (Tensor, Tensor, Tensor, Tensor, float, float, float, float) -> List[Tensor]
+
+    if candidate_points.shape[0] == 0:  # Low Response
+        maxval = np.max(hm.reshape(1, -1), 1)
+        idx = np.argmax(hm.reshape(1, -1), 1)
+
+        x = idx % resW
+        y = int(idx / resW)
+
+        candidate_points = np.zeros((1, 3))
+        candidate_points[0, 0:1] = x
+        candidate_points[0, 1:2] = y
+        candidate_points[0, 2:3] = maxval
+
+    res_pts = []
+    for i in range(candidate_points.shape[0]):
+        x, y, maxval = candidate_points[i][0], candidate_points[i][1], candidate_points[i][2]
+
+        if bool(maxval < 0.05) and len(res_pts) > 0:
+            pass
+        else:
+            if bool(x > 0) and bool(x < resW - 2):
+                if bool(hm[int(y)][int(x) + 1] - hm[int(y)][int(x) - 1] > 0):
+                    x += 0.25
+                elif bool(hm[int(y)][int(x) + 1] - hm[int(y)][int(x) - 1] < 0):
+                    x -= 0.25
+            if bool(y > 0) and bool(y < resH - 2):
+                if bool(hm[int(y) + 1][int(x)] - hm[int(y) - 1][int(x)] > 0):
+                    y += (0.25 * inpH / inpW)
+                elif bool(hm[int(y) + 1][int(x)] - hm[int(y) - 1][int(x)] < 0):
+                    y -= (0.25 * inpH / inpW)
+
+            #pt = torch.zeros(2)
+            pt = np.zeros(2)
+            pt[0] = x + 0.2
+            pt[1] = y + 0.2
+
+            pt = transformBoxInvert(pt, pt1, pt2, inpH, inpW, resH, resW)
+
+            res_pt = np.zeros(3)
+            res_pt[:2] = pt
+            res_pt[2] = maxval
+
+            res_pts.append(res_pt)
+
+            if maxval < 0.05:
+                break
+    return res_pts
diff --git a/joints_detectors/Alphapose/SPPE/src/utils/pose.py b/joints_detectors/Alphapose/SPPE/src/utils/pose.py
new file mode 100644
index 0000000000000000000000000000000000000000..9fa2abe12d34a385704b34d9ab98c62b364a85d4
--- /dev/null
+++ b/joints_detectors/Alphapose/SPPE/src/utils/pose.py
@@ -0,0 +1,169 @@
+from .img import (load_image, drawGaussian, drawBigCircle, drawSmallCircle, cv_rotate,
+                       cropBox, transformBox, transformBoxInvert, flip, shuffleLR, drawCOCO)
+from .eval import getPrediction
+import torch
+import numpy as np
+import random
+from opt import opt
+
+
+def rnd(x):
+    return max(-2 * x, min(2 * x, np.random.randn(1)[0] * x))
+
+
+def generateSampleBox(img_path, bndbox, part, nJoints, imgset, scale_factor, dataset, train=True):
+
+    nJoints_coco = 17
+    nJoints_mpii = 16
+    img = load_image(img_path)
+    if train:
+        img[0].mul_(random.uniform(0.7, 1.3)).clamp_(0, 1)
+        img[1].mul_(random.uniform(0.7, 1.3)).clamp_(0, 1)
+        img[2].mul_(random.uniform(0.7, 1.3)).clamp_(0, 1)
+
+    ori_img = img.clone()
+    img[0].add_(-0.406)
+    img[1].add_(-0.457)
+    img[2].add_(-0.480)
+
+    upLeft = torch.Tensor((int(bndbox[0][0]), int(bndbox[0][1])))
+    bottomRight = torch.Tensor((int(bndbox[0][2]), int(bndbox[0][3])))
+    ht = bottomRight[1] - upLeft[1]
+    width = bottomRight[0] - upLeft[0]
+    imght = img.shape[1]
+    imgwidth = img.shape[2]
+    scaleRate = random.uniform(*scale_factor)
+
+    upLeft[0] = max(0, upLeft[0] - width * scaleRate / 2)
+    upLeft[1] = max(0, upLeft[1] - ht * scaleRate / 2)
+    bottomRight[0] = min(imgwidth - 1, bottomRight[0] + width * scaleRate / 2)
+    bottomRight[1] = min(imght - 1, bottomRight[1] + ht * scaleRate / 2)
+
+    # Doing Random Sample
+    if opt.addDPG:
+        PatchScale = random.uniform(0, 1)
+        if PatchScale > 0.85:
+            ratio = ht / width
+            if (width < ht):
+                patchWidth = PatchScale * width
+                patchHt = patchWidth * ratio
+            else:
+                patchHt = PatchScale * ht
+                patchWidth = patchHt / ratio
+
+            xmin = upLeft[0] + random.uniform(0, 1) * (width - patchWidth)
+            ymin = upLeft[1] + random.uniform(0, 1) * (ht - patchHt)
+
+            xmax = xmin + patchWidth + 1
+            ymax = ymin + patchHt + 1
+        else:
+            xmin = max(1, min(upLeft[0] + np.random.normal(-0.0142, 0.1158) * width, imgwidth - 3))
+            ymin = max(1, min(upLeft[1] + np.random.normal(0.0043, 0.068) * ht, imght - 3))
+            xmax = min(max(xmin + 2, bottomRight[0] + np.random.normal(0.0154, 0.1337) * width), imgwidth - 3)
+            ymax = min(max(ymin + 2, bottomRight[1] + np.random.normal(-0.0013, 0.0711) * ht), imght - 3)
+
+        upLeft[0] = xmin
+        upLeft[1] = ymin
+        bottomRight[0] = xmax
+        bottomRight[1] = ymax
+
+    # Counting Joints number
+    jointNum = 0
+    if imgset == 'coco':
+        for i in range(17):
+            if part[i][0] > 0 and part[i][0] > upLeft[0] and part[i][1] > upLeft[1] \
+               and part[i][0] < bottomRight[0] and part[i][1] < bottomRight[1]:
+                jointNum += 1
+    else:
+        for i in range(16):
+            if part[i][0] > 0 and part[i][0] > upLeft[0] and part[i][1] > upLeft[1] \
+               and part[i][0] < bottomRight[0] and part[i][1] < bottomRight[1]:
+                jointNum += 1
+
+    # Doing Random Crop
+    if opt.addDPG:
+        if jointNum > 13 and train:
+            switch = random.uniform(0, 1)
+            if switch > 0.96:
+                bottomRight[0] = (upLeft[0] + bottomRight[0]) / 2
+                bottomRight[1] = (upLeft[1] + bottomRight[1]) / 2
+            elif switch > 0.92:
+                upLeft[0] = (upLeft[0] + bottomRight[0]) / 2
+                bottomRight[1] = (upLeft[1] + bottomRight[1]) / 2
+            elif switch > 0.88:
+                upLeft[1] = (upLeft[1] + bottomRight[1]) / 2
+                bottomRight[0] = (upLeft[0] + bottomRight[0]) / 2
+            elif switch > 0.84:
+                upLeft[0] = (upLeft[0] + bottomRight[0]) / 2
+                upLeft[1] = (upLeft[1] + bottomRight[1]) / 2
+            elif switch > 0.80:
+                bottomRight[0] = (upLeft[0] + bottomRight[0]) / 2
+            elif switch > 0.76:
+                upLeft[0] = (upLeft[0] + bottomRight[0]) / 2
+            elif switch > 0.72:
+                bottomRight[1] = (upLeft[1] + bottomRight[1]) / 2
+            elif switch > 0.68:
+                upLeft[1] = (upLeft[1] + bottomRight[1]) / 2
+
+    ori_inp = cropBox(ori_img, upLeft, bottomRight, opt.inputResH, opt.inputResW)
+    inp = cropBox(img, upLeft, bottomRight, opt.inputResH, opt.inputResW)
+    if jointNum == 0:
+        inp = torch.zeros(3, opt.inputResH, opt.inputResW)
+
+    out_bigcircle = torch.zeros(nJoints, opt.outputResH, opt.outputResW)
+    out_smallcircle = torch.zeros(nJoints, opt.outputResH, opt.outputResW)
+    out = torch.zeros(nJoints, opt.outputResH, opt.outputResW)
+    setMask = torch.zeros(nJoints, opt.outputResH, opt.outputResW)
+
+    # Draw Label
+    if imgset == 'coco':
+        for i in range(nJoints_coco):
+            if part[i][0] > 0 and part[i][0] > upLeft[0] and part[i][1] > upLeft[1] \
+               and part[i][0] < bottomRight[0] and part[i][1] < bottomRight[1]:
+                out_bigcircle[i] = drawBigCircle(out_bigcircle[i], transformBox(part[i], upLeft, bottomRight, opt.inputResH, opt.inputResW, opt.outputResH, opt.outputResW), opt.hmGauss * 2)
+                out_smallcircle[i] = drawSmallCircle(out_smallcircle[i], transformBox(part[i], upLeft, bottomRight, opt.inputResH, opt.inputResW, opt.outputResH, opt.outputResW), opt.hmGauss)
+                out[i] = drawGaussian(out[i], transformBox(part[i], upLeft, bottomRight, opt.inputResH, opt.inputResW, opt.outputResH, opt.outputResW), opt.hmGauss)
+            setMask[i].add_(1)
+    elif imgset == 'mpii':
+        for i in range(nJoints_coco, nJoints_coco + nJoints_mpii):
+            if part[i - nJoints_coco][0] > 0 and part[i - nJoints_coco][0] > upLeft[0] and part[i - nJoints_coco][1] > upLeft[1] \
+               and part[i - nJoints_coco][0] < bottomRight[0] and part[i - nJoints_coco][1] < bottomRight[1]:
+                out_bigcircle[i] = drawBigCircle(out_bigcircle[i], transformBox(part[i - nJoints_coco], upLeft, bottomRight, opt.inputResH, opt.inputResW, opt.outputResH, opt.outputResW), opt.hmGauss * 2)
+                out_smallcircle[i] = drawSmallCircle(out_smallcircle[i], transformBox(part[i - nJoints_coco], upLeft, bottomRight, opt.inputResH, opt.inputResW, opt.outputResH, opt.outputResW), opt.hmGauss)
+                out[i] = drawGaussian(out[i], transformBox(part[i - nJoints_coco], upLeft, bottomRight, opt.inputResH, opt.inputResW, opt.outputResH, opt.outputResW), opt.hmGauss)
+            setMask[i].add_(1)
+    else:
+        for i in range(nJoints_coco, nJoints_coco + nJoints_mpii):
+            if part[i - nJoints_coco][0] > 0 and part[i - nJoints_coco][0] > upLeft[0] and part[i - nJoints_coco][1] > upLeft[1] \
+               and part[i - nJoints_coco][0] < bottomRight[0] and part[i - nJoints_coco][1] < bottomRight[1]:
+                out_bigcircle[i] = drawBigCircle(out_bigcircle[i], transformBox(part[i - nJoints_coco], upLeft, bottomRight, opt.inputResH, opt.inputResW, opt.outputResH, opt.outputResW), opt.hmGauss * 2)
+                out_smallcircle[i] = drawSmallCircle(out_smallcircle[i], transformBox(part[i - nJoints_coco], upLeft, bottomRight, opt.inputResH, opt.inputResW, opt.outputResH, opt.outputResW), opt.hmGauss)
+                out[i] = drawGaussian(out[i], transformBox(part[i - nJoints_coco], upLeft, bottomRight, opt.inputResH, opt.inputResW, opt.outputResH, opt.outputResW), opt.hmGauss)
+            if i != 6 + nJoints_coco and i != 7 + nJoints_coco:
+                setMask[i].add_(1)
+
+    if opt.debug:
+        preds_hm, preds_img, preds_scores = getPrediction(out.unsqueeze(0), upLeft.unsqueeze(0), bottomRight.unsqueeze(0), opt.inputResH,
+                                                          opt.inputResW, opt.outputResH, opt.outputResW)
+        tmp_preds = preds_hm.mul(opt.inputResH / opt.outputResH)
+        drawCOCO(ori_inp.unsqueeze(0), tmp_preds, preds_scores)
+
+    if train:
+        # Flip
+        if random.uniform(0, 1) < 0.5:
+            inp = flip(inp)
+            ori_inp = flip(ori_inp)        
+            out_bigcircle = shuffleLR(flip(out_bigcircle), dataset)
+            out_smallcircle = shuffleLR(flip(out_smallcircle), dataset)
+            out = shuffleLR(flip(out), dataset)
+        # Rotate
+        r = rnd(opt.rotate)
+        if random.uniform(0, 1) < 0.6:
+            r = 0
+        if r != 0:
+            inp = cv_rotate(inp, r, opt.inputResW, opt.inputResH)
+            out_bigcircle = cv_rotate(out_bigcircle, r, opt.outputResW, opt.outputResH)
+            out_smallcircle = cv_rotate(out_smallcircle, r, opt.outputResW, opt.outputResH)
+            out = cv_rotate(out, r, opt.outputResW, opt.outputResH)
+
+    return inp, out_bigcircle, out_smallcircle, out, setMask
diff --git a/joints_detectors/Alphapose/dataloader.py b/joints_detectors/Alphapose/dataloader.py
new file mode 100644
index 0000000000000000000000000000000000000000..92072bb2f46b5f4f61d7e8e690622d5f74d1ed80
--- /dev/null
+++ b/joints_detectors/Alphapose/dataloader.py
@@ -0,0 +1,782 @@
+import os
+import sys
+import time
+from multiprocessing import Queue as pQueue
+from threading import Thread
+
+import cv2
+import numpy as np
+import torch
+import torch.multiprocessing as mp
+import torch.utils.data as data
+import torchvision.transforms as transforms
+from PIL import Image
+from torch.autograd import Variable
+
+from SPPE.src.utils.eval import getPrediction, getMultiPeakPrediction
+from SPPE.src.utils.img import load_image, cropBox, im_to_torch
+from matching import candidate_reselect as matching
+from opt import opt
+from pPose_nms import pose_nms
+from yolo.darknet import Darknet
+from yolo.preprocess import prep_image, prep_frame
+from yolo.util import dynamic_write_results
+
+# import the Queue class from Python 3
+if sys.version_info >= (3, 0):
+    from queue import Queue, LifoQueue
+# otherwise, import the Queue class for Python 2.7
+else:
+    from Queue import Queue, LifoQueue
+
+if opt.vis_fast:
+    from fn import vis_frame_fast as vis_frame
+else:
+    from fn import vis_frame
+
+
+class Image_loader(data.Dataset):
+    def __init__(self, im_names, format='yolo'):
+        super(Image_loader, self).__init__()
+        self.img_dir = opt.inputpath
+        self.imglist = im_names
+        self.transform = transforms.Compose([
+            transforms.ToTensor(),
+            transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225))
+        ])
+        self.format = format
+
+    def getitem_ssd(self, index):
+        im_name = self.imglist[index].rstrip('\n').rstrip('\r')
+        im_name = os.path.join(self.img_dir, im_name)
+        im = Image.open(im_name)
+        inp = load_image(im_name)
+        if im.mode == 'L':
+            im = im.convert('RGB')
+
+        ow = oh = 512
+        im = im.resize((ow, oh))
+        im = self.transform(im)
+        return im, inp, im_name
+
+    def getitem_yolo(self, index):
+        inp_dim = int(opt.inp_dim)
+        im_name = self.imglist[index].rstrip('\n').rstrip('\r')
+        im_name = os.path.join(self.img_dir, im_name)
+        im, orig_img, im_dim = prep_image(im_name, inp_dim)
+        # im_dim = torch.FloatTensor([im_dim]).repeat(1, 2)
+
+        inp = load_image(im_name)
+        return im, inp, orig_img, im_name, im_dim
+
+    def __getitem__(self, index):
+        if self.format == 'ssd':
+            return self.getitem_ssd(index)
+        elif self.format == 'yolo':
+            return self.getitem_yolo(index)
+        else:
+            raise NotImplementedError
+
+    def __len__(self):
+        return len(self.imglist)
+
+
+class ImageLoader:
+    def __init__(self, im_names, batchSize=1, format='yolo', queueSize=50):
+        self.img_dir = opt.inputpath
+        self.imglist = im_names
+        self.transform = transforms.Compose([
+            transforms.ToTensor(),
+            transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225))
+        ])
+        self.format = format
+
+        self.batchSize = batchSize
+        self.datalen = len(self.imglist)
+        leftover = 0
+        if (self.datalen) % batchSize:
+            leftover = 1
+        self.num_batches = self.datalen // batchSize + leftover
+
+        # initialize the queue used to store data
+        if opt.sp:
+            self.Q = Queue(maxsize=queueSize)
+        else:
+            self.Q = mp.Queue(maxsize=queueSize)
+
+    def start(self):
+        # start a thread to read frames from the file video stream
+        if self.format == 'ssd':
+            if opt.sp:
+                p = Thread(target=self.getitem_ssd, args=())
+            else:
+                p = mp.Process(target=self.getitem_ssd, args=())
+        elif self.format == 'yolo':
+            if opt.sp:
+                p = Thread(target=self.getitem_yolo, args=())
+            else:
+                p = mp.Process(target=self.getitem_yolo, args=())
+        else:
+            raise NotImplementedError
+        p.daemon = True
+        p.start()
+        return self
+
+    def getitem_ssd(self):
+        length = len(self.imglist)
+        for index in range(length):
+            im_name = self.imglist[index].rstrip('\n').rstrip('\r')
+            im_name = os.path.join(self.img_dir, im_name)
+            im = Image.open(im_name)
+            inp = load_image(im_name)
+            if im.mode == 'L':
+                im = im.convert('RGB')
+
+            ow = oh = 512
+            im = im.resize((ow, oh))
+            im = self.transform(im)
+            while self.Q.full():
+                time.sleep(2)
+            self.Q.put((im, inp, im_name))
+
+    def getitem_yolo(self):
+        for i in range(self.num_batches):
+            img = []
+            orig_img = []
+            im_name = []
+            im_dim_list = []
+            for k in range(i * self.batchSize, min((i + 1) * self.batchSize, self.datalen)):
+                inp_dim = int(opt.inp_dim)
+                im_name_k = self.imglist[k].rstrip('\n').rstrip('\r')
+                im_name_k = os.path.join(self.img_dir, im_name_k)
+                img_k, orig_img_k, im_dim_list_k = prep_image(im_name_k, inp_dim)
+
+                img.append(img_k)
+                orig_img.append(orig_img_k)
+                im_name.append(im_name_k)
+                im_dim_list.append(im_dim_list_k)
+
+            with torch.no_grad():
+                # Human Detection
+                img = torch.cat(img)
+                im_dim_list = torch.FloatTensor(im_dim_list).repeat(1, 2)
+                im_dim_list_ = im_dim_list
+
+            while self.Q.full():
+                time.sleep(2)
+
+            self.Q.put((img, orig_img, im_name, im_dim_list))
+
+    def getitem(self):
+        return self.Q.get()
+
+    def length(self):
+        return len(self.imglist)
+
+    def len(self):
+        return self.Q.qsize()
+
+
+class VideoLoader:
+    def __init__(self, path, batchSize=1, queueSize=50):
+        # initialize the file video stream along with the boolean
+        # used to indicate if the thread should be stopped or not
+        self.path = path
+        self.stream = cv2.VideoCapture(path)
+        assert self.stream.isOpened(), 'Cannot capture source'
+        self.stopped = False
+
+        self.batchSize = batchSize
+        self.datalen = int(self.stream.get(cv2.CAP_PROP_FRAME_COUNT))
+        leftover = 0
+        if (self.datalen) % batchSize:
+            leftover = 1
+        self.num_batches = self.datalen // batchSize + leftover
+
+        # initialize the queue used to store frames read from
+        # the video file
+        if opt.sp:
+            self.Q = Queue(maxsize=queueSize)
+        else:
+            self.Q = mp.Queue(maxsize=queueSize)
+
+    def length(self):
+        return self.datalen
+
+    def start(self):
+        # start a thread to read frames from the file video stream
+        if opt.sp:
+            t = Thread(target=self.update, args=())
+            t.daemon = True
+            t.start()
+        else:
+            p = mp.Process(target=self.update, args=())
+            p.daemon = True
+            p.start()
+        return self
+
+    def update(self):
+        stream = cv2.VideoCapture(self.path)
+        assert stream.isOpened(), 'Cannot capture source'
+
+        for i in range(self.num_batches):
+            img = []
+            orig_img = []
+            im_name = []
+            im_dim_list = []
+            for k in range(i * self.batchSize, min((i + 1) * self.batchSize, self.datalen)):
+                inp_dim = int(opt.inp_dim)
+                (grabbed, frame) = stream.read()
+                # if the `grabbed` boolean is `False`, then we have
+                # reached the end of the video file
+                if not grabbed:
+                    self.Q.put((None, None, None, None))
+                    print('===========================> This video get ' + str(k) + ' frames in total.')
+                    sys.stdout.flush()
+                    return
+                # process and add the frame to the queue
+                img_k, orig_img_k, im_dim_list_k = prep_frame(frame, inp_dim)
+
+                img.append(img_k)
+                orig_img.append(orig_img_k)
+                im_name.append(str(k) + '.jpg')
+                im_dim_list.append(im_dim_list_k)
+
+            with torch.no_grad():
+                # Human Detection
+                img = torch.cat(img)
+                im_dim_list = torch.FloatTensor(im_dim_list).repeat(1, 2)
+
+            while self.Q.full():
+                time.sleep(2)
+
+            self.Q.put((img, orig_img, im_name, im_dim_list))
+
+    def videoinfo(self):
+        # indicate the video info
+        fourcc = int(self.stream.get(cv2.CAP_PROP_FOURCC))
+        fps = self.stream.get(cv2.CAP_PROP_FPS)
+        frameSize = (int(self.stream.get(cv2.CAP_PROP_FRAME_WIDTH)), int(self.stream.get(cv2.CAP_PROP_FRAME_HEIGHT)))
+        return (fourcc, fps, frameSize)
+
+    def getitem(self):
+        # return next frame in the queue
+        return self.Q.get()
+
+    def len(self):
+        return self.Q.qsize()
+
+
+class DetectionLoader:
+    def __init__(self, dataloder, batchSize=1, queueSize=1024):
+        # initialize the file video stream along with the boolean
+        # used to indicate if the thread should be stopped or not
+        self.det_model = Darknet("joints_detectors/Alphapose/yolo/cfg/yolov3-spp.cfg")
+        self.det_model.load_weights('joints_detectors/Alphapose/models/yolo/yolov3-spp.weights')
+        self.det_model.net_info['height'] = opt.inp_dim
+        self.det_inp_dim = int(self.det_model.net_info['height'])
+        assert self.det_inp_dim % 32 == 0
+        assert self.det_inp_dim > 32
+        self.det_model.cuda()
+        self.det_model.eval()
+
+        self.stopped = False
+        self.dataloder = dataloder
+        self.batchSize = batchSize
+        self.datalen = self.dataloder.length()
+        leftover = 0
+        if (self.datalen) % batchSize:
+            leftover = 1
+        self.num_batches = self.datalen // batchSize + leftover
+        # initialize the queue used to store frames read from
+        # the video file
+        if opt.sp:
+            self.Q = Queue(maxsize=queueSize)
+        else:
+            self.Q = mp.Queue(maxsize=queueSize)
+
+    def start(self):
+        # start a thread to read frames from the file video stream
+        if opt.sp:
+            t = Thread(target=self.update, args=())
+            t.daemon = True
+            t.start()
+        else:
+            p = mp.Process(target=self.update, args=(), daemon=True)
+            # p = mp.Process(target=self.update, args=())
+            # p.daemon = True
+            p.start()
+        return self
+
+    def update(self):
+        # keep looping the whole dataset
+        for i in range(self.num_batches):
+            img, orig_img, im_name, im_dim_list = self.dataloder.getitem()
+            if img is None:
+                self.Q.put((None, None, None, None, None, None, None))
+                return
+
+            with torch.no_grad():
+                # Human Detection
+                img = img.cuda()
+                prediction = self.det_model(img, CUDA=True)
+                # NMS process
+                dets = dynamic_write_results(prediction, opt.confidence,
+                                             opt.num_classes, nms=True, nms_conf=opt.nms_thesh)
+                if isinstance(dets, int) or dets.shape[0] == 0:
+                    for k in range(len(orig_img)):
+                        if self.Q.full():
+                            time.sleep(2)
+                        self.Q.put((orig_img[k], im_name[k], None, None, None, None, None))
+                    continue
+                dets = dets.cpu()
+                im_dim_list = torch.index_select(im_dim_list, 0, dets[:, 0].long())
+                scaling_factor = torch.min(self.det_inp_dim / im_dim_list, 1)[0].view(-1, 1)
+
+                # coordinate transfer
+                dets[:, [1, 3]] -= (self.det_inp_dim - scaling_factor * im_dim_list[:, 0].view(-1, 1)) / 2
+                dets[:, [2, 4]] -= (self.det_inp_dim - scaling_factor * im_dim_list[:, 1].view(-1, 1)) / 2
+
+                dets[:, 1:5] /= scaling_factor
+                for j in range(dets.shape[0]):
+                    dets[j, [1, 3]] = torch.clamp(dets[j, [1, 3]], 0.0, im_dim_list[j, 0])
+                    dets[j, [2, 4]] = torch.clamp(dets[j, [2, 4]], 0.0, im_dim_list[j, 1])
+                boxes = dets[:, 1:5]
+                scores = dets[:, 5:6]
+
+            for k in range(len(orig_img)):
+                boxes_k = boxes[dets[:, 0] == k]
+                if isinstance(boxes_k, int) or boxes_k.shape[0] == 0:
+                    if self.Q.full():
+                        time.sleep(2)
+                    self.Q.put((orig_img[k], im_name[k], None, None, None, None, None))
+                    continue
+                inps = torch.zeros(boxes_k.size(0), 3, opt.inputResH, opt.inputResW)
+                pt1 = torch.zeros(boxes_k.size(0), 2)
+                pt2 = torch.zeros(boxes_k.size(0), 2)
+                if self.Q.full():
+                    time.sleep(2)
+                self.Q.put((orig_img[k], im_name[k], boxes_k, scores[dets[:, 0] == k], inps, pt1, pt2))
+
+    def read(self):
+        # return next frame in the queue
+        return self.Q.get()
+
+    def len(self):
+        # return queue len
+        return self.Q.qsize()
+
+
+class DetectionProcessor:
+    def __init__(self, detectionLoader, queueSize=1024):
+        # initialize the file video stream along with the boolean
+        # used to indicate if the thread should be stopped or not
+        self.detectionLoader = detectionLoader
+        self.stopped = False
+        self.datalen = self.detectionLoader.datalen
+
+        # initialize the queue used to store data
+        if opt.sp:
+            self.Q = Queue(maxsize=queueSize)
+        else:
+            self.Q = pQueue(maxsize=queueSize)
+
+    def start(self):
+        # start a thread to read frames from the file video stream
+        if opt.sp:
+            # t = Thread(target=self.update, args=(), daemon=True)
+            t = Thread(target=self.update, args=())
+            t.daemon = True
+            t.start()
+        else:
+            p = mp.Process(target=self.update, args=(), daemon=True)
+            # p = mp.Process(target=self.update, args=())
+            # p.daemon = True
+            p.start()
+        return self
+
+    def update(self):
+        # keep looping the whole dataset
+        for i in range(self.datalen):
+
+            with torch.no_grad():
+                (orig_img, im_name, boxes, scores, inps, pt1, pt2) = self.detectionLoader.read()
+                if orig_img is None:
+                    self.Q.put((None, None, None, None, None, None, None))
+                    return
+                if boxes is None or boxes.nelement() == 0:
+                    while self.Q.full():
+                        time.sleep(0.2)
+                    self.Q.put((None, orig_img, im_name, boxes, scores, None, None))
+                    continue
+                inp = im_to_torch(cv2.cvtColor(orig_img, cv2.COLOR_BGR2RGB))
+                inps, pt1, pt2 = crop_from_dets(inp, boxes, inps, pt1, pt2)
+
+                while self.Q.full():
+                    time.sleep(0.2)
+                self.Q.put((inps, orig_img, im_name, boxes, scores, pt1, pt2))
+
+    def read(self):
+        # return next frame in the queue
+        return self.Q.get()
+
+    def len(self):
+        # return queue len
+        return self.Q.qsize()
+
+
+class VideoDetectionLoader:
+    def __init__(self, path, batchSize=4, queueSize=256):
+        # initialize the file video stream along with the boolean
+        # used to indicate if the thread should be stopped or not
+        self.det_model = Darknet("yolo/cfg/yolov3-spp.cfg")
+        self.det_model.load_weights('models/yolo/yolov3-spp.weights')
+        self.det_model.net_info['height'] = opt.inp_dim
+        self.det_inp_dim = int(self.det_model.net_info['height'])
+        assert self.det_inp_dim % 32 == 0
+        assert self.det_inp_dim > 32
+        self.det_model.cuda()
+        self.det_model.eval()
+
+        self.stream = cv2.VideoCapture(path)
+        assert self.stream.isOpened(), 'Cannot capture source'
+        self.stopped = False
+        self.batchSize = batchSize
+        self.datalen = int(self.stream.get(cv2.CAP_PROP_FRAME_COUNT))
+        leftover = 0
+        if (self.datalen) % batchSize:
+            leftover = 1
+        self.num_batches = self.datalen // batchSize + leftover
+        # initialize the queue used to store frames read from
+        # the video file
+        self.Q = Queue(maxsize=queueSize)
+
+    def length(self):
+        return self.datalen
+
+    def len(self):
+        return self.Q.qsize()
+
+    def start(self):
+        # start a thread to read frames from the file video stream
+        t = Thread(target=self.update, args=())
+        t.daemon = True
+        t.start()
+        return self
+
+    def update(self):
+        # keep looping the whole video
+        for i in range(self.num_batches):
+            img = []
+            inp = []
+            orig_img = []
+            im_name = []
+            im_dim_list = []
+            for k in range(i * self.batchSize, min((i + 1) * self.batchSize, self.datalen)):
+                (grabbed, frame) = self.stream.read()
+                # if the `grabbed` boolean is `False`, then we have
+                # reached the end of the video file
+                if not grabbed:
+                    self.stop()
+                    return
+                # process and add the frame to the queue
+                inp_dim = int(opt.inp_dim)
+                img_k, orig_img_k, im_dim_list_k = prep_frame(frame, inp_dim)
+                inp_k = im_to_torch(orig_img_k)
+
+                img.append(img_k)
+                inp.append(inp_k)
+                orig_img.append(orig_img_k)
+                im_dim_list.append(im_dim_list_k)
+
+            with torch.no_grad():
+                ht = inp[0].size(1)
+                wd = inp[0].size(2)
+                # Human Detection
+                img = Variable(torch.cat(img)).cuda()
+                im_dim_list = torch.FloatTensor(im_dim_list).repeat(1, 2)
+                im_dim_list = im_dim_list.cuda()
+
+                prediction = self.det_model(img, CUDA=True)
+                # NMS process
+                dets = dynamic_write_results(prediction, opt.confidence,
+                                             opt.num_classes, nms=True, nms_conf=opt.nms_thesh)
+                if isinstance(dets, int) or dets.shape[0] == 0:
+                    for k in range(len(inp)):
+                        while self.Q.full():
+                            time.sleep(0.2)
+                        self.Q.put((inp[k], orig_img[k], None, None))
+                    continue
+
+                im_dim_list = torch.index_select(im_dim_list, 0, dets[:, 0].long())
+                scaling_factor = torch.min(self.det_inp_dim / im_dim_list, 1)[0].view(-1, 1)
+
+                # coordinate transfer
+                dets[:, [1, 3]] -= (self.det_inp_dim - scaling_factor * im_dim_list[:, 0].view(-1, 1)) / 2
+                dets[:, [2, 4]] -= (self.det_inp_dim - scaling_factor * im_dim_list[:, 1].view(-1, 1)) / 2
+
+                dets[:, 1:5] /= scaling_factor
+                for j in range(dets.shape[0]):
+                    dets[j, [1, 3]] = torch.clamp(dets[j, [1, 3]], 0.0, im_dim_list[j, 0])
+                    dets[j, [2, 4]] = torch.clamp(dets[j, [2, 4]], 0.0, im_dim_list[j, 1])
+                boxes = dets[:, 1:5].cpu()
+                scores = dets[:, 5:6].cpu()
+
+            for k in range(len(inp)):
+                while self.Q.full():
+                    time.sleep(0.2)
+                self.Q.put((inp[k], orig_img[k], boxes[dets[:, 0] == k], scores[dets[:, 0] == k]))
+
+    def videoinfo(self):
+        # indicate the video info
+        fourcc = int(self.stream.get(cv2.CAP_PROP_FOURCC))
+        fps = self.stream.get(cv2.CAP_PROP_FPS)
+        frameSize = (int(self.stream.get(cv2.CAP_PROP_FRAME_WIDTH)), int(self.stream.get(cv2.CAP_PROP_FRAME_HEIGHT)))
+        return (fourcc, fps, frameSize)
+
+    def read(self):
+        # return next frame in the queue
+        return self.Q.get()
+
+    def more(self):
+        # return True if there are still frames in the queue
+        return self.Q.qsize() > 0
+
+    def stop(self):
+        # indicate that the thread should be stopped
+        self.stopped = True
+
+
+class WebcamLoader:
+    def __init__(self, webcam, queueSize=256):
+        # initialize the file video stream along with the boolean
+        # used to indicate if the thread should be stopped or not
+        self.stream = cv2.VideoCapture(int(webcam))
+        assert self.stream.isOpened(), 'Cannot capture source'
+        self.stopped = False
+        # initialize the queue used to store frames read from
+        # the video file
+        self.Q = LifoQueue(maxsize=queueSize)
+
+    def start(self):
+        # start a thread to read frames from the file video stream
+        t = Thread(target=self.update, args=())
+        t.daemon = True
+        t.start()
+        return self
+
+    def update(self):
+        # keep looping infinitely
+        while True:
+            # otherwise, ensure the queue has room in it
+            if not self.Q.full():
+                # read the next frame from the file
+                (grabbed, frame) = self.stream.read()
+                # if the `grabbed` boolean is `False`, then we have
+                # reached the end of the video file
+                if not grabbed:
+                    self.stop()
+                    return
+                # process and add the frame to the queue
+                inp_dim = int(opt.inp_dim)
+                img, orig_img, dim = prep_frame(frame, inp_dim)
+                inp = im_to_torch(orig_img)
+                im_dim_list = torch.FloatTensor([dim]).repeat(1, 2)
+
+                self.Q.put((img, orig_img, inp, im_dim_list))
+            else:
+                with self.Q.mutex:
+                    self.Q.queue.clear()
+
+    def videoinfo(self):
+        # indicate the video info
+        fourcc = int(self.stream.get(cv2.CAP_PROP_FOURCC))
+        fps = self.stream.get(cv2.CAP_PROP_FPS)
+        frameSize = (int(self.stream.get(cv2.CAP_PROP_FRAME_WIDTH)), int(self.stream.get(cv2.CAP_PROP_FRAME_HEIGHT)))
+        return (fourcc, fps, frameSize)
+
+    def read(self):
+        # return next frame in the queue
+        return self.Q.get()
+
+    def len(self):
+        # return queue size
+        return self.Q.qsize()
+
+    def stop(self):
+        # indicate that the thread should be stopped
+        self.stopped = True
+
+
+class DataWriter:
+    def __init__(self, save_video=False,
+                 savepath='examples/res/1.avi', fourcc=cv2.VideoWriter_fourcc(*'XVID'), fps=25, frameSize=(640, 480),
+                 queueSize=1024):
+        if save_video:
+            # initialize the file video stream along with the boolean
+            # used to indicate if the thread should be stopped or not
+            self.stream = cv2.VideoWriter(savepath, fourcc, fps, frameSize)
+            assert self.stream.isOpened(), 'Cannot open video for writing'
+        self.save_video = save_video
+        self.stopped = False
+        self.final_result = []
+        # initialize the queue used to store frames read from
+        # the video file
+        self.Q = Queue(maxsize=queueSize)
+        if opt.save_img:
+            if not os.path.exists(opt.outputpath + '/vis'):
+                os.mkdir(opt.outputpath + '/vis')
+
+    def start(self):
+        # start a thread to read frames from the file video stream
+        t = Thread(target=self.update, args=(), daemon=True)
+        # t = Thread(target=self.update, args=())
+        # t.daemon = True
+        t.start()
+        return self
+
+    def update(self):
+        # keep looping infinitely
+        while True:
+            # if the thread indicator variable is set, stop the
+            # thread
+            if self.stopped:
+                if self.save_video:
+                    self.stream.release()
+                return
+            # otherwise, ensure the queue is not empty
+            if not self.Q.empty():
+                (boxes, scores, hm_data, pt1, pt2, orig_img, im_name) = self.Q.get()
+                orig_img = np.array(orig_img, dtype=np.uint8)
+                if boxes is None:
+                    if opt.save_img or opt.save_video or opt.vis:
+                        img = orig_img
+                        if opt.vis:
+                            cv2.imshow("AlphaPose Demo", img)
+                            cv2.waitKey(30)
+                        if opt.save_img:
+                            cv2.imwrite(os.path.join(opt.outputpath, 'vis', im_name), img)
+                        if opt.save_video:
+                            self.stream.write(img)
+                else:
+                    # location prediction (n, kp, 2) | score prediction (n, kp, 1)
+                    if opt.matching:
+                        preds = getMultiPeakPrediction(
+                            hm_data, pt1.numpy(), pt2.numpy(), opt.inputResH, opt.inputResW, opt.outputResH, opt.outputResW)
+                        result = matching(boxes, scores.numpy(), preds)
+                    else:
+                        preds_hm, preds_img, preds_scores = getPrediction(
+                            hm_data, pt1, pt2, opt.inputResH, opt.inputResW, opt.outputResH, opt.outputResW)
+                        result = pose_nms(
+                            boxes, scores, preds_img, preds_scores)
+                    result = {
+                        'imgname': im_name,
+                        'result': result
+                    }
+                    self.final_result.append(result)
+                    if opt.save_img or opt.save_video or opt.vis:
+                        img = vis_frame(orig_img, result)
+                        if opt.vis:
+                            cv2.imshow("AlphaPose Demo", img)
+                            cv2.waitKey(30)
+                        if opt.save_img:
+                            cv2.imwrite(os.path.join(opt.outputpath, 'vis', im_name), img)
+                        if opt.save_video:
+                            self.stream.write(img)
+            else:
+                time.sleep(0.1)
+
+    def running(self):
+        # indicate that the thread is still running
+        time.sleep(0.2)
+        return not self.Q.empty()
+
+    def save(self, boxes, scores, hm_data, pt1, pt2, orig_img, im_name):
+        # save next frame in the queue
+        self.Q.put((boxes, scores, hm_data, pt1, pt2, orig_img, im_name))
+
+    def stop(self):
+        # indicate that the thread should be stopped
+        self.stopped = True
+        time.sleep(0.2)
+
+    def results(self):
+        # return final result
+        return self.final_result
+
+    def len(self):
+        # return queue len
+        return self.Q.qsize()
+
+
+class Mscoco(data.Dataset):
+    def __init__(self, train=True, sigma=1,
+                 scale_factor=(0.2, 0.3), rot_factor=40, label_type='Gaussian'):
+        self.img_folder = '../data/coco/images'  # root image folders
+        self.is_train = train  # training set or test set
+        self.inputResH = opt.inputResH
+        self.inputResW = opt.inputResW
+        self.outputResH = opt.outputResH
+        self.outputResW = opt.outputResW
+        self.sigma = sigma
+        self.scale_factor = scale_factor
+        self.rot_factor = rot_factor
+        self.label_type = label_type
+
+        self.nJoints_coco = 17
+        self.nJoints_mpii = 16
+        self.nJoints = 33
+
+        self.accIdxs = (1, 2, 3, 4, 5, 6, 7, 8,
+                        9, 10, 11, 12, 13, 14, 15, 16, 17)
+        self.flipRef = ((2, 3), (4, 5), (6, 7),
+                        (8, 9), (10, 11), (12, 13),
+                        (14, 15), (16, 17))
+
+    def __getitem__(self, index):
+        pass
+
+    def __len__(self):
+        pass
+
+
+def crop_from_dets(img, boxes, inps, pt1, pt2):
+    '''
+    Crop human from origin image according to Dectecion Results
+    '''
+
+    imght = img.size(1)
+    imgwidth = img.size(2)
+    tmp_img = img
+    tmp_img[0].add_(-0.406)
+    tmp_img[1].add_(-0.457)
+    tmp_img[2].add_(-0.480)
+    for i, box in enumerate(boxes):
+        upLeft = torch.Tensor(
+            (float(box[0]), float(box[1])))
+        bottomRight = torch.Tensor(
+            (float(box[2]), float(box[3])))
+
+        ht = bottomRight[1] - upLeft[1]
+        width = bottomRight[0] - upLeft[0]
+
+        scaleRate = 0.3
+
+        upLeft[0] = max(0, upLeft[0] - width * scaleRate / 2)
+        upLeft[1] = max(0, upLeft[1] - ht * scaleRate / 2)
+        bottomRight[0] = max(
+            min(imgwidth - 1, bottomRight[0] + width * scaleRate / 2), upLeft[0] + 5)
+        bottomRight[1] = max(
+            min(imght - 1, bottomRight[1] + ht * scaleRate / 2), upLeft[1] + 5)
+
+        try:
+            inps[i] = cropBox(tmp_img.clone(), upLeft, bottomRight, opt.inputResH, opt.inputResW)
+        except IndexError:
+            print(tmp_img.shape)
+            print(upLeft)
+            print(bottomRight)
+            print('===')
+        pt1[i] = upLeft
+        pt2[i] = bottomRight
+
+    return inps, pt1, pt2
diff --git a/joints_detectors/Alphapose/dataloader_webcam.py b/joints_detectors/Alphapose/dataloader_webcam.py
new file mode 100644
index 0000000000000000000000000000000000000000..75cd3b919338fe90a54e6032cd176a39751f7a6a
--- /dev/null
+++ b/joints_detectors/Alphapose/dataloader_webcam.py
@@ -0,0 +1,520 @@
+import os
+import torch
+from torch.autograd import Variable
+import torch.utils.data as data
+import torchvision.transforms as transforms
+from PIL import Image, ImageDraw
+from SPPE.src.utils.img import load_image, cropBox, im_to_torch
+from opt import opt
+from yolo.preprocess import prep_image, prep_frame, inp_to_image
+from pPose_nms import pose_nms, write_json
+from SPPE.src.utils.eval import getPrediction
+from yolo.util import write_results, dynamic_write_results
+from yolo.darknet import Darknet
+from tqdm import tqdm
+import cv2
+import json
+import numpy as np
+import sys
+import time
+import torch.multiprocessing as mp
+from multiprocessing import Process
+from multiprocessing import Queue as pQueue
+from threading import Thread
+# import the Queue class from Python 3
+if sys.version_info >= (3, 0):
+    from queue import Queue, LifoQueue
+# otherwise, import the Queue class for Python 2.7
+else:
+    from Queue import Queue, LifoQueue
+
+if opt.vis_fast:
+    from fn import vis_frame_fast as vis_frame
+else:
+    from fn import vis_frame
+
+
+class WebcamLoader:
+    def __init__(self, webcam, batchSize=1, queueSize=256):
+        # initialize the file video stream along with the boolean
+        # used to indicate if the thread should be stopped or not
+        self.stream = cv2.VideoCapture(int(webcam))
+        assert self.stream.isOpened(), 'Cannot capture source'
+        self.stopped = False
+        # initialize the queue used to store frames read from
+        # the video file
+        self.batchSize = batchSize
+        self.Q = LifoQueue(maxsize=queueSize)
+
+    def start(self):
+        # start a thread to read frames from the file video stream
+        t = Thread(target=self.update, args=())
+        t.daemon = True
+        t.start()
+        return self
+
+    def update(self):
+        # keep looping infinitely
+        i = 0
+        while True:
+            # otherwise, ensure the queue has room in it
+            if not self.Q.full():
+                img = []
+                orig_img = []
+                im_name = []
+                im_dim_list = []
+                for k in range(self.batchSize):
+                    (grabbed, frame) = self.stream.read()
+                    # if the `grabbed` boolean is `False`, then we have
+                    # reached the end of the video file
+                    if not grabbed:
+                        self.stop()
+                        return
+                    inp_dim = int(opt.inp_dim)
+                    img_k, orig_img_k, im_dim_list_k = prep_frame(frame, inp_dim)
+                
+                    img.append(img_k)
+                    orig_img.append(orig_img_k)
+                    im_name.append(str(i)+'.jpg')
+                    im_dim_list.append(im_dim_list_k)
+
+                with torch.no_grad():
+                    # Human Detection
+                    img = torch.cat(img)
+                    im_dim_list = torch.FloatTensor(im_dim_list).repeat(1,2)
+
+                    self.Q.put((img, orig_img, im_name, im_dim_list))
+                    i = i+1
+
+            else:
+                with self.Q.mutex:
+                    self.Q.queue.clear()
+    def videoinfo(self):
+        # indicate the video info
+        fourcc=int(self.stream.get(cv2.CAP_PROP_FOURCC))
+        fps=self.stream.get(cv2.CAP_PROP_FPS)
+        frameSize=(int(self.stream.get(cv2.CAP_PROP_FRAME_WIDTH)),int(self.stream.get(cv2.CAP_PROP_FRAME_HEIGHT)))
+        return (fourcc,fps,frameSize)
+
+    def getitem(self):
+        # return next frame in the queue
+        return self.Q.get()
+
+    def len(self):
+        # return queue size
+        return self.Q.qsize()
+
+    def stop(self):
+        # indicate that the thread should be stopped
+        self.stopped = True
+
+
+class DetectionLoader:
+    def __init__(self, dataloder, batchSize=1, queueSize=1024):
+        # initialize the file video stream along with the boolean
+        # used to indicate if the thread should be stopped or not
+        self.det_model = Darknet("yolo/cfg/yolov3-spp.cfg")
+        self.det_model.load_weights('models/yolo/yolov3-spp.weights')
+        self.det_model.net_info['height'] = opt.inp_dim
+        self.det_inp_dim = int(self.det_model.net_info['height'])
+        assert self.det_inp_dim % 32 == 0
+        assert self.det_inp_dim > 32
+        self.det_model.cuda()
+        self.det_model.eval()
+
+        self.stopped = False
+        self.dataloder = dataloder
+        self.batchSize = batchSize
+        # initialize the queue used to store frames read from
+        # the video file
+        self.Q = LifoQueue(maxsize=queueSize)
+
+    def start(self):
+        # start a thread to read frames from the file video stream
+        t = Thread(target=self.update, args=())
+        t.daemon = True
+        t.start()
+        return self
+
+    def update(self):
+        # keep looping the whole dataset
+        while True:
+            img, orig_img, im_name, im_dim_list = self.dataloder.getitem()
+            with self.dataloder.Q.mutex:
+                self.dataloder.Q.queue.clear()
+            with torch.no_grad():
+                # Human Detection
+                img = img.cuda()
+                prediction = self.det_model(img, CUDA=True)
+                # NMS process
+                dets = dynamic_write_results(prediction, opt.confidence,
+                                    opt.num_classes, nms=True, nms_conf=opt.nms_thesh)
+                if isinstance(dets, int) or dets.shape[0] == 0:
+                    for k in range(len(orig_img)):
+                        if self.Q.full():
+                            time.sleep(2)
+                        self.Q.put((orig_img[k], im_name[k], None, None, None, None, None))
+                    continue
+                dets = dets.cpu()
+                im_dim_list = torch.index_select(im_dim_list,0, dets[:, 0].long())
+                scaling_factor = torch.min(self.det_inp_dim / im_dim_list, 1)[0].view(-1, 1)
+
+                # coordinate transfer
+                dets[:, [1, 3]] -= (self.det_inp_dim - scaling_factor * im_dim_list[:, 0].view(-1, 1)) / 2
+                dets[:, [2, 4]] -= (self.det_inp_dim - scaling_factor * im_dim_list[:, 1].view(-1, 1)) / 2
+
+                
+                dets[:, 1:5] /= scaling_factor
+                for j in range(dets.shape[0]):
+                    dets[j, [1, 3]] = torch.clamp(dets[j, [1, 3]], 0.0, im_dim_list[j, 0])
+                    dets[j, [2, 4]] = torch.clamp(dets[j, [2, 4]], 0.0, im_dim_list[j, 1])
+                boxes = dets[:, 1:5]
+                scores = dets[:, 5:6]
+
+            for k in range(len(orig_img)):
+                boxes_k = boxes[dets[:,0]==k]
+                if isinstance(boxes_k, int) or boxes_k.shape[0] == 0:
+                    if self.Q.full():
+                        time.sleep(2)
+                    self.Q.put((orig_img[k], im_name[k], None, None, None, None, None))
+                    continue
+                inps = torch.zeros(boxes_k.size(0), 3, opt.inputResH, opt.inputResW)
+                pt1 = torch.zeros(boxes_k.size(0), 2)
+                pt2 = torch.zeros(boxes_k.size(0), 2)
+                if self.Q.full():
+                    time.sleep(2)
+                self.Q.put((orig_img[k], im_name[k], boxes_k, scores[dets[:,0]==k], inps, pt1, pt2))
+
+    def read(self):
+        # return next frame in the queue
+        return self.Q.get()
+
+    def len(self):
+        # return queue len
+        return self.Q.qsize()
+
+
+class DetectionProcessor:
+    def __init__(self, detectionLoader, queueSize=1024):
+        # initialize the file video stream along with the boolean
+        # used to indicate if the thread should be stopped or not
+        self.detectionLoader = detectionLoader
+        self.stopped = False
+
+        # initialize the queue used to store data
+        self.Q = LifoQueue(maxsize=queueSize)
+
+    def start(self):
+        # start a thread to read frames from the file video stream
+        t = Thread(target=self.update, args=())
+        t.daemon = True
+        t.start()
+        return self
+
+    def update(self):
+        # keep looping the whole dataset
+        while True:
+            
+            with torch.no_grad():
+                (orig_img, im_name, boxes, scores, inps, pt1, pt2) = self.detectionLoader.read()
+                with self.detectionLoader.Q.mutex:
+                    self.detectionLoader.Q.queue.clear()
+                if boxes is None or boxes.nelement() == 0:
+                    while self.Q.full():
+                        time.sleep(0.2)
+                    self.Q.put((None, orig_img, im_name, boxes, scores, None, None))
+                    continue
+                inp = im_to_torch(cv2.cvtColor(orig_img, cv2.COLOR_BGR2RGB))
+                inps, pt1, pt2 = crop_from_dets(inp, boxes, inps, pt1, pt2)
+
+                while self.Q.full():
+                    time.sleep(0.2)
+                self.Q.put((inps, orig_img, im_name, boxes, scores, pt1, pt2))
+
+    def read(self):
+        # return next frame in the queue
+        return self.Q.get()
+
+    def len(self):
+        # return queue len
+        return self.Q.qsize()
+
+
+class WebcamDetectionLoader:
+    def __init__(self, webcam = 0, batchSize=1, queueSize=256):
+        # initialize the file video stream along with the boolean
+        # used to indicate if the thread should be stopped or not
+        self.det_model = Darknet("yolo/cfg/yolov3-spp.cfg")
+        self.det_model.load_weights('models/yolo/yolov3-spp.weights')
+        self.det_model.net_info['height'] = opt.inp_dim
+        self.det_inp_dim = int(self.det_model.net_info['height'])
+        assert self.det_inp_dim % 32 == 0
+        assert self.det_inp_dim > 32
+        self.det_model.cuda()
+        self.det_model.eval()
+
+        self.stream = cv2.VideoCapture(int(webcam))
+        assert self.stream.isOpened(), 'Cannot open webcam'
+        self.stopped = False
+        self.batchSize = batchSize
+
+        # initialize the queue used to store frames read from
+        # the video file
+        self.Q = LifoQueue(maxsize=queueSize)
+
+
+    def len(self):
+        return self.Q.qsize()
+
+    def start(self):
+        # start a thread to read frames from the file video stream
+        t = Thread(target=self.update, args=())
+        t.daemon = True
+        t.start()
+        return self
+
+    def update(self):
+        # keep looping
+        while True:
+            img = []
+            inp = []
+            orig_img = []
+            im_name = []
+            im_dim_list = []
+            for k in range(self.batchSize):
+                (grabbed, frame) = self.stream.read()
+                if not grabbed:
+                    continue
+                # process and add the frame to the queue
+                inp_dim = int(opt.inp_dim)
+                img_k, orig_img_k, im_dim_list_k = prep_frame(frame, inp_dim)
+                inp_k = im_to_torch(orig_img_k)
+
+                img.append(img_k)
+                inp.append(inp_k)
+                orig_img.append(orig_img_k)
+                im_dim_list.append(im_dim_list_k)
+
+            with torch.no_grad():
+                ht = inp[0].size(1)
+                wd = inp[0].size(2)
+                # Human Detection
+                img = Variable(torch.cat(img)).cuda()
+                im_dim_list = torch.FloatTensor(im_dim_list).repeat(1,2)
+                im_dim_list = im_dim_list.cuda()
+
+                prediction = self.det_model(img, CUDA=True)
+                # NMS process
+                dets = dynamic_write_results(prediction, opt.confidence,
+                                    opt.num_classes, nms=True, nms_conf=opt.nms_thesh)
+                if isinstance(dets, int) or dets.shape[0] == 0:
+                    for k in range(len(inp)):
+                        if self.Q.full():
+                            with self.Q.mutex:
+                                self.Q.queue.clear()
+                        self.Q.put((inp[k], orig_img[k], None, None))
+                    continue
+
+                im_dim_list = torch.index_select(im_dim_list,0, dets[:, 0].long())
+                scaling_factor = torch.min(self.det_inp_dim / im_dim_list, 1)[0].view(-1, 1)
+
+                # coordinate transfer
+                dets[:, [1, 3]] -= (self.det_inp_dim - scaling_factor * im_dim_list[:, 0].view(-1, 1)) / 2
+                dets[:, [2, 4]] -= (self.det_inp_dim - scaling_factor * im_dim_list[:, 1].view(-1, 1)) / 2
+
+                dets[:, 1:5] /= scaling_factor
+                for j in range(dets.shape[0]):
+                    dets[j, [1, 3]] = torch.clamp(dets[j, [1, 3]], 0.0, im_dim_list[j, 0])
+                    dets[j, [2, 4]] = torch.clamp(dets[j, [2, 4]], 0.0, im_dim_list[j, 1])
+                boxes = dets[:, 1:5].cpu()
+                scores = dets[:, 5:6].cpu()
+
+            for k in range(len(inp)):
+                if self.Q.full():
+                    with self.Q.mutex:
+                        self.Q.queue.clear()
+                self.Q.put((inp[k], orig_img[k], boxes[dets[:,0]==k], scores[dets[:,0]==k]))
+
+    def videoinfo(self):
+        # indicate the video info
+        fourcc=int(self.stream.get(cv2.CAP_PROP_FOURCC))
+        fps=self.stream.get(cv2.CAP_PROP_FPS)
+        frameSize=(int(self.stream.get(cv2.CAP_PROP_FRAME_WIDTH)),int(self.stream.get(cv2.CAP_PROP_FRAME_HEIGHT)))
+        return (fourcc,fps,frameSize)
+
+    def read(self):
+        # return next frame in the queue
+        return self.Q.get()
+
+    def more(self):
+        # return True if there are still frames in the queue
+        return self.Q.qsize() > 0
+
+    def stop(self):
+        # indicate that the thread should be stopped
+        self.stopped = True
+
+
+
+class DataWriter:
+    def __init__(self, save_video=False,
+                savepath='examples/res/1.avi', fourcc=cv2.VideoWriter_fourcc(*'XVID'), fps=25, frameSize=(640,480),
+                queueSize=1024):
+        if save_video:
+            # initialize the file video stream along with the boolean
+            # used to indicate if the thread should be stopped or not
+            self.stream = cv2.VideoWriter(savepath, fourcc, fps, frameSize)
+            assert self.stream.isOpened(), 'Cannot open video for writing'
+        self.save_video = save_video
+        self.stopped = False
+        self.final_result = []
+        # initialize the queue used to store frames read from
+        # the video file
+        self.Q = Queue(maxsize=queueSize)
+        if opt.save_img:
+            if not os.path.exists(opt.outputpath + '/vis'):
+                os.mkdir(opt.outputpath + '/vis')
+
+    def start(self):
+        # start a thread to read frames from the file video stream
+        t = Thread(target=self.update, args=())
+        t.daemon = True
+        t.start()
+        return self
+
+    def update(self):
+        # keep looping infinitely
+        while True:
+            # if the thread indicator variable is set, stop the
+            # thread
+            if self.stopped:
+                if self.save_video:
+                    self.stream.release()
+                return
+            # otherwise, ensure the queue is not empty
+            if not self.Q.empty():
+                (boxes, scores, hm_data, pt1, pt2, orig_img, im_name) = self.Q.get()
+                orig_img = np.array(orig_img, dtype=np.uint8)
+                if boxes is None:
+                    if opt.save_img or opt.save_video or opt.vis:
+                        img = orig_img
+                        if opt.vis:
+                            cv2.imshow("AlphaPose Demo", img)
+                            cv2.waitKey(30)
+                        if opt.save_img:
+                            cv2.imwrite(os.path.join(opt.outputpath, 'vis', im_name), img)
+                        if opt.save_video:
+                            self.stream.write(img)
+                else:
+                    # location prediction (n, kp, 2) | score prediction (n, kp, 1)
+                    
+                    preds_hm, preds_img, preds_scores = getPrediction(
+                        hm_data, pt1, pt2, opt.inputResH, opt.inputResW, opt.outputResH, opt.outputResW)
+
+                    result = pose_nms(boxes, scores, preds_img, preds_scores)
+                    result = {
+                        'imgname': im_name,
+                        'result': result
+                    }
+                    self.final_result.append(result)
+                    if opt.save_img or opt.save_video or opt.vis:
+                        img = vis_frame(orig_img, result)
+                        if opt.vis:
+                            cv2.imshow("AlphaPose Demo", img)
+                            cv2.waitKey(30)
+                        if opt.save_img:
+                            cv2.imwrite(os.path.join(opt.outputpath, 'vis', im_name), img)
+                        if opt.save_video:
+                            self.stream.write(img)
+            else:
+                time.sleep(0.1)
+
+    def running(self):
+        # indicate that the thread is still running
+        time.sleep(0.2)
+        return not self.Q.empty()
+
+    def save(self, boxes, scores, hm_data, pt1, pt2, orig_img, im_name):
+        # save next frame in the queue
+        self.Q.put((boxes, scores, hm_data, pt1, pt2, orig_img, im_name))
+
+    def stop(self):
+        # indicate that the thread should be stopped
+        self.stopped = True
+        time.sleep(0.2)
+
+    def results(self):
+        # return final result
+        return self.final_result
+
+    def len(self):
+        # return queue len
+        return self.Q.qsize()
+
+class Mscoco(data.Dataset):
+    def __init__(self, train=True, sigma=1,
+                 scale_factor=(0.2, 0.3), rot_factor=40, label_type='Gaussian'):
+        self.img_folder = '../data/coco/images'    # root image folders
+        self.is_train = train           # training set or test set
+        self.inputResH = opt.inputResH
+        self.inputResW = opt.inputResW
+        self.outputResH = opt.outputResH
+        self.outputResW = opt.outputResW
+        self.sigma = sigma
+        self.scale_factor = scale_factor
+        self.rot_factor = rot_factor
+        self.label_type = label_type
+
+        self.nJoints_coco = 17
+        self.nJoints_mpii = 16
+        self.nJoints = 33
+
+        self.accIdxs = (1, 2, 3, 4, 5, 6, 7, 8,
+                        9, 10, 11, 12, 13, 14, 15, 16, 17)
+        self.flipRef = ((2, 3), (4, 5), (6, 7),
+                        (8, 9), (10, 11), (12, 13),
+                        (14, 15), (16, 17))
+
+    def __getitem__(self, index):
+        pass
+
+    def __len__(self):
+        pass
+
+
+def crop_from_dets(img, boxes, inps, pt1, pt2):
+    '''
+    Crop human from origin image according to Dectecion Results
+    '''
+
+    imght = img.size(1)
+    imgwidth = img.size(2)
+    tmp_img = img
+    tmp_img[0].add_(-0.406)
+    tmp_img[1].add_(-0.457)
+    tmp_img[2].add_(-0.480)
+    for i, box in enumerate(boxes):
+        upLeft = torch.Tensor(
+            (float(box[0]), float(box[1])))
+        bottomRight = torch.Tensor(
+            (float(box[2]), float(box[3])))
+
+        ht = bottomRight[1] - upLeft[1]
+        width = bottomRight[0] - upLeft[0]
+        if width > 100:
+            scaleRate = 0.2
+        else:
+            scaleRate = 0.3
+
+        upLeft[0] = max(0, upLeft[0] - width * scaleRate / 2)
+        upLeft[1] = max(0, upLeft[1] - ht * scaleRate / 2)
+        bottomRight[0] = max(
+            min(imgwidth - 1, bottomRight[0] + width * scaleRate / 2), upLeft[0] + 5)
+        bottomRight[1] = max(
+            min(imght - 1, bottomRight[1] + ht * scaleRate / 2), upLeft[1] + 5)
+
+        inps[i] = cropBox(tmp_img.clone(), upLeft, bottomRight, opt.inputResH, opt.inputResW)
+        pt1[i] = upLeft
+        pt2[i] = bottomRight
+
+    return inps, pt1, pt2
diff --git a/joints_detectors/Alphapose/demo.py b/joints_detectors/Alphapose/demo.py
new file mode 100644
index 0000000000000000000000000000000000000000..cc4c57bd1898dcb1bd26527a03edcb0c8bd9fb55
--- /dev/null
+++ b/joints_detectors/Alphapose/demo.py
@@ -0,0 +1,131 @@
+# import os
+#
+# os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
+# os.environ["CUDA_VISIBLE_DEVICES"] = "1"
+#
+# import numpy as np
+# from tqdm import tqdm
+#
+# from SPPE.src.main_fast_inference import *
+# from dataloader import ImageLoader, DetectionLoader, DetectionProcessor, DataWriter, Mscoco
+# from fn import getTime
+# from opt import opt
+# from pPose_nms import write_json
+# from in_the_wild_data import split_frame
+#
+#
+# def main(args):
+#     inputpath = args.inputpath
+#     inputlist = args.inputlist
+#     mode = args.mode
+#     if not os.path.exists(args.outputpath):
+#         os.makedirs(args.outputpath, exist_ok=True)
+#
+#     if len(inputlist):
+#         im_names = open(inputlist, 'r').readlines()
+#     elif len(inputpath) and inputpath != '/':
+#         for root, dirs, files in os.walk(inputpath):
+#             im_names = [f for f in files if 'png' in f or 'jpg' in f]
+#     else:
+#         raise IOError('Error: must contain either --indir/--list')
+#
+#     # Load input images
+#     data_loader = ImageLoader(im_names, batchSize=args.detbatch, format='yolo').start()
+#
+#     # Load detection loader
+#     print('Loading YOLO model..')
+#     sys.stdout.flush()
+#     det_loader = DetectionLoader(data_loader, batchSize=args.detbatch).start()
+#     det_processor = DetectionProcessor(det_loader).start()
+#
+#     # Load pose model
+#     pose_dataset = Mscoco()
+#     if args.fast_inference:
+#         pose_model = InferenNet_fast(4 * 1 + 1, pose_dataset)
+#     else:
+#         pose_model = InferenNet(4 * 1 + 1, pose_dataset)
+#     pose_model.cuda()
+#     pose_model.eval()
+#
+#     runtime_profile = {
+#         'dt': [],
+#         'pt': [],
+#         'pn': []
+#     }
+#
+#     # Init data writer
+#     writer = DataWriter(args.save_video).start()
+#
+#     data_len = data_loader.length()
+#     im_names_desc = tqdm(range(data_len))
+#
+#     batchSize = args.posebatch
+#     for i in im_names_desc:
+#         start_time = getTime()
+#         with torch.no_grad():
+#             (inps, orig_img, im_name, boxes, scores, pt1, pt2) = det_processor.read()
+#             if boxes is None or boxes.nelement() == 0:
+#                 writer.save(None, None, None, None, None, orig_img, im_name.split('/')[-1])
+#                 continue
+#
+#             ckpt_time, det_time = getTime(start_time)
+#             runtime_profile['dt'].append(det_time)
+#             # Pose Estimation
+#
+#             datalen = inps.size(0)
+#             leftover = 0
+#             if (datalen) % batchSize:
+#                 leftover = 1
+#             num_batches = datalen // batchSize + leftover
+#             hm = []
+#             for j in range(num_batches):
+#                 inps_j = inps[j * batchSize:min((j + 1) * batchSize, datalen)].cuda()
+#                 hm_j = pose_model(inps_j)
+#                 hm.append(hm_j)
+#             hm = torch.cat(hm)
+#             ckpt_time, pose_time = getTime(ckpt_time)
+#             runtime_profile['pt'].append(pose_time)
+#             hm = hm.cpu()
+#             writer.save(boxes, scores, hm, pt1, pt2, orig_img, im_name.split('/')[-1])
+#
+#             ckpt_time, post_time = getTime(ckpt_time)
+#             runtime_profile['pn'].append(post_time)
+#
+#         if args.profile:
+#             # TQDM
+#             im_names_desc.set_description(
+#                 'det time: {dt:.3f} | pose time: {pt:.2f} | post processing: {pn:.4f}'.format(
+#                     dt=np.mean(runtime_profile['dt']), pt=np.mean(runtime_profile['pt']), pn=np.mean(runtime_profile['pn']))
+#             )
+#
+#     print('===========================> Finish Model Running.')
+#     if (args.save_img or args.save_video) and not args.vis_fast:
+#         print('===========================> Rendering remaining images in the queue...')
+#         print('===========================> If this step takes too long, you can enable the --vis_fast flag to use fast rendering (real-time).')
+#     while writer.running():
+#         pass
+#     writer.stop()
+#     final_result = writer.results()
+#     write_json(final_result, args.outputpath)
+#
+#
+# if __name__ == "__main__":
+#     args = opt
+#     args.dataset = 'coco'
+#     args.sp = True
+#     if not args.sp:
+#         torch.multiprocessing.set_start_method('forkserver', force=True)
+#         torch.multiprocessing.set_sharing_strategy('file_system')
+#
+#     video_name = 'kobe'
+#
+#     args.inputpath = f'../in_the_wild_data/split_{video_name}'
+#     if not os.listdir(args.inputpath):
+#         split_frame.split(f'../in_the_wild_data/{video_name}.mp4')
+#
+#     args.outputpath = f'../in_the_wild_data/alphapose_{video_name}'
+#     args.save_img = True
+#
+#     args.detbatch = 4
+#
+#     main(args)
diff --git a/joints_detectors/Alphapose/doc/CrowdPose.md b/joints_detectors/Alphapose/doc/CrowdPose.md
new file mode 100644
index 0000000000000000000000000000000000000000..ba8428167feb80bdac1c18984709fd1515ebe1a6
--- /dev/null
+++ b/joints_detectors/Alphapose/doc/CrowdPose.md
@@ -0,0 +1,64 @@
+## [CrowdPose: Efficient Crowded Scenes Pose Estimation and A New Benchmark](https://arxiv.org/abs/1812.00324) *(accepted to CVPR2019)*
+
+<p align='center'>
+    <img src="crowdpose.gif", width="360">
+</p>
+
+## Introduction
+Our proposed method surpasses the state-of-the-art methods on [CrowdPose](https://arxiv.org/abs/1812.00324) dataset by **5** mAP and results on MSCOCO dataset demonstrate the generalization ability of our method (comparatively **0.8** mAP higher). Images in our proposed CrowdPose dataset have a uniform distribution of *Crowd Index* among \[0, 1\].
+
+## Code
+We provide [evaluation tools](https://github.com/Jeff-sjtu/CrowdPose) for CrowdPose dataset. Our evaluation tools is developed based on [@cocodataset/cocoapi](https://github.com/cocodataset/cocoapi). The source code of our model is integrated into [AlphaPose](https://github.com/MVIG-SJTU/AlphaPose/tree/pytorch).
+
+## Quick Start
+Run with `matching` option to use the matching algorithm in CrowdPose.
+
+- **Input dir**:  Run AlphaPose for all images in a folder with:
+```
+python3 demo.py --indir ${img_directory} --outdir examples/res --matching
+```
+
+## Dataset
+[Train + Validation + Test Images](https://drive.google.com/file/d/1VprytECcLtU4tKP32SYi_7oDRbw7yUTL/view?usp=sharing) (Google Drive)
+
+[Annotations](https://drive.google.com/open?id=196vsma1uuLLCcUt1NrXp1K8PBU6tVH8w) (Google Drive)
+
+## Results
+
+**Results on CrowdPose Validation:**
+
+*Compare with state-of-the-art methods*
+<center>
+
+| Method | AP @0.5:0.95 | AP @0.5 | AP @0.75 | AR @0.5:0.95 | AR @0.5 | AR @0.75 |
+|:-------|:-----:|:-------:|:-------:|:-------:|:-------:|:-------:|
+| Detectron (Mask R-CNN) | 57.2 | 83.5 | 60.3 | 65.9 | 89.3 | 69.4 |
+| Simple Pose (Xiao *et al.*) | 60.8 | 81.4 | 65.7 | 67.3 | 86.3 | 71.8 |
+| **Ours** | **66.0** | **84.2** | **71.5** | **72.7** | **89.5** | **77.5** |
+
+</center>
+
+*Compare with open-source systems*
+<center>
+
+| Method | AP @*Easy* | AP @*Medium* | AP @*Hard* | FPS |
+|:-------|:-----:|:-------:|:-------:|:-------:|
+| OpenPose (CMU-Pose) | 62.7 | 48.7 | 32.3 | 5.3 |
+| Detectron (Mask R-CNN) | 69.4 | 57.9 | 45.8 | 2.9 |
+| **Ours** | **75.5** | **66.3** | **57.4** | **10.1** |
+
+</center>
+
+**Results on MSCOCO Validation:**
+<center>
+
+| Method | AP @0.5:0.95 | AR @0.5:0.95 |
+|:-------|:-----:|:-------:|
+| Detectron (Mask R-CNN) | 64.8 | 71.1 |
+| Simple Pose (Xiao *et al.*) | 69.8 | 74.1 |
+| **AlphaPose** | **70.9** | **76.4** |
+
+</center>
+
+## Contributors
+ CrowdPose is authored by [Jiefeng Li](http://jeff-leaf.site/), [Can Wang](https://github.com/Canwang-sjtu), [Hao Zhu](https://github.com/BernieZhu), [Yihuan Mao](), [Hao-Shu Fang](https://fang-haoshu.github.io/), and [Cewu Lu](http://www.mvig.org/). 
diff --git a/joints_detectors/Alphapose/doc/contributors.md b/joints_detectors/Alphapose/doc/contributors.md
new file mode 100644
index 0000000000000000000000000000000000000000..cb02844c7d040a4420b6361273845af19aa1b90a
--- /dev/null
+++ b/joints_detectors/Alphapose/doc/contributors.md
@@ -0,0 +1 @@
+Alpha Pose is contributed and maintained by Hao-Shu Fang, Jiefeng Li, Yuliang Xiu, Ruiheng Chang and Cewu Lu.
diff --git a/joints_detectors/Alphapose/doc/faq.md b/joints_detectors/Alphapose/doc/faq.md
new file mode 100644
index 0000000000000000000000000000000000000000..0569b9b715891c0eb27613170a00b6fcf87e79db
--- /dev/null
+++ b/joints_detectors/Alphapose/doc/faq.md
@@ -0,0 +1,32 @@
+AlphaPose - Frequently Asked Question (FAQ)
+============================================
+
+## FAQ
+1. [Can't open webcan or video file](#Can't-open-webcan-or-video-file)
+
+## FAQ
+### Can't open webcam or video file
+**Q:** - I can't open the webcam or video file.
+
+**A**: Try re-install `opencv-python` with version >= 3.3.1.11 by
+```
+pip3 uninstall opencv_python
+pip3 install opencv_python --user
+```
+Many people meet this problem at https://github.com/opencv/opencv/issues/8471. The solution I use is 
+```
+sudo cp <path to opencv source repo>/build/lib/python3/cv2.cpython-35m-x86_64-linux-gnu.so /usr/local/lib/python3.5/dist-packages/cv2/cv2.cpython-35m-x86_64-linux-gnu.so
+```
+The idea is to replace the cv2.so library provided by pypi with the one compiled from sources. You can check for more info at https://github.com/opencv/opencv/issues/8471.
+
+### Can't open webcam
+**Q:** - I can't open the webcam with the latest `opencv-python`
+
+**A**: Check if your device is valid by
+```
+ls /dev/video*
+```
+Usually you can find `video0`, but if you have a device with other index like `video3`, you can run the program by
+```
+python3 webcam_demo.py --webcam 3 --outdir examples/res --vis
+```
\ No newline at end of file
diff --git a/joints_detectors/Alphapose/doc/output.md b/joints_detectors/Alphapose/doc/output.md
new file mode 100644
index 0000000000000000000000000000000000000000..922b844ac6afc261951e183fcbc8d14b1dfc97d5
--- /dev/null
+++ b/joints_detectors/Alphapose/doc/output.md
@@ -0,0 +1,143 @@
+AlphaPose - Output format
+====================================
+
+
+
+## Contents
+1. [Output Format](#output-format)
+    1. [Keypoint Ordering](#keypoint-ordering)
+    2. [Heatmap Ordering](#heatmap-ordering)
+
+
+## Output Format
+1. By default, we save the results for all images in one json file, which is similar to the [results format](http://cocodataset.org/#format) used by COCO.
+    1. `keypoints` contains the body part locations and detection confidence formatted as `x1,y1,c1,x2,y2,c2,...`. `c` is the confidence score in the range [0,1] for MPII dataset and range [0,6] for COCO dataset.
+    2. `score` is the confidence score for the whole person, computed by our parametric pose NMS.
