【图像去噪】第七期论文复现赛SwinIR

在低级视觉任务中引入了Swin-T结构，该技术包括图像超分辨率重建、去噪以及压缩伪影去除。构建了一个由浅层特征提取模块（积）、深层特征提取模块及重建模块组成的网络架构。重建模块根据不同的任务需求采用不同的设计：浅层特征提取通过一个卷积完成；而深层则由k个Residual Spatial Transformer Block (RSTB)块与卷积层加上残差连接构成。

论文复现Low-level算法 SwinIR (去噪)

SwinIR: Image Restoration Using Swin Transformer基于Swin Transformer的用于图像恢复的强基线模型

官方源码：https://github.com/JingyunLiang/SwinIR

复现地址：https://github.com/sldyns/SwinIR_paddle

脚本任务：https://aistudio.baidu.com/aistudio/clusterprojectdetail/3792518

PaddleGAN版本：https://github.com/PaddlePaddle/PaddleGAN/blob/develop/docs/zh_CN/tutorials/swinir.md

1. 简介

SwinIR的结构相对简单，相比于Swin-T而言并不那么复杂。如果你已经熟悉了Swin-T结构的应用，那么SwinIR中的低级视觉任务如图像超分辨率重建、去噪以及压缩伪影去除等问题将不再成为难点。SwinIR由浅层特征提取模块、深层特征提取模块和重建模块组成。重建模块根据不同的任务使用特定的结构。浅层特征提取采用一个积层。深层特征提取则包括k个RSTB块（Res-Swin-Transformer-Block）以及最后一层卷积加残差连接。每个RSTB由L个STL和一层卷积及残差连接构成。

2. 复现精度

在 CBSD68 测试集上测试，达到验收最低标准34.32： SwinIR Noise Level15 Pytorch 34.42 Paddle 34.32

注：源代码八卡训练的iteration为 1,600,000，我们四卡只训练到了 426,000 就超时停止了.

3. 数据集、预训练模型、文件结构

3.1训练数据

DIV2K(800 training images) +Flickr2K(2650 images) +BSD500(400 training&testing images) +WED(4744 images)

已经整理好的数据：放在了Ai Studio里.

训练数据放在：data/trainsets/trainH下

3.2测试数据

测试数据为 CBSD68：放在了Ai Studio里.

解压到：data/testsets/CBSD68 In [2]

# 解压!cd data && unzip -oq -d testsets/ data147756/CBSD68.zip!cd data && unzip -oq -d trainsets/ data149405/trainH.zip登录后复制 In [3]

# 添加软链接!cd work && ln -s ../data/trainsets trainsets && ln -s ../data/testsets testsets登录后复制

3.2 预训练模型

已放在文件夹work/pretrained_models下： 官方预训练模型，已转为 paddle 的，名为005_colorDN_DFWB_s128w8_SwinIR-M_noise15.pdparams. 复现的模型，名为SwinIR_paddle.pdparams.

3.3 文件结构

SwinIR_Paddle项目是一个全面的深度学习框架，专注于图像处理。该框架包含以下几个关键部分： 数据和模型文件：存储各种训练数据和预训练模型。 配置文件：管理项目的整体设置和参数。 训练数据集：包括CBSD试集等。 工具代码：提供辅助功能，如生成数据patch。 推理代码：实现模型的测试和推理过程。 LICENSE和README.md文件：包含许可证和项目说明。该框架的主要目标是通过先进的图像处理算法（例如SwinIR）来提高图像质量和性能。它提供了一个完整的平台，可以用于深度学习训练、推理以及测试模型，并且包含了详细的操作指南和日志记录功能。

4. 环境依赖

PaddlePaddle >= 2.3.2

scikit-image == 0.19.3 In [1]

!pip install scikit-image登录后复制

Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple Collecting scikit-image Downloading https://pypi.tuna.tsinghua.edu.cn/packages/2d/ba/63ce953b7d593bd493e80be158f2d9f82936582380aee0998315510633aa/scikit_image-0.19.3-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (13.5 MB) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 13.5/13.5 MB 717.8 kB/s eta 0:00:0000:0100:01 Requirement already satisfied: scipy>=1.4.1 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from scikit-image) (1.6.3) Requirement already satisfied: packaging>=20.0 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from scikit-image) (21.3) Requirement already satisfied: networkx>=2.2 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from scikit-image) (2.4) Requirement already satisfied: numpy>=1.17.0 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from scikit-image) (1.19.5) Collecting tifffile>=2019.7.26 Downloading https://pypi.tuna.tsinghua.edu.cn/packages/d8/38/85ae5ed77598ca90558c17a2f79ddaba33173b31cf8d8f545d34d9134f0d/tifffile-2021.11.2-py3-none-any.whl (178 kB) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 178.9/178.9 kB 25.2 kB/s eta 0:00:00a 0:00:01Requirement already satisfied: pillow!=7.1.0,!=7.1.1,!=8.3.0,>=6.1.0 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from scikit-image) (8.2.0) Collecting PyWavelets>=1.1.1 Downloading https://pypi.tuna.tsinghua.edu.cn/packages/ae/56/4441877073d8a5266dbf7b04c7f3dc66f1149c8efb9323e0ef987a9bb1ce/PyWavelets-1.3.0-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl (6.4 MB) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 6.4/6.4 MB 1.3 MB/s eta 0:00:0000:0100:01 Requirement already satisfied: imageio>=2.4.1 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from scikit-image) (2.6.1) Requirement already satisfied: decorator>=4.3.0 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from networkx>=2.2->scikit-image) (4.4.2) Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from packaging>=20.0->scikit-image) (3.0.9) Installing collected packages: tifffile, PyWavelets, scikit-image Successfully installed PyWavelets-1.3.0 scikit-image-0.19.3 tifffile-2021.11.2[notice] A new release of pip available: 22.1.2 -> 22.3[notice] To update, run: pip install --upgrade pip登录后复制

5. 快速开始

配置文件在work/options下，可修改学习率、batch_size等参数

5.1 模型训练

为更好的体验，建议使用单机多卡训练，例如fork并运行脚本任务： https://aistudio.baidu.com/aistudio/clusterprojectdetail/3792518 In []

