def define_G(opt):
opt_net = opt['network_G']
which_model = opt_net['which_model_G']
# image restoration
if which_model == 'MSRResNet':
netG = SRResNet_arch.MSRResNet(in_nc=opt_net['in_nc'],
out_nc=opt_net['out_nc'],
nf=opt_net['nf'],
nb=opt_net['nb'],
upscale=opt_net['scale'])
elif which_model == 'RRDBNet':
netG = RRDBNet_arch.RRDBNet(in_nc=opt_net['in_nc'],
out_nc=opt_net['out_nc'],
nf=opt_net['nf'],
nb=opt_net['nb'],
upscale=opt_net['scale'])
# video restoration
elif which_model == 'EDVR':
netG = EDVR_arch.EDVR(nf=opt_net['nf'],
nframes=opt_net['nframes'],
groups=opt_net['groups'],
front_RBs=opt_net['front_RBs'],
back_RBs=opt_net['back_RBs'],
center=opt_net['center'],
predeblur=opt_net['predeblur'],
HR_in=opt_net['HR_in'],
w_TSA=opt_net['w_TSA'])
else:
raise NotImplementedError(
'Generator model [{:s}] not recognized'.format(which_model))
return netG
def define_G(opt):
opt_net = opt['network_G']
which_model = opt_net['which_model_G']
# image restoration
if which_model == 'MSRResNet':
netG = SRResNet_arch.MSRResNet(in_nc=opt_net['in_nc'], out_nc=opt_net['out_nc'],
nf=opt_net['nf'], nb=opt_net['nb'], upscale=opt_net['scale'])
elif which_model == 'RCAN':
netG = RCAN_arch.RCAN(n_resblocks=opt_net['n_resblocks'], n_feats=opt_net['n_feats'],
res_scale=opt_net['res_scale'], n_colors=opt_net['n_colors'],rgb_range=opt_net['rgb_range'],
scale=opt_net['scale'],reduction=opt_net['reduction'],n_resgroups=opt_net['n_resgroups'])
elif which_model == 'CARN_M':
netG = CARN_arch.CARN_M(in_nc=opt_net['in_nc'], out_nc=opt_net['out_nc'],
nf=opt_net['nf'], scale=opt_net['scale'], group=opt_net['group'])
elif which_model == 'fsrcnn':
netG = FSRCNN_arch.FSRCNN_net(input_channels=opt_net['in_nc'],upscale=opt_net['scale'],d=opt_net['d'],
s=opt_net['s'],m=opt_net['m'])
elif which_model == 'classSR_3class_fsrcnn_net':
netG = classSR_3class_arch.classSR_3class_fsrcnn_net(in_nc=opt_net['in_nc'], out_nc=opt_net['out_nc'])
elif which_model == 'classSR_3class_rcan':
netG = classSR_rcan_arch.classSR_3class_rcan(in_nc=opt_net['in_nc'], out_nc=opt_net['out_nc'])
elif which_model == 'classSR_3class_srresnet':
netG = classSR_srresnet_arch.ClassSR(in_nc=opt_net['in_nc'], out_nc=opt_net['out_nc'])
elif which_model == 'classSR_3class_carn':
netG = classSR_carn_arch.ClassSR(in_nc=opt_net['in_nc'], out_nc=opt_net['out_nc'])
else:
raise NotImplementedError('Generator model [{:s}] not recognized'.format(which_model))
return netG
def define_G(opt):
opt_net = opt['network_G']
which_model = opt_net['which_model_G']
# image restoration
if which_model == 'PAN':
netG = PAN_arch.PAN(in_nc=opt_net['in_nc'],
out_nc=opt_net['out_nc'],
nf=opt_net['nf'],
unf=opt_net['unf'],
nb=opt_net['nb'],
scale=opt_net['scale'])
elif which_model == 'MSRResNet_PA':
netG = SRResNet_arch.MSRResNet_PA(in_nc=opt_net['in_nc'],
out_nc=opt_net['out_nc'],
nf=opt_net['nf'],
nb=opt_net['nb'],
upscale=opt_net['scale'])
elif which_model == 'RCAN_PA':
netG = RCAN_arch.RCAN_PA(n_resgroups=opt_net['n_resgroups'],
n_resblocks=opt_net['n_resblocks'],
n_feats=opt_net['n_feats'],
res_scale=opt_net['res_scale'],
n_colors=opt_net['n_colors'],
rgb_range=opt_net['rgb_range'],
scale=opt_net['scale'])
else:
raise NotImplementedError(
'Generator model [{:s}] not recognized'.format(which_model))
return netG
def define_G(opt):
opt_net = opt['network_G']
