### config.py import os import math class Config(): def __init__(self) -> None: # PATH settings self.sys_home_dir = os.environ['HOME'] # 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 ][3] 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)) 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))