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721 lines
29 KiB
721 lines
29 KiB
5 months ago
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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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def autopad(k, p=None, d=1): # kernel, padding, dilation
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"""Pad to 'same' shape outputs."""
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if d > 1:
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k = d * (k - 1) + 1 if isinstance(k, int) else [d * (x - 1) + 1 for x in k] # actual kernel-size
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if p is None:
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p = k // 2 if isinstance(k, int) else [x // 2 for x in k] # auto-pad
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return p
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class Conv(nn.Module):
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"""Standard convolution with args(ch_in, ch_out, kernel, stride, padding, groups, dilation, activation)."""
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default_act = nn.SiLU() # default activation
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def __init__(self, c1, c2, k=1, s=1, p=None, g=1, d=1, act=True):
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"""Initialize Conv layer with given arguments including activation."""
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super().__init__()
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self.conv = nn.Conv2d(c1, c2, k, s, autopad(k, p, d), groups=g, dilation=d, bias=False)
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self.bn = nn.BatchNorm2d(c2)
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self.act = self.default_act if act is True else act if isinstance(act, nn.Module) else nn.Identity()
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def forward(self, x):
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"""Apply convolution, batch normalization and activation to input tensor."""
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return self.act(self.bn(self.conv(x)))
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def forward_fuse(self, x):
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"""Perform transposed convolution of 2D data."""
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return self.act(self.conv(x))
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class DropPath(nn.Module):
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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):# mg
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super(DropPath, self).__init__()
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self.drop_prob = drop_prob
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def forward(self, x):
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return drop_path_f(x, self.drop_prob, self.training)
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def drop_path_f(x, drop_prob: float = 0., training: bool = False):
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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 = keep_prob + torch.rand(shape, dtype=x.dtype, device=x.device)
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random_tensor.floor_() # binarize
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output = x.div(keep_prob) * random_tensor
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return output
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##### swin transformer #####
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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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# 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)) # 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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nn.init.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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qkv = self.qkv(x).reshape(B_, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
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q, k, v = qkv[0], qkv[1], qkv[2] # 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], 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() # 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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# print(attn.dtype, v.dtype)
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try:
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x = (attn @ v).transpose(1, 2).reshape(B_, N, C)
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except:
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# print(attn.dtype, v.dtype)
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x = (attn.half() @ 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 Mlp(nn.Module): # tc
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def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.SiLU, 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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def window_partition(x, window_size):
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B, H, W, C = x.shape
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assert H % window_size == 0, 'feature map h and w can not divide by window size'
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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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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 SwinTransformerLayer(nn.Module):
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def __init__(self, dim, num_heads, window_size=8, 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=nn.SiLU, norm_layer=nn.LayerNorm):
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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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# 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, window_size=(self.window_size, self.window_size), num_heads=num_heads,
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qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop)
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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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def create_mask(self, H, W):
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# calculate attention mask for SW-MSA
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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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return attn_mask
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def forward(self, x):
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# reshape x[b c h w] to x[b l c]
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_, _, H_, W_ = x.shape
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Padding = False
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if min(H_, W_) < self.window_size or H_ % self.window_size != 0 or W_ % self.window_size != 0:
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Padding = True
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# print(f'img_size {min(H_, W_)} is less than (or not divided by) window_size {self.window_size}, Padding.')
