def refresh_info(self):
info = {}
info['mode'] = self._mode
info['fps'] = self._fps
if self.vid:
info['cur_frame_index'] = self.vid.cur_frame_index
info['num_frames'] = self.vid.vid_frames
info['canny'] = self._canny_on
info['komas'] = self._k
info['onion'] = self._onion_num
info_str = ' Playing Info \n\n'
info_str += 'FPS of playing: {:.2f}\n'.format(self._fps)
if self.vid:
info_str += 'Time: {}/{}\n'.format(
utils.sec2time(self.vid.cur_frame_index / self.vid.fps),
utils.sec2time(self.vid.vid_frames / self.vid.fps))
info_str += 'No. of frames: {}/{}\n'.format(
self.vid.cur_frame_index, self.vid.vid_frames - 1)
info_str += 'Range: {} - {}\n'.format(self.vid.start_index,
self.vid.end_index)
info_str += '\n'
else:
pass
info_str += 'Canny Mode: {}\n'.format(
self._canny_on if self._canny_on else 'Off')
info_str += 'Komas with \'j\'/\'k\': {}\n'.format(self._k)
info_str += 'Onion num: {}\n'.format(
self._onion_num if self._onion_num else 'Off')
self.ext_print(info_str, info)
def optimize(self):
"""
Train the network. For each iteration, call the optimization loop function.
"""
print(colored('starting optimization with ADAM...', 'cyan'))
self.optimizer = torch.optim.Adam(self.parameters, lr=self.args.lr)
scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(self.optimizer, mode='min',
factor=self.args.lr_factor,
threshold=self.args.lr_thresh,
patience=self.args.lr_patience)
# stop after no improvements greater than a certain percentage of the previous loss
stopper = u.EarlyStopping(patience=self.args.earlystop_patience,
min_delta=self.args.earlystop_min_delta,
percentage=True)
start = time()
for j in range(self.args.epochs):
self.optimizer.zero_grad()
loss = self.optimization_loop()
self.optimizer.step()
if self.args.reduce_lr:
scheduler.step(loss)
if stopper.step(loss): # stopper is computed on loss, as we don't have any validation metrics
break
self.elapsed = time() - start
print(colored(u.sec2time(self.elapsed), 'yellow'))
def run(self):
""" This function shows the frame and all visualizations. It is also responsable for synchronization of stimulus and computer time."""
""" scheduler configuration """
cpu_time = time.time()
self.snippets[self.snippet_id].frame_count += 1
#act_frame_element = s.enterabs(sequence.time_started+1/stimulus.fps/SPEED*sequence.frame_count, 1, showFrame, (s,stimulus,sequence,gazess,player))
""" load image """
frameImg = self.stimulus.get_next_frame()
if frameImg is None:
return False
""" adjusting stimulus, due to slow computers """
#skipped_frames = stimulus.resync(sequence.begin + (cpu_time - sequence.time_started)*SPEED)
#sequence.skipped_frames += skipped_frames
""" when snippet is over, stop scheduler through deleting next schedule element """
if not self.snippets[self.snippet_id].contains(self.stimulus.act_pos):
if self.snippet_id + 1 >= len(self.snippets):
self.play_snippet(1)
else:
self.play_snippet(self.snippet_id + 1)
if self.stop_after:
self.qscheduler.stop()
return
""" bgr to gray convertion for using gray stimulus image """
if self.gray:
frameImg = cv2.cvtColor(frameImg,cv.CV_BGR2GRAY)
frameImg = cv2.cvtColor(frameImg,cv.CV_GRAY2BGR)
showPic = cv2.addWeighted(frameImg, self.movie_opacity, frameImg, 0, 0) #TODO: find better implementation
""" go through every option and do calculations """
if self.show_gazes_each > 0:
ovImg = self.gazes.get_each(self.stimulus)
showPic = cv2.addWeighted(showPic, 1, ovImg, self.show_gazes_each, 0)
if self.show_gazes_clustered > 0:
heatMap = self.gazes.get_heat_map(self.stimulus, .5)
ovImg = self.gazes.get_clustered(self.stimulus, heatMap)
showPic = cv2.addWeighted(showPic, 1, ovImg, self.show_gazes_clustered, 0)
""" shows final stimulus visualized image """
self.stim_win.show_img(showPic)
self.control_win.update_texts(self)
self.stim_win.pos.setText('%(start)s / %(end)s' %{'start':utils.sec2time(self.stimulus.act_pos), 'end': self.stimulus.end_pos_str})
#self.stim_win.debug.setText('skipped: %(fr)i' %{'fr':sequence.skipped_frames})
cv2.waitKey(1)
def reconstruct_patches(args,
return_history=False,
verbose=False) -> Tuple[np.ndarray, list]:
inputs = np.load(os.path.join(args.imgdir, args.imgname),
allow_pickle=True)
pe = _get_patch_extractor(inputs.shape, args.patch_shape,
args.patch_stride, args.datadim, args.imgchannel)
# this is necessary for setting pe attributes
_ = pe.extract(inputs)
patch_array_shape = u.patch_array_shape(inputs.shape, pe.dim, pe.stride)
patches_out = []
elapsed = []
history = []
for path in glob(os.path.join('./results', args.outdir) + '/*.npy'):
try:
out = np.load(path, allow_pickle=True).item()
except AttributeError:
out = np.load(path, allow_pickle=True).item()
patches_out.append(out['output'])
try:
elapsed.append(out['elapsed'])
except KeyError:
elapsed.append(out['elapsed time'])
history.append(out['history'])
patches_out = np.asarray(patches_out)
if args.datadim == '2.5d':
patches_out = _transpose_patches_25d(patches_out, args.slice, adj=True)
outputs = pe.reconstruct(
patches_out.reshape(patch_array_shape)) / args.gain
try:
gpu_ = u.get_gpu_name(int(out['device']))
except:
gpu_ = out['device']
if verbose:
print('\n%d patches; total elapsed time on %s: %s' %
(len(history), gpu_,
u.sec2time(sum([u.time2sec(e) for e in elapsed]))))
if return_history:
return outputs, history
else:
return outputs
def save_result(self):
"""
Save the results, the model (if asked) and some info to disk in a .npy file.
"""
np.save(os.path.join(self.outpath, self.image_name + '_run.npy'), {
'device' : u.get_gpu_name(int(os.environ["CUDA_VISIBLE_DEVICES"])),
'elapsed': u.sec2time(self.elapsed),
'outpath': self.outpath,
'history': self.history,
'mask' : self.mask,
'image' : self.img,
'output' : self.out_best,
'noise' : self.input_list,
})
# save the model
if self.args.savemodel:
torch.save(self.net.state_dict(),
os.path.join(self.outpath, self.image_name + '_model.pth'))
def __init__(self, movie_path):
self.path = movie_path
self.data = cv2.VideoCapture(self.path)
self.fps = self.data.get(cv.CV_CAP_PROP_FPS )
self.nFrames = int(self.data.get(cv.CV_CAP_PROP_FRAME_COUNT ))
self.length = self.nFrames / self.fps
self.width = int(self.data.get(cv.CV_CAP_PROP_FRAME_WIDTH ))
self.height = int(self.data.get(cv.CV_CAP_PROP_FRAME_HEIGHT))
# actual information
self.act_pos = 0 # actual position of stimulus in seconds
self.act_frame = None # numpy array containing last frame image
self.speed = 1
# synchronizing information
self.start_pos = 0
self.start_time = 0
# performance caching
self.end_pos_str = utils.sec2time(self.nFrames/self.fps)
def run(self):
"""
System Entry
"""
# ray.init()
start_time = time.time()
start_time_str = time.strftime('%Y-%m-%d-%H:%M:%S',
time.localtime(start_time))
logger.info(
f'*******************************Dolphin Detection System: Running Environment [{self.cfg.env}] at '
f'[{start_time_str}]********************************')
# self.http_server.run()
if self.cfg.run_direct:
self.monitor.monitor()
else:
self.scheduler.start()
end_time = time.time()
end_time_str = time.strftime('%Y-%m-%d-%H:%M:%S',
time.localtime(end_time))
run_time = sec2time(end_time - start_time)
logger.info(
f'*******************************Dolphin Detection System: Shut down at [{end_time_str}].'
