Python utility.quantize() Examples
The following are 19
code examples of utility.quantize().
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Example #1
| Source File: videotester.py From OISR-PyTorch with BSD 2-Clause "Simplified" License | 4 votes |
|
def test(self):
torch.set_grad_enabled(False)
self.ckp.write_log('\nEvaluation on video:')
self.model.eval()
timer_test = utility.timer()
for idx_scale, scale in enumerate(self.scale):
vidcap = cv2.VideoCapture(self.args.dir_demo)
total_frames = int(vidcap.get(cv2.CAP_PROP_FRAME_COUNT))
vidwri = cv2.VideoWriter(
self.ckp.get_path('{}_x{}.avi'.format(self.filename, scale)),
cv2.VideoWriter_fourcc(*'XVID'),
vidcap.get(cv2.CAP_PROP_FPS),
(
int(scale * vidcap.get(cv2.CAP_PROP_FRAME_WIDTH)),
int(scale * vidcap.get(cv2.CAP_PROP_FRAME_HEIGHT))
)
)
tqdm_test = tqdm(range(total_frames), ncols=80)
for _ in tqdm_test:
success, lr = vidcap.read()
if not success: break
lr, = common.set_channel(lr, n_channels=self.args.n_colors)
lr, = common.np2Tensor(lr, rgb_range=self.args.rgb_range)
lr, = self.prepare(lr.unsqueeze(0))
sr = self.model(lr, idx_scale)
sr = utility.quantize(sr, self.args.rgb_range).squeeze(0)
normalized = sr * 255 / self.args.rgb_range
ndarr = normalized.byte().permute(1, 2, 0).cpu().numpy()
vidwri.write(ndarr)
vidcap.release()
vidwri.release()
self.ckp.write_log(
'Total: {:.2f}s\n'.format(timer_test.toc()), refresh=True
)
torch.set_grad_enabled(True)
Example #2
| Source File: trainer.py From MSRN-PyTorch with MIT License | 4 votes |
|
def test(self):
epoch = self.scheduler.last_epoch + 1
self.ckp.write_log('\nEvaluation:')
self.ckp.add_log(torch.zeros(1, len(self.scale)))
self.model.eval()
timer_test = utility.timer()
with torch.no_grad():
for idx_scale, scale in enumerate(self.scale):
eval_acc = 0
self.loader_test.dataset.set_scale(idx_scale)
tqdm_test = tqdm(self.loader_test, ncols=80)
for idx_img, (lr, hr, filename, _) in enumerate(tqdm_test):
filename = filename[0]
no_eval = (hr.nelement() == 1)
if not no_eval:
lr, hr = self.prepare(lr, hr)
else:
lr, = self.prepare(lr)
sr = self.model(lr, idx_scale)
sr = utility.quantize(sr, self.args.rgb_range)
save_list = [sr]
if not no_eval:
eval_acc += utility.calc_psnr(
sr, hr, scale, self.args.rgb_range,
benchmark=self.loader_test.dataset.benchmark
)
save_list.extend([lr, hr])
if self.args.save_results:
self.ckp.save_results(filename, save_list, scale)
self.ckp.log[-1, idx_scale] = eval_acc / len(self.loader_test)
best = self.ckp.log.max(0)
self.ckp.write_log(
'[{} x{}]\tPSNR: {:.3f} (Best: {:.3f} @epoch {})'.format(
self.args.data_test,
scale,
self.ckp.log[-1, idx_scale],
best[0][idx_scale],
best[1][idx_scale] + 1
)
)
self.ckp.write_log(
'Total time: {:.2f}s\n'.format(timer_test.toc()), refresh=True
)
if not self.args.test_only:
self.ckp.save(self, epoch, is_best=(best[1][0] + 1 == epoch))
Example #3
| Source File: trainer.py From MSRN-PyTorch with MIT License | 4 votes |
|
def test(self):
epoch = self.scheduler.last_epoch + 1
self.ckp.write_log('\nEvaluation:')
self.ckp.add_log(torch.zeros(1, len(self.scale)))
self.model.eval()
timer_test = utility.timer()
with torch.no_grad():
for idx_scale, scale in enumerate(self.scale):
eval_acc = 0
self.loader_test.dataset.set_scale(idx_scale)
tqdm_test = tqdm(self.loader_test, ncols=80)
for idx_img, (lr, hr, filename, _) in enumerate(tqdm_test):
