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425 lines
15 KiB
Python
425 lines
15 KiB
Python
"""
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* Copyright (c) 2022, salesforce.com, inc.
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* All rights reserved.
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* SPDX-License-Identifier: BSD-3-Clause
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* For full license text, see LICENSE.txt file in the repo root or https://opensource.org/licenses/BSD-3-Clause
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* By Junnan Li
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* Based on timm code base
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* https://github.com/rwightman/pytorch-image-models/tree/master/timm.
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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.models.helpers import adapt_input_conv
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from timm.models.layers import DropPath, trunc_normal_
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from timm.models.vision_transformer import PatchEmbed
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class Mlp(nn.Module):
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"""MLP as used in Vision Transformer, MLP-Mixer and related networks."""
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def __init__(
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self,
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in_features,
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hidden_features=None,
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out_features=None,
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act_layer=nn.GELU,
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drop=0.0,
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):
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super().__init__()
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out_features = out_features or in_features
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hidden_features = hidden_features or in_features
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self.fc1 = nn.Linear(in_features, hidden_features)
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self.act = act_layer()
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self.fc2 = nn.Linear(hidden_features, out_features)
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self.drop = nn.Dropout(drop)
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def forward(self, x):
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x = self.fc1(x)
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x = self.act(x)
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x = self.drop(x)
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x = self.fc2(x)
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x = self.drop(x)
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return x
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class Attention(nn.Module):
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def __init__(
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self,
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dim,
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num_heads=8,
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qkv_bias=False,
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qk_scale=None,
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attn_drop=0.0,
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proj_drop=0.0,
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):
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super().__init__()
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self.num_heads = num_heads
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head_dim = dim // num_heads
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# NOTE scale factor was wrong in my original version, can set manually to be compat with prev weights
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self.scale = qk_scale or head_dim**-0.5
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self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
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self.attn_drop = nn.Dropout(attn_drop)
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self.proj = nn.Linear(dim, dim)
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self.proj_drop = nn.Dropout(proj_drop)
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self.attn_gradients = None
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self.attention_map = None
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def save_attn_gradients(self, attn_gradients):
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self.attn_gradients = attn_gradients
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def get_attn_gradients(self):
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return self.attn_gradients
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def save_attention_map(self, attention_map):
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self.attention_map = attention_map
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def get_attention_map(self):
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return self.attention_map
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def forward(self, x, register_hook=False):
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B, N, C = x.shape
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qkv = (
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self.qkv(x)
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.reshape(B, N, 3, self.num_heads, C // self.num_heads)
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.permute(2, 0, 3, 1, 4)
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)
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q, k, v = (
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qkv[0],
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qkv[1],
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qkv[2],
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) # make torchscript happy (cannot use tensor as tuple)
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attn = (q @ k.transpose(-2, -1)) * self.scale
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attn = attn.softmax(dim=-1)
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attn = self.attn_drop(attn)
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if register_hook:
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self.save_attention_map(attn)
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attn.register_hook(self.save_attn_gradients)
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x = (attn @ v).transpose(1, 2).reshape(B, N, C)
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x = self.proj(x)
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x = self.proj_drop(x)
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return x
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class Block(nn.Module):
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def __init__(
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self,
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dim,
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num_heads,
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mlp_ratio=4.0,
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qkv_bias=False,
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qk_scale=None,
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drop=0.0,
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attn_drop=0.0,
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drop_path=0.0,
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act_layer=nn.GELU,
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norm_layer=nn.LayerNorm,
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use_grad_checkpointing=False,
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):
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super().__init__()
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self.norm1 = norm_layer(dim)
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self.attn = Attention(
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dim,
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num_heads=num_heads,
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qkv_bias=qkv_bias,
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qk_scale=qk_scale,
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attn_drop=attn_drop,
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proj_drop=drop,
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)
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# NOTE: drop path for stochastic depth, we shall see if this is better than dropout here
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self.drop_path = DropPath(drop_path) if drop_path > 0.0 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(
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in_features=dim,
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hidden_features=mlp_hidden_dim,
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act_layer=act_layer,
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drop=drop,
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)
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if use_grad_checkpointing:
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raise RuntimeError("not supported")
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def forward(self, x, register_hook=False):
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x = x + self.drop_path(self.attn(self.norm1(x), register_hook=register_hook))
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x = x + self.drop_path(self.mlp(self.norm2(x)))
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return x
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class VisionTransformer(nn.Module):
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"""Vision Transformer
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A PyTorch impl of : `An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale` -
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https://arxiv.org/abs/2010.11929.