f'Total Running Time '
f'[{run_time}]********************************')
def train(model,
train_loader,
test_loader,
mode='EDSR_Baseline',
save_image_every=50,
save_model_every=10,
test_model_every=1,
epoch_start=0,
num_epochs=1000,
device=None,
refresh=True):
if device is None:
device = 'cuda' if torch.cuda.is_available() else 'cpu'
today = datetime.datetime.now().strftime('%Y.%m.%d')
result_dir = f'./results/{today}/{mode}'
weight_dir = f'./weights/{today}/{mode}'
logger_dir = f'./logger/{today}_{mode}'
csv = f'./hist_{today}_{mode}.csv'
if refresh:
try:
shutil.rmtree(result_dir)
shutil.rmtree(weight_dir)
shutil.rmtree(logger_dir)
except FileNotFoundError:
pass
os.makedirs(result_dir, exist_ok=True)
os.makedirs(weight_dir, exist_ok=True)
os.makedirs(logger_dir, exist_ok=True)
logger = SummaryWriter(log_dir=logger_dir, flush_secs=2)
model = model.to(device)
params = list(model.parameters())
optim = torch.optim.Adam(params, lr=1e-4)
scheduler = torch.optim.lr_scheduler.StepLR(optim,
step_size=1000,
gamma=0.99)
criterion = torch.nn.L1Loss()
GMSD = GMSD_quality().to(device)
mshf = MSHF(3, 3).to(device)
start_time = time.time()
print(f'Training Start || Mode: {mode}')
step = 0
pfix = OrderedDict()
pfix_test = OrderedDict()
hist = dict()
hist['mode'] = f'{today}_{mode}'
for key in ['epoch', 'psnr', 'ssim', 'ms-ssim']:
hist[key] = []
blurs = {}
for ksize in [3, 5, 7]:
blurs[ksize] = {}
for sigma in [0.4, 0.8, 1.0, 1.2, 1.6, 2.0]:
blurs[ksize][sigma] = Blur(ksize=ksize, sigma=sigma).to(device)
noise_sigma = 0.3
for epoch in range(epoch_start, epoch_start + num_epochs):
if epoch == 0:
torch.save(model.state_dict(),
f'{weight_dir}/epoch_{epoch+1:04d}.pth')
if epoch == 0:
with torch.no_grad():
with tqdm(
test_loader,
desc=
f'{mode} || Warming Up || Test Epoch {epoch}/{num_epochs}',
position=0,
leave=True) as pbar_test:
psnrs = []
ssims = []
msssims = []
for lr, hr, fname in pbar_test:
lr = lr.to(device)
# hr = hr.to(device)
blur = blurs[7][2.0]
lr_input = blur(lr)
lr_input = lr_input + torch.rand_like(
lr, device=lr.device) * noise_sigma
_, features = model(lr_input)
dr = features[0]
# sr = quantize(sr)
psnr, ssim, msssim = evaluate(lr, dr)
psnrs.append(psnr)
ssims.append(ssim)
msssims.append(msssim)
psnr_mean = np.array(psnrs).mean()
ssim_mean = np.array(ssims).mean()
msssim_mean = np.array(msssims).mean()
pfix_test['psnr'] = f'{psnr:.4f}'
pfix_test['ssim'] = f'{ssim:.4f}'
pfix_test['msssim'] = f'{msssim:.4f}'
pfix_test['psnr_mean'] = f'{psnr_mean:.4f}'
pfix_test['ssim_mean'] = f'{ssim_mean:.4f}'
pfix_test['msssim_mean'] = f'{msssim_mean:.4f}'
pbar_test.set_postfix(pfix_test)
if len(psnrs) > 1: break
with tqdm(train_loader,
desc=f'{mode} || Epoch {epoch+1}/{num_epochs}',
position=0,
leave=True) as pbar:
psnrs = []
ssims = []
msssims = []
losses = []
for lr, hr, _ in pbar:
lr = lr.to(device)
# hr = hr.to(device)
# prediction
ksize_ = random.choice([3, 5, 7])
sigma_ = random.choice([0.4, 0.8, 1.0, 1.2, 1.6, 2.0])
blur = blurs[ksize_][sigma_]
dnd = random.choice(['blur', 'noise', 'blur_and_noise'])
if dnd == 'blur':
lr_input = blur(lr)
elif dnd == 'noise':
lr_input = lr + torch.rand_like(
lr, device=lr.device) * noise_sigma
else:
lr_input = blur(lr)
lr_input = lr_input + torch.rand_like(
lr, device=lr.device) * noise_sigma
_, features = model(lr_input)
dr = features[0]
gmsd = GMSD(lr, dr)
# training
loss = criterion(lr, dr)
loss_tot = loss
optim.zero_grad()
loss_tot.backward()
optim.step()
scheduler.step()
# training history
elapsed_time = time.time() - start_time
elapsed = sec2time(elapsed_time)
pfix['Step'] = f'{step+1}'
pfix['Loss'] = f'{loss.item():.4f}'
psnr, ssim, msssim = evaluate(lr, dr)
psnrs.append(psnr)
ssims.append(ssim)
msssims.append(msssim)
psnr_mean = np.array(psnrs).mean()
ssim_mean = np.array(ssims).mean()
msssim_mean = np.array(msssims).mean()
pfix['PSNR'] = f'{psnr:.2f}'
pfix['SSIM'] = f'{ssim:.4f}'
# pfix['MSSSIM'] = f'{msssim:.4f}'
pfix['PSNR_mean'] = f'{psnr_mean:.2f}'
pfix['SSIM_mean'] = f'{ssim_mean:.4f}'
# pfix['MSSSIM_mean'] = f'{msssim_mean:.4f}'
free_gpu = get_gpu_memory()[0]
pfix['free GPU'] = f'{free_gpu}MiB'
pfix['Elapsed'] = f'{elapsed}'
pbar.set_postfix(pfix)
losses.append(loss.item())
if step % save_image_every == 0:
imsave([lr_input[0], dr[0], lr[0], gmsd[0]],
f'{result_dir}/epoch_{epoch+1}_iter_{step:05d}.jpg')
step += 1
logger.add_scalar("Loss/train", np.array(losses).mean(), epoch + 1)
logger.add_scalar("PSNR/train", psnr_mean, epoch + 1)
logger.add_scalar("SSIM/train", ssim_mean, epoch + 1)
if (epoch + 1) % save_model_every == 0:
torch.save(model.state_dict(),
f'{weight_dir}/epoch_{epoch+1:04d}.pth')
if (epoch + 1) % test_model_every == 0:
with torch.no_grad():
with tqdm(
test_loader,
desc=f'{mode} || Test Epoch {epoch+1}/{num_epochs}',
position=0,
leave=True) as pbar_test:
psnrs = []
ssims = []
msssims = []
for lr, hr, fname in pbar_test:
fname = fname[0].split('/')[-1].split('.pt')[0]
lr = lr.to(device)
# hr = hr.to(device)
blur = blurs[7][2.0]
lr_input = blur(lr)
lr_input = lr_input + torch.rand_like(
lr, device=lr.device) * noise_sigma
_, features = model(lr_input)
dr = features[0]
mshf_lr = mshf(lr)
mshf_dr = mshf(dr)
gmsd = GMSD(lr, dr)
psnr, ssim, msssim = evaluate(lr, dr)
psnrs.append(psnr)
ssims.append(ssim)
msssims.append(msssim)
psnr_mean = np.array(psnrs).mean()
ssim_mean = np.array(ssims).mean()
msssim_mean = np.array(msssims).mean()
pfix_test['psnr'] = f'{psnr:.4f}'
pfix_test['ssim'] = f'{ssim:.4f}'
pfix_test['msssim'] = f'{msssim:.4f}'
pfix_test['psnr_mean'] = f'{psnr_mean:.4f}'
pfix_test['ssim_mean'] = f'{ssim_mean:.4f}'
pfix_test['msssim_mean'] = f'{msssim_mean:.4f}'
pbar_test.set_postfix(pfix_test)
imsave([lr_input[0], dr[0], lr[0], gmsd[0]],
f'{result_dir}/{fname}.jpg')
mshf_vis = torch.cat(
(torch.cat([
mshf_dr[:, i, :, :]
for i in range(mshf_dr.shape[1])
],
dim=-1),
torch.cat([
mshf_lr[:, i, :, :]
for i in range(mshf_lr.shape[1])
],
dim=-1)),
dim=-2)
imsave(mshf_vis, f'{result_dir}/MSHF_{fname}.jpg')
hist['epoch'].append(epoch + 1)
hist['psnr'].append(psnr_mean)
hist['ssim'].append(ssim_mean)
hist['ms-ssim'].append(msssim_mean)
logger.add_scalar("PSNR/test", psnr_mean, epoch + 1)
logger.add_scalar("SSIM/test", ssim_mean, epoch + 1)
logger.add_scalar("MS-SSIM/test", msssim_mean,
epoch + 1)
df = pd.DataFrame(hist)
df.to_csv(csv)
final_boxes, final_labels, final_probs = final_boxes[good_ids], \
final_labels[good_ids], final_probs[good_ids]
vis_boxes = np.asarray(
[[box[0], box[1], box[2] + box[0], box[3] + box[1]]
for box in final_boxes])
vis_labels = [
"%s_%.2f" % (targetid2class[cat_id], prob)
for cat_id, prob in zip(final_labels, final_probs)
]
newim = draw_boxes(im,
vis_boxes,
vis_labels,
color=np.array([255, 0, 0]),
font_scale=0.5,
thickness=2)
vis_file = os.path.join(vis_path, "%s.jpg" % (imgname))
cv2.imwrite(vis_file, newim)
if args.log_time_and_gpu:
end_time = time.time()
print(
"total run time %s (%s), log gpu utilize every %s seconds and get "
