mirror of https://github.com/alibaba/EasyCV.git
419 lines
14 KiB
Python
419 lines
14 KiB
Python
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# Copyright (c) Alibaba, Inc. and its affiliates.
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"""
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This model is taken from
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https://github.com/SamsungLabs/EdgeViTs
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"""
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from collections import OrderedDict
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from functools import partial
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import torch
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import torch.nn as nn
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from timm.models.layers import DropPath, to_2tuple, trunc_normal_
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from easycv.models.utils import ConvMlp, Mlp
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from easycv.utils.checkpoint import load_checkpoint
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from easycv.utils.logger import get_root_logger
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from ..registry import BACKBONES
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class GlobalSparseAttn(nn.Module):
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def __init__(self,
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dim,
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num_heads=8,
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qkv_bias=False,
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qk_scale=None,
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attn_drop=0.,
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proj_drop=0.,
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sr_ratio=1):
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super().__init__()
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self.num_heads = num_heads
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head_dim = dim // num_heads
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# NOTE scale factor was wrong in my original version, can set manually to be compat with prev weights
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self.scale = qk_scale or head_dim**-0.5
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self.qkv = nn.Linear(dim, dim * 3, 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.upsample = nn.Upsample(scale_factor=sr_ratio, mode='nearest')
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self.sr = sr_ratio
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if self.sr > 1:
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self.sampler = nn.AvgPool2d(1, sr_ratio)
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kernel_size = sr_ratio
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self.LocalProp = nn.ConvTranspose2d(
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dim, dim, kernel_size, stride=sr_ratio, groups=dim)
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self.norm = nn.LayerNorm(dim)
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else:
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self.sampler = nn.Identity()
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self.upsample = nn.Identity()
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self.norm = nn.Identity()
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def forward(self, x, H: int, W: int):
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B, N, C = x.shape
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if self.sr > 1.:
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x = x.transpose(1, 2).reshape(B, C, H, W)
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x = self.sampler(x)
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x = x.flatten(2).transpose(1, 2)
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qkv = self.qkv(x).reshape(B, -1, 3, self.num_heads,
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C // self.num_heads).permute(2, 0, 3, 1, 4)
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q, k, v = qkv[0], qkv[1], qkv[2]
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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, -1, C)
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if self.sr > 1:
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x = x.permute(0, 2, 1).reshape(B, C, int(H / self.sr),
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int(W / self.sr))
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x = self.LocalProp(x)
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x = x.reshape(B, C, -1).permute(0, 2, 1)
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x = self.norm(x)
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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 LocalAgg(nn.Module):
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def __init__(self,
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dim,
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num_heads,
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mlp_ratio=4.,
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qkv_bias=False,
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qk_scale=None,
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drop=0.,
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attn_drop=0.,
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drop_path=0.,
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act_layer=nn.GELU,
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norm_layer=nn.LayerNorm):
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super().__init__()
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self.pos_embed = nn.Conv2d(dim, dim, 3, padding=1, groups=dim)
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self.norm1 = nn.BatchNorm2d(dim)
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self.conv1 = nn.Conv2d(dim, dim, 1)
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self.conv2 = nn.Conv2d(dim, dim, 1)
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self.attn = nn.Conv2d(dim, dim, 5, padding=2, groups=dim)
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self.drop_path = DropPath(
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drop_path) if drop_path > 0. else nn.Identity()
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self.norm2 = nn.BatchNorm2d(dim)
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mlp_hidden_dim = int(dim * mlp_ratio)
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self.mlp = ConvMlp(
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in_features=dim,
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hidden_features=mlp_hidden_dim,
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act_layer=act_layer,
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drop=drop)
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def forward(self, x):
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x = x + self.pos_embed(x)
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x = x + self.drop_path(
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self.conv2(self.attn(self.conv1(self.norm1(x)))))
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x = x + self.drop_path(self.mlp(self.norm2(x)))
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return x
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class SelfAttn(nn.Module):
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def __init__(self,
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dim,
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num_heads,
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mlp_ratio=4.,
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qkv_bias=False,
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qk_scale=None,
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drop=0.,
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attn_drop=0.,
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drop_path=0.,
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act_layer=nn.GELU,
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norm_layer=nn.LayerNorm,
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sr_ratio=1.):
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super().__init__()
