422 lines
16 KiB
Python
422 lines
16 KiB
Python
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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from einops import rearrange, repeat
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from timm.models.layers import DropPath, to_2tuple, trunc_normal_
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import timm
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class ConvBNReLU(nn.Sequential):
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def __init__(self, in_channels, out_channels, kernel_size=3, dilation=1, stride=1, norm_layer=nn.BatchNorm2d,
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bias=False):
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super(ConvBNReLU, self).__init__(
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nn.Conv2d(in_channels, out_channels, kernel_size=kernel_size, bias=bias,
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dilation=dilation, stride=stride, padding=((stride - 1) + dilation * (kernel_size - 1)) // 2),
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norm_layer(out_channels),
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nn.ReLU6()
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)
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class ConvBN(nn.Sequential):
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def __init__(self, in_channels, out_channels, kernel_size=3, dilation=1, stride=1, norm_layer=nn.BatchNorm2d,
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bias=False):
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super(ConvBN, self).__init__(
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nn.Conv2d(in_channels, out_channels, kernel_size=kernel_size, bias=bias,
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dilation=dilation, stride=stride, padding=((stride - 1) + dilation * (kernel_size - 1)) // 2),
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norm_layer(out_channels)
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)
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class Conv(nn.Sequential):
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def __init__(self, in_channels, out_channels, kernel_size=3, dilation=1, stride=1, bias=False):
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super(Conv, self).__init__(
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nn.Conv2d(in_channels, out_channels, kernel_size=kernel_size, bias=bias,
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dilation=dilation, stride=stride, padding=((stride - 1) + dilation * (kernel_size - 1)) // 2)
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)
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class SeparableConvBNReLU(nn.Sequential):
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def __init__(self, in_channels, out_channels, kernel_size=3, stride=1, dilation=1,
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norm_layer=nn.BatchNorm2d):
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super(SeparableConvBNReLU, self).__init__(
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nn.Conv2d(in_channels, in_channels, kernel_size, stride=stride, dilation=dilation,
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padding=((stride - 1) + dilation * (kernel_size - 1)) // 2,
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groups=in_channels, bias=False),
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norm_layer(out_channels),
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nn.Conv2d(in_channels, out_channels, kernel_size=1, bias=False),
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nn.ReLU6()
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)
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class SeparableConvBN(nn.Sequential):
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def __init__(self, in_channels, out_channels, kernel_size=3, stride=1, dilation=1,
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norm_layer=nn.BatchNorm2d):
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super(SeparableConvBN, self).__init__(
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nn.Conv2d(in_channels, in_channels, kernel_size, stride=stride, dilation=dilation,
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padding=((stride - 1) + dilation * (kernel_size - 1)) // 2,
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groups=in_channels, bias=False),
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norm_layer(out_channels),
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nn.Conv2d(in_channels, out_channels, kernel_size=1, bias=False)
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)
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class SeparableConv(nn.Sequential):
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def __init__(self, in_channels, out_channels, kernel_size=3, stride=1, dilation=1):
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super(SeparableConv, self).__init__(
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nn.Conv2d(in_channels, in_channels, kernel_size, stride=stride, dilation=dilation,
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padding=((stride - 1) + dilation * (kernel_size - 1)) // 2,
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groups=in_channels, bias=False),
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nn.Conv2d(in_channels, out_channels, kernel_size=1, bias=False)
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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=nn.ReLU6, 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.Conv2d(in_features, hidden_features, 1, 1, 0, bias=True)
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self.act = act_layer()
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self.fc2 = nn.Conv2d(hidden_features, out_features, 1, 1, 0, bias=True)
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self.drop = nn.Dropout(drop, inplace=True)
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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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class GlobalLocalAttention(nn.Module):
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def __init__(self,
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dim=256,
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num_heads=16,
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qkv_bias=False,
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window_size=8,
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relative_pos_embedding=True
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):
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super().__init__()
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self.num_heads = num_heads
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head_dim = dim // self.num_heads
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self.scale = head_dim ** -0.5
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self.ws = window_size
