def main(args):
transform = getTransforms()
data_path = args.input_data
if not os.path.exists(data_path):
print('ERROR: No dataset named {}'.format(data_path))
exit(1)
dataset = EvalDataset(data_path, transform=transform)
dataloader = torch.utils.data.DataLoader(dataset,
batch_size=1,
shuffle=False,
num_workers=1)
with open(args.class_list, 'r') as class_file:
class_names = []
for class_name in class_file.readlines():
if len(class_name.strip()) > 0:
class_names.append(class_name.strip())
model = ResNet(num_layers=18, num_classes=len(class_names)).to(DEVICE)
model = model.eval()
output_dir = os.path.join(data_path, 'out')
os.makedirs(output_dir, exist_ok=True)
model_file = args.model_file
if os.path.exists(model_file):
checkpoint = torch.load(model_file)
if 'state_dict' in checkpoint.keys():
model.load_state_dict(checkpoint['state_dict'], strict=False)
else:
model.load_state_dict(checkpoint, strict=False)
print('=> loaded {}'.format(model_file))
else:
print('model_file "{}" does not exists.'.format(model_file))
exit(1)
font = cv2.FONT_HERSHEY_SIMPLEX
with torch.no_grad():
for data, path in dataloader:
outputs = model(data.to(DEVICE))
_, predicted = torch.max(outputs.data, 1)
predicted = predicted.to('cpu')[0].item()
class_text = class_names[predicted]
print(class_text, path)
image = cv2.imread(path[0], cv2.IMREAD_COLOR)
image = cv2.rectangle(image, (0, 0), (150, 25), (255, 255, 255),
-1)
image = cv2.rectangle(image, (0, 0), (150, 25), (255, 0, 0), 2)
cv2.putText(image, class_text, (5, 15), font, 0.5, (
255,
0,
), 1, cv2.LINE_AA)
cv2.imwrite(os.path.join(output_dir, os.path.basename(path[0])),
image)
criterion = nn.MSELoss()
optimizer = optim.Adam([{
'params': model.first_part.parameters()
}, {
'params': model.last_part.parameters(),
'lr': args.lr * 0.1
}],
lr=args.lr)
train_dataset = TrainDataset(args.train_file)
train_dataloader = DataLoader(dataset=train_dataset,
batch_size=args.batch_size,
shuffle=True,
num_workers=args.num_workers,
pin_memory=True)
eval_dataset = EvalDataset(args.eval_file)
eval_dataloader = DataLoader(dataset=eval_dataset, batch_size=1)
best_weights = copy.deepcopy(model.state_dict())
best_epoch = 0
best_psnr = 0.0
for epoch in range(args.num_epochs):
for param_group in optimizer.param_groups:
param_group['lr'] = args.lr * (0.1**(epoch //
int(args.num_epochs * 0.8)))
model.train()
epoch_losses = AverageMeter()
with tqdm(total=(len(train_dataset) -
def main():
# Load dataset
print('Loading dataset ...\n')
#dataset_train = TrainDataset(1000, opt.batchSize, train=True)
#dataset_val = ValDataset(train=False)
dataset_train = TrainDataset('train_BSD500.h5', opt.patch_size, int(opt.upscale_factor[0]))
print(len(dataset_train))
dataset_val = EvalDataset('test_BSD500.h5')
loader_train = DataLoader(dataset=dataset_train, num_workers=1, batch_size=opt.batchSize, shuffle=True)
loader_val = DataLoader(dataset=dataset_val, batch_size=1)
print("# of training samples: %d\n" % int(len(dataset_train)))
# Build model
netG = make_model(opt)
print('# generator parameters:', sum(param.numel() for param in netG.parameters()))
#netG.apply(weights_init_kaiming)
# content_criterion = nn.MSELoss()
#feature_extractor = FeatureExtractor(torchvision.models.vgg19(pretrained=True))
content_criterion = nn.L1Loss()
# Move to GPU
if torch.cuda.is_available():
netG.cuda()
content_criterion.cuda()
optim_rdn = optim.Adam(netG.parameters(), lr=opt.generatorLR)
# Optimizer
# training
writer = SummaryWriter(opt.outf)
step = 0
# noiseL_B=[0,55] # ingnored when opt.mode=='S'
# Generator Pretraining(Using MSE Loss)
for epoch in range(opt.epochs):
mean_generator_content_loss = 0.0
mean_generator_PSNRs = 0.0
mean_generator_SSIMs = 0.0
for param_group in optim_rdn.param_groups:
param_group['lr'] = opt.generatorLR * (0.1 ** (epoch // int(opt.epochs * 0.8)))
for i, (lrimg, hrimg) in enumerate(loader_train):
# adding noise
for j in range(opt.batchSize):
#noise = torch.FloatTensor(lrimg[j].size()).normal_(mean=0.0, std=opt.noiseL/255.)
#lrimg[j] = lrimg[j] + noise
lrimg[j] = lrimg[j]
# Generate real and fake inputs
if opt.cuda:
high_res_real = Variable(hrimg.cuda())
high_res_fake = netG(Variable(lrimg).cuda())
else:
high_res_real = Variable(hrimg)
high_res_fake = netG(Variable(lrimg))
######### Train generator #########
netG.zero_grad()
generator_content_loss = content_criterion(high_res_fake, high_res_real)
mean_generator_content_loss += generator_content_loss.data
generator_content_loss.backward()
optim_rdn.step()
######### Status and display #########
sys.stdout.write('\r[%d/%d][%d/%d] Generator_MSE_Loss: %.4f' % (epoch, opt.epochs, i, len(loader_train), generator_content_loss.data))
# visualizer.show(low_res, high_res_real.cpu().data, high_res_fake.cpu().data)
out_train = torch.clamp(high_res_fake, 0., 1.)
psnr_train, ssim_train = batch_PSNR(out_train, high_res_real, scale=3, data_range=255.0)
mean_generator_PSNRs += psnr_train
mean_generator_SSIMs += ssim_train
if step % 10 == 0:
# Log the scalar values
writer.add_scalar('generator_content_loss', generator_content_loss.item(), step)
#writer.add_scalar('PSNR on training data', psnr_train, step)
#writer.add_scalar('SSIM on training data', ssim_train, step)
step += 1
# sys.stdout.write('\r[%d/%d][%d/%d] PSNR: %.4f, SSIM:%.4f' % (epoch, 2, i, len(loader_train), psnr_train, ssim_train))
psnr_avg_train = mean_generator_PSNRs/len(loader_train)
ssim_avg_train = mean_generator_SSIMs/len(loader_train)
sys.stdout.write('\r[%d/%d][%d/%d] Generator_MSE_Loss: %.4f\n' % (epoch, opt.epochs, i, len(loader_train), mean_generator_content_loss/len(loader_train)))
print("\n[epoch %d] PSNR_train: %.4f" % (epoch+1, psnr_avg_train))
print("\n[epoch %d] SSIM_train: %.4f" % (epoch+1, ssim_avg_train))
writer.add_scalar('PSNR on training data', psnr_avg_train, epoch)
writer.add_scalar('SSIM on training data', ssim_avg_train, epoch)
#log_value('generator_mse_loss', mean_generator_content_loss/len(dataloader), epoch)
torch.save(netG.state_dict(), '%s/model/rdn_final_%d.pth'%(opt.outf,epoch))
## the end of each epoch
# netG.eval()
# validate
psnr_val = 0
ssim_val = 0.0
val_images = []
num = 0
numofex=opt.noiseL
for index, (lrimg_val, hrimg_val) in enumerate(loader_val):
#lrimg_val, hrimg_val = dataset_val[k]
#noise = torch.FloatTensor(lrimg_val.size()).normal_(mean=0, std=opt.val_noiseL/255.)
