892 lines
32 KiB
Python
892 lines
32 KiB
Python
# --------------------------------------------------------
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# Swin Transformer
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# Copyright (c) 2021 Microsoft
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# Licensed under The MIT License [see LICENSE for details]
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# Written by Ze Liu, Yutong Lin, Yixuan Wei
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# --------------------------------------------------------
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# Copyright (c) Facebook, Inc. and its affiliates.
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# Modified by Bowen Cheng from https://github.com/SwinTransformer/Swin-Transformer-Semantic-Segmentation/blob/main/mmseg/models/backbones/swin_transformer.py
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import logging
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import numpy as np
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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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import torch.utils.checkpoint as checkpoint
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from timm.models.layers import DropPath, to_2tuple, trunc_normal_
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from detectron2.modeling import Backbone, ShapeSpec
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from detectron2.utils.file_io import PathManager
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from .registry import register_backbone
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logger = logging.getLogger(__name__)
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class Mlp(nn.Module):
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"""Multilayer perceptron."""
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def __init__(
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self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, 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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def window_partition(x, window_size):
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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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def window_reverse(windows, window_size, H, W):
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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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"""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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qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set
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attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0
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proj_drop (float, optional): Dropout ratio of output. Default: 0.0
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"""
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def __init__(
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self,
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dim,
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window_size,
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num_heads,
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qkv_bias=True,
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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.dim = dim
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self.window_size = window_size # Wh, Ww
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self.num_heads = num_heads
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head_dim = dim // num_heads
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self.scale = qk_scale or head_dim ** -0.5
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# define a parameter table of relative position bias
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self.relative_position_bias_table = nn.Parameter(
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torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1), num_heads)
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) # 2*Wh-1 * 2*Ww-1, nH
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# get pair-wise relative position index for each token inside the window
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coords_h = torch.arange(self.window_size[0])
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coords_w = torch.arange(self.window_size[1])
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coords = torch.stack(torch.meshgrid([coords_h, coords_w])) # 2, Wh, Ww
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coords_flatten = torch.flatten(coords, 1) # 2, Wh*Ww
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relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :] # 2, Wh*Ww, Wh*Ww
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relative_coords = relative_coords.permute(1, 2, 0).contiguous() # Wh*Ww, Wh*Ww, 2
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relative_coords[:, :, 0] += self.window_size[0] - 1 # shift to start from 0
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relative_coords[:, :, 1] += self.window_size[1] - 1
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relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1
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relative_position_index = relative_coords.sum(-1) # Wh*Ww, Wh*Ww
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self.register_buffer("relative_position_index", relative_position_index)
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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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trunc_normal_(self.relative_position_bias_table, std=0.02)
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self.softmax = nn.Softmax(dim=-1)
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def forward(self, x, mask=None):
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"""Forward function.
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Args:
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x: input features with shape of (num_windows*B, N, C)
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mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None
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"""
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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 = qkv[0], qkv[1], qkv[2] # make torchscript happy (cannot use tensor as tuple)
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q = q * self.scale
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attn = q @ k.transpose(-2, -1)
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relative_position_bias = self.relative_position_bias_table[
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self.relative_position_index.view(-1)
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].view(
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self.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[1], -1
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) # Wh*Ww,Wh*Ww,nH
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relative_position_bias = relative_position_bias.permute(
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2, 0, 1
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).contiguous() # nH, Wh*Ww, Wh*Ww
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attn = attn + relative_position_bias.unsqueeze(0)
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if mask is not None:
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nW = mask.shape[0]
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attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze(1).unsqueeze(0)
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attn = attn.view(-1, self.num_heads, N, N)
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attn = self.softmax(attn)
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else:
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attn = self.softmax(attn)
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attn = self.attn_drop(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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x = self.proj_drop(x)
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return x
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class SwinTransformerBlock(nn.Module):
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"""Swin Transformer 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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shift_size (int): Shift size for SW-MSA.
