import ast import json import logging import math import os import random import sys import time import braceexpand from dataclasses import dataclass from multiprocessing import Value import cv2 import numpy as np import pandas as pd import torch import torchvision.datasets as datasets import webdataset as wds from PIL import Image from torch.utils.data import Dataset, DataLoader, SubsetRandomSampler, IterableDataset, get_worker_info from torch.utils.data.distributed import DistributedSampler from webdataset.filters import _shuffle from webdataset.tariterators import base_plus_ext, url_opener, tar_file_expander, valid_sample from torchvision import transforms import io import PIL from PIL import ImageFile ImageFile.LOAD_TRUNCATED_IMAGES = True try: import horovod.torch as hvd except ImportError: hvd = None import cv2 import math import json import random import seaborn as sns def vis_landmark_on_img(img, shape, linewidth=8): ''' Visualize landmark on images. ''' def draw_curve(idx_list, color=(0, 255, 0), loop=False, lineWidth=linewidth): for i in idx_list: cv2.line(img, (shape[i][0], shape[i][1]), (shape[i + 1][0], shape[i + 1][1]), color, lineWidth) if (loop): cv2.line(img, (shape[idx_list[0]][0], shape[idx_list[0]][1]), (shape[idx_list[-1] + 1][0], shape[idx_list[-1] + 1][1]), color, lineWidth) draw_curve(list(range(0, 16)), color=(255, 144, 25)) # jaw draw_curve(list(range(17, 21)), color=(50, 205, 50)) # eye brow draw_curve(list(range(22, 26)), color=(50, 205, 50)) draw_curve(list(range(27, 35)), color=(208, 224, 63)) # nose draw_curve(list(range(36, 41)), loop=True, color=(71, 99, 255)) # eyes draw_curve(list(range(42, 47)), loop=True, color=(71, 99, 255)) draw_curve(list(range(48, 59)), loop=True, color=(238, 130, 238)) # mouth draw_curve(list(range(60, 67)), loop=True, color=(238, 130, 238)) return img.astype("uint8") def imshow_keypoints(img, pose_result, skeleton=None, kpt_score_thr=0.3, pose_kpt_color=None, pose_link_color=None, radius=4, thickness=1, show_keypoint_weight=False, height=None, width=None): """Draw keypoints and links on an image. Args: img (str or Tensor): The image to draw poses on. If an image array is given, id will be modified in-place. pose_result (list[kpts]): The poses to draw. Each element kpts is a set of K keypoints as an Kx3 numpy.ndarray, where each keypoint is represented as x, y, score. kpt_score_thr (float, optional): Minimum score of keypoints to be shown. Default: 0.3. pose_kpt_color (np.array[Nx3]`): Color of N keypoints. If None, the keypoint will not be drawn. pose_link_color (np.array[Mx3]): Color of M links. If None, the links will not be drawn. thickness (int): Thickness of lines. """ # img = mmcv.imread(img) # img_h, img_w, _ = img.shape if img is None: img = np.zeros((height, width, 3), dtype=np.uint8) img_h, img_w = height, width else: img_h, img_w, _ = img.shape for kpts in pose_result: kpts = np.array(kpts, copy=False) # draw each point on image if pose_kpt_color is not None: assert len(pose_kpt_color) == len(kpts) for kid, kpt in enumerate(kpts): x_coord, y_coord, kpt_score = int(kpt[0]), int(kpt[1]), kpt[2] if kpt_score > kpt_score_thr: color = tuple(int(c) for c in pose_kpt_color[kid]) if show_keypoint_weight: img_copy = img.copy() cv2.circle(img_copy, (int(x_coord), int(y_coord)), radius, color, -1) transparency = max(0, min(1, kpt_score)) cv2.addWeighted( img_copy, transparency, img, 1 - transparency, 0, dst=img) else: cv2.circle(img, (int(x_coord), int(y_coord)), radius, color, -1) # draw links if skeleton is not None and pose_link_color is not None: assert len(pose_link_color) == len(skeleton) for sk_id, sk in enumerate(skeleton): pos1 = (int(kpts[sk[0], 0]), int(kpts[sk[0], 1])) pos2 = (int(kpts[sk[1], 0]), int(kpts[sk[1], 1])) # if (pos1[0] > 0 and pos1[0] < img_w and pos1[1] > 0 # and pos1[1] < img_h and pos2[0] > 0 and pos2[0] < img_w # and pos2[1] > 0 and pos2[1] < img_h # and kpts[sk[0], 2] > kpt_score_thr # and kpts[sk[1], 2] > kpt_score_thr): if (kpts[sk[0], 2] > kpt_score_thr and kpts[sk[1], 2] > kpt_score_thr): color = tuple(int(c) for c in pose_link_color[sk_id]) if show_keypoint_weight: img_copy = img.copy() X = (pos1[0], pos2[0]) Y = (pos1[1], pos2[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 = thickness polygon = cv2.ellipse2Poly( (int(mX), int(mY)), (int(length / 2), int(stickwidth)), int(angle), 0, 360, 1) cv2.fillConvexPoly(img_copy, polygon, color) # transparency = max( # 0, min(1, 0.5 * (kpts[sk[0], 2] + kpts[sk[1], 2]))) transparency = 1 cv2.addWeighted( img_copy, transparency, img, 1 - transparency, 0, dst=img) else: cv2.line(img, pos1, pos2, color, thickness=thickness) return img def imshow_keypoints_body(img, pose_result, skeleton=None, kpt_score_thr=0.3, pose_kpt_color=None, pose_link_color=None, radius=4, thickness=1, show_keypoint_weight=False, height=None, width=None): """Draw keypoints and links on an image. Args: img (str or Tensor): The image to draw poses on. If an image array is given, id will be modified in-place. pose_result (list[kpts]): The poses to draw. Each element kpts is a set of K keypoints as an Kx3 numpy.ndarray, where each keypoint is represented as x, y, score. kpt_score_thr (float, optional): Minimum score of keypoints to be shown. Default: 0.3. pose_kpt_color (np.array[Nx3]`): Color of N keypoints. If None, the keypoint will not be drawn. pose_link_color (np.array[Mx3]): Color of M links. If None, the links will not be drawn. thickness (int): Thickness of lines. """ # img = mmcv.imread(img) # img_h, img_w, _ = img.shape if img is None: img = np.zeros((height, width, 3), dtype=np.uint8) img_h, img_w = height, width else: img_h, img_w, _ = img.shape for kpts in pose_result: kpts = np.array(kpts, copy=False) # draw each point on image if pose_kpt_color is not None: assert len(pose_kpt_color) == len(kpts) for kid, kpt in enumerate(kpts): if kid in [17, 18, 19, 20, 21, 22]: continue if kid in [13, 14, 15, 16]: if kpt[0] > min(kpts[23:91, 0]) and kpt[0] < max(kpts[23:91, 0]) and kpt[1] > min(kpts[23:91, 1]) and kpt[1] < max(kpts[23:91, 1]): continue x_coord, y_coord, kpt_score = int(kpt[0]), int(kpt[1]), kpt[2] if kpt_score > kpt_score_thr: color = tuple(int(c) for c in pose_kpt_color[kid]) if show_keypoint_weight: img_copy = img.copy() cv2.circle(img_copy, (int(x_coord), int(y_coord)), radius, color, -1) transparency = max(0, min(1, kpt_score)) cv2.addWeighted( img_copy, transparency, img, 1 - transparency, 0, dst=img) else: cv2.circle(img, (int(x_coord), int(y_coord)), radius, color, -1) # draw links if skeleton is not None and pose_link_color is not None: assert len(pose_link_color) == len(skeleton) for sk_id, sk in enumerate(skeleton): if sk[0] in [17, 18, 19, 20, 21, 22] or sk[1] in [17, 18, 19, 20, 21, 22]: continue if sk[0] in [13, 14, 15, 16]: if kpts[sk[0], 0] > min(kpts[23:91, 0]) and kpts[sk[0], 0] < max(kpts[23:91, 0]) and kpts[sk[0], 1] > min(kpts[23:91, 1]) and kpts[sk[0], 1] < max(kpts[23:91, 1]): continue if sk[1] in [13, 14, 15, 16]: if kpts[sk[1], 0] > min(kpts[23:91, 0]) and kpts[sk[1], 0] < max(kpts[23:91, 0]) and kpts[sk[1], 1] > min(kpts[23:91, 1]) and kpts[sk[1], 1] < max(kpts[23:91, 1]): continue pos1 = (int(kpts[sk[0], 0]), int(kpts[sk[0], 1])) pos2 = (int(kpts[sk[1], 0]), int(kpts[sk[1], 1])) # if (pos1[0] > 0 and pos1[0] < img_w and pos1[1] > 0 # and pos1[1] < img_h and pos2[0] > 0 and pos2[0] < img_w # and pos2[1] > 0 and pos2[1] < img_h # and kpts[sk[0], 2] > kpt_score_thr # and kpts[sk[1], 2] > kpt_score_thr): if (kpts[sk[0], 2] > kpt_score_thr and kpts[sk[1], 2] > kpt_score_thr): color = tuple(int(c) for c in pose_link_color[sk_id]) if show_keypoint_weight: img_copy = img.copy() X = (pos1[0], pos2[0]) Y = (pos1[1], pos2[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 = thickness polygon = cv2.ellipse2Poly( (int(mX), int(mY)), (int(length / 2), int(stickwidth)), int(angle), 0, 360, 1) cv2.fillConvexPoly(img_copy, polygon, color) # transparency = max( # 0, min(1, 0.5 * (kpts[sk[0], 2] + kpts[sk[1], 2]))) transparency = 1 cv2.addWeighted( img_copy, transparency, img, 1 - transparency, 0, dst=img) else: cv2.line(img, pos1, pos2, color, thickness=thickness) return img def imshow_keypoints_whole(img, pose_result, skeleton=None, kpt_score_thr=0.3, pose_kpt_color=None, pose_link_color=None, radius=4, thickness=1, show_keypoint_weight=False, height=None, width=None): """Draw keypoints and links on an image. Args: img (str or Tensor): The image to draw poses on. If an image array is given, id will be modified in-place. pose_result (list[kpts]): The poses to draw. Each element kpts is a set of K keypoints as an Kx3 numpy.ndarray, where each keypoint is represented as x, y, score. kpt_score_thr (float, optional): Minimum score of keypoints to be shown. Default: 0.3. pose_kpt_color (np.array[Nx3]`): Color of N keypoints. If None, the keypoint will not be drawn. pose_link_color (np.array[Mx3]): Color of M links. If None, the links will not be drawn. thickness (int): Thickness of lines. """ # img = mmcv.imread(img) # img_h, img_w, _ = img.shape if img is None: img = np.zeros((height, width, 3), dtype=np.uint8) img_h, img_w = height, width else: img_h, img_w, _ = img.shape for kpts in pose_result: kpts = np.array(kpts, copy=False) # draw each point on image if pose_kpt_color is not None: assert len(pose_kpt_color) == len(kpts) for kid, kpt in enumerate(kpts): if kid in [17, 18, 19, 20, 21, 22]: continue if kid in [13, 14, 15, 16]: if kpt[0] > min(kpts[23:91, 0]) and kpt[0] < max(kpts[23:91, 0]) and kpt[1] > min(kpts[23:91, 1]) and kpt[1] < max(kpts[23:91, 1]): continue x_coord, y_coord, kpt_score = int(kpt[0]), int(kpt[1]), kpt[2] if kpt_score > kpt_score_thr: color = tuple(int(c) for c in pose_kpt_color[kid]) if show_keypoint_weight: img_copy = img.copy() cv2.circle(img_copy, (int(x_coord), int(y_coord)), radius, color, -1) transparency = max(0, min(1, kpt_score)) cv2.addWeighted( img_copy, transparency, img, 1 - transparency, 0, dst=img) else: cv2.circle(img, (int(x_coord), int(y_coord)), radius, color, -1) # draw links if skeleton is not None and pose_link_color is not None: assert len(pose_link_color) == len(skeleton) for sk_id, sk in enumerate(skeleton): if sk[0] in [17, 18, 19, 20, 21, 22] or sk[1] in [17, 18, 19, 20, 21, 22]: continue if sk[0] in [13, 14, 15, 16]: if kpts[sk[0], 0] > min(kpts[23:91, 0]) and kpts[sk[0], 0] < max(kpts[23:91, 0]) and kpts[sk[0], 1] > min(kpts[23:91, 1]) and kpts[sk[0], 1] < max(kpts[23:91, 1]): continue if sk[1] in [13, 14, 15, 16]: if kpts[sk[1], 0] > min(kpts[23:91, 0]) and kpts[sk[1], 0] < max(kpts[23:91, 0]) and kpts[sk[1], 1] > min(kpts[23:91, 1]) and kpts[sk[1], 1] < max(kpts[23:91, 1]): continue pos1 = (int(kpts[sk[0], 0]), int(kpts[sk[0], 1])) pos2 = (int(kpts[sk[1], 0]), int(kpts[sk[1], 1])) # if (pos1[0] > 0 and pos1[0] < img_w and pos1[1] > 0 # and pos1[1] < img_h and pos2[0] > 0 and pos2[0] < img_w # and pos2[1] > 0 and pos2[1] < img_h # and kpts[sk[0], 2] > kpt_score_thr # and kpts[sk[1], 2] > kpt_score_thr): if (kpts[sk[0], 2] > kpt_score_thr and kpts[sk[1], 2] > kpt_score_thr): color = tuple(int(c) for c in pose_link_color[sk_id]) if show_keypoint_weight: img_copy = img.copy() X = (pos1[0], pos2[0]) Y = (pos1[1], pos2[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 = thickness polygon = cv2.ellipse2Poly( (int(mX), int(mY)), (int(length / 2), int(stickwidth)), int(angle), 0, 360, 1) cv2.fillConvexPoly(img_copy, polygon, color) # transparency = max( # 0, min(1, 0.5 * (kpts[sk[0], 2] + kpts[sk[1], 2]))) transparency = 1 cv2.addWeighted( img_copy, transparency, img, 1 - transparency, 0, dst=img) else: cv2.line(img, pos1, pos2, color, thickness=thickness) return img def draw_whole_body_skeleton( img, pose, radius=4, thickness=1, kpt_score_thr=0.3, height=None, width=None, ): palette = np.array([[255, 128, 0], [255, 153, 51], [255, 178, 102], [230, 230, 0], [255, 153, 255], [153, 204, 255], [255, 102, 255], [255, 51, 255], [102, 178, 255], [51, 153, 255], [255, 153, 153], [255, 102, 102], [255, 51, 51], [153, 255, 153], [102, 255, 102], [51, 255, 51], [0, 255, 0], [0, 0, 255], [255, 0, 0], [255, 255, 255]]) # below are for the whole body keypoints skeleton = [[15, 13], [13, 11], [16, 14], [14, 12], [11, 12], [5, 11], [6, 12], [5, 6], [5, 7], [6, 8], [7, 9], [8, 10], [1, 2], [0, 1], [0, 2], [1, 3], [2, 4], [3, 5], [4, 6], [15, 17], [15, 18], [15, 19], [16, 20], [16, 21], [16, 22], [91, 92], [92, 93], [93, 94], [94, 95], [91, 96], [96, 97], [97, 98], [98, 99], [91, 100], [100, 101], [101, 102], [102, 103], [91, 104], [104, 105], [105, 106], [106, 107], [91, 108], [108, 109], [109, 110], [110, 111], [112, 113], [113, 114], [114, 115], [115, 116], [112, 117], [117, 118], [118, 119], [119, 120], [112, 121], [121, 122], [122, 123], [123, 124], [112, 125], [125, 126], [126, 127], [127, 128], [112, 129], [129, 130], [130, 131], [131, 132]] pose_link_color = palette[[ 0, 0, 0, 0, 7, 7, 7, 9, 9, 9, 9, 9, 16, 16, 16, 16, 16, 16, 16 ] + [16, 16, 16, 16, 16, 16] + [ 0, 0, 0, 0, 4, 4, 4, 4, 8, 8, 8, 8, 12, 12, 12, 12, 16, 16, 16, 16 ] + [ 0, 0, 0, 0, 4, 4, 4, 4, 8, 8, 8, 8, 12, 12, 12, 12, 16, 16, 16, 16 ]] pose_kpt_color = palette[ [16, 16, 16, 16, 16, 9, 9, 9, 9, 9, 9, 0, 0, 0, 0, 0, 0] + [0, 0, 0, 0, 0, 0] + [19] * (68 + 42)] draw = imshow_keypoints_whole(img, pose, skeleton, kpt_score_thr=0.3, pose_kpt_color=pose_kpt_color, pose_link_color=pose_link_color, radius=radius, thickness=thickness, show_keypoint_weight=True, height=height, width=width) return draw def draw_humansd_skeleton(image, pose, mmpose_detection_thresh=0.3, height=None, width=None, humansd_skeleton_width=10): humansd_skeleton=[ [0,0,1], [1,0,2], [2,1,3], [3,2,4], [4,3,5], [5,4,6], [6,5,7], [7,6,8], [8,7,9], [9,8,10], [10,5,11], [11,6,12], [12,11,13], [13,12,14], [14,13,15], [15,14,16], ] # humansd_skeleton_width=10 humansd_color=sns.color_palette("hls", len(humansd_skeleton)) def plot_kpts(img_draw, kpts, color, edgs,width): for idx, kpta, kptb in edgs: if kpts[kpta,2]>mmpose_detection_thresh and \ kpts[kptb,2]>mmpose_detection_thresh : line_color = tuple([int(255*color_i) for color_i in