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self.qkv = Conv(dim, 3 * dim, kernel_size=1, bias=qkv_bias)
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self.local1 = ConvBN(dim, dim, kernel_size=3)
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self.local2 = ConvBN(dim, dim, kernel_size=1)
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self.proj = SeparableConvBN(dim, dim, kernel_size=window_size)
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self.attn_x = nn.AvgPool2d(kernel_size=(window_size, 1), stride=1, padding=(window_size // 2 - 1, 0))
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self.attn_y = nn.AvgPool2d(kernel_size=(1, window_size), stride=1, padding=(0, window_size // 2 - 1))
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self.relative_pos_embedding = relative_pos_embedding
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if self.relative_pos_embedding:
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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 - 1) * (2 * window_size - 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.ws)
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coords_w = torch.arange(self.ws)
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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.ws - 1 # shift to start from 0
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relative_coords[:, :, 1] += self.ws - 1
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relative_coords[:, :, 0] *= 2 * self.ws - 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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trunc_normal_(self.relative_position_bias_table, std=.02)
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def pad(self, x, ps):
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_, _, H, W = x.size()
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if W % ps != 0:
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# x = F.pad(x, (0, ps - W % ps), mode='reflect')
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x = F.pad(x, (0, ps - W % ps,0,0), mode='reflect')
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if H % ps != 0:
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x = F.pad(x, (0, 0, 0, ps - H % ps), mode='reflect')
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return x
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def pad_out(self, x):
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x = F.pad(x, pad=(0, 1, 0, 1), mode='reflect')
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return x
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def forward(self, x):
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B, C, H, W = x.shape
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local = self.local2(x) + self.local1(x)
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x = self.pad(x, self.ws)
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B, C, Hp, Wp = x.shape
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qkv = self.qkv(x)
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q, k, v = rearrange(qkv, 'b (qkv h d) (hh ws1) (ww ws2) -> qkv (b hh ww) h (ws1 ws2) d', h=self.num_heads,
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d=C // self.num_heads, hh=Hp // self.ws, ww=Wp // self.ws, qkv=3, ws1=self.ws, ws2=self.ws)
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dots = (q @ k.transpose(-2, -1)) * self.scale
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if self.relative_pos_embedding:
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relative_position_bias = self.relative_position_bias_table[self.relative_position_index.view(-1)].view(
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self.ws * self.ws, self.ws * self.ws, -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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dots += relative_position_bias.unsqueeze(0)
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attn = dots.softmax(dim=-1)
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attn = attn @ v
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attn = rearrange(attn, '(b hh ww) h (ws1 ws2) d -> b (h d) (hh ws1) (ww ws2)', h=self.num_heads,
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d=C // self.num_heads, hh=Hp // self.ws, ww=Wp // self.ws, ws1=self.ws, ws2=self.ws)
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attn = attn[:, :, :H, :W]
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out = self.attn_x(F.pad(attn, pad=(0, 0, 0, 1), mode='reflect')) + \
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self.attn_y(F.pad(attn, pad=(0, 1, 0, 0), mode='reflect'))
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out = out + local
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out = self.pad_out(out)
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out = self.proj(out)
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# print(out.size())
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out = out[:, :, :H, :W]
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return out
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class Block(nn.Module):
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def __init__(self, dim=256, num_heads=16, mlp_ratio=4., qkv_bias=False, drop=0., attn_drop=0.,
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drop_path=0., act_layer=nn.ReLU6, norm_layer=nn.BatchNorm2d, window_size=8):
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super().__init__()
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self.norm1 = norm_layer(dim)
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self.attn = GlobalLocalAttention(dim, num_heads=num_heads, qkv_bias=qkv_bias, window_size=window_size)
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self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
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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, out_features=dim, act_layer=act_layer,
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drop=drop)
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self.norm2 = norm_layer(dim)
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def forward(self, x):
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x = x + self.drop_path(self.attn(self.norm1(x)))
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x = x + self.drop_path(self.mlp(self.norm2(x)))
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return x
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class WF(nn.Module):
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def __init__(self, in_channels=128, decode_channels=128, eps=1e-8):
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super(WF, self).__init__()
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self.pre_conv = Conv(in_channels, decode_channels, kernel_size=1)
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self.weights = nn.Parameter(torch.ones(2, dtype=torch.float32), requires_grad=True)
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self.eps = eps
