Updated drunet-training log
This commit is contained in:
Emilio Martinez
parent
69aff57894
commit
e6c0d5a79b
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@ -5531,3 +5531,658 @@ UNetRes(
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23-03-25 23:09:54.077 : <epoch: 11, iter: 140, lr:1.000e-04> G_loss: 4.483e-01
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23-03-26 00:13:39.315 : <epoch: 13, iter: 160, lr:1.000e-04> G_loss: 4.610e-01
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23-03-26 00:59:39.362 : <epoch: 14, iter: 180, lr:1.000e-04> G_loss: 4.139e-01
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23-03-29 16:31:47.255 : task: drunet
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model: plain
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gpu_ids: [0]
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scale: 1
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n_channels: 1
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n_channels_datasetload: 3
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sigma: [0, 50]
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sigma_test: 25
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path:[
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root: denoising
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pretrained_netG: None
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task: denoising/drunet
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log: denoising/drunet
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options: denoising/drunet/options
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models: denoising/drunet/models
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images: denoising/drunet/images
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pretrained_optimizerG: None
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]
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datasets:[
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train:[
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name: train_dataset
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dataset_type: ffdnet
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dataroot_H: trainsets/web_images_train
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num_patches_per_image: 10
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dataroot_L: trainsets/simulations
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H_size: 128
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dataloader_shuffle: True
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dataloader_num_workers: 8
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dataloader_batch_size: 64
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phase: train
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scale: 1
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n_channels: 1
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]
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test:[
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name: test_dataset
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dataset_type: ffdnet
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dataroot_H: testsets/web_images_test
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dataroot_L: testsets/simulations
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phase: test
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scale: 1
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n_channels: 1
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]
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]
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netG:[
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net_type: drunet
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in_nc: 1
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out_nc: 1
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nc: [64, 128, 256, 512]
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nb: 4
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gc: 32
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ng: 2
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reduction: 16
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act_mode: R
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upsample_mode: convtranspose
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downsample_mode: strideconv
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bias: False
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init_type: orthogonal
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init_bn_type: uniform
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init_gain: 0.2
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scale: 1
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]
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train:[
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epochs: 1000
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G_lossfn_type: tv
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G_lossfn_weight: 1.0
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G_tvloss_weight: 1.0
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G_optimizer_type: adam
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G_optimizer_lr: 0.0001
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G_optimizer_clipgrad: None
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G_scheduler_type: MultiStepLR
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G_scheduler_milestones: [100000, 200000, 300000, 400000]
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G_scheduler_gamma: 0.5
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G_regularizer_orthstep: None
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G_regularizer_clipstep: None
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checkpoint_test: 500
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checkpoint_save: 780
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checkpoint_print: 16
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F_feature_layer: 34
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F_weights: 1.0
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F_lossfn_type: l1
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F_use_input_norm: True
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F_use_range_norm: False
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G_optimizer_betas: [0.9, 0.999]
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G_scheduler_restart_weights: 1
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G_optimizer_wd: 0
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G_optimizer_reuse: False
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G_param_strict: True
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E_param_strict: True
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E_decay: 0
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]
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opt_path: options/train_drunet.json
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is_train: True
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merge_bn: False
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merge_bn_startpoint: -1
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find_unused_parameters: True
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use_static_graph: False
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dist: False
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num_gpu: 1
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rank: 0
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world_size: 1
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23-03-29 16:31:47.259 : Number of train images: 500, iters: 8
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23-03-29 16:31:47.968 :
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Networks name: UNetRes
