Richard Chen
3 years ago
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""" CrossViT Model
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@inproceedings{
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chen2021crossvit,
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title={{CrossViT: Cross-Attention Multi-Scale Vision Transformer for Image Classification}},
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author={Chun-Fu (Richard) Chen and Quanfu Fan and Rameswar Panda},
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booktitle={International Conference on Computer Vision (ICCV)},
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year={2021}
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}
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Paper link: https://arxiv.org/abs/2103.14899
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Original code: https://github.com/IBM/CrossViT/blob/main/models/crossvit.py
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"""
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# Copyright IBM All Rights Reserved.
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# SPDX-License-Identifier: Apache-2.0
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"""
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Modifed from Timm. https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py
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"""
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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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import torch.hub
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from functools import partial
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from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
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from .helpers import build_model_with_cfg
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from .layers import DropPath, to_2tuple, trunc_normal_
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from .registry import register_model
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from .vision_transformer import Mlp, Block
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def _cfg(url='', **kwargs):
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return {
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'url': url,
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'num_classes': 1000, 'input_size': (3, 240, 240), 'pool_size': None,
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'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD, 'fixed_input_size': True,
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# 'first_conv': 'patch_embed.proj',
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'classifier': 'head',
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**kwargs
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}
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default_cfgs = {
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'crossvit_15_224': _cfg(url='https://github.com/IBM/CrossViT/releases/download/weights-0.1/crossvit_15_224.pth'),
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'crossvit_15_dagger_224': _cfg(url='https://github.com/IBM/CrossViT/releases/download/weights-0.1/crossvit_15_dagger_224.pth'),
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'crossvit_15_dagger_384': _cfg(url='https://github.com/IBM/CrossViT/releases/download/weights-0.1/crossvit_15_dagger_384.pth'),
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'crossvit_18_224': _cfg(url='https://github.com/IBM/CrossViT/releases/download/weights-0.1/crossvit_18_224.pth'),
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'crossvit_18_dagger_224': _cfg(url='https://github.com/IBM/CrossViT/releases/download/weights-0.1/crossvit_18_dagger_224.pth'),
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'crossvit_18_dagger_384': _cfg(url='https://github.com/IBM/CrossViT/releases/download/weights-0.1/crossvit_18_dagger_384.pth'),
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'crossvit_9_224': _cfg(url='https://github.com/IBM/CrossViT/releases/download/weights-0.1/crossvit_9_224.pth'),
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'crossvit_9_dagger_224': _cfg(url='https://github.com/IBM/CrossViT/releases/download/weights-0.1/crossvit_9_dagger_224.pth'),
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'crossvit_base_224': _cfg(url='https://github.com/IBM/CrossViT/releases/download/weights-0.1/crossvit_base_224.pth'),
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'crossvit_small_224': _cfg(url='https://github.com/IBM/CrossViT/releases/download/weights-0.1/crossvit_small_224.pth'),
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'crossvit_tiny_224': _cfg(url='https://github.com/IBM/CrossViT/releases/download/weights-0.1/crossvit_tiny_224.pth'),
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}
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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, multi_conv=False):
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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]) * (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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if multi_conv:
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if patch_size[0] == 12:
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self.proj = nn.Sequential(
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nn.Conv2d(in_chans, embed_dim // 4, kernel_size=7, stride=4, padding=3),
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nn.ReLU(inplace=True),
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nn.Conv2d(embed_dim // 4, embed_dim // 2, kernel_size=3, stride=3, padding=0),
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nn.ReLU(inplace=True),
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nn.Conv2d(embed_dim // 2, embed_dim, kernel_size=3, stride=1, padding=1),
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)
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elif patch_size[0] == 16:
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self.proj = nn.Sequential(
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nn.Conv2d(in_chans, embed_dim // 4, kernel_size=7, stride=4, padding=3),
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nn.ReLU(inplace=True),
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nn.Conv2d(embed_dim // 4, embed_dim // 2, kernel_size=3, stride=2, padding=1),
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nn.ReLU(inplace=True),
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nn.Conv2d(embed_dim // 2, embed_dim, kernel_size=3, stride=2, padding=1),
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)
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else:
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self.proj = nn.Conv2d(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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# FIXME look at relaxing size constraints
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assert H == self.img_size[0] and W == self.img_size[1], \
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f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})."
