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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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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.registry import register_model
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import math
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class Mlp(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.dwconv = DWConv(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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self.apply(self._init_weights)
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def _init_weights(self, m):
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if isinstance(m, nn.Linear):
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trunc_normal_(m.weight, std=.02)
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if isinstance(m, nn.Linear) and m.bias is not None:
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nn.init.constant_(m.bias, 0)
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elif isinstance(m, nn.LayerNorm):
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nn.init.constant_(m.bias, 0)
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nn.init.constant_(m.weight, 1.0)
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elif isinstance(m, nn.Conv2d):
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fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
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fan_out //= m.groups
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m.weight.data.normal_(0, math.sqrt(2.0 / fan_out))
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if m.bias is not None:
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m.bias.data.zero_()
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def forward(self, x, H, W):
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x = self.fc1(x)
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x = self.dwconv(x, H, W)
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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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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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self.apply(self._init_weights)
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def _init_weights(self, m):
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if isinstance(m, nn.Linear):
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trunc_normal_(m.weight, std=.02)
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if isinstance(m, nn.Linear) and m.bias is not None:
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nn.init.constant_(m.bias, 0)
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elif isinstance(m, nn.LayerNorm):
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nn.init.constant_(m.bias, 0)
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nn.init.constant_(m.weight, 1.0)
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elif isinstance(m, nn.Conv2d):
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fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
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fan_out //= m.groups
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m.weight.data.normal_(0, math.sqrt(2.0 / fan_out))
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if m.bias is not None:
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m.bias.data.zero_()
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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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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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# NOTE: drop path for stochastic depth, we shall see if this is better than dropout here
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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 = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
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self.apply(self._init_weights)
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def _init_weights(self, m):
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if isinstance(m, nn.Linear):
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trunc_normal_(m.weight, std=.02)
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if isinstance(m, nn.Linear) and m.bias is not None:
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nn.init.constant_(m.bias, 0)
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elif isinstance(m, nn.LayerNorm):
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nn.init.constant_(m.bias, 0)
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nn.init.constant_(m.weight, 1.0)
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elif isinstance(m, nn.Conv2d):
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fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
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fan_out //= m.groups
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m.weight.data.normal_(0, math.sqrt(2.0 / fan_out))
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if m.bias is not None:
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m.bias.data.zero_()
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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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"""
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def __init__(self, img_size=224, patch_size=7, stride=4, in_chans=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_chans, 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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self.apply(self._init_weights)
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def _init_weights(self, m):
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if isinstance(m, nn.Linear):
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trunc_normal_(m.weight, std=.02)
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if isinstance(m, nn.Linear) and m.bias is not None:
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nn.init.constant_(m.bias, 0)
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elif isinstance(m, nn.LayerNorm):
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nn.init.constant_(m.bias, 0)
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nn.init.constant_(m.weight, 1.0)
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elif isinstance(m, nn.Conv2d):
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fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
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fan_out //= m.groups
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m.weight.data.normal_(0, math.sqrt(2.0 / fan_out))
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if m.bias is not None:
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m.bias.data.zero_()
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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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class PyramidVisionTransformerImpr(nn.Module):
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def __init__(self, img_size=224, patch_size=16, in_chans=3, num_classes=1000, embed_dims=[64, 128, 256, 512],
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num_heads=[1, 2, 4, 8], mlp_ratios=[4, 4, 4, 4], qkv_bias=False, qk_scale=None, drop_rate=0.,
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attn_drop_rate=0., drop_path_rate=0., norm_layer=nn.LayerNorm,
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depths=[3, 4, 6, 3], sr_ratios=[8, 4, 2, 1]):
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super().__init__()
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self.num_classes = num_classes
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self.depths = depths
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# patch_embed
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self.patch_embed1 = OverlapPatchEmbed(img_size=img_size, patch_size=7, stride=4, in_chans=in_chans,
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embed_dim=embed_dims[0])
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self.patch_embed2 = OverlapPatchEmbed(img_size=img_size // 4, patch_size=3, stride=2, in_chans=embed_dims[0],
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embed_dim=embed_dims[1])