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Params number: 32638080
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Net structure:
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UNetRes(
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(m_head): Conv2d(1, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
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(m_down1): Sequential(
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(0): ResBlock(
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(res): Sequential(
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(0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
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(1): ReLU(inplace=True)
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(2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
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)
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)
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(1): ResBlock(
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(res): Sequential(
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(0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
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(1): ReLU(inplace=True)
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(2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
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)
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)
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(2): ResBlock(
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(res): Sequential(
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(0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
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(1): ReLU(inplace=True)
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(2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
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)
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)
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(3): ResBlock(
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(res): Sequential(
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(0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
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(1): ReLU(inplace=True)
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(2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
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)
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)
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(4): Conv2d(64, 128, kernel_size=(2, 2), stride=(2, 2), bias=False)
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)
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(m_down2): Sequential(
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(0): ResBlock(
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(res): Sequential(
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(0): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
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(1): ReLU(inplace=True)
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(2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
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)
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)
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(1): ResBlock(
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(res): Sequential(
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(0): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
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(1): ReLU(inplace=True)
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(2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
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)
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)
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(2): ResBlock(
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(res): Sequential(
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(0): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
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(1): ReLU(inplace=True)
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(2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
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)
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)
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(3): ResBlock(
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(res): Sequential(
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(0): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
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(1): ReLU(inplace=True)
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(2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
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)
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)
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(4): Conv2d(128, 256, kernel_size=(2, 2), stride=(2, 2), bias=False)
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)
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(m_down3): Sequential(
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(0): ResBlock(
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(res): Sequential(
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(0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
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(1): ReLU(inplace=True)
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(2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
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)
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)
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(1): ResBlock(
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(res): Sequential(
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(0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
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(1): ReLU(inplace=True)
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(2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
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)
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)
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(2): ResBlock(
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(res): Sequential(
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(0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
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(1): ReLU(inplace=True)
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(2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
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)
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)
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(3): ResBlock(
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(res): Sequential(
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(0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
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(1): ReLU(inplace=True)
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(2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
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)
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)
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(4): Conv2d(256, 512, kernel_size=(2, 2), stride=(2, 2), bias=False)
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)
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(m_body): Sequential(
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(0): ResBlock(
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(res): Sequential(
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(0): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