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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, pad_r, 0, pad_b))
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# print('2', x.shape)
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B, C, H, W = x.shape
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L = H * W
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x = x.permute(0, 2, 3, 1).contiguous().view(B, L, C) # b, L, c
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# create mask from init to forward
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if self.shift_size > 0:
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attn_mask = self.create_mask(H, W).to(x.device)
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else:
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attn_mask = None
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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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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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# 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=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, H, W) # 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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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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x = x.permute(0, 2, 1).contiguous().view(-1, C, H, W) # b c h w
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if Padding:
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x = x[:, :, :H_, :W_] # reverse padding
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return x
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class SwinTransformerBlock(nn.Module):
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def __init__(self, c1, c2, num_heads, num_layers, window_size=8):
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super().__init__()
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self.conv = None
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if c1 != c2:
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self.conv = Conv(c1, c2)
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# remove input_resolution
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self.blocks = nn.Sequential(*[SwinTransformerLayer(dim=c2, num_heads=num_heads, window_size=window_size,
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shift_size=0 if (i % 2 == 0) else window_size // 2) for i in
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range(num_layers)])
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def forward(self, x):
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if self.conv is not None:
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x = self.conv(x)
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x = self.blocks(x)
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return x
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class STCSPA(nn.Module):
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# CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks
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def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion
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super(STCSPA, self).__init__()
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c_ = int(c2 * e) # hidden channels
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self.cv1 = Conv(c1, c_, 1, 1)
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self.cv2 = Conv(c1, c_, 1, 1)
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self.cv3 = Conv(2 * c_, c2, 1, 1)
|
||
|
|
num_heads = c_ // 32
|
||
|
|