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x = self.proj(x).flatten(2).transpose(1, 2)
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return x
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class CrossAttention(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.):
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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.wq = nn.Linear(dim, dim, bias=qkv_bias)
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self.wk = nn.Linear(dim, dim, bias=qkv_bias)
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self.wv = nn.Linear(dim, dim, 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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def forward(self, x):
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B, N, C = x.shape
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q = self.wq(x[:, 0:1, ...]).reshape(B, 1, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3) # B1C -> B1H(C/H) -> BH1(C/H)
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k = self.wk(x).reshape(B, N, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3) # BNC -> BNH(C/H) -> BHN(C/H)
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v = self.wv(x).reshape(B, N, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3) # BNC -> BNH(C/H) -> BHN(C/H)
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attn = (q @ k.transpose(-2, -1)) * self.scale # BH1(C/H) @ BH(C/H)N -> BH1N
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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) # (BH1N @ BHN(C/H)) -> BH1(C/H) -> B1H(C/H) -> B1C
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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 CrossAttentionBlock(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, has_mlp=True):
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super().__init__()
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self.norm1 = norm_layer(dim)
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self.attn = CrossAttention(
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dim, num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop)
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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.has_mlp = has_mlp
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if has_mlp:
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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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def forward(self, x):
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x = x[:, 0:1, ...] + self.drop_path(self.attn(self.norm1(x)))
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if self.has_mlp:
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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 MultiScaleBlock(nn.Module):
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def __init__(self, dim, patches, depth, num_heads, mlp_ratio, 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):
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super().__init__()
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num_branches = len(dim)
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self.num_branches = num_branches
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# different branch could have different embedding size, the first one is the base
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self.blocks = nn.ModuleList()
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for d in range(num_branches):
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tmp = []
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for i in range(depth[d]):
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tmp.append(
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Block(dim=dim[d], num_heads=num_heads[d], mlp_ratio=mlp_ratio[d], qkv_bias=qkv_bias,
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drop=drop, attn_drop=attn_drop, drop_path=drop_path[i], norm_layer=norm_layer))
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if len(tmp) != 0:
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self.blocks.append(nn.Sequential(*tmp))
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if len(self.blocks) == 0:
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self.blocks = None
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self.projs = nn.ModuleList()
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for d in range(num_branches):
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if dim[d] == dim[(d+1) % num_branches] and False:
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tmp = [nn.Identity()]
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else:
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tmp = [norm_layer(dim[d]), act_layer(), nn.Linear(dim[d], dim[(d+1) % num_branches])]
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self.projs.append(nn.Sequential(*tmp))
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self.fusion = nn.ModuleList()
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for d in range(num_branches):
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d_ = (d+1) % num_branches
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nh = num_heads[d_]
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if depth[-1] == 0: # backward capability:
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self.fusion.append(CrossAttentionBlock(dim=dim[d_], num_heads=nh, mlp_ratio=mlp_ratio[d], qkv_bias=qkv_bias, qk_scale=qk_scale,
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drop=drop, attn_drop=attn_drop, drop_path=drop_path[-1], norm_layer=norm_layer,
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has_mlp=False))
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else:
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tmp = []
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for _ in range(depth[-1]):
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tmp.append(CrossAttentionBlock(dim=dim[d_], num_heads=nh, mlp_ratio=mlp_ratio[d], qkv_bias=qkv_bias, qk_scale=qk_scale,
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drop=drop, attn_drop=attn_drop, drop_path=drop_path[-1], norm_layer=norm_layer,
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has_mlp=False))
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self.fusion.append(nn.Sequential(*tmp))
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self.revert_projs = nn.ModuleList()
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for d in range(num_branches):
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if dim[(d+1) % num_branches] == dim[d] and False:
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tmp = [nn.Identity()]
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else:
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tmp = [norm_layer(dim[(d+1) % num_branches]), act_layer(), nn.Linear(dim[(d+1) % num_branches], dim[d])]
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self.revert_projs.append(nn.Sequential(*tmp))
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def forward(self, x):
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outs_b = [block(x_) for x_, block in zip(x, self.blocks)]
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# only take the cls token out
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proj_cls_token = [proj(x[:, 0:1]) for x, proj in zip(outs_b, self.projs)]
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# cross attention
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outs = []
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for i in range(self.num_branches):
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tmp = torch.cat((proj_cls_token[i], outs_b[(i + 1) % self.num_branches][:, 1:, ...]), dim=1)
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tmp = self.fusion[i](tmp)
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reverted_proj_cls_token = self.revert_projs[i](tmp[:, 0:1, ...])
