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322 lines
14 KiB
322 lines
14 KiB
""" Hybrid Vision Transformer (ViT) in PyTorch
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A PyTorch implement of the Hybrid 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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NOTE These hybrid model definitions depend on code in vision_transformer.py.
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They were moved here to keep file sizes sane.
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Hacked together by / Copyright 2020, Ross Wightman
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"""
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from functools import partial
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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 timm.layers import StdConv2dSame, StdConv2d, to_2tuple
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from ._pretrained import generate_default_cfgs
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from ._registry import register_model
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from .resnet import resnet26d, resnet50d
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from .resnetv2 import ResNetV2, create_resnetv2_stem
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from .vision_transformer import _create_vision_transformer
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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__(
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self,
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backbone,
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img_size=224,
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patch_size=1,
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feature_size=None,
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in_chans=3,
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embed_dim=768,
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bias=True,
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):
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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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patch_size = to_2tuple(patch_size)
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self.img_size = img_size
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self.patch_size = patch_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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# NOTE Most reliable way of determining output dims is to run forward pass
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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]))
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if isinstance(o, (list, tuple)):
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o = o[-1] # last feature if backbone outputs list/tuple of features
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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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if hasattr(self.backbone, 'feature_info'):
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feature_dim = self.backbone.feature_info.channels()[-1]
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else:
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feature_dim = self.backbone.num_features
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assert feature_size[0] % patch_size[0] == 0 and feature_size[1] % patch_size[1] == 0
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self.grid_size = (feature_size[0] // patch_size[0], feature_size[1] // patch_size[1])
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self.num_patches = self.grid_size[0] * self.grid_size[1]
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self.proj = nn.Conv2d(feature_dim, embed_dim, kernel_size=patch_size, stride=patch_size, bias=bias)
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def forward(self, x):
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x = self.backbone(x)
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if isinstance(x, (list, tuple)):
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x = x[-1] # last feature if backbone outputs list/tuple of features
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x = self.proj(x).flatten(2).transpose(1, 2)
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return x
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def _create_vision_transformer_hybrid(variant, backbone, pretrained=False, **kwargs):
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embed_layer = partial(HybridEmbed, backbone=backbone)
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kwargs.setdefault('patch_size', 1) # default patch size for hybrid models if not set
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return _create_vision_transformer(variant, pretrained=pretrained, embed_layer=embed_layer, **kwargs)
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def _resnetv2(layers=(3, 4, 9), **kwargs):
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""" ResNet-V2 backbone helper"""
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padding_same = kwargs.get('padding_same', True)
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stem_type = 'same' if padding_same else ''
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conv_layer = partial(StdConv2dSame, eps=1e-8) if padding_same else partial(StdConv2d, eps=1e-8)
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if len(layers):
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backbone = ResNetV2(
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layers=layers, num_classes=0, global_pool='', in_chans=kwargs.get('in_chans', 3),
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preact=False, stem_type=stem_type, conv_layer=conv_layer)
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else:
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backbone = create_resnetv2_stem(
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kwargs.get('in_chans', 3), stem_type=stem_type, preact=False, conv_layer=conv_layer)
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return backbone
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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': (0.5, 0.5, 0.5), 'std': (0.5, 0.5, 0.5),
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'first_conv': 'patch_embed.backbone.stem.conv', 'classifier': 'head',
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**kwargs
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}
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default_cfgs = generate_default_cfgs({
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# hybrid in-1k models (weights from official JAX impl where they exist)
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'vit_tiny_r_s16_p8_224.augreg_in21k_ft_in1k': _cfg(
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url='https://storage.googleapis.com/vit_models/augreg/R_Ti_16-i21k-300ep-lr_0.001-aug_none-wd_0.03-do_0.0-sd_0.0--imagenet2012-steps_20k-lr_0.03-res_224.npz',
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hf_hub_id='timm/',
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custom_load=True,
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first_conv='patch_embed.backbone.conv'),
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'vit_tiny_r_s16_p8_384.augreg_in21k_ft_in1k': _cfg(
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url='https://storage.googleapis.com/vit_models/augreg/R_Ti_16-i21k-300ep-lr_0.001-aug_none-wd_0.03-do_0.0-sd_0.0--imagenet2012-steps_20k-lr_0.03-res_384.npz',
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hf_hub_id='timm/',
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first_conv='patch_embed.backbone.conv', input_size=(3, 384, 384), crop_pct=1.0, custom_load=True),
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'vit_small_r26_s32_224.augreg_in21k_ft_in1k': _cfg(
