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from __future__ import division
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
import numpy as np
import cv2
import matplotlib.pyplot as plt
try:
from bbox import bbox_iou
except ImportError:
from yolo.bbox import bbox_iou
def count_parameters(model):
return sum(p.numel() for p in model.parameters())
def count_learnable_parameters(model):
return sum(p.numel() for p in model.parameters() if p.requires_grad)
def convert2cpu(matrix):
if matrix.is_cuda:
return torch.FloatTensor(matrix.size()).copy_(matrix)
else:
return matrix
def predict_transform(prediction, inp_dim, anchors, num_classes, CUDA = True):
batch_size = prediction.size(0)
stride = inp_dim // prediction.size(2)
grid_size = inp_dim // stride
bbox_attrs = 5 + num_classes
num_anchors = len(anchors)
anchors = [(a[0]/stride, a[1]/stride) for a in anchors]
prediction = prediction.view(batch_size, bbox_attrs*num_anchors, grid_size*grid_size)
prediction = prediction.transpose(1,2).contiguous()
prediction = prediction.view(batch_size, grid_size*grid_size*num_anchors, bbox_attrs)
#Sigmoid the centre_X, centre_Y. and object confidencce
prediction[:,:,0] = torch.sigmoid(prediction[:,:,0])
prediction[:,:,1] = torch.sigmoid(prediction[:,:,1])
prediction[:,:,4] = torch.sigmoid(prediction[:,:,4])
#Add the center offsets
grid_len = np.arange(grid_size)
a,b = np.meshgrid(grid_len, grid_len)
x_offset = torch.FloatTensor(a).view(-1,1)
y_offset = torch.FloatTensor(b).view(-1,1)
if CUDA:
x_offset = x_offset
y_offset = y_offset
x_y_offset = torch.cat((x_offset, y_offset), 1).repeat(1,num_anchors).view(-1,2).unsqueeze(0)
prediction[:,:,:2] += x_y_offset
#log space transform height and the width
anchors = torch.FloatTensor(anchors)
if CUDA:
anchors = anchors
anchors = anchors.repeat(grid_size*grid_size, 1).unsqueeze(0)
prediction[:,:,2:4] = torch.exp(prediction[:,:,2:4])*anchors
#Softmax the class scores
prediction[:,:,5: 5 + num_classes] = torch.sigmoid((prediction[:,:, 5 : 5 + num_classes]))
prediction[:,:,:4] *= stride
return prediction
def load_classes(namesfile):
fp = open(namesfile, "r")
names = fp.read().split("\n")[:-1]
return names
def get_im_dim(im):
im = cv2.imread(im)
w,h = im.shape[1], im.shape[0]
return w,h
def unique(tensor):
tensor_np = tensor.cpu().numpy()
unique_np = np.unique(tensor_np)
unique_tensor = torch.from_numpy(unique_np)
tensor_res = tensor.new(unique_tensor.shape)
tensor_res.copy_(unique_tensor)
return tensor_res
def dynamic_write_results(prediction, confidence, num_classes, nms=True, nms_conf=0.4):
prediction_bak = prediction.clone()
dets = write_results(prediction.clone(), confidence, num_classes, nms, nms_conf)
if isinstance(dets, int):
return dets
if dets.shape[0] > 100:
nms_conf -= 0.05
dets = write_results(prediction_bak.clone(), confidence, num_classes, nms, nms_conf)
return dets
def write_results(prediction, confidence, num_classes, nms=True, nms_conf=0.4):
conf_mask = (prediction[:, :, 4] > confidence).float().float().unsqueeze(2)
prediction = prediction * conf_mask
try:
ind_nz = torch.nonzero(prediction[:,:,4]).transpose(0,1).contiguous()
except:
return 0
box_a = prediction.new(prediction.shape)
box_a[:,:,0] = (prediction[:,:,0] - prediction[:,:,2]/2)
box_a[:,:,1] = (prediction[:,:,1] - prediction[:,:,3]/2)
box_a[:,:,2] = (prediction[:,:,0] + prediction[:,:,2]/2)
box_a[:,:,3] = (prediction[:,:,1] + prediction[:,:,3]/2)
prediction[:,:,:4] = box_a[:,:,:4]
batch_size = prediction.size(0)
output = prediction.new(1, prediction.size(2) + 1)
write = False
num = 0