"median %.2f%% and average %.2f%%. GPU temperature median %.2f and "
"average %.2f (C)" %
(sec2time(end_time - start_time), end_time - start_time,
gpu_log_interval, np.median(gpu_util_logs) * 100,
np.mean(gpu_util_logs) * 100, np.median(gpu_temp_logs),
np.mean(gpu_temp_logs)))
cv2.destroyAllWindows()
def train(model,
train_loader,
test_loader,
mode='EDSR_Baseline',
save_image_every=50,
save_model_every=10,
test_model_every=1,
epoch_start=0,
num_epochs=1000,
device=None,
refresh=True,
scale=2,
today=None):
if device is None:
device = 'cuda' if torch.cuda.is_available() else 'cpu'
if today is None:
today = datetime.datetime.now().strftime('%Y.%m.%d')
result_dir = f'./results/{today}/{mode}'
weight_dir = f'./weights/{today}/{mode}'
logger_dir = f'./logger/{today}_{mode}'
csv = f'./hist_{today}_{mode}.csv'
if refresh:
try:
shutil.rmtree(result_dir)
shutil.rmtree(weight_dir)
shutil.rmtree(logger_dir)
except FileNotFoundError:
pass
os.makedirs(result_dir, exist_ok=True)
os.makedirs(weight_dir, exist_ok=True)
os.makedirs(logger_dir, exist_ok=True)
logger = SummaryWriter(log_dir=logger_dir, flush_secs=2)
model = model.to(device)
params = list(model.parameters())
optim = torch.optim.Adam(params, lr=1e-4)
scheduler = torch.optim.lr_scheduler.StepLR(optim,
step_size=1000,
gamma=0.99)
criterion = torch.nn.L1Loss()
GMSD = GMSD_quality().to(device)
opening = Opening().to(device)
blur = Blur().to(device)
mshf = MSHF(3, 3).to(device)
downx2_bicubic = nn.Upsample(scale_factor=1 / 2,
mode='bicubic',
align_corners=False)
downx4_bicubic = nn.Upsample(scale_factor=1 / 4,
mode='bicubic',
align_corners=False)
start_time = time.time()
print(f'Training Start || Mode: {mode}')
step = 0
pfix = OrderedDict()
pfix_test = OrderedDict()
hist = dict()
hist['mode'] = f'{today}_{mode}'
for key in ['epoch', 'psnr', 'ssim', 'ms-ssim']:
hist[key] = []
soft_mask = False
# hf_kernel = get_hf_kernel(mode='high')
for epoch in range(epoch_start, epoch_start + num_epochs):
if epoch == 0:
torch.save(model.state_dict(),
f'{weight_dir}/epoch_{epoch+1:04d}.pth')
if epoch == 0:
with torch.no_grad():
with tqdm(
test_loader,
desc=
f'{mode} || Warming Up || Test Epoch {epoch}/{num_epochs}',
position=0,
leave=True) as pbar_test:
psnrs = []
ssims = []
msssims = []
for lr, hr, fname in pbar_test:
lr = lr.to(device)
hr = hr.to(device)
sr, srx2, srx1 = model(lr)
sr = quantize(sr)
psnr, ssim, msssim = evaluate(hr, sr)
psnrs.append(psnr)
ssims.append(ssim)
msssims.append(msssim)
psnr_mean = np.array(psnrs).mean()
ssim_mean = np.array(ssims).mean()
msssim_mean = np.array(msssims).mean()
pfix_test['psnr_mean'] = f'{psnr_mean:.4f}'
pfix_test['ssim_mean'] = f'{ssim_mean:.4f}'
pfix_test['msssim_mean'] = f'{msssim_mean:.4f}'
pbar_test.set_postfix(pfix_test)
if len(psnrs) > 1: break
with tqdm(train_loader,
desc=f'{mode} || Epoch {epoch+1}/{num_epochs}',
position=0,
leave=True) as pbar:
psnrs = []
ssims = []
msssims = []
losses = []
for lr, hr, _ in pbar:
lr = lr.to(device)
hr = hr.to(device)
hrx1 = downx4_bicubic(hr)
hrx2 = downx2_bicubic(hr)
# prediction
sr, srx2, srx1 = model(lr)
gmsd = GMSD(hr, sr)
sr_ = quantize(sr)
psnr, ssim, msssim = evaluate(hr, sr_)
if psnr >= 40 - 2 * scale:
soft_mask = True
else:
soft_mask = False
if soft_mask:
# with torch.no_grad():
# for _ in range(10): gmsd = opening(gmsd)
gmask = gmsd / gmsd.max()
gmask = (gmask > 0.2) * 1.0
gmask = blur(gmask)
gmask = (gmask - gmask.min()) / (gmask.max() -
gmask.min() + 1e-7)
gmask = (gmask + 0.25) / 1.25
gmask = gmask.detach()
gmaskx2 = downx2_bicubic(gmask)
gmaskx1 = downx4_bicubic(gmask)
# training
loss = criterion(sr * gmask, hr * gmask)
lossx2 = criterion(srx2 * gmaskx2, hrx2 * gmaskx2)
lossx1 = criterion(srx1 * gmaskx1, hrx1 * gmaskx1)
else:
loss = criterion(sr, hr)
lossx2 = criterion(srx2, hrx2)
lossx1 = criterion(srx1, hrx1)
# training
loss_tot = loss + 0.25 * lossx2 + 0.125 * lossx1
optim.zero_grad()
loss_tot.backward()
optim.step()
scheduler.step()
# training history
elapsed_time = time.time() - start_time
elapsed = sec2time(elapsed_time)
pfix['Loss'] = f'{loss.item():.4f}'
pfix['x2'] = f'{lossx2.item():.4f}'
pfix['x1'] = f'{lossx1.item():.4f}'
psnrs.append(psnr)
ssims.append(ssim)
msssims.append(msssim)
psnr_mean = np.array(psnrs).mean()
ssim_mean = np.array(ssims).mean()
msssim_mean = np.array(msssims).mean()
pfix['PSNR_mean'] = f'{psnr_mean:.2f}'
pfix['SSIM_mean'] = f'{ssim_mean:.4f}'
free_gpu = get_gpu_memory()[0]
pfix['Elapsed'] = f'{elapsed}'
pfix['free GPU'] = f'{free_gpu}MiB'
pbar.set_postfix(pfix)
losses.append(loss.item())
if step % save_image_every == 0:
z = torch.zeros_like(lr[0])
_, _, llr, _ = lr.shape
_, _, hlr, _ = hr.shape
if hlr // 2 == llr:
xz = torch.cat((lr[0], z), dim=-2)
elif hlr // 4 == llr:
xz = torch.cat((lr[0], z, z, z), dim=-2)
imsave([xz, sr[0], hr[0], gmsd[0]],
f'{result_dir}/epoch_{epoch+1}_iter_{step:05d}.jpg')
step += 1
logger.add_scalar("Loss/train", np.array(losses).mean(), epoch + 1)
logger.add_scalar("PSNR/train", psnr_mean, epoch + 1)
logger.add_scalar("SSIM/train", ssim_mean, epoch + 1)
if (epoch + 1) % save_model_every == 0:
torch.save(model.state_dict(),
f'{weight_dir}/epoch_{epoch+1:04d}.pth')
if (epoch + 1) % test_model_every == 0:
with torch.no_grad():
with tqdm(
test_loader,
desc=f'{mode} || Test Epoch {epoch+1}/{num_epochs}',
position=0,
leave=True) as pbar_test:
psnrs = []
ssims = []
msssims = []
for lr, hr, fname in pbar_test:
fname = fname[0].split('/')[-1].split('.pt')[0]
lr = lr.to(device)
hr = hr.to(device)
sr, _, _ = model(lr)
mshf_hr = mshf(hr)
mshf_sr = mshf(sr)
gmsd = GMSD(hr, sr)
sr = quantize(sr)
psnr, ssim, msssim = evaluate(hr, sr)
psnrs.append(psnr)
ssims.append(ssim)
msssims.append(msssim)
psnr_mean = np.array(psnrs).mean()
ssim_mean = np.array(ssims).mean()
msssim_mean = np.array(msssims).mean()
pfix_test['psnr_mean'] = f'{psnr_mean:.4f}'
pfix_test['ssim_mean'] = f'{ssim_mean:.4f}'
pfix_test['msssim_mean'] = f'{msssim_mean:.4f}'
pbar_test.set_postfix(pfix_test)
z = torch.zeros_like(lr[0])
_, _, llr, _ = lr.shape
_, _, hlr, _ = hr.shape
if hlr // 2 == llr:
xz = torch.cat((lr[0], z), dim=-2)
elif hlr // 4 == llr:
xz = torch.cat((lr[0], z, z, z), dim=-2)
imsave([xz, sr[0], hr[0], gmsd[0]],
f'{result_dir}/{fname}.jpg')
mshf_vis = torch.cat(
(torch.cat([
mshf_sr[:, i, :, :]
for i in range(mshf_sr.shape[1])
],
dim=-1),
torch.cat([
mshf_hr[:, i, :, :]
for i in range(mshf_hr.shape[1])
],
dim=-1)),
dim=-2)
imsave(mshf_vis, f'{result_dir}/MSHF_{fname}.jpg')
hist['epoch'].append(epoch + 1)
hist['psnr'].append(psnr_mean)
hist['ssim'].append(ssim_mean)
hist['ms-ssim'].append(msssim_mean)
logger.add_scalar("PSNR/test", psnr_mean, epoch + 1)
logger.add_scalar("SSIM/test", ssim_mean, epoch + 1)
logger.add_scalar("MS-SSIM/test", msssim_mean,
epoch + 1)
df = pd.DataFrame(hist)
df.to_csv(csv)
return model
def run(self):
""" This function shows the frame and all visualizations. It is also responsable for synchronization of stimulus and computer time."""