+```
+[
+ // for person_1 in image_1
+ {
+    "image_id" : string, image_1_name, 
+    "category_id" : int, 1 for person
+    "keypoints" : [x1,y1,c1,...,xk,yk,ck], 
+    "score" : float,
+ },
+ // for person_2 in image_1
+ {
+    "image_id" : string, image_1_name, 
+    "category_id" : int, 1 for person
+    "keypoints" : [x1,y1,c1,...,xk,yk,ck], 
+    "score" : float,
+ },
+ ...
+ // for persons in image_2
+{
+    "image_id" : string, image_2_name, 
+    "category_id" : int, 1 for person
+    "keypoints" : [x1,y1,c1,...,xk,yk,ck], 
+    "score" : float,
+ },
+ ...
+]
+```
+
+2. If the `--format` flag is set as 'cmu', we will save the results for each image in the format used by CMU-Pose.
+```
+{
+    "version":0.1,
+    "bodies":[
+        {"joints":[x1,y1,c1,...,xk,yk,ck]},
+        {"joints":[x1,y1,c1,...,xk,yk,ck]},
+    ]
+}
+```
+
+3. If the `--format` flag is set as 'open', we will save the results for each image in the format used by OpenPose.
+```
+{
+    "version":0.1,
+    "people":[
+        {"pose_keypoints_2d":[x1,y1,c1,...,xk,yk,ck]},
+        {"pose_keypoints_2d":[x1,y1,c1,...,xk,yk,ck]},
+    ]
+}
+```
+
+### Keypoint Ordering
+The default keypoint order is
+```
+// Result for COCO (17 body parts)
+    {0,  "Nose"},
+    {1,  "LEye"},
+    {2,  "REye"},
+    {3,  "LEar"},
+    {4,  "REar"},
+    {5,  "LShoulder"},
+    {6,  "RShoulder"},
+    {7,  "LElbow"},
+    {8,  "RElbow"},
+    {9,  "LWrist"},
+    {10, "RWrist"},
+    {11, "LHip"},
+    {12, "RHip"},
+    {13, "LKnee"},
+    {14, "Rknee"},
+    {15, "LAnkle"},
+    {16, "RAnkle"},
+// Result for MPII (16 body parts)
+    {0,  "RAnkle"},
+    {1,  "Rknee"},
+    {2,  "RHip"},
+    {3,  "LHip"},
+    {4,  "LKnee"},
+    {5,  "LAnkle"},
+    {6,  "Pelv"},
+    {7,  "Thrx"},
+    {8,  "Neck"},
+    {9,  "Head"},
+    {10, "RWrist"},
+    {11, "RElbow"},
+    {12, "RShoulder"},
+    {13, "LShoulder"},
+    {14, "LElbow"},
+    {15, "LWrist"},
+```
+If the `--format` flag is set to 'cmu' or 'open', the keypoint order is
+```
+//Result for COCO (18 body parts)
+    {0,  "Nose"},
+    {1,  "Neck"},
+    {2,  "RShoulder"},
+    {3,  "RElbow"},
+    {4,  "RWrist"},
+    {5,  "LShoulder"},
+    {6,  "LElbow"},
+    {7,  "LWrist"},
+    {8,  "RHip"},
+    {9,  "RKnee"},
+    {10, "RAnkle"},
+    {11, "LHip"},
+    {12, "LKnee"},
+    {13, "LAnkle"},
+    {14, "REye"},
+    {15, "LEye"},
+    {16, "REar"},
+    {17, "LEar"},
+// Result for MPII (15 body parts)
+    {0,  "Head"},
+    {1,  "Neck"},
+    {2,  "RShoulder"},
+    {3,  "RElbow"},
+    {4,  "RWrist"},
+    {5,  "LShoulder"},
+    {6,  "LElbow"},
+    {7,  "LWrist"},
+    {8,  "RHip"},
+    {9,  "RKnee"},
+    {10, "RAnkle"},
+    {11, "LHip"},
+    {12, "LKnee"},
+    {13, "LAnkle"},
+    {14, "Thrx"},
+```
+
diff --git a/joints_detectors/Alphapose/doc/run.md b/joints_detectors/Alphapose/doc/run.md
new file mode 100644
index 0000000000000000000000000000000000000000..fe830a27def9e26d6154814ead78f5836076d717
--- /dev/null
+++ b/joints_detectors/Alphapose/doc/run.md
@@ -0,0 +1,35 @@
+AlphaPose Usage & Examples
+====================================
+
+Here, we first list the flags of this script and then give some examples.
+
+## Flags
+- `--indir`: Directory of the input images. All the images in the directory will be processed.
+- `--list`: A text file list for the input images
+- `--video`: Read video and process the video frame by frame.
+- `--outdir`: Output directory to store the pose estimation results.
+- `--vis`: If turned-on, it will render the results and visualize them.
+- `--save_img`: If turned-on, it will render the results and save them as images in $outdir/vis. 
+- `--save_video`: If turned-on, it will render the results and save them as a video.
+- `--vis_fast`: If turned on, it will use faster rendering method. Default is false.
+- `--format`: The format of the saved results. By default, it will save the output in COCO-like format. Alternative options are 'cmu' and 'open', which saves the results in the format of CMU-Pose or OpenPose. For more details, see [output.md](output.md)
+- `--conf`: Confidence threshold for human detection. Lower the value can improve the final accuracy but decrease the speed. Default is 0.1.
+- `--nms`: Confidence threshold for human detection. Increase the value can improve the final accuracy but decrease the speed. Default is 0.6.
+- `--detbatch`: Batch size for the detection network. 
+- `--posebatch`: Maximum batch size for the pose estimation network. If you met OOM problem, decrease this value until it fit in the memory.
+- `--sp`: Run the program using a single process. Windows users need to turn this flag on.
+- `--inp_dim`: The input size of detection network. The inp_dim should be multiple of 32. Default is 608.
+
+## Examples
+- **Run AlphaPose for all images in a folder ,save the results in the format of CMU-Pose and save the images**:
+```
+python3 demo.py --indir examples/demo/ --outdir examples/results/ --save_img --format cmu
+```
+- **Run AlphaPose for a video, save the video and use faster rendering method**:
+```
+python3 video_demo.py --video examples/input.mp4 --outdir examples/results/ --save_video  --vis_fast
+```
+- **Run AlphaPose for a video, speeding up by increasing the confidence and lowering the NMS threshold.**:
+```
+python3 video_demo.py --video examples/input.mp4 --outdir examples/results/  --conf 0.5 --nms 0.45
+```
diff --git a/joints_detectors/Alphapose/doc/speed_up.md b/joints_detectors/Alphapose/doc/speed_up.md
new file mode 100644
index 0000000000000000000000000000000000000000..3dd12e9d298bfafaa9831bfcecccc56468491f76
--- /dev/null
+++ b/joints_detectors/Alphapose/doc/speed_up.md
@@ -0,0 +1,16 @@
+AlphaPose - Speeding Up
+============================================
+
+
+Run AlphaPose for a video, speeding up by increasing the confidence and lowering the NMS threshold:
+```
+python3 video_demo.py --video ${path to video} --outdir examples/results/  --conf 0.5 --nms 0.45
+```
+For users with GPU memory >= 8GB, I suggest increasing the detection batch:
+```
+python3 demo.py --indir ${img_directory} --outdir examples/res --detbatch 2
+```
+For users that do not need to detect small size persons, I suggest lowering the input size of detection network. The inp_dim should be multiple of 32.
+```
+python3 demo.py --indir ${img_directory} --outdir examples/res --inp_dim 480
+```
\ No newline at end of file
diff --git a/joints_detectors/Alphapose/doc/win_install.md b/joints_detectors/Alphapose/doc/win_install.md
new file mode 100644
index 0000000000000000000000000000000000000000..e98e039df0139e6d97b34bbdb55e9ef2e013fab5
--- /dev/null
+++ b/joints_detectors/Alphapose/doc/win_install.md
@@ -0,0 +1,37 @@
+AlphaPose - Windows Installation
+============================================
+
+Tested on Win10 with GTX 1060
+
+1. Download and install Git for Windows: https://git-scm.com/download/win
+2. Download and install Python3(3.6 or 3.7): https://www.python.org/downloads/
+3. Download and install CUDA toolkit: https://developer.nvidia.com/cuda-downloads
+4. Download and install PyTorch: https://pytorch.org/
+	Right click, choose the "Git Bash Here"
+	<div align="left">
+    <img src="step1.jpg">
+	</div>
+	Input the command here and press Enter
+	<div align="left">
+    <img src="step2.jpg">
+	</div>
+5. 	Download win-64/intel-openmp-2018.0.0-8.tar.bz2: https://anaconda.org/anaconda/intel-openmp/files
+	put the .dll files in Library\bin into a dir, then append the path of it to the environment variable PATH. 
+	I suggest you to put it in C:\Users\<Your user name>\bin since this dir is already in the PATH
+6. Right click, choose the "Git Bash Here" and then follow the instructions in README to install AlphaPose
+	<div align="left">
+    <img src="step3.jpg">
+	</div>
+7. Have fun! Now you can run AlphaPose by entering command. Try webcam demo by input 
+```
+python3 webcam_demo.py --vis
+```
+**Note:** For `demo.py` and `video_demo.py`, you need to turn on the `--sp` flag, like
+
+```
+python3 demo.py --indir ${img_directory} --outdir examples/res --sp
+```
+
+<div align="left">
+<img src="step4.jpg">
+</div>
\ No newline at end of file
diff --git a/joints_detectors/Alphapose/examples/list-coco-demo.txt b/joints_detectors/Alphapose/examples/list-coco-demo.txt
new file mode 100644
index 0000000000000000000000000000000000000000..8ad8ec54810e3a24dc296371dd04604f43fbf782
--- /dev/null
+++ b/joints_detectors/Alphapose/examples/list-coco-demo.txt
@@ -0,0 +1,20 @@
+000000375530.jpg
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+000000215524.jpg
diff --git a/joints_detectors/Alphapose/examples/list-coco-minival500.txt b/joints_detectors/Alphapose/examples/list-coco-minival500.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e6d257e94e4cd88494c37aca4d4e89d1a182738d
--- /dev/null
+++ b/joints_detectors/Alphapose/examples/list-coco-minival500.txt
@@ -0,0 +1,500 @@
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+000000247808.jpg
+000000259755.jpg
+000000378894.jpg
+000000247839.jpg
+000000247840.jpg
+000000378928.jpg
+000000378940.jpg
+000000378948.jpg
+000000378962.jpg
diff --git a/joints_detectors/Alphapose/fn.py b/joints_detectors/Alphapose/fn.py
new file mode 100644
index 0000000000000000000000000000000000000000..7669df4f4c555fbb11252d916e8a89595e1a0865
--- /dev/null
+++ b/joints_detectors/Alphapose/fn.py
@@ -0,0 +1,233 @@
+import collections
+import math
+import re
+import time
+
+import cv2
+import numpy as np
+import torch
+from torch._six import string_classes
+
+RED = (0, 0, 255)
+GREEN = (0, 255, 0)
+BLUE = (255, 0, 0)
+CYAN = (255, 255, 0)
+YELLOW = (0, 255, 255)
+ORANGE = (0, 165, 255)
+PURPLE = (255, 0, 255)
+
+numpy_type_map = {
+    'float64': torch.DoubleTensor,
+    'float32': torch.FloatTensor,
+    'float16': torch.HalfTensor,
+    'int64': torch.LongTensor,
+    'int32': torch.IntTensor,
+    'int16': torch.ShortTensor,
+    'int8': torch.CharTensor,
+    'uint8': torch.ByteTensor,
+}
+
+_use_shared_memory = True
+
+
+def collate_fn(batch):
+    r"""Puts each data field into a tensor with outer dimension batch size"""
+
+    error_msg = "batch must contain tensors, numbers, dicts or lists; found {}"
+    elem_type = type(batch[0])
+
+    if isinstance(batch[0], torch.Tensor):
+        out = None
+        if _use_shared_memory:
+            # If we're in a background process, concatenate directly into a
+            # shared memory tensor to avoid an extra copy
+            numel = sum([x.numel() for x in batch])
+            storage = batch[0].storage()._new_shared(numel)
+            out = batch[0].new(storage)
+        return torch.stack(batch, 0, out=out)
+    elif elem_type.__module__ == 'numpy' and elem_type.__name__ != 'str_' \
+            and elem_type.__name__ != 'string_':
+        elem = batch[0]
+        if elem_type.__name__ == 'ndarray':
+            # array of string classes and object
+            if re.search('[SaUO]', elem.dtype.str) is not None:
+                raise TypeError(error_msg.format(elem.dtype))
+
+            return torch.stack([torch.from_numpy(b) for b in batch], 0)
+        if elem.shape == ():  # scalars
+            py_type = float if elem.dtype.name.startswith('float') else int
+            return numpy_type_map[elem.dtype.name](list(map(py_type, batch)))
+    elif isinstance(batch[0], int):
+        return torch.LongTensor(batch)
+    elif isinstance(batch[0], float):
+        return torch.DoubleTensor(batch)
+    elif isinstance(batch[0], string_classes):
+        return batch
+    elif isinstance(batch[0], collections.Mapping):
+        return {key: collate_fn([d[key] for d in batch]) for key in batch[0]}
+    elif isinstance(batch[0], collections.Sequence):
+        transposed = zip(*batch)
+        return [collate_fn(samples) for samples in transposed]
+
+    raise TypeError((error_msg.format(type(batch[0]))))
+
+
+def collate_fn_list(batch):
+    img, inp, im_name = zip(*batch)
+    img = collate_fn(img)
+    im_name = collate_fn(im_name)
+
+    return img, inp, im_name
+
+
+def vis_frame_fast(frame, im_res, format='coco'):
+    '''
+    frame: frame image
+    im_res: im_res of predictions
+    format: coco or mpii
+
+    return rendered image
+    '''
+    if format == 'coco':
+        l_pair = [
+            (0, 1), (0, 2), (1, 3), (2, 4),  # Head
+            (5, 6), (5, 7), (7, 9), (6, 8), (8, 10),
+            (17, 11), (17, 12),  # Body
+            (11, 13), (12, 14), (13, 15), (14, 16)
+        ]
+        p_color = [(0, 255, 255), (0, 191, 255), (0, 255, 102), (0, 77, 255), (0, 255, 0),  # Nose, LEye, REye, LEar, REar
+                   (77, 255, 255), (77, 255, 204), (77, 204, 255), (191, 255, 77), (77, 191, 255), (191, 255, 77),
+                   # LShoulder, RShoulder, LElbow, RElbow, LWrist, RWrist
+                   (204, 77, 255), (77, 255, 204), (191, 77, 255), (77, 255, 191), (127, 77, 255), (77, 255, 127),
+                   (0, 255, 255)]  # LHip, RHip, LKnee, Rknee, LAnkle, RAnkle, Neck
+        line_color = [(0, 215, 255), (0, 255, 204), (0, 134, 255), (0, 255, 50),
+                      (77, 255, 222), (77, 196, 255), (77, 135, 255), (191, 255, 77), (77, 255, 77),
+                      (77, 222, 255), (255, 156, 127),
+                      (0, 127, 255), (255, 127, 77), (0, 77, 255), (255, 77, 36)]
+    elif format == 'mpii':
+        l_pair = [
+            (8, 9), (11, 12), (11, 10), (2, 1), (1, 0),
+            (13, 14), (14, 15), (3, 4), (4, 5),
+            (8, 7), (7, 6), (6, 2), (6, 3), (8, 12), (8, 13)
+        ]
+        p_color = [PURPLE, BLUE, BLUE, RED, RED, BLUE, BLUE, RED, RED, PURPLE, PURPLE, PURPLE, RED, RED, BLUE, BLUE]
+    else:
+        NotImplementedError
+
+    im_name = im_res['imgname'].split('/')[-1]
+    img = frame
+    for human in im_res['result']:
+        part_line = {}
+        kp_preds = human['keypoints']
+        kp_scores = human['kp_score']
+        kp_preds = torch.cat((kp_preds, torch.unsqueeze((kp_preds[5, :] + kp_preds[6, :]) / 2, 0)))
+        kp_scores = torch.cat((kp_scores, torch.unsqueeze((kp_scores[5, :] + kp_scores[6, :]) / 2, 0)))
+        # Draw keypoints
+        for n in range(kp_scores.shape[0]):
+            if kp_scores[n] <= 0.05:
+                continue
+            cor_x, cor_y = int(kp_preds[n, 0]), int(kp_preds[n, 1])
+            part_line[n] = (cor_x, cor_y)
+            cv2.circle(img, (cor_x, cor_y), 4, p_color[n], -1)
+        # Draw limbs
+        for i, (start_p, end_p) in enumerate(l_pair):
+            if start_p in part_line and end_p in part_line:
+                start_xy = part_line[start_p]
+                end_xy = part_line[end_p]
+                cv2.line(img, start_xy, end_xy, line_color[i], 2 * (kp_scores[start_p] + kp_scores[end_p]) + 1)
+    return img
+
+
+def vis_frame(frame, im_res, format='coco'):
+    '''
+    frame: frame image
+    im_res: im_res of predictions
+    format: coco or mpii
+
+    return rendered image
+    '''
+    if format == 'coco':
+        l_pair = [
+            (0, 1), (0, 2), (1, 3), (2, 4),  # Head
+            (5, 6), (5, 7), (7, 9), (6, 8), (8, 10),
+            (17, 11), (17, 12),  # Body
+            (11, 13), (12, 14), (13, 15), (14, 16)
+        ]
+
+        p_color = [(0, 255, 255), (0, 191, 255), (0, 255, 102), (0, 77, 255), (0, 255, 0),  # Nose, LEye, REye, LEar, REar
+                   (77, 255, 255), (77, 255, 204), (77, 204, 255), (191, 255, 77), (77, 191, 255), (191, 255, 77),
+                   # LShoulder, RShoulder, LElbow, RElbow, LWrist, RWrist
+                   (204, 77, 255), (77, 255, 204), (191, 77, 255), (77, 255, 191), (127, 77, 255), (77, 255, 127),
+                   (0, 255, 255)]  # LHip, RHip, LKnee, Rknee, LAnkle, RAnkle, Neck
+        line_color = [(0, 215, 255), (0, 255, 204), (0, 134, 255), (0, 255, 50),
+                      (77, 255, 222), (77, 196, 255), (77, 135, 255), (191, 255, 77), (77, 255, 77),
+                      (77, 222, 255), (255, 156, 127),
+                      (0, 127, 255), (255, 127, 77), (0, 77, 255), (255, 77, 36)]
+    elif format == 'mpii':
+        l_pair = [
+            (8, 9), (11, 12), (11, 10), (2, 1), (1, 0),
+            (13, 14), (14, 15), (3, 4), (4, 5),
+            (8, 7), (7, 6), (6, 2), (6, 3), (8, 12), (8, 13)
+        ]
+        p_color = [PURPLE, BLUE, BLUE, RED, RED, BLUE, BLUE, RED, RED, PURPLE, PURPLE, PURPLE, RED, RED, BLUE, BLUE]
+        line_color = [PURPLE, BLUE, BLUE, RED, RED, BLUE, BLUE, RED, RED, PURPLE, PURPLE, RED, RED, BLUE, BLUE]
+    else:
+        raise NotImplementedError
+
+    im_name = im_res['imgname'].split('/')[-1]
+    img = frame
+    height, width = img.shape[:2]
+    img = cv2.resize(img, (int(width / 2), int(height / 2)))
+    for human in im_res['result']:
+        part_line = {}
+        kp_preds = human['keypoints']
+        kp_scores = human['kp_score']
+        kp_preds = torch.cat((kp_preds, torch.unsqueeze((kp_preds[5, :] + kp_preds[6, :]) / 2, 0)))
+        kp_scores = torch.cat((kp_scores, torch.unsqueeze((kp_scores[5, :] + kp_scores[6, :]) / 2, 0)))
+
+        # Draw keypoints
+        for n in range(kp_scores.shape[0]):
+            if kp_scores[n] <= 0.05:
+                continue
+            cor_x, cor_y = int(kp_preds[n, 0]), int(kp_preds[n, 1])
+            part_line[n] = (int(cor_x / 2), int(cor_y / 2))
+            bg = img.copy()
+            cv2.circle(bg, (int(cor_x / 2), int(cor_y / 2)), 2, p_color[n], -1)
+            # Now create a mask of logo and create its inverse mask also
+            transparency = max(0, min(1, kp_scores[n].item()))
+            img = cv2.addWeighted(bg, transparency, img, 1 - transparency, 0)
+
+        # Draw proposal score on the head
+        middle_eye = (kp_preds[1] + kp_preds[2]) / 4
+        middle_cor = int(middle_eye[0]) - 10, int(middle_eye[1]) - 12
+        cv2.putText(img, f"{human['proposal_score'].item():.2f}", middle_cor, cv2.FONT_HERSHEY_SIMPLEX, 0.3, (0, 0, 255))
+
+        # Draw limbs
+        for i, (start_p, end_p) in enumerate(l_pair):
+            if start_p in part_line and end_p in part_line:
+                start_xy = part_line[start_p]
+                end_xy = part_line[end_p]
+                bg = img.copy()
+
+                X = (start_xy[0], end_xy[0])
+                Y = (start_xy[1], end_xy[1])
+                mX = np.mean(X)
+                mY = np.mean(Y)
+                length = ((Y[0] - Y[1]) ** 2 + (X[0] - X[1]) ** 2) ** 0.5
+                angle = math.degrees(math.atan2(Y[0] - Y[1], X[0] - X[1]))
+                stickwidth = (kp_scores[start_p] + kp_scores[end_p]) + 1
+                polygon = cv2.ellipse2Poly((int(mX), int(mY)), (int(length / 2), int(stickwidth)), int(angle), 0, 360, 1)
+                cv2.fillConvexPoly(bg, polygon, line_color[i])
+                # cv2.line(bg, start_xy, end_xy, line_color[i], (2 * (kp_scores[start_p] + kp_scores[end_p])) + 1)
+                transparency = max(0, min(1, 0.5 * (kp_scores[start_p] + kp_scores[end_p]).item()))
+                img = cv2.addWeighted(bg, transparency, img, 1 - transparency, 0)
+    img = cv2.resize(img, (width, height), interpolation=cv2.INTER_CUBIC)
+    return img
+
+
+def getTime(time1=0):
+    if not time1:
+        return time.time()
+    else:
+        interval = time.time() - time1
+        return time.time(), interval
diff --git a/joints_detectors/Alphapose/gene_npz.py b/joints_detectors/Alphapose/gene_npz.py
new file mode 100644
index 0000000000000000000000000000000000000000..b2cb1a897997369405df75b3e09b02912892988f
--- /dev/null
+++ b/joints_detectors/Alphapose/gene_npz.py
@@ -0,0 +1,198 @@
+import ntpath
+import os
+import shutil
+
+import numpy as np
+import torch.utils.data
+from tqdm import tqdm
+
+from SPPE.src.main_fast_inference import *
+from common.utils import calculate_area
+from dataloader import DetectionLoader, DetectionProcessor, DataWriter, Mscoco, VideoLoader
+from fn import getTime
+from opt import opt
+from pPose_nms import write_json
+
+args = opt
+args.dataset = 'coco'
+args.fast_inference = False
+args.save_img = True
+args.sp = True
+if not args.sp:
+    torch.multiprocessing.set_start_method('forkserver', force=True)
+    torch.multiprocessing.set_sharing_strategy('file_system')
+
+
+def model_load():
+    model = None
+    return model
+
+
+def image_interface(model, image):
+    pass
+
+
+def generate_kpts(video_file):
+    final_result, video_name = handle_video(video_file)
+
+    # ============ Changing ++++++++++
+
+    kpts = []
+    no_person = []
+    for i in range(len(final_result)):
+        if not final_result[i]['result']:  # No people
+            no_person.append(i)
+            kpts.append(None)
+            continue
+
+        kpt = max(final_result[i]['result'],
+                  key=lambda x: x['proposal_score'].data[0] * calculate_area(x['keypoints']), )['keypoints']
+
+        kpts.append(kpt.data.numpy())
+
+        for n in no_person:
+            kpts[n] = kpts[-1]
+        no_person.clear()
+
+    for n in no_person:
+        kpts[n] = kpts[-1] if kpts[-1] else kpts[n-1]
+
+    # ============ Changing End ++++++++++
+
+    name = f'{args.outputpath}/{video_name}.npz'
+    kpts = np.array(kpts).astype(np.float32)
+    print('kpts npz save in ', name)
+    np.savez_compressed(name, kpts=kpts)
+
+    return kpts
+
+
+def handle_video(video_file):
+    # =========== common ===============
+    args.video = video_file
+    base_name = os.path.basename(args.video)
+    video_name = base_name[:base_name.rfind('.')]
+    # =========== end common ===============
+    # =========== image ===============
+    # img_path = f'outputs/alpha_pose_{video_name}/split_image/'
+    # args.inputpath = img_path
+    # args.outputpath = f'outputs/alpha_pose_{video_name}'
+    # if os.path.exists(args.outputpath):
+    #     shutil.rmtree(f'{args.outputpath}/vis', ignore_errors=True)
+    # else:
+    #     os.mkdir(args.outputpath)
+    #
+    # # if not len(video_file):
+    # #     raise IOError('Error: must contain --video')
+    #
+    # if len(img_path) and img_path != '/':
+    #     for root, dirs, files in os.walk(img_path):
+    #         im_names = sorted([f for f in files if 'png' in f or 'jpg' in f])
+    # else:
+    #     raise IOError('Error: must contain either --indir/--list')
+    #
+    # # Load input images
+    # data_loader = ImageLoader(im_names, batchSize=args.detbatch, format='yolo').start()
+    # print(f'Totally {data_loader.datalen} images')
+    # =========== end image ===============
+    # =========== video ===============
+    args.outputpath = f'output_alphapose/{video_name}'
+    if os.path.exists(args.outputpath):
+        shutil.rmtree(f'{args.outputpath}/vis', ignore_errors=True)
+    else:
+        os.mkdir(args.outputpath)
+    videofile = args.video
+    mode = args.mode
+    if not len(videofile):
+        raise IOError('Error: must contain --video')
+    # Load input video
+    data_loader = VideoLoader(videofile, batchSize=args.detbatch).start()
+    (fourcc, fps, frameSize) = data_loader.videoinfo()
+    print('the video is {} f/s'.format(fps))
+    # =========== end video ===============
+    # Load detection loader
+    print('Loading YOLO model..')
+    sys.stdout.flush()
+    det_loader = DetectionLoader(data_loader, batchSize=args.detbatch).start()
+    #  start a thread to read frames from the file video stream
+    det_processor = DetectionProcessor(det_loader).start()
+    # Load pose model
+    pose_dataset = Mscoco()
+    if args.fast_inference:
+        pose_model = InferenNet_fast(4 * 1 + 1, pose_dataset)
+    else:
+        pose_model = InferenNet(4 * 1 + 1, pose_dataset)
+    pose_model.cuda()
+    pose_model.eval()
+    runtime_profile = {
+        'dt': [],
+        'pt': [],
+        'pn': []
+    }
+    # Data writer
+    save_path = os.path.join(args.outputpath, 'AlphaPose_' + ntpath.basename(video_file).split('.')[0] + '.avi')
+    # writer = DataWriter(args.save_video, save_path, cv2.VideoWriter_fourcc(*'XVID'), fps, frameSize).start()
+    writer = DataWriter(args.save_video).start()
+    print('Start pose estimation...')
+    im_names_desc = tqdm(range(data_loader.length()))
+    batchSize = args.posebatch
+    for i in im_names_desc:
+
+        start_time = getTime()
+        with torch.no_grad():
+            (inps, orig_img, im_name, boxes, scores, pt1, pt2) = det_processor.read()
+            if orig_img is None:
+                print(f'{i}-th image read None: handle_video')
+                break
+            if boxes is None or boxes.nelement() == 0:
+                writer.save(None, None, None, None, None, orig_img, im_name.split('/')[-1])
+                continue
+
+            ckpt_time, det_time = getTime(start_time)
+            runtime_profile['dt'].append(det_time)
+            # Pose Estimation
+
+            datalen = inps.size(0)
+            leftover = 0
+            if datalen % batchSize:
+                leftover = 1
+            num_batches = datalen // batchSize + leftover
+            hm = []
+            for j in range(num_batches):
+                inps_j = inps[j * batchSize:min((j + 1) * batchSize, datalen)].cuda()
+                hm_j = pose_model(inps_j)
+                hm.append(hm_j)
+            hm = torch.cat(hm)
+            ckpt_time, pose_time = getTime(ckpt_time)
+            runtime_profile['pt'].append(pose_time)
+
+            hm = hm.cpu().data
+            writer.save(boxes, scores, hm, pt1, pt2, orig_img, im_name.split('/')[-1])
+
+            ckpt_time, post_time = getTime(ckpt_time)
+            runtime_profile['pn'].append(post_time)
+
+        if args.profile:
+            # TQDM
+            im_names_desc.set_description(
+                'det time: {dt:.4f} | pose time: {pt:.4f} | post processing: {pn:.4f}'.format(
+                    dt=np.mean(runtime_profile['dt']), pt=np.mean(runtime_profile['pt']), pn=np.mean(runtime_profile['pn']))
+            )
+    if (args.save_img or args.save_video) and not args.vis_fast:
+        print('===========================> Rendering remaining images in the queue...')
+        print('===========================> If this step takes too long, you can enable the --vis_fast flag to use fast rendering (real-time).')
+    while writer.running():
+        pass
+    writer.stop()
+    final_result = writer.results()
+    write_json(final_result, args.outputpath)
+
+    return final_result, video_name
+
+
+if __name__ == "__main__":
+    os.chdir('../..')
+    print(os.getcwd())
+
+    # handle_video(img_path='outputs/image/kobe')
+    generate_kpts('outputs/dance.mp4')
diff --git a/joints_detectors/Alphapose/matching.py b/joints_detectors/Alphapose/matching.py
new file mode 100644
index 0000000000000000000000000000000000000000..fa1bdf809a36808492a973368519a8a8f599f235
--- /dev/null
+++ b/joints_detectors/Alphapose/matching.py
@@ -0,0 +1,229 @@
+# -----------------------------------------------------
+# Copyright (c) Shanghai Jiao Tong University. All rights reserved.
+# Written by Jiefeng Li (jeff.lee.sjtu@gmail.com)
+# -----------------------------------------------------
+
+import numpy as np
+import torch
+from scipy.optimize import linear_sum_assignment
+
+sigmas = np.array([.26, .25, .25, .35, .35, .79, .79, .72, .72, .62, .62, 1.07, 1.07, .87, .87, .89, .89])
+
+
+def candidate_reselect(bboxes, bboxes_scores, pose_preds):
+    '''
+    Grouping
+    '''
+    # Group same keypointns together
+    kp_groups = grouping(bboxes, bboxes_scores, pose_preds)
+
+    '''
+    Re-select
+    '''
+
+    # Generate Matrix
+    human_num = len(pose_preds.keys())
+    costMatrix = []
+    for k in range(17):
+        kp_group = kp_groups[k]
+        joint_num = len(kp_group.keys())
+
+        costMatrix.append(np.zeros((human_num, joint_num)))
+
+    group_size = {k: {} for k in range(17)}
+
+    for n, person in pose_preds.items():
+        h_id = n
+        assert 0 <= h_id < human_num
+
+        for k in range(17):
+            g_id = person['group_id'][k]
+            if g_id is not None:
+                if g_id not in group_size[k].keys():
+                    group_size[k][g_id] = 0
+
+                group_size[k][g_id] += 1
+
+                g_id = int(g_id) - 1
+                _, _, score = person[k][0]
+                h_score = person['human_score']
+
+                if score < 0.05:
+                    costMatrix[k][h_id][g_id] = 0
+                else:
+                    costMatrix[k][h_id][g_id] = -(h_score * score)
+
+    pose_preds = matching(pose_preds, costMatrix, kp_groups)
+
+    # To JSON
+    final_result = []
+
+    for n, person in pose_preds.items():
+
+        final_pose = torch.zeros(17, 2)
+        final_score = torch.zeros(17, 1)
+
+        max_score = 0
+        mean_score = 0
+
+        xmax, xmin = 0, 1e5
+        ymax, ymin = 0, 1e5
+
+        for k in range(17):
+            assert len(person[k]) > 0
+            x, y, s = person[k][0]
+
+            xmax = max(xmax, x)
+            xmin = min(xmin, x)
+            ymax = max(ymax, y)
+            ymin = min(ymin, y)
+
+            final_pose[k][0] = x.item() - 0.3
+            final_pose[k][1] = y.item() - 0.3
+            final_score[k] = s.item()
+            mean_score += (s.item() / 17)
+            max_score = max(max_score, s.item())
+
+        if torch.max(final_score).item() < 0.1:
+            continue
+
+        if (1.5 ** 2 * (xmax - xmin) * (ymax - ymin) < 40 * 40):
+            continue
+
+        final_result.append({
+            'keypoints': final_pose,
+            'kp_score': final_score,
+            'proposal_score': mean_score + max_score + person['bbox_score']
+        })
+
+    return final_result
+
+
+def grouping(bboxes, bboxes_scores, pose_preds):
+    kp_groups = {}
+    for k in range(17):
+        kp_groups[k] = {}
+
+    ids = np.zeros(17)
+
+    for n, person in pose_preds.items():
+        pose_preds[n]['bbox'] = bboxes[n]
+        pose_preds[n]['bbox_score'] = bboxes_scores[n]
+        pose_preds[n]['group_id'] = {}
+        s = 0
+
+        for k in range(17):
+            pose_preds[n]['group_id'][k] = None
+            pose_preds[n][k] = np.array(pose_preds[n][k])
+            assert len(pose_preds[n][k]) > 0
+            s += pose_preds[n][k][0][-1]
+
+        s = s / 17
+
+        pose_preds[n]['human_score'] = s
+
+        for k in range(17):
+            latest_id = ids[k]
+            kp_group = kp_groups[k]
+
+            assert len(person[k]) > 0
+            x0, y0, s0 = person[k][0]
+            if s0 < 0.05:
+                continue
+
+            for g_id, g in kp_group.items():
+                x_c, y_c = kp_group[g_id]['group_center']
+
+                '''
+                Get Average Box Size
+                '''
+                group_area = kp_group[g_id]['group_area']
+                group_area = group_area[0] * group_area[1] / (group_area[2] ** 2)
+
+                '''
+                Groupingn Criterion
+                '''
+                # Joint Group
+                dist = np.sqrt(
+                    ((x_c - x0) ** 2 + (y_c - y0) ** 2) / group_area)
+
+                if dist <= 0.1 * sigmas[k]:  # Small Distance
+                    if s0 >= 0.3:
+                        kp_group[g_id]['kp_list'][0] += x0 * s0
+                        kp_group[g_id]['kp_list'][1] += y0 * s0
+                        kp_group[g_id]['kp_list'][2] += s0
+
+                        kp_group[g_id]['group_area'][0] += (person['bbox'][2] - person['bbox'][0]) * person['human_score']
+                        kp_group[g_id]['group_area'][1] += (person['bbox'][3] - person['bbox'][1]) * person['human_score']
+                        kp_group[g_id]['group_area'][2] += person['human_score']
+
+                        x_c = kp_group[g_id]['kp_list'][0] / kp_group[g_id]['kp_list'][2]
+                        y_c = kp_group[g_id]['kp_list'][1] / kp_group[g_id]['kp_list'][2]
+                        kp_group[g_id]['group_center'] = (x_c, y_c)
+
+                    pose_preds[n]['group_id'][k] = g_id
+                    break
+            else:
+                # A new keypoint group
+                latest_id += 1
+                kp_group[latest_id] = {
+                    'kp_list': None,
+                    'group_center': person[k][0].copy()[:2],
+                    'group_area': None
+                }
+
+                x, y, s = person[k][0]
+                kp_group[latest_id]['kp_list'] = np.array((x * s, y * s, s))
+
+                # Ref Area
+                ref_width = person['bbox'][2] - person['bbox'][0]
+                ref_height = person['bbox'][3] - person['bbox'][1]
+                ref_score = person['human_score']
+                kp_group[latest_id]['group_area'] = np.array((
+                    ref_width * ref_score, ref_height * ref_score, ref_score))
+
+                pose_preds[n]['group_id'][k] = latest_id
+                ids[k] = latest_id
+    return kp_groups
+
+
+def matching(pose_preds, matrix, kp_groups):
+    index = []
+    for k in range(17):
+        human_ind, joint_ind = linear_sum_assignment(matrix[k])
+        # human_ind, joint_ind = greedy_matching(matrix[k])
+
+        index.append(list(zip(human_ind, joint_ind)))
+
+    for n, person in pose_preds.items():
+        for k in range(17):
+            g_id = person['group_id'][k]
+            if g_id is not None:
+                g_id = int(g_id) - 1
+                h_id = n
+
+                x, y, s = pose_preds[n][k][0]
+                if ((h_id, g_id) not in index[k]) and len(pose_preds[n][k]) > 1:
+                    pose_preds[n][k] = np.delete(pose_preds[n][k], 0, 0)
+                elif ((h_id, g_id) not in index[k]) and len(person[k]) == 1:
+                    x, y, _ = pose_preds[n][k][0]
+                    pose_preds[n][k][0] = (x, y, 1e-5)
+                    pass
+                elif ((h_id, g_id) in index[k]):
+                    x, y = kp_groups[k][g_id + 1]['group_center']
+                    s = pose_preds[n][k][0][2]
+                    pose_preds[n][k][0] = (x, y, s)
+
+    return pose_preds
+
+
+def greedy_matching(matrix):
+    num_human, num_joint = matrix.shape
+
+    if num_joint <= num_human or True:
+        human_ind = np.argmin(matrix, axis=0)
+        joint_ind = np.arange(num_joint)
+    else:
+        pass
+
+    return human_ind.tolist(), joint_ind.tolist()
diff --git a/joints_detectors/Alphapose/models/sppe/.gitkeep b/joints_detectors/Alphapose/models/sppe/.gitkeep
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/joints_detectors/Alphapose/models/ssd/.gitkeep b/joints_detectors/Alphapose/models/ssd/.gitkeep
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/joints_detectors/Alphapose/models/yolo/.gitkeep b/joints_detectors/Alphapose/models/yolo/.gitkeep
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/joints_detectors/Alphapose/online_demo.py b/joints_detectors/Alphapose/online_demo.py
new file mode 100644
index 0000000000000000000000000000000000000000..86017b1aecaf9e0e6f82a7eaf1006a50ed49493d
--- /dev/null
+++ b/joints_detectors/Alphapose/online_demo.py
@@ -0,0 +1,152 @@
+import torch
+from torch.autograd import Variable
+import torch.nn.functional as F
+import torchvision.transforms as transforms
+
+import torch.nn as nn
+import torch.utils.data
+import numpy as np
+from opt import opt
+
+from dataloader import WebcamLoader, DataWriter, crop_from_dets, Mscoco
+from yolo.darknet import Darknet
+from yolo.util import write_results, dynamic_write_results
+from SPPE.src.main_fast_inference import *
+
+from SPPE.src.utils.img import im_to_torch
+import os
+import sys
+from tqdm import tqdm
+import time
+from fn import getTime
+import cv2
+
+from pPose_nms import write_json
+
+args = opt
+args.dataset = 'coco'
+
+
+def loop():
+    n = 0
+    while True:
+        yield n
+        n += 1
+
+
+if __name__ == "__main__":
+    webcam = args.webcam
+    mode = args.mode
+    if not os.path.exists(args.outputpath):
+        os.mkdir(args.outputpath)
+
+    # Load input video
+    fvs = WebcamLoader(webcam).start()
+    (fourcc, fps, frameSize) = fvs.videoinfo()
+    # Data writer
+    save_path = os.path.join(args.outputpath, 'AlphaPose_webcam' + webcam + '.avi')
+    writer = DataWriter(args.save_video, save_path, cv2.VideoWriter_fourcc(*'XVID'), fps, frameSize).start()
+
+    # Load YOLO model
+    print('Loading YOLO model..')
+    sys.stdout.flush()
+    det_model = Darknet("yolo/cfg/yolov3-spp.cfg")
+    det_model.load_weights('models/yolo/yolov3-spp.weights')
+    det_model.net_info['height'] = args.inp_dim
+    det_inp_dim = int(det_model.net_info['height'])
+    assert det_inp_dim % 32 == 0
+    assert det_inp_dim > 32
+    det_model.cuda()
+    det_model.eval()
+
+    # Load pose model
+    pose_dataset = Mscoco()
+    if args.fast_inference:
+        pose_model = InferenNet_fast(4 * 1 + 1, pose_dataset)
+    else:
+        pose_model = InferenNet(4 * 1 + 1, pose_dataset)
+    pose_model.cuda()
+    pose_model.eval()
+
+    runtime_profile = {
+        'ld': [],
+        'dt': [],
+        'dn': [],
+        'pt': [],
+        'pn': []
+    }
+
+    print('Starting webcam demo, press Ctrl + C to terminate...')
+    sys.stdout.flush()
+    im_names_desc = tqdm(loop())
+    for i in im_names_desc:
+        try:
+            start_time = getTime()
+
+            (img, orig_img, inp, im_dim_list) = fvs.read()
+            ckpt_time, load_time = getTime(start_time)
+            runtime_profile['ld'].append(load_time)
+            with torch.no_grad():
+                # Human Detection
+                img = Variable(img).cuda()
+                im_dim_list = im_dim_list.cuda()
+
+                prediction = det_model(img, CUDA=True)
+                ckpt_time, det_time = getTime(ckpt_time)
+                runtime_profile['dt'].append(det_time)
+                # NMS process
+                dets = dynamic_write_results(prediction, opt.confidence,
+                                             opt.num_classes, nms=True, nms_conf=opt.nms_thesh)
+                if isinstance(dets, int) or dets.shape[0] == 0:
+                    writer.save(None, None, None, None, None, orig_img, im_name=str(i) + '.jpg')
+                    continue
+                im_dim_list = torch.index_select(im_dim_list, 0, dets[:, 0].long())
+                scaling_factor = torch.min(det_inp_dim / im_dim_list, 1)[0].view(-1, 1)
+
+                # coordinate transfer
+                dets[:, [1, 3]] -= (det_inp_dim - scaling_factor * im_dim_list[:, 0].view(-1, 1)) / 2
+                dets[:, [2, 4]] -= (det_inp_dim - scaling_factor * im_dim_list[:, 1].view(-1, 1)) / 2
+
+                dets[:, 1:5] /= scaling_factor
+                for j in range(dets.shape[0]):
+                    dets[j, [1, 3]] = torch.clamp(dets[j, [1, 3]], 0.0, im_dim_list[j, 0])
+                    dets[j, [2, 4]] = torch.clamp(dets[j, [2, 4]], 0.0, im_dim_list[j, 1])
+                boxes = dets[:, 1:5].cpu()
+                scores = dets[:, 5:6].cpu()
+                ckpt_time, detNMS_time = getTime(ckpt_time)
+                runtime_profile['dn'].append(detNMS_time)
+                # Pose Estimation
+                inps = torch.zeros(boxes.size(0), 3, opt.inputResH, opt.inputResW)
+                pt1 = torch.zeros(boxes.size(0), 2)
+                pt2 = torch.zeros(boxes.size(0), 2)
+                inps, pt1, pt2 = crop_from_dets(inp, boxes, inps, pt1, pt2)
+                inps = Variable(inps.cuda())
+
+                hm = pose_model(inps)
+                ckpt_time, pose_time = getTime(ckpt_time)
+                runtime_profile['pt'].append(pose_time)
+
+                writer.save(boxes, scores, hm.cpu(), pt1, pt2, orig_img, im_name=str(i) + '.jpg')
+
+                ckpt_time, post_time = getTime(ckpt_time)
+                runtime_profile['pn'].append(post_time)
+
+            # TQDM
+            im_names_desc.set_description(
+                'load time: {ld:.4f} | det time: {dt:.4f} | det NMS: {dn:.4f} | pose time: {pt:.4f} | post process: {pn:.4f}'.format(
+                    ld=np.mean(runtime_profile['ld']), dt=np.mean(runtime_profile['dt']), dn=np.mean(runtime_profile['dn']),
+                    pt=np.mean(runtime_profile['pt']), pn=np.mean(runtime_profile['pn']))
+            )
+        except KeyboardInterrupt:
+            break
+
+    print(' ')
+    print('===========================> Finish Model Running.')
+    if (args.save_img or args.save_video) and not args.vis_fast:
+        print('===========================> Rendering remaining images in the queue...')
+        print('===========================> If this step takes too long, you can enable the --vis_fast flag to use fast rendering (real-time).')
+    while writer.running():
+        pass
+    writer.stop()
+    final_result = writer.results()
+    write_json(final_result, args.outputpath)
diff --git a/joints_detectors/Alphapose/opt.py b/joints_detectors/Alphapose/opt.py
new file mode 100644
index 0000000000000000000000000000000000000000..192ed325af080361fcd32b891ba5ab04e90e5712
--- /dev/null
+++ b/joints_detectors/Alphapose/opt.py
@@ -0,0 +1,146 @@
+import argparse
+
+parser = argparse.ArgumentParser(description='PyTorch AlphaPose Training')
+
+"----------------------------- General options -----------------------------"
+parser.add_argument('--expID', default='default', type=str,
+                    help='Experiment ID')
+parser.add_argument('--dataset', default='coco', type=str,
+                    help='Dataset choice: mpii | coco')
+parser.add_argument('--nThreads', default=30, type=int,
+                    help='Number of data loading threads')
+parser.add_argument('--debug', default=False, type=bool,
+                    help='Print the debug information')
+parser.add_argument('--snapshot', default=1, type=int,
+                    help='How often to take a snapshot of the model (0 = never)')
+
+"----------------------------- AlphaPose options -----------------------------"
+parser.add_argument('--addDPG', default=False, type=bool,
+                    help='Train with data augmentation')
+parser.add_argument('--sp', default=False, action='store_true',
+                    help='Use single process for pytorch')
+parser.add_argument('--profile', default=False, action='store_true',
+                    help='add speed profiling at screen output')
+
+"----------------------------- Model options -----------------------------"
+parser.add_argument('--netType', default='hgPRM', type=str,
+                    help='Options: hgPRM | resnext')
+parser.add_argument('--loadModel', default=None, type=str,
+                    help='Provide full path to a previously trained model')
+parser.add_argument('--Continue', default=False, type=bool,
+                    help='Pick up where an experiment left off')
+parser.add_argument('--nFeats', default=256, type=int,
+                    help='Number of features in the hourglass')
+parser.add_argument('--nClasses', default=33, type=int,
+                    help='Number of output channel')
+parser.add_argument('--nStack', default=4, type=int,
+                    help='Number of hourglasses to stack')
+
+"----------------------------- Hyperparameter options -----------------------------"
+parser.add_argument('--fast_inference', default=True, type=bool,
+                    help='Fast inference')
+parser.add_argument('--use_pyranet', default=True, type=bool,
+                    help='use pyranet')
+
+"----------------------------- Hyperparameter options -----------------------------"
+parser.add_argument('--LR', default=2.5e-4, type=float,
+                    help='Learning rate')
+parser.add_argument('--momentum', default=0, type=float,
+                    help='Momentum')
+parser.add_argument('--weightDecay', default=0, type=float,
+                    help='Weight decay')
+parser.add_argument('--crit', default='MSE', type=str,
+                    help='Criterion type')
+parser.add_argument('--optMethod', default='rmsprop', type=str,
+                    help='Optimization method: rmsprop | sgd | nag | adadelta')
+
+"----------------------------- Training options -----------------------------"
+parser.add_argument('--nEpochs', default=50, type=int,
+                    help='Number of hourglasses to stack')
+parser.add_argument('--epoch', default=0, type=int,
+                    help='Current epoch')
+parser.add_argument('--trainBatch', default=40, type=int,
+                    help='Train-batch size')
+parser.add_argument('--validBatch', default=20, type=int,
+                    help='Valid-batch size')
+parser.add_argument('--trainIters', default=0, type=int,
+                    help='Total train iters')
+parser.add_argument('--valIters', default=0, type=int,
+                    help='Total valid iters')
+parser.add_argument('--init', default=None, type=str,
+                    help='Initialization')
+
+"----------------------------- Data options -----------------------------"
+parser.add_argument('--inputResH', default=320, type=int,
+                    help='Input image height')
+parser.add_argument('--inputResW', default=256, type=int,
+                    help='Input image width')
+parser.add_argument('--outputResH', default=80, type=int,
+                    help='Output heatmap height')
+parser.add_argument('--outputResW', default=64, type=int,
+                    help='Output heatmap width')
+parser.add_argument('--scale', default=0.25, type=float,
+                    help='Degree of scale augmentation')
+parser.add_argument('--rotate', default=30, type=float,
+                    help='Degree of rotation augmentation')
+parser.add_argument('--hmGauss', default=1, type=int,
+                    help='Heatmap gaussian size')
+
+"----------------------------- PyraNet options -----------------------------"
+parser.add_argument('--baseWidth', default=9, type=int,
+                    help='Heatmap gaussian size')
+parser.add_argument('--cardinality', default=5, type=int,
+                    help='Heatmap gaussian size')
+parser.add_argument('--nResidual', default=1, type=int,
+                    help='Number of residual modules at each location in the pyranet')
+
+"----------------------------- Distribution options -----------------------------"
+parser.add_argument('--dist', dest='dist', type=int, default=1,
+                    help='distributed training or not')
+parser.add_argument('--backend', dest='backend', type=str, default='gloo',
+                    help='backend for distributed training')
+parser.add_argument('--port', dest='port',
+                    help='port of server')
+
+"----------------------------- Detection options -----------------------------"
+parser.add_argument('--net', dest='demo_net', help='Network to use [vgg16 res101]',
+                    default='res152')
+parser.add_argument('--indir', dest='inputpath',
+                    help='image-directory', default="")
+parser.add_argument('--list', dest='inputlist',
+                    help='image-list', default="")
+parser.add_argument('--mode', dest='mode',
+                    help='detection mode, fast/normal/accurate', default="normal")
+parser.add_argument('--outdir', dest='outputpath',
+                    help='output-directory', default="examples/res/")
+parser.add_argument('--inp_dim', dest='inp_dim', type=str, default='608',
+                    help='inpdim')
+parser.add_argument('--conf', dest='confidence', type=float, default=0.05,
+                    help='bounding box confidence threshold')
+parser.add_argument('--nms', dest='nms_thesh', type=float, default=0.6,
+                    help='bounding box nms threshold')
+parser.add_argument('--save_img', default=False, action='store_true',
+                    help='save result as image')
+parser.add_argument('--vis', default=False, action='store_true',
+                    help='visualize image')
+parser.add_argument('--matching', default=False, action='store_true',
+                    help='use best matching')
+parser.add_argument('--format', type=str,
+                    help='save in the format of cmu or coco or openpose, option: coco/cmu/open')
+parser.add_argument('--detbatch', type=int, default=1,
+                    help='detection batch size')
+parser.add_argument('--posebatch', type=int, default=80,
+                    help='pose estimation maximum batch size')
+
+"----------------------------- Video options -----------------------------"
+parser.add_argument('--video', dest='video',
+                    help='video-name', default="")
+parser.add_argument('--webcam', dest='webcam', type=str,
+                    help='webcam number', default='0')
+parser.add_argument('--save_video', dest='save_video',
+                    help='whether to save rendered video', default=False, action='store_true')
+parser.add_argument('--vis_fast', dest='vis_fast',
+                    help='use fast rendering', action='store_true', default=False)
+opt = parser.parse_args()
+
+opt.num_classes = 80
diff --git a/joints_detectors/Alphapose/pPose_nms.py b/joints_detectors/Alphapose/pPose_nms.py
new file mode 100644
index 0000000000000000000000000000000000000000..3c041e145445cc2ccd2ea6034a37fa2c4bb3b24e
--- /dev/null
+++ b/joints_detectors/Alphapose/pPose_nms.py
@@ -0,0 +1,363 @@
+# -*- coding: utf-8 -*-
+import torch
+import json
+import os
+import zipfile
+import time
+from multiprocessing.dummy import Pool as ThreadPool
+import numpy as np
+from opt import opt
+
+''' Constant Configuration '''
+delta1 = 1
+mu = 1.7
+delta2 = 2.65
+gamma = 22.48
+scoreThreds = 0.3
+matchThreds = 5
+areaThres = 0#40 * 40.5
+alpha = 0.1
+#pool = ThreadPool(4)
+
+
+def pose_nms(bboxes, bbox_scores, pose_preds, pose_scores):
+    '''
+    Parametric Pose NMS algorithm
+    bboxes:         bbox locations list (n, 4)
+    bbox_scores:    bbox scores list (n,)
+    pose_preds:     pose locations list (n, 17, 2)
+    pose_scores:    pose scores list    (n, 17, 1)
+    '''
+    #global ori_pose_preds, ori_pose_scores, ref_dists
+
+    pose_scores[pose_scores == 0] = 1e-5
+
+    final_result = []
+
+    ori_bbox_scores = bbox_scores.clone()
+    ori_pose_preds = pose_preds.clone()
+    ori_pose_scores = pose_scores.clone()
+
+    xmax = bboxes[:, 2]
+    xmin = bboxes[:, 0]
+    ymax = bboxes[:, 3]
+    ymin = bboxes[:, 1]
+
+    widths = xmax - xmin
+    heights = ymax - ymin
+    ref_dists = alpha * np.maximum(widths, heights)
+
+    nsamples = bboxes.shape[0]
+    human_scores = pose_scores.mean(dim=1)
+
+    human_ids = np.arange(nsamples)
+    # Do pPose-NMS
+    pick = []
+    merge_ids = []
+    while(human_scores.shape[0] != 0):
+        # Pick the one with highest score
+        pick_id = torch.argmax(human_scores)
+        pick.append(human_ids[pick_id])
+        # num_visPart = torch.sum(pose_scores[pick_id] > 0.2)
+
+        # Get numbers of match keypoints by calling PCK_match
+        ref_dist = ref_dists[human_ids[pick_id]]
+        simi = get_parametric_distance(pick_id, pose_preds, pose_scores, ref_dist)
+        num_match_keypoints = PCK_match(pose_preds[pick_id], pose_preds, ref_dist)
+
+        # Delete humans who have more than matchThreds keypoints overlap and high similarity
+        delete_ids = torch.from_numpy(np.arange(human_scores.shape[0]))[(simi > gamma) | (num_match_keypoints >= matchThreds)]
+
+        if delete_ids.shape[0] == 0:
+            delete_ids = pick_id
+        #else:
+        #    delete_ids = torch.from_numpy(delete_ids)
+
+        merge_ids.append(human_ids[delete_ids])
+        pose_preds = np.delete(pose_preds, delete_ids, axis=0)
+        pose_scores = np.delete(pose_scores, delete_ids, axis=0)
+        human_ids = np.delete(human_ids, delete_ids)
+        human_scores = np.delete(human_scores, delete_ids, axis=0)
+        bbox_scores = np.delete(bbox_scores, delete_ids, axis=0)
+
+    assert len(merge_ids) == len(pick)
+    preds_pick = ori_pose_preds[pick]
+    scores_pick = ori_pose_scores[pick]
+    bbox_scores_pick = ori_bbox_scores[pick]
+    #final_result = pool.map(filter_result, zip(scores_pick, merge_ids, preds_pick, pick, bbox_scores_pick))
+    #final_result = [item for item in final_result if item is not None]
+
+    for j in range(len(pick)):
+        ids = np.arange(17)
+        max_score = torch.max(scores_pick[j, ids, 0])
+
+        if max_score < scoreThreds:
+            continue
+
+        # Merge poses
+        merge_id = merge_ids[j]
+        merge_pose, merge_score = p_merge_fast(
+            preds_pick[j], ori_pose_preds[merge_id], ori_pose_scores[merge_id], ref_dists[pick[j]])
+
+        max_score = torch.max(merge_score[ids])
+        if max_score < scoreThreds:
+            continue
+
+        xmax = max(merge_pose[:, 0])
+        xmin = min(merge_pose[:, 0])
+        ymax = max(merge_pose[:, 1])
+        ymin = min(merge_pose[:, 1])
+
+        if (1.5 ** 2 * (xmax - xmin) * (ymax - ymin) < areaThres):
+            continue
+
+        final_result.append({
+            'keypoints': merge_pose - 0.3,
+            'kp_score': merge_score,
+            'proposal_score': torch.mean(merge_score) + bbox_scores_pick[j] + 1.25 * max(merge_score)
+        })
+
+    return final_result
+
+
+def filter_result(args):
+    score_pick, merge_id, pred_pick, pick, bbox_score_pick = args
+    global ori_pose_preds, ori_pose_scores, ref_dists
+    ids = np.arange(17)
+    max_score = torch.max(score_pick[ids, 0])
+
+    if max_score < scoreThreds:
+        return None
+
+    # Merge poses
+    merge_pose, merge_score = p_merge_fast(
+        pred_pick, ori_pose_preds[merge_id], ori_pose_scores[merge_id], ref_dists[pick])
+
+    max_score = torch.max(merge_score[ids])
+    if max_score < scoreThreds:
+        return None
+
+    xmax = max(merge_pose[:, 0])
+    xmin = min(merge_pose[:, 0])
+    ymax = max(merge_pose[:, 1])
+    ymin = min(merge_pose[:, 1])
+
+    if (1.5 ** 2 * (xmax - xmin) * (ymax - ymin) < 40 * 40.5):
+        return None
+
+    return {
+        'keypoints': merge_pose - 0.3,
+        'kp_score': merge_score,
+        'proposal_score': torch.mean(merge_score) + bbox_score_pick + 1.25 * max(merge_score)
+    }
+
+
+def p_merge(ref_pose, cluster_preds, cluster_scores, ref_dist):
+    '''
+    Score-weighted pose merging
+    INPUT:
+        ref_pose:       reference pose          -- [17, 2]
+        cluster_preds:  redundant poses         -- [n, 17, 2]
+        cluster_scores: redundant poses score   -- [n, 17, 1]
+        ref_dist:       reference scale         -- Constant
+    OUTPUT:
+        final_pose:     merged pose             -- [17, 2]
+        final_score:    merged score            -- [17]
+    '''
+    dist = torch.sqrt(torch.sum(
+        torch.pow(ref_pose[np.newaxis, :] - cluster_preds, 2),
+        dim=2
+    ))  # [n, 17]
+
+    kp_num = 17
+    ref_dist = min(ref_dist, 15)
+
+    mask = (dist <= ref_dist)
+    final_pose = torch.zeros(kp_num, 2)
+    final_score = torch.zeros(kp_num)
+
+    if cluster_preds.dim() == 2:
+        cluster_preds.unsqueeze_(0)
+        cluster_scores.unsqueeze_(0)
+    if mask.dim() == 1:
+        mask.unsqueeze_(0)
+
+    for i in range(kp_num):
+        cluster_joint_scores = cluster_scores[:, i][mask[:, i]]  # [k, 1]
+        cluster_joint_location = cluster_preds[:, i, :][mask[:, i].unsqueeze(
+            -1).repeat(1, 2)].view((torch.sum(mask[:, i]), -1))
+
+        # Get an normalized score
+        normed_scores = cluster_joint_scores / torch.sum(cluster_joint_scores)
+
+        # Merge poses by a weighted sum
+        final_pose[i, 0] = torch.dot(cluster_joint_location[:, 0], normed_scores.squeeze(-1))
+        final_pose[i, 1] = torch.dot(cluster_joint_location[:, 1], normed_scores.squeeze(-1))
+
+        final_score[i] = torch.dot(cluster_joint_scores.transpose(0, 1).squeeze(0), normed_scores.squeeze(-1))
+
+    return final_pose, final_score
+
+
+def p_merge_fast(ref_pose, cluster_preds, cluster_scores, ref_dist):
+    '''
+    Score-weighted pose merging
+    INPUT:
+        ref_pose:       reference pose          -- [17, 2]
+        cluster_preds:  redundant poses         -- [n, 17, 2]
+        cluster_scores: redundant poses score   -- [n, 17, 1]
+        ref_dist:       reference scale         -- Constant
+    OUTPUT:
+        final_pose:     merged pose             -- [17, 2]
+        final_score:    merged score            -- [17]
+    '''
+    dist = torch.sqrt(torch.sum(
+        torch.pow(ref_pose[np.newaxis, :] - cluster_preds, 2),
+        dim=2
+    ))
+
+    kp_num = 17
+    ref_dist = min(ref_dist, 15)
+
+    mask = (dist <= ref_dist)
+    final_pose = torch.zeros(kp_num, 2)
+    final_score = torch.zeros(kp_num)
+
+    if cluster_preds.dim() == 2:
+        cluster_preds.unsqueeze_(0)
+        cluster_scores.unsqueeze_(0)
+    if mask.dim() == 1:
+        mask.unsqueeze_(0)
+
+    # Weighted Merge
+    masked_scores = cluster_scores.mul(mask.float().unsqueeze(-1))
+    normed_scores = masked_scores / torch.sum(masked_scores, dim=0)
+
+    final_pose = torch.mul(cluster_preds, normed_scores.repeat(1, 1, 2)).sum(dim=0)
+    final_score = torch.mul(masked_scores, normed_scores).sum(dim=0)
+    return final_pose, final_score
+
+
+def get_parametric_distance(i, all_preds, keypoint_scores, ref_dist):
+    pick_preds = all_preds[i]
+    pred_scores = keypoint_scores[i]
+    dist = torch.sqrt(torch.sum(
+        torch.pow(pick_preds[np.newaxis, :] - all_preds, 2),
+        dim=2
+    ))
+    mask = (dist <= 1)
+
+    # Define a keypoints distance
+    score_dists = torch.zeros(all_preds.shape[0], 17)
+    keypoint_scores.squeeze_()
+    if keypoint_scores.dim() == 1:
+        keypoint_scores.unsqueeze_(0)
+    if pred_scores.dim() == 1:
+        pred_scores.unsqueeze_(1)
+    # The predicted scores are repeated up to do broadcast
+    pred_scores = pred_scores.repeat(1, all_preds.shape[0]).transpose(0, 1)
+
+    score_dists[mask] = torch.tanh(pred_scores[mask] / delta1) * torch.tanh(keypoint_scores[mask] / delta1)
+
+    point_dist = torch.exp((-1) * dist / delta2)
+    final_dist = torch.sum(score_dists, dim=1) + mu * torch.sum(point_dist, dim=1)
+
+    return final_dist
+
+
+def PCK_match(pick_pred, all_preds, ref_dist):
+    dist = torch.sqrt(torch.sum(
+        torch.pow(pick_pred[np.newaxis, :] - all_preds, 2),
+        dim=2
+    ))
+    ref_dist = min(ref_dist, 7)
+    num_match_keypoints = torch.sum(
+        dist / ref_dist <= 1,
+        dim=1
+    )
+
+    return num_match_keypoints
+
+
+def write_json(all_results, outputpath, for_eval=False):
+    '''
+    all_result: result dict of predictions
+    outputpath: output directory
+    '''
+    form = opt.format
+    json_results = []
+    json_results_cmu = {}
+    for im_res in all_results:
+        im_name = im_res['imgname']
+        for human in im_res['result']:
+            keypoints = []
+            result = {}
+            if for_eval:
+                result['image_id'] = int(im_name.split('/')[-1].split('.')[0].split('_')[-1])
+            else:
+                result['image_id'] = im_name.split('/')[-1]
+            result['category_id'] = 1
+
+            kp_preds = human['keypoints']
+            kp_scores = human['kp_score']
+            pro_scores = human['proposal_score']
+            for n in range(kp_scores.shape[0]):
+                keypoints.append(float(kp_preds[n, 0]))
+                keypoints.append(float(kp_preds[n, 1]))
+                keypoints.append(float(kp_scores[n]))
+            result['keypoints'] = keypoints
+            result['score'] = float(pro_scores)
+
+            if form == 'cmu': # the form of CMU-Pose
+                if result['image_id'] not in json_results_cmu.keys():
+                    json_results_cmu[result['image_id']]={}
+                    json_results_cmu[result['image_id']]['version']="AlphaPose v0.2"
+                    json_results_cmu[result['image_id']]['bodies']=[]
+                tmp={'joints':[]}
+                result['keypoints'].append((result['keypoints'][15]+result['keypoints'][18])/2)
+                result['keypoints'].append((result['keypoints'][16]+result['keypoints'][19])/2)
+                result['keypoints'].append((result['keypoints'][17]+result['keypoints'][20])/2)
+                indexarr=[0,51,18,24,30,15,21,27,36,42,48,33,39,45,6,3,12,9]
+                for i in indexarr:
+                    tmp['joints'].append(result['keypoints'][i])
+                    tmp['joints'].append(result['keypoints'][i+1])
+                    tmp['joints'].append(result['keypoints'][i+2])
+                json_results_cmu[result['image_id']]['bodies'].append(tmp)
+            elif form == 'open': # the form of OpenPose
+                if result['image_id'] not in json_results_cmu.keys():
+                    json_results_cmu[result['image_id']]={}
+                    json_results_cmu[result['image_id']]['version']="AlphaPose v0.2"
+                    json_results_cmu[result['image_id']]['people']=[]
+                tmp={'pose_keypoints_2d':[]}
+                result['keypoints'].append((result['keypoints'][15]+result['keypoints'][18])/2)
+                result['keypoints'].append((result['keypoints'][16]+result['keypoints'][19])/2)
+                result['keypoints'].append((result['keypoints'][17]+result['keypoints'][20])/2)
+                indexarr=[0,51,18,24,30,15,21,27,36,42,48,33,39,45,6,3,12,9]
+                for i in indexarr:
+                    tmp['pose_keypoints_2d'].append(result['keypoints'][i])
+                    tmp['pose_keypoints_2d'].append(result['keypoints'][i+1])
+                    tmp['pose_keypoints_2d'].append(result['keypoints'][i+2])
+                json_results_cmu[result['image_id']]['people'].append(tmp)
+            else:
+                json_results.append(result)
+
+    if form == 'cmu': # the form of CMU-Pose
+        with open(os.path.join(outputpath,'alphapose-results.json'), 'w') as json_file:
+            json_file.write(json.dumps(json_results_cmu))
+            if not os.path.exists(os.path.join(outputpath,'sep-json')):
+                os.mkdir(os.path.join(outputpath,'sep-json'))
+            for name in json_results_cmu.keys():
+                with open(os.path.join(outputpath,'sep-json',name.split('.')[0]+'.json'),'w') as json_file:
+                    json_file.write(json.dumps(json_results_cmu[name]))
+    elif form == 'open': # the form of OpenPose
+        with open(os.path.join(outputpath,'alphapose-results.json'), 'w') as json_file:
+            json_file.write(json.dumps(json_results_cmu))
+            if not os.path.exists(os.path.join(outputpath,'sep-json')):
+                os.mkdir(os.path.join(outputpath,'sep-json'))
+            for name in json_results_cmu.keys():
+                with open(os.path.join(outputpath,'sep-json',name.split('.')[0]+'.json'),'w') as json_file:
+                    json_file.write(json.dumps(json_results_cmu[name]))
+    else:
+        with open(os.path.join(outputpath,'alphapose-results.json'), 'w') as json_file:
+            json_file.write(json.dumps(json_results))
+
diff --git a/joints_detectors/Alphapose/requirements.txt b/joints_detectors/Alphapose/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..6e1f58e06f607c58586255a41209cbd28aa46a08
--- /dev/null
+++ b/joints_detectors/Alphapose/requirements.txt
@@ -0,0 +1,9 @@
+torch==0.4.0
+torchvision
+visdom
+nibabel
+pandas
+tqdm
+matplotlib
+opencv-python
+ntpath
diff --git a/joints_detectors/Alphapose/train_sppe/README.md b/joints_detectors/Alphapose/train_sppe/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..9b3d95225614fbfcab375d0edc4238ba5aa89ffc
--- /dev/null
+++ b/joints_detectors/Alphapose/train_sppe/README.md
@@ -0,0 +1,81 @@
+This folder includes PyTorch code for training the Single Person Pose Estimation network in AlphaPose.
+
+## Installation
+1. Install PyTorch >= 0.4.0 following [official instruction](https://pytorch.org/get-started/locally/).
+2. Install other dependencies.
+``` bash
+cd ${TRAIN_ROOT}
+pip install -r requirements.txt
+```
+3.Disable cudnn for batch_norm: (See: [@Microsoft / human-pose-estimation.pytorch#installation](https://github.com/Microsoft/human-pose-estimation.pytorch#installation))
+```
+# PYTORCH=/path/to/pytorch
+# for pytorch v0.4.0
+sed -i "1194s/torch\.backends\.cudnn\.enabled/False/g" ${PYTORCH}/torch/nn/functional.py
+# for pytorch v0.4.1
+sed -i "1254s/torch\.backends\.cudnn\.enabled/False/g" ${PYTORCH}/torch/nn/functional.py
+
+# Note that instructions like # PYTORCH=/path/to/pytorch indicate that you should pick 
+# a path where you'd like to have pytorch installed and then set an environment
+# variable (PYTORCH in this case) accordingly.
+```
+## Data preparation
+
+### COCO Data
+Please download [annot_coco.h5](https://drive.google.com/open?id=1OviCQgzKO2t0gh4Me0MXfi6xgXyTWC5T) and `person_keypoints_val2014.json` from `cocodataset`(http://cocodataset.org/#download).
+```
+${TRAIN_ROOT}
+|-- src
+|-- exp
+|-- data
+`-- |-- coco
+    `-- |-- annot_coco.h5
+        |-- person_keypoints_val2014.json
+        `-- images
+            |-- trainval2017
+            |   |-- 000000000001.jpg
+            |   |-- 000000000002.jpg
+            |   |-- 000000000003.jpg
+            |   |-- ... 
+```
+
+## Train on COCO
+``` bash
+cd src
+# Train without DPG first
+python train.py --dataset coco --expID exp1 --nClasses 17 --LR 1e-4
+# Then, train with DPG
+python train.py --dataset coco --expID exp1 --nClasses 17 --LR 1e-4 --addDPG
+
+# Or, train with pretrian model
+python train.py --dataset coco --expID exp1 --nClasses 17 --LR 1e-5 --addDPG --loadModel #{MODEL_DIR}
+```
+
+## Train on new dataset
+Please create the `h5` file from your own datset. Here is the python demo to read the `h5` file.
+``` python
+>>> import h5py
+>>> annot = h5py.File('annot_coco.h5')
+>>> for k in annot.keys():
+...     print(k)
+bndbox
+imgname
+part
+
+>>> bndboxes = annot['bndbox'][:]
+>>> bndbox.shape
+(144213, 1, 4)
+>>> imgnames = annot['imgname'][:]
+>>> imgname.shape
+(144213, 16)
+>>> parts = annot['part'][:]
+>>> part.shape
+(144213, 17, 2)
+```
+```
+bndbox:     [1 x 4]     (upleft_x, upleft_y, bottomright_x, bottomright_y)
+imgname:    [16]        #ascii number of imagename
+part:       [17 x 2]    (kp1_x, kp1_y, kp2_x, kp2_y, ..., kp17_x, kp17_y)
+```
+
+Please refer to this python demo to create your own `h5` files. How to save data in `h5` files, please refer to [h5py quick start](http://docs.h5py.org/en/stable/quick.html#quick).
diff --git a/joints_detectors/Alphapose/train_sppe/data/coco/coco-minival500_images.txt b/joints_detectors/Alphapose/train_sppe/data/coco/coco-minival500_images.txt
new file mode 100644
index 0000000000000000000000000000000000000000..d6dc42e0364c595b52cce2e2335394373091b208
--- /dev/null
+++ b/joints_detectors/Alphapose/train_sppe/data/coco/coco-minival500_images.txt
@@ -0,0 +1 @@
+375521,244455,375530,244462,113397,113408,375554,171819,375566,244496,139077,506656,375606,244539,565045,113473,375621,244550,492605,506707,113493,215524,506717,506723,433980,244586,113533,113559,390298,281072,113571,543218,506803,113588,113590,244665,375755,375769,324776,506872,506874,375810,375812,113678,375823,261292,506933,113720,113722,506945,375875,506950,113736,375881,244815,113745,113756,113757,375902,244833,244834,507015,178370,412184,507037,507065,244925,244931,507080,193743,104176,113890,113905,113914,376059,244999,507147,507154,245013,376093,507167,245026,507171,106375,507180,376112,507187,113975,113989,507211,507223,376160,114025,376185,114049,114055,507273,376206,376208,376209,376233,376236,507312,245174,376247,114108,245182,507330,507352,114147,376295,245227,114158,376307,376322,106411,114183,376342,507427,062726,507436,434098,245295,376372,245301,245311,245313,245315,245320,376393,507473,038721,376416,114282,114291,376441,376449,376450,245383,376456,114313,114317,376469,507551,245411,245414,114352,245426,245430,245432,245447,245448,376521,245453,376531,245460,114389,245462,376545,376549,114414,507633,245497,376575,507663,507667,114453,281220,114458,376603,114481,376628,507719,139113,172004,507750,376679,346774,507782,245642,507797,245660,245667,303089,376751,507826,507833,376773,114634,245716,303101,108130,245754,114684,477867,114710,507927,376859,114717,507935,507945,114744,114745,245818,376891,507966,376900,507975,106508,507979,019129,508006,376939,376959,245898,376990,114855,114868,114870,508087,114884,508101,508119,114907,150224,377060,246001,246004,246005,246014,114946,377091,508167,377097,377111,246040,377113,246053,246057,061605,246064,246066,115006,377155,508230,246105,246106,377183,115043,377195,246124,246125,115060,508288,508302,508303,377239,246183,508339,062878,115128,115146,508370,508373,246231,246252,246265,377352,377361,377385,115243,115245,508470,521655,377427,182784,390585,508514,115314,565353,508538,456127,246398,377486,115363,115370,246454,508605,377577,508656,377588,115455,115459,246535,246562,377635,128476,246576,377652,508730,246589,246590,115521,246597,246612,246626,246629,377706,305000,377715,246649,115579,115584,377730,508811,508822,246686,150342,115626,115636,508872,377802,377809,115667,246746,377832,368807,115700,508917,246782,115721,508949,508950,543577,508962,115752,508972,508977,309371,115765,377910,508985,246843,115772,106677,115791,115793,115796,509014,509020,377949,246878,377951,246883,509028,509037,115823,115830,195896,377984,377999,368836,115870,115875,246951,246963,246968,115898,378048,115912,182805,115924,115930,562519,509158,378096,378116,509192,509194,368855,378126,247057,378134,378147,509223,509227,116017,378163,116023,378169,200267,116037,116046,259640,378204,116061,116067,116068,247141,116083,116096,063040,378244,509319,247179,116132,116133,247206,543644,378284,247216,116149,509366,116173,247259,378334,247264,116202,378347,116208,281512,247285,247306,509451,509459,247317,509471,116261,509497,509514,247378,509526,509536,378467,543676,378482,116341,194153,116354,378502,509577,247438,378515,509589,509590,116377,172315,116389,378538,247474,116405,247484,378561,116434,116439,247519,116455,291936,116466,378614,509695,378652,378657,378658,247587,116517,378667,378673,172342,509766,247625,247639,509786,116574,378727,509811,509826,247692,247714,247720,509867,509891,378823,378825,378831,116696,247782,116712,247788,247790,378873,247808,259755,378894,247839,247840,378928,378940,378948,378962
\ No newline at end of file
diff --git a/joints_detectors/Alphapose/train_sppe/requirements.txt b/joints_detectors/Alphapose/train_sppe/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..2072cb449db4b22f5f0efbc33269f1938c175912
--- /dev/null
+++ b/joints_detectors/Alphapose/train_sppe/requirements.txt
@@ -0,0 +1,12 @@
+torch==0.4.0
+torchvision
+opencv-python
+matplotlib
+nibabel
+cython
+pandas
+h5py
+scipy
+tqdm
+pycocotools
+tensorboardx
\ No newline at end of file
diff --git a/joints_detectors/Alphapose/train_sppe/src/.tensorboard/coco/test/events.out.tfevents.1537703645.mvig-14 b/joints_detectors/Alphapose/train_sppe/src/.tensorboard/coco/test/events.out.tfevents.1537703645.mvig-14
new file mode 100644
index 0000000000000000000000000000000000000000..ad9530ce2777ad8c4ded323f2bad3affe12595b7
--- /dev/null
+++ b/joints_detectors/Alphapose/train_sppe/src/.tensorboard/coco/test/events.out.tfevents.1537703645.mvig-14
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a18ebf54707c24344dd13e573c88c944539cbb266257694fe97efa1bcf6af42d
+size 66022
diff --git a/joints_detectors/Alphapose/train_sppe/src/.tensorboard/coco/test/events.out.tfevents.1537703981.mvig-14 b/joints_detectors/Alphapose/train_sppe/src/.tensorboard/coco/test/events.out.tfevents.1537703981.mvig-14
new file mode 100644
index 0000000000000000000000000000000000000000..17f4889d9f39269a70cc409cff824980c929ee01
--- /dev/null
+++ b/joints_detectors/Alphapose/train_sppe/src/.tensorboard/coco/test/events.out.tfevents.1537703981.mvig-14
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@@ -0,0 +1,3 @@
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diff --git a/joints_detectors/Alphapose/train_sppe/src/evaluation.py b/joints_detectors/Alphapose/train_sppe/src/evaluation.py
new file mode 100644
index 0000000000000000000000000000000000000000..10c13ddac01132844ab2a0d89c57e37f28ca9728
--- /dev/null
+++ b/joints_detectors/Alphapose/train_sppe/src/evaluation.py
@@ -0,0 +1,133 @@
+# -----------------------------------------------------
+# Copyright (c) Shanghai Jiao Tong University. All rights reserved.
