chore: Clear out repo again

original/BiRefNet_config.py

@@ -1,11 +0,0 @@
-from transformers import PretrainedConfig
-
-class BiRefNetConfig(PretrainedConfig):
-    model_type = "SegformerForSemanticSegmentation"
-    def __init__(
-        self,
-        bb_pretrained=False,
-        **kwargs
-    ):
-        self.bb_pretrained = bb_pretrained
-        super().__init__(**kwargs)

original/README.md

@@ -1,176 +0,0 @@
----
-library_name: birefnet
-tags:
-- background-removal
-- mask-generation
-- Dichotomous Image Segmentation
-- Camouflaged Object Detection
-- Salient Object Detection
-- pytorch_model_hub_mixin
-- model_hub_mixin
-repo_url: https://github.com/ZhengPeng7/BiRefNet
-pipeline_tag: image-segmentation
-license: mit
----
-<h1 align="center">Bilateral Reference for High-Resolution Dichotomous Image Segmentation</h1>
-
-<div align='center'>
-    <a href='https://scholar.google.com/citations?user=TZRzWOsAAAAJ' target='_blank'><strong>Peng Zheng</strong></a><sup> 1,4,5,6</sup>,&thinsp;
-    <a href='https://scholar.google.com/citations?user=0uPb8MMAAAAJ' target='_blank'><strong>Dehong Gao</strong></a><sup> 2</sup>,&thinsp;
-    <a href='https://scholar.google.com/citations?user=kakwJ5QAAAAJ' target='_blank'><strong>Deng-Ping Fan</strong></a><sup> 1*</sup>,&thinsp;
-    <a href='https://scholar.google.com/citations?user=9cMQrVsAAAAJ' target='_blank'><strong>Li Liu</strong></a><sup> 3</sup>,&thinsp;
-    <a href='https://scholar.google.com/citations?user=qQP6WXIAAAAJ' target='_blank'><strong>Jorma Laaksonen</strong></a><sup> 4</sup>,&thinsp;
-    <a href='https://scholar.google.com/citations?user=pw_0Z_UAAAAJ' target='_blank'><strong>Wanli Ouyang</strong></a><sup> 5</sup>,&thinsp;
-    <a href='https://scholar.google.com/citations?user=stFCYOAAAAAJ' target='_blank'><strong>Nicu Sebe</strong></a><sup> 6</sup>
-</div>
-
-<div align='center'>
-    <sup>1 </sup>Nankai University&ensp;  <sup>2 </sup>Northwestern Polytechnical University&ensp;  <sup>3 </sup>National University of Defense Technology&ensp; <sup>4 </sup>Aalto University&ensp;  <sup>5 </sup>Shanghai AI Laboratory&ensp;  <sup>6 </sup>University of Trento&ensp; 
-</div>
-
-<div align="center" style="display: flex; justify-content: center; flex-wrap: wrap;">
-  <a href='https://www.sciopen.com/article/pdf/10.26599/AIR.2024.9150038.pdf'><img src='https://img.shields.io/badge/Journal-Paper-red'></a>&ensp; 
-  <a href='https://arxiv.org/pdf/2401.03407'><img src='https://img.shields.io/badge/arXiv-BiRefNet-red'></a>&ensp; 
-  <a href='https://drive.google.com/file/d/1aBnJ_R9lbnC2dm8dqD0-pzP2Cu-U1Xpt/view?usp=drive_link'><img src='https://img.shields.io/badge/中文版-BiRefNet-red'></a>&ensp; 
-  <a href='https://www.birefnet.top'><img src='https://img.shields.io/badge/Page-BiRefNet-red'></a>&ensp; 
-  <a href='https://drive.google.com/drive/folders/1s2Xe0cjq-2ctnJBR24563yMSCOu4CcxM'><img src='https://img.shields.io/badge/Drive-Stuff-green'></a>&ensp; 
-  <a href='LICENSE'><img src='https://img.shields.io/badge/License-MIT-yellow'></a>&ensp; 
-  <a href='https://huggingface.co/spaces/ZhengPeng7/BiRefNet_demo'><img src='https://img.shields.io/badge/%F0%9F%A4%97%20HF%20Spaces-BiRefNet-blue'></a>&ensp; 
-  <a href='https://huggingface.co/ZhengPeng7/BiRefNet'><img src='https://img.shields.io/badge/%F0%9F%A4%97%20HF%20Models-BiRefNet-blue'></a>&ensp; 
-  <a href='https://colab.research.google.com/drive/14Dqg7oeBkFEtchaHLNpig2BcdkZEogba?usp=drive_link'><img src='https://img.shields.io/badge/Single_Image_Inference-F9AB00?style=for-the-badge&logo=googlecolab&color=525252'></a>&ensp; 
-  <a href='https://colab.research.google.com/drive/1MaEiBfJ4xIaZZn0DqKrhydHB8X97hNXl#scrollTo=DJ4meUYjia6S'><img src='https://img.shields.io/badge/Inference_&_Evaluation-F9AB00?style=for-the-badge&logo=googlecolab&color=525252'></a>&ensp; 
-</div>
-
-
-|            *DIS-Sample_1*        |             *DIS-Sample_2*        |
-| :------------------------------: | :-------------------------------: |
-| <img src="https://drive.google.com/thumbnail?id=1ItXaA26iYnE8XQ_GgNLy71MOWePoS2-g&sz=w400" /> |  <img src="https://drive.google.com/thumbnail?id=1Z-esCujQF_uEa_YJjkibc3NUrW4aR_d4&sz=w400" /> |
-
-This repo is the official implementation of "[**Bilateral Reference for High-Resolution Dichotomous Image Segmentation**](https://arxiv.org/pdf/2401.03407.pdf)" (___CAAI AIR 2024___). 
-
-Visit our GitHub repo: [https://github.com/ZhengPeng7/BiRefNet](https://github.com/ZhengPeng7/BiRefNet) for more details -- **codes**, **docs**, and **model zoo**!
-
-## How to use
-
-### 0. Install Packages:
-```
-pip install -qr https://raw.githubusercontent.com/ZhengPeng7/BiRefNet/main/requirements.txt
-```
-
-### 1. Load BiRefNet:
-
-#### Use codes + weights from HuggingFace
-> Only use the weights on HuggingFace -- Pro: No need to download BiRefNet codes manually; Con: Codes on HuggingFace might not be latest version (I'll try to keep them always latest).
-
-```python
-# Load BiRefNet with weights
-from transformers import AutoModelForImageSegmentation
-birefnet = AutoModelForImageSegmentation.from_pretrained('ZhengPeng7/BiRefNet', trust_remote_code=True)
-```
-
-#### Use codes from GitHub + weights from HuggingFace
-> Only use the weights on HuggingFace -- Pro: codes are always latest; Con: Need to clone the BiRefNet repo from my GitHub.
-
-```shell
-# Download codes
-git clone https://github.com/ZhengPeng7/BiRefNet.git
-cd BiRefNet
-```
-
-```python
-# Use codes locally
-from models.birefnet import BiRefNet
-
-# Load weights from Hugging Face Models
-birefnet = BiRefNet.from_pretrained('ZhengPeng7/BiRefNet')
-```
-
-#### Use codes from GitHub + weights from local space
-> Only use the weights and codes both locally.
-
-```python
-# Use codes and weights locally
-import torch
-from utils import check_state_dict
-
-birefnet = BiRefNet(bb_pretrained=False)
-state_dict = torch.load(PATH_TO_WEIGHT, map_location='cpu')
-state_dict = check_state_dict(state_dict)
-birefnet.load_state_dict(state_dict)
-```
-
-#### Use the loaded BiRefNet for inference
-```python
-# Imports
-from PIL import Image
-import matplotlib.pyplot as plt
-import torch
-from torchvision import transforms
-from models.birefnet import BiRefNet
-
-birefnet = ... # -- BiRefNet should be loaded with codes above, either way.
-torch.set_float32_matmul_precision(['high', 'highest'][0])
-birefnet.to('cuda')
-birefnet.eval()
-
-def extract_object(birefnet, imagepath):
-    # Data settings
-    image_size = (1024, 1024)
-    transform_image = transforms.Compose([
-        transforms.Resize(image_size),
-        transforms.ToTensor(),
-        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
-    ])
-
-    image = Image.open(imagepath)
-    input_images = transform_image(image).unsqueeze(0).to('cuda')
-
-    # Prediction
-    with torch.no_grad():
-        preds = birefnet(input_images)[-1].sigmoid().cpu()
-    pred = preds[0].squeeze()
-    pred_pil = transforms.ToPILImage()(pred)
-    mask = pred_pil.resize(image.size)
-    image.putalpha(mask)
-    return image, mask
-
-# Visualization
-plt.axis("off")
-plt.imshow(extract_object(birefnet, imagepath='PATH-TO-YOUR_IMAGE.jpg')[0])
-plt.show()
-
-```
-
-
-> This BiRefNet for standard dichotomous image segmentation (DIS) is trained on **DIS-TR** and validated on **DIS-TEs and DIS-VD**.
-
-## This repo holds the official model weights of "[<ins>Bilateral Reference for High-Resolution Dichotomous Image Segmentation</ins>](https://arxiv.org/pdf/2401.03407)" (_CAAI AIR 2024_).
-
-This repo contains the weights of BiRefNet proposed in our paper, which has achieved the SOTA performance on three tasks (DIS, HRSOD, and COD).
-
-Go to my GitHub page for BiRefNet codes and the latest updates: https://github.com/ZhengPeng7/BiRefNet :)
-
-
-#### Try our online demos for inference:
-
-+ Online **Image Inference** on Colab: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/14Dqg7oeBkFEtchaHLNpig2BcdkZEogba?usp=drive_link)
-+ **Online Inference with GUI on Hugging Face** with adjustable resolutions: [![Hugging Face Spaces](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue)](https://huggingface.co/spaces/ZhengPeng7/BiRefNet_demo)  
-+ **Inference and evaluation** of your given weights: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1MaEiBfJ4xIaZZn0DqKrhydHB8X97hNXl#scrollTo=DJ4meUYjia6S)
-<img src="https://drive.google.com/thumbnail?id=12XmDhKtO1o2fEvBu4OE4ULVB2BK0ecWi&sz=w1080" />
-
-## Acknowledgement:
-
-+ Many thanks to @fal for their generous support on GPU resources for training better BiRefNet models.
-+ Many thanks to @not-lain for his help on the better deployment of our BiRefNet model on HuggingFace.
-
-
-## Citation
-
-```
-@article{BiRefNet,
-  title={Bilateral Reference for High-Resolution Dichotomous Image Segmentation},
-  author={Zheng, Peng and Gao, Dehong and Fan, Deng-Ping and Liu, Li and Laaksonen, Jorma and Ouyang, Wanli and Sebe, Nicu},
-  journal={CAAI Artificial Intelligence Research},
-  year={2024}
-}
-```

original/birefnet.py

@@ -1,2244 +0,0 @@
-### config.py
-
-import os
-import math
-
-
-class Config():
-    def __init__(self) -> None:
-        # PATH settings
-        self.sys_home_dir = os.path.expanduser('~')    # Make up your file system as: SYS_HOME_DIR/codes/dis/BiRefNet, SYS_HOME_DIR/datasets/dis/xx, SYS_HOME_DIR/weights/xx
-
-        # TASK settings
-        self.task = ['DIS5K', 'COD', 'HRSOD', 'DIS5K+HRSOD+HRS10K', 'P3M-10k'][0]
-        self.training_set = {
-            'DIS5K': ['DIS-TR', 'DIS-TR+DIS-TE1+DIS-TE2+DIS-TE3+DIS-TE4'][0],
-            'COD': 'TR-COD10K+TR-CAMO',
-            'HRSOD': ['TR-DUTS', 'TR-HRSOD', 'TR-UHRSD', 'TR-DUTS+TR-HRSOD', 'TR-DUTS+TR-UHRSD', 'TR-HRSOD+TR-UHRSD', 'TR-DUTS+TR-HRSOD+TR-UHRSD'][5],
-            'DIS5K+HRSOD+HRS10K': 'DIS-TE1+DIS-TE2+DIS-TE3+DIS-TE4+DIS-TR+TE-HRS10K+TE-HRSOD+TE-UHRSD+TR-HRS10K+TR-HRSOD+TR-UHRSD',     # leave DIS-VD for evaluation.
-            'P3M-10k': 'TR-P3M-10k',
-        }[self.task]
-        self.prompt4loc = ['dense', 'sparse'][0]
-
-        # Faster-Training settings
-        self.load_all = True
-        self.compile = True     # 1. Trigger CPU memory leak in some extend, which is an inherent problem of PyTorch.
-                                #   Machines with > 70GB CPU memory can run the whole training on DIS5K with default setting.
-                                # 2. Higher PyTorch version may fix it: https://github.com/pytorch/pytorch/issues/119607.
-                                # 3. But compile in Pytorch > 2.0.1 seems to bring no acceleration for training.
