894 lines
33 KiB
Python
894 lines
33 KiB
Python
# Taken from https://github.com/microsoft/Swin-Transformer with slight modifications
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# --------------------------------------------------------
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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
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# --------------------------------------------------------
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from copy import copy
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import torch
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import torch.nn as nn
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import torch.utils.checkpoint as checkpoint
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from loguru import logger
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from timm.models import register_model
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from timm.models.layers import DropPath, to_2tuple, trunc_normal_
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from resizing_interface import ResizingInterface
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try:
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import os
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import sys
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kernel_path = os.path.abspath(os.path.join(".."))
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sys.path.append(kernel_path)
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from kernels.window_process.window_process import (
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WindowProcess,
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WindowProcessReverse,
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)
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except ImportError:
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WindowProcess = None
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WindowProcessReverse = None
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logger.warning("Fused window process have not been installed. Please refer to get_started.md for installation.")
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class Mlp(nn.Module):
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def __init__(
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self,
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in_features,
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hidden_features=None,
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out_features=None,
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act_layer=nn.GELU,
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drop=0.0,
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):
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super().__init__()
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out_features = out_features or in_features
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hidden_features = hidden_features or in_features
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self.fc1 = nn.Linear(in_features, hidden_features)
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self.act = act_layer()
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self.fc2 = nn.Linear(hidden_features, out_features)
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self.drop = nn.Dropout(drop)
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def forward(self, x):
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x = self.fc1(x)
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x = self.act(x)
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x = self.drop(x)
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x = self.fc2(x)
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x = self.drop(x)
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return x
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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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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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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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"""
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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 = 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 = (
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qkv[0],
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qkv[1],
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qkv[2],
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) # make torchscript happy (cannot use tensor as tuple)
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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[self.relative_position_index.view(-1)].view(
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self.window_size[0] * self.window_size[1],
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self.window_size[0] * self.window_size[1],
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-1,
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) # Wh*Ww,Wh*Ww,nH
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relative_position_bias = relative_position_bias.permute(2, 0, 1).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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def extra_repr(self) -> str:
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return f"dim={self.dim}, window_size={self.window_size}, num_heads={self.num_heads}"
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def flops(self, N):
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# calculate flops for 1 window with token length of N
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flops = 0
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# qkv = self.qkv(x)
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flops += N * self.dim * 3 * self.dim
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# attn = (q @ k.transpose(-2, -1))
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flops += self.num_heads * N * (self.dim // self.num_heads) * N
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# x = (attn @ v)
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flops += self.num_heads * N * N * (self.dim // self.num_heads)
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# x = self.proj(x)
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flops += N * self.dim * self.dim
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return flops
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class SwinTransformerBlock(nn.Module):
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r"""Swin Transformer Block.
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Args:
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dim (int): Number of input channels.
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input_resolution (tuple[int]): Input resulotion.
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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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fused_window_process (bool, optional): If True, use one kernel to fused window shift & window partition for acceleration, similar for the reversed part. 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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input_resolution,
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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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fused_window_process=False,
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):
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super().__init__()
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self.dim = dim
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self.input_resolution = input_resolution
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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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if min(self.input_resolution) <= 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(self.input_resolution)
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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,
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hidden_features=mlp_hidden_dim,
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act_layer=act_layer,
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drop=drop,
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)
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if self.shift_size > 0:
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# calculate attention mask for SW-MSA
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H, W = self.input_resolution
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img_mask = torch.zeros((1, H, W, 1)) # 1 H W 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(img_mask, self.window_size) # 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(attn_mask == 0, float(0.0))
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else:
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attn_mask = None
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self.register_buffer("attn_mask", attn_mask)
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self.fused_window_process = fused_window_process
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def forward(self, x):
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H, W = self.input_resolution
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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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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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# cyclic shift
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if self.shift_size > 0:
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if not self.fused_window_process:
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shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2))
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# partition windows
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x_windows = window_partition(shifted_x, self.window_size) # nW*B, window_size, window_size, C
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else:
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x_windows = WindowProcess.apply(x, B, H, W, C, -self.shift_size, self.window_size)
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else:
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shifted_x = x
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# partition windows
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x_windows = window_partition(shifted_x, self.window_size) # nW*B, window_size, window_size, C
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x_windows = x_windows.view(-1, self.window_size * self.window_size, C) # 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=self.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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# reverse cyclic shift
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if self.shift_size > 0:
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if not self.fused_window_process:
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shifted_x = window_reverse(attn_windows, self.window_size, H, W) # B H' W' C
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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 = WindowProcessReverse.apply(attn_windows, B, H, W, C, self.shift_size, self.window_size)
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else:
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shifted_x = window_reverse(attn_windows, self.window_size, H, W) # B H' W' C
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x = shifted_x
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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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# FFN
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x = x + self.drop_path(self.mlp(self.norm2(x)))
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return x
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def extra_repr(self) -> str:
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return (
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f"dim={self.dim}, input_resolution={self.input_resolution}, num_heads={self.num_heads}, "
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f"window_size={self.window_size}, shift_size={self.shift_size}, mlp_ratio={self.mlp_ratio}"
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)
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def flops(self):
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flops = 0
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H, W = self.input_resolution
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# norm1
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flops += self.dim * H * W
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# W-MSA/SW-MSA
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nW = H * W / self.window_size / self.window_size
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flops += nW * self.attn.flops(self.window_size * self.window_size)
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# mlp
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flops += 2 * H * W * self.dim * self.dim * self.mlp_ratio
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# norm2
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flops += self.dim * H * W
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return flops
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class PatchMerging(nn.Module):
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r"""Patch Merging Layer.