+# Written by Jiefeng Li (jeff.lee.sjtu@gmail.com)
+# -----------------------------------------------------
+
+import torch
+import torch.nn as nn
+import torch.utils.data
+from .predict.annot.coco_minival import Mscoco_minival
+from .predict.p_poseNMS import pose_nms, write_json
+import numpy as np
+from .opt import opt
+from tqdm import tqdm
+from .utils.img import flip, shuffleLR, vis_frame
+from .util.eval import getPrediction
+from .util.eval import getmap
+import os
+import cv2
+
+
+def gaussian(size):
+    '''
+    Generate a 2D gaussian array
+    '''
+    sigma = 1
+    x = np.arange(0, size, 1, float)
+    y = x[:, np.newaxis]
+    x0 = y0 = size // 2
+    sigma = size / 4.0
+    # The gaussian is not normalized, we want the center value to equal 1
+    g = np.exp(- ((x - x0) ** 2 + (y - y0) ** 2) / (2 * sigma ** 2))
+    g = g[np.newaxis, :]
+    return g
+
+
+gaussian_kernel = nn.Conv2d(17, 17, kernel_size=4 * 1 + 1,
+                            stride=1, padding=2, groups=17, bias=False)
+
+g = torch.from_numpy(gaussian(4 * 1 + 1)).clone()
+g = torch.unsqueeze(g, 1)
+g = g.repeat(17, 1, 1, 1)
+gaussian_kernel.weight.data = g.float()
+gaussian_kernel.cuda()
+
+
+def prediction(model):
+    model.eval()
+    dataset = Mscoco_minival()
+    minival_loader = torch.utils.data.DataLoader(
+        dataset, batch_size=1, shuffle=False, num_workers=20, pin_memory=True)
+    minival_loader_desc = tqdm(minival_loader)
+
+    final_result = []
+
+    tmp_inp = {}
+    for i, (inp, box, im_name, metaData) in enumerate(minival_loader_desc):
+        #inp = torch.autograd.Variable(inp.cuda(), volatile=True)
+        pt1, pt2, ori_inp = metaData
+        #with torch.autograd.profiler.profile(use_cuda=True) as prof:
+        if im_name[0] in tmp_inp.keys():
+            inps = tmp_inp[im_name[0]]['inps']
+            ori_inps = tmp_inp[im_name[0]]['ori_inps']
+            boxes = tmp_inp[im_name[0]]['boxes']
+            pt1s = tmp_inp[im_name[0]]['pt1s']
+            pt2s = tmp_inp[im_name[0]]['pt2s']
+            tmp_inp[im_name[0]]['inps'] = torch.cat((inps, inp), dim=0)
+            tmp_inp[im_name[0]]['pt1s'] = torch.cat((pt1s, pt1), dim=0)
+            tmp_inp[im_name[0]]['pt2s'] = torch.cat((pt2s, pt2), dim=0)
+            tmp_inp[im_name[0]]['ori_inps'] = torch.cat(
+                (ori_inps, ori_inp), dim=0)
+            tmp_inp[im_name[0]]['boxes'] = torch.cat((boxes, box), dim=0)
+        else:
+            tmp_inp[im_name[0]] = {
+                'inps': inp,
+                'ori_inps': ori_inp,
+                'boxes': box,
+                'pt1s': pt1,
+                'pt2s': pt2
+            }
+
+    for im_name, item in tqdm(tmp_inp.items()):
+        inp = item['inps']
+        pt1 = item['pt1s']
+        pt2 = item['pt2s']
+        box = item['boxes']
+        ori_inp = item['ori_inps']
+
+        with torch.no_grad():
+            try:
+                kp_preds = model(inp)
+                kp_preds = kp_preds.data[:, :17, :]
+            except RuntimeError as e:
+                '''
+                Divide inputs into two batches
+                '''
+                assert str(e) == 'CUDA error: out of memory'
+                bn = inp.shape[0]
+                inp1 = inp[: bn // 2]
+                inp2 = inp[bn // 2:]
+                kp_preds1 = model(inp1)
+                kp_preds2 = model(inp2)
+                kp_preds = torch.cat((kp_preds1, kp_preds2), dim=0)
+                kp_preds = kp_preds.data[:, :17, :]
+
+            # kp_preds = gaussian_kernel(F.relu(kp_preds))
+
+            # Get predictions
+            # location prediction (n, kp, 2) | score prediction (n, kp, 1)
+
+            preds, preds_img, preds_scores = getPrediction(
+                kp_preds.cpu().data, pt1, pt2,
+                opt.inputResH, opt.inputResW, opt.outputResH, opt.outputResW
+            )
+
+            result = pose_nms(box, preds_img, preds_scores)
+            result = {
+                'imgname': im_name,
+                'result': result
+            }
+        #img = display_frame(orig_img, result, opt.outputpath)
+        #ori_inp = np.transpose(
+        #    ori_inp[0][:3].clone().numpy(), (1, 2, 0)) * 255
+        #img = vis_frame(ori_inp, result)
+        #cv2.imwrite(os.path.join(
+        #    './val', 'vis', im_name), img)
+        final_result.append(result)
+
+    write_json(final_result, './val', for_eval=True)
+    return getmap()
+
+
+if __name__ == '__main__':
+    prediction()
diff --git a/joints_detectors/Alphapose/train_sppe/src/install.sh b/joints_detectors/Alphapose/train_sppe/src/install.sh
new file mode 100644
index 0000000000000000000000000000000000000000..7db1830fe2be5cc92ffc2886a9d37bc23ce9abc9
--- /dev/null
+++ b/joints_detectors/Alphapose/train_sppe/src/install.sh
@@ -0,0 +1,11 @@
+torch==0.4.0
+torchvision
+opencv-python
+matplotlib
+nibabel
+cython
+pandas
+h5py
+scipy
+tqdm
+pycocotools
\ No newline at end of file
diff --git a/joints_detectors/Alphapose/train_sppe/src/models/FastPose.py b/joints_detectors/Alphapose/train_sppe/src/models/FastPose.py
new file mode 100644
index 0000000000000000000000000000000000000000..1b5d590b627745a6bb1ce3d037eb87678b28b137
--- /dev/null
+++ b/joints_detectors/Alphapose/train_sppe/src/models/FastPose.py
@@ -0,0 +1,41 @@
+# -----------------------------------------------------
+# Copyright (c) Shanghai Jiao Tong University. All rights reserved.
+# Written by Jiefeng Li (jeff.lee.sjtu@gmail.com)
+# -----------------------------------------------------
+
+import torch.nn as nn
+
+from .layers.DUC import DUC
+from .layers.SE_Resnet import SEResnet
+
+# Import training option
+from opt import opt
+
+
+def createModel():
+    return FastPose_SE()
+
+
+class FastPose_SE(nn.Module):
+    conv_dim = 128
+
+    def __init__(self):
+        super(FastPose_SE, self).__init__()
+
+        self.preact = SEResnet('resnet101')
+
+        self.suffle1 = nn.PixelShuffle(2)
+        self.duc1 = DUC(512, 1024, upscale_factor=2)
+        self.duc2 = DUC(256, 512, upscale_factor=2)
+
+        self.conv_out = nn.Conv2d(
+            self.conv_dim, opt.nClasses, kernel_size=3, stride=1, padding=1)
+
+    def forward(self, x):
+        out = self.preact(x)
+        out = self.suffle1(out)
+        out = self.duc1(out)
+        out = self.duc2(out)
+
+        out = self.conv_out(out)
+        return out
diff --git a/joints_detectors/Alphapose/train_sppe/src/models/layers/DUC.py b/joints_detectors/Alphapose/train_sppe/src/models/layers/DUC.py
new file mode 100644
index 0000000000000000000000000000000000000000..86811b6fd629c18d1556fef844a52ff96ef47b87
--- /dev/null
+++ b/joints_detectors/Alphapose/train_sppe/src/models/layers/DUC.py
@@ -0,0 +1,28 @@
+# -----------------------------------------------------
+# Copyright (c) Shanghai Jiao Tong University. All rights reserved.
+# Written by Jiefeng Li (jeff.lee.sjtu@gmail.com)
+# -----------------------------------------------------
+
+import torch.nn as nn
+
+
+class DUC(nn.Module):
+    '''
+    Initialize: inplanes, planes, upscale_factor
+    OUTPUT: (planes // upscale_factor^2) * ht * wd
+    '''
+
+    def __init__(self, inplanes, planes, upscale_factor=2):
+        super(DUC, self).__init__()
+        self.conv = nn.Conv2d(
+            inplanes, planes, kernel_size=3, padding=1, bias=False)
+        self.bn = nn.BatchNorm2d(planes, momentum=0.1)
+        self.relu = nn.ReLU(inplace=True)
+        self.pixel_shuffle = nn.PixelShuffle(upscale_factor)
+
+    def forward(self, x):
+        x = self.conv(x)
+        x = self.bn(x)
+        x = self.relu(x)
+        x = self.pixel_shuffle(x)
+        return x
diff --git a/joints_detectors/Alphapose/train_sppe/src/models/layers/SE_Resnet.py b/joints_detectors/Alphapose/train_sppe/src/models/layers/SE_Resnet.py
new file mode 100644
index 0000000000000000000000000000000000000000..1be60a1ad9f108fa83bc96f0341bc9c71baf4e76
--- /dev/null
+++ b/joints_detectors/Alphapose/train_sppe/src/models/layers/SE_Resnet.py
@@ -0,0 +1,105 @@
+# -----------------------------------------------------
+# Copyright (c) Shanghai Jiao Tong University. All rights reserved.
+# Written by Jiefeng Li (jeff.lee.sjtu@gmail.com)
+# -----------------------------------------------------
+
+import torch.nn as nn
+from models.layers.SE_module import SELayer
+import torch.nn.functional as F
+
+
+class Bottleneck(nn.Module):
+    expansion = 4
+
+    def __init__(self, inplanes, planes, stride=1, downsample=None, reduction=False):
+        super(Bottleneck, self).__init__()
+        self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
+        self.bn1 = nn.BatchNorm2d(planes, momentum=0.1)
+        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride,
+                               padding=1, bias=False)
+        self.bn2 = nn.BatchNorm2d(planes, momentum=0.1)
+        self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)
+        self.bn3 = nn.BatchNorm2d(planes * 4, momentum=0.1)
+        if reduction:
+            self.se = SELayer(planes * 4)
+
+        self.reduc = reduction
+        self.downsample = downsample
+        self.stride = stride
+
+    def forward(self, x):
+        residual = x
+
+        out = F.relu(self.bn1(self.conv1(x)), inplace=True)
+        out = F.relu(self.bn2(self.conv2(out)), inplace=True)
+
+        out = self.conv3(out)
+        out = self.bn3(out)
+        if self.reduc:
+            out = self.se(out)
+
+        if self.downsample is not None:
+            residual = self.downsample(x)
+
+        out += residual
+        out = F.relu(out)
+
+        return out
+
+
+class SEResnet(nn.Module):
+    """ SEResnet """
+
+    def __init__(self, architecture):
+        super(SEResnet, self).__init__()
+        assert architecture in ["resnet50", "resnet101"]
+        self.inplanes = 64
+        self.layers = [3, 4, {"resnet50": 6, "resnet101": 23}[architecture], 3]
+        self.block = Bottleneck
+
+        self.conv1 = nn.Conv2d(3, 64, kernel_size=7,
+                               stride=2, padding=3, bias=False)
+        self.bn1 = nn.BatchNorm2d(64, eps=1e-5, momentum=0.1, affine=True)
+        self.relu = nn.ReLU(inplace=True)
+        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
+
+        self.layer1 = self.make_layer(self.block, 64, self.layers[0])
+        self.layer2 = self.make_layer(
+            self.block, 128, self.layers[1], stride=2)
+        self.layer3 = self.make_layer(
+            self.block, 256, self.layers[2], stride=2)
+
+        self.layer4 = self.make_layer(
+            self.block, 512, self.layers[3], stride=2)
+
+    def forward(self, x):
+        x = self.maxpool(self.relu(self.bn1(self.conv1(x))))  # 64 * h/4 * w/4
+        x = self.layer1(x)  # 256 * h/4 * w/4
+        x = self.layer2(x)  # 512 * h/8 * w/8
+        x = self.layer3(x)  # 1024 * h/16 * w/16
+        x = self.layer4(x)  # 2048 * h/32 * w/32
+        return x
+
+    def stages(self):
+        return [self.layer1, self.layer2, self.layer3, self.layer4]
+
+    def make_layer(self, block, planes, blocks, stride=1):
+        downsample = None
+        if stride != 1 or self.inplanes != planes * block.expansion:
+            downsample = nn.Sequential(
+                nn.Conv2d(self.inplanes, planes * block.expansion,
+                          kernel_size=1, stride=stride, bias=False),
+                nn.BatchNorm2d(planes * block.expansion, momentum=0.1),
+            )
+
+        layers = []
+        if downsample is not None:
+            layers.append(block(self.inplanes, planes,
+                                stride, downsample, reduction=True))
+        else:
+            layers.append(block(self.inplanes, planes, stride, downsample))
+        self.inplanes = planes * block.expansion
+        for i in range(1, blocks):
+            layers.append(block(self.inplanes, planes))
+
+        return nn.Sequential(*layers)
diff --git a/joints_detectors/Alphapose/train_sppe/src/models/layers/SE_module.py b/joints_detectors/Alphapose/train_sppe/src/models/layers/SE_module.py
new file mode 100644
index 0000000000000000000000000000000000000000..f370fd4e1fb777306e37f4a7c7be99bd0fbca64a
--- /dev/null
+++ b/joints_detectors/Alphapose/train_sppe/src/models/layers/SE_module.py
@@ -0,0 +1,24 @@
+# -----------------------------------------------------
+# Copyright (c) Shanghai Jiao Tong University. All rights reserved.
+# Written by Jiefeng Li (jeff.lee.sjtu@gmail.com)
+# -----------------------------------------------------
+
+from torch import nn
+
+
+class SELayer(nn.Module):
+    def __init__(self, channel, reduction=1):
+        super(SELayer, self).__init__()
+        self.avg_pool = nn.AdaptiveAvgPool2d(1)
+        self.fc = nn.Sequential(
+            nn.Linear(channel, channel // reduction),
+            nn.ReLU(inplace=True),
+            nn.Linear(channel // reduction, channel),
+            nn.Sigmoid()
+        )
+
+    def forward(self, x):
+        b, c, _, _ = x.size()
+        y = self.avg_pool(x).view(b, c)
+        y = self.fc(y).view(b, c, 1, 1)
+        return x * y
diff --git a/joints_detectors/Alphapose/train_sppe/src/opt.py b/joints_detectors/Alphapose/train_sppe/src/opt.py
new file mode 100644
index 0000000000000000000000000000000000000000..42b296be0f2c898a5bdeb248728278550cc59454
--- /dev/null
+++ b/joints_detectors/Alphapose/train_sppe/src/opt.py
@@ -0,0 +1,76 @@
+# -----------------------------------------------------
+# Copyright (c) Shanghai Jiao Tong University. All rights reserved.
+# Written by Jiefeng Li (jeff.lee.sjtu@gmail.com)
+# -----------------------------------------------------
+
+import argparse
+
+parser = argparse.ArgumentParser(description='PyTorch AlphaPose Training')
+
+"----------------------------- General options -----------------------------"
+parser.add_argument('--expID', default='default', type=str,
+                    help='Experiment ID')
+parser.add_argument('--dataset', default='coco', type=str,
+                    help='Dataset choice: mpii | coco')
+parser.add_argument('--nThreads', default=30, type=int,
+                    help='Number of data loading threads')
+parser.add_argument('--snapshot', default=1, type=int,
+                    help='How often to take a snapshot of the model (0 = never)')
+
+"----------------------------- AlphaPose options -----------------------------"
+parser.add_argument('--addDPG', default=False, dest='addDPG',
+                    help='Train with data augmentation', action='store_true')
+
+"----------------------------- Model options -----------------------------"
+parser.add_argument('--loadModel', default=None, type=str,
+                    help='Provide full path to a previously trained model')
+parser.add_argument('--nClasses', default=17, type=int,
+                    help='Number of output channel')
+
+"----------------------------- Hyperparameter options -----------------------------"
+parser.add_argument('--LR', default=1e-3, type=float,
+                    help='Learning rate')
+parser.add_argument('--momentum', default=0, type=float,
+                    help='Momentum')
+parser.add_argument('--weightDecay', default=0, type=float,
+                    help='Weight decay')
+parser.add_argument('--eps', default=1e-8, type=float,
+                    help='epsilon')
+parser.add_argument('--crit', default='MSE', type=str,
+                    help='Criterion type')
+parser.add_argument('--optMethod', default='rmsprop', type=str,
+                    help='Optimization method: rmsprop | sgd | nag | adadelta')
+
+
+"----------------------------- Training options -----------------------------"
+parser.add_argument('--nEpochs', default=50, type=int,
+                    help='Number of hourglasses to stack')
+parser.add_argument('--epoch', default=0, type=int,
+                    help='Current epoch')
+parser.add_argument('--trainBatch', default=128, type=int,
+                    help='Train-batch size')
+parser.add_argument('--validBatch', default=24, type=int,
+                    help='Valid-batch size')
+parser.add_argument('--trainIters', default=0, type=int,
+                    help='Total train iters')
+parser.add_argument('--valIters', default=0, type=int,
+                    help='Total valid iters')
+
+"----------------------------- Data options -----------------------------"
+parser.add_argument('--inputResH', default=320, type=int,
+                    help='Input image height')
+parser.add_argument('--inputResW', default=256, type=int,
+                    help='Input image width')
+parser.add_argument('--outputResH', default=80, type=int,
+                    help='Output heatmap height')
+parser.add_argument('--outputResW', default=64, type=int,
+                    help='Output heatmap width')
+parser.add_argument('--scale', default=0.3, type=float,
+                    help='Degree of scale augmentation')
+parser.add_argument('--rotate', default=40, type=float,
+                    help='Degree of rotation augmentation')
+parser.add_argument('--hmGauss', default=1, type=int,
+                    help='Heatmap gaussian size')
+
+
+opt = parser.parse_args()
diff --git a/joints_detectors/Alphapose/train_sppe/src/predict/annot/coco-minival-images-newnms/index.txt b/joints_detectors/Alphapose/train_sppe/src/predict/annot/coco-minival-images-newnms/index.txt
new file mode 100644
index 0000000000000000000000000000000000000000..9dfbc523157fd7437717fa33f69eb72e26e300bb
--- /dev/null
+++ b/joints_detectors/Alphapose/train_sppe/src/predict/annot/coco-minival-images-newnms/index.txt
@@ -0,0 +1,500 @@
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diff --git a/joints_detectors/Alphapose/train_sppe/src/predict/annot/coco-minival-images-newnms/score-proposals.txt b/joints_detectors/Alphapose/train_sppe/src/predict/annot/coco-minival-images-newnms/score-proposals.txt
new file mode 100644
index 0000000000000000000000000000000000000000..d1c1bfb1ca548b2e313a4c33eb1a26203b8d1050
--- /dev/null
+++ b/joints_detectors/Alphapose/train_sppe/src/predict/annot/coco-minival-images-newnms/score-proposals.txt
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diff --git a/joints_detectors/Alphapose/train_sppe/src/predict/annot/coco-minival-images-newnms/test-dev_images.txt b/joints_detectors/Alphapose/train_sppe/src/predict/annot/coco-minival-images-newnms/test-dev_images.txt
new file mode 100644
index 0000000000000000000000000000000000000000..262da0592248b0eb6187e2571cf0285ec93d68e6
--- /dev/null
+++ b/joints_detectors/Alphapose/train_sppe/src/predict/annot/coco-minival-images-newnms/test-dev_images.txt
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diff --git a/joints_detectors/Alphapose/train_sppe/src/predict/annot/coco_minival.py b/joints_detectors/Alphapose/train_sppe/src/predict/annot/coco_minival.py
new file mode 100644
index 0000000000000000000000000000000000000000..0c6c7528fe85f2f1ede01a2ef7d0f9f1c4ba3d70
--- /dev/null
+++ b/joints_detectors/Alphapose/train_sppe/src/predict/annot/coco_minival.py
@@ -0,0 +1,89 @@
+# -----------------------------------------------------
+# Copyright (c) Shanghai Jiao Tong University. All rights reserved.
+# Written by Jiefeng Li (jeff.lee.sjtu@gmail.com)
+# -----------------------------------------------------
+
+import os
+import h5py
+import torch
+import torch.utils.data as data
+from train_sppe.src.utils.img import (load_image, cropBox)
+from opt import opt
+
+
+class Mscoco_minival(data.Dataset):
+    def __init__(self, annoSet='coco-minival-images-newnms/test-dev'):
+        self.img_folder = '../data/coco/images'    # root image folders
+        self.annot = dict()
+
+        # Read in annotation information from hdf5 file
+        tags = ['xmin', 'ymin', 'xmax', 'ymax']
+        with h5py.File('./predict/annot/' + annoSet + '.h5', 'r') as a:
+            for tag in tags:
+                self.annot[tag] = a[tag][:]
+
+        # Load in image file names
+        with open('./predict/annot/' + annoSet + '_images.txt', 'r') as f:
+            self.images = f.readlines()
+        self.images = list(map(lambda x: x.strip('\n'), self.images))
+        assert len(self.images) == self.annot['xmin'].shape[0]
+        self.size = len(self.images)
+
+        self.flipRef = ((2, 3), (4, 5), (6, 7),
+                        (8, 9), (10, 11), (12, 13),
+                        (14, 15), (16, 17))
+        self.year = 2017
+
+    def __getitem__(self, index):
+        if self.year == 2014:
+            imgname = self.images[index]
+        else:
+            imgname = self.images[index].split('_')[2]
+
+        img_path = os.path.join(self.img_folder, imgname)
+        img = load_image(img_path)
+
+        ori_img = img.clone()
+        img[0].add_(-0.406)
+        img[1].add_(-0.457)
+        img[2].add_(-0.480)
+
+        imght = img.size(1)
+        imgwidth = img.size(2)
+        upLeft = torch.Tensor(
+            (float(self.annot['xmin'][index]), float(self.annot['ymin'][index])))
+        bottomRight = torch.Tensor(
+            (float(self.annot['xmax'][index]), float(self.annot['ymax'][index])))
+
+        ht = bottomRight[1] - upLeft[1]
+        width = bottomRight[0] - upLeft[0]
+        if width > 100:
+            scaleRate = 0.2
+        else:
+            scaleRate = 0.3
+
+        upLeft[0] = max(0, upLeft[0] - width * scaleRate / 2)
+        upLeft[1] = max(0, upLeft[1] - ht * scaleRate / 2)
+        bottomRight[0] = max(
+            min(imgwidth - 1, bottomRight[0] + width * scaleRate / 2), upLeft[0] + 5)
+        bottomRight[1] = max(
+            min(imght - 1, bottomRight[1] + ht * scaleRate / 2), upLeft[1] + 5)
+
+        inp = cropBox(img, upLeft, bottomRight, opt.inputResH, opt.inputResW)
+        ori_inp = cropBox(ori_img, upLeft, bottomRight,
+                          opt.inputResH, opt.inputResW)
+        metaData = (
+            upLeft,
+            bottomRight,
+            ori_inp
+        )
+        box = torch.zeros(4)
+        box[0] = upLeft[0]
+        box[1] = upLeft[1]
+        box[2] = bottomRight[0]
+        box[3] = bottomRight[1]
+
+        return inp, box, imgname, metaData
+
+    def __len__(self):
+        return self.size
diff --git a/joints_detectors/Alphapose/train_sppe/src/predict/p_poseNMS.py b/joints_detectors/Alphapose/train_sppe/src/predict/p_poseNMS.py
new file mode 100644
index 0000000000000000000000000000000000000000..ce8cbc9f1afffaeb8dc52009012ee43e26264399
--- /dev/null
+++ b/joints_detectors/Alphapose/train_sppe/src/predict/p_poseNMS.py
@@ -0,0 +1,313 @@
+# -----------------------------------------------------
+# Copyright (c) Shanghai Jiao Tong University. All rights reserved.
+# Written by Jiefeng Li (jeff.lee.sjtu@gmail.com)
+# -----------------------------------------------------
+
+import torch
+import json
+import os
+import numpy as np
+
+''' Constant Configuration '''
+delta1 = 1
+mu = 1.7
+delta2 = 1.3
+gamma = 22.48
+scoreThreds = 0.15
+matchThreds = 5
+alpha = 0.1
+
+
+def pose_nms(bboxes, pose_preds, pose_scores):
+    '''
+    Parametric Pose NMS algorithm
+    bboxes:         bbox locations list (n, 4)
+    bbox_scores:    bbox scores list (n,)
+    pose_preds:     pose locations list (n, 17, 2)
+    pose_scores:    pose scores list    (n, 17, 1)
+    '''
+    pose_scores[pose_scores <= scoreThreds] = 1e-5
+    pose_scores[pose_scores > 1] = 1
+    final_result = []
+
+    ori_pose_preds = pose_preds.clone()
+    ori_pose_scores = pose_scores.clone()
+
+    xmax = bboxes[:, 2]
+    xmin = bboxes[:, 0]
+    ymax = bboxes[:, 3]
+    ymin = bboxes[:, 1]
+
+    widths = xmax - xmin
+    heights = ymax - ymin
+    ref_dists = alpha * np.maximum(widths, heights)
+
+    nsamples = bboxes.shape[0]
+    human_scores = pose_scores.mean(dim=1)
+
+    human_ids = np.arange(nsamples)
+    # Do pPose-NMS
+    pick = []
+    merge_ids = []
+    while(human_scores.shape[0] != 0):
+        # Pick the one with highest score
+        pick_id = torch.argmax(human_scores)
+        pick.append(human_ids[pick_id])
+        # num_visPart = torch.sum(pose_scores[pick_id] > 0.2)
+
+        # Get numbers of match keypoints by calling PCK_match
+        ref_dist = ref_dists[human_ids[pick_id]]
+        simi = get_parametric_distance(
+            pick_id, pose_preds, pose_scores, ref_dist)
+        num_match_keypoints = PCK_match(
+            pose_preds[pick_id], pose_preds, ref_dist)
+
+        # Delete humans who have more than matchThreds keypoints overlap and high similarity
+        delete_ids = torch.from_numpy(np.arange(human_scores.shape[0]))[
+            (simi > gamma) | (num_match_keypoints >= matchThreds)]
+
+        if delete_ids.shape[0] == 0:
+            delete_ids = pick_id
+        #else:
+        #    delete_ids = torch.from_numpy(delete_ids)
+
+        merge_ids.append(human_ids[delete_ids])
+        pose_preds = np.delete(pose_preds, delete_ids, axis=0)
+        pose_scores = np.delete(pose_scores, delete_ids, axis=0)
+        human_ids = np.delete(human_ids, delete_ids)
+        human_scores = np.delete(human_scores, delete_ids, axis=0)
+
+    assert len(merge_ids) == len(pick)
+    preds_pick = ori_pose_preds[pick]
+    scores_pick = ori_pose_scores[pick]
+
+    for j in range(len(pick)):
+        ids = np.arange(17)
+        max_score = torch.max(scores_pick[j, ids, 0])
+
+        if max_score < scoreThreds:
+            continue
+
+        # Merge poses
+        merge_id = merge_ids[j]
+        merge_pose, merge_score = p_merge_fast(
+            preds_pick[j], ori_pose_preds[merge_id], ori_pose_scores[merge_id], ref_dists[pick[j]])
+
+        max_score = torch.max(merge_score[ids])
+        if max_score < scoreThreds:
+            continue
+
+        xmax = max(merge_pose[:, 0])
+        xmin = min(merge_pose[:, 0])
+        ymax = max(merge_pose[:, 1])
+        ymin = min(merge_pose[:, 1])
+
+        if (1.5 ** 2 * (xmax - xmin) * (ymax - ymin) < 40 * 40.5):
+            continue
+
+        final_result.append({
+            'keypoints': merge_pose - 0.3,
+            'kp_score': merge_score,
+            'proposal_score': torch.mean(merge_score) + 1.25 * max(merge_score)
+        })
+
+    return final_result
+
+
+def filter_result(args):
+    score_pick, merge_id, pred_pick, pick, bbox_score_pick = args
+    global ori_pose_preds, ori_pose_scores, ref_dists
+    ids = np.arange(17)
+    max_score = torch.max(score_pick[ids, 0])
+
+    if max_score < scoreThreds:
+        return None
+
+    # Merge poses
+    merge_pose, merge_score = p_merge_fast(
+        pred_pick, ori_pose_preds[merge_id], ori_pose_scores[merge_id], ref_dists[pick])
+
+    max_score = torch.max(merge_score[ids])
+    if max_score < scoreThreds:
+        return None
+
+    xmax = max(merge_pose[:, 0])
+    xmin = min(merge_pose[:, 0])
+    ymax = max(merge_pose[:, 1])
+    ymin = min(merge_pose[:, 1])
+
+    if (1.5 ** 2 * (xmax - xmin) * (ymax - ymin) < 40 * 40.5):
+        return None
+
+    return {
+        'keypoints': merge_pose - 0.3,
+        'kp_score': merge_score,
+        'proposal_score': torch.mean(merge_score) + bbox_score_pick + 1.25 * max(merge_score)
+    }
+
+
+def p_merge(ref_pose, cluster_preds, cluster_scores, ref_dist):
+    '''
+    Score-weighted pose merging
+    INPUT:
+        ref_pose:       reference pose          -- [17, 2]
+        cluster_preds:  redundant poses         -- [n, 17, 2]
+        cluster_scores: redundant poses score   -- [n, 17, 1]
+        ref_dist:       reference scale         -- Constant
+    OUTPUT:
+        final_pose:     merged pose             -- [17, 2]
+        final_score:    merged score            -- [17]
+    '''
+    dist = torch.sqrt(torch.sum(
+        torch.pow(ref_pose[np.newaxis, :] - cluster_preds, 2),
+        dim=2
+    ))  # [n, 17]
+
+    kp_num = 17
+    ref_dist = min(ref_dist, 15)
+
+    mask = (dist <= ref_dist)
+    final_pose = torch.zeros(kp_num, 2)
+    final_score = torch.zeros(kp_num)
+
+    if cluster_preds.dim() == 2:
+        cluster_preds.unsqueeze_(0)
+        cluster_scores.unsqueeze_(0)
+    if mask.dim() == 1:
+        mask.unsqueeze_(0)
+
+    for i in range(kp_num):
+        cluster_joint_scores = cluster_scores[:, i][mask[:, i]]  # [k, 1]
+        cluster_joint_location = cluster_preds[:, i, :][mask[:, i].unsqueeze(
+            -1).repeat(1, 2)].view((torch.sum(mask[:, i]), -1))
+
+        # Get an normalized score
+        normed_scores = cluster_joint_scores / torch.sum(cluster_joint_scores)
+
+        # Merge poses by a weighted sum
+        final_pose[i, 0] = torch.dot(
+            cluster_joint_location[:, 0], normed_scores.squeeze(-1))
+        final_pose[i, 1] = torch.dot(
+            cluster_joint_location[:, 1], normed_scores.squeeze(-1))
+
+        final_score[i] = torch.dot(cluster_joint_scores.transpose(
+            0, 1).squeeze(0), normed_scores.squeeze(-1))
+
+    return final_pose, final_score
+
+
+def p_merge_fast(ref_pose, cluster_preds, cluster_scores, ref_dist):
+    '''
+    Score-weighted pose merging
+    INPUT:
+        ref_pose:       reference pose          -- [17, 2]
+        cluster_preds:  redundant poses         -- [n, 17, 2]
+        cluster_scores: redundant poses score   -- [n, 17, 1]
+        ref_dist:       reference scale         -- Constant
+    OUTPUT:
+        final_pose:     merged pose             -- [17, 2]
+        final_score:    merged score            -- [17]
+    '''
+    dist = torch.sqrt(torch.sum(
+        torch.pow(ref_pose[np.newaxis, :] - cluster_preds, 2),
+        dim=2
+    ))
+
+    kp_num = 17
+    ref_dist = min(ref_dist, 15)
+
+    mask = (dist <= ref_dist)
+    final_pose = torch.zeros(kp_num, 2)
+    final_score = torch.zeros(kp_num)
+
+    if cluster_preds.dim() == 2:
+        cluster_preds.unsqueeze_(0)
+        cluster_scores.unsqueeze_(0)
+    if mask.dim() == 1:
+        mask.unsqueeze_(0)
+
+    # Weighted Merge
+    masked_scores = cluster_scores.mul(mask.float().unsqueeze(-1))
+    normed_scores = masked_scores / torch.sum(masked_scores, dim=0)
+
+    final_pose = torch.mul(
+        cluster_preds, normed_scores.repeat(1, 1, 2)).sum(dim=0)
+    final_score = torch.mul(masked_scores, normed_scores).sum(dim=0)
+    return final_pose, final_score
+
+
+def get_parametric_distance(i, all_preds, keypoint_scores, ref_dist):
+    pick_preds = all_preds[i]
+    pred_scores = keypoint_scores[i]
+    dist = torch.sqrt(torch.sum(
+        torch.pow(pick_preds[np.newaxis, :] - all_preds, 2),
+        dim=2
+    ))
+    mask = (dist <= 1)
+
+    # Define a keypoints distance
+    score_dists = torch.zeros(all_preds.shape[0], 17)
+    keypoint_scores.squeeze_()
+    if keypoint_scores.dim() == 1:
+        keypoint_scores.unsqueeze_(0)
+    if pred_scores.dim() == 1:
+        pred_scores.unsqueeze_(1)
+    # The predicted scores are repeated up to do broadcast
+    pred_scores = pred_scores.repeat(1, all_preds.shape[0]).transpose(0, 1)
+
+    score_dists[mask] = torch.tanh(
+        pred_scores[mask] / delta1) * torch.tanh(keypoint_scores[mask] / delta1)
+
+    point_dist = torch.exp((-1) * dist / delta2)
+    final_dist = torch.sum(score_dists, dim=1) + mu * \
+        torch.sum(point_dist, dim=1)
+
+    return final_dist
+
+
+def PCK_match(pick_pred, all_preds, ref_dist):
+    dist = torch.sqrt(torch.sum(
+        torch.pow(pick_pred[np.newaxis, :] - all_preds, 2),
+        dim=2
+    ))
+    ref_dist = min(ref_dist, 7)
+    num_match_keypoints = torch.sum(
+        dist / ref_dist <= 1,
+        dim=1
+    )
+
+    return num_match_keypoints
+
+
+def write_json(all_results, outputpath, for_eval=False):
+    '''
+    all_result: result dict of predictions
+    outputpath: output directory
+    '''
+    json_results = []
+    for im_res in all_results:
+        im_name = im_res['imgname']
+        for human in im_res['result']:
+            keypoints = []
+            result = {}
+            if for_eval:
+                result['image_id'] = int(im_name.split(
+                    '/')[-1].split('.')[0].split('_')[-1])
+            else:
+                result['image_id'] = im_name.split('/')[-1]
+            result['category_id'] = 1
+
+            kp_preds = human['keypoints']
+            kp_scores = human['kp_score']
+            pro_scores = human['proposal_score']
+            for n in range(kp_scores.shape[0]):
+                keypoints.append(float(kp_preds[n, 0]))
+                keypoints.append(float(kp_preds[n, 1]))
+                keypoints.append(float(kp_scores[n]))
+            result['keypoints'] = keypoints
+            result['score'] = float(pro_scores)
+
+            json_results.append(result)
+
+    with open(os.path.join(outputpath, 'alphapose-results.json'), 'w') as json_file:
+        json_file.write(json.dumps(json_results))
diff --git a/joints_detectors/Alphapose/train_sppe/src/train.py b/joints_detectors/Alphapose/train_sppe/src/train.py
new file mode 100644
index 0000000000000000000000000000000000000000..e0b477cdaefbfadd0db1a4aa2aeaedff9a8d7f37
--- /dev/null
+++ b/joints_detectors/Alphapose/train_sppe/src/train.py
@@ -0,0 +1,210 @@
+# -----------------------------------------------------
+# Copyright (c) Shanghai Jiao Tong University. All rights reserved.
+# Written by Jiefeng Li (jeff.lee.sjtu@gmail.com)
+# -----------------------------------------------------
+
+import torch
+import torch.utils.data
+from .utils.dataset import coco
+from opt import opt
+from tqdm import tqdm
+from models.FastPose import createModel
+from .utils.eval import DataLogger, accuracy
+from .utils.img import flip, shuffleLR
+from .evaluation import prediction
+
+from tensorboardX import SummaryWriter
+import os
+
+
+def train(train_loader, m, criterion, optimizer, writer):
+    lossLogger = DataLogger()
+    accLogger = DataLogger()
+    m.train()
+
+    train_loader_desc = tqdm(train_loader)
+
+    for i, (inps, labels, setMask, imgset) in enumerate(train_loader_desc):
+        inps = inps.cuda().requires_grad_()
+        labels = labels.cuda()
+        setMask = setMask.cuda()
+        out = m(inps)
+
+        loss = criterion(out.mul(setMask), labels)
+
+        acc = accuracy(out.data.mul(setMask), labels.data, train_loader.dataset)
+
+        accLogger.update(acc[0], inps.size(0))
+        lossLogger.update(loss.item(), inps.size(0))
+
+        optimizer.zero_grad()
+        loss.backward()
+        optimizer.step()
+
+        opt.trainIters += 1
+        # Tensorboard
+        writer.add_scalar(
+            'Train/Loss', lossLogger.avg, opt.trainIters)
+        writer.add_scalar(
+            'Train/Acc', accLogger.avg, opt.trainIters)
+
+        # TQDM
+        train_loader_desc.set_description(
+            'loss: {loss:.8f} | acc: {acc:.2f}'.format(
+                loss=lossLogger.avg,
+                acc=accLogger.avg * 100)
+        )
+
+    train_loader_desc.close()
+
+    return lossLogger.avg, accLogger.avg
+
+
+def valid(val_loader, m, criterion, optimizer, writer):
+    lossLogger = DataLogger()
+    accLogger = DataLogger()
+    m.eval()
+
+    val_loader_desc = tqdm(val_loader)
+
+    for i, (inps, labels, setMask, imgset) in enumerate(val_loader_desc):
+        inps = inps.cuda()
+        labels = labels.cuda()
+        setMask = setMask.cuda()
+
+        with torch.no_grad():
+            out = m(inps)
+
+            loss = criterion(out.mul(setMask), labels)
+
+            flip_out = m(flip(inps))
+            flip_out = flip(shuffleLR(flip_out, val_loader.dataset))
+
+            out = (flip_out + out) / 2
+
+        acc = accuracy(out.mul(setMask), labels, val_loader.dataset)
+
+        lossLogger.update(loss.item(), inps.size(0))
+        accLogger.update(acc[0], inps.size(0))
+
+        opt.valIters += 1
+
+        # Tensorboard
+        writer.add_scalar(
+            'Valid/Loss', lossLogger.avg, opt.valIters)
+        writer.add_scalar(
+            'Valid/Acc', accLogger.avg, opt.valIters)
+
+        val_loader_desc.set_description(
+            'loss: {loss:.8f} | acc: {acc:.2f}'.format(
+                loss=lossLogger.avg,
+                acc=accLogger.avg * 100)
+        )
+
+    val_loader_desc.close()
+
+    return lossLogger.avg, accLogger.avg
+
+
+def main():
+
+    # Model Initialize
+    m = createModel().cuda()
+    if opt.loadModel:
+        print('Loading Model from {}'.format(opt.loadModel))
+        m.load_state_dict(torch.load(opt.loadModel))
+        if not os.path.exists("../exp/{}/{}".format(opt.dataset, opt.expID)):
+            try:
+                os.mkdir("../exp/{}/{}".format(opt.dataset, opt.expID))
+            except FileNotFoundError:
+                os.mkdir("../exp/{}".format(opt.dataset))
+                os.mkdir("../exp/{}/{}".format(opt.dataset, opt.expID))
+    else:
+        print('Create new model')
+        if not os.path.exists("../exp/{}/{}".format(opt.dataset, opt.expID)):
+            try:
+                os.mkdir("../exp/{}/{}".format(opt.dataset, opt.expID))
+            except FileNotFoundError:
+                os.mkdir("../exp/{}".format(opt.dataset))
+                os.mkdir("../exp/{}/{}".format(opt.dataset, opt.expID))
+
+    criterion = torch.nn.MSELoss().cuda()
+
+    if opt.optMethod == 'rmsprop':
+        optimizer = torch.optim.RMSprop(m.parameters(),
+                                        lr=opt.LR,
+                                        momentum=opt.momentum,
+                                        weight_decay=opt.weightDecay)
+    elif opt.optMethod == 'adam':
+        optimizer = torch.optim.Adam(
+            m.parameters(),
+            lr=opt.LR
+        )
+    else:
+        raise Exception
+
+    writer = SummaryWriter(
+        '.tensorboard/{}/{}'.format(opt.dataset, opt.expID))
+
+    # Prepare Dataset
+    if opt.dataset == 'coco':
+        train_dataset = coco.Mscoco(train=True)
+        val_dataset = coco.Mscoco(train=False)
+
+    train_loader = torch.utils.data.DataLoader(
+        train_dataset, batch_size=opt.trainBatch, shuffle=True, num_workers=opt.nThreads, pin_memory=True)
+
+    val_loader = torch.utils.data.DataLoader(
+        val_dataset, batch_size=opt.validBatch, shuffle=False, num_workers=opt.nThreads, pin_memory=True)
+
+    # Model Transfer
+    m = torch.nn.DataParallel(m).cuda()
+
+    # Start Training
+    for i in range(opt.nEpochs):
+        opt.epoch = i
+
+        print('############# Starting Epoch {} #############'.format(opt.epoch))
+        loss, acc = train(train_loader, m, criterion, optimizer, writer)
+
+        print('Train-{idx:d} epoch | loss:{loss:.8f} | acc:{acc:.4f}'.format(
+            idx=opt.epoch,
+            loss=loss,
+            acc=acc
+        ))
+
+        opt.acc = acc
+        opt.loss = loss
+        m_dev = m.module
+        if i % opt.snapshot == 0:
+            torch.save(
+                m_dev.state_dict(), '../exp/{}/{}/model_{}.pkl'.format(opt.dataset, opt.expID, opt.epoch))
+            torch.save(
+                opt, '../exp/{}/{}/option.pkl'.format(opt.dataset, opt.expID, opt.epoch))
+            torch.save(
+                optimizer, '../exp/{}/{}/optimizer.pkl'.format(opt.dataset, opt.expID))
+
+        loss, acc = valid(val_loader, m, criterion, optimizer, writer)
+
+        print('Valid-{idx:d} epoch | loss:{loss:.8f} | acc:{acc:.4f}'.format(
+            idx=i,
+            loss=loss,
+            acc=acc
+        ))
+
+        '''
+        if opt.dataset != 'mpii':
+            with torch.no_grad():
+                mAP, mAP5 = prediction(m)
+
+            print('Prediction-{idx:d} epoch | mAP:{mAP:.3f} | mAP0.5:{mAP5:.3f}'.format(
+                idx=i,
+                mAP=mAP,
+                mAP5=mAP5
+            ))
+        '''
+    writer.close()
+
+
+if __name__ == '__main__':
+    main()
diff --git a/joints_detectors/Alphapose/train_sppe/src/utils/dataset/__init__.py b/joints_detectors/Alphapose/train_sppe/src/utils/dataset/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/joints_detectors/Alphapose/train_sppe/src/utils/dataset/coco.py b/joints_detectors/Alphapose/train_sppe/src/utils/dataset/coco.py
new file mode 100644
index 0000000000000000000000000000000000000000..650e13b610c3f8fba273f674fc071628743345ef
--- /dev/null
+++ b/joints_detectors/Alphapose/train_sppe/src/utils/dataset/coco.py
@@ -0,0 +1,79 @@
+# -----------------------------------------------------
+# Copyright (c) Shanghai Jiao Tong University. All rights reserved.
+# Written by Jiefeng Li (jeff.lee.sjtu@gmail.com)
+# -----------------------------------------------------
+
+import os
+import h5py
+from functools import reduce
+
+import torch.utils.data as data
+from ..pose import generateSampleBox
+from opt import opt
+
+
+class Mscoco(data.Dataset):
+    def __init__(self, train=True, sigma=1,
+                 scale_factor=(0.2, 0.3), rot_factor=40, label_type='Gaussian'):
+        self.img_folder = '../data/coco/images'    # root image folders
+        self.is_train = train           # training set or test set
+        self.inputResH = opt.inputResH
+        self.inputResW = opt.inputResW
+        self.outputResH = opt.outputResH
+        self.outputResW = opt.outputResW
+        self.sigma = sigma
+        self.scale_factor = scale_factor
+        self.rot_factor = rot_factor
+        self.label_type = label_type
+
+        self.nJoints_coco = 17
+        self.nJoints = 17
+
+        self.accIdxs = (1, 2, 3, 4, 5, 6, 7, 8,
+                        9, 10, 11, 12, 13, 14, 15, 16, 17)
+        self.flipRef = ((2, 3), (4, 5), (6, 7),
+                        (8, 9), (10, 11), (12, 13),
+                        (14, 15), (16, 17))
+
+        # create train/val split
+        with h5py.File('../data/coco/annot_coco.h5', 'r') as annot:
+            # train
+            self.imgname_coco_train = annot['imgname'][:-5887]
+            self.bndbox_coco_train = annot['bndbox'][:-5887]
+            self.part_coco_train = annot['part'][:-5887]
+            # val
+            self.imgname_coco_val = annot['imgname'][-5887:]
+            self.bndbox_coco_val = annot['bndbox'][-5887:]
+            self.part_coco_val = annot['part'][-5887:]
+
+        self.size_train = self.imgname_coco_train.shape[0]
+        self.size_val = self.imgname_coco_val.shape[0]
+
+    def __getitem__(self, index):
+        sf = self.scale_factor
+
+        if self.is_train:
+            part = self.part_coco_train[index]
+            bndbox = self.bndbox_coco_train[index]
+            imgname = self.imgname_coco_train[index]
+        else:
+            part = self.part_coco_val[index]
+            bndbox = self.bndbox_coco_val[index]
+            imgname = self.imgname_coco_val[index]
+
+        imgname = reduce(lambda x, y: x + y,
+                         map(lambda x: chr(int(x)), imgname))
+        img_path = os.path.join(self.img_folder, imgname)
+
+        metaData = generateSampleBox(img_path, bndbox, part, self.nJoints,
+                                     'coco', sf, self, train=self.is_train)
+
+        inp, out, setMask = metaData
+
+        return inp, out, setMask, 'coco'
+
+    def __len__(self):
+        if self.is_train:
+            return self.size_train
+        else:
+            return self.size_val
diff --git a/joints_detectors/Alphapose/train_sppe/src/utils/eval.py b/joints_detectors/Alphapose/train_sppe/src/utils/eval.py
new file mode 100644
index 0000000000000000000000000000000000000000..881b69bf1cd46e4448751550b08f75fd8902cb3e
--- /dev/null
+++ b/joints_detectors/Alphapose/train_sppe/src/utils/eval.py
@@ -0,0 +1,208 @@
+# -----------------------------------------------------
+# Copyright (c) Shanghai Jiao Tong University. All rights reserved.
+# Written by Jiefeng Li (jeff.lee.sjtu@gmail.com)
+# -----------------------------------------------------
+
+from opt import opt
+import sys
+import numpy as np
+
+import torch
+from pycocotools.coco import COCO
+from pycocotools.cocoeval import COCOeval
+
+from .img import transformBoxInvert
+
+
+class DataLogger(object):
+    def __init__(self):
+        self.clear()
+
+    def clear(self):
+        self.value = 0
+        self.sum = 0
+        self.cnt = 0
+        self.avg = 0
+
+    def update(self, value, n=1):
+        self.value = value
+        self.sum += value * n
+        self.cnt += n
+        self._cal_avg()
+
+    def _cal_avg(self):
+        self.avg = self.sum / self.cnt
+
+
+class NullWriter(object):
+    def write(self, arg):
+        pass
+
+
+def accuracy(output, label, dataset, out_offset=None):
+    if type(output) == list:
+        return accuracy(output[opt.nStack - 1], label[opt.nStack - 1], dataset, out_offset)
+    else:
+        return heatmapAccuracy(output.cpu().data, label.cpu().data, dataset.accIdxs)
+
+
+def heatmapAccuracy(output, label, idxs):
+    preds = getPreds(output)
+    gt = getPreds(label)
+
+    norm = torch.ones(preds.size(0)) * opt.outputResH / 10
+    dists = calc_dists(preds, gt, norm)
+
+    acc = torch.zeros(len(idxs) + 1)
+    avg_acc = 0
+    cnt = 0
+    for i in range(len(idxs)):
+        acc[i + 1] = dist_acc(dists[idxs[i] - 1])
+        if acc[i + 1] >= 0:
+            avg_acc = avg_acc + acc[i + 1]
+            cnt += 1
+    if cnt != 0:
+        acc[0] = avg_acc / cnt
+    return acc
+
+
+def getPreds(hm):
+    ''' get predictions from score maps in torch Tensor
+        return type: torch.LongTensor
+    '''
+    assert hm.dim() == 4, 'Score maps should be 4-dim'
+    maxval, idx = torch.max(hm.view(hm.size(0), hm.size(1), -1), 2)
+
+    maxval = maxval.view(hm.size(0), hm.size(1), 1)
+    idx = idx.view(hm.size(0), hm.size(1), 1) + 1
+
+    preds = idx.repeat(1, 1, 2).float()
+
+    preds[:, :, 0] = (preds[:, :, 0] - 1) % hm.size(3)
+    preds[:, :, 1] = torch.floor((preds[:, :, 1] - 1) / hm.size(3))
+
+    # pred_mask = maxval.gt(0).repeat(1, 1, 2).float()
+    # preds *= pred_mask
+    return preds
+
+
+def calc_dists(preds, target, normalize):
+    preds = preds.float().clone()
+    target = target.float().clone()
+    dists = torch.zeros(preds.size(1), preds.size(0))
+    for n in range(preds.size(0)):
+        for c in range(preds.size(1)):
+            if target[n, c, 0] > 0 and target[n, c, 1] > 0:
+                dists[c, n] = torch.dist(
+                    preds[n, c, :], target[n, c, :]) / normalize[n]
+            else:
+                dists[c, n] = -1
+    return dists
+
+
+def dist_acc(dists, thr=0.5):
+    ''' Return percentage below threshold while ignoring values with a -1 '''
+    if dists.ne(-1).sum() > 0:
+        return dists.le(thr).eq(dists.ne(-1)).float().sum() * 1.0 / dists.ne(-1).float().sum()
+    else:
+        return - 1
+
+
+def postprocess(output):
+    p = getPreds(output)
+
+    for i in range(p.size(0)):
+        for j in range(p.size(1)):
+            hm = output[i][j]
+            pX, pY = int(round(p[i][j][0])), int(round(p[i][j][1]))
+            if 0 < pX < opt.outputResW - 1 and 0 < pY < opt.outputResH - 1:
+                diff = torch.Tensor(
+                    (hm[pY][pX + 1] - hm[pY][pX - 1], hm[pY + 1][pX] - hm[pY - 1][pX]))
+                p[i][j] += diff.sign() * 0.25
+    p -= 0.5
+
+    return p
+
+
+def getPrediction(hms, pt1, pt2, inpH, inpW, resH, resW):
+    assert hms.dim() == 4, 'Score maps should be 4-dim'
+    maxval, idx = torch.max(hms.view(hms.size(0), hms.size(1), -1), 2)
+
+    maxval = maxval.view(hms.size(0), hms.size(1), 1)
+    idx = idx.view(hms.size(0), hms.size(1), 1) + 1
+
+    preds = idx.repeat(1, 1, 2).float()
+
+    preds[:, :, 0] = (preds[:, :, 0] - 1) % hms.size(3)
+    preds[:, :, 1] = torch.floor((preds[:, :, 1] - 1) / hms.size(3))
+
+    pred_mask = maxval.gt(0).repeat(1, 1, 2).float()
+    preds *= pred_mask
+
+    # Very simple post-processing step to improve performance at tight PCK thresholds
+    for i in range(preds.size(0)):
+        for j in range(preds.size(1)):
+            hm = hms[i][j]
+            pX, pY = int(round(float(preds[i][j][0]))), int(
+                round(float(preds[i][j][1])))
+            if 1 < pX < opt.outputResW - 2 and 1 < pY < opt.outputResH - 2:
+                diff = torch.Tensor(
+                    (hm[pY][pX + 1] - hm[pY][pX - 1], hm[pY + 1][pX] - hm[pY - 1][pX]))
+                diff = diff.sign() * 0.25
+                diff[1] = diff[1] * inpH / inpW
+                preds[i][j] += diff
+
+    preds_tf = torch.zeros(preds.size())
+    for i in range(hms.size(0)):        # Number of samples
+        for j in range(hms.size(1)):    # Number of output heatmaps for one sample
+            preds_tf[i][j] = transformBoxInvert(
+                preds[i][j], pt1[i], pt2[i], inpH, inpW, resH, resW)
+
+    return preds, preds_tf, maxval
+
+
+def getmap(JsonDir='./val/alphapose-results.json'):
+    ListDir = '../coco-minival500_images.txt'
+
+    annType = ['segm', 'bbox', 'keypoints']
+    annType = annType[2]  # specify type here
+    prefix = 'person_keypoints' if annType == 'keypoints' else 'instances'
+    print('Running evaluation for *%s* results.' % (annType))
+
+    # load Ground_truth
+    dataType = 'val2014'
+    annFile = '../%s_%s.json' % (prefix, dataType)
+    cocoGt = COCO(annFile)
+
+    # load Answer(json)
+    resFile = JsonDir
+    cocoDt = cocoGt.loadRes(resFile)
+
+    # load List
+    fin = open(ListDir, 'r')
+    imgIds_str = fin.readline()
+    if imgIds_str[-1] == '\n':
+        imgIds_str = imgIds_str[:-1]
+    imgIds_str = imgIds_str.split(',')
+
+    imgIds = []
+    for x in imgIds_str:
+        imgIds.append(int(x))
+
+    # running evaluation
+    iouThrs = np.linspace(.5, 0.95, np.round((0.95 - .5) / .05) + 1, endpoint=True)
+    t = np.where(0.5 == iouThrs)[0]
+
+    cocoEval = COCOeval(cocoGt, cocoDt, annType)
+    cocoEval.params.imgIds = imgIds
+    cocoEval.evaluate()
+    cocoEval.accumulate()
+
+    score = cocoEval.eval['precision'][:, :, :, 0, :]
+    mApAll, mAp5 = 0.01, 0.01
+    if len(score[score > -1]) != 0:
+        score2 = score[t]
+        mApAll = np.mean(score[score > -1])
+        mAp5 = np.mean(score2[score2 > -1])
+    cocoEval.summarize()
+    return mApAll, mAp5
diff --git a/joints_detectors/Alphapose/train_sppe/src/utils/img.py b/joints_detectors/Alphapose/train_sppe/src/utils/img.py
new file mode 100644
index 0000000000000000000000000000000000000000..45edc833e1306367697438f03879766cfe3883d3
--- /dev/null
+++ b/joints_detectors/Alphapose/train_sppe/src/utils/img.py
@@ -0,0 +1,308 @@
+# -----------------------------------------------------
+# Copyright (c) Shanghai Jiao Tong University. All rights reserved.
+# Written by Jiefeng Li (jeff.lee.sjtu@gmail.com)
+# -----------------------------------------------------
+
+import numpy as np
+import torch
+import scipy.misc
+import torch.nn.functional as F
+import cv2
+from opt import opt
+
+
+RED = (0, 0, 255)
+GREEN = (0, 255, 0)
+BLUE = (255, 0, 0)
+CYAN = (255, 255, 0)
+YELLOW = (0, 255, 255)
+ORANGE = (0, 165, 255)
+PURPLE = (255, 0, 255)
+
+
+def im_to_torch(img):
+    img = np.transpose(img, (2, 0, 1))  # C*H*W
+    img = to_torch(img).float()
+    if img.max() > 1:
+        img /= 255
+    return img
+
+
+def torch_to_im(img):
+    img = to_numpy(img)
+    img = np.transpose(img, (1, 2, 0))  # C*H*W
+    return img
+
+
+def load_image(img_path):
+    # H x W x C => C x H x W
+    return im_to_torch(scipy.misc.imread(img_path, mode='RGB'))
+
+
+def to_numpy(tensor):
+    if torch.is_tensor(tensor):
+        return tensor.cpu().numpy()
+    elif type(tensor).__module__ != 'numpy':
+        raise ValueError("Cannot convert {} to numpy array"
+                         .format(type(tensor)))
+    return tensor
+
+
+def to_torch(ndarray):
+    if type(ndarray).__module__ == 'numpy':
+        return torch.from_numpy(ndarray)
+    elif not torch.is_tensor(ndarray):
+        raise ValueError("Cannot convert {} to torch tensor"
+                         .format(type(ndarray)))
+    return ndarray
+
+
+def drawGaussian(img, pt, sigma):
+    img = to_numpy(img)
+    tmpSize = 3 * sigma
+    # Check that any part of the gaussian is in-bounds
+    ul = [int(pt[0] - tmpSize), int(pt[1] - tmpSize)]
+    br = [int(pt[0] + tmpSize + 1), int(pt[1] + tmpSize + 1)]
+
+    if (ul[0] >= img.shape[1] or ul[1] >= img.shape[0] or
+            br[0] < 0 or br[1] < 0):
+        # If not, just return the image as is
+        return to_torch(img)
+
+    # Generate gaussian
+    size = 2 * tmpSize + 1
+    x = np.arange(0, size, 1, float)
+    y = x[:, np.newaxis]
+    x0 = y0 = size // 2
+    sigma = size / 4.0
+    # The gaussian is not normalized, we want the center value to equal 1
+    g = np.exp(- ((x - x0) ** 2 + (y - y0) ** 2) / (2 * sigma ** 2))
+
+    # Usable gaussian range
+    g_x = max(0, -ul[0]), min(br[0], img.shape[1]) - ul[0]
+    g_y = max(0, -ul[1]), min(br[1], img.shape[0]) - ul[1]
+    # Image range
+    img_x = max(0, ul[0]), min(br[0], img.shape[1])
+    img_y = max(0, ul[1]), min(br[1], img.shape[0])
+
+    img[img_y[0]:img_y[1], img_x[0]:img_x[1]] = g[g_y[0]:g_y[1], g_x[0]:g_x[1]]
+    return to_torch(img)
+
+
+def transformBox(pt, ul, br, inpH, inpW, resH, resW):
+    center = torch.zeros(2)
+    center[0] = (br[0] - 1 - ul[0]) / 2
+    center[1] = (br[1] - 1 - ul[1]) / 2
+
+    lenH = max(br[1] - ul[1], (br[0] - ul[0]) * inpH / inpW)
+    lenW = lenH * inpW / inpH
+
+    _pt = torch.zeros(2)
+    _pt[0] = pt[0] - ul[0]
+    _pt[1] = pt[1] - ul[1]
+    # Move to center
+    _pt[0] = _pt[0] + max(0, (lenW - 1) / 2 - center[0])
+    _pt[1] = _pt[1] + max(0, (lenH - 1) / 2 - center[1])
+    pt = (_pt * resH) / lenH
+    pt[0] = round(float(pt[0]))
+    pt[1] = round(float(pt[1]))
+    return pt.int()
+
+
+def transformBoxInvert(pt, ul, br, inpH, inpW, resH, resW):
+    center = torch.zeros(2)
+    center[0] = (br[0] - 1 - ul[0]) / 2
+    center[1] = (br[1] - 1 - ul[1]) / 2
+
+    lenH = max(br[1] - ul[1], (br[0] - ul[0]) * inpH / inpW)
+    lenW = lenH * inpW / inpH
+
+    _pt = (pt * lenH) / resH
+    _pt[0] = _pt[0] - max(0, (lenW - 1) / 2 - center[0])
+    _pt[1] = _pt[1] - max(0, (lenH - 1) / 2 - center[1])
+
+    new_point = torch.zeros(2)
+    new_point[0] = _pt[0] + ul[0]
+    new_point[1] = _pt[1] + ul[1]
+    return new_point
+
+
+def cropBox(img, ul, br, resH, resW):
+    ul = ul.int()
+    br = (br - 1).int()
+    # br = br.int()
+    lenH = max((br[1] - ul[1]).item(), (br[0] - ul[0]).item() * resH / resW)
+    lenW = lenH * resW / resH
+    if img.dim() == 2:
+        img = img[np.newaxis, :]
+
+    box_shape = [br[1] - ul[1], br[0] - ul[0]]
+    pad_size = [(lenH - box_shape[0]) // 2, (lenW - box_shape[1]) // 2]
+    # Padding Zeros
+    img[:, :ul[1], :], img[:, :, :ul[0]] = 0, 0
+    img[:, br[1] + 1:, :], img[:, :, br[0] + 1:] = 0, 0
+
+    src = np.zeros((3, 2), dtype=np.float32)
+    dst = np.zeros((3, 2), dtype=np.float32)
+
+    src[0, :] = np.array([ul[0] - pad_size[1], ul[1] - pad_size[0]], np.float32)
+    src[1, :] = np.array([br[0] + pad_size[1], br[1] + pad_size[0]], np.float32)
+    dst[0, :] = 0
+    dst[1, :] = np.array([resW - 1, resH - 1], np.float32)
+
+    src[2:, :] = get_3rd_point(src[0, :], src[1, :])
+    dst[2:, :] = get_3rd_point(dst[0, :], dst[1, :])
+
+    trans = cv2.getAffineTransform(np.float32(src), np.float32(dst))
+
+    dst_img = cv2.warpAffine(torch_to_im(img), trans,
+                             (resW, resH), flags=cv2.INTER_LINEAR)
+
+    return im_to_torch(torch.Tensor(dst_img))
+
+
+def cv_rotate(img, rot, resW, resH):
+
+    center = np.array((resW - 1, resH - 1)) / 2
+    rot_rad = np.pi * rot / 180
+
+    src_dir = get_dir([0, (resH - 1) * -0.5], rot_rad)
+    dst_dir = np.array([0, (resH - 1) * -0.5], np.float32)
+
+    src = np.zeros((3, 2), dtype=np.float32)
+    dst = np.zeros((3, 2), dtype=np.float32)
+
+    src[0, :] = center
+    src[1, :] = center + src_dir
+    dst[0, :] = [(resW - 1) * 0.5, (resH - 1) * 0.5]
+    dst[1, :] = np.array([(resW - 1) * 0.5, (resH - 1) * 0.5]) + dst_dir
+
+    src[2:, :] = get_3rd_point(src[0, :], src[1, :])
+    dst[2:, :] = get_3rd_point(dst[0, :], dst[1, :])
+
+    trans = cv2.getAffineTransform(np.float32(src), np.float32(dst))
+
+    dst_img = cv2.warpAffine(torch_to_im(img), trans,
+                             (resW, resH), flags=cv2.INTER_LINEAR)
+
+    return im_to_torch(torch.Tensor(dst_img))
+
+
+def flip(x):
+    assert (x.dim() == 3 or x.dim() == 4)
+    if '0.4.1' in torch.__version__:
+        dim = x.dim() - 1
+
+        return x.flip(dims=(dim,))
+    else:
+        is_cuda = False
+        if x.is_cuda:
+            x = x.cpu()
+            is_cuda = True
+        x = x.numpy().copy()
+        if x.ndim == 3:
+            x = np.transpose(np.fliplr(np.transpose(x, (0, 2, 1))), (0, 2, 1))
+        elif x.ndim == 4:
+            for i in range(x.shape[0]):
+                x[i] = np.transpose(
+                    np.fliplr(np.transpose(x[i], (0, 2, 1))), (0, 2, 1))
+        x = torch.from_numpy(x.copy())
+        if is_cuda:
+            x = x.cuda()
+    return x
+
+
+def shuffleLR(x, dataset):
+    flipRef = dataset.flipRef
+    assert (x.dim() == 3 or x.dim() == 4)
+    for pair in flipRef:
+        dim0, dim1 = pair
+        dim0 -= 1
+        dim1 -= 1
+        if x.dim() == 4:
+            tmp = x[:, dim1].clone()
+            x[:, dim1] = x[:, dim0].clone()
+            x[:, dim0] = tmp.clone()
+            #x[:, dim0], x[:, dim1] = deepcopy((x[:, dim1], x[:, dim0]))
+        else:
+            tmp = x[dim1].clone()
+            x[dim1] = x[dim0].clone()
+            x[dim0] = tmp.clone()
+            #x[dim0], x[dim1] = deepcopy((x[dim1], x[dim0]))
+    return x
+
+
+def vis_frame(frame, im_res, format='coco'):
+    '''
+    frame: frame image
+    im_res: im_res of predictions
+    format: coco or mpii
+
+    return rendered image
+    '''
+    if format == 'coco':
+        l_pair = [
+            (0, 1), (0, 2), (1, 3), (2, 4),  # Head
+            (5, 6), (5, 7), (7, 9), (6, 8), (8, 10),
+            (5, 11), (6, 12),  # Body
+            (11, 13), (12, 14), (13, 15), (14, 16)
+        ]
+        p_color = [RED, RED, RED, RED, RED, YELLOW, YELLOW, YELLOW,
+                   YELLOW, YELLOW, YELLOW, GREEN, GREEN, GREEN, GREEN, GREEN, GREEN]
+        line_color = [YELLOW, YELLOW, YELLOW, YELLOW, BLUE, BLUE,
+                      BLUE, BLUE, BLUE, PURPLE, PURPLE, RED, RED, RED, RED]
+    elif format == 'mpii':
+        l_pair = [
+            (8, 9), (11, 12), (11, 10), (2, 1), (1, 0),
+            (13, 14), (14, 15), (3, 4), (4, 5),
+            (8, 7), (7, 6), (6, 2), (6, 3), (8, 12), (8, 13)
+        ]
+        p_color = [PURPLE, BLUE, BLUE, RED, RED, BLUE, BLUE, RED,
+                   RED, PURPLE, PURPLE, PURPLE, RED, RED, BLUE, BLUE]
+        line_color = [PURPLE, BLUE, BLUE, RED, RED, BLUE, BLUE,
+                      RED, RED, PURPLE, PURPLE, RED, RED, BLUE, BLUE]
+    else:
+        raise NotImplementedError
+
+    im_name = im_res['imgname'].split('/')[-1]
+    img = frame.copy()
+    for human in im_res['result']:
+        part_line = {}
+        kp_preds = human['keypoints']
+        kp_scores = human['kp_score']
+        # Draw keypoints
+        for n in range(kp_scores.shape[0]):
+            if kp_scores[n] <= 0.15:
+                continue
+            cor_x, cor_y = int(kp_preds[n, 0]), int(kp_preds[n, 1])
+            part_line[n] = (cor_x, cor_y)
+            cv2.circle(img, (cor_x, cor_y), 4, p_color[n], -1)
+            # Now create a mask of logo and create its inverse mask also
+            #transparency = max(0, min(1, kp_scores[n]))
+            #img = cv2.addWeighted(bg, transparency, img, 1, 0)
+        # Draw limbs
+        for i, (start_p, end_p) in enumerate(l_pair):
+            if start_p in part_line and end_p in part_line:
+                start_xy = part_line[start_p]
+                end_xy = part_line[end_p]
+                cv2.line(img, start_xy, end_xy,
+                         line_color[i], (0.5 * (kp_scores[start_p] + kp_scores[end_p])) + 1)
+                #transparency = max(
+                #    0, min(1, (kp_scores[start_p] + kp_scores[end_p])))
+                #img = cv2.addWeighted(bg, transparency, img, 1, 0)
+    return img
+
+
+def get_3rd_point(a, b):
+    direct = a - b
+    return b + np.array([-direct[1], direct[0]], dtype=np.float32)
+
+
+def get_dir(src_point, rot_rad):
+    sn, cs = np.sin(rot_rad), np.cos(rot_rad)
+
+    src_result = [0, 0]
+    src_result[0] = src_point[0] * cs - src_point[1] * sn
+    src_result[1] = src_point[0] * sn + src_point[1] * cs
+
+    return src_result
diff --git a/joints_detectors/Alphapose/train_sppe/src/utils/pose.py b/joints_detectors/Alphapose/train_sppe/src/utils/pose.py
new file mode 100644
index 0000000000000000000000000000000000000000..cea08a948b9abcc98bf07c7a4b5f4b7310efb132
--- /dev/null
+++ b/joints_detectors/Alphapose/train_sppe/src/utils/pose.py
@@ -0,0 +1,142 @@
+# -----------------------------------------------------
+# Copyright (c) Shanghai Jiao Tong University. All rights reserved.