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(1): ReLU(inplace=True)
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(2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
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)
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)
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(1): ResBlock(
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(res): Sequential(
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(0): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
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(1): ReLU(inplace=True)
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(2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
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)
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)
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(2): ResBlock(
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(res): Sequential(
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(0): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
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(1): ReLU(inplace=True)
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(2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
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)
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)
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(3): ResBlock(
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(res): Sequential(
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(0): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
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(1): ReLU(inplace=True)
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(2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
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)
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)
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)
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(m_up3): Sequential(
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(0): ConvTranspose2d(512, 256, kernel_size=(2, 2), stride=(2, 2), bias=False)
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(1): ResBlock(
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(res): Sequential(
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(0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
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(1): ReLU(inplace=True)
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(2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
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)
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)
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(2): ResBlock(
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(res): Sequential(
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(0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
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(1): ReLU(inplace=True)
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(2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
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)
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)
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(3): ResBlock(
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(res): Sequential(
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(0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
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(1): ReLU(inplace=True)
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(2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
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)
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)
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(4): ResBlock(
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(res): Sequential(
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(0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
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(1): ReLU(inplace=True)
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(2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
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)
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)
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)
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(m_up2): Sequential(
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(0): ConvTranspose2d(256, 128, kernel_size=(2, 2), stride=(2, 2), bias=False)
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(1): ResBlock(
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(res): Sequential(
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(0): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
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(1): ReLU(inplace=True)
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(2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
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)
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)
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(2): ResBlock(
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(res): Sequential(
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(0): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
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(1): ReLU(inplace=True)
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(2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
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)
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)
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(3): ResBlock(
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(res): Sequential(
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(0): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
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(1): ReLU(inplace=True)
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(2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
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)
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)
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(4): ResBlock(
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(res): Sequential(
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(0): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
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(1): ReLU(inplace=True)
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(2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
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)
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)
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)
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(m_up1): Sequential(
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(0): ConvTranspose2d(128, 64, kernel_size=(2, 2), stride=(2, 2), bias=False)
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(1): ResBlock(
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(res): Sequential(
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(0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
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(1): ReLU(inplace=True)
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(2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
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)
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)
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(2): ResBlock(
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(res): Sequential(
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(0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
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(1): ReLU(inplace=True)
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(2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
