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383 lines
13 KiB
383 lines
13 KiB
""" Pooling-based Vision Transformer (PiT) in PyTorch
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A PyTorch implement of Pooling-based Vision Transformers as described in
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'Rethinking Spatial Dimensions of Vision Transformers' - https://arxiv.org/abs/2103.16302
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This code was adapted from the original version at https://github.com/naver-ai/pit, original copyright below.
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Modifications for timm by / Copyright 2020 Ross Wightman
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"""
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# PiT
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# Copyright 2021-present NAVER Corp.
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# Apache License v2.0
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import math
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import re
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from copy import deepcopy
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from functools import partial
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from typing import Tuple
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import torch
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from torch import 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 trunc_normal_, to_2tuple
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from .registry import register_model
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from .vision_transformer import Block
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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_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD,
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'first_conv': 'patch_embed.conv', 'classifier': 'head',
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**kwargs
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}
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default_cfgs = {
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# deit models (FB weights)
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'pit_ti_224': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-pit-weights/pit_ti_730.pth'),
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'pit_xs_224': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-pit-weights/pit_xs_781.pth'),
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'pit_s_224': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-pit-weights/pit_s_809.pth'),
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'pit_b_224': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-pit-weights/pit_b_820.pth'),
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'pit_ti_distilled_224': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-pit-weights/pit_ti_distill_746.pth',
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classifier=('head', 'head_dist')),
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'pit_xs_distilled_224': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-pit-weights/pit_xs_distill_791.pth',
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classifier=('head', 'head_dist')),
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'pit_s_distilled_224': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-pit-weights/pit_s_distill_819.pth',
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classifier=('head', 'head_dist')),
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'pit_b_distilled_224': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-pit-weights/pit_b_distill_840.pth',
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classifier=('head', 'head_dist')),
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}
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class SequentialTuple(nn.Sequential):
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""" This module exists to work around torchscript typing issues list -> list"""
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def __init__(self, *args):
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super(SequentialTuple, self).__init__(*args)
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def forward(self, x: Tuple[torch.Tensor, torch.Tensor]) -> Tuple[torch.Tensor, torch.Tensor]:
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for module in self:
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x = module(x)
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return x
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class Transformer(nn.Module):
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def __init__(
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self, base_dim, depth, heads, mlp_ratio, pool=None, drop_rate=.0, attn_drop_rate=.0, drop_path_prob=None):
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super(Transformer, self).__init__()
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self.layers = nn.ModuleList([])
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embed_dim = base_dim * heads
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self.blocks = nn.Sequential(*[
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Block(
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dim=embed_dim,
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num_heads=heads,
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mlp_ratio=mlp_ratio,
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qkv_bias=True,
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drop=drop_rate,
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attn_drop=attn_drop_rate,
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drop_path=drop_path_prob[i],
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norm_layer=partial(nn.LayerNorm, eps=1e-6)
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)
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for i in range(depth)])
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self.pool = pool
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def forward(self, x: Tuple[torch.Tensor, torch.Tensor]) -> Tuple[torch.Tensor, torch.Tensor]:
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x, cls_tokens = x
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B, C, H, W = x.shape
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token_length = cls_tokens.shape[1]
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x = x.flatten(2).transpose(1, 2)
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x = torch.cat((cls_tokens, x), dim=1)
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x = self.blocks(x)
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cls_tokens = x[:, :token_length]
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x = x[:, token_length:]
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x = x.transpose(1, 2).reshape(B, C, H, W)
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if self.pool is not None:
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x, cls_tokens = self.pool(x, cls_tokens)
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return x, cls_tokens
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class ConvHeadPooling(nn.Module):
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def __init__(self, in_feature, out_feature, stride, padding_mode='zeros'):