#lrimgn_val = lrimg_val + noise
lrimgn_val = lrimg_val
#lrimgn_val = torch.Tensor(np.expand_dims(lrimgn_val, axis=0))
#hrimg_val = torch.Tensor(np.expand_dims(hrimg_val, axis=0))
hrimg_val, lrimg_val = Variable(hrimg_val.cuda(), volatile=True), Variable(lrimgn_val.cuda(), volatile=True)
out_val = netG(lrimg_val)
psnr_val_e, ssim_val_e = batch_PSNR(out_val, hrimg_val, scale=3, data_range=255.0)
psnr_val += psnr_val_e
ssim_val += ssim_val_e
hrimg_val = np.transpose(hrimg_val[0].detach().cpu().numpy(), (1,2,0))
out_val = np.transpose(out_val[0].detach().cpu().numpy(),(1,2,0))
if num<5:
num+=1
# hrimg_val = hrimg_val[int(hrimg_val.shape[0] / 2) - 160:int(hrimg_val.shape[0] / 2) + 160,
# int(hrimg_val.shape[1] / 2) - 160:int(hrimg_val.shape[1] / 2) + 160]
# out_val = out_val[int(out_val.shape[0] / 2) - 160:int(out_val.shape[0] / 2) + 160,
# int(out_val.shape[1] / 2) - 160:int(out_val.shape[1] / 2) + 160]
val_images.extend([hrimg_val,out_val])
output_image=make_grid(val_images,nrow=2,nline=1)
if not os.path.exists('%s/training_results/%d/' % (opt.outf, numofex)):
os.makedirs('%s/training_results/%d/' % (opt.outf, numofex))
save_result(output_image,path='%s/training_results/%d/epoch%d.png' % (opt.outf,numofex,epoch))
psnr_val /= len(dataset_val)
ssim_val /= len(dataset_val)
print("\n[epoch %d] PSNR_val: %.4f" % (epoch+1, psnr_val))
print("\n[epoch %d] SSIM_val: %.4f" % (epoch+1, ssim_val))
writer.add_scalar('PSNR on validation data', psnr_val, epoch)
writer.add_scalar('SSIM on validation data', ssim_val, epoch)
def train(self, args):
with open(args.train_list, 'r') as train_list_file:
self.train_list = [line.strip() for line in train_list_file.readlines()]
self.eval_file = args.eval_file
self.num_train_sentences = args.num_train_sentences
self.batch_size = args.batch_size
self.lr = args.lr
self.max_epoch = args.max_epoch
self.model_path = args.model_path
self.log_path = args.log_path
self.fig_path = args.fig_path
self.eval_plot_num = args.eval_plot_num
self.eval_steps = args.eval_steps
self.resume_model = args.resume_model
self.wav_path = args.wav_path
self.train_wav_path = args.train_wav_path
self.tool_path = args.tool_path
# create a training dataset and an evaluation dataset
trainSet = TrainingDataset(self.train_list,
frame_size=self.frame_size,
frame_shift=self.frame_shift)
evalSet = EvalDataset(self.eval_file,
self.num_test_sentences)
# trainSet = evalSet
# create data loaders for training and evaluation
train_loader = DataLoader(trainSet,
batch_size=self.batch_size,
shuffle=True,
num_workers=16,
collate_fn=TrainCollate())
eval_loader = DataLoader(evalSet,
batch_size=1,
shuffle=False,
num_workers=4,
collate_fn=EvalCollate())
# create a network
print('model', self.model_name)
net = Net(device=self.device, L=self.frame_size, width=self.width)
# net = torch.nn.DataParallel(net)
net.to(self.device)
print('Number of learnable parameters: %d' % numParams(net))
print(net)
criterion = mse_loss()
criterion1 = stftm_loss(device=self.device)
optimizer = torch.optim.Adam(net.parameters(), lr=self.lr)
self.lr_list = [0.0002] * 3 + [0.0001] * 6 + [0.00005] * 3 + [0.00001] * 3
if self.resume_model:
print('Resume model from "%s"' % self.resume_model)
checkpoint = Checkpoint()
checkpoint.load(self.resume_model)
start_epoch = checkpoint.start_epoch
start_iter = checkpoint.start_iter
best_loss = checkpoint.best_loss
net.load_state_dict(checkpoint.state_dict)
optimizer.load_state_dict(checkpoint.optimizer)
else:
print('Training from scratch.')
start_epoch = 0
start_iter = 0
best_loss = np.inf
num_train_batches = self.num_train_sentences // self.batch_size
total_train_batch = self.max_epoch * num_train_batches
print('num_train_sentences', self.num_train_sentences)
print('batches_per_epoch', num_train_batches)
print('total_train_batch', total_train_batch)
print('batch_size', self.batch_size)
print('model_name', self.model_name)
batch_timings = 0.
counter = int(start_epoch * num_train_batches + start_iter)
counter1 = 0
print('counter', counter)
ttime = 0.
cnt = 0.