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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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qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
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drop (float, optional): Dropout rate. Default: 0.0
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attn_drop (float, optional): Attention dropout rate. Default: 0.0
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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__(
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self,
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dim,
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num_heads,
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window_size=7,
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shift_size=0,
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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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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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):
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super().__init__()
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self.dim = dim
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self.num_heads = num_heads
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self.window_size = window_size
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self.shift_size = shift_size
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self.mlp_ratio = mlp_ratio
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assert 0 <= self.shift_size < self.window_size, "shift_size must in 0-window_size"
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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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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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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, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop
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)
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self.H = None
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self.W = None
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def forward(self, x, mask_matrix):
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"""Forward function.
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Args:
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x: Input feature, tensor size (B, H*W, C).
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H, W: Spatial resolution of the input feature.
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mask_matrix: Attention mask for cyclic shift.
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"""
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B, L, C = x.shape
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H, W = self.H, self.W
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assert L == H * W, "input feature has wrong size"
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# HACK model will not upsampling
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# if min([H, W]) <= self.window_size:
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# if window size is larger than input resolution, we don't partition windows
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# self.shift_size = 0
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# self.window_size = min([H,W])
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shortcut = x
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x = self.norm1(x)
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x = x.view(B, H, W, C)
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# pad feature maps to multiples of window size
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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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# cyclic shift
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if self.shift_size > 0:
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shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2))
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attn_mask = mask_matrix
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else:
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shifted_x = x
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attn_mask = None
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# partition windows
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x_windows = window_partition(
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shifted_x, self.window_size
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) # nW*B, window_size, window_size, C
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x_windows = x_windows.view(
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-1, self.window_size * self.window_size, C
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) # nW*B, window_size*window_size, C
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# W-MSA/SW-MSA
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attn_windows = self.attn(x_windows, mask=attn_mask) # nW*B, window_size*window_size, C
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# merge windows
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attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C)
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shifted_x = window_reverse(attn_windows, self.window_size, Hp, Wp) # B H' W' C
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# reverse cyclic shift
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if self.shift_size > 0:
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x = torch.roll(shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2))
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else:
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x = shifted_x
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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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# FFN
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x = shortcut + self.drop_path(x)
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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 PatchMerging(nn.Module):
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"""Patch Merging Layer
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Args:
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dim (int): Number of input channels.
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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, norm_layer=nn.LayerNorm):
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super().__init__()
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self.dim = dim
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self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False)
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self.norm = norm_layer(4 * dim)
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def forward(self, x, H, W):
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"""Forward function.
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Args:
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x: Input feature, tensor size (B, H*W, C).
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H, W: Spatial resolution of the input feature.
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"""
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B, L, C = x.shape
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assert L == H * W, "input feature has wrong size"
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x = x.view(B, H, W, C)
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# padding
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pad_input = (H % 2 == 1) or (W % 2 == 1)
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if pad_input:
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x = F.pad(x, (0, 0, 0, W % 2, 0, H % 2))
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x0 = x[:, 0::2, 0::2, :] # B H/2 W/2 C
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x1 = x[:, 1::2, 0::2, :] # B H/2 W/2 C
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x2 = x[:, 0::2, 1::2, :] # B H/2 W/2 C
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x3 = x[:, 1::2, 1::2, :] # B H/2 W/2 C
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x = torch.cat([x0, x1, x2, x3], -1) # B H/2 W/2 4*C
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x = x.view(B, -1, 4 * C) # B H/2*W/2 4*C
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x = self.norm(x)
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x = self.reduction(x)
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return x
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class BasicLayer(nn.Module):
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"""A basic Swin Transformer layer for one stage.
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Args:
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dim (int): Number of feature channels
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depth (int): Depths of this stage.
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num_heads (int): Number of attention head.
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window_size (int): Local 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, optional): If True, add a learnable bias to query, key, value. Default: True
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qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
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drop (float, optional): Dropout rate. Default: 0.0
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attn_drop (float, optional): Attention dropout rate. Default: 0.0
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drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0
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norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
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downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None
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use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
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"""
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def __init__(
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self,
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dim,
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depth,
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num_heads,
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window_size=7,
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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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drop=0.0,
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attn_drop=0.0,
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drop_path=0.0,
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norm_layer=nn.LayerNorm,
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downsample=None,
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use_checkpoint=False,
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):
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super().__init__()
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self.window_size = window_size
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self.shift_size = window_size // 2
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self.depth = depth
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self.use_checkpoint = use_checkpoint
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# build blocks
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self.blocks = nn.ModuleList(
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[
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SwinTransformerBlock(
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dim=dim,
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num_heads=num_heads,
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window_size=window_size,
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shift_size=0 if (i % 2 == 0) else window_size // 2,
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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,
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attn_drop=attn_drop,
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drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path,
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norm_layer=norm_layer,
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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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# patch merging layer
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if downsample is not None:
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self.downsample = downsample(dim=dim, norm_layer=norm_layer)
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else:
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self.downsample = None
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def forward(self, x, H, W):
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"""Forward function.
