diff --git "a/checkpoints/imagenet/hole_benchmark/20250427163636067215.log" "b/checkpoints/imagenet/hole_benchmark/20250427163636067215.log" new file mode 100644--- /dev/null +++ "b/checkpoints/imagenet/hole_benchmark/20250427163636067215.log" @@ -0,0 +1,774 @@ +2025-04-27 16:36:36,067 INFO Arguments: Namespace(config='configs/config.yaml', seed=None) +2025-04-27 16:36:36,067 INFO Random seed: 8482 +2025-04-27 16:36:36,069 INFO Configuration: {'dataset_name': 'imagenet', 'data_with_subfolder': True, 'train_data_path': 'traindata/train', 'val_data_path': 'traindata/val', 'resume': 'checkpoints/imagenet/hole_benchmark', 'batch_size': 4, 'image_shape': [256, 256, 3], 'mask_shape': [128, 128], 'mask_batch_same': True, 'max_delta_shape': [32, 32], 'margin': [0, 0], 'discounted_mask': True, 'spatial_discounting_gamma': 0.9, 'random_crop': True, 'mask_type': 'hole', 'mosaic_unit_size': 12, 'expname': 'benchmark', 'cuda': 'Ture', 'gpu_ids': [0], 'num_workers': 4, 'lr': 0.0001, 'beta1': 0.5, 'beta2': 0.9, 'n_critic': 5, 'niter': 480000, 'print_iter': 100, 'viz_iter': 1000, 'viz_max_out': 16, 'snapshot_save_iter': 5000, 'coarse_l1_alpha': 1.2, 'l1_loss_alpha': 1.2, 'ae_loss_alpha': 1.2, 'global_wgan_loss_alpha': 1.0, 'gan_loss_alpha': 0.001, 'wgan_gp_lambda': 10, 'netG': {'input_dim': 3, 'ngf': 32}, 'netD': {'input_dim': 3, 'ndf': 64}} +2025-04-27 16:36:36,069 INFO Training on dataset: imagenet +2025-04-27 16:36:36,543 INFO +Generator( + (coarse_generator): CoarseGenerator( + (conv1): Conv2dBlock( + (pad): ZeroPad2d((0, 0, 0, 0)) + (activation): ELU(alpha=1.0, inplace=True) + (conv): Conv2d(5, 32, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2)) + ) + (conv2_downsample): Conv2dBlock( + (pad): ZeroPad2d((0, 0, 0, 0)) + (activation): ELU(alpha=1.0, inplace=True) + (conv): Conv2d(32, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1)) + ) + (conv3): Conv2dBlock( + (pad): ZeroPad2d((0, 0, 0, 0)) + (activation): ELU(alpha=1.0, inplace=True) + (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (conv4_downsample): Conv2dBlock( + (pad): ZeroPad2d((0, 0, 0, 0)) + (activation): ELU(alpha=1.0, inplace=True) + (conv): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1)) + ) + (conv5): Conv2dBlock( + (pad): ZeroPad2d((0, 0, 0, 0)) + (activation): ELU(alpha=1.0, inplace=True) + (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (conv6): Conv2dBlock( + (pad): ZeroPad2d((0, 0, 0, 0)) + (activation): ELU(alpha=1.0, inplace=True) + (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (conv7_atrous): Conv2dBlock( + (pad): ZeroPad2d((0, 0, 0, 0)) + (activation): ELU(alpha=1.0, inplace=True) + (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2)) + ) + (conv8_atrous): Conv2dBlock( + (pad): ZeroPad2d((0, 0, 0, 0)) + (activation): ELU(alpha=1.0, inplace=True) + (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(4, 4), dilation=(4, 4)) + ) + (conv9_atrous): Conv2dBlock( + (pad): ZeroPad2d((0, 0, 0, 0)) + (activation): ELU(alpha=1.0, inplace=True) + (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(8, 8), dilation=(8, 8)) + ) + (conv10_atrous): Conv2dBlock( + (pad): ZeroPad2d((0, 0, 0, 0)) + (activation): ELU(alpha=1.0, inplace=True) + (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(16, 16), dilation=(16, 16)) + ) + (conv11): Conv2dBlock( + (pad): ZeroPad2d((0, 0, 0, 0)) + (activation): ELU(alpha=1.0, inplace=True) + (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (conv12): Conv2dBlock( + (pad): ZeroPad2d((0, 0, 0, 0)) + (activation): ELU(alpha=1.0, inplace=True) + (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (conv13): Conv2dBlock( + (pad): ZeroPad2d((0, 0, 0, 0)) + (activation): ELU(alpha=1.0, inplace=True) + (conv): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (conv14): Conv2dBlock( + (pad): ZeroPad2d((0, 