which_model = opt_net['which_model_G']
# image restoration
if which_model == 'MSRResNet':
netG = SRResNet_arch.MSRResNet(in_nc=opt_net['in_nc'],
out_nc=opt_net['out_nc'],
nf=opt_net['nf'],
nb=opt_net['nb'],
upscale=opt_net['scale'])
elif which_model == 'RRDBNet':
netG = RRDBNet_arch.RRDBNet(in_nc=opt_net['in_nc'],
out_nc=opt_net['out_nc'],
nf=opt_net['nf'],
nb=opt_net['nb'])
elif which_model == 'ORDSRNet':
netG = ORDSRModel(in_nc=opt_net['in_nc'],
out_nc=opt_net['out_nc'],
N=opt_net['base_size'],
S=opt_net['stride'],
upscale=opt_net['scale'])
else:
raise NotImplementedError(
'Generator model [{:s}] not recognized'.format(which_model))
return netG
def define_C(opt):
opt_net = opt['network_C']
which_model = opt_net['which_model_C']
if which_model == 'dfn':
netC = DynamicF.DFN_Color_correction()
elif 'ResNet' in which_model:
netC = SRResNet_arch.ResNet_alpha_beta_multi_in(which_model)
else:
raise NotImplementedError(
'Discriminator model [{:s}] not recognized'.format(which_model))
return netC
def define_G(opt):
opt_net = opt['network_G']
which_model = opt_net['which_model_G']
# image restoration
if which_model == 'MSRResNet':
netG = SRResNet_arch.MSRResNet(in_nc=opt_net['in_nc'],
out_nc=opt_net['out_nc'],
nf=opt_net['nf'],
nb=opt_net['nb'],
upscale=opt_net['scale'])
elif which_model == 'RRDBNet':
netG = RRDBNet_arch.RRDBNet(in_nc=opt_net['in_nc'],
out_nc=opt_net['out_nc'],
nf=opt_net['nf'],
nb=opt_net['nb'])
# video restoration
elif which_model == 'EDVR':
import models.archs.EDVR_arch as EDVR_arch
netG = EDVR_arch.EDVR(nf=opt_net['nf'],
nframes=opt_net['nframes'],
groups=opt_net['groups'],
front_RBs=opt_net['front_RBs'],
back_RBs=opt_net['back_RBs'],
center=opt_net['center'],
predeblur=opt_net['predeblur'],
HR_in=opt_net['HR_in'],
w_TSA=opt_net['w_TSA'],
w_GCB=opt_net['w_GCB'])
# elif which_model == 'EDVR_woDCN':
# import models.archs.EDVR_woDCN_arch as EDVR_arch
# netG = EDVR_arch.EDVR(nf=opt_net['nf'], nframes=opt_net['nframes'],
# groups=opt_net['groups'], front_RBs=opt_net['front_RBs'],
# back_RBs=opt_net['back_RBs'], center=opt_net['center'],
# predeblur=opt_net['predeblur'], HR_in=opt_net['HR_in'],
# w_TSA=opt_net['w_TSA'], w_GCB=opt_net['w_GCB'])
elif which_model == 'MGANet':
netG = Gen_Guided_UNet(input_size=opt_net['input_size'])
elif which_model == 'Unet':
import repo.CycleGAN.networks as unet_networks
netG = unet_networks.define_G(2 * 3, 1, opt_net['nf'],
opt_net['G_type'], opt_net['norm'],
opt_net['dropout'], opt_net['init_type'],
opt_net['init_gain'])
else:
raise NotImplementedError(
'Generator model [{:s}] not recognized'.format(which_model))
return netG
def define_G(opt):
opt_net = opt['network_G']
which_model = opt_net['which_model_G']
# image restoration
if which_model == 'MSRResNet':
import models.archs.SRResNet_arch as SRResNet_arch
netG = SRResNet_arch.MSRResNet(in_nc=opt_net['in_nc'],
out_nc=opt_net['out_nc'],
nf=opt_net['nf'],
nb=opt_net['nb'],
upscale=opt_net['scale'])
elif which_model == 'RRDBNet':
import models.archs.RRDBNet_arch as RRDBNet_arch
netG = RRDBNet_arch.RRDBNet(in_nc=opt_net['in_nc'],
out_nc=opt_net['out_nc'],
nf=opt_net['nf'],
nb=opt_net['nb'])
# video restoration
elif which_model == 'EDVR':
import models.archs.EDVR_arch as EDVR_arch
netG = EDVR_arch.EDVR(nf=opt_net['nf'],
nframes=opt_net['nframes'],