color[idx]]) cv2.line(img_draw, (int(kpts[kpta,0]),int(kpts[kpta,1])), (int(kpts[kptb,0]),int(kpts[kptb,1])), line_color,width) cv2.circle(img_draw, (int(kpts[kpta,0]),int(kpts[kpta,1])), width//2, line_color, -1) cv2.circle(img_draw, (int(kpts[kptb,0]),int(kpts[kptb,1])), width//2, line_color, -1) if image is None: pose_image = np.zeros((height, width, 3), dtype=np.uint8) else: pose_image = np.array(image, dtype=np.uint8) for person_i in range(len(pose)): if np.sum(pose[person_i])>0: plot_kpts(pose_image, pose[person_i],humansd_color,humansd_skeleton,humansd_skeleton_width) return pose_image def draw_controlnet_skeleton(image, pose, mmpose_detection_thresh=0.3, height=None, width=None): if image is None: canvas = np.zeros((height, width, 3), dtype=np.uint8) else: H, W, C = image.shape canvas = np.array(image, dtype=np.uint8) for pose_i in range(len(pose)): present_pose=pose[pose_i] candidate=[ [present_pose[0,0],present_pose[0,1],present_pose[0,2],0], [(present_pose[6,0]+present_pose[5,0])/2,(present_pose[6,1]+present_pose[5,1])/2,(present_pose[6,2]+present_pose[5,2])/2,1] if present_pose[6,2]>mmpose_detection_thresh and present_pose[5,2]>mmpose_detection_thresh else [-1,-1,0,1], [present_pose[6,0],present_pose[6,1],present_pose[6,2],2], [present_pose[8,0],present_pose[8,1],present_pose[8,2],3], [present_pose[10,0],present_pose[10,1],present_pose[10,2],4], [present_pose[5,0],present_pose[5,1],present_pose[5,2],5], [present_pose[7,0],present_pose[7,1],present_pose[7,2],6], [present_pose[9,0],present_pose[9,1],present_pose[9,2],7], [present_pose[12,0],present_pose[12,1],present_pose[12,2],8], [present_pose[14,0],present_pose[14,1],present_pose[14,2],9], [present_pose[16,0],present_pose[16,1],present_pose[16,2],10], [present_pose[11,0],present_pose[11,1],present_pose[11,2],11], [present_pose[13,0],present_pose[13,1],present_pose[13,2],12], [present_pose[15,0],present_pose[15,1],present_pose[15,2],13], [present_pose[2,0],present_pose[2,1],present_pose[2,2],14], [present_pose[1,0],present_pose[1,1],present_pose[1,2],15], [present_pose[4,0],present_pose[4,1],present_pose[4,2],16], [present_pose[3,0],present_pose[3,1],present_pose[3,2],17], ] stickwidth = 4 limbSeq = [[2, 3], [2, 6], [3, 4], [4, 5], [6, 7], [7, 8], [2, 9], [9, 10], \ [10, 11], [2, 12], [12, 13], [13, 14], [2, 1], [1, 15], [15, 17], \ [1, 16], [16, 18], [3, 17], [6, 18]] 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, 0, 170], [255, 0, 85]] for i in range(17): if candidate[limbSeq[i][0]-1][2]>mmpose_detection_thresh and candidate[limbSeq[i][1]-1][2]>mmpose_detection_thresh: Y=[candidate[limbSeq[i][1]-1][0],candidate[limbSeq[i][0]-1][0]] X=[candidate[limbSeq[i][1]-1][1],candidate[limbSeq[i][0]-1][1]] mX = np.mean(X) mY = np.mean(Y) length = ((X[0] - X[1]) ** 2 + (Y[0] - Y[1]) ** 2) ** 0.5 angle = math.degrees(math.atan2(X[0] - X[1], Y[0] - Y[1])) polygon = cv2.ellipse2Poly((int(mY), int(mX)), (int(length / 2), stickwidth), int(angle), 0, 360, 1) cur_canvas = canvas.copy() cv2.fillConvexPoly(cur_canvas, polygon, colors[i]) canvas = cv2.addWeighted(canvas, 0.4, cur_canvas, 0.6, 0) for i in range(18): if candidate[i][2]>mmpose_detection_thresh: x, y = candidate[i][0:2] cv2.circle(canvas, (int(x), int(y)), 4, colors[i], thickness=-1) return canvas def draw_body_skeleton( img, pose, radius=4, thickness=1, kpt_score_thr=0.3, height=None, width=None, ): palette = np.array([[255, 128, 0], [255, 153, 51], [255, 178, 102], [230, 230, 0], [255, 153, 255], [153, 204, 255], [255, 102, 255], [255, 51, 255], [102, 178, 255], [51, 153, 255], [255, 153, 153], [255, 102, 102], [255, 51, 51], [153, 255, 153], [102, 255, 102], [51, 255, 51], [0, 255, 0], [0, 0, 255], [255, 0, 0], [255, 255, 255]]) # below are for the body keypoints # skeleton = [[15, 13], [13, 11], [16, 14], [14, 12], [11, 12], # [5, 11], [6, 12], [5, 6], [5, 7], [6, 8], [7, 9], # [8, 10], [1, 2], [0, 1], [0, 2], [1, 3], [2, 4], # [3, 5], [4, 6]] skeleton = [[15, 13], [13, 11], [16, 14], [14, 12], [11, 12], [5, 11], [6, 12], [5, 6], [5, 7], [6, 8], [7, 9], [8, 10], [3, 4], [3, 5], [4, 6]] # pose_link_color = palette[[ # 0, 0, 0, 0, 7, 7, 7, 9, 9, 9, 9, 9, 16, 16, 16, 16, 16, 16, 16 # ]] # pose_kpt_color = palette[[ # 16, 16, 16, 16, 16, 9, 9, 9, 9, 9, 9, 0, 0, 0, 0, 0, 0 # ]] pose_link_color = palette[[ 12, 16, 1, 5, 9, 13, 19, 15, 11, 7, 3, 18, 14, 8, 0 ]] pose_kpt_color = palette[[ 19, 15, 11, 7, 3, 18, 14, 10, 6, 2, 17, 13, 9, 5, 1, 16, 12 ]] draw = imshow_keypoints_body(img, pose, skeleton, kpt_score_thr=0.3, pose_kpt_color=pose_kpt_color, pose_link_color=pose_link_color, radius=radius, thickness=thickness, show_keypoint_weight=True, height=height, width=width) return draw def draw_face_skeleton( img, pose, radius=4, thickness=1, kpt_score_thr=0.3, height=None, width=None, ): palette = np.array([[255, 128, 0], [255, 153, 51], [255, 178, 102], [230, 230, 0], [255, 153, 255], [153, 204, 255], [255, 102, 255], [255, 51, 255], [102, 178, 255], [51, 153, 255], [255, 153, 153], [255, 102, 102], [255, 51, 51], [153, 255, 153], [102, 255, 102], [51, 255, 51], [0, 255, 0], [0, 0, 255], [255, 0, 0], [255, 255, 255]]) # below are for the face keypoints skeleton = [] pose_link_color = palette[[]] pose_kpt_color = palette[[19] * 68] kpt_score_thr = 0 draw = imshow_keypoints(img, pose, skeleton, kpt_score_thr=kpt_score_thr, pose_kpt_color=pose_kpt_color, pose_link_color=pose_link_color, radius=radius, thickness=thickness, show_keypoint_weight=True, height=height, width=width) return draw def draw_hand_skeleton( img, pose, radius=4, thickness=1, kpt_score_thr=0.3, height=None, width=None, ): palette = np.array([[255, 128, 0], [255, 153, 51], [255, 178, 102], [230, 230, 0], [255, 153, 255], [153, 204, 255], [255, 102, 255], [255, 51, 255], [102, 178, 255], [51, 153, 255], [255, 153, 153], [255, 102, 102], [255, 51, 51], [153, 255, 153], [102, 255, 102], [51, 255, 51], [0, 255, 0], [0, 0, 255], [255, 0, 0], [255, 255, 255]]) # hand option 1 skeleton = [[0, 1], [1, 2], [2, 3], [3, 4], [0, 5], [5, 6], [6, 7], [7, 8], [0, 9], [9, 10], [10, 11], [11, 12], [0, 13], [13, 14], [14, 15], [15, 16], [0, 17], [17, 18], [18, 19], [19, 20]] pose_link_color = palette[[ 0, 0, 0, 0, 4, 4, 4, 4, 8, 8, 8, 8, 12, 12, 12, 12, 16, 16, 16, 16 ]] pose_kpt_color = palette[[ 0, 0, 0, 0, 0, 4, 4, 4, 4, 8, 8, 8, 8, 12, 12, 12, 12, 16, 16, 16, 16 ]] # # hand option 2 # skeleton = [[0, 1], [1, 2], [2, 3], [4, 5], [5, 6], [6, 7], [8, 9], # [9, 10], [10, 11], [12, 13], [13, 14], [14, 15], # [16, 17], [17, 18], [18, 19], [3, 20], [7, 20], # [11, 20], [15, 20], [19, 20]] # pose_link_color = palette[[ # 0, 0, 0, 4, 4, 4, 8, 8, 8, 12, 12, 12, 16, 16, 16, 0, 4, 8, 12, # 16 # ]] # pose_kpt_color = palette[[ # 0, 0, 0, 0, 4, 4, 4, 4, 8, 8, 8, 8, 12, 12, 12, 12, 16, 16, 16, # 16, 0 # ]] draw = imshow_keypoints(img, pose, skeleton, kpt_score_thr=kpt_score_thr, pose_kpt_color=pose_kpt_color, pose_link_color=pose_link_color, radius=radius, thickness=thickness, show_keypoint_weight=True, height=height, width=width) return draw class CsvDataset(Dataset): def __init__(self, input_filename, transforms, img_key, caption_key, sep="\t", tokenizer=None): logging.debug(f'Loading csv data from {input_filename}.') df = pd.read_csv(input_filename, sep=sep) self.images = df[img_key].tolist() self.captions = df[caption_key].tolist() self.transforms = transforms logging.debug('Done loading data.') self.tokenize = tokenizer def __len__(self): return len(self.captions) def __getitem__(self, idx): images = self.transforms(Image.open(str(self.images[idx]))) texts = self.tokenize([str(self.captions[idx])])[0] return images, texts class SharedEpoch: def __init__(self, epoch: int = 0): self.shared_epoch = Value('i', epoch) def set_value(self, epoch): self.shared_epoch.value = epoch def get_value(self): return self.shared_epoch.value @dataclass class DataInfo: dataloader: DataLoader sampler: DistributedSampler = None shared_epoch: SharedEpoch = None def set_epoch(self, epoch): if self.shared_epoch is not None: self.shared_epoch.set_value(epoch) if self.sampler is not None and isinstance(self.sampler, DistributedSampler): self.sampler.set_epoch(epoch) def expand_urls(urls, weights=None): if weights is None: expanded_urls = wds.shardlists.expand_urls(urls) return expanded_urls, None if isinstance(urls, str): urllist = urls.split("::") weights = weights.split('::') assert len(weights) == len(urllist), f"Expected the number of data components ({len(urllist)}) and weights({len(weights)}) to match." weights = [float(weight) for weight in weights] all_urls, all_weights = [], [] for url, weight in zip(urllist, weights): expanded_url = list(braceexpand.braceexpand(url)) expanded_weights = [weight for _ in expanded_url] all_urls.extend(expanded_url) all_weights.extend(expanded_weights) return all_urls, all_weights else: all_urls = list(urls) return all_urls, weights def get_dataset_size(shards): shards_list, _ = expand_urls(shards) dir_path = os.path.dirname(shards_list[0]) sizes_filename = os.path.join(dir_path, 'sizes.json') len_filename = os.path.join(dir_path, '__len__') if os.path.exists(sizes_filename): sizes = json.load(open(sizes_filename, 'r')) total_size = sum([int(sizes[os.path.basename(shard)]) for shard in shards_list]) elif os.path.exists(len_filename): # FIXME this used to be eval(open(...)) but that seemed rather unsafe total_size = ast.literal_eval(open(len_filename, 'r').read()) else: total_size = None # num samples undefined # some common dataset sizes (at time of authors last download) # CC3M (train): 2905954 # CC12M: 10968539 # LAION-400M: 407332084 # LAION-2B (english): 2170337258 num_shards = len(shards_list) return total_size, num_shards def get_imagenet(args, preprocess_fns, split): assert split in ["train", "val", "v2"] is_train = split == "train" preprocess_train, preprocess_val = preprocess_fns if split == "v2": from imagenetv2_pytorch import ImageNetV2Dataset dataset = ImageNetV2Dataset(location=args.imagenet_v2, transform=preprocess_val) else: if is_train: data_path = args.imagenet_train preprocess_fn = preprocess_train else: data_path = args.imagenet_val preprocess_fn = preprocess_val assert data_path dataset = datasets.ImageFolder(data_path, transform=preprocess_fn) if is_train: idxs = np.zeros(len(dataset.targets)) target_array = np.array(dataset.targets) k = 50 for c in range(1000): m = target_array == c n = len(idxs[m]) arr = np.zeros(n) arr[:k] = 1 np.random.shuffle(arr) idxs[m] = arr idxs = idxs.astype('int') sampler = SubsetRandomSampler(np.where(idxs)[0]) else: sampler = None dataloader = torch.utils.data.DataLoader( dataset, batch_size=args.batch_size, num_workers=args.workers, sampler=sampler, ) return DataInfo(dataloader=dataloader, sampler=sampler) def count_samples(dataloader): os.environ["WDS_EPOCH"] = "0" n_elements, n_batches = 0, 0 for images, texts in dataloader: n_batches += 1 n_elements += len(images) assert len(images) == len(texts) return n_elements, n_batches def filter_no_caption_or_no_image(sample): has_caption = ('txt' in sample) has_image = ('png' in sample or 'jpg' in sample or 'jpeg' in sample or 'webp' in sample) return has_caption and has_image def filter_no_image_or_no_ldmk(sample): has_image = ('png' in sample or 'jpg' in sample or 'jpeg' in sample or 'webp' in sample) has_ldmk = ('ldmk' in sample) return has_image and has_ldmk def log_and_continue(exn): """Call in an exception handler to ignore any exception, issue a warning, and continue.""" logging.warning(f'Handling webdataset error ({repr(exn)}). Ignoring.') return True def group_by_keys_nothrow(data, keys=base_plus_ext, lcase=True, suffixes=None, handler=None): """Return function over iterator that groups key, value pairs into samples. :param keys: function that splits the key into key and extension (base_plus_ext) :param lcase: convert suffixes to lower case (Default value = True) """ current_sample = None for filesample in data: assert isinstance(filesample, dict) fname, value = filesample["fname"], filesample["data"] prefix, suffix = keys(fname) if prefix is None: continue if lcase: suffix = suffix.lower() # FIXME webdataset version throws if suffix in current_sample, but we have a potential for # this happening in the current LAION400m dataset if a tar ends with same prefix as the next # begins, rare, but can happen since prefix aren't unique across tar files in that dataset if current_sample is None or prefix != current_sample["__key__"] or suffix in current_sample: if valid_sample(current_sample): yield current_sample current_sample = dict(__key__=prefix, __url__=filesample["__url__"]) if suffixes is None or suffix in suffixes: current_sample[suffix] = value if valid_sample(current_sample): yield current_sample def tarfile_to_samples_nothrow(src, handler=log_and_continue): # NOTE this is a re-impl of the webdataset impl with group_by_keys that doesn't throw streams = url_opener(src, handler=handler) files = tar_file_expander(streams, handler=handler) samples = group_by_keys_nothrow(files, handler=handler) return samples def pytorch_worker_seed(increment=0): """get dataloader worker seed from pytorch""" worker_info = get_worker_info() if worker_info is not None: # favour using the seed already created for pytorch dataloader workers if it exists seed = worker_info.seed if increment: # space out seed increments so they can't overlap across workers in different iterations seed += increment * max(1, worker_info.num_workers) return seed # fallback to wds rank based seed return wds.utils.pytorch_worker_seed() _SHARD_SHUFFLE_SIZE = 2000 _SHARD_SHUFFLE_INITIAL = 500 _SAMPLE_SHUFFLE_SIZE = 5000 _SAMPLE_SHUFFLE_INITIAL = 1000 class detshuffle2(wds.PipelineStage): def __init__( self, bufsize=1000, initial=100, seed=0, epoch=-1, ): self.bufsize = bufsize self.initial = initial self.seed = seed self.epoch = epoch def run(self, src): if isinstance(self.epoch, SharedEpoch): epoch = self.epoch.get_value() else: # NOTE: this is epoch tracking is problematic in a multiprocess (dataloader workers or train) # situation as different workers may wrap at different times (or not at all). self.epoch += 1 epoch = self.epoch rng = random.Random() if self.seed < 0: # If seed is negative, we use the worker's seed, this will be different across all nodes/workers seed = pytorch_worker_seed(epoch) else: # This seed to be deterministic AND the same across all nodes/workers in each epoch seed = self.seed + epoch rng.seed(seed) return _shuffle(src, self.bufsize, self.initial, rng) class ResampledShards2(IterableDataset): """An iterable dataset yielding a list of urls.""" def __init__( self, urls, weights=None, nshards=sys.maxsize, worker_seed=None, deterministic=False, epoch=-1, ): """Sample shards from the shard list with replacement. :param urls: a list of URLs as a Python list or brace notation string """ super().