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- # Copyright (c) 2015-present, Facebook, Inc.
- # All rights reserved.
-
-
- from functools import partial
- from typing import Optional
-
- import torch
- import torch.nn as nn
- import torch.nn.functional as F
- from timm.models.efficientnet_blocks import SqueezeExcite
- from timm.models.layers import DropPath, to_2tuple, trunc_normal_
- from timm.models.registry import register_model
-
- __all__ = ['S60', 'S120', 'B60', 'B120', 'L60', 'L120', 'S60_multi']
-
-
- class Mlp(nn.Module):
- def __init__(
- self,
- in_features: int,
- hidden_features: Optional[int] = None,
- out_features: Optional[int] = None,
- act_layer: nn.Module = nn.GELU,
- drop: float = 0.0,
- ):
- 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: torch.Tensor) -> torch.Tensor:
- x = self.fc1(x)
- x = self.act(x)
- x = self.drop(x)
- x = self.fc2(x)
- x = self.drop(x)
- return x
-
-
- class Learned_Aggregation_Layer(nn.Module):
- def __init__(
- self,
- dim: int,
- num_heads: int = 1,
- qkv_bias: bool = False,
- qk_scale: Optional[float] = None,
- attn_drop: float = 0.0,
- proj_drop: float = 0.0,
- ):
- super().__init__()
- self.num_heads = num_heads
- head_dim: int = dim // num_heads
- # NOTE scale factor was wrong in my original version, can set manually to be compat with prev weights
- self.scale = qk_scale or head_dim**-0.5
-
- self.q = nn.Linear(dim, dim, bias=qkv_bias)
- self.k = nn.Linear(dim, dim, bias=qkv_bias)
- self.v = nn.Linear(dim, dim, bias=qkv_bias)
- self.id = nn.Identity()
- self.attn_drop = nn.Dropout(attn_drop)
- self.proj = nn.Linear(dim, dim)
- self.proj_drop = nn.Dropout(proj_drop)
-
- def forward(self, x: torch.Tensor) -> torch.Tensor:
- B, N, C = x.shape
- q = self.q(x[:, 0]).unsqueeze(1).reshape(B, 1, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3)
- k = self.k(x).reshape(B, N, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3)
-
- q = q * self.scale
- v = self.v(x).reshape(B, N, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3)
-
- attn = q @ k.transpose(-2, -1)
- attn = self.id(attn)
- attn = attn.softmax(dim=-1)
- attn = self.attn_drop(attn)
-
- x_cls = (attn @ v).transpose(1, 2).reshape(B, 1, C)
- x_cls = self.proj(x_cls)
- x_cls = self.proj_drop(x_cls)
-
- return x_cls
-
-
- class Learned_Aggregation_Layer_multi(nn.Module):
- def __init__(
- self,
- dim: int,
- num_heads: int = 8,
- qkv_bias: bool = False,
- qk_scale: Optional[float] = None,
- attn_drop: float = 0.0,
- proj_drop: float = 0.0,
- num_classes: int = 1000,
- ):
- super().__init__()
- self.num_heads = num_heads
- head_dim: int = dim // num_heads
- self.scale = qk_scale or head_dim**-0.5
-
- self.q = nn.Linear(dim, dim, bias=qkv_bias)
- self.k = nn.Linear(dim, dim, bias=qkv_bias)
- self.v = nn.Linear(dim, dim, bias=qkv_bias)
- self.attn_drop = nn.Dropout(attn_drop)
- self.proj = nn.Linear(dim, dim)
- self.proj_drop = nn.Dropout(proj_drop)
- self.num_classes = num_classes
-
- def forward(self, x: torch.Tensor) -> torch.Tensor:
- B, N, C = x.shape
- q = (
- self.q(x[:, : self.num_classes])
- .reshape(B, self.num_classes, self.num_heads, C // self.num_heads)
- .permute(0, 2, 1, 3)
- )
- k = (
- self.k(x[:, self.num_classes:])
- .reshape(B, N - self.num_classes, self.num_heads, C // self.num_heads)
- .permute(0, 2, 1, 3)
- )
-
- q = q * self.scale
- v = (
- self.v(x[:, self.num_classes:])
- .reshape(B, N - self.num_classes, self.num_heads, C // self.num_heads)