self.m = SwinTransformerBlock(c_, c_, num_heads, n)
|
||
|
|
# self.m = nn.Sequential(*[Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)])
|
||
|
|
|
||
|
|
def forward(self, x):
|
||
|
|
y1 = self.m(self.cv1(x))
|
||
|
|
y2 = self.cv2(x)
|
||
|
|
return self.cv3(torch.cat((y1, y2), dim=1))
|
||
|
|
|
||
|
|
|
||
|
|
class STCSPB(nn.Module):
|
||
|
|
# CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks
|
||
|
|
def __init__(self, c1, c2, n=1, shortcut=False, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion
|
||
|
|
super(STCSPB, self).__init__()
|
||
|
|
c_ = int(c2) # hidden channels
|
||
|
|
self.cv1 = Conv(c1, c_, 1, 1)
|
||
|
|
self.cv2 = Conv(c_, c_, 1, 1)
|
||
|
|
self.cv3 = Conv(2 * c_, c2, 1, 1)
|
||
|
|
num_heads = c_ // 32
|
||
|
|
self.m = SwinTransformerBlock(c_, c_, num_heads, n)
|
||
|
|
# self.m = nn.Sequential(*[Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)])
|
||
|
|
|
||
|
|
def forward(self, x):
|
||
|
|
x1 = self.cv1(x)
|
||
|
|
y1 = self.m(x1)
|
||
|
|
y2 = self.cv2(x1)
|
||
|
|
return self.cv3(torch.cat((y1, y2), dim=1))
|
||
|
|
|
||
|
|
|
||
|
|
class STCSPC(nn.Module):
|
||
|
|
# CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks
|
||
|
|
def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion
|
||
|
|
super(STCSPC, self).__init__()
|
||
|
|
c_ = int(c2 * e) # hidden channels
|
||
|
|
self.cv1 = Conv(c1, c_, 1, 1)
|
||
|
|
self.cv2 = Conv(c1, c_, 1, 1)
|
||
|
|
self.cv3 = Conv(c_, c_, 1, 1)
|
||
|
|
self.cv4 = Conv(2 * c_, c2, 1, 1)
|
||
|
|
num_heads = c_ // 32
|
||
|
|
self.m = SwinTransformerBlock(c_, c_, num_heads, n)
|
||
|
|
# self.m = nn.Sequential(*[Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)])
|
||
|
|
|
||
|
|
def forward(self, x):
|
||
|
|
y1 = self.cv3(self.m(self.cv1(x)))
|
||
|
|
y2 = self.cv2(x)
|
||
|
|
return self.cv4(torch.cat((y1, y2), dim=1))
|
||
|
|
|
||
|
|
|
||
|
|
##### end of swin transformer #####
|
||
|
|
|
||
|
|
|
||
|
|
##### swin transformer v2 #####
|
||
|
|
|
||
|
|
class WindowAttention_v2(nn.Module):
|
||
|
|
|
||
|
|
def __init__(self, dim, window_size, num_heads, qkv_bias=True, attn_drop=0., proj_drop=0.,
|
||
|
|
pretrained_window_size=[0, 0]):
|
||
|
|
|
||
|
|
super().__init__()
|
||
|
|
self.dim = dim
|
||
|
|
self.window_size = window_size # Wh, Ww
|
||
|
|
self.pretrained_window_size = pretrained_window_size
|
||
|
|
self.num_heads = num_heads
|
||
|
|
|
||
|
|
self.logit_scale = nn.Parameter(torch.log(10 * torch.ones((num_heads, 1, 1))), requires_grad=True).to('cuda')
|
||
|
|
|
||
|
|
# mlp to generate continuous relative position bias
|
||
|
|
self.cpb_mlp = nn.Sequential(nn.Linear(2, 512, bias=True),
|
||
|
|
nn.ReLU(inplace=True),
|
||
|
|
nn.Linear(512, num_heads, bias=False))
|
||
|
|
|
||
|
|
# get relative_coords_table
|
||
|
|
relative_coords_h = torch.arange(-(self.window_size[0] - 1), self.window_size[0], dtype=torch.float32)
|
||
|
|
relative_coords_w = torch.arange(-(self.window_size[1] - 1), self.window_size[1], dtype=torch.float32)
|
||
|
|
relative_coords_table = torch.stack(
|
||
|
|
torch.meshgrid([relative_coords_h,
|
||
|
|
relative_coords_w])).permute(1, 2, 0).contiguous().unsqueeze(0) # 1, 2*Wh-1, 2*Ww-1, 2
|
||
|
|
if pretrained_window_size[0] > 0:
|
||
|
|
relative_coords_table[:, :, :, 0] /= (pretrained_window_size[0] - 1)
|
||
|
|
relative_coords_table[:, :, :, 1] /= (pretrained_window_size[1] - 1)
|
||
|
|
else:
|
||
|
|
relative_coords_table[:, :, :, 0] /= (self.window_size[0] - 1)
|
||
|
|
relative_coords_table[:, :, :, 1] /= (self.window_size[1] - 1)
|
||
|
|
relative_coords_table *= 8 # normalize to -8, 8
|
||
|
|
relative_coords_table = torch.sign(relative_coords_table) * torch.log2(
|
||
|
|
torch.abs(relative_coords_table) + 1.0) / np.log2(8)
|
||
|
|
|
||
|
|