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Args:
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input_resolution (tuple[int]): Resolution of input feature.
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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, input_resolution, dim, norm_layer=nn.LayerNorm):
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super().__init__()
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self.input_resolution = input_resolution
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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):
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"""
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x: B, H*W, C
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"""
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H, W = self.input_resolution
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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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assert H % 2 == 0 and W % 2 == 0, f"x size ({H}*{W}) are not even."
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x = x.view(B, H, W, C)
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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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def extra_repr(self) -> str:
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return f"input_resolution={self.input_resolution}, dim={self.dim}"
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def flops(self):
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H, W = self.input_resolution
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flops = H * W * self.dim
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flops += (H // 2) * (W // 2) * 4 * self.dim * 2 * self.dim
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return flops
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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 input channels.
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input_resolution (tuple[int]): Input resolution.
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depth (int): Number of blocks.
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num_heads (int): Number of attention heads.
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window_size (int): Local 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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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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fused_window_process (bool, optional): If True, use one kernel to fused window shift & window partition for acceleration, similar for the reversed part. Default: False
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"""
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def __init__(
|
|
self,
|
|
dim,
|
|
input_resolution,
|
|
depth,
|
|
num_heads,
|
|
window_size,
|
|
mlp_ratio=4.0,
|
|
qkv_bias=True,
|
|
qk_scale=None,
|
|
drop=0.0,
|
|
attn_drop=0.0,
|
|
drop_path=0.0,
|
|
norm_layer=nn.LayerNorm,
|
|
downsample=None,
|
|
use_checkpoint=False,
|
|
fused_window_process=False,
|
|
):
|
|
|
|
super().__init__()
|
|
self.dim = dim
|
|
self.input_resolution = input_resolution
|
|
self.depth = depth
|
|
self.use_checkpoint = use_checkpoint
|
|
|
|
# build blocks
|
|
self.blocks = nn.ModuleList(
|
|
[
|
|
SwinTransformerBlock(
|
|
dim=dim,
|
|
input_resolution=input_resolution,
|
|
num_heads=num_heads,
|
|
window_size=window_size,
|
|
shift_size=0 if (i % 2 == 0) else window_size // 2,
|
|
mlp_ratio=mlp_ratio,
|
|
qkv_bias=qkv_bias,
|
|
qk_scale=qk_scale,
|
|
drop=drop,
|
|
attn_drop=attn_drop,
|
|
drop_path=(drop_path[i] if isinstance(drop_path, list) else drop_path),
|
|
norm_layer=norm_layer,
|
|
fused_window_process=fused_window_process,
|
|
)
|
|
for i in range(depth)
|
|
]
|
|
)
|
|
|
|
# patch merging layer
|
|
if downsample is not None:
|
|
self.downsample = downsample(input_resolution, dim=dim, norm_layer=norm_layer)
|
|
else:
|
|
self.downsample = None
|
|
|
|
def forward(self, x):
|
|
for blk in self.blocks:
|
|
if self.use_checkpoint:
|
|
x = checkpoint.checkpoint(blk, x)
|
|
else:
|
|
x = blk(x)
|
|
if self.downsample is not None:
|
|
x = self.downsample(x)
|
|
return x
|
|
|
|
def extra_repr(self) -> str:
|
|
return f"dim={self.dim}, input_resolution={self.input_resolution}, depth={self.depth}"
|
|
|
|
def flops(self):
|
|
flops = 0
|
|
for blk in self.blocks:
|
|
flops += blk.flops()
|
|
if self.downsample is not None:
|
|
flops += self.downsample.flops()
|
|
return flops
|
|
|
|
|
|
class PatchEmbed(nn.Module):
|
|
r"""Image to Patch Embedding
|
|
|
|
Args:
|
|
img_size (int): Image size. Default: 224.