groups=opt_net['groups'],
front_RBs=opt_net['front_RBs'],
back_RBs=opt_net['back_RBs'],
center=opt_net['center'],
predeblur=opt_net['predeblur'],
HR_in=opt_net['HR_in'],
w_TSA=opt_net['w_TSA'],
scale=opt['scale'])
elif which_model == 'DUF':
import models.archs.DUF_arch as DUF_arch
if opt_net['layers'] == 16:
netG = DUF_arch.DUF_16L(scale=opt['scale'], adapt_official=True)
elif opt_net['layers'] == 28:
netG = DUF_arch.DUF_28L(scale=opt['scale'], adapt_official=True)
else:
netG = DUF_arch.DUF_52L(scale=opt['scale'], adapt_official=True)
elif which_model == 'TOF':
import models.archs.TOF_arch as TOF_arch
netG = TOF_arch.TOFlow(adapt_official=True)
else:
raise NotImplementedError(
'Generator model [{:s}] not recognized'.format(which_model))
return netG
import os.path as osp
import sys
import torch
try:
sys.path.append(osp.dirname(osp.dirname(osp.abspath(__file__))))
import models.archs.SRResNet_arch as SRResNet_arch
except ImportError:
pass
pretrained_net = torch.load(
'../../experiments/pretrained_models/MSRResNetx4.pth')
crt_model = SRResNet_arch.MSRResNet(in_nc=3, out_nc=3, nf=64, nb=16, upscale=3)
crt_net = crt_model.state_dict()
for k, v in crt_net.items():
if k in pretrained_net and 'upconv1' not in k:
crt_net[k] = pretrained_net[k]
print('replace ... ', k)
# x4 -> x3
crt_net['upconv1.weight'][
0:256, :, :, :] = pretrained_net['upconv1.weight'] / 2
crt_net['upconv1.weight'][
256:512, :, :, :] = pretrained_net['upconv1.weight'] / 2
crt_net['upconv1.weight'][
512:576, :, :, :] = pretrained_net['upconv1.weight'][0:64, :, :, :] / 2
crt_net['upconv1.bias'][0:256] = pretrained_net['upconv1.bias'] / 2
crt_net['upconv1.bias'][256:512] = pretrained_net['upconv1.bias'] / 2
crt_net['upconv1.bias'][512:576] = pretrained_net['upconv1.bias'][0:64] / 2
torch.save(crt_net, '../../experiments/pretrained_models/MSRResNetx3_ini.pth')
def define_G(opt):
opt_net = opt['network_G']
which_model = opt_net['which_model_G']
# image restoration
if which_model == 'MSRResNet':
netG = SRResNet_arch.MSRResNet(in_nc=opt_net['in_nc'],
out_nc=opt_net['out_nc'],
nf=opt_net['nf'],
nb=opt_net['nb'],
upscale=opt_net['scale'])
elif which_model == 'RRDBNet':
netG = RRDBNet_arch.RRDBNet(in_nc=opt_net['in_nc'],
out_nc=opt_net['out_nc'],
nf=opt_net['nf'],
nb=opt_net['nb'])
# video restoration
elif which_model == 'EDVR':
netG = EDVR_arch.EDVR(nf=opt_net['nf'],
nframes=opt_net['nframes'],
groups=opt_net['groups'],
front_RBs=opt_net['front_RBs'],
back_RBs=opt_net['back_RBs'],
center=opt_net['center'],
predeblur=opt_net['predeblur'],
HR_in=opt_net['HR_in'],
w_TSA=opt_net['w_TSA'])
elif which_model == 'EDVR2X':
netG = EDVR_arch.EDVR2X(nf=opt_net['nf'],
nframes=opt_net['nframes'],
groups=opt_net['groups'],
front_RBs=opt_net['front_RBs'],
back_RBs=opt_net['back_RBs'],
center=opt_net['center'],
predeblur=opt_net['predeblur'],
HR_in=opt_net['HR_in'],
w_TSA=opt_net['w_TSA'])
elif which_model == 'EDVRImg':
netG = EDVR_arch.EDVRImage(nf=opt_net['nf'],
front_RBs=opt_net['front_RBs'],
back_RBs=opt_net['back_RBs'],
down_scale=opt_net['down_scale'])
elif which_model == 'EDVR3D':
netG = EDVR_arch.EDVR3D(nf=opt_net['nf'],
front_RBs=opt_net['front_RBs'],
back_RBs=opt_net['back_RBs'],
down_scale=opt_net['down_scale'])
elif which_model == 'UPEDVR':
netG = EDVR_arch.UPEDVR(nf=opt_net['nf'],
nframes=opt_net['nframes'],