self.register_buffer("relative_coords_table", relative_coords_table)
|
||
|
|
|
||
|
|
# get pair-wise relative position index for each token inside the window
|
||
|
|
coords_h = torch.arange(self.window_size[0])
|
||
|
|
coords_w = torch.arange(self.window_size[1])
|
||
|
|
coords = torch.stack(torch.meshgrid([coords_h, coords_w])) # 2, Wh, Ww
|
||
|
|
coords_flatten = torch.flatten(coords, 1) # 2, Wh*Ww
|
||
|
|
relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :] # 2, Wh*Ww, Wh*Ww
|
||
|
|
relative_coords = relative_coords.permute(1, 2, 0).contiguous() # Wh*Ww, Wh*Ww, 2
|
||
|
|
relative_coords[:, :, 0] += self.window_size[0] - 1 # shift to start from 0
|
||
|
|
relative_coords[:, :, 1] += self.window_size[1] - 1
|
||
|
|
relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1
|
||
|
|
relative_position_index = relative_coords.sum(-1) # Wh*Ww, Wh*Ww
|
||
|
|
self.register_buffer("relative_position_index", relative_position_index)
|
||
|
|
|
||
|
|
self.qkv = nn.Linear(dim, dim * 3, bias=False)
|
||
|
|
if qkv_bias:
|
||
|
|
self.q_bias = nn.Parameter(torch.zeros(dim))
|
||
|
|
self.v_bias = nn.Parameter(torch.zeros(dim))
|
||
|
|
else:
|
||
|
|
self.q_bias = None
|
||
|
|
self.v_bias = None
|
||
|
|
self.attn_drop = nn.Dropout(attn_drop)
|
||
|
|
self.proj = nn.Linear(dim, dim)
|
||
|
|
self.proj_drop = nn.Dropout(proj_drop)
|
||
|
|
self.softmax = nn.Softmax(dim=-1)
|
||
|
|
|
||
|
|
def forward(self, x, mask=None):
|
||
|
|
|
||
|
|
B_, N, C = x.shape
|
||
|
|
qkv_bias = None
|
||
|
|
if self.q_bias is not None:
|
||
|
|
qkv_bias = torch.cat((self.q_bias, torch.zeros_like(self.v_bias, requires_grad=False), self.v_bias))
|
||
|
|
qkv = F.linear(input=x, weight=self.qkv.weight, bias=qkv_bias)
|
||
|
|
qkv = qkv.reshape(B_, N, 3, self.num_heads, -1).permute(2, 0, 3, 1, 4)
|
||
|
|
q, k, v = qkv[0], qkv[1], qkv[2] # make torchscript happy (cannot use tensor as tuple)
|
||
|
|
|
||
|
|
# cosine attention
|
||
|
|
attn = (F.normalize(q, dim=-1) @ F.normalize(k, dim=-1).transpose(-2, -1))
|
||
|
|
logit_scale = torch.clamp(self.logit_scale, max=torch.log(torch.tensor(1. / 0.01))).exp()
|
||
|
|
attn = attn * logit_scale
|
||
|
|
|
||
|
|
relative_position_bias_table = self.cpb_mlp(self.relative_coords_table).view(-1, self.num_heads)
|
||
|
|
relative_position_bias = relative_position_bias_table[self.relative_position_index.view(-1)].view(
|
||
|
|
self.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[1], -1) # Wh*Ww,Wh*Ww,nH
|
||
|
|
relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous() # nH, Wh*Ww, Wh*Ww
|
||
|
|
relative_position_bias = 16 * torch.sigmoid(relative_position_bias)
|
||
|
|
attn = attn + relative_position_bias.unsqueeze(0)
|
||
|
|
|
||
|
|
if mask is not None:
|
||
|
|
nW = mask.shape[0]
|
||
|
|
attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze(1).unsqueeze(0)
|
||
|
|
attn = attn.view(-1, self.num_heads, N, N)
|
||
|
|
attn = self.softmax(attn)
|
||
|
|
else:
|
||
|
|
attn = self.softmax(attn)
|
||
|
|
|
||
|
|
attn = self.attn_drop(attn)
|
||
|
|
|
||
|
|
try:
|
||
|
|
x = (attn @ v).transpose(1, 2).reshape(B_, N, C)
|
||
|
|
except:
|
||
|
|
x = (attn.half() @ v).transpose(1, 2).reshape(B_, N, C)
|
||
|
|
|
||
|
|
x = self.proj(x)
|
||
|
|
x = self.proj_drop(x)
|
||
|
|
return x
|
||
|
|
|
||
|
|
def extra_repr(self) -> str:
|
||
|
|
return f'dim={self.dim}, window_size={self.window_size}, ' \
|
||
|
|
f'pretrained_window_size={self.pretrained_window_size}, num_heads={self.num_heads}'
|
||
|
|
|
||
|
|
def flops(self, N):
|
||
|
|
# calculate flops for 1 window with token length of N
|
||
|
|
flops = 0
|
||
|
|
# qkv = self.qkv(x)
|
||
|
|
flops += N * self.dim * 3 * self.dim
|
||
|
|
# attn = (q @ k.transpose(-2, -1))
|
||
|
|
flops += self.num_heads * N * (self.dim // self.num_heads) * N
|
||
|
|