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self.patch_embed3 = OverlapPatchEmbed(img_size=img_size // 8, patch_size=3, stride=2, in_chans=embed_dims[1],
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embed_dim=embed_dims[2])
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self.patch_embed4 = OverlapPatchEmbed(img_size=img_size // 16, patch_size=3, stride=2, in_chans=embed_dims[2],
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embed_dim=embed_dims[3])
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# transformer encoder
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dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))] # stochastic depth decay rule
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cur = 0
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self.block1 = nn.ModuleList([Block(
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dim=embed_dims[0], num_heads=num_heads[0], mlp_ratio=mlp_ratios[0], qkv_bias=qkv_bias, qk_scale=qk_scale,
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drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[cur + i], norm_layer=norm_layer,
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sr_ratio=sr_ratios[0])
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for i in range(depths[0])])
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self.norm1 = norm_layer(embed_dims[0])
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cur += depths[0]
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self.block2 = nn.ModuleList([Block(
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dim=embed_dims[1], num_heads=num_heads[1], mlp_ratio=mlp_ratios[1], qkv_bias=qkv_bias, qk_scale=qk_scale,
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drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[cur + i], norm_layer=norm_layer,
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sr_ratio=sr_ratios[1])
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for i in range(depths[1])])
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self.norm2 = norm_layer(embed_dims[1])
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cur += depths[1]
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self.block3 = nn.ModuleList([Block(
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dim=embed_dims[2], num_heads=num_heads[2], mlp_ratio=mlp_ratios[2], qkv_bias=qkv_bias, qk_scale=qk_scale,
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drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[cur + i], norm_layer=norm_layer,
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sr_ratio=sr_ratios[2])
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for i in range(depths[2])])
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self.norm3 = norm_layer(embed_dims[2])
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cur += depths[2]
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self.block4 = nn.ModuleList([Block(
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dim=embed_dims[3], num_heads=num_heads[3], mlp_ratio=mlp_ratios[3], qkv_bias=qkv_bias, qk_scale=qk_scale,
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drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[cur + i], norm_layer=norm_layer,
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sr_ratio=sr_ratios[3])
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for i in range(depths[3])])
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self.norm4 = norm_layer(embed_dims[3])
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# classification head
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# self.head = nn.Linear(embed_dims[3], num_classes) if num_classes > 0 else nn.Identity()
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self.apply(self._init_weights)
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def _init_weights(self, m):
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if isinstance(m, nn.Linear):
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trunc_normal_(m.weight, std=.02)
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if isinstance(m, nn.Linear) and m.bias is not None:
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nn.init.constant_(m.bias, 0)
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elif isinstance(m, nn.LayerNorm):
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nn.init.constant_(m.bias, 0)
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nn.init.constant_(m.weight, 1.0)
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elif isinstance(m, nn.Conv2d):
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fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
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fan_out //= m.groups
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m.weight.data.normal_(0, math.sqrt(2.0 / fan_out))
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if m.bias is not None:
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m.bias.data.zero_()
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def init_weights(self, pretrained=None):
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if isinstance(pretrained, str):
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# 这里可以添加加载预训练权重的逻辑，如果需要的话
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# 例如：load_checkpoint(self, pretrained, map_location='cpu', strict=False, logger=logger)
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pass
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def reset_drop_path(self, drop_path_rate):
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dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(self.depths))]
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cur = 0
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for i in range(self.depths[0]):
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self.block1[i].drop_path.drop_prob = dpr[cur + i]
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cur += self.depths[0]
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for i in range(self.depths[1]):
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self.block2[i].drop_path.drop_prob = dpr[cur + i]
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cur += self.depths[1]
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for i in range(self.depths[2]):
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self.block3[i].drop_path.drop_prob = dpr[cur + i]
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cur += self.depths[2]
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for i in range(self.depths[3]):
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self.block4[i].drop_path.drop_prob = dpr[cur + i]
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def freeze_patch_emb(self):
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self.patch_embed1.requires_grad = False
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@torch.jit.ignore
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def no_weight_decay(self):
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return {'pos_embed1', 'pos_embed2', 'pos_embed3', 'pos_embed4', 'cls_token'} # has pos_embed may be better
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def get_classifier(self):
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return self.head
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def reset_classifier(self, num_classes, global_pool=''):
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self.num_classes = num_classes
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self.head = nn.Linear(self.embed_dim, num_classes) if num_classes > 0 else nn.Identity()
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# def _get_pos_embed(self, pos_embed, patch_embed, H, W):
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# if H * W == self.patch_embed1.num_patches:
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# return pos_embed
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# else:
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# return F.interpolate(
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# pos_embed.reshape(1, patch_embed.H, patch_embed.W, -1).permute(0, 3, 1, 2),
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# size=(H, W), mode="bilinear").reshape(1, -1, H * W).permute(0, 2, 1)
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def forward_features(self, x):
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B = x.shape[0]