|
|
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, img_size=224, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None):
|
|
super().__init__()
|
|
img_size = to_2tuple(img_size)
|
|
patch_size = to_2tuple(patch_size)
|
|
patches_resolution = [
|
|
img_size[0] // patch_size[0],
|
|
img_size[1] // patch_size[1],
|
|
]
|
|
self.img_size = img_size
|
|
self.patch_size = patch_size
|
|
self.patches_resolution = patches_resolution
|
|
self.num_patches = patches_resolution[0] * patches_resolution[1]
|
|
|
|
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):
|
|
B, C, H, W = x.shape
|
|
# FIXME look at relaxing size constraints
|
|
assert (
|
|
H == self.img_size[0] and W == self.img_size[1]
|
|
), f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})."
|
|
x = self.proj(x).flatten(2).transpose(1, 2) # B Ph*Pw C
|
|
if self.norm is not None:
|
|
x = self.norm(x)
|
|
return x
|
|
|
|
def flops(self):
|
|
Ho, Wo = self.patches_resolution
|
|
flops = Ho * Wo * self.embed_dim * self.in_chans * (self.patch_size[0] * self.patch_size[1])
|
|
if self.norm is not None:
|
|
flops += Ho * Wo * self.embed_dim
|
|
return flops
|
|
|
|
|
|
class SwinTransformer(nn.Module, ResizingInterface):
|
|
r"""Swin Transformer
|
|
A PyTorch impl of : `Swin Transformer: Hierarchical Vision Transformer using Shifted Windows` -
|
|
https://arxiv.org/pdf/2103.14030
|
|
|
|
Args:
|
|
img_size (int | tuple(int)): Input image size. Default 224
|
|
patch_size (int | tuple(int)): Patch size. Default: 4
|
|
in_chans (int): Number of input image channels. Default: 3
|
|
num_classes (int): Number of classes for classification head. Default: 1000
|
|
embed_dim (int): Patch embedding dimension. Default: 96
|
|
depths (tuple(int)): Depth of each Swin Transformer layer.
|
|
num_heads (tuple(int)): Number of attention heads in different layers.
|
|
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. Default: None
|
|
drop_rate (float): Dropout rate. Default: 0
|
|
attn_drop_rate (float): Attention dropout rate. Default: 0
|
|
drop_path_rate (float): Stochastic depth rate. Default: 0.1
|
|
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
|
|
use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False
|
|
fused_window_process (bool, optional): If True, use one kernel to fused window shift & window partition for acceleration, similar for the reversed part. Default: False
|
|
"""
|
|
|
|
def __init__(
|
|
self,
|
|
img_size=224,
|
|
patch_size=4,
|
|
in_chans=3,
|
|
num_classes=1000,
|
|
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=0.1,
|
|
norm_layer=nn.LayerNorm,
|
|
ape=False,
|
|
patch_norm=True,
|
|
use_checkpoint=False,
|
|
fused_window_process=False,
|
|
**kwargs,
|
|
):
|
|
super().__init__()
|
|
|
|
self.num_classes = num_classes
|
|
self.num_layers = len(depths)
|
|
self.embed_dim = embed_dim
|
|
self.ape = ape
|
|
self.patch_norm = patch_norm
|
|
self.num_features = int(embed_dim * 2 ** (self.num_layers - 1))
|
|
self.mlp_ratio = mlp_ratio
|
|
self.img_size = img_size
|
|
self.fused_window_process = fused_window_process
|
|
|
|
# split image into non-overlapping patches
|
|
self.patch_embed = PatchEmbed(
|
|
img_size=img_size,
|
|
patch_size=patch_size,
|
|
in_chans=in_chans,
|
|
embed_dim=embed_dim,
|
|
norm_layer=norm_layer if self.patch_norm else None,
|
|
)
|
|
num_patches = self.patch_embed.num_patches
|
|
patches_resolution = self.patch_embed.patches_resolution
|
|
self.patches_resolution = patches_resolution
|
|
self.embed_layer = PatchEmbed
|
|
self.patch_size = patch_size
|
|
self.in_chans = in_chans
|
|
self.norm_layer = norm_layer
|
|
|
|
# absolute position embedding
|
|
if self.ape:
|
|
self.absolute_pos_embed = nn.Parameter(torch.zeros(1, num_patches, embed_dim))
|
|
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),
|
|
input_resolution=(
|
|
patches_resolution[0] // (2**i_layer),
|
|
patches_resolution[1] // (2**i_layer),
|
|
),
|
|
depth=depths[i_layer],
|
|
num_heads=num_heads[i_layer],
|
|
window_size=window_size,
|
|
mlp_ratio=self.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,
|
|
fused_window_process=fused_window_process,
|
|
)
|
|
self.layers.append(layer)
|
|
|
|
self.norm = norm_layer(self.num_features)
|
|
self.avgpool = nn.AdaptiveAvgPool1d(1)
|
|
self.head = nn.Linear(self.num_features, num_classes) if num_classes > 0 else nn.Identity()
|
|
|
|
self.apply(self._init_weights)
|
|
|
|
def set_num_classes(self, n_classes):
|
|
"""Reset the classification head with a new number of classes.
