plot_loss_start_epoch = 1 only_validate = False # from visdom import Visdom vis = Visdom(server='http://127.0.0.1', port=8097) # =================== config for model and dataset ===================================================================== from squid.data import Photo2PhotoData from squid.data import RandomCropPhoto2PhotoData from squid.model import SuperviseModel import torch import torch.nn as nn from squid.loss import VGGLoss from squid.net import DnCnn target_net = DnCnn(layer_num=20) target_net = nn.DataParallel(target_net).cuda() model = SuperviseModel({ 'net': target_net, 'optimizer': torch.optim.Adam([{ 'name': 'net_params', 'params': target_net.parameters(), 'base_lr': 1e-4 }], betas=(0.9, 0.999), weight_decay=0.0005), 'lr_step_ratio': 0.5,
only_validate = False # from visdom import Visdom vis = Visdom(server='http://127.0.0.1', port=8097) # =================== config for model and dataset ===================================================================== from squid.data import Photo2PhotoData from squid.data import RandomCropPhoto2PhotoData from squid.model import SuperviseModel import torch.nn as nn from squid.loss import VGGLoss from squid.net import DnCnn hr_size = 256 target_net = DnCnn(layer_num=20) target_net.load_state_dict(checkpoint) model = SuperviseModel({ 'net': target_net, 'optimizer': torch.optim.Adam([{'name':'net_params', 'params':target_net.parameters(), 'base_lr':0.00005}], betas=(0.9, 0.999), weight_decay=0.0005), 'lr_step_ratio': 0.5, 'lr_step_size': 5, 'supervise':{ 'out': {'MSE_loss': {'obj': nn.MSELoss(size_average=True), 'factor':0.1, 'weight': 1.0}}, }, 'metrics': {} })
only_validate = False # from visdom import Visdom vis = Visdom(server='http://127.0.0.1', port=8097) # =================== config for model and dataset ===================================================================== from squid.data import Photo2PhotoData from squid.data import RandomCropPhoto2PhotoData from squid.model import SuperviseModel import torch import torch.nn as nn from squid.loss import VGGLoss from squid.net import DnCnn target_net = DnCnn(layer_num=20) target_net = nn.DataParallel(target_net).cuda() model = SuperviseModel({ 'net': target_net, 'optimizer': torch.optim.Adam([{'name':'net_params', 'params':target_net.parameters(), 'base_lr':1e-4}], betas=(0.9, 0.999), weight_decay=0.0005), 'lr_step_ratio': 0.5, 'lr_step_size': 20, 'supervise':{ 'out': {'MSE_loss': {'obj': nn.MSELoss(size_average=True), 'factor':1.0, 'weight':1.0}}, }, 'metrics': {} })
# inference config file # Created by zyh in Meitu. from squid.net import DnCnn from squid.net import AODNet from squid.net import DnCnn_AOD test_snapshot_path = '/root/zyh3/train_tasks/dncnn_configv5/models/snapshot_12_G_model' # target_net = AODNet() target_net = DnCnn(layer_num=20) # target_net = DnCnn_AOD() test_input_dir = '/root/zyh3/SOTS/SOTS/indoor/nyuhaze500/hazy' # test_input_dir = '/root/zyh3/IndoorTrain/IndoorTrainHazy' TEST_OUT_FOLDER = '/root/zyh3/SOTS_dncnn_20layer_out' # TEST_OUT_FOLDER = '/root/zyh3/IndoorTrain/IndoorTrainHazy_out' GPU_ID = 0 vis = None
only_validate = False # from visdom import Visdom vis = Visdom(server='http://127.0.0.1', port=8097) # =================== config for model and dataset ===================================================================== from squid.data import Photo2PhotoData from squid.data import RandomCropPhoto2PhotoData from squid.model import SuperviseModel import torch import torch.nn as nn from squid.loss import VGGLoss from squid.net import DnCnn hr_size = (407, 541) target_net = DnCnn() model = SuperviseModel({ 'net': target_net, 'optimizer': torch.optim.Adam([{ 'name': 'net_params', 'params': target_net.parameters(), 'base_lr': 2e-4 }], betas=(0.9, 0.999), weight_decay=0.0005), 'lr_step_ratio': 0.5, 'lr_step_size':
plot_loss_start_epoch = 1 only_validate = False # from visdom import Visdom vis = Visdom(server='http://127.0.0.1', port=8097) # =================== config for model and dataset ===================================================================== from squid.data import Photo2PhotoData from squid.data import RandomCropPhoto2PhotoData from squid.model import SuperviseModel import torch.nn as nn from squid.loss import VGGLoss from squid.net import DnCnn hr_size = (407, 541) target_net = DnCnn() target_net.load_state_dict(checkpoint) model = SuperviseModel({ 'net': target_net, 'optimizer': torch.optim.Adam([{ 'name': 'net_params', 'params': target_net.parameters(), 'base_lr': 1e-4 }], betas=(0.9, 0.999), weight_decay=0.0005), 'lr_step_ratio': 0.5,