filename = filename[0]
no_eval = (hr.nelement() == 1)
if not no_eval:
lr, hr = self.prepare([lr, hr])
else:
lr = self.prepare([lr])[0]
sr = self.model(lr, idx_scale)
sr = utility.quantize(sr, self.args.rgb_range)
save_list = [sr]
if not no_eval:
eval_acc += utility.calc_psnr(
sr, hr, scale, self.args.rgb_range,
benchmark=self.loader_test.dataset.benchmark
)
save_list.extend([lr, hr])
if self.args.save_results:
#self.ckp.save_results(filename, save_list, scale)
self.ckp.save_results_nopostfix(filename, save_list, scale)
self.ckp.log[-1, idx_scale] = eval_acc / len(self.loader_test)
best = self.ckp.log.max(0)
self.ckp.write_log(
'[{} x{}]\tPSNR: {:.3f} (Best: {:.3f} @epoch {})'.format(
self.args.data_test,
scale,
self.ckp.log[-1, idx_scale],
best[0][idx_scale],
best[1][idx_scale] + 1
)
)
self.ckp.write_log(
'Total time: {:.2f}s, ave time: {:.2f}s\n'.format(timer_test.toc(), timer_test.toc()/len(self.loader_test)), refresh=True
)
if not self.args.test_only:
self.ckp.save(self, epoch, is_best=(best[1][0] + 1 == epoch))
Example #4
| Source File: trainer.py From 2018_subeesh_epsr_eccvw with MIT License | 4 votes |
|
def test(self):
epoch = self.scheduler.last_epoch + 1
self.ckp.write_log('\nEvaluation:')
self.ckp.add_log(torch.zeros(1, len(self.scale)))
self.model.eval()
timer_test = utility.timer()
with torch.no_grad():
for idx_scale, scale in enumerate(self.scale):
eval_acc = 0
self.loader_test.dataset.set_scale(idx_scale)
tqdm_test = tqdm(self.loader_test, ncols=80)
for idx_img, (lr, hr, filename, _) in enumerate(tqdm_test):
filename = filename[0]
no_eval = (hr.nelement() == 1)
if not no_eval:
lr, hr = self.prepare([lr, hr])
else:
lr = self.prepare([lr])[0]
print(lr.shape)
sr = self.model(lr, idx_scale)
sr = utility.quantize(sr, self.args.rgb_range)
save_list = [sr]
if not no_eval:
eval_acc += utility.calc_psnr(
sr, hr, scale, self.args.rgb_range,
benchmark=self.loader_test.dataset.benchmark
)
save_list.extend([lr, hr])
if self.args.save_results:
self.ckp.save_results(filename, save_list, scale)
self.ckp.log[-1, idx_scale] = eval_acc / len(self.loader_test)
best = self.ckp.log.max(0)
self.ckp.write_log(
'[{} x{}]\tPSNR: {:.3f} (Best: {:.3f} @epoch {})'.format(
self.args.data_test,
scale,
self.ckp.log[-1, idx_scale],
best[0][idx_scale],
best[1][idx_scale] + 1
)
)
self.ckp.write_log(
'Total time: {:.2f}s\n'.format(timer_test.toc()), refresh=True
)
if not self.args.test_only:
self.ckp.save(self, epoch, is_best=(best[1][0] + 1 == epoch))
Example #5
| Source File: trainer.py From NTIRE2019_EDRN with MIT License | 4 votes |
|
def test(self):
#if self.args.test_only:
# self.scheduler.step() #just for remake the curve of psnr
epoch = self.scheduler.last_epoch + 1
self.ckp.write_log('\nEvaluation:')
self.ckp.add_log(torch.zeros(1, len(self.scale)))
self.model.eval()
timer_test = utility.timer()
with torch.no_grad():
for idx_scale, scale in enumerate(self.scale):
eval_acc = 0
#eval_ssim = 0
self.loader_test.dataset.set_scale(idx_scale)
tqdm_test = tqdm(self.loader_test, ncols=80)
for idx_img, (lr, hr,filename, _) in enumerate(tqdm_test):
filename = filename[0]
no_eval = (hr.nelement() == 1)
if not no_eval:
lr, hr = self.prepare(lr, hr)
else:
lr, = self.prepare(lr)
sr = self.model(lr, idx_scale)
sr = utility.quantize(sr, self.args.rgb_range)
save_list = [sr]
if not no_eval:
eval_acc += utility.calc_psnr(