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tmp = torch.cat((reverted_proj_cls_token, outs_b[i][:, 1:, ...]), dim=1)
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outs.append(tmp)
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return outs
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def _compute_num_patches(img_size, patches):
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return [i // p * i // p for i, p in zip(img_size,patches)]
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class CrossViT(nn.Module):
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""" Vision Transformer with support for patch or hybrid CNN input stage
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"""
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def __init__(self, img_size=(224, 224), patch_size=(8, 16), in_chans=3, num_classes=1000, embed_dim=(192, 384), depth=([1, 3, 1], [1, 3, 1], [1, 3, 1]),
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num_heads=(6, 12), mlp_ratio=(2., 2., 4.), qkv_bias=False, qk_scale=None, drop_rate=0., attn_drop_rate=0.,
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drop_path_rate=0., norm_layer=nn.LayerNorm, multi_conv=False):
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super().__init__()
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self.num_classes = num_classes
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if not isinstance(img_size, list):
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img_size = to_2tuple(img_size)
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self.img_size = img_size
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num_patches = _compute_num_patches(img_size, patch_size)
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self.num_branches = len(patch_size)
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self.patch_embed = nn.ModuleList()
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self.pos_embed = nn.ParameterList([nn.Parameter(torch.zeros(1, 1 + num_patches[i], embed_dim[i])) for i in range(self.num_branches)])
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for im_s, p, d in zip(img_size, patch_size, embed_dim):
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self.patch_embed.append(PatchEmbed(img_size=im_s, patch_size=p, in_chans=in_chans, embed_dim=d, multi_conv=multi_conv))
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self.cls_token = nn.ParameterList([nn.Parameter(torch.zeros(1, 1, embed_dim[i])) for i in range(self.num_branches)])
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self.pos_drop = nn.Dropout(p=drop_rate)
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total_depth = sum([sum(x[-2:]) for x in depth])
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dpr = [x.item() for x in torch.linspace(0, drop_path_rate, total_depth)] # stochastic depth decay rule
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dpr_ptr = 0
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self.blocks = nn.ModuleList()
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for idx, block_cfg in enumerate(depth):
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curr_depth = max(block_cfg[:-1]) + block_cfg[-1]
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dpr_ = dpr[dpr_ptr:dpr_ptr + curr_depth]
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blk = MultiScaleBlock(embed_dim, num_patches, block_cfg, num_heads=num_heads, mlp_ratio=mlp_ratio,
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qkv_bias=qkv_bias, qk_scale=qk_scale, drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr_,
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norm_layer=norm_layer)
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dpr_ptr += curr_depth
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self.blocks.append(blk)
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self.norm = nn.ModuleList([norm_layer(embed_dim[i]) for i in range(self.num_branches)])
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self.head = nn.ModuleList([nn.Linear(embed_dim[i], num_classes) if num_classes > 0 else nn.Identity() for i in range(self.num_branches)])
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for i in range(self.num_branches):
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if self.pos_embed[i].requires_grad:
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trunc_normal_(self.pos_embed[i], std=.02)
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trunc_normal_(self.cls_token[i], std=.02)
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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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@torch.jit.ignore
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def no_weight_decay(self):
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out = {'cls_token'}
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if self.pos_embed[0].requires_grad:
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out.add('pos_embed')
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return out
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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 forward_features(self, x):
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B, C, H, W = x.shape
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xs = []
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for i in range(self.num_branches):
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x_ = torch.nn.functional.interpolate(x, size=(self.img_size[i], self.img_size[i]), mode='bicubic') if H != self.img_size[i] else x
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tmp = self.patch_embed[i](x_)
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cls_tokens = self.cls_token[i].expand(B, -1, -1) # stole cls_tokens impl from Phil Wang, thanks
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tmp = torch.cat((cls_tokens, tmp), dim=1)
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tmp = tmp + self.pos_embed[i]
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tmp = self.pos_drop(tmp)
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xs.append(tmp)
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for blk in self.blocks:
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xs = blk(xs)
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# NOTE: was before branch token section, move to here to assure all branch token are before layer norm
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xs = [self.norm[i](x) for i, x in enumerate(xs)]
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out = [x[:, 0] for x in xs]
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return out
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def forward(self, x):
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xs = self.forward_features(x)
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ce_logits = [self.head[i](x) for i, x in enumerate(xs)]
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ce_logits = torch.mean(torch.stack(ce_logits, dim=0), dim=0)
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return ce_logits
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def _create_crossvit(variant, pretrained=False, **kwargs):
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if kwargs.get('features_only', None):
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raise RuntimeError('features_only not implemented for Vision Transformer models.')
