mirror of
https://github.com/facebookresearch/deit.git
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381 lines
15 KiB
Python
381 lines
15 KiB
Python
""" 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'
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- https://arxiv.org/abs/2010.11929
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`How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers`
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- https://arxiv.org/abs/2106.10270
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The official jax code is released and available at https://github.com/google-research/vision_transformer
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DeiT model defs and weights from https://github.com/facebookresearch/deit,
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paper `DeiT: Data-efficient Image Transformers` - https://arxiv.org/abs/2012.12877
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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 math
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import logging
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from functools import partial
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from collections import OrderedDict
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from copy import deepcopy
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from statistics import mode
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, IMAGENET_INCEPTION_MEAN, IMAGENET_INCEPTION_STD
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from timm.models.helpers import load_pretrained
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from timm.models.layers import DropPath, to_2tuple, trunc_normal_
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from timm.models.registry import register_model
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from sparse_linear import SparseLinearSuper
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_logger = logging.getLogger(__name__)
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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', 'fixed_input_size': True,
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'mean': IMAGENET_INCEPTION_MEAN, 'std': IMAGENET_INCEPTION_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-vitjx/jx_vit_base_p16_224-80ecf9dd.pth',
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mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5),
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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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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_large_p16_224-4ee7a4dc.pth',
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mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5)),
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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 MlpSuper(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.act = act_layer()
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self.drop = nn.Dropout(drop)
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self.fc1 = nn.Linear(in_features, hidden_features)
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self.fc2 = nn.Linear(hidden_features, out_features)
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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 AttentionSuper(nn.Module):
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def __init__(self, dim, num_heads=8, qkv_bias=False, 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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self.scale = head_dim ** -0.5
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self.proj = nn.Linear(dim, dim)
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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_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.unbind(0) # 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, 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 = AttentionSuper(dim, num_heads=num_heads, qkv_bias=qkv_bias, 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 = MlpSuper(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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def num_params(self):
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return sum(p.numel() for p in self.parameters() if p.requires_grad)
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class VisionTransformer(nn.Module):
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""" Vision Transformer
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A PyTorch impl of : `An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale`
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- https://arxiv.org/abs/2010.11929
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Includes distillation token & head support for `DeiT: Data-efficient Image Transformers`
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- https://arxiv.org/abs/2012.12877
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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=True, representation_size=None, distilled=False,
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drop_rate=0., attn_drop_rate=0., drop_path_rate=0., embed_layer=PatchEmbed, norm_layer=None,
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act_layer=None, weight_init='', ):
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"""
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Args:
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img_size (int, tuple): input image size
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patch_size (int, tuple): patch size
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in_chans (int): number of input channels
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num_classes (int): number of classes for classification head
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embed_dim (int): embedding dimension
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depth (int): depth of transformer
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num_heads (int): number of attention heads
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mlp_ratio (int): ratio of mlp hidden dim to embedding dim
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qkv_bias (bool): enable bias for qkv if True
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representation_size (Optional[int]): enable and set representation layer (pre-logits) to this value if set
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distilled (bool): model includes a distillation token and head as in DeiT models
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drop_rate (float): dropout rate
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attn_drop_rate (float): attention dropout rate
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drop_path_rate (float): stochastic depth rate
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embed_layer (nn.Module): patch embedding layer
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norm_layer: (nn.Module): normalization layer
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weight_init: (str): weight init scheme
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"""
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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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self.num_tokens = 2 if distilled else 1
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norm_layer = norm_layer or partial(nn.LayerNorm, eps=1e-6)
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act_layer = act_layer or nn.GELU
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self.patch_embed = embed_layer(
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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.dist_token = nn.Parameter(torch.zeros(1, 1, embed_dim)) if distilled else None
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self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + self.num_tokens, 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.Sequential(*[
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Block(
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dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, drop=drop_rate,
