| import torch
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| import torch.nn as nn
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| from functools import partial
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|
|
| from timm.models.layers import DropPath, to_2tuple, trunc_normal_
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| from timm.models import register_model
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|
|
| import math
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|
|
|
|
| class MLPLayer(nn.Module):
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| def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):
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| super().__init__()
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| out_features = out_features or in_features
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| hidden_features = hidden_features or in_features
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| self.fc1 = nn.Linear(in_features, hidden_features)
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| self.act = act_layer()
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| self.fc2 = nn.Linear(hidden_features, out_features)
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| self.drop = nn.Dropout(drop)
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|
|
| def forward(self, x):
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| x = self.fc1(x)
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| x = self.act(x)
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| x = self.drop(x)
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| x = self.fc2(x)
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| x = self.drop(x)
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| return x
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|
|
|
|
| class Attention(nn.Module):
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| def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0., sr_ratio=1):
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| super().__init__()
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| assert dim % num_heads == 0, f"dim {dim} should be divided by num_heads {num_heads}."
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|
|
| self.dim = dim
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| self.num_heads = num_heads
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| head_dim = dim // num_heads
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| self.scale = qk_scale or head_dim ** -0.5
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|
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| self.q = nn.Linear(dim, dim, bias=qkv_bias)
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| self.kv = nn.Linear(dim, dim * 2, bias=qkv_bias)
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| self.attn_drop = nn.Dropout(attn_drop)
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| self.proj = nn.Linear(dim, dim)
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| self.proj_drop = nn.Dropout(proj_drop)
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|
|
| self.sr_ratio = sr_ratio
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| if sr_ratio > 1:
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| self.sr = nn.Conv2d(dim, dim, kernel_size=sr_ratio, stride=sr_ratio)
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| self.norm = nn.LayerNorm(dim)
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|
|
| def forward(self, x, H, W):
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| B, N, C = x.shape
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| q = self.q(x).reshape(B, N, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3)
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|
|
| if self.sr_ratio > 1:
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| x_ = x.permute(0, 2, 1).reshape(B, C, H, W)
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| x_ = self.sr(x_).reshape(B, C, -1).permute(0, 2, 1)
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| x_ = self.norm(x_)
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| kv = self.kv(x_).reshape(B, -1, 2, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
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| else:
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| kv = self.kv(x).reshape(B, -1, 2, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
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| k, v = kv[0], kv[1]
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|
|
| attn = (q @ k.transpose(-2, -1)) * self.scale
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| attn = attn.softmax(dim=-1)
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| attn = self.attn_drop(attn)
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|
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| x = (attn @ v).transpose(1, 2).reshape(B, N, C)
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| x = self.proj(x)
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| x = self.proj_drop(x)
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| return x
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|
|
|
|
| class Block(nn.Module):
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| def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0.,
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| drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm, sr_ratio=1):
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| super().__init__()
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| self.norm1 = norm_layer(dim)
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| self.attn = Attention(
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| dim,
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| num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale,
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| attn_drop=attn_drop, proj_drop=drop, sr_ratio=sr_ratio)
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|
|
| self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
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| self.norm2 = norm_layer(dim)
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| mlp_hidden_dim = int(dim * mlp_ratio)
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| self.mlp = MLPLayer(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
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|
|
| def forward(self, x, H, W):
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| x = x + self.drop_path(self.attn(self.norm1(x), H, W))
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| x = x + self.drop_path(self.mlp(self.norm2(x), H, W))
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| return x
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|
|
|
|
| class OverlapPatchEmbed(nn.Module):
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| """ Image to Patch Embedding
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| """
|
|
|
| def __init__(self, img_size=224, patch_size=7, stride=4, in_channels=3, embed_dim=768):
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| super().__init__()
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| img_size = to_2tuple(img_size)
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| patch_size = to_2tuple(patch_size)
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|
|
| self.img_size = img_size
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| self.patch_size = patch_size
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| self.H, self.W = img_size[0] // patch_size[0], img_size[1] // patch_size[1]
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| self.num_patches = self.H * self.W
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| self.proj = nn.Conv2d(in_channels, embed_dim, kernel_size=patch_size, stride=stride,
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| padding=(patch_size[0] // 2, patch_size[1] // 2))
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| self.norm = nn.LayerNorm(embed_dim)
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|
|
| def forward(self, x):
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| x = self.proj(x)
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| _, _, H, W = x.shape
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| x = x.flatten(2).transpose(1, 2)
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| x = self.norm(x)
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| return x, H, W
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|
|
|
|