for ind in range(batch_size):
#select the image from the batch
image_pred = prediction[ind]
#Get the class having maximum score, and the index of that class
#Get rid of num_classes softmax scores
#Add the class index and the class score of class having maximum score
max_conf, max_conf_score = torch.max(image_pred[:,5:5+ num_classes], 1)
max_conf = max_conf.float().unsqueeze(1)
max_conf_score = max_conf_score.float().unsqueeze(1)
seq = (image_pred[:,:5], max_conf, max_conf_score)
image_pred = torch.cat(seq, 1)
#Get rid of the zero entries
non_zero_ind = (torch.nonzero(image_pred[:,4]))
image_pred_ = image_pred[non_zero_ind.squeeze(),:].view(-1,7)
#Get the various classes detected in the image
try:
img_classes = unique(image_pred_[:,-1])
except:
continue
#WE will do NMS classwise
#print(img_classes)
for cls in img_classes:
if cls != 0:
continue
#get the detections with one particular class
cls_mask = image_pred_*(image_pred_[:,-1] == cls).float().unsqueeze(1)
class_mask_ind = torch.nonzero(cls_mask[:,-2]).squeeze()
image_pred_class = image_pred_[class_mask_ind].view(-1,7)
#sort the detections such that the entry with the maximum objectness
#confidence is at the top
conf_sort_index = torch.sort(image_pred_class[:,4], descending = True )[1]
image_pred_class = image_pred_class[conf_sort_index]
idx = image_pred_class.size(0)
#if nms has to be done
if nms:
# Perform non-maximum suppression
max_detections = []
while image_pred_class.size(0):
# Get detection with highest confidence and save as max detection
max_detections.append(image_pred_class[0].unsqueeze(0))
# Stop if we're at the last detection
if len(image_pred_class) == 1:
break
# Get the IOUs for all boxes with lower confidence
ious = bbox_iou(max_detections[-1], image_pred_class[1:])
# Remove detections with IoU >= NMS threshold
image_pred_class = image_pred_class[1:][ious < nms_conf]
image_pred_class = torch.cat(max_detections).data
#Concatenate the batch_id of the image to the detection
#this helps us identify which image does the detection correspond to
#We use a linear straucture to hold ALL the detections from the batch
#the batch_dim is flattened
#batch is identified by extra batch column
batch_ind = image_pred_class.new(image_pred_class.size(0), 1).fill_(ind)
seq = batch_ind, image_pred_class
if not write:
output = torch.cat(seq,1)
write = True
else:
out = torch.cat(seq,1)
output = torch.cat((output,out))
num += 1
if not num:
return 0
return output
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Sat Mar 24 00:12:16 2018
@author: ayooshmac
"""
def predict_transform_half(prediction, inp_dim, anchors, num_classes, CUDA = True):
batch_size = prediction.size(0)
stride = inp_dim // prediction.size(2)
bbox_attrs = 5 + num_classes
num_anchors = len(anchors)
grid_size = inp_dim // stride
prediction = prediction.view(batch_size, bbox_attrs*num_anchors, grid_size*grid_size)
prediction = prediction.transpose(1,2).contiguous()
prediction = prediction.view(batch_size, grid_size*grid_size*num_anchors, bbox_attrs)
#Sigmoid the centre_X, centre_Y. and object confidencce
prediction[:,:,0] = torch.sigmoid(prediction[:,:,0])
prediction[:,:,1] = torch.sigmoid(prediction[:,:,1])
prediction[:,:,4] = torch.sigmoid(prediction[:,:,4])
#Add the center offsets
grid_len = np.arange(grid_size)
a,b = np.meshgrid(grid_len, grid_len)
x_offset = torch.FloatTensor(a).view(-1,1)
y_offset = torch.FloatTensor(b).view(-1,1)
if CUDA:
x_offset = x_offset.half()
y_offset = y_offset.half()
x_y_offset = torch.cat((x_offset, y_offset), 1).repeat(1,num_anchors).view(-1,2).unsqueeze(0)
