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ImageNet.py
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import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data
import os
import h5py
import numpy as np
class _NonLocalBlockND(nn.Module):
def __init__(self, in_channels, inter_channels=None, dimension=3, sub_sample=True, bn_layer=True):
super(_NonLocalBlockND, self).__init__()
assert dimension in [1, 2, 3]
self.dimension = dimension
self.sub_sample = sub_sample
self.in_channels = in_channels
self.inter_channels = inter_channels
if self.inter_channels is None:
self.inter_channels = in_channels // 2
if self.inter_channels == 0:
self.inter_channels = 1
if dimension == 3:
conv_nd = nn.Conv3d
max_pool_layer = nn.MaxPool3d(kernel_size=(1, 2, 2))
bn = nn.BatchNorm3d
elif dimension == 2:
conv_nd = nn.Conv2d
max_pool_layer = nn.MaxPool2d(kernel_size=(2, 2))
bn = nn.BatchNorm2d
else:
conv_nd = nn.Conv1d
max_pool_layer = nn.MaxPool1d(kernel_size=(2))
bn = nn.BatchNorm1d
self.g = conv_nd(in_channels=self.in_channels, out_channels=self.inter_channels,
kernel_size=1, stride=1, padding=0)
if bn_layer:
self.W = nn.Sequential(
conv_nd(in_channels=self.inter_channels, out_channels=self.in_channels,
kernel_size=1, stride=1, padding=0),
bn(self.in_channels)
)
nn.init.constant_(self.W[1].weight, 0)
nn.init.constant_(self.W[1].bias, 0)
else:
self.W = conv_nd(in_channels=self.inter_channels, out_channels=self.in_channels,
kernel_size=1, stride=1, padding=0)
nn.init.constant_(self.W.weight, 0)
nn.init.constant_(self.W.bias, 0)
self.theta = conv_nd(in_channels=self.in_channels, out_channels=self.inter_channels,
kernel_size=1, stride=1, padding=0)
self.phi = conv_nd(in_channels=self.in_channels, out_channels=self.inter_channels,
kernel_size=1, stride=1, padding=0)
if sub_sample:
self.g = nn.Sequential(self.g, max_pool_layer)
self.phi = nn.Sequential(self.phi, max_pool_layer)
def forward(self, x):
'''
:param x: (b, c, t, h, w)
:return:
'''
batch_size = x.size(0)
g_x = self.g(x).view(batch_size, self.inter_channels, -1)
g_x = g_x.permute(0, 2, 1)
theta_x = self.theta(x).view(batch_size, self.inter_channels, -1)
theta_x = theta_x.permute(0, 2, 1)
phi_x = self.phi(x).view(batch_size, self.inter_channels, -1)
f = torch.matmul(theta_x, phi_x)
f_div_C = F.softmax(f, dim=-1)
y = torch.matmul(f_div_C, g_x)
y = y.permute(0, 2, 1).contiguous()
y = y.view(batch_size, self.inter_channels, *x.size()[2:])
W_y = self.W(y)
z = W_y + x
return z
class NONLocalBlock2D(_NonLocalBlockND):
def __init__(self, in_channels, inter_channels=None, sub_sample=True, bn_layer=True):
super(NONLocalBlock2D, self).__init__(in_channels,
inter_channels=inter_channels,
dimension=2, sub_sample=sub_sample,
bn_layer=bn_layer)
import torch
# import torchvision
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data
class conv_block1(nn.Module):
def __init__(self,in_c,o_c):
super(conv_block1,self).__init__()
self.conv = nn.Sequential(
nn.Conv2d(in_c,o_c,kernel_size=3,stride=1,padding=1,bias=True),
nn.BatchNorm2d(o_c),
nn.ReLU(),
nn.Conv2d(o_c,o_c,kernel_size=3,stride=1,padding=1,bias=True),
