| DataParallel( | |
| (module): LAMBDANET( | |
| (sub_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) | |
| (add_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) | |
| (head): Sequential( | |
| (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| ) | |
| (body): Sequential( | |
| (0): ResBlock( | |
| (body): Sequential( | |
| (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| (1): PReLU(num_parameters=1) | |
| (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| ) | |
| ) | |
| (1): ResBlock( | |
| (body): Sequential( | |
| (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| (1): PReLU(num_parameters=1) | |
| (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| ) | |
| ) | |
| (2): ResBlock( | |
| (body): Sequential( | |
| (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| (1): PReLU(num_parameters=1) | |
| (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| ) | |
| ) | |
| (3): ResBlock( | |
| (body): Sequential( | |
| (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| (1): PReLU(num_parameters=1) | |
| (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| ) | |
| ) | |
| (4): ResBlock( | |
| (body): Sequential( | |
| (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| (1): PReLU(num_parameters=1) | |
| (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| ) | |
| ) | |
| (5): ResBlock( | |
| (body): Sequential( | |
| (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| (1): PReLU(num_parameters=1) | |
| (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| ) | |
| ) | |
| (6): ResBlock( | |
| (body): Sequential( | |
| (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| (1): PReLU(num_parameters=1) | |
| (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| ) | |
| ) | |
| (7): ResBlock( | |
| (body): Sequential( | |
| (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| (1): PReLU(num_parameters=1) | |
| (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| ) | |
| ) | |
| (8): ResBlock( | |
| (body): Sequential( | |
| (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| (1): PReLU(num_parameters=1) | |
| (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| ) | |
| ) | |
| (9): ResBlock( | |
| (body): Sequential( | |
| (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| (1): PReLU(num_parameters=1) | |
| (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| ) | |
| ) | |
| (10): LambdaLayer( | |
| (to_q): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) | |
| (to_k): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) | |
| (to_v): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) | |
| (norm_q): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (norm_v): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (pos_conv): Conv3d(4, 16, kernel_size=(1, 23, 23), stride=(1, 1, 1), padding=(0, 11, 11)) | |
| ) | |
| (11): ResBlock( | |
| (body): Sequential( | |
| (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| (1): PReLU(num_parameters=1) | |
| (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| ) | |
| ) | |
| (12): ResBlock( | |
| (body): Sequential( | |
| (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| (1): PReLU(num_parameters=1) | |
| (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| ) | |
| ) | |
| (13): ResBlock( | |
| (body): Sequential( | |
| (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| (1): PReLU(num_parameters=1) | |
| (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| ) | |
| ) | |
| (14): ResBlock( | |
| (body): Sequential( | |
| (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| (1): PReLU(num_parameters=1) | |
| (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| ) | |
| ) | |
| (15): ResBlock( | |
| (body): Sequential( | |
| (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| (1): PReLU(num_parameters=1) | |
| (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| ) | |
| ) | |
| (16): ResBlock( | |
| (body): Sequential( | |
| (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| (1): PReLU(num_parameters=1) | |
| (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| ) | |
| ) | |
| (17): ResBlock( | |
| (body): Sequential( | |
| (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| (1): PReLU(num_parameters=1) | |
| (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| ) | |
| ) | |
| (18): ResBlock( | |
| (body): Sequential( | |
| (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| (1): PReLU(num_parameters=1) | |
| (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| ) | |
| ) | |
| (19): ResBlock( | |
| (body): Sequential( | |
| (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| (1): PReLU(num_parameters=1) | |
| (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| ) | |
| ) | |
| (20): ResBlock( | |
| (body): Sequential( | |
| (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| (1): PReLU(num_parameters=1) | |
| (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| ) | |
| ) | |
| (21): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| ) | |
| (tail): Sequential( | |
| (0): Conv2d(64, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| ) | |
| ) | |
| ) | |
| [Epoch 1] Learning rate: 1.00e-4 | |
| [1600/16000] [L1: 1.7904] 82.4+0.7s | |
| [3200/16000] [L1: 1.6993] 74.0+0.1s | |
| DataParallel( | |
| (module): LAMBDANET( | |
| (sub_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) | |
| (add_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) | |
| (head): Sequential( | |
| (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| ) | |
| (body): Sequential( | |
| (0): ResBlock( | |
| (body): Sequential( | |
| (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| (1): PReLU(num_parameters=1) | |
| (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| ) | |
| ) | |
| (1): ResBlock( | |
| (body): Sequential( | |
| (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| (1): PReLU(num_parameters=1) | |
| (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| ) | |
| ) | |
| (2): ResBlock( | |
| (body): Sequential( | |
| (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| (1): PReLU(num_parameters=1) | |
| (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| ) | |
| ) | |
| (3): ResBlock( | |
| (body): Sequential( | |
| (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| (1): PReLU(num_parameters=1) | |
| (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| ) | |
| ) | |
| (4): ResBlock( | |
| (body): Sequential( | |
| (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| (1): PReLU(num_parameters=1) | |