""" scheduler configuration """
cpu_time = time.time()
self.snippets[self.snippet_id].frame_count += 1
#act_frame_element = s.enterabs(sequence.time_started+1/stimulus.fps/SPEED*sequence.frame_count, 1, showFrame, (s,stimulus,sequence,gazess,player))
""" load image """
frameImg = self.stimulus.get_next_frame()
if frameImg is None:
return False
""" adjusting stimulus, due to slow computers """
if self.qscheduler.isActive():
skipped_frames = self.stimulus.resync()
self.snippets[self.snippet_id].skipped_frames += skipped_frames
""" when snippet is over, stop scheduler through deleting next schedule element """
if not self.snippets[self.snippet_id].contains(self.stimulus.act_pos):
if self.snippet_id + 1 >= len(self.snippets):
self.play_snippet(1)
else:
self.play_snippet(self.snippet_id + 1)
if self.stop_after:
self.qscheduler.stop()
return
""" bgr to gray convertion for using gray stimulus image """
if self.movie_gray > 0:
frameImg = cv2.cvtColor(frameImg,cv.CV_BGR2GRAY)
frameImg = cv2.cvtColor(frameImg,cv.CV_GRAY2BGR)
showPic = cv2.addWeighted(frameImg, self.movie_opacity, frameImg, 0, 0) #TODO: find better implementation
for ts in self.timeseries:
ts.show(self.stimulus.act_pos)
for an in self.annotations:
an.show(int(self.stimulus.act_pos*self.stimulus.fps))
heatMap = None
if self.show_aperture > 0:
heatMap = self.gazes.get_heat_map(self.stimulus, .5)
aperture = cv2.resize(heatMap, (self.stimulus.width, self.stimulus.height)) / 255.
ap_pic = showPic.copy()
for i in range(3):
ap_pic[:,:,i] = showPic[:,:,i] * aperture
showPic = ap_pic
if self.show_gazes_each > 0:
ovImg = self.gazes.get_each(self.stimulus)
showPic = cv2.addWeighted(showPic, 1, ovImg, self.show_gazes_each, 0)
if self.show_gazes_clustered > 0:
ovImg = self.gazes.get_clustered(self.stimulus, heatMap)
showPic = cv2.addWeighted(showPic, 1, ovImg, self.show_gazes_clustered, 0)
""" shows final stimulus visualized image """
self.stim_win.show_img(showPic)
self.control_win.update_texts(self)
self.stim_win.pos.setText('%(start)s / %(end)s' %{'start':utils.sec2time(self.stimulus.act_pos), 'end': self.stimulus.end_pos_str})
self.stim_win.debug.setText('skipped: %(fr)i' %{'fr':self.snippets[self.snippet_id].skipped_frames})
cv2.waitKey(1)
def train(model,
train_loader,
test_loader,
mode='EDSR_Baseline',
save_image_every=50,
save_model_every=10,
test_model_every=1,
num_epochs=1000,
device=None,
refresh=True):
if device is None:
device = 'cuda' if torch.cuda.is_available() else 'cpu'
today = datetime.datetime.now().strftime('%Y.%m.%d')
result_dir = f'./results/{today}/{mode}'
weight_dir = f'./weights/{today}/{mode}'
logger_dir = f'./logger/{today}_{mode}'
csv = f'./hist_{today}_{mode}.csv'
if refresh:
try:
shutil.rmtree(result_dir)
shutil.rmtree(weight_dir)
shutil.rmtree(logger_dir)
except FileNotFoundError:
pass
os.makedirs(result_dir, exist_ok=True)
os.makedirs(weight_dir, exist_ok=True)
os.makedirs(logger_dir, exist_ok=True)
logger = SummaryWriter(log_dir=logger_dir, flush_secs=2)
model = model.to(device)
params = list(model.parameters())
optim = torch.optim.Adam(params, lr=1e-4)
scheduler = torch.optim.lr_scheduler.StepLR(optim,
step_size=1000,
gamma=0.99)
criterion = torch.nn.L1Loss()
######
ED = Edge().to(device)
######
start_time = time.time()
print(f'Training Start || Mode: {mode}')
step = 0
pfix = OrderedDict()
pfix_test = OrderedDict()
hist = dict()
hist['mode'] = f'{today}_{mode}'
for key in ['epoch', 'psnr', 'ssim', 'ms-ssim']:
hist[key] = []
for epoch in range(num_epochs):
if epoch == 0:
torch.save(model.state_dict(),
f'{weight_dir}/epoch_{epoch+1:04d}.pth')
if epoch == 0:
with torch.no_grad():
with tqdm(
test_loader,
desc=
f'Mode: {mode} || Warming Up || Test Epoch {epoch}/{num_epochs}',
position=0,
leave=True) as pbar_test:
psnrs = []
ssims = []
msssims = []
for lr, hr, fname in pbar_test:
lr = lr.to(device)
hr = hr.to(device)
sr, _, features = model(lr)
sr = quantize(sr)
psnr, ssim, msssim = evaluate(hr, sr)
psnrs.append(psnr)
ssims.append(ssim)
msssims.append(msssim)
psnr_mean = np.array(psnrs).mean()
ssim_mean = np.array(ssims).mean()
msssim_mean = np.array(msssims).mean()
pfix_test['psnr'] = f'{psnr:.4f}'
pfix_test['ssim'] = f'{ssim:.4f}'
pfix_test['msssim'] = f'{msssim:.4f}'
pfix_test['psnr_mean'] = f'{psnr_mean:.4f}'
pfix_test['ssim_mean'] = f'{ssim_mean:.4f}'
pfix_test['msssim_mean'] = f'{msssim_mean:.4f}'
pbar_test.set_postfix(pfix_test)
if len(psnrs) > 1: break
with tqdm(train_loader,
desc=f'Mode: {mode} || Epoch {epoch+1}/{num_epochs}',
position=0,
leave=True) as pbar:
psnrs = []
ssims = []
msssims = []
losses = []
for lr, hr, _ in pbar:
lr = lr.to(device)
hr = hr.to(device)
# prediction
sr, lr_edge, features = model(lr)
####
hr_edge = ED(hr)
loss_edge = criterion(lr_edge, hr_edge)
#####
# training
loss = criterion(hr, sr)
loss_tot = loss + 0.1 * loss_edge
optim.zero_grad()
loss_tot.backward()
optim.step()
scheduler.step()
# training history
elapsed_time = time.time() - start_time
elapsed = sec2time(elapsed_time)
pfix['Step'] = f'{step+1}'
pfix['Loss'] = f'{loss.item():.4f}'
pfix['Loss Edge'] = f'{loss_edge.item():.4f}'
sr = quantize(sr)
psnr, ssim, msssim = evaluate(hr, sr)
psnrs.append(psnr)
ssims.append(ssim)
msssims.append(msssim)
psnr_mean = np.array(psnrs).mean()
ssim_mean = np.array(ssims).mean()
msssim_mean = np.array(msssims).mean()
pfix['PSNR'] = f'{psnr:.2f}'
pfix['SSIM'] = f'{ssim:.4f}'
# pfix['MSSSIM'] = f'{msssim:.4f}'
pfix['PSNR_mean'] = f'{psnr_mean:.2f}'
pfix['SSIM_mean'] = f'{ssim_mean:.4f}'
# pfix['MSSSIM_mean'] = f'{msssim_mean:.4f}'
free_gpu = get_gpu_memory()[0]
pfix['free GPU'] = f'{free_gpu}MiB'
pfix['Elapsed'] = f'{elapsed}'
pbar.set_postfix(pfix)
losses.append(loss.item())
if step % save_image_every == 0:
z = torch.zeros_like(lr[0])
_, _, llr, _ = lr.shape
_, _, hlr, _ = hr.shape
if hlr // 2 == llr:
xz = torch.cat((lr[0], z), dim=-2)
elif hlr // 4 == llr:
xz = torch.cat((lr[0], z, z, z), dim=-2)
imsave([xz, sr[0], hr[0]],
f'{result_dir}/epoch_{epoch+1}_iter_{step:05d}.jpg')
step += 1
logger.add_scalar("Loss/train", np.array(losses).mean(), epoch + 1)
logger.add_scalar("PSNR/train", psnr_mean, epoch + 1)
