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423 lines
18 KiB
423 lines
18 KiB
""" Vision Transformer (ViT) in PyTorch
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A PyTorch implement of Vision Transformers as described in
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'An Image Is Worth 16 x 16 Words: Transformers for Image Recognition at Scale' - https://arxiv.org/abs/2010.11929
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The official jax code is released and available at https://github.com/google-research/vision_transformer
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Status/TODO:
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* Models updated to be compatible with official impl. Args added to support backward compat for old PyTorch weights.
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* Weights ported from official jax impl for 384x384 base and small models, 16x16 and 32x32 patches.
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* Trained (supervised on ImageNet-1k) my custom 'small' patch model to 77.9, 'base' to 79.4 top-1 with this code.
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* Hopefully find time and GPUs for SSL or unsupervised pretraining on OpenImages w/ ImageNet fine-tune in future.
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Acknowledgments:
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* The paper authors for releasing code and weights, thanks!
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* I fixed my class token impl based on Phil Wang's https://github.com/lucidrains/vit-pytorch ... check it out
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for some einops/einsum fun
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* Simple transformer style inspired by Andrej Karpathy's https://github.com/karpathy/minGPT
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* Bert reference code checks against Huggingface Transformers and Tensorflow Bert
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Hacked together by / Copyright 2020 Ross Wightman
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"""
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import torch
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import torch.nn as nn
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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 load_pretrained
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from .layers import DropPath, to_2tuple, trunc_normal_
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from .resnet import resnet26d, resnet50d
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from .registry import register_model
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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, 224, 224), 'pool_size': None,
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'crop_pct': .9, 'interpolation': 'bicubic',
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'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD,
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'first_conv': 'patch_embed.proj', 'classifier': 'head',
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**kwargs
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}
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default_cfgs = {
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# patch models
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'vit_small_patch16_224': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/vit_small_p16_224-15ec54c9.pth',
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),
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'vit_base_patch16_224': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/vit_base_p16_224-4e355ebd.pth',
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),
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'vit_base_patch16_384': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_base_p16_384-83fb41ba.pth',
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input_size=(3, 384, 384), mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), crop_pct=1.0),
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'vit_base_patch32_384': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_base_p32_384-830016f5.pth',
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input_size=(3, 384, 384), mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), crop_pct=1.0),
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'vit_large_patch16_224': _cfg(),
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'vit_large_patch16_384': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_large_p16_384-b3be5167.pth',
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input_size=(3, 384, 384), mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), crop_pct=1.0),
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'vit_large_patch32_384': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_large_p32_384-9b920ba8.pth',
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input_size=(3, 384, 384), mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), crop_pct=1.0),
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'vit_huge_patch16_224': _cfg(),
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'vit_huge_patch32_384': _cfg(input_size=(3, 384, 384)),
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# hybrid models
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'vit_small_resnet26d_224': _cfg(),
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'vit_small_resnet50d_s3_224': _cfg(),
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'vit_base_resnet26d_224': _cfg(),
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'vit_base_resnet50d_224': _cfg(),
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}
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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.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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def forward(self, x):
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x = self.fc1(x)
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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.):
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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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def forward(self, x):
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B, N, C = x.shape
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qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
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q, k, v = qkv[0], qkv[1], qkv[2] # make torchscript happy (cannot use tensor as tuple)
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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):
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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, 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.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 + self.drop_path(self.attn(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 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]) * (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.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 HybridEmbed(nn.Module):
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""" CNN Feature Map Embedding
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Extract feature map from CNN, flatten, project to embedding dim.
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"""
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def __init__(self, backbone, img_size=224, feature_size=None, in_chans=3, embed_dim=768):
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super().__init__()
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assert isinstance(backbone, nn.Module)
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img_size = to_2tuple(img_size)
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self.img_size = img_size
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self.backbone = backbone
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if feature_size is None:
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with torch.no_grad():
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# FIXME this is hacky, but most reliable way of determining the exact dim of the output feature
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# map for all networks, the feature metadata has reliable channel and stride info, but using
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# stride to calc feature dim requires info about padding of each stage that isn't captured.