sr, hr, scale, self.args.rgb_range,
benchmark=self.loader_test.dataset.benchmark
)
#save_list.extend([lr, hr])
#eval_ssim += utility.calc_ssim(
# sr, hr, scale
#)
if self.args.save_results:
self.ckp.save_results(filename, save_list, scale,epoch)
#del lr,hr,sr,idx_img,filename,save_list
self.ckp.log[-1, idx_scale] = eval_acc / len(self.loader_test)
best = self.ckp.log.max(0)
self.ckp.write_log(
'[{} x{}]\tPSNR: {:.3f}(Best: {:.3f} @epoch {})'.format(
self.args.data_test,
scale,
self.ckp.log[-1, idx_scale],
best[0][idx_scale],
best[1][idx_scale] + 1
)
)
#del idx_scale,scale,tqdm_test
self.ckp.write_log(
'Total time: {:.2f}s\n'.format(timer_test.toc()), refresh=True
)
if not self.args.test_only:
self.ckp.save(self, epoch, is_best=(best[1][0] + 1 == epoch))
#self.ckp.save(self, epoch, is_best=False)
else:
self.ckp.save_for_test(epoch)
Example #6
| Source File: trainer.py From AWSRN with MIT License | 4 votes |
|
def test(self):
epoch = self.scheduler.last_epoch + 1
self.ckp.write_log('\nEvaluation:')
self.ckp.add_log(torch.zeros(1, len(self.scale)))
self.model.eval()
timer_test = utility.timer()
with torch.no_grad():
for idx_scale, scale in enumerate(self.scale):
eval_psnr = 0
# eval_ssim = 0
self.loader_test.dataset.set_scale(idx_scale)
tqdm_test = tqdm(self.loader_test, ncols=80)
for idx_img, (lr, hr, filename, _) in enumerate(tqdm_test):
filename = filename[0]
no_eval = (hr.nelement() == 1)
if not no_eval:
lr, hr = self.prepare(lr, hr)
else:
lr, = self.prepare(lr)
sr = self.model(lr, idx_scale)
sr = utility.quantize(sr, self.args.rgb_range)
save_list = [sr]
if not no_eval:
eval_psnr += utility.calc_psnr(
sr, hr, scale, self.args.rgb_range,
benchmark=self.loader_test.dataset.benchmark
)
# eval_ssim += utility.calc_ssim(sr, hr)
# save_list.extend([lr, hr])
save_list.extend([hr])
if self.args.save_results:
self.ckp.save_results(filename, save_list, scale)
self.ckp.log[-1, idx_scale] = eval_psnr / len(self.loader_test)
# mean_ssim = eval_ssim / len(self.loader_test)
best = self.ckp.log.max(0)
self.ckp.write_log(
'[{} x{}]\tPSNR: {:.3f} (Best PSNR: {:.3f} @epoch {})'.format(
self.args.data_test,
scale,
self.ckp.log[-1, idx_scale],
best[0][idx_scale],
best[1][idx_scale] + 1
)
)
self.ckp.write_log(
'Total time: {:.2f}s\n'.format(timer_test.toc()), refresh=True
)
if not self.args.test_only:
self.ckp.save(self, epoch, is_best=(best[1][0] + 1 == epoch))
Example #7
| Source File: trainer.py From 3D_Appearance_SR with MIT License | 4 votes |
|
def test(self):
epoch = self.scheduler.last_epoch + 1
self.ckp.write_log('\nEvaluation:')
self.ckp.add_log(torch.zeros(1, len(self.scale)))
self.model.eval()
timer_test = utility.timer()
with torch.no_grad():
for idx_scale, scale in enumerate(self.scale):
eval_acc = 0
self.loader_test.dataset.set_scale(idx_scale)
tqdm_test = tqdm(self.loader_test, ncols=80)
for idx_img, (lr, hr, filename, _) in enumerate(tqdm_test):
# from IPython import embed; embed();
filename = filename[0]
no_eval = (hr.nelement() == 1)
if not no_eval:
lr, hr = self.prepare([lr, hr])
else:
lr = self.prepare([lr])[0]
sr = self.model(idx_scale, lr)
sr = utility.quantize(sr, self.args.rgb_range)
save_list = [sr]
if not no_eval:
eval_acc += utility.calc_psnr(
sr, hr, scale, self.args.rgb_range,
benchmark=self.loader_test.dataset.benchmark
)
save_list.extend([lr, hr])
if self.args.save_results:
self.ckp.save_results(filename, save_list, scale)