# 单机单卡!cd work && python main_train_psnr.py --opt options/train_swinir_multi_card_32.json登录后复制

/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/__init__.py:107: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working from collections import MutableMapping /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/rcsetup.py:20: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working from collections import Iterable, Mapping /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/colors.py:53: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working from collections import Sized export CUDA_VISIBLE_DEVICES=0,1,2,3 number of GPUs is: 4 /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/distributed/parallel.py:159: UserWarning: Currently not a parallel execution environment, `paddle.distributed.init_parallel_env` will not do anything. "Currently not a parallel execution environment, `paddle.distributed.init_parallel_env` will not do anything." 1 LogHandlers setup! 22-10-17 16:47:13.877 : task: swinir_denoising_color_15 model: plain gpu_ids: [0, 1, 2, 3] dist: True n_channels: 3 path:[ root: denoising pretrained_netG: None pretrained_netE: None task: denoising/swinir_denoising_color_15 log: denoising/swinir_denoising_color_15 options: denoising/swinir_denoising_color_15/options models: denoising/swinir_denoising_color_15/models images: denoising/swinir_denoising_color_15/images pretrained_optimizerG: None ] datasets:[ train:[ name: train_dataset dataset_type: dncnn dataroot_H: trainsets/trainH dataroot_L: None H_size: 128 sigma: 15 sigma_test: 15 dataloader_shuffle: True dataloader_num_workers: 8 dataloader_batch_size: 2 phase: train scale: 1 n_channels: 3 ] test:[ name: test_dataset dataset_type: dncnn dataroot_H: testsets/CBSD68 dataroot_L: None sigma: 15 sigma_test: 15 phase: test scale: 1 n_channels: 3 ] ] netG:[ net_type: swinir upscale: 1 in_chans: 3 img_size: 128 window_size: 8 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: None resi_connection: 1conv init_type: default scale: 1 ] train:[ G_lossfn_type: charbonnier G_lossfn_weight: 1.0 G_charbonnier_eps: 1e-09 E_decay: 0.999 G_optimizer_type: adam G_optimizer_lr: 0.0002 G_optimizer_wd: 0 G_optimizer_clipgrad: None G_optimizer_reuse: True G_scheduler_type: MultiStepLR G_scheduler_milestones: [800000, 1200000, 1400000, 1500000, 1600000] G_scheduler_gamma: 0.5 G_regularizer_orthstep: None G_regularizer_clipstep: None G_param_strict: True E_param_strict: True manual_seed: 42 checkpoint_test: 2000 checkpoint_save: 2000 checkpoint_print: 400 F_feature_layer: 34 F_weights: 1.0 F_lossfn_type: l1 F_use_input_norm: True F_use_range_norm: False G_optimizer_betas: [0.9, 0.999] G_scheduler_restart_weights: 1 ] opt_path: options/train_swinir_multi_card_32.json is_train: True merge_bn: False merge_bn_startpoint: -1 scale: 1 find_unused_parameters: True use_static_graph: False num_gpu: 4 nranks: 1 Random seed: 42 Dataset: Denosing on AWGN with fixed sigma. Only dataroot_H is needed. Dataset [DatasetDnCNN - train_dataset] is created. 22-10-17 16:47:13.913 : Number of train images: 8,694, iters: 4,347 Dataset: Denosing on AWGN with fixed sigma. Only dataroot_H is needed. Dataset [DatasetDnCNN - test_dataset] is created. W1017 16:47:14.995301 3546 gpu_resources.cc:61] Please NOTE: device: 0, GPU Compute Capability: 7.0, Driver API Version: 11.2, Runtime API Version: 11.2 W1017 16:47:14.999406 3546 gpu_resources.cc:91] device: 0, cuDNN Version: 8.2. Pass this initialization! Initialization was done during network definition! Pass this initialization! Initialization was done during network definition! Training model [ModelPlain] is created. Copying model for E ... 22-10-17 16:47:15.620 : Networks name: SwinIR Params number: Tensor(shape=[1], dtype=int64, place=Place(gpu:0), stop_gradient=False, [11504163]) Net structure: SwinIR( (conv_first): Conv2D(3, 180, kernel_size=[3, 3], padding=1, data_format=NCHW) (patch_embed): PatchEmbed( (norm): LayerNorm(normalized_shape=[180], epsilon=1e-05) ) (patch_unembed): PatchUnEmbed() (pos_drop): Dropout(p=0.0, axis=None, mode=upscale_in_train) (layers): LayerList( (0): RSTB( (residual_group): BasicLayer(dim=180, input_resolution=(128, 128), depth=6 (blocks): LayerList( (0): SwinTransformerBlock(dim=180, input_resolution=(128, 128), num_heads=6, window_size=8, shift_size=0, mlp_ratio=2 (norm1): LayerNorm(normalized_shape=[180], epsilon=1e-05) (attn): WindowAttention(dim=180, window_size=(8, 8), num_heads=6 (qkv): Linear(in_features=180, out_features=540, dtype=float32) (attn_dropout): Dropout(p=0.0, axis=None, mode=upscale_in_train) (proj): Linear(in_features=180, out_features=180, dtype=float32) (proj_dropout): Dropout(p=0.0, axis=None, mode=upscale_in_train) (softmax): Softmax(axis=-1) ) (drop_path): Identity() (norm2): LayerNorm(normalized_shape=[180], epsilon=1e-05) (mlp): Mlp( (fc1): Linear(in_features=180, out_features=360, dtype=float32) (fc2): Linear(in_features=360, out_features=180, dtype=float32) (act): GELU(approximate=False) (dropout): Dropout(p=0.0, axis=None, mode=upscale_in_train) ) ) (1): SwinTransformerBlock(dim=180, input_resolution=(128, 128), num_heads=6, window_size=8, shift_size=4, mlp_ratio=2 (norm1): LayerNorm(normalized_shape=[180], epsilon=1e-05) (attn): WindowAttention(dim=180, window_size=(8, 8), num_heads=6 (qkv): Linear(in_features=180, out_features=540, dtype=float32) (attn_dropout): Dropout(p=0.0, axis=None, mode=upscale_in_train) (proj): Linear(in_features=180, out_features=180, dtype=float32) (proj_dropout): Dropout(p=0.0, axis=None, mode=upscale_in_train) (softmax): Softmax(axis=-1) ) (drop_path): DropPath() (norm2): LayerNorm(normalized_shape=[180], epsilon=1e-05) (mlp): Mlp( (fc1): Linear(in_features=180, out_features=360, dtype=float32) (fc2): Linear(in_features=360, out_features=180, dtype=float32) (act): GELU(approximate=False) (dropout): Dropout(p=0.0, axis=None, mode=upscale_in_train) ) ) (2): SwinTransformerBlock(dim=180, input_resolution=(128, 128), num_heads=6, window_size=8, shift_size=0, mlp_ratio=2 (norm1): LayerNorm(normalized_shape=[180], epsilon=1e-05) (attn): WindowAttention(dim=180, window_size=(8, 8), num_heads=6 (qkv): Linear(in_features=180, out_features=540, dtype=float32) (attn_dropout): Dropout(p=0.0, axis=None, mode=upscale_in_train) (proj): Linear(in_features=180, out_features=180, dtype=float32) (proj_dropout): Dropout(p=0.0, axis=None, mode=upscale_in_train) (softmax): Softmax(axis=-1) ) (drop_path): DropPath() (norm2): LayerNorm(normalized_shape=[180], epsilon=1e-05) (mlp): Mlp( (fc1): Linear(in_features=180, out_features=360, dtype=float32) (fc2): Linear(in_features=360, out_features=180, dtype=float32) (act): GELU(approximate=False) (dropout): Dropout(p=0.0, axis=None, mode=upscale_in_train) ) ) (3): SwinTransformerBlock(dim=180, input_resolution=(128, 128), num_heads=6, window_size=8, shift_size=4, mlp_ratio=2 (norm1): LayerNorm(normalized_shape=[180], epsilon=1e-05) (attn): WindowAttention(dim=180, window_size=(8, 8), num_heads=6 (qkv): Linear(in_features=180, out_features=540, dtype=float32) (attn_dropout): Dropout(p=0.0, axis=None, mode=upscale_in_train) (proj): Linear(in_features=180, out_features=180, dtype=float32) (proj_dropout): Dropout(p=0.0, axis=None, mode=upscale_in_train) (softmax): Softmax(axis=-1) ) (drop_path): DropPath() (norm2): LayerNorm(normalized_shape=[180], epsilon=1e-05) (mlp): Mlp( (fc1): Linear(in_features=180, out_features=360, dtype=float32) (fc2): Linear(in_features=360, out_features=180, dtype=float32) (act): GELU(approximate=False) (dropout): Dropout(p=0.0, axis=None, mode=upscale_in_train) ) ) (4): SwinTransformerBlock(dim=180, input_resolution=(128, 128), num_heads=6, window_size=8, shift_size=0, mlp_ratio=2 (norm1): LayerNorm(normalized_shape=[180], epsilon=1e-05) (attn): WindowAttention(dim=180, window_size=(8, 8), num_heads=6 (qkv): Linear(in_features=180, out_features=540, dtype=float32) (attn_dropout): Dropout(p=0.0, axis=None, mode=upscale_in_train) (proj): Linear(in_features=180, out_features=180, dtype=float32) (proj_dropout): Dropout(p=0.0, axis=None, mode=upscale_in_train) (softmax): Softmax(axis=-1) ) (drop_path): DropPath() (norm2): LayerNorm(normalized_shape=[180], epsilon=1e-05) (mlp): Mlp( (fc1): Linear(in_features=180, out_features=360, dtype=float32) (fc2): Linear(in_features=360, out_features=180, dtype=float32) (act): GELU(approximate=False) (dropout): Dropout(p=0.0, axis=None, mode=upscale_in_train) ) ) (5): SwinTransformerBlock(dim=180, input_resolution=(128, 128), num_heads=6, window_size=8, shift_size=4, mlp_ratio=2 (norm1): LayerNorm(normalized_shape=[180], epsilon=1e-05) (attn): WindowAttention(dim=180, window_size=(8, 8), num_heads=6 (qkv): Linear(in_features=180, out_features=540, dtype=float32) (attn_dropout): Dropout(p=0.0, axis=None, mode=upscale_in_train) (proj): Linear(in_features=180, out_features=180, dtype=float32) (proj_dropout): Dropout(p=0.0, axis=None, mode=upscale_in_train) (softmax): Softmax(axis=-1) ) (drop_path): DropPath() (norm2): LayerNorm(normalized_shape=[180], epsilon=1e-05) (mlp): Mlp( (fc1): Linear(in_features=180, out_features=360, dtype=float32) (fc2): Linear(in_features=360, out_features=180, dtype=float32) (act): GELU(approximate=False) (dropout): Dropout(p=0.0, axis=None, mode=upscale_in_train) ) ) ) ) (conv): Conv2D(180, 180, kernel_size=[3, 3], padding=1, data_format=NCHW) (patch_embed): PatchEmbed() (patch_unembed): PatchUnEmbed() ) (1): RSTB( (residual_group): BasicLayer(dim=180, input_resolution=(128, 128), depth=6 (blocks): LayerList( (0): SwinTransformerBlock(dim=180, input_resolution=(128, 128), num_heads=6, window_size=8, shift_size=0, mlp_ratio=2 (norm1): LayerNorm(normalized_shape=[180], epsilon=1e-05) (attn): WindowAttention(dim=180, window_size=(8, 8), num_heads=6 (qkv): Linear(in_features=180, out_features=540, dtype=float32) (attn_dropout): Dropout(p=0.0, axis=None, mode=upscale_in_train) (proj): Linear(in_features=180, out_features=180, dtype=float32) (proj_dropout): Dropout(p=0.0, axis=None, mode=upscale_in_train) (softmax): Softmax(axis=-1) ) (drop_path): DropPath() (norm2): LayerNorm(normalized_shape=[180], epsilon=1e-05) (mlp): Mlp( (fc1): Linear(in_features=180, out_features=360, dtype=float32) (fc2): Linear(in_features=360, out_features=180, dtype=float32) (act): GELU(approximate=False) (dropout): Dropout(p=0.0, axis=None, mode=upscale_in_train) ) ) (1): SwinTransformerBlock(dim=180, input_resolution=(128, 128), num_heads=6, window_size=8, shift_size=4, mlp_ratio=2 (norm1): LayerNorm(normalized_shape=[180], epsilon=1e-05) (attn): WindowAttention(dim=180, window_size=(8, 8), num_heads=6 (qkv): Linear(in_features=180, out_features=540, dtype=float32) (attn_dropout): Dropout(p=0.0, axis=None, mode=upscale_in_train) (proj): Linear(in_features=180, out_features=180, dtype=float32) (proj_dropout): Dropout(p=0.0, axis=None, mode=upscale_in_train) (softmax): Softmax(axis=-1) ) (drop_path): DropPath() (norm2): LayerNorm(normalized_shape=[180], epsilon=1e-05) (mlp): Mlp( (fc1): Linear(in_features=180, out_features=360, dtype=float32) (fc2): Linear(in_features=360, out_features=180, dtype=float32) (act): GELU(approximate=False) (dropout): Dropout(p=0.0, axis=None, mode=upscale_in_train) ) ) (2): SwinTransformerBlock(dim=180, input_resolution=(128, 128), num_heads=6, window_size=8, shift_size=0, mlp_ratio=2 (norm1): LayerNorm(normalized_shape=[180], epsilon=1e-05) (attn): WindowAttention(dim=180, window_size=(8, 8), num_heads=6 (qkv): Linear(in_features=180, out_features=540, dtype=float32) (attn_dropout): Dropout(p=0.0, axis=None, mode=upscale_in_train) (proj): Linear(in_features=180, out_features=180, dtype=float32) (proj_dropout): Dropout(p=0.0, axis=None, mode=upscale_in_train) (softmax): Softmax(axis=-1) ) (drop_path): DropPath() (norm2): LayerNorm(normalized_shape=[180], epsilon=1e-05) (mlp): Mlp( (fc1): Linear(in_features=180, out_features=360, dtype=float32) (fc2): Linear(in_features=360, out_features=180, dtype=float32) (act): GELU(approximate=False) (dropout): Dropout(p=0.0, axis=None, mode=upscale_in_train) ) ) (3): SwinTransformerBlock(dim=180, input_resolution=(128, 128), num_heads=6, window_size=8, shift_size=4, mlp_ratio=2 (norm1): LayerNorm(normalized_shape=[180], epsilon=1e-05) (attn): WindowAttention(dim=180, window_size=(8, 8), num_heads=6 (qkv): Linear(in_features=180, out_features=540, dtype=float32) (attn_dropout): Dropout(p=0.0, axis=None, mode=upscale_in_train) (proj): Linear(in_features=180, out_features=180, dtype=float32) (proj_dropout): Dropout(p=0.0, axis=None, mode=upscale_in_train) (softmax): Softmax(axis=-1) ) (drop_path): DropPath() (norm2): LayerNorm(normalized_shape=[180], epsilon=1e-05) (mlp): Mlp( (fc1): Linear(in_features=180, out_features=360, dtype=float32) (fc2): Linear(in_features=360, out_features=180, dtype=float32) (act): GELU(approximate=False) (dropout): Dropout(p=0.0, axis=None, mode=upscale_in_train) ) ) (4): SwinTransformerBlock(dim=180, input_resolution=(128, 128), num_heads=6, window_size=8, shift_size=0, mlp_ratio=2 (norm1): LayerNorm(normalized_shape=[180], epsilon=1e-05) (attn): WindowAttention(dim=180, window_size=(8, 8), num_heads=6 (qkv): Linear(in_features=180, out_features=540, dtype=float32) (attn_dropout): Dropout(p=0.0, axis=None, mode=upscale_in_train) (proj): Linear(in_features=180, out_features=180, dtype=float32) (proj_dropout): Dropout(p=0.0, axis=None, mode=upscale_in_train) (softmax): Softmax(axis=-1) ) (drop_path): DropPath() (norm2): LayerNorm(normalized_shape=[180], epsilon=1e-05) (mlp): Mlp( (fc1): Linear(in_features=180, out_features=360, dtype=float32) (fc2): Linear(in_features=360, out_features=180, dtype=float32) (act): GELU(approximate=False) (dropout): Dropout(p=0.0, axis=None, mode=upscale_in_train) ) ) (5): SwinTransformerBlock(dim=180, input_resolution=(128, 128), num_heads=6, window_size=8, shift_size=4, mlp_ratio=2 (norm1): LayerNorm(normalized_shape=[180], epsilon=1e-05) (attn): WindowAttention(dim=180, window_size=(8, 8), num_heads=6 (qkv): Linear(in_features=180, out_features=540, dtype=float32) (attn_dropout): Dropout(p=0.0, axis=None, mode=upscale_in_train) (proj): Linear(in_features=180, out_features=180, dtype=float32) (proj_dropout): Dropout(p=0.0, axis=None, mode=upscale_in_train) (softmax): Softmax(axis=-1) ) (drop_path): DropPath() (norm2): LayerNorm(normalized_shape=[180], epsilon=1e-05) (mlp): Mlp( (fc1): Linear(in_features=180, out_features=360, dtype=float32) (fc2): Linear(in_features=360, out_features=180, dtype=float32) (act): GELU(approximate=False) (dropout): Dropout(p=0.0, axis=None, mode=upscale_in_train) ) ) ) ) (conv): Conv2D(180, 