groups=opt_net['groups'],
front_RBs=opt_net['front_RBs'],
back_RBs=opt_net['back_RBs'],
center=opt_net['center'],
w_TSA=opt_net['w_TSA'],
down_scale=opt_net['down_scale'],
align_target=opt_net['align_target'],
ret_valid=opt_net['ret_valid'])
elif which_model == 'UPContEDVR':
netG = EDVR_arch.UPControlEDVR(
nf=opt_net['nf'],
nframes=opt_net['nframes'],
groups=opt_net['groups'],
front_RBs=opt_net['front_RBs'],
back_RBs=opt_net['back_RBs'],
center=opt_net['center'],
w_TSA=opt_net['w_TSA'],
down_scale=opt_net['down_scale'],
align_target=opt_net['align_target'],
ret_valid=opt_net['ret_valid'],
multi_scale_cont=opt_net['multi_scale_cont'])
elif which_model == 'FlowUPContEDVR':
netG = EDVR_arch.FlowUPControlEDVR(
nf=opt_net['nf'],
nframes=opt_net['nframes'],
groups=opt_net['groups'],
front_RBs=opt_net['front_RBs'],
back_RBs=opt_net['back_RBs'],
center=opt_net['center'],
w_TSA=opt_net['w_TSA'],
down_scale=opt_net['down_scale'],
align_target=opt_net['align_target'],
ret_valid=opt_net['ret_valid'],
multi_scale_cont=opt_net['multi_scale_cont'])
# video SR for multiple target frames
elif which_model == 'MultiEDVR':
netG = EDVR_arch.MultiEDVR(nf=opt_net['nf'],
nframes=opt_net['nframes'],
groups=opt_net['groups'],
front_RBs=opt_net['front_RBs'],
back_RBs=opt_net['back_RBs'],
center=opt_net['center'],
predeblur=opt_net['predeblur'],
HR_in=opt_net['HR_in'],
w_TSA=opt_net['w_TSA'])
# arbitrary magnification video super-resolution
elif which_model == 'MetaEDVR':
netG = EDVR_arch.MetaEDVR(nf=opt_net['nf'],
nframes=opt_net['nframes'],
groups=opt_net['groups'],
front_RBs=opt_net['front_RBs'],
back_RBs=opt_net['back_RBs'],
center=opt_net['center'],
predeblur=opt_net['predeblur'],
HR_in=opt_net['HR_in'],
w_TSA=opt_net['w_TSA'],
fix_edvr=opt_net['fix_edvr'])
else:
raise NotImplementedError(
'Generator model [{:s}] not recognized'.format(which_model))
return netG
def define_G(opt):
opt_net = opt['network_G']
which_model = opt_net['which_model_G']
# image restoration
if which_model == 'MSRResNet':
netG = SRResNet_arch.MSRResNet(in_nc=opt_net['in_nc'],
out_nc=opt_net['out_nc'],
nf=opt_net['nf'],
nb=opt_net['nb'],
upscale=opt_net['scale'])
elif which_model == 'RRDBNet':
netG = RRDBNet_arch.RRDBNet(in_nc=opt_net['in_nc'],
out_nc=opt_net['out_nc'],
nf=opt_net['nf'],
nb=opt_net['nb'])
# video restoration
elif which_model == 'EDVR':
netG = EDVR_arch.EDVR(nf=opt_net['nf'],
nframes=opt_net['nframes'],
groups=opt_net['groups'],
front_RBs=opt_net['front_RBs'],
back_RBs=opt_net['back_RBs'],
center=opt_net['center'],
predeblur=opt_net['predeblur'],
HR_in=opt_net['HR_in'],
w_TSA=opt_net['w_TSA'])
elif which_model == 'MY_EDVR_FusionDenoise':
netG = my_EDVR_arch.MYEDVR_FusionDenoise(
nf=opt_net['nf'],
nframes=opt_net['nframes'],
groups=opt_net['groups'],
front_RBs=opt_net['front_RBs'],
back_RBs=opt_net['back_RBs'],
center=opt_net['center'],
predeblur=opt_net['predeblur'],
HR_in=opt_net['HR_in'],
w_TSA=opt_net['w_TSA'])
elif which_model == 'MY_EDVR_RES':
netG = my_EDVR_arch.MYEDVR_RES(nf=opt_net['nf'],
nframes=opt_net['nframes'],
groups=opt_net['groups'],
front_RBs=opt_net['front_RBs'],
back_RBs=opt_net['back_RBs'],
center=opt_net['center'],
predeblur=opt_net['predeblur'],
HR_in=opt_net['HR_in'],
w_TSA=opt_net['w_TSA'])
elif which_model == 'MY_EDVR_PreEnhance':
netG = my_EDVR_arch.MYEDVR_PreEnhance(nf=opt_net['nf'],