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super(ConvHeadPooling, self).__init__()
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self.conv = nn.Conv2d(
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in_feature, out_feature, kernel_size=stride + 1, padding=stride // 2, stride=stride,
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padding_mode=padding_mode, groups=in_feature)
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self.fc = nn.Linear(in_feature, out_feature)
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def forward(self, x, cls_token) -> Tuple[torch.Tensor, torch.Tensor]:
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x = self.conv(x)
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cls_token = self.fc(cls_token)
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return x, cls_token
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class ConvEmbedding(nn.Module):
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def __init__(self, in_channels, out_channels, patch_size, stride, padding):
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super(ConvEmbedding, self).__init__()
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self.conv = nn.Conv2d(
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in_channels, out_channels, kernel_size=patch_size, stride=stride, padding=padding, bias=True)
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def forward(self, x):
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x = self.conv(x)
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return x
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class PoolingVisionTransformer(nn.Module):
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""" Pooling-based Vision Transformer
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A PyTorch implement of 'Rethinking Spatial Dimensions of Vision Transformers'
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- https://arxiv.org/abs/2103.16302
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"""
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def __init__(self, img_size, patch_size, stride, base_dims, depth, heads,
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mlp_ratio, num_classes=1000, in_chans=3, distilled=False,
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attn_drop_rate=.0, drop_rate=.0, drop_path_rate=.0):
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super(PoolingVisionTransformer, self).__init__()
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padding = 0
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img_size = to_2tuple(img_size)
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patch_size = to_2tuple(patch_size)
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height = math.floor((img_size[0] + 2 * padding - patch_size[0]) / stride + 1)
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width = math.floor((img_size[1] + 2 * padding - patch_size[1]) / stride + 1)
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self.base_dims = base_dims
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self.heads = heads
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self.num_classes = num_classes
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self.num_tokens = 2 if distilled else 1
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self.patch_size = patch_size
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self.pos_embed = nn.Parameter(torch.randn(1, base_dims[0] * heads[0], height, width))
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self.patch_embed = ConvEmbedding(in_chans, base_dims[0] * heads[0], patch_size, stride, padding)
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self.cls_token = nn.Parameter(torch.randn(1, self.num_tokens, base_dims[0] * heads[0]))
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self.pos_drop = nn.Dropout(p=drop_rate)
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transformers = []
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# stochastic depth decay rule
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dpr = [x.tolist() for x in torch.linspace(0, drop_path_rate, sum(depth)).split(depth)]
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for stage in range(len(depth)):
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pool = None
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if stage < len(heads) - 1:
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pool = ConvHeadPooling(
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base_dims[stage] * heads[stage], base_dims[stage + 1] * heads[stage + 1], stride=2)
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transformers += [Transformer(
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base_dims[stage], depth[stage], heads[stage], mlp_ratio, pool=pool,
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drop_rate=drop_rate, attn_drop_rate=attn_drop_rate, drop_path_prob=dpr[stage])
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]
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self.transformers = SequentialTuple(*transformers)
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self.norm = nn.LayerNorm(base_dims[-1] * heads[-1], eps=1e-6)
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self.num_features = self.embed_dim = base_dims[-1] * heads[-1]
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# Classifier head
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self.head = nn.Linear(self.embed_dim, 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.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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if self.head_dist is not None:
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return self.head, self.head_dist
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else:
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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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if self.head_dist is not None:
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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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x = self.pos_drop(x + self.pos_embed)
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cls_tokens = self.cls_token.expand(x.shape[0], -1, -1)
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x, cls_tokens = self.transformers((x, cls_tokens))
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cls_tokens = self.norm(cls_tokens)
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if self.head_dist is not None:
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return cls_tokens[:, 0], cls_tokens[:, 1]
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else:
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return cls_tokens[:, 0]
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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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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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return self.head(x)
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def checkpoint_filter_fn(state_dict, model):
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""" preprocess checkpoints """
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out_dict = {}
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p_blocks = re.compile(r'pools\.(\d)\.')