-        self.precisionHigh = True
-
-        # MODEL settings
-        self.ms_supervision = True
-        self.out_ref = self.ms_supervision and True
-        self.dec_ipt = True
-        self.dec_ipt_split = True
-        self.cxt_num = [0, 3][1]    # multi-scale skip connections from encoder
-        self.mul_scl_ipt = ['', 'add', 'cat'][2]
-        self.dec_att = ['', 'ASPP', 'ASPPDeformable'][2]
-        self.squeeze_block = ['', 'BasicDecBlk_x1', 'ResBlk_x4', 'ASPP_x3', 'ASPPDeformable_x3'][1]
-        self.dec_blk = ['BasicDecBlk', 'ResBlk', 'HierarAttDecBlk'][0]
-
-        # TRAINING settings
-        self.batch_size = 4
-        self.IoU_finetune_last_epochs = [
-            0,
-            {
-                'DIS5K': -50,
-                'COD': -20,
-                'HRSOD': -20,
-                'DIS5K+HRSOD+HRS10K': -20,
-                'P3M-10k': -20,
-            }[self.task]
-        ][1]    # choose 0 to skip
-        self.lr = (1e-4 if 'DIS5K' in self.task else 1e-5) * math.sqrt(self.batch_size / 4)     # DIS needs high lr to converge faster. Adapt the lr linearly
-        self.size = 1024
-        self.num_workers = max(4, self.batch_size)          # will be decrease to min(it, batch_size) at the initialization of the data_loader
-
-        # Backbone settings
-        self.bb = [
-            'vgg16', 'vgg16bn', 'resnet50',         # 0, 1, 2
-            'swin_v1_t', 'swin_v1_s',               # 3, 4
-            'swin_v1_b', 'swin_v1_l',               # 5-bs9, 6-bs4
-            'pvt_v2_b0', 'pvt_v2_b1',               # 7, 8
-            'pvt_v2_b2', 'pvt_v2_b5',               # 9-bs10, 10-bs5
-        ][6]
-        self.lateral_channels_in_collection = {
-            'vgg16': [512, 256, 128, 64], 'vgg16bn': [512, 256, 128, 64], 'resnet50': [1024, 512, 256, 64],
-            'pvt_v2_b2': [512, 320, 128, 64], 'pvt_v2_b5': [512, 320, 128, 64],
-            'swin_v1_b': [1024, 512, 256, 128], 'swin_v1_l': [1536, 768, 384, 192],
-            'swin_v1_t': [768, 384, 192, 96], 'swin_v1_s': [768, 384, 192, 96],
-            'pvt_v2_b0': [256, 160, 64, 32], 'pvt_v2_b1': [512, 320, 128, 64],
-        }[self.bb]
-        if self.mul_scl_ipt == 'cat':
-            self.lateral_channels_in_collection = [channel * 2 for channel in self.lateral_channels_in_collection]
-        self.cxt = self.lateral_channels_in_collection[1:][::-1][-self.cxt_num:] if self.cxt_num else []
-
-        # MODEL settings - inactive
-        self.lat_blk = ['BasicLatBlk'][0]
-        self.dec_channels_inter = ['fixed', 'adap'][0]
-        self.refine = ['', 'itself', 'RefUNet', 'Refiner', 'RefinerPVTInChannels4'][0]
-        self.progressive_ref = self.refine and True
-        self.ender = self.progressive_ref and False
-        self.scale = self.progressive_ref and 2
-        self.auxiliary_classification = False       # Only for DIS5K, where class labels are saved in `dataset.py`.
-        self.refine_iteration = 1
-        self.freeze_bb = False
-        self.model = [
-            'BiRefNet',
-        ][0]
-        if self.dec_blk == 'HierarAttDecBlk':
-            self.batch_size = 2 ** [0, 1, 2, 3, 4][2]
-
-        # TRAINING settings - inactive
-        self.preproc_methods = ['flip', 'enhance', 'rotate', 'pepper', 'crop'][:4]
-        self.optimizer = ['Adam', 'AdamW'][1]
-        self.lr_decay_epochs = [1e5]    # Set to negative N to decay the lr in the last N-th epoch.
-        self.lr_decay_rate = 0.5
-        # Loss
-        self.lambdas_pix_last = {
-            # not 0 means opening this loss
-            # original rate -- 1 : 30 : 1.5 : 0.2, bce x 30
-            'bce': 30 * 1,          # high performance
-            'iou': 0.5 * 1,         # 0 / 255
-            'iou_patch': 0.5 * 0,   # 0 / 255, win_size = (64, 64)
-            'mse': 150 * 0,         # can smooth the saliency map
-            'triplet': 3 * 0,
-            'reg': 100 * 0,
-            'ssim': 10 * 1,          # help contours,
-            'cnt': 5 * 0,          # help contours
-            'structure': 5 * 0,    # structure loss from codes of MVANet. A little improvement on DIS-TE[1,2,3], a bit more decrease on DIS-TE4.
-        }
-        self.lambdas_cls = {
-            'ce': 5.0
-        }
-        # Adv
-        self.lambda_adv_g = 10. * 0        # turn to 0 to avoid adv training
-        self.lambda_adv_d = 3. * (self.lambda_adv_g > 0)
-
-        # PATH settings - inactive
-        self.data_root_dir = os.path.join(self.sys_home_dir, 'datasets/dis')
-        self.weights_root_dir = os.path.join(self.sys_home_dir, 'weights')
-        self.weights = {
-            'pvt_v2_b2': os.path.join(self.weights_root_dir, 'pvt_v2_b2.pth'),
-            'pvt_v2_b5': os.path.join(self.weights_root_dir, ['pvt_v2_b5.pth', 'pvt_v2_b5_22k.pth'][0]),
-            'swin_v1_b': os.path.join(self.weights_root_dir, ['swin_base_patch4_window12_384_22kto1k.pth', 'swin_base_patch4_window12_384_22k.pth'][0]),
-            'swin_v1_l': os.path.join(self.weights_root_dir, ['swin_large_patch4_window12_384_22kto1k.pth', 'swin_large_patch4_window12_384_22k.pth'][0]),
-            'swin_v1_t': os.path.join(self.weights_root_dir, ['swin_tiny_patch4_window7_224_22kto1k_finetune.pth'][0]),
-            'swin_v1_s': os.path.join(self.weights_root_dir, ['swin_small_patch4_window7_224_22kto1k_finetune.pth'][0]),
-            'pvt_v2_b0': os.path.join(self.weights_root_dir, ['pvt_v2_b0.pth'][0]),
-            'pvt_v2_b1': os.path.join(self.weights_root_dir, ['pvt_v2_b1.pth'][0]),
-        }
-
-        # Callbacks - inactive
-        self.verbose_eval = True
-        self.only_S_MAE = False
-        self.use_fp16 = False   # Bugs. It may cause nan in training.
-        self.SDPA_enabled = False    # Bugs. Slower and errors occur in multi-GPUs
-
-        # others
-        self.device = [0, 'cpu'][0]     # .to(0) == .to('cuda:0')
-
-        self.batch_size_valid = 1
-        self.rand_seed = 7
-        # run_sh_file = [f for f in os.listdir('.') if 'train.sh' == f] + [os.path.join('..', f) for f in os.listdir('..') if 'train.sh' == f]
-        # with open(run_sh_file[0], 'r') as f:
-        #     lines = f.readlines()
-        #     self.save_last = int([l.strip() for l in lines if '"{}")'.format(self.task) in l and 'val_last=' in l][0].split('val_last=')[-1].split()[0])
-        #     self.save_step = int([l.strip() for l in lines if '"{}")'.format(self.task) in l and 'step=' in l][0].split('step=')[-1].split()[0])
-        # self.val_step = [0, self.save_step][0]
-
-    def print_task(self) -> None:
-        # Return task for choosing settings in shell scripts.
-        print(self.task)
-
-
-
-### models/backbones/pvt_v2.py
-
-import torch
-import torch.nn as nn
-from functools import partial
-
-from timm.models.layers import DropPath, to_2tuple, trunc_normal_
-from timm.models.registry import register_model
-
-import math
-
-# from config import Config
-
-# config = Config()
-
-class Mlp(nn.Module):
-    def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):
-        super().__init__()
-        out_features = out_features or in_features
-        hidden_features = hidden_features or in_features
-        self.fc1 = nn.Linear(in_features, hidden_features)
-        self.dwconv = DWConv(hidden_features)
-        self.act = act_layer()
-        self.fc2 = nn.Linear(hidden_features, out_features)
-        self.drop = nn.Dropout(drop)
-
-        self.apply(self._init_weights)
-
-    def _init_weights(self, m):
-        if isinstance(m, nn.Linear):
-            trunc_normal_(m.weight, std=.02)
-            if isinstance(m, nn.Linear) and m.bias is not None:
-                nn.init.constant_(m.bias, 0)
-        elif isinstance(m, nn.LayerNorm):
-            nn.init.constant_(m.bias, 0)
-            nn.init.constant_(m.weight, 1.0)
-        elif isinstance(m, nn.Conv2d):
-            fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
-            fan_out //= m.groups
-            m.weight.data.normal_(0, math.sqrt(2.0 / fan_out))
-            if m.bias is not None:
-                m.bias.data.zero_()
-
-    def forward(self, x, H, W):
-        x = self.fc1(x)
-        x = self.dwconv(x, H, W)
-        x = self.act(x)
-        x = self.drop(x)
-        x = self.fc2(x)
-        x = self.drop(x)
-        return x
-
-
-class Attention(nn.Module):
-    def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0., sr_ratio=1):
-        super().__init__()
-        assert dim % num_heads == 0, f"dim {dim} should be divided by num_heads {num_heads}."
-
-        self.dim = dim
-        self.num_heads = num_heads
-        head_dim = dim // num_heads
-        self.scale = qk_scale or head_dim ** -0.5
-
-        self.q = nn.Linear(dim, dim, bias=qkv_bias)
-        self.kv = nn.Linear(dim, dim * 2, bias=qkv_bias)
-        self.attn_drop_prob = attn_drop
-        self.attn_drop = nn.Dropout(attn_drop)
-        self.proj = nn.Linear(dim, dim)
-        self.proj_drop = nn.Dropout(proj_drop)
-
-        self.sr_ratio = sr_ratio
-        if sr_ratio > 1:
-            self.sr = nn.Conv2d(dim, dim, kernel_size=sr_ratio, stride=sr_ratio)
-            self.norm = nn.LayerNorm(dim)
-
-        self.apply(self._init_weights)
-
-    def _init_weights(self, m):
-        if isinstance(m, nn.Linear):
-            trunc_normal_(m.weight, std=.02)
-            if isinstance(m, nn.Linear) and m.bias is not None:
-                nn.init.constant_(m.bias, 0)
-        elif isinstance(m, nn.LayerNorm):
-            nn.init.constant_(m.bias, 0)
-            nn.init.constant_(m.weight, 1.0)
-        elif isinstance(m, nn.Conv2d):
-            fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
-            fan_out //= m.groups
-            m.weight.data.normal_(0, math.sqrt(2.0 / fan_out))
-            if m.bias is not None:
-                m.bias.data.zero_()
-
-    def forward(self, x, H, W):
-        B, N, C = x.shape
-        q = self.q(x).reshape(B, N, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3)
-
-        if self.sr_ratio > 1:
-            x_ = x.permute(0, 2, 1).reshape(B, C, H, W)
-            x_ = self.sr(x_).reshape(B, C, -1).permute(0, 2, 1)
-            x_ = self.norm(x_)
-            kv = self.kv(x_).reshape(B, -1, 2, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
-        else:
-            kv = self.kv(x).reshape(B, -1, 2, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
-        k, v = kv[0], kv[1]
-
-        if config.SDPA_enabled:
-            x = torch.nn.functional.scaled_dot_product_attention(
-                q, k, v,
-                attn_mask=None, dropout_p=self.attn_drop_prob, is_causal=False
-            ).transpose(1, 2).reshape(B, N, C)
-        else:
-            attn = (q @ k.transpose(-2, -1)) * self.scale
-            attn = attn.softmax(dim=-1)
-            attn = self.attn_drop(attn)
-
-            x = (attn @ v).transpose(1, 2).reshape(B, N, C)
-        x = self.proj(x)
-        x = self.proj_drop(x)
-
-        return x
-
-
-class Block(nn.Module):
-
-    def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0.,
-                 drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm, sr_ratio=1):
-        super().__init__()
-        self.norm1 = norm_layer(dim)
-        self.attn = Attention(
-            dim,
-            num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale,
-            attn_drop=attn_drop, proj_drop=drop, sr_ratio=sr_ratio)
-        # NOTE: drop path for stochastic depth, we shall see if this is better than dropout here
-        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
-        self.norm2 = norm_layer(dim)
-        mlp_hidden_dim = int(dim * mlp_ratio)
-        self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
-
-        self.apply(self._init_weights)
-
-    def _init_weights(self, m):
-        if isinstance(m, nn.Linear):
-            trunc_normal_(m.weight, std=.02)
-            if isinstance(m, nn.Linear) and m.bias is not None:
-                nn.init.constant_(m.bias, 0)
-        elif isinstance(m, nn.LayerNorm):
-            nn.init.constant_(m.bias, 0)
-            nn.init.constant_(m.weight, 1.0)
-        elif isinstance(m, nn.Conv2d):
-            fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
-            fan_out //= m.groups
-            m.weight.data.normal_(0, math.sqrt(2.0 / fan_out))
-            if m.bias is not None:
-                m.bias.data.zero_()
-
-    def forward(self, x, H, W):
-        x = x + self.drop_path(self.attn(self.norm1(x), H, W))
-        x = x + self.drop_path(self.mlp(self.norm2(x), H, W))
-
-        return x
-
-
-class OverlapPatchEmbed(nn.Module):
-    """ Image to Patch Embedding
-    """
-
-    def __init__(self, img_size=224, patch_size=7, stride=4, in_channels=3, embed_dim=768):
-        super().__init__()
-        img_size = to_2tuple(img_size)
-        patch_size = to_2tuple(patch_size)
-
-        self.img_size = img_size
-        self.patch_size = patch_size
-        self.H, self.W = img_size[0] // patch_size[0], img_size[1] // patch_size[1]
-        self.num_patches = self.H * self.W
-        self.proj = nn.Conv2d(in_channels, embed_dim, kernel_size=patch_size, stride=stride,
-                              padding=(patch_size[0] // 2, patch_size[1] // 2))