nframes=opt_net['nframes'],
groups=opt_net['groups'],
front_RBs=opt_net['front_RBs'],
back_RBs=opt_net['back_RBs'],
center=opt_net['center'],
predeblur=opt_net['predeblur'],
HR_in=opt_net['HR_in'],
w_TSA=opt_net['w_TSA'])
elif which_model == 'Recurr_ResBlocks':
netG = Recurr_arch.Recurr_ResBlocks(
nf=opt_net['nf'],
N_RBs=opt_net['N_RBs'],
N_flow_lv=opt_net['N_flow_lv'],
pretrain_flow=opt_net['pretrain_flow'])
else:
raise NotImplementedError(
'Generator model [{:s}] not recognized'.format(which_model))
return netG
def main():
#################
# configurations
#################
#torch.backends.cudnn.benchmark = True
#torch.backends.cudnn.enabled = True
device = torch.device('cuda')
os.environ['CUDA_VISIBLE_DEVICES'] = '5'
test_set = 'AI4K_test' # Vid4 | YouKu10 | REDS4 | AI4K_test
data_mode = 'sharp_bicubic' # sharp_bicubic | blur_bicubic
test_name = 'Contest2_Test18_A38_color_EDVR_35_220000_A01_5in_64f_10b_128_pretrain_A01xxx_900000_fix_before_pcd_165000' #'AI4K_TEST_Denoise_A02_265000' | AI4K_test_A01b_145000
N_in = 5
# load test set
if test_set == 'AI4K_test':
#test_dataset_folder = '/data1/yhliu/AI4K/Corrected_TestA_Contest2_001_ResNet_alpha_beta_gaussian_65000/' #'/data1/yhliu/AI4K/testA_LR_png/'
test_dataset_folder = '/home/yhliu/AI4K/contest2/testA_LR_png/'
flip_test = False #False
#model_path = '../experiments/pretrained_models/EDVR_Vimeo90K_SR_L.pth'
#model_path = '../experiments/002_EDVR_EDVRwoTSAIni_lr4e-4_600k_REDS_LrCAR4S_fixTSA50k_new/models/latest_G.pth'
#model_path = '../experiments/A02_predenoise/models/415000_G.pth'
model_path = '../experiments/A38_color_EDVR_35_220000_A01_5in_64f_10b_128_pretrain_A01xxx_900000_fix_before_pcd/models/165000_G.pth'
color_model_path = '/home/yhliu/BasicSR/experiments/35_ResNet_alpha_beta_decoder_3x3_IN_encoder_8HW_re_100k/models/220000_G.pth'
predeblur, HR_in = False, False
back_RBs = 10
if data_mode == 'blur_bicubic':
predeblur = True
if data_mode == 'blur' or data_mode == 'blur_comp':
predeblur, HR_in = True, True
model = EDVR_arch.EDVR(64,
N_in,
8,
5,
back_RBs,
predeblur=predeblur,
HR_in=HR_in)
#model = my_EDVR_arch.MYEDVR_FusionDenoise(64, N_in, 8, 5, back_RBs, predeblur=predeblur, HR_in=HR_in, deconv=False)
color_model = SRResNet_arch.ResNet_alpha_beta_multi_in(
structure='ResNet_alpha_beta_decoder_3x3_IN_encoder_8HW')
#### evaluation
crop_border = 0
border_frame = N_in // 2 # border frames when evaluate
# temporal padding mode
if data_mode == 'Vid4' or data_mode == 'sharp_bicubic':
padding = 'new_info'
else:
padding = 'replicate'
save_imgs = True
save_folder = '../results/{}'.format(test_name)
util.mkdirs(save_folder)
util.setup_logger('base',
save_folder,
'test',
level=logging.INFO,
screen=True,
tofile=True)
logger = logging.getLogger('base')
#### log info
logger.info('Data: {} - {}'.format(data_mode, test_dataset_folder))
logger.info('Padding mode: {}'.format(padding))
logger.info('Model path: {}'.format(model_path))
logger.info('Save images: {}'.format(save_imgs))
logger.info('Flip test: {}'.format(flip_test))
#### set up the models
model.load_state_dict(torch.load(model_path), strict=True)
model.eval()
model = model.to(device)
model = nn.DataParallel(model)
#### set up the models
load_net = torch.load(color_model_path)
load_net_clean = OrderedDict() # add prefix 'color_net.'