iteration = 0
print('best_loss', best_loss)
for epoch in range(start_epoch, self.max_epoch):
accu_train_loss = 0.0
net.train()
for param_group in optimizer.param_groups:
param_group['lr'] = self.lr_list[epoch]
start = timeit.default_timer()
for i, (features, labels, nframes, feat_size, label_size, get_filename) in enumerate(
train_loader): # features:torch.Size([4, 1, 250, 512])
iteration += 1
labels_cpu = labels
i += start_iter
features, labels = features.to(self.device), labels.to(self.device) # torch.Size([4, 1, 250, 512])
loss_mask = compLossMask(labels, nframes=nframes)
# forward + backward + optimize
optimizer.zero_grad()
outputs = net(features) # torch.Size([4, 1, 64256])
feature_maker = Fbank(sample_rate=16000, n_fft=400, n_mels=40)
loss_fbank = 0
for t in range(len(get_filename)):
reader = h5py.File(get_filename[t], 'r')
feature_asr = reader['noisy_raw'][:]
label_asr = reader['clean_raw'][:]
feat_asr_size = int(feat_size[t][0].item())
label_asr_size = int(label_size[t][0].item())
output_asr = self.train_asr_forward(feature_asr, net)
est_output_asr = output_asr[:feat_asr_size]
ideal_labels_asr = label_asr
# 保存train的wav
est_path = os.path.join(self.train_wav_path, '{}_est.wav'.format(t + 1))
ideal_path = os.path.join(self.train_wav_path, '{}_ideal.wav'.format(t + 1))
sf.write(est_path, normalize_wav(est_output_asr)[0], self.srate)
sf.write(ideal_path, normalize_wav(ideal_labels_asr)[0], self.srate)
# read wav
est_sig = sb.dataio.dataio.read_audio(est_path).unsqueeze(axis=0).to(self.device)
ideal_sig = sb.dataio.dataio.read_audio(ideal_path).unsqueeze(axis=0).to(self.device)
est_sig_feats = feature_maker(est_sig)
ideal_sig_feats = feature_maker(ideal_sig)
# fbank_loss
loss_fbank += F.mse_loss(est_sig_feats, ideal_sig_feats, True)
loss_fbank /= 100 * len(get_filename)
# print(loss_fbank)
# loss_fbank = 1 / (1 + math.exp(loss_fbank))
outputs = outputs[:, :, :labels.shape[-1]]
loss1 = criterion(outputs, labels, loss_mask, nframes)
loss2 = criterion1(outputs, labels, loss_mask, nframes)
# print(loss1)
# print(loss2)
# loss = 0.8 * loss1 + 0.2 * loss2
loss = 0.4 * loss1 + 0.1 * loss2 + 0.5 * loss_fbank
loss.backward()
optimizer.step()
# calculate losses
running_loss = loss.data.item()
accu_train_loss += running_loss
# train-loss show
summary.add_scalar('Train Loss', accu_train_loss, iteration)
cnt += 1.
counter += 1
counter1 += 1
del loss, loss_fbank, loss1, loss2, outputs, loss_mask, features, labels
end = timeit.default_timer()
curr_time = end - start
ttime += curr_time
mtime = ttime / counter1
print(
'iter = {}/{}, epoch = {}/{}, loss = {:.5f}, time/batch = {:.5f}, mtime/batch = {:.5f}'.format(
i + 1,
num_train_batches, epoch + 1, self.max_epoch, running_loss, curr_time, mtime))
start = timeit.default_timer()
if (i + 1) % self.eval_steps == 0:
start = timeit.default_timer()
avg_train_loss = accu_train_loss / cnt
avg_eval_loss = self.validate(net, eval_loader, iteration)
net.train()
print('Epoch [%d/%d], Iter [%d/%d] ( TrainLoss: %.4f | EvalLoss: %.4f )' % (
epoch + 1, self.max_epoch, i + 1, self.num_train_sentences // self.batch_size,
avg_train_loss,
avg_eval_loss))
is_best = True if avg_eval_loss < best_loss else False
best_loss = avg_eval_loss if is_best else best_loss
checkpoint = Checkpoint(epoch, i, avg_train_loss, avg_eval_loss, best_loss, net.state_dict(),
optimizer.state_dict())
model_name = self.model_name + '_latest.model'
best_model = self.model_name + '_best.model'
checkpoint.save(is_best, os.path.join(self.model_path, model_name),
os.path.join(self.model_path, best_model))
logging(self.log_path, self.model_name + '_loss_log.txt', checkpoint, self.eval_steps)
# metric_logging(self.log_path, self.model_name +'_metric_log.txt', epoch+1, [avg_st, avg_sn, avg_pe])
accu_train_loss = 0.0
cnt = 0.
net.train()
if (i + 1) % num_train_batches == 0:
break
avg_st, avg_sn, avg_pe = self.validate_with_metrics(net, eval_loader)
net.train()
print('#' * 50)
print('')
print('After {} epoch the performance on validation score is a s follows:'.format(epoch + 1))
print('')
print('STOI: {:.4f}'.format(avg_st))
print('SNR: {:.4f}'.format(avg_sn))
print('PESQ: {:.4f}'.format(avg_pe))
for param_group in optimizer.param_groups:
print('learning_rate', param_group['lr'])
print('')
print('#' * 50)
checkpoint = Checkpoint(epoch, 0, None, None, best_loss, net.state_dict(), optimizer.state_dict())
checkpoint.save(False, os.path.join(self.model_path, self.model_name + '-{}.model'.format(epoch + 1)),
os.path.join(self.model_path, best_model))
metric_logging(self.log_path, self.model_name + '_metric_log.txt', epoch, [avg_st, avg_sn, avg_pe])
start_iter = 0.
def test(self, args):
with open(args.test_list, 'r') as test_list_file:
self.test_list = [line.strip() for line in test_list_file.readlines()]
self.model_name = args.model_name
self.model_file = args.model_file
self.test_mixture_path = args.test_mixture_path
self.prediction_path = args.prediction_path
# create a network
print('model', self.model_name)
net = Net(device=self.device, L=self.frame_size, width=self.width)
# net = torch.nn.DataParallel(net)
net.to(self.device)
print('Number of learnable parameters: %d' % numParams(net))
print(net)
# loss and optimizer
criterion = mse_loss()
net.eval()
print('Load model from "%s"' % self.model_file)
checkpoint = Checkpoint()
checkpoint.load(self.model_file)
net.load_state_dict(checkpoint.state_dict)
with torch.no_grad():
for i in range(len(self.test_list)):
# read the mixture for resynthesis
filename_input = self.test_list[i].split('/')[-1]
start1 = timeit.default_timer()
print('{}/{}, Started working on {}.'.format(i + 1, len(self.test_list), self.test_list[i]))
print('')
filename_mix = filename_input.replace('.samp', '_mix.dat')
filename_s_ideal = filename_input.replace('.samp', '_s_ideal.dat')
filename_s_est = filename_input.replace('.samp', '_s_est.dat')
# print(filename_mix)
# sys.exit()
f_mix = h5py.File(os.path.join(self.test_mixture_path, filename_mix), 'r')
f_s_ideal = h5py.File(os.path.join(self.prediction_path, filename_s_ideal), 'w')
f_s_est = h5py.File(os.path.join(self.prediction_path, filename_s_est), 'w')
# create a test dataset
testSet = EvalDataset(os.path.join(self.test_mixture_path, self.test_list[i]),
self.num_test_sentences)
# create a data loader for test
test_loader = DataLoader(testSet,
batch_size=1,
shuffle=False,
num_workers=2,
collate_fn=EvalCollate())
# print '\n[%d/%d] Predict on %s' % (i+1, len(self.test_list), self.test_list[i])
accu_test_loss = 0.0
accu_test_nframes = 0
ttime = 0.
