Vision Transformer refactoring and Rel Pos impl
Ross Wightman
parent
b7cb8d0337
commit
41dc49a337
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@ -23,6 +23,15 @@ I'm fortunate to be able to dedicate significant time and money of my own suppor
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## What's New
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### May 2, 2022
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* Vision Transformer experiments adding Relative Position (Swin-V2 log-coord) (`vision_transformer_relpos.py`) and Residual Post-Norm branches (from Swin-V2) (`vision_transformer*.py`)
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* `vit_relpos_base_patch32_plus_rpn_256` - 79.5 @ 256, 80.6 @ 320 -- rel pos + extended width + res-post-norm, no class token, avg pool
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* `vit_relpos_base_patch16_224` - 82.5 @ 224, 83.6 @ 320 -- rel pos, layer scale, no class token, avg pool
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* `vit_base_patch16_rpn_224` - 82.3 @ 224 -- rel pos + res-post-norm, no class token, avg pool
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* Vision Transformer refactor to remove representation layer that was only used in initial vit and rarely used since with newer pretrain (ie `How to Train Your ViT`)
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* `vit_*` models support removal of class token, use of global average pool, use of fc_norm (ala beit, mae).
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### April 22, 2022
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* `timm` models are now officially supported in [fast.ai](https://www.fast.ai/)! Just in time for the new Practical Deep Learning course. `timmdocs` documentation link updated to [timm.fast.ai](http://timm.fast.ai/).
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* Two more model weights added in the TPU trained [series](https://github.com/rwightman/pytorch-image-models/releases/tag/v0.1-tpu-weights). Some In22k pretrain still in progress.
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@ -49,6 +49,7 @@ from .vgg import *
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from .visformer import *
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from .vision_transformer import *
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from .vision_transformer_hybrid import *
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from .vision_transformer_relpos import *
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from .volo import *
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from .vovnet import *
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from .xception import *
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@ -23,6 +23,7 @@ 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 typing import Optional
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import torch
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import torch.nn as nn
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@ -107,7 +108,6 @@ default_cfgs = {
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'vit_giant_patch14_224': _cfg(url=''),
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'vit_gigantic_patch14_224': _cfg(url=''),
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'vit_base2_patch32_256': _cfg(url='', input_size=(3, 256, 256), crop_pct=0.95),
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# patch models, imagenet21k (weights from official Google JAX impl)
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'vit_tiny_patch16_224_in21k': _cfg(
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@ -171,7 +171,12 @@ default_cfgs = {
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mean=(0, 0, 0), std=(1, 1, 1), crop_pct=0.875, interpolation='bilinear',
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),
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# experimental
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'vit_base_patch16_rpn_224': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tpu-weights/vit_base_patch16_rpn_224-sw-3b07e89d.pth'),
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# experimental (may be removed)
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'vit_base_patch32_plus_256': _cfg(url='', input_size=(3, 256, 256), crop_pct=0.95),
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'vit_base_patch16_plus_240': _cfg(url='', input_size=(3, 240, 240), crop_pct=0.95),
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'vit_small_patch16_36x1_224': _cfg(url=''),
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'vit_small_patch16_18x2_224': _cfg(url=''),
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'vit_base_patch16_18x2_224': _cfg(url=''),
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@ -229,8 +234,7 @@ class Block(nn.Module):
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self.drop_path1 = DropPath(drop_path) if drop_path > 0. else nn.Identity()
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self.norm2 = norm_layer(dim)
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mlp_hidden_dim = int(dim * mlp_ratio)
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self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
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self.mlp = Mlp(in_features=dim, hidden_features=int(dim * mlp_ratio), act_layer=act_layer, drop=drop)
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self.ls2 = LayerScale(dim, init_values=init_values) if init_values else nn.Identity()
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self.drop_path2 = DropPath(drop_path) if drop_path > 0. else nn.Identity()
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@ -240,6 +244,36 @@ class Block(nn.Module):
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return x
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class ResPostBlock(nn.Module):
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def __init__(
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self, dim, num_heads, mlp_ratio=4., qkv_bias=False, drop=0., attn_drop=0., init_values=None,
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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.init_values = init_values
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self.attn = Attention(dim, num_heads=num_heads, qkv_bias=qkv_bias, attn_drop=attn_drop, proj_drop=drop)
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self.norm1 = norm_layer(dim)
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self.drop_path1 = DropPath(drop_path) if drop_path > 0. else nn.Identity()
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self.mlp = Mlp(in_features=dim, hidden_features=int(dim * mlp_ratio), act_layer=act_layer, drop=drop)
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self.norm2 = norm_layer(dim)
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self.drop_path2 = DropPath(drop_path) if drop_path > 0. else nn.Identity()
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self.init_weights()
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def init_weights(self):
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# NOTE this init overrides that base model init with specific changes for the block type
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if self.init_values is not None:
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nn.init.constant_(self.norm1.weight, self.init_values)
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nn.init.constant_(self.norm2.weight, self.init_values)
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def forward(self, x):
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x = x + self.drop_path1(self.norm1(self.attn(x)))
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x = x + self.drop_path2(self.norm2(self.mlp(x)))
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return x
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class ParallelBlock(nn.Module):
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def __init__(
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@ -290,9 +324,9 @@ class VisionTransformer(nn.Module):
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def __init__(
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self, img_size=224, patch_size=16, in_chans=3, num_classes=1000, global_pool='token',
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embed_dim=768, depth=12, num_heads=12, mlp_ratio=4., qkv_bias=True, representation_size=None,
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drop_rate=0., attn_drop_rate=0., drop_path_rate=0., weight_init='', init_values=None,
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embed_layer=PatchEmbed, norm_layer=None, act_layer=None, block_fn=Block):
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embed_dim=768, depth=12, num_heads=12, mlp_ratio=4., qkv_bias=True, init_values=None,
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drop_rate=0., attn_drop_rate=0., drop_path_rate=0., weight_init='', class_token=True,
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fc_norm=None, embed_layer=PatchEmbed, norm_layer=None, act_layer=None, block_fn=Block):
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"""
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Args:
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img_size (int, tuple): input image size
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@ -305,33 +339,36 @@ class VisionTransformer(nn.Module):
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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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init_values: (float): layer-scale init values
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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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weight_init: (str): weight init scheme
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init_values: (float): layer-scale init values
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weight_init (str): weight init scheme
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class_token (bool): use class token
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fc_norm (Optional[bool]): pre-fc norm after pool, set if global_pool == 'avg' if None (default: None)
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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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act_layer: (nn.Module): MLP activation layer
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"""
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super().__init__()
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assert global_pool in ('', 'avg', 'token')
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assert class_token or global_pool != 'token'
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use_fc_norm = global_pool == 'avg' if fc_norm is None else fc_norm
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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.num_classes = num_classes
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self.global_pool = global_pool
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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 = 1
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self.num_tokens = 1 if class_token else 0
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self.grad_checkpointing = False
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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.pos_embed = nn.Parameter(torch.zeros(1, num_patches + self.num_tokens, embed_dim))
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self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim)) if self.num_tokens > 0 else None
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self.pos_embed = nn.Parameter(torch.randn(1, num_patches + self.num_tokens, embed_dim) * .02)
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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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@ -340,38 +377,21 @@ class VisionTransformer(nn.Module):
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dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, init_values=init_values,
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drop=drop_rate, 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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use_fc_norm = self.global_pool == 'avg'
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self.norm = norm_layer(embed_dim) if not use_fc_norm else nn.Identity()
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# Representation layer. Used for original ViT models w/ in21k pretraining.
