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638 lines
23 KiB
Python
638 lines
23 KiB
Python
""" DaViT: Dual Attention Vision Transformers
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As described in https://arxiv.org/abs/2204.03645
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Input size invariant transformer architecture that combines channel and spacial
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attention in each block. The attention mechanisms used are linear in complexity.
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DaViT model defs and weights adapted from https://github.com/dingmyu/davit, original copyright below
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"""
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# Copyright (c) 2022 Mingyu Ding
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# All rights reserved.
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# This source code is licensed under the MIT license
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import itertools
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from typing import Any, Dict, Iterable, Iterator, Mapping, Optional, overload, Tuple, TypeVar, Union, List
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from collections import OrderedDict
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from torch import Tensor
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import torch.utils.checkpoint as checkpoint
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from .features import FeatureInfo
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from .fx_features import register_notrace_function, register_notrace_module
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from .helpers import build_model_with_cfg, pretrained_cfg_for_features
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from .layers import DropPath, to_2tuple, trunc_normal_, SelectAdaptivePool2d, ClassifierHead, Mlp
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from .pretrained import generate_default_cfgs
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from .registry import register_model
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from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
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__all__ = ['DaViT']
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class ConvPosEnc(nn.Module):
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def __init__(self, dim : int, k : int=3, act : bool=False, normtype : str='none'):
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super(ConvPosEnc, self).__init__()
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self.proj = nn.Conv2d(dim,
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dim,
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to_2tuple(k),
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to_2tuple(1),
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to_2tuple(k // 2),
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groups=dim)
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self.normtype = normtype
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self.norm = nn.Identity()
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if self.normtype == 'batch':
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self.norm = nn.BatchNorm2d(dim)
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elif self.normtype == 'layer':
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self.norm = nn.LayerNorm(dim)
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self.activation = nn.GELU() if act else nn.Identity()
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def forward(self, x : Tensor, size: Tuple[int, int]):
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B, N, C = x.shape
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H, W = size
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feat = x.transpose(1, 2).view(B, C, H, W)
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feat = self.proj(feat)
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if self.normtype == 'batch':
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feat = self.norm(feat).flatten(2).transpose(1, 2)
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elif self.normtype == 'layer':
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feat = self.norm(feat.flatten(2).transpose(1, 2))
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else:
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feat = feat.flatten(2).transpose(1, 2)
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x = x + self.activation(feat)
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return x
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# reason: dim in control sequence
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# FIXME reimplement to allow tracing
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@register_notrace_module
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class PatchEmbed(nn.Module):
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""" Size-agnostic implementation of 2D image to patch embedding,
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allowing input size to be adjusted during model forward operation
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"""
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def __init__(
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self,
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patch_size=16,
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in_chans=3,
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embed_dim=96,
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overlapped=False):
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super().__init__()
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patch_size = to_2tuple(patch_size)
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self.patch_size = patch_size
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if patch_size[0] == 4:
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self.proj = nn.Conv2d(
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in_chans,
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embed_dim,
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kernel_size=(7, 7),
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stride=patch_size,
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padding=(3, 3))
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self.norm = nn.LayerNorm(embed_dim)
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if patch_size[0] == 2:
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kernel = 3 if overlapped else 2
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pad = 1 if overlapped else 0
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self.proj = nn.Conv2d(
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in_chans,
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embed_dim,
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kernel_size=to_2tuple(kernel),
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stride=patch_size,
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padding=to_2tuple(pad))
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self.norm = nn.LayerNorm(in_chans)
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def forward(self, x : Tensor, size: Tuple[int, int]):
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H, W = size
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dim = x.dim()
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if dim == 3:
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B, HW, C = x.shape
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x = self.norm(x)
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x = x.reshape(B,
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H,
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W,
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C).permute(0, 3, 1, 2).contiguous()
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B, C, H, W = x.shape
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if W % self.patch_size[1] != 0:
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x = F.pad(x, (0, self.patch_size[1] - W % self.patch_size[1]))
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if H % self.patch_size[0] != 0:
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x = F.pad(x, (0, 0, 0, self.patch_size[0] - H % self.patch_size[0]))
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x = self.proj(x)
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newsize = (x.size(2), x.size(3))
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x = x.flatten(2).transpose(1, 2)