mtime = 0.
cnt = 0.
for k, (mix_raw, cln_raw) in enumerate(test_loader):
start = timeit.default_timer()
est_s = self.eval_forward(mix_raw, net)
est_s = est_s[:mix_raw.size]
mix = f_mix[str(k)][:]
ideal_s = cln_raw
f_s_ideal.create_dataset(str(k), data=ideal_s.astype(np.float32), chunks=True)
f_s_est.create_dataset(str(k), data=est_s.astype(np.float32), chunks=True)
# compute eval_loss
test_loss = np.mean((est_s - ideal_s) ** 2)
accu_test_loss += test_loss
cnt += 1
end = timeit.default_timer()
curr_time = end - start
ttime += curr_time
mtime = ttime / cnt
mtime = (mtime * (k) + (end - start)) / (k + 1)
print('{}/{}, test_loss = {:.4f}, time/utterance = {:.4f}, '
'mtime/utternace = {:.4f}'.format(k + 1, self.num_test_sentences, test_loss, curr_time,
mtime))
avg_test_loss = accu_test_loss / cnt
# bar.update(k,test_loss=avg_test_loss)
# bar.finish()
end1 = timeit.default_timer()
print('********** Finisehe working on {}. time taken = {:.4f} **********'.format(filename_input,
end1 - start1))
print('')
f_mix.close()
f_s_est.close()
f_s_ideal.close()
torch.manual_seed(args.seed)
model = CSRCNN(scale_factor=args.scale).to(device)
criterion =CharbonnierLoss(delta=0.0001)#CharbonnierLoss(delta=0.0001)#HuberLoss(delta=0.9)#nn.L1Loss()# nn.MSELoss()
optimizer = optim.Adam([
{'params': model.first_part.parameters(), 'lr': args.lr * 0.1},
# {'params': model.mid_part.parameters(), 'lr': args.lr * 0.1},
{'params': model.last_part.parameters(), 'lr': args.lr * 0.1}
], lr=args.lr)
train_dataset = TrainDataset(args.train_file)
train_dataloader = DataLoader(dataset=train_dataset,
batch_size=args.batch_size,
shuffle=True,
num_workers=args.num_workers,
pin_memory=True,
drop_last=False)#drop_last=False
eval_dataset = EvalDataset(args.eval_file)
eval_dataloader = DataLoader(dataset=eval_dataset, batch_size=1)
eval_dataset1 = EvalDataset(args.eval_file1)
eval_dataloader1 = DataLoader(dataset=eval_dataset1, batch_size=1)
eval_dataset2 = EvalDataset(args.eval_file2)
eval_dataloader2 = DataLoader(dataset=eval_dataset2, batch_size=1)
best_weights = copy.deepcopy(model.state_dict())
best_epoch = 0
best_psnr = 0.0
epoch_num=range(1,args.num_epochs+1)
psrn=[]
loss_num=[]
psrn_Set14=[]
psrn_BSD200=[]
def main():
# Load dataset
print('Loading dataset ...\n')
#dataset_train = Dataset(train=True)
#dataset_val = Dataset(train=False)
dataset_train = TrainDataset('train_DIV_new.h5', opt.patch_size,
int(opt.upscale_factor[0]))
dataset_val = EvalDataset('test_DIV.h5')
loader_train = DataLoader(dataset=dataset_train,
num_workers=1,
batch_size=opt.batchSize,
shuffle=True)
loader_val = DataLoader(dataset=dataset_val, batch_size=1)
print("# of training samples: %d\n" % int(len(dataset_train)))
# Build model
netG = Generator_RDN(opt)
print('# generator parameters:',
sum(param.numel() for param in netG.parameters()))
netD = Discriminator()
print('# discriminator parameters:',
sum(param.numel() for param in netD.parameters()))
# net.apply(weights_init_kaiming)
# content_criterion = nn.MSELoss()
# feature_extractor = FeatureExtractor(torchvision.models.vgg19(pretrained=True))
content_criterion = nn.L1Loss()
adversarial_criterion = nn.BCELoss()
ones_const = Variable(torch.ones(opt.batchSize, 1))
# Move to GPU
if torch.cuda.is_available():
netG.cuda()
netD.cuda()
content_criterion.cuda()
adversarial_criterion.cuda()
ones_const = ones_const.cuda()
# feature_extractor.cuda()
optim_rdn = optim.Adam(netG.parameters(), lr=1e-4)
# Optimizer
# training
writer = SummaryWriter(opt.outf)
step = 0
# noiseL_B=[0,55] # ingnored when opt.mode=='S'
# Generator Pretraining(Using MSE Loss)
for epoch in range(10):
mean_generator_content_loss = 0.0
for i, (lrimg, hrimg) in enumerate(loader_train):
# adding noise
#print(lrimg[-1].shape)
#print(hrimg[-1].shape)
#cv2.imshow('win1',lrimg[-1].detach().numpy().transpose((1,2,0)))
#cv2.imshow('win2',hrimg[-1].detach().numpy().transpose((1,2,0)))
#cv2.waitKey(0)
for j in range(opt.batchSize):
noise = torch.FloatTensor(lrimg[j].size()).normal_(
mean=0.0, std=opt.noiseL / 255.)
#lrimg[j] = lrimg[j] + noise
lrimg[j] = lrimg[j]
# Generate real and fake inputs
if opt.cuda:
high_res_real = Variable(hrimg.cuda())
high_res_fake = netG(Variable(lrimg).cuda())
else:
high_res_real = Variable(hrimg)
high_res_fake = netG(Variable(lrimg))
######### Train generator #########
netG.zero_grad()
generator_content_loss = content_criterion(high_res_fake,
high_res_real)
mean_generator_content_loss += generator_content_loss.data
generator_content_loss.backward()
optim_rdn.step()
######### Status and display #########
# sys.stdout.write('\r[%d/%d][%d/%d] Generator_MSE_Loss: %.4f' % (epoch, 2, i, len(loader_train), generator_content_loss.data))
# visualizer.show(low_res, high_res_real.cpu().data, high_res_fake.cpu().data)
out_train = torch.clamp(high_res_fake, 0., 1.)
psnr_train, ssim_train = batch_PSNR(out_train,
high_res_real,
scale=3.0,
data_range=1.)