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self.representation_size = representation_size
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self.pre_logits = nn.Identity()
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if representation_size:
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self._reset_representation(representation_size)
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# Classifier Head
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self.fc_norm = norm_layer(embed_dim) if use_fc_norm else nn.Identity()
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final_chs = self.representation_size if self.representation_size else self.embed_dim
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self.head = nn.Linear(final_chs, num_classes) if num_classes > 0 else nn.Identity()
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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 weight_init != 'skip':
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self.init_weights(weight_init)
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def _reset_representation(self, representation_size):
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self.representation_size = representation_size
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if self.representation_size:
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self.pre_logits = nn.Sequential(OrderedDict([
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('fc', nn.Linear(self.embed_dim, self.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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def init_weights(self, mode=''):
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assert mode in ('jax', 'jax_nlhb', 'moco', '')
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head_bias = -math.log(self.num_classes) if 'nlhb' in mode else 0.
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trunc_normal_(self.pos_embed, std=.02)
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nn.init.normal_(self.cls_token, std=1e-6)
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if self.cls_token is not None:
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nn.init.normal_(self.cls_token, std=1e-6)
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named_apply(get_init_weights_vit(mode, head_bias), self)
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def _init_weights(self, m):
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@ -401,19 +421,17 @@ class VisionTransformer(nn.Module):
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def get_classifier(self):
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return self.head
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def reset_classifier(self, num_classes: int, global_pool=None, representation_size=None):
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def reset_classifier(self, num_classes: int, global_pool=None):
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self.num_classes = num_classes
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if global_pool is not None:
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assert global_pool in ('', 'avg', 'token')
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self.global_pool = global_pool
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if representation_size is not None:
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self._reset_representation(representation_size)
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final_chs = self.representation_size if self.representation_size else self.embed_dim
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self.head = nn.Linear(final_chs, num_classes) if num_classes > 0 else nn.Identity()
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self.head = nn.Linear(self.embed_dim, num_classes) if num_classes > 0 else nn.Identity()
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def forward_features(self, x):
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x = self.patch_embed(x)
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x = torch.cat((self.cls_token.expand(x.shape[0], -1, -1), x), dim=1)
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if self.cls_token is not None:
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x = torch.cat((self.cls_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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if self.grad_checkpointing and not torch.jit.is_scripting():
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x = checkpoint_seq(self.blocks, x)
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@ -424,9 +442,8 @@ class VisionTransformer(nn.Module):
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def forward_head(self, x, pre_logits: bool = False):
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if self.global_pool:
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x = x[:, 1:].mean(dim=1) if self.global_pool == 'avg' else x[:, 0]
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x = x[:, self.num_tokens:].mean(dim=1) if self.global_pool == 'avg' else x[:, 0]
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x = self.fc_norm(x)
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x = self.pre_logits(x)
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return x if pre_logits else self.head(x)
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def forward(self, x):
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@ -441,6 +458,8 @@ def init_weights_vit_timm(module: nn.Module, name: str = ''):
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trunc_normal_(module.weight, std=.02)
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if module.bias is not None:
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nn.init.zeros_(module.bias)
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elif hasattr(module, 'init_weights'):
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module.init_weights()
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def init_weights_vit_jax(module: nn.Module, name: str = '', head_bias: float = 0.):
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@ -449,9 +468,6 @@ def init_weights_vit_jax(module: nn.Module, name: str = '', head_bias: float = 0
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if name.startswith('head'):
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nn.init.zeros_(module.weight)
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nn.init.constant_(module.bias, head_bias)
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elif name.startswith('pre_logits'):
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lecun_normal_(module.weight)
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nn.init.zeros_(module.bias)
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else:
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nn.init.xavier_uniform_(module.weight)
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if module.bias is not None:
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@ -460,6 +476,8 @@ def init_weights_vit_jax(module: nn.Module, name: str = '', head_bias: float = 0
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lecun_normal_(module.weight)
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if module.bias is not None:
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nn.init.zeros_(module.bias)
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elif hasattr(module, 'init_weights'):
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module.init_weights()
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def init_weights_vit_moco(module: nn.Module, name: str = ''):
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@ -473,6 +491,8 @@ def init_weights_vit_moco(module: nn.Module, name: str = ''):
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nn.init.xavier_uniform_(module.weight)
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if module.bias is not None:
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nn.init.zeros_(module.bias)
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elif hasattr(module, 'init_weights'):