self.ckp.log[-1, idx_scale] = eval_acc / len(self.loader_test)
best = self.ckp.log.max(0)
self.ckp.write_log(
'[{} x{}]\tPSNR: {:.3f} (Best: {:.3f} @epoch {})'.format(
self.args.data_test,
scale,
self.ckp.log[-1, idx_scale],
best[0][idx_scale],
best[1][idx_scale] + 1
)
)
self.ckp.write_log(
'Total time: {:.2f}s\n'.format(timer_test.toc()), refresh=True
)
if not self.args.test_only:
self.ckp.save(self, epoch, is_best=(best[1][0] + 1 == epoch))
Example #8
| Source File: trainer_finetune.py From 3D_Appearance_SR with MIT License | 4 votes |
|
def test(self):
epoch = self.scheduler.last_epoch + 1
self.ckp.write_log('\nEvaluation:')
self.ckp.add_log(torch.zeros(1, len(self.scale)))
self.model.eval()
timer_test = utility.timer()
with torch.no_grad():
for idx_scale, scale in enumerate(self.scale):
eval_acc = 0
self.loader_test.dataset.set_scale(idx_scale)
tqdm_test = tqdm(self.loader_test, ncols=80)
for idx_img, (lr, nl, mk, hr, filename, _) in enumerate(tqdm_test):
# print('FLAG')
# print(filename)
filename = filename[0]
print(filename)
no_eval = (hr.nelement() == 1)
if not no_eval:
lr, nl, mk, hr = self.prepare([lr, nl, mk, hr])
else:
lr, nl, mk, = self.prepare([lr, nl, mk])
sr = self.model(idx_scale, lr, nl, mk)
sr = utility.quantize(sr, self.args.rgb_range)
# print(sr.shape)
b, c, h, w = sr.shape
hr = hr[:, :, :h, :w]
save_list = [sr]
if not no_eval:
eval_acc += utility.calc_psnr(
sr, hr, scale, self.args.rgb_range,
benchmark=self.loader_test.dataset.benchmark
)
save_list.extend([lr, hr])
if self.args.save_results:
self.ckp.save_results(filename, save_list, scale)
self.ckp.log[-1, idx_scale] = eval_acc / len(self.loader_test)
best = self.ckp.log.max(0)
self.ckp.write_log(
'[{} x{}]\tPSNR: {:.3f} (Best: {:.3f} @epoch {})'.format(
self.args.data_test,
scale,
self.ckp.log[-1, idx_scale],
best[0][idx_scale],
best[1][idx_scale] + 1
)
)
self.ckp.write_log(
'Total time: {:.2f}s\n'.format(timer_test.toc()), refresh=True
)
if not self.args.test_only:
self.ckp.save(self, epoch, is_best=(best[1][0] + 1 == epoch))
Example #9
| Source File: trainer.py From EDSR-PyTorch with MIT License | 4 votes |
|
def test(self):
torch.set_grad_enabled(False)
epoch = self.optimizer.get_last_epoch()
self.ckp.write_log('\nEvaluation:')
self.ckp.add_log(
torch.zeros(1, len(self.loader_test), len(self.scale))
)
self.model.eval()
timer_test = utility.timer()
if self.args.save_results: self.ckp.begin_background()
for idx_data, d in enumerate(self.loader_test):
for idx_scale, scale in enumerate(self.scale):
d.dataset.set_scale(idx_scale)
for lr, hr, filename in tqdm(d, ncols=80):
lr, hr = self.prepare(lr, hr)
sr = self.model(lr, idx_scale)
sr = utility.quantize(sr, self.args.rgb_range)
save_list = [sr]
self.ckp.log[-1, idx_data, idx_scale] += utility.calc_psnr(
sr, hr, scale, self.args.rgb_range, dataset=d
)
if self.args.save_gt:
save_list.extend([lr, hr])
if self.args.save_results:
self.ckp.save_results(d, filename[0], save_list, scale)
self.ckp.log[-1, idx_data, idx_scale] /= len(d)
best = self.ckp.log.max(0)
self.ckp.write_log(
'[{} x{}]\tPSNR: {:.3f} (Best: {:.3f} @epoch {})'.format(
d.dataset.name,
scale,
self.ckp.log[-1, idx_data, idx_scale],
best[0][idx_data, idx_scale],
best[1][idx_data, idx_scale] + 1
)
)
self.ckp.write_log('Forward: {:.2f}s\n'.format(timer_test.toc()))
self.ckp.write_log('Saving...')