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self.pos_embed = nn.Conv2d(dim, dim, 3, padding=1, groups=dim)
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self.norm1 = norm_layer(dim)
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self.attn = GlobalSparseAttn(
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dim,
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num_heads=num_heads,
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qkv_bias=qkv_bias,
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qk_scale=qk_scale,
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attn_drop=attn_drop,
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proj_drop=drop,
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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(
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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(
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in_features=dim,
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hidden_features=mlp_hidden_dim,
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act_layer=act_layer,
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drop=drop)
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# global layer_scale
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# self.ls = layer_scale
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def forward(self, x):
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x = x + self.pos_embed(x)
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B, N, H, W = x.shape
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x = x.flatten(2).transpose(1, 2)
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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)))
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x = x.transpose(1, 2).reshape(B, N, H, W)
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return x
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class LGLBlock(nn.Module):
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def __init__(self,
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dim,
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num_heads,
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mlp_ratio=4.,
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qkv_bias=False,
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qk_scale=None,
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drop=0.,
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attn_drop=0.,
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drop_path=0.,
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act_layer=nn.GELU,
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norm_layer=nn.LayerNorm,
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sr_ratio=1.):
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super().__init__()
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if sr_ratio > 1:
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self.LocalAgg = LocalAgg(dim, num_heads, mlp_ratio, qkv_bias,
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qk_scale, drop, attn_drop, drop_path,
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act_layer, norm_layer)
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else:
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self.LocalAgg = nn.Identity()
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self.SelfAttn = SelfAttn(dim, num_heads, mlp_ratio, qkv_bias, qk_scale,
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drop, attn_drop, drop_path, act_layer,
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norm_layer, sr_ratio)
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def forward(self, x):
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x = self.LocalAgg(x)
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x = self.SelfAttn(x)
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return x
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class PatchEmbed(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=16, 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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num_patches = (img_size[1] // patch_size[1]) * (
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img_size[0] // patch_size[0])
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self.img_size = img_size
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self.patch_size = patch_size
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self.num_patches = num_patches
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self.norm = nn.LayerNorm(embed_dim)
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self.proj = nn.Conv2d(
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in_chans, embed_dim, kernel_size=patch_size, stride=patch_size)
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def forward(self, x):
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B, C, H, W = x.shape
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assert H == self.img_size[0] and W == self.img_size[1], \
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f"Input image size ({B}*{C}*{H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})."
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x = self.proj(x)
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B, C, 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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x = x.reshape(B, H, W, -1).permute(0, 3, 1, 2).contiguous()
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return x
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@BACKBONES.register_module()
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class EdgeVit(nn.Module):
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""" Vision Transformer
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A PyTorch impl of : `An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale` -
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https://arxiv.org/abs/2010.11929
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"""
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def __init__(self,
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depth=[1, 2, 3, 2],
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img_size=224,
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in_chans=3,
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num_classes=1000,
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embed_dim=[48, 96, 240, 384],
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head_dim=48,
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mlp_ratio=[4] * 4,
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qkv_bias=True,
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qk_scale=None,
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representation_size=None,
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drop_rate=0.,
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attn_drop_rate=0.,
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drop_path_rate=0.,
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norm_layer=partial(nn.LayerNorm, eps=1e-8),
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sr_ratios=[4, 2, 2, 1],
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pretrained=None):
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"""
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Args:
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depth (list): depth of each stage
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img_size (int, tuple): input image size
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in_chans (int): number of input channels
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num_classes (int): number of classes for classification head
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embed_dim (list): embedding dimension of each stage
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head_dim (int): head dimension
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mlp_ratio (int): ratio of mlp hidden dim to embedding dim
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qkv_bias (bool): enable bias for qkv if True
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qk_scale (float): override default qk scale of head_dim ** -0.5 if set
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representation_size (Optional[int]): enable and set representation layer (pre-logits) to this value if set