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module.init_weights()
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def get_init_weights_vit(mode='jax', head_bias: float = 0.):
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@ -543,9 +563,10 @@ def _load_weights(model: VisionTransformer, checkpoint_path: str, prefix: str =
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if isinstance(model.head, nn.Linear) and model.head.bias.shape[0] == w[f'{prefix}head/bias'].shape[-1]:
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model.head.weight.copy_(_n2p(w[f'{prefix}head/kernel']))
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model.head.bias.copy_(_n2p(w[f'{prefix}head/bias']))
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if isinstance(getattr(model.pre_logits, 'fc', None), nn.Linear) and f'{prefix}pre_logits/bias' in w:
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model.pre_logits.fc.weight.copy_(_n2p(w[f'{prefix}pre_logits/kernel']))
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model.pre_logits.fc.bias.copy_(_n2p(w[f'{prefix}pre_logits/bias']))
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# NOTE representation layer has been removed, not used in latest 21k/1k pretrained weights
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# if isinstance(getattr(model.pre_logits, 'fc', None), nn.Linear) and f'{prefix}pre_logits/bias' in w:
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# model.pre_logits.fc.weight.copy_(_n2p(w[f'{prefix}pre_logits/kernel']))
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# model.pre_logits.fc.bias.copy_(_n2p(w[f'{prefix}pre_logits/bias']))
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for i, block in enumerate(model.blocks.children()):
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block_prefix = f'{prefix}Transformer/encoderblock_{i}/'
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mha_prefix = block_prefix + 'MultiHeadDotProductAttention_1/'
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@ -601,6 +622,9 @@ def checkpoint_filter_fn(state_dict, model):
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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(
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v, model.pos_embed, getattr(model, 'num_tokens', 1), model.patch_embed.grid_size)
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elif 'pre_logits' in k:
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# NOTE representation layer removed as not used in latest 21k/1k pretrained weights
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continue
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out_dict[k] = v
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return out_dict
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@ -609,21 +633,10 @@ def _create_vision_transformer(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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# NOTE this extra code to support handling of repr size for in21k pretrained models
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pretrained_cfg = resolve_pretrained_cfg(variant, kwargs=kwargs)
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default_num_classes = pretrained_cfg['num_classes']
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num_classes = kwargs.get('num_classes', default_num_classes)
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repr_size = kwargs.pop('representation_size', None)
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if repr_size is not None and num_classes != default_num_classes:
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# Remove representation layer if fine-tuning. This may not always be the desired action,
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# but I feel better than doing nothing by default for fine-tuning. Perhaps a better interface?
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_logger.warning("Removing representation layer for fine-tuning.")
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repr_size = None
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model = build_model_with_cfg(
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VisionTransformer, variant, pretrained,
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pretrained_cfg=pretrained_cfg,
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representation_size=repr_size,
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pretrained_filter_fn=checkpoint_filter_fn,
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pretrained_custom_load='npz' in pretrained_cfg['url'],
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**kwargs)
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@ -696,16 +709,6 @@ def vit_base_patch32_224(pretrained=False, **kwargs):
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return model
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@register_model
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def vit_base2_patch32_256(pretrained=False, **kwargs):
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""" ViT-Base (ViT-B/32)
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# FIXME experiment
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"""
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model_kwargs = dict(patch_size=32, embed_dim=896, depth=12, num_heads=14, **kwargs)
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model = _create_vision_transformer('vit_base2_patch32_256', pretrained=pretrained, **model_kwargs)
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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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""" ViT-Base model (ViT-B/32) from original paper (https://arxiv.org/abs/2010.11929).
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@ -860,8 +863,7 @@ def vit_base_patch32_224_in21k(pretrained=False, **kwargs):
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ImageNet-21k weights @ 224x224, source https://github.com/google-research/vision_transformer.
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NOTE: this model has valid 21k classifier head and no representation (pre-logits) layer
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"""
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model_kwargs = dict(
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patch_size=32, embed_dim=768, depth=12, num_heads=12, **kwargs)
|
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model_kwargs = dict(patch_size=32, embed_dim=768, depth=12, num_heads=12, **kwargs)
|
||||
model = _create_vision_transformer('vit_base_patch32_224_in21k', pretrained=pretrained, **model_kwargs)
|
||||
return model
|
||||
|
||||
|
|
@ -872,8 +874,7 @@ def vit_base_patch16_224_in21k(pretrained=False, **kwargs):
|
|||
ImageNet-21k weights @ 224x224, source https://github.com/google-research/vision_transformer.
|
||||
NOTE: this model has valid 21k classifier head and no representation (pre-logits) layer
|
||||
"""
|
||||
model_kwargs = dict(
|
||||
patch_size=16, embed_dim=768, depth=12, num_heads=12, **kwargs)
|
||||
model_kwargs = dict(patch_size=16, embed_dim=768, depth=12, num_heads=12, **kwargs)
|
||||
model = _create_vision_transformer('vit_base_patch16_224_in21k', pretrained=pretrained, **model_kwargs)
|
||||
return model
|
||||
|
||||
|
|
@ -884,8 +885,7 @@ def vit_base_patch8_224_in21k(pretrained=False, **kwargs):
|
|||
ImageNet-21k weights @ 224x224, source https://github.com/google-research/vision_transformer.
|
||||
NOTE: this model has valid 21k classifier head and no representation (pre-logits) layer
|
||||
"""
|
||||
model_kwargs = dict(
|
||||
patch_size=8, embed_dim=768, depth=12, num_heads=12, **kwargs)
|
||||
model_kwargs = dict(patch_size=8, embed_dim=768, depth=12, num_heads=12, **kwargs)
|
||||
model = _create_vision_transformer('vit_base_patch8_224_in21k', pretrained=pretrained, **model_kwargs)
|
||||
return model
|
||||
|
||||
|
|
@ -896,8 +896,7 @@ def vit_large_patch32_224_in21k(pretrained=False, **kwargs):
|
|||
ImageNet-21k weights @ 224x224, source https://github.com/google-research/vision_transformer.
