def forward(self, x):
return functions.upsamplefeatures(x)
def convert_onnx(**args):
r"""Denoises an input image with FFDNet
"""
# Init logger
logger = init_logger_ipol()
# Check if input exists and if it is RGB
try:
rgb_den = is_rgb(args['input'])
except:
raise Exception('Could not open the input image')
# Open image as a CxHxW torch.Tensor
if rgb_den:
in_ch = 3
model_fn = 'models/net_rgb.pth'
imorig = cv2.imread(args['input'])
# from HxWxC to CxHxW, RGB image
imorig = (cv2.cvtColor(imorig, cv2.COLOR_BGR2RGB)).transpose(2, 0, 1)
else:
# from HxWxC to CxHxW grayscale image (C=1)
in_ch = 1
model_fn = 'models/net_gray.pth'
imorig = cv2.imread(args['input'], cv2.IMREAD_GRAYSCALE)
imorig = np.expand_dims(imorig, 0)
imorig = np.expand_dims(imorig, 0)
# Handle odd sizes
expanded_h = False
expanded_w = False
sh_im = imorig.shape
# Those image whose height and width cannot be split into half
if sh_im[2]%2 == 1:
expanded_h = True
imorig = np.concatenate((imorig, \
imorig[:, :, -1, :][:, :, np.newaxis, :]), axis=2)
if sh_im[3]%2 == 1:
expanded_w = True
imorig = np.concatenate((imorig, \
imorig[:, :, :, -1][:, :, :, np.newaxis]), axis=3)
imorig = normalize(imorig)
imorig = torch.Tensor(imorig)
# Absolute path to model file
model_fn = os.path.join(os.path.abspath(os.path.dirname(__file__)), \
model_fn)
# Create model
print('Loading model ...\n')
net = IntermediateDnCNN(num_input_channels=in_ch)
# Load saved weights
if args['cuda']:
state_dict = torch.load(model_fn)
device_ids = [0]
model = nn.DataParallel(net, device_ids=device_ids).cuda()
else:
state_dict = torch.load(model_fn, map_location='cpu')
# CPU mode: remove the DataParallel wrapper
state_dict = remove_dataparallel_wrapper(state_dict)
model = net
model.load_state_dict(state_dict)
# Sets the model in evaluation mode (e.g. it removes BN)
model.eval()
# Sets data type according to CPU or GPU modes
if args['cuda']:
dtype = torch.cuda.FloatTensor
else:
dtype = torch.FloatTensor
# Add noise
if args['add_noise']:
noise = torch.FloatTensor(imorig.size()).\
normal_(mean=0, std=args['noise_sigma'])
imnoisy = imorig + noise
else:
imnoisy = imorig.clone()
# Test mode
with torch.no_grad(): # PyTorch v0.4.0
imorig, imnoisy = Variable(imorig.type(dtype)), \
Variable(imnoisy.type(dtype))
nsigma = Variable(
torch.FloatTensor([args['noise_sigma']]).type(dtype))
# Move and handle the downsampling and concatenation in here
concat_noise_x = functions.concatenate_input_noise_map(imnoisy.data, nsigma.data)
# The downsampled images should be handled in here already
concat_noise_x = Variable(concat_noise_x)
# Measure runtime
start_t = time.time()
# Estimate noise and subtract it to the input image
h_dncnn = model(concat_noise_x)
im_noise_estim = functions.upsamplefeatures(h_dncnn)
outim = torch.clamp(imnoisy-im_noise_estim, 0., 1.)
stop_t = time.time()
if expanded_h:
imorig = imorig[:, :, :-1, :]
outim = outim[:, :, :-1, :]
imnoisy = imnoisy[:, :, :-1, :]
if expanded_w:
imorig = imorig[:, :, :, :-1]
outim = outim[:, :, :, :-1]
imnoisy = imnoisy[:, :, :, :-1]
# Compute PSNR and log it
if rgb_den:
logger.info("### RGB denoising ###")
else:
logger.info("### Grayscale denoising ###")
if args['add_noise']:
psnr = batch_psnr(outim, imorig, 1.)
psnr_noisy = batch_psnr(imnoisy, imorig, 1.)
logger.info("\tPSNR noisy {0:0.2f}dB".format(psnr_noisy))
logger.info("\tPSNR denoised {0:0.2f}dB".format(psnr))
else:
logger.info("\tNo noise was added, cannot compute PSNR")
logger.info("\tRuntime {0:0.4f}s".format(stop_t-start_t))
# Compute difference
diffout = 2*(outim - imorig) + .5
diffnoise = 2*(imnoisy-imorig) + .5
# Save images
if not args['dont_save_results']:
noisyimg = variable_to_cv2_image(imnoisy)
outimg = variable_to_cv2_image(outim)
cv2.imwrite("noisy.png", noisyimg)
cv2.imwrite("ffdnet.png", outimg)
if args['add_noise']:
cv2.imwrite("noisy_diff.png", variable_to_cv2_image(diffnoise))
cv2.imwrite("ffdnet_diff.png", variable_to_cv2_image(diffout))