|
|
|
|
Parameters
|
|
----------
|
|
n_classes : int
|
|
new number of classes
|
|
"""
|
|
if n_classes == self.num_classes:
|
|
return
|
|
self.head = nn.Linear(self.num_features, n_classes) if n_classes > 0 else nn.Identity()
|
|
self.num_classes = n_classes
|
|
|
|
nn.init.trunc_normal_(self.head.weight, std=0.02)
|
|
nn.init.constant_(self.head.bias, 0)
|
|
|
|
def set_image_res(self, res):
|
|
if res == self.img_size:
|
|
return
|
|
|
|
old_patch_embed_state = copy(self.patch_embed.state_dict())
|
|
self.patch_embed = self.embed_layer(
|
|
img_size=res,
|
|
patch_size=self.patch_size,
|
|
in_chans=self.in_chans,
|
|
embed_dim=self.embed_dim,
|
|
norm_layer=self.norm_layer if self.patch_norm else None,
|
|
)
|
|
self.patch_embed.load_state_dict(old_patch_embed_state)
|
|
patches_resolution = self.patch_embed.patches_resolution
|
|
self.patches_resolution = patches_resolution
|
|
|
|
for i_layer, layer in enumerate(self.layers):
|
|
input_resolution = (
|
|
patches_resolution[0] // (2**i_layer),
|
|
patches_resolution[1] // (2**i_layer),
|
|
)
|
|
layer.input_resolution = input_resolution
|
|
downsample = PatchMerging if (i_layer < self.num_layers - 1) else None
|
|
if downsample is not None:
|
|
layer.downsample = downsample(input_resolution, dim=layer.dim, norm_layer=self.norm_layer)
|
|
|
|
for block in layer.blocks:
|
|
block.input_resolution = input_resolution
|
|
|
|
if min(input_resolution) <= block.window_size:
|
|
# if window size is larger than input resolution, we don't partition windows
|
|
block.shift_size = 0
|
|
block.window_size = min(block.input_resolution)
|
|
assert 0 <= block.shift_size < block.window_size, "shift_size must in 0-window_size"
|
|
|
|
if block.shift_size > 0:
|
|
# calculate attention mask for SW-MSA
|
|
H, W = block.input_resolution
|
|
img_mask = torch.zeros((1, H, W, 1)) # 1 H W 1
|
|
h_slices = (
|
|
slice(0, -block.window_size),
|
|
slice(-block.window_size, -block.shift_size),
|
|
slice(-block.shift_size, None),
|
|
)
|
|
w_slices = (
|
|
slice(0, -block.window_size),
|
|
slice(-block.window_size, -block.shift_size),
|
|
slice(-block.shift_size, None),
|
|
)
|
|
cnt = 0
|
|
for h in h_slices:
|
|
for w in w_slices:
|
|
img_mask[:, h, w, :] = cnt
|
|
cnt += 1
|
|
|
|
mask_windows = window_partition(img_mask, block.window_size) # nW, window_size, window_size, 1
|
|
mask_windows = mask_windows.view(-1, block.window_size * block.window_size)
|
|
attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)
|
|
attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(
|
|
attn_mask == 0, float(0.0)
|
|
)
|
|
else:
|
|
attn_mask = None
|
|
|
|
block.register_buffer("attn_mask", attn_mask)
|
|
|
|
if self.ape:
|
|
orig_size = int((self.absolute_pos_embed.shape[-2]) ** 0.5)
|
|
new_size = int(self.patch_embed.num_patches**0.5)
|
|
pos_tokens = self.absolute_pos_embed[:, :]
|
|
# make it shape rest x embed_dim x orig_size x orig_size
|
|
pos_tokens = pos_tokens.reshape(-1, orig_size, orig_size, self.embed_dim).permute(0, 3, 1, 2)
|
|
pos_tokens = nn.functional.interpolate(
|
|
pos_tokens,
|
|
size=(new_size, new_size),
|
|
mode="bicubic",
|
|
align_corners=False,
|
|
)
|
|
# make it shape rest x new_size^2 x embed_dim
|