| (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| ) | |
| ) | |
| (5): ResBlock( | |
| (body): Sequential( | |
| (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| (1): PReLU(num_parameters=1) | |
| (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| ) | |
| ) | |
| (6): ResBlock( | |
| (body): Sequential( | |
| (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| (1): PReLU(num_parameters=1) | |
| (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| ) | |
| ) | |
| (7): ResBlock( | |
| (body): Sequential( | |
| (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| (1): PReLU(num_parameters=1) | |
| (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| ) | |
| ) | |
| (8): ResBlock( | |
| (body): Sequential( | |
| (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| (1): PReLU(num_parameters=1) | |
| (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| ) | |
| ) | |
| (9): ResBlock( | |
| (body): Sequential( | |
| (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| (1): PReLU(num_parameters=1) | |
| (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| ) | |
| ) | |
| (10): LambdaLayer( | |
| (to_q): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) | |
| (to_k): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) | |
| (to_v): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) | |
| (norm_q): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (norm_v): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (pos_conv): Conv3d(4, 16, kernel_size=(1, 23, 23), stride=(1, 1, 1), padding=(0, 11, 11)) | |
| ) | |
| (11): ResBlock( | |
| (body): Sequential( | |
| (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| (1): PReLU(num_parameters=1) | |
| (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| ) | |
| ) | |
| (12): ResBlock( | |
| (body): Sequential( | |
| (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| (1): PReLU(num_parameters=1) | |
| (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| ) | |
| ) | |
| (13): ResBlock( | |
| (body): Sequential( | |
| (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| (1): PReLU(num_parameters=1) | |
| (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| ) | |
| ) | |
| (14): ResBlock( | |
| (body): Sequential( | |
| (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| (1): PReLU(num_parameters=1) | |
| (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| ) | |
| ) | |
| (15): ResBlock( | |
| (body): Sequential( | |
| (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| (1): PReLU(num_parameters=1) | |
| (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| ) | |
| ) | |
| (16): ResBlock( | |
| (body): Sequential( | |
| (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| (1): PReLU(num_parameters=1) | |
| (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| ) | |
| ) | |
| (17): ResBlock( | |
| (body): Sequential( | |
| (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| (1): PReLU(num_parameters=1) | |
| (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| ) | |
| ) | |
| (18): ResBlock( | |
| (body): Sequential( | |
| (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| (1): PReLU(num_parameters=1) | |
| (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| ) | |
| ) | |
| (19): ResBlock( | |
| (body): Sequential( | |
| (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| (1): PReLU(num_parameters=1) | |
| (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| ) | |
| ) | |
| (20): ResBlock( | |
| (body): Sequential( | |
| (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| (1): PReLU(num_parameters=1) | |
| (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| ) | |
| ) | |
| (21): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| ) | |
| (tail): Sequential( | |
| (0): Conv2d(64, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| ) | |
| ) | |
| ) | |
| [Epoch 1] Learning rate: 1.00e-4 | |
| DataParallel( | |
| (module): LAMBDANET( | |
| (sub_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) | |
| (add_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) | |
| (head): Sequential( | |
| (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| ) | |
| (body): Sequential( | |
| (0): ResBlock( | |
| (body): Sequential( | |
| (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| (1): PReLU(num_parameters=1) | |
| (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| ) | |
| ) | |
| (1): ResBlock( | |
| (body): Sequential( | |
| (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| (1): PReLU(num_parameters=1) | |
| (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| ) | |
| ) | |
| (2): ResBlock( | |
| (body): Sequential( | |
| (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| (1): PReLU(num_parameters=1) | |
| (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| ) | |
| ) | |
| (3): ResBlock( | |
| (body): Sequential( | |
| (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| (1): PReLU(num_parameters=1) | |
| (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| ) | |
| ) | |
| (4): ResBlock( | |
| (body): Sequential( | |
| (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| (1): PReLU(num_parameters=1) | |
| (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| ) | |
| ) | |
| (5): ResBlock( | |
| (body): Sequential( | |
| (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| (1): PReLU(num_parameters=1) | |
| (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| ) | |
| ) | |
| (6): ResBlock( | |
| (body): Sequential( | |
| (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| (1): PReLU(num_parameters=1) | |
| (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| ) | |
| ) | |
| (7): ResBlock( | |
| (body): Sequential( | |
| (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| (1): PReLU(num_parameters=1) | |
| (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| ) | |
| ) | |
| (8): ResBlock( | |
| (body): Sequential( | |
| (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| (1): PReLU(num_parameters=1) | |
| (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| ) | |
| ) | |
| (9): ResBlock( | |
| (body): Sequential( | |
| (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| (1): PReLU(num_parameters=1) | |
| (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| ) | |
| ) | |
| (10): LambdaLayer( | |
| (to_q): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) | |
| (to_k): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) | |
| (to_v): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) | |