logger.add_scalar("SSIM/train", ssim_mean, epoch + 1)
if (epoch + 1) % save_model_every == 0:
torch.save(model.state_dict(),
f'{weight_dir}/epoch_{epoch+1:04d}.pth')
if (epoch + 1) % test_model_every == 0:
with torch.no_grad():
with tqdm(
test_loader,
desc=
f'Mode: {mode} || Test Epoch {epoch+1}/{num_epochs}',
position=0,
leave=True) as pbar_test:
psnrs = []
ssims = []
msssims = []
for lr, hr, fname in pbar_test:
fname = fname[0].split('/')[-1].split('.pt')[0]
lr = lr.to(device)
hr = hr.to(device)
sr, _, features = model(lr)
sr = quantize(sr)
psnr, ssim, msssim = evaluate(hr, sr)
psnrs.append(psnr)
ssims.append(ssim)
msssims.append(msssim)
psnr_mean = np.array(psnrs).mean()
ssim_mean = np.array(ssims).mean()
msssim_mean = np.array(msssims).mean()
pfix_test['psnr'] = f'{psnr:.4f}'
pfix_test['ssim'] = f'{ssim:.4f}'
pfix_test['msssim'] = f'{msssim:.4f}'
pfix_test['psnr_mean'] = f'{psnr_mean:.4f}'
pfix_test['ssim_mean'] = f'{ssim_mean:.4f}'
pfix_test['msssim_mean'] = f'{msssim_mean:.4f}'
pbar_test.set_postfix(pfix_test)
z = torch.zeros_like(lr[0])
_, _, llr, _ = lr.shape
_, _, hlr, _ = hr.shape
if hlr // 2 == llr:
xz = torch.cat((lr[0], z), dim=-2)
elif hlr // 4 == llr:
xz = torch.cat((lr[0], z, z, z), dim=-2)
imsave([xz, sr[0], hr[0]],
f'{result_dir}/{fname}.jpg')
hist['epoch'].append(epoch + 1)
hist['psnr'].append(psnr_mean)
hist['ssim'].append(ssim_mean)
hist['ms-ssim'].append(msssim_mean)
logger.add_scalar("PSNR/test", psnr_mean, epoch + 1)
logger.add_scalar("SSIM/test", ssim_mean, epoch + 1)
logger.add_scalar("MS-SSIM/test", msssim_mean,
epoch + 1)
df = pd.DataFrame(hist)
df.to_csv(csv)
def train(config):
self_summary_strs = [] # summary string to print out for later
# first, read both data and filter stuff, to get the word2vec idx,
train_data = read_data(config, 'train', config.load)
val_data = read_data(
config, 'val', True
) # dev should always load model shared data(word2idx etc.) from train
config_vars = vars(config)
str_ = "threshold setting--\n" + "\t" + " ,".join(
["%s:%s" % (key, config_vars[key]) for key in config.thresmeta])
print str_
self_summary_strs.append(str_)
# cap the numbers
# max sentence word count etc.
update_config(config, [train_data, val_data],
showMeta=True) # all word num is <= max_thres
str_ = "renewed ----\n" + "\t" + " ,".join(
["%s:%s" % (key, config_vars[key]) for key in config.maxmeta])
print str_
self_summary_strs.append(str_)
# now we initialize the matrix for word embedding for word not in glove
word2vec_dict = train_data.shared['word2vec']
word2idx_dict = train_data.shared[
'word2idx'] # this is the word not in word2vec
# we are not fine tuning , so this should be empty
idx2vec_dict = {
word2idx_dict[word]: vec
for word, vec in word2vec_dict.items() if word in word2idx_dict
}
# random initial embedding matrix for new words
config.emb_mat = np.array([
idx2vec_dict[idx]
if idx2vec_dict.has_key(idx) else np.random.multivariate_normal(
np.zeros(config.word_emb_size), np.eye(config.word_emb_size))
for idx in xrange(config.word_vocab_size)
],
dtype="float32")
model = get_model(config) # construct model under gpu0
trainer = Trainer(model, config)
tester = Tester(model, config)
saver = tf.train.Saver(max_to_keep=5) # how many model to keep
bestsaver = tf.train.Saver(max_to_keep=5) # just for saving the best model
save_period = config.save_period # also the eval period
# for debug, show the batch content
if (config.showspecs):
for batch in train_data.get_batches(2, num_steps=20):
batchIdx, batchDs = batch
print "showing a batch with batch_size=2"
# show each data point
print "keys:%s" % batchDs.data.keys()
for key in sorted(batchDs.data.keys()):
print "\t%s:%s" % (key, batchDs.data[key])
# show some image feature
photo_idx1 = batchDs.data['photo_idxs'][0][0][
0] # [bacth_num][album_num][photo_num]
photo_id1 = batchDs.data['photo_ids'][0][0][0]
photo_idx2 = batchDs.data['photo_idxs'][1][0][0]
photo_id2 = batchDs.data['photo_ids'][1][0][0]
print "pidx:%s,pid:%s,feature:\n %s (%s)\n,should be:\n %s (%s)" % (
photo_idx1, photo_id1,
batchDs.data['pidx2feat'][photo_idx1][:10],
batchDs.data['pidx2feat'][photo_idx1].shape,
train_data.shared['pid2feat'][photo_id1][:10],
train_data.shared['pid2feat'][photo_id1].shape)
print "pidx:%s,pid:%s,feature:\n %s (%s)\n,should be:\n %s (%s)" % (
photo_idx2, photo_id2,
batchDs.data['pidx2feat'][photo_idx2][:10],
batchDs.data['pidx2feat'][photo_idx2].shape,
train_data.shared['pid2feat'][photo_id2][:10],
train_data.shared['pid2feat'][photo_id2].shape)
# get the feed_dict to check
#feed_dict = model.get_feed_dict(batchDs,is_train=True)
feed_dict = model.get_feed_dict(batchDs, is_train=False)
sys.exit()
# start training!
# allow_soft_placement : tf will auto select other device if the tf.device(*) not available
tfconfig = tf.ConfigProto(allow_soft_placement=True)
tfconfig.gpu_options.allow_growth = True # this way it will only allocate nessasary gpu, not take all
with tf.Session(config=tfconfig) as sess:
# calculate total parameters
totalParam = cal_total_param()
str_ = "total parameters: %s" % (totalParam)
print str_
self_summary_strs.append(str_)
initialize(load=config.load,
load_best=config.load_best,
model=model,
config=config,
sess=sess)
# the total step (iteration) the model will run
last_time = time.time()
# total / batchSize * epoch
num_steps = int(
math.ceil(train_data.num_examples /
float(config.batch_size))) * config.num_epochs
# get_batches is a generator, run on the fly
# there will be num_steps batch
str_ = " batch_size:%s, epoch:%s,total step:%s,eval/save every %s steps" % (
config.batch_size, config.num_epochs, num_steps,
config.save_period)
print str_
self_summary_strs.append(str_)
best = {
"acc": 0.0,
"step": -1
} # remember the best eval acc during training
finalAcc = None
isStart = True
for batch in tqdm(train_data.get_batches(config.batch_size,
num_steps=num_steps),
total=num_steps):
# each batch has (batch_idxs,Dataset(batch_data, full_shared))
# batch_data has {"q":,"y":..."pidx2feat",.."photo_idxs"..}
global_step = sess.run(model.global_step) + 1 # start from 0
# if load from existing model, save if first
if config.load and isStart:
tqdm.write("saving original model...")
