def main():
# ----------------------------------------
# Preparation
# ----------------------------------------
model_name = '1000_G' # 'msrresnet_x4_gan' | 'msrresnet_x4_psnr'
testset_name = 'set_real' # test set, 'set5' | 'srbsd68'
need_degradation = True # default: True
x8 = False # default: False, x8 to boost performance, default: False
sf = [int(s) for s in re.findall(r'\d+', model_name)][0] # scale factor
show_img = False # default: False
task_current = 'sr' # 'dn' for denoising | 'sr' for super-resolution
n_channels = 3 # fixed
model_pool = 'model_zoo' # fixed
testsets = 'testsets' # fixed
results = 'results' # fixed
noise_level_img = 0 # fixed: 0, noise level for LR image
result_name = testset_name + '_' + model_name
border = sf if task_current == 'sr' else 0 # shave boader to calculate PSNR and SSIM
model_path = os.path.join(model_pool, model_name + '.pth')
# ----------------------------------------
# L_path, E_path, H_path
# ----------------------------------------
L_path = os.path.join(testsets,
testset_name) # L_path, for Low-quality images
H_path = L_path # H_path, for High-quality images
E_path = os.path.join(results, result_name) # E_path, for Estimated images
util.mkdir(E_path)
if H_path == L_path:
need_degradation = True
logger_name = result_name
utils_logger.logger_info(logger_name,
log_path=os.path.join(E_path,
logger_name + '.log'))
logger = logging.getLogger(logger_name)
need_H = True if H_path is not None else False
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
# ----------------------------------------
# load model
# ----------------------------------------
from models.network_msrresnet import MSRResNet1 as net
model = net(in_nc=n_channels, out_nc=n_channels, nc=64, nb=16, upscale=4)
model.load_state_dict(torch.load(model_path), strict=True)
model.eval()
for k, v in model.named_parameters():
v.requires_grad = False
model = model.to(device)
logger.info('Model path: {:s}'.format(model_path))
number_parameters = sum(map(lambda x: x.numel(), model.parameters()))
logger.info('Params number: {}'.format(number_parameters))
test_results = OrderedDict()
test_results['psnr'] = []
test_results['ssim'] = []
test_results['psnr_y'] = []
test_results['ssim_y'] = []
logger.info('model_name:{}, image sigma:{}'.format(model_name,
noise_level_img))
logger.info(L_path)
L_paths = util.get_image_paths(L_path)
H_paths = util.get_image_paths(H_path) if need_H else None
for idx, img in enumerate(L_paths):
# ------------------------------------
# (1) img_L
# ------------------------------------
img_name, ext = os.path.splitext(os.path.basename(img))
# logger.info('{:->4d}--> {:>10s}'.format(idx+1, img_name+ext))
img_L = util.imread_uint(img, n_channels=n_channels)
img_L = util.uint2single(img_L)
# degradation process, bicubic downsampling
if need_degradation:
img_L = util.modcrop(img_L, sf)
img_L = util.imresize_np(img_L, 1 / sf)
# img_L = util.uint2single(util.single2uint(img_L))
# np.random.seed(seed=0) # for reproducibility
# img_L += np.random.normal(0, noise_level_img/255., img_L.shape)
util.imshow(util.single2uint(img_L),
title='LR image with noise level {}'.format(
noise_level_img)) if show_img else None
img_L = util.single2tensor4(img_L)
img_L = img_L.to(device)
# ------------------------------------
# (2) img_E
# ------------------------------------
if not x8:
img_E = model(img_L)
else:
img_E = utils_model.test_mode(model, img_L, mode=3, sf=sf)
img_E = util.tensor2uint(img_E)
if need_H:
# --------------------------------
# (3) img_H
# --------------------------------
img_H = util.imread_uint(H_paths[idx], n_channels=n_channels)
img_H = img_H.squeeze()
img_H = util.modcrop(img_H, sf)
# --------------------------------
# PSNR and SSIM
# --------------------------------
psnr = util.calculate_psnr(img_E, img_H, border=border)
ssim = util.calculate_ssim(img_E, img_H, border=border)
test_results['psnr'].append(psnr)
test_results['ssim'].append(ssim)
logger.info('{:s} - PSNR: {:.2f} dB; SSIM: {:.4f}.'.format(
img_name + ext, psnr, ssim))
util.imshow(np.concatenate([img_E, img_H], axis=1),
title='Recovered / Ground-truth') if show_img else None
if np.ndim(img_H) == 3: # RGB image
img_E_y = util.rgb2ycbcr(img_E, only_y=True)
img_H_y = util.rgb2ycbcr(img_H, only_y=True)
psnr_y = util.calculate_psnr(img_E_y, img_H_y, border=border)
ssim_y = util.calculate_ssim(img_E_y, img_H_y, border=border)
test_results['psnr_y'].append(psnr_y)
test_results['ssim_y'].append(ssim_y)
# ------------------------------------
# save results
# ------------------------------------
util.imsave(img_E, os.path.join(E_path, img_name + '.png'))
if need_H:
ave_psnr = sum(test_results['psnr']) / len(test_results['psnr'])