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outs = []
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# stage 1
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x, H, W = self.patch_embed1(x)
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for i, blk in enumerate(self.block1):
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x = blk(x, H, W)
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x = self.norm1(x)
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x = x.reshape(B, H, W, -1).permute(0, 3, 1, 2).contiguous()
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outs.append(x)
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# stage 2
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x, H, W = self.patch_embed2(x)
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for i, blk in enumerate(self.block2):
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x = blk(x, H, W)
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x = self.norm2(x)
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x = x.reshape(B, H, W, -1).permute(0, 3, 1, 2).contiguous()
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outs.append(x)
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# stage 3
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x, H, W = self.patch_embed3(x)
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for i, blk in enumerate(self.block3):
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x = blk(x, H, W)
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x = self.norm3(x)
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x = x.reshape(B, H, W, -1).permute(0, 3, 1, 2).contiguous()
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outs.append(x)
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# stage 4
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x, H, W = self.patch_embed4(x)
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for i, blk in enumerate(self.block4):
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x = blk(x, H, W)
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x = self.norm4(x)
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x = x.reshape(B, H, W, -1).permute(0, 3, 1, 2).contiguous()
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outs.append(x)
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return outs
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# return x.mean(dim=1)
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def forward(self, x):
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x = self.forward_features(x)
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# x = self.head(x)
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return x
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class DWConv(nn.Module):
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def __init__(self, dim=768):
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super(DWConv, self).__init__()
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self.dwconv = nn.Conv2d(dim, dim, 3, 1, 1, bias=True, groups=dim)
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def forward(self, x, H, W):
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B, N, C = x.shape
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x = x.transpose(1, 2).view(B, C, H, W)
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x = self.dwconv(x)
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x = x.flatten(2).transpose(1, 2)
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return x
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def _conv_filter(state_dict, patch_size=16):
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""" convert patch embedding weight from manual patchify + linear proj to conv"""
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out_dict = {}
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for k, v in state_dict.items():
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if 'patch_embed.proj.weight' in k:
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v = v.reshape((v.shape[0], 3, patch_size, patch_size))
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out_dict[k] = v
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return out_dict
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@register_model
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class pvt_v2_b0(PyramidVisionTransformerImpr):
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def __init__(self, **kwargs):
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super(pvt_v2_b0, self).__init__(
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patch_size=4, embed_dims=[32, 64, 160, 256], num_heads=[1, 2, 5, 8], mlp_ratios=[8, 8, 4, 4],
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qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6), depths=[2, 2, 2, 2], sr_ratios=[8, 4, 2, 1],
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drop_rate=0.0, drop_path_rate=0.1)
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@register_model
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class pvt_v2_b1(PyramidVisionTransformerImpr):
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def __init__(self, **kwargs):
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super(pvt_v2_b1, self).__init__(
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patch_size=4, embed_dims=[64, 128, 320, 512], num_heads=[1, 2, 5, 8], mlp_ratios=[8, 8, 4, 4],
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qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6), depths=[2, 2, 2, 2], sr_ratios=[8, 4, 2, 1],
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drop_rate=0.0, drop_path_rate=0.1)
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@register_model
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class pvt_v2_b2(PyramidVisionTransformerImpr):
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def __init__(self, **kwargs):
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super(pvt_v2_b2, self).__init__(
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patch_size=4, embed_dims=[64, 128, 320, 512], num_heads=[1, 2, 5, 8], mlp_ratios=[8, 8, 4, 4],
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qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6), depths=[3, 4, 6, 3], sr_ratios=[8, 4, 2, 1],
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drop_rate=0.0, drop_path_rate=0.1)
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@register_model
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class pvt_v2_b3(PyramidVisionTransformerImpr):
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def __init__(self, **kwargs):
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|
super(pvt_v2_b3, self).__init__(
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patch_size=4, embed_dims=[64, 128, 320, 512], num_heads=[1, 2, 5, 8], mlp_ratios=[8, 8, 4, 4],
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qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6), depths=[3, 4, 18, 3], sr_ratios=[8, 4, 2, 1],
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drop_rate=0.0, drop_path_rate=0.1)
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@register_model
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|
class pvt_v2_b4(PyramidVisionTransformerImpr):
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|
def __init__(self, **kwargs):
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|
super(pvt_v2_b4, self).__init__(
|
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|
patch_size=4, embed_dims=[64, 128, 320, 512], num_heads=[1, 2, 5, 8], mlp_ratios=[8, 8, 4, 4],
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|
qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6), depths=[3, 8, 27, 3], sr_ratios=[8, 4, 2, 1],
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drop_rate=0.0, drop_path_rate=0.1)
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|
@register_model
|
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|
class pvt_v2_b5(PyramidVisionTransformerImpr):
|
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|
def __init__(self, **kwargs):
|
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|
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],
|
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|
qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6), depths=[3, 6, 40, 3], sr_ratios=[8, 4, 2, 1],
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|
drop_rate=0.0, drop_path_rate=0.1) |