tqdm.write("\tsaving model...")
saver.save(sess,
config.save_dir_model,
global_step=global_step)
tqdm.write("\tdone")
isStart = False
id2predanswers = {}
id2realanswers = {}
for evalbatch in val_data.get_batches(
config.batch_size,
num_steps=config.val_num_batches,
shuffle=False,
cap=True):
yp = tester.step(
sess, evalbatch
) # [N,4] # id2realanswersprob for each answer
pred, gt = getAnswers(
yp, evalbatch) # from here we get the qid:yindx,
id2predanswers.update(pred)
id2realanswers.update(gt)
evalAcc = getEvalScore(id2predanswers, id2realanswers)
tqdm.write(
"\teval on validation %s batches Acc:%s, (best:%s at step %s) "
% (config.val_num_batches, evalAcc, best['acc'],
best['step']))
# remember the best acc
if (evalAcc > best['acc']):
best['acc'] = evalAcc
best['step'] = global_step
# save the best model
tqdm.write("\t saving best model...")
bestsaver.save(sess,
config.save_dir_best_model,
global_step=global_step)
tqdm.write("\t done.")
finalAcc = evalAcc
loss, summary, train_op = trainer.step(sess,
batch,
get_summary=False)
if global_step % save_period == 0: # time to save model
duration = time.time() - last_time # in seconds
sec_per_step = duration / float(save_period)
last_time = time.time()
#use tqdm to print
tqdm.write(
"step:%s/%s (epoch:%.3f), took %s, loss:%s, estimate remaining:%s"
% (global_step, num_steps,
(config.num_epochs * global_step / float(num_steps)),
sec2time(duration), loss,
sec2time((num_steps - global_step) * sec_per_step)))
tqdm.write("\tsaving model...")
saver.save(sess,
config.save_dir_model,
global_step=global_step)
tqdm.write("\tdone")
id2predanswers = {}
id2realanswers = {}
for evalbatch in val_data.get_batches(
config.batch_size,
num_steps=config.val_num_batches,
shuffle=False,
cap=True):
yp = tester.step(
sess, evalbatch
) # [N,4] # id2realanswersprob for each answer
pred, gt = getAnswers(
yp, evalbatch) # from here we get the qid:yindx,
id2predanswers.update(pred)
id2realanswers.update(gt)
evalAcc = getEvalScore(id2predanswers, id2realanswers)
tqdm.write(
"\teval on validation %s batches Acc:%s, (best:%s at step %s) "
% (config.val_num_batches, evalAcc, best['acc'],
best['step']))
# remember the best acc
if (evalAcc > best['acc']):
best['acc'] = evalAcc
best['step'] = global_step
# save the best model
tqdm.write("\t saving best model...")
bestsaver.save(sess,
config.save_dir_best_model,
global_step=global_step)
tqdm.write("\t done.")
finalAcc = evalAcc
if global_step % save_period != 0: # time to save model
saver.save(sess, config.save_dir_model, global_step=global_step)
str_ = "best eval on val Accurucy: %s at %s step, final step %s Acc is %s" % (
best['acc'], best['step'], global_step, finalAcc)
print str_
self_summary_strs.append(str_)
if config.write_self_sum:
f = open(config.self_summary_path, "w")
f.writelines("%s" % ("\n".join(self_summary_strs)))
f.close()
video_writer.write(frame)
video_writer.release()
cv2.destroyAllWindows()
del cube_track_datas[gid]
del frame_data
gc.collect()
print("Total [ori_track/global]: Person %s, Vehicle %s" %
(total_person_num, total_vehicle_num))
for cat_name in ["Person", "Vehicle"]:
g_lengths_sorted = sorted(global_track_lengths[cat_name],
key=lambda x: x[0])
print(
"\t %s: %s global track total length %s. shortest %.1f, longest %.1f (g%s), median %.1f"
% (cat_name, len(global_track_lengths[cat_name]),
sec2time(sum([o[0] for o in global_track_lengths[cat_name]
])), g_lengths_sorted[0][0],
g_lengths_sorted[-1][0], g_lengths_sorted[-1][1],
np.median([o[0] for o in global_track_lengths[cat_name]])))
print("\tlongest track gid %s, from %s: %s" %
(longest_track[cat_name][1][0], longest_track[cat_name][1][1],
longest_track[cat_name][2]))
g_cams_sorted = sorted(global_track_cams[cat_name], key=lambda x: x[0])
print(
"\t %s: %s global track total local tracks %s. least cams %d, most %d (g%s), median %.1f"
% (cat_name, len(global_track_cams[cat_name]),
sum([o[0] for o in global_track_cams[cat_name]]),
g_cams_sorted[0][0], g_cams_sorted[-1][0], g_cams_sorted[-1][1],
np.median([o[0] for o in global_track_cams[cat_name]])))
print("\tmost_camera track gid %s, length %.1f, from %s: %s" %
(most_camera_track[cat_name][1][0],
most_camera_track[cat_name][1][2],
for lr, hr, _ in pbar:
# prediction
pred = model(lr)
# training
loss = criterion(hr, pred)
optim.zero_grad()
loss.backward()
optim.step()
scheduler.step()
# training history
free_gpu = get_gpu_memory()[0]
elapsed_time = time.time() - start_time
elapsed = sec2time(elapsed_time)
pfix['Step'] = f'{step+1}'
pfix['Loss'] = f'{loss.item():.4f}'
pfix['free GPU'] = f'{free_gpu}MiB'
pfix['Elapsed'] = f'{elapsed}'
hist['Iter'].append(step)
hist['Loss'].append(loss.item())
pbar.set_postfix(pfix)
if step % save_image_every == 0:
z = torch.zeros_like(lr[0])
xz = torch.cat((lr[0], z), dim=-2)
img = torch.cat((xz, pred[0], hr[0]), dim=-1)
img = torch.clamp(img, 0, 1).cpu()
img = t2img(img)
def train(config):
self_summary_strs = [] # summary string to print out for later
# first, read both data and filter stuff, to get the word2vec idx,
train_data = read_data(config, 'train', config.load)
val_data = read_data(
config, 'val', True
) # dev should always load model shared data(word2idx etc.) from train
# now that the dataset is loaded , we get the max_word_size from the dataset
# then adjust the max based on the threshold as well
# also get the vocab size
config_vars = vars(config)
str_ = "threshold setting--\n" + "\t" + " ,".join(
["%s:%s" % (key, config_vars[key]) for key in config.thresmeta])
print str_
self_summary_strs.append(str_)
# cap the numbers
# max sentence word count etc.
update_config(config, [train_data, val_data],
showMeta=True) # all word num is <= max_thres
str_ = "renewed ----\n" + "\t" + " ,".join(
["%s:%s" % (key, config_vars[key]) for key in config.maxmeta])
print str_
self_summary_strs.append(str_)
# now we initialize the matrix for word embedding for word not in glove
word2vec_dict = train_data.shared['word2vec']
word2idx_dict = train_data.shared[
'word2idx'] # this is the word not in word2vec
# we are not fine tuning , so this should be empty
idx2vec_dict = {
word2idx_dict[word]: vec
for word, vec in word2vec_dict.items() if word in word2idx_dict
}
#print len(idx2vec_dict) # 0
# config.word_vocab_size = len(train_data.shared['word2idx']) # the word not in word2vec
# so the emb_mat should all be a random vector
# np.random.multivariate_normal gets mean of zero and co of 1 for each dim, like
#>>> np.random.multivariate_normal(np.zeros(5),np.eye(5))
#array([-0.73663652, -1.16417783, -0.74083293, -0.80016731, 0.060182 ])
# random initial embedding matrix for new words
config.emb_mat = np.array([
idx2vec_dict[idx]
if idx2vec_dict.has_key(idx) else np.random.multivariate_normal(
np.zeros(config.word_emb_size), np.eye(config.word_emb_size))
for idx in xrange(config.word_vocab_size)
],
dtype="float32")
model = get_model(config) # construct model under gpu0
trainer = Trainer(model, config)
tester = Tester(model, config)