- .permute(0, 2, 1, 3)
- )
-
- attn = q @ k.transpose(-2, -1)
- attn = attn.softmax(dim=-1)
- attn = self.attn_drop(attn)
-
- x_cls = (attn @ v).transpose(1, 2).reshape(B, self.num_classes, C)
- x_cls = self.proj(x_cls)
- x_cls = self.proj_drop(x_cls)
-
- return x_cls
-
-
- class Layer_scale_init_Block_only_token(nn.Module):
- def __init__(
- self,
- dim: int,
- num_heads: int,
- mlp_ratio: float = 4.0,
- qkv_bias: bool = False,
- qk_scale: Optional[float] = None,
- drop: float = 0.0,
- attn_drop: float = 0.0,
- drop_path: float = 0.0,
- act_layer: nn.Module = nn.GELU,
- norm_layer=nn.LayerNorm,
- Attention_block=Learned_Aggregation_Layer,
- Mlp_block=Mlp,
- init_values: float = 1e-4,
- ):
- super().__init__()
- self.norm1 = norm_layer(dim)
- self.attn = Attention_block(
- dim, num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop
- )
- # 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 > 0.0 else nn.Identity()
- self.norm2 = norm_layer(dim)
- mlp_hidden_dim = int(dim * mlp_ratio)
- self.mlp = Mlp_block(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
- self.gamma_1 = nn.Parameter(init_values * torch.ones((dim)), requires_grad=True)
- self.gamma_2 = nn.Parameter(init_values * torch.ones((dim)), requires_grad=True)
-
- def forward(self, x: torch.Tensor, x_cls: torch.Tensor) -> torch.Tensor:
- u = torch.cat((x_cls, x), dim=1)
- x_cls = x_cls + self.drop_path(self.gamma_1 * self.attn(self.norm1(u)))
- x_cls = x_cls + self.drop_path(self.gamma_2 * self.mlp(self.norm2(x_cls)))
- return x_cls
-
-
- class Conv_blocks_se(nn.Module):
- def __init__(self, dim: int):
- super().__init__()
-
- self.qkv_pos = nn.Sequential(
- nn.Conv2d(dim, dim, kernel_size=1),
- nn.GELU(),
- nn.Conv2d(dim, dim, groups=dim, kernel_size=3, padding=1, stride=1, bias=True),
- nn.GELU(),
- SqueezeExcite(dim, rd_ratio=0.25),
- nn.Conv2d(dim, dim, kernel_size=1),
- )
-
- def forward(self, x: torch.Tensor) -> torch.Tensor:
- B, N, C = x.shape
- H = W = int(N ** 0.5)
- x = x.transpose(-1, -2)
- x = x.reshape(B, C, H, W)
- x = self.qkv_pos(x)
- x = x.reshape(B, C, N)
- x = x.transpose(-1, -2)
- return x
-
-
- class Layer_scale_init_Block(nn.Module):
- def __init__(
- self,
- dim: int,
- drop_path: float = 0.0,
- act_layer: nn.Module = nn.GELU,
- norm_layer=nn.LayerNorm,
- Attention_block=None,
- init_values: float = 1e-4,
- ):
- super().__init__()
- self.norm1 = norm_layer(dim)
- self.attn = Attention_block(dim)
- self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
- self.gamma_1 = nn.Parameter(init_values * torch.ones((dim)), requires_grad=True)
-
- def forward(self, x: torch.Tensor) -> torch.Tensor:
- return x + self.drop_path(self.gamma_1 * self.attn(self.norm1(x)))
-
-
- def conv3x3(in_planes: int, out_planes: int, stride: int = 1) -> nn.Sequential:
- """3x3 convolution with padding"""
- return nn.Sequential(
- nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=False),
- )
-
-
- class ConvStem(nn.Module):
- """Image to Patch Embedding"""
-
- def __init__(self, img_size: int = 224, patch_size: int = 16, in_chans: int = 3, embed_dim: int = 768):
- super().__init__()
- img_size = to_2tuple(img_size)
- patch_size = to_2tuple(patch_size)
- num_patches = (img_size[1] // patch_size[1]) * (img_size[0] // patch_size[0])
- self.img_size = img_size
- self.patch_size = patch_size
- self.num_patches = num_patches
-
- self.proj = nn.Sequential(
- conv3x3(in_chans, embed_dim // 8, 2),