# x = (attn @ v)
|
||
|
|
flops += self.num_heads * N * N * (self.dim // self.num_heads)
|
||
|
|
# x = self.proj(x)
|
||
|
|
flops += N * self.dim * self.dim
|
||
|
|
return flops
|
||
|
|
|
||
|
|
|
||
|
|
class Mlp_v2(nn.Module):
|
||
|
|
def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.SiLU, drop=0.):
|
||
|
|
super().__init__()
|
||
|
|
out_features = out_features or in_features
|
||
|
|
hidden_features = hidden_features or in_features
|
||
|
|
self.fc1 = nn.Linear(in_features, hidden_features)
|
||
|
|
self.act = act_layer()
|
||
|
|
self.fc2 = nn.Linear(hidden_features, out_features)
|
||
|
|
self.drop = nn.Dropout(drop)
|
||
|
|
|
||
|
|
def forward(self, x):
|
||
|
|
x = self.fc1(x)
|
||
|
|
x = self.act(x)
|
||
|
|
x = self.drop(x)
|
||
|
|
x = self.fc2(x)
|
||
|
|
x = self.drop(x)
|
||
|
|
return x
|
||
|
|
|
||
|
|
|
||
|
|
def window_partition_v2(x, window_size):
|
||
|
|
B, H, W, C = x.shape
|
||
|
|
x = x.view(B, H // window_size, window_size, W // window_size, window_size, C)
|
||
|
|
windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)
|
||
|
|
return windows
|
||
|
|
|
||
|
|
|
||
|
|
def window_reverse_v2(windows, window_size, H, W):
|
||
|
|
B = int(windows.shape[0] / (H * W / window_size / window_size))
|
||
|
|
x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1)
|
||
|
|
x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1)
|
||
|
|
return x
|
||
|
|
|
||
|
|
|
||
|
|
class SwinTransformerLayer_v2(nn.Module):
|
||
|
|
|
||
|
|
def __init__(self, dim, num_heads, window_size=7, shift_size=0,
|
||
|
|
mlp_ratio=4., qkv_bias=True, drop=0., attn_drop=0., drop_path=0.,
|
||
|
|
act_layer=nn.SiLU, norm_layer=nn.LayerNorm, pretrained_window_size=0):
|
||
|
|
super().__init__()
|
||
|
|
self.dim = dim
|
||
|
|
# self.input_resolution = input_resolution
|
||
|
|
self.num_heads = num_heads
|
||
|
|
self.window_size = window_size
|
||
|
|
self.shift_size = shift_size
|
||
|
|
self.mlp_ratio = mlp_ratio
|
||
|
|
# if min(self.input_resolution) <= self.window_size:
|
||
|
|
# # if window size is larger than input resolution, we don't partition windows
|
||
|
|
# self.shift_size = 0
|
||
|
|
# self.window_size = min(self.input_resolution)
|
||
|
|
assert 0 <= self.shift_size < self.window_size, "shift_size must in 0-window_size"
|
||
|
|
|
||
|
|
self.norm1 = norm_layer(dim)
|
||
|
|
self.attn = WindowAttention_v2(
|
||
|
|
dim, window_size=(self.window_size, self.window_size), num_heads=num_heads,
|
||
|
|
qkv_bias=qkv_bias, attn_drop=attn_drop, proj_drop=drop,
|
||
|
|
pretrained_window_size=(pretrained_window_size, pretrained_window_size))
|
||
|
|
|
||
|
|
self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
|
||
|
|
self.norm2 = norm_layer(dim)
|
||
|
|
mlp_hidden_dim = int(dim * mlp_ratio)
|
||
|
|
self.mlp = Mlp_v2(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
|
||
|
|
|
||
|
|
def create_mask(self, H, W):
|
||
|
|
# calculate attention mask for SW-MSA
|
||
|
|
img_mask = torch.zeros((1, H, W, 1)) # 1 H W 1
|
||
|
|
h_slices = (slice(0, -self.window_size),
|
||
|
|
slice(-self.window_size, -self.shift_size),
|
||
|
|
slice(-self.shift_size, None))
|
||
|
|
w_slices = (slice(0, -self.window_size),
|
||
|
|
slice(-self.window_size, -self.shift_size),
|
||
|
|
slice(-self.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, self.window_size) # nW, window_size, window_size, 1
|
||
|
|
mask_windows = mask_windows.view(-1, self.window_size * self.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))
|
||
|
|
|
||
|
|
return attn_mask
|
||
|
|
|
||
|
|
def forward(self, x):
|
||
|
|
# reshape x[b c h w] to x[b l c]
|
||
|
|
_, _, H_, W_ = x.shape
|
||
|
|
|
||
|
|
Padding = False
|
||
|
|
if min(H_, W_) < self.window_size or H_ % self.window_size != 0 or W_ % self.window_size != 0:
|
||
|
|
Padding = True
|
||
|
|
# print(f'img_size {min(H_, W_)} is less than (or not divided by) window_size {self.window_size}, Padding.')
|
||
|
|
pad_r = (self.window_size - W_ % self.window_size) % self.window_size
|
||
|
|
pad_b = (self.window_size - H_ % self.window_size) % self.window_size
|
||
|
|
x = F.pad(x, (0, pad_r, 0, pad_b))
|
||
|
|
|
||
|
|
# print('2', x.shape)
|
||
|
|
B, C, H, W = x.shape
|
||
|
|
L = H * W
|
||
|
|
x = x.permute(0, 2, 3, 1).contiguous().view(B, L, C) # b, L, c
|
||
|
|
|
||
|
|
# create mask from init to forward
|
||
|
|
if self.shift_size > 0:
|
||
|
|
attn_mask = self.create_mask(H, W).to(x.device)
|
||
|
|
else:
|
||
|
|
attn_mask = None
|
||
|
|
|
||
|
|
shortcut = x
|
||
|
|
x = x.view(B, H, W, C)
|
||
|
|
|
||
|
|
# cyclic shift
|
||
|
|
if self.shift_size > 0:
|
||
|
|
shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2))
|
||
|
|
else:
|
||
|
|
shifted_x = x
|
||
|
|
|
||
|
|
# partition windows
|
||
|
|
x_windows = window_partition_v2(shifted_x, self.window_size) # nW*B, window_size, window_size, C
|
||
|
|
x_windows = x_windows.view(-1, self.window_size * self.window_size, C) # nW*B, window_size*window_size, C
|
||
|
|
|
||
|
|
# W-MSA/SW-MSA
|
||
|
|
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_v2(attn_windows, self.window_size, H, W) # B H' W' C
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|
|
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||
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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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||
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|
else:
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x = shifted_x
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x = x.view(B, H * W, C)
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||
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x = shortcut + self.drop_path(self.norm1(x))
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|
|
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||
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# FFN
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x = x + self.drop_path(self.norm2(self.mlp(x)))
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||
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|
x = x.permute(0, 2, 1).contiguous().view(-1, C, H, W) # b c h w
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||
|
|
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||
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|
if Padding:
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||
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|
x = x[:, :, :H_, :W_] # reverse padding
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||
|
|
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||
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|
return x
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||
|
|
|
||
|
|
def extra_repr(self) -> str:
|
||
|
|
return f"dim={self.dim}, input_resolution={self.input_resolution}, num_heads={self.num_heads}, " \
|
||
|
|
f"window_size={self.window_size}, shift_size={self.shift_size}, mlp_ratio={self.mlp_ratio}"
|
||
|
|
|
||
|
|
def flops(self):
|
||
|
|
flops = 0
|
||
|
|
H, W = self.input_resolution
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||
|
