real-sharper-lowmem: Pulling from racoonman254/image-upscaler-hat 52d2b7f179e3: Already exists 2b8a9a2240c1: Already exists 618a49bbc6c6: Already exists 708d13076071: Already exists 90b76fc2ebde: Already exists 610d266c7811: Already exists 5186d5863148: Already exists 60d6e2d1c2aa: Already exists 9dbc6d4d03b6: Already exists c8b0ee973ba4: Already exists cb813d4c301f: Already exists 4819eed7651a: Already exists 871c0a3c8455: Already exists a1e3ef145161: Already exists e7809b12417f: Already exists d63f442a636b: Already exists 991265557976: Already exists 6e39ad22e868: Already exists 977d2091734e: Already exists 48851a15662c: Already exists fd047ab689b1: Already exists 2b16729fb01d: Already exists a83367168736: Already exists 3ee165eafdff: Already exists da7951ba3291: Already exists 8ffb98cceb11: Already exists d30cc7cc2f81: Already exists d4851d68f090: Already exists 6fc3d28dd1ea: Already exists 9467d22bcf3d: Already exists 988e2d951269: Already exists 3a6109b4f17d: Already exists 400fdb55e52a: Already exists 9d76f905f41b: Already exists 89998a7c1de5: Already exists f05f8792ea60: Already exists Setting up the model... Predicting... /usr/local/lib/python3.10/site-packages/torchvision/transforms/functional_tensor.py:5: UserWarning: The torchvision.transforms.functional_tensor module is deprecated in 0.15 and will be **removed in 0.17**. Please don't rely on it. You probably just need to use APIs in torchvision.transforms.functional or in torchvision.transforms.v2.functional. warnings.warn( Disable distributed. 2023-08-25 15:31:17,920 INFO: ____ _ _____ ____ / __ ) ____ _ _____ (_)_____/ ___/ / __ \ / __ |/ __ `// ___// // ___/\__ \ / /_/ / / /_/ // /_/ /(__ )/ // /__ ___/ // _, _/ /_____/ \__,_//____//_/ \___//____//_/ |_| ______ __ __ __ __ / ____/____ ____ ____/ / / / __ __ _____ / /__ / / / / __ / __ \ / __ \ / __ / / / / / / // ___// //_/ / / / /_/ // /_/ // /_/ // /_/ / / /___/ /_/ // /__ / /< /_/ \____/ \____/ \____/ \____/ /_____/\____/ \___//_/|_| (_) Version Information: BasicSR: 1.3.4.9 PyTorch: 2.0.1+cu118 TorchVision: 0.15.2+cu118 2023-08-25 15:31:17,920 INFO: name: HAT_GAN_Real_sharper model_type: HATModel scale: 4 num_gpu: 1 manual_seed: 0 tile:[ tile_size: 512 tile_pad: 32 ] datasets:[ test_1:[ name: custom type: SingleImageDataset dataroot_lq: input_dir io_backend:[ type: disk ] phase: test scale: 4 ] ] network_g:[ type: HAT upscale: 4 in_chans: 3 img_size: 64 window_size: 16 compress_ratio: 3 squeeze_factor: 30 conv_scale: 0.01 overlap_ratio: 0.5 img_range: 1.0 depths: [6, 6, 6, 6, 6, 6] embed_dim: 180 num_heads: [6, 6, 6, 6, 6, 6] mlp_ratio: 2 upsampler: pixelshuffle resi_connection: 1conv ] path:[ pretrain_network_g: ./experiments/pretrained_models/Real_HAT_GAN_sharper.pth strict_load_g: True param_key_g: params_ema results_root: /tungsten/results/HAT_GAN_Real_sharper log: /tungsten/results/HAT_GAN_Real_sharper visualization: /tungsten/results/HAT_GAN_Real_sharper/visualization ] val:[ save_img: True suffix: None ] dist: False rank: 0 world_size: 1 auto_resume: False is_train: False 2023-08-25 15:31:17,920 INFO: Dataset [SingleImageDataset] - custom is built. 