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return build_model_with_cfg(
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CrossViT, variant, pretrained,
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default_cfg=default_cfgs[variant],
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**kwargs)
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@register_model
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def crossvit_tiny_224(pretrained=False, **kwargs):
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model_args = dict(
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img_size=[240, 224], patch_size=[12, 16], embed_dim=[96, 192], depth=[[1, 4, 0], [1, 4, 0], [1, 4, 0]],
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num_heads=[3, 3], mlp_ratio=[4, 4, 1], qkv_bias=True,
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norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
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model = _create_crossvit(variant='crossvit_tiny_224', pretrained=pretrained, **model_args)
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return model
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@register_model
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def crossvit_small_224(pretrained=False, **kwargs):
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model_args = dict(img_size=[240, 224],
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patch_size=[12, 16], embed_dim=[192, 384], depth=[[1, 4, 0], [1, 4, 0], [1, 4, 0]],
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num_heads=[6, 6], mlp_ratio=[4, 4, 1], qkv_bias=True,
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norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
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model = _create_crossvit(variant='crossvit_small_224', pretrained=pretrained, **model_args)
|
||||
return model
|
||||
|
||||
|
||||
@register_model
|
||||
def crossvit_base_224(pretrained=False, **kwargs):
|
||||
model_args = dict(img_size=[240, 224],
|
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patch_size=[12, 16], embed_dim=[384, 768], depth=[[1, 4, 0], [1, 4, 0], [1, 4, 0]],
|
||||
num_heads=[12, 12], mlp_ratio=[4, 4, 1], qkv_bias=True,
|
||||
norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
|
||||
model = _create_crossvit(variant='crossvit_base_224', pretrained=pretrained, **model_args)
|
||||
return model
|
||||
|
||||
|
||||
@register_model
|
||||
def crossvit_9_224(pretrained=False, **kwargs):
|
||||
model_args = dict(img_size=[240, 224],
|
||||
patch_size=[12, 16], embed_dim=[128, 256], depth=[[1, 3, 0], [1, 3, 0], [1, 3, 0]],
|
||||
num_heads=[4, 4], mlp_ratio=[3, 3, 1], qkv_bias=True,
|
||||
norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
|
||||
model = _create_crossvit(variant='crossvit_9_224', pretrained=pretrained, **model_args)
|
||||
return model
|
||||
|
||||
|
||||
@register_model
|
||||
def crossvit_15_224(pretrained=False, **kwargs):
|
||||
model_args = dict(img_size=[240, 224],
|
||||
patch_size=[12, 16], embed_dim=[192, 384], depth=[[1, 5, 0], [1, 5, 0], [1, 5, 0]],
|
||||