-        self.norm = nn.LayerNorm(embed_dim)
-
-        self.apply(self._init_weights)
-
-    def _init_weights(self, m):
-        if isinstance(m, nn.Linear):
-            trunc_normal_(m.weight, std=.02)
-            if isinstance(m, nn.Linear) and m.bias is not None:
-                nn.init.constant_(m.bias, 0)
-        elif isinstance(m, nn.LayerNorm):
-            nn.init.constant_(m.bias, 0)
-            nn.init.constant_(m.weight, 1.0)
-        elif isinstance(m, nn.Conv2d):
-            fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
-            fan_out //= m.groups
-            m.weight.data.normal_(0, math.sqrt(2.0 / fan_out))
-            if m.bias is not None:
-                m.bias.data.zero_()
-
-    def forward(self, x):
-        x = self.proj(x)
-        _, _, H, W = x.shape
-        x = x.flatten(2).transpose(1, 2)
-        x = self.norm(x)
-
-        return x, H, W
-
-
-class PyramidVisionTransformerImpr(nn.Module):
-    def __init__(self, img_size=224, patch_size=16, in_channels=3, num_classes=1000, embed_dims=[64, 128, 256, 512],
-                 num_heads=[1, 2, 4, 8], mlp_ratios=[4, 4, 4, 4], qkv_bias=False, qk_scale=None, drop_rate=0.,
-                 attn_drop_rate=0., drop_path_rate=0., norm_layer=nn.LayerNorm,
-                 depths=[3, 4, 6, 3], sr_ratios=[8, 4, 2, 1]):
-        super().__init__()
-        self.num_classes = num_classes
-        self.depths = depths
-
-        # patch_embed
-        self.patch_embed1 = OverlapPatchEmbed(img_size=img_size, patch_size=7, stride=4, in_channels=in_channels,
-                                              embed_dim=embed_dims[0])
-        self.patch_embed2 = OverlapPatchEmbed(img_size=img_size // 4, patch_size=3, stride=2, in_channels=embed_dims[0],
-                                              embed_dim=embed_dims[1])
-        self.patch_embed3 = OverlapPatchEmbed(img_size=img_size // 8, patch_size=3, stride=2, in_channels=embed_dims[1],
-                                              embed_dim=embed_dims[2])
-        self.patch_embed4 = OverlapPatchEmbed(img_size=img_size // 16, patch_size=3, stride=2, in_channels=embed_dims[2],
-                                              embed_dim=embed_dims[3])
-
-        # transformer encoder
-        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))]  # stochastic depth decay rule
-        cur = 0
-        self.block1 = nn.ModuleList([Block(
-            dim=embed_dims[0], num_heads=num_heads[0], mlp_ratio=mlp_ratios[0], qkv_bias=qkv_bias, qk_scale=qk_scale,
-            drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[cur + i], norm_layer=norm_layer,
-            sr_ratio=sr_ratios[0])
-            for i in range(depths[0])])
-        self.norm1 = norm_layer(embed_dims[0])
-
-        cur += depths[0]
-        self.block2 = nn.ModuleList([Block(
-            dim=embed_dims[1], num_heads=num_heads[1], mlp_ratio=mlp_ratios[1], qkv_bias=qkv_bias, qk_scale=qk_scale,
-            drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[cur + i], norm_layer=norm_layer,
-            sr_ratio=sr_ratios[1])
-            for i in range(depths[1])])
-        self.norm2 = norm_layer(embed_dims[1])
-
-        cur += depths[1]
-        self.block3 = nn.ModuleList([Block(
-            dim=embed_dims[2], num_heads=num_heads[2], mlp_ratio=mlp_ratios[2], qkv_bias=qkv_bias, qk_scale=qk_scale,
-            drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[cur + i], norm_layer=norm_layer,
-            sr_ratio=sr_ratios[2])
-            for i in range(depths[2])])
-        self.norm3 = norm_layer(embed_dims[2])
-
-        cur += depths[2]
-        self.block4 = nn.ModuleList([Block(
-            dim=embed_dims[3], num_heads=num_heads[3], mlp_ratio=mlp_ratios[3], qkv_bias=qkv_bias, qk_scale=qk_scale,
-            drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[cur + i], norm_layer=norm_layer,
-            sr_ratio=sr_ratios[3])
-            for i in range(depths[3])])
-        self.norm4 = norm_layer(embed_dims[3])
-
-        # classification head
-        # self.head = nn.Linear(embed_dims[3], num_classes) if num_classes > 0 else nn.Identity()
-
-        self.apply(self._init_weights)
-
-    def _init_weights(self, m):
-        if isinstance(m, nn.Linear):
-            trunc_normal_(m.weight, std=.02)
-            if isinstance(m, nn.Linear) and m.bias is not None:
-                nn.init.constant_(m.bias, 0)
-        elif isinstance(m, nn.LayerNorm):
-            nn.init.constant_(m.bias, 0)
-            nn.init.constant_(m.weight, 1.0)
-        elif isinstance(m, nn.Conv2d):
-            fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
-            fan_out //= m.groups
-            m.weight.data.normal_(0, math.sqrt(2.0 / fan_out))
-            if m.bias is not None:
-                m.bias.data.zero_()
-
-    def init_weights(self, pretrained=None):
-        if isinstance(pretrained, str):
-            logger = 1
-            #load_checkpoint(self, pretrained, map_location='cpu', strict=False, logger=logger)
-
-    def reset_drop_path(self, drop_path_rate):
-        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(self.depths))]
-        cur = 0
-        for i in range(self.depths[0]):
-            self.block1[i].drop_path.drop_prob = dpr[cur + i]
-
-        cur += self.depths[0]
-        for i in range(self.depths[1]):
-            self.block2[i].drop_path.drop_prob = dpr[cur + i]
-
-        cur += self.depths[1]
-        for i in range(self.depths[2]):
-            self.block3[i].drop_path.drop_prob = dpr[cur + i]
-
-        cur += self.depths[2]
-        for i in range(self.depths[3]):
-            self.block4[i].drop_path.drop_prob = dpr[cur + i]
-
-    def freeze_patch_emb(self):
-        self.patch_embed1.requires_grad = False
-
-    @torch.jit.ignore
-    def no_weight_decay(self):
-        return {'pos_embed1', 'pos_embed2', 'pos_embed3', 'pos_embed4', 'cls_token'}  # has pos_embed may be better
-
-    def get_classifier(self):
-        return self.head
-
-    def reset_classifier(self, num_classes, global_pool=''):
-        self.num_classes = num_classes
-        self.head = nn.Linear(self.embed_dim, num_classes) if num_classes > 0 else nn.Identity()
-
-    def forward_features(self, x):
-        B = x.shape[0]
-        outs = []
-
-        # stage 1
-        x, H, W = self.patch_embed1(x)
-        for i, blk in enumerate(self.block1):
-            x = blk(x, H, W)
-        x = self.norm1(x)
-        x = x.reshape(B, H, W, -1).permute(0, 3, 1, 2).contiguous()
-        outs.append(x)
-
-        # stage 2
-        x, H, W = self.patch_embed2(x)
-        for i, blk in enumerate(self.block2):
-            x = blk(x, H, W)
-        x = self.norm2(x)
-        x = x.reshape(B, H, W, -1).permute(0, 3, 1, 2).contiguous()
-        outs.append(x)
-
-        # stage 3
-        x, H, W = self.patch_embed3(x)
-        for i, blk in enumerate(self.block3):
-            x = blk(x, H, W)
-        x = self.norm3(x)
-        x = x.reshape(B, H, W, -1).permute(0, 3, 1, 2).contiguous()
-        outs.append(x)
-
-        # stage 4
-        x, H, W = self.patch_embed4(x)
-        for i, blk in enumerate(self.block4):
-            x = blk(x, H, W)
-        x = self.norm4(x)
-        x = x.reshape(B, H, W, -1).permute(0, 3, 1, 2).contiguous()
-        outs.append(x)
-
-        return outs
-
-        # return x.mean(dim=1)
-
-    def forward(self, x):
-        x = self.forward_features(x)
-        # x = self.head(x)
-
-        return x
-
-
-class DWConv(nn.Module):
-    def __init__(self, dim=768):
-        super(DWConv, self).__init__()
-        self.dwconv = nn.Conv2d(dim, dim, 3, 1, 1, bias=True, groups=dim)
-
-    def forward(self, x, H, W):
-        B, N, C = x.shape
-        x = x.transpose(1, 2).view(B, C, H, W).contiguous()
-        x = self.dwconv(x)
-        x = x.flatten(2).transpose(1, 2)
-
-        return x
-
-
-def _conv_filter(state_dict, patch_size=16):
-    """ convert patch embedding weight from manual patchify + linear proj to conv"""
-    out_dict = {}
-    for k, v in state_dict.items():
-        if 'patch_embed.proj.weight' in k:
-            v = v.reshape((v.shape[0], 3, patch_size, patch_size))
-        out_dict[k] = v
-
-    return out_dict
-
-
-## @register_model
-class pvt_v2_b0(PyramidVisionTransformerImpr):
-    def __init__(self, **kwargs):
-        super(pvt_v2_b0, self).__init__(
-            patch_size=4, embed_dims=[32, 64, 160, 256], num_heads=[1, 2, 5, 8], mlp_ratios=[8, 8, 4, 4],
-            qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6), depths=[2, 2, 2, 2], sr_ratios=[8, 4, 2, 1],
-            drop_rate=0.0, drop_path_rate=0.1)
-
-
-
-## @register_model
-class pvt_v2_b1(PyramidVisionTransformerImpr):
-    def __init__(self, **kwargs):
-        super(pvt_v2_b1, self).__init__(
-            patch_size=4, embed_dims=[64, 128, 320, 512], num_heads=[1, 2, 5, 8], mlp_ratios=[8, 8, 4, 4],
-            qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6), depths=[2, 2, 2, 2], sr_ratios=[8, 4, 2, 1],
-            drop_rate=0.0, drop_path_rate=0.1)
-
-## @register_model
-class pvt_v2_b2(PyramidVisionTransformerImpr):
-    def __init__(self, in_channels=3, **kwargs):
-        super(pvt_v2_b2, self).__init__(
-            patch_size=4, embed_dims=[64, 128, 320, 512], num_heads=[1, 2, 5, 8], mlp_ratios=[8, 8, 4, 4],
-            qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6), depths=[3, 4, 6, 3], sr_ratios=[8, 4, 2, 1],
-            drop_rate=0.0, drop_path_rate=0.1, in_channels=in_channels)
-
-## @register_model
-class pvt_v2_b3(PyramidVisionTransformerImpr):
-    def __init__(self, **kwargs):
-        super(pvt_v2_b3, self).__init__(
-            patch_size=4, embed_dims=[64, 128, 320, 512], num_heads=[1, 2, 5, 8], mlp_ratios=[8, 8, 4, 4],
-            qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6), depths=[3, 4, 18, 3], sr_ratios=[8, 4, 2, 1],
-            drop_rate=0.0, drop_path_rate=0.1)
-
-## @register_model
-class pvt_v2_b4(PyramidVisionTransformerImpr):
-    def __init__(self, **kwargs):
-        super(pvt_v2_b4, self).__init__(
-            patch_size=4, embed_dims=[64, 128, 320, 512], num_heads=[1, 2, 5, 8], mlp_ratios=[8, 8, 4, 4],
-            qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6), depths=[3, 8, 27, 3], sr_ratios=[8, 4, 2, 1],
-            drop_rate=0.0, drop_path_rate=0.1)
-
-
-## @register_model
-class pvt_v2_b5(PyramidVisionTransformerImpr):
-    def __init__(self, **kwargs):
-        super(pvt_v2_b5, self).__init__(
-            patch_size=4, embed_dims=[64, 128, 320, 512], num_heads=[1, 2, 5, 8], mlp_ratios=[4, 4, 4, 4],
-            qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6), depths=[3, 6, 40, 3], sr_ratios=[8, 4, 2, 1],
-            drop_rate=0.0, drop_path_rate=0.1)
-
-
-
-### models/backbones/swin_v1.py
-
-# --------------------------------------------------------
-# Swin Transformer
-# Copyright (c) 2021 Microsoft
-# Licensed under The MIT License [see LICENSE for details]
-# Written by Ze Liu, Yutong Lin, Yixuan Wei
-# --------------------------------------------------------
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-import torch.utils.checkpoint as checkpoint
-import numpy as np
-from timm.models.layers import DropPath, to_2tuple, trunc_normal_
-
-# from config import Config
-
-
-# config = Config()
-
-class Mlp(nn.Module):
-    """ Multilayer perceptron."""
-
-    def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):
-        super().__init__()
-        out_features = out_features or in_features
-        hidden_features = hidden_features or in_features
-        self.fc1 = nn.Linear(in_features, hidden_features)
-        self.act = act_layer()
-        self.fc2 = nn.Linear(hidden_features, out_features)
-        self.drop = nn.Dropout(drop)
-
-    def forward(self, x):
-        x = self.fc1(x)
-        x = self.act(x)
-        x = self.drop(x)
-        x = self.fc2(x)
-        x = self.drop(x)
-        return x
-
-
-def window_partition(x, window_size):
-    """
-    Args:
-        x: (B, H, W, C)
-        window_size (int): window size
-
-    Returns:
-        windows: (num_windows*B, window_size, window_size, C)
-    """
-    B, H, W, C = x.shape
-    x = x.view(B, H // window_size, window_size, W // window_size, window_size, C)
-    windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)
-    return windows
-
-
-def window_reverse(windows, window_size, H, W):
-    """
-    Args:
-        windows: (num_windows*B, window_size, window_size, C)
-        window_size (int): Window size
-        H (int): Height of image
-        W (int): Width of image
-
-    Returns:
-        x: (B, H, W, C)
-    """
-    B = int(windows.shape[0] / (H * W / window_size / window_size))
-    x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1)
-    x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1)
-    return x
-
-
-class WindowAttention(nn.Module):
-    """ Window based multi-head self attention (W-MSA) module with relative position bias.
-    It supports both of shifted and non-shifted window.
-
-    Args:
-        dim (int): Number of input channels.
-        window_size (tuple[int]): The height and width of the window.
-        num_heads (int): Number of attention heads.
