-
Notifications
You must be signed in to change notification settings - Fork 0
/
train_lr.py
445 lines (422 loc) · 17 KB
/
train_lr.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
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
import torch.optim as optim
from torch.optim import lr_scheduler
import torch.nn as nn
from torch.utils.data import DataLoader
import numpy as np
import argparse
import time
import shutil
from tensorboardX import SummaryWriter
from torchvision.transforms import transforms
# import setproctitle
from utils.training_util import MovingAverage, save_checkpoint, load_checkpoint
from utils.training_util import calculate_psnr, calculate_ssim
from utils.data_provider_DGF import *
from utils.data_provider_DGF_synthetic import SingleLoader_DGF_synth
from utils.loss import LossBasic,WaveletLoss,tv_loss,LossAnneal_i
from model.KPN import KPN
from model.Att_KPN import Att_KPN, Att_KPN_Wavelet
from model.Att_Weight_KPN import Att_Weight_KPN
import scipy.io
from collections import OrderedDict
from test_NonKPN_DGF_mat import load_data
import matplotlib.pyplot as plt
def train(num_workers, cuda, restart_train, mGPU):
# torch.set_num_threads(num_threads)
color = True
batch_size = args.batch_size
lr = 2e-4
lr_decay = 0.89125093813
n_epoch = args.epoch
# num_workers = 8
save_freq = args.save_every
loss_freq = args.loss_every
lr_step_size = 100
burst_length = args.burst_length
# checkpoint path
checkpoint_dir = "checkpoints/" + args.checkpoint
if not os.path.exists(checkpoint_dir):
os.makedirs(checkpoint_dir)
# logs path
logs_dir = "checkpoints/logs/" + args.checkpoint
if not os.path.exists(logs_dir):
os.makedirs(logs_dir)
shutil.rmtree(logs_dir)
log_writer = SummaryWriter(logs_dir)
# dataset and dataloader
if args.data_type == 'real':
data_set = SingleLoader_DGF(noise_dir=args.noise_dir,gt_dir=args.gt_dir,image_size=args.image_size,burst_length=burst_length)
elif args.data_type == "synth":
data_set = SingleLoader_DGF_synth(gt_dir=args.gt_dir,image_size=args.image_size,burst_length=burst_length)
else:
print("Wrong type data")
return
data_loader = DataLoader(
data_set,
batch_size=batch_size,
shuffle=True,
num_workers=num_workers
)
# model here
if args.model_type == "attKPN":
model = Att_KPN(
color=color,
burst_length=burst_length,
blind_est=True,
kernel_size=[5],
sep_conv=False,
channel_att=True,
spatial_att=True,
upMode="bilinear",
core_bias=False
)
elif args.model_type == "attKPN_Wave":
model = Att_KPN_Wavelet(
color=color,
burst_length=1,
blind_est=True,
kernel_size=[5],
sep_conv=False,
channel_att=True,
spatial_att=True,
upMode="bilinear",
core_bias=False
)
elif args.model_type == "attWKPN":
model = Att_Weight_KPN(
color=color,
burst_length=1,
blind_est=True,
kernel_size=[5],
sep_conv=False,
channel_att=True,
spatial_att=True,
upMode="bilinear",
core_bias=False
)
elif args.model_type == "KPN":
model = KPN(
color=color,
burst_length=1,
blind_est=True,
kernel_size=[5],
sep_conv=False,
channel_att=False,
spatial_att=False,
upMode="bilinear",
core_bias=False
)
else:
print(" Model type not valid")
return
if cuda:
model = model.cuda()
if mGPU:
model = nn.DataParallel(model)
model.train()
# loss function here
# loss_func = LossFunc(
# coeff_basic=1.0,
# coeff_anneal=1.0,
# gradient_L1=True,
# alpha=0.9998,
# beta=100.0
# )
loss_func = LossBasic()
loss_func_i = LossAnneal_i()
if args.wavelet_loss:
print("Use wavelet loss")
loss_func2 = WaveletLoss()
# Optimizer here
optimizer = optim.Adam(
model.parameters(),
lr=lr
)
optimizer.zero_grad()
# learning rate scheduler here
scheduler = lr_scheduler.StepLR(optimizer, step_size=lr_step_size, gamma=lr_decay)
average_loss = MovingAverage(save_freq)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
if not restart_train:
try:
checkpoint = load_checkpoint(checkpoint_dir,cuda=device=='cuda',best_or_latest=args.load_type)
start_epoch = checkpoint['epoch']
global_step = checkpoint['global_iter']
best_loss = checkpoint['best_loss']
model.load_state_dict(checkpoint['state_dict'])
optimizer.load_state_dict(checkpoint['optimizer'])
scheduler.load_state_dict(checkpoint['lr_scheduler'])
print('=> loaded checkpoint (epoch {}, global_step {})'.format(start_epoch, global_step))
except:
start_epoch = 0
global_step = 0
best_loss = np.inf
print('=> no checkpoint file to be loaded.')
