|
""" Vision Transformer (ViT) in PyTorch |
|
Hacked together by / Copyright 2020 Ross Wightman |
|
""" |
|
import torch |
|
import torch.nn as nn |
|
from functools import partial |
|
from einops import rearrange |
|
|
|
from ViT.helpers import load_pretrained |
|
from ViT.weight_init import trunc_normal_ |
|
from ViT.layer_helpers import to_2tuple |
|
|
|
|
|
def _cfg(url='', **kwargs): |
|
return { |
|
'url': url, |
|
'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': None, |
|
'crop_pct': .9, 'interpolation': 'bicubic', |
|
'first_conv': 'patch_embed.proj', 'classifier': 'head', |
|
**kwargs |
|
} |
|
|
|
|
|
default_cfgs = { |
|
|
|
'vit_small_patch16_224': _cfg( |
|
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/vit_small_p16_224-15ec54c9.pth', |
|
), |
|
'vit_base_patch16_224': _cfg( |
|
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_base_p16_224-80ecf9dd.pth', |
|
mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), |
|
), |
|
'vit_large_patch16_224': _cfg( |
|
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_large_p16_224-4ee7a4dc.pth', |
|
mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5)), |
|
|
|
|
|
'deit_tiny_patch16_224': _cfg( |
|
url='https://dl.fbaipublicfiles.com/deit/deit_tiny_patch16_224-a1311bcf.pth'), |
|
'deit_small_patch16_224': _cfg( |
|
url='https://dl.fbaipublicfiles.com/deit/deit_small_patch16_224-cd65a155.pth'), |
|
'deit_base_patch16_224': _cfg( |
|
url='https://dl.fbaipublicfiles.com/deit/deit_base_patch16_224-b5f2ef4d.pth', ), |
|
'deit_base_patch16_384': _cfg( |
|
url='', |
|
input_size=(3, 384, 384)), |
|
} |
|
|
|
class Mlp(nn.Module): |
|
def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.): |
|
super().__init__() |
|
out_features = out_features or in_features |
|
hidden_features = hidden_features or in_features |
|
self.fc1 = nn.Linear(in_features, hidden_features) |
|
self.act = act_layer() |
|
self.fc2 = nn.Linear(hidden_features, out_features) |
|
self.drop = nn.Dropout(drop) |
|
|
|
def forward(self, x): |
|
x = self.fc1(x) |
|
x = self.act(x) |
|
x = self.drop(x) |
|
x = self.fc2(x) |
|
x = self.drop(x) |
|
return x |
|
|
|
|
|
class Attention(nn.Module): |
|
def __init__(self, dim, num_heads=8, qkv_bias=False,attn_drop=0., proj_drop=0.): |
|
super().__init__() |
|
self.num_heads = num_heads |
|
head_dim = dim // num_heads |
|
|
|
self.scale = head_dim ** -0.5 |
|
|
|
self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) |
|
self.attn_drop = nn.Dropout(attn_drop) |
|
self.proj = nn.Linear(dim, dim) |
|
self.proj_drop = nn.Dropout(proj_drop) |
|
|
|
self.attn_gradients = None |
|
self.attention_map = None |
|
|
|
def save_attn_gradients(self, attn_gradients): |
|
self.attn_gradients = attn_gradients |
|
|
|
def get_attn_gradients(self): |
|
return self.attn_gradients |
|
|
|
def save_attention_map(self, attention_map): |
|
self.attention_map = attention_map |
|
|
|
def get_attention_map(self): |
|
return self.attention_map |
|
|
|
def forward(self, x, register_hook=False, return_attentions=False): |
|
b, n, _, h = *x.shape, self.num_heads |
|
|
|
qkv = self.qkv(x) |
|
q, k, v = rearrange(qkv, 'b n (qkv h d) -> qkv b h n d', qkv = 3, h = h) |
|
|
|
dots = torch.einsum('bhid,bhjd->bhij', q, k) * self.scale |
|
|
|
attn = dots.softmax(dim=-1) |
|
attn = self.attn_drop(attn) |
|
|
|