+# Written by Jiefeng Li (jeff.lee.sjtu@gmail.com)
+# -----------------------------------------------------
+
+from utils.img import (load_image, drawGaussian, cropBox, transformBox, flip, shuffleLR, cv_rotate)
+import torch
+import numpy as np
+import random
+from opt import opt
+
+
+def rnd(x):
+    return max(-2 * x, min(2 * x, np.random.randn(1)[0] * x))
+
+
+def generateSampleBox(img_path, bndbox, part, nJoints, imgset, scale_factor, dataset, train=True, nJoints_coco=17):
+
+    img = load_image(img_path)
+    if train:
+        img[0].mul_(random.uniform(0.7, 1.3)).clamp_(0, 1)
+        img[1].mul_(random.uniform(0.7, 1.3)).clamp_(0, 1)
+        img[2].mul_(random.uniform(0.7, 1.3)).clamp_(0, 1)
+
+    img[0].add_(-0.406)
+    img[1].add_(-0.457)
+    img[2].add_(-0.480)
+
+    upLeft = torch.Tensor((int(bndbox[0][0]), int(bndbox[0][1])))
+    bottomRight = torch.Tensor((int(bndbox[0][2]), int(bndbox[0][3])))
+    ht = bottomRight[1] - upLeft[1]
+    width = bottomRight[0] - upLeft[0]
+    imght = img.shape[1]
+    imgwidth = img.shape[2]
+    scaleRate = random.uniform(*scale_factor)
+
+    upLeft[0] = max(0, upLeft[0] - width * scaleRate / 2)
+    upLeft[1] = max(0, upLeft[1] - ht * scaleRate / 2)
+    bottomRight[0] = min(imgwidth - 1, bottomRight[0] + width * scaleRate / 2)
+    bottomRight[1] = min(imght - 1, bottomRight[1] + ht * scaleRate / 2)
+
+    # Doing Random Sample
+    if opt.addDPG:
+        PatchScale = random.uniform(0, 1)
+        if PatchScale > 0.85:
+            ratio = ht / width
+            if (width < ht):
+                patchWidth = PatchScale * width
+                patchHt = patchWidth * ratio
+            else:
+                patchHt = PatchScale * ht
+                patchWidth = patchHt / ratio
+
+            xmin = upLeft[0] + random.uniform(0, 1) * (width - patchWidth)
+            ymin = upLeft[1] + random.uniform(0, 1) * (ht - patchHt)
+            xmax = xmin + patchWidth + 1
+            ymax = ymin + patchHt + 1
+        else:
+            xmin = max(
+                1, min(upLeft[0] + np.random.normal(-0.0142, 0.1158) * width, imgwidth - 3))
+            ymin = max(
+                1, min(upLeft[1] + np.random.normal(0.0043, 0.068) * ht, imght - 3))
+            xmax = min(max(
+                xmin + 2, bottomRight[0] + np.random.normal(0.0154, 0.1337) * width), imgwidth - 3)
+            ymax = min(
+                max(ymin + 2, bottomRight[1] + np.random.normal(-0.0013, 0.0711) * ht), imght - 3)
+
+        upLeft[0] = xmin
+        upLeft[1] = ymin
+        bottomRight[0] = xmax
+        bottomRight[1] = ymax
+
+    # Counting Joints number
+    jointNum = 0
+    if imgset == 'coco':
+        for i in range(17):
+            if part[i][0] > 0 and part[i][0] > upLeft[0] and part[i][1] > upLeft[1] \
+               and part[i][0] < bottomRight[0] and part[i][1] < bottomRight[1]:
+                jointNum += 1
+
+    # Doing Random Crop
+    if opt.addDPG:
+        if jointNum > 13 and train:
+            switch = random.uniform(0, 1)
+            if switch > 0.96:
+                bottomRight[0] = (upLeft[0] + bottomRight[0]) / 2
+                bottomRight[1] = (upLeft[1] + bottomRight[1]) / 2
+            elif switch > 0.92:
+                upLeft[0] = (upLeft[0] + bottomRight[0]) / 2
+                bottomRight[1] = (upLeft[1] + bottomRight[1]) / 2
+            elif switch > 0.88:
+                upLeft[1] = (upLeft[1] + bottomRight[1]) / 2
+                bottomRight[0] = (upLeft[0] + bottomRight[0]) / 2
+            elif switch > 0.84:
+                upLeft[0] = (upLeft[0] + bottomRight[0]) / 2
+                upLeft[1] = (upLeft[1] + bottomRight[1]) / 2
+            elif switch > 0.80:
+                bottomRight[0] = (upLeft[0] + bottomRight[0]) / 2
+            elif switch > 0.76:
+                upLeft[0] = (upLeft[0] + bottomRight[0]) / 2
+            elif switch > 0.72:
+                bottomRight[1] = (upLeft[1] + bottomRight[1]) / 2
+            elif switch > 0.68:
+                upLeft[1] = (upLeft[1] + bottomRight[1]) / 2
+
+    inputResH, inputResW = opt.inputResH, opt.inputResW
+    outputResH, outputResW = opt.outputResH, opt.outputResW
+
+    inp = cropBox(img, upLeft, bottomRight, inputResH, inputResW)
+
+    if jointNum == 0:
+        inp = torch.zeros(3, inputResH, inputResW)
+
+    out = torch.zeros(nJoints, outputResH, outputResW)
+    setMask = torch.zeros(nJoints, outputResH, outputResW)
+    # Draw Label
+    if imgset == 'coco':
+        for i in range(nJoints_coco):
+            if part[i][0] > 0 and part[i][0] > upLeft[0] and part[i][1] > upLeft[1] \
+               and part[i][0] < bottomRight[0] and part[i][1] < bottomRight[1]:
+                hm_part = transformBox(
+                    part[i], upLeft, bottomRight, inputResH, inputResW, outputResH, outputResW)
+
+                out[i] = drawGaussian(out[i], hm_part, opt.hmGauss)
+
+            setMask[i].add_(1)
+
+    if train:
+        # Flip
+        if random.uniform(0, 1) < 0.5:
+            inp = flip(inp)
+            out = shuffleLR(flip(out), dataset)
+
+        # Rotate
+        r = rnd(opt.rotate)
+        if random.uniform(0, 1) < 0.6:
+            r = 0
+        if r != 0:
+            inp = cv_rotate(inp, r, opt.inputResW, opt.inputResH)
+            out = cv_rotate(out, r, opt.outputResW, opt.outputResH)
+
+    return inp, out, setMask
diff --git a/joints_detectors/Alphapose/video_demo.py b/joints_detectors/Alphapose/video_demo.py
new file mode 100644
index 0000000000000000000000000000000000000000..be14651a56725e82bb1b1e0f6a8ab1c76951c2ac
--- /dev/null
+++ b/joints_detectors/Alphapose/video_demo.py
@@ -0,0 +1,120 @@
+import os
+
+from SPPE.src.main_fast_inference import *
+from dataloader import ImageLoader, DetectionLoader, DetectionProcessor, DataWriter, Mscoco
+from fn import getTime
+from opt import opt
+from pPose_nms import write_json
+from tqdm import tqdm
+
+
+def main(args):
+    inputpath = args.inputpath
+    inputlist = args.inputlist
+    mode = args.mode
+    if not os.path.exists(args.outputpath):
+        os.mkdir(args.outputpath)
+
+    if len(inputlist):
+        im_names = open(inputlist, 'r').readlines()
+    elif len(inputpath) and inputpath != '/':
+        for root, dirs, files in os.walk(inputpath):
+            im_names = files
+    else:
+        raise IOError('Error: must contain either --indir/--list')
+
+    # Load input images
+    data_loader = ImageLoader(im_names, batchSize=args.detbatch, format='yolo').start()
+
+    # Load detection loader
+    print('Loading YOLO model..')
+    sys.stdout.flush()
+    det_loader = DetectionLoader(data_loader, batchSize=args.detbatch).start()
+    det_processor = DetectionProcessor(det_loader).start()
+
+    # Load pose model
+    pose_dataset = Mscoco()
+    if args.fast_inference:
+        pose_model = InferenNet_fast(4 * 1 + 1, pose_dataset)
+    else:
+        pose_model = InferenNet(4 * 1 + 1, pose_dataset)
+    pose_model.cuda()
+    pose_model.eval()
+
+    runtime_profile = {
+        'dt': [],
+        'pt': [],
+        'pn': []
+    }
+
+    # Init data writer
+    writer = DataWriter(args.save_video).start()
+
+    data_len = data_loader.length()
+    im_names_desc = tqdm(range(data_len))
+
+    batchSize = args.posebatch
+    for i in im_names_desc:
+        start_time = getTime()
+        with torch.no_grad():
+            (inps, orig_img, im_name, boxes, scores, pt1, pt2) = det_processor.read()
+            if boxes is None or boxes.nelement() == 0:
+                writer.save(None, None, None, None, None, orig_img, im_name.split('/')[-1])
+                continue
+
+            ckpt_time, det_time = getTime(start_time)
+            runtime_profile['dt'].append(det_time)
+            # Pose Estimation
+
+            datalen = inps.size(0)
+            leftover = 0
+            if (datalen) % batchSize:
+                leftover = 1
+            num_batches = datalen // batchSize + leftover
+            hm = []
+            for j in range(num_batches):
+                inps_j = inps[j * batchSize:min((j + 1) * batchSize, datalen)].cuda()
+                hm_j = pose_model(inps_j)
+                hm.append(hm_j)
+            hm = torch.cat(hm)
+            ckpt_time, pose_time = getTime(ckpt_time)
+            runtime_profile['pt'].append(pose_time)
+            hm = hm.cpu()
+            writer.save(boxes, scores, hm, pt1, pt2, orig_img, im_name.split('/')[-1])
+
+            ckpt_time, post_time = getTime(ckpt_time)
+            runtime_profile['pn'].append(post_time)
+
+        if args.profile:
+            # TQDM
+            im_names_desc.set_description(
+                'det time: {dt:.3f} | pose time: {pt:.2f} | post processing: {pn:.4f}'.format(
+                    dt=np.mean(runtime_profile['dt']), pt=np.mean(runtime_profile['pt']), pn=np.mean(runtime_profile['pn']))
+            )
+
+    print('===========================> Finish Model Running.')
+    if (args.save_img or args.save_video) and not args.vis_fast:
+        print('===========================> Rendering remaining images in the queue...')
+        print('===========================> If this step takes too long, you can enable the --vis_fast flag to use fast rendering (real-time).')
+    while (writer.running()):
+        pass
+    writer.stop()
+    final_result = writer.results()
+    write_json(final_result, args.outputpath)
+
+
+if __name__ == "__main__":
+    args = opt
+    args.dataset = 'coco'
+    args.sp = True
+    if not args.sp:
+        torch.multiprocessing.set_start_method('forkserver', force=True)
+        torch.multiprocessing.set_sharing_strategy('file_system')
+
+    video_name = 'kunkun'
+    args.inputpath = f'data/split_{video_name}'
+    args.outputpath = f'data/alphapose_{video_name}'
+
+    args.save_img = True
+
+    main(args)
diff --git a/joints_detectors/Alphapose/webcam_demo.py b/joints_detectors/Alphapose/webcam_demo.py
new file mode 100644
index 0000000000000000000000000000000000000000..5fde77943ede76cd791cde45ce005082bd7c5d03
--- /dev/null
+++ b/joints_detectors/Alphapose/webcam_demo.py
@@ -0,0 +1,115 @@
+from opt import opt
+
+import os
+import numpy as np
+
+import cv2
+from tqdm import tqdm
+
+from SPPE.src.main_fast_inference import *
+from dataloader_webcam import WebcamLoader, DetectionLoader, DetectionProcessor, DataWriter, Mscoco
+from fn import getTime
+from opt import opt
+from pPose_nms import write_json
+
+args = opt
+args.dataset = 'coco'
+
+
+def loop():
+    n = 0
+    while True:
+        yield n
+        n += 1
+
+
+if __name__ == "__main__":
+    webcam = args.webcam
+    mode = args.mode
+    if not os.path.exists(args.outputpath):
+        os.mkdir(args.outputpath)
+
+    # Load input video
+    data_loader = WebcamLoader(webcam).start()
+    (fourcc, fps, frameSize) = data_loader.videoinfo()
+
+    # Load detection loader
+    print('Loading YOLO model..')
+    sys.stdout.flush()
+    det_loader = DetectionLoader(data_loader, batchSize=args.detbatch).start()
+    det_processor = DetectionProcessor(det_loader).start()
+
+    # Load pose model
+    pose_dataset = Mscoco()
+    if args.fast_inference:
+        pose_model = InferenNet_fast(4 * 1 + 1, pose_dataset)
+    else:
+        pose_model = InferenNet(4 * 1 + 1, pose_dataset)
+    pose_model.cuda()
+    pose_model.eval()
+
+    # Data writer
+    save_path = os.path.join(args.outputpath, 'AlphaPose_webcam' + webcam + '.avi')
+    writer = DataWriter(args.save_video, save_path, cv2.VideoWriter_fourcc(*'XVID'), fps, frameSize).start()
+
+    runtime_profile = {
+        'dt': [],
+        'pt': [],
+        'pn': []
+    }
+
+    print('Starting webcam demo, press Ctrl + C to terminate...')
+    sys.stdout.flush()
+    im_names_desc = tqdm(loop())
+    batchSize = args.posebatch
+    for i in im_names_desc:
+        try:
+            start_time = getTime()
+            with torch.no_grad():
+                (inps, orig_img, im_name, boxes, scores, pt1, pt2) = det_processor.read()
+                if boxes is None or boxes.nelement() == 0:
+                    writer.save(None, None, None, None, None, orig_img, im_name.split('/')[-1])
+                    continue
+
+                ckpt_time, det_time = getTime(start_time)
+                runtime_profile['dt'].append(det_time)
+                # Pose Estimation
+
+                datalen = inps.size(0)
+                leftover = 0
+                if (datalen) % batchSize:
+                    leftover = 1
+                num_batches = datalen // batchSize + leftover
+                hm = []
+                for j in range(num_batches):
+                    inps_j = inps[j * batchSize:min((j + 1) * batchSize, datalen)].cuda()
+                    hm_j = pose_model(inps_j)
+                    hm.append(hm_j)
+                hm = torch.cat(hm)
+                ckpt_time, pose_time = getTime(ckpt_time)
+                runtime_profile['pt'].append(pose_time)
+
+                hm = hm.cpu().data
+                writer.save(boxes, scores, hm, pt1, pt2, orig_img, im_name.split('/')[-1])
+
+                ckpt_time, post_time = getTime(ckpt_time)
+                runtime_profile['pn'].append(post_time)
+            if args.profile:
+                # TQDM
+                im_names_desc.set_description(
+                    'det time: {dt:.3f} | pose time: {pt:.2f} | post processing: {pn:.4f}'.format(
+                        dt=np.mean(runtime_profile['dt']), pt=np.mean(runtime_profile['pt']), pn=np.mean(runtime_profile['pn']))
+                )
+        except KeyboardInterrupt:
+            break
+
+    print(' ')
+    print('===========================> Finish Model Running.')
+    if (args.save_img or args.save_video) and not args.vis_fast:
+        print('===========================> Rendering remaining images in the queue...')
+        print('===========================> If this step takes too long, you can enable the --vis_fast flag to use fast rendering (real-time).')
+    while (writer.running()):
+        pass
+    writer.stop()
+    final_result = writer.results()
+    write_json(final_result, args.outputpath)
diff --git a/joints_detectors/Alphapose/yolo/README.md b/joints_detectors/Alphapose/yolo/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..ed47930a3c5eca8f22ca974e94d8f7b1d18a45da
--- /dev/null
+++ b/joints_detectors/Alphapose/yolo/README.md
@@ -0,0 +1,3 @@
+# A PyTorch implementation of a YOLO v3 Object Detector
+
+Forked from https://github.com/ayooshkathuria/pytorch-yolo-v3
diff --git a/joints_detectors/Alphapose/yolo/__init__.py b/joints_detectors/Alphapose/yolo/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/joints_detectors/Alphapose/yolo/bbox.py b/joints_detectors/Alphapose/yolo/bbox.py
new file mode 100644
index 0000000000000000000000000000000000000000..999424173fe863433891fbde8b574adccaa9a488
--- /dev/null
+++ b/joints_detectors/Alphapose/yolo/bbox.py
@@ -0,0 +1,113 @@
+from __future__ import division
+
+import torch 
+import random
+
+import numpy as np
+import cv2
+
+def confidence_filter(result, confidence):
+    conf_mask = (result[:,:,4] > confidence).float().unsqueeze(2)
+    result = result*conf_mask    
+    
+    return result
+
+def confidence_filter_cls(result, confidence):
+    max_scores = torch.max(result[:,:,5:25], 2)[0]
+    res = torch.cat((result, max_scores),2)
+    print(res.shape)
+    
+    
+    cond_1 = (res[:,:,4] > confidence).float()
+    cond_2 = (res[:,:,25] > 0.995).float()
+    
+    conf = cond_1 + cond_2
+    conf = torch.clamp(conf, 0.0, 1.0)
+    conf = conf.unsqueeze(2)
+    result = result*conf   
+    return result
+
+
+
+def get_abs_coord(box):
+    box[2], box[3] = abs(box[2]), abs(box[3])
+    x1 = (box[0] - box[2]/2) - 1 
+    y1 = (box[1] - box[3]/2) - 1 
+    x2 = (box[0] + box[2]/2) - 1 
+    y2 = (box[1] + box[3]/2) - 1
+    return x1, y1, x2, y2
+    
+
+
+def sanity_fix(box):
+    if (box[0] > box[2]):
+        box[0], box[2] = box[2], box[0]
+    
+    if (box[1] >  box[3]):
+        box[1], box[3] = box[3], box[1]
+        
+    return box
+
+def bbox_iou(box1, box2):
+    """
+    Returns the IoU of two bounding boxes 
+    
+    
+    """
+    #Get the coordinates of bounding boxes
+    b1_x1, b1_y1, b1_x2, b1_y2 = box1[:,0], box1[:,1], box1[:,2], box1[:,3]
+    b2_x1, b2_y1, b2_x2, b2_y2 = box2[:,0], box2[:,1], box2[:,2], box2[:,3]
+    
+    #get the corrdinates of the intersection rectangle
+    inter_rect_x1 =  torch.max(b1_x1, b2_x1)
+    inter_rect_y1 =  torch.max(b1_y1, b2_y1)
+    inter_rect_x2 =  torch.min(b1_x2, b2_x2)
+    inter_rect_y2 =  torch.min(b1_y2, b2_y2)
+    
+    #Intersection area
+    
+    inter_area = torch.max(inter_rect_x2 - inter_rect_x1 + 1,torch.zeros(inter_rect_x2.shape).cuda())*torch.max(inter_rect_y2 - inter_rect_y1 + 1, torch.zeros(inter_rect_x2.shape).cuda())
+    
+    #Union Area
+    b1_area = (b1_x2 - b1_x1 + 1)*(b1_y2 - b1_y1 + 1)
+    b2_area = (b2_x2 - b2_x1 + 1)*(b2_y2 - b2_y1 + 1)
+    
+    iou = inter_area / (b1_area + b2_area - inter_area)
+    
+    return iou
+
+
+def pred_corner_coord(prediction):
+    #Get indices of non-zero confidence bboxes
+    ind_nz = torch.nonzero(prediction[:,:,4]).transpose(0,1).contiguous()
+    
+    box = prediction[ind_nz[0], ind_nz[1]]
+    
+    
+    box_a = box.new(box.shape)
+    box_a[:,0] = (box[:,0] - box[:,2]/2)
+    box_a[:,1] = (box[:,1] - box[:,3]/2)
+    box_a[:,2] = (box[:,0] + box[:,2]/2) 
+    box_a[:,3] = (box[:,1] + box[:,3]/2)
+    box[:,:4] = box_a[:,:4]
+    
+    prediction[ind_nz[0], ind_nz[1]] = box
+    
+    return prediction
+
+
+
+
+def write(x, batches, results, colors, classes):
+    c1 = tuple(x[1:3].int())
+    c2 = tuple(x[3:5].int())
+    img = results[int(x[0])]
+    cls = int(x[-1])
+    label = "{0}".format(classes[cls])
+    color = random.choice(colors)
+    cv2.rectangle(img, c1, c2,color, 1)
+    t_size = cv2.getTextSize(label, cv2.FONT_HERSHEY_PLAIN, 1 , 1)[0]
+    c2 = c1[0] + t_size[0] + 3, c1[1] + t_size[1] + 4
+    cv2.rectangle(img, c1, c2,color, -1)
+    cv2.putText(img, label, (c1[0], c1[1] + t_size[1] + 4), cv2.FONT_HERSHEY_PLAIN, 1, [225,255,255], 1);
+    return img
diff --git a/joints_detectors/Alphapose/yolo/cam_demo.py b/joints_detectors/Alphapose/yolo/cam_demo.py
new file mode 100644
index 0000000000000000000000000000000000000000..9c335e464205f061fc565a523d07326ceb23294f
--- /dev/null
+++ b/joints_detectors/Alphapose/yolo/cam_demo.py
@@ -0,0 +1,168 @@
+from __future__ import division
+import time
+import torch 
+import torch.nn as nn
+from torch.autograd import Variable
+import numpy as np
+import cv2 
+from .util import *
+from .darknet import Darknet
+from .preprocess import prep_image, inp_to_image
+import pandas as pd
+import random 
+import argparse
+import pickle as pkl
+
+def get_test_input(input_dim, CUDA):
+    img = cv2.imread("imgs/messi.jpg")
+    img = cv2.resize(img, (input_dim, input_dim)) 
+    img_ =  img[:,:,::-1].transpose((2,0,1))
+    img_ = img_[np.newaxis,:,:,:]/255.0
+    img_ = torch.from_numpy(img_).float()
+    img_ = Variable(img_)
+    
+    if CUDA:
+        img_ = img_.cuda()
+    
+    return img_
+
+def prep_image(img, inp_dim):
+    """
+    Prepare image for inputting to the neural network. 
+    
+    Returns a Variable 
+    """
+
+    orig_im = img
+    dim = orig_im.shape[1], orig_im.shape[0]
+    img = cv2.resize(orig_im, (inp_dim, inp_dim))
+    img_ = img[:,:,::-1].transpose((2,0,1)).copy()
+    img_ = torch.from_numpy(img_).float().div(255.0).unsqueeze(0)
+    return img_, orig_im, dim
+
+def write(x, img):
+    c1 = tuple(x[1:3].int())
+    c2 = tuple(x[3:5].int())
+    cls = int(x[-1])
+    label = "{0}".format(classes[cls])
+    color = random.choice(colors)
+    cv2.rectangle(img, c1, c2,color, 1)
+    t_size = cv2.getTextSize(label, cv2.FONT_HERSHEY_PLAIN, 1 , 1)[0]
+    c2 = c1[0] + t_size[0] + 3, c1[1] + t_size[1] + 4
+    cv2.rectangle(img, c1, c2,color, -1)
+    cv2.putText(img, label, (c1[0], c1[1] + t_size[1] + 4), cv2.FONT_HERSHEY_PLAIN, 1, [225,255,255], 1);
+    return img
+
+def arg_parse():
+    """
+    Parse arguements to the detect module
+    
+    """
+    
+    
+    parser = argparse.ArgumentParser(description='YOLO v3 Cam Demo')
+    parser.add_argument("--confidence", dest = "confidence", help = "Object Confidence to filter predictions", default = 0.25)
+    parser.add_argument("--nms_thresh", dest = "nms_thresh", help = "NMS Threshhold", default = 0.4)
+    parser.add_argument("--reso", dest = 'reso', help = 
+                        "Input resolution of the network. Increase to increase accuracy. Decrease to increase speed",
+                        default = "160", type = str)
+    return parser.parse_args()
+
+
+
+if __name__ == '__main__':
+    cfgfile = "cfg/yolov3-spp.cfg"
+    weightsfile = "yolov3-spp.weights"
+    num_classes = 80
+
+    args = arg_parse()
+    confidence = float(args.confidence)
+    nms_thesh = float(args.nms_thresh)
+    start = 0
+    CUDA = torch.cuda.is_available()
+    
+
+    
+    
+    num_classes = 80
+    bbox_attrs = 5 + num_classes
+    
+    model = Darknet(cfgfile)
+    model.load_weights(weightsfile)
+    
+    model.net_info["height"] = args.reso
+    inp_dim = int(model.net_info["height"])
+    
+    assert inp_dim % 32 == 0 
+    assert inp_dim > 32
+
+    if CUDA:
+        model.cuda()
+            
+    model.eval()
+    
+    videofile = 'video.avi'
+    
+    cap = cv2.VideoCapture(0)
+    
+    assert cap.isOpened(), 'Cannot capture source'
+    
+    frames = 0
+    start = time.time()    
+    while cap.isOpened():
+        
+        ret, frame = cap.read()
+        if ret:
+            
+            img, orig_im, dim = prep_image(frame, inp_dim)
+            
+#            im_dim = torch.FloatTensor(dim).repeat(1,2)                        
+            
+            
+            if CUDA:
+                im_dim = im_dim.cuda()
+                img = img.cuda()
+            
+            
+            output = model(Variable(img), CUDA)
+            output = write_results(output, confidence, num_classes, nms = True, nms_conf = nms_thesh)
+
+            if type(output) == int:
+                frames += 1
+                print("FPS of the video is {:5.2f}".format( frames / (time.time() - start)))
+                cv2.imshow("frame", orig_im)
+                key = cv2.waitKey(1)
+                if key & 0xFF == ord('q'):
+                    break
+                continue
+            
+
+        
+            output[:,1:5] = torch.clamp(output[:,1:5], 0.0, float(inp_dim))/inp_dim
+            
+#            im_dim = im_dim.repeat(output.size(0), 1)
+            output[:,[1,3]] *= frame.shape[1]
+            output[:,[2,4]] *= frame.shape[0]
+
+            
+            classes = load_classes('data/coco.names')
+            colors = pkl.load(open("pallete", "rb"))
+            
+            list(map(lambda x: write(x, orig_im), output))
+            
+            
+            cv2.imshow("frame", orig_im)
+            key = cv2.waitKey(1)
+            if key & 0xFF == ord('q'):
+                break
+            frames += 1
+            print("FPS of the video is {:5.2f}".format( frames / (time.time() - start)))
+
+            
+        else:
+            break
+    
+
+    
+    
+
diff --git a/joints_detectors/Alphapose/yolo/cfg/tiny-yolo-voc.cfg b/joints_detectors/Alphapose/yolo/cfg/tiny-yolo-voc.cfg
new file mode 100644
index 0000000000000000000000000000000000000000..ab2c066a216eacbee86e78c28f4d236e5d6b351a
--- /dev/null
+++ b/joints_detectors/Alphapose/yolo/cfg/tiny-yolo-voc.cfg
@@ -0,0 +1,134 @@
+[net]
+batch=64
+subdivisions=8
+width=416
+height=416
+channels=3
+momentum=0.9
+decay=0.0005
+angle=0
+saturation = 1.5
+exposure = 1.5
+hue=.1
+
+learning_rate=0.001
+max_batches = 40200
+policy=steps
+steps=-1,100,20000,30000
+scales=.1,10,.1,.1
+
+[convolutional]
+batch_normalize=1
+filters=16
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=2
+
+[convolutional]
+batch_normalize=1
+filters=32
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=2
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=2
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=2
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=2
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=1
+
+[convolutional]
+batch_normalize=1
+filters=1024
+size=3
+stride=1
+pad=1
+activation=leaky
+
+###########
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=1024
+activation=leaky
+
+[convolutional]
+size=1
+stride=1
+pad=1
+filters=125
+activation=linear
+
+[region]
+anchors = 1.08,1.19,  3.42,4.41,  6.63,11.38,  9.42,5.11,  16.62,10.52
+bias_match=1
+classes=20
+coords=4
+num=5
+softmax=1
+jitter=.2
+rescore=1
+
+object_scale=5
+noobject_scale=1
+class_scale=1
+coord_scale=1
+
+absolute=1
+thresh = .6
+random=1
diff --git a/joints_detectors/Alphapose/yolo/cfg/yolo-voc.cfg b/joints_detectors/Alphapose/yolo/cfg/yolo-voc.cfg
new file mode 100644
index 0000000000000000000000000000000000000000..d5bdfc1c5bf2d34885d7614d76d980c90373f89a
--- /dev/null
+++ b/joints_detectors/Alphapose/yolo/cfg/yolo-voc.cfg
@@ -0,0 +1,258 @@
+[net]
+# Testing
+batch=64
+subdivisions=8
+# Training
+# batch=64
+# subdivisions=8
+height=416
+width=416
+channels=3
+momentum=0.9
+decay=0.0005
+angle=0
+saturation = 1.5
+exposure = 1.5
+hue=.1
+
+learning_rate=0.001
+burn_in=1000
+max_batches = 80200
+policy=steps
+steps=-1,500,40000,60000
+scales=0.1,10,.1,.1
+
+[convolutional]
+batch_normalize=1
+filters=32
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=2
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=2
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=2
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=2
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=2
+
+[convolutional]
+batch_normalize=1
+filters=1024
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=1024
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=1024
+size=3
+stride=1
+pad=1
+activation=leaky
+
+
+#######
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=1024
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=1024
+activation=leaky
+
+[route]
+layers=-9
+
+[convolutional]
+batch_normalize=1
+size=1
+stride=1
+pad=1
+filters=64
+activation=leaky
+
+[reorg]
+stride=2
+
+[route]
+layers=-1,-4
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=1024
+activation=leaky
+
+[convolutional]
+size=1
+stride=1
+pad=1
+filters=125
+activation=linear
+
+
+[region]
+anchors =  1.3221, 1.73145, 3.19275, 4.00944, 5.05587, 8.09892, 9.47112, 4.84053, 11.2364, 10.0071
+bias_match=1
+classes=20
+coords=4
+num=5
+softmax=1
+jitter=.3
+rescore=1
+
+object_scale=5
+noobject_scale=1
+class_scale=1
+coord_scale=1
+
+absolute=1
+thresh = .6
+random=1
diff --git a/joints_detectors/Alphapose/yolo/cfg/yolo.cfg b/joints_detectors/Alphapose/yolo/cfg/yolo.cfg
new file mode 100644
index 0000000000000000000000000000000000000000..2a0cd98fbd07c94aa0840c528a12b1b60a004928
--- /dev/null
+++ b/joints_detectors/Alphapose/yolo/cfg/yolo.cfg
@@ -0,0 +1,258 @@
+[net]
+# Testing
+batch=1
+subdivisions=1
+# Training
+# batch=64
+# subdivisions=8
+width=416
+height=416
+channels=3
+momentum=0.9
+decay=0.0005
+angle=0
+saturation = 1.5
+exposure = 1.5
+hue=.1
+
+learning_rate=0.001
+burn_in=1000
+max_batches = 500200
+policy=steps
+steps=400000,450000
+scales=.1,.1
+
+[convolutional]
+batch_normalize=1
+filters=32
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=2
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=2
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=2
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=2
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=2
+
+[convolutional]
+batch_normalize=1
+filters=1024
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=1024
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=1024
+size=3
+stride=1
+pad=1
+activation=leaky
+
+
+#######
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=1024
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=1024
+activation=leaky
+
+[route]
+layers=-9
+
+[convolutional]
+batch_normalize=1
+size=1
+stride=1
+pad=1
+filters=64
+activation=leaky
+
+[reorg]
+stride=2
+
+[route]
+layers=-1,-4
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=1024
+activation=leaky
+
+[convolutional]
+size=1
+stride=1
+pad=1
+filters=425
+activation=linear
+
+
+[region]
+anchors =  0.57273, 0.677385, 1.87446, 2.06253, 3.33843, 5.47434, 7.88282, 3.52778, 9.77052, 9.16828
+bias_match=1
+classes=80
+coords=4
+num=5
+softmax=1
+jitter=.3
+rescore=1
+
+object_scale=5
+noobject_scale=1
+class_scale=1
+coord_scale=1
+
+absolute=1
+thresh = .6
+random=1
diff --git a/joints_detectors/Alphapose/yolo/cfg/yolov3-spp.cfg b/joints_detectors/Alphapose/yolo/cfg/yolov3-spp.cfg
new file mode 100644
index 0000000000000000000000000000000000000000..4ad2a052d88328a79cff5686ff4dd1df6993a2fd
--- /dev/null
+++ b/joints_detectors/Alphapose/yolo/cfg/yolov3-spp.cfg
@@ -0,0 +1,822 @@
+[net]
+# Testing
+batch=1
+subdivisions=1
+# Training
+# batch=64
+# subdivisions=16
+width=608
+height=608
+channels=3
+momentum=0.9
+decay=0.0005
+angle=0
+saturation = 1.5
+exposure = 1.5
+hue=.1
+
+learning_rate=0.001
+burn_in=1000
+max_batches = 500200
+policy=steps
+steps=400000,450000
+scales=.1,.1
+
+[convolutional]
+batch_normalize=1
+filters=32
+size=3
+stride=1
+pad=1
+activation=leaky
+
+# Downsample
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=3
+stride=2
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=32
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+# Downsample
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=3
+stride=2
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+# Downsample
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=2
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+# Downsample
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=2
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+# Downsample
+
+[convolutional]
+batch_normalize=1
+filters=1024
+size=3
+stride=2
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=1024
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=1024
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=1024
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=1024
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+######################
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=1024
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=leaky
+
+### SPP ###
+[maxpool]
+stride=1
+size=5
+
+[route]
+layers=-2
+
+[maxpool]
+stride=1
+size=9
+
+[route]
+layers=-4
+
+[maxpool]
+stride=1
+size=13
+
+[route]
+layers=-1,-3,-5,-6
+
+### End SPP ###
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=leaky
+
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=1024
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=1024
+activation=leaky
+
+[convolutional]
+size=1
+stride=1
+pad=1
+filters=255
+activation=linear
+
+
+[yolo]
+mask = 6,7,8
+anchors = 10,13,  16,30,  33,23,  30,61,  62,45,  59,119,  116,90,  156,198,  373,326
+classes=80
+num=9
+jitter=.3
+ignore_thresh = .7
+truth_thresh = 1
+random=1
+
+
+[route]
+layers = -4
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[upsample]
+stride=2
+
+[route]
+layers = -1, 61
+
+
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=512
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=512
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=512
+activation=leaky
+
+[convolutional]
+size=1
+stride=1
+pad=1
+filters=255
+activation=linear
+
+
+[yolo]
+mask = 3,4,5
+anchors = 10,13,  16,30,  33,23,  30,61,  62,45,  59,119,  116,90,  156,198,  373,326
+classes=80
+num=9
+jitter=.3
+ignore_thresh = .7
+truth_thresh = 1
+random=1
+
+
+
+[route]
+layers = -4
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[upsample]
+stride=2
+
+[route]
+layers = -1, 36
+
+
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=256
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=256
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=256
+activation=leaky
+
+[convolutional]
+size=1
+stride=1
+pad=1
+filters=255
+activation=linear
+
+
+[yolo]
+mask = 0,1,2
+anchors = 10,13,  16,30,  33,23,  30,61,  62,45,  59,119,  116,90,  156,198,  373,326
+classes=80
+num=9
+jitter=.3
+ignore_thresh = .7
+truth_thresh = 1
+random=1
+
diff --git a/joints_detectors/Alphapose/yolo/cfg/yolov3.cfg b/joints_detectors/Alphapose/yolo/cfg/yolov3.cfg
new file mode 100644
index 0000000000000000000000000000000000000000..e94193b0e82e56b3b457f3d8c049ffb9ac7ed1f8
--- /dev/null
+++ b/joints_detectors/Alphapose/yolo/cfg/yolov3.cfg
@@ -0,0 +1,789 @@
+[net]
+# Testing
+batch=1
+subdivisions=1
+# Training
+# batch=64
+# subdivisions=16
+width= 320
+height = 320
+channels=3
+momentum=0.9
+decay=0.0005
+angle=0
+saturation = 1.5
+exposure = 1.5
+hue=.1
+
+learning_rate=0.001
+burn_in=1000
+max_batches = 500200
+policy=steps
+steps=400000,450000
+scales=.1,.1
+
+[convolutional]
+batch_normalize=1
+filters=32
+size=3
+stride=1
+pad=1
+activation=leaky
+
+# Downsample
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=3
+stride=2
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=32
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+# Downsample
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=3
+stride=2
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+# Downsample
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=2
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+# Downsample
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=2
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+# Downsample
+
+[convolutional]
+batch_normalize=1
+filters=1024
+size=3
+stride=2
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=1024
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=1024
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=1024
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=1024
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+######################
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=1024
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=1024
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=1024
+activation=leaky
+
+[convolutional]
+size=1
+stride=1
+pad=1
+filters=255
+activation=linear
+
+
+[yolo]
+mask = 6,7,8
+anchors = 10,13,  16,30,  33,23,  30,61,  62,45,  59,119,  116,90,  156,198,  373,326
+classes=80
+num=9
+jitter=.3
+ignore_thresh = .5
+truth_thresh = 1
+random=1
+
+
+[route]
+layers = -4
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[upsample]
+stride=2
+
+[route]
+layers = -1, 61
+
+
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=512
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=512
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=512
+activation=leaky
+
+[convolutional]
+size=1
+stride=1
+pad=1
+filters=255
+activation=linear
+
+
+[yolo]
+mask = 3,4,5
+anchors = 10,13,  16,30,  33,23,  30,61,  62,45,  59,119,  116,90,  156,198,  373,326
+classes=80
+num=9
+jitter=.3
+ignore_thresh = .5
+truth_thresh = 1
+random=1
+
+
+
+[route]
+layers = -4
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[upsample]
+stride=2
+
+[route]
+layers = -1, 36
+
+
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=256
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=256
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=256
+activation=leaky
+
+[convolutional]
+size=1
+stride=1
+pad=1
+filters=255
+activation=linear
+
+
+[yolo]
+mask = 0,1,2
+anchors = 10,13,  16,30,  33,23,  30,61,  62,45,  59,119,  116,90,  156,198,  373,326
+classes=80
+num=9
+jitter=.3
+ignore_thresh = .5
+truth_thresh = 1
+random=1
+
diff --git a/joints_detectors/Alphapose/yolo/darknet.py b/joints_detectors/Alphapose/yolo/darknet.py
new file mode 100644
index 0000000000000000000000000000000000000000..f12a8ac6a577217e2a797214f788fdf30d8ec989
--- /dev/null
+++ b/joints_detectors/Alphapose/yolo/darknet.py
@@ -0,0 +1,548 @@
+from __future__ import division
+
+import torch 
+import torch.nn as nn
+import torch.nn.functional as F 
+from torch.autograd import Variable
+import numpy as np
+import cv2 
+import matplotlib.pyplot as plt
+try:
+    from util import count_parameters as count
+    from util import convert2cpu as cpu
+    from util import predict_transform
+except ImportError:
+    from yolo.util import count_parameters as count
+    from yolo.util import convert2cpu as cpu
+    from yolo.util import predict_transform
+
+class test_net(nn.Module):
+    def __init__(self, num_layers, input_size):
+        super(test_net, self).__init__()
+        self.num_layers= num_layers
+        self.linear_1 = nn.Linear(input_size, 5)
+        self.middle = nn.ModuleList([nn.Linear(5,5) for x in range(num_layers)])
+        self.output = nn.Linear(5,2)
+    
+    def forward(self, x):
+        x = x.view(-1)
+        fwd = nn.Sequential(self.linear_1, *self.middle, self.output)
+        return fwd(x)
+        
+def get_test_input():
+    img = cv2.imread("dog-cycle-car.png")
+    img = cv2.resize(img, (416,416)) 
+    img_ =  img[:,:,::-1].transpose((2,0,1))
+    img_ = img_[np.newaxis,:,:,:]/255.0
+    img_ = torch.from_numpy(img_).float()
+    img_ = Variable(img_)
+    return img_
+
+
+def parse_cfg(cfgfile):
+    """
+    Takes a configuration file
+    
+    Returns a list of blocks. Each blocks describes a block in the neural
+    network to be built. Block is represented as a dictionary in the list
+    
+    """
+    file = open(cfgfile, 'r')
+    lines = file.read().split('\n')     #store the lines in a list
+    lines = [x for x in lines if len(x) > 0] #get read of the empty lines 
+    lines = [x for x in lines if x[0] != '#']  
+    lines = [x.rstrip().lstrip() for x in lines]
+
+    
+    block = {}
+    blocks = []
+    
+    for line in lines:
+        if line[0] == "[":               #This marks the start of a new block
+            if len(block) != 0:
+                blocks.append(block)
+                block = {}
+            block["type"] = line[1:-1].rstrip()
+        else:
+            key,value = line.split("=")
+            block[key.rstrip()] = value.lstrip()
+    blocks.append(block)
+
+    return blocks
+#    print('\n\n'.join([repr(x) for x in blocks]))
+
+import pickle as pkl
+
+class MaxPoolStride1(nn.Module):
+    def __init__(self, kernel_size):
+        super(MaxPoolStride1, self).__init__()
+        self.kernel_size = kernel_size
+        self.pad = kernel_size - 1
+
+    def forward(self, x):
+        padding = int(self.pad / 2)
+        #padded_x = F.pad(x, (0,self.pad,0,self.pad), mode="replicate")
+        #pooled_x = nn.MaxPool2d(self.kernel_size, self.pad)(padded_x)
+        #padded_x = F.pad(x, (0, self.pad, 0, self.pad), mode="replicate")
+        padded_x = F.pad(x, (padding, padding, padding, padding), mode="constant", value=0)
+        pooled_x = nn.MaxPool2d(self.kernel_size, 1)(padded_x)
+        return pooled_x
+
+
+class EmptyLayer(nn.Module):
+    def __init__(self):
+        super(EmptyLayer, self).__init__()
+        
+
+class DetectionLayer(nn.Module):
+    def __init__(self, anchors):
+        super(DetectionLayer, self).__init__()
+        self.anchors = anchors
+    
+    def forward(self, x, inp_dim, num_classes, confidence):
+        x = x.data
+        global CUDA
+        prediction = x
+        prediction = predict_transform(prediction, inp_dim, self.anchors, num_classes, confidence, CUDA)
+        return prediction
+        
+
+        
+
+
+class Upsample(nn.Module):
+    def __init__(self, stride=2):
+        super(Upsample, self).__init__()
+        self.stride = stride
+        
+    def forward(self, x):
+        stride = self.stride
+        assert(x.data.dim() == 4)
+        B = x.data.size(0)
+        C = x.data.size(1)
+        H = x.data.size(2)
+        W = x.data.size(3)
+        ws = stride
+        hs = stride
+        x = x.view(B, C, H, 1, W, 1).expand(B, C, H, stride, W, stride).contiguous().view(B, C, H*stride, W*stride)
+        return x
+#       
+        
+class ReOrgLayer(nn.Module):
+    def __init__(self, stride = 2):
+        super(ReOrgLayer, self).__init__()
+        self.stride= stride
+        
+    def forward(self,x):
+        assert(x.data.dim() == 4)
+        B,C,H,W = x.data.shape
+        hs = self.stride
+        ws = self.stride
+        assert(H % hs == 0),  "The stride " + str(self.stride) + " is not a proper divisor of height " + str(H)
+        assert(W % ws == 0),  "The stride " + str(self.stride) + " is not a proper divisor of height " + str(W)
+        x = x.view(B,C, H // hs, hs, W // ws, ws).transpose(-2,-3).contiguous()
+        x = x.view(B,C, H // hs * W // ws, hs, ws)
+        x = x.view(B,C, H // hs * W // ws, hs*ws).transpose(-1,-2).contiguous()
+        x = x.view(B, C, ws*hs, H // ws, W // ws).transpose(1,2).contiguous()
+        x = x.view(B, C*ws*hs, H // ws, W // ws)
+        return x
+
+
+def create_modules(blocks):
+    net_info = blocks[0]     #Captures the information about the input and pre-processing    
+    
+    module_list = nn.ModuleList()
+    
+    index = 0    #indexing blocks helps with implementing route  layers (skip connections)
+
+    
+    prev_filters = 3
+    
+    output_filters = []
+    
+    for x in blocks:
+        module = nn.Sequential()
+        
+        if (x["type"] == "net"):
+            continue
+        
+        #If it's a convolutional layer
+        if (x["type"] == "convolutional"):
+            #Get the info about the layer
+            activation = x["activation"]
+            try:
+                batch_normalize = int(x["batch_normalize"])
+                bias = False
+            except:
+                batch_normalize = 0
+                bias = True
+                
+            filters= int(x["filters"])
+            padding = int(x["pad"])
+            kernel_size = int(x["size"])
+            stride = int(x["stride"])
+            
+            if padding:
+                pad = (kernel_size - 1) // 2
+            else:
+                pad = 0
+                
+            #Add the convolutional layer
+            conv = nn.Conv2d(prev_filters, filters, kernel_size, stride, pad, bias=bias)
+            module.add_module("conv_{0}".format(index), conv)
+            
+            #Add the Batch Norm Layer
+            if batch_normalize:
+                bn = nn.BatchNorm2d(filters)
+                module.add_module("batch_norm_{0}".format(index), bn)
+            
+            #Check the activation. 
+            #It is either Linear or a Leaky ReLU for YOLO
+            if activation == "leaky":
+                activn = nn.LeakyReLU(0.1, inplace = True)
+                module.add_module("leaky_{0}".format(index), activn)
+            
+            
+            
+        #If it's an upsampling layer
+        #We use Bilinear2dUpsampling
+        
+        elif (x["type"] == "upsample"):
+            stride = int(x["stride"])
+#            upsample = Upsample(stride)
+            upsample = nn.Upsample(scale_factor = 2, mode = "nearest")
+            module.add_module("upsample_{}".format(index), upsample)
+        
+        #If it is a route layer
+        elif (x["type"] == "route"):
+            x["layers"] = x["layers"].split(',')
+            
+            #Start  of a route
+            start = int(x["layers"][0])
+            if len(x["layers"]) <= 2:
+                #end, if there exists one.
+                try:
+                    end = int(x["layers"][1])
+                except:
+                    end = 0
+
+                #Positive anotation
+                if start > 0: 
+                    start = start - index
+                
+                if end > 0:
+                    end = end - index
+
+                
+                route = EmptyLayer()
+                module.add_module("route_{0}".format(index), route)
+                
+                
+                
+                if end < 0:
+                    filters = output_filters[index + start] + output_filters[index + end]
+                else:
+                    filters= output_filters[index + start]
+            else:  #SPP-route
+                assert len(x["layers"]) == 4
+
+                round = EmptyLayer()
+                module.add_module("route_{0}".format(index), route)
+
+                filters = output_filters[index + start] + output_filters[index + int(x["layers"][1])] \
+                          + output_filters[index + int(x["layers"][2])] + output_filters[index + int(x["layers"][3])]
+        
+        #shortcut corresponds to skip connection
+        elif x["type"] == "shortcut":
+            from_ = int(x["from"])
+            shortcut = EmptyLayer()
+            module.add_module("shortcut_{}".format(index), shortcut)
+            
+            
+        elif x["type"] == "maxpool":
+            stride = int(x["stride"])
+            size = int(x["size"])
+            if stride != 1:
+                maxpool = nn.MaxPool2d(size, stride)
+            else:
+                maxpool = MaxPoolStride1(size)
+                #maxpool = nn.MaxPool2d(size, stride=1, padding=size-1)
+
+            module.add_module("maxpool_{}".format(index), maxpool)
+        
+        #Yolo is the detection layer
+        elif x["type"] == "yolo":
+            mask = x["mask"].split(",")
+            mask = [int(x) for x in mask]
+            
+            
+            anchors = x["anchors"].split(",")
+            anchors = [int(a) for a in anchors]
+            anchors = [(anchors[i], anchors[i+1]) for i in range(0, len(anchors),2)]
+            anchors = [anchors[i] for i in mask]
+            
+            detection = DetectionLayer(anchors)
+            module.add_module("Detection_{}".format(index), detection)
+        
+            
+            
+        else:
+            print("Something I dunno")
+            assert False
+
+        module_list.append(module)
+        prev_filters = filters
+        output_filters.append(filters)
+        index += 1
+        
+    
+    return (net_info, module_list)
+
+
+
+class Darknet(nn.Module):
+    def __init__(self, cfgfile):
+        super(Darknet, self).__init__()
+        self.blocks = parse_cfg(cfgfile)
+        self.net_info, self.module_list = create_modules(self.blocks)
+        self.header = torch.IntTensor([0,0,0,0])
+        self.seen = 0
+
+        
+        
+    def get_blocks(self):
+        return self.blocks
+    
+    def get_module_list(self):
+        return self.module_list
+
+                
+    def forward(self, x, CUDA):
+        detections = []
+        modules = self.blocks[1:]
+        outputs = {}   #We cache the outputs for the route layer
+        
+        
+        write = 0
+        for i in range(len(modules)):        
+            
+            module_type = (modules[i]["type"])
+            if module_type == "convolutional" or module_type == "upsample" or module_type == "maxpool":
+                
+                x = self.module_list[i](x)
+                outputs[i] = x
+
+                
+            elif module_type == "route":
+                layers = modules[i]["layers"]
+                layers = [int(a) for a in layers]
+                
+                if (layers[0]) > 0:
+                    layers[0] = layers[0] - i
+
+                if len(layers) == 1:
+                    x = outputs[i + (layers[0])]
+
+                elif len(layers) == 2:
+                    if (layers[1]) > 0:
+                        layers[1] = layers[1] - i
+                        
+                    map1 = outputs[i + layers[0]]
+                    map2 = outputs[i + layers[1]]
+
+                    x = torch.cat((map1, map2), 1)
+                elif len(layers) == 4:  # SPP
+                    map1 = outputs[i + layers[0]]
+                    map2 = outputs[i + layers[1]]
+                    map3 = outputs[i + layers[2]]
+                    map4 = outputs[i + layers[3]]
+
+                    x = torch.cat((map1, map2, map3, map4), 1)
+                outputs[i] = x
+            
+            elif  module_type == "shortcut":
+                from_ = int(modules[i]["from"])
+                x = outputs[i-1] + outputs[i+from_]
+                outputs[i] = x
+                
+            
+            
+            elif module_type == 'yolo':        
+                
+                anchors = self.module_list[i][0].anchors
+                #Get the input dimensions
+                inp_dim = int (self.net_info["height"])
+                
+                #Get the number of classes
+                num_classes = int (modules[i]["classes"])
+                
+                #Output the result
+                x = x.data
+                x = predict_transform(x, inp_dim, anchors, num_classes, CUDA)
+                
+                if type(x) == int:
+                    continue
+
+                
+                if not write:
+                    detections = x
+                    write = 1
+                
+                else:
+                    detections = torch.cat((detections, x), 1)
+                
+                outputs[i] = outputs[i-1]
+                
+        
+        
+        try:
+            return detections
+        except:
+            return 0
+
+            
+    def load_weights(self, weightfile):
+        
+        #Open the weights file
+        fp = open(weightfile, "rb")
+
+        #The first 4 values are header information 
+        # 1. Major version number
+        # 2. Minor Version Number
+        # 3. Subversion number 
+        # 4. IMages seen 
+        header = np.fromfile(fp, dtype = np.int32, count = 5)
+        self.header = torch.from_numpy(header)
+        self.seen = self.header[3]
+        
+        #The rest of the values are the weights
+        # Let's load them up
+        weights = np.fromfile(fp, dtype = np.float32)
+        
+        ptr = 0
+        for i in range(len(self.module_list)):
+            module_type = self.blocks[i + 1]["type"]
+            
+            if module_type == "convolutional":
+                model = self.module_list[i]
+                try:
+                    batch_normalize = int(self.blocks[i+1]["batch_normalize"])
+                except:
+                    batch_normalize = 0
+                
+                conv = model[0]
+                
+                if (batch_normalize):
+                    bn = model[1]
+                    
+                    #Get the number of weights of Batch Norm Layer
+                    num_bn_biases = bn.bias.numel()
+                    
+                    #Load the weights
+                    bn_biases = torch.from_numpy(weights[ptr:ptr + num_bn_biases])
+                    ptr += num_bn_biases
+                    
+                    bn_weights = torch.from_numpy(weights[ptr: ptr + num_bn_biases])
+                    ptr  += num_bn_biases
+                    
+                    bn_running_mean = torch.from_numpy(weights[ptr: ptr + num_bn_biases])
+                    ptr  += num_bn_biases
+                    
+                    bn_running_var = torch.from_numpy(weights[ptr: ptr + num_bn_biases])
+                    ptr  += num_bn_biases
+                    
+                    #Cast the loaded weights into dims of model weights. 
+                    bn_biases = bn_biases.view_as(bn.bias.data)
+                    bn_weights = bn_weights.view_as(bn.weight.data)
+                    bn_running_mean = bn_running_mean.view_as(bn.running_mean)
+                    bn_running_var = bn_running_var.view_as(bn.running_var)
+
+                    #Copy the data to model
+                    bn.bias.data.copy_(bn_biases)
+                    bn.weight.data.copy_(bn_weights)
+                    bn.running_mean.copy_(bn_running_mean)
+                    bn.running_var.copy_(bn_running_var)
+                
+                else:
+                    #Number of biases
+                    num_biases = conv.bias.numel()
+                
+                    #Load the weights
+                    conv_biases = torch.from_numpy(weights[ptr: ptr + num_biases])
+                    ptr = ptr + num_biases
+                    
+                    #reshape the loaded weights according to the dims of the model weights
+                    conv_biases = conv_biases.view_as(conv.bias.data)
+                    
+                    #Finally copy the data
+                    conv.bias.data.copy_(conv_biases)
+                    
+                    
+                #Let us load the weights for the Convolutional layers
+                num_weights = conv.weight.numel()
+                
+                #Do the same as above for weights
+                conv_weights = torch.from_numpy(weights[ptr:ptr+num_weights])
+                ptr = ptr + num_weights
+
+                conv_weights = conv_weights.view_as(conv.weight.data)
+                conv.weight.data.copy_(conv_weights)
+                
+    def save_weights(self, savedfile, cutoff = 0):
+            
+        if cutoff <= 0:
+            cutoff = len(self.blocks) - 1
+        
+        fp = open(savedfile, 'wb')
+        
+        # Attach the header at the top of the file
+        self.header[3] = self.seen
+        header = self.header
+
+        header = header.numpy()
+        header.tofile(fp)
+        
+        # Now, let us save the weights 
+        for i in range(len(self.module_list)):
+            module_type = self.blocks[i+1]["type"]
+            
+            if (module_type) == "convolutional":
+                model = self.module_list[i]
+                try:
+                    batch_normalize = int(self.blocks[i+1]["batch_normalize"])
+                except:
+                    batch_normalize = 0
+                    
+                conv = model[0]
+
+                if (batch_normalize):
+                    bn = model[1]
+                
+                    #If the parameters are on GPU, convert them back to CPU
+                    #We don't convert the parameter to GPU
+                    #Instead. we copy the parameter and then convert it to CPU
+                    #This is done as weight are need to be saved during training
+                    cpu(bn.bias.data).numpy().tofile(fp)
+                    cpu(bn.weight.data).numpy().tofile(fp)
+                    cpu(bn.running_mean).numpy().tofile(fp)
+                    cpu(bn.running_var).numpy().tofile(fp)
+                
+            
+                else:
+                    cpu(conv.bias.data).numpy().tofile(fp)
+                
+                
+                #Let us save the weights for the Convolutional layers
+                cpu(conv.weight.data).numpy().tofile(fp)
+               
+
+
+
+
+#
+#dn = Darknet('cfg/yolov3.cfg')
+#dn.load_weights("yolov3.weights")
+#inp = get_test_input()
+#a, interms = dn(inp)
+#dn.eval()
+#a_i, interms_i = dn(inp)
diff --git a/joints_detectors/Alphapose/yolo/data/coco.names b/joints_detectors/Alphapose/yolo/data/coco.names
new file mode 100644
index 0000000000000000000000000000000000000000..ca76c80b5b2cd0b25047f75736656cfebc9da7aa
--- /dev/null
+++ b/joints_detectors/Alphapose/yolo/data/coco.names
@@ -0,0 +1,80 @@
+person
+bicycle
+car
+motorbike
+aeroplane
+bus
+train
+truck
+boat
+traffic light
+fire hydrant
+stop sign
+parking meter
+bench
+bird
+cat
+dog
+horse
+sheep
+cow
+elephant
+bear
+zebra
+giraffe
+backpack
+umbrella
+handbag
+tie
+suitcase
+frisbee
+skis
+snowboard
+sports ball
+kite
+baseball bat
+baseball glove
+skateboard
+surfboard
+tennis racket
+bottle
+wine glass
+cup
+fork
+knife
+spoon
+bowl
+banana
+apple
+sandwich
+orange
+broccoli
+carrot
+hot dog
+pizza
+donut
+cake
+chair
+sofa
+pottedplant
+bed
+diningtable
+toilet
+tvmonitor
+laptop
+mouse
+remote
+keyboard
+cell phone
+microwave
+oven
+toaster
+sink
+refrigerator
+book
+clock
+vase
+scissors
+teddy bear
+hair drier
+toothbrush
diff --git a/joints_detectors/Alphapose/yolo/data/voc.names b/joints_detectors/Alphapose/yolo/data/voc.names
new file mode 100644
index 0000000000000000000000000000000000000000..8420ab35ede7400974f25836a6bb543024686a0e
--- /dev/null
+++ b/joints_detectors/Alphapose/yolo/data/voc.names
@@ -0,0 +1,20 @@
+aeroplane
+bicycle
+bird
+boat
+bottle
+bus
+car
+cat
+chair
+cow
+diningtable
+dog
+horse
+motorbike
+person
+pottedplant
+sheep
+sofa
+train
+tvmonitor
diff --git a/joints_detectors/Alphapose/yolo/detect.py b/joints_detectors/Alphapose/yolo/detect.py
new file mode 100644
index 0000000000000000000000000000000000000000..d9fd2b84f553751cac436a24bd03a9f122458606
--- /dev/null
+++ b/joints_detectors/Alphapose/yolo/detect.py
@@ -0,0 +1,103 @@
+from __future__ import division
+import time
+import torch 
+import torch.nn as nn
+from torch.autograd import Variable
+import numpy as np
+import cv2 
+from .util import *
+import argparse
+import os 
+import os.path as osp
+from .darknet import Darknet
+from .preprocess import prep_image, inp_to_image
+import pandas as pd
+import random 
+import pickle as pkl
+import itertools
+
+
+if __name__ == '__main__':
+
+    scales = "1,2,3"
+    images = "imgs/messi.jpg"
+    batch_size = 1
+    confidence = 0.5
+    nms_thesh = 0.4
+
+    CUDA = torch.cuda.is_available()
+
+    num_classes = 80
+    classes = load_classes('data/coco.names') 
+
+    #Set up the neural network
+    print("Loading network.....")
+    model = Darknet("cfg/yolov3-spp.cfg")
+    model.load_weights("yolov3-spp.weights")
+    print("Network successfully loaded")
+
+    model.net_info["height"] = "608"
+    inp_dim = int(model.net_info["height"])
+    assert inp_dim % 32 == 0
+    assert inp_dim > 32
+
+    #If there's a GPU availible, put the model on GPU
+    if CUDA:
+        model.cuda()
+
+    #Set the model in evaluation mode
+    model.eval()
+
+    #Detection phase
+    try:
+        imlist = []
+        imlist.append(osp.join(osp.realpath('.'), images))
+    except FileNotFoundError:
+        print ("No file or directory with the name {}".format(images))
+        exit()
+
+    batches = list(map(prep_image, imlist, [inp_dim for x in range(len(imlist))]))
+    im_batches = [x[0] for x in batches]
+    orig_ims = [x[1] for x in batches]
+    im_dim_list = [x[2] for x in batches]
+    im_dim_list = torch.FloatTensor(im_dim_list).repeat(1, 2)
+
+    if CUDA:
+        im_dim_list = im_dim_list.cuda()
+
+
+    for batch in im_batches:
+        #load the image
+        if CUDA:
+            batch = batch.cuda()
+        with torch.no_grad():
+            prediction = model(Variable(batch), CUDA)
+
+        prediction = write_results(prediction, confidence, num_classes, nms=True, nms_conf=nms_thesh)
+        output = prediction
+
+        if CUDA:
+            torch.cuda.synchronize()
+
+    try:
+        output
+    except NameError:
+        print("No detections were made")
+        exit()
+    print(im_dim_list.shape)
+    im_dim_list = torch.index_select(im_dim_list, 0, output[:,0].long())
+
+    scaling_factor = torch.min(inp_dim/im_dim_list,1)[0].view(-1,1)
+
+
+    output[:,[1,3]] -= (inp_dim - scaling_factor*im_dim_list[:,0].view(-1,1))/2
+    output[:,[2,4]] -= (inp_dim - scaling_factor*im_dim_list[:,1].view(-1,1))/2
+
+    output[:,1:5] /= scaling_factor
+
+    for i in range(output.shape[0]):
+        output[i, [1,3]] = torch.clamp(output[i, [1,3]], 0.0, im_dim_list[i,0])
+        output[i, [2,4]] = torch.clamp(output[i, [2,4]], 0.0, im_dim_list[i,1])
+
+    print(output)
+    print(output.shape)
diff --git a/joints_detectors/Alphapose/yolo/pallete b/joints_detectors/Alphapose/yolo/pallete
new file mode 100644
index 0000000000000000000000000000000000000000..25f0143e9c80c98923dac550f6cd52e20a9dbbe6
Binary files /dev/null and b/joints_detectors/Alphapose/yolo/pallete differ
diff --git a/joints_detectors/Alphapose/yolo/preprocess.py b/joints_detectors/Alphapose/yolo/preprocess.py
new file mode 100644
index 0000000000000000000000000000000000000000..f9a2f7f52882e1b932d48bb9c2d2d8c15a9b7ce8
--- /dev/null
+++ b/joints_detectors/Alphapose/yolo/preprocess.py
@@ -0,0 +1,87 @@
+from __future__ import division
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torch.autograd import Variable
+import numpy as np
+import cv2
+import matplotlib.pyplot as plt
+try:
+    from util import count_parameters as count
+    from util import convert2cpu as cpu
+except ImportError:
+    from yolo.util import count_parameters as count
+    from yolo.util import convert2cpu as cpu
+from PIL import Image, ImageDraw
+
+
+def letterbox_image(img, inp_dim):
+    '''resize image with unchanged aspect ratio using padding'''
+    img_w, img_h = img.shape[1], img.shape[0]
+    w, h = inp_dim
+    new_w = int(img_w * min(w / img_w, h / img_h))
+    new_h = int(img_h * min(w / img_w, h / img_h))
+    resized_image = cv2.resize(img, (new_w, new_h), interpolation=cv2.INTER_CUBIC)
+
+    canvas = np.full((inp_dim[1], inp_dim[0], 3), 128)
+
+    canvas[(h - new_h) // 2:(h - new_h) // 2 + new_h, (w - new_w) // 2:(w - new_w) // 2 + new_w, :] = resized_image
+
+    return canvas
+
+
+def prep_image(img, inp_dim):
+    """
+    Prepare image for inputting to the neural network.
+
+    Returns a Variable
+    """
+
+    orig_im = cv2.imread(img)
+    shape = orig_im.shape
+    dim = orig_im.shape[1], orig_im.shape[0]
+    img = (letterbox_image(orig_im, (inp_dim, inp_dim)))
+    img_ = img[:, :, ::-1].transpose((2, 0, 1)).copy()
+    img_ = torch.from_numpy(img_).float().div(255.0).unsqueeze(0)
+    return img_, orig_im, dim
+
+
+def prep_frame(img, inp_dim):
+    """
+    Prepare image for inputting to the neural network.
+
+    Returns a Variable
+    """
+
+    orig_im = img
+    dim = orig_im.shape[1], orig_im.shape[0]
+    img = (letterbox_image(orig_im, (inp_dim, inp_dim)))
+    img_ = img[:, :, ::-1].transpose((2, 0, 1)).copy()
+    img_ = torch.from_numpy(img_).float().div(255.0).unsqueeze(0)
+    return img_, orig_im, dim
+
+
+def prep_image_pil(img, network_dim):
+    orig_im = Image.open(img)
+    img = orig_im.convert('RGB')
+    dim = img.size
+    img = img.resize(network_dim)
+    img = torch.ByteTensor(torch.ByteStorage.from_buffer(img.tobytes()))
+    img = img.view(*network_dim, 3).transpose(0, 1).transpose(0, 2).contiguous()
+    img = img.view(1, 3, *network_dim)
+    img = img.float().div(255.0)
+    return (img, orig_im, dim)
+
+
+def inp_to_image(inp):
+    inp = inp.cpu().squeeze()
+    inp = inp * 255
+    try:
+        inp = inp.data.numpy()
+    except RuntimeError:
+        inp = inp.numpy()
+    inp = inp.transpose(1, 2, 0)
+
+    inp = inp[:, :, ::-1]
+    return inp
diff --git a/joints_detectors/Alphapose/yolo/util.py b/joints_detectors/Alphapose/yolo/util.py
new file mode 100644
index 0000000000000000000000000000000000000000..8eb4e245cafcfe5800298f2e4194836181a5fdd7
--- /dev/null
+++ b/joints_detectors/Alphapose/yolo/util.py
@@ -0,0 +1,387 @@
+
+from __future__ import division
+
+import torch 
+import torch.nn as nn
+import torch.nn.functional as F 
+from torch.autograd import Variable
+import numpy as np
+import cv2 
+import matplotlib.pyplot as plt
+try:
+    from bbox import bbox_iou
+except ImportError:
+    from yolo.bbox import bbox_iou
+
+
+def count_parameters(model):
+    return sum(p.numel() for p in model.parameters())
+
+def count_learnable_parameters(model):
+    return sum(p.numel() for p in model.parameters() if p.requires_grad)
+
+def convert2cpu(matrix):
+    if matrix.is_cuda:
+        return torch.FloatTensor(matrix.size()).copy_(matrix)
+    else:
+        return matrix
+
+def predict_transform(prediction, inp_dim, anchors, num_classes, CUDA = True):
+    batch_size = prediction.size(0)
+    stride =  inp_dim // prediction.size(2)
+    grid_size = inp_dim // stride
+    bbox_attrs = 5 + num_classes
+    num_anchors = len(anchors)
+    
+    anchors = [(a[0]/stride, a[1]/stride) for a in anchors]
+
+
+
+    prediction = prediction.view(batch_size, bbox_attrs*num_anchors, grid_size*grid_size)
+    prediction = prediction.transpose(1,2).contiguous()
+    prediction = prediction.view(batch_size, grid_size*grid_size*num_anchors, bbox_attrs)
+
+
+    #Sigmoid the  centre_X, centre_Y. and object confidencce
+    prediction[:,:,0] = torch.sigmoid(prediction[:,:,0])
+    prediction[:,:,1] = torch.sigmoid(prediction[:,:,1])
+    prediction[:,:,4] = torch.sigmoid(prediction[:,:,4])
+    
+
+    
+    #Add the center offsets
+    grid_len = np.arange(grid_size)
+    a,b = np.meshgrid(grid_len, grid_len)
+    
+    x_offset = torch.FloatTensor(a).view(-1,1)
+    y_offset = torch.FloatTensor(b).view(-1,1)
+    
+    if CUDA:
+        x_offset = x_offset.cuda()
+        y_offset = y_offset.cuda()
+    
+    x_y_offset = torch.cat((x_offset, y_offset), 1).repeat(1,num_anchors).view(-1,2).unsqueeze(0)
+    
+    prediction[:,:,:2] += x_y_offset
+      
+    #log space transform height and the width
+    anchors = torch.FloatTensor(anchors)
+    
+    if CUDA:
+        anchors = anchors.cuda()
+    
+    anchors = anchors.repeat(grid_size*grid_size, 1).unsqueeze(0)
+    prediction[:,:,2:4] = torch.exp(prediction[:,:,2:4])*anchors
+
+    #Softmax the class scores
+    prediction[:,:,5: 5 + num_classes] = torch.sigmoid((prediction[:,:, 5 : 5 + num_classes]))
+
+    prediction[:,:,:4] *= stride
+   
+    
+    return prediction
+
+def load_classes(namesfile):
+    fp = open(namesfile, "r")
+    names = fp.read().split("\n")[:-1]
+    return names
+
+def get_im_dim(im):
+    im = cv2.imread(im)
+    w,h = im.shape[1], im.shape[0]
+    return w,h
+
+def unique(tensor):
+    tensor_np = tensor.cpu().numpy()
+    unique_np = np.unique(tensor_np)
+    unique_tensor = torch.from_numpy(unique_np)
+
+    tensor_res = tensor.new(unique_tensor.shape)
+    tensor_res.copy_(unique_tensor)
+    return tensor_res
+
+
+def dynamic_write_results(prediction, confidence, num_classes, nms=True, nms_conf=0.4):
+    prediction_bak = prediction.clone()
+    dets = write_results(prediction.clone(), confidence, num_classes, nms, nms_conf)
+    if isinstance(dets, int):
+        return dets
+
+    if dets.shape[0] > 100:
+        nms_conf -= 0.05
+        dets = write_results(prediction_bak.clone(), confidence, num_classes, nms, nms_conf)
+
+    return dets
+
+
+def write_results(prediction, confidence, num_classes, nms=True, nms_conf=0.4):
+    conf_mask = (prediction[:, :, 4] > confidence).float().float().unsqueeze(2)
+    prediction = prediction * conf_mask
+
+    try:
+        ind_nz = torch.nonzero(prediction[:,:,4]).transpose(0,1).contiguous()
+    except:
+        return 0
+
+    box_a = prediction.new(prediction.shape)
+    box_a[:,:,0] = (prediction[:,:,0] - prediction[:,:,2]/2)
+    box_a[:,:,1] = (prediction[:,:,1] - prediction[:,:,3]/2)
+    box_a[:,:,2] = (prediction[:,:,0] + prediction[:,:,2]/2) 
+    box_a[:,:,3] = (prediction[:,:,1] + prediction[:,:,3]/2)
+    prediction[:,:,:4] = box_a[:,:,:4]
+
+    batch_size = prediction.size(0)
+
+    output = prediction.new(1, prediction.size(2) + 1)
+    write = False
+    num = 0
+    for ind in range(batch_size):
+        #select the image from the batch
+        image_pred = prediction[ind]
+
+        #Get the class having maximum score, and the index of that class
+        #Get rid of num_classes softmax scores 
+        #Add the class index and the class score of class having maximum score
+        max_conf, max_conf_score = torch.max(image_pred[:,5:5+ num_classes], 1)
+        max_conf = max_conf.float().unsqueeze(1)
+        max_conf_score = max_conf_score.float().unsqueeze(1)
+        seq = (image_pred[:,:5], max_conf, max_conf_score)
+        image_pred = torch.cat(seq, 1)
+
+        #Get rid of the zero entries
+        non_zero_ind =  (torch.nonzero(image_pred[:,4]))
+
+        image_pred_ = image_pred[non_zero_ind.squeeze(),:].view(-1,7)
+
+        #Get the various classes detected in the image
+        try:
+            img_classes = unique(image_pred_[:,-1])
+        except:
+            continue
+
+        #WE will do NMS classwise
+        #print(img_classes)
+        for cls in img_classes:
+            if cls != 0:
+                continue
+            #get the detections with one particular class
+            cls_mask = image_pred_*(image_pred_[:,-1] == cls).float().unsqueeze(1)
+            class_mask_ind = torch.nonzero(cls_mask[:,-2]).squeeze()
+
+            image_pred_class = image_pred_[class_mask_ind].view(-1,7)
+
+            #sort the detections such that the entry with the maximum objectness
+            #confidence is at the top
+            conf_sort_index = torch.sort(image_pred_class[:,4], descending = True )[1]
+            image_pred_class = image_pred_class[conf_sort_index]
+            idx = image_pred_class.size(0)
+
+            #if nms has to be done
+            if nms:
+                # Perform non-maximum suppression
+                max_detections = []
+                while image_pred_class.size(0):
+                    # Get detection with highest confidence and save as max detection
+                    max_detections.append(image_pred_class[0].unsqueeze(0))
+                    # Stop if we're at the last detection
+                    if len(image_pred_class) == 1:
+                        break
+                    # Get the IOUs for all boxes with lower confidence
+                    ious = bbox_iou(max_detections[-1], image_pred_class[1:])
+                    # Remove detections with IoU >= NMS threshold
+                    image_pred_class = image_pred_class[1:][ious < nms_conf]
+
+                image_pred_class = torch.cat(max_detections).data
+
+
+            #Concatenate the batch_id of the image to the detection
+            #this helps us identify which image does the detection correspond to 
+            #We use a linear straucture to hold ALL the detections from the batch
+            #the batch_dim is flattened
+            #batch is identified by extra batch column
+
+            batch_ind = image_pred_class.new(image_pred_class.size(0), 1).fill_(ind)
+            seq = batch_ind, image_pred_class
+            if not write:
+                output = torch.cat(seq,1)
+                write = True
+            else:
+                out = torch.cat(seq,1)
+                output = torch.cat((output,out))
+            num += 1
+    
+    if not num:
+        return 0
+
+    return output
+
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Sat Mar 24 00:12:16 2018
+
+@author: ayooshmac
+"""
+
+def predict_transform_half(prediction, inp_dim, anchors, num_classes, CUDA = True):
+    batch_size = prediction.size(0)
+    stride =  inp_dim // prediction.size(2)
+
+    bbox_attrs = 5 + num_classes
+    num_anchors = len(anchors)
+    grid_size = inp_dim // stride
+
+    
+    prediction = prediction.view(batch_size, bbox_attrs*num_anchors, grid_size*grid_size)
+    prediction = prediction.transpose(1,2).contiguous()
+    prediction = prediction.view(batch_size, grid_size*grid_size*num_anchors, bbox_attrs)
+    
+    
+    #Sigmoid the  centre_X, centre_Y. and object confidencce
+    prediction[:,:,0] = torch.sigmoid(prediction[:,:,0])
+    prediction[:,:,1] = torch.sigmoid(prediction[:,:,1])
+    prediction[:,:,4] = torch.sigmoid(prediction[:,:,4])
+
+    
+    #Add the center offsets
+    grid_len = np.arange(grid_size)
+    a,b = np.meshgrid(grid_len, grid_len)
+    
+    x_offset = torch.FloatTensor(a).view(-1,1)
+    y_offset = torch.FloatTensor(b).view(-1,1)
+    
+    if CUDA:
+        x_offset = x_offset.cuda().half()
+        y_offset = y_offset.cuda().half()
+    
+    x_y_offset = torch.cat((x_offset, y_offset), 1).repeat(1,num_anchors).view(-1,2).unsqueeze(0)
+    
+    prediction[:,:,:2] += x_y_offset
+      
+    #log space transform height and the width
+    anchors = torch.HalfTensor(anchors)
+    
+    if CUDA:
+        anchors = anchors.cuda()
+    
+    anchors = anchors.repeat(grid_size*grid_size, 1).unsqueeze(0)
+    prediction[:,:,2:4] = torch.exp(prediction[:,:,2:4])*anchors
+
+    #Softmax the class scores
+    prediction[:,:,5: 5 + num_classes] = nn.Softmax(-1)(Variable(prediction[:,:, 5 : 5 + num_classes])).data
+
+    prediction[:,:,:4] *= stride
+    
+    
+    return prediction
+
+
+def write_results_half(prediction, confidence, num_classes, nms = True, nms_conf = 0.4):
+    conf_mask = (prediction[:,:,4] > confidence).half().unsqueeze(2)
+    prediction = prediction*conf_mask
+    
+    try:
+        ind_nz = torch.nonzero(prediction[:,:,4]).transpose(0,1).contiguous()
+    except:
+        return 0
+    
+    
+    
+    box_a = prediction.new(prediction.shape)
+    box_a[:,:,0] = (prediction[:,:,0] - prediction[:,:,2]/2)
+    box_a[:,:,1] = (prediction[:,:,1] - prediction[:,:,3]/2)
+    box_a[:,:,2] = (prediction[:,:,0] + prediction[:,:,2]/2) 
+    box_a[:,:,3] = (prediction[:,:,1] + prediction[:,:,3]/2)
+    prediction[:,:,:4] = box_a[:,:,:4]
+    
+    
+    
+    batch_size = prediction.size(0)
+    
+    output = prediction.new(1, prediction.size(2) + 1)
+    write = False
+    
+    for ind in range(batch_size):
+        #select the image from the batch
+        image_pred = prediction[ind]
+
+        
+        #Get the class having maximum score, and the index of that class
+        #Get rid of num_classes softmax scores 
+        #Add the class index and the class score of class having maximum score
+        max_conf, max_conf_score = torch.max(image_pred[:,5:5+ num_classes], 1)
+        max_conf = max_conf.half().unsqueeze(1)
+        max_conf_score = max_conf_score.half().unsqueeze(1)
+        seq = (image_pred[:,:5], max_conf, max_conf_score)
+        image_pred = torch.cat(seq, 1)
+        
+        
+        #Get rid of the zero entries
+        non_zero_ind =  (torch.nonzero(image_pred[:,4]))
+        try:
+            image_pred_ = image_pred[non_zero_ind.squeeze(),:]
+        except:
+            continue
+        
+        #Get the various classes detected in the image
+        img_classes = unique(image_pred_[:,-1].long()).half()
+        
+        
+        
+                
+        #WE will do NMS classwise
+        for cls in img_classes:
+            #get the detections with one particular class
+            cls_mask = image_pred_*(image_pred_[:,-1] == cls).half().unsqueeze(1)
+            class_mask_ind = torch.nonzero(cls_mask[:,-2]).squeeze()
+            
+
+            image_pred_class = image_pred_[class_mask_ind]
+
+        
+             #sort the detections such that the entry with the maximum objectness
+             #confidence is at the top
+            conf_sort_index = torch.sort(image_pred_class[:,4], descending = True )[1]
+            image_pred_class = image_pred_class[conf_sort_index]
+            idx = image_pred_class.size(0)
+            
+            #if nms has to be done
+            if nms:
+                #For each detection
+                for i in range(idx):
+                    #Get the IOUs of all boxes that come after the one we are looking at 
+                    #in the loop
+                    try:
+                        ious = bbox_iou(image_pred_class[i].unsqueeze(0), image_pred_class[i+1:])
+                    except ValueError:
+                        break
+        
+                    except IndexError:
+                        break
+                    
+                    #Zero out all the detections that have IoU > treshhold
+                    iou_mask = (ious < nms_conf).half().unsqueeze(1)
+                    image_pred_class[i+1:] *= iou_mask       
+                    
+                    #Remove the non-zero entries
+                    non_zero_ind = torch.nonzero(image_pred_class[:,4]).squeeze()
+                    image_pred_class = image_pred_class[non_zero_ind]
+                    
+                    
+            
+            #Concatenate the batch_id of the image to the detection
+            #this helps us identify which image does the detection correspond to 
+            #We use a linear straucture to hold ALL the detections from the batch
+            #the batch_dim is flattened
+            #batch is identified by extra batch column
+            batch_ind = image_pred_class.new(image_pred_class.size(0), 1).fill_(ind)
+            seq = batch_ind, image_pred_class
+            
+            if not write:
+                output = torch.cat(seq,1)
+                write = True
+            else:
+                out = torch.cat(seq,1)
+                output = torch.cat((output,out))
+    
+    return output
diff --git a/joints_detectors/Alphapose/yolo/video_demo.py b/joints_detectors/Alphapose/yolo/video_demo.py
new file mode 100644
index 0000000000000000000000000000000000000000..c55e6b7aba203fd5840c928f60e1359d7f57920a
--- /dev/null
+++ b/joints_detectors/Alphapose/yolo/video_demo.py
@@ -0,0 +1,186 @@
+from __future__ import division
+import time
+import torch 
+import torch.nn as nn
+from torch.autograd import Variable
+import numpy as np
+import cv2 
+from .util import *
+from .darknet import Darknet
+from .preprocess import prep_image, inp_to_image, letterbox_image
+import pandas as pd
+import random 
+import pickle as pkl
+import argparse
+
+
+def get_test_input(input_dim, CUDA):
+    img = cv2.imread("dog-cycle-car.png")
+    img = cv2.resize(img, (input_dim, input_dim)) 
+    img_ =  img[:,:,::-1].transpose((2,0,1))
+    img_ = img_[np.newaxis,:,:,:]/255.0
+    img_ = torch.from_numpy(img_).float()
+    img_ = Variable(img_)
+    
+    if CUDA:
+        img_ = img_.cuda()
+    
+    return img_
+
+def prep_image(img, inp_dim):
+    """
+    Prepare image for inputting to the neural network. 