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training = backbone.training
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if training:
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backbone.eval()
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o = self.backbone(torch.zeros(1, in_chans, img_size[0], img_size[1]))[-1]
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feature_size = o.shape[-2:]
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feature_dim = o.shape[1]
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backbone.train(training)
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else:
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feature_size = to_2tuple(feature_size)
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feature_dim = self.backbone.feature_info.channels()[-1]
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self.num_patches = feature_size[0] * feature_size[1]
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self.proj = nn.Linear(feature_dim, embed_dim)
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def forward(self, x):
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x = self.backbone(x)[-1]
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x = x.flatten(2).transpose(1, 2)
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x = self.proj(x)
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return x
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class VisionTransformer(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, patch_size=16, in_chans=3, num_classes=1000, embed_dim=768, depth=12,
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num_heads=12, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop_rate=0., attn_drop_rate=0.,
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drop_path_rate=0., hybrid_backbone=None, norm_layer=nn.LayerNorm):
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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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if hybrid_backbone is not None:
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self.patch_embed = HybridEmbed(
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hybrid_backbone, img_size=img_size, in_chans=in_chans, embed_dim=embed_dim)
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else:
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self.patch_embed = PatchEmbed(
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img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim)
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num_patches = self.patch_embed.num_patches
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self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim))
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self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + 1, embed_dim))
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self.pos_drop = nn.Dropout(p=drop_rate)
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dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)] # stochastic depth decay rule
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self.blocks = nn.ModuleList([
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Block(
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dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale,
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drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer)
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for i in range(depth)])
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self.norm = norm_layer(embed_dim)
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# NOTE as per official impl, we could have a pre-logits representation dense layer + tanh here
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#self.repr = nn.Linear(embed_dim, representation_size)
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#self.repr_act = nn.Tanh()
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# Classifier head
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self.head = nn.Linear(embed_dim, num_classes) if num_classes > 0 else nn.Identity()
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trunc_normal_(self.pos_embed, std=.02)
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trunc_normal_(self.cls_token, 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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return {'pos_embed', 'cls_token'}
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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 = x.shape[0]
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x = self.patch_embed(x)
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cls_tokens = self.cls_token.expand(B, -1, -1) # stole cls_tokens impl from Phil Wang, thanks
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x = torch.cat((cls_tokens, x), dim=1)
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x = x + self.pos_embed
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x = self.pos_drop(x)
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for blk in self.blocks:
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x = blk(x)
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x = self.norm(x)
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return x[:, 0]
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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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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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def vit_small_patch16_224(pretrained=False, **kwargs):
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if pretrained:
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# NOTE my scale was wrong for original weights, leaving this here until I have better ones for this model
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kwargs.setdefault('qk_scale', 768 ** -0.5)
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model = VisionTransformer(patch_size=16, embed_dim=768, depth=8, num_heads=8, mlp_ratio=3., **kwargs)
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model.default_cfg = default_cfgs['vit_small_patch16_224']
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if pretrained:
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load_pretrained(
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model, num_classes=model.num_classes, in_chans=kwargs.get('in_chans', 3), filter_fn=_conv_filter)
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return model
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@register_model
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def vit_base_patch16_224(pretrained=False, **kwargs):
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if pretrained:
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# NOTE my scale was wrong for original weights, leaving this here until I have better ones for this model
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kwargs.setdefault('qk_scale', 768 ** -0.5)
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model = VisionTransformer(patch_size=16, embed_dim=768, depth=12, num_heads=12, mlp_ratio=4, **kwargs)
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model.default_cfg = default_cfgs['vit_base_patch16_224']
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if pretrained:
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load_pretrained(
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model, num_classes=model.num_classes, in_chans=kwargs.get('in_chans', 3), filter_fn=_conv_filter)
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return model
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@register_model
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def vit_base_patch16_384(pretrained=False, **kwargs):
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model = VisionTransformer(
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img_size=384, patch_size=16, embed_dim=768, depth=12, num_heads=12, mlp_ratio=4, qkv_bias=True,
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norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
