638 lines
29 KiB
Python
638 lines
29 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
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# --------------------------------------------------------
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import math
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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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def _make_divisible(v, divisor, min_value=None):
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if min_value is None:
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min_value = divisor
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new_v = max(min_value, int(v + divisor / 2) // divisor * divisor)
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if new_v < 0.9 * v:
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new_v += divisor
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return new_v
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def trunc_normal_(tensor, mean=0., std=1., a=-2., b=2.):
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def _no_grad_trunc_normal_(tensor, mean, std, a, b):
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def norm_cdf(x):
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return (1. + math.erf(x / math.sqrt(2.))) / 2.
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with torch.no_grad():
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l = norm_cdf((a - mean) / std)
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u = norm_cdf((b - mean) / std)
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tensor.uniform_(2 * l - 1, 2 * u - 1)
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tensor.erfinv_()
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tensor.mul_(std * math.sqrt(2.))
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tensor.add_(mean)
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tensor.clamp_(min=a, max=b)
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return tensor
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return _no_grad_trunc_normal_(tensor, mean, std, a, b)
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#--------------------------------------#
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# Gelu激活函数的实现
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# 利用近似的数学公式
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#--------------------------------------#
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class GELU(nn.Module):
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def __init__(self):
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super(GELU, self).__init__()
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def forward(self, x):
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return 0.5 * x * (1 + torch.tanh(np.sqrt(2 / np.pi) * (x + 0.044715 * torch.pow(x,3))))
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#-------------------------------------------------------#
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# 对输入进来的图片进行高和宽的压缩
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# 并且进行通道的扩张。
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#-------------------------------------------------------#
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class PatchEmbed(nn.Module):
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def __init__(self, img_size=[224, 224], patch_size=4, in_chans=3, embed_dim=96, norm_layer=None):
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super().__init__()
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# [224, 224]
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self.img_size = img_size
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# [4, 4]
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self.patch_size = [patch_size, patch_size]
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# [56, 56]
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self.patches_resolution = [self.img_size[0] // self.patch_size[0], self.img_size[1] // self.patch_size[1]]
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# 3136
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self.num_patches = self.patches_resolution[0] * self.patches_resolution[1]
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# 3
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self.in_chans = in_chans
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# 96
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self.embed_dim = embed_dim
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#-------------------------------------------------------#
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# bs, 224, 224, 3 -> bs, 56, 56, 96
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#-------------------------------------------------------#
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self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size)
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if norm_layer is not None:
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self.norm = norm_layer(embed_dim)
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else:
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self.norm = None
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def forward(self, x):
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B, C, H, W = x.shape
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# FIXME look at relaxing size constraints
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assert H == self.img_size[0] and W == self.img_size[1], \
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f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]} * {self.img_size[1]})."
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#-------------------------------------------------------#
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# bs, 224, 224, 3 -> bs, 56, 56, 96 -> bs, 3136, 96
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#-------------------------------------------------------#
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x = self.proj(x).flatten(2).transpose(1, 2)
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if self.norm is not None:
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x = self.norm(x)
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return x
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def window_partition(x, window_size):
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B, H, W, C = x.shape
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#------------------------------------------------------------------#
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# bs, 56, 56, 96 -> bs, 8, 7, 8, 7, 96 -> bs * 64, 7, 7, 96
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#------------------------------------------------------------------#
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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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# bs * 64, 7, 7, 96 -> bs, 8, 8, 7, 7, 96 -> bs, 56, 56, 96
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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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def __init__(self, dim, window_size, num_heads, qkv_bias=True, qk_scale=None, attn_drop=0., proj_drop=0.):