+    
+    Returns a Variable 
+    """
+
+    orig_im = img
+    dim = orig_im.shape[1], orig_im.shape[0]
+    img = (letterbox_image(orig_im, (inp_dim, inp_dim)))
+    img_ = img[:,:,::-1].transpose((2,0,1)).copy()
+    img_ = torch.from_numpy(img_).float().div(255.0).unsqueeze(0)
+    return img_, orig_im, dim
+
+def write(x, img):
+    c1 = tuple(x[1:3].int())
+    c2 = tuple(x[3:5].int())
+    cls = int(x[-1])
+    label = "{0}".format(classes[cls])
+    color = random.choice(colors)
+    cv2.rectangle(img, c1, c2,color, 1)
+    t_size = cv2.getTextSize(label, cv2.FONT_HERSHEY_PLAIN, 1 , 1)[0]
+    c2 = c1[0] + t_size[0] + 3, c1[1] + t_size[1] + 4
+    cv2.rectangle(img, c1, c2,color, -1)
+    cv2.putText(img, label, (c1[0], c1[1] + t_size[1] + 4), cv2.FONT_HERSHEY_PLAIN, 1, [225,255,255], 1);
+    return img
+
+def arg_parse():
+    """
+    Parse arguements to the detect module
+    
+    """
+    
+    
+    parser = argparse.ArgumentParser(description='YOLO v3 Video Detection Module')
+   
+    parser.add_argument("--video", dest = 'video', help = 
+                        "Video to run detection upon",
+                        default = "video.avi", type = str)
+    parser.add_argument("--dataset", dest = "dataset", help = "Dataset on which the network has been trained", default = "pascal")
+    parser.add_argument("--confidence", dest = "confidence", help = "Object Confidence to filter predictions", default = 0.5)
+    parser.add_argument("--nms_thresh", dest = "nms_thresh", help = "NMS Threshhold", default = 0.4)
+    parser.add_argument("--cfg", dest = 'cfgfile', help = 
+                        "Config file",
+                        default = "cfg/yolov3-spp.cfg", type = str)
+    parser.add_argument("--weights", dest = 'weightsfile', help = 
+                        "weightsfile",
+                        default = "yolov3-spp.weights", type = str)
+    parser.add_argument("--reso", dest = 'reso', help = 
+                        "Input resolution of the network. Increase to increase accuracy. Decrease to increase speed",
+                        default = "416", type = str)
+    return parser.parse_args()
+
+
+if __name__ == '__main__':
+    args = arg_parse()
+    confidence = float(args.confidence)
+    nms_thesh = float(args.nms_thresh)
+    start = 0
+
+    CUDA = torch.cuda.is_available()
+
+    num_classes = 80
+
+    CUDA = torch.cuda.is_available()
+    
+    bbox_attrs = 5 + num_classes
+    
+    print("Loading network.....")
+    model = Darknet(args.cfgfile)
+    model.load_weights(args.weightsfile)
+    print("Network successfully loaded")
+
+    model.net_info["height"] = args.reso
+    inp_dim = int(model.net_info["height"])
+    assert inp_dim % 32 == 0 
+    assert inp_dim > 32
+
+    if CUDA:
+        model.cuda()
+        
+    model(get_test_input(inp_dim, CUDA), CUDA)
+
+    model.eval()
+    
+    videofile = args.video
+    
+    cap = cv2.VideoCapture(videofile)
+    
+    assert cap.isOpened(), 'Cannot capture source'
+    
+    frames = 0
+    start = time.time()    
+    while cap.isOpened():
+        
+        ret, frame = cap.read()
+        if ret:
+            
+
+            img, orig_im, dim = prep_image(frame, inp_dim)
+            
+            im_dim = torch.FloatTensor(dim).repeat(1,2)                        
+            
+            
+            if CUDA:
+                im_dim = im_dim.cuda()
+                img = img.cuda()
+            
+            with torch.no_grad():   
+                output = model(Variable(img), CUDA)
+            output = write_results(output, confidence, num_classes, nms = True, nms_conf = nms_thesh)
+
+            if type(output) == int:
+                frames += 1
+                print("FPS of the video is {:5.2f}".format( frames / (time.time() - start)))
+                cv2.imshow("frame", orig_im)
+                key = cv2.waitKey(1)
+                if key & 0xFF == ord('q'):
+                    break
+                continue
+            
+            
+
+            
+            im_dim = im_dim.repeat(output.size(0), 1)
+            scaling_factor = torch.min(inp_dim/im_dim,1)[0].view(-1,1)
+            
+            output[:,[1,3]] -= (inp_dim - scaling_factor*im_dim[:,0].view(-1,1))/2
+            output[:,[2,4]] -= (inp_dim - scaling_factor*im_dim[:,1].view(-1,1))/2
+            
+            output[:,1:5] /= scaling_factor
+    
+            for i in range(output.shape[0]):
+                output[i, [1,3]] = torch.clamp(output[i, [1,3]], 0.0, im_dim[i,0])
+                output[i, [2,4]] = torch.clamp(output[i, [2,4]], 0.0, im_dim[i,1])
+            
+            classes = load_classes('data/coco.names')
+            colors = pkl.load(open("pallete", "rb"))
+            
+            list(map(lambda x: write(x, orig_im), output))
+            
+            
+            cv2.imshow("frame", orig_im)
+            key = cv2.waitKey(1)
+            if key & 0xFF == ord('q'):
+                break
+            frames += 1
+            print("FPS of the video is {:5.2f}".format( frames / (time.time() - start)))
+
+            
+        else:
+            break
+    
+
+    
+    
+
diff --git a/joints_detectors/Alphapose/yolo/video_demo_half.py b/joints_detectors/Alphapose/yolo/video_demo_half.py
new file mode 100644
index 0000000000000000000000000000000000000000..f710d496dd5fcb018569353131c4258483deb47c
--- /dev/null
+++ b/joints_detectors/Alphapose/yolo/video_demo_half.py
@@ -0,0 +1,189 @@
+from __future__ import division
+import time
+import torch 
+import torch.nn as nn
+from torch.autograd import Variable
+import numpy as np
+import cv2 
+from .util import *
+from .darknet import Darknet
+from .preprocess import prep_image, inp_to_image, letterbox_image
+import pandas as pd
+import random 
+import pickle as pkl
+import argparse
+
+
+def get_test_input(input_dim, CUDA):
+    img = cv2.imread("dog-cycle-car.png")
+    img = cv2.resize(img, (input_dim, input_dim)) 
+    img_ =  img[:,:,::-1].transpose((2,0,1))
+    img_ = img_[np.newaxis,:,:,:]/255.0
+    img_ = torch.from_numpy(img_).float()
+    img_ = Variable(img_)
+    
+    if CUDA:
+        img_ = img_.cuda()
+    
+    return img_
+
+def prep_image(img, inp_dim):
+    """
+    Prepare image for inputting to the neural network. 
+    
+    Returns a Variable 
+    """
+
+    orig_im = img
+    dim = orig_im.shape[1], orig_im.shape[0]
+    img = (letterbox_image(orig_im, (inp_dim, inp_dim)))
+    img_ = img[:,:,::-1].transpose((2,0,1)).copy()
+    img_ = torch.from_numpy(img_).float().div(255.0).unsqueeze(0)
+    return img_, orig_im, dim
+
+def write(x, img):
+    c1 = tuple(x[1:3].int())
+    c2 = tuple(x[3:5].int())
+    cls = int(x[-1])
+    label = "{0}".format(classes[cls])
+    color = random.choice(colors)
+    cv2.rectangle(img, c1, c2,color, 1)
+    t_size = cv2.getTextSize(label, cv2.FONT_HERSHEY_PLAIN, 1 , 1)[0]
+    c2 = c1[0] + t_size[0] + 3, c1[1] + t_size[1] + 4
+    cv2.rectangle(img, c1, c2,color, -1)
+    cv2.putText(img, label, (c1[0], c1[1] + t_size[1] + 4), cv2.FONT_HERSHEY_PLAIN, 1, [225,255,255], 1);
+    return img
+
+def arg_parse():
+    """
+    Parse arguements to the detect module
+    
+    """
+    
+    
+    parser = argparse.ArgumentParser(description='YOLO v2 Video Detection Module')
+   
+    parser.add_argument("--video", dest = 'video', help = 
+                        "Video to run detection upon",
+                        default = "video.avi", type = str)
+    parser.add_argument("--dataset", dest = "dataset", help = "Dataset on which the network has been trained", default = "pascal")
+    parser.add_argument("--confidence", dest = "confidence", help = "Object Confidence to filter predictions", default = 0.5)
+    parser.add_argument("--nms_thresh", dest = "nms_thresh", help = "NMS Threshhold", default = 0.4)
+    parser.add_argument("--cfg", dest = 'cfgfile', help = 
+                        "Config file",
+                        default = "cfg/yolov3-spp.cfg", type = str)
+    parser.add_argument("--weights", dest = 'weightsfile', help = 
+                        "weightsfile",
+                        default = "yolov3-spp.weights", type = str)
+    parser.add_argument("--reso", dest = 'reso', help = 
+                        "Input resolution of the network. Increase to increase accuracy. Decrease to increase speed",
+                        default = "416", type = str)
+    return parser.parse_args()
+
+
+if __name__ == '__main__':
+    args = arg_parse()
+    confidence = float(args.confidence)
+    nms_thesh = float(args.nms_thresh)
+    start = 0
+
+    CUDA = torch.cuda.is_available()
+
+        
+
+    CUDA = torch.cuda.is_available()
+    num_classes = 80 
+    bbox_attrs = 5 + num_classes
+    
+    print("Loading network.....")
+    model = Darknet(args.cfgfile)
+    model.load_weights(args.weightsfile)
+    print("Network successfully loaded")
+    
+    model.net_info["height"] = args.reso
+    inp_dim = int(model.net_info["height"])
+    assert inp_dim % 32 == 0 
+    assert inp_dim > 32
+
+    
+    if CUDA:
+        model.cuda().half()
+        
+    model(get_test_input(inp_dim, CUDA), CUDA)
+
+    model.eval()
+    
+    videofile = 'video.avi'
+    
+    cap = cv2.VideoCapture(videofile)
+    
+    assert cap.isOpened(), 'Cannot capture source'
+    
+    frames = 0
+    start = time.time()    
+    while cap.isOpened():
+        
+        ret, frame = cap.read()
+        if ret:
+            
+
+            img, orig_im, dim = prep_image(frame, inp_dim)
+            
+            im_dim = torch.FloatTensor(dim).repeat(1,2)                        
+            
+            
+            if CUDA:
+                img = img.cuda().half()
+                im_dim = im_dim.half().cuda()
+                write_results = write_results_half
+                predict_transform = predict_transform_half
+            
+            
+            output = model(Variable(img, volatile = True), CUDA)
+            output = write_results(output, confidence, num_classes, nms = True, nms_conf = nms_thesh)
+
+           
+            if type(output) == int:
+                frames += 1
+                print("FPS of the video is {:5.2f}".format( frames / (time.time() - start)))
+                cv2.imshow("frame", orig_im)
+                key = cv2.waitKey(1)
+                if key & 0xFF == ord('q'):
+                    break
+                continue
+
+        
+            im_dim = im_dim.repeat(output.size(0), 1)
+            scaling_factor = torch.min(inp_dim/im_dim,1)[0].view(-1,1)
+            
+            output[:,[1,3]] -= (inp_dim - scaling_factor*im_dim[:,0].view(-1,1))/2
+            output[:,[2,4]] -= (inp_dim - scaling_factor*im_dim[:,1].view(-1,1))/2
+            
+            output[:,1:5] /= scaling_factor
+    
+            for i in range(output.shape[0]):
+                output[i, [1,3]] = torch.clamp(output[i, [1,3]], 0.0, im_dim[i,0])
+                output[i, [2,4]] = torch.clamp(output[i, [2,4]], 0.0, im_dim[i,1])
+            
+            
+            classes = load_classes('data/coco.names')
+            colors = pkl.load(open("pallete", "rb"))
+            
+            list(map(lambda x: write(x, orig_im), output))
+            
+            
+            cv2.imshow("frame", orig_im)
+            key = cv2.waitKey(1)
+            if key & 0xFF == ord('q'):
+                break
+            frames += 1
+            print("FPS of the video is {:5.2f}".format( frames / (time.time() - start)))
+
+            
+        else:
+            break
+    
+
+    
+    
+
diff --git a/joints_detectors/__init__.py b/joints_detectors/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/joints_detectors/mediapipe/pose.py b/joints_detectors/mediapipe/pose.py
new file mode 100644
index 0000000000000000000000000000000000000000..23fbfe6869ebdd2a31c66c89b8456a7a4c7568e0
--- /dev/null
+++ b/joints_detectors/mediapipe/pose.py
@@ -0,0 +1,51 @@
+from tqdm import tqdm
+import mediapipe as mp
+import numpy as np
+import cv2
+
+pose = mp.solutions.pose.Pose(static_image_mode=False, min_detection_confidence=0.5, min_tracking_confidence=0.5)
+
+def generate_kpts(video_file):
+    vid = cv2.VideoCapture(video_file)
+    kpts = []
+    video_length = int(vid.get(cv2.CAP_PROP_FRAME_COUNT))
+
+    for i in tqdm(range(video_length)):
+        ret, frame = vid.read()
+        if not ret:
+            break
+
+        results = pose.process(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))
+        if(not results.pose_landmarks):
+            kpts.append(kpts[-1] if len(kpts) > 0 else [[0, 0] for _ in range(17)])
+            continue
+        
+        # Take the coco keypoints
+        l = results.pose_landmarks.landmark
+        pl = mp.solutions.pose.PoseLandmark
+        kpts.append([
+            [l[pl.NOSE].x, l[pl.NOSE].y],
+            [l[pl.LEFT_EYE].x, l[pl.LEFT_EYE].y],
+            [l[pl.RIGHT_EYE].x, l[pl.RIGHT_EYE].y],
+            [l[pl.LEFT_EAR].x, l[pl.LEFT_EAR].y],
+            [l[pl.RIGHT_EAR].x, l[pl.RIGHT_EAR].y],
+            [l[pl.LEFT_SHOULDER].x, l[pl.LEFT_SHOULDER].y],
+            [l[pl.RIGHT_SHOULDER].x, l[pl.RIGHT_SHOULDER].y],
+            [l[pl.LEFT_ELBOW].x, l[pl.LEFT_ELBOW].y],
+            [l[pl.RIGHT_ELBOW].x, l[pl.RIGHT_ELBOW].y],
+            [l[pl.LEFT_WRIST].x, l[pl.LEFT_WRIST].y],
+            [l[pl.RIGHT_WRIST].x, l[pl.RIGHT_WRIST].y],
+            [l[pl.LEFT_HIP].x, l[pl.LEFT_HIP].y],
+            [l[pl.RIGHT_HIP].x, l[pl.RIGHT_HIP].y],
+            [l[pl.LEFT_KNEE].x, l[pl.LEFT_KNEE].y],
+            [l[pl.RIGHT_KNEE].x, l[pl.RIGHT_KNEE].y],
+            [l[pl.LEFT_ANKLE].x, l[pl.LEFT_ANKLE].y],
+            [l[pl.RIGHT_ANKLE].x, l[pl.RIGHT_ANKLE].y]
+        ])
+
+        # multiply all the x coordinates with frame width and y coordinates with frame height.
+        for i in range(len(kpts[-1])):
+            kpts[-1][i][0] *= frame.shape[1]
+            kpts[-1][i][1] *= frame.shape[0]
+
+    return np.array(kpts)
\ No newline at end of file
diff --git a/joints_detectors/openpose/README.md b/joints_detectors/openpose/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..648314d88913e688c3542c4f1521234e7a626586
--- /dev/null
+++ b/joints_detectors/openpose/README.md
@@ -0,0 +1,4 @@
+需要将该环境conda的python与openpose编译,才能调用openpose python API  
+
+
+
diff --git a/joints_detectors/openpose/main.py b/joints_detectors/openpose/main.py
new file mode 100644
index 0000000000000000000000000000000000000000..a2f33174c1ced8b53bd46e05c4253415aec71a63
--- /dev/null
+++ b/joints_detectors/openpose/main.py
@@ -0,0 +1,126 @@
+import os
+import sys
+
+import cv2
+
+dir_path = os.path.dirname(os.path.realpath(__file__))
+sys.path.insert(0, dir_path)
+import ipdb;
+
+pdb = ipdb.set_trace
+import argparse
+from tqdm import tqdm
+from utils import convert
+import numpy as np
+
+sys.path.remove(dir_path)
+
+try:
+    from openpose import pyopenpose as op
+except ImportError as e:
+    print('Error: OpenPose library could not be found. Did you enable `BUILD_PYTHON` in CMake and have this Python script in the right folder?')
+    raise e
+
+# Flags
+parser = argparse.ArgumentParser()
+parser.add_argument("--image_path", default="../../examples/media/COCO_val2014_000000000192.jpg",
+                    help="Process an image. Read all standard formats (jpg, png, bmp, etc.).")
+args = parser.parse_known_args()
+
+params = dict()
+cur_dir = os.path.dirname(os.path.abspath(__file__))
+params["model_folder"] = cur_dir + "/models/"
+params['tracking'] = 5
+params['number_people_max'] = 1
+
+
+#  params['num_gpu'] = 1
+#  params['num_gpu_start'] = 1
+#  import ipdb;ipdb.set_trace()
+
+
+def load_model():
+    try:
+        opWrapper = op.WrapperPython()
+        opWrapper.configure(params)
+        opWrapper.start()
+    except Exception as e:
+        print(e)
+        sys.exit(-1)
+
+    return opWrapper
+
+
+def test_video(model, video_name=0):
+    opWrapper = model
+
+    cam = cv2.VideoCapture(video_name)
+    # warm up
+    for i in range(5):
+        datum = op.Datum()
+        _, imageToProcess = cam.read()
+        datum.cvInputData = imageToProcess
+        opWrapper.emplaceAndPop([datum])
+
+    for i in tqdm(range(2000)):
+        datum = op.Datum()
+        _, imageToProcess = cam.read()
+        datum.cvInputData = imageToProcess
+        opWrapper.emplaceAndPop([datum])
+
+    # Display Image
+    #  print("Body keypoints: \n" + str(datum.poseKeypoints))
+    #  cv2.imshow("OpenPose 1.4.0 - Tutorial Python API", datum.cvOutputData)
+    #  cv2.waitKey(10)
+    #  cv2.destroyAllWindows()
+
+
+def generate_kpts(video_name):
+    kpt_results = []
+
+    cap = cv2.VideoCapture(video_name)
+    length = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
+    opWrapper = load_model()
+    for i in tqdm(range(length)):
+
+        try:
+            datum = op.Datum()
+            _, imageToProcess = cap.read()
+            datum.cvInputData = imageToProcess
+            opWrapper.emplaceAndPop([datum])
+            results = datum.poseKeypoints
+
+            assert len(results) == 1, 'videopose3D only support one pserson restruction'
+            # 25 to 17
+            kpts = convert(results[0])
+            kpt_results.append(kpts)
+        except Exception as e:
+            print(e)
+
+    # pose processes
+    result = np.array(kpt_results)
+
+    #  # save
+    #  name = '/home/xyliu/experiments/VideoPose3D/data/tmp.npz'
+    #  kpts = result.astype(np.float32)
+    #  print('kpts npz save in ', name)
+    #  np.savez_compressed(name, kpts=kpts)
+    return result
+
+
+def generate_frame_kpt(frame, opWrapper):
+    '''
+    提供frame and model
+    '''
+    datum = op.Datum()
+    datum.cvInputData = frame
+    opWrapper.emplaceAndPop([datum])
+    re = datum.poseKeypoints
+    assert len(re) == 1, 'videopose3D only support one pserson restruction'
+    kpt = convert(re[0])
+
+    return kpt
+
+
+if __name__ == "__main__":
+    generate_kpts(os.environ.get('VIDEO_PATH') + 'dance.mp4')
diff --git a/joints_detectors/openpose/models/cameraParameters/flir/17012332.xml.example b/joints_detectors/openpose/models/cameraParameters/flir/17012332.xml.example
new file mode 100644
index 0000000000000000000000000000000000000000..84aa5e0a155e8bf0eee4e12040d01f557c6c98e0
--- /dev/null
+++ b/joints_detectors/openpose/models/cameraParameters/flir/17012332.xml.example
@@ -0,0 +1,28 @@
+<?xml version="1.0"?>
+<!-- Note: Camera matrix translation units are meters. -->
+<opencv_storage>
+<CameraMatrix type_id="opencv-matrix">
+  <rows>3</rows>
+  <cols>4</cols>
+  <dt>d</dt>
+  <data>
+    1. 0. 0. 0.
+    0. 1. 0. 0.
+    0. 0. 1. 0.</data></CameraMatrix>
+<Intrinsics type_id="opencv-matrix">
+  <rows>3</rows>
+  <cols>3</cols>
+  <dt>d</dt>
+  <data>
+    8.1793481631740565e+02 0. 6.0070689997785121e+02 0.
+    8.1651774059837908e+02 5.1784529566329593e+02 0. 0. 1.</data></Intrinsics>
+<Distortion type_id="opencv-matrix">
+  <rows>8</rows>
+  <cols>1</cols>
+  <dt>d</dt>
+  <data>
+    -1.8102158829399091e+00 9.1966147162623262e+00
+    -4.4293900343777355e-04 1.3638377686816653e-03
+    1.3303863414979364e+00 -1.4189051636354870e+00
+    8.4725535468475819e+00 4.7911023525901033e+00</data></Distortion>
+</opencv_storage>
diff --git a/joints_detectors/openpose/models/face/haarcascade_frontalface_alt.xml b/joints_detectors/openpose/models/face/haarcascade_frontalface_alt.xml
new file mode 100644
index 0000000000000000000000000000000000000000..665f4f9475b07d498855e68c57e0e12e4363be85
--- /dev/null
+++ b/joints_detectors/openpose/models/face/haarcascade_frontalface_alt.xml
@@ -0,0 +1,39705 @@
+<?xml version="1.0"?>
+<!--
+    Stump-based 20x20 gentle adaboost frontal face detector.
+    Created by Rainer Lienhart.
+
+////////////////////////////////////////////////////////////////////////////////////////
+
+  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+
+  By downloading, copying, installing or using the software you agree to this license.
+  If you do not agree to this license, do not download, install,
+  copy or use the software.
+
+
+                        Intel License Agreement
+                For Open Source Computer Vision Library
+
+ Copyright (C) 2000, Intel Corporation, all rights reserved.
+ Third party copyrights are property of their respective owners.
+
+ Redistribution and use in source and binary forms, with or without modification,
+ are permitted provided that the following conditions are met:
+
+   * Redistribution's of source code must retain the above copyright notice,
+     this list of conditions and the following disclaimer.
+
+   * Redistribution's in binary form must reproduce the above copyright notice,
+     this list of conditions and the following disclaimer in the documentation
+     and/or other materials provided with the distribution.
+
+   * The name of Intel Corporation may not be used to endorse or promote products
+     derived from this software without specific prior written permission.
+
+ This software is provided by the copyright holders and contributors "as is" and
+ any express or implied warranties, including, but not limited to, the implied
+ warranties of merchantability and fitness for a particular purpose are disclaimed.
+ In no event shall the Intel Corporation or contributors be liable for any direct,
+ indirect, incidental, special, exemplary, or consequential damages
+ (including, but not limited to, procurement of substitute goods or services;
+ loss of use, data, or profits; or business interruption) however caused
+ and on any theory of liability, whether in contract, strict liability,
+ or tort (including negligence or otherwise) arising in any way out of
+ the use of this software, even if advised of the possibility of such damage.
+-->
+<opencv_storage>
+    <cascade type_id="opencv-cascade-classifier">
+        <stageType>BOOST</stageType>
+        <featureType>HAAR</featureType>
+        <height>20</height>
+        <width>20</width>
+        <stageParams>
+            <maxWeakCount>213</maxWeakCount>
+        </stageParams>
+        <featureParams>
+            <maxCatCount>0</maxCatCount>
+        </featureParams>
+        <stageNum>22</stageNum>
+        <stages>
+            <_>
+                <maxWeakCount>3</maxWeakCount>
+                <stageThreshold>8.2268941402435303e-01</stageThreshold>
+                <weakClassifiers>
+                    <_>
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+                <rects>
+                    <_>
+                        7 13 3 3 -1.
+                    </_>
+                    <_>
+                        7 14 3 1 3.
+                    </_>
+                </rects>
+            </_>
+            <_>
+                <rects>
+                    <_>
+                        9 9 2 2 -1.
+                    </_>
+                    <_>
+                        9 10 2 1 2.
+                    </_>
+                </rects>
+            </_>
+            <_>
+                <rects>
+                    <_>
+                        0 1 18 2 -1.
+                    </_>
+                    <_>
+                        6 1 6 2 3.
+                    </_>
+                </rects>
+            </_>
+            <_>
+                <rects>
+                    <_>
+                        7 1 6 14 -1.
+                    </_>
+                    <_>
+                        7 8 6 7 2.
+                    </_>
+                </rects>
+            </_>
+            <_>
+                <rects>
+                    <_>
+                        1 9 18 1 -1.
+                    </_>
+                    <_>
+                        7 9 6 1 3.
+                    </_>
+                </rects>
+            </_>
+            <_>
+                <rects>
+                    <_>
+                        9 7 2 2 -1.
+                    </_>
+                    <_>
+                        9 7 1 2 2.
+                    </_>
+                </rects>
+            </_>
+            <_>
+                <rects>
+                    <_>
+                        9 3 2 9 -1.
+                    </_>
+                    <_>
+                        10 3 1 9 2.
+                    </_>
+                </rects>
+            </_>
+            <_>
+                <rects>
+                    <_>
+                        18 14 2 3 -1.
+                    </_>
+                    <_>
+                        18 15 2 1 3.
+                    </_>
+                </rects>
+            </_>
+            <_>
+                <rects>
+                    <_>
+                        7 11 3 1 -1.
+                    </_>
+                    <_>
+                        8 11 1 1 3.
+                    </_>
+                </rects>
+            </_>
+            <_>
+                <rects>
+                    <_>
+                        10 8 3 4 -1.
+                    </_>
+                    <_>
+                        11 8 1 4 3.
+                    </_>
+                </rects>
+            </_>
+            <_>
+                <rects>
+                    <_>
+                        7 14 3 6 -1.
+                    </_>
+                    <_>
+                        8 14 1 6 3.
+                    </_>
+                </rects>
+            </_>
+            <_>
+                <rects>
+                    <_>
+                        10 8 3 4 -1.
+                    </_>
+                    <_>
+                        11 8 1 4 3.
+                    </_>
+                </rects>
+            </_>
+            <_>
+                <rects>
+                    <_>
+                        7 8 3 4 -1.
+                    </_>
+                    <_>
+                        8 8 1 4 3.
+                    </_>
+                </rects>
+            </_>
+            <_>
+                <rects>
+                    <_>
+                        7 9 6 9 -1.
+                    </_>
+                    <_>
+                        7 12 6 3 3.
+                    </_>
+                </rects>
+            </_>
+            <_>
+                <rects>
+                    <_>
+                        0 14 2 3 -1.
+                    </_>
+                    <_>
+                        0 15 2 1 3.
+                    </_>
+                </rects>
+            </_>
+            <_>
+                <rects>
+                    <_>
+                        11 12 1 2 -1.
+                    </_>
+                    <_>
+                        11 13 1 1 2.
+                    </_>
+                </rects>
+            </_>
+            <_>
+                <rects>
+                    <_>
+                        4 3 8 3 -1.
+                    </_>
+                    <_>
+                        8 3 4 3 2.
+                    </_>
+                </rects>
+            </_>
+            <_>
+                <rects>
+                    <_>
+                        0 4 20 6 -1.
+                    </_>
+                    <_>
+                        0 4 10 6 2.
+                    </_>
+                </rects>
+            </_>
+            <_>
+                <rects>
+                    <_>
+                        9 14 1 3 -1.
+                    </_>
+                    <_>
+                        9 15 1 1 3.
+                    </_>
+                </rects>
+            </_>
+            <_>
+                <rects>
+                    <_>
+                        8 14 4 3 -1.
+                    </_>
+                    <_>
+                        8 15 4 1 3.
+                    </_>
+                </rects>
+            </_>
+            <_>
+                <rects>
+                    <_>
+                        0 15 14 4 -1.
+                    </_>
+                    <_>
+                        0 17 14 2 2.
+                    </_>
+                </rects>
+            </_>
+            <_>
+                <rects>
+                    <_>
+                        1 14 18 6 -1.
+                    </_>
+                    <_>
+                        1 17 18 3 2.
+                    </_>
+                </rects>
+            </_>
+            <_>
+                <rects>
+                    <_>
+                        0 0 10 6 -1.
+                    </_>
+                    <_>
+                        0 0 5 3 2.
+                    </_>
+                    <_>
+                        5 3 5 3 2.
+                    </_>
+                </rects>
+            </_>
+        </features>
+    </cascade>
+</opencv_storage>
diff --git a/joints_detectors/openpose/models/face/pose_deploy.prototxt b/joints_detectors/openpose/models/face/pose_deploy.prototxt
new file mode 100644
index 0000000000000000000000000000000000000000..d347748cac0bd03b09a45bfe4772654145c7dda5
--- /dev/null
+++ b/joints_detectors/openpose/models/face/pose_deploy.prototxt
@@ -0,0 +1,1728 @@
+input: "image"
+input_dim: 1
+input_dim: 3
+input_dim: 1 # Original: 368
+input_dim: 1 # Original: 368
+# input: "weights"
+# input_dim: 1
+# input_dim: 71
+# input_dim: 184
+# input_dim: 184
+# input: "labels"
+# input_dim: 1
+# input_dim: 71
+# input_dim: 184
+# input_dim: 184
+
+layer {
+  name: "conv1_1"
+  type: "Convolution"
+  bottom: "image"
+  top: "conv1_1"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 64
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "conv1_1_re"
+  type: "ReLU"
+  bottom: "conv1_1"
+  top: "conv1_1"
+}
+layer {
+  name: "conv1_2"
+  type: "Convolution"
+  bottom: "conv1_1"
+  top: "conv1_2"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 64
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "conv1_2_re"
+  type: "ReLU"
+  bottom: "conv1_2"
+  top: "conv1_2"
+}
+layer {
+  name: "pool1"
+  type: "Pooling"
+  bottom: "conv1_2"
+  top: "pool1"
+  pooling_param {
+    pool: MAX
+    kernel_size: 2
+    stride: 2
+  }
+}
+layer {
+  name: "conv2_1"
+  type: "Convolution"
+  bottom: "pool1"
+  top: "conv2_1"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "conv2_1_re"
+  type: "ReLU"
+  bottom: "conv2_1"
+  top: "conv2_1"
+}
+layer {
+  name: "conv2_2"
+  type: "Convolution"
+  bottom: "conv2_1"
+  top: "conv2_2"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "conv2_2_re"
+  type: "ReLU"
+  bottom: "conv2_2"
+  top: "conv2_2"
+}
+layer {
+  name: "pool2"
+  type: "Pooling"
+  bottom: "conv2_2"
+  top: "pool2"
+  pooling_param {
+    pool: MAX
+    kernel_size: 2
+    stride: 2
+  }
+}
+layer {
+  name: "conv3_1"
+  type: "Convolution"
+  bottom: "pool2"
+  top: "conv3_1"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 256
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "conv3_1_re"
+  type: "ReLU"
+  bottom: "conv3_1"
+  top: "conv3_1"
+}
+layer {
+  name: "conv3_2"
+  type: "Convolution"
+  bottom: "conv3_1"
+  top: "conv3_2"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 256
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "conv3_2_re"
+  type: "ReLU"
+  bottom: "conv3_2"
+  top: "conv3_2"
+}
+layer {
+  name: "conv3_3"
+  type: "Convolution"
+  bottom: "conv3_2"
+  top: "conv3_3"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 256
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "conv3_3_re"
+  type: "ReLU"
+  bottom: "conv3_3"
+  top: "conv3_3"
+}
+layer {
+  name: "conv3_4"
+  type: "Convolution"
+  bottom: "conv3_3"
+  top: "conv3_4"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 256
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "conv3_4_re"
+  type: "ReLU"
+  bottom: "conv3_4"
+  top: "conv3_4"
+}
+layer {
+  name: "pool3"
+  type: "Pooling"
+  bottom: "conv3_4"
+  top: "pool3"
+  pooling_param {
+    pool: MAX
+    kernel_size: 2
+    stride: 2
+  }
+}
+layer {
+  name: "conv4_1"
+  type: "Convolution"
+  bottom: "pool3"
+  top: "conv4_1"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 512
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "conv4_1_re"
+  type: "ReLU"
+  bottom: "conv4_1"
+  top: "conv4_1"
+}
+layer {
+  name: "conv4_2"
+  type: "Convolution"
+  bottom: "conv4_1"
+  top: "conv4_2"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 512
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "conv4_2_re"
+  type: "ReLU"
+  bottom: "conv4_2"
+  top: "conv4_2"
+}
+layer {
+  name: "conv4_3"
+  type: "Convolution"
+  bottom: "conv4_2"
+  top: "conv4_3"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 512
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "conv4_3_re"
+  type: "ReLU"
+  bottom: "conv4_3"
+  top: "conv4_3"
+}
+layer {
+  name: "conv4_4"
+  type: "Convolution"
+  bottom: "conv4_3"
+  top: "conv4_4"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 512
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "conv4_4_re"
+  type: "ReLU"
+  bottom: "conv4_4"
+  top: "conv4_4"
+}
+layer {
+  name: "conv5_1"
+  type: "Convolution"
+  bottom: "conv4_4"
+  top: "conv5_1"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 512
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "conv5_1_re"
+  type: "ReLU"
+  bottom: "conv5_1"
+  top: "conv5_1"
+}
+layer {
+  name: "conv5_2"
+  type: "Convolution"
+  bottom: "conv5_1"
+  top: "conv5_2"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 512
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "conv5_2_re"
+  type: "ReLU"
+  bottom: "conv5_2"
+  top: "conv5_2"
+}
+layer {
+  name: "conv5_3_CPM"
+  type: "Convolution"
+  bottom: "conv5_2"
+  top: "conv5_3_CPM"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "conv5_3_CPM_re"
+  type: "ReLU"
+  bottom: "conv5_3_CPM"
+  top: "conv5_3_CPM"
+}
+layer {
+  name: "conv6_1_CPM"
+  type: "Convolution"
+  bottom: "conv5_3_CPM"
+  top: "conv6_1_CPM"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 512
+    pad: 0
+    kernel_size: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "conv6_1_CPM_re"
+  type: "ReLU"
+  bottom: "conv6_1_CPM"
+  top: "conv6_1_CPM"
+}
+layer {
+  name: "conv6_2_CPM"
+  type: "Convolution"
+  bottom: "conv6_1_CPM"
+  top: "conv6_2_CPM"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 71
+    pad: 0
+    kernel_size: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "features_in_stage_2"
+  type: "Concat"
+  bottom: "conv6_2_CPM"
+  bottom: "conv5_3_CPM"
+  top: "features_in_stage_2"
+  concat_param {
+    axis: 1
+  }
+}
+layer {
+  name: "Mconv1_stage2"
+  type: "Convolution"
+  bottom: "features_in_stage_2"
+  top: "Mconv1_stage2"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mconv1_stage2_re"
+  type: "ReLU"
+  bottom: "Mconv1_stage2"
+  top: "Mconv1_stage2"
+}
+layer {
+  name: "Mconv2_stage2"
+  type: "Convolution"
+  bottom: "Mconv1_stage2"
+  top: "Mconv2_stage2"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mconv2_stage2_re"
+  type: "ReLU"
+  bottom: "Mconv2_stage2"
+  top: "Mconv2_stage2"
+}
+layer {
+  name: "Mconv3_stage2"
+  type: "Convolution"
+  bottom: "Mconv2_stage2"
+  top: "Mconv3_stage2"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mconv3_stage2_re"
+  type: "ReLU"
+  bottom: "Mconv3_stage2"
+  top: "Mconv3_stage2"
+}
+layer {
+  name: "Mconv4_stage2"
+  type: "Convolution"
+  bottom: "Mconv3_stage2"
+  top: "Mconv4_stage2"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mconv4_stage2_re"
+  type: "ReLU"
+  bottom: "Mconv4_stage2"
+  top: "Mconv4_stage2"
+}
+layer {
+  name: "Mconv5_stage2"
+  type: "Convolution"
+  bottom: "Mconv4_stage2"
+  top: "Mconv5_stage2"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mconv5_stage2_re"
+  type: "ReLU"
+  bottom: "Mconv5_stage2"
+  top: "Mconv5_stage2"
+}
+layer {
+  name: "Mconv6_stage2"
+  type: "Convolution"
+  bottom: "Mconv5_stage2"
+  top: "Mconv6_stage2"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 0
+    kernel_size: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mconv6_stage2_re"
+  type: "ReLU"
+  bottom: "Mconv6_stage2"
+  top: "Mconv6_stage2"
+}
+layer {
+  name: "Mconv7_stage2"
+  type: "Convolution"
+  bottom: "Mconv6_stage2"
+  top: "Mconv7_stage2"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 71
+    pad: 0
+    kernel_size: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "features_in_stage_3"
+  type: "Concat"
+  bottom: "Mconv7_stage2"
+  bottom: "conv5_3_CPM"
+  top: "features_in_stage_3"
+  concat_param {
+    axis: 1
+  }
+}
+layer {
+  name: "Mconv1_stage3"
+  type: "Convolution"
+  bottom: "features_in_stage_3"
+  top: "Mconv1_stage3"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mconv1_stage3_re"
+  type: "ReLU"
+  bottom: "Mconv1_stage3"
+  top: "Mconv1_stage3"
+}
+layer {
+  name: "Mconv2_stage3"
+  type: "Convolution"
+  bottom: "Mconv1_stage3"
+  top: "Mconv2_stage3"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mconv2_stage3_re"
+  type: "ReLU"
+  bottom: "Mconv2_stage3"
+  top: "Mconv2_stage3"
+}
+layer {
+  name: "Mconv3_stage3"
+  type: "Convolution"
+  bottom: "Mconv2_stage3"
+  top: "Mconv3_stage3"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mconv3_stage3_re"
+  type: "ReLU"
+  bottom: "Mconv3_stage3"
+  top: "Mconv3_stage3"
+}
+layer {
+  name: "Mconv4_stage3"
+  type: "Convolution"
+  bottom: "Mconv3_stage3"
+  top: "Mconv4_stage3"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mconv4_stage3_re"
+  type: "ReLU"
+  bottom: "Mconv4_stage3"
+  top: "Mconv4_stage3"
+}
+layer {
+  name: "Mconv5_stage3"
+  type: "Convolution"
+  bottom: "Mconv4_stage3"
+  top: "Mconv5_stage3"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mconv5_stage3_re"
+  type: "ReLU"
+  bottom: "Mconv5_stage3"
+  top: "Mconv5_stage3"
+}
+layer {
+  name: "Mconv6_stage3"
+  type: "Convolution"
+  bottom: "Mconv5_stage3"
+  top: "Mconv6_stage3"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 0
+    kernel_size: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mconv6_stage3_re"
+  type: "ReLU"
+  bottom: "Mconv6_stage3"
+  top: "Mconv6_stage3"
+}
+layer {
+  name: "Mconv7_stage3"
+  type: "Convolution"
+  bottom: "Mconv6_stage3"
+  top: "Mconv7_stage3"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 71
+    pad: 0
+    kernel_size: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "features_in_stage_4"
+  type: "Concat"
+  bottom: "Mconv7_stage3"
+  bottom: "conv5_3_CPM"
+  top: "features_in_stage_4"
+  concat_param {
+    axis: 1
+  }
+}
+layer {
+  name: "Mconv1_stage4"
+  type: "Convolution"
+  bottom: "features_in_stage_4"
+  top: "Mconv1_stage4"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mconv1_stage4_re"
+  type: "ReLU"
+  bottom: "Mconv1_stage4"
+  top: "Mconv1_stage4"
+}
+layer {
+  name: "Mconv2_stage4"
+  type: "Convolution"
+  bottom: "Mconv1_stage4"
+  top: "Mconv2_stage4"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mconv2_stage4_re"
+  type: "ReLU"
+  bottom: "Mconv2_stage4"
+  top: "Mconv2_stage4"
+}
+layer {
+  name: "Mconv3_stage4"
+  type: "Convolution"
+  bottom: "Mconv2_stage4"
+  top: "Mconv3_stage4"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mconv3_stage4_re"
+  type: "ReLU"
+  bottom: "Mconv3_stage4"
+  top: "Mconv3_stage4"
+}
+layer {
+  name: "Mconv4_stage4"
+  type: "Convolution"
+  bottom: "Mconv3_stage4"
+  top: "Mconv4_stage4"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mconv4_stage4_re"
+  type: "ReLU"
+  bottom: "Mconv4_stage4"
+  top: "Mconv4_stage4"
+}
+layer {
+  name: "Mconv5_stage4"
+  type: "Convolution"
+  bottom: "Mconv4_stage4"
+  top: "Mconv5_stage4"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mconv5_stage4_re"
+  type: "ReLU"
+  bottom: "Mconv5_stage4"
+  top: "Mconv5_stage4"
+}
+layer {
+  name: "Mconv6_stage4"
+  type: "Convolution"
+  bottom: "Mconv5_stage4"
+  top: "Mconv6_stage4"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 0
+    kernel_size: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mconv6_stage4_re"
+  type: "ReLU"
+  bottom: "Mconv6_stage4"
+  top: "Mconv6_stage4"
+}
+layer {
+  name: "Mconv7_stage4"
+  type: "Convolution"
+  bottom: "Mconv6_stage4"
+  top: "Mconv7_stage4"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 71
+    pad: 0
+    kernel_size: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "features_in_stage_5"
+  type: "Concat"
+  bottom: "Mconv7_stage4"
+  bottom: "conv5_3_CPM"
+  top: "features_in_stage_5"
+  concat_param {
+    axis: 1
+  }
+}
+layer {
+  name: "Mconv1_stage5"
+  type: "Convolution"
+  bottom: "features_in_stage_5"
+  top: "Mconv1_stage5"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mconv1_stage5_re"
+  type: "ReLU"
+  bottom: "Mconv1_stage5"
+  top: "Mconv1_stage5"
+}
+layer {
+  name: "Mconv2_stage5"
+  type: "Convolution"
+  bottom: "Mconv1_stage5"
+  top: "Mconv2_stage5"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mconv2_stage5_re"
+  type: "ReLU"
+  bottom: "Mconv2_stage5"
+  top: "Mconv2_stage5"
+}
+layer {
+  name: "Mconv3_stage5"
+  type: "Convolution"
+  bottom: "Mconv2_stage5"
+  top: "Mconv3_stage5"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mconv3_stage5_re"
+  type: "ReLU"
+  bottom: "Mconv3_stage5"
+  top: "Mconv3_stage5"
+}
+layer {
+  name: "Mconv4_stage5"
+  type: "Convolution"
+  bottom: "Mconv3_stage5"
+  top: "Mconv4_stage5"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mconv4_stage5_re"
+  type: "ReLU"
+  bottom: "Mconv4_stage5"
+  top: "Mconv4_stage5"
+}
+layer {
+  name: "Mconv5_stage5"
+  type: "Convolution"
+  bottom: "Mconv4_stage5"
+  top: "Mconv5_stage5"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mconv5_stage5_re"
+  type: "ReLU"
+  bottom: "Mconv5_stage5"
+  top: "Mconv5_stage5"
+}
+layer {
+  name: "Mconv6_stage5"
+  type: "Convolution"
+  bottom: "Mconv5_stage5"
+  top: "Mconv6_stage5"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 0
+    kernel_size: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mconv6_stage5_re"
+  type: "ReLU"
+  bottom: "Mconv6_stage5"
+  top: "Mconv6_stage5"
+}
+layer {
+  name: "Mconv7_stage5"
+  type: "Convolution"
+  bottom: "Mconv6_stage5"
+  top: "Mconv7_stage5"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 71
+    pad: 0
+    kernel_size: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "features_in_stage_6"
+  type: "Concat"
+  bottom: "Mconv7_stage5"
+  bottom: "conv5_3_CPM"
+  top: "features_in_stage_6"
+  concat_param {
+    axis: 1
+  }
+}
+layer {
+  name: "Mconv1_stage6"
+  type: "Convolution"
+  bottom: "features_in_stage_6"
+  top: "Mconv1_stage6"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mconv1_stage6_re"
+  type: "ReLU"
+  bottom: "Mconv1_stage6"
+  top: "Mconv1_stage6"
+}
+layer {
+  name: "Mconv2_stage6"
+  type: "Convolution"
+  bottom: "Mconv1_stage6"
+  top: "Mconv2_stage6"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mconv2_stage6_re"
+  type: "ReLU"
+  bottom: "Mconv2_stage6"
+  top: "Mconv2_stage6"
+}
+layer {
+  name: "Mconv3_stage6"
+  type: "Convolution"
+  bottom: "Mconv2_stage6"
+  top: "Mconv3_stage6"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mconv3_stage6_re"
+  type: "ReLU"
+  bottom: "Mconv3_stage6"
+  top: "Mconv3_stage6"
+}
+layer {
+  name: "Mconv4_stage6"
+  type: "Convolution"
+  bottom: "Mconv3_stage6"
+  top: "Mconv4_stage6"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mconv4_stage6_re"
+  type: "ReLU"
+  bottom: "Mconv4_stage6"
+  top: "Mconv4_stage6"
+}
+layer {
+  name: "Mconv5_stage6"
+  type: "Convolution"
+  bottom: "Mconv4_stage6"
+  top: "Mconv5_stage6"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mconv5_stage6_re"
+  type: "ReLU"
+  bottom: "Mconv5_stage6"
+  top: "Mconv5_stage6"
+}
+layer {
+  name: "Mconv6_stage6"
+  type: "Convolution"
+  bottom: "Mconv5_stage6"
+  top: "Mconv6_stage6"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 0
+    kernel_size: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mconv6_stage6_re"
+  type: "ReLU"
+  bottom: "Mconv6_stage6"
+  top: "Mconv6_stage6"
+}
+layer {
+  name: "Mconv7_stage6"
+  type: "Convolution"
+  bottom: "Mconv6_stage6"
+#   top: "Mconv7_stage6"
+  top: "net_output"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 71
+    pad: 0
+    kernel_size: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
diff --git a/joints_detectors/openpose/models/getModels.bat b/joints_detectors/openpose/models/getModels.bat
new file mode 100644
index 0000000000000000000000000000000000000000..2eccb5a147732e8606d6cac9692288e4959be78c
--- /dev/null
+++ b/joints_detectors/openpose/models/getModels.bat
@@ -0,0 +1,43 @@
+:: Avoid printing all the comments in the Windows cmd
+@echo off
+
+echo ------------------------- BODY, FOOT, FACE, AND HAND MODELS -------------------------
+echo ----- Downloading body pose (COCO and MPI), face and hand models -----
+SET WGET_EXE=..\3rdparty\windows\wget\wget.exe
+SET OPENPOSE_URL=http://posefs1.perception.cs.cmu.edu/OpenPose/models/
+SET POSE_FOLDER=pose/
+SET FACE_FOLDER=face/
+SET HAND_FOLDER=hand/
+
+echo:
+echo ------------------------- POSE (BODY+FOOT) MODELS -------------------------
+echo Body (BODY_25)
+set BODY_25_FOLDER=%POSE_FOLDER%body_25/
+set BODY_25_MODEL=%BODY_25_FOLDER%pose_iter_584000.caffemodel
+%WGET_EXE% -c %OPENPOSE_URL%%BODY_25_MODEL% -P %BODY_25_FOLDER%
+
+echo Body (COCO)
+SET COCO_FOLDER=%POSE_FOLDER%coco/
+SET COCO_MODEL=%COCO_FOLDER%pose_iter_440000.caffemodel
+%WGET_EXE% -c %OPENPOSE_URL%%COCO_MODEL% -P %COCO_FOLDER%
+
+echo:
+echo Body (MPI)
+SET MPI_FOLDER=%POSE_FOLDER%mpi/
+SET MPI_MODEL=%MPI_FOLDER%pose_iter_160000.caffemodel
+%WGET_EXE% -c %OPENPOSE_URL%%MPI_MODEL% -P %MPI_FOLDER%
+echo ----------------------- POSE DOWNLOADED -----------------------
+
+echo:
+echo ------------------------- FACE MODELS -------------------------
+echo Face
+SET FACE_MODEL=%FACE_FOLDER%pose_iter_116000.caffemodel
+%WGET_EXE% -c %OPENPOSE_URL%%FACE_MODEL% -P %FACE_FOLDER%
+echo ----------------------- FACE DOWNLOADED -----------------------
+
+echo:
+echo ------------------------- HAND MODELS -------------------------
+echo Hand
+SET HAND_MODEL=%HAND_FOLDER%pose_iter_102000.caffemodel
+%WGET_EXE% -c %OPENPOSE_URL%%HAND_MODEL% -P %HAND_FOLDER%
+echo ----------------------- HAND DOWNLOADED -----------------------
diff --git a/joints_detectors/openpose/models/getModels.sh b/joints_detectors/openpose/models/getModels.sh
new file mode 100644
index 0000000000000000000000000000000000000000..63aef4d552905b312b95f2a4e367067f28eba3d7
--- /dev/null
+++ b/joints_detectors/openpose/models/getModels.sh
@@ -0,0 +1,36 @@
+# ------------------------- BODY, FOOT, FACE, AND HAND MODELS -------------------------
+# Downloading body pose (COCO and MPI), face and hand models
+OPENPOSE_URL="http://posefs1.perception.cs.cmu.edu/OpenPose/models/"
+POSE_FOLDER="pose/"
+FACE_FOLDER="face/"
+HAND_FOLDER="hand/"
+
+# ------------------------- POSE (BODY+FOOT) MODELS -------------------------
+# Body (BODY_25)
+BODY_25_FOLDER=${POSE_FOLDER}"body_25/"
+BODY_25_MODEL=${BODY_25_FOLDER}"pose_iter_584000.caffemodel"
+wget -c ${OPENPOSE_URL}${BODY_25_MODEL} -P ${BODY_25_FOLDER}
+
+# Body (COCO)
+COCO_FOLDER=${POSE_FOLDER}"coco/"
+COCO_MODEL=${COCO_FOLDER}"pose_iter_440000.caffemodel"
+wget -c ${OPENPOSE_URL}${COCO_MODEL} -P ${COCO_FOLDER}
+# Alternative: it will not check whether file was fully downloaded
+# if [ ! -f $COCO_MODEL ]; then
+#     wget ${OPENPOSE_URL}$COCO_MODEL -P $COCO_FOLDER
+# fi
+
+# Body (MPI)
+MPI_FOLDER=${POSE_FOLDER}"mpi/"
+MPI_MODEL=${MPI_FOLDER}"pose_iter_160000.caffemodel"
+wget -c ${OPENPOSE_URL}${MPI_MODEL} -P ${MPI_FOLDER}
+
+# "------------------------- FACE MODELS -------------------------"
+# Face
+FACE_MODEL=${FACE_FOLDER}"pose_iter_116000.caffemodel"
+wget -c ${OPENPOSE_URL}${FACE_MODEL} -P ${FACE_FOLDER}
+
+# "------------------------- HAND MODELS -------------------------"
+# Hand
+HAND_MODEL=$HAND_FOLDER"pose_iter_102000.caffemodel"
+wget -c ${OPENPOSE_URL}${HAND_MODEL} -P ${HAND_FOLDER}
diff --git a/joints_detectors/openpose/models/hand/pose_deploy.prototxt b/joints_detectors/openpose/models/hand/pose_deploy.prototxt
new file mode 100644
index 0000000000000000000000000000000000000000..3554c3a02e3dc545b3da4f32a0c954b452289706
--- /dev/null
+++ b/joints_detectors/openpose/models/hand/pose_deploy.prototxt
@@ -0,0 +1,1756 @@
+input: "image"
+input_dim: 1 # Original: 2
+input_dim: 3 # It crashes if not left to 3
+input_dim: 1 # Original: 368
+input_dim: 1 # Original: 368
+layer {
+  name: "conv1_1"
+  type: "Convolution"
+  bottom: "image"
+  top: "conv1_1"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 64
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+    }
+    dilation: 1
+  }
+}
+layer {
+  name: "relu1_1"
+  type: "ReLU"
+  bottom: "conv1_1"
+  top: "conv1_1"
+}
+layer {
+  name: "conv1_2"
+  type: "Convolution"
+  bottom: "conv1_1"
+  top: "conv1_2"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 64
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+    }
+    dilation: 1
+  }
+}
+layer {
+  name: "relu1_2"
+  type: "ReLU"
+  bottom: "conv1_2"
+  top: "conv1_2"
+}
+layer {
+  name: "pool1_stage1"
+  type: "Pooling"
+  bottom: "conv1_2"
+  top: "pool1_stage1"
+  pooling_param {
+    pool: MAX
+    kernel_size: 2
+    stride: 2
+  }
+}
+layer {
+  name: "conv2_1"
+  type: "Convolution"
+  bottom: "pool1_stage1"
+  top: "conv2_1"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+    }
+    dilation: 1
+  }
+}
+layer {
+  name: "relu2_1"
+  type: "ReLU"
+  bottom: "conv2_1"
+  top: "conv2_1"
+}
+layer {
+  name: "conv2_2"
+  type: "Convolution"
+  bottom: "conv2_1"
+  top: "conv2_2"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+    }
+    dilation: 1
+  }
+}
+layer {
+  name: "relu2_2"
+  type: "ReLU"
+  bottom: "conv2_2"
+  top: "conv2_2"
+}
+layer {
+  name: "pool2_stage1"
+  type: "Pooling"
+  bottom: "conv2_2"
+  top: "pool2_stage1"
+  pooling_param {
+    pool: MAX
+    kernel_size: 2
+    stride: 2
+  }
+}
+layer {
+  name: "conv3_1"
+  type: "Convolution"
+  bottom: "pool2_stage1"
+  top: "conv3_1"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 256
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+    }
+    dilation: 1
+  }
+}
+layer {
+  name: "relu3_1"
+  type: "ReLU"
+  bottom: "conv3_1"
+  top: "conv3_1"
+}
+layer {
+  name: "conv3_2"
+  type: "Convolution"
+  bottom: "conv3_1"
+  top: "conv3_2"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 256
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+    }
+    dilation: 1
+  }
+}
+layer {
+  name: "relu3_2"
+  type: "ReLU"
+  bottom: "conv3_2"
+  top: "conv3_2"
+}
+layer {
+  name: "conv3_3"
+  type: "Convolution"
+  bottom: "conv3_2"
+  top: "conv3_3"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 256
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+    }
+    dilation: 1
+  }
+}
+layer {
+  name: "relu3_3"
+  type: "ReLU"
+  bottom: "conv3_3"
+  top: "conv3_3"
+}
+layer {
+  name: "conv3_4"
+  type: "Convolution"
+  bottom: "conv3_3"
+  top: "conv3_4"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 256
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+    }
+    dilation: 1
+  }
+}
+layer {
+  name: "relu3_4"
+  type: "ReLU"
+  bottom: "conv3_4"
+  top: "conv3_4"
+}
+layer {
+  name: "pool3_stage1"
+  type: "Pooling"
+  bottom: "conv3_4"
+  top: "pool3_stage1"
+  pooling_param {
+    pool: MAX
+    kernel_size: 2
+    stride: 2
+  }
+}
+layer {
+  name: "conv4_1"
+  type: "Convolution"
+  bottom: "pool3_stage1"
+  top: "conv4_1"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 512
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+    }
+    dilation: 1
+  }
+}
+layer {
+  name: "relu4_1"
+  type: "ReLU"
+  bottom: "conv4_1"
+  top: "conv4_1"
+}
+layer {
+  name: "conv4_2"
+  type: "Convolution"
+  bottom: "conv4_1"
+  top: "conv4_2"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 512
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+    }
+    dilation: 1
+  }
+}
+layer {
+  name: "relu4_2"
+  type: "ReLU"
+  bottom: "conv4_2"
+  top: "conv4_2"
+}
+layer {
+  name: "conv4_3"
+  type: "Convolution"
+  bottom: "conv4_2"
+  top: "conv4_3"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 512
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+    }
+    dilation: 1
+  }
+}
+layer {
+  name: "relu4_3"
+  type: "ReLU"
+  bottom: "conv4_3"
+  top: "conv4_3"
+}
+layer {
+  name: "conv4_4"
+  type: "Convolution"
+  bottom: "conv4_3"
+  top: "conv4_4"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 512
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+    }
+    dilation: 1
+  }
+}
+layer {
+  name: "relu4_4"
+  type: "ReLU"
+  bottom: "conv4_4"
+  top: "conv4_4"
+}
+layer {
+  name: "conv5_1"
+  type: "Convolution"
+  bottom: "conv4_4"
+  top: "conv5_1"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 512
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+    }
+    dilation: 1
+  }
+}
+layer {
+  name: "relu5_1"
+  type: "ReLU"
+  bottom: "conv5_1"
+  top: "conv5_1"
+}
+layer {
+  name: "conv5_2"
+  type: "Convolution"
+  bottom: "conv5_1"
+  top: "conv5_2"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 512
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+    }
+    dilation: 1
+  }
+}
+layer {
+  name: "relu5_2"
+  type: "ReLU"
+  bottom: "conv5_2"
+  top: "conv5_2"
+}
+layer {
+  name: "conv5_3_CPM"
+  type: "Convolution"
+  bottom: "conv5_2"
+  top: "conv5_3_CPM"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+    dilation: 1
+  }
+}
+layer {
+  name: "relu5_4_stage1_3"
+  type: "ReLU"
+  bottom: "conv5_3_CPM"
+  top: "conv5_3_CPM"
+}
+layer {
+  name: "conv6_1_CPM"
+  type: "Convolution"
+  bottom: "conv5_3_CPM"
+  top: "conv6_1_CPM"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 512
+    pad: 0
+    kernel_size: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+    dilation: 1
+  }
+}
+layer {
+  name: "relu6_4_stage1_1"
+  type: "ReLU"
+  bottom: "conv6_1_CPM"
+  top: "conv6_1_CPM"
+}
+layer {
+  name: "conv6_2_CPM"
+  type: "Convolution"
+  bottom: "conv6_1_CPM"
+  top: "conv6_2_CPM"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 22
+    pad: 0
+    kernel_size: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+    dilation: 1
+  }
+}
+layer {
+  name: "concat_stage2"
+  type: "Concat"
+  bottom: "conv6_2_CPM"
+  bottom: "conv5_3_CPM"
+  top: "concat_stage2"
+  concat_param {
+    axis: 1
+  }
+}
+layer {
+  name: "Mconv1_stage2"
+  type: "Convolution"
+  bottom: "concat_stage2"
+  top: "Mconv1_stage2"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+    dilation: 1
+  }
+}
+layer {
+  name: "Mrelu1_2_stage2_1"
+  type: "ReLU"
+  bottom: "Mconv1_stage2"
+  top: "Mconv1_stage2"
+}
+layer {
+  name: "Mconv2_stage2"
+  type: "Convolution"
+  bottom: "Mconv1_stage2"
+  top: "Mconv2_stage2"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+    dilation: 1
+  }
+}
+layer {
+  name: "Mrelu1_3_stage2_2"
+  type: "ReLU"
+  bottom: "Mconv2_stage2"
+  top: "Mconv2_stage2"
+}
+layer {
+  name: "Mconv3_stage2"
+  type: "Convolution"
+  bottom: "Mconv2_stage2"
+  top: "Mconv3_stage2"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+    dilation: 1
+  }
+}
+layer {
+  name: "Mrelu1_4_stage2_3"
+  type: "ReLU"
+  bottom: "Mconv3_stage2"
+  top: "Mconv3_stage2"
+}
+layer {
+  name: "Mconv4_stage2"
+  type: "Convolution"
+  bottom: "Mconv3_stage2"
+  top: "Mconv4_stage2"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+    dilation: 1
+  }
+}
+layer {
+  name: "Mrelu1_5_stage2_4"
+  type: "ReLU"
+  bottom: "Mconv4_stage2"
+  top: "Mconv4_stage2"
+}
+layer {
+  name: "Mconv5_stage2"
+  type: "Convolution"
+  bottom: "Mconv4_stage2"
+  top: "Mconv5_stage2"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+    dilation: 1
+  }
+}
+layer {
+  name: "Mrelu1_6_stage2_5"
+  type: "ReLU"
+  bottom: "Mconv5_stage2"
+  top: "Mconv5_stage2"
+}
+layer {
+  name: "Mconv6_stage2"
+  type: "Convolution"
+  bottom: "Mconv5_stage2"
+  top: "Mconv6_stage2"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 0
+    kernel_size: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+    dilation: 1
+  }
+}
+layer {
+  name: "Mrelu1_7_stage2_6"
+  type: "ReLU"
+  bottom: "Mconv6_stage2"
+  top: "Mconv6_stage2"
+}
+layer {
+  name: "Mconv7_stage2"
+  type: "Convolution"
+  bottom: "Mconv6_stage2"
+  top: "Mconv7_stage2"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 22
+    pad: 0
+    kernel_size: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+    dilation: 1
+  }
+}
+layer {
+  name: "concat_stage3"
+  type: "Concat"
+  bottom: "Mconv7_stage2"
+  bottom: "conv5_3_CPM"
+  top: "concat_stage3"
+  concat_param {
+    axis: 1
+  }
+}
+layer {
+  name: "Mconv1_stage3"
+  type: "Convolution"
+  bottom: "concat_stage3"
+  top: "Mconv1_stage3"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+    dilation: 1
+  }
+}
+layer {
+  name: "Mrelu1_2_stage3_1"
+  type: "ReLU"
+  bottom: "Mconv1_stage3"
+  top: "Mconv1_stage3"
+}
+layer {
+  name: "Mconv2_stage3"
+  type: "Convolution"
+  bottom: "Mconv1_stage3"
+  top: "Mconv2_stage3"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+    dilation: 1
+  }
+}
+layer {
+  name: "Mrelu1_3_stage3_2"
+  type: "ReLU"
+  bottom: "Mconv2_stage3"
+  top: "Mconv2_stage3"
+}
+layer {
+  name: "Mconv3_stage3"
+  type: "Convolution"
+  bottom: "Mconv2_stage3"
+  top: "Mconv3_stage3"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+    dilation: 1
+  }
+}
+layer {
+  name: "Mrelu1_4_stage3_3"
+  type: "ReLU"
+  bottom: "Mconv3_stage3"
+  top: "Mconv3_stage3"
+}
+layer {
+  name: "Mconv4_stage3"
+  type: "Convolution"
+  bottom: "Mconv3_stage3"
+  top: "Mconv4_stage3"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+    dilation: 1
+  }
+}
+layer {
+  name: "Mrelu1_5_stage3_4"
+  type: "ReLU"
+  bottom: "Mconv4_stage3"
+  top: "Mconv4_stage3"
+}
+layer {
+  name: "Mconv5_stage3"
+  type: "Convolution"
+  bottom: "Mconv4_stage3"
+  top: "Mconv5_stage3"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+    dilation: 1
+  }
+}
+layer {
+  name: "Mrelu1_6_stage3_5"
+  type: "ReLU"
+  bottom: "Mconv5_stage3"
+  top: "Mconv5_stage3"
+}
+layer {
+  name: "Mconv6_stage3"
+  type: "Convolution"
+  bottom: "Mconv5_stage3"
+  top: "Mconv6_stage3"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 0
+    kernel_size: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+    dilation: 1
+  }
+}
+layer {
+  name: "Mrelu1_7_stage3_6"
+  type: "ReLU"
+  bottom: "Mconv6_stage3"
+  top: "Mconv6_stage3"
+}
+layer {
+  name: "Mconv7_stage3"
+  type: "Convolution"
+  bottom: "Mconv6_stage3"
+  top: "Mconv7_stage3"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 22
+    pad: 0
+    kernel_size: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+    dilation: 1
+  }
+}
+layer {
+  name: "concat_stage4"
+  type: "Concat"
+  bottom: "Mconv7_stage3"
+  bottom: "conv5_3_CPM"
+  top: "concat_stage4"
+  concat_param {
+    axis: 1
+  }
+}
+layer {
+  name: "Mconv1_stage4"
+  type: "Convolution"
+  bottom: "concat_stage4"
+  top: "Mconv1_stage4"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+    dilation: 1
+  }
+}
+layer {
+  name: "Mrelu1_2_stage4_1"
+  type: "ReLU"
+  bottom: "Mconv1_stage4"
+  top: "Mconv1_stage4"
+}
+layer {
+  name: "Mconv2_stage4"
+  type: "Convolution"
+  bottom: "Mconv1_stage4"
+  top: "Mconv2_stage4"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+    dilation: 1
+  }
+}
+layer {
+  name: "Mrelu1_3_stage4_2"
+  type: "ReLU"
+  bottom: "Mconv2_stage4"
+  top: "Mconv2_stage4"
+}
+layer {
+  name: "Mconv3_stage4"
+  type: "Convolution"
+  bottom: "Mconv2_stage4"
+  top: "Mconv3_stage4"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+    dilation: 1
+  }
+}
+layer {
+  name: "Mrelu1_4_stage4_3"
+  type: "ReLU"
+  bottom: "Mconv3_stage4"
+  top: "Mconv3_stage4"
+}
+layer {
+  name: "Mconv4_stage4"
+  type: "Convolution"
+  bottom: "Mconv3_stage4"
+  top: "Mconv4_stage4"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+    dilation: 1
+  }
+}
+layer {
+  name: "Mrelu1_5_stage4_4"
+  type: "ReLU"
+  bottom: "Mconv4_stage4"
+  top: "Mconv4_stage4"
+}
+layer {
+  name: "Mconv5_stage4"
+  type: "Convolution"
+  bottom: "Mconv4_stage4"
+  top: "Mconv5_stage4"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+    dilation: 1
+  }
+}
+layer {
+  name: "Mrelu1_6_stage4_5"
+  type: "ReLU"
+  bottom: "Mconv5_stage4"
+  top: "Mconv5_stage4"
+}
+layer {
+  name: "Mconv6_stage4"
+  type: "Convolution"
+  bottom: "Mconv5_stage4"
+  top: "Mconv6_stage4"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 0
+    kernel_size: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+    dilation: 1
+  }
+}
+layer {
+  name: "Mrelu1_7_stage4_6"
+  type: "ReLU"
+  bottom: "Mconv6_stage4"
+  top: "Mconv6_stage4"
+}
+layer {
+  name: "Mconv7_stage4"
+  type: "Convolution"
+  bottom: "Mconv6_stage4"
+  top: "Mconv7_stage4"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 22
+    pad: 0
+    kernel_size: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+    dilation: 1
+  }
+}
+layer {
+  name: "concat_stage5"
+  type: "Concat"
+  bottom: "Mconv7_stage4"
+  bottom: "conv5_3_CPM"
+  top: "concat_stage5"
+  concat_param {
+    axis: 1
+  }
+}
+layer {
+  name: "Mconv1_stage5"
+  type: "Convolution"
+  bottom: "concat_stage5"
+  top: "Mconv1_stage5"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+    dilation: 1
+  }
+}
+layer {
+  name: "Mrelu1_2_stage5_1"
+  type: "ReLU"
+  bottom: "Mconv1_stage5"
+  top: "Mconv1_stage5"
+}
+layer {
+  name: "Mconv2_stage5"
+  type: "Convolution"
+  bottom: "Mconv1_stage5"
+  top: "Mconv2_stage5"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+    dilation: 1
+  }
+}
+layer {
+  name: "Mrelu1_3_stage5_2"
