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@ -12,8 +12,10 @@ DaViT model defs and weights adapted from https://github.com/dingmyu/davit, orig
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# All rights reserved.
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# This source code is licensed under the MIT license
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# FIXME remove unused imports
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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 typing import Any, Dict, Iterable, Iterator, List, Mapping, Optional, overload, Tuple, TypeVar, Union
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from collections import OrderedDict
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import torch
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@ -32,6 +34,7 @@ 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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@ -50,25 +53,21 @@ class ConvPosEnc(nn.Module):
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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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def forward(self, x : Tensor):
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B, C, H, W = x.shape
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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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#feat = x.transpose(1, 2).view(B, C, H, W)
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feat = self.proj(x)
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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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x = x + self.activation(feat).transpose(1, 2).view(B, C, H, W)
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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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@ -76,13 +75,15 @@ class PatchEmbed(nn.Module):
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def __init__(
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self,
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patch_size=16,
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patch_size=4,
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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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self.in_chans = in_chans
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self.embed_dim = embed_dim
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if patch_size[0] == 4:
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self.proj = nn.Conv2d(
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@ -104,30 +105,19 @@ class PatchEmbed(nn.Module):
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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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def forward(self, x : Tensor):
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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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if self.norm.normalized_shape[0] == self.in_chans:
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x = self.norm(x.permute(0, 2, 3, 1)).permute(0, 3, 1, 2)
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x = F.pad(x, (0, (self.patch_size[1] - W % self.patch_size[1]) % self.patch_size[1]))
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x = F.pad(x, (0, 0, 0, (self.patch_size[0] - H % self.patch_size[0]) % 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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if self.norm.normalized_shape[0] == self.embed_dim:
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x = self.norm(x.permute(0, 2, 3, 1)).permute(0, 3, 1, 2)
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return x
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class ChannelAttention(nn.Module):
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@ -162,12 +152,12 @@ class ChannelBlock(nn.Module):
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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.cpe1 = 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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self.cpe2 = ConvPosEnc(dim=dim, k=3, act=cpe_act)
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if self.ffn:
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self.norm2 = norm_layer(dim)
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@ -178,17 +168,23 @@ class ChannelBlock(nn.Module):
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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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def forward(self, x : Tensor):
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B, C, H, W = x.shape
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x = self.cpe1(x).flatten(2).transpose(1, 2)
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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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x = self.cpe2(x.transpose(1, 2).view(B, C, H, W)).flatten(2).transpose(1, 2)
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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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x = x.transpose(1, 2).view(B, C, H, W)
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return x
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def window_partition(x : Tensor, window_size: int):
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"""
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@ -283,9 +279,8 @@ class SpatialBlock(nn.Module):
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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.cpe1 = 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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@ -294,6 +289,7 @@ class SpatialBlock(nn.Module):
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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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self.cpe2 = ConvPosEnc(dim=dim, k=3, act=cpe_act)
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if self.ffn:
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self.norm2 = norm_layer(dim)
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@ -304,12 +300,11 @@ class SpatialBlock(nn.Module):
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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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def forward(self, x : Tensor):
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B, C, H, W = x.shape
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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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shortcut = self.cpe1(x).flatten(2).transpose(1, 2)
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x = self.norm1(shortcut)
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x = x.view(B, H, W, C)
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@ -338,11 +333,92 @@ class SpatialBlock(nn.Module):
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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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x = self.cpe2(x.transpose(1, 2).view(B, C, H, W)).flatten(2).transpose(1, 2)
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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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x = x.transpose(1, 2).view(B, C, H, W)
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return x
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class DaViTStage(nn.Module):
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def __init__(
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self,
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in_chs,
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out_chs,
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depth = 1,
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patch_size = 4,
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overlapped_patch = False,
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attention_types = ('spatial', 'channel'),
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num_heads = 3,
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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_rates = (0, 0),
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norm_layer = nn.LayerNorm,
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ffn = True,
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cpe_act = False
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):
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super().__init__()
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self.grad_checkpointing = False
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# patch embedding layer at the beginning of each stage
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self.patch_embed = PatchEmbed(
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patch_size=patch_size,
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in_chans=in_chs,
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embed_dim=out_chs,
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overlapped=overlapped_patch
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)
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'''
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repeating alternating attention blocks in each stage
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default: (spatial -> channel) x depth
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potential opportunity to integrate with a more general version of ByobNet/ByoaNet
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since the logic is similar
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'''
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stage_blocks = []