ave_ssim = sum(test_results['ssim']) / len(test_results['ssim'])
logger.info(
'Average PSNR/SSIM(RGB) - {} - x{} --PSNR: {:.2f} dB; SSIM: {:.4f}'
.format(result_name, sf, ave_psnr, ave_ssim))
if np.ndim(img_H) == 3:
ave_psnr_y = sum(test_results['psnr_y']) / len(
test_results['psnr_y'])
ave_ssim_y = sum(test_results['ssim_y']) / len(
test_results['ssim_y'])
logger.info(
'Average PSNR/SSIM( Y ) - {} - x{} - PSNR: {:.2f} dB; SSIM: {:.4f}'
.format(result_name, sf, ave_psnr_y, ave_ssim_y))
def main():
utils_logger.logger_info('efficientsr_challenge',
log_path='efficientsr_challenge.log')
logger = logging.getLogger('efficientsr_challenge')
# print(torch.__version__) # pytorch version
# print(torch.version.cuda) # cuda version
# print(torch.backends.cudnn.version()) # cudnn version
# --------------------------------
# basic settings
# --------------------------------
model_names = ['msrresnet', 'imdn']
model_id = 1 # set the model name
model_name = model_names[model_id]
logger.info('{:>16s} : {:s}'.format('Model Name', model_name))
testsets = 'testsets' # set path of testsets
testset_L = 'DIV2K_valid_LR' # set current testing dataset; 'DIV2K_test_LR'
testset_L = 'set12'
save_results = True
print_modelsummary = True # set False when calculating `Max Memery` and `Runtime`
torch.cuda.set_device(0) # set GPU ID
logger.info('{:>16s} : {:<d}'.format('GPU ID',
torch.cuda.current_device()))
torch.cuda.empty_cache()
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
# --------------------------------
# define network and load model
# --------------------------------
if model_name == 'msrresnet':
from models.network_msrresnet import MSRResNet1 as net
model = net(in_nc=3, out_nc=3, nc=64, nb=16,
upscale=4) # define network
model_path = os.path.join('model_zoo',
'msrresnet_x4_psnr.pth') # set model path
elif model_name == 'imdn':
from models.network_imdn import IMDN as net
model = net(in_nc=3,
out_nc=3,
nc=64,
nb=8,
upscale=4,
act_mode='L',
upsample_mode='pixelshuffle') # define network
model_path = os.path.join('model_zoo', 'imdn_x4.pth') # set model path
model.load_state_dict(torch.load(model_path), strict=True)
model.eval()
for k, v in model.named_parameters():
v.requires_grad = False
model = model.to(device)
# --------------------------------
# print model summary
# --------------------------------
if print_modelsummary:
from utils.utils_modelsummary import get_model_activation, get_model_flops
input_dim = (3, 256, 256) # set the input dimension
activations, num_conv2d = get_model_activation(model, input_dim)
logger.info('{:>16s} : {:<.4f} [M]'.format('#Activations',
activations / 10**6))
logger.info('{:>16s} : {:<d}'.format('#Conv2d', num_conv2d))
flops = get_model_flops(model, input_dim, False)
logger.info('{:>16s} : {:<.4f} [G]'.format('FLOPs', flops / 10**9))
num_parameters = sum(map(lambda x: x.numel(), model.parameters()))
logger.info('{:>16s} : {:<.4f} [M]'.format('#Params',
num_parameters / 10**6))
# --------------------------------
# read image
# --------------------------------
L_path = os.path.join(testsets, testset_L)
E_path = os.path.join(testsets, testset_L + '_' + model_name)
util.mkdir(E_path)
# record runtime
test_results = OrderedDict()
test_results['runtime'] = []
logger.info('{:>16s} : {:s}'.format('Input Path', L_path))
logger.info('{:>16s} : {:s}'.format('Output Path', E_path))
idx = 0
start = torch.cuda.Event(enable_timing=True)
end = torch.cuda.Event(enable_timing=True)
for img in util.get_image_paths(L_path):
# --------------------------------
# (1) img_L
# --------------------------------
idx += 1
img_name, ext = os.path.splitext(os.path.basename(img))
logger.info('{:->4d}--> {:>10s}'.format(idx, img_name + ext))
img_L = util.imread_uint(img, n_channels=3)
img_L = util.uint2tensor4(img_L)
torch.cuda.empty_cache()
img_L = img_L.to(device)
start.record()
img_E = model(img_L)
# logger.info('{:>16s} : {:<.3f} [M]'.format('Max Memery', torch.cuda.max_memory_allocated(torch.cuda.current_device())/1024**2)) # Memery
end.record()
torch.cuda.synchronize()
test_results['runtime'].append(start.elapsed_time(end)) # milliseconds
# torch.cuda.synchronize()
# start = time.time()
# img_E = model(img_L)
# torch.cuda.synchronize()
# end = time.time()
# test_results['runtime'].append(end-start) # seconds
# --------------------------------
# (2) img_E
# --------------------------------
img_E = util.tensor2uint(img_E)
if save_results:
util.imsave(img_E, os.path.join(E_path, img_name + ext))
ave_runtime = sum(test_results['runtime']) / len(
test_results['runtime']) / 1000.0
logger.info('------> Average runtime of ({}) is : {:.6f} seconds'.format(
L_path, ave_runtime))