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if dim == 4:
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x = self.norm(x)
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return x, newsize
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class ChannelAttention(nn.Module):
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def __init__(self, dim, num_heads=8, qkv_bias=False):
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super().__init__()
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self.num_heads = num_heads
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head_dim = dim // num_heads
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self.scale = head_dim ** -0.5
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self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
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self.proj = nn.Linear(dim, dim)
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def forward(self, x : Tensor):
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B, N, C = x.shape
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qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
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q, k, v = qkv[0], qkv[1], qkv[2]
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k = k * self.scale
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attention = k.transpose(-1, -2) @ v
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attention = attention.softmax(dim=-1)
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x = (attention @ q.transpose(-1, -2)).transpose(-1, -2)
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x = x.transpose(1, 2).reshape(B, N, C)
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x = self.proj(x)
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return x
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class ChannelBlock(nn.Module):
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def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False,
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drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm,
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ffn=True, cpe_act=False):
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super().__init__()
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self.cpe = nn.ModuleList([ConvPosEnc(dim=dim, k=3, act=cpe_act),
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ConvPosEnc(dim=dim, k=3, act=cpe_act)])
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self.ffn = ffn
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self.norm1 = norm_layer(dim)
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self.attn = ChannelAttention(dim, num_heads=num_heads, qkv_bias=qkv_bias)
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self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
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if self.ffn:
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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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def forward(self, x : Tensor, size: Tuple[int, int]):
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x = self.cpe[0](x, size)
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cur = self.norm1(x)
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cur = self.attn(cur)
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x = x + self.drop_path(cur)
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x = self.cpe[1](x, size)
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if self.ffn:
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x = x + self.drop_path(self.mlp(self.norm2(x)))
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return x, size
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def window_partition(x : Tensor, window_size: int):
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"""
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Args:
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x: (B, H, W, C)
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window_size (int): window size
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Returns:
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windows: (num_windows*B, window_size, window_size, C)
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"""
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B, H, W, C = x.shape
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x = x.view(B, H // window_size, window_size, W // window_size, window_size, C)
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windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)
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return windows
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@register_notrace_function # reason: int argument is a Proxy
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def window_reverse(windows : Tensor, window_size: int, H: int, W: int):
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"""
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Args:
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windows: (num_windows*B, window_size, window_size, C)
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window_size (int): Window size
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H (int): Height of image
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W (int): Width of image
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Returns:
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x: (B, H, W, C)
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"""
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B = int(windows.shape[0] / (H * W / window_size / window_size))
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x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1)
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x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1)
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return x
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class WindowAttention(nn.Module):
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r""" Window based multi-head self attention (W-MSA) module with relative position bias.
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It supports both of shifted and non-shifted window.
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Args:
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dim (int): Number of input channels.
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window_size (tuple[int]): The height and width of the window.
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num_heads (int): Number of attention heads.
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qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
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"""
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def __init__(self, dim, window_size, num_heads, qkv_bias=True):
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super().__init__()
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self.dim = dim
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self.window_size = window_size
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self.num_heads = num_heads
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head_dim = dim // num_heads
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self.scale = head_dim ** -0.5
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self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
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self.proj = nn.Linear(dim, dim)
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self.softmax = nn.Softmax(dim=-1)
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def forward(self, x : Tensor):
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B_, N, C = x.shape
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qkv = self.qkv(x).reshape(B_, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
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q, k, v = qkv[0], qkv[1], qkv[2]
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q = q * self.scale
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attn = (q @ k.transpose(-2, -1))
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attn = self.softmax(attn)
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x = (attn @ v).transpose(1, 2).reshape(B_, N, C)
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x = self.proj(x)
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return x
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class SpatialBlock(nn.Module):
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r""" Windows Block.