num_heads=[6, 6], mlp_ratio=[3, 3, 1], qkv_bias=True,
|
||||
norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
|
||||
model = _create_crossvit(variant='crossvit_15_224', pretrained=pretrained, **model_args)
|
||||
return model
|
||||
|
||||
|
||||
@register_model
|
||||
def crossvit_18_224(pretrained=False, **kwargs):
|
||||
model_args = dict(img_size=[240, 224],
|
||||
patch_size=[12, 16], embed_dim=[224, 448], depth=[[1, 6, 0], [1, 6, 0], [1, 6, 0]],
|
||||
num_heads=[7, 7], mlp_ratio=[3, 3, 1], qkv_bias=True,
|
||||
norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
|
||||
model = _create_crossvit(variant='crossvit_18_224', pretrained=pretrained, **model_args)
|
||||
return model
|
||||
|
||||
|
||||
@register_model
|
||||
def crossvit_9_dagger_224(pretrained=False, **kwargs):
|
||||
model_args = dict(img_size=[240, 224],
|
||||
patch_size=[12, 16], embed_dim=[128, 256], depth=[[1, 3, 0], [1, 3, 0], [1, 3, 0]],
|
||||
num_heads=[4, 4], mlp_ratio=[3, 3, 1], qkv_bias=True,
|
||||
norm_layer=partial(nn.LayerNorm, eps=1e-6), multi_conv=True, **kwargs)
|
||||
model = _create_crossvit(variant='crossvit_9_dagger_224', pretrained=pretrained, **model_args)
|
||||
return model
|
||||
|
||||
@register_model
|
||||
def crossvit_15_dagger_224(pretrained=False, **kwargs):
|
||||
model_args = dict(img_size=[240, 224],
|
||||
patch_size=[12, 16], embed_dim=[192, 384], depth=[[1, 5, 0], [1, 5, 0], [1, 5, 0]],
|
||||
num_heads=[6, 6], mlp_ratio=[3, 3, 1], qkv_bias=True,
|
||||
norm_layer=partial(nn.LayerNorm, eps=1e-6), multi_conv=True, **kwargs)
|
||||
model = _create_crossvit(variant='crossvit_15_dagger_224', pretrained=pretrained, **model_args)
|
||||
return model
|
||||
|
||||
@register_model
|
||||
def crossvit_15_dagger_384(pretrained=False, **kwargs):
|
||||
model_args = dict(img_size=[408, 384],
|
||||
patch_size=[12, 16], embed_dim=[192, 384], depth=[[1, 5, 0], [1, 5, 0], [1, 5, 0]],
|
||||
num_heads=[6, 6], mlp_ratio=[3, 3, 1], qkv_bias=True,
|
||||
norm_layer=partial(nn.LayerNorm, eps=1e-6), multi_conv=True, **kwargs)
|
||||
model = _create_crossvit(variant='crossvit_15_dagger_384', pretrained=pretrained, **model_args)
|
||||
return model
|
||||
|
||||
@register_model
|
||||
def crossvit_18_dagger_224(pretrained=False, **kwargs):
|
||||
model_args = dict(img_size=[240, 224],
|
||||
patch_size=[12, 16], embed_dim=[224, 448], depth=[[1, 6, 0], [1, 6, 0], [1, 6, 0]],
|
||||
num_heads=[7, 7], mlp_ratio=[3, 3, 1], qkv_bias=True,
|
||||
norm_layer=partial(nn.LayerNorm, eps=1e-6), multi_conv=True, **kwargs)
|
||||
model = _create_crossvit(variant='crossvit_18_dagger_224', pretrained=pretrained, **model_args)
|
||||
return model
|
||||
|
||||
@register_model
|
||||
def crossvit_18_dagger_384(pretrained=False, **kwargs):
|
||||
model_args = dict(img_size=[408, 384],
|
||||
patch_size=[12, 16], embed_dim=[224, 448], depth=[[1, 6, 0], [1, 6, 0], [1, 6, 0]],
|
||||
num_heads=[7, 7], mlp_ratio=[3, 3, 1], qkv_bias=True,
|
||||
norm_layer=partial(nn.LayerNorm, eps=1e-6), multi_conv=True, **kwargs)
|
||||
model = _create_crossvit(variant='crossvit_18_dagger_384', pretrained=pretrained, **model_args)
|
||||
return model
|
||||
Loading…
Reference in new issue