-        qkv_bias (bool, optional):  If True, add a learnable bias to query, key, value. Default: True
-        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set
-        attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0
-        proj_drop (float, optional): Dropout ratio of output. Default: 0.0
-    """
-
-    def __init__(self, dim, window_size, num_heads, qkv_bias=True, qk_scale=None, attn_drop=0., proj_drop=0.):
-
-        super().__init__()
-        self.dim = dim
-        self.window_size = window_size  # Wh, Ww
-        self.num_heads = num_heads
-        head_dim = dim // num_heads
-        self.scale = qk_scale or head_dim ** -0.5
-
-        # define a parameter table of relative position bias
-        self.relative_position_bias_table = nn.Parameter(
-            torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1), num_heads))  # 2*Wh-1 * 2*Ww-1, nH
-
-        # get pair-wise relative position index for each token inside the window
-        coords_h = torch.arange(self.window_size[0])
-        coords_w = torch.arange(self.window_size[1])
-        coords = torch.stack(torch.meshgrid([coords_h, coords_w], indexing='ij'))  # 2, Wh, Ww
-        coords_flatten = torch.flatten(coords, 1)  # 2, Wh*Ww
-        relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :]  # 2, Wh*Ww, Wh*Ww
-        relative_coords = relative_coords.permute(1, 2, 0).contiguous()  # Wh*Ww, Wh*Ww, 2
-        relative_coords[:, :, 0] += self.window_size[0] - 1  # shift to start from 0
-        relative_coords[:, :, 1] += self.window_size[1] - 1
-        relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1
-        relative_position_index = relative_coords.sum(-1)  # Wh*Ww, Wh*Ww
-        self.register_buffer("relative_position_index", relative_position_index)
-
-        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
-        self.attn_drop_prob = attn_drop
-        self.attn_drop = nn.Dropout(attn_drop)
-        self.proj = nn.Linear(dim, dim)
-        self.proj_drop = nn.Dropout(proj_drop)
-
-        trunc_normal_(self.relative_position_bias_table, std=.02)
-        self.softmax = nn.Softmax(dim=-1)
-
-    def forward(self, x, mask=None):
-        """ Forward function.
-
-        Args:
-            x: input features with shape of (num_windows*B, N, C)
-            mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None
-        """
-        B_, N, C = x.shape
-        qkv = self.qkv(x).reshape(B_, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
-        q, k, v = qkv[0], qkv[1], qkv[2]  # make torchscript happy (cannot use tensor as tuple)
-
-        q = q * self.scale
-
-        if config.SDPA_enabled:
-            x = torch.nn.functional.scaled_dot_product_attention(
-                q, k, v,
-                attn_mask=None, dropout_p=self.attn_drop_prob, is_causal=False
-            ).transpose(1, 2).reshape(B_, N, C)
-        else:
-            attn = (q @ k.transpose(-2, -1))
-
-            relative_position_bias = self.relative_position_bias_table[self.relative_position_index.view(-1)].view(
-                self.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[1], -1)  # Wh*Ww,Wh*Ww,nH
-            relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous()  # nH, Wh*Ww, Wh*Ww
-            attn = attn + relative_position_bias.unsqueeze(0)
-
-            if mask is not None:
-                nW = mask.shape[0]
-                attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze(1).unsqueeze(0)
-                attn = attn.view(-1, self.num_heads, N, N)
-                attn = self.softmax(attn)
-            else:
-                attn = self.softmax(attn)
-
-            attn = self.attn_drop(attn)
-
-            x = (attn @ v).transpose(1, 2).reshape(B_, N, C)
-        x = self.proj(x)
-        x = self.proj_drop(x)
-        return x
-
-
-class SwinTransformerBlock(nn.Module):
-    """ Swin Transformer Block.
-
-    Args:
-        dim (int): Number of input channels.
-        num_heads (int): Number of attention heads.
-        window_size (int): Window size.
-        shift_size (int): Shift size for SW-MSA.
-        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
-        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
-        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
-        drop (float, optional): Dropout rate. Default: 0.0
-        attn_drop (float, optional): Attention dropout rate. Default: 0.0
-        drop_path (float, optional): Stochastic depth rate. Default: 0.0
-        act_layer (nn.Module, optional): Activation layer. Default: nn.GELU
-        norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm
-    """
-
-    def __init__(self, dim, num_heads, window_size=7, shift_size=0,
-                 mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0., drop_path=0.,
-                 act_layer=nn.GELU, norm_layer=nn.LayerNorm):
-        super().__init__()
-        self.dim = dim
-        self.num_heads = num_heads
-        self.window_size = window_size
-        self.shift_size = shift_size
-        self.mlp_ratio = mlp_ratio
-        assert 0 <= self.shift_size < self.window_size, "shift_size must in 0-window_size"
-
-        self.norm1 = norm_layer(dim)
-        self.attn = WindowAttention(
-            dim, window_size=to_2tuple(self.window_size), num_heads=num_heads,
-            qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop)
-
-        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
-        self.norm2 = norm_layer(dim)
-        mlp_hidden_dim = int(dim * mlp_ratio)
-        self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
-
-        self.H = None
-        self.W = None
-
-    def forward(self, x, mask_matrix):
-        """ Forward function.
-
-        Args:
-            x: Input feature, tensor size (B, H*W, C).
-            H, W: Spatial resolution of the input feature.
-            mask_matrix: Attention mask for cyclic shift.
-        """
-        B, L, C = x.shape
-        H, W = self.H, self.W
-        assert L == H * W, "input feature has wrong size"
-
-        shortcut = x
-        x = self.norm1(x)
-        x = x.view(B, H, W, C)
-
-        # pad feature maps to multiples of window size
-        pad_l = pad_t = 0
-        pad_r = (self.window_size - W % self.window_size) % self.window_size
-        pad_b = (self.window_size - H % self.window_size) % self.window_size
-        x = F.pad(x, (0, 0, pad_l, pad_r, pad_t, pad_b))
-        _, Hp, Wp, _ = x.shape
-
-        # cyclic shift
-        if self.shift_size > 0:
-            shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2))
-            attn_mask = mask_matrix
-        else:
-            shifted_x = x
-            attn_mask = None
-
-        # partition windows
-        x_windows = window_partition(shifted_x, self.window_size)  # nW*B, window_size, window_size, C
-        x_windows = x_windows.view(-1, self.window_size * self.window_size, C)  # nW*B, window_size*window_size, C
-
-        # W-MSA/SW-MSA
-        attn_windows = self.attn(x_windows, mask=attn_mask)  # nW*B, window_size*window_size, C
-
-        # merge windows
-        attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C)
-        shifted_x = window_reverse(attn_windows, self.window_size, Hp, Wp)  # B H' W' C
-
-        # reverse cyclic shift
-        if self.shift_size > 0:
-            x = torch.roll(shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2))
-        else:
-            x = shifted_x
-
-        if pad_r > 0 or pad_b > 0:
-            x = x[:, :H, :W, :].contiguous()
-
-        x = x.view(B, H * W, C)
-
-        # FFN
-        x = shortcut + self.drop_path(x)
-        x = x + self.drop_path(self.mlp(self.norm2(x)))
-
-        return x
-
-
-class PatchMerging(nn.Module):
-    """ Patch Merging Layer
-
-    Args:
-        dim (int): Number of input channels.
-        norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm
-    """
-    def __init__(self, dim, norm_layer=nn.LayerNorm):
-        super().__init__()
-        self.dim = dim
-        self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False)
-        self.norm = norm_layer(4 * dim)
-
-    def forward(self, x, H, W):
-        """ Forward function.
-
-        Args:
-            x: Input feature, tensor size (B, H*W, C).
-            H, W: Spatial resolution of the input feature.
-        """
-        B, L, C = x.shape
-        assert L == H * W, "input feature has wrong size"
-
-        x = x.view(B, H, W, C)
-
-        # padding
-        pad_input = (H % 2 == 1) or (W % 2 == 1)
-        if pad_input:
-            x = F.pad(x, (0, 0, 0, W % 2, 0, H % 2))
-
-        x0 = x[:, 0::2, 0::2, :]  # B H/2 W/2 C
-        x1 = x[:, 1::2, 0::2, :]  # B H/2 W/2 C
-        x2 = x[:, 0::2, 1::2, :]  # B H/2 W/2 C
-        x3 = x[:, 1::2, 1::2, :]  # B H/2 W/2 C
-        x = torch.cat([x0, x1, x2, x3], -1)  # B H/2 W/2 4*C
-        x = x.view(B, -1, 4 * C)  # B H/2*W/2 4*C
-
-        x = self.norm(x)
-        x = self.reduction(x)
-
-        return x
-
-
-class BasicLayer(nn.Module):
-    """ A basic Swin Transformer layer for one stage.
-
-    Args:
-        dim (int): Number of feature channels
-        depth (int): Depths of this stage.
-        num_heads (int): Number of attention head.
-        window_size (int): Local window size. Default: 7.
-        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4.
-        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
-        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
-        drop (float, optional): Dropout rate. Default: 0.0
-        attn_drop (float, optional): Attention dropout rate. Default: 0.0
-        drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0
-        norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
-        downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None
-        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
-    """
-
-    def __init__(self,
-                 dim,
-                 depth,
-                 num_heads,
-                 window_size=7,
-                 mlp_ratio=4.,
-                 qkv_bias=True,
-                 qk_scale=None,
-                 drop=0.,
-                 attn_drop=0.,
-                 drop_path=0.,
-                 norm_layer=nn.LayerNorm,
-                 downsample=None,
-                 use_checkpoint=False):
-        super().__init__()
-        self.window_size = window_size
-        self.shift_size = window_size // 2
-        self.depth = depth
-        self.use_checkpoint = use_checkpoint
-
-        # build blocks
-        self.blocks = nn.ModuleList([
-            SwinTransformerBlock(
-                dim=dim,
-                num_heads=num_heads,
-                window_size=window_size,
-                shift_size=0 if (i % 2 == 0) else window_size // 2,
-                mlp_ratio=mlp_ratio,
-                qkv_bias=qkv_bias,
-                qk_scale=qk_scale,
-                drop=drop,
-                attn_drop=attn_drop,
-                drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path,
-                norm_layer=norm_layer)
-            for i in range(depth)])
-
-        # patch merging layer
-        if downsample is not None:
-            self.downsample = downsample(dim=dim, norm_layer=norm_layer)
-        else:
-            self.downsample = None
-
-    def forward(self, x, H, W):
-        """ Forward function.
-
-        Args:
-            x: Input feature, tensor size (B, H*W, C).
-            H, W: Spatial resolution of the input feature.
-        """
-
-        # calculate attention mask for SW-MSA
-        Hp = int(np.ceil(H / self.window_size)) * self.window_size
-        Wp = int(np.ceil(W / self.window_size)) * self.window_size
-        img_mask = torch.zeros((1, Hp, Wp, 1), device=x.device)  # 1 Hp Wp 1
-        h_slices = (slice(0, -self.window_size),
-                    slice(-self.window_size, -self.shift_size),
-                    slice(-self.shift_size, None))
-        w_slices = (slice(0, -self.window_size),
-                    slice(-self.window_size, -self.shift_size),
-                    slice(-self.shift_size, None))
-        cnt = 0
-        for h in h_slices:
-            for w in w_slices:
-                img_mask[:, h, w, :] = cnt
-                cnt += 1
-
-        mask_windows = window_partition(img_mask, self.window_size)  # nW, window_size, window_size, 1
-        mask_windows = mask_windows.view(-1, self.window_size * self.window_size)
-        attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)
-        attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(attn_mask == 0, float(0.0)).to(x.dtype)
-
-        for blk in self.blocks:
-            blk.H, blk.W = H, W
-            if self.use_checkpoint:
-                x = checkpoint.checkpoint(blk, x, attn_mask)
-            else:
-                x = blk(x, attn_mask)
-        if self.downsample is not None:
-            x_down = self.downsample(x, H, W)
-            Wh, Ww = (H + 1) // 2, (W + 1) // 2
-            return x, H, W, x_down, Wh, Ww
-        else:
-            return x, H, W, x, H, W
-
-
-class PatchEmbed(nn.Module):
-    """ Image to Patch Embedding
-
-    Args:
-        patch_size (int): Patch token size. Default: 4.
-        in_channels (int): Number of input image channels. Default: 3.
-        embed_dim (int): Number of linear projection output channels. Default: 96.
-        norm_layer (nn.Module, optional): Normalization layer. Default: None
-    """
-
-    def __init__(self, patch_size=4, in_channels=3, embed_dim=96, norm_layer=None):
-        super().__init__()
-        patch_size = to_2tuple(patch_size)
-        self.patch_size = patch_size
-
-        self.in_channels = in_channels
-        self.embed_dim = embed_dim
-
-        self.proj = nn.Conv2d(in_channels, embed_dim, kernel_size=patch_size, stride=patch_size)
-        if norm_layer is not None:
-            self.norm = norm_layer(embed_dim)
-        else:
-            self.norm = None
-
-    def forward(self, x):
-        """Forward function."""
-        # padding
-        _, _, H, W = x.size()
-        if W % self.patch_size[1] != 0:
-            x = F.pad(x, (0, self.patch_size[1] - W % self.patch_size[1]))
-        if H % self.patch_size[0] != 0:
-            x = F.pad(x, (0, 0, 0, self.patch_size[0] - H % self.patch_size[0]))
-
-        x = self.proj(x)  # B C Wh Ww
-        if self.norm is not None:
-            Wh, Ww = x.size(2), x.size(3)
-            x = x.flatten(2).transpose(1, 2)
-            x = self.norm(x)
-            x = x.transpose(1, 2).view(-1, self.embed_dim, Wh, Ww)
-
-        return x
-
-
-class SwinTransformer(nn.Module):
-    """ Swin Transformer backbone.
-        A PyTorch impl of : `Swin Transformer: Hierarchical Vision Transformer using Shifted Windows`  -
-          https://arxiv.org/pdf/2103.14030
-
-    Args:
-        pretrain_img_size (int): Input image size for training the pretrained model,
-            used in absolute postion embedding. Default 224.
-        patch_size (int | tuple(int)): Patch size. Default: 4.
-        in_channels (int): Number of input image channels. Default: 3.
-        embed_dim (int): Number of linear projection output channels. Default: 96.
-        depths (tuple[int]): Depths of each Swin Transformer stage.
-        num_heads (tuple[int]): Number of attention head of each stage.
-        window_size (int): Window size. Default: 7.
-        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4.