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Args:
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dim (int): Number of input channels.
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num_heads (int): Number of attention heads.
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window_size (int): Window size.
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mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
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qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
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drop_path (float, optional): Stochastic depth rate. Default: 0.0
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act_layer (nn.Module, optional): Activation layer. Default: nn.GELU
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norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
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"""
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def __init__(self, dim, num_heads, window_size=7,
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mlp_ratio=4., qkv_bias=True, drop_path=0.,
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act_layer=nn.GELU, norm_layer=nn.LayerNorm,
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ffn=True, cpe_act=False):
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super().__init__()
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self.dim = dim
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self.ffn = ffn
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self.num_heads = num_heads
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self.window_size = window_size
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self.mlp_ratio = mlp_ratio
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self.cpe = nn.ModuleList([ConvPosEnc(dim=dim, k=3, act=cpe_act),
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ConvPosEnc(dim=dim, k=3, act=cpe_act)])
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self.norm1 = norm_layer(dim)
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self.attn = WindowAttention(
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dim,
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window_size=to_2tuple(self.window_size),
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num_heads=num_heads,
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qkv_bias=qkv_bias)
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self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
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if self.ffn:
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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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def forward(self, x : Tensor, size: Tuple[int, int]):
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H, W = size
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B, L, C = x.shape
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shortcut = self.cpe[0](x, size)
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x = self.norm1(shortcut)
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x = x.view(B, H, W, C)
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pad_l = pad_t = 0
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pad_r = (self.window_size - W % self.window_size) % self.window_size
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pad_b = (self.window_size - H % self.window_size) % self.window_size
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x = F.pad(x, (0, 0, pad_l, pad_r, pad_t, pad_b))
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_, Hp, Wp, _ = x.shape
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x_windows = window_partition(x, self.window_size)
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x_windows = x_windows.view(-1, self.window_size * self.window_size, C)
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# W-MSA/SW-MSA
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attn_windows = self.attn(x_windows)
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# merge windows
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attn_windows = attn_windows.view(-1,
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self.window_size,
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self.window_size,
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C)
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x = window_reverse(attn_windows, self.window_size, Hp, Wp)
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#if pad_r > 0 or pad_b > 0:
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x = x[:, :H, :W, :].contiguous()
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x = x.view(B, H * W, C)
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x = shortcut + self.drop_path(x)
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x = self.cpe[1](x, size)
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if self.ffn:
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x = x + self.drop_path(self.mlp(self.norm2(x)))
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return x, size
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class DaViT(nn.Module):
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r""" DaViT
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A PyTorch implementation of `DaViT: Dual Attention Vision Transformers` - https://arxiv.org/abs/2204.03645
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Supports arbitrary input sizes and pyramid feature extraction
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Args:
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in_chans (int): Number of input image channels. Default: 3
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num_classes (int): Number of classes for classification head. Default: 1000
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depths (tuple(int)): Number of blocks in each stage. Default: (1, 1, 3, 1)
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patch_size (int | tuple(int)): Patch size. Default: 4
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embed_dims (tuple(int)): Patch embedding dimension. Default: (96, 192, 384, 768)
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num_heads (tuple(int)): Number of attention heads in different layers. Default: (3, 6, 12, 24)
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window_size (int): Window size. Default: 7
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mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4
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qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True
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drop_path_rate (float): Stochastic depth rate. Default: 0.1
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norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm.