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)
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)
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(3): ResBlock(
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(res): Sequential(
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(0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
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(1): ReLU(inplace=True)
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(2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
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)
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)
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(4): ResBlock(
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(res): Sequential(
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(0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
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(1): ReLU(inplace=True)
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(2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
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)
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)
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)
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(m_tail): Conv2d(64, 1, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
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)
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23-03-29 16:31:47.987 :
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| mean | min | max | std || shape
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| -0.000 | -0.074 | 0.075 | 0.025 | torch.Size([64, 1, 3, 3]) || m_head.weight
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| 0.000 | -0.033 | 0.033 | 0.008 | torch.Size([64, 64, 3, 3]) || m_down1.0.res.0.weight
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| 0.000 | -0.034 | 0.034 | 0.008 | torch.Size([64, 64, 3, 3]) || m_down1.0.res.2.weight
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| 0.000 | -0.031 | 0.035 | 0.008 | torch.Size([64, 64, 3, 3]) || m_down1.1.res.0.weight
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| 0.000 | -0.034 | 0.034 | 0.008 | torch.Size([64, 64, 3, 3]) || m_down1.1.res.2.weight
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| 0.000 | -0.033 | 0.033 | 0.008 | torch.Size([64, 64, 3, 3]) || m_down1.2.res.0.weight
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| -0.000 | -0.035 | 0.034 | 0.008 | torch.Size([64, 64, 3, 3]) || m_down1.2.res.2.weight
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| 0.000 | -0.034 | 0.033 | 0.008 | torch.Size([64, 64, 3, 3]) || m_down1.3.res.0.weight
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| 0.000 | -0.035 | 0.032 | 0.008 | torch.Size([64, 64, 3, 3]) || m_down1.3.res.2.weight
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| 0.000 | -0.055 | 0.050 | 0.012 | torch.Size([128, 64, 2, 2]) || m_down1.4.weight
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| -0.000 | -0.026 | 0.026 | 0.006 | torch.Size([128, 128, 3, 3]) || m_down2.0.res.0.weight
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| -0.000 | -0.026 | 0.026 | 0.006 | torch.Size([128, 128, 3, 3]) || m_down2.0.res.2.weight
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| -0.000 | -0.026 | 0.025 | 0.006 | torch.Size([128, 128, 3, 3]) || m_down2.1.res.0.weight
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| -0.000 | -0.026 | 0.025 | 0.006 | torch.Size([128, 128, 3, 3]) || m_down2.1.res.2.weight
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| -0.000 | -0.025 | 0.025 | 0.006 | torch.Size([128, 128, 3, 3]) || m_down2.2.res.0.weight
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| 0.000 | -0.025 | 0.029 | 0.006 | torch.Size([128, 128, 3, 3]) || m_down2.2.res.2.weight
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| -0.000 | -0.028 | 0.028 | 0.006 | torch.Size([128, 128, 3, 3]) || m_down2.3.res.0.weight
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| -0.000 | -0.026 | 0.025 | 0.006 | torch.Size([128, 128, 3, 3]) || m_down2.3.res.2.weight
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| -0.000 | -0.042 | 0.037 | 0.009 | torch.Size([256, 128, 2, 2]) || m_down2.4.weight
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| -0.000 | -0.020 | 0.020 | 0.004 | torch.Size([256, 256, 3, 3]) || m_down3.0.res.0.weight
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| 0.000 | -0.019 | 0.019 | 0.004 | torch.Size([256, 256, 3, 3]) || m_down3.0.res.2.weight
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| -0.000 | -0.021 | 0.020 | 0.004 | torch.Size([256, 256, 3, 3]) || m_down3.1.res.0.weight
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| -0.000 | -0.020 | 0.020 | 0.004 | torch.Size([256, 256, 3, 3]) || m_down3.1.res.2.weight
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| 0.000 | -0.018 | 0.019 | 0.004 | torch.Size([256, 256, 3, 3]) || m_down3.2.res.0.weight
|
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| 0.000 | -0.020 | 0.021 | 0.004 | torch.Size([256, 256, 3, 3]) || m_down3.2.res.2.weight
|
||||
| -0.000 | -0.019 | 0.020 | 0.004 | torch.Size([256, 256, 3, 3]) || m_down3.3.res.0.weight
|
||||
| -0.000 | -0.020 | 0.020 | 0.004 | torch.Size([256, 256, 3, 3]) || m_down3.3.res.2.weight
|
||||
| 0.000 | -0.028 | 0.029 | 0.006 | torch.Size([512, 256, 2, 2]) || m_down3.4.weight
|
||||
| 0.000 | -0.014 | 0.015 | 0.003 | torch.Size([512, 512, 3, 3]) || m_body.0.res.0.weight
|
||||
| 0.000 | -0.015 | 0.014 | 0.003 | torch.Size([512, 512, 3, 3]) || m_body.0.res.2.weight
|
||||
| -0.000 | -0.015 | 0.015 | 0.003 | torch.Size([512, 512, 3, 3]) || m_body.1.res.0.weight
|
||||
| -0.000 | -0.015 | 0.015 | 0.003 | torch.Size([512, 512, 3, 3]) || m_body.1.res.2.weight
|
||||
| 0.000 | -0.014 | 0.016 | 0.003 | torch.Size([512, 512, 3, 3]) || m_body.2.res.0.weight
|
||||
| 0.000 | -0.015 | 0.014 | 0.003 | torch.Size([512, 512, 3, 3]) || m_body.2.res.2.weight
|
||||
| -0.000 | -0.014 | 0.015 | 0.003 | torch.Size([512, 512, 3, 3]) || m_body.3.res.0.weight
|
||||
| 0.000 | -0.015 | 0.014 | 0.003 | torch.Size([512, 512, 3, 3]) || m_body.3.res.2.weight
|
||||
| -0.000 | -0.028 | 0.027 | 0.006 | torch.Size([512, 256, 2, 2]) || m_up3.0.weight
|
||||
| -0.000 | -0.019 | 0.021 | 0.004 | torch.Size([256, 256, 3, 3]) || m_up3.1.res.0.weight
|
||||
| -0.000 | -0.020 | 0.018 | 0.004 | torch.Size([256, 256, 3, 3]) || m_up3.1.res.2.weight
|
||||
| 0.000 | -0.020 | 0.021 | 0.004 | torch.Size([256, 256, 3, 3]) || m_up3.2.res.0.weight
|
||||
| -0.000 | -0.018 | 0.019 | 0.004 | torch.Size([256, 256, 3, 3]) || m_up3.2.res.2.weight
|
||||
| 0.000 | -0.019 | 0.021 | 0.004 | torch.Size([256, 256, 3, 3]) || m_up3.3.res.0.weight
|
||||
| 0.000 | -0.019 | 0.019 | 0.004 | torch.Size([256, 256, 3, 3]) || m_up3.3.res.2.weight
|
||||
| -0.000 | -0.020 | 0.022 | 0.004 | torch.Size([256, 256, 3, 3]) || m_up3.4.res.0.weight
|
||||
| -0.000 | -0.019 | 0.019 | 0.004 | torch.Size([256, 256, 3, 3]) || m_up3.4.res.2.weight
|
||||
| -0.000 | -0.036 | 0.040 | 0.009 | torch.Size([256, 128, 2, 2]) || m_up2.0.weight
|
||||
| 0.000 | -0.030 | 0.026 | 0.006 | torch.Size([128, 128, 3, 3]) || m_up2.1.res.0.weight
|
||||
| -0.000 | -0.024 | 0.025 | 0.006 | torch.Size([128, 128, 3, 3]) || m_up2.1.res.2.weight
|
||||
| 0.000 | -0.025 | 0.030 | 0.006 | torch.Size([128, 128, 3, 3]) || m_up2.2.res.0.weight
|
||||
| -0.000 | -0.027 | 0.026 | 0.006 | torch.Size([128, 128, 3, 3]) || m_up2.2.res.2.weight
|
||||
| 0.000 | -0.029 | 0.025 | 0.006 | torch.Size([128, 128, 3, 3]) || m_up2.3.res.0.weight
|
||||
| 0.000 | -0.028 | 0.030 | 0.006 | torch.Size([128, 128, 3, 3]) || m_up2.3.res.2.weight
|
||||
| 0.000 | -0.025 | 0.027 | 0.006 | torch.Size([128, 128, 3, 3]) || m_up2.4.res.0.weight
|
||||
| 0.000 | -0.027 | 0.025 | 0.006 | torch.Size([128, 128, 3, 3]) || m_up2.4.res.2.weight
|
||||
| -0.000 | -0.049 | 0.049 | 0.012 | torch.Size([128, 64, 2, 2]) || m_up1.0.weight