|
||||
NOTE: this model has a representation layer but the 21k classifier head is zero'd out in original weights
|
||||
"""
|
||||
model_kwargs = dict(
|
||||
patch_size=32, embed_dim=1024, depth=24, num_heads=16, representation_size=1024, **kwargs)
|
||||
model_kwargs = dict(patch_size=32, embed_dim=1024, depth=24, num_heads=16, **kwargs)
|
||||
model = _create_vision_transformer('vit_large_patch32_224_in21k', pretrained=pretrained, **model_kwargs)
|
||||
return model
|
||||
|
||||
|
|
@ -908,8 +907,7 @@ def vit_large_patch16_224_in21k(pretrained=False, **kwargs):
|
|||
ImageNet-21k weights @ 224x224, source https://github.com/google-research/vision_transformer.
|
||||
NOTE: this model has valid 21k classifier head and no representation (pre-logits) layer
|
||||
"""
|
||||
model_kwargs = dict(
|
||||
patch_size=16, embed_dim=1024, depth=24, num_heads=16, **kwargs)
|
||||
model_kwargs = dict(patch_size=16, embed_dim=1024, depth=24, num_heads=16, **kwargs)
|
||||
model = _create_vision_transformer('vit_large_patch16_224_in21k', pretrained=pretrained, **model_kwargs)
|
||||
return model
|
||||
|
||||
|
|
@ -920,8 +918,7 @@ def vit_huge_patch14_224_in21k(pretrained=False, **kwargs):
|
|||
ImageNet-21k weights @ 224x224, source https://github.com/google-research/vision_transformer.
|
||||
NOTE: this model has a representation layer but the 21k classifier head is zero'd out in original weights
|
||||
"""
|
||||
model_kwargs = dict(
|
||||
patch_size=14, embed_dim=1280, depth=32, num_heads=16, representation_size=1280, **kwargs)
|
||||
model_kwargs = dict(patch_size=14, embed_dim=1280, depth=32, num_heads=16, **kwargs)
|
||||
model = _create_vision_transformer('vit_huge_patch14_224_in21k', pretrained=pretrained, **model_kwargs)
|
||||
return model
|
||||
|
||||
|
|
@ -930,7 +927,6 @@ def vit_huge_patch14_224_in21k(pretrained=False, **kwargs):
|
|||
def vit_base_patch16_224_sam(pretrained=False, **kwargs):
|
||||
""" ViT-Base (ViT-B/16) w/ SAM pretrained weights. Paper: https://arxiv.org/abs/2106.01548
|
||||
"""
|
||||
# NOTE original SAM weights release worked with representation_size=768
|
||||
model_kwargs = dict(patch_size=16, embed_dim=768, depth=12, num_heads=12, **kwargs)
|
||||
model = _create_vision_transformer('vit_base_patch16_224_sam', pretrained=pretrained, **model_kwargs)
|
||||
return model
|
||||
|
|
@ -940,7 +936,6 @@ def vit_base_patch16_224_sam(pretrained=False, **kwargs):
|
|||
def vit_base_patch32_224_sam(pretrained=False, **kwargs):
|
||||
""" ViT-Base (ViT-B/32) w/ SAM pretrained weights. Paper: https://arxiv.org/abs/2106.01548
|
||||
"""
|
||||
# NOTE original SAM weights release worked with representation_size=768
|
||||
model_kwargs = dict(patch_size=32, embed_dim=768, depth=12, num_heads=12, **kwargs)
|
||||
model = _create_vision_transformer('vit_base_patch32_224_sam', pretrained=pretrained, **model_kwargs)
|
||||
return model
|
||||
|
|
@ -1002,6 +997,37 @@ def vit_base_patch16_224_miil(pretrained=False, **kwargs):
|
|||
return model
|
||||
|
||||
|
||||
# Experimental models below
|
||||
|
||||
@register_model
|
||||
def vit_base_patch32_plus_256(pretrained=False, **kwargs):
|
||||
""" ViT-Base (ViT-B/32+)
|
||||
"""
|
||||
model_kwargs = dict(patch_size=32, embed_dim=896, depth=12, num_heads=14, init_values=1e-5, **kwargs)
|
||||
model = _create_vision_transformer('vit_base_patch32_plus_256', pretrained=pretrained, **model_kwargs)
|
||||
return model
|
||||
|
||||
|
||||
@register_model
|
||||
def vit_base_patch16_plus_240(pretrained=False, **kwargs):
|
||||
""" ViT-Base (ViT-B/16+)
|
||||
"""
|
||||
model_kwargs = dict(patch_size=16, embed_dim=896, depth=12, num_heads=14, init_values=1e-5, **kwargs)
|
||||
model = _create_vision_transformer('vit_base_patch16_plus_240', pretrained=pretrained, **model_kwargs)
|
||||
return model
|
||||
|
||||
|
||||
@register_model
|
||||
def vit_base_patch16_rpn_224(pretrained=False, **kwargs):
|
||||
""" ViT-Base (ViT-B/16) w/ residual post-norm
|
||||
"""
|
||||
model_kwargs = dict(
|
||||
patch_size=16, embed_dim=768, depth=12, num_heads=12, qkv_bias=False, init_values=1e-5, class_token=False,
|
||||
block_fn=ResPostBlock, global_pool=kwargs.pop('global_pool', 'avg'), **kwargs)
|
||||
model = _create_vision_transformer('vit_base_patch16_rpn_224', pretrained=pretrained, **model_kwargs)
|
||||
return model
|
||||
|
||||
|
||||
@register_model
|
||||
def vit_small_patch16_36x1_224(pretrained=False, **kwargs):
|
||||
""" ViT-Base w/ LayerScale + 36 x 1 (36 block serial) config. Experimental, may remove.
|
||||
|
|
|
|||
|
|
@ -295,7 +295,7 @@ def vit_base_r50_s16_224_in21k(pretrained=False, **kwargs):
|
|||
ImageNet-21k weights @ 224x224, source https://github.com/google-research/vision_transformer.