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"""
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def __init__(
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self,
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in_chans=3,
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depths=(1, 1, 3, 1),
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patch_size=4,
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embed_dims=(96, 192, 384, 768),
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num_heads=(3, 6, 12, 24),
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window_size=7,
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mlp_ratio=4.,
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qkv_bias=True,
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drop_path_rate=0.1,
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norm_layer=nn.LayerNorm,
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attention_types=('spatial', 'channel'),
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ffn=True,
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overlapped_patch=False,
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cpe_act=False,
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drop_rate=0.,
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attn_drop_rate=0.,
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num_classes=1000,
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global_pool='avg'
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):
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super().__init__()
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architecture = [[index] * item for index, item in enumerate(depths)]
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self.architecture = architecture
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self.embed_dims = embed_dims
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self.num_heads = num_heads
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self.num_stages = len(self.embed_dims)
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dpr = [x.item() for x in torch.linspace(0, drop_path_rate, 2 * len(list(itertools.chain(*self.architecture))))]
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assert self.num_stages == len(self.num_heads) == (sorted(list(itertools.chain(*self.architecture)))[-1] + 1)
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self.num_classes = num_classes
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self.num_features = embed_dims[-1]
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self.drop_rate=drop_rate
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self.grad_checkpointing = False
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self.feature_info = []
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self.patch_embeds = nn.ModuleList([
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PatchEmbed(patch_size=patch_size if i == 0 else 2,
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in_chans=in_chans if i == 0 else self.embed_dims[i - 1],
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embed_dim=self.embed_dims[i],
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overlapped=overlapped_patch)
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for i in range(self.num_stages)])
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self.stages = nn.ModuleList()
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for stage_id, stage_param in enumerate(self.architecture):
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layer_offset_id = len(list(itertools.chain(*self.architecture[:stage_id])))
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stage = nn.ModuleList([
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nn.ModuleList([
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ChannelBlock(
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dim=self.embed_dims[item],
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num_heads=self.num_heads[item],
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mlp_ratio=mlp_ratio,
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qkv_bias=qkv_bias,
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drop_path=dpr[2 * (layer_id + layer_offset_id) + attention_id],
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norm_layer=nn.LayerNorm,
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ffn=ffn,
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cpe_act=cpe_act
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) if attention_type == 'channel' else
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SpatialBlock(
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dim=self.embed_dims[item],
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num_heads=self.num_heads[item],
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mlp_ratio=mlp_ratio,
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qkv_bias=qkv_bias,
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drop_path=dpr[2 * (layer_id + layer_offset_id) + attention_id],
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norm_layer=nn.LayerNorm,