out = torch.einsum('bhij,bhjd->bhid', attn, v) |
|
|
|
self.save_attention_map(attn) |
|
if register_hook: |
|
attn.register_hook(self.save_attn_gradients) |
|
|
|
out = rearrange(out, 'b h n d -> b n (h d)') |
|
out = self.proj(out) |
|
out = self.proj_drop(out) |
|
if not return_attentions: |
|
return out |
|
else: |
|
return out, attn |
|
|
|
|
|
class Block(nn.Module): |
|
|
|
def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, drop=0., attn_drop=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm): |
|
super().__init__() |
|
self.norm1 = norm_layer(dim) |
|
self.attn = Attention( |
|
dim, num_heads=num_heads, qkv_bias=qkv_bias, attn_drop=attn_drop, proj_drop=drop) |
|
self.norm2 = norm_layer(dim) |
|
mlp_hidden_dim = int(dim * mlp_ratio) |
|
self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop) |
|
|
|
def forward(self, x, register_hook=False, return_attentions=False): |
|
if not return_attentions: |
|
x = x + self.attn(self.norm1(x), register_hook=register_hook) |
|
else: |
|
attn_res, attn = self.attn(self.norm1(x), register_hook=register_hook, return_attentions=True) |
|
x = x + attn_res |
|
x = x + self.mlp(self.norm2(x)) |
|
if not return_attentions: |
|
return x |
|
else: |
|
return x, attn |
|
|
|
|
|
class PatchEmbed(nn.Module): |
|
""" Image to Patch Embedding |
|
""" |
|
def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=768): |
|
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 |
|
|
|
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 |
|
|
|
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 VisionTransformer(nn.Module): |
|
""" Vision Transformer |
|
""" |
|
def __init__(self, img_size=224, patch_size=16, in_chans=3, num_classes=1000, embed_dim=768, depth=12, |
|
num_heads=12, mlp_ratio=4., qkv_bias=False, drop_rate=0., attn_drop_rate=0., norm_layer=nn.LayerNorm): |
|
super().__init__() |
|
self.num_classes = num_classes |
|
self.num_features = self.embed_dim = embed_dim |
|
self.patch_embed = PatchEmbed( |
|
img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim) |
|
num_patches = self.patch_embed.num_patches |
|
|
|
self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim)) |
|
self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + 1, embed_dim)) |
|
self.pos_drop = nn.Dropout(p=drop_rate) |
|
|
|
self.blocks = nn.ModuleList([ |
|
Block( |
|
dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, |
|
drop=drop_rate, attn_drop=attn_drop_rate, norm_layer=norm_layer) |
|
for i in range(depth)]) |
|
self.norm = norm_layer(embed_dim) |
|
|
|
|
|
self.head = nn.Linear(embed_dim, num_classes) if num_classes > 0 else nn.Identity() |
|
|
|
trunc_normal_(self.pos_embed, std=.02) |
|
trunc_normal_(self.cls_token, 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): |
|
return {'pos_embed', 'cls_token'} |
|
|
|
def forward(self, x, register_hook=False, return_attentions=False): |
|
if return_attentions: |
|
attentions = [] |
|
|
|
B = x.shape[0] |
|
x = self.patch_embed(x) |
|
|
|
cls_tokens = self.cls_token.expand(B, -1, -1) |
|
x = torch.cat((cls_tokens, x), dim=1) |
|
x = x + self.pos_embed |
|
x = self.pos_drop(x) |
|
|
|
for blk in self.blocks: |
|
if not return_attentions: |
|
x = blk(x, register_hook=register_hook) |
|
else: |
|
x, attn = blk(x, register_hook=register_hook, return_attentions=True) |
|