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self.post_conv = ConvBNReLU(decode_channels, decode_channels, kernel_size=3)
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def forward(self, x, res):
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# x = F.interpolate(x, scale_factor=2, mode='bilinear', align_corners=False)
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x = F.interpolate(x, size=res.shape[2:], mode='bilinear', align_corners=False)
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weights = nn.ReLU()(self.weights)
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fuse_weights = weights / (torch.sum(weights, dim=0) + self.eps)
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x = fuse_weights[0] * self.pre_conv(res) + fuse_weights[1] * x
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x = self.post_conv(x)
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return x
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class FeatureRefinementHead(nn.Module):
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def __init__(self, in_channels=64, decode_channels=64):
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super().__init__()
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self.pre_conv = Conv(in_channels, decode_channels, kernel_size=1)
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self.weights = nn.Parameter(torch.ones(2, dtype=torch.float32), requires_grad=True)
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self.eps = 1e-8
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self.post_conv = ConvBNReLU(decode_channels, decode_channels, kernel_size=3)
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self.pa = nn.Sequential(
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nn.Conv2d(decode_channels, decode_channels, kernel_size=3, padding=1, groups=decode_channels),
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nn.Sigmoid())
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self.ca = nn.Sequential(nn.AdaptiveAvgPool2d(1),
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Conv(decode_channels, decode_channels // 16, kernel_size=1),
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nn.ReLU6(),
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Conv(decode_channels // 16, decode_channels, kernel_size=1),
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nn.Sigmoid())
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self.shortcut = ConvBN(decode_channels, decode_channels, kernel_size=1)
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self.proj = SeparableConvBN(decode_channels, decode_channels, kernel_size=3)
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self.act = nn.ReLU6()
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def forward(self, x, res):
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x = F.interpolate(x, scale_factor=2, mode='bilinear', align_corners=False)
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weights = nn.ReLU()(self.weights)
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fuse_weights = weights / (torch.sum(weights, dim=0) + self.eps)
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x = fuse_weights[0] * self.pre_conv(res) + fuse_weights[1] * x
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x = self.post_conv(x)
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shortcut = self.shortcut(x)
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pa = self.pa(x) * x
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ca = self.ca(x) * x
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x = pa + ca
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x = self.proj(x) + shortcut
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x = self.act(x)
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return x
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class AuxHead(nn.Module):
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def __init__(self, in_channels=64, num_classes=8):
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super().__init__()
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self.conv = ConvBNReLU(in_channels, in_channels)
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self.drop = nn.Dropout(0.1)
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self.conv_out = Conv(in_channels, num_classes, kernel_size=1)
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def forward(self, x, h, w):
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feat = self.conv(x)
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feat = self.drop(feat)
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feat = self.conv_out(feat)
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feat = F.interpolate(feat, size=(h, w), mode='bilinear', align_corners=False)
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return feat
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class Decoder(nn.Module):
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def __init__(self,
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encoder_channels=(64, 128, 256, 512),
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decode_channels=64,
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dropout=0.1,
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window_size=8,
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num_classes=6):
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super(Decoder, self).__init__()
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self.pre_conv = ConvBN(encoder_channels[-1], decode_channels, kernel_size=1)
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self.b4 = Block(dim=decode_channels, num_heads=8, window_size=window_size)
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self.b3 = Block(dim=decode_channels, num_heads=8, window_size=window_size)
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self.p3 = WF(encoder_channels[-2], decode_channels)
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self.b2 = Block(dim=decode_channels, num_heads=8, window_size=window_size)
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self.p2 = WF(encoder_channels[-3], decode_channels)
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self.p1 = FeatureRefinementHead(encoder_channels[-4], decode_channels)
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self.segmentation_head = nn.Sequential(ConvBNReLU(decode_channels, decode_channels),
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nn.Dropout2d(p=dropout, inplace=True),
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Conv(decode_channels, num_classes, kernel_size=1))
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self.init_weight()
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if self.training:
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self.up4 = nn.UpsamplingBilinear2d(scale_factor=4)
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self.up3 = nn.UpsamplingBilinear2d(scale_factor=2)
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self.aux_head = AuxHead(decode_channels, num_classes)