-        qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True
-        qk_scale (float): Override default qk scale of head_dim ** -0.5 if set.
-        drop_rate (float): Dropout rate.
-        attn_drop_rate (float): Attention dropout rate. Default: 0.
-        drop_path_rate (float): Stochastic depth rate. Default: 0.2.
-        norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm.
-        ape (bool): If True, add absolute position embedding to the patch embedding. Default: False.
-        patch_norm (bool): If True, add normalization after patch embedding. Default: True.
-        out_indices (Sequence[int]): Output from which stages.
-        frozen_stages (int): Stages to be frozen (stop grad and set eval mode).
-            -1 means not freezing any parameters.
-        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
-    """
-
-    def __init__(self,
-                 pretrain_img_size=224,
-                 patch_size=4,
-                 in_channels=3,
-                 embed_dim=96,
-                 depths=[2, 2, 6, 2],
-                 num_heads=[3, 6, 12, 24],
-                 window_size=7,
-                 mlp_ratio=4.,
-                 qkv_bias=True,
-                 qk_scale=None,
-                 drop_rate=0.,
-                 attn_drop_rate=0.,
-                 drop_path_rate=0.2,
-                 norm_layer=nn.LayerNorm,
-                 ape=False,
-                 patch_norm=True,
-                 out_indices=(0, 1, 2, 3),
-                 frozen_stages=-1,
-                 use_checkpoint=False):
-        super().__init__()
-
-        self.pretrain_img_size = pretrain_img_size
-        self.num_layers = len(depths)
-        self.embed_dim = embed_dim
-        self.ape = ape
-        self.patch_norm = patch_norm
-        self.out_indices = out_indices
-        self.frozen_stages = frozen_stages
-
-        # split image into non-overlapping patches
-        self.patch_embed = PatchEmbed(
-            patch_size=patch_size, in_channels=in_channels, embed_dim=embed_dim,
-            norm_layer=norm_layer if self.patch_norm else None)
-
-        # absolute position embedding
-        if self.ape:
-            pretrain_img_size = to_2tuple(pretrain_img_size)
-            patch_size = to_2tuple(patch_size)
-            patches_resolution = [pretrain_img_size[0] // patch_size[0], pretrain_img_size[1] // patch_size[1]]
-
-            self.absolute_pos_embed = nn.Parameter(torch.zeros(1, embed_dim, patches_resolution[0], patches_resolution[1]))
-            trunc_normal_(self.absolute_pos_embed, std=.02)
-
-        self.pos_drop = nn.Dropout(p=drop_rate)
-
-        # stochastic depth
-        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))]  # stochastic depth decay rule
-
-        # build layers
-        self.layers = nn.ModuleList()
-        for i_layer in range(self.num_layers):
-            layer = BasicLayer(
-                dim=int(embed_dim * 2 ** i_layer),
-                depth=depths[i_layer],
-                num_heads=num_heads[i_layer],
-                window_size=window_size,
-                mlp_ratio=mlp_ratio,
-                qkv_bias=qkv_bias,
-                qk_scale=qk_scale,
-                drop=drop_rate,
-                attn_drop=attn_drop_rate,
-                drop_path=dpr[sum(depths[:i_layer]):sum(depths[:i_layer + 1])],
-                norm_layer=norm_layer,
-                downsample=PatchMerging if (i_layer < self.num_layers - 1) else None,
-                use_checkpoint=use_checkpoint)
-            self.layers.append(layer)
-
-        num_features = [int(embed_dim * 2 ** i) for i in range(self.num_layers)]
-        self.num_features = num_features
-
-        # add a norm layer for each output
-        for i_layer in out_indices:
-            layer = norm_layer(num_features[i_layer])
-            layer_name = f'norm{i_layer}'
-            self.add_module(layer_name, layer)
-
-        self._freeze_stages()
-
-    def _freeze_stages(self):
-        if self.frozen_stages >= 0:
-            self.patch_embed.eval()
-            for param in self.patch_embed.parameters():
-                param.requires_grad = False
-
-        if self.frozen_stages >= 1 and self.ape:
-            self.absolute_pos_embed.requires_grad = False
-
-        if self.frozen_stages >= 2:
-            self.pos_drop.eval()
-            for i in range(0, self.frozen_stages - 1):
-                m = self.layers[i]
-                m.eval()
-                for param in m.parameters():
-                    param.requires_grad = False
-
-
-    def forward(self, x):
-        """Forward function."""
-        x = self.patch_embed(x)
-
-        Wh, Ww = x.size(2), x.size(3)
-        if self.ape:
-            # interpolate the position embedding to the corresponding size
-            absolute_pos_embed = F.interpolate(self.absolute_pos_embed, size=(Wh, Ww), mode='bicubic')
-            x = (x + absolute_pos_embed) # B Wh*Ww C
-            
-        outs = []#x.contiguous()]
-        x = x.flatten(2).transpose(1, 2)
-        x = self.pos_drop(x)
-        for i in range(self.num_layers):
-            layer = self.layers[i]
-            x_out, H, W, x, Wh, Ww = layer(x, Wh, Ww)
-
-            if i in self.out_indices:
-                norm_layer = getattr(self, f'norm{i}')
-                x_out = norm_layer(x_out)
-
-                out = x_out.view(-1, H, W, self.num_features[i]).permute(0, 3, 1, 2).contiguous()
-                outs.append(out)
-
-        return tuple(outs)
-
-    def train(self, mode=True):
-        """Convert the model into training mode while keep layers freezed."""
-        super(SwinTransformer, self).train(mode)
-        self._freeze_stages()
-
-def swin_v1_t():
-    model = SwinTransformer(embed_dim=96, depths=[2, 2, 6, 2], num_heads=[3, 6, 12, 24], window_size=7)
-    return model
-
-def swin_v1_s():
-    model = SwinTransformer(embed_dim=96, depths=[2, 2, 18, 2], num_heads=[3, 6, 12, 24], window_size=7)
-    return model
-
-def swin_v1_b():
-    model = SwinTransformer(embed_dim=128, depths=[2, 2, 18, 2], num_heads=[4, 8, 16, 32], window_size=12)
-    return model
-
-def swin_v1_l():
-    model = SwinTransformer(embed_dim=192, depths=[2, 2, 18, 2], num_heads=[6, 12, 24, 48], window_size=12)
-    return model
-
-
-
-### models/modules/deform_conv.py
-
-import torch
-import torch.nn as nn
-from torchvision.ops import deform_conv2d
-
-
-class DeformableConv2d(nn.Module):
-    def __init__(self,
-                 in_channels,
-                 out_channels,
-                 kernel_size=3,
-                 stride=1,
-                 padding=1,
-                 bias=False):
-
-        super(DeformableConv2d, self).__init__()
-        
-        assert type(kernel_size) == tuple or type(kernel_size) == int
-
-        kernel_size = kernel_size if type(kernel_size) == tuple else (kernel_size, kernel_size)
-        self.stride = stride if type(stride) == tuple else (stride, stride)
-        self.padding = padding
-        
-        self.offset_conv = nn.Conv2d(in_channels,
-                                     2 * kernel_size[0] * kernel_size[1],
-                                     kernel_size=kernel_size,
-                                     stride=stride,
-                                     padding=self.padding,
-                                     bias=True)
-
-        nn.init.constant_(self.offset_conv.weight, 0.)
-        nn.init.constant_(self.offset_conv.bias, 0.)
-        
-        self.modulator_conv = nn.Conv2d(in_channels,
-                                     1 * kernel_size[0] * kernel_size[1],
-                                     kernel_size=kernel_size,
-                                     stride=stride,
-                                     padding=self.padding,
-                                     bias=True)
-
-        nn.init.constant_(self.modulator_conv.weight, 0.)
-        nn.init.constant_(self.modulator_conv.bias, 0.)
-
-        self.regular_conv = nn.Conv2d(in_channels,
-                                      out_channels=out_channels,
-                                      kernel_size=kernel_size,
-                                      stride=stride,
-                                      padding=self.padding,
-                                      bias=bias)
-
-    def forward(self, x):
-        #h, w = x.shape[2:]
-        #max_offset = max(h, w)/4.
-
-        offset = self.offset_conv(x)#.clamp(-max_offset, max_offset)
-        modulator = 2. * torch.sigmoid(self.modulator_conv(x))
-        
-        x = deform_conv2d(
-            input=x,
-            offset=offset,
-            weight=self.regular_conv.weight,
-            bias=self.regular_conv.bias,
-            padding=self.padding,
-            mask=modulator,
-            stride=self.stride,
-        )
-        return x
-
-
-
-
-### utils.py
-
-import torch.nn as nn
-
-
-def build_act_layer(act_layer):
-    if act_layer == 'ReLU':
-        return nn.ReLU(inplace=True)
-    elif act_layer == 'SiLU':
-        return nn.SiLU(inplace=True)
-    elif act_layer == 'GELU':
-        return nn.GELU()
-
-    raise NotImplementedError(f'build_act_layer does not support {act_layer}')
-
-
-def build_norm_layer(dim,
-                     norm_layer,
-                     in_format='channels_last',
-                     out_format='channels_last',
-                     eps=1e-6):
-    layers = []
-    if norm_layer == 'BN':
-        if in_format == 'channels_last':
-            layers.append(to_channels_first())
-        layers.append(nn.BatchNorm2d(dim))
-        if out_format == 'channels_last':
-            layers.append(to_channels_last())
-    elif norm_layer == 'LN':
-        if in_format == 'channels_first':
-            layers.append(to_channels_last())
-        layers.append(nn.LayerNorm(dim, eps=eps))
-        if out_format == 'channels_first':
-            layers.append(to_channels_first())
-    else:
-        raise NotImplementedError(
-            f'build_norm_layer does not support {norm_layer}')
-    return nn.Sequential(*layers)
-
-
-class to_channels_first(nn.Module):
-
-    def __init__(self):
-        super().__init__()
-
-    def forward(self, x):
-        return x.permute(0, 3, 1, 2)
-
-
-class to_channels_last(nn.Module):
-
-    def __init__(self):
-        super().__init__()
-
-    def forward(self, x):
-        return x.permute(0, 2, 3, 1)
-
-
-
-### dataset.py
-
-_class_labels_TR_sorted = (
-    'Airplane, Ant, Antenna, Archery, Axe, BabyCarriage, Bag, BalanceBeam, Balcony, Balloon, Basket, BasketballHoop, Beatle, Bed, Bee, Bench, Bicycle, '
-    'BicycleFrame, BicycleStand, Boat, Bonsai, BoomLift, Bridge, BunkBed, Butterfly, Button, Cable, CableLift, Cage, Camcorder, Cannon, Canoe, Car, '
-    'CarParkDropArm, Carriage, Cart, Caterpillar, CeilingLamp, Centipede, Chair, Clip, Clock, Clothes, CoatHanger, Comb, ConcretePumpTruck, Crack, Crane, '
-    'Cup, DentalChair, Desk, DeskChair, Diagram, DishRack, DoorHandle, Dragonfish, Dragonfly, Drum, Earphone, Easel, ElectricIron, Excavator, Eyeglasses, '
-    'Fan, Fence, Fencing, FerrisWheel, FireExtinguisher, Fishing, Flag, FloorLamp, Forklift, GasStation, Gate, Gear, Goal, Golf, GymEquipment, Hammock, '
-    'Handcart, Handcraft, Handrail, HangGlider, Harp, Harvester, Headset, Helicopter, Helmet, Hook, HorizontalBar, Hydrovalve, IroningTable, Jewelry, Key, '
-    'KidsPlayground, Kitchenware, Kite, Knife, Ladder, LaundryRack, Lightning, Lobster, Locust, Machine, MachineGun, MagazineRack, Mantis, Medal, MemorialArchway, '
-    'Microphone, Missile, MobileHolder, Monitor, Mosquito, Motorcycle, MovingTrolley, Mower, MusicPlayer, MusicStand, ObservationTower, Octopus, OilWell, '
-    'OlympicLogo, OperatingTable, OutdoorFitnessEquipment, Parachute, Pavilion, Piano, Pipe, PlowHarrow, PoleVault, Punchbag, Rack, Racket, Rifle, Ring, Robot, '
-    'RockClimbing, Rope, Sailboat, Satellite, Scaffold, Scale, Scissor, Scooter, Sculpture, Seadragon, Seahorse, Seal, SewingMachine, Ship, Shoe, ShoppingCart, '
-    'ShoppingTrolley, Shower, Shrimp, Signboard, Skateboarding, Skeleton, Skiing, Spade, SpeedBoat, Spider, Spoon, Stair, Stand, Stationary, SteeringWheel, '
-    'Stethoscope, Stool, Stove, StreetLamp, SweetStand, Swing, Sword, TV, Table, TableChair, TableLamp, TableTennis, Tank, Tapeline, Teapot, Telescope, Tent, '
-    'TobaccoPipe, Toy, Tractor, TrafficLight, TrafficSign, Trampoline, TransmissionTower, Tree, Tricycle, TrimmerCover, Tripod, Trombone, Truck, Trumpet, Tuba, '
-    'UAV, Umbrella, UnevenBars, UtilityPole, VacuumCleaner, Violin, Wakesurfing, Watch, WaterTower, WateringPot, Well, WellLid, Wheel, Wheelchair, WindTurbine, Windmill, WineGlass, WireWhisk, Yacht'
-)
-class_labels_TR_sorted = _class_labels_TR_sorted.split(', ')
-
-
-### models/backbones/build_backbones.py
-
-import torch
-import torch.nn as nn
-from collections import OrderedDict
-from torchvision.models import vgg16, vgg16_bn, VGG16_Weights, VGG16_BN_Weights, resnet50, ResNet50_Weights
-# from models.pvt_v2 import pvt_v2_b0, pvt_v2_b1, pvt_v2_b2, pvt_v2_b5
-# from models.swin_v1 import swin_v1_t, swin_v1_s, swin_v1_b, swin_v1_l
-# from config import Config
-
-
-config = Config()
-
-def build_backbone(bb_name, pretrained=True, params_settings=''):
-    if bb_name == 'vgg16':
-        bb_net = list(vgg16(pretrained=VGG16_Weights.DEFAULT if pretrained else None).children())[0]
-        bb = nn.Sequential(OrderedDict({'conv1': bb_net[:4], 'conv2': bb_net[4:9], 'conv3': bb_net[9:16], 'conv4': bb_net[16:23]}))
-    elif bb_name == 'vgg16bn':
-        bb_net = list(vgg16_bn(pretrained=VGG16_BN_Weights.DEFAULT if pretrained else None).children())[0]
-        bb = nn.Sequential(OrderedDict({'conv1': bb_net[:6], 'conv2': bb_net[6:13], 'conv3': bb_net[13:23], 'conv4': bb_net[23:33]}))
-    elif bb_name == 'resnet50':
-        bb_net = list(resnet50(pretrained=ResNet50_Weights.DEFAULT if pretrained else None).children())
-        bb = nn.Sequential(OrderedDict({'conv1': nn.Sequential(*bb_net[0:3]), 'conv2': bb_net[4], 'conv3': bb_net[5], 'conv4': bb_net[6]}))
-    else:
-        bb = eval('{}({})'.format(bb_name, params_settings))
-        if pretrained:
-            bb = load_weights(bb, bb_name)
-    return bb
-
-def load_weights(model, model_name):
-    save_model = torch.load(config.weights[model_name], map_location='cpu')
-    model_dict = model.state_dict()
-    state_dict = {k: v if v.size() == model_dict[k].size() else model_dict[k] for k, v in save_model.items() if k in model_dict.keys()}
-    # to ignore the weights with mismatched size when I modify the backbone itself.
