Ross Wightman
4 years ago
committed by
GitHub
commit
70ae7f0cc2
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""" Vision Transformer (ViT) in PyTorch
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This is a WIP attempt to implement Vision Transformers as described in
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'An Image Is Worth 16 x 16 Words: Transformers for Image Recognition at Scale' -
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https://openreview.net/pdf?id=YicbFdNTTy
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The paper is currently under review and there is no official reference impl. The
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code here is likely to change in the future and I will not make an effort to maintain
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backwards weight compatibility when it does.
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Status/TODO:
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* Trained (supervised on ImageNet-1k) my custom 'small' patch model to ~75 top-1 after 4 days, 2x GPU,
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no dropout or stochastic depth active
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* Need more time for supervised training results with dropout and drop connect active, hparam tuning
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* Need more GPUs for SSL or unsupervised pretraining on OpenImages w/ ImageNet fine-tune
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* There are likely mistakes. If you notice any, I'd love to improve this. This is my first time
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fiddling with transformers/multi-head attn.
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* Hopefully end up with worthwhile pretrained model at some point...
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Acknowledgments:
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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 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 .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': 1.0, 'interpolation': 'bicubic',
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'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD,
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'first_conv': '', '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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'vit_base_patch16_224': _cfg(),
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'vit_base_patch16_384': _cfg(input_size=(3, 384, 384)),
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'vit_base_patch32_384': _cfg(input_size=(3, 384, 384)),
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'vit_large_patch16_224': _cfg(),
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'vit_large_patch16_384': _cfg(input_size=(3, 384, 384)),
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'vit_large_patch32_384': _cfg(input_size=(3, 384, 384)),
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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.dropout = nn.Dropout(drop) # seems more common to have Transformer MLP drouput here?
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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.fc2(x)
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x = self.dropout(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, attn_drop=0., proj_drop=0.):
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super().__init__()
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self.scale = 1. / dim ** 0.5
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self.num_heads = num_heads
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self.qkv = nn.Linear(dim, dim * 3, bias=False)
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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, attn_mask=None):
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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)
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q, k, v = qkv[:, :, 0].transpose(1, 2), qkv[:, :, 1].transpose(1, 2), qkv[:, :, 2].transpose(1, 2)
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# TODO benchmark vs above
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#qkv = qkv.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
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attn = (q @ k.transpose(-2, -1)) * self.scale
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# FIXME support masking
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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., act_layer=nn.GELU, drop=0., drop_path=0.):
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super().__init__()
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self.norm1 = nn.LayerNorm(dim)
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self.attn = Attention(dim, num_heads=num_heads, attn_drop=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 = nn.LayerNorm(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, attn_mask=None):
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x = x + self.drop_path(self.attn(self.norm1(x), attn_mask=attn_mask))
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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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Unfold image into fixed size patches, flatten into seq, project to embedding dim.
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"""
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def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=768, flatten_channels_last=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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assert img_size[0] % patch_size[0] == 0, 'image height must be divisible by the patch height'
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assert img_size[1] % patch_size[1] == 0, 'image width must be divisible by the patch width'
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num_patches = (img_size[1] // patch_size[1]) * (img_size[0] // patch_size[0])
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patch_dim = in_chans * patch_size[0] * patch_size[1]
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self.img_size = img_size
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self.patch_size = patch_size
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self.flatten_channels_last = flatten_channels_last
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self.num_patches = num_patches
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self.proj = nn.Linear(patch_dim, embed_dim)
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def forward(self, x):
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B, C, H, W = x.shape
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Ph, Pw = self.patch_size
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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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if self.flatten_channels_last:
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# flatten patches with channels last like the paper (likely using TF)
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x = x.unfold(2, Ph, Ph).unfold(3, Pw, Pw).permute(0, 2, 3, 4, 5, 1).reshape(B, -1, Ph * Pw * C)
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else:
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x = x.permute(0, 2, 3, 1).unfold(1, Ph, Ph).unfold(2, Pw, Pw).reshape(B, -1, C * Ph * Pw)
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x = self.proj(x)
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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., mlp_head=False, drop_rate=0., drop_path_rate=0.,
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flatten_channels_last=False, hybrid_backbone=None):
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super().__init__()
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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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flatten_channels_last=flatten_channels_last)
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num_patches = self.patch_embed.num_patches
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self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + 1, embed_dim))
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self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim))
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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(dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, drop=drop_rate, drop_path=dpr[i])
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for i in range(depth)])
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self.norm = nn.LayerNorm(embed_dim)
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if mlp_head:
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# paper diagram suggests 'MLP head', but results in 4M extra parameters vs paper
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self.head = Mlp(embed_dim, int(embed_dim * mlp_ratio), num_classes)
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else:
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# with a single Linear layer as head, the param count within rounding of paper
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self.head = nn.Linear(embed_dim, num_classes)
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# FIXME not quite sure what the proper weight init is supposed to be,
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# normal / trunc normal w/ std == .02 similar to other Bert like transformers
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trunc_normal_(self.pos_embed, std=.02) # embeddings same as weights?
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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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@property
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def no_weight_decay(self):
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return {'pos_embed', 'cls_token'}
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def forward(self, x, attn_mask=None):
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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 += self.pos_embed
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for blk in self.blocks:
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x = blk(x, attn_mask=attn_mask)
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x = self.norm(x[:, 0])
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x = self.head(x)
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return x
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@register_model
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def vit_small_patch16_224(pretrained=False, **kwargs):
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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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return model
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@register_model
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def vit_base_patch16_224(pretrained=False, **kwargs):
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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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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, **kwargs)
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model.default_cfg = default_cfgs['vit_base_patch16_384']
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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, **kwargs)
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model.default_cfg = default_cfgs['vit_base_patch32_384']
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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, **kwargs)
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model.default_cfg = default_cfgs['vit_large_patch16_384']
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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, **kwargs)
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model.default_cfg = default_cfgs['vit_large_patch32_384']
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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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