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Args:
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x: Input feature, tensor size (B, H*W, C).
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H, W: Spatial resolution of the input feature.
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"""
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# calculate attention mask for SW-MSA
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Hp = int(np.ceil(H / self.window_size)) * self.window_size
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Wp = int(np.ceil(W / self.window_size)) * self.window_size
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img_mask = torch.zeros((1, Hp, Wp, 1), device=x.device) # 1 Hp Wp 1
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h_slices = (
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slice(0, -self.window_size),
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slice(-self.window_size, -self.shift_size),
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slice(-self.shift_size, None),
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)
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w_slices = (
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slice(0, -self.window_size),
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slice(-self.window_size, -self.shift_size),
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slice(-self.shift_size, None),
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)
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cnt = 0
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for h in h_slices:
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for w in w_slices:
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img_mask[:, h, w, :] = cnt
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cnt += 1
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mask_windows = window_partition(
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img_mask, self.window_size
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) # nW, window_size, window_size, 1
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mask_windows = mask_windows.view(-1, self.window_size * self.window_size)
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attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)
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attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(
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attn_mask == 0, float(0.0)
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).type(x.dtype)
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for blk in self.blocks:
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blk.H, blk.W = H, W
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if self.use_checkpoint:
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x = checkpoint.checkpoint(blk, x, attn_mask)
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else:
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x = blk(x, attn_mask)
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if self.downsample is not None:
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|
x_down = self.downsample(x, H, W)
|
|
Wh, Ww = (H + 1) // 2, (W + 1) // 2
|
|
return x, H, W, x_down, Wh, Ww
|
|
else:
|
|
return x, H, W, x, H, W
|
|
|
|
|
|
class PatchEmbed(nn.Module):
|
|
"""Image to Patch Embedding
|
|
Args:
|
|
patch_size (int): Patch token size. Default: 4.
|
|
in_chans (int): Number of input image channels. Default: 3.
|
|
embed_dim (int): Number of linear projection output channels. Default: 96.
|
|
norm_layer (nn.Module, optional): Normalization layer. Default: None
|
|
"""
|
|
|
|
def __init__(self, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None):
|
|
super().__init__()
|
|
patch_size = to_2tuple(patch_size)
|
|
self.patch_size = patch_size
|
|
|
|
self.in_chans = in_chans
|
|
self.embed_dim = embed_dim
|
|
|
|
self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size)
|
|
if norm_layer is not None:
|
|
self.norm = norm_layer(embed_dim)
|
|
else:
|
|
self.norm = None
|
|
|
|
def forward(self, x):
|
|
"""Forward function."""
|
|
# padding
|
|
_, _, H, W = x.size()
|
|
if W % self.patch_size[1] != 0:
|
|
x = F.pad(x, (0, self.patch_size[1] - W % self.patch_size[1]))
|
|
if H % self.patch_size[0] != 0:
|
|
x = F.pad(x, (0, 0, 0, self.patch_size[0] - H % self.patch_size[0]))
|
|
|
|
x = self.proj(x) # B C Wh Ww
|
|
if self.norm is not None:
|
|
Wh, Ww = x.size(2), x.size(3)
|
|
x = x.flatten(2).transpose(1, 2)
|
|
x = self.norm(x)
|
|
x = x.transpose(1, 2).view(-1, self.embed_dim, Wh, Ww)
|
|
|
|
return x
|
|
|
|
|
|
class SwinTransformer(nn.Module):
|
|
"""Swin Transformer backbone.