for k, v in load_net.items():
k = 'color_net.' + k
load_net_clean[k] = v
color_model.load_state_dict(load_net_clean, strict=True)
color_model.eval()
color_model = color_model.to(device)
color_model = nn.DataParallel(color_model)
avg_psnr_l, avg_psnr_center_l, avg_psnr_border_l = [], [], []
subfolder_name_l = []
subfolder_l = sorted(glob.glob(osp.join(test_dataset_folder, '*')))
#print(subfolder_l)
#print(subfolder_GT_l)
#exit()
# for each subfolder
for subfolder in subfolder_l:
subfolder_name = osp.basename(subfolder)
subfolder_name_l.append(subfolder_name)
save_subfolder = osp.join(save_folder, subfolder_name)
img_path_l = sorted(glob.glob(osp.join(subfolder, '*')))
#print(img_path_l)
max_idx = len(img_path_l)
if save_imgs:
util.mkdirs(save_subfolder)
#### read LQ and GT images
imgs_LQ = data_util.read_img_seq(subfolder)
# process each image
for img_idx, img_path in enumerate(img_path_l):
img_name = osp.splitext(osp.basename(img_path))[0]
select_idx = data_util.index_generation(img_idx,
max_idx,
N_in,
padding=padding)
imgs_in = imgs_LQ.index_select(
0, torch.LongTensor(select_idx)).unsqueeze(0).cpu()
print(imgs_in.size())
if flip_test:
imgs_in = util.single_forward(color_model, imgs_in)
output = util.flipx4_forward(model, imgs_in)
else:
start_time = time.time()
imgs_in = util.single_forward(color_model, imgs_in)
output = util.single_forward(model, imgs_in)
end_time = time.time()
print('Forward One image:', end_time - start_time)
output = util.tensor2img(output.squeeze(0))
if save_imgs:
cv2.imwrite(
osp.join(save_subfolder, '{}.png'.format(img_name)),
output)
logger.info('{:3d} - {:25}'.format(img_idx + 1, img_name))
logger.info('################ Tidy Outputs ################')
logger.info('################ Final Results ################')
logger.info('Data: {} - {}'.format(data_mode, test_dataset_folder))
logger.info('Padding mode: {}'.format(padding))
logger.info('Model path: {}'.format(model_path))
logger.info('Save images: {}'.format(save_imgs))
logger.info('Flip test: {}'.format(flip_test))
def main():
#################
# configurations
#################
device = torch.device('cuda')
os.environ['CUDA_VISIBLE_DEVICES'] = '5'
#os.environ['CUDA_VISIBLE_DEVICES'] = '1,2,3,4'
test_set = 'AI4K_val' # Vid4 | YouKu10 | REDS4 | AI4K_val
test_name = 'A38_color_EDVR_35_220000_A01_5in_64f_10b_128_pretrain_A01xxx_900000_fix_before_pcd_165000'
data_mode = 'sharp_bicubic' # sharp_bicubic | blur_bicubic
N_in = 5
# load test set
if test_set == 'Vid4':
test_dataset_folder = '../datasets/Vid4/BIx4'
GT_dataset_folder = '../datasets/Vid4/GT'
elif test_set == 'YouKu10':