|
||||
"""
|
||||
backbone = _resnetv2(layers=(3, 4, 9), **kwargs)
|
||||
model_kwargs = dict(embed_dim=768, depth=12, num_heads=12, representation_size=768, **kwargs)
|
||||
model_kwargs = dict(embed_dim=768, depth=12, num_heads=12, **kwargs)
|
||||
model = _create_vision_transformer_hybrid(
|
||||
'vit_base_r50_s16_224_in21k', backbone=backbone, pretrained=pretrained, **model_kwargs)
|
||||
return model
|
||||
|
|
|
|||
|
|
@ -0,0 +1,425 @@
|
|||
""" Relative Position Vision Transformer (ViT) in PyTorch
|
||||
|
||||
Hacked together by / Copyright 2022, Ross Wightman
|
||||
"""
|
||||
import math
|
||||
import logging
|
||||
from functools import partial
|
||||
from collections import OrderedDict
|
||||
from typing import Optional, Tuple
|
||||
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import torch.nn.functional as F
|
||||
from torch.utils.checkpoint import checkpoint
|
||||
|
||||
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, IMAGENET_INCEPTION_MEAN, IMAGENET_INCEPTION_STD
|
||||
from .helpers import build_model_with_cfg, resolve_pretrained_cfg, named_apply
|
||||
from .layers import PatchEmbed, Mlp, DropPath, trunc_normal_, lecun_normal_, to_2tuple
|
||||
from .registry import register_model
|
||||
|
||||
_logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
def _cfg(url='', **kwargs):
|
||||
return {
|
||||
'url': url,
|
||||
'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': None,
|
||||
'crop_pct': .9, 'interpolation': 'bicubic', 'fixed_input_size': True,
|
||||
'mean': IMAGENET_INCEPTION_MEAN, 'std': IMAGENET_INCEPTION_STD,
|
||||
'first_conv': 'patch_embed.proj', 'classifier': 'head',
|
||||
**kwargs
|
||||
}
|
||||
|
||||
|
||||
default_cfgs = {
|
||||
'vit_relpos_base_patch32_plus_rpn_256': _cfg(
|
||||
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tpu-weights/vit_replos_base_patch32_plus_rpn_256-sw-dd486f51.pth',
|
||||
input_size=(3, 256, 256)),
|
||||
'vit_relpos_base_patch16_plus_240': _cfg(url='', input_size=(3, 240, 240)),
|
||||
'vit_relpos_base_patch16_rpn_224': _cfg(url=''),
|
||||
'vit_relpos_base_patch16_224': _cfg(
|
||||
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tpu-weights/vit_relpos_base_patch16_224-sw-49049aed.pth'),
|
||||
}
|
||||
|
||||
|
||||
def gen_relative_position_index(win_size: Tuple[int, int], class_token: int = 0) -> torch.Tensor:
|
||||
# cut and paste w/ modifications from swin / beit codebase
|
||||
# cls to token & token 2 cls & cls to cls
|
||||
# get pair-wise relative position index for each token inside the window
|
||||
window_area = win_size[0] * win_size[1]
|
||||
coords = torch.stack(torch.meshgrid([torch.arange(win_size[0]), torch.arange(win_size[1])])).flatten(1) # 2, Wh, Ww
|
||||
relative_coords = coords[:, :, None] - coords[:, None, :] # 2, Wh*Ww, Wh*Ww
|
||||
relative_coords = relative_coords.permute(1, 2, 0).contiguous() # Wh*Ww, Wh*Ww, 2
|
||||
relative_coords[:, :, 0] += win_size[0] - 1 # shift to start from 0
|
||||
relative_coords[:, :, 1] += win_size[1] - 1
|
||||
relative_coords[:, :, 0] *= 2 * win_size[1] - 1
|
||||
if class_token:
|
||||
num_relative_distance = (2 * win_size[0] - 1) * (2 * win_size[1] - 1) + 3
|
||||
relative_position_index = torch.zeros(size=(window_area + 1,) * 2, dtype=relative_coords.dtype)
|
||||
relative_position_index[1:, 1:] = relative_coords.sum(-1) # Wh*Ww, Wh*Ww
|
||||
relative_position_index[0, 0:] = num_relative_distance - 3
|
||||
relative_position_index[0:, 0] = num_relative_distance - 2
|
||||
relative_position_index[0, 0] = num_relative_distance - 1
|
||||
else:
|
||||
relative_position_index = relative_coords.sum(-1) # Wh*Ww, Wh*Ww
|
||||
return relative_position_index
|
||||
|
||||
|
||||
def gen_relative_position_log(win_size: Tuple[int, int]) -> torch.Tensor:
|
||||
"""Method initializes the pair-wise relative positions to compute the positional biases."""
|
||||
coordinates = torch.stack(torch.meshgrid([torch.arange(win_size[0]), torch.arange(win_size[1])])).flatten(1)
|
||||
relative_coords = coordinates[:, :, None] - coordinates[:, None, :]
|
||||
relative_coords = relative_coords.permute(1, 2, 0).float()
|
||||
relative_coordinates_log = torch.sign(relative_coords) * torch.log(1.0 + relative_coords.abs())
|
||||
return relative_coordinates_log
|
||||
|
||||
|
||||
class RelPosMlp(nn.Module):
|
||||
# based on timm swin-v2 impl
|
||||
def __init__(self, window_size, num_heads=8, hidden_dim=32, class_token=False):
|
||||
super().__init__()
|
||||
self.window_size = window_size
|
||||
self.window_area = self.window_size[0] * self.window_size[1]
|
||||
self.class_token = 1 if class_token else 0
|
||||
self.num_heads = num_heads
|
||||
|
||||
self.mlp = Mlp(
|
||||
2, # x, y
|
||||
hidden_features=min(128, hidden_dim * num_heads),
|
||||
out_features=num_heads,
|
||||
act_layer=nn.ReLU,
|
||||
drop=(0.125, 0.)