|
|
A PyTorch impl of : `Swin Transformer: Hierarchical Vision Transformer using Shifted Windows` -
|
|
https://arxiv.org/pdf/2103.14030
|
|
Args:
|
|
pretrain_img_size (int): Input image size for training the pretrained model,
|
|
used in absolute postion embedding. Default 224.
|
|
patch_size (int | tuple(int)): Patch size. Default: 4.
|
|
in_chans (int): Number of input image channels. Default: 3.
|
|
embed_dim (int): Number of linear projection output channels. Default: 96.
|
|
depths (tuple[int]): Depths of each Swin Transformer stage.
|
|
num_heads (tuple[int]): Number of attention head of each stage.
|
|
window_size (int): Window size. Default: 7.
|
|
mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4.
|
|
qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True
|
|
qk_scale (float): Override default qk scale of head_dim ** -0.5 if set.
|
|
drop_rate (float): Dropout rate.
|
|
attn_drop_rate (float): Attention dropout rate. Default: 0.
|
|
drop_path_rate (float): Stochastic depth rate. Default: 0.2.
|
|
norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm.
|
|
ape (bool): If True, add absolute position embedding to the patch embedding. Default: False.
|
|
patch_norm (bool): If True, add normalization after patch embedding. Default: True.
|
|
out_indices (Sequence[int]): Output from which stages.
|
|
frozen_stages (int): Stages to be frozen (stop grad and set eval mode).
|
|
-1 means not freezing any parameters.
|
|
use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
|
|
"""
|
|
|
|
def __init__(
|
|
self,
|
|
pretrain_img_size=224,
|
|
patch_size=4,
|
|
in_chans=3,
|
|
embed_dim=96,
|
|
depths=[2, 2, 6, 2],
|
|
num_heads=[3, 6, 12, 24],
|
|
window_size=7,
|
|
mlp_ratio=4.0,
|
|
qkv_bias=True,
|
|
qk_scale=None,
|
|
drop_rate=0.0,
|
|
attn_drop_rate=0.0,
|
|
drop_path_rate=0.2,
|
|
norm_layer=nn.LayerNorm,
|
|
ape=False,
|
|
patch_norm=True,
|
|
out_indices=(0, 1, 2, 3),
|
|
frozen_stages=-1,
|
|
use_checkpoint=False,
|
|
):
|
|
super().__init__()
|
|
|
|
self.pretrain_img_size = pretrain_img_size
|
|
self.num_layers = len(depths)
|
|
self.embed_dim = embed_dim
|
|
self.ape = ape
|
|
self.patch_norm = patch_norm
|
|
self.out_indices = out_indices
|
|
self.frozen_stages = frozen_stages
|
|
|
|
# split image into non-overlapping patches
|
|
self.patch_embed = PatchEmbed(
|
|
patch_size=patch_size,
|
|
in_chans=in_chans,
|
|
embed_dim=embed_dim,
|
|
norm_layer=norm_layer if self.patch_norm else None,
|
|
)
|
|
|
|
# absolute position embedding
|
|
if self.ape:
|
|
pretrain_img_size = to_2tuple(pretrain_img_size)
|
|
patch_size = to_2tuple(patch_size)
|
|
patches_resolution = [
|
|
pretrain_img_size[0] // patch_size[0],
|
|
pretrain_img_size[1] // patch_size[1],
|
|
]
|
|
|
|
self.absolute_pos_embed = nn.Parameter(
|
|
torch.zeros(1, embed_dim, patches_resolution[0], patches_resolution[1])
|
|
)
|
|
trunc_normal_(self.absolute_pos_embed, std=0.02)
|
|
|
|
self.pos_drop = nn.Dropout(p=drop_rate)
|
|
|
|
# stochastic depth
|
|
dpr = [
|
|
x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))
|
|
] # stochastic depth decay rule
|
|
|
|
# build layers
|
|
self.layers = nn.ModuleList()
|
|
for i_layer in range(self.num_layers):
|
|
layer = BasicLayer(
|
|
dim=int(embed_dim * 2 ** i_layer),