saver = tf.train.Saver(max_to_keep=5) # how many model to keep
bestsaver = tf.train.Saver(max_to_keep=5) # just for saving the best model
save_period = config.save_period # also the eval period
# start training!
# allow_soft_placement : tf will auto select other device if the tf.device(*) not available
tfconfig = tf.ConfigProto(allow_soft_placement=True)
tfconfig.gpu_options.allow_growth = True # this way it will only allocate nessasary gpu, not take all
# or you can set hard limit
#tfconfig.gpu_options.per_process_gpu_memory_fraction = 0.4
with tf.Session(config=tfconfig) as sess:
# calculate total parameters
totalParam = cal_total_param()
str_ = "total parameters: %s" % (totalParam)
print str_
self_summary_strs.append(str_)
initialize(load=config.load,
load_best=config.load_best,
model=model,
config=config,
sess=sess)
# the total step (iteration) the model will run
last_time = time.time()
# total / batchSize * epoch
num_steps = int(
math.ceil(train_data.num_examples /
float(config.batch_size))) * config.num_epochs
# get_batches is a generator, run on the fly
# there will be num_steps batch
str_ = " batch_size:%s, epoch:%s,total step:%s,eval/save every %s steps" % (
config.batch_size, config.num_epochs, num_steps,
config.save_period)
print str_
self_summary_strs.append(str_)
best = {
"acc": 0.0,
"step": -1
} # remember the best eval acc during training
finalAcc = None
isStart = True
for batch in tqdm(train_data.get_batches(config.batch_size,
num_steps=num_steps),
total=num_steps):
# each batch has (batch_idxs,Dataset(batch_data, full_shared))
# batch_data has {"q":,"y":..."pidx2feat",.."photo_idxs"..}
global_step = sess.run(model.global_step) + 1 # start from 0
# if load from existing model, save if first
if config.load and isStart:
tqdm.write("saving original model...")
tqdm.write("\tsaving model...")
saver.save(sess,
config.save_dir_model,
global_step=global_step)
tqdm.write("\tdone")
isStart = False
id2predanswers = {}
id2realanswers = {}
for evalbatch in val_data.get_batches(
config.batch_size,
num_steps=config.val_num_batches,
shuffle=False,
cap=True):
yp = tester.step(
sess, evalbatch
) # [N,4] # id2realanswersprob for each answer
pred, gt = getAnswers(
yp, evalbatch) # from here we get the qid:yindx,
id2predanswers.update(pred)
id2realanswers.update(gt)
evalAcc = getEvalScore(id2predanswers, id2realanswers)
tqdm.write(
"\teval on validation %s batches Acc:%s, (best:%s at step %s) "
% (config.val_num_batches, evalAcc, best['acc'],
best['step']))
# remember the best acc
if (evalAcc > best['acc']):
best['acc'] = evalAcc
best['step'] = global_step
# save the best model
tqdm.write("\t saving best model...")
bestsaver.save(sess,
config.save_dir_best_model,
global_step=global_step)
tqdm.write("\t done.")
finalAcc = evalAcc
loss, train_op = trainer.step(sess, batch)
if global_step % save_period == 0: # time to save model
duration = time.time() - last_time # in seconds
sec_per_step = duration / float(save_period)
last_time = time.time()
#use tqdm to print
tqdm.write(
"step:%s/%s (epoch:%.3f), took %s, loss:%s, estimate remaining:%s"
% (global_step, num_steps,
(config.num_epochs * global_step / float(num_steps)),
sec2time(duration), loss,
sec2time((num_steps - global_step) * sec_per_step)))
tqdm.write("\tsaving model...")
saver.save(sess,
config.save_dir_model,
global_step=global_step)
tqdm.write("\tdone")
id2predanswers = {}
id2realanswers = {}
for evalbatch in val_data.get_batches(
config.batch_size,
num_steps=config.val_num_batches,
shuffle=False,
cap=True):
yp = tester.step(
sess, evalbatch
) # [N,4] # id2realanswersprob for each answer
pred, gt = getAnswers(
yp, evalbatch) # from here we get the qid:yindx,
id2predanswers.update(pred)
id2realanswers.update(gt)
evalAcc = getEvalScore(id2predanswers, id2realanswers)
tqdm.write(
"\teval on validation %s batches Acc:%s, (best:%s at step %s) "
% (config.val_num_batches, evalAcc, best['acc'],
best['step']))
# remember the best acc
if (evalAcc > best['acc']):
best['acc'] = evalAcc
best['step'] = global_step
# save the best model
tqdm.write("\t saving best model...")
bestsaver.save(sess,
config.save_dir_best_model,
global_step=global_step)
tqdm.write("\t done.")
finalAcc = evalAcc
if global_step % save_period != 0: # time to save model
saver.save(sess, config.save_dir_model, global_step=global_step)
str_ = "best eval on val Accurucy: %s at %s step, final step %s Acc is %s" % (
best['acc'], best['step'], global_step, finalAcc)
print str_
self_summary_strs.append(str_)
if config.write_self_sum:
f = open(config.self_summary_path, "w")
f.writelines("%s" % ("\n".join(self_summary_strs)))
f.close()
def train(model, model_sr, train_loader, test_loader, mode='EDSR_Baseline', save_image_every=50, save_model_every=10, test_model_every=1, epoch_start=0, num_epochs=1000, device=None, refresh=True):
if device is None:
device = 'cuda' if torch.cuda.is_available() else 'cpu'
today = datetime.datetime.now().strftime('%Y.%m.%d')
result_dir = f'./results/{today}/{mode}'
weight_dir = f'./weights/{today}/{mode}'
logger_dir = f'./logger/{today}_{mode}'
csv = f'./hist_{today}_{mode}.csv'
if refresh:
try:
shutil.rmtree(result_dir)
shutil.rmtree(weight_dir)
shutil.rmtree(logger_dir)
except FileNotFoundError:
pass
os.makedirs(result_dir, exist_ok=True)
os.makedirs(weight_dir, exist_ok=True)
os.makedirs(logger_dir, exist_ok=True)
logger = SummaryWriter(log_dir=logger_dir, flush_secs=2)
model = model.to(device)
model_sr = model_sr.to(device)
params = list(model.parameters())
optim = torch.optim.Adam(params, lr=1e-4)
scheduler = torch.optim.lr_scheduler.StepLR(optim, step_size=1000, gamma= 0.99)
criterion = torch.nn.L1Loss()
GMSD = GMSD_quality().to(device)
mshf = MSHF(3, 3).to(device)
start_time = time.time()
print(f'Training Start || Mode: {mode}')
step = 0
pfix = OrderedDict()
pfix_test = OrderedDict()
hist = dict()
hist['mode'] = f'{today}_{mode}'
for key in ['epoch', 'psnr', 'ssim', 'ms-ssim']:
hist[key] = []
for epoch in range(epoch_start, epoch_start+num_epochs):
if epoch == 0:
torch.save(model.state_dict(), f'{weight_dir}/epoch_{epoch+1:04d}.pth')
if epoch == 0:
with torch.no_grad():
with tqdm(test_loader, desc=f'{mode} || Warming Up || Test Epoch {epoch}/{num_epochs}', position=0, leave=True) as pbar_test:
psnrs = []
ssims = []
msssims = []
for lr, hr, fname in pbar_test:
lr = lr.to(device)
hr = hr.to(device)
sr, deep = model_sr(lr)
fake = model(sr)
sr = quantize(sr)
psnr, ssim, msssim = evaluate(hr, sr)
psnrs.append(psnr)
ssims.append(ssim)
msssims.append(msssim)
psnr_mean = np.array(psnrs).mean()
ssim_mean = np.array(ssims).mean()
msssim_mean = np.array(msssims).mean()
pfix_test['psnr'] = f'{psnr:.4f}'
pfix_test['ssim'] = f'{ssim:.4f}'
pfix_test['msssim'] = f'{msssim:.4f}'
pfix_test['psnr_mean'] = f'{psnr_mean:.4f}'
pfix_test['ssim_mean'] = f'{ssim_mean:.4f}'