if step % 10 == 0:
# Log the scalar values
writer.add_scalar('generator_content_loss',
generator_content_loss.item(), step)
writer.add_scalar('PSNR on training data', psnr_train, step)
step += 1
# sys.stdout.write('\r[%d/%d][%d/%d] PSNR: %.4f, SSIM:%.4f' % (epoch, 2, i, len(loader_train), psnr_train, ssim_train))
sys.stdout.write('\r[%d/%d][%d/%d] Generator_MSE_Loss: %.4f\n' %
(epoch, 2, i, len(loader_train),
mean_generator_content_loss / len(loader_train)))
#log_value('generator_mse_loss', mean_generator_content_loss/len(dataloader), epoch)
# Do checkpointing
torch.save(netG.state_dict(), '%s/model/generator_pretrain.pth' % opt.outf)
#SRGAN-RDN training
optim_generator = optim.Adam(netG.parameters(), lr=opt.generatorLR)
optim_discriminator = optim.Adam(netD.parameters(), lr=opt.discriminatorLR)
scheduler_dis = torch.optim.lr_scheduler.StepLR(optim_discriminator, 50,
0.1)
scheduler_gen = torch.optim.lr_scheduler.StepLR(optim_generator, 50, 0.1)
print('SRGAN training')
step_new = 0
for epoch in range(opt.epochs):
mean_generator_content_loss = 0.0
mean_generator_adversarial_loss = 0.0
mean_generator_total_loss = 0.0
mean_discriminator_loss = 0.0
netG.train()
scheduler_gen.step()
scheduler_dis.step()
for i, (lrimg, hrimg) in enumerate(loader_train):
#print(lrimg[-1].shape)
#print(hrimg[-1].shape)
#cv2.imshow('win1',lrimg[-1].detach().numpy().transpose((1,2,0)))
#cv2.imshow('win2',hrimg[-1].detach().numpy().transpose((1,2,0)))
#cv2.waitKey(0)
for j in range(opt.batchSize):
noise = torch.FloatTensor(lrimg[j].size()).normal_(
mean=0, std=opt.noiseL / 255.)
#lrimg[j] = lrimg[j] + noise
lrimg[j] = lrimg[j]
# Generate real and fake inputs
if opt.cuda:
high_res_real = Variable(hrimg.cuda())
high_res_fake = netG(Variable(lrimg).cuda())
target_real = Variable(
torch.rand(opt.batchSize, 1) * 0.5 + 0.7).cuda()
target_fake = Variable(torch.rand(opt.batchSize, 1) *
0.3).cuda()
else:
high_res_real = Variable(hrimg)
high_res_fake = netG(Variable(lrimg))
target_real = Variable(
torch.rand(opt.batchSize, 1) * 0.5 + 0.7)
target_fake = Variable(torch.rand(opt.batchSize, 1) * 0.3)
######### Train discriminator #########
netD.zero_grad()
discriminator_loss = adversarial_criterion(netD(high_res_real), target_real) + \
adversarial_criterion(netD(Variable(high_res_fake.data)), target_fake)
mean_discriminator_loss += discriminator_loss.data
discriminator_loss.backward()
optim_discriminator.step()
######### Train generator #########
netG.zero_grad()
#real_features = Variable(feature_extractor(high_res_real).data)
#fake_features = feature_extractor(high_res_fake)
generator_content_loss = content_criterion(
high_res_fake, high_res_real
) #+ 0.006*content_criterion(fake_features, real_features)
mean_generator_content_loss += generator_content_loss.data
generator_adversarial_loss = adversarial_criterion(
netD(high_res_fake), ones_const)
mean_generator_adversarial_loss += generator_adversarial_loss.data
generator_total_loss = generator_content_loss + 1e-3 * generator_adversarial_loss
mean_generator_total_loss += generator_total_loss.data
generator_total_loss.backward()
optim_generator.step()
######### Status and display #########
sys.stdout.write(
'\r[%d/%d][%d/%d] Discriminator_Loss: %.4f Generator_Loss (Content/Advers/Total): %.4f/%.4f/%.4f'
% (epoch, opt.epochs, i, len(loader_train),
discriminator_loss.data, generator_content_loss.data,
generator_adversarial_loss.data, generator_total_loss.data))
# visualizer.show(low_res, high_res_real.cpu().data, high_res_fake.cpu().data)
out_train = torch.clamp(high_res_fake, 0., 1.)
psnr_train, ssim_train = batch_PSNR(out_train,
high_res_real,
scale=3.0,
data_range=1.)
if step_new % 10 == 0:
# Log the scalar values
writer.add_scalar('generator_content_loss',
generator_content_loss.item(), step)
writer.add_scalar('PSNR on training data', psnr_train, step)
writer.add_scalar('discriminator_loss',
discriminator_loss.data, step_new)
writer.add_scalar('generator_adversarial_loss',
generator_adversarial_loss.item(), step_new)
writer.add_scalar('generator_total_loss', generator_total_loss,
step_new)
step += 1
step_new += 1
sys.stdout.write(
'\r[%d/%d][%d/%d] Discriminator_Loss: %.4f Generator_Loss (Content/Advers/Total): %.4f/%.4f/%.4f\n'
%
(epoch, opt.epochs, i, len(loader_train), mean_discriminator_loss /
len(loader_train), mean_generator_content_loss /
len(loader_train), mean_generator_adversarial_loss /
len(loader_train), mean_generator_total_loss / len(loader_train)))
# Do checkpointing
torch.save(netG.state_dict(),
'%s/model/generator_final_%d.pth' % (opt.outf, epoch))
torch.save(netD.state_dict(),
'%s/model/discriminator_final%d.pth' % (opt.outf, epoch))
## the end of each epoch
netG.eval()
# validate
psnr_val = 0
ssim_val = 0.0
val_images = []
num = 0
numofex = opt.noiseL
for index, (lrimg_val, hrimg_val) in enumerate(loader_val):
#lrimg_val, hrimg_val = dataset_val[k]
noise = torch.FloatTensor(lrimg_val.size()).normal_(
mean=0, std=opt.val_noiseL / 255.)