| (norm_q): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (norm_v): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (pos_conv): Conv3d(4, 16, kernel_size=(1, 23, 23), stride=(1, 1, 1), padding=(0, 11, 11)) | |
| ) | |
| (11): ResBlock( | |
| (body): Sequential( | |
| (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| (1): PReLU(num_parameters=1) | |
| (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| ) | |
| ) | |
| (12): ResBlock( | |
| (body): Sequential( | |
| (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| (1): PReLU(num_parameters=1) | |
| (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| ) | |
| ) | |
| (13): ResBlock( | |
| (body): Sequential( | |
| (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| (1): PReLU(num_parameters=1) | |
| (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| ) | |
| ) | |
| (14): ResBlock( | |
| (body): Sequential( | |
| (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| (1): PReLU(num_parameters=1) | |
| (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| ) | |
| ) | |
| (15): ResBlock( | |
| (body): Sequential( | |
| (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| (1): PReLU(num_parameters=1) | |
| (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| ) | |
| ) | |
| (16): ResBlock( | |
| (body): Sequential( | |
| (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| (1): PReLU(num_parameters=1) | |
| (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| ) | |
| ) | |
| (17): ResBlock( | |
| (body): Sequential( | |
| (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| (1): PReLU(num_parameters=1) | |
| (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| ) | |
| ) | |
| (18): ResBlock( | |
| (body): Sequential( | |
| (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| (1): PReLU(num_parameters=1) | |
| (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| ) | |
| ) | |
| (19): ResBlock( | |
| (body): Sequential( | |
| (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| (1): PReLU(num_parameters=1) | |
| (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| ) | |
| ) | |
| (20): ResBlock( | |
| (body): Sequential( | |
| (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| (1): PReLU(num_parameters=1) | |
| (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| ) | |
| ) | |
| (21): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| ) | |
| (tail): Sequential( | |
| (0): Conv2d(64, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| ) | |
| ) | |
| ) | |
| [Epoch 1] Learning rate: 1.00e-4 | |
| DataParallel( | |
| (module): LAMBDANET( | |
| (sub_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) | |
| (add_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) | |
| (head): Sequential( | |
| (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| ) | |
| (body): Sequential( | |
| (0): ResBlock( | |
| (body): Sequential( | |
| (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| (1): PReLU(num_parameters=1) | |
| (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| ) | |
| ) | |
| (1): ResBlock( | |
| (body): Sequential( | |
| (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| (1): PReLU(num_parameters=1) | |
| (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| ) | |
| ) | |
| (2): ResBlock( | |
| (body): Sequential( | |
| (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| (1): PReLU(num_parameters=1) | |
| (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| ) | |
| ) | |
| (3): ResBlock( | |
| (body): Sequential( | |
| (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| (1): PReLU(num_parameters=1) | |
| (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| ) | |
| ) | |
| (4): ResBlock( | |
| (body): Sequential( | |
| (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| (1): PReLU(num_parameters=1) | |
| (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| ) | |
| ) | |
| (5): ResBlock( | |
| (body): Sequential( | |
| (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| (1): PReLU(num_parameters=1) | |
| (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| ) | |
| ) | |
| (6): ResBlock( | |
| (body): Sequential( | |
| (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| (1): PReLU(num_parameters=1) | |
| (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| ) | |
| ) | |
| (7): ResBlock( | |
| (body): Sequential( | |
| (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| (1): PReLU(num_parameters=1) | |
| (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| ) | |
| ) | |
| (8): ResBlock( | |
| (body): Sequential( | |
| (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| (1): PReLU(num_parameters=1) | |
| (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| ) | |
| ) | |
| (9): ResBlock( | |
| (body): Sequential( | |
| (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| (1): PReLU(num_parameters=1) | |
| (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| ) | |
| ) | |
| (10): LambdaLayer( | |
| (to_q): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) | |
| (to_k): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) | |
| (to_v): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) | |
| (norm_q): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (norm_v): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (pos_conv): Conv3d(4, 16, kernel_size=(1, 23, 23), stride=(1, 1, 1), padding=(0, 11, 11)) | |
| ) | |
| (11): ResBlock( | |
| (body): Sequential( | |
| (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| (1): PReLU(num_parameters=1) | |
| (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| ) | |
| ) | |
| (12): ResBlock( | |
| (body): Sequential( | |
| (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| (1): PReLU(num_parameters=1) | |
| (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| ) | |
| ) | |
| (13): ResBlock( | |
| (body): Sequential( | |
| (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| (1): PReLU(num_parameters=1) | |
| (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| ) | |
| ) | |
| (14): ResBlock( | |
| (body): Sequential( | |
| (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| (1): PReLU(num_parameters=1) | |
| (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| ) | |
| ) | |
| (15): ResBlock( | |
| (body): Sequential( | |
| (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| (1): PReLU(num_parameters=1) | |
| (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| ) | |
| ) | |
| (16): ResBlock( | |
| (body): Sequential( | |
| (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| (1): PReLU(num_parameters=1) | |