test_dataset_folder = '../datasets/YouKu10/LR'
GT_dataset_folder = '../datasets/YouKu10/HR'
elif test_set == 'YouKu_val':
test_dataset_folder = '/data0/yhliu/DATA/YouKuVid/valid/valid_lr_bmp'
GT_dataset_folder = '/data0/yhliu/DATA/YouKuVid/valid/valid_hr_bmp'
elif test_set == 'REDS4':
test_dataset_folder = '../datasets/REDS4/{}'.format(data_mode)
GT_dataset_folder = '../datasets/REDS4/GT'
elif test_set == 'AI4K_val':
test_dataset_folder = '/home/yhliu/AI4K/contest2/val2_LR_png/'
GT_dataset_folder = '/home/yhliu/AI4K/contest1/val1_HR_png/'
elif test_set == 'AI4K_bic':
test_dataset_folder = '/home/yhliu/AI4K/contest2/val2_LR_png_bic/'
GT_dataset_folder = '/home/yhliu/AI4K/contest1/val1_HR_png_bic/'
elif test_set == 'AI4K_testA':
test_dataset_folder = '/data0/yhliu/AI4K/val2_LR_png' #'/home/yhliu/AI4K/val2_LR_png/'
GT_dataset_folder = '/data0/yhliu/AI4K/val1_HR_png/'
flip_test = False
#model_path = '../experiments/pretrained_models/A01xxx/900000_G.pth'
model_path = '../experiments/A38_color_EDVR_35_220000_A01_5in_64f_10b_128_pretrain_A01xxx_900000_fix_before_pcd/models/165000_G.pth'
color_model_path = '/home/yhliu/BasicSR/experiments/35_ResNet_alpha_beta_decoder_3x3_IN_encoder_8HW_re_100k/models/220000_G.pth'
predeblur, HR_in = False, False
back_RBs = 10
if data_mode == 'blur_bicubic':
predeblur = True
if data_mode == 'blur' or data_mode == 'blur_comp':
predeblur, HR_in = True, True
model = EDVR_arch.EDVR(64,
N_in,
8,
5,
back_RBs,
predeblur=predeblur,
HR_in=HR_in)
color_model = SRResNet_arch.ResNet_alpha_beta_multi_in(
structure='ResNet_alpha_beta_decoder_3x3_IN_encoder_8HW')
#### evaluation
crop_border = 0
border_frame = N_in // 2 # border frames when evaluate
# temporal padding mode
if data_mode == 'Vid4' or data_mode == 'sharp_bicubic':
padding = 'new_info'
else:
padding = 'replicate'
save_imgs = True
save_folder = '../results/{}'.format(test_name)
util.mkdirs(save_folder)
util.setup_logger('base',
save_folder,
'test',
level=logging.INFO,
screen=True,
tofile=True)
logger = logging.getLogger('base')
#### log info
logger.info('Data: {} - {}'.format(data_mode, test_dataset_folder))
logger.info('Padding mode: {}'.format(padding))
logger.info('Model path: {}'.format(model_path))
logger.info('Save images: {}'.format(save_imgs))
logger.info('Flip test: {}'.format(flip_test))
#### set up the models
model.load_state_dict(torch.load(model_path), strict=True)
model.eval()
model = model.to(device)
model = nn.DataParallel(model)
#### set up the models
load_net = torch.load(color_model_path)
load_net_clean = OrderedDict() # add prefix 'color_net.'