+  type: "ReLU"
+  bottom: "Mconv2_stage5"
+  top: "Mconv2_stage5"
+}
+layer {
+  name: "Mconv3_stage5"
+  type: "Convolution"
+  bottom: "Mconv2_stage5"
+  top: "Mconv3_stage5"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+    dilation: 1
+  }
+}
+layer {
+  name: "Mrelu1_4_stage5_3"
+  type: "ReLU"
+  bottom: "Mconv3_stage5"
+  top: "Mconv3_stage5"
+}
+layer {
+  name: "Mconv4_stage5"
+  type: "Convolution"
+  bottom: "Mconv3_stage5"
+  top: "Mconv4_stage5"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+    dilation: 1
+  }
+}
+layer {
+  name: "Mrelu1_5_stage5_4"
+  type: "ReLU"
+  bottom: "Mconv4_stage5"
+  top: "Mconv4_stage5"
+}
+layer {
+  name: "Mconv5_stage5"
+  type: "Convolution"
+  bottom: "Mconv4_stage5"
+  top: "Mconv5_stage5"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+    dilation: 1
+  }
+}
+layer {
+  name: "Mrelu1_6_stage5_5"
+  type: "ReLU"
+  bottom: "Mconv5_stage5"
+  top: "Mconv5_stage5"
+}
+layer {
+  name: "Mconv6_stage5"
+  type: "Convolution"
+  bottom: "Mconv5_stage5"
+  top: "Mconv6_stage5"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 0
+    kernel_size: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+    dilation: 1
+  }
+}
+layer {
+  name: "Mrelu1_7_stage5_6"
+  type: "ReLU"
+  bottom: "Mconv6_stage5"
+  top: "Mconv6_stage5"
+}
+layer {
+  name: "Mconv7_stage5"
+  type: "Convolution"
+  bottom: "Mconv6_stage5"
+  top: "Mconv7_stage5"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 22
+    pad: 0
+    kernel_size: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+    dilation: 1
+  }
+}
+layer {
+  name: "concat_stage6"
+  type: "Concat"
+  bottom: "Mconv7_stage5"
+  bottom: "conv5_3_CPM"
+  top: "concat_stage6"
+  concat_param {
+    axis: 1
+  }
+}
+layer {
+  name: "Mconv1_stage6"
+  type: "Convolution"
+  bottom: "concat_stage6"
+  top: "Mconv1_stage6"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+    dilation: 1
+  }
+}
+layer {
+  name: "Mrelu1_2_stage6_1"
+  type: "ReLU"
+  bottom: "Mconv1_stage6"
+  top: "Mconv1_stage6"
+}
+layer {
+  name: "Mconv2_stage6"
+  type: "Convolution"
+  bottom: "Mconv1_stage6"
+  top: "Mconv2_stage6"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+    dilation: 1
+  }
+}
+layer {
+  name: "Mrelu1_3_stage6_2"
+  type: "ReLU"
+  bottom: "Mconv2_stage6"
+  top: "Mconv2_stage6"
+}
+layer {
+  name: "Mconv3_stage6"
+  type: "Convolution"
+  bottom: "Mconv2_stage6"
+  top: "Mconv3_stage6"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+    dilation: 1
+  }
+}
+layer {
+  name: "Mrelu1_4_stage6_3"
+  type: "ReLU"
+  bottom: "Mconv3_stage6"
+  top: "Mconv3_stage6"
+}
+layer {
+  name: "Mconv4_stage6"
+  type: "Convolution"
+  bottom: "Mconv3_stage6"
+  top: "Mconv4_stage6"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+    dilation: 1
+  }
+}
+layer {
+  name: "Mrelu1_5_stage6_4"
+  type: "ReLU"
+  bottom: "Mconv4_stage6"
+  top: "Mconv4_stage6"
+}
+layer {
+  name: "Mconv5_stage6"
+  type: "Convolution"
+  bottom: "Mconv4_stage6"
+  top: "Mconv5_stage6"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+    dilation: 1
+  }
+}
+layer {
+  name: "Mrelu1_6_stage6_5"
+  type: "ReLU"
+  bottom: "Mconv5_stage6"
+  top: "Mconv5_stage6"
+}
+layer {
+  name: "Mconv6_stage6"
+  type: "Convolution"
+  bottom: "Mconv5_stage6"
+  top: "Mconv6_stage6"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 0
+    kernel_size: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+    dilation: 1
+  }
+}
+layer {
+  name: "Mrelu1_7_stage6_6"
+  type: "ReLU"
+  bottom: "Mconv6_stage6"
+  top: "Mconv6_stage6"
+}
+layer {
+  name: "Mconv7_stage6"
+  type: "Convolution"
+  bottom: "Mconv6_stage6"
+#   top: "Mconv7_stage6"
+  top: "net_output"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 22
+    pad: 0
+    kernel_size: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+    dilation: 1
+  }
+}
+
diff --git a/joints_detectors/openpose/models/pose/body_25/pose_deploy.prototxt b/joints_detectors/openpose/models/pose/body_25/pose_deploy.prototxt
new file mode 100644
index 0000000000000000000000000000000000000000..bd18b9d3a225cf64c172a51cc7b03d9c481ca604
--- /dev/null
+++ b/joints_detectors/openpose/models/pose/body_25/pose_deploy.prototxt
@@ -0,0 +1,2332 @@
+name: "OpenPose - BODY_25"
+input: "image"
+input_dim: 1 # This value will be defined at runtime
+input_dim: 3
+input_dim: 16 # This value will be defined at runtime
+input_dim: 16 # This value will be defined at runtime
+layer {
+  name: "conv1_1"
+  type: "Convolution"
+  bottom: "image"
+  top: "conv1_1"
+  convolution_param {
+    num_output: 64
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "relu1_1"
+  type: "ReLU"
+  bottom: "conv1_1"
+  top: "conv1_1"
+}
+layer {
+  name: "conv1_2"
+  type: "Convolution"
+  bottom: "conv1_1"
+  top: "conv1_2"
+  convolution_param {
+    num_output: 64
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "relu1_2"
+  type: "ReLU"
+  bottom: "conv1_2"
+  top: "conv1_2"
+}
+layer {
+  name: "pool1_stage1"
+  type: "Pooling"
+  bottom: "conv1_2"
+  top: "pool1_stage1"
+  pooling_param {
+    pool: MAX
+    kernel_size: 2
+    stride: 2
+  }
+}
+layer {
+  name: "conv2_1"
+  type: "Convolution"
+  bottom: "pool1_stage1"
+  top: "conv2_1"
+  convolution_param {
+    num_output: 128
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "relu2_1"
+  type: "ReLU"
+  bottom: "conv2_1"
+  top: "conv2_1"
+}
+layer {
+  name: "conv2_2"
+  type: "Convolution"
+  bottom: "conv2_1"
+  top: "conv2_2"
+  convolution_param {
+    num_output: 128
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "relu2_2"
+  type: "ReLU"
+  bottom: "conv2_2"
+  top: "conv2_2"
+}
+layer {
+  name: "pool2_stage1"
+  type: "Pooling"
+  bottom: "conv2_2"
+  top: "pool2_stage1"
+  pooling_param {
+    pool: MAX
+    kernel_size: 2
+    stride: 2
+  }
+}
+layer {
+  name: "conv3_1"
+  type: "Convolution"
+  bottom: "pool2_stage1"
+  top: "conv3_1"
+  convolution_param {
+    num_output: 256
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "relu3_1"
+  type: "ReLU"
+  bottom: "conv3_1"
+  top: "conv3_1"
+}
+layer {
+  name: "conv3_2"
+  type: "Convolution"
+  bottom: "conv3_1"
+  top: "conv3_2"
+  convolution_param {
+    num_output: 256
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "relu3_2"
+  type: "ReLU"
+  bottom: "conv3_2"
+  top: "conv3_2"
+}
+layer {
+  name: "conv3_3"
+  type: "Convolution"
+  bottom: "conv3_2"
+  top: "conv3_3"
+  convolution_param {
+    num_output: 256
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "relu3_3"
+  type: "ReLU"
+  bottom: "conv3_3"
+  top: "conv3_3"
+}
+layer {
+  name: "conv3_4"
+  type: "Convolution"
+  bottom: "conv3_3"
+  top: "conv3_4"
+  convolution_param {
+    num_output: 256
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "relu3_4"
+  type: "ReLU"
+  bottom: "conv3_4"
+  top: "conv3_4"
+}
+layer {
+  name: "pool3_stage1"
+  type: "Pooling"
+  bottom: "conv3_4"
+  top: "pool3_stage1"
+  pooling_param {
+    pool: MAX
+    kernel_size: 2
+    stride: 2
+  }
+}
+layer {
+  name: "conv4_1"
+  type: "Convolution"
+  bottom: "pool3_stage1"
+  top: "conv4_1"
+  convolution_param {
+    num_output: 512
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "relu4_1"
+  type: "ReLU"
+  bottom: "conv4_1"
+  top: "conv4_1"
+}
+layer {
+  name: "conv4_2"
+  type: "Convolution"
+  bottom: "conv4_1"
+  top: "conv4_2"
+  convolution_param {
+    num_output: 512
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "prelu4_2"
+  type: "PReLU"
+  bottom: "conv4_2"
+  top: "conv4_2"
+}
+layer {
+  name: "conv4_3_CPM"
+  type: "Convolution"
+  bottom: "conv4_2"
+  top: "conv4_3_CPM"
+  convolution_param {
+    num_output: 256
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "prelu4_3_CPM"
+  type: "PReLU"
+  bottom: "conv4_3_CPM"
+  top: "conv4_3_CPM"
+}
+layer {
+  name: "conv4_4_CPM"
+  type: "Convolution"
+  bottom: "conv4_3_CPM"
+  top: "conv4_4_CPM"
+  convolution_param {
+    num_output: 128
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "prelu4_4_CPM"
+  type: "PReLU"
+  bottom: "conv4_4_CPM"
+  top: "conv4_4_CPM"
+}
+layer {
+  name: "Mconv1_stage0_L2_0"
+  type: "Convolution"
+  bottom: "conv4_4_CPM"
+  top: "Mconv1_stage0_L2_0"
+  convolution_param {
+    num_output: 96
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "Mprelu1_stage0_L2_0"
+  type: "PReLU"
+  bottom: "Mconv1_stage0_L2_0"
+  top: "Mconv1_stage0_L2_0"
+}
+layer {
+  name: "Mconv1_stage0_L2_1"
+  type: "Convolution"
+  bottom: "Mconv1_stage0_L2_0"
+  top: "Mconv1_stage0_L2_1"
+  convolution_param {
+    num_output: 96
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "Mprelu1_stage0_L2_1"
+  type: "PReLU"
+  bottom: "Mconv1_stage0_L2_1"
+  top: "Mconv1_stage0_L2_1"
+}
+layer {
+  name: "Mconv1_stage0_L2_2"
+  type: "Convolution"
+  bottom: "Mconv1_stage0_L2_1"
+  top: "Mconv1_stage0_L2_2"
+  convolution_param {
+    num_output: 96
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "Mprelu1_stage0_L2_2"
+  type: "PReLU"
+  bottom: "Mconv1_stage0_L2_2"
+  top: "Mconv1_stage0_L2_2"
+}
+layer {
+  name: "Mconv1_stage0_L2_concat"
+  type: "Concat"
+  bottom: "Mconv1_stage0_L2_0"
+  bottom: "Mconv1_stage0_L2_1"
+  bottom: "Mconv1_stage0_L2_2"
+  top: "Mconv1_stage0_L2_concat"
+  concat_param {
+    axis: 1
+  }
+}
+layer {
+  name: "Mconv2_stage0_L2_0"
+  type: "Convolution"
+  bottom: "Mconv1_stage0_L2_concat"
+  top: "Mconv2_stage0_L2_0"
+  convolution_param {
+    num_output: 96
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "Mprelu2_stage0_L2_0"
+  type: "PReLU"
+  bottom: "Mconv2_stage0_L2_0"
+  top: "Mconv2_stage0_L2_0"
+}
+layer {
+  name: "Mconv2_stage0_L2_1"
+  type: "Convolution"
+  bottom: "Mconv2_stage0_L2_0"
+  top: "Mconv2_stage0_L2_1"
+  convolution_param {
+    num_output: 96
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "Mprelu2_stage0_L2_1"
+  type: "PReLU"
+  bottom: "Mconv2_stage0_L2_1"
+  top: "Mconv2_stage0_L2_1"
+}
+layer {
+  name: "Mconv2_stage0_L2_2"
+  type: "Convolution"
+  bottom: "Mconv2_stage0_L2_1"
+  top: "Mconv2_stage0_L2_2"
+  convolution_param {
+    num_output: 96
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "Mprelu2_stage0_L2_2"
+  type: "PReLU"
+  bottom: "Mconv2_stage0_L2_2"
+  top: "Mconv2_stage0_L2_2"
+}
+layer {
+  name: "Mconv2_stage0_L2_concat"
+  type: "Concat"
+  bottom: "Mconv2_stage0_L2_0"
+  bottom: "Mconv2_stage0_L2_1"
+  bottom: "Mconv2_stage0_L2_2"
+  top: "Mconv2_stage0_L2_concat"
+  concat_param {
+    axis: 1
+  }
+}
+layer {
+  name: "Mconv3_stage0_L2_0"
+  type: "Convolution"
+  bottom: "Mconv2_stage0_L2_concat"
+  top: "Mconv3_stage0_L2_0"
+  convolution_param {
+    num_output: 96
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "Mprelu3_stage0_L2_0"
+  type: "PReLU"
+  bottom: "Mconv3_stage0_L2_0"
+  top: "Mconv3_stage0_L2_0"
+}
+layer {
+  name: "Mconv3_stage0_L2_1"
+  type: "Convolution"
+  bottom: "Mconv3_stage0_L2_0"
+  top: "Mconv3_stage0_L2_1"
+  convolution_param {
+    num_output: 96
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "Mprelu3_stage0_L2_1"
+  type: "PReLU"
+  bottom: "Mconv3_stage0_L2_1"
+  top: "Mconv3_stage0_L2_1"
+}
+layer {
+  name: "Mconv3_stage0_L2_2"
+  type: "Convolution"
+  bottom: "Mconv3_stage0_L2_1"
+  top: "Mconv3_stage0_L2_2"
+  convolution_param {
+    num_output: 96
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "Mprelu3_stage0_L2_2"
+  type: "PReLU"
+  bottom: "Mconv3_stage0_L2_2"
+  top: "Mconv3_stage0_L2_2"
+}
+layer {
+  name: "Mconv3_stage0_L2_concat"
+  type: "Concat"
+  bottom: "Mconv3_stage0_L2_0"
+  bottom: "Mconv3_stage0_L2_1"
+  bottom: "Mconv3_stage0_L2_2"
+  top: "Mconv3_stage0_L2_concat"
+  concat_param {
+    axis: 1
+  }
+}
+layer {
+  name: "Mconv4_stage0_L2_0"
+  type: "Convolution"
+  bottom: "Mconv3_stage0_L2_concat"
+  top: "Mconv4_stage0_L2_0"
+  convolution_param {
+    num_output: 96
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "Mprelu4_stage0_L2_0"
+  type: "PReLU"
+  bottom: "Mconv4_stage0_L2_0"
+  top: "Mconv4_stage0_L2_0"
+}
+layer {
+  name: "Mconv4_stage0_L2_1"
+  type: "Convolution"
+  bottom: "Mconv4_stage0_L2_0"
+  top: "Mconv4_stage0_L2_1"
+  convolution_param {
+    num_output: 96
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "Mprelu4_stage0_L2_1"
+  type: "PReLU"
+  bottom: "Mconv4_stage0_L2_1"
+  top: "Mconv4_stage0_L2_1"
+}
+layer {
+  name: "Mconv4_stage0_L2_2"
+  type: "Convolution"
+  bottom: "Mconv4_stage0_L2_1"
+  top: "Mconv4_stage0_L2_2"
+  convolution_param {
+    num_output: 96
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "Mprelu4_stage0_L2_2"
+  type: "PReLU"
+  bottom: "Mconv4_stage0_L2_2"
+  top: "Mconv4_stage0_L2_2"
+}
+layer {
+  name: "Mconv4_stage0_L2_concat"
+  type: "Concat"
+  bottom: "Mconv4_stage0_L2_0"
+  bottom: "Mconv4_stage0_L2_1"
+  bottom: "Mconv4_stage0_L2_2"
+  top: "Mconv4_stage0_L2_concat"
+  concat_param {
+    axis: 1
+  }
+}
+layer {
+  name: "Mconv5_stage0_L2_0"
+  type: "Convolution"
+  bottom: "Mconv4_stage0_L2_concat"
+  top: "Mconv5_stage0_L2_0"
+  convolution_param {
+    num_output: 96
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "Mprelu5_stage0_L2_0"
+  type: "PReLU"
+  bottom: "Mconv5_stage0_L2_0"
+  top: "Mconv5_stage0_L2_0"
+}
+layer {
+  name: "Mconv5_stage0_L2_1"
+  type: "Convolution"
+  bottom: "Mconv5_stage0_L2_0"
+  top: "Mconv5_stage0_L2_1"
+  convolution_param {
+    num_output: 96
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "Mprelu5_stage0_L2_1"
+  type: "PReLU"
+  bottom: "Mconv5_stage0_L2_1"
+  top: "Mconv5_stage0_L2_1"
+}
+layer {
+  name: "Mconv5_stage0_L2_2"
+  type: "Convolution"
+  bottom: "Mconv5_stage0_L2_1"
+  top: "Mconv5_stage0_L2_2"
+  convolution_param {
+    num_output: 96
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "Mprelu5_stage0_L2_2"
+  type: "PReLU"
+  bottom: "Mconv5_stage0_L2_2"
+  top: "Mconv5_stage0_L2_2"
+}
+layer {
+  name: "Mconv5_stage0_L2_concat"
+  type: "Concat"
+  bottom: "Mconv5_stage0_L2_0"
+  bottom: "Mconv5_stage0_L2_1"
+  bottom: "Mconv5_stage0_L2_2"
+  top: "Mconv5_stage0_L2_concat"
+  concat_param {
+    axis: 1
+  }
+}
+layer {
+  name: "Mconv6_stage0_L2"
+  type: "Convolution"
+  bottom: "Mconv5_stage0_L2_concat"
+  top: "Mconv6_stage0_L2"
+  convolution_param {
+    num_output: 256
+    pad: 0
+    kernel_size: 1
+  }
+}
+layer {
+  name: "Mprelu6_stage0_L2"
+  type: "PReLU"
+  bottom: "Mconv6_stage0_L2"
+  top: "Mconv6_stage0_L2"
+}
+layer {
+  name: "Mconv7_stage0_L2"
+  type: "Convolution"
+  bottom: "Mconv6_stage0_L2"
+  top: "Mconv7_stage0_L2"
+  convolution_param {
+    num_output: 52
+    pad: 0
+    kernel_size: 1
+  }
+}
+layer {
+  name: "concat_stage1_L2"
+  type: "Concat"
+  bottom: "conv4_4_CPM"
+  bottom: "Mconv7_stage0_L2"
+  top: "concat_stage1_L2"
+  concat_param {
+    axis: 1
+  }
+}
+layer {
+  name: "Mconv1_stage1_L2_0"
+  type: "Convolution"
+  bottom: "concat_stage1_L2"
+  top: "Mconv1_stage1_L2_0"
+  convolution_param {
+    num_output: 128
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "Mprelu1_stage1_L2_0"
+  type: "PReLU"
+  bottom: "Mconv1_stage1_L2_0"
+  top: "Mconv1_stage1_L2_0"
+}
+layer {
+  name: "Mconv1_stage1_L2_1"
+  type: "Convolution"
+  bottom: "Mconv1_stage1_L2_0"
+  top: "Mconv1_stage1_L2_1"
+  convolution_param {
+    num_output: 128
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "Mprelu1_stage1_L2_1"
+  type: "PReLU"
+  bottom: "Mconv1_stage1_L2_1"
+  top: "Mconv1_stage1_L2_1"
+}
+layer {
+  name: "Mconv1_stage1_L2_2"
+  type: "Convolution"
+  bottom: "Mconv1_stage1_L2_1"
+  top: "Mconv1_stage1_L2_2"
+  convolution_param {
+    num_output: 128
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "Mprelu1_stage1_L2_2"
+  type: "PReLU"
+  bottom: "Mconv1_stage1_L2_2"
+  top: "Mconv1_stage1_L2_2"
+}
+layer {
+  name: "Mconv1_stage1_L2_concat"
+  type: "Concat"
+  bottom: "Mconv1_stage1_L2_0"
+  bottom: "Mconv1_stage1_L2_1"
+  bottom: "Mconv1_stage1_L2_2"
+  top: "Mconv1_stage1_L2_concat"
+  concat_param {
+    axis: 1
+  }
+}
+layer {
+  name: "Mconv2_stage1_L2_0"
+  type: "Convolution"
+  bottom: "Mconv1_stage1_L2_concat"
+  top: "Mconv2_stage1_L2_0"
+  convolution_param {
+    num_output: 128
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "Mprelu2_stage1_L2_0"
+  type: "PReLU"
+  bottom: "Mconv2_stage1_L2_0"
+  top: "Mconv2_stage1_L2_0"
+}
+layer {
+  name: "Mconv2_stage1_L2_1"
+  type: "Convolution"
+  bottom: "Mconv2_stage1_L2_0"
+  top: "Mconv2_stage1_L2_1"
+  convolution_param {
+    num_output: 128
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "Mprelu2_stage1_L2_1"
+  type: "PReLU"
+  bottom: "Mconv2_stage1_L2_1"
+  top: "Mconv2_stage1_L2_1"
+}
+layer {
+  name: "Mconv2_stage1_L2_2"
+  type: "Convolution"
+  bottom: "Mconv2_stage1_L2_1"
+  top: "Mconv2_stage1_L2_2"
+  convolution_param {
+    num_output: 128
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "Mprelu2_stage1_L2_2"
+  type: "PReLU"
+  bottom: "Mconv2_stage1_L2_2"
+  top: "Mconv2_stage1_L2_2"
+}
+layer {
+  name: "Mconv2_stage1_L2_concat"
+  type: "Concat"
+  bottom: "Mconv2_stage1_L2_0"
+  bottom: "Mconv2_stage1_L2_1"
+  bottom: "Mconv2_stage1_L2_2"
+  top: "Mconv2_stage1_L2_concat"
+  concat_param {
+    axis: 1
+  }
+}
+layer {
+  name: "Mconv3_stage1_L2_0"
+  type: "Convolution"
+  bottom: "Mconv2_stage1_L2_concat"
+  top: "Mconv3_stage1_L2_0"
+  convolution_param {
+    num_output: 128
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "Mprelu3_stage1_L2_0"
+  type: "PReLU"
+  bottom: "Mconv3_stage1_L2_0"
+  top: "Mconv3_stage1_L2_0"
+}
+layer {
+  name: "Mconv3_stage1_L2_1"
+  type: "Convolution"
+  bottom: "Mconv3_stage1_L2_0"
+  top: "Mconv3_stage1_L2_1"
+  convolution_param {
+    num_output: 128
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "Mprelu3_stage1_L2_1"
+  type: "PReLU"
+  bottom: "Mconv3_stage1_L2_1"
+  top: "Mconv3_stage1_L2_1"
+}
+layer {
+  name: "Mconv3_stage1_L2_2"
+  type: "Convolution"
+  bottom: "Mconv3_stage1_L2_1"
+  top: "Mconv3_stage1_L2_2"
+  convolution_param {
+    num_output: 128
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "Mprelu3_stage1_L2_2"
+  type: "PReLU"
+  bottom: "Mconv3_stage1_L2_2"
+  top: "Mconv3_stage1_L2_2"
+}
+layer {
+  name: "Mconv3_stage1_L2_concat"
+  type: "Concat"
+  bottom: "Mconv3_stage1_L2_0"
+  bottom: "Mconv3_stage1_L2_1"
+  bottom: "Mconv3_stage1_L2_2"
+  top: "Mconv3_stage1_L2_concat"
+  concat_param {
+    axis: 1
+  }
+}
+layer {
+  name: "Mconv4_stage1_L2_0"
+  type: "Convolution"
+  bottom: "Mconv3_stage1_L2_concat"
+  top: "Mconv4_stage1_L2_0"
+  convolution_param {
+    num_output: 128
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "Mprelu4_stage1_L2_0"
+  type: "PReLU"
+  bottom: "Mconv4_stage1_L2_0"
+  top: "Mconv4_stage1_L2_0"
+}
+layer {
+  name: "Mconv4_stage1_L2_1"
+  type: "Convolution"
+  bottom: "Mconv4_stage1_L2_0"
+  top: "Mconv4_stage1_L2_1"
+  convolution_param {
+    num_output: 128
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "Mprelu4_stage1_L2_1"
+  type: "PReLU"
+  bottom: "Mconv4_stage1_L2_1"
+  top: "Mconv4_stage1_L2_1"
+}
+layer {
+  name: "Mconv4_stage1_L2_2"
+  type: "Convolution"
+  bottom: "Mconv4_stage1_L2_1"
+  top: "Mconv4_stage1_L2_2"
+  convolution_param {
+    num_output: 128
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "Mprelu4_stage1_L2_2"
+  type: "PReLU"
+  bottom: "Mconv4_stage1_L2_2"
+  top: "Mconv4_stage1_L2_2"
+}
+layer {
+  name: "Mconv4_stage1_L2_concat"
+  type: "Concat"
+  bottom: "Mconv4_stage1_L2_0"
+  bottom: "Mconv4_stage1_L2_1"
+  bottom: "Mconv4_stage1_L2_2"
+  top: "Mconv4_stage1_L2_concat"
+  concat_param {
+    axis: 1
+  }
+}
+layer {
+  name: "Mconv5_stage1_L2_0"
+  type: "Convolution"
+  bottom: "Mconv4_stage1_L2_concat"
+  top: "Mconv5_stage1_L2_0"
+  convolution_param {
+    num_output: 128
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "Mprelu5_stage1_L2_0"
+  type: "PReLU"
+  bottom: "Mconv5_stage1_L2_0"
+  top: "Mconv5_stage1_L2_0"
+}
+layer {
+  name: "Mconv5_stage1_L2_1"
+  type: "Convolution"
+  bottom: "Mconv5_stage1_L2_0"
+  top: "Mconv5_stage1_L2_1"
+  convolution_param {
+    num_output: 128
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "Mprelu5_stage1_L2_1"
+  type: "PReLU"
+  bottom: "Mconv5_stage1_L2_1"
+  top: "Mconv5_stage1_L2_1"
+}
+layer {
+  name: "Mconv5_stage1_L2_2"
+  type: "Convolution"
+  bottom: "Mconv5_stage1_L2_1"
+  top: "Mconv5_stage1_L2_2"
+  convolution_param {
+    num_output: 128
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "Mprelu5_stage1_L2_2"
+  type: "PReLU"
+  bottom: "Mconv5_stage1_L2_2"
+  top: "Mconv5_stage1_L2_2"
+}
+layer {
+  name: "Mconv5_stage1_L2_concat"
+  type: "Concat"
+  bottom: "Mconv5_stage1_L2_0"
+  bottom: "Mconv5_stage1_L2_1"
+  bottom: "Mconv5_stage1_L2_2"
+  top: "Mconv5_stage1_L2_concat"
+  concat_param {
+    axis: 1
+  }
+}
+layer {
+  name: "Mconv6_stage1_L2"
+  type: "Convolution"
+  bottom: "Mconv5_stage1_L2_concat"
+  top: "Mconv6_stage1_L2"
+  convolution_param {
+    num_output: 512
+    pad: 0
+    kernel_size: 1
+  }
+}
+layer {
+  name: "Mprelu6_stage1_L2"
+  type: "PReLU"
+  bottom: "Mconv6_stage1_L2"
+  top: "Mconv6_stage1_L2"
+}
+layer {
+  name: "Mconv7_stage1_L2"
+  type: "Convolution"
+  bottom: "Mconv6_stage1_L2"
+  top: "Mconv7_stage1_L2"
+  convolution_param {
+    num_output: 52
+    pad: 0
+    kernel_size: 1
+  }
+}
+layer {
+  name: "concat_stage2_L2"
+  type: "Concat"
+  bottom: "conv4_4_CPM"
+  bottom: "Mconv7_stage1_L2"
+  top: "concat_stage2_L2"
+  concat_param {
+    axis: 1
+  }
+}
+layer {
+  name: "Mconv1_stage2_L2_0"
+  type: "Convolution"
+  bottom: "concat_stage2_L2"
+  top: "Mconv1_stage2_L2_0"
+  convolution_param {
+    num_output: 128
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "Mprelu1_stage2_L2_0"
+  type: "PReLU"
+  bottom: "Mconv1_stage2_L2_0"
+  top: "Mconv1_stage2_L2_0"
+}
+layer {
+  name: "Mconv1_stage2_L2_1"
+  type: "Convolution"
+  bottom: "Mconv1_stage2_L2_0"
+  top: "Mconv1_stage2_L2_1"
+  convolution_param {
+    num_output: 128
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "Mprelu1_stage2_L2_1"
+  type: "PReLU"
+  bottom: "Mconv1_stage2_L2_1"
+  top: "Mconv1_stage2_L2_1"
+}
+layer {
+  name: "Mconv1_stage2_L2_2"
+  type: "Convolution"
+  bottom: "Mconv1_stage2_L2_1"
+  top: "Mconv1_stage2_L2_2"
+  convolution_param {
+    num_output: 128
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "Mprelu1_stage2_L2_2"
+  type: "PReLU"
+  bottom: "Mconv1_stage2_L2_2"
+  top: "Mconv1_stage2_L2_2"
+}
+layer {
+  name: "Mconv1_stage2_L2_concat"
+  type: "Concat"
+  bottom: "Mconv1_stage2_L2_0"
+  bottom: "Mconv1_stage2_L2_1"
+  bottom: "Mconv1_stage2_L2_2"
+  top: "Mconv1_stage2_L2_concat"
+  concat_param {
+    axis: 1
+  }
+}
+layer {
+  name: "Mconv2_stage2_L2_0"
+  type: "Convolution"
+  bottom: "Mconv1_stage2_L2_concat"
+  top: "Mconv2_stage2_L2_0"
+  convolution_param {
+    num_output: 128
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "Mprelu2_stage2_L2_0"
+  type: "PReLU"
+  bottom: "Mconv2_stage2_L2_0"
+  top: "Mconv2_stage2_L2_0"
+}
+layer {
+  name: "Mconv2_stage2_L2_1"
+  type: "Convolution"
+  bottom: "Mconv2_stage2_L2_0"
+  top: "Mconv2_stage2_L2_1"
+  convolution_param {
+    num_output: 128
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "Mprelu2_stage2_L2_1"
+  type: "PReLU"
+  bottom: "Mconv2_stage2_L2_1"
+  top: "Mconv2_stage2_L2_1"
+}
+layer {
+  name: "Mconv2_stage2_L2_2"
+  type: "Convolution"
+  bottom: "Mconv2_stage2_L2_1"
+  top: "Mconv2_stage2_L2_2"
+  convolution_param {
+    num_output: 128
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "Mprelu2_stage2_L2_2"
+  type: "PReLU"
+  bottom: "Mconv2_stage2_L2_2"
+  top: "Mconv2_stage2_L2_2"
+}
+layer {
+  name: "Mconv2_stage2_L2_concat"
+  type: "Concat"
+  bottom: "Mconv2_stage2_L2_0"
+  bottom: "Mconv2_stage2_L2_1"
+  bottom: "Mconv2_stage2_L2_2"
+  top: "Mconv2_stage2_L2_concat"
+  concat_param {
+    axis: 1
+  }
+}
+layer {
+  name: "Mconv3_stage2_L2_0"
+  type: "Convolution"
+  bottom: "Mconv2_stage2_L2_concat"
+  top: "Mconv3_stage2_L2_0"
+  convolution_param {
+    num_output: 128
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "Mprelu3_stage2_L2_0"
+  type: "PReLU"
+  bottom: "Mconv3_stage2_L2_0"
+  top: "Mconv3_stage2_L2_0"
+}
+layer {
+  name: "Mconv3_stage2_L2_1"
+  type: "Convolution"
+  bottom: "Mconv3_stage2_L2_0"
+  top: "Mconv3_stage2_L2_1"
+  convolution_param {
+    num_output: 128
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "Mprelu3_stage2_L2_1"
+  type: "PReLU"
+  bottom: "Mconv3_stage2_L2_1"
+  top: "Mconv3_stage2_L2_1"
+}
+layer {
+  name: "Mconv3_stage2_L2_2"
+  type: "Convolution"
+  bottom: "Mconv3_stage2_L2_1"
+  top: "Mconv3_stage2_L2_2"
+  convolution_param {
+    num_output: 128
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "Mprelu3_stage2_L2_2"
+  type: "PReLU"
+  bottom: "Mconv3_stage2_L2_2"
+  top: "Mconv3_stage2_L2_2"
+}
+layer {
+  name: "Mconv3_stage2_L2_concat"
+  type: "Concat"
+  bottom: "Mconv3_stage2_L2_0"
+  bottom: "Mconv3_stage2_L2_1"
+  bottom: "Mconv3_stage2_L2_2"
+  top: "Mconv3_stage2_L2_concat"
+  concat_param {
+    axis: 1
+  }
+}
+layer {
+  name: "Mconv4_stage2_L2_0"
+  type: "Convolution"
+  bottom: "Mconv3_stage2_L2_concat"
+  top: "Mconv4_stage2_L2_0"
+  convolution_param {
+    num_output: 128
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "Mprelu4_stage2_L2_0"
+  type: "PReLU"
+  bottom: "Mconv4_stage2_L2_0"
+  top: "Mconv4_stage2_L2_0"
+}
+layer {
+  name: "Mconv4_stage2_L2_1"
+  type: "Convolution"
+  bottom: "Mconv4_stage2_L2_0"
+  top: "Mconv4_stage2_L2_1"
+  convolution_param {
+    num_output: 128
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "Mprelu4_stage2_L2_1"
+  type: "PReLU"
+  bottom: "Mconv4_stage2_L2_1"
+  top: "Mconv4_stage2_L2_1"
+}
+layer {
+  name: "Mconv4_stage2_L2_2"
+  type: "Convolution"
+  bottom: "Mconv4_stage2_L2_1"
+  top: "Mconv4_stage2_L2_2"
+  convolution_param {
+    num_output: 128
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "Mprelu4_stage2_L2_2"
+  type: "PReLU"
+  bottom: "Mconv4_stage2_L2_2"
+  top: "Mconv4_stage2_L2_2"
+}
+layer {
+  name: "Mconv4_stage2_L2_concat"
+  type: "Concat"
+  bottom: "Mconv4_stage2_L2_0"
+  bottom: "Mconv4_stage2_L2_1"
+  bottom: "Mconv4_stage2_L2_2"
+  top: "Mconv4_stage2_L2_concat"
+  concat_param {
+    axis: 1
+  }
+}
+layer {
+  name: "Mconv5_stage2_L2_0"
+  type: "Convolution"
+  bottom: "Mconv4_stage2_L2_concat"
+  top: "Mconv5_stage2_L2_0"
+  convolution_param {
+    num_output: 128
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "Mprelu5_stage2_L2_0"
+  type: "PReLU"
+  bottom: "Mconv5_stage2_L2_0"
+  top: "Mconv5_stage2_L2_0"
+}
+layer {
+  name: "Mconv5_stage2_L2_1"
+  type: "Convolution"
+  bottom: "Mconv5_stage2_L2_0"
+  top: "Mconv5_stage2_L2_1"
+  convolution_param {
+    num_output: 128
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "Mprelu5_stage2_L2_1"
+  type: "PReLU"
+  bottom: "Mconv5_stage2_L2_1"
+  top: "Mconv5_stage2_L2_1"
+}
+layer {
+  name: "Mconv5_stage2_L2_2"
+  type: "Convolution"
+  bottom: "Mconv5_stage2_L2_1"
+  top: "Mconv5_stage2_L2_2"
+  convolution_param {
+    num_output: 128
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "Mprelu5_stage2_L2_2"
+  type: "PReLU"
+  bottom: "Mconv5_stage2_L2_2"
+  top: "Mconv5_stage2_L2_2"
+}
+layer {
+  name: "Mconv5_stage2_L2_concat"
+  type: "Concat"
+  bottom: "Mconv5_stage2_L2_0"
+  bottom: "Mconv5_stage2_L2_1"
+  bottom: "Mconv5_stage2_L2_2"
+  top: "Mconv5_stage2_L2_concat"
+  concat_param {
+    axis: 1
+  }
+}
+layer {
+  name: "Mconv6_stage2_L2"
+  type: "Convolution"
+  bottom: "Mconv5_stage2_L2_concat"
+  top: "Mconv6_stage2_L2"
+  convolution_param {
+    num_output: 512
+    pad: 0
+    kernel_size: 1
+  }
+}
+layer {
+  name: "Mprelu6_stage2_L2"
+  type: "PReLU"
+  bottom: "Mconv6_stage2_L2"
+  top: "Mconv6_stage2_L2"
+}
+layer {
+  name: "Mconv7_stage2_L2"
+  type: "Convolution"
+  bottom: "Mconv6_stage2_L2"
+  top: "Mconv7_stage2_L2"
+  convolution_param {
+    num_output: 52
+    pad: 0
+    kernel_size: 1
+  }
+}
+layer {
+  name: "concat_stage3_L2"
+  type: "Concat"
+  bottom: "conv4_4_CPM"
+  bottom: "Mconv7_stage2_L2"
+  top: "concat_stage3_L2"
+  concat_param {
+    axis: 1
+  }
+}
+layer {
+  name: "Mconv1_stage3_L2_0"
+  type: "Convolution"
+  bottom: "concat_stage3_L2"
+  top: "Mconv1_stage3_L2_0"
+  convolution_param {
+    num_output: 128
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "Mprelu1_stage3_L2_0"
+  type: "PReLU"
+  bottom: "Mconv1_stage3_L2_0"
+  top: "Mconv1_stage3_L2_0"
+}
+layer {
+  name: "Mconv1_stage3_L2_1"
+  type: "Convolution"
+  bottom: "Mconv1_stage3_L2_0"
+  top: "Mconv1_stage3_L2_1"
+  convolution_param {
+    num_output: 128
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "Mprelu1_stage3_L2_1"
+  type: "PReLU"
+  bottom: "Mconv1_stage3_L2_1"
+  top: "Mconv1_stage3_L2_1"
+}
+layer {
+  name: "Mconv1_stage3_L2_2"
+  type: "Convolution"
+  bottom: "Mconv1_stage3_L2_1"
+  top: "Mconv1_stage3_L2_2"
+  convolution_param {
+    num_output: 128
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "Mprelu1_stage3_L2_2"
+  type: "PReLU"
+  bottom: "Mconv1_stage3_L2_2"
+  top: "Mconv1_stage3_L2_2"
+}
+layer {
+  name: "Mconv1_stage3_L2_concat"
+  type: "Concat"
+  bottom: "Mconv1_stage3_L2_0"
+  bottom: "Mconv1_stage3_L2_1"
+  bottom: "Mconv1_stage3_L2_2"
+  top: "Mconv1_stage3_L2_concat"
+  concat_param {
+    axis: 1
+  }
+}
+layer {
+  name: "Mconv2_stage3_L2_0"
+  type: "Convolution"
+  bottom: "Mconv1_stage3_L2_concat"
+  top: "Mconv2_stage3_L2_0"
+  convolution_param {
+    num_output: 128
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "Mprelu2_stage3_L2_0"
+  type: "PReLU"
+  bottom: "Mconv2_stage3_L2_0"
+  top: "Mconv2_stage3_L2_0"
+}
+layer {
+  name: "Mconv2_stage3_L2_1"
+  type: "Convolution"
+  bottom: "Mconv2_stage3_L2_0"
+  top: "Mconv2_stage3_L2_1"
+  convolution_param {
+    num_output: 128
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "Mprelu2_stage3_L2_1"
+  type: "PReLU"
+  bottom: "Mconv2_stage3_L2_1"
+  top: "Mconv2_stage3_L2_1"
+}
+layer {
+  name: "Mconv2_stage3_L2_2"
+  type: "Convolution"
+  bottom: "Mconv2_stage3_L2_1"
+  top: "Mconv2_stage3_L2_2"
+  convolution_param {
+    num_output: 128
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "Mprelu2_stage3_L2_2"
+  type: "PReLU"
+  bottom: "Mconv2_stage3_L2_2"
+  top: "Mconv2_stage3_L2_2"
+}
+layer {
+  name: "Mconv2_stage3_L2_concat"
+  type: "Concat"
+  bottom: "Mconv2_stage3_L2_0"
+  bottom: "Mconv2_stage3_L2_1"
+  bottom: "Mconv2_stage3_L2_2"
+  top: "Mconv2_stage3_L2_concat"
+  concat_param {
+    axis: 1
+  }
+}
+layer {
+  name: "Mconv3_stage3_L2_0"
+  type: "Convolution"
+  bottom: "Mconv2_stage3_L2_concat"
+  top: "Mconv3_stage3_L2_0"
+  convolution_param {
+    num_output: 128
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "Mprelu3_stage3_L2_0"
+  type: "PReLU"
+  bottom: "Mconv3_stage3_L2_0"
+  top: "Mconv3_stage3_L2_0"
+}
+layer {
+  name: "Mconv3_stage3_L2_1"
+  type: "Convolution"
+  bottom: "Mconv3_stage3_L2_0"
+  top: "Mconv3_stage3_L2_1"
+  convolution_param {
+    num_output: 128
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "Mprelu3_stage3_L2_1"
+  type: "PReLU"
+  bottom: "Mconv3_stage3_L2_1"
+  top: "Mconv3_stage3_L2_1"
+}
+layer {
+  name: "Mconv3_stage3_L2_2"
+  type: "Convolution"
+  bottom: "Mconv3_stage3_L2_1"
+  top: "Mconv3_stage3_L2_2"
+  convolution_param {
+    num_output: 128
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "Mprelu3_stage3_L2_2"
+  type: "PReLU"
+  bottom: "Mconv3_stage3_L2_2"
+  top: "Mconv3_stage3_L2_2"
+}
+layer {
+  name: "Mconv3_stage3_L2_concat"
+  type: "Concat"
+  bottom: "Mconv3_stage3_L2_0"
+  bottom: "Mconv3_stage3_L2_1"
+  bottom: "Mconv3_stage3_L2_2"
+  top: "Mconv3_stage3_L2_concat"
+  concat_param {
+    axis: 1
+  }
+}
+layer {
+  name: "Mconv4_stage3_L2_0"
+  type: "Convolution"
+  bottom: "Mconv3_stage3_L2_concat"
+  top: "Mconv4_stage3_L2_0"
+  convolution_param {
+    num_output: 128
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "Mprelu4_stage3_L2_0"
+  type: "PReLU"
+  bottom: "Mconv4_stage3_L2_0"
+  top: "Mconv4_stage3_L2_0"
+}
+layer {
+  name: "Mconv4_stage3_L2_1"
+  type: "Convolution"
+  bottom: "Mconv4_stage3_L2_0"
+  top: "Mconv4_stage3_L2_1"
+  convolution_param {
+    num_output: 128
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "Mprelu4_stage3_L2_1"
+  type: "PReLU"
+  bottom: "Mconv4_stage3_L2_1"
+  top: "Mconv4_stage3_L2_1"
+}
+layer {
+  name: "Mconv4_stage3_L2_2"
+  type: "Convolution"
+  bottom: "Mconv4_stage3_L2_1"
+  top: "Mconv4_stage3_L2_2"
+  convolution_param {
+    num_output: 128
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "Mprelu4_stage3_L2_2"
+  type: "PReLU"
+  bottom: "Mconv4_stage3_L2_2"
+  top: "Mconv4_stage3_L2_2"
+}
+layer {
+  name: "Mconv4_stage3_L2_concat"
+  type: "Concat"
+  bottom: "Mconv4_stage3_L2_0"
+  bottom: "Mconv4_stage3_L2_1"
+  bottom: "Mconv4_stage3_L2_2"
+  top: "Mconv4_stage3_L2_concat"
+  concat_param {
+    axis: 1
+  }
+}
+layer {
+  name: "Mconv5_stage3_L2_0"
+  type: "Convolution"
+  bottom: "Mconv4_stage3_L2_concat"
+  top: "Mconv5_stage3_L2_0"
+  convolution_param {
+    num_output: 128
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "Mprelu5_stage3_L2_0"
+  type: "PReLU"
+  bottom: "Mconv5_stage3_L2_0"
+  top: "Mconv5_stage3_L2_0"
+}
+layer {
+  name: "Mconv5_stage3_L2_1"
+  type: "Convolution"
+  bottom: "Mconv5_stage3_L2_0"
+  top: "Mconv5_stage3_L2_1"
+  convolution_param {
+    num_output: 128
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "Mprelu5_stage3_L2_1"
+  type: "PReLU"
+  bottom: "Mconv5_stage3_L2_1"
+  top: "Mconv5_stage3_L2_1"
+}
+layer {
+  name: "Mconv5_stage3_L2_2"
+  type: "Convolution"
+  bottom: "Mconv5_stage3_L2_1"
+  top: "Mconv5_stage3_L2_2"
+  convolution_param {
+    num_output: 128
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "Mprelu5_stage3_L2_2"
+  type: "PReLU"
+  bottom: "Mconv5_stage3_L2_2"
+  top: "Mconv5_stage3_L2_2"
+}
+layer {
+  name: "Mconv5_stage3_L2_concat"
+  type: "Concat"
+  bottom: "Mconv5_stage3_L2_0"
+  bottom: "Mconv5_stage3_L2_1"
+  bottom: "Mconv5_stage3_L2_2"
+  top: "Mconv5_stage3_L2_concat"
+  concat_param {
+    axis: 1
+  }
+}
+layer {
+  name: "Mconv6_stage3_L2"
+  type: "Convolution"
+  bottom: "Mconv5_stage3_L2_concat"
+  top: "Mconv6_stage3_L2"
+  convolution_param {
+    num_output: 512
+    pad: 0
+    kernel_size: 1
+  }
+}
+layer {
+  name: "Mprelu6_stage3_L2"
+  type: "PReLU"
+  bottom: "Mconv6_stage3_L2"
+  top: "Mconv6_stage3_L2"
+}
+layer {
+  name: "Mconv7_stage3_L2"
+  type: "Convolution"
+  bottom: "Mconv6_stage3_L2"
+  top: "Mconv7_stage3_L2"
+  convolution_param {
+    num_output: 52
+    pad: 0
+    kernel_size: 1
+  }
+}
+layer {
+  name: "concat_stage0_L1"
+  type: "Concat"
+  bottom: "conv4_4_CPM"
+  bottom: "Mconv7_stage3_L2"
+  top: "concat_stage0_L1"
+  concat_param {
+    axis: 1
+  }
+}
+layer {
+  name: "Mconv1_stage0_L1_0"
+  type: "Convolution"
+  bottom: "concat_stage0_L1"
+  top: "Mconv1_stage0_L1_0"
+  convolution_param {
+    num_output: 96
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "Mprelu1_stage0_L1_0"
+  type: "PReLU"
+  bottom: "Mconv1_stage0_L1_0"
+  top: "Mconv1_stage0_L1_0"
+}
+layer {
+  name: "Mconv1_stage0_L1_1"
+  type: "Convolution"
+  bottom: "Mconv1_stage0_L1_0"
+  top: "Mconv1_stage0_L1_1"
+  convolution_param {
+    num_output: 96
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "Mprelu1_stage0_L1_1"
+  type: "PReLU"
+  bottom: "Mconv1_stage0_L1_1"
+  top: "Mconv1_stage0_L1_1"
+}
+layer {
+  name: "Mconv1_stage0_L1_2"
+  type: "Convolution"
+  bottom: "Mconv1_stage0_L1_1"
+  top: "Mconv1_stage0_L1_2"
+  convolution_param {
+    num_output: 96
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "Mprelu1_stage0_L1_2"
+  type: "PReLU"
+  bottom: "Mconv1_stage0_L1_2"
+  top: "Mconv1_stage0_L1_2"
+}
+layer {
+  name: "Mconv1_stage0_L1_concat"
+  type: "Concat"
+  bottom: "Mconv1_stage0_L1_0"
+  bottom: "Mconv1_stage0_L1_1"
+  bottom: "Mconv1_stage0_L1_2"
+  top: "Mconv1_stage0_L1_concat"
+  concat_param {
+    axis: 1
+  }
+}
+layer {
+  name: "Mconv2_stage0_L1_0"
+  type: "Convolution"
+  bottom: "Mconv1_stage0_L1_concat"
+  top: "Mconv2_stage0_L1_0"
+  convolution_param {
+    num_output: 96
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "Mprelu2_stage0_L1_0"
+  type: "PReLU"
+  bottom: "Mconv2_stage0_L1_0"
+  top: "Mconv2_stage0_L1_0"
+}
+layer {
+  name: "Mconv2_stage0_L1_1"
+  type: "Convolution"
+  bottom: "Mconv2_stage0_L1_0"
+  top: "Mconv2_stage0_L1_1"
+  convolution_param {
+    num_output: 96
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "Mprelu2_stage0_L1_1"
+  type: "PReLU"
+  bottom: "Mconv2_stage0_L1_1"
+  top: "Mconv2_stage0_L1_1"
+}
+layer {
+  name: "Mconv2_stage0_L1_2"
+  type: "Convolution"
+  bottom: "Mconv2_stage0_L1_1"
+  top: "Mconv2_stage0_L1_2"
+  convolution_param {
+    num_output: 96
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "Mprelu2_stage0_L1_2"
+  type: "PReLU"
+  bottom: "Mconv2_stage0_L1_2"
+  top: "Mconv2_stage0_L1_2"
+}
+layer {
+  name: "Mconv2_stage0_L1_concat"
+  type: "Concat"
+  bottom: "Mconv2_stage0_L1_0"
+  bottom: "Mconv2_stage0_L1_1"
+  bottom: "Mconv2_stage0_L1_2"
+  top: "Mconv2_stage0_L1_concat"
+  concat_param {
+    axis: 1
+  }
+}
+layer {
+  name: "Mconv3_stage0_L1_0"
+  type: "Convolution"
+  bottom: "Mconv2_stage0_L1_concat"
+  top: "Mconv3_stage0_L1_0"
+  convolution_param {
+    num_output: 96
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "Mprelu3_stage0_L1_0"
+  type: "PReLU"
+  bottom: "Mconv3_stage0_L1_0"
+  top: "Mconv3_stage0_L1_0"
+}
+layer {
+  name: "Mconv3_stage0_L1_1"
+  type: "Convolution"
+  bottom: "Mconv3_stage0_L1_0"
+  top: "Mconv3_stage0_L1_1"
+  convolution_param {
+    num_output: 96
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "Mprelu3_stage0_L1_1"
+  type: "PReLU"
+  bottom: "Mconv3_stage0_L1_1"
+  top: "Mconv3_stage0_L1_1"
+}
+layer {
+  name: "Mconv3_stage0_L1_2"
+  type: "Convolution"
+  bottom: "Mconv3_stage0_L1_1"
+  top: "Mconv3_stage0_L1_2"
+  convolution_param {
+    num_output: 96
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "Mprelu3_stage0_L1_2"
+  type: "PReLU"
+  bottom: "Mconv3_stage0_L1_2"
+  top: "Mconv3_stage0_L1_2"
+}
+layer {
+  name: "Mconv3_stage0_L1_concat"
+  type: "Concat"
+  bottom: "Mconv3_stage0_L1_0"
+  bottom: "Mconv3_stage0_L1_1"
+  bottom: "Mconv3_stage0_L1_2"
+  top: "Mconv3_stage0_L1_concat"
+  concat_param {
+    axis: 1
+  }
+}
+layer {
+  name: "Mconv4_stage0_L1_0"
+  type: "Convolution"
+  bottom: "Mconv3_stage0_L1_concat"
+  top: "Mconv4_stage0_L1_0"
+  convolution_param {
+    num_output: 96
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "Mprelu4_stage0_L1_0"
+  type: "PReLU"
+  bottom: "Mconv4_stage0_L1_0"
+  top: "Mconv4_stage0_L1_0"
+}
+layer {
+  name: "Mconv4_stage0_L1_1"
+  type: "Convolution"
+  bottom: "Mconv4_stage0_L1_0"
+  top: "Mconv4_stage0_L1_1"
+  convolution_param {
+    num_output: 96
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "Mprelu4_stage0_L1_1"
+  type: "PReLU"
+  bottom: "Mconv4_stage0_L1_1"
+  top: "Mconv4_stage0_L1_1"
+}
+layer {
+  name: "Mconv4_stage0_L1_2"
+  type: "Convolution"
+  bottom: "Mconv4_stage0_L1_1"
+  top: "Mconv4_stage0_L1_2"
+  convolution_param {
+    num_output: 96
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "Mprelu4_stage0_L1_2"
+  type: "PReLU"
+  bottom: "Mconv4_stage0_L1_2"
+  top: "Mconv4_stage0_L1_2"
+}
+layer {
+  name: "Mconv4_stage0_L1_concat"
+  type: "Concat"
+  bottom: "Mconv4_stage0_L1_0"
+  bottom: "Mconv4_stage0_L1_1"
+  bottom: "Mconv4_stage0_L1_2"
+  top: "Mconv4_stage0_L1_concat"
+  concat_param {
+    axis: 1
+  }
+}
+layer {
+  name: "Mconv5_stage0_L1_0"
+  type: "Convolution"
+  bottom: "Mconv4_stage0_L1_concat"
+  top: "Mconv5_stage0_L1_0"
+  convolution_param {
+    num_output: 96
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "Mprelu5_stage0_L1_0"
+  type: "PReLU"
+  bottom: "Mconv5_stage0_L1_0"
+  top: "Mconv5_stage0_L1_0"
+}
+layer {
+  name: "Mconv5_stage0_L1_1"
+  type: "Convolution"
+  bottom: "Mconv5_stage0_L1_0"
+  top: "Mconv5_stage0_L1_1"
+  convolution_param {
+    num_output: 96
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "Mprelu5_stage0_L1_1"
+  type: "PReLU"
+  bottom: "Mconv5_stage0_L1_1"
+  top: "Mconv5_stage0_L1_1"
+}
+layer {
+  name: "Mconv5_stage0_L1_2"
+  type: "Convolution"
+  bottom: "Mconv5_stage0_L1_1"
+  top: "Mconv5_stage0_L1_2"
+  convolution_param {
+    num_output: 96
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "Mprelu5_stage0_L1_2"
+  type: "PReLU"
+  bottom: "Mconv5_stage0_L1_2"
+  top: "Mconv5_stage0_L1_2"
+}
+layer {
+  name: "Mconv5_stage0_L1_concat"
+  type: "Concat"
+  bottom: "Mconv5_stage0_L1_0"
+  bottom: "Mconv5_stage0_L1_1"
+  bottom: "Mconv5_stage0_L1_2"
+  top: "Mconv5_stage0_L1_concat"
+  concat_param {
+    axis: 1
+  }
+}
+layer {
+  name: "Mconv6_stage0_L1"
+  type: "Convolution"
+  bottom: "Mconv5_stage0_L1_concat"
+  top: "Mconv6_stage0_L1"
+  convolution_param {
+    num_output: 256
+    pad: 0
+    kernel_size: 1
+  }
+}
+layer {
+  name: "Mprelu6_stage0_L1"
+  type: "PReLU"
+  bottom: "Mconv6_stage0_L1"
+  top: "Mconv6_stage0_L1"
+}
+layer {
+  name: "Mconv7_stage0_L1"
+  type: "Convolution"
+  bottom: "Mconv6_stage0_L1"
+  top: "Mconv7_stage0_L1"
+  convolution_param {
+    num_output: 26
+    pad: 0
+    kernel_size: 1
+  }
+}
+layer {
+  name: "concat_stage1_L1"
+  type: "Concat"
+  bottom: "conv4_4_CPM"
+  bottom: "Mconv7_stage0_L1"
+  bottom: "Mconv7_stage3_L2"
+  top: "concat_stage1_L1"
+  concat_param {
+    axis: 1
+  }
+}
+layer {
+  name: "Mconv1_stage1_L1_0"
+  type: "Convolution"
+  bottom: "concat_stage1_L1"
+  top: "Mconv1_stage1_L1_0"
+  convolution_param {
+    num_output: 128
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "Mprelu1_stage1_L1_0"
+  type: "PReLU"
+  bottom: "Mconv1_stage1_L1_0"
+  top: "Mconv1_stage1_L1_0"
+}
+layer {
+  name: "Mconv1_stage1_L1_1"
+  type: "Convolution"
+  bottom: "Mconv1_stage1_L1_0"
+  top: "Mconv1_stage1_L1_1"
+  convolution_param {
+    num_output: 128
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "Mprelu1_stage1_L1_1"
+  type: "PReLU"
+  bottom: "Mconv1_stage1_L1_1"
+  top: "Mconv1_stage1_L1_1"
+}
+layer {
+  name: "Mconv1_stage1_L1_2"
+  type: "Convolution"
+  bottom: "Mconv1_stage1_L1_1"
+  top: "Mconv1_stage1_L1_2"
+  convolution_param {
+    num_output: 128
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "Mprelu1_stage1_L1_2"
+  type: "PReLU"
+  bottom: "Mconv1_stage1_L1_2"
+  top: "Mconv1_stage1_L1_2"
+}
+layer {
+  name: "Mconv1_stage1_L1_concat"
+  type: "Concat"
+  bottom: "Mconv1_stage1_L1_0"
+  bottom: "Mconv1_stage1_L1_1"
+  bottom: "Mconv1_stage1_L1_2"
+  top: "Mconv1_stage1_L1_concat"
+  concat_param {
+    axis: 1
+  }
+}
+layer {
+  name: "Mconv2_stage1_L1_0"
+  type: "Convolution"
+  bottom: "Mconv1_stage1_L1_concat"
+  top: "Mconv2_stage1_L1_0"
+  convolution_param {
+    num_output: 128
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "Mprelu2_stage1_L1_0"
+  type: "PReLU"
+  bottom: "Mconv2_stage1_L1_0"
+  top: "Mconv2_stage1_L1_0"
+}
+layer {
+  name: "Mconv2_stage1_L1_1"
+  type: "Convolution"
+  bottom: "Mconv2_stage1_L1_0"
+  top: "Mconv2_stage1_L1_1"
+  convolution_param {
+    num_output: 128
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "Mprelu2_stage1_L1_1"
+  type: "PReLU"
+  bottom: "Mconv2_stage1_L1_1"
+  top: "Mconv2_stage1_L1_1"
+}
+layer {
+  name: "Mconv2_stage1_L1_2"
+  type: "Convolution"
+  bottom: "Mconv2_stage1_L1_1"
+  top: "Mconv2_stage1_L1_2"
+  convolution_param {
+    num_output: 128
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "Mprelu2_stage1_L1_2"
+  type: "PReLU"
+  bottom: "Mconv2_stage1_L1_2"
+  top: "Mconv2_stage1_L1_2"
+}
+layer {
+  name: "Mconv2_stage1_L1_concat"
+  type: "Concat"
+  bottom: "Mconv2_stage1_L1_0"
+  bottom: "Mconv2_stage1_L1_1"
+  bottom: "Mconv2_stage1_L1_2"
+  top: "Mconv2_stage1_L1_concat"
+  concat_param {
+    axis: 1
+  }
+}
+layer {
+  name: "Mconv3_stage1_L1_0"
+  type: "Convolution"
+  bottom: "Mconv2_stage1_L1_concat"
+  top: "Mconv3_stage1_L1_0"
+  convolution_param {
+    num_output: 128
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "Mprelu3_stage1_L1_0"
+  type: "PReLU"
+  bottom: "Mconv3_stage1_L1_0"
+  top: "Mconv3_stage1_L1_0"
+}
+layer {
+  name: "Mconv3_stage1_L1_1"
+  type: "Convolution"
+  bottom: "Mconv3_stage1_L1_0"
+  top: "Mconv3_stage1_L1_1"
+  convolution_param {
+    num_output: 128
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "Mprelu3_stage1_L1_1"
+  type: "PReLU"
+  bottom: "Mconv3_stage1_L1_1"
+  top: "Mconv3_stage1_L1_1"
+}
+layer {
+  name: "Mconv3_stage1_L1_2"
+  type: "Convolution"
+  bottom: "Mconv3_stage1_L1_1"
+  top: "Mconv3_stage1_L1_2"
+  convolution_param {
+    num_output: 128
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "Mprelu3_stage1_L1_2"
+  type: "PReLU"
+  bottom: "Mconv3_stage1_L1_2"
+  top: "Mconv3_stage1_L1_2"
+}
+layer {
+  name: "Mconv3_stage1_L1_concat"
+  type: "Concat"
+  bottom: "Mconv3_stage1_L1_0"
+  bottom: "Mconv3_stage1_L1_1"
+  bottom: "Mconv3_stage1_L1_2"
+  top: "Mconv3_stage1_L1_concat"
+  concat_param {
+    axis: 1
+  }
+}
+layer {
+  name: "Mconv4_stage1_L1_0"
+  type: "Convolution"
+  bottom: "Mconv3_stage1_L1_concat"
+  top: "Mconv4_stage1_L1_0"
+  convolution_param {
+    num_output: 128
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "Mprelu4_stage1_L1_0"
+  type: "PReLU"
+  bottom: "Mconv4_stage1_L1_0"
+  top: "Mconv4_stage1_L1_0"
+}
+layer {
+  name: "Mconv4_stage1_L1_1"
+  type: "Convolution"
+  bottom: "Mconv4_stage1_L1_0"
+  top: "Mconv4_stage1_L1_1"
+  convolution_param {
+    num_output: 128
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "Mprelu4_stage1_L1_1"
+  type: "PReLU"
+  bottom: "Mconv4_stage1_L1_1"
+  top: "Mconv4_stage1_L1_1"
+}
+layer {
+  name: "Mconv4_stage1_L1_2"
+  type: "Convolution"
+  bottom: "Mconv4_stage1_L1_1"
+  top: "Mconv4_stage1_L1_2"
+  convolution_param {
+    num_output: 128
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "Mprelu4_stage1_L1_2"
+  type: "PReLU"
+  bottom: "Mconv4_stage1_L1_2"
+  top: "Mconv4_stage1_L1_2"
+}
+layer {
+  name: "Mconv4_stage1_L1_concat"
+  type: "Concat"
+  bottom: "Mconv4_stage1_L1_0"
+  bottom: "Mconv4_stage1_L1_1"
+  bottom: "Mconv4_stage1_L1_2"
+  top: "Mconv4_stage1_L1_concat"
+  concat_param {
+    axis: 1
+  }
+}
+layer {
+  name: "Mconv5_stage1_L1_0"
+  type: "Convolution"
+  bottom: "Mconv4_stage1_L1_concat"
+  top: "Mconv5_stage1_L1_0"
+  convolution_param {
+    num_output: 128
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "Mprelu5_stage1_L1_0"
+  type: "PReLU"
+  bottom: "Mconv5_stage1_L1_0"
+  top: "Mconv5_stage1_L1_0"
+}
+layer {
+  name: "Mconv5_stage1_L1_1"
+  type: "Convolution"
+  bottom: "Mconv5_stage1_L1_0"
+  top: "Mconv5_stage1_L1_1"
+  convolution_param {
+    num_output: 128
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "Mprelu5_stage1_L1_1"
+  type: "PReLU"
+  bottom: "Mconv5_stage1_L1_1"
+  top: "Mconv5_stage1_L1_1"
+}
+layer {
+  name: "Mconv5_stage1_L1_2"
+  type: "Convolution"
+  bottom: "Mconv5_stage1_L1_1"
+  top: "Mconv5_stage1_L1_2"
+  convolution_param {
+    num_output: 128
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "Mprelu5_stage1_L1_2"
+  type: "PReLU"
+  bottom: "Mconv5_stage1_L1_2"
+  top: "Mconv5_stage1_L1_2"
+}
+layer {
+  name: "Mconv5_stage1_L1_concat"
+  type: "Concat"
+  bottom: "Mconv5_stage1_L1_0"
+  bottom: "Mconv5_stage1_L1_1"
+  bottom: "Mconv5_stage1_L1_2"
+  top: "Mconv5_stage1_L1_concat"
+  concat_param {
+    axis: 1
+  }
+}
+layer {
+  name: "Mconv6_stage1_L1"
+  type: "Convolution"
+  bottom: "Mconv5_stage1_L1_concat"
+  top: "Mconv6_stage1_L1"
+  convolution_param {
+    num_output: 512
+    pad: 0
+    kernel_size: 1
+  }
+}
+layer {
+  name: "Mprelu6_stage1_L1"
+  type: "PReLU"
+  bottom: "Mconv6_stage1_L1"
+  top: "Mconv6_stage1_L1"
+}
+layer {
+  name: "Mconv7_stage1_L1"
+  type: "Convolution"
+  bottom: "Mconv6_stage1_L1"
+  top: "Mconv7_stage1_L1"
+  convolution_param {
+    num_output: 26
+    pad: 0
+    kernel_size: 1
+  }
+}
+layer {
+  name: "net_output"
+  type: "Concat"
+  bottom: "Mconv7_stage1_L1"
+  bottom: "Mconv7_stage3_L2"
+  top: "net_output"
+  concat_param {
+    axis: 1
+  }
+}
diff --git a/joints_detectors/openpose/models/pose/coco/pose_deploy_linevec.prototxt b/joints_detectors/openpose/models/pose/coco/pose_deploy_linevec.prototxt
new file mode 100644
index 0000000000000000000000000000000000000000..fbe0c824588d61f079de03b06ab428e817b99819
--- /dev/null
+++ b/joints_detectors/openpose/models/pose/coco/pose_deploy_linevec.prototxt
@@ -0,0 +1,2976 @@
+input: "image"
+input_dim: 1
+input_dim: 3
+input_dim: 1 # This value will be defined at runtime
+input_dim: 1 # This value will be defined at runtime
+layer {
+  name: "conv1_1"
+  type: "Convolution"
+  bottom: "image"
+  top: "conv1_1"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 64
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "relu1_1"
+  type: "ReLU"
+  bottom: "conv1_1"
+  top: "conv1_1"
+}
+layer {
+  name: "conv1_2"
+  type: "Convolution"
+  bottom: "conv1_1"
+  top: "conv1_2"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 64
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "relu1_2"
+  type: "ReLU"
+  bottom: "conv1_2"
+  top: "conv1_2"
+}
+layer {
+  name: "pool1_stage1"
+  type: "Pooling"
+  bottom: "conv1_2"
+  top: "pool1_stage1"
+  pooling_param {
+    pool: MAX
+    kernel_size: 2
+    stride: 2
+  }
+}
+layer {
+  name: "conv2_1"
+  type: "Convolution"
+  bottom: "pool1_stage1"
+  top: "conv2_1"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "relu2_1"
+  type: "ReLU"
+  bottom: "conv2_1"
+  top: "conv2_1"
+}
+layer {
+  name: "conv2_2"
+  type: "Convolution"
+  bottom: "conv2_1"
+  top: "conv2_2"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "relu2_2"
+  type: "ReLU"
+  bottom: "conv2_2"
+  top: "conv2_2"
+}
+layer {
+  name: "pool2_stage1"
+  type: "Pooling"
+  bottom: "conv2_2"
+  top: "pool2_stage1"
+  pooling_param {
+    pool: MAX
+    kernel_size: 2
+    stride: 2
+  }
+}
+layer {
+  name: "conv3_1"
+  type: "Convolution"
+  bottom: "pool2_stage1"
+  top: "conv3_1"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 256
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "relu3_1"
+  type: "ReLU"
+  bottom: "conv3_1"
+  top: "conv3_1"
+}
+layer {
+  name: "conv3_2"
+  type: "Convolution"
+  bottom: "conv3_1"
+  top: "conv3_2"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 256
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "relu3_2"
+  type: "ReLU"
+  bottom: "conv3_2"
+  top: "conv3_2"
+}
+layer {
+  name: "conv3_3"
+  type: "Convolution"
+  bottom: "conv3_2"
+  top: "conv3_3"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 256
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "relu3_3"
+  type: "ReLU"
+  bottom: "conv3_3"
+  top: "conv3_3"
+}
+layer {
+  name: "conv3_4"
+  type: "Convolution"
+  bottom: "conv3_3"
+  top: "conv3_4"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 256
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "relu3_4"
+  type: "ReLU"
+  bottom: "conv3_4"
+  top: "conv3_4"
+}
+layer {
+  name: "pool3_stage1"
+  type: "Pooling"
+  bottom: "conv3_4"
+  top: "pool3_stage1"
+  pooling_param {
+    pool: MAX
+    kernel_size: 2
+    stride: 2
+  }
+}
+layer {
+  name: "conv4_1"
+  type: "Convolution"
+  bottom: "pool3_stage1"
+  top: "conv4_1"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 512
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "relu4_1"
+  type: "ReLU"
+  bottom: "conv4_1"
+  top: "conv4_1"
+}
+layer {
+  name: "conv4_2"
+  type: "Convolution"
+  bottom: "conv4_1"
+  top: "conv4_2"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 512
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "relu4_2"
+  type: "ReLU"
+  bottom: "conv4_2"
+  top: "conv4_2"
+}
+layer {
+  name: "conv4_3_CPM"
+  type: "Convolution"
+  bottom: "conv4_2"
+  top: "conv4_3_CPM"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 256
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "relu4_3_CPM"
+  type: "ReLU"
+  bottom: "conv4_3_CPM"
+  top: "conv4_3_CPM"
+}
+layer {
+  name: "conv4_4_CPM"
+  type: "Convolution"
+  bottom: "conv4_3_CPM"
+  top: "conv4_4_CPM"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "relu4_4_CPM"
+  type: "ReLU"
+  bottom: "conv4_4_CPM"
+  top: "conv4_4_CPM"
+}
+layer {
+  name: "conv5_1_CPM_L1"
+  type: "Convolution"
+  bottom: "conv4_4_CPM"
+  top: "conv5_1_CPM_L1"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "relu5_1_CPM_L1"
+  type: "ReLU"
+  bottom: "conv5_1_CPM_L1"
+  top: "conv5_1_CPM_L1"
+}
+layer {
+  name: "conv5_1_CPM_L2"
+  type: "Convolution"
+  bottom: "conv4_4_CPM"
+  top: "conv5_1_CPM_L2"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "relu5_1_CPM_L2"
+  type: "ReLU"
+  bottom: "conv5_1_CPM_L2"
+  top: "conv5_1_CPM_L2"
+}
+layer {
+  name: "conv5_2_CPM_L1"
+  type: "Convolution"
+  bottom: "conv5_1_CPM_L1"
+  top: "conv5_2_CPM_L1"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "relu5_2_CPM_L1"
+  type: "ReLU"
+  bottom: "conv5_2_CPM_L1"
+  top: "conv5_2_CPM_L1"
+}
+layer {
+  name: "conv5_2_CPM_L2"
+  type: "Convolution"
+  bottom: "conv5_1_CPM_L2"
+  top: "conv5_2_CPM_L2"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "relu5_2_CPM_L2"
+  type: "ReLU"
+  bottom: "conv5_2_CPM_L2"
+  top: "conv5_2_CPM_L2"
+}
+layer {
+  name: "conv5_3_CPM_L1"
+  type: "Convolution"
+  bottom: "conv5_2_CPM_L1"
+  top: "conv5_3_CPM_L1"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "relu5_3_CPM_L1"
+  type: "ReLU"
+  bottom: "conv5_3_CPM_L1"
+  top: "conv5_3_CPM_L1"
+}
+layer {
+  name: "conv5_3_CPM_L2"
+  type: "Convolution"
+  bottom: "conv5_2_CPM_L2"
+  top: "conv5_3_CPM_L2"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "relu5_3_CPM_L2"
+  type: "ReLU"
+  bottom: "conv5_3_CPM_L2"
+  top: "conv5_3_CPM_L2"
+}
+layer {
+  name: "conv5_4_CPM_L1"
+  type: "Convolution"
+  bottom: "conv5_3_CPM_L1"
+  top: "conv5_4_CPM_L1"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 512
+    pad: 0
+    kernel_size: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "relu5_4_CPM_L1"
+  type: "ReLU"
+  bottom: "conv5_4_CPM_L1"
+  top: "conv5_4_CPM_L1"
+}
+layer {
+  name: "conv5_4_CPM_L2"
+  type: "Convolution"
+  bottom: "conv5_3_CPM_L2"
+  top: "conv5_4_CPM_L2"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 512
+    pad: 0
+    kernel_size: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "relu5_4_CPM_L2"
+  type: "ReLU"
+  bottom: "conv5_4_CPM_L2"
+  top: "conv5_4_CPM_L2"
+}
+layer {
+  name: "conv5_5_CPM_L1"
+  type: "Convolution"
+  bottom: "conv5_4_CPM_L1"
+  top: "conv5_5_CPM_L1"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 38
+    pad: 0
+    kernel_size: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "conv5_5_CPM_L2"
+  type: "Convolution"
+  bottom: "conv5_4_CPM_L2"
+  top: "conv5_5_CPM_L2"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 19
+    pad: 0
+    kernel_size: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "concat_stage2"
+  type: "Concat"
+  bottom: "conv5_5_CPM_L1"
+  bottom: "conv5_5_CPM_L2"
+  bottom: "conv4_4_CPM"
+  top: "concat_stage2"
+  concat_param {
+    axis: 1
+  }
+}
+layer {
+  name: "Mconv1_stage2_L1"
+  type: "Convolution"
+  bottom: "concat_stage2"
+  top: "Mconv1_stage2_L1"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mrelu1_stage2_L1"
+  type: "ReLU"
+  bottom: "Mconv1_stage2_L1"
+  top: "Mconv1_stage2_L1"
+}
+layer {
+  name: "Mconv1_stage2_L2"
+  type: "Convolution"
+  bottom: "concat_stage2"
+  top: "Mconv1_stage2_L2"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mrelu1_stage2_L2"
+  type: "ReLU"
+  bottom: "Mconv1_stage2_L2"
+  top: "Mconv1_stage2_L2"
+}
+layer {
+  name: "Mconv2_stage2_L1"
+  type: "Convolution"
+  bottom: "Mconv1_stage2_L1"
+  top: "Mconv2_stage2_L1"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mrelu2_stage2_L1"
+  type: "ReLU"
+  bottom: "Mconv2_stage2_L1"
+  top: "Mconv2_stage2_L1"
+}
+layer {
+  name: "Mconv2_stage2_L2"
+  type: "Convolution"
+  bottom: "Mconv1_stage2_L2"
+  top: "Mconv2_stage2_L2"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mrelu2_stage2_L2"
+  type: "ReLU"
+  bottom: "Mconv2_stage2_L2"
+  top: "Mconv2_stage2_L2"
+}
+layer {
+  name: "Mconv3_stage2_L1"
+  type: "Convolution"
+  bottom: "Mconv2_stage2_L1"
+  top: "Mconv3_stage2_L1"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mrelu3_stage2_L1"
+  type: "ReLU"
+  bottom: "Mconv3_stage2_L1"
+  top: "Mconv3_stage2_L1"
+}
+layer {
+  name: "Mconv3_stage2_L2"
+  type: "Convolution"
+  bottom: "Mconv2_stage2_L2"
+  top: "Mconv3_stage2_L2"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mrelu3_stage2_L2"
+  type: "ReLU"
+  bottom: "Mconv3_stage2_L2"
+  top: "Mconv3_stage2_L2"
+}
+layer {
+  name: "Mconv4_stage2_L1"
+  type: "Convolution"
+  bottom: "Mconv3_stage2_L1"
+  top: "Mconv4_stage2_L1"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mrelu4_stage2_L1"
+  type: "ReLU"
+  bottom: "Mconv4_stage2_L1"
+  top: "Mconv4_stage2_L1"
+}
+layer {
+  name: "Mconv4_stage2_L2"
+  type: "Convolution"
+  bottom: "Mconv3_stage2_L2"
+  top: "Mconv4_stage2_L2"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mrelu4_stage2_L2"
+  type: "ReLU"
+  bottom: "Mconv4_stage2_L2"
+  top: "Mconv4_stage2_L2"
+}
+layer {
+  name: "Mconv5_stage2_L1"
+  type: "Convolution"
+  bottom: "Mconv4_stage2_L1"
+  top: "Mconv5_stage2_L1"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mrelu5_stage2_L1"
+  type: "ReLU"
+  bottom: "Mconv5_stage2_L1"
+  top: "Mconv5_stage2_L1"
+}
+layer {
+  name: "Mconv5_stage2_L2"
+  type: "Convolution"
+  bottom: "Mconv4_stage2_L2"
+  top: "Mconv5_stage2_L2"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mrelu5_stage2_L2"
+  type: "ReLU"
+  bottom: "Mconv5_stage2_L2"
+  top: "Mconv5_stage2_L2"
+}
+layer {
+  name: "Mconv6_stage2_L1"
+  type: "Convolution"
+  bottom: "Mconv5_stage2_L1"
+  top: "Mconv6_stage2_L1"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 0
+    kernel_size: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mrelu6_stage2_L1"
+  type: "ReLU"
+  bottom: "Mconv6_stage2_L1"
+  top: "Mconv6_stage2_L1"
+}
+layer {
+  name: "Mconv6_stage2_L2"
+  type: "Convolution"
+  bottom: "Mconv5_stage2_L2"
+  top: "Mconv6_stage2_L2"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 0
+    kernel_size: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mrelu6_stage2_L2"
+  type: "ReLU"
+  bottom: "Mconv6_stage2_L2"
+  top: "Mconv6_stage2_L2"
+}
+layer {
+  name: "Mconv7_stage2_L1"
+  type: "Convolution"
+  bottom: "Mconv6_stage2_L1"
+  top: "Mconv7_stage2_L1"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 38
+    pad: 0
+    kernel_size: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mconv7_stage2_L2"
+  type: "Convolution"
+  bottom: "Mconv6_stage2_L2"
+  top: "Mconv7_stage2_L2"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 19
+    pad: 0
+    kernel_size: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "concat_stage3"
+  type: "Concat"
+  bottom: "Mconv7_stage2_L1"
+  bottom: "Mconv7_stage2_L2"
+  bottom: "conv4_4_CPM"
+  top: "concat_stage3"
+  concat_param {
+    axis: 1
+  }
+}
+layer {
+  name: "Mconv1_stage3_L1"
+  type: "Convolution"
+  bottom: "concat_stage3"
+  top: "Mconv1_stage3_L1"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mrelu1_stage3_L1"
+  type: "ReLU"
+  bottom: "Mconv1_stage3_L1"
+  top: "Mconv1_stage3_L1"
+}
+layer {
+  name: "Mconv1_stage3_L2"
+  type: "Convolution"
+  bottom: "concat_stage3"
+  top: "Mconv1_stage3_L2"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mrelu1_stage3_L2"
+  type: "ReLU"
+  bottom: "Mconv1_stage3_L2"
+  top: "Mconv1_stage3_L2"
+}
+layer {
+  name: "Mconv2_stage3_L1"
+  type: "Convolution"
+  bottom: "Mconv1_stage3_L1"
+  top: "Mconv2_stage3_L1"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mrelu2_stage3_L1"
+  type: "ReLU"
+  bottom: "Mconv2_stage3_L1"
+  top: "Mconv2_stage3_L1"
+}
+layer {
+  name: "Mconv2_stage3_L2"
+  type: "Convolution"
+  bottom: "Mconv1_stage3_L2"
+  top: "Mconv2_stage3_L2"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mrelu2_stage3_L2"
+  type: "ReLU"
+  bottom: "Mconv2_stage3_L2"
+  top: "Mconv2_stage3_L2"
+}
+layer {
+  name: "Mconv3_stage3_L1"
+  type: "Convolution"
+  bottom: "Mconv2_stage3_L1"
+  top: "Mconv3_stage3_L1"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mrelu3_stage3_L1"
+  type: "ReLU"
+  bottom: "Mconv3_stage3_L1"
+  top: "Mconv3_stage3_L1"
+}
+layer {
+  name: "Mconv3_stage3_L2"
+  type: "Convolution"
+  bottom: "Mconv2_stage3_L2"
+  top: "Mconv3_stage3_L2"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mrelu3_stage3_L2"
+  type: "ReLU"
+  bottom: "Mconv3_stage3_L2"
+  top: "Mconv3_stage3_L2"
+}
+layer {
+  name: "Mconv4_stage3_L1"
+  type: "Convolution"
+  bottom: "Mconv3_stage3_L1"
+  top: "Mconv4_stage3_L1"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mrelu4_stage3_L1"
+  type: "ReLU"
+  bottom: "Mconv4_stage3_L1"
+  top: "Mconv4_stage3_L1"
+}
+layer {
+  name: "Mconv4_stage3_L2"
+  type: "Convolution"
+  bottom: "Mconv3_stage3_L2"
+  top: "Mconv4_stage3_L2"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mrelu4_stage3_L2"
+  type: "ReLU"
+  bottom: "Mconv4_stage3_L2"
+  top: "Mconv4_stage3_L2"
+}
+layer {
+  name: "Mconv5_stage3_L1"
+  type: "Convolution"
+  bottom: "Mconv4_stage3_L1"