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ffn=ffn,
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cpe_act=cpe_act,
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window_size=window_size,
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) if attention_type == 'spatial' else None
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for attention_id, attention_type in enumerate(attention_types)]
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) for layer_id, item in enumerate(stage_param)
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])
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self.stages.add_module(f'stage_{stage_id}', stage)
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self.feature_info += [dict(num_chs=self.embed_dims[stage_id], reduction=2, module=f'stages.stage_{stage_id}')]
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self.norms = norm_layer(self.num_features)
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self.head = ClassifierHead(self.num_features, num_classes, pool_type=global_pool, drop_rate=drop_rate)
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self.apply(self._init_weights)
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def _init_weights(self, m):
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if isinstance(m, nn.Linear):
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trunc_normal_(m.weight, std=.02)
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if isinstance(m, nn.Linear) and m.bias is not None:
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nn.init.constant_(m.bias, 0)
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elif isinstance(m, nn.LayerNorm):
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nn.init.constant_(m.bias, 0)
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nn.init.constant_(m.weight, 1.0)
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@torch.jit.ignore
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def set_grad_checkpointing(self, enable=True):
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self.grad_checkpointing = enable
|
|
|
|
@torch.jit.ignore
|
|
def get_classifier(self):
|
|
return self.head.fc
|
|
|
|
def reset_classifier(self, num_classes, global_pool=None):
|
|
self.num_classes = num_classes
|
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if global_pool is None:
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|
global_pool = self.head.global_pool.pool_type
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|
self.head = ClassifierHead(self.num_features, num_classes, pool_type=global_pool, drop_rate=self.drop_rate)
|
|
|
|
|
|
def forward_network(self, x):
|
|
size: Tuple[int, int] = (x.size(2), x.size(3))
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|
features = [x]
|
|
sizes = [size]
|
|
|
|
for patch_layer, stage in zip(self.patch_embeds, self.stages):
|
|
features[-1], sizes[-1] = patch_layer(features[-1], sizes[-1])
|
|
for _, block in enumerate(stage):
|
|
for _, layer in enumerate(block):
|
|
if self.grad_checkpointing and not torch.jit.is_scripting():
|
|
features[-1], sizes[-1] = checkpoint.checkpoint(layer, features[-1], sizes[-1])
|
|
else:
|
|
features[-1], sizes[-1] = layer(features[-1], sizes[-1])
|
|
|
|
# don't append outputs of last stage, since they are already there
|
|
if(len(features) < self.num_stages):
|
|
features.append(features[-1])
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|
sizes.append(sizes[-1])
|
|
|
|
|
|
# non-normalized pyramid features + corresponding sizes
|
|
return features, sizes
|
|
|
|
def forward_pyramid_features(self, x) -> List[Tensor]:
|
|
x, sizes = self.forward_network(x)
|
|
outs = []
|
|
for i, out in enumerate(x):
|
|
H, W = sizes[i]
|
|
outs.append(out.view(-1, H, W, self.embed_dims[i]).permute(0, 3, 1, 2).contiguous())
|
|
|
|
return outs
|
|
|
|
def forward_features(self, x):
|
|
x, sizes = self.forward_network(x)
|
|
# take final feature and norm
|
|
x = self.norms(x[-1])
|
|
H, W = sizes[-1]
|
|
x = x.view(-1, H, W, self.embed_dims[-1]).permute(0, 3, 1, 2).contiguous()
|
|
return x
|
|
|
|
def forward_head(self, x, pre_logits: bool = False):
|
|
return self.head(x, pre_logits=pre_logits)
|
|
|
|
def forward_classifier(self, x):
|
|
x = self.forward_features(x)
|
|
x = self.forward_head(x)
|
|
return x
|
|
|
|
def forward(self, x):
|
|
return self.forward_classifier(x)
|
|
|
|
|
|
class DaViTFeatures(DaViT):
|
|
|
|
def __init__(self, *args, **kwargs):
|
|
super().__init__(*args, **kwargs)
|
|
self.feature_info = FeatureInfo(self.feature_info, kwargs.get('out_indices', (0, 1, 2, 3)))
|
|
|
|
def forward(self, x) -> List[Tensor]:
|
|
return self.forward_pyramid_features(x)
|
|
|
|
|
|
def checkpoint_filter_fn(state_dict, model):
|
|
""" Remap MSFT checkpoints -> timm """
|
|
if 'head.norm.weight' in state_dict:
|
|
return state_dict # non-MSFT checkpoint
|
|
|
|
if 'state_dict' in state_dict:
|
|
state_dict = state_dict['state_dict']
|
|
|
|
out_dict = {}
|
|
for k, v in state_dict.items():
|
|
k = k.replace('main_blocks.', 'stages.stage_')
|
|
k = k.replace('head.', 'head.fc.')