- nn.GELU(),
- conv3x3(embed_dim // 8, embed_dim // 4, 2),
- nn.GELU(),
- conv3x3(embed_dim // 4, embed_dim // 2, 2),
- nn.GELU(),
- conv3x3(embed_dim // 2, embed_dim, 2),
- )
-
- def forward(self, x: torch.Tensor, padding_size: Optional[int] = None) -> torch.Tensor:
- B, C, H, W = x.shape
- x = self.proj(x).flatten(2).transpose(1, 2)
- return x
-
-
- class PatchConvnet(nn.Module):
- def __init__(
- self,
- img_size: int = 224,
- patch_size: int = 16,
- in_chans: int = 3,
- num_classes: int = 1000,
- embed_dim: int = 768,
- depth: int = 12,
- num_heads: int = 1,
- qkv_bias: bool = False,
- qk_scale: Optional[float] = None,
- drop_rate: float = 0.0,
- attn_drop_rate: float = 0.0,
- drop_path_rate: float = 0.0,
- hybrid_backbone: Optional = None,
- norm_layer=nn.LayerNorm,
- global_pool: Optional[str] = None,
- block_layers=Layer_scale_init_Block,
- block_layers_token=Layer_scale_init_Block_only_token,
- Patch_layer=ConvStem,
- act_layer: nn.Module = nn.GELU,
- Attention_block=Conv_blocks_se,
- dpr_constant: bool = True,
- init_scale: float = 1e-4,
- Attention_block_token_only=Learned_Aggregation_Layer,
- Mlp_block_token_only=Mlp,
- depth_token_only: int = 1,
- mlp_ratio_clstk: float = 3.0,
- multiclass: bool = False,
- ):
- super().__init__()
-
- self.multiclass = multiclass
- self.patch_size = patch_size
- self.num_classes = num_classes
- self.num_features = self.embed_dim = embed_dim # num_features for consistency with other models
-
- self.patch_embed = Patch_layer(
- img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim
- )
-
- if not self.multiclass:
- self.cls_token = nn.Parameter(torch.zeros(1, 1, int(embed_dim)))
- else:
- self.cls_token = nn.Parameter(torch.zeros(1, num_classes, int(embed_dim)))
-
- if not dpr_constant:
- dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)]
- else:
- dpr = [drop_path_rate for i in range(depth)]
-
- self.blocks = nn.ModuleList(
- [
- block_layers(
- dim=embed_dim,
- drop_path=dpr[i],
- norm_layer=norm_layer,
- act_layer=act_layer,
- Attention_block=Attention_block,
- init_values=init_scale,
- )
- for i in range(depth)
- ]
- )
-
- self.blocks_token_only = nn.ModuleList(
- [
- block_layers_token(
- dim=int(embed_dim),
- num_heads=num_heads,
- mlp_ratio=mlp_ratio_clstk,
- qkv_bias=qkv_bias,
- qk_scale=qk_scale,
- drop=drop_rate,
- attn_drop=attn_drop_rate,
- drop_path=0.0,
- norm_layer=norm_layer,
- act_layer=act_layer,
- Attention_block=Attention_block_token_only,
- Mlp_block=Mlp_block_token_only,
- init_values=init_scale,
- )
- for i in range(depth_token_only)
- ]
- )
-
- self.norm = norm_layer(int(embed_dim))
-
- self.total_len = depth_token_only + depth
-
- self.feature_info = [dict(num_chs=int(embed_dim), reduction=0, module='head')]
- if not self.multiclass:
- self.head = nn.Linear(int(embed_dim), num_classes) if num_classes > 0 else nn.Identity()
- else:
- self.head = nn.ModuleList([nn.Linear(int(embed_dim), 1) for _ in range(num_classes)])
-
- self.rescale: float = 0.02
-
- trunc_normal_(self.cls_token, std=self.rescale)
- self.apply(self._init_weights)
-
- def _init_weights(self, m):
- if isinstance(m, nn.Linear):
- trunc_normal_(m.weight, std=self.rescale)
- if 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)
-
- @torch.jit.ignore
- def no_weight_decay(self):
- return {'cls_token'}
-
- def get_classifier(self):
- return self.head
-
- def get_num_layers(self):
- return len(self.blocks)
-