-    if not state_dict:
-        save_model_keys = list(save_model.keys())
-        sub_item = save_model_keys[0] if len(save_model_keys) == 1 else None
-        state_dict = {k: v if v.size() == model_dict[k].size() else model_dict[k] for k, v in save_model[sub_item].items() if k in model_dict.keys()}
-        if not state_dict or not sub_item:
-            print('Weights are not successully loaded. Check the state dict of weights file.')
-            return None
-        else:
-            print('Found correct weights in the "{}" item of loaded state_dict.'.format(sub_item))
-    model_dict.update(state_dict)
-    model.load_state_dict(model_dict)
-    return model
-
-
-
-### models/modules/decoder_blocks.py
-
-import torch
-import torch.nn as nn
-# from models.aspp import ASPP, ASPPDeformable
-# from config import Config
-
-
-# config = Config()
-
-
-class BasicDecBlk(nn.Module):
-    def __init__(self, in_channels=64, out_channels=64, inter_channels=64):
-        super(BasicDecBlk, self).__init__()
-        inter_channels = in_channels // 4 if config.dec_channels_inter == 'adap' else 64
-        self.conv_in = nn.Conv2d(in_channels, inter_channels, 3, 1, padding=1)
-        self.relu_in = nn.ReLU(inplace=True)
-        if config.dec_att == 'ASPP':
-            self.dec_att = ASPP(in_channels=inter_channels)
-        elif config.dec_att == 'ASPPDeformable':
-            self.dec_att = ASPPDeformable(in_channels=inter_channels)
-        self.conv_out = nn.Conv2d(inter_channels, out_channels, 3, 1, padding=1)
-        self.bn_in = nn.BatchNorm2d(inter_channels) if config.batch_size > 1 else nn.Identity()
-        self.bn_out = nn.BatchNorm2d(out_channels) if config.batch_size > 1 else nn.Identity()
-
-    def forward(self, x):
-        x = self.conv_in(x)
-        x = self.bn_in(x)
-        x = self.relu_in(x)
-        if hasattr(self, 'dec_att'):
-            x = self.dec_att(x)
-        x = self.conv_out(x)
-        x = self.bn_out(x)
-        return x
-
-
-class ResBlk(nn.Module):
-    def __init__(self, in_channels=64, out_channels=None, inter_channels=64):
-        super(ResBlk, self).__init__()
-        if out_channels is None:
-            out_channels = in_channels
-        inter_channels = in_channels // 4 if config.dec_channels_inter == 'adap' else 64
-
-        self.conv_in = nn.Conv2d(in_channels, inter_channels, 3, 1, padding=1)
-        self.bn_in = nn.BatchNorm2d(inter_channels) if config.batch_size > 1 else nn.Identity()
-        self.relu_in = nn.ReLU(inplace=True)
-
-        if config.dec_att == 'ASPP':
-            self.dec_att = ASPP(in_channels=inter_channels)
-        elif config.dec_att == 'ASPPDeformable':
-            self.dec_att = ASPPDeformable(in_channels=inter_channels)
-
-        self.conv_out = nn.Conv2d(inter_channels, out_channels, 3, 1, padding=1)
-        self.bn_out = nn.BatchNorm2d(out_channels) if config.batch_size > 1 else nn.Identity()
-        
-        self.conv_resi = nn.Conv2d(in_channels, out_channels, 1, 1, 0)
-
-    def forward(self, x):
-        _x = self.conv_resi(x)
-        x = self.conv_in(x)
-        x = self.bn_in(x)
-        x = self.relu_in(x)
-        if hasattr(self, 'dec_att'):
-            x = self.dec_att(x)
-        x = self.conv_out(x)
-        x = self.bn_out(x)
-        return x + _x
-
-
-
-### models/modules/lateral_blocks.py
-
-import numpy as np
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from functools import partial
-
-# from config import Config
-
-
-# config = Config()
-
-
-class BasicLatBlk(nn.Module):
-    def __init__(self, in_channels=64, out_channels=64, inter_channels=64):
-        super(BasicLatBlk, self).__init__()
-        inter_channels = in_channels // 4 if config.dec_channels_inter == 'adap' else 64
-        self.conv = nn.Conv2d(in_channels, out_channels, 1, 1, 0)
-
-    def forward(self, x):
-        x = self.conv(x)
-        return x
-
-
-
-### models/modules/aspp.py
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-# from models.deform_conv import DeformableConv2d
-# from config import Config
-
-
-# config = Config()
-
-
-class _ASPPModule(nn.Module):
-    def __init__(self, in_channels, planes, kernel_size, padding, dilation):
-        super(_ASPPModule, self).__init__()
-        self.atrous_conv = nn.Conv2d(in_channels, planes, kernel_size=kernel_size,
-                                            stride=1, padding=padding, dilation=dilation, bias=False)
-        self.bn = nn.BatchNorm2d(planes) if config.batch_size > 1 else nn.Identity()
-        self.relu = nn.ReLU(inplace=True)
-
-    def forward(self, x):
-        x = self.atrous_conv(x)
-        x = self.bn(x)
-
-        return self.relu(x)
-
-
-class ASPP(nn.Module):
-    def __init__(self, in_channels=64, out_channels=None, output_stride=16):
-        super(ASPP, self).__init__()
-        self.down_scale = 1
-        if out_channels is None:
-            out_channels = in_channels
-        self.in_channelster = 256 // self.down_scale
-        if output_stride == 16:
-            dilations = [1, 6, 12, 18]
-        elif output_stride == 8:
-            dilations = [1, 12, 24, 36]
-        else:
-            raise NotImplementedError
-
-        self.aspp1 = _ASPPModule(in_channels, self.in_channelster, 1, padding=0, dilation=dilations[0])
-        self.aspp2 = _ASPPModule(in_channels, self.in_channelster, 3, padding=dilations[1], dilation=dilations[1])
-        self.aspp3 = _ASPPModule(in_channels, self.in_channelster, 3, padding=dilations[2], dilation=dilations[2])
-        self.aspp4 = _ASPPModule(in_channels, self.in_channelster, 3, padding=dilations[3], dilation=dilations[3])
-
-        self.global_avg_pool = nn.Sequential(nn.AdaptiveAvgPool2d((1, 1)),
-                                             nn.Conv2d(in_channels, self.in_channelster, 1, stride=1, bias=False),
-                                             nn.BatchNorm2d(self.in_channelster) if config.batch_size > 1 else nn.Identity(),
-                                             nn.ReLU(inplace=True))
-        self.conv1 = nn.Conv2d(self.in_channelster * 5, out_channels, 1, bias=False)
-        self.bn1 = nn.BatchNorm2d(out_channels) if config.batch_size > 1 else nn.Identity()
-        self.relu = nn.ReLU(inplace=True)
-        self.dropout = nn.Dropout(0.5)
-
-    def forward(self, x):
-        x1 = self.aspp1(x)
-        x2 = self.aspp2(x)
-        x3 = self.aspp3(x)
-        x4 = self.aspp4(x)
-        x5 = self.global_avg_pool(x)
-        x5 = F.interpolate(x5, size=x1.size()[2:], mode='bilinear', align_corners=True)
-        x = torch.cat((x1, x2, x3, x4, x5), dim=1)
-
-        x = self.conv1(x)
-        x = self.bn1(x)
-        x = self.relu(x)
-
-        return self.dropout(x)
-
-
-##################### Deformable
-class _ASPPModuleDeformable(nn.Module):
-    def __init__(self, in_channels, planes, kernel_size, padding):
-        super(_ASPPModuleDeformable, self).__init__()
-        self.atrous_conv = DeformableConv2d(in_channels, planes, kernel_size=kernel_size,
-                                            stride=1, padding=padding, bias=False)
-        self.bn = nn.BatchNorm2d(planes) if config.batch_size > 1 else nn.Identity()
-        self.relu = nn.ReLU(inplace=True)
-
-    def forward(self, x):
-        x = self.atrous_conv(x)
-        x = self.bn(x)
-
-        return self.relu(x)
-
-
-class ASPPDeformable(nn.Module):
-    def __init__(self, in_channels, out_channels=None, parallel_block_sizes=[1, 3, 7]):
-        super(ASPPDeformable, self).__init__()
-        self.down_scale = 1
-        if out_channels is None:
-            out_channels = in_channels
-        self.in_channelster = 256 // self.down_scale
-
-        self.aspp1 = _ASPPModuleDeformable(in_channels, self.in_channelster, 1, padding=0)
-        self.aspp_deforms = nn.ModuleList([
-            _ASPPModuleDeformable(in_channels, self.in_channelster, conv_size, padding=int(conv_size//2)) for conv_size in parallel_block_sizes
-        ])
-
-        self.global_avg_pool = nn.Sequential(nn.AdaptiveAvgPool2d((1, 1)),
-                                             nn.Conv2d(in_channels, self.in_channelster, 1, stride=1, bias=False),
-                                             nn.BatchNorm2d(self.in_channelster) if config.batch_size > 1 else nn.Identity(),
-                                             nn.ReLU(inplace=True))
-        self.conv1 = nn.Conv2d(self.in_channelster * (2 + len(self.aspp_deforms)), out_channels, 1, bias=False)
-        self.bn1 = nn.BatchNorm2d(out_channels) if config.batch_size > 1 else nn.Identity()
-        self.relu = nn.ReLU(inplace=True)
-        self.dropout = nn.Dropout(0.5)
-
-    def forward(self, x):
-        x1 = self.aspp1(x)
-        x_aspp_deforms = [aspp_deform(x) for aspp_deform in self.aspp_deforms]
-        x5 = self.global_avg_pool(x)
-        x5 = F.interpolate(x5, size=x1.size()[2:], mode='bilinear', align_corners=True)
-        x = torch.cat((x1, *x_aspp_deforms, x5), dim=1)
-
-        x = self.conv1(x)
-        x = self.bn1(x)
-        x = self.relu(x)
-
-        return self.dropout(x)
-
-
-
-### models/refinement/refiner.py
-
-import torch
-import torch.nn as nn
-from collections import OrderedDict
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from torchvision.models import vgg16, vgg16_bn
-from torchvision.models import resnet50
-
-# from config import Config
-# from dataset import class_labels_TR_sorted
-# from models.build_backbone import build_backbone
-# from models.decoder_blocks import BasicDecBlk
-# from models.lateral_blocks import BasicLatBlk
-# from models.ing import *
-# from models.stem_layer import StemLayer
-
-
-class RefinerPVTInChannels4(nn.Module):
-    def __init__(self, in_channels=3+1):
-        super(RefinerPVTInChannels4, self).__init__()
-        self.config = Config()
-        self.epoch = 1
-        self.bb = build_backbone(self.config.bb, params_settings='in_channels=4')
-
-        lateral_channels_in_collection = {
-            'vgg16': [512, 256, 128, 64], 'vgg16bn': [512, 256, 128, 64], 'resnet50': [1024, 512, 256, 64],
-            'pvt_v2_b2': [512, 320, 128, 64], 'pvt_v2_b5': [512, 320, 128, 64],
-            'swin_v1_b': [1024, 512, 256, 128], 'swin_v1_l': [1536, 768, 384, 192],
-        }
-        channels = lateral_channels_in_collection[self.config.bb]
-        self.squeeze_module = BasicDecBlk(channels[0], channels[0])
-
-        self.decoder = Decoder(channels)
-
-        if 0:
-            for key, value in self.named_parameters():
-                if 'bb.' in key:
-                    value.requires_grad = False
-
-    def forward(self, x):
-        if isinstance(x, list):
-            x = torch.cat(x, dim=1)
-        ########## Encoder ##########
-        if self.config.bb in ['vgg16', 'vgg16bn', 'resnet50']:
-            x1 = self.bb.conv1(x)