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attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer, act_layer=act_layer)
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for i in range(depth)])
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self.norm = norm_layer(embed_dim)
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# Representation layer
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if representation_size and not distilled:
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self.num_features = representation_size
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self.pre_logits = nn.Sequential(OrderedDict([
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('fc', nn.Linear(embed_dim, representation_size)),
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('act', nn.Tanh())
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]))
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else:
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self.pre_logits = nn.Identity()
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# Classifier head(s)
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self.head = nn.Linear(self.num_features, num_classes) if num_classes > 0 else nn.Identity()
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self.head_dist = None
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if distilled:
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self.head_dist = nn.Linear(self.embed_dim, self.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', 'dist_token'}
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def get_classifier(self):
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if self.dist_token is None:
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return self.head
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else:
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return self.head, self.head_dist
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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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if self.num_tokens == 2:
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self.head_dist = nn.Linear(self.embed_dim, self.num_classes) if num_classes > 0 else nn.Identity()
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def forward_features(self, x):
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x = self.patch_embed(x)
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cls_token = self.cls_token.expand(x.shape[0], -1, -1) # stole cls_tokens impl from Phil Wang, thanks
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if self.dist_token is None:
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x = torch.cat((cls_token, x), dim=1)
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else:
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x = torch.cat((cls_token, self.dist_token.expand(x.shape[0], -1, -1), x), dim=1)
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x = self.pos_drop(x + self.pos_embed)
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x = self.blocks(x)
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x = self.norm(x)
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if self.dist_token is None:
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return self.pre_logits(x[:, 0])
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else:
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return x[:, 0], x[:, 1]
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def forward(self, x):
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x = self.forward_features(x)
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if self.head_dist is not None:
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x, x_dist = self.head(x[0]), self.head_dist(x[1]) # x must be a tuple
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if self.training and not torch.jit.is_scripting():
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# during inference, return the average of both classifier predictions
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return x, x_dist
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else:
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return (x + x_dist) / 2
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else:
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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 deit_base_patch16_224(pretrained=False, **kwargs):
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""" DeiT base model @ 224x224 from paper (https://arxiv.org/abs/2012.12877).
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ImageNet-1k weights from https://github.com/facebookresearch/deit.
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"""
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model = VisionTransformer(
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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 = _cfg()
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if pretrained:
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checkpoint = torch.hub.load_state_dict_from_url(
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url="https://dl.fbaipublicfiles.com/deit/deit_base_patch16_224-b5f2ef4d.pth",
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map_location="cpu", check_hash=True
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)
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model.load_state_dict(checkpoint["model"])
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return model
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@register_model
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def deit_small_patch16_224(pretrained=False, **kwargs):
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""" DeiT base model @ 224x224 from paper (https://arxiv.org/abs/2012.12877).
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ImageNet-1k weights from https://github.com/facebookresearch/deit.
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"""
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model = VisionTransformer(
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patch_size=16, embed_dim=384, depth=12, num_heads=6, 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 = _cfg()
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if pretrained:
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checkpoint = torch.hub.load_state_dict_from_url(
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url="https://dl.fbaipublicfiles.com/deit/deit_small_patch16_224-cd65a155.pth",
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map_location="cpu", check_hash=True
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)
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model.load_state_dict(checkpoint["model"])
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return model
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@register_model
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def deit_tiny_patch16_224(pretrained=False, **kwargs):
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""" DeiT base model @ 224x224 from paper (https://arxiv.org/abs/2012.12877).
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ImageNet-1k weights from https://github.com/facebookresearch/deit.
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"""
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model = VisionTransformer(
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patch_size=16, embed_dim=192, depth=12, num_heads=3, 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 = _cfg()
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if pretrained:
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checkpoint = torch.hub.load_state_dict_from_url(
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url="https://dl.fbaipublicfiles.com/deit/deit_tiny_patch16_224-a1311bcf.pth",
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map_location="cpu", check_hash=True
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)
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model.load_state_dict(checkpoint["model"])
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return model
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