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self.p1 = FeatureRefinementHead(encoder_channels[-4], decode_channels)
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self.segmentation_head = nn.Sequential(ConvBNReLU(decode_channels, decode_channels),
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nn.Dropout2d(p=dropout, inplace=True),
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Conv(decode_channels, num_classes, kernel_size=1))
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self.init_weight()
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def forward(self, res1, res2, res3, res4, h, w):
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x = self.b4(self.pre_conv(res4))
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h4 = self.up4(x)
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x = self.p3(x, res3)
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x = self.b3(x)
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h3 = self.up3(x)
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x = self.p2(x, res2)
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x = self.b2(x)
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h2 = x
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x = self.p1(x, res1)
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x = self.segmentation_head(x)
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x = F.interpolate(x, size=(h, w), mode='bilinear', align_corners=False)
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ah = h4 + h3 + h2
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ah = self.aux_head(ah, h, w)
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return x, ah
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def init_weight(self):
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for m in self.children():
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if isinstance(m, nn.Conv2d):
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nn.init.kaiming_normal_(m.weight, a=1)
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if m.bias is not None:
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nn.init.constant_(m.bias, 0)
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from mmseg.registry import MODELS
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@MODELS.register_module()
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class UNetFormer(nn.Module):
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def __init__(self,
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decode_channels=64,
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dropout=0.1,
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backbone_name='swsl_resnet18',
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pretrained=False,
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window_size=8,
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num_classes=6
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):
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super().__init__()
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self.backbone = timm.create_model(backbone_name, features_only=True, output_stride=32,
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out_indices=(1, 2, 3, 4), pretrained=False)
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encoder_channels = self.backbone.feature_info.channels()
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self.decoder = Decoder(encoder_channels, decode_channels, dropout, window_size, num_classes)
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def forward(self, x):
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h, w = x.size()[-2:]
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res1, res2, res3, res4 = self.backbone(x)
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x,ah = self.decoder(res1, res2, res3, res4, h, w)
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return x,ah
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if __name__ == '__main__':
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import time
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from thop import profile
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device = torch.device('cuda:3')
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model = UNetFormer().to('cuda:3')
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model.eval()
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model.to(device)
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iterations = None
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input = torch.randn(1, 3, 2448, 2448).to('cuda:3')
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with torch.no_grad():
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for _ in range(10):
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model(input)
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if iterations is None:
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elapsed_time = 0
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iterations = 100
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while elapsed_time < 1:
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torch.cuda.synchronize()
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torch.cuda.synchronize()
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t_start = time.time()
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for _ in range(iterations):
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model(input)
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torch.cuda.synchronize()
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torch.cuda.synchronize()
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elapsed_time = time.time() - t_start
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iterations *= 2
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FPS = iterations / elapsed_time
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iterations = int(FPS * 6)
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print('=========Speed Testing=========')
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torch.cuda.synchronize()
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torch.cuda.synchronize()
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t_start = time.time()
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for _ in range(iterations):
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model(input)
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torch.cuda.synchronize()
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torch.cuda.synchronize()
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elapsed_time = time.time() - t_start
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latency = elapsed_time / iterations * 1000
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torch.cuda.empty_cache()
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FPS = 1000 / latency
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print(FPS) |