| (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| ) | |
| ) | |
| (17): ResBlock( | |
| (body): Sequential( | |
| (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| (1): PReLU(num_parameters=1) | |
| (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| ) | |
| ) | |
| (18): ResBlock( | |
| (body): Sequential( | |
| (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| (1): PReLU(num_parameters=1) | |
| (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| ) | |
| ) | |
| (19): ResBlock( | |
| (body): Sequential( | |
| (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| (1): PReLU(num_parameters=1) | |
| (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| ) | |
| ) | |
| (20): ResBlock( | |
| (body): Sequential( | |
| (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| (1): PReLU(num_parameters=1) | |
| (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| ) | |
| ) | |
| (21): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| ) | |
| (tail): Sequential( | |
| (0): Conv2d(64, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| ) | |
| ) | |
| ) | |
| [Epoch 1] Learning rate: 1.00e-4 | |
| [12800/128000] [L1: 1.5900] 343.1+2.0s | |
| [25600/128000] [L1: 1.6570] 341.3+0.1s | |
| DataParallel( | |
| (module): LAMBDANET( | |
| (sub_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) | |
| (add_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) | |
| (head): Sequential( | |
| (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| ) | |
| (body): Sequential( | |
| (0): ResBlock( | |
| (body): Sequential( | |
| (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| (1): PReLU(num_parameters=1) | |
| (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| ) | |
| ) | |
| (1): ResBlock( | |
| (body): Sequential( | |
| (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| (1): PReLU(num_parameters=1) | |
| (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| ) | |
| ) | |
| (2): ResBlock( | |
| (body): Sequential( | |
| (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| (1): PReLU(num_parameters=1) | |
| (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| ) | |
| ) | |
| (3): ResBlock( | |
| (body): Sequential( | |
| (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| (1): PReLU(num_parameters=1) | |
| (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| ) | |
| ) | |
| (4): ResBlock( | |
| (body): Sequential( | |
| (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| (1): PReLU(num_parameters=1) | |
| (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| ) | |
| ) | |
| (5): ResBlock( | |
| (body): Sequential( | |
| (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| (1): PReLU(num_parameters=1) | |
| (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| ) | |
| ) | |
| (6): ResBlock( | |
| (body): Sequential( | |
| (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| (1): PReLU(num_parameters=1) | |
| (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| ) | |
| ) | |
| (7): ResBlock( | |
| (body): Sequential( | |
| (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| (1): PReLU(num_parameters=1) | |
| (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| ) | |
| ) | |
| (8): ResBlock( | |
| (body): Sequential( | |
| (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| (1): PReLU(num_parameters=1) | |
| (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| ) | |
| ) | |
| (9): ResBlock( | |
| (body): Sequential( | |
| (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| (1): PReLU(num_parameters=1) | |
| (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| ) | |
| ) | |
| (10): LambdaLayer( | |
| (to_q): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) | |
| (to_k): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) | |
| (to_v): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) | |
| (norm_q): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (norm_v): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (pos_conv): Conv3d(4, 16, kernel_size=(1, 23, 23), stride=(1, 1, 1), padding=(0, 11, 11)) | |
| ) | |
| (11): ResBlock( | |
| (body): Sequential( | |
| (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| (1): PReLU(num_parameters=1) | |
| (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| ) | |
| ) | |
| (12): ResBlock( | |
| (body): Sequential( | |
| (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| (1): PReLU(num_parameters=1) | |
| (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| ) | |
| ) | |
| (13): ResBlock( | |
| (body): Sequential( | |
| (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| (1): PReLU(num_parameters=1) | |
| (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| ) | |
| ) | |
| (14): ResBlock( | |
| (body): Sequential( | |
| (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| (1): PReLU(num_parameters=1) | |
| (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| ) | |
| ) | |
| (15): ResBlock( | |
| (body): Sequential( | |
| (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| (1): PReLU(num_parameters=1) | |
| (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| ) | |
| ) | |
| (16): ResBlock( | |
| (body): Sequential( | |
| (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| (1): PReLU(num_parameters=1) | |
| (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| ) | |
| ) | |
| (17): ResBlock( | |
| (body): Sequential( | |
| (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| (1): PReLU(num_parameters=1) | |
| (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| ) | |
| ) | |
| (18): ResBlock( | |
| (body): Sequential( | |
| (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| (1): PReLU(num_parameters=1) | |
| (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| ) | |
| ) | |
| (19): ResBlock( | |
| (body): Sequential( | |
| (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| (1): PReLU(num_parameters=1) | |
| (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| ) | |
| ) | |
| (20): ResBlock( | |
| (body): Sequential( | |
| (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| (1): PReLU(num_parameters=1) | |
| (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| ) | |
| ) | |
| (21): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| ) | |
| (tail): Sequential( | |
| (0): Conv2d(64, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| ) | |
| ) | |
| ) | |
| [Epoch 1] Learning rate: 1.00e-4 | |
| [1600/16000] [L1: 1.7904] 75.5+0.7s | |
| [3200/16000] [L1: 1.6993] 70.2+0.0s | |
| [4800/16000] [L1: 1.6920] 70.5+0.0s | |