|
||||
| -0.000 | -0.032 | 0.034 | 0.008 | torch.Size([64, 64, 3, 3]) || m_up1.1.res.0.weight
|
||||
| -0.000 | -0.033 | 0.035 | 0.008 | torch.Size([64, 64, 3, 3]) || m_up1.1.res.2.weight
|
||||
| 0.000 | -0.039 | 0.035 | 0.008 | torch.Size([64, 64, 3, 3]) || m_up1.2.res.0.weight
|
||||
| 0.000 | -0.034 | 0.032 | 0.008 | torch.Size([64, 64, 3, 3]) || m_up1.2.res.2.weight
|
||||
| -0.000 | -0.034 | 0.034 | 0.008 | torch.Size([64, 64, 3, 3]) || m_up1.3.res.0.weight
|
||||
| 0.000 | -0.033 | 0.040 | 0.008 | torch.Size([64, 64, 3, 3]) || m_up1.3.res.2.weight
|
||||
| 0.000 | -0.033 | 0.040 | 0.008 | torch.Size([64, 64, 3, 3]) || m_up1.4.res.0.weight
|
||||
| 0.000 | -0.035 | 0.032 | 0.008 | torch.Size([64, 64, 3, 3]) || m_up1.4.res.2.weight
|
||||
| -0.000 | -0.030 | 0.021 | 0.008 | torch.Size([1, 64, 3, 3]) || m_tail.weight
|
||||
|
||||
23-03-29 16:32:34.394 : <epoch: 2, iter: 16, lr:1.000e-04> G_loss: 6.949e-01
|
||||
23-03-29 16:33:10.165 : <epoch: 4, iter: 32, lr:1.000e-04> G_loss: 7.753e-01
|
||||
23-03-29 16:33:46.061 : <epoch: 6, iter: 48, lr:1.000e-04> G_loss: 7.726e-01
|
||||
23-03-29 16:34:36.657 : <epoch: 9, iter: 64, lr:1.000e-04> G_loss: 1.860e-01
|
||||
23-03-29 16:35:14.507 : <epoch: 11, iter: 80, lr:1.000e-04> G_loss: 2.613e-01
|
||||
23-03-29 16:35:52.766 : <epoch: 13, iter: 96, lr:1.000e-04> G_loss: 1.826e-01
|
||||
23-03-29 16:36:31.566 : <epoch: 15, iter: 112, lr:1.000e-04> G_loss: 1.701e-01
|
||||
23-03-29 16:37:23.497 : <epoch: 18, iter: 128, lr:1.000e-04> G_loss: 1.647e-01
|
||||
23-03-29 16:38:00.729 : <epoch: 20, iter: 144, lr:1.000e-04> G_loss: 1.608e-01
|
||||
23-03-29 16:38:41.727 : <epoch: 22, iter: 160, lr:1.000e-04> G_loss: 1.480e-01
|
||||
23-03-29 16:39:34.785 : <epoch: 25, iter: 176, lr:1.000e-04> G_loss: 1.530e-01
|
||||
23-03-29 16:40:13.959 : <epoch: 27, iter: 192, lr:1.000e-04> G_loss: 1.378e-01
|
||||
23-03-29 16:40:51.615 : <epoch: 29, iter: 208, lr:1.000e-04> G_loss: 1.273e-01
|
||||
23-03-29 16:41:30.969 : <epoch: 31, iter: 224, lr:1.000e-04> G_loss: 1.282e-01
|
||||
23-03-29 16:42:25.068 : <epoch: 34, iter: 240, lr:1.000e-04> G_loss: 1.080e-01
|
||||
23-03-29 16:43:02.496 : <epoch: 36, iter: 256, lr:1.000e-04> G_loss: 1.196e-01
|
||||
23-03-29 16:43:43.206 : <epoch: 38, iter: 272, lr:1.000e-04> G_loss: 1.017e-01
|
||||
23-03-29 16:44:33.739 : <epoch: 41, iter: 288, lr:1.000e-04> G_loss: 1.004e-01
|
||||
23-03-29 16:45:13.981 : <epoch: 43, iter: 304, lr:1.000e-04> G_loss: 9.401e-02
|
||||
23-03-29 16:45:51.188 : <epoch: 45, iter: 320, lr:1.000e-04> G_loss: 9.004e-02
|
||||
23-03-29 16:46:30.564 : <epoch: 47, iter: 336, lr:1.000e-04> G_loss: 9.985e-02
|
||||
23-03-29 16:47:25.445 : <epoch: 50, iter: 352, lr:1.000e-04> G_loss: 9.280e-02
|
||||
23-03-29 16:48:04.575 : <epoch: 52, iter: 368, lr:1.000e-04> G_loss: 9.237e-02
|
||||
23-03-29 16:48:45.999 : <epoch: 54, iter: 384, lr:1.000e-04> G_loss: 9.082e-02
|
||||
23-03-29 16:49:38.930 : <epoch: 57, iter: 400, lr:1.000e-04> G_loss: 9.076e-02
|
||||
23-03-29 16:50:20.771 : <epoch: 59, iter: 416, lr:1.000e-04> G_loss: 9.092e-02
|
||||
23-03-29 16:50:59.966 : <epoch: 61, iter: 432, lr:1.000e-04> G_loss: 8.740e-02
|
||||
23-03-29 16:51:37.775 : <epoch: 63, iter: 448, lr:1.000e-04> G_loss: 8.498e-02
|
||||
23-03-29 16:52:32.279 : <epoch: 66, iter: 464, lr:1.000e-04> G_loss: 8.878e-02
|
||||
23-03-29 16:53:11.601 : <epoch: 68, iter: 480, lr:1.000e-04> G_loss: 7.787e-02
|
||||
23-03-29 16:53:51.581 : <epoch: 70, iter: 496, lr:1.000e-04> G_loss: 7.546e-02
|
||||
23-03-29 16:54:11.246 : ---1--> 10094.png | 9.65dB
|
||||
23-03-29 16:54:11.559 : ---2--> 10231.png | 17.40dB
|
||||
23-03-29 16:54:11.864 : ---3--> 1037.png | 11.91dB
|
||||
23-03-29 16:54:12.170 : ---4--> 10379.png | 9.05dB
|
||||
23-03-29 16:54:12.499 : ---5--> 10420.png | 12.16dB
|
||||
23-03-29 16:54:12.796 : ---6--> 10436.png | 12.01dB
|
||||
23-03-29 16:54:13.109 : ---7--> 10534.png | 5.50dB
|
||||
23-03-29 16:54:13.419 : ---8--> 11054.png | 16.01dB
|
||||
23-03-29 16:54:13.742 : ---9--> 11143.png | 11.91dB
|
||||
23-03-29 16:54:14.059 : --10--> 11294.png | 14.80dB
|
||||
23-03-29 16:54:14.371 : --11--> 11404.png | 11.51dB
|
||||
23-03-29 16:54:14.684 : --12--> 11604.png | 10.64dB
|
||||
23-03-29 16:54:15.022 : --13--> 11708.png | 12.26dB
|
||||
23-03-29 16:54:15.327 : --14--> 11712.png | 12.32dB
|
||||
23-03-29 16:54:15.644 : --15--> 11880.png | 13.44dB
|
||||
23-03-29 16:54:15.962 : --16--> 1190.png | 12.41dB
|
||||
23-03-29 16:54:16.284 : --17--> 12009.png | 14.28dB
|
||||
23-03-29 16:54:16.603 : --18--> 12248.png | 13.83dB
|
||||
23-03-29 16:54:16.920 : --19--> 12449.png | 7.12dB
|
||||
23-03-29 16:54:17.227 : --20--> 12460.png | 13.67dB
|
||||
23-03-29 16:54:17.561 : --21--> 125.png | 15.13dB
|
||||
23-03-29 16:54:17.873 : --22--> 12539.png | 12.14dB
|
||||
23-03-29 16:54:18.186 : --23--> 134.png | 12.03dB
|
||||
23-03-29 16:54:18.508 : --24--> 13450.png | 14.38dB
|
||||
23-03-29 16:54:18.831 : --25--> 13902.png | 10.25dB
|
||||
23-03-29 16:54:19.148 : --26--> 14107.png | 17.18dB
|
||||
23-03-29 16:54:19.470 : --27--> 1421.png | 14.20dB
|
||||
23-03-29 16:54:19.779 : --28--> 14305.png | 14.49dB
|
||||
23-03-29 16:54:20.118 : --29--> 1431.png | 10.44dB
|
||||
23-03-29 16:54:20.438 : --30--> 14926.png | 9.74dB
|
||||
23-03-29 16:54:20.746 : --31--> 15307.png | 14.49dB
|
||||
23-03-29 16:54:21.069 : --32--> 15387.png | 11.28dB
|
||||
23-03-29 16:54:21.400 : --33--> 15612.png | 13.46dB
|
||||
23-03-29 16:54:21.711 : --34--> 15661.png | 10.46dB
|
||||
23-03-29 16:54:22.034 : --35--> 15681.png | 9.71dB
|
||||
23-03-29 16:54:22.345 : --36--> 159.png | 13.11dB
|
||||
23-03-29 16:54:22.680 : --37--> 15930.png | 16.83dB
|
||||
23-03-29 16:54:23.003 : --38--> 16028.png | 12.36dB
|
||||
23-03-29 16:54:23.311 : --39--> 1619.png | 6.96dB
|
||||
23-03-29 16:54:23.632 : --40--> 168.png | 5.82dB
|
||||
23-03-29 16:54:23.969 : --41--> 174.png | 14.65dB
|
||||
23-03-29 16:54:24.278 : --42--> 188.png | 12.15dB
|
||||
23-03-29 16:54:24.601 : --43--> 1928.png | 12.17dB
|
||||
23-03-29 16:54:24.919 : --44--> 1942.png | 16.18dB
|
||||
23-03-29 16:54:25.245 : --45--> 209.png | 13.01dB
|
||||
23-03-29 16:54:25.568 : --46--> 2179.png | 14.64dB
|
||||
23-03-29 16:54:25.884 : --47--> 2541.png | 10.54dB
|
||||
23-03-29 16:54:26.196 : --48--> 3164.png | 16.26dB
|
||||
23-03-29 16:54:26.535 : --49--> 3259.png | 15.55dB
|
||||
23-03-29 16:54:26.848 : --50--> 3410.png | 12.70dB
|
||||
23-03-29 16:54:26.864 : <epoch: 71, iter: 500, Average PSNR : 12.48dB
|
||||
|
||||
23-03-29 16:55:02.101 : <epoch: 73, iter: 512, lr:1.000e-04> G_loss: 7.442e-02
|
||||
23-03-29 16:55:42.482 : <epoch: 75, iter: 528, lr:1.000e-04> G_loss: 7.871e-02
|
||||
23-03-29 16:56:22.887 : <epoch: 77, iter: 544, lr:1.000e-04> G_loss: 7.816e-02
|
||||
23-03-29 16:57:01.732 : <epoch: 79, iter: 560, lr:1.000e-04> G_loss: 6.419e-02
|
||||
23-03-29 16:57:58.773 : <epoch: 82, iter: 576, lr:1.000e-04> G_loss: 7.175e-02
|
||||
23-03-29 16:58:35.552 : <epoch: 84, iter: 592, lr:1.000e-04> G_loss: 7.002e-02
|
||||
23-03-29 16:59:14.209 : <epoch: 86, iter: 608, lr:1.000e-04> G_loss: 6.030e-02
|
||||
23-03-29 17:00:07.515 : <epoch: 89, iter: 624, lr:1.000e-04> G_loss: 5.338e-02
|
||||
23-03-29 17:00:49.419 : <epoch: 91, iter: 640, lr:1.000e-04> G_loss: 6.444e-02
|
||||
23-03-29 17:01:27.112 : <epoch: 93, iter: 656, lr:1.000e-04> G_loss: 5.047e-02
|
||||
23-03-29 17:02:08.205 : <epoch: 95, iter: 672, lr:1.000e-04> G_loss: 5.530e-02
|
||||
23-03-29 17:03:01.214 : <epoch: 98, iter: 688, lr:1.000e-04> G_loss: 5.210e-02
|
||||
23-03-29 17:03:39.950 : <epoch:100, iter: 704, lr:1.000e-04> G_loss: 6.532e-02
|
||||
23-03-29 17:04:20.319 : <epoch:102, iter: 720, lr:1.000e-04> G_loss: 5.286e-02
|
||||
23-03-29 17:05:16.892 : <epoch:105, iter: 736, lr:1.000e-04> G_loss: 5.812e-02
|
||||
23-03-29 17:05:55.978 : <epoch:107, iter: 752, lr:1.000e-04> G_loss: 5.830e-02
|
||||
23-03-29 17:06:34.905 : <epoch:109, iter: 768, lr:1.000e-04> G_loss: 4.957e-02
|
||||
23-03-29 17:07:12.708 : Saving the model.
