""" CrossViT Model @inproceedings{ chen2021crossvit, title={{CrossViT: Cross-Attention Multi-Scale Vision Transformer for Image Classification}}, author={Chun-Fu (Richard) Chen and Quanfu Fan and Rameswar Panda}, booktitle={International Conference on Computer Vision (ICCV)}, year={2021} } Paper link: https://arxiv.org/abs/2103.14899 Original code: https://github.com/IBM/CrossViT/blob/main/models/crossvit.py """ # Copyright IBM All Rights Reserved. # SPDX-License-Identifier: Apache-2.0 """ Modifed from Timm. https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py """ import torch import torch.nn as nn import torch.nn.functional as F import torch.hub from functools import partial from typing import List from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD from .helpers import build_model_with_cfg from .layers import DropPath, to_2tuple, trunc_normal_ from .registry import register_model from .vision_transformer import Mlp, Block def _cfg(url='', **kwargs): return { 'url': url, 'num_classes': 1000, 'input_size': (3, 240, 240), 'pool_size': None, 'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD, 'fixed_input_size': True, # 'first_conv': 'patch_embed.proj', 'classifier': 'head', **kwargs } default_cfgs = { 'crossvit_15_224': _cfg(url='https://github.com/IBM/CrossViT/releases/download/weights-0.1/crossvit_15_224.pth'), 'crossvit_15_dagger_224': _cfg(url='https://github.com/IBM/CrossViT/releases/download/weights-0.1/crossvit_15_dagger_224.pth'), 'crossvit_15_dagger_384': _cfg(url='https://github.com/IBM/CrossViT/releases/download/weights-0.1/crossvit_15_dagger_384.pth'), 'crossvit_18_224': _cfg(url='https://github.com/IBM/CrossViT/releases/download/weights-0.1/crossvit_18_224.pth'), 'crossvit_18_dagger_224': _cfg(url='https://github.com/IBM/CrossViT/releases/download/weights-0.1/crossvit_18_dagger_224.pth'), 'crossvit_18_dagger_384': _cfg(url='https://github.com/IBM/CrossViT/releases/download/weights-0.1/crossvit_18_dagger_384.pth'), 'crossvit_9_224': _cfg(url='https://github.com/IBM/CrossViT/releases/download/weights-0.1/crossvit_9_224.pth'), 'crossvit_9_dagger_224': _cfg(url='https://github.com/IBM/CrossViT/releases/download/weights-0.1/crossvit_9_dagger_224.pth'), 'crossvit_base_224': _cfg(url='https://github.com/IBM/CrossViT/releases/download/weights-0.1/crossvit_base_224.pth'), 'crossvit_small_224': _cfg(url='https://github.com/IBM/CrossViT/releases/download/weights-0.1/crossvit_small_224.pth'), 'crossvit_tiny_224': _cfg(url='https://github.com/IBM/CrossViT/releases/download/weights-0.1/crossvit_tiny_224.pth'), } class PatchEmbed(nn.Module): """ Image to Patch Embedding """ def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=768, multi_conv=False): super().__init__() img_size = to_2tuple(img_size) patch_size = to_2tuple(patch_size) num_patches = (img_size[1] // patch_size[1]) * (img_size[0] // patch_size[0]) self.img_size = img_size self.patch_size = patch_size self.num_patches = num_patches if multi_conv: if patch_size[0] == 12: self.proj = nn.Sequential( nn.Conv2d(in_chans, embed_dim // 4, kernel_size=7, stride=4, padding=3), nn.ReLU(inplace=True), nn.Conv2d(embed_dim // 4, embed_dim // 2, kernel_size=3, stride=3, padding=0), nn.ReLU(inplace=True), nn.Conv2d(embed_dim // 2, embed_dim, kernel_size=3, stride=1, padding=1), ) elif patch_size[0] == 16: self.proj = nn.Sequential( nn.Conv2d(in_chans, embed_dim // 4, kernel_size=7, stride=4, padding=3), nn.ReLU(inplace=True), nn.Conv2d(embed_dim // 4, embed_dim // 2, kernel_size=3, stride=2, padding=1), nn.ReLU(inplace=True), nn.Conv2d(embed_dim // 2, embed_dim, kernel_size=3, stride=2, padding=1), ) else: self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size) def forward(self, x): B, C, H, W = x.shape # FIXME look at relaxing size constraints assert H == self.img_size[0] and W == self.img_size[1], \ f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})." x = self.proj(x).flatten(2).transpose(1, 2) return x class CrossAttention(nn.Module): def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0.): super().