for k, v in load_net.items():
k = 'color_net.' + k
load_net_clean[k] = v
color_model.load_state_dict(load_net_clean, strict=True)
color_model.eval()
color_model = color_model.to(device)
color_model = nn.DataParallel(color_model)
avg_psnr_l, avg_psnr_center_l, avg_psnr_border_l = [], [], []
subfolder_name_l = []
subfolder_l = sorted(glob.glob(osp.join(test_dataset_folder, '*')))
subfolder_GT_l = sorted(glob.glob(osp.join(GT_dataset_folder, '*')))
#print(subfolder_l)
#print(subfolder_GT_l)
#exit()
# for each subfolder
for subfolder, subfolder_GT in zip(subfolder_l, subfolder_GT_l):
subfolder_name = osp.basename(subfolder)
subfolder_name_l.append(subfolder_name)
save_subfolder = osp.join(save_folder, subfolder_name)
img_path_l = sorted(glob.glob(osp.join(subfolder, '*')))
#print(img_path_l)
max_idx = len(img_path_l)
if save_imgs:
util.mkdirs(save_subfolder)
#### read LQ and GT images
imgs_LQ = data_util.read_img_seq(subfolder)
img_GT_l = []
for img_GT_path in sorted(glob.glob(osp.join(subfolder_GT, '*'))):
#print(img_GT_path)
img_GT_l.append(data_util.read_img(None, img_GT_path))
#print(img_GT_l[0].shape)
avg_psnr, avg_psnr_border, avg_psnr_center, N_border, N_center = 0, 0, 0, 0, 0
# process each image
for img_idx, img_path in enumerate(img_path_l):
img_name = osp.splitext(osp.basename(img_path))[0]
select_idx = data_util.index_generation(img_idx,
max_idx,
N_in,
padding=padding)
imgs_in = imgs_LQ.index_select(
0,
torch.LongTensor(select_idx)).unsqueeze(0).cpu() #to(device)
print(imgs_in.size())
if flip_test:
imgs_in = util.single_forward(color_model, imgs_in)
output = util.flipx4_forward(model, imgs_in)
else:
imgs_in = util.single_forward(color_model, imgs_in)
output = util.single_forward(model, imgs_in)
output = util.tensor2img(output.squeeze(0))
if save_imgs:
cv2.imwrite(
osp.join(save_subfolder, '{}.png'.format(img_name)),
output)
# calculate PSNR
output = output / 255.
GT = np.copy(img_GT_l[img_idx])
# For REDS, evaluate on RGB channels; for Vid4, evaluate on the Y channel
'''
if data_mode == 'Vid4': # bgr2y, [0, 1]
GT = data_util.bgr2ycbcr(GT, only_y=True)
output = data_util.bgr2ycbcr(output, only_y=True)
'''
output, GT = util.crop_border([output, GT], crop_border)
crt_psnr = util.calculate_psnr(output * 255, GT * 255)
logger.info('{:3d} - {:25} \tPSNR: {:.6f} dB'.format(
img_idx + 1, img_name, crt_psnr))
if img_idx >= border_frame and img_idx < max_idx - border_frame: # center frames
avg_psnr_center += crt_psnr
N_center += 1
else: # border frames
avg_psnr_border += crt_psnr
N_border += 1
avg_psnr = (avg_psnr_center + avg_psnr_border) / (N_center + N_border)
avg_psnr_center = avg_psnr_center / N_center
avg_psnr_border = 0 if N_border == 0 else avg_psnr_border / N_border
avg_psnr_l.append(avg_psnr)
avg_psnr_center_l.append(avg_psnr_center)
avg_psnr_border_l.append(avg_psnr_border)
logger.info('Folder {} - Average PSNR: {:.6f} dB for {} frames; '
'Center PSNR: {:.6f} dB for {} frames; '
'Border PSNR: {:.6f} dB for {} frames.'.format(
subfolder_name, avg_psnr, (N_center + N_border),
avg_psnr_center, N_center, avg_psnr_border, N_border))
logger.info('################ Tidy Outputs ################')
for subfolder_name, psnr, psnr_center, psnr_border in zip(
subfolder_name_l, avg_psnr_l, avg_psnr_center_l,
avg_psnr_border_l):
logger.info('Folder {} - Average PSNR: {:.6f} dB. '
'Center PSNR: {:.6f} dB. '
'Border PSNR: {:.6f} dB.'.format(subfolder_name, psnr,
psnr_center, psnr_border))
logger.info('################ Final Results ################')
logger.info('Data: {} - {}'.format(data_mode, test_dataset_folder))
logger.info('Padding mode: {}'.format(padding))
logger.info('Model path: {}'.format(model_path))
logger.info('Save images: {}'.format(save_imgs))
logger.info('Flip test: {}'.format(flip_test))
logger.info('Total Average PSNR: {:.6f} dB for {} clips. '
'Center PSNR: {:.6f} dB. Border PSNR: {:.6f} dB.'.format(
sum(avg_psnr_l) / len(avg_psnr_l), len(subfolder_l),
sum(avg_psnr_center_l) / len(avg_psnr_center_l),
sum(avg_psnr_border_l) / len(avg_psnr_border_l)))