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url='https://storage.googleapis.com/vit_models/augreg/R26_S_32-i21k-300ep-lr_0.001-aug_light0-wd_0.03-do_0.1-sd_0.1--imagenet2012-steps_20k-lr_0.03-res_224.npz',
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hf_hub_id='timm/',
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custom_load=True,
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),
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'vit_small_r26_s32_384.augreg_in21k_ft_in1k': _cfg(
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url='https://storage.googleapis.com/vit_models/augreg/R26_S_32-i21k-300ep-lr_0.001-aug_medium2-wd_0.03-do_0.0-sd_0.0--imagenet2012-steps_20k-lr_0.03-res_384.npz',
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hf_hub_id='timm/',
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input_size=(3, 384, 384), crop_pct=1.0, custom_load=True),
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'vit_base_r26_s32_224.untrained': _cfg(),
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'vit_base_r50_s16_384.orig_in21k_ft_in1k': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_base_resnet50_384-9fd3c705.pth',
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hf_hub_id='timm/',
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input_size=(3, 384, 384), crop_pct=1.0),
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'vit_large_r50_s32_224.augreg_in21k_ft_in1k': _cfg(
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url='https://storage.googleapis.com/vit_models/augreg/R50_L_32-i21k-300ep-lr_0.001-aug_medium1-wd_0.1-do_0.1-sd_0.1--imagenet2012-steps_20k-lr_0.01-res_224.npz',
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hf_hub_id='timm/',
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custom_load=True,
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),
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'vit_large_r50_s32_384.augreg_in21k_ft_in1k': _cfg(
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url='https://storage.googleapis.com/vit_models/augreg/R50_L_32-i21k-300ep-lr_0.001-aug_medium2-wd_0.1-do_0.0-sd_0.0--imagenet2012-steps_20k-lr_0.01-res_384.npz',
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hf_hub_id='timm/',
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input_size=(3, 384, 384), crop_pct=1.0, custom_load=True,
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),
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# hybrid in-21k models (weights from official Google JAX impl where they exist)
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'vit_tiny_r_s16_p8_224.augreg_in21k': _cfg(
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url='https://storage.googleapis.com/vit_models/augreg/R_Ti_16-i21k-300ep-lr_0.001-aug_none-wd_0.03-do_0.0-sd_0.0.npz',
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hf_hub_id='timm/',
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num_classes=21843, crop_pct=0.9, first_conv='patch_embed.backbone.conv', custom_load=True),
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'vit_small_r26_s32_224.augreg_in21k': _cfg(
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url='https://storage.googleapis.com/vit_models/augreg/R26_S_32-i21k-300ep-lr_0.001-aug_medium2-wd_0.03-do_0.0-sd_0.0.npz',
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hf_hub_id='timm/',
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num_classes=21843, crop_pct=0.9, custom_load=True),
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'vit_base_r50_s16_224.orig_in21k': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_base_resnet50_224_in21k-6f7c7740.pth',
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hf_hub_id='timm/',
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num_classes=21843, crop_pct=0.9),
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'vit_large_r50_s32_224.augreg_in21k': _cfg(
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url='https://storage.googleapis.com/vit_models/augreg/R50_L_32-i21k-300ep-lr_0.001-aug_medium2-wd_0.1-do_0.0-sd_0.0.npz',
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hf_hub_id='timm/',
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num_classes=21843, crop_pct=0.9, custom_load=True),
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# hybrid models (using timm resnet backbones)
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'vit_small_resnet26d_224.untrained': _cfg(
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mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD, first_conv='patch_embed.backbone.conv1.0'),
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'vit_small_resnet50d_s16_224.untrained': _cfg(
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mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD, first_conv='patch_embed.backbone.conv1.0'),
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'vit_base_resnet26d_224.untrained': _cfg(
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mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD, first_conv='patch_embed.backbone.conv1.0'),
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'vit_base_resnet50d_224.untrained': _cfg(
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mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD, first_conv='patch_embed.backbone.conv1.0'),
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})
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@register_model
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def vit_tiny_r_s16_p8_224(pretrained=False, **kwargs):
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""" R+ViT-Ti/S16 w/ 8x8 patch hybrid @ 224 x 224.
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"""
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backbone = _resnetv2(layers=(), **kwargs)
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model_kwargs = dict(patch_size=8, embed_dim=192, depth=12, num_heads=3, **kwargs)
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model = _create_vision_transformer_hybrid(
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'vit_tiny_r_s16_p8_224', backbone=backbone, pretrained=pretrained, **model_kwargs)
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return model
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@register_model
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def vit_tiny_r_s16_p8_384(pretrained=False, **kwargs):
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""" R+ViT-Ti/S16 w/ 8x8 patch hybrid @ 384 x 384.
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"""
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backbone = _resnetv2(layers=(), **kwargs)
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model_kwargs = dict(patch_size=8, embed_dim=192, depth=12, num_heads=3, **kwargs)
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model = _create_vision_transformer_hybrid(
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'vit_tiny_r_s16_p8_384', backbone=backbone, pretrained=pretrained, **model_kwargs)
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return model
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@register_model
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def vit_small_r26_s32_224(pretrained=False, **kwargs):
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""" R26+ViT-S/S32 hybrid.
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"""
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backbone = _resnetv2((2, 2, 2, 2), **kwargs)
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model_kwargs = dict(embed_dim=384, depth=12, num_heads=6, **kwargs)
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model = _create_vision_transformer_hybrid(
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'vit_small_r26_s32_224', backbone=backbone, pretrained=pretrained, **model_kwargs)
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return model
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@register_model
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def vit_small_r26_s32_384(pretrained=False, **kwargs):
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""" R26+ViT-S/S32 hybrid.