-            x2 = self.bb.conv2(x1)
-            x3 = self.bb.conv3(x2)
-            x4 = self.bb.conv4(x3)
-        else:
-            x1, x2, x3, x4 = self.bb(x)
-
-        x4 = self.squeeze_module(x4)
-
-        ########## Decoder ##########
-
-        features = [x, x1, x2, x3, x4]
-        scaled_preds = self.decoder(features)
-
-        return scaled_preds
-
-
-class Refiner(nn.Module):
-    def __init__(self, in_channels=3+1):
-        super(Refiner, self).__init__()
-        self.config = Config()
-        self.epoch = 1
-        self.stem_layer = StemLayer(in_channels=in_channels, inter_channels=48, out_channels=3, norm_layer='BN' if self.config.batch_size > 1 else 'LN')
-        self.bb = build_backbone(self.config.bb)
-
-        lateral_channels_in_collection = {
-            'vgg16': [512, 256, 128, 64], 'vgg16bn': [512, 256, 128, 64], 'resnet50': [1024, 512, 256, 64],
-            'pvt_v2_b2': [512, 320, 128, 64], 'pvt_v2_b5': [512, 320, 128, 64],
-            'swin_v1_b': [1024, 512, 256, 128], 'swin_v1_l': [1536, 768, 384, 192],
-        }
-        channels = lateral_channels_in_collection[self.config.bb]
-        self.squeeze_module = BasicDecBlk(channels[0], channels[0])
-
-        self.decoder = Decoder(channels)
-
-        if 0:
-            for key, value in self.named_parameters():
-                if 'bb.' in key:
-                    value.requires_grad = False
-
-    def forward(self, x):
-        if isinstance(x, list):
-            x = torch.cat(x, dim=1)
-        x = self.stem_layer(x)
-        ########## Encoder ##########
-        if self.config.bb in ['vgg16', 'vgg16bn', 'resnet50']:
-            x1 = self.bb.conv1(x)
-            x2 = self.bb.conv2(x1)
-            x3 = self.bb.conv3(x2)
-            x4 = self.bb.conv4(x3)
-        else:
-            x1, x2, x3, x4 = self.bb(x)
-
-        x4 = self.squeeze_module(x4)
-
-        ########## Decoder ##########
-
-        features = [x, x1, x2, x3, x4]
-        scaled_preds = self.decoder(features)
-
-        return scaled_preds
-
-
-class Decoder(nn.Module):
-    def __init__(self, channels):
-        super(Decoder, self).__init__()
-        self.config = Config()
-        DecoderBlock = eval('BasicDecBlk')
-        LateralBlock = eval('BasicLatBlk')
-
-        self.decoder_block4 = DecoderBlock(channels[0], channels[1])
-        self.decoder_block3 = DecoderBlock(channels[1], channels[2])
-        self.decoder_block2 = DecoderBlock(channels[2], channels[3])
-        self.decoder_block1 = DecoderBlock(channels[3], channels[3]//2)
-
-        self.lateral_block4 = LateralBlock(channels[1], channels[1])
-        self.lateral_block3 = LateralBlock(channels[2], channels[2])
-        self.lateral_block2 = LateralBlock(channels[3], channels[3])
-
-        if self.config.ms_supervision:
-            self.conv_ms_spvn_4 = nn.Conv2d(channels[1], 1, 1, 1, 0)
-            self.conv_ms_spvn_3 = nn.Conv2d(channels[2], 1, 1, 1, 0)
-            self.conv_ms_spvn_2 = nn.Conv2d(channels[3], 1, 1, 1, 0)
-        self.conv_out1 = nn.Sequential(nn.Conv2d(channels[3]//2, 1, 1, 1, 0))
-
-    def forward(self, features):
-        x, x1, x2, x3, x4 = features
-        outs = []
-        p4 = self.decoder_block4(x4)
-        _p4 = F.interpolate(p4, size=x3.shape[2:], mode='bilinear', align_corners=True)
-        _p3 = _p4 + self.lateral_block4(x3)
-
-        p3 = self.decoder_block3(_p3)
-        _p3 = F.interpolate(p3, size=x2.shape[2:], mode='bilinear', align_corners=True)
-        _p2 = _p3 + self.lateral_block3(x2)
-
-        p2 = self.decoder_block2(_p2)
-        _p2 = F.interpolate(p2, size=x1.shape[2:], mode='bilinear', align_corners=True)
-        _p1 = _p2 + self.lateral_block2(x1)
-
-        _p1 = self.decoder_block1(_p1)
-        _p1 = F.interpolate(_p1, size=x.shape[2:], mode='bilinear', align_corners=True)
-        p1_out = self.conv_out1(_p1)
-
-        if self.config.ms_supervision:
-            outs.append(self.conv_ms_spvn_4(p4))
-            outs.append(self.conv_ms_spvn_3(p3))
-            outs.append(self.conv_ms_spvn_2(p2))
-        outs.append(p1_out)
-        return outs
-
-
-class RefUNet(nn.Module):
-    # Refinement
-    def __init__(self, in_channels=3+1):
-        super(RefUNet, self).__init__()
-        self.encoder_1 = nn.Sequential(
-            nn.Conv2d(in_channels, 64, 3, 1, 1),
-            nn.Conv2d(64, 64, 3, 1, 1),
-            nn.BatchNorm2d(64),
-            nn.ReLU(inplace=True)
-        )
-
-        self.encoder_2 = nn.Sequential(
-            nn.MaxPool2d(2, 2, ceil_mode=True),
-            nn.Conv2d(64, 64, 3, 1, 1),
-            nn.BatchNorm2d(64),
-            nn.ReLU(inplace=True)
-        )
-
-        self.encoder_3 = nn.Sequential(
-            nn.MaxPool2d(2, 2, ceil_mode=True),
-            nn.Conv2d(64, 64, 3, 1, 1),
-            nn.BatchNorm2d(64),
-            nn.ReLU(inplace=True)
-        )
-
-        self.encoder_4 = nn.Sequential(
-            nn.MaxPool2d(2, 2, ceil_mode=True),
-            nn.Conv2d(64, 64, 3, 1, 1),
-            nn.BatchNorm2d(64),
-            nn.ReLU(inplace=True)
-        )
-
-        self.pool4 = nn.MaxPool2d(2, 2, ceil_mode=True)
-        #####
-        self.decoder_5 = nn.Sequential(
-            nn.Conv2d(64, 64, 3, 1, 1),
-            nn.BatchNorm2d(64),
-            nn.ReLU(inplace=True)
-        )
-        #####
-        self.decoder_4 = nn.Sequential(
-            nn.Conv2d(128, 64, 3, 1, 1),
-            nn.BatchNorm2d(64),
-            nn.ReLU(inplace=True)
-        )
-
-        self.decoder_3 = nn.Sequential(
-            nn.Conv2d(128, 64, 3, 1, 1),
-            nn.BatchNorm2d(64),
-            nn.ReLU(inplace=True)
-        )
-
-        self.decoder_2 = nn.Sequential(
-            nn.Conv2d(128, 64, 3, 1, 1),
-            nn.BatchNorm2d(64),
-            nn.ReLU(inplace=True)
-        )
-
-        self.decoder_1 = nn.Sequential(
-            nn.Conv2d(128, 64, 3, 1, 1),
-            nn.BatchNorm2d(64),
-            nn.ReLU(inplace=True)
-        )
-
-        self.conv_d0 = nn.Conv2d(64, 1, 3, 1, 1)
-
-        self.upscore2 = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True)
-
-    def forward(self, x):
-        outs = []
-        if isinstance(x, list):
-            x = torch.cat(x, dim=1)
-        hx = x
-
-        hx1 = self.encoder_1(hx)
-        hx2 = self.encoder_2(hx1)
-        hx3 = self.encoder_3(hx2)
-        hx4 = self.encoder_4(hx3)
-
-        hx = self.decoder_5(self.pool4(hx4))
-        hx = torch.cat((self.upscore2(hx), hx4), 1)
-
-        d4 = self.decoder_4(hx)
-        hx = torch.cat((self.upscore2(d4), hx3), 1)
-
-        d3 = self.decoder_3(hx)
-        hx = torch.cat((self.upscore2(d3), hx2), 1)
-
-        d2 = self.decoder_2(hx)
-        hx = torch.cat((self.upscore2(d2), hx1), 1)
-
-        d1 = self.decoder_1(hx)
-
-        x = self.conv_d0(d1)
-        outs.append(x)
-        return outs
-
-
-
-### models/stem_layer.py
-
-import torch.nn as nn
-# from utils import build_act_layer, build_norm_layer
-
-
-class StemLayer(nn.Module):
-    r""" Stem layer of InternImage
-    Args:
-        in_channels (int): number of input channels
-        out_channels (int): number of output channels
-        act_layer (str): activation layer
-        norm_layer (str): normalization layer
-    """
-
-    def __init__(self,
-                 in_channels=3+1,
-                 inter_channels=48,
-                 out_channels=96,
-                 act_layer='GELU',
-                 norm_layer='BN'):
-        super().__init__()
-        self.conv1 = nn.Conv2d(in_channels,
-                               inter_channels,
-                               kernel_size=3,
-                               stride=1,
-                               padding=1)
-        self.norm1 = build_norm_layer(
-            inter_channels, norm_layer, 'channels_first', 'channels_first'
-        )
-        self.act = build_act_layer(act_layer)
-        self.conv2 = nn.Conv2d(inter_channels,
-                               out_channels,
-                               kernel_size=3,
-                               stride=1,
-                               padding=1)
-        self.norm2 = build_norm_layer(
-            out_channels, norm_layer, 'channels_first', 'channels_first'
-        )
-
-    def forward(self, x):
-        x = self.conv1(x)
-        x = self.norm1(x)
-        x = self.act(x)
-        x = self.conv2(x)
-        x = self.norm2(x)
-        return x
-
-
-### models/birefnet.py
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from kornia.filters import laplacian
-from transformers import PreTrainedModel
-
-# from config import Config
-# from dataset import class_labels_TR_sorted
-# from models.build_backbone import build_backbone
-# from models.decoder_blocks import BasicDecBlk, ResBlk, HierarAttDecBlk
-# from models.lateral_blocks import BasicLatBlk
-# from models.aspp import ASPP, ASPPDeformable
-# from models.ing import *
-# from models.refiner import Refiner, RefinerPVTInChannels4, RefUNet
-# from models.stem_layer import StemLayer
-from .BiRefNet_config import BiRefNetConfig
-
-
-class BiRefNet(
-    PreTrainedModel
-):
-    config_class = BiRefNetConfig
-    def __init__(self, bb_pretrained=True, config=BiRefNetConfig()):
-        super(BiRefNet, self).__init__(config)
-        bb_pretrained = config.bb_pretrained
-        self.config = Config()
-        self.epoch = 1
-        self.bb = build_backbone(self.config.bb, pretrained=bb_pretrained)
-
-        channels = self.config.lateral_channels_in_collection
-
-        if self.config.auxiliary_classification:
-            self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
-            self.cls_head = nn.Sequential(
-                nn.Linear(channels[0], len(class_labels_TR_sorted))
-            )
-
-        if self.config.squeeze_block:
-            self.squeeze_module = nn.Sequential(*[
-                eval(self.config.squeeze_block.split('_x')[0])(channels[0]+sum(self.config.cxt), channels[0])
-                for _ in range(eval(self.config.squeeze_block.split('_x')[1]))
-            ])
-
-        self.decoder = Decoder(channels)
-
-        if self.config.ender:
-            self.dec_end = nn.Sequential(
-                nn.Conv2d(1, 16, 3, 1, 1),
-                nn.Conv2d(16, 1, 3, 1, 1),
-                nn.ReLU(inplace=True),
-            )
-
-        # refine patch-level segmentation
-        if self.config.refine:
-            if self.config.refine == 'itself':
-                self.stem_layer = StemLayer(in_channels=3+1, inter_channels=48, out_channels=3, norm_layer='BN' if self.config.batch_size > 1 else 'LN')
-            else:
-                self.refiner = eval('{}({})'.format(self.config.refine, 'in_channels=3+1'))
-
-        if self.config.freeze_bb:
-            # Freeze the backbone...