nn.BatchNorm2d(o_c),
nn.ReLU()
)
def forward(self,x):
out = self.conv(x)
return out
class conv_block(nn.Module):
def __init__(self,in_c,o_c):
super(conv_block,self).__init__()
self.conv = nn.Sequential(
nn.Conv2d(in_c, in_c, kernel_size=4,stride=2, padding=3, dilation=2),
nn.LeakyReLU(0.2, True),
nn.BatchNorm2d(in_c),
nn.Conv2d(in_c,o_c,kernel_size=3,stride=1,padding=1,bias=True),
nn.BatchNorm2d(o_c),
nn.LeakyReLU(0.2, True),
# nn.Conv2d(o_c,o_c,kernel_size=3,stride=1,padding=1,bias=True),
# nn.BatchNorm2d(o_c),
# nn.LeakyReLU(0.2, True),
)
def forward(self,x):
out = self.conv(x)
return out
class up_conv(nn.Module):
def __init__(self,in_c,o_c,size):
super(up_conv,self).__init__()
self.size = size
self.up = nn.Sequential(
# nn.Upsample(size=size,mode='bilinear',align_corners =True),
nn.Conv2d(in_c,o_c,kernel_size=3,stride=1,padding=1,bias=True),
nn.BatchNorm2d(o_c),
nn.LeakyReLU(0.2, True),
)
def forward(self,x):
o = F.interpolate(x,size=self.size,mode='bilinear',align_corners =True)
out = self.up(o)
return out
class IM_net(nn.Module):
def __init__(self,in_c=2,o_c=1):
super(IM_net,self).__init__()
n1 = 32
filters = [n1,n1*2,n1*4,n1*8,n1*16]
self.conv1 = nn.Sequential(
nn.Conv2d(in_c,filters[0],kernel_size=3,stride=1,padding=1,bias=True),
nn.BatchNorm2d(filters[0]),
nn.LeakyReLU(0.2, True),
nn.Conv2d(filters[0],filters[0],kernel_size=3,stride=1,padding=1,bias=True),
nn.BatchNorm2d(filters[0]),
nn.LeakyReLU(0.2, True),
)
self.conv2 = conv_block(filters[0],filters[1])
self.conv3 = conv_block(filters[1],filters[2])
self.conv4 = conv_block(filters[2],filters[3])
self.conv5 = conv_block(filters[3],filters[4])
self.Up5 = up_conv(filters[4],filters[3],size=[32,32])
self.up_conv5 = conv_block1(filters[4],filters[3])
self.Up4 = up_conv(filters[3],filters[2],size=[64,64])
self.up_conv4 = conv_block1(filters[3],filters[2])
self.Up3 = up_conv(filters[2],filters[1],size=[128,128])
self.up_conv3 = conv_block1(filters[2],filters[1])
self.Up2 = up_conv(filters[1],filters[0],size=[256,256])
self.up_conv2 = conv_block1(filters[1],filters[0])
self.conv_f5 = nn.Conv2d(filters[4],o_c,kernel_size=1,stride=1,padding=0)
self.conv_f4 = nn.Conv2d(filters[3],o_c,kernel_size=1,stride=1,padding=0)
self.conv_f3 = nn.Conv2d(filters[2],o_c,kernel_size=1,stride=1,padding=0)
self.conv_f2 = nn.Conv2d(filters[1],o_c,kernel_size=1,stride=1,padding=0)
self.conv_f1 = nn.Conv2d(filters[0],o_c,kernel_size=1,stride=1,padding=0)
self.non2 = NONLocalBlock2D(filters[1])
self.non3 = NONLocalBlock2D(filters[2])
def forward(self,x,batch_has_m1):
e1 = self.conv1(x)
# e1 = self.non1(e1)
e2 = self.conv2(e1)
e2 = self.non2(e2)
e3 = self.conv3(e2)
e3 = self.non3(e3)
e4 = self.conv4(e3)
e5 = self.conv5(e4)
d5 = self.Up5(e5)
d5 = torch.cat((e4,d5),dim=1)
d5 = self.up_conv5(d5)
d4 = self.Up4(d5)
d4 = torch.cat((e3,d4),dim=1)
d4 = self.up_conv4(d4)
d3 = self.Up3(d4)
d3 = torch.cat((e2,d3),dim=1)
d3 = self.up_conv3(d3)
d2 = self.Up2(d3)
d2 = torch.cat((e1,d2),dim=1)
d2 = self.up_conv2(d2)
out1 = self.conv_f1(d2)
out = out1 + batch_has_m1
return out