-
Notifications
You must be signed in to change notification settings - Fork 1
/
train.py
173 lines (139 loc) · 6.52 KB
/
train.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
### Copyright (C) 2017 NVIDIA Corporation. All rights reserved.
### Licensed under the CC BY-NC-SA 4.0 license (https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode).
import time
import os
import torch
from subprocess import call
from options.train_options import TrainOptions
from data.data_loader import CreateDataLoader
from models.models import create_model, create_optimizer, init_params, save_models, update_models
import util.util as util
from util.visualizer import Visualizer
import warnings
warnings.filterwarnings("ignore")
def train():
opt = TrainOptions().parse()
if opt.debug:
opt.display_freq = 1
opt.print_freq = 1
opt.nThreads = 1
### initialize dataset
data_loader = CreateDataLoader(opt)
dataset = data_loader.load_data()
dataset_size = len(data_loader)
print('#training videos = %d' % dataset_size)
### initialize models
models = create_model(opt)
modelG, modelD, flowNet, optimizer_G, optimizer_D, optimizer_D_T = create_optimizer(opt, models)
### set parameters
n_gpus, tG, tD, tDB, s_scales, t_scales, input_nc, output_nc, \
start_epoch, epoch_iter, print_freq, total_steps, iter_path = init_params(opt, modelG, modelD, data_loader)
visualizer = Visualizer(opt)
# init loss list
losses_G = []
losses_D = []
losses_D_T = []
losses_t_scales = []
### real training starts here
for epoch in range(start_epoch, opt.niter + opt.niter_decay + 1):
epoch_start_time = time.time()
for idx, data in enumerate(dataset, start=epoch_iter):
if total_steps % print_freq == 0:
iter_start_time = time.time()
total_steps += opt.batchSize
epoch_iter += opt.batchSize
# whether to collect output images
save_fake = total_steps % opt.display_freq == 0
n_frames_total, n_frames_load, t_len = data_loader.dataset.init_data_params(data, n_gpus, tG)
fake_B_prev_last, frames_all = data_loader.dataset.init_data(t_scales)
for i in range(0, n_frames_total, n_frames_load):
input_A, input_B, inst_A = data_loader.dataset.prepare_data(data, i, input_nc, output_nc)
###################################### Forward Pass ##########################
####### generator
fake_B, fake_B_raw, flow, weight, real_A, real_Bp, fake_B_last = modelG(input_A, input_B, inst_A, fake_B_prev_last)
####### discriminator
### individual frame discriminator
real_B_prev, real_B = real_Bp[:, :-1], real_Bp[:, 1:] # the collection of previous and current real frames
flow_ref, conf_ref = flowNet(real_B, real_B_prev) # reference flows and confidences
fake_B_prev = modelG.module.compute_fake_B_prev(real_B_prev, fake_B_prev_last, fake_B)
fake_B_prev_last = fake_B_last
losses = modelD(0, reshape([real_B, fake_B, fake_B_raw, real_A, real_B_prev, fake_B_prev, flow, weight, flow_ref, conf_ref]))
losses = [ torch.mean(x) if x is not None else 0 for x in losses ]
loss_dict = dict(zip(modelD.module.loss_names, losses))
### temporal discriminator
# get skipped frames for each temporal scale
frames_all, frames_skipped = modelD.module.get_all_skipped_frames(frames_all, \
real_B, fake_B, flow_ref, conf_ref, t_scales, tD, n_frames_load, i, flowNet)
# run discriminator for each temporal scale
loss_dict_T = []
for s in range(t_scales):
if frames_skipped[0][s] is not None:
losses = modelD(s+1, [frame_skipped[s] for frame_skipped in frames_skipped])
losses = [ torch.mean(x) if not isinstance(x, int) else x for x in losses ]
loss_dict_T.append(dict(zip(modelD.module.loss_names_T, losses)))
# collect losses
loss_G, loss_D, loss_D_T, t_scales_act = modelD.module.get_losses(loss_dict, loss_dict_T, t_scales)
losses_G.append(loss_G.item())
losses_D.append(loss_D.item())
###################################### Backward Pass #################################
# update generator weights
loss_backward(opt, loss_G, optimizer_G)
# update individual discriminator weights
loss_backward(opt, loss_D, optimizer_D)
# update temporal discriminator weights
for s in range(t_scales_act):
loss_backward(opt, loss_D_T[s], optimizer_D_T[s])
if i == 0: fake_B_first = fake_B[0, 0] # the first generated image in this sequence
if opt.debug:
call(["nvidia-smi", "--format=csv", "--query-gpu=memory.used,memory.free"])
############## Display results and errors ##########
### print out errors
if total_steps % print_freq == 0:
t = (time.time() - iter_start_time) / print_freq
errors = {k: v.data.item() if not isinstance(v, int) else v for k, v in loss_dict.items()}
for s in range(len(loss_dict_T)):
errors.update({k+str(s): v.data.item() if not isinstance(v, int) else v for k, v in loss_dict_T[s].items()})
visualizer.print_current_errors(epoch, epoch_iter, errors, t)
visualizer.plot_current_errors(errors, total_steps)
### display output images
if save_fake:
visuals = util.save_all_tensors(opt, real_A, fake_B, fake_B_first, fake_B_raw, real_B, flow_ref, conf_ref, flow, weight, modelD)
visualizer.display_current_results(visuals, epoch, total_steps)
### save latest model
save_models(opt, epoch, epoch_iter, total_steps, visualizer, iter_path, modelG, modelD)
if epoch_iter > dataset_size - opt.batchSize:
epoch_iter = 0
break
# end of epoch
iter_end_time = time.time()
visualizer.vis_print('End of epoch %d / %d \t Time Taken: %d sec' %
(epoch, opt.niter + opt.niter_decay, time.time() - epoch_start_time))
### save model for this epoch and update model params
save_models(opt, epoch, epoch_iter, total_steps, visualizer, iter_path, modelG, modelD, end_of_epoch=True)
update_models(opt, epoch, modelG, modelD, data_loader)
from matplotlib import pyplot as plt
plt.switch_backend('agg')
# Plot Losses
plt.plot(losses_G,'-b', label='losses_G')
plt.plot(losses_D,'-r', label='losses_D')
# plt.plot(losses_D_T,'-r', label='losses_D_T')
plot_name = 'checkpoints/'+ opt.name + '/losses_plot.png'
plt.savefig(plot_name)
plt.close()
def loss_backward(opt, loss, optimizer):
optimizer.zero_grad()
if opt.fp16:
from apex import amp
with amp.scale_loss(loss, optimizer) as scaled_loss:
scaled_loss.backward()
else:
loss.backward()
optimizer.step()
def reshape(tensors):
if tensors is None: return None
if isinstance(tensors, list):
return [reshape(tensor) for tensor in tensors]
_, _, ch, h, w = tensors.size()
return tensors.contiguous().view(-1, ch, h, w)
if __name__ == "__main__":
train()