def reshape_decode(self, data, shape):
if self.float_data: # @TODO(tzaman): this is LMDB specific - Make generic!
data = tf.reshape(data, shape)
data = digits.chw_to_hwc(data)
else:
# Decode image of any time option might come: https://github.com/tensorflow/tensorflow/issues/4009
# Distinguish between mime types
if self.data_encoded:
if self.data_mime == 'image/png':
data = tf.image.decode_png(data, dtype=self.image_dtype, name='image_decoder')
elif self.data_mime == 'image/jpeg':
data = tf.image.decode_jpeg(data, name='image_decoder')
else:
logging.error('Unsupported mime type (%s); cannot be decoded' % (self.data_mime))
exit(-1)
else:
if self.backend == 'lmdb':
data = tf.decode_raw(data, self.image_dtype, name='raw_decoder')
# if data is in CHW, set the shape and convert to HWC
if self.unencoded_data_format == 'chw':
data = tf.reshape(data, [shape[0], shape[1], shape[2]])
data = digits.chw_to_hwc(data)
else: # 'hwc'
data = tf.reshape(data, shape)
if (self.channels == 3) and self.unencoded_channel_scheme == 'bgr':
data = digits.bgr_to_rgb(data)
# Convert to float
data = tf.to_float(data)
# data = tf.image.convert_image_dtype(data, tf.float32) # normalize to [0:1) range
return data
def train(train_loader, model, criterion, optimizer, epoch, result_dir):
losses = AverageMeter()
model.train()
for ind, (noise_img, origin_img) in enumerate(train_loader):
st = time.time()
input_var = noise_img.cuda()
target_var = origin_img.cuda()
output = model(input_var)
loss = criterion(output, target_var)
losses.update(loss.item())
optimizer.zero_grad()
loss.backward()
optimizer.step()
print('[{0}][{1}]\t'
'lr: {lr:.5f}\t'
'Loss: {loss.val:.4f} ({loss.avg:.4f})\t'
'Time: {time:.3f}'.format(epoch,
ind,
lr=optimizer.param_groups[-1]['lr'],
loss=losses,
time=time.time() - st))
if epoch % args.save_freq == 0:
if not os.path.isdir(os.path.join(result_dir, '%04d' % epoch)):
os.makedirs(os.path.join(result_dir, '%04d' % epoch))
origin_np = origin_img.numpy()
noise_np = noise_img.numpy()
output_np = output.cpu().detach().numpy()
origin_np_img = chw_to_hwc(origin_np[0])
noise_np_img = chw_to_hwc(noise_np[0])
output_img = chw_to_hwc(np.clip(output_np[0], 0, 1))
temp = np.concatenate((origin_np_img, noise_np_img, output_img),
axis=1)
io.imsave(
os.path.join(result_dir, '%04d/train_%d.jpg' % (epoch, ind)),
np.uint8(temp * 255))
def inferenceCMP(self, names):
imgs = []
for name in names:
img = cv2.imread(name, -1)
# img = np.load(name)
img = img / 4096
# img = img / 13132
# img = img[:512, :512]
# img = (img - img.min()) / (img.max() - img.min())
imgs.append((img).astype("float32")[None, :, :])
img = np.concatenate(imgs, axis=0)
img = torch.from_numpy(img).unsqueeze(0)
img = img.cuda()
output = self.MODEL(img)
acm = output[0].cpu().detach().numpy()
return chw_to_hwc(acm)
parser.add_argument('input_filename', type=str)
parser.add_argument('output_filename', type=str)
args = parser.parse_args()
save_dir = './save_model/'
model = Network()
model.cuda()
model = nn.DataParallel(model)
model.eval()
if os.path.exists(os.path.join(save_dir, 'checkpoint.pth.tar')):
# load existing model
model_info = torch.load(os.path.join(save_dir, 'checkpoint.pth.tar'))
model.load_state_dict(model_info['state_dict'])
else:
print('Error: no trained model detected!')
exit(1)
input_image = read_img(args.input_filename)
input_var = torch.from_numpy(hwc_to_chw(input_image)).unsqueeze(0).cuda()
with torch.no_grad():
_, output = model(input_var)
output_image = chw_to_hwc(output[0, ...].cpu().numpy())
output_image = np.uint8(np.round(np.clip(output_image, 0, 1) *
255.))[:, :, ::-1]
cv2.imwrite(args.output_filename, output_image)