180, kernel_size=[3, 3], padding=1, data_format=NCHW) (patch_embed): PatchEmbed() (patch_unembed): PatchUnEmbed() ) (2): RSTB( (residual_group): BasicLayer(dim=180, input_resolution=(128, 128), depth=6 (blocks): LayerList( (0): SwinTransformerBlock(dim=180, input_resolution=(128, 128), num_heads=6, window_size=8, shift_size=0, mlp_ratio=2 (norm1): LayerNorm(normalized_shape=[180], epsilon=1e-05) (attn): WindowAttention(dim=180, window_size=(8, 8), num_heads=6 (qkv): Linear(in_features=180, out_features=540, dtype=float32) (attn_dropout): Dropout(p=0.0, axis=None, mode=upscale_in_train) (proj): Linear(in_features=180, out_features=180, dtype=float32) (proj_dropout): Dropout(p=0.0, axis=None, mode=upscale_in_train) (softmax): Softmax(axis=-1) ) (drop_path): DropPath() (norm2): LayerNorm(normalized_shape=[180], epsilon=1e-05) (mlp): Mlp( (fc1): Linear(in_features=180, out_features=360, dtype=float32) (fc2): Linear(in_features=360, out_features=180, dtype=float32) (act): GELU(approximate=False) (dropout): Dropout(p=0.0, axis=None, mode=upscale_in_train) ) ) (1): SwinTransformerBlock(dim=180, input_resolution=(128, 128), num_heads=6, window_size=8, shift_size=4, mlp_ratio=2 (norm1): LayerNorm(normalized_shape=[180], epsilon=1e-05) (attn): WindowAttention(dim=180, window_size=(8, 8), num_heads=6 (qkv): Linear(in_features=180, out_features=540, dtype=float32) (attn_dropout): Dropout(p=0.0, axis=None, mode=upscale_in_train) (proj): Linear(in_features=180, out_features=180, dtype=float32) (proj_dropout): Dropout(p=0.0, axis=None, mode=upscale_in_train) (softmax): Softmax(axis=-1) ) (drop_path): DropPath() (norm2): LayerNorm(normalized_shape=[180], epsilon=1e-05) (mlp): Mlp( (fc1): Linear(in_features=180, out_features=360, dtype=float32) (fc2): Linear(in_features=360, out_features=180, dtype=float32) (act): GELU(approximate=False) (dropout): Dropout(p=0.0, axis=None, mode=upscale_in_train) ) ) (2): SwinTransformerBlock(dim=180, input_resolution=(128, 128), num_heads=6, window_size=8, shift_size=0, mlp_ratio=2 (norm1): LayerNorm(normalized_shape=[180], epsilon=1e-05) (attn): WindowAttention(dim=180, window_size=(8, 8), num_heads=6 (qkv): Linear(in_features=180, out_features=540, dtype=float32) (attn_dropout): Dropout(p=0.0, axis=None, mode=upscale_in_train) (proj): Linear(in_features=180, out_features=180, dtype=float32) (proj_dropout): Dropout(p=0.0, axis=None, mode=upscale_in_train) (softmax): Softmax(axis=-1) ) (drop_path): DropPath() (norm2): LayerNorm(normalized_shape=[180], epsilon=1e-05) (mlp): Mlp( (fc1): Linear(in_features=180, out_features=360, dtype=float32) (fc2): Linear(in_features=360, out_features=180, dtype=float32) (act): GELU(approximate=False) (dropout): Dropout(p=0.0, axis=None, mode=upscale_in_train) ) ) (3): SwinTransformerBlock(dim=180, input_resolution=(128, 128), num_heads=6, window_size=8, shift_size=4, mlp_ratio=2 (norm1): LayerNorm(normalized_shape=[180], epsilon=1e-05) (attn): WindowAttention(dim=180, window_size=(8, 8), num_heads=6 (qkv): Linear(in_features=180, out_features=540, dtype=float32) (attn_dropout): Dropout(p=0.0, axis=None, mode=upscale_in_train) (proj): Linear(in_features=180, out_features=180, dtype=float32) (proj_dropout): Dropout(p=0.0, axis=None, mode=upscale_in_train) (softmax): Softmax(axis=-1) ) (drop_path): DropPath() (norm2): LayerNorm(normalized_shape=[180], epsilon=1e-05) (mlp): Mlp( (fc1): Linear(in_features=180, out_features=360, dtype=float32) (fc2): Linear(in_features=360, out_features=180, dtype=float32) (act): GELU(approximate=False) (dropout): Dropout(p=0.0, axis=None, mode=upscale_in_train) ) ) (4): SwinTransformerBlock(dim=180, input_resolution=(128, 128), num_heads=6, window_size=8, shift_size=0, mlp_ratio=2 (norm1): LayerNorm(normalized_shape=[180], epsilon=1e-05) (attn): WindowAttention(dim=180, window_size=(8, 8), num_heads=6 (qkv): Linear(in_features=180, out_features=540, dtype=float32) (attn_dropout): Dropout(p=0.0, axis=None, mode=upscale_in_train) (proj): Linear(in_features=180, out_features=180, dtype=float32) (proj_dropout): Dropout(p=0.0, axis=None, mode=upscale_in_train) (softmax): Softmax(axis=-1) ) (drop_path): DropPath() (norm2): LayerNorm(normalized_shape=[180], epsilon=1e-05) (mlp): Mlp( (fc1): Linear(in_features=180, out_features=360, dtype=float32) (fc2): Linear(in_features=360, out_features=180, dtype=float32) (act): GELU(approximate=False) (dropout): Dropout(p=0.0, axis=None, mode=upscale_in_train) ) ) (5): SwinTransformerBlock(dim=180, input_resolution=(128, 128), num_heads=6, window_size=8, shift_size=4, mlp_ratio=2 (norm1): LayerNorm(normalized_shape=[180], epsilon=1e-05) (attn): WindowAttention(dim=180, window_size=(8, 8), num_heads=6 (qkv): Linear(in_features=180, out_features=540, dtype=float32) (attn_dropout): Dropout(p=0.0, axis=None, mode=upscale_in_train) (proj): Linear(in_features=180, out_features=180, dtype=float32) (proj_dropout): Dropout(p=0.0, axis=None, mode=upscale_in_train) (softmax): Softmax(axis=-1) ) (drop_path): DropPath() (norm2): LayerNorm(normalized_shape=[180], epsilon=1e-05) (mlp): Mlp( (fc1): Linear(in_features=180, out_features=360, dtype=float32) (fc2): Linear(in_features=360, out_features=180, dtype=float32) (act): GELU(approximate=False) (dropout): Dropout(p=0.0, axis=None, mode=upscale_in_train) ) ) ) ) (conv): Conv2D(180, 180, kernel_size=[3, 3], padding=1, data_format=NCHW) (patch_embed): PatchEmbed() (patch_unembed): PatchUnEmbed() ) (3): RSTB( (residual_group): BasicLayer(dim=180, input_resolution=(128, 128), depth=6 (blocks): LayerList( (0): SwinTransformerBlock(dim=180, input_resolution=(128, 128), num_heads=6, window_size=8, shift_size=0, mlp_ratio=2 (norm1): LayerNorm(normalized_shape=[180], epsilon=1e-05) (attn): WindowAttention(dim=180, window_size=(8, 8), num_heads=6 (qkv): Linear(in_features=180, out_features=540, dtype=float32) (attn_dropout): Dropout(p=0.0, axis=None, mode=upscale_in_train) (proj): Linear(in_features=180, out_features=180, dtype=float32) (proj_dropout): Dropout(p=0.0, axis=None, mode=upscale_in_train) (softmax): Softmax(axis=-1) ) (drop_path): DropPath() (norm2): LayerNorm(normalized_shape=[180], epsilon=1e-05) (mlp): Mlp( (fc1): Linear(in_features=180, out_features=360, dtype=float32) (fc2): Linear(in_features=360, out_features=180, dtype=float32) (act): GELU(approximate=False) (dropout): Dropout(p=0.0, axis=None, mode=upscale_in_train) ) ) (1): SwinTransformerBlock(dim=180, input_resolution=(128, 128), num_heads=6, window_size=8, shift_size=4, mlp_ratio=2 (norm1): LayerNorm(normalized_shape=[180], epsilon=1e-05) (attn): WindowAttention(dim=180, window_size=(8, 8), num_heads=6 (qkv): Linear(in_features=180, out_features=540, dtype=float32) (attn_dropout): Dropout(p=0.0, axis=None, mode=upscale_in_train) (proj): Linear(in_features=180, out_features=180, dtype=float32) (proj_dropout): Dropout(p=0.0, axis=None, mode=upscale_in_train) (softmax): Softmax(axis=-1) ) (drop_path): DropPath() (norm2): LayerNorm(normalized_shape=[180], epsilon=1e-05) (mlp): Mlp( (fc1): Linear(in_features=180, out_features=360, dtype=float32) (fc2): Linear(in_features=360, out_features=180, dtype=float32) (act): GELU(approximate=False) (dropout): Dropout(p=0.0, axis=None, mode=upscale_in_train) ) ) (2): SwinTransformerBlock(dim=180, input_resolution=(128, 128), num_heads=6, window_size=8, shift_size=0, mlp_ratio=2 (norm1): LayerNorm(normalized_shape=[180], epsilon=1e-05) (attn): WindowAttention(dim=180, window_size=(8, 8), num_heads=6 (qkv): Linear(in_features=180, out_features=540, dtype=float32) (attn_dropout): Dropout(p=0.0, axis=None, mode=upscale_in_train) (proj): Linear(in_features=180, out_features=180, dtype=float32) (proj_dropout): Dropout(p=0.0, axis=None, mode=upscale_in_train) (softmax): Softmax(axis=-1) ) (drop_path): DropPath() (norm2): LayerNorm(normalized_shape=[180], epsilon=1e-05) (mlp): Mlp( (fc1): Linear(in_features=180, out_features=360, dtype=float32) (fc2): Linear(in_features=360, out_features=180, dtype=float32) (act): GELU(approximate=False) (dropout): Dropout(p=0.0, axis=None, mode=upscale_in_train) ) ) (3): SwinTransformerBlock(dim=180, input_resolution=(128, 128), num_heads=6, window_size=8, shift_size=4, mlp_ratio=2 (norm1): LayerNorm(normalized_shape=[180], epsilon=1e-05) (attn): WindowAttention(dim=180, window_size=(8, 8), num_heads=6 (qkv): Linear(in_features=180, out_features=540, dtype=float32) (attn_dropout): Dropout(p=0.0, axis=None, mode=upscale_in_train) (proj): Linear(in_features=180, out_features=180, dtype=float32) (proj_dropout): Dropout(p=0.0, axis=None, mode=upscale_in_train) (softmax): Softmax(axis=-1) ) (drop_path): DropPath() (norm2): LayerNorm(normalized_shape=[180], epsilon=1e-05) (mlp): Mlp( (fc1): Linear(in_features=180, out_features=360, dtype=float32) (fc2): Linear(in_features=360, out_features=180, dtype=float32) (act): GELU(approximate=False) (dropout): Dropout(p=0.0, axis=None, mode=upscale_in_train) ) ) (4): SwinTransformerBlock(dim=180, input_resolution=(128, 128), num_heads=6, window_size=8, shift_size=0, mlp_ratio=2 (norm1): LayerNorm(normalized_shape=[180], epsilon=1e-05) (attn): WindowAttention(dim=180, window_size=(8, 8), num_heads=6 (qkv): Linear(in_features=180, out_features=540, dtype=float32) (attn_dropout): Dropout(p=0.0, axis=None, mode=upscale_in_train) (proj): Linear(in_features=180, out_features=180, dtype=float32) (proj_dropout): Dropout(p=0.0, axis=None, mode=upscale_in_train) (softmax): Softmax(axis=-1) ) (drop_path): DropPath() (norm2): LayerNorm(normalized_shape=[180], epsilon=1e-05) (mlp): Mlp( (fc1): Linear(in_features=180, out_features=360, dtype=float32) (fc2): Linear(in_features=360, out_features=180, dtype=float32) (act): GELU(approximate=False) (dropout): Dropout(p=0.0, axis=None, mode=upscale_in_train) ) ) (5): SwinTransformerBlock(dim=180, input_resolution=(128, 128), num_heads=6, window_size=8, shift_size=4, mlp_ratio=2 (norm1): LayerNorm(normalized_shape=[180], epsilon=1e-05) (attn): WindowAttention(dim=180, window_size=(8, 8), num_heads=6 (qkv): Linear(in_features=180, out_features=540, dtype=float32) (attn_dropout): Dropout(p=0.0, axis=None, mode=upscale_in_train) (proj): Linear(in_features=180, out_features=180, dtype=float32) (proj_dropout): Dropout(p=0.0, axis=None, mode=upscale_in_train) (softmax): Softmax(axis=-1) ) (drop_path): DropPath() (norm2): LayerNorm(normalized_shape=[180], epsilon=1e-05) (mlp): Mlp( (fc1): Linear(in_features=180, out_features=360, dtype=float32) (fc2): Linear(in_features=360, out_features=180, dtype=float32) (act): GELU(approximate=False) (dropout): Dropout(p=0.0, axis=None, mode=upscale_in_train) ) ) ) ) (conv): Conv2D(180, 180, kernel_size=[3, 3], padding=1, data_format=NCHW) (patch_embed): PatchEmbed() (patch_unembed): PatchUnEmbed() ) (4): RSTB( (residual_group): BasicLayer(dim=180, input_resolution=(128, 128), depth=6 (blocks): LayerList( (0): SwinTransformerBlock(dim=180, input_resolution=(128, 128), num_heads=6, window_size=8, shift_size=0, mlp_ratio=2 (norm1): LayerNorm(normalized_shape=[180], epsilon=1e-05) (attn): WindowAttention(dim=180, window_size=(8, 8), num_heads=6 (qkv): Linear(in_features=180, out_features=540, dtype=float32) (attn_dropout): Dropout(p=0.0, axis=None, mode=upscale_in_train) (proj): Linear(in_features=180, out_features=180, dtype=float32) (proj_dropout): Dropout(p=0.0, axis=None, mode=upscale_in_train) (softmax): Softmax(axis=-1) ) (drop_path): DropPath() (norm2): LayerNorm(normalized_shape=[180], epsilon=1e-05) (mlp): Mlp( (fc1): Linear(in_features=180, out_features=360, dtype=float32) (fc2): Linear(in_features=360, out_features=180, dtype=float32) (act): GELU(approximate=False) (dropout): Dropout(p=0.0, axis=None, mode=upscale_in_train) ) ) (1): SwinTransformerBlock(dim=180, input_resolution=(128, 128), num_heads=6, window_size=8, shift_size=4, mlp_ratio=2 (norm1): LayerNorm(normalized_shape=[180], epsilon=1e-05) (attn): WindowAttention(dim=180, window_size=(8, 8), num_heads=6 (qkv): Linear(in_features=180, out_features=540, dtype=float32) (attn_dropout): Dropout(p=0.0, axis=None, mode=upscale_in_train) (proj): Linear(in_features=180, out_features=180, dtype=float32) (proj_dropout): Dropout(p=0.0, axis=None, mode=upscale_in_train) (softmax): Softmax(axis=-1) ) (drop_path): DropPath() (norm2): LayerNorm(normalized_shape=[180], epsilon=1e-05) (mlp): Mlp( (fc1): Linear(in_features=180, out_features=360, dtype=float32) (fc2): Linear(in_features=360, out_features=180, dtype=float32) (act): GELU(approximate=False) (dropout): Dropout(p=0.0, axis=None, mode=upscale_in_train) ) ) (2): SwinTransformerBlock(dim=180, input_resolution=(128, 128), num_heads=6, window_size=8, shift_size=0, mlp_ratio=2 (norm1): LayerNorm(normalized_shape=[180], epsilon=1e-05) (attn): WindowAttention(dim=180, window_size=(8, 8), num_heads=6 (qkv): Linear(in_features=180, out_features=540, dtype=float32) (attn_dropout): Dropout(p=0.0, axis=None, mode=upscale_in_train) (proj): Linear(in_features=180, out_features=180, dtype=float32) (proj_dropout): Dropout(p=0.0, axis=None, mode=upscale_in_train) (softmax): Softmax(axis=-1) ) (drop_path): DropPath() (norm2): LayerNorm(normalized_shape=[180], epsilon=1e-05) (mlp): Mlp( (fc1): Linear(in_features=180, out_features=360, dtype=float32) (fc2): Linear(in_features=360, out_features=180, dtype=float32) (act): GELU(approximate=False) (dropout): Dropout(p=0.0, axis=None, mode=upscale_in_train) ) ) (3): SwinTransformerBlock(dim=180, input_resolution=(128, 128), num_heads=6, window_size=8, shift_size=4, mlp_ratio=2 (norm1): LayerNorm(normalized_shape=[180], epsilon=1e-05) (attn): WindowAttention(dim=180, window_size=(8, 8), num_heads=6 (qkv): Linear(in_features=180, out_features=540, dtype=float32) (attn_dropout): Dropout(p=0.0, axis=None, mode=upscale_in_train) (proj): Linear(in_features=180, out_features=180, dtype=float32) (proj_dropout): Dropout(p=0.0, axis=None, mode=upscale_in_train) (softmax): Softmax(axis=-1) ) (drop_path): DropPath() (norm2): LayerNorm(normalized_shape=[180], epsilon=1e-05) (mlp): Mlp( (fc1): Linear(in_features=180, out_features=360, dtype=float32) (fc2): Linear(in_features=360, out_features=180, dtype=float32) (act): GELU(approximate=False) (dropout): Dropout(p=0.0, axis=None, mode=upscale_in_train) ) ) (4): SwinTransformerBlock(dim=180, input_resolution=(128, 128), num_heads=6, window_size=8, shift_size=0, mlp_ratio=2 (norm1): LayerNorm(normalized_shape=[180], epsilon=1e-05) (attn): WindowAttention(dim=180, window_size=(8, 8), num_heads=6 (qkv): Linear(in_features=180, out_features=540, dtype=float32) (attn_dropout): Dropout(p=0.0, axis=None, mode=upscale_in_train) (proj): Linear(in_features=180, out_features=180, dtype=float32) (proj_dropout): Dropout(p=0.0, axis=None, mode=upscale_in_train) (softmax): Softmax(axis=-1) ) (drop_path): DropPath() (norm2): LayerNorm(normalized_shape=[180], epsilon=1e-05) (mlp): Mlp( (fc1): Linear(in_features=180, out_features=360, dtype=float32) (fc2): Linear(in_features=360, out_features=180, dtype=float32) (act): GELU(approximate=False) (dropout): Dropout(p=0.0, axis=None, mode=upscale_in_train) ) ) (5): SwinTransformerBlock(dim=180, input_resolution=(128, 128), num_heads=6, window_size=8, shift_size=4, mlp_ratio=2 (norm1): LayerNorm(normalized_shape=[180], epsilon=1e-05) (attn): WindowAttention(dim=180, window_size=(8, 8), num_heads=6 (qkv): Linear(in_features=180, out_features=540, dtype=float32) (attn_dropout): Dropout(p=0.0, axis=None, mode=upscale_in_train) (proj): Linear(in_features=180, out_features=180, dtype=float32) (proj_dropout): Dropout(p=0.0, axis=None, mode=upscale_in_train) (softmax): Softmax(axis=-1) ) (drop_path): DropPath() (norm2): LayerNorm(normalized_shape=[180], epsilon=1e-05) (mlp): Mlp( (fc1): Linear(in_features=180, out_features=360, dtype=float32) (fc2): Linear(in_features=360, out_features=180, dtype=float32) (act): GELU(approximate=False) (dropout): Dropout(p=0.0, axis=None, mode=upscale_in_train) ) ) ) ) (conv): Conv2D(180, 180, kernel_size=[3, 3], padding=1, data_format=NCHW) (patch_embed): PatchEmbed() (patch_unembed): PatchUnEmbed() ) (5): RSTB( (residual_group): BasicLayer(dim=180, input_resolution=(128, 128), depth=6 (blocks): LayerList( (0): SwinTransformerBlock(dim=180, input_resolution=(128, 128), num_heads=6, window_size=8, shift_size=0, mlp_ratio=2 (norm1): LayerNorm(normalized_shape=[180], epsilon=1e-05) (attn): WindowAttention(dim=180, window_size=(8, 8), num_heads=6 (qkv): Linear(in_features=180, out_features=540, dtype=float32) (attn_dropout): Dropout(p=0.0, axis=None, mode=upscale_in_train) (proj): Linear(in_features=180, out_features=180, dtype=float32) (proj_dropout): Dropout(p=0.0, axis=None, mode=upscale_in_train) (softmax): Softmax(axis=-1) ) (drop_path): DropPath() (norm2): LayerNorm(normalized_shape=[180], epsilon=1e-05) (mlp): Mlp( (fc1): Linear(in_features=180, out_features=360, dtype=float32) (fc2): Linear(in_features=360, out_features=180, dtype=float32) (act): GELU(approximate=False) (dropout): Dropout(p=0.0, axis=None, mode=upscale_in_train) ) ) (1): SwinTransformerBlock(dim=180, input_resolution=(128, 128), num_heads=6, window_size=8, shift_size=4, mlp_ratio=2 (norm1): LayerNorm(normalized_shape=[180], epsilon=1e-05) (attn): WindowAttention(dim=180, window_size=(8, 8), num_heads=6 (qkv): Linear(in_features=180, out_features=540, dtype=float32) (attn_dropout): Dropout(p=0.0, axis=None, mode=upscale_in_train) (proj): Linear(in_features=180, out_features=180, dtype=float32) (proj_dropout): Dropout(p=0.0, axis=None, mode=upscale_in_train) (softmax): Softmax(axis=-1) ) (drop_path): DropPath() (norm2): LayerNorm(normalized_shape=[180], epsilon=1e-05) (mlp): Mlp( (fc1): Linear(in_features=180, out_features=360, dtype=float32) (fc2): Linear(in_features=360, out_features=180, dtype=float32) (act): GELU(approximate=False) (dropout): Dropout(p=0.0, axis=None, mode=upscale_in_train) ) ) (2): SwinTransformerBlock(dim=180, input_resolution=(128, 128), num_heads=6, window_size=8, shift_size=0, mlp_ratio=2 (norm1): LayerNorm(normalized_shape=[180], epsilon=1e-05) (attn): WindowAttention(dim=180, window_size=(8, 8), num_heads=6 (qkv): Linear(in_features=180, out_features=540, dtype=float32) (attn_dropout): Dropout(p=0.0, axis=None, mode=upscale_in_train) (proj): Linear(in_features=180, out_features=180, dtype=float32) (proj_dropout): Dropout(p=0.0, axis=None, mode=upscale_in_train) (softmax): Softmax(axis=-1) ) (drop_path): DropPath() (norm2): LayerNorm(normalized_shape=[180], epsilon=1e-05) (mlp): Mlp( (fc1): Linear(in_features=180, out_features=360, dtype=float32) (fc2): Linear(in_features=360, out_features=180, dtype=float32) (act): GELU(approximate=False) (dropout): Dropout(p=0.0, axis=None, mode=upscale_in_train) ) ) (3): SwinTransformerBlock(dim=180, input_resolution=(128, 128), num_heads=6, window_size=8, shift_size=4, mlp_ratio=2 (norm1): LayerNorm(normalized_shape=[180], epsilon=1e-05) (attn): WindowAttention(dim=180, window_size=(8, 8), num_heads=6 (qkv): Linear(in_features=180, out_features=540, dtype=float32) (attn_dropout): Dropout(p=0.0, axis=None, mode=upscale_in_train) (proj): Linear(in_features=180, out_features=180, dtype=float32) (proj_dropout): Dropout(p=0.0, axis=None, mode=upscale_in_train) (softmax): Softmax(axis=-1) ) (drop_path): DropPath() (norm2): LayerNorm(normalized_shape=[180], epsilon=1e-05) (mlp): Mlp( (fc1): Linear(in_features=180, out_features=360, dtype=float32) (fc2): Linear(in_features=360, out_features=180, dtype=float32) (act): GELU(approximate=False) (dropout): Dropout(p=0.0, axis=None, mode=upscale_in_train) ) ) (4): SwinTransformerBlock(dim=180, input_resolution=(128, 128), num_heads=6, window_size=8, shift_size=0, mlp_ratio=2 (norm1): LayerNorm(normalized_shape=[180], epsilon=1e-05) (attn): WindowAttention(dim=180, window_size=(8, 8), num_heads=6 (qkv): Linear(in_features=180, out_features=540, dtype=float32) (attn_dropout): Dropout(p=0.0, axis=None, mode=upscale_in_train) (proj): Linear(in_features=180, out_features=180, dtype=float32) (proj_dropout): Dropout(p=0.0, axis=None, mode=upscale_in_train) (softmax): Softmax(axis=-1) ) (drop_path): DropPath() (norm2): LayerNorm(normalized_shape=[180], epsilon=1e-05) (mlp): Mlp( (fc1): Linear(in_features=180, out_features=360, dtype=float32) (fc2): Linear(in_features=360, out_features=180, dtype=float32) (act): GELU(approximate=False) (dropout): Dropout(p=0.0, axis=None, mode=upscale_in_train) ) ) (5): SwinTransformerBlock(dim=180, input_resolution=(128, 128), num_heads=6, window_size=8, shift_size=4, mlp_ratio=2 (norm1): LayerNorm(normalized_shape=[180], epsilon=1e-05) (attn): WindowAttention(dim=180, window_size=(8, 8), num_heads=6 (qkv): Linear(in_features=180, out_features=540, dtype=float32) (attn_dropout): Dropout(p=0.0, axis=None, mode=upscale_in_train) (proj): Linear(in_features=180, out_features=180, dtype=float32) (proj_dropout): Dropout(p=0.0, axis=None, mode=upscale_in_train) (softmax): Softmax(axis=-1) ) (drop_path): DropPath() (norm2): LayerNorm(normalized_shape=[180], epsilon=1e-05) (mlp): Mlp( (fc1): Linear(in_features=180, out_features=360, dtype=float32) (fc2): Linear(in_features=360, out_features=180, dtype=float32) (act): GELU(approximate=False) (dropout): Dropout(p=0.0, axis=None, mode=upscale_in_train) ) ) ) ) (conv): Conv2D(180, 180, kernel_size=[3, 3], padding=1, data_format=NCHW) (patch_embed): PatchEmbed() (patch_unembed): PatchUnEmbed() ) ) (norm): LayerNorm(normalized_shape=[180], epsilon=1e-05) (conv_after_body): Conv2D(180, 180, kernel_size=[3, 3], padding=1, data_format=NCHW) (conv_last): Conv2D(180, 3, kernel_size=[3, 3], padding=1, data_format=NCHW) ) 22-10-17 16:47:15.819 : | mean | min | max | std || shape | -0.002 | -0.941 | 1.199 | 0.274 | (180, 3, 3, 3) || conv_first.weight | 0.000 | 0.000 | 0.000 | 0.000 | (180,) || conv_first.bias | 1.000 | 1.000 | 1.000 | 0.000 | (180,) || patch_embed.norm.weight | 0.000 | 0.000 | 0.000 | 0.000 | (180,) || patch_embed.norm.bias | 1.000 | 1.000 | 1.000 | 0.000 | (180,) || layers.0.residual_group.blocks.0.norm1.weight | 0.000 | 0.000 | 0.000 | 0.000 | (180,) || layers.0.residual_group.blocks.0.norm1.bias | -0.000 | -0.040 | 0.040 | 0.017 | (225, 6) || layers.0.residual_group.blocks.0.attn.relative_position_bias_table | 112.000 | 0.000 | 224.000 | 48.713 | (64, 64) || layers.0.residual_group.blocks.0.attn.relative_position_index | -0.000 | -0.040 | 0.040 | 0.018 | (180, 540) || layers.0.residual_group.blocks.0.attn.qkv.weight | 0.000 | 0.000 | 0.000 | 0.000 | (540,) || layers.0.residual_group.blocks.0.attn.qkv.bias | -0.000 | -0.040 | 0.040 | 0.018 | (180, 180) || layers.0.residual_group.blocks.0.attn.proj.weight | 0.000 | 0.000 | 0.000 | 0.000 | (180,) || layers.0.residual_group.blocks.0.attn.proj.bias | 1.000 | 1.000 | 1.000 | 0.000 | (180,) || layers.0.residual_group.blocks.0.norm2.weight | 0.000 | 0.000 | 0.000 | 0.000 | (180,) || layers.0.residual_group.blocks.0.norm2.bias | 0.000 | -0.105 | 0.105 | 0.061 | (180, 360) || layers.0.residual_group.blocks.0.mlp.fc1.weight | 0.000 | -0.000 | 0.000 | 0.000 | (360,) || layers.0.residual_group.blocks.0.mlp.fc1.bias | -0.000 | -0.105 | 0.105 | 0.061 | (360, 180) || layers.0.residual_group.blocks.0.mlp.fc2.weight | -0.000 | -0.000 | 0.000 | 0.000 | (180,) || layers.0.residual_group.blocks.0.mlp.fc2.bias | -6.152 | -100.000 | -0.000 | 24.029 | (256, 64, 64) || layers.0.residual_group.blocks.1.attn_mask | 1.000 | 1.000 | 1.000 | 0.000 | (180,) || layers.0.residual_group.blocks.1.norm1.weight | 0.000 | 0.000 | 0.000 | 0.000 | (180,) || layers.0.residual_group.blocks.1.norm1.bias | -0.001 | -0.040 | 0.040 | 0.018 | (225, 6) || layers.0.residual_group.blocks.1.attn.relative_position_bias_table | 112.000 | 0.000 | 224.000 | 48.713 | (64, 64) || layers.0.residual_group.blocks.1.attn.relative_position_index | -0.000 | -0.040 | 0.040 | 0.018 | (180, 540) || layers.0.residual_group.blocks.1.attn.qkv.weight | 0.000 | 0.000 | 0.000 | 0.000 | (540,) || layers.0.residual_group.blocks.1.attn.qkv.bias | 0.000 | -0.040 | 0.040 | 0.018 | (180, 180) || layers.0.residual_group.blocks.1.attn.proj.weight | 0.000 | 0.000 | 0.000 | 0.000 | (180,) || layers.0.residual_group.blocks.1.attn.proj.bias | 1.000 | 1.000 | 1.000 | 0.000 | (180,) || layers.0.residual_group.blocks.1.norm2.weight | 0.000 | 0.000 | 0.000 | 0.000 | (180,) || layers.0.residual_group.blocks.1.norm2.bias | -0.000 | -0.105 | 0.105 | 0.061 | (180, 360) || layers.0.residual_group.blocks.1.mlp.fc1.weight | 0.000 | -0.000 | 0.000 | 0.000 | (360,) || layers.0.residual_group.blocks.1.mlp.fc1.bias | -0.000 | -0.105 | 0.105 | 0.061 | (360, 180) || layers.0.residual_group.blocks.1.mlp.fc2.weight | 0.000 | -0.000 | 0.000 | 0.000 | (180,) || layers.0.residual_group.blocks.1.mlp.fc2.bias | 1.000 | 1.000 | 1.000 | 0.000 | (180,) || layers.0.residual_group.blocks.2.norm1.weight | 0.000 | 0.000 | 0.000 | 0.000 | (180,) || layers.0.residual_group.blocks.2.norm1.bias | 0.001 | -0.040 | 0.040 | 0.018 | (225, 6) || layers.0.residual_group.blocks.2.attn.relative_position_bias_table | 112.000 | 0.000 | 224.000 | 48.713 | (64, 64) || layers.0.residual_group.blocks.2.attn.relative_position_index | -0.000 | -0.040 | 0.040 | 0.018 | (180, 540) || layers.0.residual_group.blocks.2.attn.qkv.weight | 0.000 | 0.000 | 0.000 | 0.000 | (540,) || layers.0.residual_group.blocks.2.attn.qkv.bias | -0.000 | -0.040 | 0.040 | 0.018 | (180, 180) || layers.0.residual_group.blocks.2.attn.proj.weight | 0.000 | 0.000 | 0.000 | 0.000 | (180,) || layers.0.residual_group.blocks.2.attn.proj.bias | 1.000 | 1.000 | 1.000 | 0.000 | (180,) || layers.0.residual_group.blocks.2.norm2.weight | 0.000 | 0.000 | 0.000 | 0.000 | (180,) || layers.0.residual_group.blocks.2.norm2.bias | 0.001 | -0.105 | 0.105 | 0.061 | (180, 360) || layers.0.residual_group.blocks.2.mlp.fc1.weight | 0.000 | -0.000 | 0.000 | 0.000 | (360,) || layers.0.residual_group.blocks.2.mlp.fc1.bias | 0.000 | -0.105 | 0.105 | 0.061 | (360, 180) || layers.0.residual_group.blocks.2.mlp.fc2.weight | 0.000 | -0.000 | 0.000 | 0.000 | (180,) || layers.0.residual_group.blocks.2.mlp.fc2.bias | -6.152 | -100.000 | -0.000 | 24.029 | (256, 64, 64) || layers.0.residual_group.blocks.3.attn_mask | 1.000 | 1.000 | 1.000 | 0.000 | (180,) || layers.0.residual_group.blocks.3.norm1.weight | 0.000 | 0.000 | 0.000 | 0.000 | (180,) || layers.0.residual_group.blocks.3.norm1.bias | -0.000 | -0.040 | 0.040 | 0.018 | (225, 6) || layers.0.residual_group.blocks.3.attn.relative_position_bias_table | 112.000 | 0.000 | 224.000 | 48.713 | (64, 64) || layers.0.residual_group.blocks.3.attn.relative_position_index | -0.000 | -0.040 | 0.040 | 0.018 | (180, 540) || layers.0.residual_group.blocks.3.attn.qkv.weight | 0.000 | 0.000 | 0.000 | 0.000 | (540,) || layers.0.residual_group.blocks.3.attn.qkv.bias | 0.000 | -0.040 | 0.040 | 0.018 | (180, 180) || layers.0.residual_group.blocks.3.attn.proj.weight | 0.000 | 0.000 | 0.000 | 0.000 | (180,) || layers.0.residual_group.blocks.3.attn.proj.bias | 1.000 | 1.000 | 1.000 | 0.000 | (180,) || layers.0.residual_group.blocks.3.norm2.weight | 0.000 | 0.000 | 0.000 | 0.000 | (180,) || layers.0.residual_group.blocks.3.norm2.bias | -0.000 | -0.105 | 0.105 | 0.061 | (180, 360) || layers.0.residual_group.blocks.3.mlp.fc1.weight | 0.000 | -0.000 | 0.000 | 0.000 | (360,) || layers.0.residual_group.blocks.3.mlp.fc1.bias | 0.000 | -0.105 | 0.105 | 0.061 | (360, 180) || layers.0.residual_group.blocks.3.mlp.fc2.weight | 0.000 | -0.000 | 0.000 | 0.000 | (180,) || layers.0.residual_group.blocks.3.mlp.fc2.bias | 1.000 | 1.000 | 1.000 | 0.000 | (180,) || layers.0.residual_group.blocks.4.norm1.weight | 0.000 | 0.000 | 0.000 | 0.000 | (180,) || layers.0.residual_group.blocks.4.norm1.bias | 0.000 | -0.039 | 0.040 | 0.018 | (225, 6) || layers.0.residual_group.blocks.4.attn.relative_position_bias_table | 112.000 | 0.000 | 224.000 | 48.713 | (64, 64) || layers.0.residual_group.blocks.4.attn.relative_position_index | -0.000 | -0.040 | 0.040 | 0.018 | (180, 540) || layers.0.residual_group.blocks.4.attn.qkv.weight | 0.000 | 0.000 | 0.000 | 0.000 | (540,) || layers.0.residual_group.blocks.4.attn.qkv.bias | 0.000 | -0.040 | 0.040 | 0.018 | (180, 180) || layers.0.residual_group.blocks.4.attn.proj.weight | 0.000 | 0.000 | 0.000 | 0.000 | (180,) || layers.0.residual_group.blocks.4.attn.proj.bias | 1.000 | 1.000 | 1.000 | 0.000 | (180,) || layers.0.residual_group.blocks.4.norm2.weight | 0.000 | 0.000 | 0.000 | 0.000 | (180,) || layers.0.residual_group.blocks.4.norm2.bias | -0.000 | -0.105 | 0.105 | 0.061 | (180, 360) || layers.0.residual_group.blocks.4.mlp.fc1.weight | -0.000 | -0.000 | 0.000 | 0.000 | (360,) || layers.0.residual_group.blocks.4.mlp.fc1.bias | 0.000 | -0.105 | 0.105 | 0.061 | (360, 180) || layers.0.residual_group.blocks.4.mlp.fc2.weight | -0.000 | -0.000 | 0.000 | 0.000 | (180,) || layers.0.residual_group.blocks.4.mlp.fc2.bias | -6.152 | -100.000 | -0.000 | 24.029 | (256, 64, 64) || layers.0.residual_group.blocks.5.attn_mask | 1.000 | 1.000 | 1.000 | 0.000 | (180,) || layers.0.residual_group.blocks.5.norm1.weight | 0.000 | 0.000 | 0.000 | 0.000 | (180,) || layers.0.residual_group.blocks.5.norm1.bias | -0.000 | -0.040 | 0.039 | 0.018 | (225, 6) || layers.0.residual_group.blocks.5.attn.relative_position_bias_table | 112.000 | 0.000 | 224.000 | 48.713 | (64, 