if self.args.save_results:
self.ckp.end_background()
if not self.args.test_only:
self.ckp.save(self, epoch, is_best=(best[1][0, 0] + 1 == epoch))
self.ckp.write_log(
'Total: {:.2f}s\n'.format(timer_test.toc()), refresh=True
)
torch.set_grad_enabled(True)
Example #10
| Source File: videotester.py From EDSR-PyTorch with MIT License | 4 votes |
|
def test(self):
torch.set_grad_enabled(False)
self.ckp.write_log('\nEvaluation on video:')
self.model.eval()
timer_test = utility.timer()
for idx_scale, scale in enumerate(self.scale):
vidcap = cv2.VideoCapture(self.args.dir_demo)
total_frames = int(vidcap.get(cv2.CAP_PROP_FRAME_COUNT))
vidwri = cv2.VideoWriter(
self.ckp.get_path('{}_x{}.avi'.format(self.filename, scale)),
cv2.VideoWriter_fourcc(*'XVID'),
vidcap.get(cv2.CAP_PROP_FPS),
(
int(scale * vidcap.get(cv2.CAP_PROP_FRAME_WIDTH)),
int(scale * vidcap.get(cv2.CAP_PROP_FRAME_HEIGHT))
)
)
tqdm_test = tqdm(range(total_frames), ncols=80)
for _ in tqdm_test:
success, lr = vidcap.read()
if not success: break
lr, = common.set_channel(lr, n_channels=self.args.n_colors)
lr, = common.np2Tensor(lr, rgb_range=self.args.rgb_range)
lr, = self.prepare(lr.unsqueeze(0))
sr = self.model(lr, idx_scale)
sr = utility.quantize(sr, self.args.rgb_range).squeeze(0)
normalized = sr * 255 / self.args.rgb_range
ndarr = normalized.byte().permute(1, 2, 0).cpu().numpy()
vidwri.write(ndarr)
vidcap.release()
vidwri.release()
self.ckp.write_log(
'Total: {:.2f}s\n'.format(timer_test.toc()), refresh=True
)
torch.set_grad_enabled(True)
Example #11
| Source File: videotester.py From OISR-PyTorch with BSD 2-Clause "Simplified" License | 4 votes |
|
def test(self):
torch.set_grad_enabled(False)
self.ckp.write_log('\nEvaluation on video:')
self.model.eval()
timer_test = utility.timer()
for idx_scale, scale in enumerate(self.scale):
vidcap = cv2.VideoCapture(self.args.dir_demo)
total_frames = int(vidcap.get(cv2.CAP_PROP_FRAME_COUNT))
vidwri = cv2.VideoWriter(
self.ckp.get_path('{}_x{}.avi'.format(self.filename, scale)),
cv2.VideoWriter_fourcc(*'XVID'),
vidcap.get(cv2.CAP_PROP_FPS),
(
int(scale * vidcap.get(cv2.CAP_PROP_FRAME_WIDTH)),
int(scale * vidcap.get(cv2.CAP_PROP_FRAME_HEIGHT))
)
)
tqdm_test = tqdm(range(total_frames), ncols=80)
for _ in tqdm_test:
success, lr = vidcap.read()
if not success: break
lr, = common.set_channel(lr, n_channels=self.args.n_colors)
lr, = common.np2Tensor(lr, rgb_range=self.args.rgb_range)
lr, = self.prepare(lr.unsqueeze(0))
sr = self.model(lr, idx_scale)
sr = utility.quantize(sr, self.args.rgb_range).squeeze(0)
normalized = sr * 255 / self.args.rgb_range
ndarr = normalized.byte().permute(1, 2, 0).cpu().numpy()
vidwri.write(ndarr)
vidcap.release()
vidwri.release()
self.ckp.write_log(
'Total: {:.2f}s\n'.format(timer_test.toc()), refresh=True
)
torch.set_grad_enabled(True)
Example #12
| Source File: trainer.py From OISR-PyTorch with BSD 2-Clause "Simplified" License | 4 votes |
|
def test(self):
torch.set_grad_enabled(False)
epoch = self.scheduler.last_epoch + 1
self.ckp.write_log('\nEvaluation:')
self.ckp.add_log(
torch.zeros(1, len(self.loader_test), len(self.scale))
)
self.model.eval()
timer_test = utility.timer()
if self.args.save_results: self.ckp.begin_background()
for idx_data, d in enumerate(self.loader_test):
for idx_scale, scale in enumerate(self.scale):
d.dataset.set_scale(idx_scale)
for lr, hr, filename, _ in tqdm(d, ncols=80):
lr, hr = self.prepare(lr, hr)
sr = self.model(lr, idx_scale)
sr = utility.quantize(sr, self.args.rgb_range)
save_list = [sr]
self.ckp.log[-1, idx_data, idx_scale] += utility.calc_psnr(
sr, hr, scale, self.args.rgb_range, dataset=d
)
if self.args.save_gt:
save_list.extend([lr, hr])
if self.args.save_results:
self.ckp.save_results(d, filename[0], save_list, scale)
self.ckp.log[-1, idx_data, idx_scale] /= len(d)
best = self.ckp.log.max(0)
self.ckp.write_log(
'[{} x{}]\tPSNR: {:.3f} (Best: {:.3f} @epoch {})'.format(
d.dataset.name,
scale,
self.ckp.log[-1, idx_data, idx_scale],
best[0][idx_data, idx_scale],
best[1][idx_data, idx_scale] + 1
)
)
self.ckp.write_log('Forward: {:.2f}s\n'.format(timer_test.toc()))
self.ckp.write_log('Saving...')