else:
start_epoch = 0
global_step = 0
best_loss = np.inf
if os.path.exists(checkpoint_dir):
pass
# files = os.listdir(checkpoint_dir)
# for f in files:
# os.remove(os.path.join(checkpoint_dir, f))
else:
os.mkdir(checkpoint_dir)
print('=> training')
for epoch in range(start_epoch, n_epoch):
epoch_start_time = time.time()
# decay the learning rate
# print('='*20, 'lr={}'.format([param['lr'] for param in optimizer.param_groups]), '='*20)
t1 = time.time()
for step, (image_noise_hr,image_noise_lr, image_gt_hr, image_gt_lr) in enumerate(data_loader):
# print(burst_noise.size())
# print(gt.size())
if cuda:
burst_noise = image_noise_lr[:,0:1,:,:,:].cuda()
# gt = image_gt_hr.cuda()
gt = image_gt_lr[:,0,:,:,:].cuda()
# image_noise_hr = image_noise_hr.cuda()
else:
burst_noise = image_noise_lr[:,0:1,:,:,:]
gt = image_gt_lr[:,0,:,:,:]
if color:
b, N, c, h, w = burst_noise.size()
# print(image_noise_lr.size())
feedData = burst_noise.view(b, -1, h, w)
else:
feedData = image_noise_lr
# print('white_level', white_level, white_level.size())
# print("feedData : ",feedData.size())
# print("burst_noise : ",burst_noise.size())
#
pred_i, pred = model(feedData, burst_noise)
#
# loss_basic, loss_anneal = loss_func(pred_i, pred, gt, global_step)
# print(pred.size())
# print(gt.size())
loss_basic = loss_func(pred, gt)
# loss_i =loss_func_i(global_step, pred_i, image_gt_lr)
loss = loss_basic
if args.wavelet_loss:
loss_wave = loss_func2(pred,gt)
# print(loss_wave)
loss = loss_basic + loss_wave
# backward
optimizer.zero_grad()
loss.backward()
optimizer.step()
# update the average loss
average_loss.update(loss)
# global_step
if not color:
pred = pred.unsqueeze(1)
gt = gt.unsqueeze(1)
if global_step %loss_freq ==0:
# calculate PSNR
# print("burst_noise : ",burst_noise.size())
# print("gt : ",gt.size())
# print("feedData : ", feedData.size())
psnr = calculate_psnr(pred, gt)
ssim = calculate_ssim(pred, gt)
# add scalars to tensorboardX
log_writer.add_scalar('loss_basic', loss_basic, global_step)
# log_writer.add_scalar('loss_anneal', loss_anneal, global_step)
log_writer.add_scalar('loss_total', loss, global_step)
log_writer.add_scalar('psnr', psnr, global_step)
log_writer.add_scalar('ssim', ssim, global_step)
# print
print('{:-4d}\t| epoch {:2d}\t| step {:4d}\t| loss_basic: {:.4f}\t|'
' loss: {:.4f}\t| PSNR: {:.2f}dB\t| SSIM: {:.4f}\t| time:{:.2f} seconds.'