#lrimgn_val = lrimg_val + noise
lrimgn_val = lrimg_val
#lrimgn_val = torch.Tensor(np.expand_dims(lrimgn_val, axis=0))
#hrimg_val = torch.Tensor(np.expand_dims(hrimg_val, axis=0))
#lrimg_val = lrimg_val + noise
hrimg_val, lrimgn_val = Variable(
hrimg_val.cuda(), volatile=True), Variable(lrimgn_val.cuda(),
volatile=True)
#print(lrimgn_val[-1].shape)
#print(hrimg_val[-1].shape)
#cv2.imshow('win1', lrimgn_val[-1].detach().cpu().numpy().transpose((1,2,0)))
#cv2.imshow('win2', hrimg_val[-1].detach().cpu().numpy().transpose((1,2,0)))
#cv2.waitKey(0)
out_val = netG(lrimgn_val)
psnr_val_e, ssim_val_e = batch_PSNR(out_val,
hrimg_val,
scale=3.0,
data_range=1.)
psnr_val += psnr_val_e
ssim_val += ssim_val_e
hrimg_val = np.transpose(hrimg_val[0].detach().cpu().numpy(),
(1, 2, 0))
out_val = np.transpose(out_val[0].detach().cpu().numpy(),
(1, 2, 0))
if num < 5:
num += 1
# hrimg_val = hrimg_val[int(hrimg_val.shape[0] / 2) - 160:int(hrimg_val.shape[0] / 2) + 160,
# int(hrimg_val.shape[1] / 2) - 160:int(hrimg_val.shape[1] / 2) + 160]
# out_val = out_val[int(out_val.shape[0] / 2) - 160:int(out_val.shape[0] / 2) + 160,
# int(out_val.shape[1] / 2) - 160:int(out_val.shape[1] / 2) + 160]
val_images.extend([hrimg_val, out_val])
output_image = make_grid(val_images, nrow=2, nline=1)
if not os.path.exists('%s/training_results/%d/' % (opt.outf, numofex)):
os.makedirs('%s/training_results/%d/' % (opt.outf, numofex))
save_result(output_image,
path='%s/training_results/%d/epoch%d.png' %
(opt.outf, numofex, epoch))
psnr_val /= len(dataset_val)
ssim_val /= len(dataset_val)
print("\n[epoch %d] PSNR_val: %.4f" % (epoch + 1, psnr_val))
print("\n[epoch %d] SSIM_val: %.4f" % (epoch + 1, ssim_val))
writer.add_scalar('PSNR on validation data', psnr_val, epoch)
writer.add_scalar('SSIM on validation data', ssim_val, epoch)
# Load model checkpoint that is to be evaluated
checkpoint = torch.load(checkpoint)
model = checkpoint['model']
model = model.to(device)
# Switch to eval mode
model.eval()
# Load test data
test_dataset = PascalVOCDataset(data_folder,
split='test',
keep_difficult=keep_difficult)
test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=batch_size, shuffle=False,
collate_fn=test_dataset.collate_fn, num_workers=workers, pin_memory=True)
eval_dataset = EvalDataset()
eval_loader = torch.utils.data.DataLoader(eval_dataset, batch_size = 1, shuffle=False,
collate_fn = eval_dataset.collate_fn, num_workers = 1, pin_memory=True)
def evaluate_new(loader, model):
model.eval()
with torch.no_grad():
for i, images in enumerate(tqdm(eval_loader, desc='Evaluating')):
images = images.to(device) # (N, 3, 300, 300)
predicted_locs, predicted_scores = model(images)
det_boxes_batch, det_labels_batch, det_scores_batch = model.detect_objects(predicted_locs, predicted_scores,
min_score=0.01, max_overlap=0.45,
top_k=200)
print(i, images)
print(det_boxes_batch, det_labels_batch, det_scores_batch)
pth_path = './checkpoint/' + str(network) + '/x' + str(
scale) + '/best.pth'
print('Loading weights:', pth_path)
checkpoint = torch.load(pth_path)
model.load_state_dict(checkpoint)
# model_dict = model.state_dict()
# pretrained_dict = checkpoint
# pretrained_dict = {k: v for k, v in pretrained_dict.items() if k in model_dict}
# model_dict.update(pretrained_dict)
# model.load_state_dict(model_dict)
eval_file_ = "./h5file_" + datasetfortest + "_x" + str(
scale) + "_test"
eval_dataset = EvalDataset(eval_file_)
eval_dataloader = DataLoader(dataset=eval_dataset,
batch_size=1)
model.eval()
epoch_psnr = AverageMeter()
epoch_ssim = AverageMeter()
for data in eval_dataloader:
inputs, labels = data
if network == "SRCNN":
import torch.nn.functional as F
inputs = F.interpolate(inputs,
scale_factor=opt.scale,
mode='bilinear')
def train(self, args):
with open(args.train_list, 'r') as train_list_file:
self.train_list = [
line.strip() for line in train_list_file.readlines()
]
self.eval_file = args.eval_file
self.num_train_sentences = args.num_train_sentences
self.batch_size = args.batch_size
self.lr = args.lr
self.max_epoch = args.max_epoch
self.model_path = args.model_path
self.log_path = args.log_path
self.fig_path = args.fig_path
self.eval_plot_num = args.eval_plot_num
self.eval_steps = args.eval_steps
self.resume_model = args.resume_model
self.wav_path = args.wav_path
self.tool_path = args.tool_path
# create a training dataset and an evaluation dataset
trainSet = TrainingDataset(self.train_list,
frame_size=self.frame_size,
frame_shift=self.frame_shift)
evalSet = EvalDataset(self.eval_file, self.num_test_sentences)
#trainSet = evalSet
# create data loaders for training and evaluation
train_loader = DataLoader(trainSet,
batch_size=self.batch_size,
shuffle=True,
num_workers=16,
collate_fn=TrainCollate())
eval_loader = DataLoader(evalSet,
batch_size=1,
shuffle=False,
num_workers=4,
collate_fn=EvalCollate())
# create a network
print('model', self.model_name)
net = Net(device=self.device, L=self.frame_size, width=self.width)
#net = torch.nn.DataParallel(net)
net.to(self.device)
print('Number of learnable parameters: %d' % numParams(net))
print(net)
criterion = mse_loss()
criterion1 = stftm_loss(device=self.device)
optimizer = torch.optim.Adam(net.parameters(), lr=self.lr)
self.lr_list = [0.0002] * 3 + [0.0001] * 6 + [0.00005] * 3 + [0.00001
] * 3
if self.resume_model:
print('Resume model from "%s"' % self.resume_model)
checkpoint = Checkpoint()
checkpoint.load(self.resume_model)
start_epoch = checkpoint.start_epoch
start_iter = checkpoint.start_iter
best_loss = checkpoint.best_loss
net.load_state_dict(checkpoint.state_dict)
optimizer.load_state_dict(checkpoint.optimizer)
else:
print('Training from scratch.')
start_epoch = 0
start_iter = 0
best_loss = np.inf
num_train_batches = self.num_train_sentences // self.batch_size
total_train_batch = self.max_epoch * num_train_batches
print('num_train_sentences', self.num_train_sentences)
print('batches_per_epoch', num_train_batches)
print('total_train_batch', total_train_batch)
print('batch_size', self.batch_size)
print('model_name', self.model_name)
batch_timings = 0.
counter = int(start_epoch * num_train_batches + start_iter)
counter1 = 0
print('counter', counter)
ttime = 0.
cnt = 0.