prediction[:,:,:2] += x_y_offset
#log space transform height and the width
anchors = torch.HalfTensor(anchors)
if CUDA:
anchors = anchors
anchors = anchors.repeat(grid_size*grid_size, 1).unsqueeze(0)
prediction[:,:,2:4] = torch.exp(prediction[:,:,2:4])*anchors
#Softmax the class scores
prediction[:,:,5: 5 + num_classes] = nn.Softmax(-1)(Variable(prediction[:,:, 5 : 5 + num_classes])).data
prediction[:,:,:4] *= stride
return prediction
def write_results_half(prediction, confidence, num_classes, nms = True, nms_conf = 0.4):
conf_mask = (prediction[:,:,4] > confidence).half().unsqueeze(2)
prediction = prediction*conf_mask
try:
ind_nz = torch.nonzero(prediction[:,:,4]).transpose(0,1).contiguous()
except:
return 0
box_a = prediction.new(prediction.shape)
box_a[:,:,0] = (prediction[:,:,0] - prediction[:,:,2]/2)
box_a[:,:,1] = (prediction[:,:,1] - prediction[:,:,3]/2)
box_a[:,:,2] = (prediction[:,:,0] + prediction[:,:,2]/2)
box_a[:,:,3] = (prediction[:,:,1] + prediction[:,:,3]/2)
prediction[:,:,:4] = box_a[:,:,:4]
batch_size = prediction.size(0)
output = prediction.new(1, prediction.size(2) + 1)
write = False
for ind in range(batch_size):
#select the image from the batch
image_pred = prediction[ind]
#Get the class having maximum score, and the index of that class
#Get rid of num_classes softmax scores
#Add the class index and the class score of class having maximum score
max_conf, max_conf_score = torch.max(image_pred[:,5:5+ num_classes], 1)
max_conf = max_conf.half().unsqueeze(1)
max_conf_score = max_conf_score.half().unsqueeze(1)
seq = (image_pred[:,:5], max_conf, max_conf_score)
image_pred = torch.cat(seq, 1)
#Get rid of the zero entries
non_zero_ind = (torch.nonzero(image_pred[:,4]))
try:
image_pred_ = image_pred[non_zero_ind.squeeze(),:]
except:
continue
#Get the various classes detected in the image
img_classes = unique(image_pred_[:,-1].long()).half()
#WE will do NMS classwise
for cls in img_classes:
#get the detections with one particular class
cls_mask = image_pred_*(image_pred_[:,-1] == cls).half().unsqueeze(1)
class_mask_ind = torch.nonzero(cls_mask[:,-2]).squeeze()
image_pred_class = image_pred_[class_mask_ind]
#sort the detections such that the entry with the maximum objectness
#confidence is at the top
conf_sort_index = torch.sort(image_pred_class[:,4], descending = True )[1]
image_pred_class = image_pred_class[conf_sort_index]
idx = image_pred_class.size(0)
#if nms has to be done
if nms:
#For each detection
for i in range(idx):
#Get the IOUs of all boxes that come after the one we are looking at
#in the loop
try:
ious = bbox_iou(image_pred_class[i].unsqueeze(0), image_pred_class[i+1:])
except ValueError:
break
except IndexError:
break
#Zero out all the detections that have IoU > treshhold
iou_mask = (ious < nms_conf).half().unsqueeze(1)
image_pred_class[i+1:] *= iou_mask
#Remove the non-zero entries
non_zero_ind = torch.nonzero(image_pred_class[:,4]).squeeze()
image_pred_class = image_pred_class[non_zero_ind]
#Concatenate the batch_id of the image to the detection
#this helps us identify which image does the detection correspond to
#We use a linear straucture to hold ALL the detections from the batch
#the batch_dim is flattened
#batch is identified by extra batch column
batch_ind = image_pred_class.new(image_pred_class.size(0), 1).fill_(ind)
seq = batch_ind, image_pred_class
if not write:
output = torch.cat(seq,1)
write = True
else:
out = torch.cat(seq,1)
output = torch.cat((output,out))
return output
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