|
||||
)
|
||||
|
||||
self.register_buffer(
|
||||
'rel_coords_log',
|
||||
gen_relative_position_log(window_size),
|
||||
persistent=False
|
||||
)
|
||||
|
||||
def get_bias(self) -> torch.Tensor:
|
||||
relative_position_bias = self.mlp(self.rel_coords_log).permute(2, 0, 1).unsqueeze(0)
|
||||
if self.class_token:
|
||||
relative_position_bias = F.pad(relative_position_bias, [self.class_token, 0, self.class_token, 0])
|
||||
return relative_position_bias
|
||||
|
||||
def forward(self, attn, shared_rel_pos: Optional[torch.Tensor] = None):
|
||||
return attn + self.get_bias()
|
||||
|
||||
|
||||
class RelPosBias(nn.Module):
|
||||
|
||||
def __init__(self, window_size, num_heads, class_token=False):
|
||||
super().__init__()
|
||||
self.window_size = window_size
|
||||
self.window_area = window_size[0] * window_size[1]
|
||||
self.class_token = 1 if class_token else 0
|
||||
self.bias_shape = (self.window_area + self.class_token,) * 2 + (num_heads,)
|
||||
|
||||
num_relative_distance = (2 * window_size[0] - 1) * (2 * window_size[1] - 1) + 3 * self.class_token
|
||||
self.relative_position_bias_table = nn.Parameter(torch.zeros(num_relative_distance, num_heads))
|
||||
self.register_buffer(
|
||||
"relative_position_index",
|
||||
gen_relative_position_index(self.window_size, class_token=self.class_token),
|
||||
persistent=False,
|
||||
)
|
||||
|
||||
self.init_weights()
|
||||
|
||||
def init_weights(self):
|
||||
trunc_normal_(self.relative_position_bias_table, std=.02)
|
||||
|
||||
def get_bias(self) -> torch.Tensor:
|
||||
relative_position_bias = self.relative_position_bias_table[self.relative_position_index.view(-1)].view(
|
||||
self.bias_shape) # win_h * win_w, win_h * win_w, num_heads
|
||||
relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous()
|
||||
return relative_position_bias
|
||||
|
||||
def forward(self, attn, shared_rel_pos: Optional[torch.Tensor] = None):
|
||||
return attn + self.get_bias()
|
||||
|
||||
|
||||
class RelPosAttention(nn.Module):
|
||||
def __init__(self, dim, num_heads=8, qkv_bias=False, rel_pos_cls=None, attn_drop=0., proj_drop=0.):
|
||||
super().__init__()
|
||||
assert dim % num_heads == 0, 'dim should be divisible by num_heads'
|
||||
self.num_heads = num_heads
|
||||
head_dim = dim // num_heads
|
||||
self.scale = head_dim ** -0.5
|
||||
|
||||
self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
|
||||
self.rel_pos = rel_pos_cls(num_heads=num_heads) if rel_pos_cls else None
|
||||
self.attn_drop = nn.Dropout(attn_drop)
|
||||
self.proj = nn.Linear(dim, dim)
|
||||
self.proj_drop = nn.Dropout(proj_drop)
|
||||
|
||||
def forward(self, x, shared_rel_pos: Optional[torch.Tensor] = None):
|
||||
B, N, C = x.shape
|
||||
qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
|
||||
q, k, v = qkv.unbind(0) # make torchscript happy (cannot use tensor as tuple)
|
||||
|
||||
attn = (q @ k.transpose(-2, -1)) * self.scale
|
||||
if self.rel_pos is not None:
|
||||
attn = self.rel_pos(attn, shared_rel_pos=shared_rel_pos)
|
||||
elif shared_rel_pos is not None:
|
||||
attn = attn + shared_rel_pos
|
||||
attn = attn.softmax(dim=-1)
|
||||
attn = self.attn_drop(attn)
|
||||
|
||||
x = (attn @ v).transpose(1, 2).reshape(B, N, C)
|
||||
x = self.proj(x)
|
||||
x = self.proj_drop(x)
|
||||
return x
|
||||
|
||||
|
||||
class LayerScale(nn.Module):
|
||||
def __init__(self, dim, init_values=1e-5, inplace=False):
|
||||
super().__init__()
|
||||
self.inplace = inplace
|
||||
self.gamma = nn.Parameter(init_values * torch.ones(dim))
|
||||
|
||||
def forward(self, x):
|
||||
return x.mul_(self.gamma) if self.inplace else x * self.gamma
|
||||
|
||||
|
||||
class RelPosBlock(nn.Module):
|
||||
|
||||
def __init__(
|
||||
self, dim, num_heads, mlp_ratio=4., qkv_bias=False, rel_pos_cls=None, init_values=None,
|
||||
drop=0., attn_drop=0., drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm):
|
||||
super().__init__()
|
||||
self.norm1 = norm_layer(dim)
|
||||
self.attn = RelPosAttention(
|
||||
dim, num_heads, qkv_bias=qkv_bias, rel_pos_cls=rel_pos_cls, attn_drop=attn_drop, proj_drop=drop)
|
||||
self.ls1 = LayerScale(dim, init_values=init_values) if init_values else nn.Identity()
|
||||
# NOTE: drop path for stochastic depth, we shall see if this is better than dropout here
|
||||
self.drop_path1 = DropPath(drop_path) if drop_path > 0. else nn.Identity()
|
||||
|
||||
self.norm2 = norm_layer(dim)
|
||||
self.mlp = Mlp(in_features=dim, hidden_features=int(dim * mlp_ratio), act_layer=act_layer, drop=drop)
|
||||