if self.args.save_results: self.ckp.end_background()
if not self.args.test_only:
self.ckp.save(self, epoch, is_best=(best[1][0, 0] + 1 == epoch))
self.ckp.write_log(
'Total: {:.2f}s\n'.format(timer_test.toc()), refresh=True
)
torch.set_grad_enabled(True)
Example #13
| Source File: videotester.py From OISR-PyTorch with BSD 2-Clause "Simplified" License | 4 votes |
|
def test(self):
torch.set_grad_enabled(False)
self.ckp.write_log('\nEvaluation on video:')
self.model.eval()
timer_test = utility.timer()
for idx_scale, scale in enumerate(self.scale):
vidcap = cv2.VideoCapture(self.args.dir_demo)
total_frames = int(vidcap.get(cv2.CAP_PROP_FRAME_COUNT))
vidwri = cv2.VideoWriter(
self.ckp.get_path('{}_x{}.avi'.format(self.filename, scale)),
cv2.VideoWriter_fourcc(*'XVID'),
vidcap.get(cv2.CAP_PROP_FPS),
(
int(scale * vidcap.get(cv2.CAP_PROP_FRAME_WIDTH)),
int(scale * vidcap.get(cv2.CAP_PROP_FRAME_HEIGHT))
)
)
tqdm_test = tqdm(range(total_frames), ncols=80)
for _ in tqdm_test:
success, lr = vidcap.read()
if not success: break
lr, = common.set_channel(lr, n_channels=self.args.n_colors)
lr, = common.np2Tensor(lr, rgb_range=self.args.rgb_range)
lr, = self.prepare(lr.unsqueeze(0))
sr = self.model(lr, idx_scale)
sr = utility.quantize(sr, self.args.rgb_range).squeeze(0)
normalized = sr * 255 / self.args.rgb_range
ndarr = normalized.byte().permute(1, 2, 0).cpu().numpy()
vidwri.write(ndarr)
vidcap.release()
vidwri.release()
self.ckp.write_log(
'Total: {:.2f}s\n'.format(timer_test.toc()), refresh=True
)
torch.set_grad_enabled(True)
Example #14
| Source File: trainer.py From OISR-PyTorch with BSD 2-Clause "Simplified" License | 4 votes |
|
def test(self):
torch.set_grad_enabled(False)
epoch = self.scheduler.last_epoch + 1
self.ckp.write_log('\nEvaluation:')
self.ckp.add_log(
torch.zeros(1, len(self.loader_test), len(self.scale))
)
self.model.eval()
timer_test = utility.timer()
if self.args.save_results: self.ckp.begin_background()
for idx_data, d in enumerate(self.loader_test):
for idx_scale, scale in enumerate(self.scale):
d.dataset.set_scale(idx_scale)
for lr, hr, filename, _ in tqdm(d, ncols=80):
lr, hr = self.prepare(lr, hr)
sr = self.model(lr, idx_scale)
sr = utility.quantize(sr, self.args.rgb_range)
save_list = [sr]
self.ckp.log[-1, idx_data, idx_scale] += utility.calc_psnr(
sr, hr, scale, self.args.rgb_range, dataset=d
)
if self.args.save_gt:
save_list.extend([lr, hr])
if self.args.save_results:
self.ckp.save_results(d, filename[0], save_list, scale)
self.ckp.log[-1, idx_data, idx_scale] /= len(d)
best = self.ckp.log.max(0)
self.ckp.write_log(
'[{} x{}]\tPSNR: {:.3f} (Best: {:.3f} @epoch {})'.format(
d.dataset.name,
scale,
self.ckp.log[-1, idx_data, idx_scale],
best[0][idx_data, idx_scale],
best[1][idx_data, idx_scale] + 1
)
)
self.ckp.write_log('Forward: {:.2f}s\n'.format(timer_test.toc()))
self.ckp.write_log('Saving...')