| [6400/16000] [L1: 1.6977] 70.5+0.1s | |
| [8000/16000] [L1: 1.7032] 70.0+0.1s | |
| [9600/16000] [L1: 1.7071] 69.4+0.1s | |
| [11200/16000] [L1: 1.7094] 69.1+0.0s | |
| [12800/16000] [L1: 1.7120] 69.1+0.1s | |
| [14400/16000] [L1: 1.7150] 69.5+0.1s | |
| [16000/16000] [L1: 1.7150] 68.3+0.0s | |
| Evaluation: | |
| [DIV2K x1] PSNR: 8.165 (Best: 8.165 @epoch 1) | |
| Forward: 34.67s | |
| Saving... | |
| Total: 35.61s | |
| [Epoch 2] Learning rate: 1.00e-4 | |
| [1600/16000] [L1: 1.7355] 69.7+0.9s | |
| [3200/16000] [L1: 1.7380] 68.1+0.1s | |
| [4800/16000] [L1: 1.7339] 68.4+0.0s | |
| [6400/16000] [L1: 1.7332] 68.1+0.0s | |
| [8000/16000] [L1: 1.7292] 69.2+0.1s | |
| [9600/16000] [L1: 1.7285] 68.1+0.1s | |
| [11200/16000] [L1: 1.7290] 68.9+0.0s | |
| [12800/16000] [L1: 1.7290] 69.2+0.0s | |
| [14400/16000] [L1: 1.7288] 68.8+0.0s | |
| [16000/16000] [L1: 1.7278] 68.2+0.0s | |
| Evaluation: | |
| [DIV2K x1] PSNR: 8.177 (Best: 8.177 @epoch 2) | |
| Forward: 34.33s | |
| Saving... | |
| Total: 34.94s | |
| [Epoch 3] Learning rate: 1.00e-4 | |
| [1600/16000] [L1: 1.7239] 69.2+0.9s | |
| [3200/16000] [L1: 1.7194] 69.6+0.1s | |
| [4800/16000] [L1: 1.7211] 68.7+0.1s | |
| [6400/16000] [L1: 1.7224] 68.9+0.0s | |
| [8000/16000] [L1: 1.7244] 69.5+0.1s | |
| [9600/16000] [L1: 1.7256] 69.7+0.1s | |
| [11200/16000] [L1: 1.7250] 70.0+0.0s | |
| [12800/16000] [L1: 1.7250] 68.9+0.0s | |
| [14400/16000] [L1: 1.7258] 68.6+0.1s | |
| [16000/16000] [L1: 1.7265] 67.6+0.0s | |
| Evaluation: | |
| [DIV2K x1] PSNR: 8.165 (Best: 8.177 @epoch 2) | |
| Forward: 34.60s | |
| Saving... | |
| Total: 35.19s | |
| [Epoch 4] Learning rate: 1.00e-4 | |
| [1600/16000] [L1: 1.7141] 69.3+0.9s | |
| [3200/16000] [L1: 1.7201] 69.7+0.1s | |
| [4800/16000] [L1: 1.7263] 70.1+0.1s | |
| [6400/16000] [L1: 1.7284] 69.6+0.1s | |
| [8000/16000] [L1: 1.7294] 68.3+0.1s | |
| [9600/16000] [L1: 1.7281] 69.5+0.1s | |
| [11200/16000] [L1: 1.7236] 69.5+0.1s | |
| [12800/16000] [L1: 1.7228] 69.6+0.1s | |
| [14400/16000] [L1: 1.7243] 69.6+0.0s | |
| [16000/16000] [L1: 1.7235] 69.0+0.0s | |
| Evaluation: | |
| [DIV2K x1] PSNR: 8.147 (Best: 8.177 @epoch 2) | |
| Forward: 34.49s | |
| Saving... | |
| Total: 34.89s | |
| [Epoch 5] Learning rate: 1.00e-4 | |
| [1600/16000] [L1: 1.7153] 69.5+0.8s | |
| [3200/16000] [L1: 1.7273] 68.8+0.1s | |
| [4800/16000] [L1: 1.7253] 68.8+0.1s | |
| [6400/16000] [L1: 1.7284] 68.7+0.1s | |
| [8000/16000] [L1: 1.7234] 70.1+0.1s | |
| [9600/16000] [L1: 1.7242] 68.4+0.0s | |
| [11200/16000] [L1: 1.7246] 69.6+0.1s | |
| [12800/16000] [L1: 1.7266] 70.0+0.1s | |
| [14400/16000] [L1: 1.7256] 69.7+0.1s | |
| [16000/16000] [L1: 1.7252] 69.5+0.1s | |
| Evaluation: | |
| [DIV2K x1] PSNR: 8.170 (Best: 8.177 @epoch 2) | |
| Forward: 34.28s | |
| Saving... | |
| Total: 34.76s | |
| [Epoch 6] Learning rate: 1.00e-4 | |
| [1600/16000] [L1: 1.7266] 70.6+1.0s | |
| [3200/16000] [L1: 1.7270] 68.5+0.0s | |
| [4800/16000] [L1: 1.7310] 69.8+0.1s | |
| [6400/16000] [L1: nan] 69.0+0.1s | |
| [8000/16000] [L1: nan] 69.6+0.1s | |
| [9600/16000] [L1: nan] 68.9+0.0s | |
| [11200/16000] [L1: nan] 67.5+0.0s | |
| [12800/16000] [L1: nan] 68.2+0.0s | |
| [14400/16000] [L1: nan] 70.3+0.1s | |
| [16000/16000] [L1: nan] 68.6+0.0s | |
| Evaluation: | |
| [DIV2K x1] PSNR: nan (Best: nan @epoch 6) | |
| Forward: 34.45s | |
| Saving... | |
| Total: 34.96s | |
| [Epoch 7] Learning rate: 1.00e-4 | |
| [1600/16000] [L1: nan] 69.3+0.9s | |
| [3200/16000] [L1: nan] 68.8+0.1s | |
| [4800/16000] [L1: nan] 69.2+0.1s | |
| [6400/16000] [L1: nan] 68.4+0.0s | |
| [8000/16000] [L1: nan] 69.7+0.1s | |
| [9600/16000] [L1: nan] 70.6+0.1s | |
| [11200/16000] [L1: nan] 69.8+0.1s | |
| [12800/16000] [L1: nan] 69.3+0.1s | |
| [14400/16000] [L1: nan] 68.8+0.1s | |
| [16000/16000] [L1: nan] 70.0+0.0s | |
| Evaluation: | |
| [DIV2K x1] PSNR: nan (Best: nan @epoch 6) | |
| Forward: 34.50s | |
| Saving... | |
| Total: 35.03s | |
| [Epoch 8] Learning rate: 1.00e-4 | |
| [1600/16000] [L1: nan] 67.9+0.9s | |
| [3200/16000] [L1: nan] 69.5+0.1s | |
| [4800/16000] [L1: nan] 68.7+0.0s | |
| [6400/16000] [L1: nan] 69.5+0.0s | |
| [8000/16000] [L1: nan] 69.7+0.1s | |
| [9600/16000] [L1: nan] 70.7+0.1s | |
| [11200/16000] [L1: nan] 70.2+0.1s | |
| [12800/16000] [L1: nan] 68.8+0.0s | |
| [14400/16000] [L1: nan] 69.6+0.0s | |
| [16000/16000] [L1: nan] 69.9+0.0s | |
| Evaluation: | |
| [DIV2K x1] PSNR: nan (Best: nan @epoch 6) | |
| Forward: 34.44s | |
| Saving... | |
| Total: 34.89s | |
| [Epoch 9] Learning rate: 1.00e-4 | |
| [1600/16000] [L1: nan] 69.8+1.0s | |
| [3200/16000] [L1: nan] 69.4+0.1s | |
| [4800/16000] [L1: nan] 70.4+0.1s | |
| [6400/16000] [L1: nan] 70.6+0.1s | |
| [8000/16000] [L1: nan] 69.3+0.1s | |
| [9600/16000] [L1: nan] 70.4+0.1s | |
| [11200/16000] [L1: nan] 70.2+0.1s | |
| [12800/16000] [L1: nan] 69.0+0.0s | |
| [14400/16000] [L1: nan] 69.2+0.0s | |
| [16000/16000] [L1: nan] 68.8+0.0s | |
| Evaluation: | |
| [DIV2K x1] PSNR: nan (Best: nan @epoch 6) | |
| Forward: 34.73s | |
| Saving... | |
| Total: 35.26s | |
| [Epoch 10] Learning rate: 1.00e-4 | |
| [1600/16000] [L1: nan] 69.0+0.9s | |
| [3200/16000] [L1: nan] 69.1+0.1s | |
| [4800/16000] [L1: nan] 69.8+0.1s | |
| [6400/16000] [L1: nan] 69.6+0.1s | |
| [8000/16000] [L1: nan] 69.5+0.1s | |
| [9600/16000] [L1: nan] 69.5+0.1s | |
| [11200/16000] [L1: nan] 70.4+0.1s | |
| [12800/16000] [L1: nan] 69.4+0.1s | |
| [14400/16000] [L1: nan] 70.0+0.1s | |
| [16000/16000] [L1: nan] 70.4+0.1s | |
| Evaluation: | |
| [DIV2K x1] PSNR: nan (Best: nan @epoch 6) | |
| Forward: 34.74s | |
| Saving... | |
| Total: 35.27s | |
| [Epoch 11] Learning rate: 1.00e-4 | |
| [1600/16000] [L1: nan] 69.8+0.8s | |
| [3200/16000] [L1: nan] 68.7+0.1s | |
| [4800/16000] [L1: nan] 70.5+0.1s | |
| [6400/16000] [L1: nan] 69.4+0.1s | |
| [8000/16000] [L1: nan] 69.0+0.1s | |
| [9600/16000] [L1: nan] 70.6+0.1s | |
| [11200/16000] [L1: nan] 70.6+0.1s | |
| [12800/16000] [L1: nan] 71.2+0.1s | |
| [14400/16000] [L1: nan] 70.9+0.1s | |
| [16000/16000] [L1: nan] 67.7+0.0s | |
| Evaluation: | |
| [DIV2K x1] PSNR: nan (Best: nan @epoch 6) | |
| Forward: 34.78s | |
| Saving... | |
| Total: 35.32s | |
| [Epoch 12] Learning rate: 1.00e-4 | |
| [1600/16000] [L1: nan] 69.6+1.0s | |
| [3200/16000] [L1: nan] 69.5+0.1s | |
| [4800/16000] [L1: nan] 68.7+0.1s | |
| [6400/16000] [L1: nan] 70.0+0.1s | |
| [8000/16000] [L1: nan] 67.7+0.0s | |
| [9600/16000] [L1: nan] 66.6+0.0s | |
| [11200/16000] [L1: nan] 68.7+0.1s | |
| [12800/16000] [L1: nan] 70.2+0.1s | |
| [14400/16000] [L1: nan] 69.9+0.1s | |