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"""
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def __init__(
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self,
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img_size=224,
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patch_size=16,
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in_chans=3,
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num_classes=1000,
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embed_dim=768,
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depth=12,
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num_heads=12,
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mlp_ratio=4.0,
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qkv_bias=True,
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qk_scale=None,
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representation_size=None,
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drop_rate=0.0,
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attn_drop_rate=0.0,
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drop_path_rate=0.0,
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norm_layer=None,
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use_grad_checkpointing=False,
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ckpt_layer=0,
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):
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"""
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Args:
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img_size (int, tuple): input image size
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patch_size (int, tuple): patch size
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in_chans (int): number of input channels
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num_classes (int): number of classes for classification head
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embed_dim (int): embedding dimension
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depth (int): depth of transformer
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num_heads (int): number of attention heads
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mlp_ratio (int): ratio of mlp hidden dim to embedding dim
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qkv_bias (bool): enable bias for qkv if True
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qk_scale (float): override default qk scale of head_dim ** -0.5 if set
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representation_size (Optional[int]): enable and set representation layer (pre-logits) to this value if set
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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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norm_layer: (nn.Module): normalization layer.
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"""
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super().__init__()
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self.num_features = (
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self.embed_dim
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) = embed_dim # num_features for consistency with other models
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norm_layer = norm_layer or partial(nn.LayerNorm, eps=1e-6)
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self.patch_embed = PatchEmbed(
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img_size=img_size,
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patch_size=patch_size,
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in_chans=in_chans,
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embed_dim=embed_dim,
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)
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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 + 1, embed_dim))
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self.pos_drop = nn.Dropout(p=drop_rate)
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dpr = [
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x.item() for x in torch.linspace(0, drop_path_rate, depth)
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] # stochastic depth decay rule
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self.blocks = nn.ModuleList(
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[
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Block(
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dim=embed_dim,
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num_heads=num_heads,
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mlp_ratio=mlp_ratio,
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qkv_bias=qkv_bias,
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qk_scale=qk_scale,
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drop=drop_rate,
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attn_drop=attn_drop_rate,
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drop_path=dpr[i],
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norm_layer=norm_layer,
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use_grad_checkpointing=(
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use_grad_checkpointing and i >= depth - ckpt_layer
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),
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)
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for i in range(depth)
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]
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)
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self.norm = norm_layer(embed_dim)
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trunc_normal_(self.pos_embed, std=0.02)
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trunc_normal_(self.cls_token, std=0.02)
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self.apply(self._init_weights)
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def _init_weights(self, m):
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if isinstance(m, nn.Linear):
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trunc_normal_(m.weight, std=0.02)
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if isinstance(m, nn.Linear) and m.bias is not None:
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nn.init.constant_(m.bias, 0)
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elif isinstance(m, nn.LayerNorm):
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nn.init.constant_(m.bias, 0)
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nn.init.constant_(m.weight, 1.0)
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@torch.jit.ignore
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def no_weight_decay(self):
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return {"pos_embed", "cls_token"}
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def forward(self, x, register_blk=-1):
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B = x.shape[0]
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x = self.patch_embed(x)
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cls_tokens = self.cls_token.expand(
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B, -1, -1
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) # stole cls_tokens impl from Phil Wang, thanks
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x = torch.cat((cls_tokens, x), dim=1)
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x = x + self.pos_embed[:, : x.size(1), :]
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x = self.pos_drop(x)
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for i, blk in enumerate(self.blocks):
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x = blk(x, register_blk == i)
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x = self.norm(x)
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return x
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@torch.jit.ignore()
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def load_pretrained(self, checkpoint_path, prefix=""):
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_load_weights(self, checkpoint_path, prefix)
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@torch.no_grad()
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def _load_weights(model: VisionTransformer, checkpoint_path: str, prefix: str = ""):
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"""Load weights from .npz checkpoints for official Google Brain Flax implementation."""
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import numpy as np
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def _n2p(w, t=True):
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if w.ndim == 4 and w.shape[0] == w.shape[1] == w.shape[2] == 1:
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w = w.flatten()
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if t:
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if w.ndim == 4:
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w = w.transpose([3, 2, 0, 1])
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elif w.ndim == 3:
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w = w.transpose([2, 0, 1])
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elif w.ndim == 2:
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w = w.transpose([1, 0])
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return torch.from_numpy(w)
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w = np.load(checkpoint_path)
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if not prefix and "opt/target/embedding/kernel" in w:
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prefix = "opt/target/"
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if hasattr(model.patch_embed, "backbone"):
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# hybrid
