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| 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 | |
| class MLPLayer(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.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 | |
| 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 = 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) | |
| 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] | |
| 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 = MLPLayer(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop) | |
| 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) | |
| 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 | |