__init__() urls, weights = expand_urls(urls, weights) self.urls = urls self.weights = weights if self.weights is not None: assert len(self.urls) == len(self.weights), f"Number of urls {len(self.urls)} and weights {len(self.weights)} should match." assert isinstance(self.urls[0], str) self.nshards = nshards self.rng = random.Random() self.worker_seed = worker_seed self.deterministic = deterministic self.epoch = epoch def __iter__(self): """Return an iterator over the shards.""" if isinstance(self.epoch, SharedEpoch): epoch = self.epoch.get_value() else: # NOTE: this is epoch tracking is problematic in a multiprocess (dataloader workers or train) # situation as different workers may wrap at different times (or not at all). self.epoch += 1 epoch = self.epoch if self.deterministic: # reset seed w/ epoch if deterministic if self.worker_seed is None: # pytorch worker seed should be deterministic due to being init by arg.seed + rank + worker id seed = pytorch_worker_seed(epoch) else: seed = self.worker_seed() + epoch self.rng.seed(seed) for _ in range(self.nshards): if self.weights is None: yield dict(url=self.rng.choice(self.urls)) else: yield dict(url=self.rng.choices(self.urls, weights=self.weights, k=1)[0]) def get_wds_dataset_filter(args, preprocess_img): input_shards = args.train_data assert input_shards is not None pipeline = [wds.SimpleShardList(input_shards)] def replicate_img(sample): import copy sample["original"] = copy.copy(sample["image"]) return sample def decode_byte_to_rgb(sample): # import io # import PIL # from PIL import ImageFile # ImageFile.LOAD_TRUNCATED_IMAGES = True with io.BytesIO(sample["image"]) as stream: try: img = PIL.Image.open(stream) img.load() img = img.convert("RGB") sample["image"] = img return sample except: print("A broken image is encountered, replace w/ a placeholder") image = Image.new('RGB', (512, 512)) sample["image"] = image return sample # at this point we have an iterator over all the shards pipeline.extend([ wds.split_by_node, wds.split_by_worker, tarfile_to_samples_nothrow, wds.select(filter_no_caption_or_no_image), # wds.decode("pilrgb", handler=log_and_continue), wds.rename(image="jpg;png;jpeg;webp", text="txt"), # wds.map_dict(image=preprocess_img, text=lambda text: tokenizer(text)[0]), wds.map(replicate_img), wds.map(decode_byte_to_rgb), # wds.map_dict(image=preprocess_img, text=lambda x: x.encode('utf-8'), \ # __key__=lambda x: x.encode('utf-8'), __url__=lambda x: x.encode('utf-8')), wds.map_dict(image=preprocess_img), wds.to_tuple("original", "image", "text", "__key__", "__url__", "json"), wds.batched(args.batch_size, partial=True) ]) dataset = wds.DataPipeline(*pipeline) dataloader = wds.WebLoader( dataset, batch_size=None, shuffle=False, num_workers=args.workers, persistent_workers=True, drop_last=False ) return DataInfo(dataloader=dataloader) def get_wds_dataset_cond_face(args, main_args, is_train, epoch=0, floor=False, tokenizer=None, dropout=False, string_concat=False, string_substitute=False, grid_dnc=False, filter_lowres=False): input_shards = args.train_data if is_train else args.val_data assert input_shards is not None resampled = getattr(args, 'dataset_resampled', False) and is_train num_samples, num_shards = get_dataset_size(input_shards) if not num_samples: if is_train: num_samples = args.train_num_samples if not num_samples: raise RuntimeError( 'Currently, number of dataset samples must be specified for training dataset. ' 'Please specify via `--train-num-samples` if no dataset length info present.') else: num_samples = args.val_num_samples or 0 # eval will just exhaust the iterator if not specified shared_epoch = SharedEpoch(epoch=epoch) # create a shared epoch store to sync epoch to dataloader worker proc if resampled: pipeline = [ResampledShards2(input_shards, weights=args.train_data_upsampling_factors, deterministic=True, epoch=shared_epoch)] else: assert args.train_data_upsampling_factors is None, "--train_data_upsampling_factors is only supported when sampling with replacement (together with --dataset-resampled)." pipeline = [wds.SimpleShardList(input_shards)] # at this point we have an iterator over all the shards if is_train: if not resampled: pipeline.extend([ detshuffle2( bufsize=_SHARD_SHUFFLE_SIZE, initial=_SHARD_SHUFFLE_INITIAL, seed=args.seed, epoch=shared_epoch, ), wds.split_by_node, wds.split_by_worker, ]) pipeline.extend([ # at this point, we have an iterator over the shards assigned to each worker at each node tarfile_to_samples_nothrow, # wds.tarfile_to_samples(handler=log_and_continue), wds.shuffle( bufsize=_SAMPLE_SHUFFLE_SIZE, initial=_SAMPLE_SHUFFLE_INITIAL, ), ]) else: pipeline.extend([ wds.split_by_worker, # at this point, we have an iterator over the shards assigned to each worker wds.tarfile_to_samples(handler=log_and_continue), ]) def preprocess_image(sample): # print(main_args.resolution, main_args.center_crop, main_args.random_flip) resize_transform = transforms.Resize(main_args.resolution, interpolation=transforms.InterpolationMode.BICUBIC) sample["image"] = resize_transform(sample["image"]) sample["ldmk"] = resize_transform(sample["ldmk"]) transform_list = [] image_height, image_width = sample["image"].height, sample["image"].width i = torch.randint(0, image_height - main_args.resolution + 1, size=(1,)).item() j = torch.randint(0, image_width - main_args.resolution + 1, size=(1,)).item() if main_args.center_crop or not is_train: transform_list.append(transforms.CenterCrop(main_args.resolution)) else: if image_height < main_args.resolution or image_width < main_args.resolution: raise ValueError(f"Required crop size {(main_args.resolution, main_args.resolution)} is larger than input image size {(image_height, image_width)}") elif image_width == main_args.resolution and image_height == main_args.resolution: i, j = 0, 0 transform_list.append(transforms.Lambda(lambda img: transforms.functional.crop(img, i, j, main_args.resolution, main_args.resolution))) if is_train and torch.rand(1) < 0.5: transform_list.append(transforms.RandomHorizontalFlip(p=1.)) transform_list.extend([transforms.ToTensor(), transforms.Normalize([0.5], [0.5])]) train_transforms = transforms.Compose(transform_list) sample["image"] = train_transforms(sample["image"]) sample["ldmk"] = train_transforms(sample["ldmk"]) return sample # def extract_ldmk(sample): # image_height, image_width = sample["image"].height, sample["image"].width # preds = fa.get_landmarks(np.array(sample["image"])) # lands = [] # if preds is not None: # for pred in preds: # land = pred.reshape(-1, 3)[:,:2].astype(int) # lands.append(land) # lms_color_map = np.zeros(shape=(image_height, image_width, 3)).astype("uint8") # if len(lands) > 0: # for land in lands: # lms_color_map = vis_landmark_on_img(lms_color_map, land) # # print(lms_color_map.shape) # sample["ldmk"] = Image.fromarray(lms_color_map) # return sample def visualize_ldmk(sample): image_height, image_width = sample["image"].height, sample["image"].width lands = np.frombuffer(sample["ldmk"], dtype=np.float32) lms_color_map = np.zeros(shape=(image_height, image_width, 3)).astype("uint8") if len(lands) > 0: lands = lands.reshape(-1, 68, 3).astype(int) for i in range(lands.shape[0]): lms_color_map = vis_landmark_on_img(lms_color_map, lands[i]) # print(lms_color_map.shape) sample["ldmk"] = Image.fromarray(lms_color_map) return sample def filter_ldmk_none(sample): return not (sample["ldmk"] == -1).all() def filter_low_res(sample): if filter_lowres: string_json = sample["json"].decode('utf-8') dict_json = json.loads(string_json) if "height" in dict_json.keys() and "width" in dict_json.keys(): min_length = min(dict_json["height"], dict_json["width"]) return min_length >= main_args.resolution else: return True return True pipeline.extend([ wds.select(filter_no_caption_or_no_image), wds.select(filter_low_res), wds.decode("pilrgb", handler=log_and_continue), wds.rename(image="jpg;png;jpeg;webp", text="txt"), # wds.map(extract_ldmk), wds.map(visualize_ldmk), wds.map(preprocess_image), wds.select(filter_ldmk_none), # wds.map_dict(image=preprocess_img, text=lambda text: tokenizer(text)[0]), wds.map_dict( text=lambda text: tokenizer(text, \ max_length=tokenizer.model_max_length, \ padding="max_length", truncation=True, \ return_tensors='pt')['input_ids'], ), wds.to_tuple("image", "text", "ldmk"), # wds.to_tuple("image", "text", "blip", "normal", "depth", "canny"), wds.batched(args.batch_size, partial=not is_train) ]) dataset = wds.DataPipeline(*pipeline) if is_train: if not resampled: assert num_shards >= args.workers * args.world_size, 'number of shards must be >= total workers' # roll over and repeat a few samples to get same number of full batches on each node round_fn = math.floor if floor else math.ceil global_batch_size = args.batch_size * args.world_size num_batches = round_fn(num_samples / global_batch_size) num_workers = max(1, args.workers) num_worker_batches = round_fn(num_batches / num_workers) # per dataloader worker num_batches = num_worker_batches * num_workers num_samples = num_batches * global_batch_size dataset = dataset.with_epoch(num_worker_batches) # each worker is iterating over this else: # last batches are partial, eval is done on single (master) node num_batches = math.ceil(num_samples / args.batch_size) dataloader = wds.WebLoader( dataset, batch_size=None, shuffle=False, num_workers=args.workers, persistent_workers=True, ) # FIXME not clear which approach is better, with_epoch before vs after dataloader? # hoping to resolve via https://github.com/webdataset/webdataset/issues/169 # if is_train: # # roll over and repeat a few samples to get same number of full batches on each node # global_batch_size = args.batch_size * args.world_size # num_batches = math.ceil(num_samples / global_batch_size) # num_workers = max(1, args.workers) # num_batches = math.ceil(num_batches / num_workers) * num_workers # num_samples = num_batches * global_batch_size # dataloader = dataloader.with_epoch(num_batches) # else: # # last batches are partial, eval is done on single (master) node # num_batches = math.ceil(num_samples / args.batch_size) # add meta-data to dataloader instance for convenience dataloader.num_batches = num_batches dataloader.num_samples = num_samples return DataInfo(dataloader=dataloader, shared_epoch=shared_epoch) def get_wds_dataset_depth(args, main_args, is_train, epoch=0, floor=False, tokenizer=None, dropout=False, string_concat=False, string_substitute=False, grid_dnc=False, filter_lowres=False, filter_mface=False, filter_wpose=False): input_shards = args.train_data if is_train else args.val_data assert input_shards is not None resampled = getattr(args, 'dataset_resampled', False) and is_train num_samples, num_shards = get_dataset_size(input_shards) if not num_samples: if is_train: num_samples = args.train_num_samples if not num_samples: raise RuntimeError( 'Currently, number of dataset samples must be specified for training dataset. ' 'Please specify via `--train-num-samples` if no dataset length info present.') else: num_samples = args.val_num_samples or 0 # eval will just exhaust the iterator if not specified shared_epoch = SharedEpoch(epoch=epoch) # create a shared epoch store to sync epoch to dataloader worker proc if resampled: pipeline = [ResampledShards2(input_shards, weights=args.train_data_upsampling_factors, deterministic=True, epoch=shared_epoch)] else: assert args.train_data_upsampling_factors is None, "--train_data_upsampling_factors is only supported when sampling with replacement (together with --dataset-resampled)." pipeline = [wds.SimpleShardList(input_shards)] # at this point we have an iterator over all the shards if is_train: if not resampled: pipeline.extend([ detshuffle2( bufsize=_SHARD_SHUFFLE_SIZE, initial=_SHARD_SHUFFLE_INITIAL, seed=args.seed, epoch=shared_epoch, ), wds.split_by_node, wds.split_by_worker, ]) pipeline.extend([ # at this point, we have an iterator over the shards assigned to each worker at each node tarfile_to_samples_nothrow, # wds.tarfile_to_samples(handler=log_and_continue), wds.shuffle( bufsize=_SAMPLE_SHUFFLE_SIZE, initial=_SAMPLE_SHUFFLE_INITIAL, ), ]) else: pipeline.extend([ wds.split_by_worker, # at this point, we have an iterator over the shards assigned to each worker wds.tarfile_to_samples(handler=log_and_continue), ]) def decode_image(sample): with io.BytesIO(sample["omni_depth"]) as stream: try: img = PIL.Image.open(stream) img.load() img = img.convert("RGB") sample["depth"] = img except: print("A broken image is encountered, replace w/ a placeholder") image = Image.new('RGB', (512, 512)) sample["depth"] = image return sample train_transforms = transforms.Compose( [ transforms.Resize(main_args.resolution, interpolation=transforms.InterpolationMode.BILINEAR), transforms.CenterCrop(main_args.resolution) if main_args.center_crop else transforms.RandomCrop(main_args.resolution), transforms.RandomHorizontalFlip() if main_args.random_flip else transforms.Lambda(lambda x: x), transforms.ToTensor(), transforms.Normalize([0.5], [0.5]), ] ) def filter_depth_none(sample): return not (sample["depth"] == -1).all() def filter_low_res(sample): if filter_lowres: string_json = sample["json"].decode('utf-8') dict_json = json.loads(string_json) if "height" in dict_json.keys() and "width" in dict_json.keys(): min_length = min(dict_json["height"], dict_json["width"]) return min_length >= main_args.resolution else: return True return True def filter_multi_face(sample): if filter_mface: face_kp = np.frombuffer(sample["face_kp"], dtype=np.float32).reshape(-1, 98, 2) if face_kp.shape[0] > 1: return False return True def filter_whole_skeleton(sample): if filter_wpose: height, width = sample["image"].height, sample["image"].width body_kp = np.frombuffer(sample["body_kp"], dtype=np.float32).reshape(17, 2) if (body_kp[:, 0] > 0).all() and (body_kp[:, 0] < width).all() and (body_kp[:, 1] > 0).all() and (body_kp[:, 1] < height).all(): return True else: return False return True pipeline.extend([ wds.select(filter_no_caption_or_no_image), wds.select(filter_multi_face), wds.select(filter_low_res), wds.decode("pilrgb", handler=log_and_continue), wds.rename(image="jpg;png;jpeg;webp", text="txt"), wds.select(filter_whole_skeleton), wds.map(decode_image), wds.map_dict(depth=train_transforms), wds.select(filter_depth_none), # wds.map_dict(depth=train_transforms, text=lambda text: tokenizer(text)[0]), wds.map_dict( text=lambda text: tokenizer(text, \ max_length=tokenizer.model_max_length, \ padding="max_length", truncation=True, \ return_tensors='pt')['input_ids']), wds.to_tuple("depth", "text"), # wds.to_tuple("image", "text", "blip", "normal", "depth", "canny"), wds.batched(args.batch_size, partial=not is_train) ]) dataset = wds.DataPipeline(*pipeline) if is_train: if not resampled: assert num_shards >= args.workers * args.world_size, 'number of shards must be >= total workers' # roll over and repeat a few samples to get same number of full batches on each node round_fn = math.floor if floor else math.ceil global_batch_size = args.batch_size * args.world_size num_batches = round_fn(num_samples / global_batch_size) num_workers = max(1, args.workers) num_worker_batches = round_fn(num_batches / num_workers) # per dataloader worker num_batches = num_worker_batches * num_workers num_samples = num_batches * global_batch_size dataset = dataset.with_epoch(num_worker_batches) # each worker is iterating over this else: # last batches are partial, eval is done on single (master) node num_batches = math.ceil(num_samples / args.batch_size) dataloader = wds.WebLoader( dataset, batch_size=None, shuffle=False, num_workers=args.workers, persistent_workers=True, ) # FIXME not clear which approach is better, with_epoch before vs after dataloader? # hoping to resolve via https://github.com/webdataset/webdataset/issues/169 # if is_train: # # roll over and repeat a few samples to get same number of full batches on each node # global_batch_size = args.batch_size * args.world_size # num_batches = math.ceil(num_samples / global_batch_size) # num_workers = max(1, args.workers) # num_batches = math.ceil(num_batches / num_workers) * num_workers # num_samples = num_batches * global_batch_size # dataloader = dataloader.with_epoch(num_batches) # else: # # last batches are partial, eval is done on single (master) node # num_batches = math.ceil(num_samples / args.batch_size) # add meta-data to dataloader instance for convenience dataloader.num_batches = num_batches dataloader.num_samples = num_samples return DataInfo(dataloader=dataloader, shared_epoch=shared_epoch) def get_wds_dataset_depth2canny(args, main_args, is_train, epoch=0, floor=False, tokenizer=None, dropout=False, string_concat=False, string_substitute=False, grid_dnc=False): input_shards = args.train_data if is_train else args.val_data assert input_shards is not None resampled = getattr(args, 'dataset_resampled', False) and is_train num_samples, num_shards = get_dataset_size(input_shards) if not num_samples: if is_train: num_samples = args.train_num_samples if not num_samples: raise RuntimeError( 'Currently, number of dataset samples must be specified for training dataset. ' 'Please specify via `--train-num-samples` if no dataset length info present.') else: num_samples = args.val_num_samples or 0 # eval will just exhaust the iterator if not specified shared_epoch = SharedEpoch(epoch=epoch) # create a shared epoch store to sync epoch to dataloader worker proc if resampled: pipeline = [ResampledShards2(input_shards, weights=args.train_data_upsampling_factors, deterministic=True, epoch=shared_epoch)] else: assert args.train_data_upsampling_factors is None, "--train_data_upsampling_factors is only supported when sampling with replacement (together with --dataset-resampled)." pipeline = [wds.SimpleShardList(input_shards)] # at this point we have an iterator over all the shards if is_train: if not resampled: pipeline.extend([ detshuffle2( bufsize=_SHARD_SHUFFLE_SIZE, initial=_SHARD_SHUFFLE_INITIAL, seed=args.seed, epoch=shared_epoch, ), wds.split_by_node, wds.split_by_worker, ]) pipeline.extend([ # at this point, we have an iterator over the shards assigned to each worker at each node tarfile_to_samples_nothrow, # wds.tarfile_to_samples(handler=log_and_continue), wds.shuffle( bufsize=_SAMPLE_SHUFFLE_SIZE, initial=_SAMPLE_SHUFFLE_INITIAL, ), ]) else: pipeline.extend([ wds.split_by_worker, # at this point, we have an iterator over the shards assigned to each worker wds.tarfile_to_samples(handler=log_and_continue), ]) def decode_image(sample): with io.BytesIO(sample["omni_depth"]) as stream: try: img = PIL.Image.open(stream) img.load() img = img.convert("RGB") sample["depth"] = img except: print("A broken image is encountered, replace w/ a placeholder") image = Image.new('RGB', (512, 512)) sample["depth"] = image return sample def add_canny(sample): canny = np.array(sample["image"]) low_threshold = 100 high_threshold = 200 canny = cv2.Canny(canny, low_threshold, high_threshold) canny = canny[:, :, None] canny = np.concatenate([canny, canny, canny], axis=2) sample["canny"] = Image.fromarray(canny) return sample def preprocess_image(sample): # print(main_args.resolution, main_args.center_crop, main_args.random_flip) if grid_dnc: resize_transform = transforms.Resize(main_args.resolution // 2, interpolation=transforms.InterpolationMode.BICUBIC) else: resize_transform = transforms.Resize(main_args.resolution, interpolation=transforms.InterpolationMode.BICUBIC) sample["image"] = resize_transform(sample["image"]) sample["canny"] = resize_transform(sample["canny"]) sample["depth"] = resize_transform(sample["depth"]) transform_list = [] image_height, image_width = sample["image"].height, sample["image"].width if grid_dnc: i = torch.randint(0, image_height - main_args.resolution // 2 + 1, size=(1,)).item() j = torch.randint(0, image_width - main_args.resolution // 2 + 1, size=(1,)).item() else: i = torch.randint(0, image_height - main_args.resolution + 1, size=(1,)).item() j = torch.randint(0, image_width - main_args.resolution + 1, size=(1,)).item() if main_args.center_crop or not is_train: if grid_dnc: transform_list.append(transforms.CenterCrop(main_args.resolution // 2)) else: transform_list.append(transforms.CenterCrop(main_args.resolution)) else: if grid_dnc: if image_height < main_args.resolution // 2 or image_width < main_args.resolution // 2: raise ValueError(f"Required crop size {(main_args.resolution // 2, main_args.resolution // 2)} is larger than input image size {(image_height, image_width)}") elif image_width == main_args.resolution // 2 and image_height == main_args.resolution // 2: i, j = 0, 0 transform_list.append(transforms.Lambda(lambda img: transforms.functional.crop(img, i, j, main_args.resolution // 2, main_args.resolution // 2))) else: if image_height < main_args.resolution or image_width < main_args.resolution: raise ValueError(f"Required crop size {(main_args.resolution, main_args.resolution)} is larger than input image size {(image_height, image_width)}") elif image_width == main_args.resolution and image_height == main_args.resolution: i, j = 0, 0 transform_list.append(transforms.Lambda(lambda img: transforms.functional.crop(img, i, j, main_args.resolution, main_args.resolution))) if is_train and torch.rand(1) < 0.5: transform_list.append(transforms.RandomHorizontalFlip(p=1.)) transform_list.extend([transforms.ToTensor(), transforms.Normalize([0.5], [0.5])]) train_transforms = transforms.Compose(transform_list) sample["image"] = train_transforms(sample["image"]) sample["canny"] = train_transforms(sample["canny"]) sample["depth"] = train_transforms(sample["depth"]) return sample def random_mask(sample): if is_train and dropout: random_num = torch.rand(1) if random_num < 0.1: sample["depth"] = torch.ones_like(sample["depth"]) * (-1) return sample pipeline.extend([ wds.select(filter_no_caption_or_no_image), wds.decode("pilrgb", handler=log_and_continue), wds.rename(image="jpg;png;jpeg;webp", text="txt"), wds.map(decode_image), wds.map(add_canny), wds.map(preprocess_image), wds.map(random_mask), # wds.map_dict(image=preprocess_img, text=lambda text: tokenizer(text)[0]), wds.map_dict( text=lambda text: tokenizer(text, \ max_length=tokenizer.model_max_length, \ padding="max_length", truncation=True, \ return_tensors='pt')['input_ids'], ), wds.to_tuple("canny", "text", "depth"), # wds.to_tuple("image", "text", "blip", "normal", "depth", "canny"), wds.batched(args.batch_size, partial=not is_train) ]) dataset = wds.DataPipeline(*pipeline) if is_train: if not resampled: assert num_shards >= args.workers * args.world_size, 'number of shards must be >= total workers' # roll over and repeat a few samples to get same number of full batches on each node round_fn = math.floor if floor else math.ceil global_batch_size = args.batch_size * args.world_size num_batches = round_fn(num_samples / global_batch_size) num_workers = max(1, args.workers) num_worker_batches = round_fn(num_batches / num_workers) # per dataloader worker num_batches = num_worker_batches * num_workers num_samples = num_batches * global_batch_size dataset = dataset.with_epoch(num_worker_batches) # each worker is iterating over this else: # last batches are partial, eval is done on single (master) node num_batches = math.ceil(num_samples / args.batch_size) dataloader = wds.WebLoader( dataset, batch_size=None, shuffle=False, num_workers=args.workers, persistent_workers=True, ) # FIXME not clear which approach is better, with_epoch before vs after dataloader? # hoping to resolve via https://github.com/webdataset/webdataset/issues/169 # if is_train: # # roll over and repeat a few samples to get same number of full batches on each node # global_batch_size = args.batch_size * args.world_size # num_batches = math.ceil(num_samples / global_batch_size) # num_workers = max(1, args.workers) # num_batches = math.ceil(num_batches / num_workers) * num_workers # num_samples = num_batches * global_batch_size # dataloader = dataloader.with_epoch(num_batches) # else: # # last batches are partial, eval is done on single (master) node # num_batches = math.ceil(num_samples / args.batch_size) # add meta-data to dataloader instance for convenience dataloader.num_batches = num_batches dataloader.num_samples = num_samples return DataInfo(dataloader=dataloader, shared_epoch=shared_epoch) def get_wds_dataset_depth2normal(args, main_args, is_train, epoch=0, floor=False, tokenizer=None, dropout=False, string_concat=False, string_substitute=False, grid_dnc=False, filter_lowres=False): input_shards = args.train_data if is_train else args.val_data assert input_shards is not None resampled = getattr(args, 'dataset_resampled', False) and is_train num_samples, num_shards = get_dataset_size(input_shards) if not num_samples: if is_train: num_samples = args.train_num_samples if not num_samples: raise RuntimeError( 'Currently, number of dataset samples must be specified for training dataset. ' 'Please specify via `--train-num-samples` if no dataset length info present.') else: num_samples = args.val_num_samples or 0 # eval will just exhaust the iterator if not specified shared_epoch = SharedEpoch(epoch=epoch) # create a shared epoch store to sync epoch to dataloader worker proc if resampled: pipeline = [ResampledShards2(input_shards, weights=args.train_data_upsampling_factors, deterministic=True, epoch=shared_epoch)] else: assert args.train_data_upsampling_factors is None, "--train_data_upsampling_factors is only supported when sampling with replacement (together with --dataset-resampled)." pipeline = [wds.SimpleShardList(input_shards)] # at this point we have an iterator over all the shards if is_train: if not resampled: pipeline.extend([ detshuffle2( bufsize=_SHARD_SHUFFLE_SIZE, initial=_SHARD_SHUFFLE_INITIAL, seed=args.seed, epoch=shared_epoch, ), wds.split_by_node, wds.split_by_worker, ]) pipeline.extend([ # at this point, we have an iterator over the shards assigned to each worker at each node tarfile_to_samples_nothrow, # wds.tarfile_to_samples(handler=log_and_continue), wds.shuffle( bufsize=_SAMPLE_SHUFFLE_SIZE, initial=_SAMPLE_SHUFFLE_INITIAL, ), ]) else: pipeline.extend([ wds.split_by_worker, # at this point, we have an iterator over the shards assigned to each worker wds.tarfile_to_samples(handler=log_and_continue), ]) def decode_image(sample): with io.BytesIO(sample["omni_normal"]) as stream: try: img = PIL.Image.open(stream) img.load() img = img.convert("RGB") sample["normal"] = img except: print("A broken image is encountered, replace w/ a placeholder") image = Image.new('RGB', (512, 512)) sample["normal"] = image with io.BytesIO(sample["omni_depth"]) as stream: try: img = PIL.Image.open(stream) img.load() img = img.convert("RGB") sample["depth"] = img except: print("A broken image is encountered, replace w/ a placeholder") image = Image.new('RGB', (512, 512)) sample["depth"] = image return sample def preprocess_image(sample): # print(main_args.resolution, main_args.center_crop, main_args.random_flip) if grid_dnc: resize_transform = transforms.Resize(main_args.resolution // 2, interpolation=transforms.InterpolationMode.BICUBIC) else: resize_transform = transforms.Resize(main_args.resolution, interpolation=transforms.InterpolationMode.BICUBIC) sample["image"] = resize_transform(sample["image"]) sample["normal"] = resize_transform(sample["normal"]) sample["depth"] = resize_transform(sample["depth"]) transform_list = [] image_height, image_width = sample["image"].height, sample["image"].width