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drop_rate (float): dropout rate
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attn_drop_rate (float): attention dropout rate
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drop_path_rate (float): stochastic depth rate
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norm_layer (nn.Module): normalization layer
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"""
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super().__init__()
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self.num_classes = num_classes
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self.num_features = self.embed_dim = embed_dim # num_features for consistency with other models
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norm_layer = norm_layer or partial(nn.LayerNorm, eps=1e-6)
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self.patch_embed1 = PatchEmbed(
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img_size=img_size,
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patch_size=4,
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in_chans=in_chans,
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embed_dim=embed_dim[0])
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self.patch_embed2 = PatchEmbed(
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img_size=img_size // 4,
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patch_size=2,
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in_chans=embed_dim[0],
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embed_dim=embed_dim[1])
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self.patch_embed3 = PatchEmbed(
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img_size=img_size // 8,
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patch_size=2,
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in_chans=embed_dim[1],
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embed_dim=embed_dim[2])
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self.patch_embed4 = PatchEmbed(
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img_size=img_size // 16,
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patch_size=2,
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in_chans=embed_dim[2],
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embed_dim=embed_dim[3])
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self.pos_drop = nn.Dropout(p=drop_rate)
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dpr = [
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x.item() for x in torch.linspace(0, drop_path_rate, sum(depth))
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] # stochastic depth decay rule
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num_heads = [dim // head_dim for dim in embed_dim]
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self.blocks1 = nn.ModuleList([
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LGLBlock(
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dim=embed_dim[0],
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num_heads=num_heads[0],
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mlp_ratio=mlp_ratio[0],
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qkv_bias=qkv_bias,
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qk_scale=qk_scale,
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drop=drop_rate,
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attn_drop=attn_drop_rate,
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drop_path=dpr[i],
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norm_layer=norm_layer,
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sr_ratio=sr_ratios[0]) for i in range(depth[0])
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])
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self.blocks2 = nn.ModuleList([
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LGLBlock(
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dim=embed_dim[1],
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num_heads=num_heads[1],
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mlp_ratio=mlp_ratio[1],
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qkv_bias=qkv_bias,
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qk_scale=qk_scale,
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drop=drop_rate,
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attn_drop=attn_drop_rate,
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drop_path=dpr[i + depth[0]],
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norm_layer=norm_layer,
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sr_ratio=sr_ratios[1]) for i in range(depth[1])
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])
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self.blocks3 = nn.ModuleList([
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LGLBlock(
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dim=embed_dim[2],
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num_heads=num_heads[2],
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mlp_ratio=mlp_ratio[2],
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qkv_bias=qkv_bias,
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qk_scale=qk_scale,
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drop=drop_rate,
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attn_drop=attn_drop_rate,
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drop_path=dpr[i + depth[0] + depth[1]],
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norm_layer=norm_layer,
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sr_ratio=sr_ratios[2]) for i in range(depth[2])
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])
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self.blocks4 = nn.ModuleList([
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LGLBlock(
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dim=embed_dim[3],
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num_heads=num_heads[3],
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mlp_ratio=mlp_ratio[3],
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qkv_bias=qkv_bias,
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qk_scale=qk_scale,
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drop=drop_rate,
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attn_drop=attn_drop_rate,
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drop_path=dpr[i + depth[0] + depth[1] + depth[2]],
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norm_layer=norm_layer,
|
||
|
|
sr_ratio=sr_ratios[3]) for i in range(depth[3])
|
||
|
|
])
|
||
|
|
self.norm = nn.BatchNorm2d(embed_dim[-1])
|
||
|
|
|
||
|
|
# Representation layer
|
||
|
|
if representation_size:
|
||
|
|
self.num_features = representation_size
|
||
|
|
self.pre_logits = nn.Sequential(
|
||
|
|
OrderedDict([('fc', nn.Linear(embed_dim, representation_size)),
|
||
|
|
('act', nn.Tanh())]))
|
||
|
|
else:
|
||
|
|
self.pre_logits = nn.Identity()
|
||
|
|
|
||
|
|
self.pretrained = pretrained
|
||
|
|
self.init_weights()
|
||
|
|
|
||
|
|
def init_weights(self, pretrained=None):
|
||
|
|
"""Initialize the weights in backbone.
|
||
|
|
Args:
|
||
|
|
pretrained (str, optional): Path to pre-trained weights.
|
||
|
|
Defaults to None.
|
||
|
|
"""
|
||
|
|
pretrained = pretrained or self.pretrained
|
||
|
|
|
||
|
|
def _init_weights(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)
|
||
|
|
|
||
|
|
if isinstance(pretrained, str):
|
||
|
|
self.apply(_init_weights)
|
||
|
|
logger = get_root_logger()
|
||
|
|
load_checkpoint(self, pretrained, strict=False, logger=logger)
|
||
|
|
elif pretrained is None:
|
||
|
|
self.apply(_init_weights)
|
||
|
|
else:
|
||
|
|
raise TypeError('pretrained must be a str or None')
|
||
|
|
|
||
|
|
@torch.jit.ignore
|
||
|
|
def no_weight_decay(self):
|
||
|
|
return {'pos_embed', 'cls_token'}
|
||
|
|
|
||
|
|
def forward_features(self, x):
|
||
|
|
x = self.patch_embed1(x)
|
||
|
|
x = self.pos_drop(x)
|
||
|
|
for blk in self.blocks1:
|
||
|
|
x = blk(x)
|
||
|
|
x = self.patch_embed2(x)
|
||
|
|
for blk in self.blocks2:
|
||
|
|
x = blk(x)
|
||
|
|
x = self.patch_embed3(x)
|
||
|
|
for blk in self.blocks3:
|
||
|
|
x = blk(x)
|
||
|
|
x = self.patch_embed4(x)
|
||
|
|
for blk in self.blocks4:
|
||
|
|
x = blk(x)
|
||
|
|
x = self.norm(x)
|
||
|
|
x = self.pre_logits(x)
|
||
|
|
return x
|
||
|
|
|
||
|
|
def forward(self, x):
|
||
|
|
x = self.forward_features(x)
|
||
|
|
return [x]
|