__init__() self.num_heads = num_heads head_dim = dim // num_heads # NOTE scale factor was wrong in my original version, can set manually to be compat with prev weights self.scale = qk_scale or head_dim ** -0.5 self.wq = nn.Linear(dim, dim, bias=qkv_bias) self.wk = nn.Linear(dim, dim, bias=qkv_bias) self.wv = nn.Linear(dim, dim, bias=qkv_bias) self.attn_drop = nn.Dropout(attn_drop) self.proj = nn.Linear(dim, dim) self.proj_drop = nn.Dropout(proj_drop) def forward(self, x): B, N, C = x.shape 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) 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) 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) attn = (q @ k.transpose(-2, -1)) * self.scale # BH1(C/H) @ BH(C/H)N -> BH1N attn = attn.softmax(dim=-1) attn = self.attn_drop(attn) x = (attn @ v).transpose(1, 2).reshape(B, 1, C) # (BH1N @ BHN(C/H)) -> BH1(C/H) -> B1H(C/H) -> B1C x = self.proj(x) x = self.proj_drop(x) return x class CrossAttentionBlock(nn.Module): def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0., drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm): super().__init__() self.norm1 = norm_layer(dim) self.attn = CrossAttention( dim, num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop) # NOTE: drop path for stochastic depth, we shall see if this is better than dropout here self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity() def forward(self, x): x = x[:, 0:1, ...] + self.drop_path(self.attn(self.norm1(x))) return x class MultiScaleBlock(nn.Module): def __init__(self, dim, patches, depth, num_heads, mlp_ratio, qkv_bias=False, qk_scale=None, drop=0., attn_drop=0., drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm): super().__init__() num_branches = len(dim) self.num_branches = num_branches # different branch could have different embedding size, the first one is the base self.blocks = nn.ModuleList() for d in range(num_branches): tmp = [] for i in range(depth[d]): tmp.append( Block(dim=dim[d], num_heads=num_heads[d], mlp_ratio=mlp_ratio[d], qkv_bias=qkv_bias, drop=drop, attn_drop=attn_drop, drop_path=drop_path[i], norm_layer=norm_layer)) if len(tmp) != 0: self.blocks.append(nn.Sequential(*tmp)) if len(self.blocks) == 0: self.blocks = None self.projs = nn.ModuleList() for d in range(num_branches): if dim[d] == dim[(d+1) % num_branches] and False: tmp = [nn.Identity()] else: tmp = [norm_layer(dim[d]), act_layer(), nn.Linear(dim[d], dim[(d+1) % num_branches])] self.projs.append(nn.Sequential(*tmp)) self.fusion = nn.ModuleList() for d in range(num_branches): d_ = (d+1) % num_branches nh = num_heads[d_] if depth[-1] == 0: # backward capability: self.fusion.append(CrossAttentionBlock(dim=dim[d_], num_heads=nh, mlp_ratio=mlp_ratio[d], qkv_bias=qkv_bias, qk_scale=qk_scale, drop=drop, attn_drop=attn_drop, drop_path=drop_path[-1], norm_layer=norm_layer)) else: tmp = [] for _ in range(depth[-1]): tmp.append(CrossAttentionBlock(dim=dim[d_], num_heads=nh, mlp_ratio=mlp_ratio[d], qkv_bias=qkv_bias, qk_scale=qk_scale, drop=drop, attn_drop=attn_drop, drop_path=drop_path[-1], norm_layer=norm_layer)) self.fusion.append(nn.Sequential(*tmp)) self.revert_projs = nn.ModuleList() for d in range(num_branches): if dim[(d+1) % num_branches] == dim[d] and False: tmp = [nn.Identity()] else: tmp = [norm_layer(dim[(d+1) % num_branches]), act_layer(), nn.Linear(dim[(d+1) % num_branches], dim[d])] self.revert_projs.append(nn.Sequential(*tmp)) def forward(self, x: List[torch.Tensor]) -> List[torch.Tensor]: outs_b = [] for i, block in enumerate(self.blocks): outs_b.append(block(x[i])) # only take the cls token out proj_cls_token = torch.jit.annotate(List[torch.Tensor], []) for i, proj in enumerate(self.projs): proj_cls_token.append(proj(outs_b[i][:, 0:1, ...])) # cross attention outs = [] for i, (fusion, revert_proj) in enumerate(zip(self.fusion, self.revert_projs)): tmp = torch.cat((proj_cls_token[i], outs_b[(i + 1) % self.num_branches][:, 1:, ...]), dim=1) tmp = fusion(tmp) reverted_proj_cls_token = revert_proj(tmp[:, 0:1, ...]) tmp = torch.cat((reverted_proj_cls_token, outs_b[i][:, 1:, ...]), dim=1) outs.append(tmp) return outs def _compute_num_patches(img_size, patches): return [i // p * i // p for i, p in zip(img_size,patches)] class CrossViT(nn.Module): """ Vision Transformer with support for patch or hybrid CNN input stage """ 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]), num_heads=(6, 12), mlp_ratio=(2., 2., 4.), qkv_bias=False, qk_scale=None, drop_rate=0., attn_drop_rate=0., drop_path_rate=0., norm_layer=nn.LayerNorm, multi_conv=False): super().