if grid_dnc: i = torch.randint(0, image_height - main_args.resolution // 2 + 1, size=(1,)).item() j = torch.randint(0, image_width - main_args.resolution // 2 + 1, size=(1,)).item() else: i = torch.randint(0, image_height - main_args.resolution + 1, size=(1,)).item() j = torch.randint(0, image_width - main_args.resolution + 1, size=(1,)).item() if main_args.center_crop or not is_train: if grid_dnc: transform_list.append(transforms.CenterCrop(main_args.resolution // 2)) else: transform_list.append(transforms.CenterCrop(main_args.resolution)) else: if grid_dnc: if image_height < main_args.resolution // 2 or image_width < main_args.resolution // 2: raise ValueError(f"Required crop size {(main_args.resolution // 2, main_args.resolution // 2)} is larger than input image size {(image_height, image_width)}") elif image_width == main_args.resolution // 2 and image_height == main_args.resolution // 2: i, j = 0, 0 transform_list.append(transforms.Lambda(lambda img: transforms.functional.crop(img, i, j, main_args.resolution // 2, main_args.resolution // 2))) else: if image_height < main_args.resolution or image_width < main_args.resolution: raise ValueError(f"Required crop size {(main_args.resolution, main_args.resolution)} is larger than input image size {(image_height, image_width)}") elif image_width == main_args.resolution and image_height == main_args.resolution: i, j = 0, 0 transform_list.append(transforms.Lambda(lambda img: transforms.functional.crop(img, i, j, main_args.resolution, main_args.resolution))) if is_train and torch.rand(1) < 0.5: transform_list.append(transforms.RandomHorizontalFlip(p=1.)) transform_list.extend([transforms.ToTensor(), transforms.Normalize([0.5], [0.5])]) train_transforms = transforms.Compose(transform_list) sample["image"] = train_transforms(sample["image"]) sample["normal"] = train_transforms(sample["normal"]) sample["depth"] = train_transforms(sample["depth"]) return sample def random_mask(sample): if is_train and dropout: random_num = torch.rand(1) if random_num < 0.1: sample["depth"] = torch.ones_like(sample["depth"]) * (-1) return sample def filter_low_res(sample): if filter_lowres: string_json = sample["json"].decode('utf-8') dict_json = json.loads(string_json) if "height" in dict_json.keys() and "width" in dict_json.keys(): min_length = min(dict_json["height"], dict_json["width"]) return min_length >= main_args.resolution else: return True return True pipeline.extend([ wds.select(filter_no_caption_or_no_image), wds.select(filter_low_res), wds.decode("pilrgb", handler=log_and_continue), wds.rename(image="jpg;png;jpeg;webp", text="txt"), wds.map(decode_image), wds.map(preprocess_image), wds.map(random_mask), # wds.map_dict(image=preprocess_img, text=lambda text: tokenizer(text)[0]), wds.map_dict( text=lambda text: tokenizer(text, \ max_length=tokenizer.model_max_length, \ padding="max_length", truncation=True, \ return_tensors='pt')['input_ids'], ), wds.to_tuple("normal", "text", "depth"), # wds.to_tuple("image", "text", "blip", "normal", "depth", "canny"), wds.batched(args.batch_size, partial=not is_train) ]) dataset = wds.DataPipeline(*pipeline) if is_train: if not resampled: assert num_shards >= args.workers * args.world_size, 'number of shards must be >= total workers' # roll over and repeat a few samples to get same number of full batches on each node round_fn = math.floor if floor else math.ceil global_batch_size = args.batch_size * args.world_size num_batches = round_fn(num_samples / global_batch_size) num_workers = max(1, args.workers) num_worker_batches = round_fn(num_batches / num_workers) # per dataloader worker num_batches = num_worker_batches * num_workers num_samples = num_batches * global_batch_size dataset = dataset.with_epoch(num_worker_batches) # each worker is iterating over this else: # last batches are partial, eval is done on single (master) node num_batches = math.ceil(num_samples / args.batch_size) dataloader = wds.WebLoader( dataset, batch_size=None, shuffle=False, num_workers=args.workers, persistent_workers=True, ) # FIXME not clear which approach is better, with_epoch before vs after dataloader? # hoping to resolve via https://github.com/webdataset/webdataset/issues/169 # if is_train: # # roll over and repeat a few samples to get same number of full batches on each node # global_batch_size = args.batch_size * args.world_size # num_batches = math.ceil(num_samples / global_batch_size) # num_workers = max(1, args.workers) # num_batches = math.ceil(num_batches / num_workers) * num_workers # num_samples = num_batches * global_batch_size # dataloader = dataloader.with_epoch(num_batches) # else: # # last batches are partial, eval is done on single (master) node # num_batches = math.ceil(num_samples / args.batch_size) # add meta-data to dataloader instance for convenience dataloader.num_batches = num_batches dataloader.num_samples = num_samples return DataInfo(dataloader=dataloader, shared_epoch=shared_epoch) # def get_wds_dataset_cond_sdxl(args, main_args, is_train, epoch=0, floor=False, tokenizer=None, dropout=False, string_concat=False, string_substitute=False, grid_dnc=False, filter_lowres=False): # input_shards = args.train_data if is_train else args.val_data # assert input_shards is not None # resampled = getattr(args, 'dataset_resampled', False) and is_train # num_samples, num_shards = get_dataset_size(input_shards) # if not num_samples: # if is_train: # num_samples = args.train_num_samples # if not num_samples: # raise RuntimeError( # 'Currently, number of dataset samples must be specified for training dataset. ' # 'Please specify via `--train-num-samples` if no dataset length info present.') # else: # num_samples = args.val_num_samples or 0 # eval will just exhaust the iterator if not specified # shared_epoch = SharedEpoch(epoch=epoch) # create a shared epoch store to sync epoch to dataloader worker proc # if resampled: # pipeline = [ResampledShards2(input_shards, weights=args.train_data_upsampling_factors, deterministic=True, epoch=shared_epoch)] # else: # assert args.train_data_upsampling_factors is None, "--train_data_upsampling_factors is only supported when sampling with replacement (together with --dataset-resampled)." # pipeline = [wds.SimpleShardList(input_shards)] # # at this point we have an iterator over all the shards # if is_train: # if not resampled: # pipeline.extend([ # detshuffle2( # bufsize=_SHARD_SHUFFLE_SIZE, # initial=_SHARD_SHUFFLE_INITIAL, # seed=args.seed, # epoch=shared_epoch, # ), # wds.split_by_node, # wds.split_by_worker, # ]) # pipeline.extend([ # # at this point, we have an iterator over the shards assigned to each worker at each node # tarfile_to_samples_nothrow, # wds.tarfile_to_samples(handler=log_and_continue), # wds.shuffle( # bufsize=_SAMPLE_SHUFFLE_SIZE, # initial=_SAMPLE_SHUFFLE_INITIAL, # ), # ]) # else: # pipeline.extend([ # wds.split_by_worker, # # at this point, we have an iterator over the shards assigned to each worker # wds.tarfile_to_samples(handler=log_and_continue), # ]) # def pose2img(sample): # height, width = sample["image"].height, sample["image"].width # min_length = min(height, width) # radius_body = max(int(4. * min_length / main_args.resolution), 4) # thickness_body = max(int(2. * min_length / main_args.resolution), 2) # radius_face = max(int(2. * min_length / main_args.resolution), 2) # thickness_face = max(int(1. * min_length / main_args.resolution), 1) # radius_hand = max(int(2. * min_length / main_args.resolution), 2) # thickness_hand = max(int(1. * min_length / main_args.resolution), 1) # # if "getty" in sample["__url__"]: # # radius_body *= 4 # # thickness_body *= 4 # # radius_face *= 4 # # thickness_face *= 4 # # radius_hand *= 4 # # thickness_hand *= 4 # body_kp = np.frombuffer(sample["body_kp"], dtype=np.float32).reshape(17, 2) # body_kpconf = np.frombuffer(sample["body_kpconf"], dtype=np.float32) # body_all = np.concatenate([body_kp, body_kpconf[:, np.newaxis]], axis=1) # body_all = body_all[np.newaxis, ...] # body_draw = draw_body_skeleton( # img=None, # pose=body_all, # radius=radius_body, # thickness=thickness_body, # height=height, # width=width # ) # body_draw = Image.fromarray(body_draw) # face_kp = np.frombuffer(sample["face_kp"], dtype=np.float32).reshape(-1, 98, 2) # face_kpconf = np.frombuffer(sample["face_kpconf"], dtype=np.float32).reshape(-1, 98) # face_all = np.concatenate([face_kp, face_kpconf[..., np.newaxis]], axis=2) # face_draw = draw_face_skeleton( # # img=np.array(img), # img=None, # pose=face_all, # radius=radius_face, # thickness=thickness_face, # height=height, # width=width # ) # face_draw = Image.fromarray(face_draw) # hand_kp = np.frombuffer(sample["hand_kp"], dtype=np.float32).reshape(-1, 21, 2) # hand_kpconf = np.frombuffer(sample["hand_kpconf"], dtype=np.float32).reshape(-1, 21) # hand_all = np.concatenate([hand_kp, hand_kpconf[..., np.newaxis]], axis=2) # hand_draw = draw_hand_skeleton( # # img=np.array(img), # img=None, # pose=hand_all, # radius=radius_hand, # thickness=thickness_hand, # height=height, # width=width # ) # hand_draw = Image.fromarray(hand_draw) # sample["body"] = body_draw # sample["face"] = face_draw # sample["hand"] = hand_draw # return sample # def decode_image(sample): # with io.BytesIO(sample["omni_normal"]) as stream: # try: # img = PIL.Image.open(stream) # img.load() # img = img.convert("RGB") # sample["normal"] = img # except: # print("A broken image is encountered, replace w/ a placeholder") # image = Image.new('RGB', (512, 512)) # sample["normal"] = image # with io.BytesIO(sample["omni_depth"]) as stream: # try: # img = PIL.Image.open(stream) # img.load() # img = img.convert("RGB") # sample["depth"] = img # except: # print("A broken image is encountered, replace w/ a placeholder") # image = Image.new('RGB', (512, 512)) # sample["depth"] = image # return sample # def add_canny(sample): # canny = np.array(sample["image"]) # low_threshold = 100 # high_threshold = 200 # canny = cv2.Canny(canny, low_threshold, high_threshold) # canny = canny[:, :, None] # canny = np.concatenate([canny, canny, canny], axis=2) # sample["canny"] = Image.fromarray(canny) # return sample # def decode_text(sample): # sample["blip"] = sample["blip"].decode("utf-8") # sample["blip_raw"] = sample["blip"] # sample["text_raw"] = sample["text"] # return sample # def augment_text(sample): # if is_train and string_concat: # sample["text"] = sample["text"] + " " + sample["blip"] # if is_train and string_substitute: # sample["text"] = sample["blip"] # return sample # def preprocess_image(sample): # # print(main_args.resolution, main_args.center_crop, main_args.random_flip) # if grid_dnc: # resize_transform = transforms.Resize(main_args.resolution // 2, interpolation=transforms.InterpolationMode.BICUBIC) # else: # resize_transform = transforms.Resize(main_args.resolution, interpolation=transforms.InterpolationMode.BICUBIC) # sample["image"] = resize_transform(sample["image"]) # sample["normal"] = resize_transform(sample["normal"]) # sample["depth"] = resize_transform(sample["depth"]) # sample["canny"] = resize_transform(sample["canny"]) # sample["body"] = resize_transform(sample["body"]) # sample["face"] = resize_transform(sample["face"]) # sample["hand"] = resize_transform(sample["hand"]) # transform_list = [] # image_height, image_width = sample["image"].height, sample["image"].width # if grid_dnc: # i = torch.randint(0, image_height - main_args.resolution // 2 + 1, size=(1,)).item() # j = torch.randint(0, image_width - main_args.resolution // 2 + 1, size=(1,)).item() # else: # i = torch.randint(0, image_height - main_args.resolution + 1, size=(1,)).item() # j = torch.randint(0, image_width - main_args.resolution + 1, size=(1,)).item() # if main_args.center_crop or not is_train: # sample["description"]["crop_tl_h"] = (image_height - main_args.resolution) // 2 # sample["description"]["crop_tl_w"] = (image_width - main_args.resolution) // 2 # if grid_dnc: # transform_list.append(transforms.CenterCrop(main_args.resolution // 2)) # else: # transform_list.append(transforms.CenterCrop(main_args.resolution)) # else: # if grid_dnc: # if image_height < main_args.resolution // 2 or image_width < main_args.resolution // 2: # raise ValueError(f"Required crop size {(main_args.resolution // 2, main_args.resolution // 2)} is larger than input image size {(image_height, image_width)}") # elif image_width == main_args.resolution // 2 and image_height == main_args.resolution // 2: # i, j = 0, 0 # transform_list.append(transforms.Lambda(lambda img: transforms.functional.crop(img, i, j, main_args.resolution // 2, main_args.resolution // 2))) # else: # if image_height < main_args.resolution or image_width < main_args.resolution: # raise ValueError(f"Required crop size {(main_args.resolution, main_args.resolution)} is larger than input image size {(image_height, image_width)}") # elif image_width == main_args.resolution and image_height == main_args.resolution: # i, j = 0, 0 # transform_list.append(transforms.Lambda(lambda img: transforms.functional.crop(img, i, j, main_args.resolution, main_args.resolution))) # sample["description"]["crop_tl_h"] = i # sample["description"]["crop_tl_w"] = j # if is_train and torch.rand(1) < 0.5: # transform_list.append(transforms.RandomHorizontalFlip(p=1.)) # transform_list.extend([transforms.ToTensor(), transforms.Normalize([0.5], [0.5])]) # train_transforms = transforms.Compose(transform_list) # sample["image"] = train_transforms(sample["image"]) # sample["normal"] = train_transforms(sample["normal"]) # sample["depth"] = train_transforms(sample["depth"]) # sample["canny"] = train_transforms(sample["canny"]) # sample["body"] = train_transforms(sample["body"]) # sample["face"] = train_transforms(sample["face"]) # sample["hand"] = train_transforms(sample["hand"]) # return sample # def random_mask(sample): # if is_train and dropout: # random_num = torch.rand(1) # if random_num < 0.1: # sample["normal"] = torch.ones_like(sample["normal"]) * (-1) # sample["depth"] = torch.ones_like(sample["depth"]) * (-1) # sample["canny"] = torch.ones_like(sample["canny"]) * (-1) # sample["body"] = torch.ones_like(sample["body"]) * (-1) # sample["face"] = torch.ones_like(sample["face"]) * (-1) # sample["hand"] = torch.ones_like(sample["hand"]) * (-1) # elif random_num > 0.9: # pass # else: # if torch.rand(1) < 0.5: # sample["normal"] = torch.ones_like(sample["normal"]) * (-1) # if torch.rand(1) < 0.5: # sample["depth"] = torch.ones_like(sample["depth"]) * (-1) # if torch.rand(1) < 0.8: # sample["canny"] = torch.ones_like(sample["canny"]) * (-1) # if torch.rand(1) < 0.5: # sample["body"] = torch.ones_like(sample["body"]) * (-1) # if torch.rand(1) < 0.5: # sample["face"] = torch.ones_like(sample["face"]) * (-1) # if torch.rand(1) < 0.2: # sample["hand"] = torch.ones_like(sample["hand"]) * (-1) # return sample # def make_grid_dnc(sample): # if grid_dnc: # resized_image = transforms.functional.resize(sample["image"], (main_args.resolution // 2, main_args.resolution // 2), interpolation=transforms.InterpolationMode.BICUBIC) # resized_depth = transforms.functional.resize(sample["depth"], (main_args.resolution // 2, main_args.resolution // 2), interpolation=transforms.InterpolationMode.BICUBIC) # resized_normal = transforms.functional.resize(sample["normal"], (main_args.resolution // 2, main_args.resolution // 2), interpolation=transforms.InterpolationMode.BICUBIC) # resized_canny = transforms.functional.resize(sample["canny"], (main_args.resolution // 2, main_args.resolution // 2), interpolation=transforms.InterpolationMode.BICUBIC) # grid = torch.cat([torch.cat([resized_image, resized_depth], dim=2), # torch.cat([resized_normal, resized_canny], dim=2)], dim=1) # assert grid.shape[1] == main_args.resolution and grid.shape[2] == main_args.resolution # sample["image"] = grid # return sample # def filter_low_res(sample): # if main_args.filter_res is None: # main_args.filter_res = main_args.resolution # if filter_lowres: # string_json = sample["json"].decode('utf-8') # dict_json = json.loads(string_json) # if "height" in dict_json.keys() and "width" in dict_json.keys(): # min_length = min(dict_json["height"], dict_json["width"]) # return min_length >= main_args.filter_res # else: # return True # return True # def add_original_hw(sample): # image_height, image_width = sample["image"].height, sample["image"].width # sample["description"] = {"h": image_height, "w": image_width} # return sample # def add_description(sample): # # string_json = sample["json"].decode('utf-8') # # dict_json = json.loads(string_json) # dict_json = sample["json"] # if "height" in dict_json.keys() and "width" in dict_json.keys(): # sample["description"]["h"] = dict_json["height"] # sample["description"]["w"] = dict_json["width"] # return sample # pipeline.extend([ # wds.select(filter_no_caption_or_no_image), # wds.select(filter_low_res), # wds.decode("pilrgb", handler=log_and_continue), # wds.rename(image="jpg;png;jpeg;webp", text="txt"), # wds.map(add_original_hw), # wds.map(decode_text), # wds.map(augment_text), # wds.map(pose2img), # wds.map(decode_image), # wds.map(add_canny), # wds.map(preprocess_image), # wds.map(make_grid_dnc), # wds.map(random_mask), # wds.map(add_description), # # wds.map_dict(image=preprocess_img, text=lambda text: tokenizer(text)[0]), # # wds.map_dict( # # text=lambda text: tokenizer(text, \ # # max_length=tokenizer.model_max_length, \ # # padding="max_length", truncation=True, \ # # return_tensors='pt')['input_ids'], # # blip=lambda blip: tokenizer(blip, \ # # max_length=tokenizer.model_max_length, \ # # padding="max_length", truncation=True, \ # # return_tensors='pt')['input_ids'] # # ), # wds.to_tuple("image", "text", "text_raw", "blip", "blip_raw", "body", "face", "hand", "normal", "depth", "canny", "description"), # # wds.to_tuple("image", "text", "blip", "normal", "depth", "canny"), # wds.batched(args.batch_size, partial=not is_train) # ]) # dataset = wds.DataPipeline(*pipeline) # if is_train: # if not resampled: # assert num_shards >= args.workers * args.world_size, 'number of shards must be >= total workers' # # roll over and repeat a few samples to get same number of full batches on each node # round_fn = math.floor if floor else math.ceil # global_batch_size = args.batch_size * args.world_size # num_batches = round_fn(num_samples / global_batch_size) # num_workers = max(1, args.workers) # num_worker_batches = round_fn(num_batches / num_workers) # per dataloader worker # num_batches = num_worker_batches * num_workers # num_samples = num_batches * global_batch_size # dataset = dataset.with_epoch(num_worker_batches) # each worker is iterating over this # else: # # last batches are partial, eval is done on single (master) node # num_batches = math.ceil(num_samples / args.batch_size) # dataloader = wds.WebLoader( # dataset, # batch_size=None, # shuffle=False, # num_workers=args.workers, # persistent_workers=True, # ) # # FIXME not clear which approach is better, with_epoch before vs after dataloader? # # hoping to resolve via https://github.com/webdataset/webdataset/issues/169 # # if is_train: # # # roll over and repeat a few samples to get same number of full batches on each node # # global_batch_size = args.batch_size * args.world_size # # num_batches = math.ceil(num_samples / global_batch_size) # # num_workers = max(1, args.workers) # # num_batches = math.ceil(num_batches / num_workers) * num_workers # # num_samples = num_batches * global_batch_size # # dataloader = dataloader.with_epoch(num_batches) # # else: # # # last batches are partial, eval is done on single (master) node # # num_batches = math.ceil(num_samples / args.batch_size) # # add meta-data to dataloader instance for convenience # dataloader.num_batches = num_batches # dataloader.num_samples = num_samples # return DataInfo(dataloader=dataloader, shared_epoch=shared_epoch) def get_wds_dataset_cond(args, main_args, is_train, epoch=0, floor=False, tokenizer=None, dropout=False, string_concat=False, string_substitute=False, grid_dnc=False, filter_lowres=False, filter_res=512, filter_mface=False, filter_wpose=False): input_shards = args.train_data if is_train else args.val_data assert input_shards is not None resampled = getattr(args, 'dataset_resampled', False) and is_train num_samples, num_shards = get_dataset_size(input_shards) if not num_samples: if is_train: num_samples = args.train_num_samples if not num_samples: raise RuntimeError( 'Currently, number of dataset samples must be specified for training dataset. ' 'Please specify via `--train-num-samples` if no dataset length info present.') else: num_samples = args.val_num_samples or 0 # eval will just exhaust the iterator if not specified shared_epoch = SharedEpoch(epoch=epoch) # create a shared epoch store to sync epoch to dataloader worker proc if resampled: pipeline = [ResampledShards2(input_shards, weights=args.train_data_upsampling_factors, deterministic=True, epoch=shared_epoch)] else: assert args.train_data_upsampling_factors is None, "--train_data_upsampling_factors is only supported when sampling with replacement (together with --dataset-resampled)." pipeline = [wds.SimpleShardList(input_shards)] # at this point we have an iterator over all the shards if is_train: if not resampled: pipeline.extend([ detshuffle2( bufsize=_SHARD_SHUFFLE_SIZE, initial=_SHARD_SHUFFLE_INITIAL, seed=args.seed, epoch=shared_epoch, ), wds.split_by_node, wds.split_by_worker, ]) pipeline.extend([ # at this point, we have an iterator over the shards assigned to each worker at each node tarfile_to_samples_nothrow, # wds.tarfile_to_samples(handler=log_and_continue), wds.shuffle( bufsize=_SAMPLE_SHUFFLE_SIZE, initial=_SAMPLE_SHUFFLE_INITIAL, ), ]) else: pipeline.extend([ wds.split_by_worker, # at this point, we have an iterator over the shards assigned to each worker wds.tarfile_to_samples(handler=log_and_continue), ]) def pose2img(sample, scale): height, width = sample["image"].height, sample["image"].width # min_length = min(height, width) # radius_body = int(4. * min_length / main_args.resolution) # thickness_body = int(4. * min_length / main_args.resolution) # radius_face = int(1.5 * min_length / main_args.resolution) # thickness_face = int(2. * min_length / main_args.resolution) # radius_hand = int(1.5 * min_length / main_args.resolution) # thickness_hand = int(2. * min_length / main_args.resolution) # if "getty" in sample["__url__"]: # radius_body *= 4 # thickness_body *= 4 # radius_face *= 4 # thickness_face *= 4 # radius_hand *= 4 # thickness_hand *= 4 try: location = np.frombuffer(sample["location"], dtype=np.float32) body_kp = np.frombuffer(sample["new_i_body_kp"], dtype=np.float32).reshape(-1, 17, 2) x_coord = (body_kp[:, :, 0] - location[0]) / location[2] * location[7] y_coord = (body_kp[:, :, 1] - location[1]) / location[3] * location[8] body_kp = np.stack([x_coord, y_coord], axis=2) body_kp = body_kp * scale # body_kp[:, :, 0] -= j # body_kp[:, :, 1] -= i body_kpconf = np.frombuffer(sample["new_i_body_kp_score"], dtype=np.float32).reshape(-1, 17) body_all = np.concatenate([body_kp, body_kpconf[..., np.newaxis]], axis=2) except: body_kp = np.frombuffer(sample["new_body_kp"], dtype=np.float32).reshape(-1, 17, 2) body_kp = body_kp * scale # body_kp[:, :, 0] -= j # body_kp[:, :, 1] -= i body_kpconf = np.frombuffer(sample["new_body_kp_score"], dtype=np.float32).reshape(-1, 17) body_all = np.concatenate([body_kp, body_kpconf[..., np.newaxis]], axis=2) # body_ratio = 0. # for i_body in range(body_kp.shape[0]): # body_ratio = max((np.max(body_kp[i_body, :, 0]) - np.min(body_kp[i_body, :, 0])) / min_length, body_ratio) # print(body_ratio) # body_kp = np.frombuffer(sample["new_body_kp"], dtype=np.float32).reshape(-1, 17, 2) # body_kpconf = np.frombuffer(sample["new_body_kp_score"], dtype=np.float32).reshape(-1, 17) # body_all = np.concatenate([body_kp, body_kpconf[..., np.newaxis]], axis=2) # body_draw = draw_controlnet_skeleton(image=None, pose=body_all, height=height, width=width) # body_draw = draw_humansd_skeleton(image=None, pose=body_all, height=height, width=width, humansd_skeleton_width=int(10. * body_ratio * min_length / main_args.resolution)) body_draw = draw_humansd_skeleton( # image=np.array(sample["image"]), image=None, pose=body_all, height=height, width=width, humansd_skeleton_width=int(10 * main_args.resolution / 512), ) # body_draw = draw_body_skeleton( # img=None, # pose=body_all, # radius=radius_body, # thickness=thickness_body, # height=height, # width=width # ) body_draw = Image.fromarray(body_draw) try: location = np.frombuffer(sample["location"], dtype=np.float32) face_kp = np.frombuffer(sample["new_i_face_kp"], dtype=np.float32).reshape(-1, 68, 2) x_coord = (face_kp[:, :, 0] - location[0]) / location[2] * location[7] y_coord = (face_kp[:, :, 1] - location[1]) / location[3] * location[8] face_kp = np.stack([x_coord, y_coord], axis=2) face_kp = face_kp * scale # face_kp[:, :, 0] -= j # face_kp[:, :, 1] -= i face_kpconf = np.frombuffer(sample["new_i_face_kp_score"], dtype=np.float32).reshape(-1, 68) face_all = np.concatenate([face_kp, face_kpconf[..., np.newaxis]], axis=2) except: face_kp = np.frombuffer(sample["new_face_kp"], dtype=np.float32).reshape(-1, 68, 2) face_kp = face_kp * scale # face_kp[:, :, 0] -= j # face_kp[:, :, 1] -= i face_kpconf = np.frombuffer(sample["new_face_kp_score"], dtype=np.float32).reshape(-1, 68) face_all = np.concatenate([face_kp, face_kpconf[..., np.newaxis]], axis=2) face_draw = draw_face_skeleton( # img=np.array(sample["image"]), img=None, pose=face_all, # radius=radius_face, # thickness=thickness_face, height=height, width=width, ) face_draw = Image.fromarray(face_draw) try: location = np.frombuffer(sample["location"], dtype=np.float32) hand_kp = np.frombuffer(sample["new_i_hand_kp"], dtype=np.float32).reshape(-1, 21, 2) x_coord = (hand_kp[:, :, 0] - location[0]) / location[2] * location[7] y_coord = (hand_kp[:, :, 1] - location[1]) / location[3] * location[8] hand_kp = np.stack([x_coord, y_coord], axis=2) hand_kp = hand_kp * scale # hand_kp[:, :, 0] -= j # hand_kp[:, :, 1] -= i hand_kpconf = np.frombuffer(sample["new_i_hand_kp_score"], dtype=np.float32).reshape(-1, 21) hand_all = np.concatenate([hand_kp, hand_kpconf[..., np.newaxis]], axis=2) except: hand_kp = np.frombuffer(sample["new_hand_kp"], dtype=np.float32).reshape(-1, 21, 2) hand_kp = hand_kp * scale # hand_kp[:, :, 0] -= j # hand_kp[:, :, 1] -= i hand_kpconf = np.frombuffer(sample["new_hand_kp_score"], dtype=np.float32).reshape(-1, 21) hand_all = np.concatenate([hand_kp, hand_kpconf[..., np.newaxis]], axis=2) hand_draw = draw_hand_skeleton( # img=np.array(sample["image"]), img=None, pose=hand_all, # radius=radius_hand, # thickness=thickness_hand, height=height, width=width, ) hand_draw = Image.fromarray(hand_draw) # whole_kp = np.frombuffer(sample["new_wholebody_kp"], dtype=np.float32).reshape(-1, 133, 2) # whole_kpconf = np.frombuffer(sample["new_wholebody_kp_score"], dtype=np.float32).reshape(-1, 133) # whole_all = np.concatenate([whole_kp, whole_kpconf[..., np.newaxis]], axis=2) try: location = np.frombuffer(sample["location"], dtype=np.float32) whole_kp = np.frombuffer(sample["new_i_wholebody_kp"], dtype=np.float32).reshape(-1, 133, 2) x_coord = (whole_kp[:, :, 0] - location[0]) / location[2] * location[7] y_coord = (whole_kp[:, :, 1] - location[1]) / location[3] * location[8] whole_kp = np.stack([x_coord, y_coord], axis=2) whole_kp = whole_kp * scale # whole_kp[:, :, 0] -= j # whole_kp[:, :, 1] -= i whole_kpconf = np.frombuffer(sample["new_i_wholebody_kp_score"], dtype=np.float32).reshape(-1, 133) whole_all = np.concatenate([whole_kp, whole_kpconf[..., np.newaxis]], axis=2) except: whole_kp = np.frombuffer(sample["new_wholebody_kp"], dtype=np.float32).reshape(-1, 133, 2) whole_kp = whole_kp * scale # whole_kp[:, :, 0] -= j # whole_kp[:, :, 1] -= i whole_kpconf = np.frombuffer(sample["new_wholebody_kp_score"], dtype=np.float32).reshape(-1, 133) whole_all = np.concatenate([whole_kp, whole_kpconf[..., np.newaxis]], axis=2) whole_draw = draw_whole_body_skeleton( # img=np.array(sample["image"]), img=None, pose=whole_all, # radius=radius_body, # thickness=thickness_body, height=height, width=width, ) whole_draw = Image.fromarray(whole_draw) sample["body"] = body_draw sample["face"] = face_draw sample["hand"] = hand_draw if main_args.change_whole_to_body: sample["whole"] = body_draw else: sample["whole"] = whole_draw return sample def decode_image(sample): with io.BytesIO(sample["omni_normal"]) as stream: try: img = PIL.Image.open(stream) img.load() img = img.convert("RGB") img = transforms.Resize((sample["image"].height, sample["image"].width), interpolation=transforms.InterpolationMode.BICUBIC)(img) sample["normal"] = img except: print("A broken image is encountered, replace w/ a placeholder") image = Image.new('RGB', (main_args.resolution, main_args.resolution)) sample["normal"] = image with io.BytesIO(sample["omni_depth"]) as stream: try: img = PIL.Image.open(stream) img.load() img = img.convert("RGB") img = transforms.Resize((sample["image"].height, sample["image"].width), interpolation=transforms.InterpolationMode.BICUBIC)(img) sample["depth"] = img except: print("A broken image is encountered, replace w/ a placeholder") image = Image.new('RGB', (main_args.resolution, main_args.resolution)) sample["depth"] = image with io.BytesIO(sample["midas_depth"]) as stream: try: img = PIL.Image.open(stream) img.load() img = img.convert("RGB") img = transforms.Resize((sample["image"].height, sample["image"].width), interpolation=transforms.InterpolationMode.BICUBIC)(img) sample["midas_depth"] = img except: print("A broken image is encountered, replace w/ a placeholder") image = Image.new('RGB', (main_args.resolution, main_args.resolution)) sample["midas_depth"] = image return sample def add_canny(sample): canny = np.array(sample["image"]) low_threshold = 100 high_threshold = 200 canny = cv2.Canny(canny, low_threshold, high_threshold) canny = canny[:, :, None] canny = np.concatenate([canny, canny, canny], axis=2) sample["canny"] = Image.fromarray(canny) return sample def decode_text(sample): try: sample["blip"] = sample["blip"].decode("utf-8") sample["blip_raw"] = sample["blip"] except: sample["blip"] = sample["text"] sample["blip_raw"] = sample["text"].encode("utf-8") sample["text_raw"] = sample["text"] return sample def augment_text(sample): if is_train and string_concat: sample["text"] = sample["text"] + " " + sample["blip"] if is_train and string_substitute: if main_args.rv_prompt: sample["text"] = "RAW photo, " + sample["blip"] + ", 8k uhd, dslr, soft lighting, high quality, film grain, Fujifilm XT3" else: sample["text"] = sample["blip"] return sample def dropout_text(sample): if is_train: try: random_num = torch.rand(1) if random_num < main_args.dropout_text: sample["text"] = sample["text_raw"] = "" except: pass return sample def preprocess_image(sample): # print(main_args.resolution, main_args.center_crop, main_args.random_flip) if grid_dnc: resize_transform = transforms.Resize(main_args.resolution // 2, interpolation=transforms.InterpolationMode.BICUBIC) else: resize_transform = transforms.Resize(main_args.resolution, interpolation=transforms.InterpolationMode.BICUBIC) scale = main_args.resolution * 1. / min(sample["image"].height, sample["image"].width) sample["image"] = resize_transform(sample["image"]) sample["normal"] = resize_transform(sample["normal"]) sample["depth"] = resize_transform(sample["depth"]) sample["midas_depth"] = resize_transform(sample["midas_depth"]) sample["canny"] = resize_transform(sample["canny"]) # sample["body"] = resize_transform(sample["body"]) # sample["face"] = resize_transform(sample["face"]) # sample["hand"] = resize_transform(sample["hand"]) # sample["whole"] = resize_transform(sample["whole"]) transform_list = [] image_height, image_width = sample["image"].height, sample["image"].width if grid_dnc: i = torch.randint(0, image_height - main_args.resolution // 2 + 1, size=(1,)).item() j = torch.randint(0, image_width - main_args.resolution // 2 + 1, size=(1,)).item() else: i = torch.randint(0, image_height - main_args.resolution + 1, size=(1,)).item() j = torch.randint(0, image_width - main_args.resolution + 1, size=(1,)).item() if main_args.center_crop or not is_train: sample["description"]["crop_tl_h"] = i = (image_height - main_args.resolution) // 2 sample["description"]["crop_tl_w"] = j = (image_width - main_args.resolution) // 2 if grid_dnc: transform_list.append(transforms.CenterCrop(main_args.resolution // 2)) else: transform_list.append(transforms.CenterCrop(main_args.resolution)) else: if grid_dnc: if image_height < main_args.resolution // 2 or image_width < main_args.resolution // 2: raise ValueError(f"Required crop size {(main_args.resolution // 2, main_args.resolution // 2)} is larger than input image size {(image_height, image_width)}") elif image_width == main_args.resolution // 2 and image_height == main_args.resolution // 2: i, j = 0, 0 transform_list.append(transforms.Lambda(lambda img: transforms.functional.crop(img, i, j, main_args.resolution // 2, main_args.resolution // 2))) else: if image_height < main_args.resolution or image_width < main_args.resolution: raise ValueError(f"Required crop size {(main_args.resolution, main_args.resolution)} is larger than input image size {(image_height, image_width)}") elif image_width == main_args.resolution and image_height == main_args.resolution: i, j = 0, 0 transform_list.append(transforms.Lambda(lambda img: transforms.functional.crop(img, i, j, main_args.resolution, main_args.resolution))) sample["description"]["crop_tl_h"] = i sample["description"]["crop_tl_w"] = j sample = pose2img(sample, scale) if is_train and torch.rand(1) < 0.5: transform_list.append(transforms.RandomHorizontalFlip(p=1.)) transform_list.extend([transforms.ToTensor(), transforms.Normalize([0.5], [0.5])]) train_transforms = transforms.Compose(transform_list) sample["image"] = train_transforms(sample["image"]) sample["normal"] = train_transforms(sample["normal"]) sample["depth"] = train_transforms(sample["depth"]) sample["midas_depth"] = train_transforms(sample["midas_depth"]) sample["canny"] = train_transforms(sample["canny"]) sample["body"] = train_transforms(sample["body"]) sample["face"] = train_transforms(sample["face"]) sample["hand"] = train_transforms(sample["hand"]) sample["whole"] = train_transforms(sample["whole"]) return sample def random_mask(sample): sample["normal_ori"] = sample["normal"].clone() sample["depth_ori"] = sample["depth"].clone() sample["midas_depth_ori"] = sample["midas_depth"].clone() sample["canny_ori"] = sample["canny"].clone() sample["body_ori"] = sample["body"].clone() sample["face_ori"] = sample["face"].clone() sample["hand_ori"] = sample["hand"].clone() sample["whole_ori"] = sample["whole"].clone() mask_list = [] if is_train and dropout: random_num = torch.rand(1) if random_num < 0.15: sample["normal"] = torch.ones_like(sample["normal"]) * (-1) sample["depth"] = torch.ones_like(sample["depth"]) * (-1) sample["midas_depth"] = torch.ones_like(sample["midas_depth"]) * (-1) sample["canny"] = torch.ones_like(sample["canny"]) * (-1) sample["body"] = torch.ones_like(sample["body"]) * (-1) sample["face"] = torch.ones_like(sample["face"]) * (-1) sample["hand"] = torch.ones_like(sample["hand"]) * (-1) sample["whole"] = torch.ones_like(sample["whole"]) * (-1) mask_list = ["normal", "depth", "midas_depth", "canny", "body", "face", "hand", "whole"] elif random_num > 0.9: pass else: if torch.rand(1) < 0.5: sample["normal"] = torch.ones_like(sample["normal"]) * (-1) mask_list.append("normal") if torch.rand(1) < 0.5: sample["depth"] = torch.ones_like(sample["depth"]) * (-1) mask_list.append("depth") if torch.rand(1) < 0.5: sample["midas_depth"] = torch.ones_like(sample["midas_depth"]) * (-1) mask_list.append("midas_depth") if torch.rand(1) < 0.8: sample["canny"] = torch.ones_like(sample["canny"]) * (-1) mask_list.append("canny") if torch.rand(1) < 0.5: sample["body"] = torch.ones_like(sample["body"]) * (-1) mask_list.append("body") if torch.rand(1) < 0.5: sample["face"] = torch.ones_like(sample["face"]) * (-1) mask_list.append("face") if torch.rand(1) < 0.2: sample["hand"] = torch.ones_like(sample["hand"]) * (-1) mask_list.append("hand") if torch.rand(1) < 0.5: sample["whole"] = torch.ones_like(sample["whole"]) * (-1) mask_list.append("whole") sample["normal_dt"] = sample["normal"].clone() sample["depth_dt"] = sample["depth"].clone() sample["midas_depth_dt"] = sample["midas_depth"].clone() sample["canny_dt"] = sample["canny"].clone() sample["body_dt"] = sample["body"].clone() sample["face_dt"] = sample["face"].clone() sample["hand_dt"] = sample["hand"].clone() sample["whole_dt"] = sample["whole"].clone() mask_list = [x for x in mask_list if x in main_args.cond_type] if len(mask_list) > 0: target = random.choice(mask_list) sample[target + "_dt"] = sample[target + "_ori"].clone() else: if len(main_args.cond_type) > 0: target = random.choice(main_args.cond_type) sample[target + "_dt"] = torch.ones_like(sample[target]) * (-1) return sample def make_grid_dnc(sample): if grid_dnc: resized_image = transforms.functional.resize(sample["image"], (main_args.resolution // 2, main_args.resolution // 2), interpolation=transforms.InterpolationMode.BICUBIC) resized_depth = transforms.functional.resize(sample["depth"], (main_args.resolution // 2, main_args.resolution // 2), interpolation=transforms.InterpolationMode.BICUBIC) resized_normal = transforms.functional.resize(sample["normal"], (main_args.resolution // 2, main_args.resolution // 2), interpolation=transforms.InterpolationMode.BICUBIC) resized_canny = transforms.functional.resize(sample["body"], (main_args.resolution // 2, main_args.resolution // 2), interpolation=transforms.InterpolationMode.BICUBIC) # resized_canny = transforms.functional.resize(sample["canny"], (main_args.resolution // 2, main_args.resolution // 2), interpolation=transforms.InterpolationMode.BICUBIC) grid = torch.cat([torch.cat([resized_image, resized_depth], dim=2), torch.cat([resized_normal, resized_canny], dim=2)], dim=1) assert grid.shape[1] == main_args.resolution and grid.shape[2] == main_args.resolution sample["image"] = grid return sample def filter_low_res(sample): if main_args.filter_res is None: main_args.filter_res = main_args.resolution if filter_lowres: # string_json = sample["json"].decode('utf-8') # dict_json = json.loads(string_json) dict_json = sample["json"] if "height" in dict_json.keys() and "width" in dict_json.keys(): min_length = min(dict_json["height"], dict_json["width"]) return min_length >= main_args.filter_res else: min_length = min(sample["image"].height, sample["image"].width) return min_length >= main_args.filter_res return True def filter_watermark(sample): if main_args.filter_wm: if sample["description"]["watermark"] >= 100: return False return True def add_original_hw(sample): image_height, image_width = sample["image"].height, sample["image"].width sample["description"] = {"h": image_height, "w": image_width} return sample def add_description(sample): # string_json = sample["json"].decode('utf-8') # dict_json = json.loads(string_json) try: dict_json = sample["json"] if "height" in dict_json.keys() and "width" in dict_json.keys(): sample["description"]["h"] = dict_json["height"] sample["description"]["w"] = dict_json["width"] # try: if "coyo" in sample["__url__"]: sample["description"]["aes"] = torch.tensor(sample["json"]["aesthetic_score_laion_v2"] * 1e2) sample["description"]["watermark"] = torch.tensor(sample["json"]["watermark_score"] * 1e3) elif "laion" in sample["__url__"]: sample["description"]["aes"] = torch.tensor(np.frombuffer(sample["aesthetic_score_laion_v2"], dtype=np.float32) * 1e2) sample["description"]["watermark"] = torch.tensor(np.frombuffer(sample["watermark_score"], dtype=np.float32) * 1e3) elif "getty" in sample["__url__"]: sample["description"]["aes"] = torch.tensor(np.frombuffer(sample["aesthetic_score_laion_v2"], dtype=np.float32) * 1e2) sample["description"]["watermark"] = torch.tensor(float(sample["json"]["display_sizes"][-1]["is_watermarked"] or 0) * 1e3) elif "fake" in sample["__url__"]: sample["description"]["aes"] = torch.tensor(random.uniform(5.5, 6.0) * 1e2) sample["description"]["watermark"] = torch.tensor(random.uniform(0., 0.1) * 1e3) except: # sample["description"]["h"] = # sample["description"]["w"] = sample["description"]["aes"] = torch.tensor(random.uniform(5.5, 6.0) * 1e2) sample["description"]["watermark"] = torch.tensor(random.uniform(0., 0.1) * 1e3) # except: # sample["description"]["aes"] = 0. # sample["description"]["watermark"] = 0. return sample def filter_multi_face(sample): if filter_mface: face_kp = np.frombuffer(sample["new_face_kp"], dtype=np.float32).reshape(-1, 68, 2) if face_kp.shape[0] > 1: return False return True def filter_whole_skeleton(sample): if filter_wpose: height, width = sample["image"].height, sample["image"].width area = height * width body_kp = np.frombuffer(sample["new_body_kp"], dtype=np.float32).reshape(-1, 17, 2) body_kpconf = np.frombuffer(sample["new_body_kp_score"], dtype=np.float32).reshape(-1, 17) if (body_kp.shape[0] == 1) and (body_kpconf > 0.5).all() and (body_kp[0, :15, 0] > 0).all() \ and (body_kp[0, :15, 0] < width).all() and (body_kp[0, :15, 1] > 0).all() and \ (body_kp[0, :15, 1] < height).all(): x_min = max(np.amin(body_kp[0, :, 0]), 0) x_max = min(np.amax(body_kp[0, :, 0]), width) y_min = max(np.amin(body_kp[0, :, 1]), 0) y_max = min(np.amax(body_kp[0, :, 1]), height) if (x_max - x_min) * (y_max - y_min) / area > 0.2: return True else: return False else: return False return True pipeline.extend([ wds.select(filter_no_caption_or_no_image), wds.select(filter_multi_face), wds.decode("pilrgb", handler=log_and_continue), wds.rename(image="jpg;png;jpeg;webp", text="txt"), wds.select(filter_whole_skeleton), wds.select(filter_low_res), wds.map(add_original_hw), wds.map(decode_text), wds.map(augment_text), wds.map(dropout_text), # wds.map(pose2img), wds.map(decode_image), wds.map(add_canny), wds.map(preprocess_image), wds.map(make_grid_dnc), wds.map(random_mask), wds.map(add_description), wds.select(filter_watermark), # wds.map_dict(image=preprocess_img, text=lambda text: tokenizer(text)[0]), wds.map_dict( text=lambda