|
||||
23-03-29 17:07:15.324 : <epoch:111, iter: 784, lr:1.000e-04> G_loss: 4.438e-02
|
||||
23-03-29 17:08:08.412 : <epoch:114, iter: 800, lr:1.000e-04> G_loss: 5.813e-02
|
||||
23-03-29 17:08:49.452 : <epoch:116, iter: 816, lr:1.000e-04> G_loss: 7.021e-02
|
||||
23-03-29 17:09:26.953 : <epoch:118, iter: 832, lr:1.000e-04> G_loss: 4.102e-02
|
||||
23-03-29 17:10:22.610 : <epoch:121, iter: 848, lr:1.000e-04> G_loss: 5.057e-02
|
||||
23-03-29 17:11:01.744 : <epoch:123, iter: 864, lr:1.000e-04> G_loss: 3.676e-02
|
||||
23-03-29 17:11:39.760 : <epoch:125, iter: 880, lr:1.000e-04> G_loss: 4.955e-02
|
||||
23-03-29 17:12:20.226 : <epoch:127, iter: 896, lr:1.000e-04> G_loss: 5.992e-02
|
||||
23-03-29 17:13:13.404 : <epoch:130, iter: 912, lr:1.000e-04> G_loss: 6.342e-02
|
||||
23-03-29 17:13:54.321 : <epoch:132, iter: 928, lr:1.000e-04> G_loss: 4.831e-02
|
||||
23-03-29 17:14:32.044 : <epoch:134, iter: 944, lr:1.000e-04> G_loss: 5.070e-02
|
||||
23-03-29 17:15:25.343 : <epoch:137, iter: 960, lr:1.000e-04> G_loss: 4.835e-02
|
||||
23-03-29 17:16:04.863 : <epoch:139, iter: 976, lr:1.000e-04> G_loss: 6.285e-02
|
||||
23-03-29 17:16:42.031 : <epoch:141, iter: 992, lr:1.000e-04> G_loss: 5.670e-02
|
||||
23-03-29 17:17:02.768 : ---1--> 10094.png | 9.65dB
|
||||
23-03-29 17:17:03.085 : ---2--> 10231.png | 17.40dB
|
||||
23-03-29 17:17:03.394 : ---3--> 1037.png | 11.91dB
|
||||
23-03-29 17:17:03.717 : ---4--> 10379.png | 9.05dB
|
||||
23-03-29 17:17:04.031 : ---5--> 10420.png | 12.16dB
|
||||
23-03-29 17:17:04.338 : ---6--> 10436.png | 12.01dB
|
||||
23-03-29 17:17:04.655 : ---7--> 10534.png | 5.50dB
|
||||
23-03-29 17:17:04.988 : ---8--> 11054.png | 16.01dB
|
||||
23-03-29 17:17:05.291 : ---9--> 11143.png | 11.91dB
|
||||
23-03-29 17:17:05.609 : --10--> 11294.png | 14.80dB
|
||||
23-03-29 17:17:05.922 : --11--> 11404.png | 11.51dB
|
||||
23-03-29 17:17:06.243 : --12--> 11604.png | 10.64dB
|
||||
23-03-29 17:17:06.561 : --13--> 11708.png | 12.26dB
|
||||
23-03-29 17:17:06.870 : --14--> 11712.png | 12.32dB
|
||||
23-03-29 17:17:07.180 : --15--> 11880.png | 13.44dB
|
||||
23-03-29 17:17:07.510 : --16--> 1190.png | 12.41dB
|
||||
23-03-29 17:17:07.819 : --17--> 12009.png | 14.28dB
|
||||
23-03-29 17:17:08.135 : --18--> 12248.png | 13.83dB
|
||||
23-03-29 17:17:08.451 : --19--> 12449.png | 7.12dB
|
||||
23-03-29 17:17:08.772 : --20--> 12460.png | 13.67dB
|
||||
23-03-29 17:17:09.089 : --21--> 125.png | 15.13dB
|
||||
23-03-29 17:17:09.401 : --22--> 12539.png | 12.14dB
|
||||
23-03-29 17:17:09.708 : --23--> 134.png | 12.03dB
|
||||
23-03-29 17:17:10.041 : --24--> 13450.png | 14.38dB
|
||||
23-03-29 17:17:10.349 : --25--> 13902.png | 10.25dB
|
||||
23-03-29 17:17:10.662 : --26--> 14107.png | 17.18dB
|
||||
23-03-29 17:17:10.979 : --27--> 1421.png | 14.20dB
|
||||
23-03-29 17:17:11.298 : --28--> 14305.png | 14.49dB
|
||||
23-03-29 17:17:11.615 : --29--> 1431.png | 10.44dB
|
||||
23-03-29 17:17:11.930 : --30--> 14926.png | 9.74dB
|
||||
23-03-29 17:17:12.237 : --31--> 15307.png | 14.49dB
|
||||
23-03-29 17:17:12.572 : --32--> 15387.png | 11.28dB
|
||||
23-03-29 17:17:12.883 : --33--> 15612.png | 13.46dB
|
||||
23-03-29 17:17:13.191 : --34--> 15661.png | 10.46dB
|
||||
23-03-29 17:17:13.509 : --35--> 15681.png | 9.71dB
|
||||
23-03-29 17:17:13.832 : --36--> 159.png | 13.11dB
|
||||
23-03-29 17:17:14.145 : --37--> 15930.png | 16.83dB
|
||||
23-03-29 17:17:14.461 : --38--> 16028.png | 12.36dB
|
||||
23-03-29 17:17:14.765 : --39--> 1619.png | 6.96dB
|
||||
23-03-29 17:17:15.100 : --40--> 168.png | 5.82dB
|
||||
23-03-29 17:17:15.415 : --41--> 174.png | 14.65dB
|
||||
23-03-29 17:17:15.728 : --42--> 188.png | 12.15dB