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model.default_cfg = default_cfgs['vit_base_patch16_384']
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if pretrained:
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load_pretrained(model, num_classes=model.num_classes, in_chans=kwargs.get('in_chans', 3))
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return model
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@register_model
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def vit_base_patch32_384(pretrained=False, **kwargs):
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model = VisionTransformer(
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img_size=384, patch_size=32, embed_dim=768, depth=12, num_heads=12, mlp_ratio=4, qkv_bias=True,
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norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
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model.default_cfg = default_cfgs['vit_base_patch32_384']
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if pretrained:
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load_pretrained(model, num_classes=model.num_classes, in_chans=kwargs.get('in_chans', 3))
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return model
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@register_model
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def vit_large_patch16_224(pretrained=False, **kwargs):
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model = VisionTransformer(patch_size=16, embed_dim=1024, depth=24, num_heads=16, mlp_ratio=4, **kwargs)
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model.default_cfg = default_cfgs['vit_large_patch16_224']
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return model
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@register_model
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def vit_large_patch16_384(pretrained=False, **kwargs):
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model = VisionTransformer(
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img_size=384, patch_size=16, embed_dim=1024, depth=24, num_heads=16, mlp_ratio=4, qkv_bias=True,
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norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
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model.default_cfg = default_cfgs['vit_large_patch16_384']
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if pretrained:
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load_pretrained(model, num_classes=model.num_classes, in_chans=kwargs.get('in_chans', 3))
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return model
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@register_model
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def vit_large_patch32_384(pretrained=False, **kwargs):
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model = VisionTransformer(
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img_size=384, patch_size=32, embed_dim=1024, depth=24, num_heads=16, mlp_ratio=4, qkv_bias=True,
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norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
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model.default_cfg = default_cfgs['vit_large_patch32_384']
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if pretrained:
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load_pretrained(model, num_classes=model.num_classes, in_chans=kwargs.get('in_chans', 3))
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return model
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@register_model
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def vit_huge_patch16_224(pretrained=False, **kwargs):
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model = VisionTransformer(patch_size=16, embed_dim=1280, depth=32, num_heads=16, mlp_ratio=4, **kwargs)
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model.default_cfg = default_cfgs['vit_huge_patch16_224']
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return model
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@register_model
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def vit_huge_patch32_384(pretrained=False, **kwargs):
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model = VisionTransformer(
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img_size=384, patch_size=32, embed_dim=1280, depth=32, num_heads=16, mlp_ratio=4, **kwargs)
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model.default_cfg = default_cfgs['vit_huge_patch32_384']
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return model
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@register_model
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def vit_small_resnet26d_224(pretrained=False, **kwargs):
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pretrained_backbone = kwargs.get('pretrained_backbone', True) # default to True for now, for testing
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backbone = resnet26d(pretrained=pretrained_backbone, features_only=True, out_indices=[4])
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model = VisionTransformer(
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img_size=224, embed_dim=768, depth=8, num_heads=8, mlp_ratio=3, hybrid_backbone=backbone, **kwargs)
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model.default_cfg = default_cfgs['vit_small_resnet26d_224']
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return model
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@register_model
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def vit_small_resnet50d_s3_224(pretrained=False, **kwargs):
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pretrained_backbone = kwargs.get('pretrained_backbone', True) # default to True for now, for testing
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backbone = resnet50d(pretrained=pretrained_backbone, features_only=True, out_indices=[3])
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model = VisionTransformer(
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img_size=224, embed_dim=768, depth=8, num_heads=8, mlp_ratio=3, hybrid_backbone=backbone, **kwargs)
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model.default_cfg = default_cfgs['vit_small_resnet50d_s3_224']
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return model
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@register_model
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def vit_base_resnet26d_224(pretrained=False, **kwargs):
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pretrained_backbone = kwargs.get('pretrained_backbone', True) # default to True for now, for testing
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backbone = resnet26d(pretrained=pretrained_backbone, features_only=True, out_indices=[4])
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model = VisionTransformer(
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img_size=224, embed_dim=768, depth=12, num_heads=12, mlp_ratio=4, hybrid_backbone=backbone, **kwargs)
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model.default_cfg = default_cfgs['vit_base_resnet26d_224']
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return model
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@register_model
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def vit_base_resnet50d_224(pretrained=False, **kwargs):
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pretrained_backbone = kwargs.get('pretrained_backbone', True) # default to True for now, for testing
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backbone = resnet50d(pretrained=pretrained_backbone, features_only=True, out_indices=[4])
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model = VisionTransformer(
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img_size=224, embed_dim=768, depth=12, num_heads=12, mlp_ratio=4, hybrid_backbone=backbone, **kwargs)
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model.default_cfg = default_cfgs['vit_base_resnet50d_224']
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return model
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