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for k, v in state_dict.items():
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# FIXME need to update resize for PiT impl
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# if k == 'pos_embed' and v.shape != model.pos_embed.shape:
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# # To resize pos embedding when using model at different size from pretrained weights
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# v = resize_pos_embed(v, model.pos_embed)
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k = p_blocks.sub(lambda exp: f'transformers.{int(exp.group(1))}.pool.', k)
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out_dict[k] = v
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return out_dict
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def _create_pit(variant, pretrained=False, **kwargs):
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if kwargs.get('features_only', None):
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raise RuntimeError('features_only not implemented for Vision Transformer models.')
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model = build_model_with_cfg(
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PoolingVisionTransformer, variant, pretrained,
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pretrained_filter_fn=checkpoint_filter_fn,
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**kwargs)
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return model
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@register_model
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def pit_b_224(pretrained, **kwargs):
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model_kwargs = dict(
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patch_size=14,
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stride=7,
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base_dims=[64, 64, 64],
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depth=[3, 6, 4],
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heads=[4, 8, 16],
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mlp_ratio=4,
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**kwargs
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)
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return _create_pit('pit_b_224', pretrained, **model_kwargs)
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@register_model
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def pit_s_224(pretrained, **kwargs):
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model_kwargs = dict(
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patch_size=16,
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stride=8,
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base_dims=[48, 48, 48],
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depth=[2, 6, 4],
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heads=[3, 6, 12],
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mlp_ratio=4,
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**kwargs
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)
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return _create_pit('pit_s_224', pretrained, **model_kwargs)
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@register_model
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def pit_xs_224(pretrained, **kwargs):
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model_kwargs = dict(
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patch_size=16,
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stride=8,
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base_dims=[48, 48, 48],
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depth=[2, 6, 4],
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heads=[2, 4, 8],
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mlp_ratio=4,
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**kwargs
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)
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return _create_pit('pit_xs_224', pretrained, **model_kwargs)
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@register_model
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def pit_ti_224(pretrained, **kwargs):
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model_kwargs = dict(
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patch_size=16,
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stride=8,
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base_dims=[32, 32, 32],
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depth=[2, 6, 4],
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heads=[2, 4, 8],
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mlp_ratio=4,
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**kwargs
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)
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return _create_pit('pit_ti_224', pretrained, **model_kwargs)
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@register_model
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def pit_b_distilled_224(pretrained, **kwargs):
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model_kwargs = dict(
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patch_size=14,
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stride=7,
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base_dims=[64, 64, 64],
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depth=[3, 6, 4],
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heads=[4, 8, 16],
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mlp_ratio=4,
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distilled=True,
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**kwargs
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)
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return _create_pit('pit_b_distilled_224', pretrained, **model_kwargs)
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@register_model
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def pit_s_distilled_224(pretrained, **kwargs):
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model_kwargs = dict(
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patch_size=16,
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stride=8,
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base_dims=[48, 48, 48],
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depth=[2, 6, 4],
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heads=[3, 6, 12],
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mlp_ratio=4,
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distilled=True,
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**kwargs
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)
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return _create_pit('pit_s_distilled_224', pretrained, **model_kwargs)
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@register_model
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def pit_xs_distilled_224(pretrained, **kwargs):
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model_kwargs = dict(
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patch_size=16,
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stride=8,
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base_dims=[48, 48, 48],
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depth=[2, 6, 4],
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heads=[2, 4, 8],
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mlp_ratio=4,
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distilled=True,
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**kwargs
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)
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return _create_pit('pit_xs_distilled_224', pretrained, **model_kwargs)
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@register_model
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def pit_ti_distilled_224(pretrained, **kwargs):
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model_kwargs = dict(
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patch_size=16,
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stride=8,
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base_dims=[32, 32, 32],
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depth=[2, 6, 4],
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heads=[2, 4, 8],
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mlp_ratio=4,
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distilled=True,
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**kwargs
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)
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return _create_pit('pit_ti_distilled_224', pretrained, **model_kwargs) |