| [16000/16000] [L1: nan] 67.0+0.0s | |
| Evaluation: | |
| [DIV2K x1] PSNR: nan (Best: nan @epoch 6) | |
| Forward: 34.76s | |
| Saving... | |
| Total: 35.21s | |
| [Epoch 13] Learning rate: 1.00e-4 | |
| [1600/16000] [L1: nan] 70.8+0.9s | |
| [3200/16000] [L1: nan] 69.6+0.1s | |
| [4800/16000] [L1: nan] 70.0+0.1s | |
| [6400/16000] [L1: nan] 69.5+0.1s | |
| [8000/16000] [L1: nan] 70.2+0.1s | |
| [9600/16000] [L1: nan] 71.0+0.1s | |
| [11200/16000] [L1: nan] 69.8+0.1s | |
| [12800/16000] [L1: nan] 68.0+0.1s | |
| [14400/16000] [L1: nan] 71.2+0.1s | |
| [16000/16000] [L1: nan] 70.2+0.0s | |
| Evaluation: | |
| [DIV2K x1] PSNR: nan (Best: nan @epoch 6) | |
| Forward: 34.75s | |
| Saving... | |
| Total: 35.43s | |
| [Epoch 14] Learning rate: 1.00e-4 | |
| [1600/16000] [L1: nan] 69.0+0.9s | |
| [3200/16000] [L1: nan] 68.9+0.1s | |
| [4800/16000] [L1: nan] 70.0+0.1s | |
| [6400/16000] [L1: nan] 70.0+0.1s | |
| [8000/16000] [L1: nan] 70.7+0.1s | |
| [9600/16000] [L1: nan] 70.0+0.1s | |
| [11200/16000] [L1: nan] 68.5+0.1s | |
| [12800/16000] [L1: nan] 67.8+0.0s | |
| [14400/16000] [L1: nan] 68.3+0.0s | |
| [16000/16000] [L1: nan] 68.3+0.0s | |
| Evaluation: | |
| [DIV2K x1] PSNR: nan (Best: nan @epoch 6) | |
| Forward: 34.81s | |
| Saving... | |
| Total: 35.53s | |
| [Epoch 15] Learning rate: 1.00e-4 | |
| [1600/16000] [L1: nan] 69.8+1.0s | |
| [3200/16000] [L1: nan] 69.9+0.1s | |
| [4800/16000] [L1: nan] 70.1+0.1s | |
| [6400/16000] [L1: nan] 70.0+0.1s | |
| [8000/16000] [L1: nan] 70.5+0.1s | |
| [9600/16000] [L1: nan] 70.0+0.1s | |
| [11200/16000] [L1: nan] 71.3+0.1s | |
| [12800/16000] [L1: nan] 70.8+0.1s | |
| [14400/16000] [L1: nan] 69.4+0.1s | |
| [16000/16000] [L1: nan] 70.0+0.1s | |
| Evaluation: | |
| [DIV2K x1] PSNR: nan (Best: nan @epoch 6) | |
| Forward: 34.69s | |
| Saving... | |
| Total: 35.23s | |
| [Epoch 16] Learning rate: 1.00e-4 | |
| [1600/16000] [L1: nan] 70.0+0.9s | |
| [3200/16000] [L1: nan] 69.0+0.1s | |
| [4800/16000] [L1: nan] 70.5+0.1s | |
| [6400/16000] [L1: nan] 70.2+0.1s | |
| [8000/16000] [L1: nan] 70.5+0.1s | |
| [9600/16000] [L1: nan] 70.0+0.1s | |
| [11200/16000] [L1: nan] 70.1+0.1s | |
| [12800/16000] [L1: nan] 69.4+0.1s | |
| [14400/16000] [L1: nan] 69.0+0.0s | |
| [16000/16000] [L1: nan] 67.7+0.0s | |
| Evaluation: | |
| [DIV2K x1] PSNR: nan (Best: nan @epoch 6) | |
| Forward: 34.98s | |
| Saving... | |
| Total: 35.43s | |
| [Epoch 17] Learning rate: 1.00e-4 | |
| [1600/16000] [L1: nan] 71.0+0.9s | |
| [3200/16000] [L1: nan] 71.1+0.1s | |
| [4800/16000] [L1: nan] 70.2+0.1s | |
| [6400/16000] [L1: nan] 70.2+0.1s | |
| [8000/16000] [L1: nan] 68.3+0.0s | |
| [9600/16000] [L1: nan] 69.1+0.0s | |
| [11200/16000] [L1: nan] 69.7+0.0s | |
| [12800/16000] [L1: nan] 69.4+0.1s | |
| [14400/16000] [L1: nan] 68.8+0.0s | |
| [16000/16000] [L1: nan] 69.5+0.0s | |
| Evaluation: | |
| [DIV2K x1] PSNR: nan (Best: nan @epoch 6) | |
| Forward: 34.82s | |
| Saving... | |
| Total: 35.34s | |
| [Epoch 18] Learning rate: 1.00e-4 | |
| [1600/16000] [L1: nan] 69.8+0.8s | |
| [3200/16000] [L1: nan] 67.6+0.1s | |
| [4800/16000] [L1: nan] 71.4+0.1s | |
| [6400/16000] [L1: nan] 70.3+0.1s | |
| [8000/16000] [L1: nan] 71.1+0.1s | |
| [9600/16000] [L1: nan] 71.4+0.1s | |
| [11200/16000] [L1: nan] 70.3+0.1s | |
| [12800/16000] [L1: nan] 70.3+0.0s | |
| [14400/16000] [L1: nan] 69.6+0.1s | |
| [16000/16000] [L1: nan] 68.8+0.1s | |
| Evaluation: | |
| [DIV2K x1] PSNR: nan (Best: nan @epoch 6) | |
| Forward: 34.78s | |
| Saving... | |
| Total: 35.23s | |
| [Epoch 19] Learning rate: 1.00e-4 | |
| [1600/16000] [L1: nan] 69.8+0.9s | |
| [3200/16000] [L1: nan] 69.5+0.1s | |
| [4800/16000] [L1: nan] 69.7+0.1s | |
| [6400/16000] [L1: nan] 70.9+0.1s | |
| [8000/16000] [L1: nan] 70.4+0.1s | |
| [9600/16000] [L1: nan] 70.4+0.1s | |
| [11200/16000] [L1: nan] 70.1+0.1s | |
| [12800/16000] [L1: nan] 69.8+0.1s | |
| [14400/16000] [L1: nan] 70.4+0.1s | |
| [16000/16000] [L1: nan] 69.9+0.1s | |
| Evaluation: | |
| [DIV2K x1] PSNR: nan (Best: nan @epoch 6) | |
| Forward: 34.61s | |
| Saving... | |
| Total: 35.23s | |
| [Epoch 20] Learning rate: 1.00e-4 | |
| [1600/16000] [L1: nan] 70.5+0.9s | |
| [3200/16000] [L1: nan] 69.1+0.1s | |
| [4800/16000] [L1: nan] 70.2+0.1s | |
| [6400/16000] [L1: nan] 69.0+0.1s | |
| [8000/16000] [L1: nan] 68.7+0.1s | |
| [9600/16000] [L1: nan] 69.0+0.1s | |
| [11200/16000] [L1: nan] 69.3+0.0s | |
| [12800/16000] [L1: nan] 69.9+0.0s | |
| [14400/16000] [L1: nan] 68.2+0.0s | |
| [16000/16000] [L1: nan] 69.3+0.0s | |
| Evaluation: | |
| [DIV2K x1] PSNR: nan (Best: nan @epoch 6) | |
| Forward: 34.65s | |
| Saving... | |
| Total: 35.18s | |
| [Epoch 21] Learning rate: 1.00e-4 | |
| [1600/16000] [L1: nan] 70.1+0.9s | |
| [3200/16000] [L1: nan] 70.3+0.1s | |
| [4800/16000] [L1: nan] 71.0+0.1s | |
| [6400/16000] [L1: nan] 68.4+0.0s | |
| [8000/16000] [L1: nan] 67.9+0.0s | |
| [9600/16000] [L1: nan] 69.7+0.0s | |
| [11200/16000] [L1: nan] 70.3+0.1s | |
| [12800/16000] [L1: nan] 70.0+0.1s | |
| [14400/16000] [L1: nan] 70.6+0.1s | |
| [16000/16000] [L1: nan] 67.9+0.0s | |
| Evaluation: | |
| [DIV2K x1] PSNR: nan (Best: nan @epoch 6) | |
| Forward: 34.75s | |
| Saving... | |
| Total: 35.26s | |
| [Epoch 22] Learning rate: 1.00e-4 | |
| [1600/16000] [L1: nan] 70.1+1.0s | |
| [3200/16000] [L1: nan] 69.5+0.1s | |
| [4800/16000] [L1: nan] 69.0+0.1s | |
| [6400/16000] [L1: nan] 69.9+0.0s | |
| [8000/16000] [L1: nan] 70.6+0.1s | |
| [9600/16000] [L1: nan] 69.4+0.1s | |
| [11200/16000] [L1: nan] 70.4+0.1s | |
| [12800/16000] [L1: nan] 70.3+0.1s | |
| [14400/16000] [L1: nan] 70.1+0.1s | |
| [16000/16000] [L1: nan] 68.8+0.0s | |
| Evaluation: | |
| [DIV2K x1] PSNR: nan (Best: nan @epoch 6) | |
| Forward: 34.60s | |
| Saving... | |
| Total: 35.16s | |
| [Epoch 23] Learning rate: 1.00e-4 | |
| [1600/16000] [L1: nan] 69.9+0.9s | |
| [3200/16000] [L1: nan] 69.1+0.1s | |
| [4800/16000] [L1: nan] 70.3+0.1s | |
| [6400/16000] [L1: nan] 69.3+0.1s | |
| [8000/16000] [L1: nan] 69.4+0.1s | |
| [9600/16000] [L1: nan] 69.8+0.1s | |
| [11200/16000] [L1: nan] 71.0+0.1s | |
| [12800/16000] [L1: nan] 70.7+0.1s | |
| [14400/16000] [L1: nan] 70.6+0.1s | |
| [16000/16000] [L1: nan] 69.6+0.1s | |
| Evaluation: | |
| [DIV2K x1] PSNR: nan (Best: nan @epoch 6) | |
| Forward: 34.59s | |
| Saving... | |
| Total: 35.13s | |
| [Epoch 24] Learning rate: 1.00e-4 | |
| [1600/16000] [L1: nan] 70.3+0.9s | |
| [3200/16000] [L1: nan] 69.7+0.1s | |
| [4800/16000] [L1: nan] 70.6+0.1s | |
| [6400/16000] [L1: nan] 69.5+0.0s | |
| [8000/16000] [L1: nan] 70.2+0.1s | |
| [9600/16000] [L1: nan] 70.3+0.1s | |