print('best_loss', best_loss)
for epoch in range(start_epoch, self.max_epoch):
accu_train_loss = 0.0
net.train()
for param_group in optimizer.param_groups:
param_group['lr'] = self.lr_list[epoch]
start = timeit.default_timer()
for i, (features, labels, nframes) in enumerate(train_loader):
i += start_iter
features, labels = features.to(self.device), labels.to(
self.device)
loss_mask = compLossMask(labels, nframes=nframes)
# forward + backward + optimize
optimizer.zero_grad()
outputs = net(features)
outputs = outputs[:, :, :labels.shape[-1]]
loss1 = criterion(outputs, labels, loss_mask, nframes)
loss2 = criterion1(outputs, labels, loss_mask, nframes)
loss = 0.8 * loss1 + 0.2 * loss2
loss.backward()
optimizer.step()
# calculate losses
running_loss = loss.data.item()
accu_train_loss += running_loss
cnt += 1.
counter += 1
counter1 += 1
del loss, loss1, loss2, outputs, loss_mask, features, labels
end = timeit.default_timer()
curr_time = end - start
ttime += curr_time
mtime = ttime / counter1
print(
'iter = {}/{}, epoch = {}/{}, loss = {:.5f}, time/batch = {:.5f}, mtime/batch = {:.5f}'
.format(i + 1, num_train_batches, epoch + 1,
self.max_epoch, running_loss, curr_time, mtime))
start = timeit.default_timer()
if (i + 1) % self.eval_steps == 0:
start = timeit.default_timer()
avg_train_loss = accu_train_loss / cnt
avg_eval_loss = self.validate(net, eval_loader)
net.train()
print(
'Epoch [%d/%d], Iter [%d/%d] ( TrainLoss: %.4f | EvalLoss: %.4f )'
% (epoch + 1, self.max_epoch, i + 1,
self.num_train_sentences // self.batch_size,
avg_train_loss, avg_eval_loss))
is_best = True if avg_eval_loss < best_loss else False
best_loss = avg_eval_loss if is_best else best_loss
checkpoint = Checkpoint(epoch, i, avg_train_loss,
avg_eval_loss, best_loss,
net.state_dict(),
optimizer.state_dict())
model_name = self.model_name + '_latest.model'
best_model = self.model_name + '_best.model'
checkpoint.save(is_best,
os.path.join(self.model_path, model_name),
os.path.join(self.model_path, best_model))
logging(self.log_path, self.model_name + '_loss_log.txt',
checkpoint, self.eval_steps)
#metric_logging(self.log_path, self.model_name +'_metric_log.txt', epoch+1, [avg_st, avg_sn, avg_pe])
accu_train_loss = 0.0
cnt = 0.
net.train()
if (i + 1) % num_train_batches == 0:
break
avg_st, avg_sn, avg_pe = self.validate_with_metrics(
net, eval_loader)
net.train()
print('#' * 50)
print('')
print(
'After {} epoch the performance on validation score is a s follows:'
.format(epoch + 1))
print('')
print('STOI: {:.4f}'.format(avg_st))
print('SNR: {:.4f}'.format(avg_sn))
print('PESQ: {:.4f}'.format(avg_pe))
for param_group in optimizer.param_groups:
print('learning_rate', param_group['lr'])
print('')
print('#' * 50)
checkpoint = Checkpoint(epoch, 0, None, None, best_loss,
net.state_dict(), optimizer.state_dict())
checkpoint.save(
False,
os.path.join(self.model_path,
self.model_name + '-{}.model'.format(epoch + 1)),
os.path.join(self.model_path, best_model))
metric_logging(self.log_path, self.model_name + '_metric_log.txt',
epoch, [avg_st, avg_sn, avg_pe])
start_iter = 0.
def main(args):
assert torch.cuda.is_available(), 'CUDA is not available.'
torch.backends.cudnn.enabled = True
torch.backends.cudnn.benchmark = True
torch.set_num_threads(args.workers)
print('Training Base Detector : prepare_seed : {:}'.format(args.rand_seed))
prepare_seed(args.rand_seed)
logger = prepare_logger(args)
checkpoint = load_checkpoint(args.init_model)
xargs = checkpoint['args']
logger.log('Previous args : {:}'.format(xargs))
# General Data Augmentation
if xargs.use_gray == False:
mean_fill = tuple([int(x * 255) for x in [0.485, 0.456, 0.406]])
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
else:
mean_fill = (0.5, )
normalize = transforms.Normalize(mean=[mean_fill[0]], std=[0.5])
eval_transform = transforms.Compose2V([transforms.ToTensor(), normalize, \
transforms.PreCrop(xargs.pre_crop_expand), \
transforms.CenterCrop(xargs.crop_max)])
# Model Configure Load
model_config = load_configure(xargs.model_config, logger)
shape = (xargs.height, xargs.width)
logger.log('--> {:}\n--> Sigma : {:}, Shape : {:}'.format(
model_config, xargs.sigma, shape))
# Evaluation Dataloader
eval_loaders = []
if args.eval_ilists is not None:
for eval_ilist in args.eval_ilists:
eval_idata = EvalDataset(eval_transform, xargs.sigma,
model_config.downsample,
xargs.heatmap_type, shape, xargs.use_gray,
xargs.data_indicator)
eval_idata.load_list(eval_ilist, args.num_pts, xargs.boxindicator,
xargs.normalizeL, True)
eval_iloader = torch.utils.data.DataLoader(
eval_idata,
batch_size=args.batch_size,
shuffle=False,
num_workers=args.workers,
pin_memory=True)
eval_loaders.append((eval_iloader, False))
if args.eval_vlists is not None:
for eval_vlist in args.eval_vlists:
eval_vdata = EvalDataset(eval_transform, xargs.sigma,
model_config.downsample,
xargs.heatmap_type, shape, xargs.use_gray,
xargs.data_indicator)
eval_vdata.load_list(eval_vlist, args.num_pts, xargs.boxindicator,
xargs.normalizeL, True)
eval_vloader = torch.utils.data.DataLoader(
eval_vdata,
batch_size=args.batch_size,
shuffle=False,
num_workers=args.workers,
pin_memory=True)
eval_loaders.append((eval_vloader, True))
# define the detector
detector = obtain_pro_model(model_config, xargs.num_pts, xargs.sigma,
xargs.use_gray)
assert model_config.downsample == detector.downsample, 'downsample is not correct : {:} vs {:}'.format(
model_config.downsample, detector.downsample)
logger.log("=> detector :\n {:}".format(detector))
logger.log("=> Net-Parameters : {:} MB".format(
count_parameters_in_MB(detector)))
logger.log('=> Eval-Transform : {:}'.format(eval_transform))
detector = detector.cuda()
net = torch.nn.DataParallel(detector)
net.eval()
net.load_state_dict(checkpoint['detector'])
cpu = torch.device('cpu')
assert len(args.use_stable) == 2
for iLOADER, (loader, is_video) in enumerate(eval_loaders):
logger.log(
'{:} The [{:2d}/{:2d}]-th test set [{:}] = {:} with {:} batches.'.