2023-08-25 15:31:17,920 INFO: Number of test images in custom: 1 /usr/local/lib/python3.10/site-packages/torch/functional.py:504: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at ../aten/src/ATen/native/TensorShape.cpp:3483.) return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined] 2023-08-25 15:31:18,169 INFO: Network [HAT] is created. 2023-08-25 15:31:18,560 INFO: Network: HAT, with parameters: 20,772,507 2023-08-25 15:31:18,560 INFO: HAT( (conv_first): Conv2d(3, 180, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (patch_embed): PatchEmbed( (norm): LayerNorm((180,), eps=1e-05, elementwise_affine=True) ) (patch_unembed): PatchUnEmbed() (pos_drop): Dropout(p=0.0, inplace=False) (layers): ModuleList( (0): RHAG( (residual_group): AttenBlocks( (blocks): ModuleList( (0): HAB( (norm1): LayerNorm((180,), eps=1e-05, elementwise_affine=True) (attn): WindowAttention( (qkv): Linear(in_features=180, out_features=540, bias=True) (attn_drop): Dropout(p=0.0, inplace=False) (proj): Linear(in_features=180, out_features=180, bias=True) (proj_drop): Dropout(p=0.0, inplace=False) (softmax): Softmax(dim=-1) ) (conv_block): CAB( (cab): Sequential( (0): Conv2d(180, 60, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (1): GELU(approximate='none') (2): Conv2d(60, 180, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (3): ChannelAttention( (attention): Sequential( (0): AdaptiveAvgPool2d(output_size=1) (1): Conv2d(180, 6, kernel_size=(1, 1), stride=(1, 1)) (2): ReLU(inplace=True) (3): Conv2d(6, 180, kernel_size=(1, 1), stride=(1, 1)) (4): Sigmoid() ) ) ) ) (drop_path): Identity() (norm2): LayerNorm((180,), eps=1e-05, elementwise_affine=True) (mlp): Mlp( (fc1): Linear(in_features=180, out_features=360, bias=True) (act): GELU(approximate='none') (fc2): Linear(in_features=360, out_features=180, bias=True) (drop): Dropout(p=0.0, inplace=False) ) ) (1-5): 5 x HAB( (norm1): LayerNorm((180,), eps=1e-05, elementwise_affine=True) (attn): WindowAttention( (qkv): Linear(in_features=180, out_features=540, bias=True) (attn_drop): Dropout(p=0.0, inplace=False) (proj): Linear(in_features=180, out_features=180, bias=True) (proj_drop): Dropout(p=0.0, inplace=False) (softmax): Softmax(dim=-1) ) (conv_block): CAB( (cab): Sequential( (0): Conv2d(180, 60, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (1): GELU(approximate='none') (2): Conv2d(60, 180, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (3): ChannelAttention( (attention): Sequential( (0): AdaptiveAvgPool2d(output_size=1) (1): Conv2d(180, 6, kernel_size=(1, 1), stride=(1, 1)) (2): ReLU(inplace=True) (3): Conv2d(6, 180, kernel_size=(1, 1), stride=(1, 1)) (4): Sigmoid() ) ) ) ) (drop_path): DropPath() (norm2): LayerNorm((180,), eps=1e-05, elementwise_affine=True) (mlp): Mlp( (fc1): Linear(in_features=180, out_features=360, bias=True) (act): GELU(approximate='none') (fc2): Linear(in_features=360, out_features=180, bias=True) (drop): Dropout(p=0.0, inplace=False) ) ) ) (overlap_attn): OCAB( (norm1): LayerNorm((180,), eps=1e-05, elementwise_affine=True) (qkv): Linear(in_features=180, out_features=540, bias=True) (unfold): Unfold(kernel_size=(24, 24), dilation=1, padding=4, stride=16) (softmax): Softmax(dim=-1) (proj): Linear(in_features=180, out_features=180, bias=True) (norm2): LayerNorm((180,), eps=1e-05, elementwise_affine=True) (mlp): Mlp( (fc1): Linear(in_features=180, out_features=360, bias=True) (act): GELU(approximate='none') (fc2): Linear(in_features=360, out_features=180, bias=True) (drop): Dropout(p=0.0, inplace=False) ) ) ) (conv): Conv2d(180, 180, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (patch_embed): PatchEmbed() (patch_unembed): PatchUnEmbed() ) (1-5): 5 x RHAG( (residual_group): AttenBlocks( (blocks): ModuleList( (0-5): 6 x HAB( (norm1): LayerNorm((180,), eps=1e-05, elementwise_affine=True) (attn): WindowAttention( (qkv): Linear(in_features=180, out_features=540, bias=True) (attn_drop): Dropout(p=0.0, inplace=False) (proj): Linear(in_features=180, out_features=180, bias=True) (proj_drop): Dropout(p=0.0, inplace=False) (softmax): Softmax(dim=-1) ) (conv_block): CAB( (cab): Sequential( (0): Conv2d(180, 60, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (1): GELU(approximate='none') (2): Conv2d(60, 180, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (3): ChannelAttention( (attention): Sequential( (0): AdaptiveAvgPool2d(output_size=1) (1): Conv2d(180, 6, kernel_size=(1, 1), stride=(1, 1)) (2): ReLU(inplace=True) (3): Conv2d(6, 180, kernel_size=(1, 1), stride=(1, 1)) (4): Sigmoid() ) ) ) ) (drop_path): DropPath() (norm2): LayerNorm((180,), eps=1e-05, elementwise_affine=True) (mlp): Mlp( (fc1): Linear(in_features=180, out_features=360, bias=True) (act): GELU(approximate='none') (fc2): Linear(in_features=360, out_features=180, bias=True) (drop): Dropout(p=0.0, inplace=False) ) ) ) (overlap_attn): OCAB( (norm1): LayerNorm((180,), eps=1e-05, elementwise_affine=True) (qkv): Linear(in_features=180, out_features=540, bias=True) (unfold): Unfold(kernel_size=(24, 24), dilation=1, padding=4, stride=16) (softmax): Softmax(dim=-1) (proj): Linear(in_features=180, out_features=180, bias=True) (norm2): LayerNorm((180,), eps=1e-05, elementwise_affine=True) (mlp): Mlp( (fc1): Linear(in_features=180, out_features=360, bias=True) (act): GELU(approximate='none') (fc2): Linear(in_features=360, out_features=180, bias=True) (drop): Dropout(p=0.0, inplace=False) ) ) ) (conv): Conv2d(180, 180, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (patch_embed): PatchEmbed() (patch_unembed): PatchUnEmbed() ) ) (norm): LayerNorm((180,), eps=1e-05, elementwise_affine=True) (conv_after_body): Conv2d(180, 180, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv_before_upsample): Sequential( (0): Conv2d(180, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (1): LeakyReLU(negative_slope=0.01, inplace=True) ) (upsample): Upsample( (0): Conv2d(64, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (1): PixelShuffle(upscale_factor=2) (2): Conv2d(64, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (3): PixelShuffle(upscale_factor=2) ) (conv_last): Conv2d(64, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) ) 2023-08-25 15:31:18,665 INFO: Loading HAT model from ./experiments/pretrained_models/Real_HAT_GAN_sharper.pth, with param key: [params_ema]. 2023-08-25 15:31:18,761 INFO: Model [HATModel] is created. 2023-08-25 15:31:18,761 INFO: Testing custom... /usr/local/lib/python3.10/site-packages/torch/nn/modules/conv.py:459: UserWarning: Applied workaround for CuDNN issue, install nvrtc.so (Triggered internally at ../aten/src/ATen/native/cudnn/Conv_v8.cpp:80.) return F.conv2d(input, weight, bias, self.stride, Tile 1/1 Prediction done.