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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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#--------------------------------------------------------------------------#
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# 相对坐标矩阵，用于表示每个窗口内，其它点相对于自己的坐标
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# 由于相对坐标取值范围为-6 ~ +6。中间共13个值，因此需要13 * 13
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# 13 * 13, num_heads
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#--------------------------------------------------------------------------#
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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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)
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#--------------------------------------------------------------------------#
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# 该部分用于获取7x7的矩阵内部，其它特征点相对于自身相对坐标
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#--------------------------------------------------------------------------#
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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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#--------------------------------------------------------------------------#
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# 乘积获得q、k、v，用于计算多头注意力机制
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#--------------------------------------------------------------------------#
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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=.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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B_, N, C = x.shape
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#--------------------------------------------------------------------------#
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# bs * 64, 49, 96 -> bs * 64, 49, 96 * 3 ->
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# bs * 64, 49, 3, num_heads, 32 -> 3, bs * 64, num_head, 49, 32
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#--------------------------------------------------------------------------#
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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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#--------------------------------------------------------------------------#
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# bs * 64, num_head, 49, 32
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#--------------------------------------------------------------------------#
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q, k, v = qkv[0], qkv[1], qkv[2]
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#--------------------------------------------------------------------------#
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# bs * 64, num_head, 49, 49
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#--------------------------------------------------------------------------#
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q = q * self.scale
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attn = (q @ k.transpose(-2, -1))
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#--------------------------------------------------------------------------#
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# 这一步是根据已经求得的注意力，加上相对坐标的偏执量
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# 形成最后的注意力
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#--------------------------------------------------------------------------#
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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], self.window_size[0] * self.window_size[1], -1) # Wh*Ww,Wh*Ww,nH
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relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous()
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attn = attn + relative_position_bias.unsqueeze(0)
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#--------------------------------------------------------------------------#
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# 加上mask，保证分区。
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# bs * 64, num_head, 49, 49
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#--------------------------------------------------------------------------#
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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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#---------------------------------------------------------------------------------------#
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# bs * 64, num_head, 49, 49 @ bs * 64, num_head, 49, 32 -> bs * 64, num_head, 49, 32
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#
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# bs * 64, num_head, 49, 32 -> bs * 64, 49, 96
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#---------------------------------------------------------------------------------------#
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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 drop_path(x, drop_prob: float = 0., training: bool = False, scale_by_keep: bool = True):
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"""
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Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
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This is the same as the DropConnect impl I created for EfficientNet, etc networks, however,
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the original name is misleading as 'Drop Connect' is a different form of dropout in a separate paper...
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See discussion: https://github.com/tensorflow/tpu/issues/494#issuecomment-532968956 ... I've opted for
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changing the layer and argument names to 'drop path' rather than mix DropConnect as a layer name and use
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'survival rate' as the argument.
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"""
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if drop_prob == 0. or not training:
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return x
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keep_prob = 1 - drop_prob
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shape = (x.shape[0],) + (1,) * (x.ndim - 1) # work with diff dim tensors, not just 2D ConvNets
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random_tensor = x.new_empty(shape).bernoulli_(keep_prob)
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if keep_prob > 0.0 and scale_by_keep:
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random_tensor.div_(keep_prob)
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return x * random_tensor
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class DropPath(nn.Module):
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"""
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Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
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"""
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def __init__(self, drop_prob=None, scale_by_keep=True):
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super(DropPath, self).__init__()
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self.drop_prob = drop_prob