format(time_string(), iLOADER, len(eval_loaders),
'video' if is_video else 'image', loader.dataset,
len(loader)))
with torch.no_grad():
all_points, all_results, all_image_ps = [], [], []
for i, (inputs, targets, masks, normpoints, transthetas,
image_index, nopoints, shapes) in enumerate(loader):
image_index = image_index.squeeze(1).tolist()
(batch_size, C, H, W), num_pts = inputs.size(), xargs.num_pts
# batch_heatmaps is a list for stage-predictions, each element should be [Batch, C, H, W]
if xargs.procedure == 'heatmap':
batch_features, batch_heatmaps, batch_locs, batch_scos = net(
inputs)
batch_locs = batch_locs[:, :-1, :]
else:
batch_locs = net(inputs)
batch_locs = batch_locs.detach().to(cpu)
# evaluate the training data
for ibatch, (imgidx,
nopoint) in enumerate(zip(image_index, nopoints)):
if xargs.procedure == 'heatmap':
norm_locs = normalize_points(
(H, W), batch_locs[ibatch].transpose(1, 0))
norm_locs = torch.cat(
(norm_locs, torch.ones(1, num_pts)), dim=0)
else:
norm_locs = torch.cat((batch_locs[ibatch].permute(
1, 0), torch.ones(1, num_pts)),
dim=0)
transtheta = transthetas[ibatch][:2, :]
norm_locs = torch.mm(transtheta, norm_locs)
real_locs = denormalize_points(shapes[ibatch].tolist(),
norm_locs)
#real_locs = torch.cat((real_locs, batch_scos[ibatch].permute(1,0)), dim=0)
real_locs = torch.cat((real_locs, torch.ones(1, num_pts)),
dim=0)
xpoints = loader.dataset.labels[imgidx].get_points().numpy(
)
image_path = loader.dataset.datas[imgidx]
# put into the list
all_points.append(torch.from_numpy(xpoints))
all_results.append(real_locs)
all_image_ps.append(image_path)
total = len(all_points)
logger.log(
'{:} The [{:2d}/{:2d}]-th test set finishes evaluation : {:} frames/images'
.format(time_string(), iLOADER, len(eval_loaders), total))
"""
if args.use_stable[0] > 0:
save_dir = Path( osp.join(args.save_path, '{:}-X-{:03d}'.format(args.model_name, iLOADER)) )
save_dir.mkdir(parents=True, exist_ok=True)
wrap_parallel = WrapParallel(save_dir, all_image_ps, all_results, all_points, 180, (255, 0, 0))
wrap_loader = torch.utils.data.DataLoader(wrap_parallel, batch_size=args.workers, shuffle=False, num_workers=args.workers, pin_memory=True)
for iL, INDEXES in enumerate(wrap_loader): _ = INDEXES
cmd = 'ffmpeg -y -i {:}/%06d.png -framerate 30 {:}.avi'.format(save_dir, save_dir)
logger.log('{:} possible >>>>> : {:}'.format(time_string(), cmd))
os.system( cmd )
if args.use_stable[1] > 0:
save_dir = Path( osp.join(args.save_path, '{:}-Y-{:03d}'.format(args.model_name, iLOADER)) )
save_dir.mkdir(parents=True, exist_ok=True)
Xpredictions, Xgts = torch.stack(all_results), torch.stack(all_points)
new_preds = fc_solve(Xgts, Xpredictions, is_cuda=True)
wrap_parallel = WrapParallel(save_dir, all_image_ps, new_preds, all_points, 180, (0, 0, 255))
wrap_loader = torch.utils.data.DataLoader(wrap_parallel, batch_size=args.workers, shuffle=False, num_workers=args.workers, pin_memory=True)
for iL, INDEXES in enumerate(wrap_loader): _ = INDEXES
cmd = 'ffmpeg -y -i {:}/%06d.png -framerate 30 {:}.avi'.format(save_dir, save_dir)
logger.log('{:} possible >>>>> : {:}'.format(time_string(), cmd))
os.system( cmd )
"""
Xpredictions, Xgts = torch.stack(all_results), torch.stack(all_points)
save_path = Path(
osp.join(args.save_path,
'{:}-result-{:03d}.pth'.format(args.model_name, iLOADER)))
torch.save(
{
'paths': all_image_ps,
'ground-truths': Xgts,
'predictions': all_results
}, save_path)
logger.log('{:} save into {:}'.format(time_string(), save_path))
if False:
new_preds = fc_solve_v2(Xgts, Xpredictions, is_cuda=True)
# create the dir
save_dir = Path(
osp.join(args.save_path,
'{:}-T-{:03d}'.format(args.model_name, iLOADER)))
save_dir.mkdir(parents=True, exist_ok=True)
wrap_parallel = WrapParallelV2(save_dir, all_image_ps, Xgts,
all_results, new_preds, all_points,
180, [args.model_name, 'SRT'])
wrap_parallel[0]
wrap_loader = torch.utils.data.DataLoader(wrap_parallel,
batch_size=args.workers,
shuffle=False,
num_workers=args.workers,
pin_memory=True)
for iL, INDEXES in enumerate(wrap_loader):
_ = INDEXES
cmd = 'ffmpeg -y -i {:}/%06d.png -vb 5000k {:}.avi'.format(
save_dir, save_dir)
logger.log('{:} possible >>>>> : {:}'.format(time_string(), cmd))
os.system(cmd)
logger.close()
return
# my_lr_scheduler = lr_scheduler.CosineAnnealingLR(optimizer, T_max=4500/opt.batch_size*opt.num_epochs,eta_min=0.000001)
my_lr_scheduler = lr_scheduler.StepLR(optimizer,
step_size=int(4000 / opt.batch_size *
200),
gamma=0.5)
train_dataset = TrainDataset(opt.train_file,
patch_size=opt.patch_size,
scale=opt.scale)
train_dataloader = DataLoader(dataset=train_dataset,
batch_size=opt.batch_size,
shuffle=True,
num_workers=opt.num_workers,
pin_memory=True)
eval_dataset = EvalDataset(opt.eval_file)
eval_dataloader = DataLoader(dataset=eval_dataset, batch_size=1)
best_weights = copy.deepcopy(model.state_dict())
best_epoch = 0
best_psnr = 0.0
for epoch in range(opt.num_epochs):
for param_group in optimizer.param_groups:
param_group['lr'] = opt.lr * (0.1**(epoch //
int(opt.num_epochs * 0.8)))
model.train()
epoch_losses = AverageMeter()
with tqdm(total=(len(train_dataset) -