|
||||
23-03-29 17:17:16.051 : --43--> 1928.png | 12.17dB
|
||||
23-03-29 17:17:16.382 : --44--> 1942.png | 16.18dB
|
||||
23-03-29 17:17:16.697 : --45--> 209.png | 13.01dB
|
||||
23-03-29 17:17:17.021 : --46--> 2179.png | 14.64dB
|
||||
23-03-29 17:17:17.332 : --47--> 2541.png | 10.54dB
|
||||
23-03-29 17:17:17.669 : --48--> 3164.png | 16.26dB
|
||||
23-03-29 17:17:17.987 : --49--> 3259.png | 15.55dB
|
||||
23-03-29 17:17:18.293 : --50--> 3410.png | 12.70dB
|
||||
23-03-29 17:17:18.310 : <epoch:142, iter: 1,000, Average PSNR : 12.48dB
|
||||
|
||||
23-03-29 17:17:37.826 : <epoch:143, iter: 1,008, lr:1.000e-04> G_loss: 4.532e-02
|
||||
23-03-29 17:18:30.295 : <epoch:146, iter: 1,024, lr:1.000e-04> G_loss: 5.543e-02
|
||||
23-03-29 17:19:09.790 : <epoch:148, iter: 1,040, lr:1.000e-04> G_loss: 6.241e-02
|
||||
23-03-29 17:19:49.234 : <epoch:150, iter: 1,056, lr:1.000e-04> G_loss: 6.542e-02
|
||||
23-03-29 17:20:44.484 : <epoch:153, iter: 1,072, lr:1.000e-04> G_loss: 5.574e-02
|
||||
23-03-29 17:21:23.848 : <epoch:155, iter: 1,088, lr:1.000e-04> G_loss: 4.075e-02
|
||||
23-03-29 17:22:04.922 : <epoch:157, iter: 1,104, lr:1.000e-04> G_loss: 5.010e-02
|
||||
23-03-29 17:22:42.915 : <epoch:159, iter: 1,120, lr:1.000e-04> G_loss: 5.169e-02
|
||||
23-03-29 17:23:40.444 : <epoch:162, iter: 1,136, lr:1.000e-04> G_loss: 5.905e-02
|
||||
23-03-29 17:24:18.205 : <epoch:164, iter: 1,152, lr:1.000e-04> G_loss: 8.263e-02
|
||||
23-03-29 17:24:57.677 : <epoch:166, iter: 1,168, lr:1.000e-04> G_loss: 5.444e-02
|
||||
23-03-29 17:25:54.289 : <epoch:169, iter: 1,184, lr:1.000e-04> G_loss: 4.735e-02
|
||||
23-03-29 17:26:31.933 : <epoch:171, iter: 1,200, lr:1.000e-04> G_loss: 5.968e-02
|
||||
23-03-29 17:27:13.487 : <epoch:173, iter: 1,216, lr:1.000e-04> G_loss: 3.609e-02
|
||||
23-03-29 17:27:51.289 : <epoch:175, iter: 1,232, lr:1.000e-04> G_loss: 5.952e-02
|
||||
23-03-29 17:28:46.803 : <epoch:178, iter: 1,248, lr:1.000e-04> G_loss: 5.702e-02
|
||||
23-03-29 17:29:25.998 : <epoch:180, iter: 1,264, lr:1.000e-04> G_loss: 5.872e-02
|
||||
23-03-29 17:30:05.369 : <epoch:182, iter: 1,280, lr:1.000e-04> G_loss: 5.911e-02
|
||||
23-03-29 17:31:00.711 : <epoch:185, iter: 1,296, lr:1.000e-04> G_loss: 7.604e-02
|
||||
23-03-29 17:31:39.758 : <epoch:187, iter: 1,312, lr:1.000e-04> G_loss: 4.435e-02
|
||||
23-03-29 17:32:21.881 : <epoch:189, iter: 1,328, lr:1.000e-04> G_loss: 3.991e-02
|
||||
23-03-29 17:32:59.768 : <epoch:191, iter: 1,344, lr:1.000e-04> G_loss: 5.020e-02
|
||||
23-03-29 17:33:55.127 : <epoch:194, iter: 1,360, lr:1.000e-04> G_loss: 4.906e-02
|
||||
23-03-29 17:34:33.967 : <epoch:196, iter: 1,376, lr:1.000e-04> G_loss: 6.111e-02
|
||||
23-03-29 17:35:12.875 : <epoch:198, iter: 1,392, lr:1.000e-04> G_loss: 4.522e-02
|
||||
23-03-29 17:36:07.905 : <epoch:201, iter: 1,408, lr:1.000e-04> G_loss: 4.585e-02
|
||||
23-03-29 17:36:46.948 : <epoch:203, iter: 1,424, lr:1.000e-04> G_loss: 5.055e-02
|
||||
23-03-29 17:37:29.556 : <epoch:205, iter: 1,440, lr:1.000e-04> G_loss: 7.818e-02
|
||||
23-03-29 17:38:06.350 : <epoch:207, iter: 1,456, lr:1.000e-04> G_loss: 6.179e-02
|
||||
23-03-29 17:39:02.029 : <epoch:210, iter: 1,472, lr:1.000e-04> G_loss: 4.973e-02
|
||||
23-03-29 17:39:43.017 : <epoch:212, iter: 1,488, lr:1.000e-04> G_loss: 5.012e-02
|
||||
23-03-29 17:40:19.689 : ---1--> 10094.png | 9.65dB
|
||||
23-03-29 17:40:20.021 : ---2--> 10231.png | 17.40dB
|
||||
23-03-29 17:40:20.323 : ---3--> 1037.png | 11.91dB
|
||||
23-03-29 17:40:20.647 : ---4--> 10379.png | 9.05dB
|
||||
23-03-29 17:40:20.959 : ---5--> 10420.png | 12.16dB
|
||||
23-03-29 17:40:21.258 : ---6--> 10436.png | 12.01dB