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self.scale_by_keep = scale_by_keep
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def forward(self, x):
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return drop_path(x, self.drop_prob, self.training, self.scale_by_keep)
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#-------------------------------------------------------#
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# 两次全连接
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#-------------------------------------------------------#
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class Mlp(nn.Module):
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def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=GELU, drop=0.):
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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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#-------------------------------------------------------#
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# 每个阶段重复的基础模块
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# 在这其中会使用WindowAttention进行特征提取
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#-------------------------------------------------------#
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class SwinTransformerBlock(nn.Module):
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def __init__(self, dim, input_resolution, num_heads, window_size=7, shift_size=0,
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mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0., drop_path=0.,
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act_layer=GELU, norm_layer=nn.LayerNorm):
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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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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 = [self.window_size, 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. 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(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
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if self.shift_size > 0:
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#----------------------------------------------------------------#
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# 由于进行特征提取时，会对输入的特征层进行的平移
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# 如：
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# [ [
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# [1, 2, 3], [5, 6, 4],
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# [4, 5, 6], --> [8, 9, 7],
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# [7, 8, 9], [1, 2, 3],
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# ] ]
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# 这一步的作用就是使得平移后的区域块只计算自己部分的注意力机制
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#----------------------------------------------------------------#
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H, W = self.input_resolution
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_H, _W = _make_divisible(H, self.window_size), _make_divisible(W, self.window_size),
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img_mask = torch.zeros((1, _H, _W, 1)) # 1 H W 1
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h_slices = (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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w_slices = (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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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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self.attn_mask = attn_mask.cpu().numpy()
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else:
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self.attn_mask = None
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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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#-----------------------------------------------#
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# bs, 3136, 96 -> bs, 56, 56, 96
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#-----------------------------------------------#
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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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_H, _W = _make_divisible(H, self.window_size), _make_divisible(W, self.window_size),
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x = x.permute(0, 3, 1, 2)
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x = F.interpolate(x, [_H, _W], mode='bicubic', align_corners=False).permute(0, 2, 3, 1)
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#-----------------------------------------------#
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# 进行特征层的平移
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#-----------------------------------------------#
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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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else:
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shifted_x = x
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#------------------------------------------------------------------------------------------#
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# bs, 56, 56, 96 -> bs * 64, 7, 7, 96 -> bs * 64, 49, 96
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#------------------------------------------------------------------------------------------#
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x_windows = window_partition(shifted_x, self.window_size) # num_windows * 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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#-----------------------------------------------#
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# bs * 64, 49, 97 -> bs * 64, 49, 97
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#-----------------------------------------------#
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if type(self.attn_mask) != type(None):
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attn_mask = torch.tensor(self.attn_mask).cuda() if x.is_cuda else torch.tensor(self.attn_mask)
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else:
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attn_mask = None
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attn_windows = self.attn(x_windows, mask=attn_mask) # nW*B, window_size*window_size, C
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#-----------------------------------------------#
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# bs * 64, 49, 97 -> bs, 56, 56, 96
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#-----------------------------------------------#
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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, _H, _W) # B H' W' C
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#-----------------------------------------------#
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# 将特征层平移回来
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#-----------------------------------------------#
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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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x = x.permute(0, 3, 1, 2)