| [11200/16000] [L1: nan] 69.9+0.1s | |
| [12800/16000] [L1: nan] 70.1+0.1s | |
| [14400/16000] [L1: nan] 70.1+0.1s | |
| [16000/16000] [L1: nan] 70.2+0.0s | |
| Evaluation: | |
| [DIV2K x1] PSNR: nan (Best: nan @epoch 6) | |
| Forward: 34.53s | |
| Saving... | |
| Total: 34.98s | |
| [Epoch 25] Learning rate: 1.00e-4 | |
| [1600/16000] [L1: nan] 70.4+0.9s | |
| [3200/16000] [L1: nan] 69.4+0.1s | |
| [4800/16000] [L1: nan] 70.4+0.1s | |
| [6400/16000] [L1: nan] 70.2+0.1s | |
| [8000/16000] [L1: nan] 69.3+0.1s | |
| [9600/16000] [L1: nan] 68.9+0.1s | |
| [11200/16000] [L1: nan] 69.1+0.0s | |
| [12800/16000] [L1: nan] 70.4+0.1s | |
| [14400/16000] [L1: nan] 69.7+0.1s | |
| [16000/16000] [L1: nan] 69.3+0.1s | |
| Evaluation: | |
| [DIV2K x1] PSNR: nan (Best: nan @epoch 6) | |
| Forward: 34.52s | |
| Saving... | |
| Total: 35.18s | |
| [Epoch 26] Learning rate: 1.00e-4 | |
| [1600/16000] [L1: nan] 69.7+0.9s | |
| [3200/16000] [L1: nan] 69.4+0.1s | |
| [4800/16000] [L1: nan] 69.9+0.1s | |
| [6400/16000] [L1: nan] 69.8+0.1s | |
| [8000/16000] [L1: nan] 68.3+0.0s | |
| [9600/16000] [L1: nan] 67.9+0.0s | |
| [11200/16000] [L1: nan] 70.0+0.1s | |
| [12800/16000] [L1: nan] 70.5+0.1s | |
| [14400/16000] [L1: nan] 70.3+0.1s | |
| [16000/16000] [L1: nan] 69.4+0.1s | |
| Evaluation: | |
| [DIV2K x1] PSNR: nan (Best: nan @epoch 6) | |
| Forward: 34.40s | |
| Saving... | |
| Total: 34.97s | |
| [Epoch 27] Learning rate: 1.00e-4 | |
| [1600/16000] [L1: nan] 69.0+0.9s | |
| [3200/16000] [L1: nan] 68.7+0.1s | |
| [4800/16000] [L1: nan] 69.4+0.1s | |
| [6400/16000] [L1: nan] 69.1+0.1s | |
| [8000/16000] [L1: nan] 70.1+0.1s | |
| [9600/16000] [L1: nan] 69.2+0.1s | |
| [11200/16000] [L1: nan] 69.8+0.1s | |
| [12800/16000] [L1: nan] 68.1+0.1s | |
| [14400/16000] [L1: nan] 70.5+0.1s | |
| [16000/16000] [L1: nan] 69.4+0.0s | |
| Evaluation: | |
| [DIV2K x1] PSNR: nan (Best: nan @epoch 6) | |
| Forward: 34.36s | |
| Saving... | |
| Total: 34.87s | |
| [Epoch 28] Learning rate: 1.00e-4 | |
| [1600/16000] [L1: nan] 70.3+0.8s | |
| [3200/16000] [L1: nan] 69.8+0.1s | |
| [4800/16000] [L1: nan] 70.3+0.1s | |
| [6400/16000] [L1: nan] 70.6+0.1s | |
| [8000/16000] [L1: nan] 71.0+0.1s | |
| [9600/16000] [L1: nan] 68.8+0.1s | |
| [11200/16000] [L1: nan] 68.5+0.1s | |
| [12800/16000] [L1: nan] 69.4+0.1s | |
| [14400/16000] [L1: nan] 69.4+0.0s | |
| [16000/16000] [L1: nan] 69.2+0.1s | |
| Evaluation: | |
| [DIV2K x1] PSNR: nan (Best: nan @epoch 6) | |
| Forward: 34.38s | |
| Saving... | |
| Total: 34.89s | |
| [Epoch 29] Learning rate: 1.00e-4 | |
| [1600/16000] [L1: nan] 69.6+0.9s | |
| [3200/16000] [L1: nan] 68.6+0.0s | |
| [4800/16000] [L1: nan] 67.4+0.0s | |
| [6400/16000] [L1: nan] 71.1+0.1s | |
| [8000/16000] [L1: nan] 70.0+0.1s | |
| [9600/16000] [L1: nan] 70.8+0.1s | |
| [11200/16000] [L1: nan] 68.6+0.1s | |
| [12800/16000] [L1: nan] 70.3+0.1s | |
| [14400/16000] [L1: nan] 69.4+0.0s | |
| [16000/16000] [L1: nan] 70.5+0.1s | |
| Evaluation: | |
| [DIV2K x1] PSNR: nan (Best: nan @epoch 6) | |
| Forward: 34.51s | |
| Saving... | |
| Total: 35.04s | |
| [Epoch 30] Learning rate: 1.00e-4 | |
| [1600/16000] [L1: nan] 69.1+0.9s | |
| [3200/16000] [L1: nan] 68.9+0.1s | |
| [4800/16000] [L1: nan] 69.6+0.1s | |
| [6400/16000] [L1: nan] 69.6+0.1s | |
| [8000/16000] [L1: nan] 70.4+0.1s | |
| [9600/16000] [L1: nan] 69.1+0.1s | |
| [11200/16000] [L1: nan] 70.3+0.1s | |
| [12800/16000] [L1: nan] 71.1+0.1s | |
| [14400/16000] [L1: nan] 67.4+0.0s | |
| [16000/16000] [L1: nan] 68.7+0.0s | |
| Evaluation: | |
| [DIV2K x1] PSNR: nan (Best: nan @epoch 6) | |
| Forward: 34.39s | |
| Saving... | |
| Total: 34.88s | |
| [Epoch 31] Learning rate: 1.00e-4 | |
| [1600/16000] [L1: nan] 70.4+0.9s | |
| [3200/16000] [L1: nan] 70.0+0.1s | |
| [4800/16000] [L1: nan] 69.8+0.1s | |
| [6400/16000] [L1: nan] 71.0+0.1s | |
| [8000/16000] [L1: nan] 70.1+0.1s | |
| [9600/16000] [L1: nan] 70.3+0.1s | |
| [11200/16000] [L1: nan] 69.6+0.0s | |
| [12800/16000] [L1: nan] 70.2+0.1s | |
| [14400/16000] [L1: nan] 69.3+0.1s | |
| [16000/16000] [L1: nan] 68.5+0.1s | |
| Evaluation: | |
| [DIV2K x1] PSNR: nan (Best: nan @epoch 6) | |
| Forward: 34.47s | |
| Saving... | |
| Total: 35.01s | |
| [Epoch 32] Learning rate: 1.00e-4 | |
| [1600/16000] [L1: nan] 69.2+1.0s | |
| [3200/16000] [L1: nan] 69.2+0.1s | |
| [4800/16000] [L1: nan] 69.8+0.1s | |
| [6400/16000] [L1: nan] 69.6+0.1s | |
| [8000/16000] [L1: nan] 69.3+0.1s | |
| [9600/16000] [L1: nan] 69.7+0.1s | |
| [11200/16000] [L1: nan] 69.2+0.1s | |
| [12800/16000] [L1: nan] 68.9+0.0s | |
| [14400/16000] [L1: nan] 68.1+0.0s | |
| [16000/16000] [L1: nan] 68.3+0.0s | |
| Evaluation: | |
| [DIV2K x1] PSNR: nan (Best: nan @epoch 6) | |
| Forward: 34.32s | |
| Saving... | |
| Total: 34.88s | |
| [Epoch 33] Learning rate: 1.00e-4 | |
| [1600/16000] [L1: nan] 70.1+1.0s | |
| [3200/16000] [L1: nan] 69.3+0.1s | |
| [4800/16000] [L1: nan] 69.1+0.1s | |
| [6400/16000] [L1: nan] 69.5+0.1s | |
| [8000/16000] [L1: nan] 68.7+0.1s | |
| [9600/16000] [L1: nan] 68.2+0.0s | |
| [11200/16000] [L1: nan] 68.9+0.0s | |
| [12800/16000] [L1: nan] 70.1+0.1s | |
| [14400/16000] [L1: nan] 70.9+0.1s | |
| [16000/16000] [L1: nan] 69.8+0.0s | |
| Evaluation: | |
| [DIV2K x1] PSNR: nan (Best: nan @epoch 6) | |
| Forward: 34.33s | |
| Saving... | |
| Total: 34.91s | |
| [Epoch 34] Learning rate: 1.00e-4 | |
| [1600/16000] [L1: nan] 70.1+1.0s | |
| [3200/16000] [L1: nan] 69.5+0.1s | |
| [4800/16000] [L1: nan] 67.9+0.1s | |
| [6400/16000] [L1: nan] 69.8+0.1s | |
| [8000/16000] [L1: nan] 68.8+0.1s | |
| [9600/16000] [L1: nan] 69.3+0.1s | |
| [11200/16000] [L1: nan] 69.1+0.0s | |
| [12800/16000] [L1: nan] 70.6+0.1s | |
| [14400/16000] [L1: nan] 70.3+0.1s | |
| [16000/16000] [L1: nan] 68.4+0.0s | |
| Evaluation: | |
| [DIV2K x1] PSNR: nan (Best: nan @epoch 6) | |
| Forward: 34.30s | |
| Saving... | |
| Total: 34.74s | |
| [Epoch 35] Learning rate: 1.00e-4 | |
| [1600/16000] [L1: nan] 69.5+0.9s | |
| [3200/16000] [L1: nan] 69.7+0.1s | |
| [4800/16000] [L1: nan] 69.4+0.1s | |
| [6400/16000] [L1: nan] 70.3+0.1s | |
| [8000/16000] [L1: nan] 70.8+0.1s | |
| [9600/16000] [L1: nan] 69.8+0.1s | |
| [11200/16000] [L1: nan] 69.4+0.1s | |
| [12800/16000] [L1: nan] 69.5+0.1s | |
| [14400/16000] [L1: nan] 70.1+0.1s | |
| [16000/16000] [L1: nan] 69.7+0.1s | |
| Evaluation: | |
| [DIV2K x1] PSNR: nan (Best: nan @epoch 6) | |
| Forward: 34.21s | |
| Saving... | |
| Total: 34.73s | |
| [Epoch 36] Learning rate: 1.00e-4 | |
| [1600/16000] [L1: nan] 69.2+0.8s | |
| [3200/16000] [L1: nan] 69.7+0.1s | |