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for block_idx in range(depth):
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dual_attention_block = []
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for attention_id, attention_type in enumerate(attention_types):
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if attention_type == 'spatial':
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dual_attention_block.append(SpatialBlock(
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dim=out_chs,
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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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drop_path=drop_path_rates[len(attention_types) * block_idx + 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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))
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elif attention_type == 'channel':
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dual_attention_block.append(ChannelBlock(
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dim=out_chs,
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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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drop_path=drop_path_rates[len(attention_types) * block_idx + 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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))
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stage_blocks.append(nn.Sequential(*dual_attention_block))
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self.blocks = nn.Sequential(*stage_blocks)
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def forward(self, x : Tensor):
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x = self.patch_embed(x)
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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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else:
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x = self.blocks(x)
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return x
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class DaViT(nn.Module):
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@ -392,7 +468,7 @@ class DaViT(nn.Module):
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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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dpr = [x.item() for x in torch.linspace(0, drop_path_rate, len(attention_types) * 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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@ -401,52 +477,38 @@ class DaViT(nn.Module):
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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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stages = []
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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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for stage_id in range(self.num_stages):
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stage_drop_rates = dpr[len(attention_types) * sum(depths[:stage_id]):len(attention_types) * sum(depths[:stage_id + 1])]
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stage = DaViTStage(
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in_chans if stage_id == 0 else embed_dims[stage_id - 1],
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embed_dims[stage_id],
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depth = depths[stage_id],
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patch_size = patch_size if stage_id == 0 else 2,
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overlapped_patch = overlapped_patch,
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attention_types = attention_types,
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num_heads = num_heads[stage_id],
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window_size = window_size,
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mlp_ratio = mlp_ratio,
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qkv_bias = qkv_bias,
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drop_path_rates = stage_drop_rates,
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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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)
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stages.append(stage)
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self.feature_info += [dict(num_chs=self.embed_dims[stage_id], reduction=2, module=f'stages.{stage_id}')]
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self.stages = nn.Sequential(*stages)
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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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@ -470,45 +532,12 @@ class DaViT(nn.Module):
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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)
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def forward_network(self, x):
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size: Tuple[int, int] = (x.size(2), x.size(3))
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features = [x]
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sizes = [size]
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for patch_layer, stage in zip(self.patch_embeds, self.stages):
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features[-1], sizes[-1] = patch_layer(features[-1], sizes[-1])
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for _, block in enumerate(stage):
|
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for _, layer in enumerate(block):
|
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|
if self.grad_checkpointing and not torch.jit.is_scripting():
|
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|
features[-1], sizes[-1] = checkpoint.checkpoint(layer, features[-1], sizes[-1])
|
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else:
|
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|
features[-1], sizes[-1] = layer(features[-1], sizes[-1])
|
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|
# don't append outputs of last stage, since they are already there
|
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|
if(len(features) < self.num_stages):
|
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|
features.append(features[-1])
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|
sizes.append(sizes[-1])
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|
# non-normalized pyramid features + corresponding sizes
|
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|
return features, sizes
|
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|
def forward_pyramid_features(self, x) -> List[Tensor]:
|
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|
|
x, sizes = self.forward_network(x)
|
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|
outs = []
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|
for i, out in enumerate(x):
|
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|
|
H, W = sizes[i]
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|
outs.append(out.view(-1, H, W, self.embed_dims[i]).permute(0, 3, 1, 2).contiguous())
|
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|
return outs
|
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|
def forward_features(self, x):
|
|
|
|
|
x, sizes = self.forward_network(x)
|
|
|
|
|
x = self.stages(x)
|
|
|
|
|
# take final feature and norm
|
|
|
|
|
x = self.norms(x[-1])
|
|
|
|
|
H, W = sizes[-1]
|
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|
|
|
x = x.view(-1, H, W, self.embed_dims[-1]).permute(0, 3, 1, 2).contiguous()
|
|
|
|
|
x = self.norms(x.permute(0, 2, 3, 1)).permute(0, 3, 1, 2)
|
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|
|
|
#H, W = sizes[-1]
|
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|
|
#x = x.view(-1, H, W, self.embed_dims[-1]).permute(0, 3, 1, 2).contiguous()
|
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|
|
|
return x
|
|
|
|
|
|
|
|
|
|
def forward_head(self, x, pre_logits: bool = False):
|
|
|
|
|
@ -522,17 +551,6 @@ class DaViT(nn.Module):
|
|
|
|
|
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:
|
|
|
|
|
@ -541,35 +559,33 @@ def checkpoint_filter_fn(state_dict, model):
|
|
|
|
|
if 'state_dict' in state_dict:
|
|
|
|
|
state_dict = state_dict['state_dict']
|
|
|
|
|
|
|
|
|
|
import re
|
|
|
|
|
out_dict = {}
|
|
|
|
|
for k, v in state_dict.items():
|
|
|
|
|
k = k.replace('main_blocks.', 'stages.stage_')
|
|
|
|
|
k = re.sub(r'patch_embeds.([0-9]+)', r'stages.\1.patch_embed', k)
|
|
|
|
|
k = re.sub(r'main_blocks.([0-9]+)', r'stages.\1.blocks', k)
|
|
|
|
|
k = k.replace('head.', 'head.fc.')
|
|
|
|
|
k = k.replace('cpe.0', 'cpe1')
|
|
|
|
|
k = k.replace('cpe.1', 'cpe2')
|
|
|
|
|
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,
|
|
|
|
|
DaViT,
|
|
|
|
|
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
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
@ -580,7 +596,7 @@ def _cfg(url='', **kwargs): # not sure how this should be set up
|
|
|
|
|
'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',
|
|
|
|
|
'first_conv': 'stages.0.patch_embed.proj', 'classifier': 'head.fc',
|
|
|
|
|
**kwargs
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
@ -594,6 +610,9 @@ default_cfgs = generate_default_cfgs({
|
|
|
|
|
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"),
|
|
|
|
|
'davit_large': _cfg(),
|
|
|
|
|
'davit_huge': _cfg(),
|
|
|
|
|
'davit_giant': _cfg(),
|
|
|
|
|
})
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
@ -616,7 +635,7 @@ def davit_base(pretrained=False, **kwargs):
|
|
|
|
|
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):
|
|
|
|
|
@ -635,4 +654,3 @@ 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)
|
|
|
|
|
'''
|
Fredo Guan
2 years ago
committed by