attentions.append(attn) |
|
|
|
x = self.norm(x) |
|
x = x[:, 0] |
|
x = self.head(x) |
|
|
|
if not return_attentions: |
|
return x |
|
else: |
|
return x, torch.cat(attentions).unsqueeze(0) |
|
|
|
|
|
def _conv_filter(state_dict, patch_size=16): |
|
""" convert patch embedding weight from manual patchify + linear proj to conv""" |
|
out_dict = {} |
|
for k, v in state_dict.items(): |
|
if 'patch_embed.proj.weight' in k: |
|
v = v.reshape((v.shape[0], 3, patch_size, patch_size)) |
|
out_dict[k] = v |
|
return out_dict |
|
|
|
|
|
def vit_base_patch16_224(pretrained=False, **kwargs): |
|
model = VisionTransformer( |
|
patch_size=16, embed_dim=768, depth=12, num_heads=12, mlp_ratio=4, qkv_bias=True, |
|
norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs) |
|
model.default_cfg = default_cfgs['vit_base_patch16_224'] |
|
if pretrained: |
|
load_pretrained( |
|
model, num_classes=model.num_classes, in_chans=kwargs.get('in_chans', 3), filter_fn=_conv_filter) |
|
return model |
|
|
|
|
|
def vit_base_finetuned_patch16_224(pretrained=False, **kwargs): |
|
model = VisionTransformer( |
|
patch_size=16, embed_dim=768, depth=12, num_heads=12, mlp_ratio=4, qkv_bias=True, |
|
norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs) |
|
model.default_cfg = default_cfgs['vit_base_finetuned_patch16_224'] |
|
if pretrained: |
|
load_pretrained( |
|
model, num_classes=model.num_classes, in_chans=kwargs.get('in_chans', 3), filter_fn=_conv_filter) |
|
return model |
|
|
|
def vit_large_patch16_224(pretrained=False, **kwargs): |
|
model = VisionTransformer( |
|
patch_size=16, embed_dim=1024, depth=24, num_heads=16, mlp_ratio=4, qkv_bias=True, |
|
norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs) |
|
model.default_cfg = default_cfgs['vit_large_patch16_224'] |
|
if pretrained: |
|
load_pretrained(model, num_classes=model.num_classes, in_chans=kwargs.get('in_chans', 3)) |
|
return model |
|
|
|
def deit_tiny_patch16_224(pretrained=False, **kwargs): |
|
model = VisionTransformer( |
|
patch_size=16, embed_dim=192, depth=12, num_heads=3, mlp_ratio=4, qkv_bias=True, |
|
norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs) |
|
model.default_cfg = default_cfgs['deit_tiny_patch16_224'] |
|
if pretrained: |
|
load_pretrained( |
|
model, num_classes=model.num_classes, in_chans=kwargs.get('in_chans', 3), filter_fn=lambda x: x['model']) |
|
return model |
|
|
|
def deit_small_patch16_224(pretrained=False, **kwargs): |
|
model = VisionTransformer( |
|
patch_size=16, embed_dim=384, depth=12, num_heads=6, mlp_ratio=4, qkv_bias=True, |
|
norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs) |
|
model.default_cfg = default_cfgs['deit_small_patch16_224'] |
|
if pretrained: |
|
load_pretrained( |
|
model, num_classes=model.num_classes, in_chans=kwargs.get('in_chans', 3), filter_fn=lambda x: x['model']) |
|
return model |
|
|
|
def deit_base_patch16_224(pretrained=False, **kwargs): |
|
model = VisionTransformer( |
|
patch_size=16, embed_dim=768, depth=12, num_heads=12, mlp_ratio=4, qkv_bias=True, |
|
norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs) |
|
model.default_cfg = default_cfgs['deit_base_patch16_224'] |
|
if pretrained: |
|
load_pretrained( |
|
model, num_classes=model.num_classes, in_chans=kwargs.get('in_chans', 3), filter_fn=lambda x: x['model']) |
|
return model |