pfix_test['msssim_mean'] = f'{msssim_mean:.4f}'
pbar_test.set_postfix(pfix_test)
if len(psnrs) > 1: break
with tqdm(train_loader, desc=f'{mode} || Epoch {epoch+1}/{num_epochs}', position=0, leave=True) as pbar:
psnrs = []
ssims = []
msssims = []
losses = []
for lr, hr, _ in pbar:
lr = lr.to(device)
hr = hr.to(device)
# prediction
sr, deep = model_sr(lr)
fake = model(sr)
loss_fake = criterion(fake, torch.zeros_like(fake, device=fake.device))
real = model(hr)
loss_real = criterion(real, torch.ones_like(real, device=real.device))
# training
loss_tot = loss_fake + loss_real
optim.zero_grad()
loss_tot.backward()
optim.step()
scheduler.step()
# training history
elapsed_time = time.time() - start_time
elapsed = sec2time(elapsed_time)
pfix['Step'] = f'{step+1}'
pfix['Loss real'] = f'{loss_real.item():.4f}'
pfix['Loss fake'] = f'{loss_fake.item():.4f}'
free_gpu = get_gpu_memory()[0]
pbar.set_postfix(pfix)
step += 1
if (epoch+1) % save_model_every == 0:
torch.save(model.state_dict(), f'{weight_dir}/epoch_{epoch+1:04d}.pth')
if (epoch+1) % test_model_every == 0:
with torch.no_grad():
with tqdm(test_loader, desc=f'{mode} || Test Epoch {epoch+1}/{num_epochs}', position=0, leave=True) as pbar_test:
psnrs = []
ssims = []
msssims = []
for lr, hr, fname in pbar_test:
lr = lr.to(device)
hr = hr.to(device)
# prediction
sr, deep = model_sr(lr)
fake = model(sr)
loss_fake = criterion(fake, torch.zeros_like(fake, device=fake.device))
real = model(hr)
loss_real = criterion(real, torch.ones_like(real, device=real.device))
# training history
elapsed_time = time.time() - start_time
elapsed = sec2time(elapsed_time)
pfix_test['Step'] = f'{step+1}'
pfix_test['Loss real'] = f'{loss_real.item():.4f}'
pfix_test['Loss fake'] = f'{loss_fake.item():.4f}'
pbar_test.set_postfix(pfix_test)
def train(model, train_loader, test_loader, mode='EDSR_Baseline', save_image_every=50, save_model_every=10, test_model_every=1, epoch_start=0, num_epochs=1000, device=None, refresh=True, scale=2, today=None):
if device is None:
device = 'cuda' if torch.cuda.is_available() else 'cpu'
if today is None:
today = datetime.datetime.now().strftime('%Y.%m.%d')
result_dir = f'./results/{today}/{mode}'
weight_dir = f'./weights/{today}/{mode}'
logger_dir = f'./logger/{today}_{mode}'
csv = f'./hist_{today}_{mode}.csv'
if refresh:
try:
shutil.rmtree(result_dir)
shutil.rmtree(weight_dir)
shutil.rmtree(logger_dir)
except FileNotFoundError:
pass
os.makedirs(result_dir, exist_ok=True)
os.makedirs(weight_dir, exist_ok=True)
os.makedirs(logger_dir, exist_ok=True)
logger = SummaryWriter(log_dir=logger_dir, flush_secs=2)
model = model.to(device)
params = list(model.parameters())
optim = torch.optim.Adam(params, lr=1e-4)
scheduler = torch.optim.lr_scheduler.StepLR(optim, step_size=1000, gamma= 0.99)
criterion = torch.nn.L1Loss()
start_time = time.time()
print(f'Training Start || Mode: {mode}')
step = 0
pfix = OrderedDict()
pfix_test = OrderedDict()
hist = dict()
hist['mode'] = f'{today}_{mode}'
for key in ['epoch', 'psnr', 'ssim', 'ms-ssim']:
hist[key] = []
soft_mask = False
sigmas = [10/255, 30/255, 50/255]
# hf_kernel = get_hf_kernel(mode='high')
for epoch in range(epoch_start, epoch_start+num_epochs):
sigma = 0.0004 * (epoch+1)
if epoch == 0:
torch.save(model.state_dict(), f'{weight_dir}/epoch_{epoch+1:04d}.pth')
if epoch == 0:
with torch.no_grad():
with tqdm(test_loader, desc=f'{mode} || Warming Up || Test Epoch {epoch}/{num_epochs}', position=0, leave=True) as pbar_test:
psnrs = []
ssims = []
msssims = []
for lr, hr, fname in pbar_test:
lr = lr.to(device)
hr = hr.to(device)
sigma = np.random.choice(sigmas)
hr_input = hr + torch.randn_like(hr, device=hr.device)*sigma
hr_input = torch.clamp(hr_input, 0, 1)
sr = model(hr_input)
sr = quantize(sr)
psnr, ssim, msssim = evaluate(hr, sr)
psnrs.append(psnr)
ssims.append(ssim)
msssims.append(msssim)
psnr_mean = np.array(psnrs).mean()
ssim_mean = np.array(ssims).mean()
msssim_mean = np.array(msssims).mean()
pfix_test['psnr_mean'] = f'{psnr_mean:.4f}'
pfix_test['ssim_mean'] = f'{ssim_mean:.4f}'
pfix_test['msssim_mean'] = f'{msssim_mean:.4f}'
pbar_test.set_postfix(pfix_test)
if len(psnrs) > 1: break
with tqdm(train_loader, desc=f'{mode} || Epoch {epoch+1}/{num_epochs}', position=0, leave=True) as pbar:
psnrs = []
ssims = []
msssims = []
losses = []
for lr, hr, _ in pbar:
lr = lr.to(device)
hr = hr.to(device)
sigma = np.random.choice(sigmas)
hr_input = hr + torch.randn_like(hr, device=hr.device)*sigma
hr_input = torch.clamp(hr_input, 0, 1)
sr = model(hr_input)
sr_ = quantize(sr)
psnr, ssim, msssim = evaluate(hr, sr_)
loss = criterion(sr, hr)
# training
loss_tot = loss
optim.zero_grad()
loss_tot.backward()
optim.step()
scheduler.step()
# training history
elapsed_time = time.time() - start_time
elapsed = sec2time(elapsed_time)
pfix['Step'] = f'{step+1}'
pfix['Loss'] = f'{loss.item():.4f}'
psnrs.append(psnr)
ssims.append(ssim)
msssims.append(msssim)
psnr_mean = np.array(psnrs).mean()
ssim_mean = np.array(ssims).mean()
msssim_mean = np.array(msssims).mean()
pfix['PSNR_mean'] = f'{psnr_mean:.2f}'
pfix['SSIM_mean'] = f'{ssim_mean:.4f}'
free_gpu = get_gpu_memory()[0]
pfix['free GPU'] = f'{free_gpu}MiB'
pfix['Elapsed'] = f'{elapsed}'
pbar.set_postfix(pfix)
losses.append(loss.item())
step += 1
logger.add_scalar("Loss/train", np.array(losses).mean(), epoch+1)
logger.add_scalar("PSNR/train", psnr_mean, epoch+1)
logger.add_scalar("SSIM/train", ssim_mean, epoch+1)
if (epoch+1) % save_model_every == 0:
torch.save(model.state_dict(), f'{weight_dir}/epoch_{epoch+1:04d}.pth')
if (epoch+1) % test_model_every == 0:
with torch.no_grad():
with tqdm(test_loader, desc=f'{mode} || Test Epoch {epoch+1}/{num_epochs}', position=0, leave=True) as pbar_test:
psnrs = []
ssims = []
msssims = []
for lr, hr, fname in pbar_test:
fname = fname[0].split('/')[-1].split('.pt')[0]
# lr = lr.to(device)
hr = hr.to(device)
sigma = np.random.choice(sigmas)
hr_input = hr + torch.randn_like(hr, device=hr.device)*sigma
hr_input = torch.clamp(hr_input, 0, 1)
sr = model(hr_input)
sr = quantize(sr)
psnr, ssim, msssim = evaluate(hr, sr)
psnrs.append(psnr)
ssims.append(ssim)
msssims.append(msssim)
psnr_mean = np.array(psnrs).mean()
ssim_mean = np.array(ssims).mean()
msssim_mean = np.array(msssims).mean()
pfix_test['psnr_mean'] = f'{psnr_mean:.4f}'
pfix_test['ssim_mean'] = f'{ssim_mean:.4f}'
pfix_test['msssim_mean'] = f'{msssim_mean:.4f}'
pbar_test.set_postfix(pfix_test)
imsave([hr_input[0], sr[0], hr[0]], f'{result_dir}/{fname}.jpg')
hist['epoch'].append(epoch+1)
hist['psnr'].append(psnr_mean)
hist['ssim'].append(ssim_mean)
hist['ms-ssim'].append(msssim_mean)
logger.add_scalar("PSNR/test", psnr_mean, epoch+1)
logger.add_scalar("SSIM/test", ssim_mean, epoch+1)
logger.add_scalar("MS-SSIM/test", msssim_mean, epoch+1)
df = pd.DataFrame(hist)
df.to_csv(csv)