__init__() self.num_classes = num_classes if not isinstance(img_size, list): img_size = to_2tuple(img_size) self.img_size = img_size num_patches = _compute_num_patches(img_size, patch_size) self.num_branches = len(patch_size) self.embed_dim = embed_dim self.num_features = embed_dim[0] # to pass the tests self.patch_embed = nn.ModuleList() # hard-coded for torch jit script for i in range(self.num_branches): setattr(self, f'pos_embed_{i}', nn.Parameter(torch.zeros(1, 1 + num_patches[i], embed_dim[i]))) setattr(self, f'cls_token_{i}', nn.Parameter(torch.zeros(1, 1, embed_dim[i]))) for im_s, p, d in zip(img_size, patch_size, embed_dim): self.patch_embed.append(PatchEmbed(img_size=im_s, patch_size=p, in_chans=in_chans, embed_dim=d, multi_conv=multi_conv)) self.pos_drop = nn.Dropout(p=drop_rate) total_depth = sum([sum(x[-2:]) for x in depth]) dpr = [x.item() for x in torch.linspace(0, drop_path_rate, total_depth)] # stochastic depth decay rule dpr_ptr = 0 self.blocks = nn.ModuleList() for idx, block_cfg in enumerate(depth): curr_depth = max(block_cfg[:-1]) + block_cfg[-1] dpr_ = dpr[dpr_ptr:dpr_ptr + curr_depth] blk = MultiScaleBlock(embed_dim, num_patches, block_cfg, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale, drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr_, norm_layer=norm_layer) dpr_ptr += curr_depth self.blocks.append(blk) self.norm = nn.ModuleList([norm_layer(embed_dim[i]) for i in range(self.num_branches)]) 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)]) for i in range(self.num_branches): if hasattr(self, f'pos_embed_{i}'): trunc_normal_(getattr(self, f'pos_embed_{i}'), std=.02) trunc_normal_(getattr(self, f'cls_token_{i}'), std=.02) self.apply(self._init_weights) def _init_weights(self, 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) @torch.jit.ignore def no_weight_decay(self): out = {'cls_token'} if self.pos_embed[0].requires_grad: out.add('pos_embed') return out def get_classifier(self): return self.head def reset_classifier(self, num_classes, global_pool=''): self.num_classes = num_classes self.head = nn.ModuleList([nn.Linear(self.embed_dim[i], num_classes) if num_classes > 0 else nn.Identity() for i in range(self.num_branches)]) def forward_features(self, x): B, C, H, W = x.shape xs = [] for i, patch_embed in enumerate(self.patch_embed): 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 tmp = patch_embed(x_) cls_tokens = self.cls_token_0 if i == 0 else self.cls_token_1 # hard-coded for torch jit script cls_tokens = cls_tokens.expand(B, -1, -1) tmp = torch.cat((cls_tokens, tmp), dim=1) pos_embed = self.pos_embed_0 if i == 0 else self.pos_embed_1 # hard-coded for torch jit script tmp = tmp + pos_embed tmp = self.pos_drop(tmp) xs.append(tmp) for i, blk in enumerate(self.blocks): xs = blk(xs) # NOTE: was before branch token section, move to here to assure all branch token are before layer norm xs = [norm(xs[i]) for i, norm in enumerate(self.norm)] out = [x[:, 0] for x in xs] return out def forward(self, x): xs = self.forward_features(x) ce_logits = [head(xs[i]) for i, head in enumerate(self.head)] ce_logits = torch.mean(torch.stack(ce_logits, dim=0), dim=0) return ce_logits def _create_crossvit(variant, pretrained=False, **kwargs): if kwargs.get('features_only', None): raise RuntimeError('features_only not implemented for Vision Transformer models.') def pretrained_filter_fn(state_dict): new_state_dict = {} for key in state_dict.keys(): if 'pos_embed' in key or 'cls_token' in key: new_key = key.replace(".", "_") else: new_key = key new_state_dict[new_key] = state_dict[key] return new_state_dict return build_model_with_cfg( CrossViT, variant, pretrained, default_cfg=default_cfgs[variant], pretrained_filter_fn=pretrained_filter_fn, **kwargs) @register_model def crossvit_tiny_224(pretrained=False, **kwargs): model_args = dict( img_size=[240, 224], patch_size=[12, 16], embed_dim=[96, 192], depth=[[1, 4, 0], [1, 4, 0], [1, 4, 0]], num_heads=[3, 3], mlp_ratio=[4, 4, 1], qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs) model = _create_crossvit(variant='crossvit_tiny_224', pretrained=pretrained, **model_args) return model @register_model def crossvit_small_224(pretrained=False, **kwargs): model_args = dict(img_size=[240, 224], patch_size=[12, 16], embed_dim=[192, 384], depth=[[1, 4, 0], [1, 4, 0], [1, 4, 0]], num_heads=[6, 6], mlp_ratio=[4, 4, 1], qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs) 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], 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