-            print(self.named_parameters())
-            for key, value in self.named_parameters():
-                if 'bb.' in key and 'refiner.' not in key:
-                    value.requires_grad = False
-
-    def forward_enc(self, x):
-        if self.config.bb in ['vgg16', 'vgg16bn', 'resnet50']:
-            x1 = self.bb.conv1(x); x2 = self.bb.conv2(x1); x3 = self.bb.conv3(x2); x4 = self.bb.conv4(x3)
-        else:
-            x1, x2, x3, x4 = self.bb(x)
-            if self.config.mul_scl_ipt == 'cat':
-                B, C, H, W = x.shape
-                x1_, x2_, x3_, x4_ = self.bb(F.interpolate(x, size=(H//2, W//2), mode='bilinear', align_corners=True))
-                x1 = torch.cat([x1, F.interpolate(x1_, size=x1.shape[2:], mode='bilinear', align_corners=True)], dim=1)
-                x2 = torch.cat([x2, F.interpolate(x2_, size=x2.shape[2:], mode='bilinear', align_corners=True)], dim=1)
-                x3 = torch.cat([x3, F.interpolate(x3_, size=x3.shape[2:], mode='bilinear', align_corners=True)], dim=1)
-                x4 = torch.cat([x4, F.interpolate(x4_, size=x4.shape[2:], mode='bilinear', align_corners=True)], dim=1)
-            elif self.config.mul_scl_ipt == 'add':
-                B, C, H, W = x.shape
-                x1_, x2_, x3_, x4_ = self.bb(F.interpolate(x, size=(H//2, W//2), mode='bilinear', align_corners=True))
-                x1 = x1 + F.interpolate(x1_, size=x1.shape[2:], mode='bilinear', align_corners=True)
-                x2 = x2 + F.interpolate(x2_, size=x2.shape[2:], mode='bilinear', align_corners=True)
-                x3 = x3 + F.interpolate(x3_, size=x3.shape[2:], mode='bilinear', align_corners=True)
-                x4 = x4 + F.interpolate(x4_, size=x4.shape[2:], mode='bilinear', align_corners=True)
-        class_preds = self.cls_head(self.avgpool(x4).view(x4.shape[0], -1)) if self.training and self.config.auxiliary_classification else None
-        if self.config.cxt:
-            x4 = torch.cat(
-                (
-                    *[
-                        F.interpolate(x1, size=x4.shape[2:], mode='bilinear', align_corners=True),
-                        F.interpolate(x2, size=x4.shape[2:], mode='bilinear', align_corners=True),
-                        F.interpolate(x3, size=x4.shape[2:], mode='bilinear', align_corners=True),
-                    ][-len(self.config.cxt):],
-                    x4
-                ),
-                dim=1
-            )
-        return (x1, x2, x3, x4), class_preds
-
-    def forward_ori(self, x):
-        ########## Encoder ##########
-        (x1, x2, x3, x4), class_preds = self.forward_enc(x)
-        if self.config.squeeze_block:
-            x4 = self.squeeze_module(x4)
-        ########## Decoder ##########
-        features = [x, x1, x2, x3, x4]
-        if self.training and self.config.out_ref:
-            features.append(laplacian(torch.mean(x, dim=1).unsqueeze(1), kernel_size=5))
-        scaled_preds = self.decoder(features)
-        return scaled_preds, class_preds
-
-    def forward(self, x):
-        scaled_preds, class_preds = self.forward_ori(x)
-        class_preds_lst = [class_preds]
-        return [scaled_preds, class_preds_lst] if self.training else scaled_preds
-
-
-class Decoder(nn.Module):
-    def __init__(self, channels):
-        super(Decoder, self).__init__()
-        self.config = Config()
-        DecoderBlock = eval(self.config.dec_blk)
-        LateralBlock = eval(self.config.lat_blk)
-
-        if self.config.dec_ipt:
-            self.split = self.config.dec_ipt_split
-            N_dec_ipt = 64
-            DBlock = SimpleConvs
-            ic = 64
-            ipt_cha_opt = 1
-            self.ipt_blk5 = DBlock(2**10*3 if self.split else 3, [N_dec_ipt, channels[0]//8][ipt_cha_opt], inter_channels=ic)
-            self.ipt_blk4 = DBlock(2**8*3 if self.split else 3, [N_dec_ipt, channels[0]//8][ipt_cha_opt], inter_channels=ic)
-            self.ipt_blk3 = DBlock(2**6*3 if self.split else 3, [N_dec_ipt, channels[1]//8][ipt_cha_opt], inter_channels=ic)
-            self.ipt_blk2 = DBlock(2**4*3 if self.split else 3, [N_dec_ipt, channels[2]//8][ipt_cha_opt], inter_channels=ic)
-            self.ipt_blk1 = DBlock(2**0*3 if self.split else 3, [N_dec_ipt, channels[3]//8][ipt_cha_opt], inter_channels=ic)
-        else:
-            self.split = None
-
-        self.decoder_block4 = DecoderBlock(channels[0]+([N_dec_ipt, channels[0]//8][ipt_cha_opt] if self.config.dec_ipt else 0), channels[1])
-        self.decoder_block3 = DecoderBlock(channels[1]+([N_dec_ipt, channels[0]//8][ipt_cha_opt] if self.config.dec_ipt else 0), channels[2])
-        self.decoder_block2 = DecoderBlock(channels[2]+([N_dec_ipt, channels[1]//8][ipt_cha_opt] if self.config.dec_ipt else 0), channels[3])
-        self.decoder_block1 = DecoderBlock(channels[3]+([N_dec_ipt, channels[2]//8][ipt_cha_opt] if self.config.dec_ipt else 0), channels[3]//2)
-        self.conv_out1 = nn.Sequential(nn.Conv2d(channels[3]//2+([N_dec_ipt, channels[3]//8][ipt_cha_opt] if self.config.dec_ipt else 0), 1, 1, 1, 0))
-
-        self.lateral_block4 = LateralBlock(channels[1], channels[1])
-        self.lateral_block3 = LateralBlock(channels[2], channels[2])
-        self.lateral_block2 = LateralBlock(channels[3], channels[3])
-
-        if self.config.ms_supervision:
-            self.conv_ms_spvn_4 = nn.Conv2d(channels[1], 1, 1, 1, 0)
-            self.conv_ms_spvn_3 = nn.Conv2d(channels[2], 1, 1, 1, 0)
-            self.conv_ms_spvn_2 = nn.Conv2d(channels[3], 1, 1, 1, 0)
-
-            if self.config.out_ref:
-                _N = 16
-                self.gdt_convs_4 = nn.Sequential(nn.Conv2d(channels[1], _N, 3, 1, 1), nn.BatchNorm2d(_N) if self.config.batch_size > 1 else nn.Identity(), nn.ReLU(inplace=True))
-                self.gdt_convs_3 = nn.Sequential(nn.Conv2d(channels[2], _N, 3, 1, 1), nn.BatchNorm2d(_N) if self.config.batch_size > 1 else nn.Identity(), nn.ReLU(inplace=True))
-                self.gdt_convs_2 = nn.Sequential(nn.Conv2d(channels[3], _N, 3, 1, 1), nn.BatchNorm2d(_N) if self.config.batch_size > 1 else nn.Identity(), nn.ReLU(inplace=True))
-
-                self.gdt_convs_pred_4 = nn.Sequential(nn.Conv2d(_N, 1, 1, 1, 0))
-                self.gdt_convs_pred_3 = nn.Sequential(nn.Conv2d(_N, 1, 1, 1, 0))
-                self.gdt_convs_pred_2 = nn.Sequential(nn.Conv2d(_N, 1, 1, 1, 0))
-                
-                self.gdt_convs_attn_4 = nn.Sequential(nn.Conv2d(_N, 1, 1, 1, 0))
-                self.gdt_convs_attn_3 = nn.Sequential(nn.Conv2d(_N, 1, 1, 1, 0))
-                self.gdt_convs_attn_2 = nn.Sequential(nn.Conv2d(_N, 1, 1, 1, 0))
-
-    def get_patches_batch(self, x, p):
-        _size_h, _size_w = p.shape[2:]
-        patches_batch = []
-        for idx in range(x.shape[0]):
-            columns_x = torch.split(x[idx], split_size_or_sections=_size_w, dim=-1)
-            patches_x = []
-            for column_x in columns_x:
-                patches_x += [p.unsqueeze(0) for p in torch.split(column_x, split_size_or_sections=_size_h, dim=-2)]
-            patch_sample = torch.cat(patches_x, dim=1)
-            patches_batch.append(patch_sample)
-        return torch.cat(patches_batch, dim=0)
-
-    def forward(self, features):
-        if self.training and self.config.out_ref:
-            outs_gdt_pred = []
-            outs_gdt_label = []
-            x, x1, x2, x3, x4, gdt_gt = features
-        else:
-            x, x1, x2, x3, x4 = features
-        outs = []
-
-        if self.config.dec_ipt:
-            patches_batch = self.get_patches_batch(x, x4) if self.split else x
-            x4 = torch.cat((x4, self.ipt_blk5(F.interpolate(patches_batch, size=x4.shape[2:], mode='bilinear', align_corners=True))), 1)
-        p4 = self.decoder_block4(x4)
-        m4 = self.conv_ms_spvn_4(p4) if self.config.ms_supervision else None
-        if self.config.out_ref:
-            p4_gdt = self.gdt_convs_4(p4)
-            if self.training:
-                # >> GT:
-                m4_dia = m4
-                gdt_label_main_4 = gdt_gt * F.interpolate(m4_dia, size=gdt_gt.shape[2:], mode='bilinear', align_corners=True)
-                outs_gdt_label.append(gdt_label_main_4)
-                # >> Pred:
-                gdt_pred_4 = self.gdt_convs_pred_4(p4_gdt)
-                outs_gdt_pred.append(gdt_pred_4)
-            gdt_attn_4 = self.gdt_convs_attn_4(p4_gdt).sigmoid()
-            # >> Finally:
-            p4 = p4 * gdt_attn_4
-        _p4 = F.interpolate(p4, size=x3.shape[2:], mode='bilinear', align_corners=True)
-        _p3 = _p4 + self.lateral_block4(x3)
-
-        if self.config.dec_ipt:
-            patches_batch = self.get_patches_batch(x, _p3) if self.split else x
-            _p3 = torch.cat((_p3, self.ipt_blk4(F.interpolate(patches_batch, size=x3.shape[2:], mode='bilinear', align_corners=True))), 1)
-        p3 = self.decoder_block3(_p3)
-        m3 = self.conv_ms_spvn_3(p3) if self.config.ms_supervision else None
-        if self.config.out_ref:
-            p3_gdt = self.gdt_convs_3(p3)
-            if self.training:
-                # >> GT:
-                # m3 --dilation--> m3_dia
-                # G_3^gt * m3_dia --> G_3^m, which is the label of gradient
-                m3_dia = m3
-                gdt_label_main_3 = gdt_gt * F.interpolate(m3_dia, size=gdt_gt.shape[2:], mode='bilinear', align_corners=True)
-                outs_gdt_label.append(gdt_label_main_3)
-                # >> Pred:
-                # p3 --conv--BN--> F_3^G, where F_3^G predicts the \hat{G_3} with xx
-                # F_3^G --sigmoid--> A_3^G
-                gdt_pred_3 = self.gdt_convs_pred_3(p3_gdt)
-                outs_gdt_pred.append(gdt_pred_3)
-            gdt_attn_3 = self.gdt_convs_attn_3(p3_gdt).sigmoid()
-            # >> Finally:
-            # p3 = p3 * A_3^G
-            p3 = p3 * gdt_attn_3
-        _p3 = F.interpolate(p3, size=x2.shape[2:], mode='bilinear', align_corners=True)
-        _p2 = _p3 + self.lateral_block3(x2)
-
-        if self.config.dec_ipt:
-            patches_batch = self.get_patches_batch(x, _p2) if self.split else x
-            _p2 = torch.cat((_p2, self.ipt_blk3(F.interpolate(patches_batch, size=x2.shape[2:], mode='bilinear', align_corners=True))), 1)
-        p2 = self.decoder_block2(_p2)
-        m2 = self.conv_ms_spvn_2(p2) if self.config.ms_supervision else None
-        if self.config.out_ref:
-            p2_gdt = self.gdt_convs_2(p2)
-            if self.training:
-                # >> GT:
-                m2_dia = m2
-                gdt_label_main_2 = gdt_gt * F.interpolate(m2_dia, size=gdt_gt.shape[2:], mode='bilinear', align_corners=True)
-                outs_gdt_label.append(gdt_label_main_2)
-                # >> Pred:
-                gdt_pred_2 = self.gdt_convs_pred_2(p2_gdt)
-                outs_gdt_pred.append(gdt_pred_2)
-            gdt_attn_2 = self.gdt_convs_attn_2(p2_gdt).sigmoid()
-            # >> Finally:
-            p2 = p2 * gdt_attn_2
-        _p2 = F.interpolate(p2, size=x1.shape[2:], mode='bilinear', align_corners=True)
-        _p1 = _p2 + self.lateral_block2(x1)
-
-        if self.config.dec_ipt:
-            patches_batch = self.get_patches_batch(x, _p1) if self.split else x
-            _p1 = torch.cat((_p1, self.ipt_blk2(F.interpolate(patches_batch, size=x1.shape[2:], mode='bilinear', align_corners=True))), 1)
-        _p1 = self.decoder_block1(_p1)
-        _p1 = F.interpolate(_p1, size=x.shape[2:], mode='bilinear', align_corners=True)
-
-        if self.config.dec_ipt:
-            patches_batch = self.get_patches_batch(x, _p1) if self.split else x
-            _p1 = torch.cat((_p1, self.ipt_blk1(F.interpolate(patches_batch, size=x.shape[2:], mode='bilinear', align_corners=True))), 1)
-        p1_out = self.conv_out1(_p1)
-
-        if self.config.ms_supervision:
-            outs.append(m4)
-            outs.append(m3)
-            outs.append(m2)
-        outs.append(p1_out)
-        return outs if not (self.config.out_ref and self.training) else ([outs_gdt_pred, outs_gdt_label], outs)
-
-
-class SimpleConvs(nn.Module):
-    def __init__(
-        self, in_channels: int, out_channels: int, inter_channels=64
-    ) -> None:
-        super().__init__()
-        self.conv1 = nn.Conv2d(in_channels, inter_channels, 3, 1, 1)
-        self.conv_out = nn.Conv2d(inter_channels, out_channels, 3, 1, 1)
-
-    def forward(self, x):
-        return self.conv_out(self.conv1(x))

original/config.json

@@ -1,20 +0,0 @@
-{
-  "_name_or_path": "ZhengPeng7/BiRefNet",
-  "architectures": [
-    "BiRefNet"
-  ],
-  "auto_map": {
-    "AutoConfig": "BiRefNet_config.BiRefNetConfig",
-    "AutoModelForImageSegmentation": "birefnet.BiRefNet"
-  },
-  "custom_pipelines": {
-    "image-segmentation": {
-      "pt": [
-        "AutoModelForImageSegmentation"
-      ],
-      "tf": [],
-      "type": "image"
-    }
-  },
-  "bb_pretrained": false
-}

original/gitattributes

@@ -1,35 +0,0 @@
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-*.arrow filter=lfs diff=lfs merge=lfs -text
-*.bin filter=lfs diff=lfs merge=lfs -text
-*.bz2 filter=lfs diff=lfs merge=lfs -text
-*.ckpt filter=lfs diff=lfs merge=lfs -text
-*.ftz filter=lfs diff=lfs merge=lfs -text
-*.gz filter=lfs diff=lfs merge=lfs -text
-*.h5 filter=lfs diff=lfs merge=lfs -text
-*.joblib filter=lfs diff=lfs merge=lfs -text
-*.lfs.* filter=lfs diff=lfs merge=lfs -text
-*.mlmodel filter=lfs diff=lfs merge=lfs -text
-*.model filter=lfs diff=lfs merge=lfs -text
-*.msgpack filter=lfs diff=lfs merge=lfs -text
-*.npy filter=lfs diff=lfs merge=lfs -text
-*.npz filter=lfs diff=lfs merge=lfs -text
-*.onnx filter=lfs diff=lfs merge=lfs -text
-*.ot filter=lfs diff=lfs merge=lfs -text
-*.parquet filter=lfs diff=lfs merge=lfs -text
-*.pb filter=lfs diff=lfs merge=lfs -text
-*.pickle filter=lfs diff=lfs merge=lfs -text
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original/gitignore

@@ -1,142 +0,0 @@
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original/model.safetensors

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