if self.args.save_results: self.ckp.end_background()
if not self.args.test_only:
self.ckp.save(self, epoch, is_best=(best[1][0, 0] + 1 == epoch))
self.ckp.write_log(
'Total: {:.2f}s\n'.format(timer_test.toc()), refresh=True
)
torch.set_grad_enabled(True)
Example #15
| Source File: videotester.py From OISR-PyTorch with BSD 2-Clause "Simplified" License | 4 votes |
|
def test(self):
torch.set_grad_enabled(False)
self.ckp.write_log('\nEvaluation on video:')
self.model.eval()
timer_test = utility.timer()
for idx_scale, scale in enumerate(self.scale):
vidcap = cv2.VideoCapture(self.args.dir_demo)
total_frames = int(vidcap.get(cv2.CAP_PROP_FRAME_COUNT))
vidwri = cv2.VideoWriter(
self.ckp.get_path('{}_x{}.avi'.format(self.filename, scale)),
cv2.VideoWriter_fourcc(*'XVID'),
vidcap.get(cv2.CAP_PROP_FPS),
(
int(scale * vidcap.get(cv2.CAP_PROP_FRAME_WIDTH)),
int(scale * vidcap.get(cv2.CAP_PROP_FRAME_HEIGHT))
)
)
tqdm_test = tqdm(range(total_frames), ncols=80)
for _ in tqdm_test:
success, lr = vidcap.read()
if not success: break
lr, = common.set_channel(lr, n_channels=self.args.n_colors)
lr, = common.np2Tensor(lr, rgb_range=self.args.rgb_range)
lr, = self.prepare(lr.unsqueeze(0))
sr = self.model(lr, idx_scale)
sr = utility.quantize(sr, self.args.rgb_range).squeeze(0)
normalized = sr * 255 / self.args.rgb_range
ndarr = normalized.byte().permute(1, 2, 0).cpu().numpy()
vidwri.write(ndarr)
vidcap.release()
vidwri.release()
self.ckp.write_log(
'Total: {:.2f}s\n'.format(timer_test.toc()), refresh=True
)
torch.set_grad_enabled(True)
Example #16
| Source File: videotester.py From OISR-PyTorch with BSD 2-Clause "Simplified" License | 4 votes |
|
def test(self):
torch.set_grad_enabled(False)
self.ckp.write_log('\nEvaluation on video:')
self.model.eval()
timer_test = utility.timer()
for idx_scale, scale in enumerate(self.scale):
vidcap = cv2.VideoCapture(self.args.dir_demo)
total_frames = int(vidcap.get(cv2.CAP_PROP_FRAME_COUNT))
vidwri = cv2.VideoWriter(
self.ckp.get_path('{}_x{}.avi'.format(self.filename, scale)),
cv2.VideoWriter_fourcc(*'XVID'),
vidcap.get(cv2.CAP_PROP_FPS),
(
int(scale * vidcap.get(cv2.CAP_PROP_FRAME_WIDTH)),
int(scale * vidcap.get(cv2.CAP_PROP_FRAME_HEIGHT))
)
)
tqdm_test = tqdm(range(total_frames), ncols=80)
for _ in tqdm_test:
success, lr = vidcap.read()
if not success: break
lr, = common.set_channel(lr, n_channels=self.args.n_colors)
lr, = common.np2Tensor(lr, rgb_range=self.args.rgb_range)
lr, = self.prepare(lr.unsqueeze(0))
sr = self.model(lr, idx_scale)
sr = utility.quantize(sr, self.args.rgb_range).squeeze(0)
normalized = sr * 255 / self.args.rgb_range
ndarr = normalized.byte().permute(1, 2, 0).cpu().numpy()
vidwri.write(ndarr)
vidcap.release()
vidwri.release()
self.ckp.write_log(
'Total: {:.2f}s\n'.format(timer_test.toc()), refresh=True
)
torch.set_grad_enabled(True)
Example #17
| Source File: trainer.py From OISR-PyTorch with BSD 2-Clause "Simplified" License | 4 votes |
|
def test(self):
torch.set_grad_enabled(False)
epoch = self.scheduler.last_epoch + 1
self.ckp.write_log('\nEvaluation:')
self.ckp.add_log(
torch.zeros(1, len(self.loader_test), len(self.scale))
)
self.model.eval()
timer_test = utility.timer()
if self.args.save_results: self.ckp.begin_background()
for idx_data, d in enumerate(self.loader_test):
for idx_scale, scale in enumerate(self.scale):
d.dataset.set_scale(idx_scale)
for lr, hr, filename, _ in tqdm(d, ncols=80):
lr, hr = self.prepare(lr, hr)
sr = self.model(lr, idx_scale)
sr = utility.quantize(sr, self.args.rgb_range)
save_list = [sr]
self.ckp.log[-1, idx_data, idx_scale] += utility.calc_psnr(
sr, hr, scale, self.args.rgb_range, dataset=d
)
if self.args.save_gt:
save_list.extend([lr, hr])
if self.args.save_results:
self.ckp.save_results(d, filename[0], save_list, scale)
self.ckp.log[-1, idx_data, idx_scale] /= len(d)
best = self.ckp.log.max(0)
self.ckp.write_log(
'[{} x{}]\tPSNR: {:.3f} (Best: {:.3f} @epoch {})'.format(
d.dataset.name,
scale,
self.ckp.log[-1, idx_data, idx_scale],
best[0][idx_data, idx_scale],
best[1][idx_data, idx_scale] + 1
)
)
self.ckp.write_log('Forward: {:.2f}s\n'.format(timer_test.toc()))
self.ckp.write_log('Saving...')