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"""
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backbone = _resnetv2((2, 2, 2, 2), **kwargs)
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model_kwargs = dict(embed_dim=384, depth=12, num_heads=6, **kwargs)
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model = _create_vision_transformer_hybrid(
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'vit_small_r26_s32_384', backbone=backbone, pretrained=pretrained, **model_kwargs)
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return model
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@register_model
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def vit_base_r26_s32_224(pretrained=False, **kwargs):
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""" R26+ViT-B/S32 hybrid.
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"""
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backbone = _resnetv2((2, 2, 2, 2), **kwargs)
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model_kwargs = dict(embed_dim=768, depth=12, num_heads=12, **kwargs)
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model = _create_vision_transformer_hybrid(
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'vit_base_r26_s32_224', backbone=backbone, pretrained=pretrained, **model_kwargs)
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return model
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@register_model
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def vit_base_r50_s16_224(pretrained=False, **kwargs):
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""" R50+ViT-B/S16 hybrid from original paper (https://arxiv.org/abs/2010.11929).
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"""
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backbone = _resnetv2((3, 4, 9), **kwargs)
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model_kwargs = dict(embed_dim=768, depth=12, num_heads=12, **kwargs)
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model = _create_vision_transformer_hybrid(
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'vit_base_r50_s16_224', backbone=backbone, pretrained=pretrained, **model_kwargs)
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return model
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@register_model
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def vit_base_r50_s16_384(pretrained=False, **kwargs):
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""" R50+ViT-B/16 hybrid from original paper (https://arxiv.org/abs/2010.11929).
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ImageNet-1k weights fine-tuned from in21k @ 384x384, source https://github.com/google-research/vision_transformer.
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"""
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backbone = _resnetv2((3, 4, 9), **kwargs)
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model_kwargs = dict(embed_dim=768, depth=12, num_heads=12, **kwargs)
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model = _create_vision_transformer_hybrid(
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'vit_base_r50_s16_384', backbone=backbone, pretrained=pretrained, **model_kwargs)
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return model
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@register_model
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def vit_large_r50_s32_224(pretrained=False, **kwargs):
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""" R50+ViT-L/S32 hybrid.
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"""
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backbone = _resnetv2((3, 4, 6, 3), **kwargs)
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model_kwargs = dict(embed_dim=1024, depth=24, num_heads=16, **kwargs)
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model = _create_vision_transformer_hybrid(
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'vit_large_r50_s32_224', backbone=backbone, pretrained=pretrained, **model_kwargs)
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return model
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@register_model
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def vit_large_r50_s32_384(pretrained=False, **kwargs):
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""" R50+ViT-L/S32 hybrid.
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"""
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backbone = _resnetv2((3, 4, 6, 3), **kwargs)
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model_kwargs = dict(embed_dim=1024, depth=24, num_heads=16, **kwargs)
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model = _create_vision_transformer_hybrid(
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'vit_large_r50_s32_384', backbone=backbone, pretrained=pretrained, **model_kwargs)
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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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""" Custom ViT small hybrid w/ ResNet26D stride 32. No pretrained weights.
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"""
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backbone = resnet26d(pretrained=pretrained, in_chans=kwargs.get('in_chans', 3), features_only=True, out_indices=[4])
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model_kwargs = dict(embed_dim=768, depth=8, num_heads=8, mlp_ratio=3, **kwargs)
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model = _create_vision_transformer_hybrid(
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'vit_small_resnet26d_224', backbone=backbone, pretrained=pretrained, **model_kwargs)
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return model
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@register_model
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def vit_small_resnet50d_s16_224(pretrained=False, **kwargs):
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""" Custom ViT small hybrid w/ ResNet50D 3-stages, stride 16. No pretrained weights.
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"""
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backbone = resnet50d(pretrained=pretrained, in_chans=kwargs.get('in_chans', 3), features_only=True, out_indices=[3])
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model_kwargs = dict(embed_dim=768, depth=8, num_heads=8, mlp_ratio=3, **kwargs)
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model = _create_vision_transformer_hybrid(
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'vit_small_resnet50d_s16_224', backbone=backbone, pretrained=pretrained, **model_kwargs)
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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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""" Custom ViT base hybrid w/ ResNet26D stride 32. No pretrained weights.
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"""
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backbone = resnet26d(pretrained=pretrained, in_chans=kwargs.get('in_chans', 3), features_only=True, out_indices=[4])
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model_kwargs = dict(embed_dim=768, depth=12, num_heads=12, **kwargs)
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model = _create_vision_transformer_hybrid(
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'vit_base_resnet26d_224', backbone=backbone, pretrained=pretrained, **model_kwargs)
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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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""" Custom ViT base hybrid w/ ResNet50D stride 32. No pretrained weights.
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"""
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backbone = resnet50d(pretrained=pretrained, in_chans=kwargs.get('in_chans', 3), features_only=True, out_indices=[4])
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model_kwargs = dict(embed_dim=768, depth=12, num_heads=12, **kwargs)
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model = _create_vision_transformer_hybrid(
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'vit_base_resnet50d_224', backbone=backbone, pretrained=pretrained, **model_kwargs)
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return model
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