self.ls2 = LayerScale(dim, init_values=init_values) if init_values else nn.Identity()
|
||||
self.drop_path2 = DropPath(drop_path) if drop_path > 0. else nn.Identity()
|
||||
|
||||
def forward(self, x, shared_rel_pos: Optional[torch.Tensor] = None):
|
||||
x = x + self.drop_path1(self.ls1(self.attn(self.norm1(x), shared_rel_pos=shared_rel_pos)))
|
||||
x = x + self.drop_path2(self.ls2(self.mlp(self.norm2(x))))
|
||||
return x
|
||||
|
||||
|
||||
class ResPostRelPosBlock(nn.Module):
|
||||
|
||||
def __init__(
|
||||
self, dim, num_heads, mlp_ratio=4., qkv_bias=False, rel_pos_cls=None, init_values=None,
|
||||
drop=0., attn_drop=0., drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm):
|
||||
super().__init__()
|
||||
self.init_values = init_values
|
||||
|
||||
self.attn = RelPosAttention(
|
||||
dim, num_heads, qkv_bias=qkv_bias, rel_pos_cls=rel_pos_cls, attn_drop=attn_drop, proj_drop=drop)
|
||||
self.norm1 = norm_layer(dim)
|
||||
self.drop_path1 = DropPath(drop_path) if drop_path > 0. else nn.Identity()
|
||||
|
||||
self.mlp = Mlp(in_features=dim, hidden_features=int(dim * mlp_ratio), act_layer=act_layer, drop=drop)
|
||||
self.norm2 = norm_layer(dim)
|
||||
self.drop_path2 = DropPath(drop_path) if drop_path > 0. else nn.Identity()
|
||||
|
||||
self.init_weights()
|
||||
|
||||
def init_weights(self):
|
||||
# NOTE this init overrides that base model init with specific changes for the block type
|
||||
if self.init_values is not None:
|
||||
nn.init.constant_(self.norm1.weight, self.init_values)
|
||||
nn.init.constant_(self.norm2.weight, self.init_values)
|
||||
|
||||
def forward(self, x, shared_rel_pos: Optional[torch.Tensor] = None):
|
||||
x = x + self.drop_path1(self.norm1(self.attn(x, shared_rel_pos=shared_rel_pos)))
|
||||
x = x + self.drop_path2(self.norm2(self.mlp(x)))
|
||||
return x
|
||||
|
||||
|
||||
class VisionTransformerRelPos(nn.Module):
|
||||
""" Vision Transformer w/ Relative Position Bias
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self, img_size=224, patch_size=16, in_chans=3, num_classes=1000, global_pool='avg',
|
||||
embed_dim=768, depth=12, num_heads=12, mlp_ratio=4., qkv_bias=True, init_values=None,
|
||||
drop_rate=0., attn_drop_rate=0., drop_path_rate=0., weight_init='skip', class_token=False,
|
||||
rel_pos_type='mlp', shared_rel_pos=False, fc_norm=False,
|
||||
embed_layer=PatchEmbed, norm_layer=None, act_layer=None, block_fn=RelPosBlock):
|
||||
"""
|
||||
Args:
|
||||
img_size (int, tuple): input image size
|
||||
patch_size (int, tuple): patch size
|
||||
in_chans (int): number of input channels
|
||||
num_classes (int): number of classes for classification head
|
||||
global_pool (str): type of global pooling for final sequence (default: 'token')
|
||||
embed_dim (int): embedding dimension
|
||||
depth (int): depth of transformer
|
||||
num_heads (int): number of attention heads
|
||||
mlp_ratio (int): ratio of mlp hidden dim to embedding dim
|
||||
qkv_bias (bool): enable bias for qkv if True
|
||||
init_values: (float): layer-scale init values
|
||||
drop_rate (float): dropout rate
|
||||
attn_drop_rate (float): attention dropout rate
|
||||
drop_path_rate (float): stochastic depth rate
|
||||
weight_init (str): weight init scheme
|
||||
class_token (bool): use class token (default: False)
|
||||
rel_pos_ty pe (str): type of relative position
|
||||
shared_rel_pos (bool): share relative pos across all blocks
|
||||
fc_norm (bool): use pre classifier norm
|
||||
embed_layer (nn.Module): patch embedding layer
|
||||
norm_layer: (nn.Module): normalization layer
|
||||
act_layer: (nn.Module): MLP activation layer
|
||||
"""
|
||||
super().__init__()
|
||||
assert global_pool in ('', 'avg', 'token')
|
||||
assert class_token or global_pool != 'token'
|
||||
norm_layer = norm_layer or partial(nn.LayerNorm, eps=1e-6)
|
||||
act_layer = act_layer or nn.GELU
|
||||
|
||||
self.num_classes = num_classes
|
||||
self.global_pool = global_pool
|
||||
self.num_features = self.embed_dim = embed_dim # num_features for consistency with other models
|
||||
self.num_tokens = 1 if class_token else 0
|
||||
self.grad_checkpointing = False
|
||||
|
||||
self.patch_embed = embed_layer(
|
||||
img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim)
|
||||
feat_size = self.patch_embed.grid_size
|
||||
|
||||
rel_pos_cls = RelPosMlp if rel_pos_type == 'mlp' else RelPosBias
|
||||
rel_pos_cls = partial(rel_pos_cls, window_size=feat_size, class_token=class_token)
|
||||
self.shared_rel_pos = None
|
||||
if shared_rel_pos:
|
||||
self.shared_rel_pos = rel_pos_cls(num_heads=num_heads)