if self.args.save_results: self.ckp.end_background()
if not self.args.test_only:
self.ckp.save(self, epoch, is_best=(best[1][0, 0] + 1 == epoch))
self.ckp.write_log(
'Total: {:.2f}s\n'.format(timer_test.toc()), refresh=True
)
torch.set_grad_enabled(True)
Example #18
| Source File: videotester.py From OISR-PyTorch with BSD 2-Clause "Simplified" License | 4 votes |
|
def test(self):
torch.set_grad_enabled(False)
self.ckp.write_log('\nEvaluation on video:')
self.model.eval()
timer_test = utility.timer()
for idx_scale, scale in enumerate(self.scale):
vidcap = cv2.VideoCapture(self.args.dir_demo)
total_frames = int(vidcap.get(cv2.CAP_PROP_FRAME_COUNT))
vidwri = cv2.VideoWriter(
self.ckp.get_path('{}_x{}.avi'.format(self.filename, scale)),
cv2.VideoWriter_fourcc(*'XVID'),
vidcap.get(cv2.CAP_PROP_FPS),
(
int(scale * vidcap.get(cv2.CAP_PROP_FRAME_WIDTH)),
int(scale * vidcap.get(cv2.CAP_PROP_FRAME_HEIGHT))
)
)
tqdm_test = tqdm(range(total_frames), ncols=80)
for _ in tqdm_test:
success, lr = vidcap.read()
if not success: break
lr, = common.set_channel(lr, n_channels=self.args.n_colors)
lr, = common.np2Tensor(lr, rgb_range=self.args.rgb_range)
lr, = self.prepare(lr.unsqueeze(0))
sr = self.model(lr, idx_scale)
sr = utility.quantize(sr, self.args.rgb_range).squeeze(0)
normalized = sr * 255 / self.args.rgb_range
ndarr = normalized.byte().permute(1, 2, 0).cpu().numpy()
vidwri.write(ndarr)
vidcap.release()
vidwri.release()
self.ckp.write_log(
'Total: {:.2f}s\n'.format(timer_test.toc()), refresh=True
)
torch.set_grad_enabled(True)
Example #19
| Source File: trainer.py From OISR-PyTorch with BSD 2-Clause "Simplified" License | 4 votes |
|
def test(self):
torch.set_grad_enabled(False)
epoch = self.scheduler.last_epoch + 1
self.ckp.write_log('\nEvaluation:')
self.ckp.add_log(
torch.zeros(1, len(self.loader_test), len(self.scale))
)
self.model.eval()
timer_test = utility.timer()
if self.args.save_results: self.ckp.begin_background()
for idx_data, d in enumerate(self.loader_test):
for idx_scale, scale in enumerate(self.scale):
d.dataset.set_scale(idx_scale)
for lr, hr, filename, _ in tqdm(d, ncols=80):
lr, hr = self.prepare(lr, hr)
sr = self.model(lr, idx_scale)
sr = utility.quantize(sr, self.args.rgb_range)
save_list = [sr]
self.ckp.log[-1, idx_data, idx_scale] += utility.calc_psnr(
sr, hr, scale, self.args.rgb_range, dataset=d
)
if self.args.save_gt:
save_list.extend([lr, hr])
if self.args.save_results:
self.ckp.save_results(d, filename[0], save_list, scale)
self.ckp.log[-1, idx_data, idx_scale] /= len(d)
best = self.ckp.log.max(0)
self.ckp.write_log(
'[{} x{}]\tPSNR: {:.3f} (Best: {:.3f} @epoch {})'.format(
d.dataset.name,
scale,
self.ckp.log[-1, idx_data, idx_scale],
best[0][idx_data, idx_scale],
best[1][idx_data, idx_scale] + 1
)
)
self.ckp.write_log('Forward: {:.2f}s\n'.format(timer_test.toc()))
self.ckp.write_log('Saving...')
if self.args.save_results: self.ckp.end_background()
if not self.args.test_only:
self.ckp.save(self, epoch, is_best=(best[1][0, 0] + 1 == epoch))
self.ckp.write_log(
'Total: {:.2f}s\n'.format(timer_test.toc()), refresh=True
)
torch.set_grad_enabled(True)