.format(global_step, epoch, step, loss_basic, loss, psnr, ssim, time.time()-t1))
t1 = time.time()
if global_step % save_freq == 0:
if average_loss.get_value() < best_loss:
is_best = True
best_loss = average_loss.get_value()
else:
is_best = False
save_dict = {
'epoch': epoch,
'global_iter': global_step,
'state_dict': model.state_dict(),
'best_loss': best_loss,
'optimizer': optimizer.state_dict(),
'lr_scheduler': scheduler.state_dict()
}
save_checkpoint(
save_dict, is_best, checkpoint_dir, global_step, max_keep=10
)
print('Save : {:-4d}\t| epoch {:2d}\t| step {:4d}\t| loss_basic: {:.4f}\t|'
' loss: {:.4f}'
.format(global_step, epoch, step, loss_basic, loss))
global_step += 1
print('Epoch {} is finished, time elapsed {:.2f} seconds.'.format(epoch, time.time()-epoch_start_time))
lr_cur = [param['lr'] for param in optimizer.param_groups]
if lr_cur[0] > 5e-6:
scheduler.step()
else:
for param in optimizer.param_groups:
param['lr'] = 5e-6
def eval(args):
color = True
burst_length = args.burst_length
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
if args.model_type == "attKPN":
model = Att_KPN(
color=color,
burst_length=burst_length,
blind_est=True,
kernel_size=[5],
sep_conv=False,
channel_att=True,
spatial_att=True,
upMode="bilinear",
core_bias=False
)
elif args.model_type == "attKPN_Wave":
model = Att_KPN_Wavelet(
color=color,
burst_length=1,
blind_est=True,
kernel_size=[5],
sep_conv=False,
channel_att=True,
spatial_att=True,
upMode="bilinear",
core_bias=False
)
elif args.model_type == "attWKPN":
model = Att_Weight_KPN(
color=color,
burst_length=1,
blind_est=True,
kernel_size=[5],
sep_conv=False,
channel_att=True,
spatial_att=True,
upMode="bilinear",
core_bias=False
)
elif args.model_type == "KPN":
model = KPN(
color=color,
burst_length=1,
blind_est=True,
kernel_size=[5],
sep_conv=False,
channel_att=False,
spatial_att=False,
upMode="bilinear",
core_bias=False
)
else:
print(" Model type not valid")
return
checkpoint_dir = "checkpoints/" + args.checkpoint
if not os.path.exists(checkpoint_dir) or len(os.listdir(checkpoint_dir)) == 0:
print('There is no any checkpoint file in path:{}'.format(checkpoint_dir))
# load trained model
ckpt = load_checkpoint(checkpoint_dir,cuda=device=='cuda',best_or_latest=args.load_type)
state_dict = ckpt['state_dict']
# if not args.cuda:
new_state_dict = OrderedDict()
for k, v in state_dict.items():
name = k[7:] # remove `module.`
new_state_dict[name] = v
model.load_state_dict(new_state_dict)
# else:
# model.load_state_dict(ckpt['state_dict'])
model.to(device)
print('The model has been loaded from epoch {}, n_iter {}.'.format(ckpt['epoch'], ckpt['global_iter']))
# switch the eval mode
model.eval()
# model= save_dict['state_dict']
trans = transforms.ToPILImage()
torch.manual_seed(0)
all_noisy_imgs = scipy.io.loadmat(args.noise_dir)['siddplus_valid_noisy_srgb']
all_clean_imgs = scipy.io.loadmat(args.gt_dir)['siddplus_valid_gt_srgb']
i_imgs,_,_,_ = all_noisy_imgs.shape
psnrs = []
ssims = []
for i_img in range(i_imgs):
image_noise = transforms.ToTensor()(Image.fromarray(all_noisy_imgs[i_img]))
image_noise_lr,image_noise_hr = load_data(image_noise,burst_length)
burst_noise = image_noise_lr[:, 0:1, :, :, :].to(device)
if color:
b, N, c, h, w = burst_noise.size()
feedData = burst_noise.view(b, -1, h, w)
else:
feedData = burst_noise
# print(feedData.size())
_,pred = model(feedData, burst_noise)
pred = pred.detach().cpu()
# print("Time : ", time.time()-begin)
gt = transforms.ToTensor()(Image.fromarray(all_clean_imgs[i_img]))