+  top: "Mconv5_stage3_L1"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mrelu5_stage3_L1"
+  type: "ReLU"
+  bottom: "Mconv5_stage3_L1"
+  top: "Mconv5_stage3_L1"
+}
+layer {
+  name: "Mconv5_stage3_L2"
+  type: "Convolution"
+  bottom: "Mconv4_stage3_L2"
+  top: "Mconv5_stage3_L2"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mrelu5_stage3_L2"
+  type: "ReLU"
+  bottom: "Mconv5_stage3_L2"
+  top: "Mconv5_stage3_L2"
+}
+layer {
+  name: "Mconv6_stage3_L1"
+  type: "Convolution"
+  bottom: "Mconv5_stage3_L1"
+  top: "Mconv6_stage3_L1"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 0
+    kernel_size: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mrelu6_stage3_L1"
+  type: "ReLU"
+  bottom: "Mconv6_stage3_L1"
+  top: "Mconv6_stage3_L1"
+}
+layer {
+  name: "Mconv6_stage3_L2"
+  type: "Convolution"
+  bottom: "Mconv5_stage3_L2"
+  top: "Mconv6_stage3_L2"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 0
+    kernel_size: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mrelu6_stage3_L2"
+  type: "ReLU"
+  bottom: "Mconv6_stage3_L2"
+  top: "Mconv6_stage3_L2"
+}
+layer {
+  name: "Mconv7_stage3_L1"
+  type: "Convolution"
+  bottom: "Mconv6_stage3_L1"
+  top: "Mconv7_stage3_L1"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 38
+    pad: 0
+    kernel_size: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mconv7_stage3_L2"
+  type: "Convolution"
+  bottom: "Mconv6_stage3_L2"
+  top: "Mconv7_stage3_L2"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 19
+    pad: 0
+    kernel_size: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "concat_stage4"
+  type: "Concat"
+  bottom: "Mconv7_stage3_L1"
+  bottom: "Mconv7_stage3_L2"
+  bottom: "conv4_4_CPM"
+  top: "concat_stage4"
+  concat_param {
+    axis: 1
+  }
+}
+layer {
+  name: "Mconv1_stage4_L1"
+  type: "Convolution"
+  bottom: "concat_stage4"
+  top: "Mconv1_stage4_L1"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mrelu1_stage4_L1"
+  type: "ReLU"
+  bottom: "Mconv1_stage4_L1"
+  top: "Mconv1_stage4_L1"
+}
+layer {
+  name: "Mconv1_stage4_L2"
+  type: "Convolution"
+  bottom: "concat_stage4"
+  top: "Mconv1_stage4_L2"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mrelu1_stage4_L2"
+  type: "ReLU"
+  bottom: "Mconv1_stage4_L2"
+  top: "Mconv1_stage4_L2"
+}
+layer {
+  name: "Mconv2_stage4_L1"
+  type: "Convolution"
+  bottom: "Mconv1_stage4_L1"
+  top: "Mconv2_stage4_L1"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mrelu2_stage4_L1"
+  type: "ReLU"
+  bottom: "Mconv2_stage4_L1"
+  top: "Mconv2_stage4_L1"
+}
+layer {
+  name: "Mconv2_stage4_L2"
+  type: "Convolution"
+  bottom: "Mconv1_stage4_L2"
+  top: "Mconv2_stage4_L2"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mrelu2_stage4_L2"
+  type: "ReLU"
+  bottom: "Mconv2_stage4_L2"
+  top: "Mconv2_stage4_L2"
+}
+layer {
+  name: "Mconv3_stage4_L1"
+  type: "Convolution"
+  bottom: "Mconv2_stage4_L1"
+  top: "Mconv3_stage4_L1"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mrelu3_stage4_L1"
+  type: "ReLU"
+  bottom: "Mconv3_stage4_L1"
+  top: "Mconv3_stage4_L1"
+}
+layer {
+  name: "Mconv3_stage4_L2"
+  type: "Convolution"
+  bottom: "Mconv2_stage4_L2"
+  top: "Mconv3_stage4_L2"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mrelu3_stage4_L2"
+  type: "ReLU"
+  bottom: "Mconv3_stage4_L2"
+  top: "Mconv3_stage4_L2"
+}
+layer {
+  name: "Mconv4_stage4_L1"
+  type: "Convolution"
+  bottom: "Mconv3_stage4_L1"
+  top: "Mconv4_stage4_L1"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mrelu4_stage4_L1"
+  type: "ReLU"
+  bottom: "Mconv4_stage4_L1"
+  top: "Mconv4_stage4_L1"
+}
+layer {
+  name: "Mconv4_stage4_L2"
+  type: "Convolution"
+  bottom: "Mconv3_stage4_L2"
+  top: "Mconv4_stage4_L2"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mrelu4_stage4_L2"
+  type: "ReLU"
+  bottom: "Mconv4_stage4_L2"
+  top: "Mconv4_stage4_L2"
+}
+layer {
+  name: "Mconv5_stage4_L1"
+  type: "Convolution"
+  bottom: "Mconv4_stage4_L1"
+  top: "Mconv5_stage4_L1"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mrelu5_stage4_L1"
+  type: "ReLU"
+  bottom: "Mconv5_stage4_L1"
+  top: "Mconv5_stage4_L1"
+}
+layer {
+  name: "Mconv5_stage4_L2"
+  type: "Convolution"
+  bottom: "Mconv4_stage4_L2"
+  top: "Mconv5_stage4_L2"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mrelu5_stage4_L2"
+  type: "ReLU"
+  bottom: "Mconv5_stage4_L2"
+  top: "Mconv5_stage4_L2"
+}
+layer {
+  name: "Mconv6_stage4_L1"
+  type: "Convolution"
+  bottom: "Mconv5_stage4_L1"
+  top: "Mconv6_stage4_L1"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 0
+    kernel_size: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mrelu6_stage4_L1"
+  type: "ReLU"
+  bottom: "Mconv6_stage4_L1"
+  top: "Mconv6_stage4_L1"
+}
+layer {
+  name: "Mconv6_stage4_L2"
+  type: "Convolution"
+  bottom: "Mconv5_stage4_L2"
+  top: "Mconv6_stage4_L2"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 0
+    kernel_size: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mrelu6_stage4_L2"
+  type: "ReLU"
+  bottom: "Mconv6_stage4_L2"
+  top: "Mconv6_stage4_L2"
+}
+layer {
+  name: "Mconv7_stage4_L1"
+  type: "Convolution"
+  bottom: "Mconv6_stage4_L1"
+  top: "Mconv7_stage4_L1"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 38
+    pad: 0
+    kernel_size: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mconv7_stage4_L2"
+  type: "Convolution"
+  bottom: "Mconv6_stage4_L2"
+  top: "Mconv7_stage4_L2"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 19
+    pad: 0
+    kernel_size: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "concat_stage5"
+  type: "Concat"
+  bottom: "Mconv7_stage4_L1"
+  bottom: "Mconv7_stage4_L2"
+  bottom: "conv4_4_CPM"
+  top: "concat_stage5"
+  concat_param {
+    axis: 1
+  }
+}
+layer {
+  name: "Mconv1_stage5_L1"
+  type: "Convolution"
+  bottom: "concat_stage5"
+  top: "Mconv1_stage5_L1"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mrelu1_stage5_L1"
+  type: "ReLU"
+  bottom: "Mconv1_stage5_L1"
+  top: "Mconv1_stage5_L1"
+}
+layer {
+  name: "Mconv1_stage5_L2"
+  type: "Convolution"
+  bottom: "concat_stage5"
+  top: "Mconv1_stage5_L2"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mrelu1_stage5_L2"
+  type: "ReLU"
+  bottom: "Mconv1_stage5_L2"
+  top: "Mconv1_stage5_L2"
+}
+layer {
+  name: "Mconv2_stage5_L1"
+  type: "Convolution"
+  bottom: "Mconv1_stage5_L1"
+  top: "Mconv2_stage5_L1"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mrelu2_stage5_L1"
+  type: "ReLU"
+  bottom: "Mconv2_stage5_L1"
+  top: "Mconv2_stage5_L1"
+}
+layer {
+  name: "Mconv2_stage5_L2"
+  type: "Convolution"
+  bottom: "Mconv1_stage5_L2"
+  top: "Mconv2_stage5_L2"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mrelu2_stage5_L2"
+  type: "ReLU"
+  bottom: "Mconv2_stage5_L2"
+  top: "Mconv2_stage5_L2"
+}
+layer {
+  name: "Mconv3_stage5_L1"
+  type: "Convolution"
+  bottom: "Mconv2_stage5_L1"
+  top: "Mconv3_stage5_L1"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mrelu3_stage5_L1"
+  type: "ReLU"
+  bottom: "Mconv3_stage5_L1"
+  top: "Mconv3_stage5_L1"
+}
+layer {
+  name: "Mconv3_stage5_L2"
+  type: "Convolution"
+  bottom: "Mconv2_stage5_L2"
+  top: "Mconv3_stage5_L2"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mrelu3_stage5_L2"
+  type: "ReLU"
+  bottom: "Mconv3_stage5_L2"
+  top: "Mconv3_stage5_L2"
+}
+layer {
+  name: "Mconv4_stage5_L1"
+  type: "Convolution"
+  bottom: "Mconv3_stage5_L1"
+  top: "Mconv4_stage5_L1"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mrelu4_stage5_L1"
+  type: "ReLU"
+  bottom: "Mconv4_stage5_L1"
+  top: "Mconv4_stage5_L1"
+}
+layer {
+  name: "Mconv4_stage5_L2"
+  type: "Convolution"
+  bottom: "Mconv3_stage5_L2"
+  top: "Mconv4_stage5_L2"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mrelu4_stage5_L2"
+  type: "ReLU"
+  bottom: "Mconv4_stage5_L2"
+  top: "Mconv4_stage5_L2"
+}
+layer {
+  name: "Mconv5_stage5_L1"
+  type: "Convolution"
+  bottom: "Mconv4_stage5_L1"
+  top: "Mconv5_stage5_L1"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mrelu5_stage5_L1"
+  type: "ReLU"
+  bottom: "Mconv5_stage5_L1"
+  top: "Mconv5_stage5_L1"
+}
+layer {
+  name: "Mconv5_stage5_L2"
+  type: "Convolution"
+  bottom: "Mconv4_stage5_L2"
+  top: "Mconv5_stage5_L2"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mrelu5_stage5_L2"
+  type: "ReLU"
+  bottom: "Mconv5_stage5_L2"
+  top: "Mconv5_stage5_L2"
+}
+layer {
+  name: "Mconv6_stage5_L1"
+  type: "Convolution"
+  bottom: "Mconv5_stage5_L1"
+  top: "Mconv6_stage5_L1"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 0
+    kernel_size: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mrelu6_stage5_L1"
+  type: "ReLU"
+  bottom: "Mconv6_stage5_L1"
+  top: "Mconv6_stage5_L1"
+}
+layer {
+  name: "Mconv6_stage5_L2"
+  type: "Convolution"
+  bottom: "Mconv5_stage5_L2"
+  top: "Mconv6_stage5_L2"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 0
+    kernel_size: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mrelu6_stage5_L2"
+  type: "ReLU"
+  bottom: "Mconv6_stage5_L2"
+  top: "Mconv6_stage5_L2"
+}
+layer {
+  name: "Mconv7_stage5_L1"
+  type: "Convolution"
+  bottom: "Mconv6_stage5_L1"
+  top: "Mconv7_stage5_L1"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 38
+    pad: 0
+    kernel_size: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mconv7_stage5_L2"
+  type: "Convolution"
+  bottom: "Mconv6_stage5_L2"
+  top: "Mconv7_stage5_L2"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 19
+    pad: 0
+    kernel_size: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "concat_stage6"
+  type: "Concat"
+  bottom: "Mconv7_stage5_L1"
+  bottom: "Mconv7_stage5_L2"
+  bottom: "conv4_4_CPM"
+  top: "concat_stage6"
+  concat_param {
+    axis: 1
+  }
+}
+layer {
+  name: "Mconv1_stage6_L1"
+  type: "Convolution"
+  bottom: "concat_stage6"
+  top: "Mconv1_stage6_L1"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mrelu1_stage6_L1"
+  type: "ReLU"
+  bottom: "Mconv1_stage6_L1"
+  top: "Mconv1_stage6_L1"
+}
+layer {
+  name: "Mconv1_stage6_L2"
+  type: "Convolution"
+  bottom: "concat_stage6"
+  top: "Mconv1_stage6_L2"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mrelu1_stage6_L2"
+  type: "ReLU"
+  bottom: "Mconv1_stage6_L2"
+  top: "Mconv1_stage6_L2"
+}
+layer {
+  name: "Mconv2_stage6_L1"
+  type: "Convolution"
+  bottom: "Mconv1_stage6_L1"
+  top: "Mconv2_stage6_L1"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mrelu2_stage6_L1"
+  type: "ReLU"
+  bottom: "Mconv2_stage6_L1"
+  top: "Mconv2_stage6_L1"
+}
+layer {
+  name: "Mconv2_stage6_L2"
+  type: "Convolution"
+  bottom: "Mconv1_stage6_L2"
+  top: "Mconv2_stage6_L2"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mrelu2_stage6_L2"
+  type: "ReLU"
+  bottom: "Mconv2_stage6_L2"
+  top: "Mconv2_stage6_L2"
+}
+layer {
+  name: "Mconv3_stage6_L1"
+  type: "Convolution"
+  bottom: "Mconv2_stage6_L1"
+  top: "Mconv3_stage6_L1"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mrelu3_stage6_L1"
+  type: "ReLU"
+  bottom: "Mconv3_stage6_L1"
+  top: "Mconv3_stage6_L1"
+}
+layer {
+  name: "Mconv3_stage6_L2"
+  type: "Convolution"
+  bottom: "Mconv2_stage6_L2"
+  top: "Mconv3_stage6_L2"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mrelu3_stage6_L2"
+  type: "ReLU"
+  bottom: "Mconv3_stage6_L2"
+  top: "Mconv3_stage6_L2"
+}
+layer {
+  name: "Mconv4_stage6_L1"
+  type: "Convolution"
+  bottom: "Mconv3_stage6_L1"
+  top: "Mconv4_stage6_L1"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mrelu4_stage6_L1"
+  type: "ReLU"
+  bottom: "Mconv4_stage6_L1"
+  top: "Mconv4_stage6_L1"
+}
+layer {
+  name: "Mconv4_stage6_L2"
+  type: "Convolution"
+  bottom: "Mconv3_stage6_L2"
+  top: "Mconv4_stage6_L2"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mrelu4_stage6_L2"
+  type: "ReLU"
+  bottom: "Mconv4_stage6_L2"
+  top: "Mconv4_stage6_L2"
+}
+layer {
+  name: "Mconv5_stage6_L1"
+  type: "Convolution"
+  bottom: "Mconv4_stage6_L1"
+  top: "Mconv5_stage6_L1"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mrelu5_stage6_L1"
+  type: "ReLU"
+  bottom: "Mconv5_stage6_L1"
+  top: "Mconv5_stage6_L1"
+}
+layer {
+  name: "Mconv5_stage6_L2"
+  type: "Convolution"
+  bottom: "Mconv4_stage6_L2"
+  top: "Mconv5_stage6_L2"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mrelu5_stage6_L2"
+  type: "ReLU"
+  bottom: "Mconv5_stage6_L2"
+  top: "Mconv5_stage6_L2"
+}
+layer {
+  name: "Mconv6_stage6_L1"
+  type: "Convolution"
+  bottom: "Mconv5_stage6_L1"
+  top: "Mconv6_stage6_L1"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 0
+    kernel_size: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mrelu6_stage6_L1"
+  type: "ReLU"
+  bottom: "Mconv6_stage6_L1"
+  top: "Mconv6_stage6_L1"
+}
+layer {
+  name: "Mconv6_stage6_L2"
+  type: "Convolution"
+  bottom: "Mconv5_stage6_L2"
+  top: "Mconv6_stage6_L2"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 0
+    kernel_size: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mrelu6_stage6_L2"
+  type: "ReLU"
+  bottom: "Mconv6_stage6_L2"
+  top: "Mconv6_stage6_L2"
+}
+layer {
+  name: "Mconv7_stage6_L1"
+  type: "Convolution"
+  bottom: "Mconv6_stage6_L1"
+  top: "Mconv7_stage6_L1"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 38
+    pad: 0
+    kernel_size: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mconv7_stage6_L2"
+  type: "Convolution"
+  bottom: "Mconv6_stage6_L2"
+  top: "Mconv7_stage6_L2"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 19
+    pad: 0
+    kernel_size: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "concat_stage7"
+  type: "Concat"
+  bottom: "Mconv7_stage6_L2"
+  bottom: "Mconv7_stage6_L1"
+  # top: "concat_stage7"
+  top: "net_output"
+  concat_param {
+    axis: 1
+  }
+}
diff --git a/joints_detectors/openpose/models/pose/mpi/pose_deploy_linevec.prototxt b/joints_detectors/openpose/models/pose/mpi/pose_deploy_linevec.prototxt
new file mode 100644
index 0000000000000000000000000000000000000000..54492d68a185cc50661078f370c7aa0041d8761a
--- /dev/null
+++ b/joints_detectors/openpose/models/pose/mpi/pose_deploy_linevec.prototxt
@@ -0,0 +1,2975 @@
+input: "image"
+input_dim: 1
+input_dim: 3
+input_dim: 1 # This value will be defined at runtime
+input_dim: 1 # This value will be defined at runtime
+layer {
+  name: "conv1_1"
+  type: "Convolution"
+  bottom: "image"
+  top: "conv1_1"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 64
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "relu1_1"
+  type: "ReLU"
+  bottom: "conv1_1"
+  top: "conv1_1"
+}
+layer {
+  name: "conv1_2"
+  type: "Convolution"
+  bottom: "conv1_1"
+  top: "conv1_2"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 64
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "relu1_2"
+  type: "ReLU"
+  bottom: "conv1_2"
+  top: "conv1_2"
+}
+layer {
+  name: "pool1_stage1"
+  type: "Pooling"
+  bottom: "conv1_2"
+  top: "pool1_stage1"
+  pooling_param {
+    pool: MAX
+    kernel_size: 2
+    stride: 2
+  }
+}
+layer {
+  name: "conv2_1"
+  type: "Convolution"
+  bottom: "pool1_stage1"
+  top: "conv2_1"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "relu2_1"
+  type: "ReLU"
+  bottom: "conv2_1"
+  top: "conv2_1"
+}
+layer {
+  name: "conv2_2"
+  type: "Convolution"
+  bottom: "conv2_1"
+  top: "conv2_2"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "relu2_2"
+  type: "ReLU"
+  bottom: "conv2_2"
+  top: "conv2_2"
+}
+layer {
+  name: "pool2_stage1"
+  type: "Pooling"
+  bottom: "conv2_2"
+  top: "pool2_stage1"
+  pooling_param {
+    pool: MAX
+    kernel_size: 2
+    stride: 2
+  }
+}
+layer {
+  name: "conv3_1"
+  type: "Convolution"
+  bottom: "pool2_stage1"
+  top: "conv3_1"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 256
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "relu3_1"
+  type: "ReLU"
+  bottom: "conv3_1"
+  top: "conv3_1"
+}
+layer {
+  name: "conv3_2"
+  type: "Convolution"
+  bottom: "conv3_1"
+  top: "conv3_2"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 256
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "relu3_2"
+  type: "ReLU"
+  bottom: "conv3_2"
+  top: "conv3_2"
+}
+layer {
+  name: "conv3_3"
+  type: "Convolution"
+  bottom: "conv3_2"
+  top: "conv3_3"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 256
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "relu3_3"
+  type: "ReLU"
+  bottom: "conv3_3"
+  top: "conv3_3"
+}
+layer {
+  name: "conv3_4"
+  type: "Convolution"
+  bottom: "conv3_3"
+  top: "conv3_4"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 256
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "relu3_4"
+  type: "ReLU"
+  bottom: "conv3_4"
+  top: "conv3_4"
+}
+layer {
+  name: "pool3_stage1"
+  type: "Pooling"
+  bottom: "conv3_4"
+  top: "pool3_stage1"
+  pooling_param {
+    pool: MAX
+    kernel_size: 2
+    stride: 2
+  }
+}
+layer {
+  name: "conv4_1"
+  type: "Convolution"
+  bottom: "pool3_stage1"
+  top: "conv4_1"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 512
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "relu4_1"
+  type: "ReLU"
+  bottom: "conv4_1"
+  top: "conv4_1"
+}
+layer {
+  name: "conv4_2"
+  type: "Convolution"
+  bottom: "conv4_1"
+  top: "conv4_2"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 512
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "relu4_2"
+  type: "ReLU"
+  bottom: "conv4_2"
+  top: "conv4_2"
+}
+layer {
+  name: "conv4_3_CPM"
+  type: "Convolution"
+  bottom: "conv4_2"
+  top: "conv4_3_CPM"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 256
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "relu4_3_CPM"
+  type: "ReLU"
+  bottom: "conv4_3_CPM"
+  top: "conv4_3_CPM"
+}
+layer {
+  name: "conv4_4_CPM"
+  type: "Convolution"
+  bottom: "conv4_3_CPM"
+  top: "conv4_4_CPM"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "relu4_4_CPM"
+  type: "ReLU"
+  bottom: "conv4_4_CPM"
+  top: "conv4_4_CPM"
+}
+layer {
+  name: "conv5_1_CPM_L1"
+  type: "Convolution"
+  bottom: "conv4_4_CPM"
+  top: "conv5_1_CPM_L1"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "relu5_1_CPM_L1"
+  type: "ReLU"
+  bottom: "conv5_1_CPM_L1"
+  top: "conv5_1_CPM_L1"
+}
+layer {
+  name: "conv5_1_CPM_L2"
+  type: "Convolution"
+  bottom: "conv4_4_CPM"
+  top: "conv5_1_CPM_L2"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "relu5_1_CPM_L2"
+  type: "ReLU"
+  bottom: "conv5_1_CPM_L2"
+  top: "conv5_1_CPM_L2"
+}
+layer {
+  name: "conv5_2_CPM_L1"
+  type: "Convolution"
+  bottom: "conv5_1_CPM_L1"
+  top: "conv5_2_CPM_L1"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "relu5_2_CPM_L1"
+  type: "ReLU"
+  bottom: "conv5_2_CPM_L1"
+  top: "conv5_2_CPM_L1"
+}
+layer {
+  name: "conv5_2_CPM_L2"
+  type: "Convolution"
+  bottom: "conv5_1_CPM_L2"
+  top: "conv5_2_CPM_L2"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "relu5_2_CPM_L2"
+  type: "ReLU"
+  bottom: "conv5_2_CPM_L2"
+  top: "conv5_2_CPM_L2"
+}
+layer {
+  name: "conv5_3_CPM_L1"
+  type: "Convolution"
+  bottom: "conv5_2_CPM_L1"
+  top: "conv5_3_CPM_L1"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "relu5_3_CPM_L1"
+  type: "ReLU"
+  bottom: "conv5_3_CPM_L1"
+  top: "conv5_3_CPM_L1"
+}
+layer {
+  name: "conv5_3_CPM_L2"
+  type: "Convolution"
+  bottom: "conv5_2_CPM_L2"
+  top: "conv5_3_CPM_L2"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "relu5_3_CPM_L2"
+  type: "ReLU"
+  bottom: "conv5_3_CPM_L2"
+  top: "conv5_3_CPM_L2"
+}
+layer {
+  name: "conv5_4_CPM_L1"
+  type: "Convolution"
+  bottom: "conv5_3_CPM_L1"
+  top: "conv5_4_CPM_L1"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 512
+    pad: 0
+    kernel_size: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "relu5_4_CPM_L1"
+  type: "ReLU"
+  bottom: "conv5_4_CPM_L1"
+  top: "conv5_4_CPM_L1"
+}
+layer {
+  name: "conv5_4_CPM_L2"
+  type: "Convolution"
+  bottom: "conv5_3_CPM_L2"
+  top: "conv5_4_CPM_L2"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 512
+    pad: 0
+    kernel_size: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "relu5_4_CPM_L2"
+  type: "ReLU"
+  bottom: "conv5_4_CPM_L2"
+  top: "conv5_4_CPM_L2"
+}
+layer {
+  name: "conv5_5_CPM_L1"
+  type: "Convolution"
+  bottom: "conv5_4_CPM_L1"
+  top: "conv5_5_CPM_L1"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 28
+    pad: 0
+    kernel_size: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "conv5_5_CPM_L2"
+  type: "Convolution"
+  bottom: "conv5_4_CPM_L2"
+  top: "conv5_5_CPM_L2"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 16
+    pad: 0
+    kernel_size: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "concat_stage2"
+  type: "Concat"
+  bottom: "conv5_5_CPM_L1"
+  bottom: "conv5_5_CPM_L2"
+  bottom: "conv4_4_CPM"
+  top: "concat_stage2"
+  concat_param {
+    axis: 1
+  }
+}
+layer {
+  name: "Mconv1_stage2_L1"
+  type: "Convolution"
+  bottom: "concat_stage2"
+  top: "Mconv1_stage2_L1"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mrelu1_stage2_L1"
+  type: "ReLU"
+  bottom: "Mconv1_stage2_L1"
+  top: "Mconv1_stage2_L1"
+}
+layer {
+  name: "Mconv1_stage2_L2"
+  type: "Convolution"
+  bottom: "concat_stage2"
+  top: "Mconv1_stage2_L2"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mrelu1_stage2_L2"
+  type: "ReLU"
+  bottom: "Mconv1_stage2_L2"
+  top: "Mconv1_stage2_L2"
+}
+layer {
+  name: "Mconv2_stage2_L1"
+  type: "Convolution"
+  bottom: "Mconv1_stage2_L1"
+  top: "Mconv2_stage2_L1"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mrelu2_stage2_L1"
+  type: "ReLU"
+  bottom: "Mconv2_stage2_L1"
+  top: "Mconv2_stage2_L1"
+}
+layer {
+  name: "Mconv2_stage2_L2"
+  type: "Convolution"
+  bottom: "Mconv1_stage2_L2"
+  top: "Mconv2_stage2_L2"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mrelu2_stage2_L2"
+  type: "ReLU"
+  bottom: "Mconv2_stage2_L2"
+  top: "Mconv2_stage2_L2"
+}
+layer {
+  name: "Mconv3_stage2_L1"
+  type: "Convolution"
+  bottom: "Mconv2_stage2_L1"
+  top: "Mconv3_stage2_L1"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mrelu3_stage2_L1"
+  type: "ReLU"
+  bottom: "Mconv3_stage2_L1"
+  top: "Mconv3_stage2_L1"
+}
+layer {
+  name: "Mconv3_stage2_L2"
+  type: "Convolution"
+  bottom: "Mconv2_stage2_L2"
+  top: "Mconv3_stage2_L2"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mrelu3_stage2_L2"
+  type: "ReLU"
+  bottom: "Mconv3_stage2_L2"
+  top: "Mconv3_stage2_L2"
+}
+layer {
+  name: "Mconv4_stage2_L1"
+  type: "Convolution"
+  bottom: "Mconv3_stage2_L1"
+  top: "Mconv4_stage2_L1"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mrelu4_stage2_L1"
+  type: "ReLU"
+  bottom: "Mconv4_stage2_L1"
+  top: "Mconv4_stage2_L1"
+}
+layer {
+  name: "Mconv4_stage2_L2"
+  type: "Convolution"
+  bottom: "Mconv3_stage2_L2"
+  top: "Mconv4_stage2_L2"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mrelu4_stage2_L2"
+  type: "ReLU"
+  bottom: "Mconv4_stage2_L2"
+  top: "Mconv4_stage2_L2"
+}
+layer {
+  name: "Mconv5_stage2_L1"
+  type: "Convolution"
+  bottom: "Mconv4_stage2_L1"
+  top: "Mconv5_stage2_L1"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mrelu5_stage2_L1"
+  type: "ReLU"
+  bottom: "Mconv5_stage2_L1"
+  top: "Mconv5_stage2_L1"
+}
+layer {
+  name: "Mconv5_stage2_L2"
+  type: "Convolution"
+  bottom: "Mconv4_stage2_L2"
+  top: "Mconv5_stage2_L2"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mrelu5_stage2_L2"
+  type: "ReLU"
+  bottom: "Mconv5_stage2_L2"
+  top: "Mconv5_stage2_L2"
+}
+layer {
+  name: "Mconv6_stage2_L1"
+  type: "Convolution"
+  bottom: "Mconv5_stage2_L1"
+  top: "Mconv6_stage2_L1"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 0
+    kernel_size: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mrelu6_stage2_L1"
+  type: "ReLU"
+  bottom: "Mconv6_stage2_L1"
+  top: "Mconv6_stage2_L1"
+}
+layer {
+  name: "Mconv6_stage2_L2"
+  type: "Convolution"
+  bottom: "Mconv5_stage2_L2"
+  top: "Mconv6_stage2_L2"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 0
+    kernel_size: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mrelu6_stage2_L2"
+  type: "ReLU"
+  bottom: "Mconv6_stage2_L2"
+  top: "Mconv6_stage2_L2"
+}
+layer {
+  name: "Mconv7_stage2_L1"
+  type: "Convolution"
+  bottom: "Mconv6_stage2_L1"
+  top: "Mconv7_stage2_L1"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 28
+    pad: 0
+    kernel_size: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mconv7_stage2_L2"
+  type: "Convolution"
+  bottom: "Mconv6_stage2_L2"
+  top: "Mconv7_stage2_L2"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 16
+    pad: 0
+    kernel_size: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "concat_stage3"
+  type: "Concat"
+  bottom: "Mconv7_stage2_L1"
+  bottom: "Mconv7_stage2_L2"
+  bottom: "conv4_4_CPM"
+  top: "concat_stage3"
+  concat_param {
+    axis: 1
+  }
+}
+layer {
+  name: "Mconv1_stage3_L1"
+  type: "Convolution"
+  bottom: "concat_stage3"
+  top: "Mconv1_stage3_L1"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mrelu1_stage3_L1"
+  type: "ReLU"
+  bottom: "Mconv1_stage3_L1"
+  top: "Mconv1_stage3_L1"
+}
+layer {
+  name: "Mconv1_stage3_L2"
+  type: "Convolution"
+  bottom: "concat_stage3"
+  top: "Mconv1_stage3_L2"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mrelu1_stage3_L2"
+  type: "ReLU"
+  bottom: "Mconv1_stage3_L2"
+  top: "Mconv1_stage3_L2"
+}
+layer {
+  name: "Mconv2_stage3_L1"
+  type: "Convolution"
+  bottom: "Mconv1_stage3_L1"
+  top: "Mconv2_stage3_L1"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mrelu2_stage3_L1"
+  type: "ReLU"
+  bottom: "Mconv2_stage3_L1"
+  top: "Mconv2_stage3_L1"
+}
+layer {
+  name: "Mconv2_stage3_L2"
+  type: "Convolution"
+  bottom: "Mconv1_stage3_L2"
+  top: "Mconv2_stage3_L2"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mrelu2_stage3_L2"
+  type: "ReLU"
+  bottom: "Mconv2_stage3_L2"
+  top: "Mconv2_stage3_L2"
+}
+layer {
+  name: "Mconv3_stage3_L1"
+  type: "Convolution"
+  bottom: "Mconv2_stage3_L1"
+  top: "Mconv3_stage3_L1"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mrelu3_stage3_L1"
+  type: "ReLU"
+  bottom: "Mconv3_stage3_L1"
+  top: "Mconv3_stage3_L1"
+}
+layer {
+  name: "Mconv3_stage3_L2"
+  type: "Convolution"
+  bottom: "Mconv2_stage3_L2"
+  top: "Mconv3_stage3_L2"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mrelu3_stage3_L2"
+  type: "ReLU"
+  bottom: "Mconv3_stage3_L2"
+  top: "Mconv3_stage3_L2"
+}
+layer {
+  name: "Mconv4_stage3_L1"
+  type: "Convolution"
+  bottom: "Mconv3_stage3_L1"
+  top: "Mconv4_stage3_L1"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mrelu4_stage3_L1"
+  type: "ReLU"
+  bottom: "Mconv4_stage3_L1"
+  top: "Mconv4_stage3_L1"
+}
+layer {
+  name: "Mconv4_stage3_L2"
+  type: "Convolution"
+  bottom: "Mconv3_stage3_L2"
+  top: "Mconv4_stage3_L2"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mrelu4_stage3_L2"
+  type: "ReLU"
+  bottom: "Mconv4_stage3_L2"
+  top: "Mconv4_stage3_L2"
+}
+layer {
+  name: "Mconv5_stage3_L1"
+  type: "Convolution"
+  bottom: "Mconv4_stage3_L1"
+  top: "Mconv5_stage3_L1"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mrelu5_stage3_L1"
+  type: "ReLU"
+  bottom: "Mconv5_stage3_L1"
+  top: "Mconv5_stage3_L1"
+}
+layer {
+  name: "Mconv5_stage3_L2"
+  type: "Convolution"
+  bottom: "Mconv4_stage3_L2"
+  top: "Mconv5_stage3_L2"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mrelu5_stage3_L2"
+  type: "ReLU"
+  bottom: "Mconv5_stage3_L2"
+  top: "Mconv5_stage3_L2"
+}
+layer {
+  name: "Mconv6_stage3_L1"
+  type: "Convolution"
+  bottom: "Mconv5_stage3_L1"
+  top: "Mconv6_stage3_L1"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 0
+    kernel_size: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mrelu6_stage3_L1"
+  type: "ReLU"
+  bottom: "Mconv6_stage3_L1"
+  top: "Mconv6_stage3_L1"
+}
+layer {
+  name: "Mconv6_stage3_L2"
+  type: "Convolution"
+  bottom: "Mconv5_stage3_L2"
+  top: "Mconv6_stage3_L2"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 0
+    kernel_size: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mrelu6_stage3_L2"
+  type: "ReLU"
+  bottom: "Mconv6_stage3_L2"
+  top: "Mconv6_stage3_L2"
+}
+layer {
+  name: "Mconv7_stage3_L1"
+  type: "Convolution"
+  bottom: "Mconv6_stage3_L1"
+  top: "Mconv7_stage3_L1"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 28
+    pad: 0
+    kernel_size: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mconv7_stage3_L2"
+  type: "Convolution"
+  bottom: "Mconv6_stage3_L2"
+  top: "Mconv7_stage3_L2"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 16
+    pad: 0
+    kernel_size: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "concat_stage4"
+  type: "Concat"
+  bottom: "Mconv7_stage3_L1"
+  bottom: "Mconv7_stage3_L2"
+  bottom: "conv4_4_CPM"
+  top: "concat_stage4"
+  concat_param {
+    axis: 1
+  }
+}
+layer {
+  name: "Mconv1_stage4_L1"
+  type: "Convolution"
+  bottom: "concat_stage4"
+  top: "Mconv1_stage4_L1"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mrelu1_stage4_L1"
+  type: "ReLU"
+  bottom: "Mconv1_stage4_L1"
+  top: "Mconv1_stage4_L1"
+}
+layer {
+  name: "Mconv1_stage4_L2"
+  type: "Convolution"
+  bottom: "concat_stage4"
+  top: "Mconv1_stage4_L2"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mrelu1_stage4_L2"
+  type: "ReLU"
+  bottom: "Mconv1_stage4_L2"
+  top: "Mconv1_stage4_L2"
+}
+layer {
+  name: "Mconv2_stage4_L1"
+  type: "Convolution"
+  bottom: "Mconv1_stage4_L1"
+  top: "Mconv2_stage4_L1"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mrelu2_stage4_L1"
+  type: "ReLU"
+  bottom: "Mconv2_stage4_L1"
+  top: "Mconv2_stage4_L1"
+}
+layer {
+  name: "Mconv2_stage4_L2"
+  type: "Convolution"
+  bottom: "Mconv1_stage4_L2"
+  top: "Mconv2_stage4_L2"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mrelu2_stage4_L2"
+  type: "ReLU"
+  bottom: "Mconv2_stage4_L2"
+  top: "Mconv2_stage4_L2"
+}
+layer {
+  name: "Mconv3_stage4_L1"
+  type: "Convolution"
+  bottom: "Mconv2_stage4_L1"
+  top: "Mconv3_stage4_L1"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mrelu3_stage4_L1"
+  type: "ReLU"
+  bottom: "Mconv3_stage4_L1"
+  top: "Mconv3_stage4_L1"
+}
+layer {
+  name: "Mconv3_stage4_L2"
+  type: "Convolution"
+  bottom: "Mconv2_stage4_L2"
+  top: "Mconv3_stage4_L2"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mrelu3_stage4_L2"
+  type: "ReLU"
+  bottom: "Mconv3_stage4_L2"
+  top: "Mconv3_stage4_L2"
+}
+layer {
+  name: "Mconv4_stage4_L1"
+  type: "Convolution"
+  bottom: "Mconv3_stage4_L1"
+  top: "Mconv4_stage4_L1"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mrelu4_stage4_L1"
+  type: "ReLU"
+  bottom: "Mconv4_stage4_L1"
+  top: "Mconv4_stage4_L1"
+}
+layer {
+  name: "Mconv4_stage4_L2"
+  type: "Convolution"
+  bottom: "Mconv3_stage4_L2"
+  top: "Mconv4_stage4_L2"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mrelu4_stage4_L2"
+  type: "ReLU"
+  bottom: "Mconv4_stage4_L2"
+  top: "Mconv4_stage4_L2"
+}
+layer {
+  name: "Mconv5_stage4_L1"
+  type: "Convolution"
+  bottom: "Mconv4_stage4_L1"
+  top: "Mconv5_stage4_L1"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mrelu5_stage4_L1"
+  type: "ReLU"
+  bottom: "Mconv5_stage4_L1"
+  top: "Mconv5_stage4_L1"
+}
+layer {
+  name: "Mconv5_stage4_L2"
+  type: "Convolution"
+  bottom: "Mconv4_stage4_L2"
+  top: "Mconv5_stage4_L2"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mrelu5_stage4_L2"
+  type: "ReLU"
+  bottom: "Mconv5_stage4_L2"
+  top: "Mconv5_stage4_L2"
+}
+layer {
+  name: "Mconv6_stage4_L1"
+  type: "Convolution"
+  bottom: "Mconv5_stage4_L1"
+  top: "Mconv6_stage4_L1"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 0
+    kernel_size: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mrelu6_stage4_L1"
+  type: "ReLU"
+  bottom: "Mconv6_stage4_L1"
+  top: "Mconv6_stage4_L1"
+}
+layer {
+  name: "Mconv6_stage4_L2"
+  type: "Convolution"
+  bottom: "Mconv5_stage4_L2"
+  top: "Mconv6_stage4_L2"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 0
+    kernel_size: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mrelu6_stage4_L2"
+  type: "ReLU"
+  bottom: "Mconv6_stage4_L2"
+  top: "Mconv6_stage4_L2"
+}
+layer {
+  name: "Mconv7_stage4_L1"
+  type: "Convolution"
+  bottom: "Mconv6_stage4_L1"
+  top: "Mconv7_stage4_L1"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 28
+    pad: 0
+    kernel_size: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mconv7_stage4_L2"
+  type: "Convolution"
+  bottom: "Mconv6_stage4_L2"
+  top: "Mconv7_stage4_L2"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 16
+    pad: 0
+    kernel_size: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "concat_stage5"
+  type: "Concat"
+  bottom: "Mconv7_stage4_L1"
+  bottom: "Mconv7_stage4_L2"
+  bottom: "conv4_4_CPM"
+  top: "concat_stage5"
+  concat_param {
+    axis: 1
+  }
+}
+layer {
+  name: "Mconv1_stage5_L1"
+  type: "Convolution"
+  bottom: "concat_stage5"
+  top: "Mconv1_stage5_L1"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mrelu1_stage5_L1"
+  type: "ReLU"
+  bottom: "Mconv1_stage5_L1"
+  top: "Mconv1_stage5_L1"
+}
+layer {
+  name: "Mconv1_stage5_L2"
+  type: "Convolution"
+  bottom: "concat_stage5"
+  top: "Mconv1_stage5_L2"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mrelu1_stage5_L2"
+  type: "ReLU"
+  bottom: "Mconv1_stage5_L2"
+  top: "Mconv1_stage5_L2"
+}
+layer {
+  name: "Mconv2_stage5_L1"
+  type: "Convolution"
+  bottom: "Mconv1_stage5_L1"
+  top: "Mconv2_stage5_L1"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mrelu2_stage5_L1"
+  type: "ReLU"
+  bottom: "Mconv2_stage5_L1"
+  top: "Mconv2_stage5_L1"
+}
+layer {
+  name: "Mconv2_stage5_L2"
+  type: "Convolution"
+  bottom: "Mconv1_stage5_L2"
+  top: "Mconv2_stage5_L2"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mrelu2_stage5_L2"
+  type: "ReLU"
+  bottom: "Mconv2_stage5_L2"
+  top: "Mconv2_stage5_L2"
+}
+layer {
+  name: "Mconv3_stage5_L1"
+  type: "Convolution"
+  bottom: "Mconv2_stage5_L1"
+  top: "Mconv3_stage5_L1"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mrelu3_stage5_L1"
+  type: "ReLU"
+  bottom: "Mconv3_stage5_L1"
+  top: "Mconv3_stage5_L1"
+}
+layer {
+  name: "Mconv3_stage5_L2"
+  type: "Convolution"
+  bottom: "Mconv2_stage5_L2"
+  top: "Mconv3_stage5_L2"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mrelu3_stage5_L2"
+  type: "ReLU"
+  bottom: "Mconv3_stage5_L2"
+  top: "Mconv3_stage5_L2"
+}
+layer {
+  name: "Mconv4_stage5_L1"
+  type: "Convolution"
+  bottom: "Mconv3_stage5_L1"
+  top: "Mconv4_stage5_L1"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mrelu4_stage5_L1"
+  type: "ReLU"
+  bottom: "Mconv4_stage5_L1"
+  top: "Mconv4_stage5_L1"
+}
+layer {
+  name: "Mconv4_stage5_L2"
+  type: "Convolution"
+  bottom: "Mconv3_stage5_L2"
+  top: "Mconv4_stage5_L2"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mrelu4_stage5_L2"
+  type: "ReLU"
+  bottom: "Mconv4_stage5_L2"
+  top: "Mconv4_stage5_L2"
+}
+layer {
+  name: "Mconv5_stage5_L1"
+  type: "Convolution"
+  bottom: "Mconv4_stage5_L1"
+  top: "Mconv5_stage5_L1"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mrelu5_stage5_L1"
+  type: "ReLU"
+  bottom: "Mconv5_stage5_L1"
+  top: "Mconv5_stage5_L1"
+}
+layer {
+  name: "Mconv5_stage5_L2"
+  type: "Convolution"
+  bottom: "Mconv4_stage5_L2"
+  top: "Mconv5_stage5_L2"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mrelu5_stage5_L2"
+  type: "ReLU"
+  bottom: "Mconv5_stage5_L2"
+  top: "Mconv5_stage5_L2"
+}
+layer {
+  name: "Mconv6_stage5_L1"
+  type: "Convolution"
+  bottom: "Mconv5_stage5_L1"
+  top: "Mconv6_stage5_L1"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 0
+    kernel_size: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mrelu6_stage5_L1"
+  type: "ReLU"
+  bottom: "Mconv6_stage5_L1"
+  top: "Mconv6_stage5_L1"
+}
+layer {
+  name: "Mconv6_stage5_L2"
+  type: "Convolution"
+  bottom: "Mconv5_stage5_L2"
+  top: "Mconv6_stage5_L2"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 0
+    kernel_size: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mrelu6_stage5_L2"
+  type: "ReLU"
+  bottom: "Mconv6_stage5_L2"
+  top: "Mconv6_stage5_L2"
+}
+layer {
+  name: "Mconv7_stage5_L1"
+  type: "Convolution"
+  bottom: "Mconv6_stage5_L1"
+  top: "Mconv7_stage5_L1"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 28
+    pad: 0
+    kernel_size: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mconv7_stage5_L2"
+  type: "Convolution"
+  bottom: "Mconv6_stage5_L2"
+  top: "Mconv7_stage5_L2"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 16
+    pad: 0
+    kernel_size: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "concat_stage6"
+  type: "Concat"
+  bottom: "Mconv7_stage5_L1"
+  bottom: "Mconv7_stage5_L2"
+  bottom: "conv4_4_CPM"
+  top: "concat_stage6"
+  concat_param {
+    axis: 1
+  }
+}
+layer {
+  name: "Mconv1_stage6_L1"
+  type: "Convolution"
+  bottom: "concat_stage6"
+  top: "Mconv1_stage6_L1"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mrelu1_stage6_L1"
+  type: "ReLU"
+  bottom: "Mconv1_stage6_L1"
+  top: "Mconv1_stage6_L1"
+}
+layer {
+  name: "Mconv1_stage6_L2"
+  type: "Convolution"
+  bottom: "concat_stage6"
+  top: "Mconv1_stage6_L2"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mrelu1_stage6_L2"
+  type: "ReLU"
+  bottom: "Mconv1_stage6_L2"
+  top: "Mconv1_stage6_L2"
+}
+layer {
+  name: "Mconv2_stage6_L1"
+  type: "Convolution"
+  bottom: "Mconv1_stage6_L1"
+  top: "Mconv2_stage6_L1"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mrelu2_stage6_L1"
+  type: "ReLU"
+  bottom: "Mconv2_stage6_L1"
+  top: "Mconv2_stage6_L1"
+}
+layer {
+  name: "Mconv2_stage6_L2"
+  type: "Convolution"
+  bottom: "Mconv1_stage6_L2"
+  top: "Mconv2_stage6_L2"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mrelu2_stage6_L2"
+  type: "ReLU"
+  bottom: "Mconv2_stage6_L2"
+  top: "Mconv2_stage6_L2"
+}
+layer {
+  name: "Mconv3_stage6_L1"
+  type: "Convolution"
+  bottom: "Mconv2_stage6_L1"
+  top: "Mconv3_stage6_L1"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mrelu3_stage6_L1"
+  type: "ReLU"
+  bottom: "Mconv3_stage6_L1"
+  top: "Mconv3_stage6_L1"
+}
+layer {
+  name: "Mconv3_stage6_L2"
+  type: "Convolution"
+  bottom: "Mconv2_stage6_L2"
+  top: "Mconv3_stage6_L2"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mrelu3_stage6_L2"
+  type: "ReLU"
+  bottom: "Mconv3_stage6_L2"
+  top: "Mconv3_stage6_L2"
+}
+layer {
+  name: "Mconv4_stage6_L1"
+  type: "Convolution"
+  bottom: "Mconv3_stage6_L1"
+  top: "Mconv4_stage6_L1"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mrelu4_stage6_L1"
+  type: "ReLU"
+  bottom: "Mconv4_stage6_L1"
+  top: "Mconv4_stage6_L1"
+}
+layer {
+  name: "Mconv4_stage6_L2"
+  type: "Convolution"
+  bottom: "Mconv3_stage6_L2"
+  top: "Mconv4_stage6_L2"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mrelu4_stage6_L2"
+  type: "ReLU"
+  bottom: "Mconv4_stage6_L2"
+  top: "Mconv4_stage6_L2"
+}
+layer {
+  name: "Mconv5_stage6_L1"
+  type: "Convolution"
+  bottom: "Mconv4_stage6_L1"
+  top: "Mconv5_stage6_L1"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mrelu5_stage6_L1"
+  type: "ReLU"
+  bottom: "Mconv5_stage6_L1"
+  top: "Mconv5_stage6_L1"
+}
+layer {
+  name: "Mconv5_stage6_L2"
+  type: "Convolution"
+  bottom: "Mconv4_stage6_L2"
+  top: "Mconv5_stage6_L2"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mrelu5_stage6_L2"
+  type: "ReLU"
+  bottom: "Mconv5_stage6_L2"
+  top: "Mconv5_stage6_L2"
+}
+layer {
+  name: "Mconv6_stage6_L1"
+  type: "Convolution"
+  bottom: "Mconv5_stage6_L1"
+  top: "Mconv6_stage6_L1"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 0
+    kernel_size: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mrelu6_stage6_L1"
+  type: "ReLU"
+  bottom: "Mconv6_stage6_L1"
+  top: "Mconv6_stage6_L1"
+}
+layer {
+  name: "Mconv6_stage6_L2"
+  type: "Convolution"
+  bottom: "Mconv5_stage6_L2"
+  top: "Mconv6_stage6_L2"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 0
+    kernel_size: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mrelu6_stage6_L2"
+  type: "ReLU"
+  bottom: "Mconv6_stage6_L2"
+  top: "Mconv6_stage6_L2"
+}
+layer {
+  name: "Mconv7_stage6_L1"
+  type: "Convolution"
+  bottom: "Mconv6_stage6_L1"
+  top: "Mconv7_stage6_L1"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 28
+    pad: 0
+    kernel_size: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mconv7_stage6_L2"
+  type: "Convolution"
+  bottom: "Mconv6_stage6_L2"
+  top: "Mconv7_stage6_L2"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 16
+    pad: 0
+    kernel_size: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "concat_stage7"
+  type: "Concat"
+  bottom: "Mconv7_stage6_L2"
+  bottom: "Mconv7_stage6_L1"
+  top: "net_output"
+  concat_param {
+    axis: 1
+  }
+}
diff --git a/joints_detectors/openpose/models/pose/mpi/pose_deploy_linevec_faster_4_stages.prototxt b/joints_detectors/openpose/models/pose/mpi/pose_deploy_linevec_faster_4_stages.prototxt
new file mode 100644
index 0000000000000000000000000000000000000000..02ec183300ecbf5509d897e73c7663f16fb3b1a6
--- /dev/null
+++ b/joints_detectors/openpose/models/pose/mpi/pose_deploy_linevec_faster_4_stages.prototxt
@@ -0,0 +1,2081 @@
+input: "image"
+input_dim: 1
+input_dim: 3
+input_dim: 1 # This value will be defined at runtime
+input_dim: 1 # This value will be defined at runtime
+layer {
+  name: "conv1_1"
+  type: "Convolution"
+  bottom: "image"
+  top: "conv1_1"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 64
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "relu1_1"
+  type: "ReLU"
+  bottom: "conv1_1"
+  top: "conv1_1"
+}
+layer {
+  name: "conv1_2"
+  type: "Convolution"
+  bottom: "conv1_1"
+  top: "conv1_2"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 64
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "relu1_2"
+  type: "ReLU"
+  bottom: "conv1_2"
+  top: "conv1_2"
+}
+layer {
+  name: "pool1_stage1"
+  type: "Pooling"
+  bottom: "conv1_2"
+  top: "pool1_stage1"
+  pooling_param {
+    pool: MAX
+    kernel_size: 2
+    stride: 2
+  }
+}
+layer {
+  name: "conv2_1"
+  type: "Convolution"
+  bottom: "pool1_stage1"
+  top: "conv2_1"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "relu2_1"
+  type: "ReLU"
+  bottom: "conv2_1"
+  top: "conv2_1"
+}
+layer {
+  name: "conv2_2"
+  type: "Convolution"
+  bottom: "conv2_1"
+  top: "conv2_2"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "relu2_2"
+  type: "ReLU"
+  bottom: "conv2_2"
+  top: "conv2_2"
+}
+layer {
+  name: "pool2_stage1"
+  type: "Pooling"
+  bottom: "conv2_2"
+  top: "pool2_stage1"
+  pooling_param {
+    pool: MAX
+    kernel_size: 2
+    stride: 2
+  }
+}
+layer {
+  name: "conv3_1"
+  type: "Convolution"
+  bottom: "pool2_stage1"
+  top: "conv3_1"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 256
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "relu3_1"
+  type: "ReLU"
+  bottom: "conv3_1"
+  top: "conv3_1"
+}
+layer {
+  name: "conv3_2"
+  type: "Convolution"
+  bottom: "conv3_1"
+  top: "conv3_2"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 256
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "relu3_2"
+  type: "ReLU"
+  bottom: "conv3_2"
+  top: "conv3_2"
+}
+layer {
+  name: "conv3_3"
+  type: "Convolution"
+  bottom: "conv3_2"
+  top: "conv3_3"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 256
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "relu3_3"
+  type: "ReLU"
+  bottom: "conv3_3"
+  top: "conv3_3"
+}
+layer {
+  name: "conv3_4"
+  type: "Convolution"
+  bottom: "conv3_3"
+  top: "conv3_4"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 256
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "relu3_4"
+  type: "ReLU"
+  bottom: "conv3_4"
+  top: "conv3_4"
+}
+layer {
+  name: "pool3_stage1"
+  type: "Pooling"
+  bottom: "conv3_4"
+  top: "pool3_stage1"
+  pooling_param {
+    pool: MAX
+    kernel_size: 2
+    stride: 2
+  }
+}
+layer {
+  name: "conv4_1"
+  type: "Convolution"
+  bottom: "pool3_stage1"
+  top: "conv4_1"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 512
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "relu4_1"
+  type: "ReLU"
+  bottom: "conv4_1"
+  top: "conv4_1"
+}
+layer {
+  name: "conv4_2"
+  type: "Convolution"
+  bottom: "conv4_1"
+  top: "conv4_2"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 512
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "relu4_2"
+  type: "ReLU"
+  bottom: "conv4_2"
+  top: "conv4_2"
+}
+layer {
+  name: "conv4_3_CPM"
+  type: "Convolution"
+  bottom: "conv4_2"
+  top: "conv4_3_CPM"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 256
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "relu4_3_CPM"
+  type: "ReLU"
+  bottom: "conv4_3_CPM"
+  top: "conv4_3_CPM"
+}
+layer {
+  name: "conv4_4_CPM"
+  type: "Convolution"
+  bottom: "conv4_3_CPM"
+  top: "conv4_4_CPM"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "relu4_4_CPM"
+  type: "ReLU"
+  bottom: "conv4_4_CPM"
+  top: "conv4_4_CPM"
+}
+layer {
+  name: "conv5_1_CPM_L1"
+  type: "Convolution"
+  bottom: "conv4_4_CPM"
+  top: "conv5_1_CPM_L1"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "relu5_1_CPM_L1"
+  type: "ReLU"
+  bottom: "conv5_1_CPM_L1"
+  top: "conv5_1_CPM_L1"
+}
+layer {
+  name: "conv5_1_CPM_L2"
+  type: "Convolution"
+  bottom: "conv4_4_CPM"
+  top: "conv5_1_CPM_L2"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "relu5_1_CPM_L2"
+  type: "ReLU"
+  bottom: "conv5_1_CPM_L2"
+  top: "conv5_1_CPM_L2"
+}
+layer {
+  name: "conv5_2_CPM_L1"
+  type: "Convolution"
+  bottom: "conv5_1_CPM_L1"
+  top: "conv5_2_CPM_L1"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "relu5_2_CPM_L1"
+  type: "ReLU"
+  bottom: "conv5_2_CPM_L1"
+  top: "conv5_2_CPM_L1"
+}
+layer {
+  name: "conv5_2_CPM_L2"
+  type: "Convolution"
+  bottom: "conv5_1_CPM_L2"
+  top: "conv5_2_CPM_L2"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "relu5_2_CPM_L2"
+  type: "ReLU"
+  bottom: "conv5_2_CPM_L2"
+  top: "conv5_2_CPM_L2"
+}
+layer {
+  name: "conv5_3_CPM_L1"
+  type: "Convolution"
+  bottom: "conv5_2_CPM_L1"
+  top: "conv5_3_CPM_L1"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "relu5_3_CPM_L1"
+  type: "ReLU"
+  bottom: "conv5_3_CPM_L1"
+  top: "conv5_3_CPM_L1"
+}
+layer {
+  name: "conv5_3_CPM_L2"
+  type: "Convolution"
+  bottom: "conv5_2_CPM_L2"
+  top: "conv5_3_CPM_L2"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "relu5_3_CPM_L2"
+  type: "ReLU"
+  bottom: "conv5_3_CPM_L2"
+  top: "conv5_3_CPM_L2"
+}
+layer {
+  name: "conv5_4_CPM_L1"
+  type: "Convolution"
+  bottom: "conv5_3_CPM_L1"
+  top: "conv5_4_CPM_L1"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 512
+    pad: 0
+    kernel_size: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "relu5_4_CPM_L1"
+  type: "ReLU"
+  bottom: "conv5_4_CPM_L1"
+  top: "conv5_4_CPM_L1"
+}
+layer {
+  name: "conv5_4_CPM_L2"
+  type: "Convolution"
+  bottom: "conv5_3_CPM_L2"
+  top: "conv5_4_CPM_L2"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 512
+    pad: 0
+    kernel_size: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "relu5_4_CPM_L2"
+  type: "ReLU"
+  bottom: "conv5_4_CPM_L2"
+  top: "conv5_4_CPM_L2"
+}
+layer {
+  name: "conv5_5_CPM_L1"
+  type: "Convolution"
+  bottom: "conv5_4_CPM_L1"
+  top: "conv5_5_CPM_L1"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 28
+    pad: 0
+    kernel_size: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "conv5_5_CPM_L2"
+  type: "Convolution"
+  bottom: "conv5_4_CPM_L2"
+  top: "conv5_5_CPM_L2"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 16
+    pad: 0
+    kernel_size: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "concat_stage2"
+  type: "Concat"
+  bottom: "conv5_5_CPM_L1"
+  bottom: "conv5_5_CPM_L2"
+  bottom: "conv4_4_CPM"
+  top: "concat_stage2"
+  concat_param {
+    axis: 1
+  }
+}
+layer {
+  name: "Mconv1_stage2_L1"
+  type: "Convolution"
+  bottom: "concat_stage2"
+  top: "Mconv1_stage2_L1"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mrelu1_stage2_L1"
+  type: "ReLU"
+  bottom: "Mconv1_stage2_L1"
+  top: "Mconv1_stage2_L1"
+}
+layer {
+  name: "Mconv1_stage2_L2"
+  type: "Convolution"
+  bottom: "concat_stage2"
+  top: "Mconv1_stage2_L2"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mrelu1_stage2_L2"
+  type: "ReLU"
+  bottom: "Mconv1_stage2_L2"
+  top: "Mconv1_stage2_L2"
+}
+layer {
+  name: "Mconv2_stage2_L1"
+  type: "Convolution"
+  bottom: "Mconv1_stage2_L1"
+  top: "Mconv2_stage2_L1"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mrelu2_stage2_L1"
+  type: "ReLU"
+  bottom: "Mconv2_stage2_L1"
+  top: "Mconv2_stage2_L1"
+}
+layer {
+  name: "Mconv2_stage2_L2"
+  type: "Convolution"
+  bottom: "Mconv1_stage2_L2"
+  top: "Mconv2_stage2_L2"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mrelu2_stage2_L2"
+  type: "ReLU"
+  bottom: "Mconv2_stage2_L2"
+  top: "Mconv2_stage2_L2"
+}
+layer {
+  name: "Mconv3_stage2_L1"
+  type: "Convolution"
+  bottom: "Mconv2_stage2_L1"
+  top: "Mconv3_stage2_L1"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mrelu3_stage2_L1"
+  type: "ReLU"
+  bottom: "Mconv3_stage2_L1"
+  top: "Mconv3_stage2_L1"
+}
+layer {
+  name: "Mconv3_stage2_L2"
+  type: "Convolution"
+  bottom: "Mconv2_stage2_L2"
+  top: "Mconv3_stage2_L2"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mrelu3_stage2_L2"
+  type: "ReLU"
+  bottom: "Mconv3_stage2_L2"
+  top: "Mconv3_stage2_L2"
+}
+layer {
+  name: "Mconv4_stage2_L1"
+  type: "Convolution"
+  bottom: "Mconv3_stage2_L1"
+  top: "Mconv4_stage2_L1"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mrelu4_stage2_L1"
+  type: "ReLU"
+  bottom: "Mconv4_stage2_L1"
+  top: "Mconv4_stage2_L1"
+}
+layer {
+  name: "Mconv4_stage2_L2"
+  type: "Convolution"
+  bottom: "Mconv3_stage2_L2"
+  top: "Mconv4_stage2_L2"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mrelu4_stage2_L2"
+  type: "ReLU"
+  bottom: "Mconv4_stage2_L2"
+  top: "Mconv4_stage2_L2"
+}
+layer {
+  name: "Mconv5_stage2_L1"
+  type: "Convolution"
+  bottom: "Mconv4_stage2_L1"
+  top: "Mconv5_stage2_L1"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mrelu5_stage2_L1"
+  type: "ReLU"
+  bottom: "Mconv5_stage2_L1"
+  top: "Mconv5_stage2_L1"
+}
+layer {
+  name: "Mconv5_stage2_L2"
+  type: "Convolution"
+  bottom: "Mconv4_stage2_L2"
+  top: "Mconv5_stage2_L2"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mrelu5_stage2_L2"
+  type: "ReLU"
+  bottom: "Mconv5_stage2_L2"
+  top: "Mconv5_stage2_L2"
+}
+layer {
+  name: "Mconv6_stage2_L1"
+  type: "Convolution"
+  bottom: "Mconv5_stage2_L1"
+  top: "Mconv6_stage2_L1"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 0
+    kernel_size: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mrelu6_stage2_L1"
+  type: "ReLU"
+  bottom: "Mconv6_stage2_L1"
+  top: "Mconv6_stage2_L1"
+}
+layer {
+  name: "Mconv6_stage2_L2"
+  type: "Convolution"
+  bottom: "Mconv5_stage2_L2"
+  top: "Mconv6_stage2_L2"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 0
+    kernel_size: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mrelu6_stage2_L2"
+  type: "ReLU"
+  bottom: "Mconv6_stage2_L2"
+  top: "Mconv6_stage2_L2"
+}
+layer {
+  name: "Mconv7_stage2_L1"
+  type: "Convolution"
+  bottom: "Mconv6_stage2_L1"
+  top: "Mconv7_stage2_L1"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 28
+    pad: 0
+    kernel_size: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mconv7_stage2_L2"
+  type: "Convolution"
+  bottom: "Mconv6_stage2_L2"
+  top: "Mconv7_stage2_L2"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 16
+    pad: 0
+    kernel_size: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "concat_stage3"
+  type: "Concat"
+  bottom: "Mconv7_stage2_L1"
+  bottom: "Mconv7_stage2_L2"
+  bottom: "conv4_4_CPM"
+  top: "concat_stage3"
+  concat_param {
+    axis: 1
+  }
+}
+layer {
+  name: "Mconv1_stage3_L1"
+  type: "Convolution"
+  bottom: "concat_stage3"
+  top: "Mconv1_stage3_L1"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mrelu1_stage3_L1"
+  type: "ReLU"
+  bottom: "Mconv1_stage3_L1"
+  top: "Mconv1_stage3_L1"
+}
+layer {
+  name: "Mconv1_stage3_L2"
+  type: "Convolution"
+  bottom: "concat_stage3"
+  top: "Mconv1_stage3_L2"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mrelu1_stage3_L2"
+  type: "ReLU"
+  bottom: "Mconv1_stage3_L2"
+  top: "Mconv1_stage3_L2"
+}
+layer {
+  name: "Mconv2_stage3_L1"
+  type: "Convolution"
+  bottom: "Mconv1_stage3_L1"
+  top: "Mconv2_stage3_L1"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mrelu2_stage3_L1"
+  type: "ReLU"
+  bottom: "Mconv2_stage3_L1"
+  top: "Mconv2_stage3_L1"
+}
+layer {
+  name: "Mconv2_stage3_L2"
+  type: "Convolution"
+  bottom: "Mconv1_stage3_L2"
+  top: "Mconv2_stage3_L2"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mrelu2_stage3_L2"
+  type: "ReLU"
+  bottom: "Mconv2_stage3_L2"
+  top: "Mconv2_stage3_L2"
+}
+layer {
+  name: "Mconv3_stage3_L1"
+  type: "Convolution"
+  bottom: "Mconv2_stage3_L1"
+  top: "Mconv3_stage3_L1"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mrelu3_stage3_L1"
+  type: "ReLU"
+  bottom: "Mconv3_stage3_L1"
+  top: "Mconv3_stage3_L1"
+}
+layer {
+  name: "Mconv3_stage3_L2"
+  type: "Convolution"
+  bottom: "Mconv2_stage3_L2"
+  top: "Mconv3_stage3_L2"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mrelu3_stage3_L2"
+  type: "ReLU"
+  bottom: "Mconv3_stage3_L2"
+  top: "Mconv3_stage3_L2"
+}
+layer {
+  name: "Mconv4_stage3_L1"
+  type: "Convolution"
+  bottom: "Mconv3_stage3_L1"
+  top: "Mconv4_stage3_L1"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mrelu4_stage3_L1"
+  type: "ReLU"
+  bottom: "Mconv4_stage3_L1"
+  top: "Mconv4_stage3_L1"
+}
+layer {
+  name: "Mconv4_stage3_L2"
+  type: "Convolution"
+  bottom: "Mconv3_stage3_L2"
+  top: "Mconv4_stage3_L2"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mrelu4_stage3_L2"
+  type: "ReLU"
+  bottom: "Mconv4_stage3_L2"
+  top: "Mconv4_stage3_L2"
+}
+layer {
+  name: "Mconv5_stage3_L1"
+  type: "Convolution"
+  bottom: "Mconv4_stage3_L1"
+  top: "Mconv5_stage3_L1"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mrelu5_stage3_L1"
+  type: "ReLU"
+  bottom: "Mconv5_stage3_L1"
+  top: "Mconv5_stage3_L1"
+}
+layer {
+  name: "Mconv5_stage3_L2"
+  type: "Convolution"
+  bottom: "Mconv4_stage3_L2"
+  top: "Mconv5_stage3_L2"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mrelu5_stage3_L2"
+  type: "ReLU"
+  bottom: "Mconv5_stage3_L2"
+  top: "Mconv5_stage3_L2"
+}
+layer {
+  name: "Mconv6_stage3_L1"
+  type: "Convolution"
+  bottom: "Mconv5_stage3_L1"
+  top: "Mconv6_stage3_L1"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 0
+    kernel_size: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mrelu6_stage3_L1"
+  type: "ReLU"
+  bottom: "Mconv6_stage3_L1"
+  top: "Mconv6_stage3_L1"
+}
+layer {
+  name: "Mconv6_stage3_L2"
+  type: "Convolution"
+  bottom: "Mconv5_stage3_L2"
+  top: "Mconv6_stage3_L2"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 0
+    kernel_size: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mrelu6_stage3_L2"
+  type: "ReLU"
+  bottom: "Mconv6_stage3_L2"
+  top: "Mconv6_stage3_L2"
+}
+layer {
+  name: "Mconv7_stage3_L1"
+  type: "Convolution"
+  bottom: "Mconv6_stage3_L1"
+  top: "Mconv7_stage3_L1"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 28
+    pad: 0
+    kernel_size: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mconv7_stage3_L2"
+  type: "Convolution"
+  bottom: "Mconv6_stage3_L2"
+  top: "Mconv7_stage3_L2"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 16
+    pad: 0
+    kernel_size: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "concat_stage4"
+  type: "Concat"
+  bottom: "Mconv7_stage3_L1"
+  bottom: "Mconv7_stage3_L2"
+  bottom: "conv4_4_CPM"
+  top: "concat_stage4"
+  concat_param {
+    axis: 1
+  }
+}
+layer {
+  name: "Mconv1_stage4_L1"
+  type: "Convolution"
+  bottom: "concat_stage4"
+  top: "Mconv1_stage4_L1"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mrelu1_stage4_L1"
+  type: "ReLU"
+  bottom: "Mconv1_stage4_L1"
+  top: "Mconv1_stage4_L1"
+}
+layer {
+  name: "Mconv1_stage4_L2"
+  type: "Convolution"
+  bottom: "concat_stage4"
+  top: "Mconv1_stage4_L2"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mrelu1_stage4_L2"
+  type: "ReLU"
+  bottom: "Mconv1_stage4_L2"
+  top: "Mconv1_stage4_L2"
+}
+layer {
+  name: "Mconv2_stage4_L1"
+  type: "Convolution"
+  bottom: "Mconv1_stage4_L1"
+  top: "Mconv2_stage4_L1"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mrelu2_stage4_L1"
+  type: "ReLU"
+  bottom: "Mconv2_stage4_L1"
+  top: "Mconv2_stage4_L1"
+}
+layer {
+  name: "Mconv2_stage4_L2"
+  type: "Convolution"
+  bottom: "Mconv1_stage4_L2"
+  top: "Mconv2_stage4_L2"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mrelu2_stage4_L2"
+  type: "ReLU"
+  bottom: "Mconv2_stage4_L2"
+  top: "Mconv2_stage4_L2"
+}
+layer {
+  name: "Mconv3_stage4_L1"
+  type: "Convolution"
+  bottom: "Mconv2_stage4_L1"
+  top: "Mconv3_stage4_L1"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mrelu3_stage4_L1"
+  type: "ReLU"
+  bottom: "Mconv3_stage4_L1"
+  top: "Mconv3_stage4_L1"
+}
+layer {
+  name: "Mconv3_stage4_L2"
+  type: "Convolution"
+  bottom: "Mconv2_stage4_L2"
+  top: "Mconv3_stage4_L2"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mrelu3_stage4_L2"
+  type: "ReLU"
+  bottom: "Mconv3_stage4_L2"
+  top: "Mconv3_stage4_L2"
+}
+layer {
+  name: "Mconv4_stage4_L1"
+  type: "Convolution"
+  bottom: "Mconv3_stage4_L1"
+  top: "Mconv4_stage4_L1"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mrelu4_stage4_L1"
+  type: "ReLU"
+  bottom: "Mconv4_stage4_L1"
+  top: "Mconv4_stage4_L1"
+}
+layer {
+  name: "Mconv4_stage4_L2"
+  type: "Convolution"
+  bottom: "Mconv3_stage4_L2"
+  top: "Mconv4_stage4_L2"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mrelu4_stage4_L2"
+  type: "ReLU"
+  bottom: "Mconv4_stage4_L2"
+  top: "Mconv4_stage4_L2"
+}
+layer {
+  name: "Mconv5_stage4_L1"
+  type: "Convolution"
+  bottom: "Mconv4_stage4_L1"
+  top: "Mconv5_stage4_L1"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mrelu5_stage4_L1"
+  type: "ReLU"
+  bottom: "Mconv5_stage4_L1"
+  top: "Mconv5_stage4_L1"
+}
+layer {
+  name: "Mconv5_stage4_L2"
+  type: "Convolution"
+  bottom: "Mconv4_stage4_L2"
+  top: "Mconv5_stage4_L2"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mrelu5_stage4_L2"
+  type: "ReLU"
+  bottom: "Mconv5_stage4_L2"
+  top: "Mconv5_stage4_L2"
+}
+layer {
+  name: "Mconv6_stage4_L1"
+  type: "Convolution"
+  bottom: "Mconv5_stage4_L1"
+  top: "Mconv6_stage4_L1"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 0
+    kernel_size: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mrelu6_stage4_L1"
+  type: "ReLU"
+  bottom: "Mconv6_stage4_L1"
+  top: "Mconv6_stage4_L1"
+}
+layer {
+  name: "Mconv6_stage4_L2"
+  type: "Convolution"
+  bottom: "Mconv5_stage4_L2"
+  top: "Mconv6_stage4_L2"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 0
+    kernel_size: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mrelu6_stage4_L2"
+  type: "ReLU"
+  bottom: "Mconv6_stage4_L2"
+  top: "Mconv6_stage4_L2"
+}
+layer {
+  name: "Mconv7_stage4_L1"
+  type: "Convolution"
+  bottom: "Mconv6_stage4_L1"
+  top: "Mconv7_stage4_L1"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 28
+    pad: 0
+    kernel_size: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "Mconv7_stage4_L2"
+  type: "Convolution"
+  bottom: "Mconv6_stage4_L2"
+  top: "Mconv7_stage4_L2"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 16
+    pad: 0
+    kernel_size: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "concat_stage7"
+  type: "Concat"
+  bottom: "Mconv7_stage4_L2"
+  bottom: "Mconv7_stage4_L1"
+  top: "net_output"
+  concat_param {
+    axis: 1
+  }
+}
diff --git a/joints_detectors/openpose/utils.py b/joints_detectors/openpose/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..e75dac339a4464edc4379721996e2d3ff74020a5
--- /dev/null
+++ b/joints_detectors/openpose/utils.py
@@ -0,0 +1,64 @@
+import cv2
+
+joint_pairs = [[0, 1], [1, 3], [0, 2], [2, 4],
+               [5, 6], [5, 7], [7, 9], [6, 8], [8, 10],
+               [5, 11], [6, 12], [11, 12],
+               [11, 13], [12, 14], [13, 15], [14, 16]]
+
+colors = [[255, 0, 0], [255, 85, 0], [255, 170, 0], [255, 255, 0], [170, 255, 0], [85, 255, 0], [0, 255, 0], \
+          [0, 255, 85], [0, 255, 170], [0, 255, 255], [0, 170, 255], [0, 85, 255], [0, 0, 255], [85, 0, 255], \
+          [170, 0, 255], [255, 0, 255], [255, 85, 255]]
+
+
+def plot_keypoint(image, keypoints, keypoint_thresh=0.1):
+    confidence = keypoints[:, :, 2:]
+    coordinates = keypoints[:, :, :2]
+    joint_visible = confidence[:, :, 0] > keypoint_thresh
+
+    # 描点
+    for people in keypoints:
+        for i in range(len(people)):
+            x, y, p = people[i]
+            if p < 0.1:
+                continue
+            x = int(x)
+            y = int(y)
+            cv2.circle(image, (x, y), 4, colors[i], thickness=-1)
+
+    for i in range(coordinates.shape[0]):
+        pts = coordinates[i]
+        for color_i, jp in zip(colors, joint_pairs):
+            if joint_visible[i, jp[0]] and joint_visible[i, jp[1]]:
+                pt0 = pts[jp, 0];
+                pt1 = pts[jp, 1]
+                pt0_0, pt0_1, pt1_0, pt1_1 = int(pt0[0]), int(pt0[1]), int(pt1[0]), int(pt1[1])
+                cv2.line(image, (pt0_0, pt1_0), (pt0_1, pt1_1), color_i, 2)
+    return image
+
+
+# convert openpose keypoints(25) format to coco keypoints(17) format
+def convert(op_kpts):
+    '''
+    0-16 map to 0,16,15,18,17,5,2,6,3,7,4,12,9,13,10,14,11
+    '''
+    coco_kpts = []
+    for i, j in enumerate([0, 16, 15, 18, 17, 5, 2, 6, 3, 7, 4, 12, 9, 13, 10, 14, 11]):
+        score = op_kpts[j][-1]
+        # if eye, ear keypoints score is lower, map it to mouth
+        if score < 0.2 and j in [15, 16, 17, 18]:
+            coco_kpts.append(op_kpts[0])
+        else:
+            coco_kpts.append(op_kpts[j])
+
+    return coco_kpts