64) || layers.0.residual_group.blocks.5.attn.relative_position_index | 0.000 | -0.040 | 0.040 | 0.018 | (180, 540) || layers.0.residual_group.blocks.5.attn.qkv.weight | 0.000 | 0.000 | 0.000 | 0.000 | (540,) || layers.0.residual_group.blocks.5.attn.qkv.bias | 0.000 | -0.040 | 0.040 | 0.018 | (180, 180) || layers.0.residual_group.blocks.5.attn.proj.weight | 0.000 | 0.000 | 0.000 | 0.000 | (180,) || layers.0.residual_group.blocks.5.attn.proj.bias | 1.000 | 1.000 | 1.000 | 0.000 | (180,) || layers.0.residual_group.blocks.5.norm2.weight | 0.000 | 0.000 | 0.000 | 0.000 | (180,) || layers.0.residual_group.blocks.5.norm2.bias | 0.000 | -0.105 | 0.105 | 0.061 | (180, 360) || layers.0.residual_group.blocks.5.mlp.fc1.weight | -0.000 | -0.000 | 0.000 | 0.000 | (360,) || layers.0.residual_group.blocks.5.mlp.fc1.bias | 0.000 | -0.105 | 0.105 | 0.061 | (360, 180) || layers.0.residual_group.blocks.5.mlp.fc2.weight | -0.000 | -0.000 | 0.000 | 0.000 | (180,) || layers.0.residual_group.blocks.5.mlp.fc2.bias | -0.000 | -0.155 | 0.158 | 0.035 | (180, 180, 3, 3) || layers.0.conv.weight | 0.000 | 0.000 | 0.000 | 0.000 | (180,) || layers.0.conv.bias | 1.000 | 1.000 | 1.000 | 0.000 | (180,) || layers.1.residual_group.blocks.0.norm1.weight | 0.000 | 0.000 | 0.000 | 0.000 | (180,) || layers.1.residual_group.blocks.0.norm1.bias | -0.000 | -0.040 | 0.039 | 0.018 | (225, 6) || layers.1.residual_group.blocks.0.attn.relative_position_bias_table | 112.000 | 0.000 | 224.000 | 48.713 | (64, 64) || layers.1.residual_group.blocks.0.attn.relative_position_index | 0.000 | -0.040 | 0.040 | 0.018 | (180, 540) || layers.1.residual_group.blocks.0.attn.qkv.weight | 0.000 | 0.000 | 0.000 | 0.000 | (540,) || layers.1.residual_group.blocks.0.attn.qkv.bias | 0.000 | -0.040 | 0.040 | 0.018 | (180, 180) || layers.1.residual_group.blocks.0.attn.proj.weight | 0.000 | 0.000 | 0.000 | 0.000 | (180,) || layers.1.residual_group.blocks.0.attn.proj.bias | 1.000 | 1.000 | 1.000 | 0.000 | (180,) || layers.1.residual_group.blocks.0.norm2.weight | 0.000 | 0.000 | 0.000 | 0.000 | (180,) || layers.1.residual_group.blocks.0.norm2.bias | 0.000 | -0.105 | 0.105 | 0.061 | (180, 360) || layers.1.residual_group.blocks.0.mlp.fc1.weight | -0.000 | -0.000 | 0.000 | 0.000 | (360,) || layers.1.residual_group.blocks.0.mlp.fc1.bias | 0.000 | -0.105 | 0.105 | 0.061 | (360, 180) || layers.1.residual_group.blocks.0.mlp.fc2.weight | 0.000 | -0.000 | 0.000 | 0.000 | (180,) || layers.1.residual_group.blocks.0.mlp.fc2.bias | -6.152 | -100.000 | -0.000 | 24.029 | (256, 64, 64) || layers.1.residual_group.blocks.1.attn_mask | 1.000 | 1.000 | 1.000 | 0.000 | (180,) || layers.1.residual_group.blocks.1.norm1.weight | 0.000 | 0.000 | 0.000 | 0.000 | (180,) || layers.1.residual_group.blocks.1.norm1.bias | 0.000 | -0.040 | 0.040 | 0.018 | (225, 6) || layers.1.residual_group.blocks.1.attn.relative_position_bias_table | 112.000 | 0.000 | 224.000 | 48.713 | (64, 64) || layers.1.residual_group.blocks.1.attn.relative_position_index | 0.000 | -0.040 | 0.040 | 0.018 | (180, 540) || layers.1.residual_group.blocks.1.attn.qkv.weight | 0.000 | 0.000 | 0.000 | 0.000 | (540,) || layers.1.residual_group.blocks.1.attn.qkv.bias | 0.000 | -0.040 | 0.040 | 0.018 | (180, 180) || layers.1.residual_group.blocks.1.attn.proj.weight | 0.000 | 0.000 | 0.000 | 0.000 | (180,) || layers.1.residual_group.blocks.1.attn.proj.bias | 1.000 | 1.000 | 1.000 | 0.000 | (180,) || layers.1.residual_group.blocks.1.norm2.weight | 0.000 | 0.000 | 0.000 | 0.000 | (180,) || layers.1.residual_group.blocks.1.norm2.bias | 0.000 | -0.105 | 0.105 | 0.061 | (180, 360) || layers.1.residual_group.blocks.1.mlp.fc1.weight | -0.000 | -0.000 | 0.000 | 0.000 | (360,) || layers.1.residual_group.blocks.1.mlp.fc1.bias | -0.000 | -0.105 | 0.105 | 0.061 | (360, 180) || layers.1.residual_group.blocks.1.mlp.fc2.weight | 0.000 | -0.000 | 0.000 | 0.000 | (180,) || layers.1.residual_group.blocks.1.mlp.fc2.bias | 1.000 | 1.000 | 1.000 | 0.000 | (180,) || layers.1.residual_group.blocks.2.norm1.weight | 0.000 | 0.000 | 0.000 | 0.000 | (180,) || layers.1.residual_group.blocks.2.norm1.bias | -0.001 | -0.040 | 0.040 | 0.018 | (225, 6) || layers.1.residual_group.blocks.2.attn.relative_position_bias_table | 112.000 | 0.000 | 224.000 | 48.713 | (64, 64) || layers.1.residual_group.blocks.2.attn.relative_position_index | 0.000 | -0.040 | 0.040 | 0.018 | (180, 540) || layers.1.residual_group.blocks.2.attn.qkv.weight | 0.000 | 0.000 | 0.000 | 0.000 | (540,) || layers.1.residual_group.blocks.2.attn.qkv.bias | 0.000 | -0.040 | 0.040 | 0.018 | (180, 180) || layers.1.residual_group.blocks.2.attn.proj.weight | 0.000 | 0.000 | 0.000 | 0.000 | (180,) || layers.1.residual_group.blocks.2.attn.proj.bias | 1.000 | 1.000 | 1.000 | 0.000 | (180,) || layers.1.residual_group.blocks.2.norm2.weight | 0.000 | 0.000 | 0.000 | 0.000 | (180,) || layers.1.residual_group.blocks.2.norm2.bias | -0.000 | -0.105 | 0.105 | 0.061 | (180, 360) || layers.1.residual_group.blocks.2.mlp.fc1.weight | -0.000 | -0.000 | 0.000 | 0.000 | (360,) || layers.1.residual_group.blocks.2.mlp.fc1.bias | 0.000 | -0.105 | 0.105 | 0.061 | (360, 180) || layers.1.residual_group.blocks.2.mlp.fc2.weight | -0.000 | -0.000 | 0.000 | 0.000 | (180,) || layers.1.residual_group.blocks.2.mlp.fc2.bias | -6.152 | -100.000 | -0.000 | 24.029 | (256, 64, 64) || layers.1.residual_group.blocks.3.attn_mask | 1.000 | 1.000 | 1.000 | 0.000 | (180,) || layers.1.residual_group.blocks.3.norm1.weight | 0.000 | 0.000 | 0.000 | 0.000 | (180,) || layers.1.residual_group.blocks.3.norm1.bias | 0.001 | -0.040 | 0.040 | 0.017 | (225, 6) || layers.1.residual_group.blocks.3.attn.relative_position_bias_table | 112.000 | 0.000 | 224.000 | 48.713 | (64, 64) || layers.1.residual_group.blocks.3.attn.relative_position_index | 0.000 | -0.040 | 0.040 | 0.018 | (180, 540) || layers.1.residual_group.blocks.3.attn.qkv.weight | 0.000 | 0.000 | 0.000 | 0.000 | (540,) || layers.1.residual_group.blocks.3.attn.qkv.bias | -0.000 | -0.040 | 0.040 | 0.018 | (180, 180) || layers.1.residual_group.blocks.3.attn.proj.weight | 0.000 | 0.000 | 0.000 | 0.000 | (180,) || layers.1.residual_group.blocks.3.attn.proj.bias | 1.000 | 1.000 | 1.000 | 0.000 | (180,) || layers.1.residual_group.blocks.3.norm2.weight | 0.000 | 0.000 | 0.000 | 0.000 | (180,) || layers.1.residual_group.blocks.3.norm2.bias | -0.000 | -0.105 | 0.105 | 0.061 | (180, 360) || layers.1.residual_group.blocks.3.mlp.fc1.weight | -0.000 | -0.000 | 0.000 | 0.000 | (360,) || layers.1.residual_group.blocks.3.mlp.fc1.bias | 0.000 | -0.105 | 0.105 | 0.061 | (360, 180) || layers.1.residual_group.blocks.3.mlp.fc2.weight | 0.000 | -0.000 | 0.000 | 0.000 | (180,) || layers.1.residual_group.blocks.3.mlp.fc2.bias | 1.000 | 1.000 | 1.000 | 0.000 | (180,) || layers.1.residual_group.blocks.4.norm1.weight | 0.000 | 0.000 | 0.000 | 0.000 | (180,) || layers.1.residual_group.blocks.4.norm1.bias | 0.000 | -0.040 | 0.040 | 0.017 | (225, 6) || layers.1.residual_group.blocks.4.attn.relative_position_bias_table | 112.000 | 0.000 | 224.000 | 48.713 | (64, 64) || layers.1.residual_group.blocks.4.attn.relative_position_index | 0.000 | -0.040 | 0.040 | 0.018 | (180, 540) || layers.1.residual_group.blocks.4.attn.qkv.weight | 0.000 | 0.000 | 0.000 | 0.000 | (540,) || layers.1.residual_group.blocks.4.attn.qkv.bias | -0.000 | -0.040 | 0.040 | 0.018 | (180, 180) || layers.1.residual_group.blocks.4.attn.proj.weight | 0.000 | 0.000 | 0.000 | 0.000 | (180,) || layers.1.residual_group.blocks.4.attn.proj.bias | 1.000 | 1.000 | 1.000 | 0.000 | (180,) || layers.1.residual_group.blocks.4.norm2.weight | 0.000 | 0.000 | 0.000 | 0.000 | (180,) || layers.1.residual_group.blocks.4.norm2.bias | -0.000 | -0.105 | 0.105 | 0.061 | (180, 360) || layers.1.residual_group.blocks.4.mlp.fc1.weight | 0.000 | -0.000 | 0.000 | 0.000 | (360,) || layers.1.residual_group.blocks.4.mlp.fc1.bias | 0.000 | -0.105 | 0.105 | 0.061 | (360, 180) || layers.1.residual_group.blocks.4.mlp.fc2.weight | 0.000 | -0.000 | 0.000 | 0.000 | (180,) || layers.1.residual_group.blocks.4.mlp.fc2.bias | -6.152 | -100.000 | -0.000 | 24.029 | (256, 64, 64) || layers.1.residual_group.blocks.5.attn_mask | 1.000 | 1.000 | 1.000 | 0.000 | (180,) || layers.1.residual_group.blocks.5.norm1.weight | 0.000 | 0.000 | 0.000 | 0.000 | (180,) || layers.1.residual_group.blocks.5.norm1.bias | 0.000 | -0.039 | 0.040 | 0.018 | (225, 6) || layers.1.residual_group.blocks.5.attn.relative_position_bias_table | 112.000 | 0.000 | 224.000 | 48.713 | (64, 64) || layers.1.residual_group.blocks.5.attn.relative_position_index | 0.000 | -0.040 | 0.040 | 0.018 | (180, 540) || layers.1.residual_group.blocks.5.attn.qkv.weight | 0.000 | 0.000 | 0.000 | 0.000 | (540,) || layers.1.residual_group.blocks.5.attn.qkv.bias | -0.000 | -0.040 | 0.040 | 0.018 | (180, 180) || layers.1.residual_group.blocks.5.attn.proj.weight | 0.000 | 0.000 | 0.000 | 0.000 | (180,) || layers.1.residual_group.blocks.5.attn.proj.bias | 1.000 | 1.000 | 1.000 | 0.000 | (180,) || layers.1.residual_group.blocks.5.norm2.weight | 0.000 | 0.000 | 0.000 | 0.000 | (180,) || layers.1.residual_group.blocks.5.norm2.bias | 0.000 | -0.105 | 0.105 | 0.061 | (180, 360) || layers.1.residual_group.blocks.5.mlp.fc1.weight | -0.000 | -0.000 | 0.000 | 0.000 | (360,) || layers.1.residual_group.blocks.5.mlp.fc1.bias | 0.000 | -0.105 | 0.105 | 0.061 | (360, 180) || layers.1.residual_group.blocks.5.mlp.fc2.weight | -0.000 | -0.000 | 0.000 | 0.000 | (180,) || layers.1.residual_group.blocks.5.mlp.fc2.bias | 0.000 | -0.155 | 0.147 | 0.035 | (180, 180, 3, 3) || layers.1.conv.weight | 0.000 | 0.000 | 0.000 | 0.000 | (180,) || layers.1.conv.bias | 1.000 | 1.000 | 1.000 | 0.000 | (180,) || layers.2.residual_group.blocks.0.norm1.weight | 0.000 | 0.000 | 0.000 | 0.000 | (180,) || layers.2.residual_group.blocks.0.norm1.bias | -0.000 | -0.040 | 0.040 | 0.018 | (225, 6) || layers.2.residual_group.blocks.0.attn.relative_position_bias_table | 112.000 | 0.000 | 224.000 | 48.713 | (64, 64) || layers.2.residual_group.blocks.0.attn.relative_position_index | 0.000 | -0.040 | 0.040 | 0.018 | (180, 540) || layers.2.residual_group.blocks.0.attn.qkv.weight | 0.000 | 0.000 | 0.000 | 0.000 | (540,) || layers.2.residual_group.blocks.0.attn.qkv.bias | 0.000 | -0.040 | 0.040 | 0.018 | (180, 180) || layers.2.residual_group.blocks.0.attn.proj.weight | 0.000 | 0.000 | 0.000 | 0.000 | (180,) || layers.2.residual_group.blocks.0.attn.proj.bias | 1.000 | 1.000 | 1.000 | 0.000 | (180,) || layers.2.residual_group.blocks.0.norm2.weight | 0.000 | 0.000 | 0.000 | 0.000 | (180,) || layers.2.residual_group.blocks.0.norm2.bias | 0.000 | -0.105 | 0.105 | 0.061 | (180, 360) || layers.2.residual_group.blocks.0.mlp.fc1.weight | -0.000 | -0.000 | 0.000 | 0.000 | (360,) || layers.2.residual_group.blocks.0.mlp.fc1.bias | 0.001 | -0.105 | 0.105 | 0.061 | (360, 180) || layers.2.residual_group.blocks.0.mlp.fc2.weight | 0.000 | -0.000 | 0.000 | 0.000 | (180,) || layers.2.residual_group.blocks.0.mlp.fc2.bias | -6.152 | -100.000 | -0.000 | 24.029 | (256, 64, 64) || layers.2.residual_group.blocks.1.attn_mask | 1.000 | 1.000 | 1.000 | 0.000 | (180,) || layers.2.residual_group.blocks.1.norm1.weight | 0.000 | 0.000 | 0.000 | 0.000 | (180,) || layers.2.residual_group.blocks.1.norm1.bias | 0.000 | -0.039 | 0.039 | 0.017 | (225, 6) || layers.2.residual_group.blocks.1.attn.relative_position_bias_table | 112.000 | 0.000 | 224.000 | 48.713 | (64, 64) || layers.2.residual_group.blocks.1.attn.relative_position_index | 0.000 | -0.040 | 0.040 | 0.018 | (180, 540) || layers.2.residual_group.blocks.1.attn.qkv.weight | 0.000 | 0.000 | 0.000 | 0.000 | (540,) || layers.2.residual_group.blocks.1.attn.qkv.bias | -0.000 | -0.040 | 0.040 | 0.018 | (180, 180) || layers.2.residual_group.blocks.1.attn.proj.weight | 0.000 | 0.000 | 0.000 | 0.000 | (180,) || layers.2.residual_group.blocks.1.attn.proj.bias | 1.000 | 1.000 | 1.000 | 0.000 | (180,) || layers.2.residual_group.blocks.1.norm2.weight | 0.000 | 0.000 | 0.000 | 0.000 | (180,) || layers.2.residual_group.blocks.1.norm2.bias | -0.000 | -0.105 | 0.105 | 0.061 | (180, 360) || layers.2.residual_group.blocks.1.mlp.fc1.weight | -0.000 | -0.000 | 0.000 | 0.000 | (360,) || layers.2.residual_group.blocks.1.mlp.fc1.bias | 0.000 | -0.105 | 0.105 | 0.061 | (360, 180) || layers.2.residual_group.blocks.1.mlp.fc2.weight | 0.000 | -0.000 | 0.000 | 0.000 | (180,) || layers.2.residual_group.blocks.1.mlp.fc2.bias | 1.000 | 1.000 | 1.000 | 0.000 | (180,) || layers.2.residual_group.blocks.2.norm1.weight | 0.000 | 0.000 | 0.000 | 0.000 | (180,) || layers.2.residual_group.blocks.2.norm1.bias | -0.000 | -0.040 | 0.040 | 0.018 | (225, 6) || layers.2.residual_group.blocks.2.attn.relative_position_bias_table | 112.000 | 0.000 | 224.000 | 48.713 | (64, 64) || layers.2.residual_group.blocks.2.attn.relative_position_index | -0.000 | -0.040 | 0.040 | 0.018 | (180, 540) || layers.2.residual_group.blocks.2.attn.qkv.weight | 0.000 | 0.000 | 0.000 | 0.000 | (540,) || layers.2.residual_group.blocks.2.attn.qkv.bias | -0.000 | -0.040 | 0.040 | 0.018 | (180, 180) || layers.2.residual_group.blocks.2.attn.proj.weight | 0.000 | 0.000 | 0.000 | 0.000 | (180,) || layers.2.residual_group.blocks.2.attn.proj.bias | 1.000 | 1.000 | 1.000 | 0.000 | (180,) || layers.2.residual_group.blocks.2.norm2.weight | 0.000 | 0.000 | 0.000 | 0.000 | (180,) || layers.2.residual_group.blocks.2.norm2.bias | 0.000 | -0.105 | 0.105 | 0.061 | (180, 360) || layers.2.residual_group.blocks.2.mlp.fc1.weight | 0.000 | -0.000 | 0.000 | 0.000 | (360,) || layers.2.residual_group.blocks.2.mlp.fc1.bias | 0.000 | -0.105 | 0.105 | 0.061 | (360, 180) || layers.2.residual_group.blocks.2.mlp.fc2.weight | 0.000 | -0.000 | 0.000 | 0.000 | (180,) || layers.2.residual_group.blocks.2.mlp.fc2.bias | -6.152 | -100.000 | -0.000 | 24.029 | (256, 64, 64) || layers.2.residual_group.blocks.3.attn_mask | 1.000 | 1.000 | 1.000 | 0.000 | (180,) || layers.2.residual_group.blocks.3.norm1.weight | 0.000 | 0.000 | 0.000 | 0.000 | (180,) || layers.2.residual_group.blocks.3.norm1.bias | -0.000 | -0.039 | 0.039 | 0.018 | (225, 6) || layers.2.residual_group.blocks.3.attn.relative_position_bias_table | 112.000 | 0.000 | 224.000 | 48.713 | (64, 64) || layers.2.residual_group.blocks.3.attn.relative_position_index | 0.000 | -0.040 | 0.040 | 0.018 | (180, 540) || layers.2.residual_group.blocks.3.attn.qkv.weight | 0.000 | 0.000 | 0.000 | 0.000 | (540,) || layers.2.residual_group.blocks.3.attn.qkv.bias | -0.000 | -0.040 | 0.040 | 0.018 | (180, 180) || layers.2.residual_group.blocks.3.attn.proj.weight | 0.000 | 0.000 | 0.000 | 0.000 | (180,) || layers.2.residual_group.blocks.3.attn.proj.bias | 1.000 | 1.000 | 