|
|
depth=depths[i_layer],
|
|
num_heads=num_heads[i_layer],
|
|
window_size=window_size,
|
|
mlp_ratio=mlp_ratio,
|
|
qkv_bias=qkv_bias,
|
|
qk_scale=qk_scale,
|
|
drop=drop_rate,
|
|
attn_drop=attn_drop_rate,
|
|
drop_path=dpr[sum(depths[:i_layer]) : sum(depths[: i_layer + 1])],
|
|
norm_layer=norm_layer,
|
|
downsample=PatchMerging if (i_layer < self.num_layers - 1) else None,
|
|
use_checkpoint=use_checkpoint,
|
|
)
|
|
self.layers.append(layer)
|
|
|
|
num_features = [int(embed_dim * 2 ** i) for i in range(self.num_layers)]
|
|
self.num_features = num_features
|
|
|
|
# add a norm layer for each output
|
|
for i_layer in out_indices:
|
|
layer = norm_layer(num_features[i_layer])
|
|
layer_name = f"norm{i_layer}"
|
|
self.add_module(layer_name, layer)
|
|
|
|
self._freeze_stages()
|
|
|
|
def _freeze_stages(self):
|
|
if self.frozen_stages >= 0:
|
|
self.patch_embed.eval()
|
|
for param in self.patch_embed.parameters():
|
|
param.requires_grad = False
|
|
|
|
if self.frozen_stages >= 1 and self.ape:
|
|
self.absolute_pos_embed.requires_grad = False
|
|
|
|
if self.frozen_stages >= 2:
|
|
self.pos_drop.eval()
|
|
for i in range(0, self.frozen_stages - 1):
|
|
m = self.layers[i]
|
|
m.eval()
|
|
for param in m.parameters():
|
|
param.requires_grad = False
|
|
|
|
def init_weights(self, pretrained=None):
|
|
"""Initialize the weights in backbone.
|
|
Args:
|
|
pretrained (str, optional): Path to pre-trained weights.
|
|
Defaults to None.
|
|
"""
|
|
|
|
def _init_weights(m):
|
|
if isinstance(m, nn.Linear):
|
|
trunc_normal_(m.weight, std=0.02)
|
|
if isinstance(m, nn.Linear) and m.bias is not None:
|
|
nn.init.constant_(m.bias, 0)
|
|
elif isinstance(m, nn.LayerNorm):
|
|
nn.init.constant_(m.bias, 0)
|
|
nn.init.constant_(m.weight, 1.0)
|
|
|
|
|
|
def load_weights(self, pretrained_dict=None, pretrained_layers=[], verbose=True):
|
|
model_dict = self.state_dict()
|
|
pretrained_dict = {
|
|
k: v for k, v in pretrained_dict.items()
|
|
if k in model_dict.keys()
|
|
}
|
|
need_init_state_dict = {}
|
|
for k, v in pretrained_dict.items():
|
|
need_init = (
|
|
(
|
|
k.split('.')[0] in pretrained_layers
|
|
or pretrained_layers[0] == '*'
|
|
)
|
|
and 'relative_position_index' not in k
|
|
and 'attn_mask' not in k
|
|
)
|
|
|
|
if need_init:
|
|
# if verbose:
|
|
# logger.info(f'=> init {k} from {pretrained}')
|
|
|
|
if 'relative_position_bias_table' in k and v.size() != model_dict[k].size():
|
|
relative_position_bias_table_pretrained = v
|
|
relative_position_bias_table_current = model_dict[k]
|
|
L1, nH1 = relative_position_bias_table_pretrained.size()
|
|
L2, nH2 = relative_position_bias_table_current.size()
|
|
if nH1 != nH2:
|
|
logger.info(f"Error in loading {k}, passing")
|
|
else:
|
|
if L1 != L2:
|
|
logger.info(
|
|
'=> load_pretrained: resized variant: {} to {}'
|
|
.format((L1, nH1), (L2, nH2))
|
|
)
|
|
S1 = int(L1 ** 0.5)
|
|
S2 = int(L2 ** 0.5)
|
|
relative_position_bias_table_pretrained_resized = torch.nn.functional.interpolate(
|
|
relative_position_bias_table_pretrained.permute(1, 0).view(1, nH1, S1, S1),
|
|
size=(S2, S2),
|
|
mode='bicubic')
|
|
v = relative_position_bias_table_pretrained_resized.view(nH2, L2).permute(1, 0)
|
|
|
|
if 'absolute_pos_embed' in k and v.size() != model_dict[k].size():
