BiRefNet-HRSOD / birefnet.py

Dtype adaptability between FP32 and FP16 in inference.

3e17287 verified 5 days ago

91.3 kB

	### 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)) # 2Wh-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, WhWw, WhWw
	relative_coords = relative_coords.permute(1, 2, 0).contiguous() # WhWw, WhWw, 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) # WhWw, WhWw
	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, WhWw, WhWw) 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) # WhWw,WhWw,nH
	relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous() # nH, WhWw, WhWw
	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) # nWB, window_sizewindow_size, C

	# W-MSA/SW-MSA
	attn_windows = self.attn(x_windows, mask=attn_mask) # nWB, window_sizewindow_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/2W/2 4C

	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*103 if self.split else 3, [N_dec_ipt, channels[0]//8][ipt_cha_opt], inter_channels=ic)
	self.ipt_blk4 = DBlock(2*83 if self.split else 3, [N_dec_ipt, channels[0]//8][ipt_cha_opt], inter_channels=ic)
	self.ipt_blk3 = DBlock(2*63 if self.split else 3, [N_dec_ipt, channels[1]//8][ipt_cha_opt], inter_channels=ic)
	self.ipt_blk2 = DBlock(2*43 if self.split else 3, [N_dec_ipt, channels[2]//8][ipt_cha_opt], inter_channels=ic)
	self.ipt_blk1 = DBlock(2*03 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))