|
pos_tokens = pos_tokens.permute(0, 2, 3, 1).flatten(1, 2)
|
|
self.absolute_pos_embed = nn.Parameter(pos_tokens.contiguous())
|
|
|
|
self.img_size = res
|
|
|
|
def _init_weights(self, 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)
|
|
|
|
@torch.jit.ignore
|
|
def no_weight_decay(self):
|
|
return {"absolute_pos_embed"}
|
|
|
|
@torch.jit.ignore
|
|
def no_weight_decay_keywords(self):
|
|
return {"relative_position_bias_table"}
|
|
|
|
def forward_features(self, x):
|
|
x = self.patch_embed(x)
|
|
if self.ape:
|
|
x = x + self.absolute_pos_embed
|
|
x = self.pos_drop(x)
|
|
|
|
for layer in self.layers:
|
|
x = layer(x)
|
|
|
|
x = self.norm(x) # B L C
|
|
return x
|
|
|
|
def forward(self, x):
|
|
x = self.forward_features(x)
|
|
x = self.avgpool(x.transpose(1, 2)) # B C 1
|
|
x = torch.flatten(x, 1)
|
|
x = self.head(x)
|
|
return x
|
|
|
|
def flops(self):
|
|
flops = 0
|
|
flops += self.patch_embed.flops()
|
|
for i, layer in enumerate(self.layers):
|
|
flops += layer.flops()
|
|
flops += self.num_features * self.patches_resolution[0] * self.patches_resolution[1] // (2**self.num_layers)
|
|
flops += self.num_features * self.num_classes
|
|
return flops
|
|
|
|
|
|
swin_sizes = {
|
|
"Ti": dict(embed_dim=96, depths=[2, 2, 6, 2], num_heads=[3, 6, 12, 24]),
|
|
"S": dict(embed_dim=96, depths=[2, 2, 18, 2], num_heads=[3, 6, 12, 24]),
|
|
"B": dict(embed_dim=128, depths=[2, 2, 18, 2], num_heads=[4, 8, 16, 32]),
|
|
"L": dict(embed_dim=192, depths=[2, 2, 18, 2], num_heads=[6, 12, 24, 48]),
|
|
}
|
|
|
|
|
|
@register_model
|
|
def swin_tiny_patch4_window7(pretrained=False, img_size=224, **kwargs):
|
|
if "pretrained_cfg" in kwargs:
|
|
kwargs.pop("pretrained_cfg")
|
|
size = swin_sizes["Ti"]
|
|
model = SwinTransformer(img_size=img_size, patch_size=4, window_size=7, **size, **kwargs)
|
|
return model
|
|
|
|
|
|
@register_model
|
|
def swin_small_patch4_window7(pretrained=False, img_size=224, **kwargs):
|
|
if "pretrained_cfg" in kwargs:
|
|
kwargs.pop("pretrained_cfg")
|
|
size = swin_sizes["S"]
|
|
model = SwinTransformer(img_size=img_size, patch_size=4, window_size=7, **size, **kwargs)
|
|
return model
|
|
|
|
|
|
@register_model
|
|
def swin_base_patch4_window7(pretrained=False, img_size=224, **kwargs):
|
|
if "pretrained_cfg" in kwargs:
|
|
kwargs.pop("pretrained_cfg")
|
|
size = swin_sizes["B"]
|
|
model = SwinTransformer(img_size=img_size, patch_size=4, window_size=7, **size, **kwargs)
|
|
return model
|
|
|
|
|
|
@register_model
|
|
def swin_large_patch4_window7(pretrained=False, img_size=224, **kwargs):
|
|
if "pretrained_cfg" in kwargs:
|
|
kwargs.pop("pretrained_cfg")
|
|
size = swin_sizes["L"]
|
|
model = SwinTransformer(img_size=img_size, patch_size=4, window_size=7, **size, **kwargs)
|
|
return model
|
|
|
|
|
|
@register_model
|
|
def swin_ashim(pretrained=False, img_size=112, **kwargs):
|
|
if "pretrained_cfg" in kwargs:
|
|
kwargs.pop("pretrained_cfg")
|
|
size = dict(embed_dim=384, depths=[12], num_heads=[12])
|
|
if "num_heads" in kwargs:
|
|
kwargs["num_heads"] = [kwargs["num_heads"]]
|
|
return SwinTransformer(img_size=img_size, in_chans=3, patch_size=2, window_size=7, **{**size, **kwargs})
|