1.000 | 0.000 | (180,) || layers.2.residual_group.blocks.3.norm2.weight | 0.000 | 0.000 | 0.000 | 0.000 | (180,) || layers.2.residual_group.blocks.3.norm2.bias | -0.001 | -0.105 | 0.105 | 0.061 | (180, 360) || layers.2.residual_group.blocks.3.mlp.fc1.weight | 0.000 | -0.000 | 0.000 | 0.000 | (360,) || layers.2.residual_group.blocks.3.mlp.fc1.bias | -0.000 | -0.105 | 0.105 | 0.061 | (360, 180) || layers.2.residual_group.blocks.3.mlp.fc2.weight | 0.000 | -0.000 | 0.000 | 0.000 | (180,) || layers.2.residual_group.blocks.3.mlp.fc2.bias | 1.000 | 1.000 | 1.000 | 0.000 | (180,) || layers.2.residual_group.blocks.4.norm1.weight | 0.000 | 0.000 | 0.000 | 0.000 | (180,) || layers.2.residual_group.blocks.4.norm1.bias | -0.000 | -0.040 | 0.040 | 0.017 | (225, 6) || layers.2.residual_group.blocks.4.attn.relative_position_bias_table | 112.000 | 0.000 | 224.000 | 48.713 | (64, 64) || layers.2.residual_group.blocks.4.attn.relative_position_index | -0.000 | -0.040 | 0.040 | 0.018 | (180, 540) || layers.2.residual_group.blocks.4.attn.qkv.weight | 0.000 | 0.000 | 0.000 | 0.000 | (540,) || layers.2.residual_group.blocks.4.attn.qkv.bias | -0.000 | -0.040 | 0.040 | 0.018 | (180, 180) || layers.2.residual_group.blocks.4.attn.proj.weight | 0.000 | 0.000 | 0.000 | 0.000 | (180,) || layers.2.residual_group.blocks.4.attn.proj.bias | 1.000 | 1.000 | 1.000 | 0.000 | (180,) || layers.2.residual_group.blocks.4.norm2.weight | 0.000 | 0.000 | 0.000 | 0.000 | (180,) || layers.2.residual_group.blocks.4.norm2.bias | 0.000 | -0.105 | 0.105 | 0.061 | (180, 360) || layers.2.residual_group.blocks.4.mlp.fc1.weight | -0.000 | -0.000 | 0.000 | 0.000 | (360,) || layers.2.residual_group.blocks.4.mlp.fc1.bias | 0.000 | -0.105 | 0.105 | 0.061 | (360, 180) || layers.2.residual_group.blocks.4.mlp.fc2.weight | 0.000 | -0.000 | 0.000 | 0.000 | (180,) || layers.2.residual_group.blocks.4.mlp.fc2.bias | -6.152 | -100.000 | -0.000 | 24.029 | (256, 64, 64) || layers.2.residual_group.blocks.5.attn_mask | 1.000 | 1.000 | 1.000 | 0.000 | (180,) || layers.2.residual_group.blocks.5.norm1.weight | 0.000 | 0.000 | 0.000 | 0.000 | (180,) || layers.2.residual_group.blocks.5.norm1.bias | 0.000 | -0.040 | 0.040 | 0.018 | (225, 6) || layers.2.residual_group.blocks.5.attn.relative_position_bias_table | 112.000 | 0.000 | 224.000 | 48.713 | (64, 64) || layers.2.residual_group.blocks.5.attn.relative_position_index | 0.000 | -0.040 | 0.040 | 0.018 | (180, 540) || layers.2.residual_group.blocks.5.attn.qkv.weight | 0.000 | 0.000 | 0.000 | 0.000 | (540,) || layers.2.residual_group.blocks.5.attn.qkv.bias | -0.000 | -0.040 | 0.040 | 0.018 | (180, 180) || layers.2.residual_group.blocks.5.attn.proj.weight | 0.000 | 0.000 | 0.000 | 0.000 | (180,) || layers.2.residual_group.blocks.5.attn.proj.bias | 1.000 | 1.000 | 1.000 | 0.000 | (180,) || layers.2.residual_group.blocks.5.norm2.weight | 0.000 | 0.000 | 0.000 | 0.000 | (180,) || layers.2.residual_group.blocks.5.norm2.bias | 0.000 | -0.105 | 0.105 | 0.061 | (180, 360) || layers.2.residual_group.blocks.5.mlp.fc1.weight | -0.000 | -0.000 | 0.000 | 0.000 | (360,) || layers.2.residual_group.blocks.5.mlp.fc1.bias | -0.000 | -0.105 | 0.105 | 0.061 | (360, 180) || layers.2.residual_group.blocks.5.mlp.fc2.weight | -0.000 | -0.000 | 0.000 | 0.000 | (180,) || layers.2.residual_group.blocks.5.mlp.fc2.bias | 0.000 | -0.159 | 0.157 | 0.035 | (180, 180, 3, 3) || layers.2.conv.weight | 0.000 | 0.000 | 0.000 | 0.000 | (180,) || layers.2.conv.bias | 1.000 | 1.000 | 1.000 | 0.000 | (180,) || layers.3.residual_group.blocks.0.norm1.weight | 0.000 | 0.000 | 0.000 | 0.000 | (180,) || layers.3.residual_group.blocks.0.norm1.bias | -0.000 | -0.040 | 0.040 | 0.018 | (225, 6) || layers.3.residual_group.blocks.0.attn.relative_position_bias_table | 112.000 | 0.000 | 224.000 | 48.713 | (64, 64) || layers.3.residual_group.blocks.0.attn.relative_position_index | -0.000 | -0.040 | 0.040 | 0.018 | (180, 540) || layers.3.residual_group.blocks.0.attn.qkv.weight | 0.000 | 0.000 | 0.000 | 0.000 | (540,) || layers.3.residual_group.blocks.0.attn.qkv.bias | 0.000 | -0.040 | 0.040 | 0.018 | (180, 180) || layers.3.residual_group.blocks.0.attn.proj.weight | 0.000 | 0.000 | 0.000 | 0.000 | (180,) || layers.3.residual_group.blocks.0.attn.proj.bias | 1.000 | 1.000 | 1.000 | 0.000 | (180,) || layers.3.residual_group.blocks.0.norm2.weight | 0.000 | 0.000 | 0.000 | 0.000 | (180,) || layers.3.residual_group.blocks.0.norm2.bias | -0.000 | -0.105 | 0.105 | 0.061 | (180, 360) || layers.3.residual_group.blocks.0.mlp.fc1.weight | -0.000 | -0.000 | 0.000 | 0.000 | (360,) || layers.3.residual_group.blocks.0.mlp.fc1.bias | 0.000 | -0.105 | 0.105 | 0.061 | (360, 180) || layers.3.residual_group.blocks.0.mlp.fc2.weight | 0.000 | -0.000 | 0.000 | 0.000 | (180,) || layers.3.residual_group.blocks.0.mlp.fc2.bias | -6.152 | -100.000 | -0.000 | 24.029 | (256, 64, 64) || layers.3.residual_group.blocks.1.attn_mask | 1.000 | 1.000 | 1.000 | 0.000 | (180,) || layers.3.residual_group.blocks.1.norm1.weight | 0.000 | 0.000 | 0.000 | 0.000 | (180,) || layers.3.residual_group.blocks.1.norm1.bias | -0.001 | -0.039 | 0.040 | 0.018 | (225, 6) || layers.3.residual_group.blocks.1.attn.relative_position_bias_table | 112.000 | 0.000 | 224.000 | 48.713 | (64, 64) || layers.3.residual_group.blocks.1.attn.relative_position_index | 0.000 | -0.040 | 0.040 | 0.018 | (180, 540) || layers.3.residual_group.blocks.1.attn.qkv.weight | 0.000 | 0.000 | 0.000 | 0.000 | (540,) || layers.3.residual_group.blocks.1.attn.qkv.bias | -0.000 | -0.040 | 0.040 | 0.018 | (180, 180) || layers.3.residual_group.blocks.1.attn.proj.weight | 0.000 | 0.000 | 0.000 | 0.000 | (180,) || layers.3.residual_group.blocks.1.attn.proj.bias | 1.000 | 1.000 | 1.000 | 0.000 | (180,) || layers.3.residual_group.blocks.1.norm2.weight | 0.000 | 0.000 | 0.000 | 0.000 | (180,) || layers.3.residual_group.blocks.1.norm2.bias | -0.000 | -0.105 | 0.105 | 0.061 | (180, 360) || layers.3.residual_group.blocks.1.mlp.fc1.weight | 0.000 | -0.000 | 0.000 | 0.000 | (360,) || layers.3.residual_group.blocks.1.mlp.fc1.bias | 0.001 | -0.105 | 0.105 | 0.061 | (360, 180) || layers.3.residual_group.blocks.1.mlp.fc2.weight | 0.000 | -0.000 | 0.000 | 0.000 | (180,) || layers.3.residual_group.blocks.1.mlp.fc2.bias | 1.000 | 1.000 | 1.000 | 0.000 | (180,) || layers.3.residual_group.blocks.2.norm1.weight | 0.000 | 0.000 | 0.000 | 0.000 | (180,) || layers.3.residual_group.blocks.2.norm1.bias | -0.001 | -0.040 | 0.040 | 0.017 | (225, 6) || layers.3.residual_group.blocks.2.attn.relative_position_bias_table | 112.000 | 0.000 | 224.000 | 48.713 | (64, 64) || layers.3.residual_group.blocks.2.attn.relative_position_index | -0.000 | -0.040 | 0.040 | 0.018 | (180, 540) || layers.3.residual_group.blocks.2.attn.qkv.weight | 0.000 | 0.000 | 0.000 | 0.000 | (540,) || layers.3.residual_group.blocks.2.attn.qkv.bias | 0.000 | -0.040 | 0.040 | 0.018 | (180, 180) || layers.3.residual_group.blocks.2.attn.proj.weight | 0.000 | 0.000 | 0.000 | 0.000 | (180,) || layers.3.residual_group.blocks.2.attn.proj.bias | 1.000 | 1.000 | 1.000 | 0.000 | (180,) || layers.3.residual_group.blocks.2.norm2.weight | 0.000 | 0.000 | 0.000 | 0.000 | (180,) || layers.3.residual_group.blocks.2.norm2.bias | 0.000 | -0.105 | 0.105 | 0.061 | (180, 360) || layers.3.residual_group.blocks.2.mlp.fc1.weight | 0.000 | -0.000 | 0.000 | 0.000 | (360,) || layers.3.residual_group.blocks.2.mlp.fc1.bias | 0.000 | -0.105 | 0.105 | 0.061 | (360, 180) || layers.3.residual_group.blocks.2.mlp.fc2.weight | 0.000 | -0.000 | 0.000 | 0.000 | (180,) || layers.3.residual_group.blocks.2.mlp.fc2.bias | -6.152 | -100.000 | -0.000 | 24.029 | (256, 64, 64) || layers.3.residual_group.blocks.3.attn_mask | 1.000 | 1.000 | 1.000 | 0.000 | (180,) || layers.3.residual_group.blocks.3.norm1.weight | 0.000 | 0.000 | 0.000 | 0.000 | (180,) || layers.3.residual_group.blocks.3.norm1.bias | 0.000 | -0.040 | 0.039 | 0.018 | (225, 6) || layers.3.residual_group.blocks.3.attn.relative_position_bias_table | 112.000 | 0.000 | 224.000 | 48.713 | (64, 64) || layers.3.residual_group.blocks.3.attn.relative_position_index | 0.000 | -0.040 | 0.040 | 0.018 | (180, 540) || layers.3.residual_group.blocks.3.attn.qkv.weight | 0.000 | 0.000 | 0.000 | 0.000 | (540,) || layers.3.residual_group.blocks.3.attn.qkv.bias | 0.000 | -0.040 | 0.040 | 0.018 | (180, 180) || layers.3.residual_group.blocks.3.attn.proj.weight | 0.000 | 0.000 | 0.000 | 0.000 | (180,) || layers.3.residual_group.blocks.3.attn.proj.bias | 1.000 | 1.000 | 1.000 | 0.000 | (180,) || layers.3.residual_group.blocks.3.norm2.weight | 0.000 | 0.000 | 0.000 | 0.000 | (180,) || layers.3.residual_group.blocks.3.norm2.bias | -0.000 | -0.105 | 0.105 | 0.061 | (180, 360) || layers.3.residual_group.blocks.3.mlp.fc1.weight | -0.000 | -0.000 | 0.000 | 0.000 | (360,) || layers.3.residual_group.blocks.3.mlp.fc1.bias | 0.000 | -0.105 | 0.105 | 0.061 | (360, 180) || layers.3.residual_group.blocks.3.mlp.fc2.weight | 0.000 | -0.000 | 0.000 | 0.000 | (180,) || layers.3.residual_group.blocks.3.mlp.fc2.bias | 1.000 | 1.000 | 1.000 | 0.000 | (180,) || layers.3.residual_group.blocks.4.norm1.weight | 0.000 | 0.000 | 0.000 | 0.000 | (180,) || layers.3.residual_group.blocks.4.norm1.bias | 0.000 | -0.040 | 0.040 | 0.018 | (225, 6) || layers.3.residual_group.blocks.4.attn.relative_position_bias_table | 112.000 | 0.000 | 224.000 | 48.713 | (64, 64) || layers.3.residual_group.blocks.4.attn.relative_position_index | 0.000 | -0.040 | 0.040 | 0.018 | (180, 540) || layers.3.residual_group.blocks.4.attn.qkv.weight | 0.000 | 0.000 | 0.000 | 0.000 | (540,) || layers.3.residual_group.blocks.4.attn.qkv.bias | -0.000 | -0.040 | 0.040 | 0.018 | (180, 180) || layers.3.residual_group.blocks.4.attn.proj.weight | 0.000 | 0.000 | 0.000 | 0.000 | (180,) || layers.3.residual_group.blocks.4.attn.proj.bias | 1.000 | 1.000 | 1.000 | 0.000 | (180,) || layers.3.residual_group.blocks.4.norm2.weight | 0.000 | 0.000 | 0.000 | 0.000 | (180,) || layers.3.residual_group.blocks.4.norm2.bias | -0.000 | -0.105 | 0.105 | 0.061 | (180, 360) || layers.3.residual_group.blocks.4.mlp.fc1.weight | 0.000 | -0.000 | 0.000 | 0.000 | (360,) || layers.3.residual_group.blocks.4.mlp.fc1.bias | 0.000 | -0.105 | 0.105 | 0.061 | (360, 180) || layers.3.residual_group.blocks.4.mlp.fc2.weight | -0.000 | -0.000 | 0.000 | 0.000 | (180,) || layers.3.residual_group.blocks.4.mlp.fc2.bias | -6.152 | -100.000 | -0.000 | 24.029 | (256, 64, 64) || layers.3.residual_group.blocks.5.attn_mask | 1.000 | 1.000 | 1.000 | 0.000 | (180,) || layers.3.residual_group.blocks.5.norm1.weight | 0.000 | 0.000 | 0.000 | 0.000 | (180,) || layers.3.residual_group.blocks.5.norm1.bias | 0.000 | -0.040 | 0.040 | 0.018 | (225, 6) || layers.3.residual_group.blocks.5.attn.relative_position_bias_table | 112.000 | 0.000 | 224.000 | 48.713 | (64, 64) || layers.3.residual_group.blocks.5.attn.relative_position_index | -0.000 | -0.040 | 0.040 | 0.018 | (180, 540) || layers.3.residual_group.blocks.5.attn.qkv.weight | 0.000 | 0.000 | 0.000 | 0.000 | (540,) || layers.3.residual_group.blocks.5.attn.qkv.bias | -0.000 | -0.040 | 0.040 | 0.018 | (180, 180) || layers.3.residual_group.blocks.5.attn.proj.weight | 0.000 | 0.000 | 0.000 | 0.000 | (180,) || layers.3.residual_group.blocks.5.attn.proj.bias | 1.000 | 1.000 | 1.000 | 0.000 | (180,) || layers.3.residual_group.blocks.5.norm2.weight | 0.000 | 0.000 | 0.000 | 0.000 | (180,) || layers.3.residual_group.blocks.5.norm2.bias | 0.000 | -0.105 | 0.105 | 0.061 | (180, 360) || layers.3.residual_group.blocks.5.mlp.fc1.weight | -0.000 | -0.000 | 0.000 | 0.000 | (360,) || layers.3.residual_group.blocks.5.mlp.fc1.bias | -0.000 | -0.105 | 0.105 | 0.061 | (360, 180) || layers.3.residual_group.blocks.5.mlp.fc2.weight | -0.000 | -0.000 | 0.000 | 0.000 | (180,) || layers.3.residual_group.blocks.5.mlp.fc2.bias | -0.000 | -0.166 | 0.169 | 0.035 | (180, 180, 3, 3) || layers.3.conv.weight | 0.000 | 0.000 | 0.000 | 0.000 | (180,) || layers.3.conv.bias | 1.000 | 1.000 | 1.000 | 0.000 | (180,) || layers.4.residual_group.blocks.0.norm1.weight | 0.000 | 0.000 | 0.000 | 0.000 | (180,) || layers.4.residual_group.blocks.0.norm1.bias | 0.001 | -0.040 | 0.040 | 0.017 | (225, 6) || layers.4.residual_group.blocks.0.attn.relative_position_bias_table | 112.000 | 0.000 | 224.000 | 48.713 | (64, 64) || layers.4.residual_group.blocks.0.attn.relative_position_index | -0.000 | -0.040 | 0.040 | 0.018 | (180, 540) || layers.4.residual_group.blocks.0.attn.qkv.weight | 0.000 | 0.000 | 0.000 | 0.000 | (540,) || layers.4.residual_group.blocks.0.attn.qkv.bias | -0.000 | -0.040 | 0.040 | 0.018 | (180, 180) || layers.4.residual_group.blocks.0.attn.proj.weight | 0.000 | 0.000 | 0.000 | 0.000 | (180,) || layers.4.residual_group.blocks.0.attn.proj.bias | 1.000 | 1.000 | 1.000 | 0.000 | (180,) || layers.4.residual_group.blocks.0.norm2.weight | 0.000 | 0.000 | 0.000 | 0.000 | (180,) || layers.4.residual_group.blocks.0.norm2.bias | -0.000 | -0.105 | 0.105 | 0.061 | (180, 360) || layers.4.residual_group.blocks.0.mlp.fc1.weight | -0.000 | -0.000 | 0.000 | 0.000 | (360,) || layers.4.residual_group.blocks.0.mlp.fc1.bias | -0.000 | -0.105 | 0.105 | 0.061 | (360, 180) || layers.4.residual_group.blocks.0.mlp.fc2.weight | -0.000 | -0.000 | 0.000 | 0.000 | (180,) || layers.4.residual_group.blocks.0.mlp.fc2.bias | -6.152 | -100.000 | -0.000 | 24.029 | (256, 64, 64) || layers.4.residual_group.blocks.1.attn_mask | 1.000 | 1.000 | 1.000 | 0.000 | (180,) || layers.4.residual_group.blocks.1.norm1.weight | 0.000 | 0.000 | 0.000 | 0.000 | (180,) || layers.4.residual_group.blocks.1.norm1.bias | -0.001 | -0.040 | 0.040 | 0.018 | (225, 6) || layers.4.residual_group.blocks.1.attn.relative_position_bias_table | 112.000 | 0.000 | 224.000 | 48.713 | (64, 64) || layers.4.residual_group.blocks.1.attn.relative_position_index | 0.000 | -0.040 | 0.040 | 0.018 | (180, 540) || layers.4.residual_group.blocks.1.attn.qkv.weight | 0.000 | 0.000 | 0.000 | 0.000 | (540,) || layers.4.residual_group.blocks.1.attn.qkv.bias | -0.000 | -0.040 | 0.040 | 0.018 | (180, 180) || layers.4.residual_group.blocks.1.attn.proj.weight | 0.000 | 0.000 | 0.000 | 0.000 | (180,) || layers.4.residual_group.blocks.1.attn.proj.bias | 1.000 | 1.000 | 1.000 | 0.000 | (180,) || layers.4.residual_group.blocks.1.norm2.weight | 0.000 | 0.000 | 0.000 | 0.000 | (180,) || layers.4.residual_group.blocks.1.norm2.bias | 0.000 | -0.105 | 0.105 | 0.061 | (180, 360) || layers.4.residual_group.blocks.1.mlp.fc1.weight | -0.000 | -0.000 | 0.000 | 0.000 | (360,) || layers.4.residual_group.blocks.1.mlp.fc1.bias | 0.000 | -0.105 | 0.105 | 0.061 | (360, 180) || layers.4.residual_group.blocks.1.mlp.fc2.weight | -0.000 | -0.000 | 0.000 | 0.000 | (180,) || layers.4.residual_group.blocks.1.mlp.fc2.bias | 1.000 | 1.000 | 1.000 | 0.000 | (180,) || layers.4.residual_group.blocks.2.norm1.weight | 0.000 | 0.000 | 0.000 | 0.000 | (180,) || layers.4.residual_group.blocks.2.norm1.bias | -0.000 | -0.040 | 0.039 | 0.018 | (225, 6) || layers.4.residual_group.blocks.2.attn.relative_position_bias_table | 112.000 | 0.000 | 224.000 | 48.713 | (64, 64) || layers.4.residual_group.blocks.2.attn.relative_position_index | 0.000 | -0.040 | 0.040 | 0.018 | (180, 540) || layers.4.residual_group.blocks.2.attn.qkv.weight | 0.000 | 0.000 | 0.000 | 0.000 | (540,) || layers.4.residual_group.blocks.2.attn.qkv.bias | -0.000 | -0.040 | 0.040 | 0.018 | (180, 180) || layers.4.residual_group.blocks.2.attn.proj.weight | 0.000 | 0.000 | 0.000 | 0.000 | (180,) || layers.4.residual_group.blocks.2.attn.proj.bias | 1.000 | 1.000 | 1.000 | 0.000 | (180,) || layers.4.residual_group.blocks.2.norm2.weight | 0.000 | 0.000 | 0.000 | 0.000 | (180,) || layers.4.residual_group.blocks.2.norm2.bias | 0.000 | -0.105 | 0.105 | 0.061 | (180, 360) || layers.4.residual_group.blocks.2.mlp.fc1.weight | -0.000 | -0.000 | 0.000 | 0.000 | (360,) || layers.4.residual_group.blocks.2.mlp.fc1.bias | -0.000 | -0.105 | 0.105 | 0.061 | (360, 180) || layers.4.residual_group.blocks.2.mlp.fc2.weight | 0.000 | -0.000 | 0.000 | 0.000 | (180,) || layers.4.residual_group.blocks.2.mlp.fc2.bias | -6.152 | -100.000 | -0.000 | 24.029 | (256, 64, 64) || layers.4.residual_group.blocks.3.attn_mask | 1.000 | 1.000 | 1.000 | 0.000 | (180,) || layers.4.residual_group.blocks.3.norm1.weight | 0.000 | 0.000 | 0.000 | 0.000 | (180,) || layers.4.residual_group.blocks.3.norm1.bias | -0.001 | -0.040 | 0.040 | 0.018 | (225, 6) || layers.4.residual_group.blocks.3.attn.relative_position_bias_table | 112.000 | 0.000 | 224.000 | 48.713 | (64, 64) || layers.4.residual_group.blocks.3.attn.relative_position_index | -0.000 | -0.040 | 0.040 | 0.018 | (180, 540) || layers.4.residual_group.blocks.3.attn.qkv.weight | 0.000 | 0.000 | 0.000 | 0.000 | (540,) || layers.4.residual_group.blocks.3.attn.qkv.bias | 0.000 | -0.040 | 0.040 | 0.018 | (180, 180) || layers.4.residual_group.blocks.3.attn.proj.weight | 0.000 | 0.000 | 0.000 | 0.000 | (180,) || layers.4.residual_group.blocks.3.attn.proj.bias | 1.000 | 1.000 | 1.000 | 0.000 | (180,) || layers.4.residual_group.blocks.3.norm2.weight | 0.000 | 0.000 | 0.000 | 0.000 | (180,) || layers.4.residual_group.blocks.3.norm2.bias | -0.000 | -0.105 | 0.105 | 0.061 | (180, 360) || layers.4.residual_group.blocks.3.mlp.fc1.weight | -0.000 | -0.000 | 0.000 | 0.000 | (360,) || layers.4.residual_group.blocks.3.mlp.fc1.bias | -0.000 | -0.105 | 0.105 | 0.061 | (360, 180) || layers.4.residual_group.blocks.3.mlp.fc2.weight | -0.000 | -0.000 | 0.000 | 0.000 | (180,) || layers.4.residual_group.blocks.3.mlp.fc2.bias | 1.000 | 1.000 | 1.000 | 0.000 | (180,) || layers.4.residual_group.blocks.4.norm1.weight | 0.000 | 0.000 | 0.000 | 0.000 | (180,) || layers.4.residual_group.blocks.4.norm1.bias | 0.000 | -0.040 | 0.040 | 0.018 | (225, 6) || layers.4.residual_group.blocks.4.attn.relative_position_bias_table | 112.000 | 0.000 | 224.000 | 48.713 | (64, 64) || layers.4.residual_group.blocks.4.attn.relative_position_index | -0.000 | -0.040 | 0.040 | 0.018 | (180, 540) || layers.4.residual_group.blocks.4.attn.qkv.weight | 0.000 | 0.000 | 0.000 | 0.000 | (540,) || layers.4.residual_group.blocks.4.attn.qkv.bias | 0.000 | -0.040 | 0.040 | 0.018 | (180, 180) || layers.4.residual_group.blocks.4.attn.proj.weight | 0.000 | 0.000 | 0.000 | 0.000 | (180,) || layers.4.residual_group.blocks.4.attn.proj.bias | 1.000 | 1.000 | 1.000 | 0.000 | (180,) || layers.4.residual_group.blocks.4.norm2.weight | 0.000 | 0.000 | 0.000 | 0.000 | (180,) || layers.4.residual_group.blocks.4.norm2.bias | 0.000 | -0.105 | 0.105 | 0.061 | (180, 360) || layers.4.residual_group.blocks.4.mlp.fc1.weight | 0.000 | -0.000 | 0.000 | 0.000 | (360,) || layers.4.residual_group.blocks.4.mlp.fc1.bias | -0.000 | -0.105 | 0.105 | 0.061 | (360, 180) || layers.4.residual_group.blocks.4.mlp.fc2.weight | 0.000 | -0.000 | 0.000 | 0.000 | (180,) || layers.4.residual_group.blocks.4.mlp.fc2.bias | -6.152 | -100.000 | -0.000 | 24.029 | (256, 64, 64) || layers.4.residual_group.blocks.5.attn_mask | 1.000 | 1.000 | 1.000 | 0.000 | (180,) || layers.4.residual_group.blocks.5.norm1.weight | 0.000 | 0.000 | 0.000 | 0.000 | (180,) || layers.4.residual_group.blocks.5.norm1.bias | 0.000 | -0.040 | 0.039 | 0.017 | (225, 6) || layers.4.residual_group.blocks.5.attn.relative_position_bias_table | 112.000 | 0.000 | 224.000 | 48.713 | (64, 64) || layers.4.residual_group.blocks.5.attn.relative_position_index | -0.000 | -0.040 | 0.040 | 0.018 | (180, 540) || layers.4.residual_group.blocks.5.attn.qkv.weight | 0.000 | 0.000 | 0.000 | 0.000 | (540,) || layers.4.residual_group.blocks.5.attn.qkv.bias | -0.000 | -0.040 | 0.040 | 0.018 | (180, 180) || layers.4.residual_group.blocks.5.attn.proj.weight | 0.000 | 0.000 | 0.000 | 0.000 | (180,) || layers.4.residual_group.blocks.5.attn.proj.bias | 1.000 | 1.000 | 1.000 | 0.000 | (180,) || layers.4.residual_group.blocks.5.norm2.weight | 0.000 | 0.000 | 0.000 | 0.000 | (180,) || layers.4.residual_group.blocks.5.norm2.bias | 0.000 | -0.105 | 0.105 | 0.061 | (180, 360) || layers.4.residual_group.blocks.5.mlp.fc1.weight | 0.000 | -0.000 | 0.000 | 0.000 | (360,) || layers.4.residual_group.blocks.5.mlp.fc1.bias | -0.000 | -0.105 | 0.105 | 0.061 | (360, 180) || layers.4.residual_group.blocks.5.mlp.fc2.weight | -0.000 | -0.000 | 0.000 | 0.000 | (180,) || layers.4.residual_group.blocks.5.mlp.fc2.bias | -0.000 | -0.148 | 0.169 | 0.035 | (180, 180, 3, 3) || layers.4.conv.weight | 0.000 | 0.000 | 0.000 | 0.000 | (180,) || layers.4.conv.bias | 1.000 | 1.000 | 1.000 | 0.000 | (180,) || layers.5.residual_group.blocks.0.norm1.weight | 0.000 | 0.000 | 0.000 | 0.000 | (180,) || layers.5.residual_group.blocks.0.norm1.bias | 0.001 | -0.040 | 0.040 | 0.018 | (225, 6) || layers.5.residual_group.blocks.0.attn.relative_position_bias_table | 112.000 | 0.000 | 224.000 | 48.713 | (64, 64) || layers.5.residual_group.blocks.0.attn.relative_position_index | -0.000 | -0.040 | 0.040 | 0.018 | (180, 540) || layers.5.residual_group.blocks.0.attn.qkv.weight | 0.000 | 0.000 | 0.000 | 0.000 | (540,) || layers.5.residual_group.blocks.0.attn.qkv.bias | 0.000 | -0.040 | 0.040 | 0.018 | (180, 180) || layers.5.residual_group.blocks.0.attn.proj.weight | 0.000 | 0.000 | 0.000 | 0.000 | (180,) || layers.5.residual_group.blocks.0.attn.proj.bias | 1.000 | 1.000 | 1.000 | 0.000 | (180,) || layers.5.residual_group.blocks.0.norm2.weight | 0.000 | 0.000 | 0.000 | 0.000 | (180,) || layers.5.residual_group.blocks.0.norm2.bias | 0.000 | -0.105 | 0.105 | 0.061 | (180, 360) || layers.5.residual_group.blocks.0.mlp.fc1.weight | 0.000 | -0.000 | 0.000 | 0.000 | (360,) || layers.5.residual_group.blocks.0.mlp.fc1.bias | 0.000 | -0.105 | 0.105 | 0.061 | (360, 180) || layers.5.residual_group.blocks.0.mlp.fc2.weight | -0.000 | -0.000 | 0.000 | 0.000 | (180,) || layers.5.residual_group.blocks.0.mlp.fc2.bias | -6.152 | -100.000 | -0.000 | 24.029 | (256, 64, 64) || layers.5.residual_group.blocks.1.attn_mask | 1.000 | 1.000 | 1.000 | 0.000 | (180,) || layers.5.residual_group.blocks.1.norm1.weight | 0.000 | 0.000 | 0.000 | 0.000 | (180,) || layers.5.residual_group.blocks.1.norm1.bias | 0.001 | -0.039 | 0.040 | 0.018 | (225, 6) || layers.5.residual_group.blocks.1.attn.relative_position_bias_table | 112.000 | 0.000 | 224.000 | 48.713 | (64, 64) || layers.5.residual_group.blocks.1.attn.relative_position_index | -0.000 | -0.040 | 0.040 | 0.018 | (180, 540) || layers.5.residual_group.blocks.1.attn.qkv.weight | 0.000 | 0.000 | 0.000 | 0.000 | (540,) || layers.5.residual_group.blocks.1.attn.qkv.bias | 0.000 | -0.040 | 0.040 | 0.018 | (180, 180) || layers.5.residual_group.blocks.1.attn.proj.weight | 0.000 | 0.000 | 0.000 | 0.000 | (180,) || layers.5.residual_group.blocks.1.attn.proj.bias | 1.000 | 1.000 | 1.000 | 0.000 | (180,) || layers.5.residual_group.blocks.1.norm2.weight | 0.000 | 0.000 | 0.000 | 0.000 | (180,) || layers.5.residual_group.blocks.1.norm2.bias | -0.000 | -0.105 | 0.105 | 0.061 | (180, 360) || layers.5.residual_group.blocks.1.mlp.fc1.weight | -0.000 | -0.000 | 0.000 | 0.000 | (360,) || layers.5.residual_group.blocks.1.mlp.fc1.bias | 0.000 | -0.105 | 0.105 | 0.061 | (360, 180) || layers.5.residual_group.blocks.1.mlp.fc2.weight | 0.000 | -0.000 | 0.000 | 0.000 | (180,) || layers.5.residual_group.blocks.1.mlp.fc2.bias | 1.000 | 1.000 | 1.000 | 0.000 | (180,) || layers.5.residual_group.blocks.2.norm1.weight | 0.000 | 0.000 | 0.000 | 0.000 | (180,) || layers.5.residual_group.blocks.2.norm1.bias | 0.000 | -0.039 | 0.040 | 0.018 | (225, 6) || layers.5.residual_group.blocks.2.attn.relative_position_bias_table | 112.000 | 0.000 | 224.000 | 48.713 | (64, 64) || layers.5.residual_group.blocks.2.attn.relative_position_index | -0.000 | -0.040 | 0.040 | 0.018 | (180, 540) || layers.5.residual_group.blocks.2.attn.qkv.weight | 0.000 | 0.000 | 0.000 | 0.000 | (540,) || layers.5.residual_group.blocks.2.attn.qkv.bias | 0.000 | -0.040 | 0.040 | 0.018 | (180, 180) || layers.5.residual_group.blocks.2.attn.proj.weight | 0.000 | 0.000 | 0.000 | 0.000 | (180,) || layers.5.residual_group.blocks.2.attn.proj.bias | 1.000 | 1.000 | 1.000 | 0.000 | (180,) || layers.5.residual_group.blocks.2.norm2.weight | 0.000 | 0.000 | 0.000 | 0.000 | (180,) || layers.5.residual_group.blocks.2.norm2.bias | 0.000 | -0.105 | 0.105 | 0.061 | (180, 360) || layers.5.residual_group.blocks.2.mlp.fc1.weight | -0.000 | -0.000 | 0.000 | 0.000 | (360,) || layers.5.residual_group.blocks.2.mlp.fc1.bias | 0.000 | -0.105 | 0.105 | 0.061 | (360, 180) || layers.5.residual_group.blocks.2.mlp.fc2.weight | 0.000 | -0.000 | 0.000 | 0.000 | (180,) || layers.5.residual_group.blocks.2.mlp.fc2.bias | -6.152 | -100.000 | -0.000 | 24.029 | (256, 64, 64) || layers.5.residual_group.blocks.3.attn_mask | 1.000 | 1.000 | 1.000 | 0.000 | (180,) || layers.5.residual_group.blocks.3.norm1.weight | 0.000 | 0.000 | 0.000 | 0.000 | (180,) || layers.5.residual_group.blocks.3.norm1.bias | -0.000 | -0.040 | 0.040 | 0.018 | (225, 6) || layers.5.residual_group.blocks.3.attn.relative_position_bias_table | 112.000 | 0.000 | 224.000 | 48.713 | (64, 64) || layers.5.residual_group.blocks.3.attn.relative_position_index | -0.000 | -0.040 | 0.040 | 0.018 | (180, 540) || layers.5.residual_group.blocks.3.attn.qkv.weight | 0.000 | 0.000 | 0.000 | 0.000 | (540,) || layers.5.residual_group.blocks.3.attn.qkv.bias | -0.000 | -0.040 | 0.040 | 0.018 | (180, 180) || layers.5.residual_group.blocks.3.attn.proj.weight | 0.000 | 0.000 | 0.000 | 0.000 | (180,) || layers.5.residual_group.blocks.3.attn.proj.bias | 1.000 | 1.000 | 1.000 | 0.000 | (180,) || layers.5.residual_group.blocks.3.norm2.weight | 0.000 | 0.000 | 0.000 | 0.000 | (180,) || layers.5.residual_group.blocks.3.norm2.bias | 0.000 | -0.105 | 0.105 | 0.061 | (180, 360) || layers.5.residual_group.blocks.3.mlp.fc1.weight | 0.000 | -0.000 | 0.000 | 0.000 | (360,) || layers.5.residual_group.blocks.3.mlp.fc1.bias | 0.000 | -0.105 | 0.105 | 0.061 | (360, 180) || layers.5.residual_group.blocks.3.mlp.fc2.weight | 0.000 | -0.000 | 0.000 | 0.000 | (180,) || layers.5.residual_group.blocks.3.mlp.fc2.bias | 1.000 | 1.000 | 1.000 | 0.000 | (180,) || layers.5.residual_group.blocks.4.norm1.weight | 0.000 | 0.000 | 0.000 | 0.000 | (180,) || layers.5.residual_group.blocks.4.norm1.bias | 0.000 | -0.040 | 0.040 | 0.017 | (225, 6) || layers.5.residual_group.blocks.4.attn.relative_position_bias_table | 112.000 | 0.000 | 224.000 | 48.713 | (64, 64) || layers.5.residual_group.blocks.4.attn.relative_position_index | -0.000 | -0.040 | 0.040 | 0.018 | (180, 540) || layers.5.residual_group.blocks.4.attn.qkv.weight | 0.000 | 0.000 | 0.000 | 0.000 | (540,) || layers.5.residual_group.blocks.4.attn.qkv.bias | -0.000 | -0.040 | 0.040 | 0.018 | (180, 180) || layers.5.residual_group.blocks.4.attn.proj.weight | 0.000 | 0.000 | 0.000 | 0.000 | (180,) || layers.5.residual_group.blocks.4.attn.proj.bias | 1.000 | 1.000 | 1.000 | 0.000 | (180,) || layers.5.residual_group.blocks.4.norm2.weight | 0.000 | 0.000 | 0.000 | 0.000 | (180,) || layers.5.residual_group.blocks.4.norm2.bias | 0.000 | -0.105 | 0.105 | 0.061 | (180, 360) || layers.5.residual_group.blocks.4.mlp.fc1.weight | 0.000 | -0.000 | 0.000 | 0.000 | (360,) || layers.5.residual_group.blocks.4.mlp.fc1.bias | -0.000 | -0.105 | 0.105 | 0.061 | (360, 180) || layers.5.residual_group.blocks.4.mlp.fc2.weight | 0.000 | -0.000 | 0.000 | 0.000 | (180,) || layers.5.residual_group.blocks.4.mlp.fc2.bias | -6.152 | -100.000 | -0.000 | 24.029 | (256, 64, 64) || layers.5.residual_group.blocks.5.attn_mask | 1.000 | 1.000 | 1.000 | 0.000 | (180,) || layers.5.residual_group.blocks.5.norm1.weight | 0.000 | 0.000 | 0.000 | 0.000 | (180,) || layers.5.residual_group.blocks.5.norm1.bias | 0.000 | -0.040 | 0.040 | 0.018 | (225, 6) || layers.5.residual_group.blocks.5.attn.relative_position_bias_table | 112.000 | 0.000 | 224.000 | 48.713 | (64, 64) || layers.5.residual_group.blocks.5.attn.relative_position_index | -0.000 | -0.040 | 0.040 | 0.018 | (180, 540) || layers.5.residual_group.blocks.5.attn.qkv.weight | 0.000 | 0.000 | 0.000 | 0.000 | (540,) || layers.5.residual_group.blocks.5.attn.qkv.bias | -0.000 | -0.040 | 0.040 | 0.018 | (180, 180) || layers.5.residual_group.blocks.5.attn.proj.weight | 0.000 | 0.000 | 0.000 | 0.000 | (180,) || layers.5.residual_group.blocks.5.attn.proj.bias | 1.000 | 1.000 | 1.000 | 0.000 | (180,) || layers.5.residual_group.blocks.5.norm2.weight | 0.000 | 0.000 | 0.000 | 0.000 | (180,) || layers.5.residual_group.blocks.5.norm2.bias | 0.000 | -0.105 | 0.105 | 0.061 | (180, 360) || layers.5.residual_group.blocks.5.mlp.fc1.weight | 0.000 | -0.000 | 0.000 | 0.000 | (360,) || layers.5.residual_group.blocks.5.mlp.fc1.bias | 0.000 | -0.105 | 0.105 | 0.061 | (360, 180) || layers.5.residual_group.blocks.5.mlp.fc2.weight | 0.000 | -0.000 | 0.000 | 0.000 | (180,) || layers.5.residual_group.blocks.5.mlp.fc2.bias | -0.000 | -0.152 | 0.160 | 0.035 | (180, 180, 3, 3) || layers.5.conv.weight | 0.000 | 0.000 | 0.000 | 0.000 | (180,) || layers.5.conv.bias | 1.000 | 1.000 | 1.000 | 0.000 | (180,) || norm.weight | 0.000 | 0.000 | 0.000 | 0.000 | (180,) || norm.bias | -0.000 | -0.166 | 0.159 | 0.035 | (180, 180, 3, 3) || conv_after_body.weight | 0.000 | 0.000 | 0.000 | 0.000 | (180,) || conv_after_body.bias | 0.000 | -0.120 | 0.133 | 0.035 | (3, 180, 3, 3) || conv_last.weight | 0.000 | 0.000 | 0.000 | 0.000 | (3,) || conv_last.bias登录后复制 In []