|
||||
23-03-29 17:40:21.573 : ---7--> 10534.png | 5.50dB
|
||||
23-03-29 17:40:21.902 : ---8--> 11054.png | 16.01dB
|
||||
23-03-29 17:40:22.209 : ---9--> 11143.png | 11.91dB
|
||||
23-03-29 17:40:22.530 : --10--> 11294.png | 14.80dB
|
||||
23-03-29 17:40:22.843 : --11--> 11404.png | 11.51dB
|
||||
23-03-29 17:40:23.173 : --12--> 11604.png | 10.64dB
|
||||
23-03-29 17:40:23.494 : --13--> 11708.png | 12.26dB
|
||||
23-03-29 17:40:23.799 : --14--> 11712.png | 12.32dB
|
||||
23-03-29 17:40:24.116 : --15--> 11880.png | 13.44dB
|
||||
23-03-29 17:40:24.446 : --16--> 1190.png | 12.41dB
|
||||
23-03-29 17:40:24.752 : --17--> 12009.png | 14.28dB
|
||||
23-03-29 17:40:25.068 : --18--> 12248.png | 13.83dB
|
||||
23-03-29 17:40:25.384 : --19--> 12449.png | 7.12dB
|
||||
23-03-29 17:40:25.716 : --20--> 12460.png | 13.67dB
|
||||
23-03-29 17:40:26.038 : --21--> 125.png | 15.13dB
|
||||
23-03-29 17:40:26.349 : --22--> 12539.png | 12.14dB
|
||||
23-03-29 17:40:26.664 : --23--> 134.png | 12.03dB
|
||||
23-03-29 17:40:27.002 : --24--> 13450.png | 14.38dB
|
||||
23-03-29 17:40:27.310 : --25--> 13902.png | 10.25dB
|
||||
23-03-29 17:40:27.628 : --26--> 14107.png | 17.18dB
|
||||
23-03-29 17:40:27.950 : --27--> 1421.png | 14.20dB
|
||||
23-03-29 17:40:28.273 : --28--> 14305.png | 14.49dB
|
||||
23-03-29 17:40:28.594 : --29--> 1431.png | 10.44dB
|
||||
23-03-29 17:40:28.911 : --30--> 14926.png | 9.74dB
|
||||
23-03-29 17:40:29.221 : --31--> 15307.png | 14.49dB
|
||||
23-03-29 17:40:29.559 : --32--> 15387.png | 11.28dB
|
||||
23-03-29 17:40:29.873 : --33--> 15612.png | 13.46dB
|
||||
23-03-29 17:40:30.185 : --34--> 15661.png | 10.46dB
|
||||
23-03-29 17:40:30.507 : --35--> 15681.png | 9.71dB
|
||||
23-03-29 17:40:30.832 : --36--> 159.png | 13.11dB
|
||||
23-03-29 17:40:31.145 : --37--> 15930.png | 16.83dB
|
||||
23-03-29 17:40:31.462 : --38--> 16028.png | 12.36dB
|
||||
23-03-29 17:40:31.767 : --39--> 1619.png | 6.96dB
|
||||
23-03-29 17:40:32.100 : --40--> 168.png | 5.82dB
|
||||
23-03-29 17:40:32.411 : --41--> 174.png | 14.65dB
|
||||
23-03-29 17:40:32.715 : --42--> 188.png | 12.15dB
|
||||
23-03-29 17:40:33.030 : --43--> 1928.png | 12.17dB
|
||||
23-03-29 17:40:33.352 : --44--> 1942.png | 16.18dB
|
||||
23-03-29 17:40:33.666 : --45--> 209.png | 13.01dB
|
||||
23-03-29 17:40:33.987 : --46--> 2179.png | 14.64dB
|
||||
23-03-29 17:40:34.295 : --47--> 2541.png | 10.54dB
|
||||
23-03-29 17:40:34.633 : --48--> 3164.png | 16.26dB
|
||||
23-03-29 17:40:34.952 : --49--> 3259.png | 15.55dB
|
||||
23-03-29 17:40:35.260 : --50--> 3410.png | 12.70dB
|
||||
23-03-29 17:40:35.275 : <epoch:214, iter: 1,500, Average PSNR : 12.48dB
|
||||
|
||||
23-03-29 17:40:37.820 : <epoch:214, iter: 1,504, lr:1.000e-04> G_loss: 5.758e-02
|
||||
23-03-29 17:41:33.715 : <epoch:217, iter: 1,520, lr:1.000e-04> G_loss: 5.188e-02
|
||||
23-03-29 17:42:12.658 : <epoch:219, iter: 1,536, lr:1.000e-04> G_loss: 4.340e-02
|
||||
23-03-29 17:42:53.851 : <epoch:221, iter: 1,552, lr:1.000e-04> G_loss: 6.086e-02
|
||||
23-03-29 17:43:13.205 : Saving the model.
|
||||
23-03-29 17:43:31.899 : <epoch:223, iter: 1,568, lr:1.000e-04> G_loss: 5.254e-02
|
||||
23-03-29 17:44:27.625 : <epoch:226, iter: 1,584, lr:1.000e-04> G_loss: 7.720e-02
|
||||
23-03-29 17:45:07.172 : <epoch:228, iter: 1,600, lr:1.000e-04> G_loss: 4.633e-02
|
||||
23-03-29 17:45:46.833 : <epoch:230, iter: 1,616, lr:1.000e-04> G_loss: 4.168e-02
|
||||
23-03-29 17:46:43.425 : <epoch:233, iter: 1,632, lr:1.000e-04> G_loss: 7.391e-02
|
||||
23-03-29 17:47:21.518 : <epoch:235, iter: 1,648, lr:1.000e-04> G_loss: 6.206e-02
|
||||
|
|
|
|||
Loading…
Reference in New Issue