text: tokenizer(text, \ max_length=tokenizer.model_max_length, \ padding="max_length", truncation=True, \ return_tensors='pt')['input_ids'], blip=lambda blip: tokenizer(blip, \ max_length=tokenizer.model_max_length, \ padding="max_length", truncation=True, \ return_tensors='pt')['input_ids'] ), wds.to_tuple("image", "text", "text_raw", "blip", "blip_raw", \ "body", "face", "hand", "normal", "depth", "midas_depth", "canny", "whole", "description", \ "body_ori", "face_ori", "hand_ori", "normal_ori", "depth_ori", "midas_depth_ori", "canny_ori", "whole_ori", \ "body_dt", "face_dt", "hand_dt", "normal_dt", "depth_dt", "midas_depth_dt", "canny_dt", "whole_dt"), # wds.to_tuple("image", "text", "blip", "normal", "depth", "canny"), wds.batched(args.batch_size, partial=not is_train) ]) dataset = wds.DataPipeline(*pipeline) if is_train: if not resampled: assert num_shards >= args.workers * args.world_size, 'number of shards must be >= total workers' # roll over and repeat a few samples to get same number of full batches on each node round_fn = math.floor if floor else math.ceil global_batch_size = args.batch_size * args.world_size num_batches = round_fn(num_samples / global_batch_size) num_workers = max(1, args.workers) num_worker_batches = round_fn(num_batches / num_workers) # per dataloader worker num_batches = num_worker_batches * num_workers num_samples = num_batches * global_batch_size dataset = dataset.with_epoch(num_worker_batches) # each worker is iterating over this else: # last batches are partial, eval is done on single (master) node num_batches = math.ceil(num_samples / args.batch_size) dataloader = wds.WebLoader( dataset, batch_size=None, shuffle=False, num_workers=args.workers, persistent_workers=True, ) # FIXME not clear which approach is better, with_epoch before vs after dataloader? # hoping to resolve via https://github.com/webdataset/webdataset/issues/169 # if is_train: # # roll over and repeat a few samples to get same number of full batches on each node # global_batch_size = args.batch_size * args.world_size # num_batches = math.ceil(num_samples / global_batch_size) # num_workers = max(1, args.workers) # num_batches = math.ceil(num_batches / num_workers) * num_workers # num_samples = num_batches * global_batch_size # dataloader = dataloader.with_epoch(num_batches) # else: # # last batches are partial, eval is done on single (master) node # num_batches = math.ceil(num_samples / args.batch_size) # add meta-data to dataloader instance for convenience dataloader.num_batches = num_batches dataloader.num_samples = num_samples return DataInfo(dataloader=dataloader, shared_epoch=shared_epoch) def get_wds_dataset_img(args, preprocess_img, is_train, epoch=0, floor=False, tokenizer=None): input_shards = args.train_data if is_train else args.val_data assert input_shards is not None resampled = getattr(args, 'dataset_resampled', False) and is_train num_samples, num_shards = get_dataset_size(input_shards) if not num_samples: if is_train: num_samples = args.train_num_samples if not num_samples: raise RuntimeError( 'Currently, number of dataset samples must be specified for training dataset. ' 'Please specify via `--train-num-samples` if no dataset length info present.') else: num_samples = args.val_num_samples or 0 # eval will just exhaust the iterator if not specified shared_epoch = SharedEpoch(epoch=epoch) # create a shared epoch store to sync epoch to dataloader worker proc if resampled: pipeline = [ResampledShards2(input_shards, weights=args.train_data_upsampling_factors, deterministic=True, epoch=shared_epoch)] else: assert args.train_data_upsampling_factors is None, "--train_data_upsampling_factors is only supported when sampling with replacement (together with --dataset-resampled)." pipeline = [wds.SimpleShardList(input_shards)] # at this point we have an iterator over all the shards if is_train: if not resampled: pipeline.extend([ detshuffle2( bufsize=_SHARD_SHUFFLE_SIZE, initial=_SHARD_SHUFFLE_INITIAL, seed=args.seed, epoch=shared_epoch, ), wds.split_by_node, wds.split_by_worker, ]) pipeline.extend([ # at this point, we have an iterator over the shards assigned to each worker at each node tarfile_to_samples_nothrow, # wds.tarfile_to_samples(handler=log_and_continue), wds.shuffle( bufsize=_SAMPLE_SHUFFLE_SIZE, initial=_SAMPLE_SHUFFLE_INITIAL, ), ]) else: pipeline.extend([ wds.split_by_worker, # at this point, we have an iterator over the shards assigned to each worker wds.tarfile_to_samples(handler=log_and_continue), ]) pipeline.extend([ wds.select(filter_no_caption_or_no_image), wds.decode("pilrgb", handler=log_and_continue), wds.rename(image="jpg;png;jpeg;webp"), # wds.map_dict(image=preprocess_img, text=lambda text: tokenizer(text)[0]), wds.map_dict(image=preprocess_img), wds.to_tuple("image"), wds.batched(args.batch_size, partial=not is_train) ]) dataset = wds.DataPipeline(*pipeline) if is_train: if not resampled: assert num_shards >= args.workers * args.world_size, 'number of shards must be >= total workers' # roll over and repeat a few samples to get same number of full batches on each node round_fn = math.floor if floor else math.ceil global_batch_size = args.batch_size * args.world_size num_batches = round_fn(num_samples / global_batch_size) num_workers = max(1, args.workers) num_worker_batches = round_fn(num_batches / num_workers) # per dataloader worker num_batches = num_worker_batches * num_workers num_samples = num_batches * global_batch_size dataset = dataset.with_epoch(num_worker_batches) # each worker is iterating over this else: # last batches are partial, eval is done on single (master) node num_batches = math.ceil(num_samples / args.batch_size) dataloader = wds.WebLoader( dataset, batch_size=None, shuffle=False, num_workers=args.workers, persistent_workers=True, ) # FIXME not clear which approach is better, with_epoch before vs after dataloader? # hoping to resolve via https://github.com/webdataset/webdataset/issues/169 # if is_train: # # roll over and repeat a few samples to get same number of full batches on each node # global_batch_size = args.batch_size * args.world_size # num_batches = math.ceil(num_samples / global_batch_size) # num_workers = max(1, args.workers) # num_batches = math.ceil(num_batches / num_workers) * num_workers # num_samples = num_batches * global_batch_size # dataloader = dataloader.with_epoch(num_batches) # else: # # last batches are partial, eval is done on single (master) node # num_batches = math.ceil(num_samples / args.batch_size) # add meta-data to dataloader instance for convenience dataloader.num_batches = num_batches dataloader.num_samples = num_samples return DataInfo(dataloader=dataloader, shared_epoch=shared_epoch) def get_wds_dataset(args, preprocess_img, is_train, epoch=0, floor=False, tokenizer=None): input_shards = args.train_data if is_train else args.val_data assert input_shards is not None resampled = getattr(args, 'dataset_resampled', False) and is_train num_samples, num_shards = get_dataset_size(input_shards) if not num_samples: if is_train: num_samples = args.train_num_samples if not num_samples: raise RuntimeError( 'Currently, number of dataset samples must be specified for training dataset. ' 'Please specify via `--train-num-samples` if no dataset length info present.') else: num_samples = args.val_num_samples or 0 # eval will just exhaust the iterator if not specified shared_epoch = SharedEpoch(epoch=epoch) # create a shared epoch store to sync epoch to dataloader worker proc if resampled: pipeline = [ResampledShards2(input_shards, weights=args.train_data_upsampling_factors, deterministic=True, epoch=shared_epoch)] else: assert args.train_data_upsampling_factors is None, "--train_data_upsampling_factors is only supported when sampling with replacement (together with --dataset-resampled)." pipeline = [wds.SimpleShardList(input_shards)] # at this point we have an iterator over all the shards if is_train: if not resampled: pipeline.extend([ detshuffle2( bufsize=_SHARD_SHUFFLE_SIZE, initial=_SHARD_SHUFFLE_INITIAL, seed=args.seed, epoch=shared_epoch, ), wds.split_by_node, wds.split_by_worker, ]) pipeline.extend([ # at this point, we have an iterator over the shards assigned to each worker at each node tarfile_to_samples_nothrow, # wds.tarfile_to_samples(handler=log_and_continue), wds.shuffle( bufsize=_SAMPLE_SHUFFLE_SIZE, initial=_SAMPLE_SHUFFLE_INITIAL, ), ]) else: pipeline.extend([ wds.split_by_worker, # at this point, we have an iterator over the shards assigned to each worker wds.tarfile_to_samples(handler=log_and_continue), ]) pipeline.extend([ wds.select(filter_no_caption_or_no_image), wds.decode("pilrgb", handler=log_and_continue), wds.rename(image="jpg;png;jpeg;webp", text="txt"), # wds.map_dict(image=preprocess_img, text=lambda text: tokenizer(text)[0]), wds.map_dict(image=preprocess_img, text=lambda text: tokenizer(text, max_length=tokenizer.model_max_length, padding="max_length", truncation=True, return_tensors='pt')['input_ids']), wds.to_tuple("image", "text"), wds.batched(args.batch_size, partial=not is_train) ]) dataset = wds.DataPipeline(*pipeline) if is_train: if not resampled: assert num_shards >= args.workers * args.world_size, 'number of shards must be >= total workers' # roll over and repeat a few samples to get same number of full batches on each node round_fn = math.floor if floor else math.ceil global_batch_size = args.batch_size * args.world_size num_batches = round_fn(num_samples / global_batch_size) num_workers = max(1, args.workers) num_worker_batches = round_fn(num_batches / num_workers) # per dataloader worker num_batches = num_worker_batches * num_workers num_samples = num_batches * global_batch_size dataset = dataset.with_epoch(num_worker_batches) # each worker is iterating over this else: # last batches are partial, eval is done on single (master) node num_batches = math.ceil(num_samples / args.batch_size) dataloader = wds.WebLoader( dataset, batch_size=None, shuffle=False, num_workers=args.workers, persistent_workers=True, ) # FIXME not clear which approach is better, with_epoch before vs after dataloader? # hoping to resolve via https://github.com/webdataset/webdataset/issues/169 # if is_train: # # roll over and repeat a few samples to get same number of full batches on each node # global_batch_size = args.batch_size * args.world_size # num_batches = math.ceil(num_samples / global_batch_size) # num_workers = max(1, args.workers) # num_batches = math.ceil(num_batches / num_workers) * num_workers # num_samples = num_batches * global_batch_size # dataloader = dataloader.with_epoch(num_batches) # else: # # last batches are partial, eval is done on single (master) node # num_batches = math.ceil(num_samples / args.batch_size) # add meta-data to dataloader instance for convenience dataloader.num_batches = num_batches dataloader.num_samples = num_samples return DataInfo(dataloader=dataloader, shared_epoch=shared_epoch) def get_csv_dataset(args, preprocess_fn, is_train, epoch=0, tokenizer=None): input_filename = args.train_data if is_train else args.val_data assert input_filename dataset = CsvDataset( input_filename, preprocess_fn, img_key=args.csv_img_key, caption_key=args.csv_caption_key, sep=args.csv_separator, tokenizer=tokenizer ) num_samples = len(dataset) sampler = DistributedSampler(dataset) if args.distributed and is_train else None shuffle = is_train and sampler is None dataloader = DataLoader( dataset, batch_size=args.batch_size, shuffle=shuffle, num_workers=args.workers, pin_memory=True, sampler=sampler, drop_last=is_train, ) dataloader.num_samples = num_samples dataloader.num_batches = len(dataloader) return DataInfo(dataloader, sampler) class SyntheticDataset(Dataset): def __init__(self, transform=None, image_size=(224, 224), caption="Dummy caption", dataset_size=100, tokenizer=None): self.transform = transform self.image_size = image_size self.caption = caption self.image = Image.new('RGB', image_size) self.dataset_size = dataset_size self.preprocess_txt = lambda text: tokenizer(text)[0] def __len__(self): return self.dataset_size def __getitem__(self, idx): if self.transform is not None: image = self.transform(self.image) return image, self.preprocess_txt(self.caption) def get_synthetic_dataset(args, preprocess_fn, is_train, epoch=0, tokenizer=None): image_size = preprocess_fn.transforms[0].size dataset = SyntheticDataset( transform=preprocess_fn, image_size=image_size, dataset_size=args.train_num_samples, tokenizer=tokenizer) num_samples = len(dataset) sampler = DistributedSampler(dataset) if args.distributed and is_train else None shuffle = is_train and sampler is None dataloader = DataLoader( dataset, batch_size=args.batch_size, shuffle=shuffle, num_workers=args.workers, pin_memory=True, sampler=sampler, drop_last=is_train, ) dataloader.num_samples = num_samples dataloader.num_batches = len(dataloader) return DataInfo(dataloader, sampler) def get_dataset_fn(data_path, dataset_type): if dataset_type == "webdataset": return get_wds_dataset elif dataset_type == "csv": return get_csv_dataset elif dataset_type == "synthetic": return get_synthetic_dataset elif dataset_type == "auto": ext = data_path.split('.')[-1] if ext in ['csv', 'tsv']: return get_csv_dataset elif ext in ['tar']: return get_wds_dataset else: raise ValueError( f"Tried to figure out dataset type, but failed for extension {ext}.") else: raise ValueError(f"Unsupported dataset type: {dataset_type}") def get_data(args, preprocess_fns, epoch=0, tokenizer=None): preprocess_train, preprocess_val = preprocess_fns data = {} if args.train_data or args.dataset_type == "synthetic": data["train"] = get_dataset_fn(args.train_data, args.dataset_type)( args, preprocess_train, is_train=True, epoch=epoch, tokenizer=tokenizer) if args.val_data: data["val"] = get_dataset_fn(args.val_data, args.dataset_type)( args, preprocess_val, is_train=False, tokenizer=tokenizer) if args.imagenet_val is not None: data["imagenet-val"] = get_imagenet(args, preprocess_fns, "val") if args.imagenet_v2 is not None: data["imagenet-v2"] = get_imagenet(args, preprocess_fns, "v2") return data