-
Notifications
You must be signed in to change notification settings - Fork 2
/
Copy pathmain.py
218 lines (156 loc) · 7.5 KB
/
main.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
"""
This code is unofficial implementation of
'Motion Guided 3D Pose Estimation from Videos' (Authors : Jingbo Wang1, Sijie Yan1, Yuanjun Xiong2, and Dahua Lin1)
Also, this code is based on https://github.com/Vegetebird/MHFormer repository.
"""
import os
import glob
import torch
import random
import logging
import matplotlib.pyplot as plt
import numpy as np
from tqdm import tqdm
import torch.nn as nn
import torch.utils.data
import torch.optim as optim
from common.opt import opts
from common.utils import *
from common.load_data_hm36 import Fusion
from common.h36m_dataset import Human36mDataset
from model.ugcn import Model
def train(opt, actions, train_loader, model, optimizer, epoch):
return step('train', opt, actions, train_loader, model, optimizer, epoch)
def val(opt, actions, val_loader, model):
with torch.no_grad():
return step('test', opt, actions, val_loader, model)
def step(split, opt, actions, dataLoader, model, optimizer=None, epoch=None):
loss_all = {'loss': AccumLoss()}
error_sum = AccumLoss()
action_error_sum = define_error_list(actions)
if split == 'train':
model.train()
else:
model.eval()
t = tqdm(dataLoader, 0)
for i, data in enumerate(t):
batch_cam, gt_3D, input_2D, action, subject, scale, bb_box, cam_ind = data
[input_2D, gt_3D, batch_cam, scale, bb_box] = get_varialbe(split, [input_2D, gt_3D, batch_cam, scale, bb_box])
N = input_2D.size(0)
out_target = gt_3D.clone().view(N, -1, opt.out_joints, opt.out_channels)
out_target[:, :, 0] = 0
gt_3D = gt_3D.view(N, -1, opt.out_joints, opt.out_channels).type(torch.cuda.FloatTensor)
if out_target.size(1) > 1:
out_target_single = out_target[:, opt.pad].unsqueeze(1)
gt_3D_single = gt_3D[:, opt.pad].unsqueeze(1)
else:
out_target_single = out_target
gt_3D_single = gt_3D
if opt.test_augmentation and split =='test':
input_2D, output_3D = input_augmentation(input_2D, model)
else:
input_2D = input_2D.view(N, -1, opt.n_joints, opt.in_channels, 1).permute(0, 3, 1, 2, 4).type(torch.cuda.FloatTensor) # N, C, T, J, M
output_3D = model(input_2D)
output_3D = output_3D.permute(0, 2, 3, 4, 1).contiguous().view(N, -1, opt.out_joints, opt.out_channels)
output_3D = output_3D * scale.unsqueeze(-1).unsqueeze(-1).unsqueeze(-1).repeat(1, output_3D.size(1), opt.out_joints, opt.out_channels)
output_3D_single = output_3D[:, opt.pad].unsqueeze(1)
if split == 'train':
pred_out = output_3D
elif split == 'test':
pred_out = output_3D_single
loss = mpjpe_cal(pred_out, out_target)
motion_loss = motion_cal(pred_out, out_target)
loss_np = loss.detach().cpu().numpy()
loss_all['loss'].update(loss_np * N, N)
# loss prompting
t.set_description('Loss({0:,.4f})'.format(loss_np))
t.refresh()
if split == 'train':
optimizer.zero_grad()
(loss + opt.alpha * motion_loss).backward()
optimizer.step()
pred_out[:, :, 0, :] = 0
joint_error = mpjpe_cal(pred_out, out_target).item()
error_sum.update(joint_error*N, N)
elif split == 'test':
pred_out[:, :, 0, :] = 0
action_error_sum = test_calculation(pred_out, out_target, action, action_error_sum, opt.dataset, subject)
if split == 'train' and i % 1000 == 0:
fig = plt.figure()
ax1 = fig.add_subplot(1, 1, 1, projection='3d')