|
|
absolute_pos_embed_pretrained = v
|
|
absolute_pos_embed_current = model_dict[k]
|
|
_, L1, C1 = absolute_pos_embed_pretrained.size()
|
|
_, L2, C2 = absolute_pos_embed_current.size()
|
|
if C1 != C1:
|
|
logger.info(f"Error in loading {k}, passing")
|
|
else:
|
|
if L1 != L2:
|
|
logger.info(
|
|
'=> load_pretrained: resized variant: {} to {}'
|
|
.format((1, L1, C1), (1, L2, C2))
|
|
)
|
|
S1 = int(L1 ** 0.5)
|
|
S2 = int(L2 ** 0.5)
|
|
absolute_pos_embed_pretrained = absolute_pos_embed_pretrained.reshape(-1, S1, S1, C1)
|
|
absolute_pos_embed_pretrained = absolute_pos_embed_pretrained.permute(0, 3, 1, 2)
|
|
absolute_pos_embed_pretrained_resized = torch.nn.functional.interpolate(
|
|
absolute_pos_embed_pretrained, size=(S2, S2), mode='bicubic')
|
|
v = absolute_pos_embed_pretrained_resized.permute(0, 2, 3, 1).flatten(1, 2)
|
|
|
|
need_init_state_dict[k] = v
|
|
self.load_state_dict(need_init_state_dict, strict=False)
|
|
|
|
|
|
def forward(self, x):
|
|
"""Forward function."""
|
|
x = self.patch_embed(x)
|
|
|
|
Wh, Ww = x.size(2), x.size(3)
|
|
if self.ape:
|
|
# interpolate the position embedding to the corresponding size
|
|
absolute_pos_embed = F.interpolate(
|
|
self.absolute_pos_embed, size=(Wh, Ww), mode="bicubic"
|
|
)
|
|
x = (x + absolute_pos_embed).flatten(2).transpose(1, 2) # B Wh*Ww C
|
|
else:
|
|
x = x.flatten(2).transpose(1, 2)
|
|
x = self.pos_drop(x)
|
|
|
|
outs = {}
|
|
for i in range(self.num_layers):
|
|
layer = self.layers[i]
|
|
x_out, H, W, x, Wh, Ww = layer(x, Wh, Ww)
|
|
|
|
if i in self.out_indices:
|
|
norm_layer = getattr(self, f"norm{i}")
|
|
x_out = norm_layer(x_out)
|
|
|
|
out = x_out.view(-1, H, W, self.num_features[i]).permute(0, 3, 1, 2).contiguous()
|
|
outs["res{}".format(i + 2)] = out
|
|
|
|
if len(self.out_indices) == 0:
|
|
outs["res5"] = x_out.view(-1, H, W, self.num_features[i]).permute(0, 3, 1, 2).contiguous()
|
|
|
|
|
|
return outs
|
|
|
|
def train(self, mode=True):
|
|
"""Convert the model into training mode while keep layers freezed."""
|
|
super(SwinTransformer, self).train(mode)
|
|
self._freeze_stages()
|
|
|
|
|
|
class D2SwinTransformer(SwinTransformer, Backbone):
|
|
def __init__(self, cfg, pretrain_img_size, patch_size, in_chans, embed_dim,
|
|
depths, num_heads, window_size, mlp_ratio, qkv_bias, qk_scale,
|
|
drop_rate, attn_drop_rate, drop_path_rate, norm_layer, ape,
|
|
patch_norm, out_indices, use_checkpoint):
|
|
super().__init__(
|
|
pretrain_img_size,
|
|
patch_size,
|
|
in_chans,
|
|
embed_dim,
|
|
depths,
|
|
num_heads,
|
|
window_size,
|
|
mlp_ratio,
|
|
qkv_bias,
|
|
qk_scale,
|
|
drop_rate,
|
|
attn_drop_rate,
|
|
drop_path_rate,
|
|
norm_layer,
|
|
ape,
|
|
patch_norm,
|
|
out_indices,
|
|
use_checkpoint=use_checkpoint,
|
|
)
|
|
|
|
self._out_features = cfg['OUT_FEATURES']
|
|
|
|
self._out_feature_strides = {
|
|
"res2": 4,
|
|
"res3": 8,
|
|
"res4": 16,
|
|
"res5": 32,
|
|
}
|
|
self._out_feature_channels = {
|
|
"res2": self.num_features[0],
|
|
"res3": self.num_features[1],
|
|
"res4": self.num_features[2],
|
|
"res5": self.num_features[3],
|
|
}
|
|
|
|
def forward(self, x):
|
|
"""
|
|
Args:
|
|
x: Tensor of shape (N,C,H,W). H, W must be a multiple of ``self.size_divisibility``.