image_gt_lr,image_gt_hr = load_data(gt,burst_length)
gt = image_gt_lr[:, 0, :, :, :].to(device)
# print(pred_i.size())
# print(pred[0].size())
psnr_t = calculate_psnr(pred, gt)
ssim_t = calculate_ssim(pred, gt)
psnrs.append(psnr_t)
ssims.append(ssim_t)
print(i_img," UP : PSNR : ", str(psnr_t)," : SSIM : ", str(ssim_t))
if args.save_img != '':
if not os.path.exists(args.save_img):
os.makedirs(args.save_img)
plt.figure(figsize=(15, 15))
plt.imshow(np.array(trans(pred[0])))
plt.title("denoise KPN DGF "+args.model_type, fontsize=25)
image_name = str(i_img)
plt.axis("off")
plt.suptitle(image_name+" UP : PSNR : "+ str(psnr_t)+" : SSIM : "+ str(ssim_t), fontsize=25)
plt.savefig( os.path.join(args.save_img,image_name + "_" + args.checkpoint + '.png'),pad_inches=0)
"""
if args.save_img:
# print(np.array(trans(mf8[0])))
plt.figure(figsize=(30, 9))
plt.subplot(1,3,1)
plt.imshow(np.array(trans(pred[0])))
plt.title("denoise DGF "+args.model_type, fontsize=26)
plt.subplot(1,3,2)
plt.imshow(np.array(trans(gt[0])))
plt.title("gt ", fontsize=26)
plt.subplot(1,3,3)
plt.imshow(np.array(trans(image_noise_hr[0])))
plt.title("noise ", fontsize=26)
plt.axis("off")
plt.suptitle(str(i)+" UP : PSNR : "+ str(psnr_t)+" : SSIM : "+ str(ssim_t), fontsize=26)
plt.savefig("checkpoints/22_DGF_" + args.checkpoint+str(i)+'.png',pad_inches=0)
"""
print(" AVG : PSNR : "+ str(np.mean(psnrs))+" : SSIM : "+ str(np.mean(ssims)))
if __name__ == '__main__':
# argparse
parser = argparse.ArgumentParser(description='parameters for training')
parser.add_argument('--noise_dir','-n', default='/home/dell/Downloads/noise', help='path to noise folder image')
parser.add_argument('--gt_dir', '-g' , default='/home/dell/Downloads/gt', help='path to gt folder image')
parser.add_argument('--data_type', '-dt' , default='real', help='real | synth')
parser.add_argument('--image_size', '-sz' , default=128, type=int, help='size of image')
parser.add_argument('--epoch', '-e' ,default=1000, type=int, help='batch size')
parser.add_argument('--batch_size','-bs' , default=2, type=int, help='batch size')
parser.add_argument('--burst_length', '-b', default=4, type=int, help='batch size')
parser.add_argument('--save_every','-se' , default=200, type=int, help='save_every')
parser.add_argument('--loss_every', '-le' , default=10, type=int, help='loss_every')
parser.add_argument('--restart','-r' , action='store_true', help='Whether to remove all old files and restart the training process')
parser.add_argument('--num_workers', '-nw', default=2, type=int, help='number of workers in data loader')
parser.add_argument('--cuda', '-c', action='store_true', help='whether to train on the GPU')
parser.add_argument('--mGPU', '-mg', action='store_true', help='whether to train on multiple GPUs')
parser.add_argument('--eval', action='store_true', help='whether to work on the evaluation mode')
parser.add_argument('--checkpoint', '-ckpt', type=str, default='kpn',
help='the checkpoint to eval')
parser.add_argument('--color','-cl' , default=True, action='store_true')
parser.add_argument('--model_type','-m' ,default="KPN", help='type of model : KPN, attKPN, attWKPN, attKPN_Wave')
parser.add_argument('--load_type', "-l" ,default="best", type=str, help='Load type best_or_latest ')
parser.add_argument('--wavelet_loss','-wl' , default=False, action='store_true')
parser.add_argument('--bn','-bn' , default=False, action='store_true', help='Use BatchNorm2d')
args = parser.parse_args()
#
if args.eval:
eval(args)
else:
train(args.num_workers,args.cuda, args.restart, args.mGPU)