|
||||
# NOTE shared rel pos currently mutually exclusive w/ per-block, but could support both...
|
||||
rel_pos_cls = None
|
||||
|
||||
self.cls_token = nn.Parameter(torch.zeros(1, self.num_tokens, embed_dim)) if self.num_tokens else None
|
||||
|
||||
dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)] # stochastic depth decay rule
|
||||
self.blocks = nn.ModuleList([
|
||||
block_fn(
|
||||
dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, rel_pos_cls=rel_pos_cls,
|
||||
init_values=init_values, drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i],
|
||||
norm_layer=norm_layer, act_layer=act_layer)
|
||||
for i in range(depth)])
|
||||
self.norm = norm_layer(embed_dim) if not fc_norm else nn.Identity()
|
||||
|
||||
# Classifier Head
|
||||
self.fc_norm = norm_layer(embed_dim) if fc_norm else nn.Identity()
|
||||
self.head = nn.Linear(self.embed_dim, num_classes) if num_classes > 0 else nn.Identity()
|
||||
|
||||
if weight_init != 'skip':
|
||||
self.init_weights(weight_init)
|
||||
|
||||
def init_weights(self, mode=''):
|
||||
assert mode in ('jax', 'moco', '')
|
||||
if self.cls_token is not None:
|
||||
nn.init.normal_(self.cls_token, std=1e-6)
|
||||
# FIXME weight init scheme using PyTorch defaults curently
|
||||
#named_apply(get_init_weights_vit(mode, head_bias), self)
|
||||
|
||||
@torch.jit.ignore
|
||||
def no_weight_decay(self):
|
||||
return {'cls_token'}
|
||||
|
||||
@torch.jit.ignore
|
||||
def group_matcher(self, coarse=False):
|
||||
return dict(
|
||||
stem=r'^cls_token|patch_embed', # stem and embed
|
||||
blocks=[(r'^blocks\.(\d+)', None), (r'^norm', (99999,))]
|
||||
)
|
||||
|
||||
@torch.jit.ignore
|
||||
def set_grad_checkpointing(self, enable=True):
|
||||
self.grad_checkpointing = enable
|
||||
|
||||
@torch.jit.ignore
|
||||
def get_classifier(self):
|
||||
return self.head
|
||||
|
||||
def reset_classifier(self, num_classes: int, global_pool=None):
|
||||
self.num_classes = num_classes
|
||||
if global_pool is not None:
|
||||
assert global_pool in ('', 'avg', 'token')
|
||||
self.global_pool = global_pool
|
||||
self.head = nn.Linear(self.embed_dim, num_classes) if num_classes > 0 else nn.Identity()
|
||||
|
||||
def forward_features(self, x):
|
||||
x = self.patch_embed(x)
|
||||
if self.cls_token is not None:
|
||||
x = torch.cat((self.cls_token.expand(x.shape[0], -1, -1), x), dim=1)
|
||||
|
||||
shared_rel_pos = self.shared_rel_pos.get_bias() if self.shared_rel_pos is not None else None
|
||||
for blk in self.blocks:
|
||||
if self.grad_checkpointing and not torch.jit.is_scripting():
|
||||
x = checkpoint(blk, x, shared_rel_pos=shared_rel_pos)
|
||||
else:
|
||||
x = blk(x, shared_rel_pos=shared_rel_pos)
|
||||
x = self.norm(x)
|
||||
return x
|
||||
|
||||
def forward_head(self, x, pre_logits: bool = False):
|
||||
if self.global_pool:
|
||||
x = x[:, self.num_tokens:].mean(dim=1) if self.global_pool == 'avg' else x[:, 0]
|
||||
x = self.fc_norm(x)
|
||||
return x if pre_logits else self.head(x)
|
||||
|
||||
def forward(self, x):
|
||||
x = self.forward_features(x)
|
||||
x = self.forward_head(x)
|
||||
return x
|
||||
|
||||
|
||||
def _create_vision_transformer_relpos(variant, pretrained=False, **kwargs):
|
||||
if kwargs.get('features_only', None):
|
||||
raise RuntimeError('features_only not implemented for Vision Transformer models.')
|
||||
|
||||
model = build_model_with_cfg(VisionTransformerRelPos, variant, pretrained, **kwargs)
|
||||
return model
|
||||
|
||||
|
||||
@register_model
|
||||
def vit_relpos_base_patch32_plus_rpn_256(pretrained=False, **kwargs):
|
||||
""" ViT-Base (ViT-B/32+) w/ relative log-coord position and residual post-norm, no class token
|
||||
"""
|
||||
model_kwargs = dict(
|
||||
patch_size=32, embed_dim=896, depth=12, num_heads=14, init_values=1e-5,
|
||||
block_fn=ResPostRelPosBlock, **kwargs)
|
||||
model = _create_vision_transformer_relpos(
|
||||
'vit_relpos_base_patch32_plus_rpn_256', pretrained=pretrained, **model_kwargs)
|
||||
return model
|
||||
|
||||
|
||||
@register_model
|
||||
def vit_relpos_base_patch16_plus_240(pretrained=False, **kwargs):
|
||||
""" ViT-Base (ViT-B/16+) w/ relative log-coord position, no class token
|
||||
"""
|
||||
model_kwargs = dict(patch_size=16, embed_dim=896, depth=12, num_heads=14, init_values=1e-5, **kwargs)
|
||||
model = _create_vision_transformer_relpos('vit_relpos_base_patch16_plus_240', pretrained=pretrained, **model_kwargs)
|
||||
return model
|
||||
|
||||
|
||||
@register_model
|
||||
def vit_relpos_base_patch16_224(pretrained=False, **kwargs):
|
||||
""" ViT-Base (ViT-B/16) w/ relative log-coord position, no class token
|
||||
"""
|
||||
model_kwargs = dict(
|
||||
patch_size=16, embed_dim=768, depth=12, num_heads=12, qkv_bias=False, init_values=1e-5,
|
||||
fc_norm=True, **kwargs)
|
||||
model = _create_vision_transformer_relpos('vit_relpos_base_patch16_224', pretrained=pretrained, **model_kwargs)
|
||||
return model
|
||||
|
||||
|
||||
@register_model
|
||||
def vit_relpos_base_patch16_rpn_224(pretrained=False, **kwargs):
|
||||
""" ViT-Base (ViT-B/16) w/ relative log-coord position and residual post-norm, no class token
|
||||
"""
|
||||
model_kwargs = dict(
|
||||
patch_size=16, embed_dim=768, depth=12, num_heads=12, qkv_bias=False, init_values=1e-5,
|
||||
block_fn=ResPostRelPosBlock, **kwargs)
|
||||
model = _create_vision_transformer_relpos('vit_relpos_base_patch16_rpn_224', pretrained=pretrained, **model_kwargs)
|
||||
return model
|
||||
Loading…
Reference in New Issue