0, 0, 0)) + (activation): ELU(alpha=1.0, inplace=True) + (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (conv15): Conv2dBlock( + (pad): ZeroPad2d((0, 0, 0, 0)) + (activation): ELU(alpha=1.0, inplace=True) + (conv): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (conv16): Conv2dBlock( + (pad): ZeroPad2d((0, 0, 0, 0)) + (activation): ELU(alpha=1.0, inplace=True) + (conv): Conv2d(32, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (conv17): Conv2dBlock( + (pad): ZeroPad2d((0, 0, 0, 0)) + (conv): Conv2d(16, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (fine_generator): FineGenerator( + (conv1): Conv2dBlock( + (pad): ZeroPad2d((0, 0, 0, 0)) + (activation): ELU(alpha=1.0, inplace=True) + (conv): Conv2d(5, 32, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2)) + ) + (conv2_downsample): Conv2dBlock( + (pad): ZeroPad2d((0, 0, 0, 0)) + (activation): ELU(alpha=1.0, inplace=True) + (conv): Conv2d(32, 32, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1)) + ) + (conv3): Conv2dBlock( + (pad): ZeroPad2d((0, 0, 0, 0)) + (activation): ELU(alpha=1.0, inplace=True) + (conv): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (conv4_downsample): Conv2dBlock( + (pad): ZeroPad2d((0, 0, 0, 0)) + (activation): ELU(alpha=1.0, inplace=True) + (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1)) + ) + (conv5): Conv2dBlock( + (pad): ZeroPad2d((0, 0, 0, 0)) + (activation): ELU(alpha=1.0, inplace=True) + (conv): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (conv6): Conv2dBlock( + (pad): ZeroPad2d((0, 0, 0, 0)) + (activation): ELU(alpha=1.0, inplace=True) + (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (conv7_atrous): Conv2dBlock( + (pad): ZeroPad2d((0, 0, 0, 0)) + (activation): ELU(alpha=1.0, inplace=True) + (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2)) + ) + (conv8_atrous): Conv2dBlock( + (pad): ZeroPad2d((0, 0, 0, 0)) + (activation): ELU(alpha=1.0, inplace=True) + (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(4, 4), dilation=(4, 4)) + ) + (conv9_atrous): Conv2dBlock( + (pad): ZeroPad2d((0, 0, 0, 0)) + (activation): ELU(alpha=1.0, inplace=True) + (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(8, 8), dilation=(8, 8)) + ) + (conv10_atrous): Conv2dBlock( + (pad): ZeroPad2d((0, 0, 0, 0)) + (activation): ELU(alpha=1.0, inplace=True) + (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(16, 16), dilation=(16, 16)) + ) + (pmconv1): Conv2dBlock( + (pad): ZeroPad2d((0, 0, 0, 0)) + (activation): ELU(alpha=1.0, inplace=True) + (conv): Conv2d(5, 32, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2)) + ) + (pmconv2_downsample): Conv2dBlock( + (pad): ZeroPad2d((0, 0, 0, 0)) + (activation): ELU(alpha=1.0, inplace=True) + (conv): Conv2d(32, 32, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1)) + ) + (pmconv3): Conv2dBlock( + (pad): ZeroPad2d((0, 0, 0, 0)) + (activation): ELU(alpha=1.0, inplace=True) + (conv): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (pmconv4_downsample): Conv2dBlock( + (pad): ZeroPad2d((0, 0, 0, 0)) + (activation): ELU(alpha=1.0, inplace=True) + (conv): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1)) + ) + (pmconv5): Conv2dBlock( + (pad): ZeroPad2d((0, 0, 0, 0)) + (activation): ELU(alpha=1.0, inplace=True) + (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (pmconv6): Conv2dBlock( + (pad): ZeroPad2d((0, 0, 0, 0)) + (activation): ReLU(inplace=True) + (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (contextul_attention): ContextualAttention() + (pmconv9): Conv2dBlock( + (pad): ZeroPad2d((0, 0, 0, 0)) + (activation): ELU(alpha=1.0, inplace=True) + (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (pmconv10): Conv2dBlock( + (pad): ZeroPad2d((0, 0, 0, 