|
|
out_dict[k] = v
|
|
return out_dict
|
|
|
|
|
|
|
|
def _create_davit(variant, pretrained=False, **kwargs):
|
|
model_cls = DaViT
|
|
features_only = False
|
|
kwargs_filter = None
|
|
default_out_indices = tuple(i for i, _ in enumerate(kwargs.get('depths', (1, 1, 3, 1))))
|
|
out_indices = kwargs.pop('out_indices', default_out_indices)
|
|
if kwargs.pop('features_only', False):
|
|
model_cls = DaViTFeatures
|
|
kwargs_filter = ('num_classes', 'global_pool')
|
|
features_only = True
|
|
model = build_model_with_cfg(
|
|
model_cls,
|
|
variant,
|
|
pretrained,
|
|
pretrained_filter_fn=checkpoint_filter_fn,
|
|
feature_cfg=dict(flatten_sequential=True, out_indices=out_indices),
|
|
**kwargs)
|
|
if features_only:
|
|
model.pretrained_cfg = pretrained_cfg_for_features(model.default_cfg)
|
|
model.default_cfg = model.pretrained_cfg # backwards compat
|
|
return model
|
|
|
|
|
|
|
|
def _cfg(url='', **kwargs): # not sure how this should be set up
|
|
return {
|
|
'url': url,
|
|
'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': (7, 7),
|
|
'crop_pct': 0.875, 'interpolation': 'bilinear',
|
|
'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD,
|
|
'first_conv': 'patch_embeds.0.proj', 'classifier': 'head.fc',
|
|
**kwargs
|
|
}
|
|
|
|
|
|
|
|
default_cfgs = generate_default_cfgs({
|
|
# official microsoft weights from https://github.com/dingmyu/davit
|
|
'davit_tiny.msft_in1k': _cfg(
|
|
url="https://github.com/fffffgggg54/pytorch-image-models/releases/download/checkpoint/davit_tiny_ed28dd55.pth.tar"),
|
|
'davit_small.msft_in1k': _cfg(
|
|
url="https://github.com/fffffgggg54/pytorch-image-models/releases/download/checkpoint/davit_small_d1ecf281.pth.tar"),
|
|
'davit_base.msft_in1k': _cfg(
|
|
url="https://github.com/fffffgggg54/pytorch-image-models/releases/download/checkpoint/davit_base_67d9ac26.pth.tar"),
|
|
})
|
|
|
|
|
|
|
|
@register_model
|
|
def davit_tiny(pretrained=False, **kwargs):
|
|
model_kwargs = dict(depths=(1, 1, 3, 1), embed_dims=(96, 192, 384, 768),
|
|
num_heads=(3, 6, 12, 24), **kwargs)
|
|
return _create_davit('davit_tiny', pretrained=pretrained, **model_kwargs)
|
|
|
|
@register_model
|
|
def davit_small(pretrained=False, **kwargs):
|
|
model_kwargs = dict(depths=(1, 1, 9, 1), embed_dims=(96, 192, 384, 768),
|
|
num_heads=(3, 6, 12, 24), **kwargs)
|
|
return _create_davit('davit_small', pretrained=pretrained, **model_kwargs)
|
|
|
|
@register_model
|
|
def davit_base(pretrained=False, **kwargs):
|
|
model_kwargs = dict(depths=(1, 1, 9, 1), embed_dims=(128, 256, 512, 1024),
|
|
num_heads=(4, 8, 16, 32), **kwargs)
|
|
return _create_davit('davit_base', pretrained=pretrained, **model_kwargs)
|
|
|
|
''' models without weights
|
|
# TODO contact authors to get larger pretrained models
|
|
@register_model
|
|
def davit_large(pretrained=False, **kwargs):
|
|
model_kwargs = dict(depths=(1, 1, 9, 1), embed_dims=(192, 384, 768, 1536),
|
|
num_heads=(6, 12, 24, 48), **kwargs)
|
|
return _create_davit('davit_large', pretrained=pretrained, **model_kwargs)
|
|
|
|
@register_model
|
|
def davit_huge(pretrained=False, **kwargs):
|
|
model_kwargs = dict(depths=(1, 1, 9, 1), embed_dims=(256, 512, 1024, 2048),
|
|
num_heads=(8, 16, 32, 64), **kwargs)
|
|
return _create_davit('davit_huge', pretrained=pretrained, **model_kwargs)
|
|
|
|
@register_model
|
|
def davit_giant(pretrained=False, **kwargs):
|
|
model_kwargs = dict(depths=(1, 1, 12, 3), embed_dims=(384, 768, 1536, 3072),
|
|
num_heads=(12, 24, 48, 96), **kwargs)
|
|
return _create_davit('davit_giant', pretrained=pretrained, **model_kwargs)
|
|
''' |