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backbone = model.patch_embed.backbone
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stem_only = not hasattr(backbone, "stem")
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stem = backbone if stem_only else backbone.stem
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stem.conv.weight.copy_(
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adapt_input_conv(
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stem.conv.weight.shape[1], _n2p(w[f"{prefix}conv_root/kernel"])
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)
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)
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stem.norm.weight.copy_(_n2p(w[f"{prefix}gn_root/scale"]))
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stem.norm.bias.copy_(_n2p(w[f"{prefix}gn_root/bias"]))
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if not stem_only:
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for i, stage in enumerate(backbone.stages):
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for j, block in enumerate(stage.blocks):
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bp = f"{prefix}block{i + 1}/unit{j + 1}/"
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for r in range(3):
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getattr(block, f"conv{r + 1}").weight.copy_(
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_n2p(w[f"{bp}conv{r + 1}/kernel"])
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)
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getattr(block, f"norm{r + 1}").weight.copy_(
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_n2p(w[f"{bp}gn{r + 1}/scale"])
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)
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getattr(block, f"norm{r + 1}").bias.copy_(
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_n2p(w[f"{bp}gn{r + 1}/bias"])
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)
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if block.downsample is not None:
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block.downsample.conv.weight.copy_(
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_n2p(w[f"{bp}conv_proj/kernel"])
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)
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block.downsample.norm.weight.copy_(
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_n2p(w[f"{bp}gn_proj/scale"])
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)
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block.downsample.norm.bias.copy_(_n2p(w[f"{bp}gn_proj/bias"]))
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embed_conv_w = _n2p(w[f"{prefix}embedding/kernel"])
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else:
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embed_conv_w = adapt_input_conv(
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model.patch_embed.proj.weight.shape[1], _n2p(w[f"{prefix}embedding/kernel"])
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)
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model.patch_embed.proj.weight.copy_(embed_conv_w)
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model.patch_embed.proj.bias.copy_(_n2p(w[f"{prefix}embedding/bias"]))
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model.cls_token.copy_(_n2p(w[f"{prefix}cls"], t=False))
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pos_embed_w = _n2p(w[f"{prefix}Transformer/posembed_input/pos_embedding"], t=False)
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if pos_embed_w.shape != model.pos_embed.shape:
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pos_embed_w = resize_pos_embed( # resize pos embedding when different size from pretrained weights
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pos_embed_w,
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model.pos_embed,
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getattr(model, "num_tokens", 1),
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model.patch_embed.grid_size,
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)
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model.pos_embed.copy_(pos_embed_w)
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model.norm.weight.copy_(_n2p(w[f"{prefix}Transformer/encoder_norm/scale"]))
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model.norm.bias.copy_(_n2p(w[f"{prefix}Transformer/encoder_norm/bias"]))
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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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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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block.norm1.weight.copy_(_n2p(w[f"{block_prefix}LayerNorm_0/scale"]))
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block.norm1.bias.copy_(_n2p(w[f"{block_prefix}LayerNorm_0/bias"]))
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block.attn.qkv.weight.copy_(
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torch.cat(
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[
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_n2p(w[f"{mha_prefix}{n}/kernel"], t=False).flatten(1).T
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for n in ("query", "key", "value")
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]
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)
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)
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block.attn.qkv.bias.copy_(
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torch.cat(
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[
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_n2p(w[f"{mha_prefix}{n}/bias"], t=False).reshape(-1)
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for n in ("query", "key", "value")
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]
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)
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)
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block.attn.proj.weight.copy_(_n2p(w[f"{mha_prefix}out/kernel"]).flatten(1))
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block.attn.proj.bias.copy_(_n2p(w[f"{mha_prefix}out/bias"]))
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for r in range(2):
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getattr(block.mlp, f"fc{r + 1}").weight.copy_(
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_n2p(w[f"{block_prefix}MlpBlock_3/Dense_{r}/kernel"])
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)
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getattr(block.mlp, f"fc{r + 1}").bias.copy_(
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_n2p(w[f"{block_prefix}MlpBlock_3/Dense_{r}/bias"])
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)
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block.norm2.weight.copy_(_n2p(w[f"{block_prefix}LayerNorm_2/scale"]))
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block.norm2.bias.copy_(_n2p(w[f"{block_prefix}LayerNorm_2/bias"]))
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def interpolate_pos_embed(pos_embed_checkpoint, visual_encoder):
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# interpolate position embedding
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embedding_size = pos_embed_checkpoint.shape[-1]
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num_patches = visual_encoder.patch_embed.num_patches
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num_extra_tokens = visual_encoder.pos_embed.shape[-2] - num_patches
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# height (== width) for the checkpoint position embedding
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orig_size = int((pos_embed_checkpoint.shape[-2] - num_extra_tokens) ** 0.5)
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# height (== width) for the new position embedding
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new_size = int(num_patches**0.5)
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if orig_size != new_size:
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# class_token and dist_token are kept unchanged
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extra_tokens = pos_embed_checkpoint[:, :num_extra_tokens]
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# only the position tokens are interpolated
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pos_tokens = pos_embed_checkpoint[:, num_extra_tokens:]
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pos_tokens = pos_tokens.reshape(
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-1, orig_size, orig_size, embedding_size
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).permute(0, 3, 1, 2)
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pos_tokens = torch.nn.functional.interpolate(
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pos_tokens, size=(new_size, new_size), mode="bicubic", align_corners=False
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)
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pos_tokens = pos_tokens.permute(0, 2, 3, 1).flatten(1, 2)
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new_pos_embed = torch.cat((extra_tokens, pos_tokens), dim=1)
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print(
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"reshape position embedding from %d to %d" % (orig_size**2, new_size**2)
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)
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return new_pos_embed
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else:
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return pos_embed_checkpoint
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