ax1.set_title('Pair-3D')
show3DposePair(out_target[0, 48].cpu().detach().numpy(), pred_out[0, 48].cpu().detach().numpy(), ax=ax1)
plt.savefig(os.path.join(opt.checkpoint, 'viz.png'), dpi=100)
plt.close(fig)
if split == 'train':
return loss_all['loss'].avg, error_sum.avg*1000
elif split == 'test':
mpjpe = print_error(opt.dataset, action_error_sum, opt.train)
return mpjpe
def input_augmentation(input_2D, model):
joints_left = [4, 5, 6, 11, 12, 13]
joints_right = [1, 2, 3, 14, 15, 16]
N, _, T, J, C = input_2D.shape
input_2D_flip = input_2D[:, 1].view(N, T, J, C, 1).permute(0, 3, 1, 2, 4)
input_2D_non_flip = input_2D[:, 0].view(N, T, J, C, 1).permute(0, 3, 1, 2, 4)
output_3D_flip = model(input_2D_flip)
output_3D_flip[:, 0] *= -1
output_3D_flip[:, :, :, joints_left + joints_right] = output_3D_flip[:, :, :, joints_right + joints_left]
output_3D_non_flip = model(input_2D_non_flip)
output_3D = (output_3D_non_flip + output_3D_flip) / 2
input_2D = input_2D_non_flip
return input_2D, output_3D
if __name__ == '__main__':
opt = opts().parse()
os.environ["CUDA_VISIBLE_DEVICES"] = opt.gpu
opt.manualSeed = 4321
random.seed(opt.manualSeed)
torch.manual_seed(opt.manualSeed)
if opt.train:
logging.basicConfig(format='%(asctime)s %(message)s', datefmt='%Y/%m/%d %H:%M:%S', \
filename=os.path.join(opt.checkpoint, 'train.log'), level=logging.INFO)
root_path = opt.root_path
dataset_path = root_path + 'data_3d_' + opt.dataset + '.npz'
dataset = Human36mDataset(dataset_path, opt)
actions = define_actions(opt.actions)
if opt.train:
train_data = Fusion(opt=opt, train=True, dataset=dataset, root_path=root_path)
train_dataloader = torch.utils.data.DataLoader(train_data, batch_size=opt.batch_size,
shuffle=True, num_workers=int(opt.workers), pin_memory=True)
if opt.test:
test_data = Fusion(opt=opt, train=False, dataset=dataset, root_path =root_path)
test_dataloader = torch.utils.data.DataLoader(test_data, batch_size=opt.batch_size,
shuffle=False, num_workers=int(opt.workers), pin_memory=True)
model = Model(opt).cuda()
model_dict = model.state_dict()
if opt.reload:
model_path = sorted(glob.glob(os.path.join(opt.previous_dir, '*.pth')))[0]
print(model_path)
pre_dict = torch.load(model_path)
for name, key in model_dict.items():
model_dict[name] = pre_dict[name]
model.load_state_dict(model_dict)
all_param = []
lr = opt.lr
all_param += list(model.parameters())
optimizer = optim.AdamW(all_param, lr=opt.lr, weight_decay=opt.weight_decay)
scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, milestones=[80, 90, 100], gamma=opt.lr_decay)
#scheduler = torch.optim.lr_scheduler.ExponentialLR(optimizer, gamma=opt.lr_decay)
for epoch in range(1, opt.nepoch):
lr = scheduler.get_last_lr()[0]
if opt.train:
loss, error = train(opt, actions, train_dataloader, model, optimizer, epoch)
if opt.test:
mpjpe = val(opt, actions, test_dataloader, model)
data_threshold = mpjpe
if opt.train and data_threshold < opt.previous_best_threshold:
opt.previous_name = save_model(opt.previous_name, opt.checkpoint, epoch, data_threshold, model)
opt.previous_best_threshold = data_threshold
if opt.train == 0:
print('mpjpe: %.2f' % (mpjpe))
break
else:
logging.info('epoch: %d, lr: %.7f, loss: %.4f, mpjpe: %.2f' % (epoch, lr, loss, mpjpe))
print('e: %d, lr: %.7f, loss: %.4f, mpjpe: %.2f' % (epoch, lr, loss, mpjpe))
scheduler.step()
print(opt.checkpoint)