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x = F.interpolate(x, [H, W], mode='bicubic', align_corners=False).permute(0, 2, 3, 1)
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#-----------------------------------------------#
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# bs, 3136, 96
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#-----------------------------------------------#
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x = x.view(B, H * W, C)
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#-----------------------------------------------#
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# FFN
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# bs, 3136, 96
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#-----------------------------------------------#
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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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#-------------------------------------------------------#
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# 对输入进来的特征层进行高和宽的压缩
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# 进行跨特征点的特征提取，提取完成后进行堆叠。
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#-------------------------------------------------------#
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class PatchMerging(nn.Module):
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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.norm = norm_layer(4 * dim)
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self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False)
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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"
|
||
assert H % 2 == 0 and W % 2 == 0, f"x size ({H}*{W}) are not even."
|
||
|
||
#-------------------------------------------------------#
|
||
# bs, 3136, 96 -> bs, 56, 56, 96
|
||
#-------------------------------------------------------#
|
||
x = x.view(B, H, W, C)
|
||
|
||
#-------------------------------------------------------#
|
||
# x0 ~ x3 bs, 56, 56, 96 -> bs, 28, 28, 96
|
||
#-------------------------------------------------------#
|
||
x0 = x[:, 0::2, 0::2, :] # B H/2 W/2 C
|
||
x1 = x[:, 1::2, 0::2, :] # B H/2 W/2 C
|
||
x2 = x[:, 0::2, 1::2, :] # B H/2 W/2 C
|
||
x3 = x[:, 1::2, 1::2, :] # B H/2 W/2 C
|
||
|
||
#-------------------------------------------------------#
|
||
# 4 X bs, 28, 28, 96 -> bs, 28, 28, 384
|
||
#-------------------------------------------------------#
|
||
x = torch.cat([x0, x1, x2, x3], -1) # B H/2 W/2 4*C
|
||
#-------------------------------------------------------#
|
||
# bs, 28, 28, 384 -> bs, 784, 384
|
||
#-------------------------------------------------------#
|
||
x = x.view(B, -1, 4 * C) # B H/2*W/2 4*C
|
||
|
||
#-------------------------------------------------------#
|
||
# bs, 784, 384 -> bs, 784, 192
|
||
#-------------------------------------------------------#
|
||
x = self.norm(x)
|
||
x = self.reduction(x)
|
||
return x
|
||
|
||
|
||
#-------------------------------------------------------#
|
||
# Swin-Transformer的基础模块。
|
||
# 使用窗口多头注意力机制进行特征提取。
|
||
# 使用PatchMerging进行高和宽的压缩。
|
||
#-------------------------------------------------------#
|
||
class BasicLayer(nn.Module):
|
||
def __init__(self, dim, input_resolution, depth, num_heads, window_size,
|
||
mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0.,
|
||
drop_path=0., norm_layer=nn.LayerNorm, downsample=None, use_checkpoint=False):
|
||
super().__init__()
|
||
#-------------------------------------------------------#
|
||
# 四个阶段对应不同的dim
|
||
# [96, 192, 384, 768]
|
||
#-------------------------------------------------------#
|
||
self.dim = dim
|
||
#-------------------------------------------------------#
|
||
# 四个阶段对应不同的输入分辨率
|
||
# [[56, 56], [28, 28], [14, 14], [7, 7]]
|
||
#-------------------------------------------------------#
|
||
self.input_resolution = input_resolution
|
||
#-------------------------------------------------------#
|
||
# 四个阶段对应不同的多头注意力机制重复次数
|
||
# [2, 2, 6, 2]
|
||
#-------------------------------------------------------#
|
||
self.depth = depth
|
||
self.use_checkpoint = use_checkpoint
|
||
|
||
#-------------------------------------------------------#
|
||
# 根据depth的次数利用窗口多头注意力机制进行特征提取。
|
||
#-------------------------------------------------------#
|
||
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
|
||
)
|
||
for i in range(depth)
|
||
]
|
||
)
|
||
|
||
if downsample is not None:
|
||
#-------------------------------------------------------#
|
||
# 判断是否要进行下采样，即：高宽压缩
|
||
#-------------------------------------------------------#
|
||
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_)
|
||
else:
|
||
x = x_
|
||
return x_, x
|
||
|
||
class SwinTransformer(nn.Module):
|
||
def __init__(self, img_size=[640, 640], 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., qkv_bias=True, qk_scale=None,
|
||
drop_rate=0., attn_drop_rate=0., drop_path_rate=0.1,
|
||
norm_layer=nn.LayerNorm, ape=False, patch_norm=True,
|
||
use_checkpoint=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
|
||
|
||
#--------------------------------------------------#
|
||
# bs, 224, 224, 3 -> bs, 3136, 96
|
||
#--------------------------------------------------#
|
||
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
|
||
)
|
||
|
||
#--------------------------------------------------#
|
||
# PatchEmbed之后的图像序列长度 3136
|
||
# PatchEmbed之后的图像对应的分辨率 [56, 56]
|
||
#--------------------------------------------------#
|
||
num_patches = self.patch_embed.num_patches
|
||
patches_resolution = self.patch_embed.patches_resolution
|
||
self.patches_resolution = patches_resolution
|
||
|
||
if self.ape:
|
||
self.absolute_pos_embed = nn.Parameter(torch.zeros(1, num_patches, embed_dim))
|
||
trunc_normal_(self.absolute_pos_embed, std=.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
|
||
|
||
#---------------------------------------------------------------#
|
||
# 构建swin-transform的每个阶段
|
||
# bs, 3136, 96 -> bs, 784, 192 -> bs, 196, 384 -> bs, 49, 768
|
||
#---------------------------------------------------------------#
|
||
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
|
||
)
|
||
self.layers.append(layer)
|
||
|
||
self.apply(self._init_weights)
|
||
|
||
def _init_weights(self, m):
|
||
if isinstance(m, nn.Linear):
|
||
trunc_normal_(m.weight, std=.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(self, x):
|
||
x = self.patch_embed(x)
|
||
if self.ape:
|
||
x = x + self.absolute_pos_embed
|
||
x = self.pos_drop(x)
|
||
|
||
inverval_outs = []
|
||
for i, layer in enumerate(self.layers):
|
||
x_, x = layer(x)
|
||
if i != 0:
|
||
inverval_outs.append(x_)
|
||
|
||
outs = []
|
||
for i, layer in enumerate(inverval_outs):
|
||
H, W = (self.patches_resolution[0] // (2 ** (i + 1)), self.patches_resolution[1] // (2 ** (i + 1)))
|
||
B, L, C = layer.shape
|
||
layer = layer.view([B, H, W, C]).permute([0, 3, 1, 2])
|
||
outs.append(layer)
|
||
|
||
return outs
|
||
|
||
def Swin_transformer_Tiny(pretrained = False, input_shape = [640, 640], **kwargs):
|
||
model = SwinTransformer(input_shape, depths=[2, 2, 6, 2], **kwargs)
|
||
if pretrained:
|
||
url = "https://github.com/bubbliiiing/yolov5-pytorch/releases/download/v1.0/swin_tiny_patch4_window7.pth"
|
||
checkpoint = torch.hub.load_state_dict_from_url(url=url, map_location="cpu", model_dir="./model_data")
|
||
model.load_state_dict(checkpoint, strict=False)
|
||
print("Load weights from ", url.split('/')[-1])
|
||
|
||
return model |