Datasets:
jblitzar
/
github-python

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m.weight.data.normal_(0, math.sqrt(2.0 / fan_out))
if m.bias is not None:
m.bias.data.zero_()
def forward_features(self, x):
B = x.shape[0]
outs = []
# stage 1
x, H, W = self.patch_embed1(x)
for i, blk in enumerate(self.block1):
x = blk(x, H, W)
x = self.norm1(x)
x = x.reshape(B, H, W, -1).permute(0, 3, 1, 2).contiguous()
outs.append(x)
# stage 2
x, H, W = self.patch_embed2(x)
for i, blk in enumerate(self.block2):
x = blk(x, H, W)
x = self.norm2(x)
x = x.reshape(B, H, W, -1).permute(0, 3, 1, 2).contiguous()
outs.append(x)
# stage 3
x, H, W = self.patch_embed3(x)
for i, blk in enumerate(self.block3):
x = blk(x, H, W)
x = self.norm3(x)
x = x.reshape(B, H, W, -1).permute(0, 3, 1, 2).contiguous()
outs.append(x)
# stage 4
x, H, W = self.patch_embed4(x)
for i, blk in enumerate(self.block4):
x = blk(x, H, W)
x = self.norm4(x)
x = x.reshape(B, H, W, -1).permute(0, 3, 1, 2).contiguous()
outs.append(x)
return x,outs[1:]
def forward(self, x):
x,outs = self.forward_features(x)
return x,outs
class DWConv(nn.Module):
def __init__(self, dim=768):
super(DWConv, self).__init__()
self.dwconv = nn.Conv2d(dim, dim, 3, 1, 1, bias=True, groups=dim)
def forward(self, x, H, W):
B, N, C = x.shape
x = x.transpose(1, 2).view(B, C, H, W)
x = self.dwconv(x)
x = x.flatten(2).transpose(1, 2)
return x
import torch
import torch.nn as nn
import torch.nn.functional as F
class UpsampleConcatConv(nn.Module):
def __init__(self):
super(UpsampleConcatConv, self).__init__()
self.upsamples2 = nn.ConvTranspose2d(128, 64, kernel_size=4, stride=2, padding=1)
self.upsamplec3 = nn.Sequential(
nn.ConvTranspose2d(384, 192, kernel_size=4, stride=2, padding=1),
nn.ConvTranspose2d(192, 96, kernel_size=4, stride=2, padding=1)
)
self.upsamples3 = nn.Sequential(
nn.ConvTranspose2d(320, 128, kernel_size=4, stride=2, padding=1),
nn.ConvTranspose2d(128, 64, kernel_size=4, stride=2, padding=1)
)
self.upsamples4 = nn.Sequential(
nn.ConvTranspose2d(512, 320, kernel_size=4, stride=2, padding=1),
nn.ConvTranspose2d(320, 128, kernel_size=4, stride=2, padding=1),
nn.ConvTranspose2d(128, 64, kernel_size=4, stride=2, padding=1)
)
def forward(self, inputs):
# 上采样
c1,c3,s2,s3,s4 = inputs
# c2 = self.upsamplec2(c2)
c3 = self.upsamplec3(c3)
# c4 = self.upsamplec4(c4)
s2 = self.upsamples2(s2)
s3 = self.upsamples3(s3)
s4 = self.upsamples4(s4)
# 拼接四个tensor
x = torch.cat([c1,c3,s2,s3,s4], dim=1)
features = [c1,c3,s2,s3,s4 ]
# shortcut = x
# x = x.permute(0, 2, 3, 1)