|
|
Returns:
|
|
dict[str->Tensor]: names and the corresponding features
|
|
"""
|
|
assert (
|
|
x.dim() == 4
|
|
), f"SwinTransformer takes an input of shape (N, C, H, W). Got {x.shape} instead!"
|
|
outputs = {}
|
|
y = super().forward(x)
|
|
for k in y.keys():
|
|
if k in self._out_features:
|
|
outputs[k] = y[k]
|
|
return outputs
|
|
|
|
def output_shape(self):
|
|
feature_names = list(set(self._out_feature_strides.keys()) & set(self._out_features))
|
|
return {
|
|
name: ShapeSpec(
|
|
channels=self._out_feature_channels[name], stride=self._out_feature_strides[name]
|
|
)
|
|
for name in feature_names
|
|
}
|
|
|
|
@property
|
|
def size_divisibility(self):
|
|
return 32
|
|
|
|
|
|
@register_backbone
|
|
def get_swin_backbone(cfg):
|
|
swin_cfg = cfg['MODEL']['BACKBONE']['SWIN']
|
|
|
|
pretrain_img_size = swin_cfg['PRETRAIN_IMG_SIZE']
|
|
patch_size = swin_cfg['PATCH_SIZE']
|
|
in_chans = 3
|
|
embed_dim = swin_cfg['EMBED_DIM']
|
|
depths = swin_cfg['DEPTHS']
|
|
num_heads = swin_cfg['NUM_HEADS']
|
|
window_size = swin_cfg['WINDOW_SIZE']
|
|
mlp_ratio = swin_cfg['MLP_RATIO']
|
|
qkv_bias = swin_cfg['QKV_BIAS']
|
|
qk_scale = swin_cfg['QK_SCALE']
|
|
drop_rate = swin_cfg['DROP_RATE']
|
|
attn_drop_rate = swin_cfg['ATTN_DROP_RATE']
|
|
drop_path_rate = swin_cfg['DROP_PATH_RATE']
|
|
norm_layer = nn.LayerNorm
|
|
ape = swin_cfg['APE']
|
|
patch_norm = swin_cfg['PATCH_NORM']
|
|
use_checkpoint = swin_cfg['USE_CHECKPOINT']
|
|
out_indices = swin_cfg.get('OUT_INDICES', [0,1,2,3])
|
|
|
|
swin = D2SwinTransformer(
|
|
swin_cfg,
|
|
pretrain_img_size,
|
|
patch_size,
|
|
in_chans,
|
|
embed_dim,
|
|
depths,
|
|
num_heads,
|
|
window_size,
|
|
mlp_ratio,
|
|
qkv_bias,
|
|
qk_scale,
|
|
drop_rate,
|
|
attn_drop_rate,
|
|
drop_path_rate,
|
|
norm_layer,
|
|
ape,
|
|
patch_norm,
|
|
out_indices,
|
|
use_checkpoint=use_checkpoint,
|
|
)
|
|
|
|
if cfg['MODEL']['BACKBONE']['LOAD_PRETRAINED'] is True:
|
|
filename = cfg['MODEL']['BACKBONE']['PRETRAINED']
|
|
with PathManager.open(filename, "rb") as f:
|
|
ckpt = torch.load(f, map_location=cfg['device'])['model']
|
|
swin.load_weights(ckpt, swin_cfg.get('PRETRAINED_LAYERS', ['*']), cfg['VERBOSE'])
|
|
|
|
return swin |