|
# norm1
|
||
|
|
flops += self.dim * H * W
|
||
|
|
# W-MSA/SW-MSA
|
||
|
|
nW = H * W / self.window_size / self.window_size
|
||
|
|
flops += nW * self.attn.flops(self.window_size * self.window_size)
|
||
|
|
# mlp
|
||
|
|
flops += 2 * H * W * self.dim * self.dim * self.mlp_ratio
|
||
|
|
# norm2
|
||
|
|
flops += self.dim * H * W
|
||
|
|
return flops
|
||
|
|
|
||
|
|
|
||
|
|
class SwinTransformer2Block(nn.Module):
|
||
|
|
def __init__(self, c1, c2, num_heads, num_layers, window_size=7):
|
||
|
|
super().__init__()
|
||
|
|
self.conv = None
|
||
|
|
if c1 != c2:
|
||
|
|
self.conv = Conv(c1, c2)
|
||
|
|
|
||
|
|
# remove input_resolution
|
||
|
|
self.blocks = nn.Sequential(*[SwinTransformerLayer_v2(dim=c2, num_heads=num_heads, window_size=window_size,
|
||
|
|
shift_size=0 if (i % 2 == 0) else window_size // 2) for i
|
||
|
|
in range(num_layers)])
|
||
|
|
|
||
|
|
def forward(self, x):
|
||
|
|
if self.conv is not None:
|
||
|
|
x = self.conv(x)
|
||
|
|
x = self.blocks(x)
|
||
|
|
return x
|
||
|
|
|
||
|
|
|
||
|
|
class ST2CSPA(nn.Module):
|
||
|
|
# CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks
|
||
|
|
def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion
|
||
|
|
super(ST2CSPA, self).__init__()
|
||
|
|
c_ = int(c2 * e) # hidden channels
|
||
|
|
self.cv1 = Conv(c1, c_, 1, 1)
|
||
|
|
self.cv2 = Conv(c1, c_, 1, 1)
|
||
|
|
self.cv3 = Conv(2 * c_, c2, 1, 1)
|
||
|
|
num_heads = c_ // 32
|
||
|
|
self.m = SwinTransformer2Block(c_, c_, num_heads, n)
|
||
|
|
# self.m = nn.Sequential(*[Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)])
|
||
|
|
|
||
|
|
def forward(self, x):
|
||
|
|
y1 = self.m(self.cv1(x))
|
||
|
|
y2 = self.cv2(x)
|
||
|
|
return self.cv3(torch.cat((y1, y2), dim=1))
|
||
|
|
|
||
|
|
|
||
|
|
class ST2CSPB(nn.Module):
|
||
|
|
# CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks
|
||
|
|
def __init__(self, c1, c2, n=1, shortcut=False, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion
|
||
|
|
super(ST2CSPB, self).__init__()
|
||
|
|
c_ = int(c2) # hidden channels
|
||
|
|
self.cv1 = Conv(c1, c_, 1, 1)
|
||
|
|
self.cv2 = Conv(c_, c_, 1, 1)
|
||
|
|
self.cv3 = Conv(2 * c_, c2, 1, 1)
|
||
|
|
num_heads = c_ // 32
|
||
|
|
self.m = SwinTransformer2Block(c_, c_, num_heads, n)
|
||
|
|
# self.m = nn.Sequential(*[Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)])
|
||
|
|
|
||
|
|
def forward(self, x):
|
||
|
|
x1 = self.cv1(x)
|
||
|
|
y1 = self.m(x1)
|
||
|
|
y2 = self.cv2(x1)
|
||
|
|
return self.cv3(torch.cat((y1, y2), dim=1))
|
||
|
|
|
||
|
|
|
||
|
|
class ST2CSPC(nn.Module):
|
||
|
|
# CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks
|
||
|
|
def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion
|
||
|
|
super(ST2CSPC, self).__init__()
|
||
|
|
c_ = int(c2 * e) # hidden channels
|
||
|
|
self.cv1 = Conv(c1, c_, 1, 1)
|
||
|
|
self.cv2 = Conv(c1, c_, 1, 1)
|
||
|
|
self.cv3 = Conv(c_, c_, 1, 1)
|
||
|
|
self.cv4 = Conv(2 * c_, c2, 1, 1)
|
||
|
|
num_heads = c_ // 32
|
||
|
|
self.m = SwinTransformer2Block(c_, c_, num_heads, n)
|
||
|
|
# self.m = nn.Sequential(*[Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)])
|
||
|
|
|
||
|
|
def forward(self, x):
|
||
|
|
y1 = self.cv3(self.m(self.cv1(x)))
|
||
|
|
y2 = self.cv2(x)
|
||
|
|
return self.cv4(torch.cat((y1, y2), dim=1))
|
||
|
|
|
||
|
|
##### end of swin transformer v2 #####
|