| [4800/16000] [L1: nan] 68.9+0.1s | |
| [6400/16000] [L1: nan] 68.8+0.1s | |
| [8000/16000] [L1: nan] 70.0+0.1s | |
| [9600/16000] [L1: nan] 69.5+0.1s | |
| [11200/16000] [L1: nan] 70.5+0.1s | |
| [12800/16000] [L1: nan] 69.5+0.1s | |
| [14400/16000] [L1: nan] 70.4+0.1s | |
| [16000/16000] [L1: nan] 67.7+0.1s | |
| Evaluation: | |
| [DIV2K x1] PSNR: nan (Best: nan @epoch 6) | |
| Forward: 34.48s | |
| Saving... | |
| Total: 35.04s | |
| [Epoch 37] Learning rate: 1.00e-4 | |
| [1600/16000] [L1: nan] 69.0+1.0s | |
| [3200/16000] [L1: nan] 69.2+0.1s | |
| [4800/16000] [L1: nan] 70.5+0.1s | |
| [6400/16000] [L1: nan] 70.5+0.1s | |
| [8000/16000] [L1: nan] 70.0+0.1s | |
| [9600/16000] [L1: nan] 68.7+0.0s | |
| [11200/16000] [L1: nan] 69.6+0.1s | |
| [12800/16000] [L1: nan] 69.4+0.1s | |
| [14400/16000] [L1: nan] 70.0+0.1s | |
| [16000/16000] [L1: nan] 66.5+0.0s | |
| Evaluation: | |
| [DIV2K x1] PSNR: nan (Best: nan @epoch 6) | |
| Forward: 34.49s | |
| Saving... | |
| Total: 34.94s | |
| [Epoch 38] Learning rate: 1.00e-4 | |
| [1600/16000] [L1: nan] 70.4+1.1s | |
| [3200/16000] [L1: nan] 70.4+0.1s | |
| [4800/16000] [L1: nan] 69.6+0.1s | |
| [6400/16000] [L1: nan] 70.0+0.1s | |
| [8000/16000] [L1: nan] 70.1+0.1s | |
| [9600/16000] [L1: nan] 70.7+0.1s | |
| [11200/16000] [L1: nan] 70.1+0.1s | |
| [12800/16000] [L1: nan] 70.7+0.1s | |
| [14400/16000] [L1: nan] 70.9+0.1s | |
| [16000/16000] [L1: nan] 69.3+0.1s | |
| Evaluation: | |
| [DIV2K x1] PSNR: nan (Best: nan @epoch 6) | |
| Forward: 34.39s | |
| Saving... | |
| Total: 34.92s | |
| [Epoch 39] Learning rate: 1.00e-4 | |
| [1600/16000] [L1: nan] 69.6+0.9s | |
| [3200/16000] [L1: nan] 68.0+0.1s | |
| [4800/16000] [L1: nan] 70.1+0.1s | |
| [6400/16000] [L1: nan] 70.3+0.1s | |
| [8000/16000] [L1: nan] 69.4+0.1s | |
| [9600/16000] [L1: nan] 69.9+0.1s | |
| [11200/16000] [L1: nan] 69.8+0.1s | |
| [12800/16000] [L1: nan] 69.6+0.1s | |
| [14400/16000] [L1: nan] 69.1+0.0s | |
| [16000/16000] [L1: nan] 68.7+0.0s | |
| Evaluation: | |
| [DIV2K x1] PSNR: nan (Best: nan @epoch 6) | |
| Forward: 34.47s | |
| Saving... | |
| Total: 35.03s | |
| [Epoch 40] Learning rate: 1.00e-4 | |
| [1600/16000] [L1: nan] 69.9+1.0s | |
| [3200/16000] [L1: nan] 69.7+0.1s | |
| [4800/16000] [L1: nan] 67.7+0.0s | |
| [6400/16000] [L1: nan] 69.8+0.1s | |
| [8000/16000] [L1: nan] 69.0+0.1s | |
| [9600/16000] [L1: nan] 68.8+0.1s | |
| [11200/16000] [L1: nan] 69.1+0.1s | |
| [12800/16000] [L1: nan] 68.7+0.0s | |
| [14400/16000] [L1: nan] 69.1+0.1s | |
| [16000/16000] [L1: nan] 65.7+0.0s | |
| Evaluation: | |
| [DIV2K x1] PSNR: nan (Best: nan @epoch 6) | |
| Forward: 34.62s | |
| Saving... | |
| Total: 35.18s | |
| [Epoch 41] Learning rate: 1.00e-4 | |
| [1600/16000] [L1: nan] 70.8+0.9s | |
| [3200/16000] [L1: nan] 70.4+0.1s | |
| [4800/16000] [L1: nan] 70.3+0.1s | |
| [6400/16000] [L1: nan] 70.8+0.1s | |
| [8000/16000] [L1: nan] 69.6+0.1s | |
| [9600/16000] [L1: nan] 68.2+0.1s | |
| [11200/16000] [L1: nan] 69.4+0.1s | |
| [12800/16000] [L1: nan] 69.9+0.1s | |
| [14400/16000] [L1: nan] 69.0+0.0s | |
| [16000/16000] [L1: nan] 69.6+0.1s | |
| Evaluation: | |
| [DIV2K x1] PSNR: nan (Best: nan @epoch 6) | |
| Forward: 34.41s | |
| Saving... | |
| Total: 34.98s | |
| [Epoch 42] Learning rate: 1.00e-4 | |
| [1600/16000] [L1: nan] 69.7+0.8s | |
| [3200/16000] [L1: nan] 68.8+0.1s | |
| [4800/16000] [L1: nan] 68.8+0.1s | |
| [6400/16000] [L1: nan] 68.0+0.0s | |
| [8000/16000] [L1: nan] 69.5+0.0s | |
| [9600/16000] [L1: nan] 70.2+0.1s | |
| [11200/16000] [L1: nan] 69.7+0.1s | |
| [12800/16000] [L1: nan] 70.3+0.1s | |
| [14400/16000] [L1: nan] 69.4+0.1s | |
| [16000/16000] [L1: nan] 70.1+0.1s | |
| Evaluation: | |
| [DIV2K x1] PSNR: nan (Best: nan @epoch 6) | |
| Forward: 34.45s | |
| Saving... | |
| Total: 34.97s | |
| [Epoch 43] Learning rate: 1.00e-4 | |
| [1600/16000] [L1: nan] 69.4+1.0s | |
| [3200/16000] [L1: nan] 70.4+0.1s | |
| [4800/16000] [L1: nan] 70.3+0.1s | |
| [6400/16000] [L1: nan] 69.9+0.1s | |
| [8000/16000] [L1: nan] 69.7+0.0s | |
| [9600/16000] [L1: nan] 70.8+0.1s | |
| [11200/16000] [L1: nan] 69.4+0.1s | |
| [12800/16000] [L1: nan] 69.1+0.1s | |
| [14400/16000] [L1: nan] 70.0+0.1s | |
| [16000/16000] [L1: nan] 68.9+0.0s | |
| Evaluation: | |
| [DIV2K x1] PSNR: nan (Best: nan @epoch 6) | |
| Forward: 34.48s | |
| Saving... | |
| Total: 34.96s | |
| [Epoch 44] Learning rate: 1.00e-4 | |
| [1600/16000] [L1: nan] 70.2+1.0s | |
| [3200/16000] [L1: nan] 69.5+0.1s | |
| [4800/16000] [L1: nan] 70.3+0.1s | |
| [6400/16000] [L1: nan] 69.7+0.1s | |
| [8000/16000] [L1: nan] 69.2+0.1s | |
| [9600/16000] [L1: nan] 70.0+0.1s | |
| [11200/16000] [L1: nan] 70.3+0.1s | |
| [12800/16000] [L1: nan] 70.4+0.1s | |
| [14400/16000] [L1: nan] 69.4+0.1s | |
| [16000/16000] [L1: nan] 69.3+0.1s | |
| Evaluation: | |
| [DIV2K x1] PSNR: nan (Best: nan @epoch 6) | |
| Forward: 34.27s | |
| Saving... | |
| Total: 34.70s | |
| [Epoch 45] Learning rate: 1.00e-4 | |
| [1600/16000] [L1: nan] 70.1+0.9s | |
| [3200/16000] [L1: nan] 69.1+0.1s | |
| [4800/16000] [L1: nan] 69.2+0.1s | |
| [6400/16000] [L1: nan] 70.0+0.1s | |
| [8000/16000] [L1: nan] 70.6+0.1s | |
| [9600/16000] [L1: nan] 70.0+0.1s | |
| [11200/16000] [L1: nan] 70.0+0.1s | |
| [12800/16000] [L1: nan] 69.1+0.1s | |
| [14400/16000] [L1: nan] 70.7+0.1s | |
| [16000/16000] [L1: nan] 69.9+0.1s | |
| Evaluation: | |
| [DIV2K x1] PSNR: nan (Best: nan @epoch 6) | |
| Forward: 34.27s | |
| Saving... | |
| Total: 34.84s | |
| [Epoch 46] Learning rate: 1.00e-4 | |
| [1600/16000] [L1: nan] 70.9+0.9s | |
| [3200/16000] [L1: nan] 70.0+0.1s | |
| [4800/16000] [L1: nan] 68.5+0.1s | |
| [6400/16000] [L1: nan] 70.4+0.1s | |
| [8000/16000] [L1: nan] 70.0+0.1s | |
| [9600/16000] [L1: nan] 69.9+0.1s | |
| [11200/16000] [L1: nan] 69.8+0.1s | |
| [12800/16000] [L1: nan] 70.7+0.1s | |
| [14400/16000] [L1: nan] 70.0+0.1s | |
| [16000/16000] [L1: nan] 68.7+0.1s | |
| Evaluation: | |
| [DIV2K x1] PSNR: nan (Best: nan @epoch 6) | |
| Forward: 34.34s | |
| Saving... | |
| Total: 34.90s | |
| [Epoch 47] Learning rate: 1.00e-4 | |
| [1600/16000] [L1: nan] 70.3+0.8s | |
| [3200/16000] [L1: nan] 68.2+0.0s | |
| [4800/16000] [L1: nan] 70.7+0.1s | |
| [6400/16000] [L1: nan] 69.0+0.1s | |
| [8000/16000] [L1: nan] 69.7+0.1s | |
| [9600/16000] [L1: nan] 71.0+0.1s | |
| [11200/16000] [L1: nan] 69.7+0.1s | |
| [12800/16000] [L1: nan] 69.5+0.1s | |
| [14400/16000] [L1: nan] 66.8+0.0s | |
| [16000/16000] [L1: nan] 67.2+0.0s | |
| Evaluation: | |
| [DIV2K x1] PSNR: nan (Best: nan @epoch 6) | |
| Forward: 34.39s | |
| Saving... | |
| Total: 34.82s | |