0)) + (activation): ELU(alpha=1.0, inplace=True) + (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (allconv11): Conv2dBlock( + (pad): ZeroPad2d((0, 0, 0, 0)) + (activation): ELU(alpha=1.0, inplace=True) + (conv): Conv2d(256, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (allconv12): Conv2dBlock( + (pad): ZeroPad2d((0, 0, 0, 0)) + (activation): ELU(alpha=1.0, inplace=True) + (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (allconv13): Conv2dBlock( + (pad): ZeroPad2d((0, 0, 0, 0)) + (activation): ELU(alpha=1.0, inplace=True) + (conv): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (allconv14): Conv2dBlock( + (pad): ZeroPad2d((0, 0, 0, 0)) + (activation): ELU(alpha=1.0, inplace=True) + (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (allconv15): Conv2dBlock( + (pad): ZeroPad2d((0, 0, 0, 0)) + (activation): ELU(alpha=1.0, inplace=True) + (conv): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (allconv16): Conv2dBlock( + (pad): ZeroPad2d((0, 0, 0, 0)) + (activation): ELU(alpha=1.0, inplace=True) + (conv): Conv2d(32, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (allconv17): Conv2dBlock( + (pad): ZeroPad2d((0, 0, 0, 0)) + (conv): Conv2d(16, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) +) +2025-04-27 16:36:36,544 INFO +LocalDis( + (dis_conv_module): DisConvModule( + (conv1): Conv2dBlock( + (pad): ZeroPad2d((0, 0, 0, 0)) + (activation): LeakyReLU(negative_slope=0.2, inplace=True) + (conv): Conv2d(3, 64, kernel_size=(5, 5), stride=(2, 2), padding=(2, 2)) + ) + (conv2): Conv2dBlock( + (pad): ZeroPad2d((0, 0, 0, 0)) + (activation): LeakyReLU(negative_slope=0.2, inplace=True) + (conv): Conv2d(64, 128, kernel_size=(5, 5), stride=(2, 2), padding=(2, 2)) + ) + (conv3): Conv2dBlock( + (pad): ZeroPad2d((0, 0, 0, 0)) + (activation): LeakyReLU(negative_slope=0.2, inplace=True) + (conv): Conv2d(128, 256, kernel_size=(5, 5), stride=(2, 2), padding=(2, 2)) + ) + (conv4): Conv2dBlock( + (pad): ZeroPad2d((0, 0, 0, 0)) + (activation): LeakyReLU(negative_slope=0.2, inplace=True) + (conv): Conv2d(256, 256, kernel_size=(5, 5), stride=(2, 2), padding=(2, 2)) + ) + ) + (linear): Linear(in_features=16384, out_features=1, bias=True) +) +2025-04-27 16:36:36,544 INFO +GlobalDis( + (dis_conv_module): DisConvModule( + (conv1): Conv2dBlock( + (pad): ZeroPad2d((0, 0, 0, 0)) + (activation): LeakyReLU(negative_slope=0.2, inplace=True) + (conv): Conv2d(3, 64, kernel_size=(5, 5), stride=(2, 2), padding=(2, 2)) + ) + (conv2): Conv2dBlock( + (pad): ZeroPad2d((0, 0, 0, 0)) + (activation): LeakyReLU(negative_slope=0.2, inplace=True) + (conv): Conv2d(64, 128, kernel_size=(5, 5), stride=(2, 2), padding=(2, 2)) + ) + (conv3): Conv2dBlock( + (pad): ZeroPad2d((0, 0, 0, 0)) + (activation): LeakyReLU(negative_slope=0.2, inplace=True) + (conv): Conv2d(128, 256, kernel_size=(5, 5), stride=(2, 2), padding=(2, 2)) + ) + (conv4): Conv2dBlock( + (pad): ZeroPad2d((0, 0, 0, 0)) + (activation): LeakyReLU(negative_slope=0.2, inplace=True) + (conv): Conv2d(256, 256, kernel_size=(5, 5), stride=(2, 2), padding=(2, 2)) + ) + ) + (linear): Linear(in_features=65536, out_features=1, bias=True) +) +2025-04-27 16:36:36,638 INFO Resume from checkpoints/imagenet/hole_benchmark at iteration 430000 +2025-04-27 16:36:39,050 INFO Iter: [430000/480000] l1: 0.046810 ae: 0.016235 wgan_g: -25.246567 wgan_d: -5.757960 wgan_gp: 0.009169 g: 0.050407 d: -5.666266 speed: 42.77 batches/s +2025-04-27 16:36:43,456 INFO Iter: [430100/480000] l1: 0.036309 ae: 0.028025 wgan_g: -70.031448 wgan_d: -18.649841 wgan_gp: 0.299248 g: 0.007170 d: -15.657358 speed: 22.73 batches/s +2025-04-27 16:36:47,135 INFO Iter: [430200/480000] l1: 0.076593 ae: 0.034039 wgan_g: -165.546371 wgan_d: -71.503021 wgan_gp: 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