# 单机四卡# !cd work && python -m paddle.distributed.launch main_train_psnr.py --opt options/train_swinir_multi_card_32.json登录后复制

训练过程会将模型参数保存在work/denoising/swinir_denoising_color_15/models/文件夹下.

训练日志将会保存在work/denoising/swinir_denoising_color_15/models/train.log

本人单机四卡的训练日志为work/train.log

5.2 模型评估与预测

在 CBSD68 测试数据上作测试，加强度为15的噪声，结果将存放在work/results/swinir_color_dn_noise15/文件夹下 In [14]

!cd work && python main_test_swinir.py --task color_dn --noise 15 --model_path pretrained_models/SwinIR_paddle.pdparams --folder_gt testsets/CBSD68/登录后复制

loading model from pretrained_models/SwinIR_paddle.pdparams W1016 16:39:45.440834 13414 gpu_resources.cc:61] Please NOTE: device: 0, GPU Compute Capability: 7.0, Driver API Version: 11.2, Runtime API Version: 11.2 W1016 16:39:45.444900 13414 gpu_resources.cc:91] device: 0, cuDNN Version: 8.2. Testing 0 101085 - PSNR: 31.32 dB; SSIM: 0.9118; PSNR_Y: 32.88 dB; SSIM_Y: 0.9210; PSNR_B: 0.00 dB. Testing 1 101087 - PSNR: 35.39 dB; SSIM: 0.9517; PSNR_Y: 37.14 dB; SSIM_Y: 0.9603; PSNR_B: 0.00 dB. Testing 2 102061 - PSNR: 35.21 dB; SSIM: 0.9348; PSNR_Y: 36.97 dB; SSIM_Y: 0.9477; PSNR_B: 0.00 dB. Testing 3 103070 - PSNR: 35.86 dB; SSIM: 0.9448; PSNR_Y: 37.79 dB; SSIM_Y: 0.9562; PSNR_B: 0.00 dB. Testing 4 105025 - PSNR: 33.11 dB; SSIM: 0.9426; PSNR_Y: 34.87 dB; SSIM_Y: 0.9509; PSNR_B: 0.00 dB. Testing 5 106024 - PSNR: 37.57 dB; SSIM: 0.9560; PSNR_Y: 39.35 dB; SSIM_Y: 0.9652; PSNR_B: 0.00 dB. Testing 6 108005 - PSNR: 33.86 dB; SSIM: 0.9337; PSNR_Y: 35.73 dB; SSIM_Y: 0.9470; PSNR_B: 0.00 dB. Testing 7 108070 - PSNR: 31.96 dB; SSIM: 0.9222; PSNR_Y: 33.71 dB; SSIM_Y: 0.9353; PSNR_B: 0.00 dB. Testing 8 108082 - PSNR: 34.37 dB; SSIM: 0.9420; PSNR_Y: 36.06 dB; SSIM_Y: 0.9520; PSNR_B: 0.00 dB. Testing 9 109053 - PSNR: 34.67 dB; SSIM: 0.9309; PSNR_Y: 36.52 dB; SSIM_Y: 0.9428; PSNR_B: 0.00 dB. Testing 10 119082 - PSNR: 34.75 dB; SSIM: 0.9570; PSNR_Y: 36.56 dB; SSIM_Y: 0.9655; PSNR_B: 0.00 dB. Testing 11 12084 - PSNR: 33.08 dB; SSIM: 0.9168; PSNR_Y: 35.40 dB; SSIM_Y: 0.9340; PSNR_B: 0.00 dB. Testing 12 123074 - PSNR: 34.70 dB; SSIM: 0.9363; PSNR_Y: 36.45 dB; SSIM_Y: 0.9465; PSNR_B: 0.00 dB. Testing 13 126007 - PSNR: 35.77 dB; SSIM: 0.9334; PSNR_Y: 37.60 dB; SSIM_Y: 0.9484; PSNR_B: 0.00 dB. Testing 14 130026 - PSNR: 32.89 dB; SSIM: 0.9189; PSNR_Y: 34.51 dB; SSIM_Y: 0.9319; PSNR_B: 0.00 dB. Testing 15 134035 - PSNR: 33.90 dB; SSIM: 0.9480; PSNR_Y: 35.66 dB; SSIM_Y: 0.9559; PSNR_B: 0.00 dB. Testing 16 14037 - PSNR: 37.21 dB; SSIM: 0.9387; PSNR_Y: 38.95 dB; SSIM_Y: 0.9533; PSNR_B: 0.00 dB. Testing 17 143090 - PSNR: 37.57 dB; SSIM: 0.9513; PSNR_Y: 39.62 dB; SSIM_Y: 0.9639; PSNR_B: 0.00 dB. Testing 18 145086 - PSNR: 33.79 dB; SSIM: 0.9310; PSNR_Y: 35.46 dB; SSIM_Y: 0.9461; PSNR_B: 0.00 dB. Testing 19 147091 - PSNR: 34.33 dB; SSIM: 0.9289; PSNR_Y: 36.20 dB; SSIM_Y: 0.9468; PSNR_B: 0.00 dB. Testing 20 148026 - PSNR: 32.43 dB; SSIM: 0.9511; PSNR_Y: 34.22 dB; SSIM_Y: 0.9619; PSNR_B: 0.00 dB. Testing 21 148089 - PSNR: 32.71 dB; SSIM: 0.9363; PSNR_Y: 34.36 dB; SSIM_Y: 0.9462; PSNR_B: 0.00 dB. Testing 22 157055 - PSNR: 34.56 dB; SSIM: 0.9495; PSNR_Y: 36.59 dB; SSIM_Y: 0.9616; PSNR_B: 0.00 dB. Testing 23 159008 - PSNR: 34.26 dB; SSIM: 0.9460; PSNR_Y: 35.95 dB; SSIM_Y: 0.9578; PSNR_B: 0.00 dB. Testing 24 160068 - PSNR: 35.17 dB; SSIM: 0.9682; PSNR_Y: 36.88 dB; SSIM_Y: 0.9758; PSNR_B: 0.00 dB. Testing 25 16077 - PSNR: 33.49 dB; SSIM: 0.9201; PSNR_Y: 35.28 dB; SSIM_Y: 0.9347; PSNR_B: 0.00 dB. Testing 26 163085 - PSNR: 34.90 dB; SSIM: 0.9267; PSNR_Y: 36.75 dB; SSIM_Y: 0.9421; PSNR_B: 0.00 dB. Testing 27 167062 - PSNR: 37.95 dB; SSIM: 0.9449; PSNR_Y: 39.81 dB; SSIM_Y: 0.9601; PSNR_B: 0.00 dB. Testing 28 167083 - PSNR: 31.24 dB; SSIM: 0.9596; PSNR_Y: 32.78 dB; SSIM_Y: 0.9645; PSNR_B: 0.00 dB. Testing 29 170057 - PSNR: 34.72 dB; SSIM: 0.9203; PSNR_Y: 36.58 dB; SSIM_Y: 0.9371; PSNR_B: 0.00 dB. Testing 30 175032 - PSNR: 30.79 dB; SSIM: 0.9515; PSNR_Y: 32.49 dB; SSIM_Y: 0.9566; PSNR_B: 0.00 dB. Testing 31 175043 - PSNR: 32.28 dB; SSIM: 0.9442; PSNR_Y: 34.06 dB; SSIM_Y: 0.9519; PSNR_B: 0.00 dB. Testing 32 182053 - PSNR: 33.90 dB; SSIM: 0.9614; PSNR_Y: 35.50 dB; SSIM_Y: 0.9692; PSNR_B: 0.00 dB. Testing 33 189080 - PSNR: 37.19 dB; SSIM: 0.9205; PSNR_Y: 38.89 dB; SSIM_Y: 0.9385; PSNR_B: 0.00 dB. Testing 34 19021 - PSNR: 33.28 dB; SSIM: 0.9276; PSNR_Y: 34.99 dB; SSIM_Y: 0.9394; PSNR_B: 0.00 dB. Testing 35 196073 - PSNR: 31.92 dB; SSIM: 0.8469; PSNR_Y: 33.31 dB; SSIM_Y: 0.8625; PSNR_B: 0.00 dB. Testing 36 197017 - PSNR: 33.46 dB; SSIM: 0.9228; PSNR_Y: 35.10 dB; SSIM_Y: 0.9352; PSNR_B: 0.00 dB. Testing 37 208001 - PSNR: 34.05 dB; SSIM: 0.9204; PSNR_Y: 36.01 dB; SSIM_Y: 0.9396; PSNR_B: 0.00 dB. Testing 38 210088 - PSNR: 38.00 dB; SSIM: 0.9620; PSNR_Y: 40.78 dB; SSIM_Y: 0.9756; PSNR_B: 0.00 dB. Testing 39 21077 - PSNR: 34.18 dB; SSIM: 0.9032; PSNR_Y: 35.83 dB; SSIM_Y: 0.9194; PSNR_B: 0.00 dB. Testing 40 216081 - PSNR: 33.35 dB; SSIM: 0.9367; PSNR_Y: 35.04 dB; SSIM_Y: 0.9494; PSNR_B: 0.00 dB. Testing 41 219090 - PSNR: 34.88 dB; SSIM: 0.9243; PSNR_Y: 36.50 dB; SSIM_Y: 0.9377; PSNR_B: 0.00 dB. Testing 42 220075 - PSNR: 35.54 dB; SSIM: 0.9521; PSNR_Y: 37.61 dB; SSIM_Y: 0.9650; PSNR_B: 0.00 dB. Testing 43 223061 - PSNR: 33.88 dB; SSIM: 0.9557; PSNR_Y: 35.49 dB; SSIM_Y: 0.9627; PSNR_B: 0.00 dB. Testing 44 227092 - PSNR: 37.97 dB; SSIM: 0.9423; PSNR_Y: 39.80 dB; SSIM_Y: 0.9547; PSNR_B: 0.00 dB. Testing 45 229036 - PSNR: 32.15 dB; SSIM: 0.9262; PSNR_Y: 33.76 dB; SSIM_Y: 0.9353; PSNR_B: 0.00 dB. Testing 46 236037 - PSNR: 32.16 dB; SSIM: 0.9350; PSNR_Y: 33.94 dB; SSIM_Y: 0.9431; PSNR_B: 0.00 dB. Testing 47 24077 - PSNR: 35.38 dB; SSIM: 0.9678; PSNR_Y: 37.68 dB; SSIM_Y: 0.9761; PSNR_B: 0.00 dB. Testing 48 241004 - PSNR: 35.61 dB; SSIM: 0.9070; PSNR_Y: 37.23 dB; SSIM_Y: 0.9253; PSNR_B: 0.00 dB. Testing 49 241048 - PSNR: 32.33 dB; SSIM: 0.9273; PSNR_Y: 33.99 dB; SSIM_Y: 0.9364; PSNR_B: 0.00 dB. Testing 50 253027 - PSNR: 33.92 dB; SSIM: 0.9310; PSNR_Y: 35.58 dB; SSIM_Y: 0.9421; PSNR_B: 0.00 dB. Testing 51 253055 - PSNR: 36.10 dB; SSIM: 0.9168; PSNR_Y: 37.78 dB; SSIM_Y: 0.9341; PSNR_B: 0.00 dB. Testing 52 260058 - PSNR: 35.37 dB; SSIM: 0.8999; PSNR_Y: 36.95 dB; SSIM_Y: 0.9184; PSNR_B: 0.00 dB. Testing 53 271035 - PSNR: 34.40 dB; SSIM: 0.9299; PSNR_Y: 36.10 dB; SSIM_Y: 0.9428; PSNR_B: 0.00 dB. Testing 54 285079 - PSNR: 33.00 dB; SSIM: 0.9271; PSNR_Y: 34.77 dB; SSIM_Y: 0.9383; PSNR_B: 0.00 dB. Testing 55 291000 - PSNR: 30.12 dB; SSIM: 0.9457; PSNR_Y: 31.99 dB; SSIM_Y: 0.9523; PSNR_B: 0.00 dB. Testing 56 295087 - PSNR: 33.49 dB; SSIM: 0.9405; PSNR_Y: 35.32 dB; SSIM_Y: 0.9515; PSNR_B: 0.00 dB. Testing 57 296007 - PSNR: 34.75 dB; SSIM: 0.8944; PSNR_Y: 36.36 dB; SSIM_Y: 0.9144; PSNR_B: 0.00 dB. Testing 58 296059 - PSNR: 34.66 dB; SSIM: 0.9062; PSNR_Y: 36.29 dB; SSIM_Y: 0.9223; PSNR_B: 0.00 dB. Testing 59 299086 - PSNR: 35.32 dB; SSIM: 0.9082; PSNR_Y: 37.03 dB; SSIM_Y: 0.9262; PSNR_B: 0.00 dB. Testing 60 300091 - PSNR: 35.18 dB; SSIM: 0.9137; PSNR_Y: 36.80 dB; SSIM_Y: 0.9295; PSNR_B: 0.00 dB. Testing 61 302008 - PSNR: 38.71 dB; SSIM: 0.9649; PSNR_Y: 40.80 dB; SSIM_Y: 0.9749; PSNR_B: 0.00 dB. Testing 62 304034 - PSNR: 32.27 dB; SSIM: 0.9455; PSNR_Y: 34.11 dB; SSIM_Y: 0.9549; PSNR_B: 0.00 dB. Testing 63 304074 - PSNR: 31.85 dB; SSIM: 0.9043; PSNR_Y: 33.38 dB; SSIM_Y: 0.9154; PSNR_B: 0.00 dB. Testing 64 306005 - PSNR: 33.75 dB; SSIM: 0.9265; PSNR_Y: 35.72 dB; SSIM_Y: 0.9444; PSNR_B: 0.00 dB. Testing 65 3096 - PSNR: 42.69 dB; SSIM: 0.9768; PSNR_Y: 44.83 dB; SSIM_Y: 0.9854; PSNR_B: 0.00 dB. Testing 66 33039 - PSNR: 30.98 dB; SSIM: 0.9585; PSNR_Y: 32.50 dB; SSIM_Y: 0.9619; PSNR_B: 0.00 dB. Testing 67 351093 - PSNR: 32.45 dB; SSIM: 0.9607; PSNR_Y: 34.08 dB; SSIM_Y: 0.9672; PSNR_B: 0.00 dB. results/swinir_color_dn_noise15 -- Average PSNR/SSIM(RGB): 34.32 dB; 0.9344 -- Average PSNR_Y/SSIM_Y: 36.10 dB; 0.9465登录后复制

输出如下：

-- Average PSNR/SSIM(RGB): 34.32 dB; 0.9344

接近了验收精度.

5.3 单张图像去噪测试

导入单张图像，测试去噪效果，首先需要在work/test_images里上传一张图片. In [13]

# 先上传一张图片import os.path as ospfrom IPython.display import displayfrom PIL import Image img_path = 'bird.png' # 改成自己上传的图片名称full_img_path = osp.join(osp.abspath('work/test_images/'), img_path) img = Image.open(full_img_path).convert('RGB')print('以下为上传的图片:') display(img)登录后复制

以下为上传的图片:登录后复制

<PIL.Image.Image image mode=RGB size=288x288 at 0x7F47D28A8C90>登录后复制

需要指定干净图像和噪声图像，可以只给一张噪声图片，也可以只给一张干净图片，也可以都给.

给定一张噪声图片：指定参数noisy_img，直接输出去噪图片.

输入一张清图片后，设定参数clean_img=指定值、noisyl=噪音级别（默认，生成含噪图与去噪图。

给定噪声图片和干净图片：直接输出去噪图片. In [24]

# 仅给定干净图片，噪声水平为15!cd work && python predict_single.py --clean_img $full_img_path --save_images --noisyL 15 --model_path pretrained_models/SwinIR_paddle.pdparams登录后复制

loading model from pretrained_models/SwinIR_paddle.pdparams W1016 17:20:03.374689 17743 gpu_resources.cc:61] Please NOTE: device: 0, GPU Compute Capability: 7.0, Driver API Version: 11.2, Runtime API Version: 11.2 W1016 17:20:03.378355 17743 gpu_resources.cc:91] device: 0, cuDNN Version: 8.2. only clean image provided, noise level is 15 PSNR on test data 34.9994登录后复制 In [25]

# 去噪效果查看import globfrom IPython.display import displayfrom PIL import Image imgs = glob.glob('work/test_images/*')for path in imgs: print(path) img = Image.open(path) display(img)登录后复制

work/test_images/bird_denoised.png登录后复制

<PIL.PngImagePlugin.PngImageFile image mode=RGB size=288x288 at 0x7F47D0110F90>登录后复制

work/test_images/bird_noised.png登录后复制

<PIL.PngImagePlugin.PngImageFile image mode=RGB size=288x288 at 0x7F47D0050450>登录后复制

work/test_images/bird.png登录后复制

<PIL.PngImagePlugin.PngImageFile image mode=RGB size=288x288 at 0x7F47D28451D0>登录后复制

以上就是【图像去噪】第七期论文复现赛SwinIR的详细内容，更多请关注其它相关文章！