- def reset_classifier(self, num_classes: int, global_pool: str = ''):
- 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: torch.Tensor) -> torch.Tensor:
- B = x.shape[0]
- x = self.patch_embed(x)
- cls_tokens = self.cls_token.expand(B, -1, -1)
-
- for i, blk in enumerate(self.blocks):
- x = blk(x)
-
- for i, blk in enumerate(self.blocks_token_only):
- cls_tokens = blk(x, cls_tokens)
- x = torch.cat((cls_tokens, x), dim=1)
-
- x = self.norm(x)
-
- if not self.multiclass:
- return x[:, 0]
- else:
- return x[:, : self.num_classes].reshape(B, self.num_classes, -1)
-
- def forward(self, x: torch.Tensor) -> torch.Tensor:
- B = x.shape[0]
- x = self.forward_features(x)
- if not self.multiclass:
- x = self.head(x)
- return x
- else:
- all_results = []
- for i in range(self.num_classes):
- all_results.append(self.head[i](x[:, i]))
- return torch.cat(all_results, dim=1).reshape(B, self.num_classes)
-
-
- @register_model
- def S60(pretrained: bool = False, **kwargs):
- model = PatchConvnet(
- patch_size=16,
- embed_dim=384,
- depth=60,
- num_heads=1,
- qkv_bias=True,
- norm_layer=partial(nn.LayerNorm, eps=1e-6),
- Patch_layer=ConvStem,
- Attention_block=Conv_blocks_se,
- depth_token_only=1,
- mlp_ratio_clstk=3.0,
- **kwargs
- )
-
- return model
-
-
- @register_model
- def S120(pretrained: bool = False, **kwargs):
- model = PatchConvnet(
- patch_size=16,
- embed_dim=384,
- depth=120,
- num_heads=1,
- qkv_bias=True,
- norm_layer=partial(nn.LayerNorm, eps=1e-6),
- Patch_layer=ConvStem,
- Attention_block=Conv_blocks_se,
- init_scale=1e-6,
- mlp_ratio_clstk=3.0,
- **kwargs
- )
-
- return model
-
-
- @register_model
- def B60(pretrained: bool = False, **kwargs):
- model = PatchConvnet(
- patch_size=16,
- embed_dim=768,
- depth=60,
- num_heads=1,
- qkv_bias=True,
- norm_layer=partial(nn.LayerNorm, eps=1e-6),
- Attention_block=Conv_blocks_se,
- init_scale=1e-6,
- **kwargs
- )
-
- return model
-
-
- @register_model
- def B120(pretrained: bool = False, **kwargs):
- model = PatchConvnet(
- patch_size=16,
- embed_dim=768,
- depth=120,
- num_heads=1,
- qkv_bias=True,
- norm_layer=partial(nn.LayerNorm, eps=1e-6),
- Patch_layer=ConvStem,
- Attention_block=Conv_blocks_se,
- init_scale=1e-6,
- **kwargs
- )
-
- return model
-
-
- @register_model
- def L60(pretrained: bool = False, **kwargs):
- model = PatchConvnet(
- patch_size=16,
- embed_dim=1024,
- depth=60,
- num_heads=1,
- qkv_bias=True,
- norm_layer=partial(nn.LayerNorm, eps=1e-6),
- Patch_layer=ConvStem,
- Attention_block=Conv_blocks_se,
- init_scale=1e-6,
- mlp_ratio_clstk=3.0,
- **kwargs
- )
-
- return model
-
-
- @register_model
- def L120(pretrained: bool = False, **kwargs):
- model = PatchConvnet(
- patch_size=16,
- embed_dim=1024,
- depth=120,
- num_heads=1,
- qkv_bias=True,
- norm_layer=partial(nn.LayerNorm, eps=1e-6),
- Patch_layer=ConvStem,
- Attention_block=Conv_blocks_se,
- init_scale=1e-6,
- mlp_ratio_clstk=3.0,
- **kwargs
- )
-
- return model
-
-
- @register_model
- def S60_multi(pretrained: bool = False, **kwargs):
- model = PatchConvnet(
- patch_size=16,
- embed_dim=384,
- depth=60,
- num_heads=1,
- qkv_bias=True,
- norm_layer=partial(nn.LayerNorm, eps=1e-6),
- Patch_layer=ConvStem,
- Attention_block=Conv_blocks_se,
- Attention_block_token_only=Learned_Aggregation_Layer_multi,
- depth_token_only=1,
- mlp_ratio_clstk=3.0,
- multiclass=True,
- **kwargs
- )
-
- return model
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