+
+
+# convert openpose keypoints(25) format to keypoints(18) format
+def convert_18(op_kpts):
+    coco_kpts = []
+    for i, j in enumerate(range(0, 18)):
+        if i < 8:
+            coco_kpts.append(op_kpts[j])
+        else:
+            coco_kpts.append(op_kpts[j + 1])
+    return coco_kpts
diff --git a/model/block/refine.py b/model/block/refine.py
new file mode 100644
index 0000000000000000000000000000000000000000..c0baa4d81302894868cd8435c795b7160bead369
--- /dev/null
+++ b/model/block/refine.py
@@ -0,0 +1,36 @@
+import torch
+import torch.nn as nn
+from torch.autograd import Variable
+
+fc_out = 256
+fc_unit = 1024
+
+class refine(nn.Module):
+    def __init__(self, opt):
+        super().__init__()
+
+        out_seqlen = 1
+        fc_in = opt.out_channels*2*out_seqlen*opt.n_joints 
+        fc_out = opt.in_channels * opt.n_joints 
+
+        self.post_refine = nn.Sequential(
+            nn.Linear(fc_in, fc_unit),
+            nn.ReLU(),
+            nn.Dropout(0.5,inplace=True),
+            nn.Linear(fc_unit, fc_out),
+            nn.Sigmoid()
+        )
+
+    def forward(self, x, x_1):
+        N, T, V,_ = x.size()
+        x_in = torch.cat((x, x_1), -1) 
+        x_in = x_in.view(N, -1) 
+
+        score = self.post_refine(x_in).view(N,T,V,2) 
+        score_cm = Variable(torch.ones(score.size()), requires_grad=False).cuda() - score
+        x_out = x.clone()
+        x_out[:, :, :, :2] = score * x[:, :, :, :2] + score_cm * x_1[:, :, :, :2]
+
+        return x_out
+
+
diff --git a/model/block/strided_transformer_encoder.py b/model/block/strided_transformer_encoder.py
new file mode 100644
index 0000000000000000000000000000000000000000..6fbcb2c608ad05f2ffd1be44662c2c75f1628cd8
--- /dev/null
+++ b/model/block/strided_transformer_encoder.py
@@ -0,0 +1,171 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torch.autograd import Variable
+import numpy as np
+import math
+import os
+import copy
+
+def clones(module, N):
+    return nn.ModuleList([copy.deepcopy(module) for _ in range(N)])
+
+class Encoder(nn.Module):
+    def __init__(self, layer, N, length, d_model):
+        super(Encoder, self).__init__()
+        self.layers = layer
+        self.norm = LayerNorm(d_model)
+
+        self.pos_embedding_1 = nn.Parameter(torch.randn(1, length, d_model))
+        self.pos_embedding_2 = nn.Parameter(torch.randn(1, length, d_model))
+        self.pos_embedding_3 = nn.Parameter(torch.randn(1, length, d_model))
+
+    def forward(self, x, mask):
+        for i, layer in enumerate(self.layers):
+            if i == 0:
+                x += self.pos_embedding_1[:, :x.shape[1]]
+            elif i == 1:
+                x += self.pos_embedding_2[:, :x.shape[1]]
+            elif i == 2:
+                x += self.pos_embedding_3[:, :x.shape[1]]
+
+            x = layer(x, mask, i)
+
+        return x
+
+class LayerNorm(nn.Module):
+    def __init__(self, features, eps=1e-6):
+        super(LayerNorm, self).__init__()
+        self.a_2 = nn.Parameter(torch.ones(features))
+        self.b_2 = nn.Parameter(torch.zeros(features))
+        self.eps = eps
+
+    def forward(self, x):
+        mean = x.mean(-1, keepdim=True)
+        std = x.std(-1, keepdim=True)
+        return self.a_2 * (x - mean) / (std + self.eps) + self.b_2
+
+def attention(query, key, value, mask=None, dropout=None):
+    d_k = query.size(-1)
+    scores = torch.matmul(query, key.transpose(-2, -1)) / math.sqrt(d_k)
+    if mask is not None:
+        scores = scores.masked_fill(mask == 0, -1e9)
+    p_attn = F.softmax(scores, dim=-1)
+
+    if dropout is not None:
+        p_attn = dropout(p_attn)
+    return torch.matmul(p_attn, value), p_attn
+
+
+class SublayerConnection(nn.Module):
+    def __init__(self, size, dropout, stride_num, i):
+        super(SublayerConnection, self).__init__()
+        self.norm = LayerNorm(size)
+        self.dropout = nn.Dropout(dropout)
+        self.pooling = nn.MaxPool1d(1, stride_num[i])
+        
+    def forward(self, x, sublayer, i=-1, stride_num=-1):
+        if i != -1:
+            if stride_num[i] != 1:
+                res = self.pooling(x.permute(0, 2, 1))
+                res = res.permute(0, 2, 1)
+                
+                return res + self.dropout(sublayer(self.norm(x)))
+            else:
+                return x + self.dropout(sublayer(self.norm(x)))
+        else:
+            return x + self.dropout(sublayer(self.norm(x)))
+
+
+class EncoderLayer(nn.Module):
+    def __init__(self, size, self_attn, feed_forward, dropout, stride_num, i):
+        super(EncoderLayer, self).__init__()
+        self.self_attn = self_attn
+        self.feed_forward = feed_forward
+        self.stride_num = stride_num
+        self.sublayer = clones(SublayerConnection(size, dropout, stride_num, i), 2)
+        self.size = size
+
+    def forward(self, x, mask, i):
+        x = self.sublayer[0](x, lambda x: self.self_attn(x, x, x, mask))
+        x = self.sublayer[1](x, self.feed_forward, i, self.stride_num)
+        return x
+
+
+class MultiHeadedAttention(nn.Module):
+    def __init__(self, h, d_model, dropout=0.1):
+        super(MultiHeadedAttention, self).__init__()
+        assert d_model % h == 0
+        self.d_k = d_model // h 
+        self.h = h
+        self.linears = clones(nn.Linear(d_model, d_model), 4)
+        self.attn = None
+        self.dropout = nn.Dropout(p=dropout)
+
+    def forward(self, query, key, value, mask=None):
+        if mask is not None:
+            mask = mask.unsqueeze(1)
+        nbatches = query.size(0)
+
+        query, key, value = [l(x).view(nbatches, -1, self.h, self.d_k).transpose(1, 2)
+             for l, x in zip(self.linears, (query, key, value))]
+
+        x, self.attn = attention(query, key, value, mask=mask,
+                                 dropout=self.dropout)
+
+        x = x.transpose(1, 2).contiguous().view(nbatches, -1, self.h * self.d_k)
+        return self.linears[-1](x)
+
+
+class PositionwiseFeedForward(nn.Module):
+    def __init__(self, d_model, d_ff, dropout=0.1, number = -1, stride_num=-1):
+        super(PositionwiseFeedForward, self).__init__()
+        self.w_1 = nn.Conv1d(d_model, d_ff, kernel_size=1, stride=1)
+        self.w_2 = nn.Conv1d(d_ff, d_model, kernel_size=3, stride=stride_num[number], padding = 1)
+
+        self.gelu = nn.ReLU()
+
+        self.dropout = nn.Dropout(dropout)
+
+    def forward(self, x):
+        x = x.permute(0, 2, 1)
+        x = self.w_2(self.dropout(self.gelu(self.w_1(x))))
+        x = x.permute(0, 2, 1)
+
+        return x
+
+class Transformer(nn.Module):   
+    def __init__(self, n_layers=3, d_model=256, d_ff=512, h=8, length=27, stride_num=None, dropout=0.1):
+        super(Transformer, self).__init__()
+
+        self.length = length
+
+        self.stride_num = stride_num
+        self.model = self.make_model(N=n_layers, d_model=d_model, d_ff=d_ff, h=h, dropout=dropout, length = self.length)
+
+    def forward(self, x, mask=None):
+        x = self.model(x, mask)
+
+        return x
+
+    def make_model(self, N=3, d_model=256, d_ff=512, h=8, dropout=0.1, length=27):
+        c = copy.deepcopy
+        attn = MultiHeadedAttention(h, d_model)
+
+        model_EncoderLayer = []
+        for i in range(N):
+            ff = PositionwiseFeedForward(d_model, d_ff, dropout, i, self.stride_num)
+            model_EncoderLayer.append(EncoderLayer(d_model, c(attn), c(ff), dropout, self.stride_num, i))
+
+        model_EncoderLayer = nn.ModuleList(model_EncoderLayer)
+
+        model = Encoder(model_EncoderLayer, N, length, d_model)
+        
+        return model
+
+
+
+
+
+
+
diff --git a/model/block/vanilla_transformer_encoder.py b/model/block/vanilla_transformer_encoder.py
new file mode 100644
index 0000000000000000000000000000000000000000..6b03d4869ab447f9465e78791b9321462c4f0e7e
--- /dev/null
+++ b/model/block/vanilla_transformer_encoder.py
@@ -0,0 +1,133 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torch.autograd import Variable
+import numpy as np
+import math
+import os
+import copy
+
+def clones(module, N):
+    return nn.ModuleList([copy.deepcopy(module) for _ in range(N)])
+
+class Encoder(nn.Module):
+    def __init__(self, layer, N):
+        super(Encoder, self).__init__()
+        self.layers = clones(layer, N)
+        self.norm = LayerNorm(layer.size)
+
+    def forward(self, x, mask):
+        for layer in self.layers:
+            x = layer(x, mask)
+        return x
+
+class LayerNorm(nn.Module):
+    def __init__(self, features, eps=1e-6):
+        super(LayerNorm, self).__init__()
+        self.a_2 = nn.Parameter(torch.ones(features))
+        self.b_2 = nn.Parameter(torch.zeros(features))
+        self.eps = eps
+
+    def forward(self, x):
+        mean = x.mean(-1, keepdim=True)
+        std = x.std(-1, keepdim=True)
+        return self.a_2 * (x - mean) / (std + self.eps) + self.b_2
+
+def attention(query, key, value, mask=None, dropout=None):
+    d_k = query.size(-1)
+    scores = torch.matmul(query, key.transpose(-2, -1)) / math.sqrt(d_k)
+
+    if mask is not None:
+        scores = scores.masked_fill(mask == 0, -1e9)
+    p_attn = F.softmax(scores, dim=-1)
+
+    if dropout is not None:
+        p_attn = dropout(p_attn)
+    return torch.matmul(p_attn, value), p_attn
+
+
+class SublayerConnection(nn.Module):
+    def __init__(self, size, dropout):
+        super(SublayerConnection, self).__init__()
+        self.norm = LayerNorm(size)
+        self.dropout = nn.Dropout(dropout)
+
+    def forward(self, x, sublayer):
+        return x + self.dropout(sublayer(self.norm(x)))
+
+
+class EncoderLayer(nn.Module):
+    def __init__(self, size, self_attn, feed_forward, dropout):
+        super(EncoderLayer, self).__init__()
+        self.self_attn = self_attn
+        self.feed_forward = feed_forward
+        self.sublayer = clones(SublayerConnection(size, dropout), 2)
+        self.size = size
+
+    def forward(self, x, mask):
+        x = self.sublayer[0](x, lambda x: self.self_attn(x, x, x, mask))
+        return self.sublayer[1](x, self.feed_forward)
+
+
+class MultiHeadedAttention(nn.Module):
+    def __init__(self, h, d_model, dropout=0.1):
+        super(MultiHeadedAttention, self).__init__()
+        assert d_model % h == 0
+        self.d_k = d_model // h 
+        self.h = h
+        self.linears = clones(nn.Linear(d_model, d_model), 4)
+        self.attn = None
+        self.dropout = nn.Dropout(p=dropout)
+
+    def forward(self, query, key, value, mask=None):
+        if mask is not None:
+            mask = mask.unsqueeze(1)
+        nbatches = query.size(0)
+
+        query, key, value = \
+            [l(x).view(nbatches, -1, self.h, self.d_k).transpose(1, 2)
+             for l, x in zip(self.linears, (query, key, value))]
+
+        x, self.attn = attention(query, key, value, mask=mask, dropout=self.dropout)
+
+        x = x.transpose(1, 2).contiguous().view(nbatches, -1, self.h * self.d_k)
+        return self.linears[-1](x)
+
+
+class PositionwiseFeedForward(nn.Module):
+    def __init__(self, d_model, d_ff, dropout=0.1):
+        super(PositionwiseFeedForward, self).__init__()
+        self.w_1 = nn.Linear(d_model, d_ff)
+        self.w_2 = nn.Linear(d_ff, d_model)
+        self.gelu = nn.ReLU()
+        self.dropout = nn.Dropout(dropout)
+
+    def forward(self, x):
+        return self.w_2(self.dropout(self.gelu(self.w_1(x))))
+
+class Transformer(nn.Module):
+    def __init__(self, n_layers=3, d_model=256, d_ff=512, h=8, dropout=0.1, length=27):
+        super(Transformer, self).__init__()
+
+        self.pos_embedding = nn.Parameter(torch.randn(1, length, d_model))
+        self.model = self.make_model(N=n_layers, d_model=d_model, d_ff=d_ff, h=h, dropout=dropout)
+                
+    def forward(self, x, mask=None):
+
+        x += self.pos_embedding
+
+        x = self.model(x, mask)
+
+        return x
+
+    def make_model(self, N=3, d_model=256, d_ff=512, h=8, dropout=0.1):
+        c = copy.deepcopy
+        attn = MultiHeadedAttention(h, d_model)
+        ff = PositionwiseFeedForward(d_model, d_ff, dropout)
+        model = Encoder(EncoderLayer(d_model, c(attn), c(ff), dropout), N)
+        return model
+
+
+
+
+
diff --git a/model/block/vanilla_transformer_encoder_pretrain.py b/model/block/vanilla_transformer_encoder_pretrain.py
new file mode 100644
index 0000000000000000000000000000000000000000..bb748b16f89c8aae557fa5d35dba0053c3b312c5
--- /dev/null
+++ b/model/block/vanilla_transformer_encoder_pretrain.py
@@ -0,0 +1,158 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torch.autograd import Variable
+import numpy as np
+import math
+import os
+import copy
+
+def clones(module, N):
+    return nn.ModuleList([copy.deepcopy(module) for _ in range(N)])
+
+class Encoder(nn.Module):
+    def __init__(self, layer, N):
+        super(Encoder, self).__init__()
+        self.layers = clones(layer, N)
+        self.norm = LayerNorm(layer.size)
+
+    def forward(self, x, mask):
+        for layer in self.layers:
+            x = layer(x, mask)
+        return x
+
+class LayerNorm(nn.Module):
+    def __init__(self, features, eps=1e-6):
+        super(LayerNorm, self).__init__()
+        self.a_2 = nn.Parameter(torch.ones(features))
+        self.b_2 = nn.Parameter(torch.zeros(features))
+        self.eps = eps
+
+    def forward(self, x):
+        mean = x.mean(-1, keepdim=True)
+        std = x.std(-1, keepdim=True)
+        return self.a_2 * (x - mean) / (std + self.eps) + self.b_2
+
+def attention(query, key, value, mask=None, dropout=None):
+    d_k = query.size(-1)
+    scores = torch.matmul(query, key.transpose(-2, -1)) / math.sqrt(d_k)
+
+    if mask is not None:
+        scores = scores.masked_fill(mask == 0, -1e9)
+    p_attn = F.softmax(scores, dim=-1)
+
+    if dropout is not None:
+        p_attn = dropout(p_attn)
+    return torch.matmul(p_attn, value), p_attn
+
+
+class SublayerConnection(nn.Module):
+    def __init__(self, size, dropout):
+        super(SublayerConnection, self).__init__()
+        self.norm = LayerNorm(size)
+        self.dropout = nn.Dropout(dropout)
+
+    def forward(self, x, sublayer):
+        return x + self.dropout(sublayer(self.norm(x)))
+
+
+class EncoderLayer(nn.Module):
+    def __init__(self, size, self_attn, feed_forward, dropout):
+        super(EncoderLayer, self).__init__()
+        self.self_attn = self_attn
+        self.feed_forward = feed_forward
+        self.sublayer = clones(SublayerConnection(size, dropout), 2)
+        self.size = size
+
+    def forward(self, x, mask):
+        x = self.sublayer[0](x, lambda x: self.self_attn(x, x, x, mask))
+        return self.sublayer[1](x, self.feed_forward)
+
+
+class MultiHeadedAttention(nn.Module):
+    def __init__(self, h, d_model, dropout=0.1):
+        super(MultiHeadedAttention, self).__init__()
+        assert d_model % h == 0
+        self.d_k = d_model // h 
+        self.h = h
+        self.linears = clones(nn.Linear(d_model, d_model), 4)
+        self.attn = None
+        self.dropout = nn.Dropout(p=dropout)
+
+    def forward(self, query, key, value, mask=None):
+        if mask is not None:
+            mask = mask.unsqueeze(1)
+        nbatches = query.size(0)
+
+        query, key, value = \
+            [l(x).view(nbatches, -1, self.h, self.d_k).transpose(1, 2)
+             for l, x in zip(self.linears, (query, key, value))]
+
+        x, self.attn = attention(query, key, value, mask=mask, dropout=self.dropout)
+
+        x = x.transpose(1, 2).contiguous().view(nbatches, -1, self.h * self.d_k)
+        return self.linears[-1](x)
+
+
+class PositionwiseFeedForward(nn.Module):
+    def __init__(self, d_model, d_ff, dropout=0.1):
+        super(PositionwiseFeedForward, self).__init__()
+        self.w_1 = nn.Linear(d_model, d_ff)
+        self.w_2 = nn.Linear(d_ff, d_model)
+        self.gelu = nn.ReLU()
+        self.dropout = nn.Dropout(dropout)
+
+    def forward(self, x):
+        return self.w_2(self.dropout(self.gelu(self.w_1(x))))
+
+class Transformer(nn.Module):
+    def __init__(self, n_layers=3, d_model=256, d_ff=512, h=8, dropout=0.1, length=27):
+        super(Transformer, self).__init__()
+
+        self.pos_embedding = nn.Parameter(torch.randn(1, length, d_model))
+        self.model = self.make_model(N=n_layers, d_model=d_model, d_ff=d_ff, h=h, dropout=dropout)
+                
+    def forward(self, x, mask_MAE=None, mask=None):
+        x += self.pos_embedding
+        #print(str(mask_MAE))
+        if mask_MAE is not None:
+            B, _, C = x.shape
+            x_vis = x[:,~mask_MAE].reshape(B, -1, C)  # ~mask means visible
+
+            x = self.model(x_vis, mask)
+        else:
+            x = self.model(x, mask)
+
+        return x
+
+    def make_model(self, N=3, d_model=256, d_ff=512, h=8, dropout=0.1):
+        c = copy.deepcopy
+        attn = MultiHeadedAttention(h, d_model)
+        ff = PositionwiseFeedForward(d_model, d_ff, dropout)
+        model = Encoder(EncoderLayer(d_model, c(attn), c(ff), dropout), N)
+        return model
+
+
+class Transformer_dec(nn.Module):
+    def __init__(self, n_layers=3, d_model=256, d_ff=512, h=8, dropout=0.1, length=27):
+        super(Transformer_dec, self).__init__()
+
+        self.model = self.make_model(N=n_layers, d_model=d_model, d_ff=d_ff, h=h, dropout=dropout)
+
+
+    def forward(self, x, return_token_num, mask=None):
+
+        x = self.model(x, mask)
+
+        return x
+
+    def make_model(self, N=3, d_model=256, d_ff=512, h=8, dropout=0.1):
+        c = copy.deepcopy
+        attn = MultiHeadedAttention(h, d_model)
+        ff = PositionwiseFeedForward(d_model, d_ff, dropout)
+        model = Encoder(EncoderLayer(d_model, c(attn), c(ff), dropout), N)
+        return model
+
+
+
+
diff --git a/model/stmo.py b/model/stmo.py
new file mode 100644
index 0000000000000000000000000000000000000000..7da885e5e54b810906f7c1ffcd81ee916c2e2835
--- /dev/null
+++ b/model/stmo.py
@@ -0,0 +1,126 @@
+import torch
+import torch.nn as nn
+from model.block.vanilla_transformer_encoder import Transformer
+from model.block.strided_transformer_encoder import Transformer as Transformer_reduce
+
+class Linear(nn.Module):
+    def __init__(self, linear_size, p_dropout=0.25):
+        super(Linear, self).__init__()
+        self.l_size = linear_size
+
+        self.relu = nn.LeakyReLU(0.2, inplace=True)
+        self.dropout = nn.Dropout(p_dropout)
+
+        #self.w1 = nn.Linear(self.l_size, self.l_size)
+        self.w1 = nn.Conv1d(self.l_size, self.l_size, kernel_size=1)
+        self.batch_norm1 = nn.BatchNorm1d(self.l_size)
+
+        #self.w2 = nn.Linear(self.l_size, self.l_size)
+        self.w2 = nn.Conv1d(self.l_size, self.l_size, kernel_size=1)
+        self.batch_norm2 = nn.BatchNorm1d(self.l_size)
+
+    def forward(self, x):
+        y = self.w1(x)
+        y = self.batch_norm1(y)
+        y = self.relu(y)
+        y = self.dropout(y)
+
+        y = self.w2(y)
+        y = self.batch_norm2(y)
+        y = self.relu(y)
+        y = self.dropout(y)
+
+        out = x + y
+
+        return out
+
+class FCBlock(nn.Module):
+
+    def __init__(self, channel_in, channel_out, linear_size, block_num):
+        super(FCBlock, self).__init__()
+
+        self.linear_size = linear_size
+        self.block_num = block_num
+        self.layers = []
+        self.channel_in = channel_in
+        self.stage_num = 3
+        self.p_dropout = 0.1
+        #self.fc_1 = nn.Linear(self.channel_in, self.linear_size)
+        self.fc_1 = nn.Conv1d(self.channel_in, self.linear_size, kernel_size=1)
+        self.bn_1 = nn.BatchNorm1d(self.linear_size)
+        for i in range(block_num):
+            self.layers.append(Linear(self.linear_size, self.p_dropout))
+        #self.fc_2 = nn.Linear(self.linear_size, channel_out)
+        self.fc_2 = nn.Conv1d(self.linear_size, channel_out, kernel_size=1)
+
+        self.layers = nn.ModuleList(self.layers)
+        self.relu = nn.LeakyReLU(0.2, inplace=True)
+        self.dropout = nn.Dropout(self.p_dropout)
+
+    def forward(self, x):
+
+        x = self.fc_1(x)
+        x = self.bn_1(x)
+        x = self.relu(x)
+        x = self.dropout(x)
+        for i in range(self.block_num):
+            x = self.layers[i](x)
+        x = self.fc_2(x)
+
+        return x
+
+class Model(nn.Module):
+    def __init__(self, args):
+        super().__init__()
+
+        layers, channel, d_hid, length  = args.layers, args.channel, args.d_hid, args.frames
+        stride_num = args.stride_num
+        self.num_joints_in, self.num_joints_out = args.n_joints, args.out_joints
+
+        self.encoder = FCBlock(2*self.num_joints_in, channel, 2*channel, 1)
+
+        self.Transformer = Transformer(layers, channel, d_hid, length=length)
+        self.Transformer_reduce = Transformer_reduce(len(stride_num), channel, d_hid, \
+            length=length, stride_num=stride_num)
+        
+        self.fcn = nn.Sequential(
+            nn.BatchNorm1d(channel, momentum=0.1),
+            nn.Conv1d(channel, 3*self.num_joints_out, kernel_size=1)
+        )
+
+        self.fcn_1 = nn.Sequential(
+            nn.BatchNorm1d(channel, momentum=0.1),
+            nn.Conv1d(channel, 3*self.num_joints_out, kernel_size=1)
+        )
+
+    def forward(self, x):
+        x = x[:, :, :, :, 0].permute(0, 2, 3, 1).contiguous() 
+        x_shape = x.shape
+
+        x = x.view(x.shape[0], x.shape[1], -1) 
+        x = x.permute(0, 2, 1).contiguous() 
+
+        x = self.encoder(x) 
+
+        x = x.permute(0, 2, 1).contiguous()
+        x = self.Transformer(x) 
+
+        x_VTE = x
+        x_VTE = x_VTE.permute(0, 2, 1).contiguous()
+        x_VTE = self.fcn_1(x_VTE) 
+
+        x_VTE = x_VTE.view(x_shape[0], self.num_joints_out, -1, x_VTE.shape[2])
+        x_VTE = x_VTE.permute(0, 2, 3, 1).contiguous().unsqueeze(dim=-1)
+
+        x = self.Transformer_reduce(x) 
+        x = x.permute(0, 2, 1).contiguous() 
+        x = self.fcn(x) 
+
+        x = x.view(x_shape[0], self.num_joints_out, -1, x.shape[2])
+        x = x.permute(0, 2, 3, 1).contiguous().unsqueeze(dim=-1)
+        
+        return x, x_VTE
+
+
+
+
diff --git a/model/stmo_pretrain.py b/model/stmo_pretrain.py
new file mode 100644
index 0000000000000000000000000000000000000000..fc19d8ea5de3e44b841890596cd4e93062a8deb2
--- /dev/null
+++ b/model/stmo_pretrain.py
@@ -0,0 +1,163 @@
+import torch
+import torch.nn as nn
+from model.block.vanilla_transformer_encoder_pretrain import Transformer, Transformer_dec
+from model.block.strided_transformer_encoder import Transformer as Transformer_reduce
+import numpy as np
+
+class LayerNorm(nn.Module):
+    def __init__(self, features, eps=1e-6):
+        super(LayerNorm, self).__init__()
+        self.a_2 = nn.Parameter(torch.ones(features))
+        self.b_2 = nn.Parameter(torch.zeros(features))
+        self.eps = eps
+
+    def forward(self, x):
+        mean = x.mean(-1, keepdim=True)
+        std = x.std(-1, keepdim=True)
+        return self.a_2 * (x - mean) / (std + self.eps) + self.b_2
+
+class Linear(nn.Module):
+    def __init__(self, linear_size, p_dropout=0.25):
+        super(Linear, self).__init__()
+        self.l_size = linear_size
+
+        self.relu = nn.LeakyReLU(0.2, inplace=True)
+        self.dropout = nn.Dropout(p_dropout)
+
+        #self.w1 = nn.Linear(self.l_size, self.l_size)
+        self.w1 = nn.Conv1d(self.l_size, self.l_size, kernel_size=1)
+        self.batch_norm1 = nn.BatchNorm1d(self.l_size)
+
+        #self.w2 = nn.Linear(self.l_size, self.l_size)
+        self.w2 = nn.Conv1d(self.l_size, self.l_size, kernel_size=1)
+        self.batch_norm2 = nn.BatchNorm1d(self.l_size)
+
+    def forward(self, x):
+        y = self.w1(x)
+        y = self.batch_norm1(y)
+        y = self.relu(y)
+        y = self.dropout(y)
+
+        y = self.w2(y)
+        y = self.batch_norm2(y)
+        y = self.relu(y)
+        y = self.dropout(y)
+
+        out = x + y
+
+        return out
+
+class FCBlock(nn.Module):
+
+    def __init__(self, channel_in, channel_out, linear_size, block_num):
+        super(FCBlock, self).__init__()
+
+        self.linear_size = linear_size
+        self.block_num = block_num
+        self.layers = []
+        self.channel_in = channel_in
+        self.stage_num = 3
+        self.p_dropout = 0.1
+        #self.fc_1 = nn.Linear(self.channel_in, self.linear_size)
+        self.fc_1 = nn.Conv1d(self.channel_in, self.linear_size, kernel_size=1)
+        self.bn_1 = nn.BatchNorm1d(self.linear_size)
+        for i in range(block_num):
+            self.layers.append(Linear(self.linear_size, self.p_dropout))
+        #self.fc_2 = nn.Linear(self.linear_size, channel_out)
+        self.fc_2 = nn.Conv1d(self.linear_size, channel_out, kernel_size=1)
+
+        self.layers = nn.ModuleList(self.layers)
+        self.relu = nn.LeakyReLU(0.2, inplace=True)
+        self.dropout = nn.Dropout(self.p_dropout)
+
+    def forward(self, x):
+
+        x = self.fc_1(x)
+        x = self.bn_1(x)
+        x = self.relu(x)
+        x = self.dropout(x)
+        for i in range(self.block_num):
+            x = self.layers[i](x)
+        x = self.fc_2(x)
+
+        return x
+
+class Model_MAE(nn.Module):
+    def __init__(self, args):
+        super().__init__()
+
+        layers, channel, d_hid, length  = args.layers, args.channel, args.d_hid, args.frames
+        stride_num = args.stride_num
+        self.spatial_mask_num = args.spatial_mask_num
+        self.num_joints_in, self.num_joints_out = args.n_joints, args.out_joints
+
+        self.length = length
+        dec_dim_shrink = 2
+
+        self.encoder = FCBlock(2*self.num_joints_in, channel, 2*channel, 1)
+
+        self.Transformer = Transformer(layers, channel, d_hid, length=length)
+        self.Transformer_dec = Transformer_dec(layers-1, channel//dec_dim_shrink, d_hid//dec_dim_shrink, length=length)
+
+        self.encoder_to_decoder = nn.Linear(channel, channel//dec_dim_shrink, bias=False)
+        self.encoder_LN = LayerNorm(channel)
+        
+        self.fcn_dec = nn.Sequential(
+            nn.BatchNorm1d(channel//dec_dim_shrink, momentum=0.1),
+            nn.Conv1d(channel//dec_dim_shrink, 2*self.num_joints_out, kernel_size=1)
+        )
+
+        # self.fcn_1 = nn.Sequential(
+        #     nn.BatchNorm1d(channel, momentum=0.1),
+        #     nn.Conv1d(channel, 3*self.num_joints_out, kernel_size=1)
+        # )
+
+        self.dec_pos_embedding = nn.Parameter(torch.randn(1, length, channel//dec_dim_shrink))
+        self.mask_token = nn.Parameter(torch.randn(1, 1, channel//dec_dim_shrink))
+
+        self.spatial_mask_token = nn.Parameter(torch.randn(1, 1, 2))
+
+    def forward(self, x_in, mask, spatial_mask):
+        x_in = x_in[:, :, :, :, 0].permute(0, 2, 3, 1).contiguous()
+        b,f,_,_ = x_in.shape
+
+        # spatial mask out
+        x = x_in.clone()
+
+        x[:,spatial_mask] = self.spatial_mask_token.expand(b,self.spatial_mask_num*f,2)
+
+
+        x = x.view(b, f, -1)
+
+        x = x.permute(0, 2, 1).contiguous()
+
+        x = self.encoder(x)
+
+        x = x.permute(0, 2, 1).contiguous()
+        feas = self.Transformer(x, mask_MAE=mask)
+
+        feas = self.encoder_LN(feas)
+        feas = self.encoder_to_decoder(feas)
+
+        B, N, C = feas.shape
+
+        # we don't unshuffle the correct visible token order,
+        # but shuffle the pos embedding accorddingly.
+        expand_pos_embed = self.dec_pos_embedding.expand(B, -1, -1).clone()
+        pos_emd_vis = expand_pos_embed[:, ~mask].reshape(B, -1, C)
+        pos_emd_mask = expand_pos_embed[:, mask].reshape(B, -1, C)
+        x_full = torch.cat([feas + pos_emd_vis, self.mask_token + pos_emd_mask], dim=1)
+
+        x_out = self.Transformer_dec(x_full, pos_emd_mask.shape[1])
+
+        x_out = x_out.permute(0, 2, 1).contiguous()
+        x_out = self.fcn_dec(x_out)
+
+        x_out = x_out.view(b, self.num_joints_out, 2, -1)
+        x_out = x_out.permute(0, 2, 3, 1).contiguous().unsqueeze(dim=-1)
+        
+        return x_out
+
+
+
+
diff --git a/skeleton_num_visualization.py b/skeleton_num_visualization.py
new file mode 100644
index 0000000000000000000000000000000000000000..7c1914b53c7675fe18107bbe28281824607e331e
--- /dev/null
+++ b/skeleton_num_visualization.py
@@ -0,0 +1,46 @@
+import numpy as np
+import matplotlib.pyplot as plt
+
+# 导入数据
+import pickle
+with open("output_3Dpose_npy/kun_1280x720_30fps_0-14_0-32.npy", 'rb') as file:
+    data = np.load(file)
+with open("skeleton.npy", 'rb') as file:
+    skeleton = pickle.load(file)
+
+# 提取第0帧坐标
+xyz_0 = data[0]
+
+# 创建3D坐标系
+fig = plt.figure(figsize=(10, 8))
+ax = fig.add_subplot(111, projection='3d')
+# 设置俯仰角和方位角
+ax.view_init(elev=0., azim=70)
+
+# 绘制3D点
+radius = 1.7
+ax.scatter(xyz_0[:, 0], xyz_0[:, 1], xyz_0[:, 2])
+# 添加文本标记
+for i in range(xyz_0.shape[0]):
+    ax.text(xyz_0[i, 0], xyz_0[i, 1], xyz_0[i, 2], str(i), fontsize=10)
+
+# 绘制两点间的线段
+for num1 in range(xyz_0.shape[0]):
+    parent = skeleton._parents
+    num2 = parent[num1]
+    if num2 != -1:
+        x1, y1, z1 = xyz_0[num1, :]
+        x2, y2, z2 = xyz_0[num2, :]
+        ax.plot([x1, x2], [y1, y2], [z1, z2])
+
+ax.set_xlabel('X')
+ax.set_ylabel('Y')
+ax.set_zlabel('Z')
+# ax.set_xlim3d([-radius/2, radius/2])
+# ax.set_ylim3d([-radius/2, radius/2])
+# ax.set_xticklabels([])
+# ax.set_yticklabels([])
+# ax.set_zticklabels([])
+
+# 保存图像
+plt.savefig('plot.png')
diff --git a/test/dignose.py b/test/dignose.py
new file mode 100644
index 0000000000000000000000000000000000000000..bf5270f5e595da02251d846eadc55999b34207dd
--- /dev/null
+++ b/test/dignose.py
@@ -0,0 +1,195 @@
+#!/usr/bin/env python
+
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+"""Diagnose script for checking OS/hardware/python/pip/mxnet/network.
+The output of this script can be a very good hint to issue/problem.
+"""
+import os
+import platform
+import socket
+import subprocess
+import sys
+import time
+
+try:
+    from urllib.request import urlopen
+    from urllib.parse import urlparse
+except ImportError:
+    from urlparse import urlparse
+    from urllib2 import urlopen
+import argparse
+
+
+def parse_args():
+    """Parse arguments."""
+    parser = argparse.ArgumentParser(
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter,
+        description='Diagnose script for checking the current system.')
+    choices = ['python', 'pip', 'mxnet', 'os', 'hardware', 'network']
+    for choice in choices:
+        parser.add_argument('--' + choice, default=1, type=int,
+                            help='Diagnose {}.'.format(choice))
+    parser.add_argument('--region', default='', type=str,
+                        help="Additional sites in which region(s) to test. \
+                        Specify 'cn' for example to test mirror sites in China.")
+    parser.add_argument('--timeout', default=10, type=int,
+                        help="Connection test timeout threshold, 0 to disable.")
+    args = parser.parse_args()
+    return args
+
+
+URLS = {
+    'MXNet': 'https://github.com/apache/incubator-mxnet',
+    'Gluon Tutorial(en)': 'http://gluon.mxnet.io',
+    'Gluon Tutorial(cn)': 'https://zh.gluon.ai',
+    'FashionMNIST': 'https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/gluon/dataset/fashion-mnist/train-labels-idx1-ubyte.gz',
+    'PYPI': 'https://pypi.python.org/pypi/pip',
+    'Conda': 'https://repo.continuum.io/pkgs/free/',
+}
+REGIONAL_URLS = {
+    'cn': {
+        'PYPI(douban)': 'https://pypi.douban.com/',
+        'Conda(tsinghua)': 'https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free/',
+    }
+}
+
+
+def connection_test(name, url, timeout=10):
+    """Simple connection test"""
+    urlinfo = urlparse(url)
+    start = time.time()
+    try:
+        ip = socket.gethostbyname(urlinfo.netloc)
+    except Exception as e:
+        print('Error resolving DNS for {}: {}, {}'.format(name, url, e))
+        return
+    dns_elapsed = time.time() - start
+    start = time.time()
+    try:
+        _ = urlopen(url, timeout=timeout)
+    except Exception as e:
+        print("Error open {}: {}, {}, DNS finished in {} sec.".format(name, url, e, dns_elapsed))
+        return
+    load_elapsed = time.time() - start
+    print("Timing for {}: {}, DNS: {:.4f} sec, LOAD: {:.4f} sec.".format(name, url, dns_elapsed, load_elapsed))
+
+
+def check_python():
+    print('----------Python Info----------')
+    print('Version      :', platform.python_version())
+    print('Compiler     :', platform.python_compiler())
+    print('Build        :', platform.python_build())
+    print('Arch         :', platform.architecture())
+
+
+def check_pip():
+    print('------------Pip Info-----------')
+    try:
+        import pip
+        print('Version      :', pip.__version__)
+        print('Directory    :', os.path.dirname(pip.__file__))
+    except ImportError:
+        print('No corresponding pip install for current python.')
+
+
+def check_mxnet():
+    print('----------MXNet Info-----------')
+    try:
+        import mxnet
+        print('Version      :', mxnet.__version__)
+        mx_dir = os.path.dirname(mxnet.__file__)
+        print('Directory    :', mx_dir)
+        commit_hash = os.path.join(mx_dir, 'COMMIT_HASH')
+        with open(commit_hash, 'r') as f:
+            ch = f.read().strip()
+            print('Commit Hash   :', ch)
+    except ImportError:
+        print('No MXNet installed.')
+    except IOError:
+        print('Hashtag not found. Not installed from pre-built package.')
+    except Exception as e:
+        import traceback
+        if not isinstance(e, IOError):
+            print("An error occured trying to import mxnet.")
+            print("This is very likely due to missing missing or incompatible library files.")
+        print(traceback.format_exc())
+
+
+def check_os():
+    print('----------System Info----------')
+    print('Platform     :', platform.platform())
+    print('system       :', platform.system())
+    print('node         :', platform.node())
+    print('release      :', platform.release())
+    print('version      :', platform.version())
+
+
+def check_hardware():
+    print('----------Hardware Info----------')
+    print('machine      :', platform.machine())
+    print('processor    :', platform.processor())
+    if sys.platform.startswith('darwin'):
+        pipe = subprocess.Popen(('sysctl', '-a'), stdout=subprocess.PIPE)
+        output = pipe.communicate()[0]
+        for line in output.split(b'\n'):
+            if b'brand_string' in line or b'features' in line:
+                print(line.strip())
+    elif sys.platform.startswith('linux'):
+        subprocess.call(['lscpu'])
+    elif sys.platform.startswith('win32'):
+        subprocess.call(['wmic', 'cpu', 'get', 'name'])
+
+
+def check_network(args):
+    print('----------Network Test----------')
+    if args.timeout > 0:
+        print('Setting timeout: {}'.format(args.timeout))
+        socket.setdefaulttimeout(10)
+    for region in args.region.strip().split(','):
+        r = region.strip().lower()
+        if not r:
+            continue
+        if r in REGIONAL_URLS:
+            URLS.update(REGIONAL_URLS[r])
+        else:
+            import warnings
+            warnings.warn('Region {} do not need specific test, please refer to global sites.'.format(r))
+    for name, url in URLS.items():
+        connection_test(name, url, args.timeout)
+
+
+if __name__ == '__main__':
+    args = parse_args()
+    if args.python:
+        check_python()
+
+    if args.pip:
+        check_pip()
+
+    if args.mxnet:
+        check_mxnet()
+
+    if args.os:
+        check_os()
+
+    if args.hardware:
+        check_hardware()
+
+    if args.network:
+        check_network(args)
diff --git a/test/opencv_capture_test.py b/test/opencv_capture_test.py
new file mode 100644
index 0000000000000000000000000000000000000000..18dbb6315ae9ebb11b8430f9f01937f091343906
--- /dev/null
+++ b/test/opencv_capture_test.py
@@ -0,0 +1,26 @@
+import cv2
+
+from tqdm import tqdm
+
+path = '../outputs/nba2k.mp4'
+stream = cv2.VideoCapture(path)
+assert stream.isOpened(), 'Cannot capture source'
+
+video_length = int(stream.get(cv2.CAP_PROP_FRAME_COUNT))
+video_fps = stream.get(cv2.CAP_PROP_FPS)
+video_size = (int(stream.get(cv2.CAP_PROP_FRAME_WIDTH)), int(stream.get(cv2.CAP_PROP_FRAME_HEIGHT)))
+writer = cv2.VideoWriter('out.mp4', cv2.VideoWriter_fourcc(*'MP4V'), video_fps, video_size)
+
+for i in tqdm(range(video_length)):
+    i += 1
+    grabbed, frame = stream.read()
+
+    writer.write(frame)
+
+    # if the `grabbed` boolean is `False`, then we have
+    # reached the end of the video file
+    if not grabbed:
+        print('\n===========================> This video get ' + str(i) + ' frames in total.')
+        break
+
+writer.release()
diff --git a/tools/amination.py b/tools/amination.py
new file mode 100644
index 0000000000000000000000000000000000000000..21e1fde9442dff6b9fb6e879dff538a27d4c1ca3
--- /dev/null
+++ b/tools/amination.py
@@ -0,0 +1,167 @@
+'''
+Realtime Display 3D Human Reconstrction
+3D image drawing by pygtagrph based on OpenGL
+speed about 25 FPS
+'''
+import math
+import sys
+from argparse import ArgumentParser
+
+import cv2
+import pyqtgraph as pg
+import pyqtgraph.opengl as gl
+from pyqtgraph.Qt import QtCore, QtGui
+from pyqtgraph.opengl import *
+
+from joints_detectors.openpose.main import load_model as Model2Dload
+
+model2D = Model2Dload()
+from joints_detectors.openpose.main import generate_frame_kpt as OpenPoseInterface
+
+interface2D = OpenPoseInterface
+from tools.utils import videopose_model_load as Model3Dload
+
+model3D = Model3Dload()
+from tools.utils import interface as VideoPoseInterface
+
+interface3D = VideoPoseInterface
+from tools.utils import draw_2Dimg, resize_img, common
+
+common = common()
+item = 0
+item_num = 0
+pos_init = np.zeros(shape=(17, 3))
+
+
+class Visualizer(object):
+    def __init__(self, input_video):
+        self.traces = dict()
+        self.app = QtGui.QApplication(sys.argv)
+        self.w = gl.GLViewWidget()
+        self.w.opts['distance'] = 45.0  ## distance of camera from center
+        self.w.opts['fov'] = 60  ## horizontal field of view in degrees
+        self.w.opts['elevation'] = 10  ## camera's angle of elevation in degrees 仰俯角
+        self.w.opts['azimuth'] = 90  ## camera's azimuthal angle in degrees 方位角
+        self.w.setWindowTitle('pyqtgraph example: GLLinePlotItem')
+        self.w.setGeometry(450, 700, 980, 700)  # 原点在左上角
+        self.w.show()
+
+        # create the background grids
+        gx = gl.GLGridItem()
+        gx.rotate(90, 0, 1, 0)
+        gx.translate(-10, 0, 0)
+        self.w.addItem(gx)
+        gy = gl.GLGridItem()
+        gy.rotate(90, 1, 0, 0)
+        gy.translate(0, -10, 0)
+        self.w.addItem(gy)
+        gz = gl.GLGridItem()
+        gz.translate(0, 0, -10)
+        self.w.addItem(gz)
+
+        # special setting
+        self.cap = cv2.VideoCapture(input_video)
+        self.video_name = input_video.split('/')[-1].split('.')[0]
+        self.kpt2Ds = []
+        pos = pos_init
+
+        for j, j_parent in enumerate(common.skeleton_parents):
+            if j_parent == -1:
+                continue
+
+            x = np.array([pos[j, 0], pos[j_parent, 0]]) * 10
+            y = np.array([pos[j, 1], pos[j_parent, 1]]) * 10
+            z = np.array([pos[j, 2], pos[j_parent, 2]]) * 10 - 10
+            pos_total = np.vstack([x, y, z]).transpose()
+            self.traces[j] = gl.GLLinePlotItem(pos=pos_total, color=pg.glColor((j, 10)), width=6, antialias=True)
+            self.w.addItem(self.traces[j])
+
+    def start(self):
+        if (sys.flags.interactive != 1) or not hasattr(QtCore, 'PYQT_VERSION'):
+            QtGui.QApplication.instance().exec_()
+
+    def set_plotdata(self, name, points, color, width):
+        self.traces[name].setData(pos=points, color=color, width=width)
+
+    def update(self):
+        global item
+        global item_num
+        num = item / 2
+        azimuth_value = abs(num % 120 + math.pow(-1, int((num / 120))) * 120) % 120
+        self.w.opts['azimuth'] = azimuth_value
+        print(item, '  ')
+        _, frame = self.cap.read()
+        if item % 2 != 1:
+            frame, W, H = resize_img(frame)
+            joint2D = interface2D(frame, model2D)
+            img2D = draw_2Dimg(frame, joint2D, 1)
+            if item == 0:
+                for _ in range(30):
+                    self.kpt2Ds.append(joint2D)
+            elif item < 30:
+                self.kpt2Ds.append(joint2D)
+                self.kpt2Ds.pop(0)
+            else:
+                self.kpt2Ds.append(joint2D)
+                self.kpt2Ds.pop(0)
+
+            item += 1
+            joint3D = interface3D(model3D, np.array(self.kpt2Ds), W, H)
+            pos = joint3D[-1]  # (17, 3)
+
+            for j, j_parent in enumerate(common.skeleton_parents):
+                if j_parent == -1:
+                    continue
+                x = np.array([pos[j, 0], pos[j_parent, 0]]) * 10
+                y = np.array([pos[j, 1], pos[j_parent, 1]]) * 10
+                z = np.array([pos[j, 2], pos[j_parent, 2]]) * 10 - 10
+                pos_total = np.vstack([x, y, z]).transpose()
+                self.set_plotdata(
+                    name=j, points=pos_total,
+                    color=pg.glColor((j, 10)),
+                    width=6)
+
+            # save
+            if item_num < 10:
+                name = '000' + str(item_num)
+
+            elif item_num < 100:
+                name = '00' + str(item_num)
+
+            elif item_num < 1000:
+                name = '0' + str(item_num)
+
+            else:
+                name = str(item_num)
+            im3Dname = 'VideoSave/' + '3D_' + name + '.png'
+            d = self.w.renderToArray((img2D.shape[1], img2D.shape[0]))  # (W, H)
+            print('Save 3D image: ', im3Dname)
+            pg.makeQImage(d).save(im3Dname)
+
+            im2Dname = 'VideoSave/' + '2D_' + name + '.png'
+            print('Save 2D image: ', im2Dname)
+            cv2.imwrite(im2Dname, img2D)
+
+            item_num += 1
+        else:
+            item += 1
+
+    def animation(self):
+        timer = QtCore.QTimer()
+        timer.timeout.connect(self.update)
+        timer.start(1)
+        self.start()
+
+
+def main():
+    parser = ArgumentParser()
+    parser.add_argument("-i", "--video", help="input video file name", default="/home/xyliu/Videos/sports/dance.mp4")
+    args = parser.parse_args()
+    print(args.video)
+    v = Visualizer(args.video)
+    v.animation()
+    cv2.destroyAllWindows()
+
+
+if __name__ == '__main__':
+    main()
diff --git a/tools/utils.py b/tools/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..8752f922157e3697eb79b149d04cc9ac2da47a67
--- /dev/null
+++ b/tools/utils.py
@@ -0,0 +1,165 @@
+import os
+
+import cv2
+import matplotlib.pyplot as plt
+import numpy as np
+import torch
+
+from common.utils import evaluate
+
+path = os.path.split(os.path.realpath(__file__))[0]
+main_path = os.path.join(path, '..')
+
+
+class common:
+    keypoints_symmetry = [[1, 3, 5, 7, 9, 11, 13, 15], [2, 4, 6, 8, 10, 12, 14, 16]]
+    rot = np.array([0.14070565, -0.15007018, -0.7552408, 0.62232804], dtype=np.float32)
+    skeleton_parents = np.array([-1, 0, 1, 2, 0, 4, 5, 0, 7, 8, 9, 8, 11, 12, 8, 14, 15])
+    pairs = [(1, 2), (5, 4), (6, 5), (8, 7), (8, 9), (10, 1), (11, 10), (12, 11), (13, 1), (14, 13), (15, 14), (16, 2), (16, 3), (16, 4), (16, 7)]
+
+    kps_left, kps_right = list(keypoints_symmetry[0]), list(keypoints_symmetry[1])
+    joints_left, joints_right = list([4, 5, 6, 11, 12, 13]), list([1, 2, 3, 14, 15, 16])
+    pad = (243 - 1) // 2  # Padding on each side
+    causal_shift = 0
+    joint_pairs = [[0, 1], [1, 3], [0, 2], [2, 4],
+                   [5, 6], [5, 7], [7, 9], [6, 8], [8, 10],
+                   [5, 11], [6, 12], [11, 12],
+                   [11, 13], [12, 14], [13, 15], [14, 16]]
+
+
+def resize_img(frame, max_length=640):
+    H, W = frame.shape[:2]
+    if max(W, H) > max_length:
+        if W > H:
+            W_resize = max_length
+            H_resize = int(H * max_length / W)
+        else:
+            H_resize = max_length
+            W_resize = int(W * max_length / H)
+        frame = cv2.resize(frame, (W_resize, H_resize), interpolation=cv2.INTER_AREA)
+        return frame, W_resize, H_resize
+
+    else:
+        return frame, W, H
+
+
+def rotate_bound(image, angle):
+    (h, w) = image.shape[:2]
+    (cX, cY) = (w // 2, h // 2)
+
+    M = cv2.getRotationMatrix2D((cX, cY), -angle, 1.0)
+    cos = np.abs(M[0, 0])
+    sin = np.abs(M[0, 1])
+
+    nW = int((h * sin) + (w * cos))
+    nH = int((h * cos) + (w * sin))
+
+    M[0, 2] += (nW / 2) - cX
+    M[1, 2] += (nH / 2) - cY
+
+    return cv2.warpAffine(image, M, (nW, nH))
+
+
+def draw_2Dimg(img, kpt, display=None):
+    # kpts : (17, 3)  3-->(x, y, score)
+    im = img.copy()
+    joint_pairs = common.joint_pairs
+    for item in kpt:
+        score = item[-1]
+        if score > 0.1:
+            x, y = int(item[0]), int(item[1])
+            cv2.circle(im, (x, y), 1, (255, 5, 0), 5)
+    for pair in joint_pairs:
+        j, j_parent = pair
+        pt1 = (int(kpt[j][0]), int(kpt[j][1]))
+        pt2 = (int(kpt[j_parent][0]), int(kpt[j_parent][1]))
+        cv2.line(im, pt1, pt2, (0, 255, 0), 2)
+
+    if display:
+        cv2.imshow('im', im)
+        cv2.waitKey(3)
+    return im
+
+
+def draw_3Dimg(pos, image, display=None, kpt2D=None):
+    from matplotlib.backends.backend_qt5agg import FigureCanvasQTAgg as FigureCanvas
+    fig = plt.figure(figsize=(12, 6))
+    canvas = FigureCanvas(fig)
+
+    # 2D
+    fig.add_subplot(121)
+    if kpt2D:
+        plt.imshow(draw_2Dimg(image, kpt2D))
+    else:
+        plt.imshow(image)
+
+    # 3D
+    ax = fig.add_subplot(122, projection='3d')
+    radius = 1.7
+    ax.view_init(elev=15., azim=70.)
+    ax.set_xlim3d([-radius / 2, radius / 2])
+    ax.set_zlim3d([0, radius])
+    ax.set_ylim3d([-radius / 2, radius / 2])
+    ax.set_aspect('equal')
+    # 坐标轴刻度
+    ax.set_xticklabels([])
+    ax.set_yticklabels([])
+    ax.set_zticklabels([])
+    ax.dist = 7.5
+    parents = common.skeleton_parents
+    joints_right = common.joints_right
+
+    for j, j_parent in enumerate(parents):
+        if j_parent == -1:
+            continue
+
+        col = 'red' if j in joints_right else 'black'
+        # 画图3D
+        ax.plot([pos[j, 0], pos[j_parent, 0]],
+                [pos[j, 1], pos[j_parent, 1]],
+                [pos[j, 2], pos[j_parent, 2]], zdir='z', c=col)
+    width, height = fig.get_size_inches() * fig.get_dpi()
+    canvas.draw()  # draw the canvas, cache the renderer
+    image = np.fromstring(canvas.tostring_rgb(), dtype='uint8').reshape(int(height), int(width), 3)
+    if display:
+        cv2.imshow('im', image)
+        cv2.waitKey(3)
+
+    return image
+
+
+def videoInfo(VideoName):
+    cap = cv2.VideoCapture(VideoName)
+    length = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
+    return cap, length
+
+
+def videopose_model_load():
+    # load trained model
+    from common.model import TemporalModel
+    chk_filename = main_path + '/checkpoint/pretrained_h36m_detectron_coco.bin'
+    checkpoint = torch.load(chk_filename, map_location=lambda storage, loc: storage)  # 把loc映射到storage
+    model_pos = TemporalModel(17, 2, 17, filter_widths=[3, 3, 3, 3, 3], causal=False, dropout=False, channels=1024, dense=False)
+    model_pos = model_pos.cuda()
+    model_pos.load_state_dict(checkpoint['model_pos'])
+    receptive_field = model_pos.receptive_field()
+    return model_pos
+
+
+def interface(model_pos, keypoints, W, H):
+    # input (N, 17, 2) return (N, 17, 3)
+    if not isinstance(keypoints, np.ndarray):
+        keypoints = np.array(keypoints)
+
+    from common.camera import camera_to_world, normalize_screen_coordinates
+    #  keypoints = normalize_screen_coordinates_new(keypoints[..., :2], w=W, h=H)
+    keypoints = normalize_screen_coordinates(keypoints[..., :2], w=1000, h=1002)
+    input_keypoints = keypoints.copy()
+    # test_time_augmentation True
+    from common.generators import UnchunkedGenerator
+    gen = UnchunkedGenerator(None, None, [input_keypoints], pad=common.pad, causal_shift=common.causal_shift, augment=True, kps_left=common.kps_left,
+                             kps_right=common.kps_right, joints_left=common.joints_left, joints_right=common.joints_right)
+    prediction = evaluate(gen, model_pos, return_predictions=True)
+    prediction = camera_to_world(prediction, R=common.rot, t=0)
+    prediction[:, :, 2] -= np.min(prediction[:, :, 2])
+    return prediction
diff --git a/tools/videoThenAmination.py b/tools/videoThenAmination.py
new file mode 100644
index 0000000000000000000000000000000000000000..a7f0d29f4ccc4e869422d72214b9b18f66a47691
--- /dev/null
+++ b/tools/videoThenAmination.py
@@ -0,0 +1,146 @@
+'''
+先生成所有的2D坐标
+再生成3D坐标，
+再绘图，不是实时的
+'''
+import sys
+import time
+from argparse import ArgumentParser
+
+import cv2
+import pyqtgraph as pg
+import pyqtgraph.opengl as gl
+from pyqtgraph.Qt import QtCore, QtGui
+from pyqtgraph.opengl import *
+from tqdm import tqdm
+
+from joints_detectors.openpose.main import load_model as Model2Dload
+
+model2D = Model2Dload()
+from joints_detectors.openpose.main import generate_frame_kpt as OpenPoseInterface
+
+interface2D = OpenPoseInterface
+from tools.utils import videopose_model_load as Model3Dload
+
+model3D = Model3Dload()
+from tools.utils import interface as VideoPoseInterface
+
+interface3D = VideoPoseInterface
+from tools.utils import draw_2Dimg, videoInfo, resize_img, common
+
+common = common()
+
+
+# 先得到所有视频的2D坐标，再统一生成3D坐标
+def VideoPoseJoints(VideoName):
+    cap, cap_length = videoInfo(VideoName)
+    kpt2Ds = []
+    for i in tqdm(range(cap_length)):
+        _, frame = cap.read()
+        frame, W, H = resize_img(frame)
+
+        try:
+            joint2D = interface2D(frame, model2D)
+        except Exception as e:
+            print(e)
+            continue
+        draw_2Dimg(frame, joint2D, 1)
+        kpt2Ds.append(joint2D)
+
+    joint3D = interface3D(model3D, np.array(kpt2Ds), W, H)
+    return joint3D
+
+
+item = 0
+pos_init = np.zeros(shape=(17, 3))
+
+
+class Visualizer(object):
+    def __init__(self, skeletons_3d):
+        self.traces = dict()
+        self.app = QtGui.QApplication(sys.argv)
+        self.w = gl.GLViewWidget()
+        self.w.opts['distance'] = 45.0  ## distance of camera from center
+        self.w.opts['fov'] = 60  ## horizontal field of view in degrees
+        self.w.opts['elevation'] = 10  ## camera's angle of elevation in degrees 仰俯角
+        self.w.opts['azimuth'] = 90  ## camera's azimuthal angle in degrees 方位角
+        self.w.setWindowTitle('pyqtgraph example: GLLinePlotItem')
+        self.w.setGeometry(450, 700, 980, 700)  # 原点在左上角
+        self.w.show()
+
+        # create the background grids
+        gx = gl.GLGridItem()
+        gx.rotate(90, 0, 1, 0)
+        gx.translate(-10, 0, 0)
+        self.w.addItem(gx)
+        gy = gl.GLGridItem()
+        gy.rotate(90, 1, 0, 0)
+        gy.translate(0, -10, 0)
+        self.w.addItem(gy)
+        gz = gl.GLGridItem()
+        gz.translate(0, 0, -10)
+        self.w.addItem(gz)
+
+        # special setting
+        pos = pos_init
+        self.skeleton_parents = common.skeleton_parents
+        self.skeletons_3d = skeletons_3d
+
+        for j, j_parent in enumerate(self.skeleton_parents):
+            if j_parent == -1:
+                continue
+            x = np.array([pos[j, 0], pos[j_parent, 0]]) * 10
+            y = np.array([pos[j, 1], pos[j_parent, 1]]) * 10
+            z = np.array([pos[j, 2], pos[j_parent, 2]]) * 10 - 10
+            pos_total = np.vstack([x, y, z]).transpose()
+            self.traces[j] = gl.GLLinePlotItem(pos=pos_total, color=pg.glColor((j, 10)), width=6, antialias=True)
+            self.w.addItem(self.traces[j])
+
+    def start(self):
+        if (sys.flags.interactive != 1) or not hasattr(QtCore, 'PYQT_VERSION'):
+            QtGui.QApplication.instance().exec_()
+
+    def set_plotdata(self, name, points, color, width):
+        self.traces[name].setData(pos=points, color=color, width=width)
+
+    def update(self):
+        time.sleep(0.03)
+        global item
+        pos = self.skeletons_3d[item]
+        print(item, '  ')
+        item += 1
+
+        for j, j_parent in enumerate(self.skeleton_parents):
+            if j_parent == -1:
+                continue
+
+            x = np.array([pos[j, 0], pos[j_parent, 0]]) * 10
+            y = np.array([pos[j, 1], pos[j_parent, 1]]) * 10
+            z = np.array([pos[j, 2], pos[j_parent, 2]]) * 10 - 10
+            pos_total = np.vstack([x, y, z]).transpose()
+            self.set_plotdata(
+                name=j, points=pos_total,
+                color=pg.glColor((j, 10)),
+                width=6)
+
+    def animation(self):
+        timer = QtCore.QTimer()
+        timer.timeout.connect(self.update)
+        timer.start(1)
+        self.start()
+
+
+def main(VideoName):
+    print(VideoName)
+    joint3D = VideoPoseJoints(VideoName)
+    v = Visualizer(joint3D)
+    v.animation()
+    cv2.destroyAllWindows()
+
+
+if __name__ == '__main__':
+    parser = ArgumentParser()
+    parser.add_argument("-video", "--video_input", help="input video file name", default="/home/xyliu/Videos/sports/dance.mp4")
+    args = parser.parse_args()
+    VideoName = args.video_input
+    main(VideoName)
diff --git a/videopose_PSTMO.py b/videopose_PSTMO.py
new file mode 100644
index 0000000000000000000000000000000000000000..6b7cabd0f1f4ae101df322473af49f5c39c62f38
--- /dev/null
+++ b/videopose_PSTMO.py
@@ -0,0 +1,209 @@
+import os
+import time
+
+from common.arguments import parse_args
+from common.camera import *
+from common.generators import *
+from common.loss import *
+from common.model import *
+from common.utils import Timer, evaluate, add_path
+from common.inference_3d import *
+
+from model.block.refine import refine
+from model.stmo import Model
+
+import HPE2keyframes as Hk 
+
+# from joints_detectors.openpose.main import generate_kpts as open_pose
+
+
+os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"  # see issue #152
+os.environ["CUDA_VISIBLE_DEVICES"] = "0"
+
+metadata = {'layout_name': 'coco', 'num_joints': 17, 'keypoints_symmetry': [[1, 3, 5, 7, 9, 11, 13, 15], [2, 4, 6, 8, 10, 12, 14, 16]]}
+
+add_path()
+
+
+# record time
+def ckpt_time(ckpt=None):
+    if not ckpt:
+        return time.time()
+    else:
+        return time.time() - float(ckpt), time.time()
+
+
+time0 = ckpt_time()
+
+
+def get_detector_2d(detector_name):
+    def get_alpha_pose():
+        from joints_detectors.Alphapose.gene_npz import generate_kpts as alpha_pose
+        return alpha_pose
+
+    def get_hr_pose():
+        from joints_detectors.hrnet.pose_estimation.video import generate_kpts as hr_pose
+        return hr_pose
+
+    detector_map = {
+        'alpha_pose': get_alpha_pose,
+        'hr_pose': get_hr_pose,
+        # 'open_pose': open_pose
+    }
+
+    assert detector_name in detector_map, f'2D detector: {detector_name} not implemented yet!'
+
+    return detector_map[detector_name]()
+
+
+class Skeleton:
+    def parents(self):
+        return np.array([-1, 0, 1, 2, 0, 4, 5, 0, 7, 8, 9, 8, 11, 12, 8, 14, 15])
+
+    def joints_right(self):
+        return [1, 2, 3, 14, 15, 16]
+
+
+def main(args):
+    detector_2d = get_detector_2d(args.detector_2d)
+
+    assert detector_2d, 'detector_2d should be in ({alpha, hr, open}_pose)'
+
+    # 2D kpts loads or generate
+    #args.input_npz = './outputs/alpha_pose_skiing_cut/skiing_cut.npz'
+    if not args.input_npz:
+        video_name = args.viz_video
+        keypoints = detector_2d(video_name)
+    else:
+        npz = np.load(args.input_npz)
+        keypoints = npz['kpts']  # (N, 17, 2)
+
+    keypoints_symmetry = metadata['keypoints_symmetry']
+    kps_left, kps_right = list(keypoints_symmetry[0]), list(keypoints_symmetry[1])
+    joints_left, joints_right = list([4, 5, 6, 11, 12, 13]), list([1, 2, 3, 14, 15, 16])
+
+    # normlization keypoints  Suppose using the camera parameter
+    keypoints = normalize_screen_coordinates(keypoints[..., :2], w=1000, h=1002)
+
+    # model_pos = TemporalModel(17, 2, 17, filter_widths=[3, 3, 3, 3, 3], causal=args.causal, dropout=args.dropout, channels=args.channels,
+    #                           dense=args.dense)
+
+    model = {}
+    model['trans'] = Model(args).cuda()
+
+
+    # if torch.cuda.is_available():
+    #     model_pos = model_pos.cuda()
+
+    ckpt, time1 = ckpt_time(time0)
+    print('-------------- load data spends {:.2f} seconds'.format(ckpt))
+
+    # load trained model
+    # chk_filename = os.path.join(args.checkpoint, args.resume if args.resume else args.evaluate)
+    # print('Loading checkpoint', chk_filename)
+    # checkpoint = torch.load(chk_filename, map_location=lambda storage, loc: storage)  # 把loc映射到storage
+    # model_pos.load_state_dict(checkpoint['model_pos'])
+
+    model_dict = model['trans'].state_dict()
+
+    no_refine_path = "checkpoint/PSTMOS_no_refine_48_5137_in_the_wild.pth"
+    pre_dict = torch.load(no_refine_path)
+    for key, value in pre_dict.items():
+        name = key[7:]
+        model_dict[name] = pre_dict[key]
+    model['trans'].load_state_dict(model_dict)
+
+
+    ckpt, time2 = ckpt_time(time1)
+    print('-------------- load 3D model spends {:.2f} seconds'.format(ckpt))
+
+    #  Receptive field: 243 frames for args.arc [3, 3, 3, 3, 3]
+    receptive_field = args.frames
+    pad = (receptive_field - 1) // 2  # Padding on each side
+    causal_shift = 0
+
+    print('Rendering...')
+    input_keypoints = keypoints.copy()
+    print(input_keypoints.shape)
+    # gen = UnchunkedGenerator(None, None, [input_keypoints],
+    #                          pad=pad, causal_shift=causal_shift, augment=args.test_time_augmentation,
+    #                          kps_left=kps_left, kps_right=kps_right, joints_left=joints_left, joints_right=joints_right)
+    # test_data = Fusion(opt=args, train=False, dataset=dataset, root_path=root_path, MAE=opt.MAE)
+    # test_dataloader = torch.utils.data.DataLoader(test_data, batch_size=1,
+    #                                               shuffle=False, num_workers=0, pin_memory=True)
+    #prediction = evaluate(gen, model_pos, return_predictions=True)
+
+    gen = Evaluate_Generator(128, None, None, [input_keypoints], args.stride,
+                             pad=pad, causal_shift=causal_shift, augment=args.test_time_augmentation, shuffle=False,
+                             kps_left=kps_left, kps_right=kps_right, joints_left=joints_left, joints_right=joints_right)
+
+    prediction = val(args, gen, model)
+
+    # save 3D joint points
+    # np.save(f'outputs/test_3d_{args.video_name}_output.npy', prediction, allow_pickle=True)
+
+    rot = np.array([0.14070565, -0.15007018, -0.7552408, 0.62232804], dtype=np.float32)
+    prediction = camera_to_world(prediction, R=rot, t=0)
+
+    # We don't have the trajectory, but at least we can rebase the height
+    prediction[:, :, 2] -= np.min(prediction[:, :, 2])
+
+    output_dir_dict = {}
+    npy_filename = f'output_3Dpose_npy/{args.video_name}.npy'
+    output_dir_dict['npy'] = npy_filename
+    np.save(npy_filename, prediction, allow_pickle=True)
+
+    anim_output = {'Ours': prediction}
+    input_keypoints = image_coordinates(input_keypoints[..., :2], w=1000, h=1002)
+
+    ckpt, time3 = ckpt_time(time2)
+    print('-------------- generate reconstruction 3D data spends {:.2f} seconds'.format(ckpt))
+
+    if not args.viz_output:
+        args.viz_output = 'outputs/alpha_result.mp4'
+
+    from common.visualization import render_animation
+    render_animation(input_keypoints, anim_output,
+                     Skeleton(), 25, args.viz_bitrate, np.array(70., dtype=np.float32), args.viz_output,
+                     limit=args.viz_limit, downsample=args.viz_downsample, size=args.viz_size,
+                     input_video_path=args.viz_video, viewport=(1000, 1002),
+                     input_video_skip=args.viz_skip)
+
+    ckpt, time4 = ckpt_time(time3)
+    print('total spend {:2f} second'.format(ckpt))
+
+    return output_dir_dict
+
+
+def inference_video(video_path, detector_2d):
+    """
+    Do image -> 2d points -> 3d points to video.
+    :param detector_2d: used 2d joints detector. Can be {alpha_pose, hr_pose}
+    :param video_path: relative to outputs
+    :return: None
+    """
+    args = parse_args()
+
+    args.detector_2d = detector_2d
+    dir_name = os.path.dirname(video_path)
+    basename = os.path.basename(video_path)
+    args.video_name = basename[:basename.rfind('.')]
+    args.viz_video = video_path
+    args.viz_output = f'output_videos/{args.video_name}.mp4'
+    args.evaluate = 'pretrained_h36m_detectron_coco.bin'
+
+    with Timer(video_path):
+        output_dir_dict = main(args)
+
+    output_dir_dict["output_videos"] = args.viz_output
+    output_dir_dict["video_name"] = args.video_name
+    return output_dir_dict
+
+
+if __name__ == '__main__':
+
+    files = os.listdir('./input_videos')
+    FPS_mine_imator = 30
+    for file in files:
+        output_dir_dict = inference_video(os.path.join('input_videos', file), 'alpha_pose')
+        Hk.hpe2keyframes(output_dir_dict['npy'], FPS_mine_imator, f"output_miframes/{output_dir_dict['video_name']}.miframes")