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model_bn.py
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import torch
import torch.nn as nn
import torch.nn.functional as F
import math
# 1 MODEL
class Unet(nn.Module):
def __init__(self):
super(Unet, self).__init__()
# All layers which have weights are created and initlialitzed in init.
# parameterless modules are used in functional style F. in forward
# (object version of parameterless modules can be created with nn. init too )
# https://pytorch.org/docs/master/nn.html#conv2d
# in_channels, out_channels, kernel_size, stride, padding, dilation, groups, bias
self.conv1 = nn.Conv2d(in_channels=1, out_channels=64, kernel_size=3, stride=1, padding=0)
# https://pytorch.org/docs/master/nn.html#batchnorm2d
# num_features/channels, eps, momentum, affine, track_running_stats
self.conv1_bn = nn.BatchNorm2d(64)
self.conv2 = nn.Conv2d(64, 64, 3, stride=1, padding=0)
self.conv2_bn = nn.BatchNorm2d(64)
# https://pytorch.org/docs/master/nn.html#maxpool2d
# kernel_size, stride, padding, dilation, return_indices, ceil_mode
self.maxPool1 = nn.MaxPool2d(2, stride=2, padding=0)
self.conv3 = nn.Conv2d(64, 128, 3, stride=1, padding=0)
self.conv3_bn = nn.BatchNorm2d(128)
self.conv4 = nn.Conv2d(128, 128, 3, stride=1, padding=0)
self.conv4_bn = nn.BatchNorm2d(128)
self.maxPool2 = nn.MaxPool2d(2, stride=2, padding=0)
self.conv5 = nn.Conv2d(128, 256, 3, stride=1, padding=0)
self.conv5_bn = nn.BatchNorm2d(256)
self.conv6 = nn.Conv2d(256, 256, 3, stride=1, padding=0)
self.conv6_bn = nn.BatchNorm2d(256)
self.maxPool3 = nn.MaxPool2d(2, stride=2, padding=0)
self.conv7 = nn.Conv2d(256, 512, 3, stride=1, padding=0)
self.conv7_bn = nn.BatchNorm2d(512)
self.conv8 = nn.Conv2d(512, 512, 3, stride=1, padding=0)
self.conv8_bn = nn.BatchNorm2d(512)
self.maxPool4 = nn.MaxPool2d(2, stride=2, padding=0)
self.conv9 = nn.Conv2d(512, 1024, 3, stride=1, padding=0)
self.conv9_bn = nn.BatchNorm2d(1024)
self.conv10 = nn.Conv2d(1024, 1024, 3, stride=1, padding=0)
self.conv10_bn = nn.BatchNorm2d(1024)
# https://pytorch.org/docs/master/nn.html#convtranspose2d
# in_channels, out_channels, kernel_size, stride, padding, output_padding, groups, bias, dilation
self.upsampconv1 = nn.ConvTranspose2d(1024, 512, 2, stride=2, padding=0)
self.conv11 = nn.Conv2d(1024, 512, 3, stride=1, padding=0)
self.conv11_bn = nn.BatchNorm2d(512)
self.conv12 = nn.Conv2d(512, 512, 3, stride=1, padding=0)
self.conv12_bn = nn.BatchNorm2d(512)
self.upsampconv2 = nn.ConvTranspose2d(512, 256, 2, stride=2, padding=0)
self.conv13 = nn.Conv2d(512, 256, 3, stride=1, padding=0)
self.conv13_bn = nn.BatchNorm2d(256)
self.conv14 = nn.Conv2d(256, 256, 3, stride=1, padding=0)
self.conv14_bn = nn.BatchNorm2d(256)
self.upsampconv3 = nn.ConvTranspose2d(256, 128, 2, stride=2, padding=0)
self.conv15 = nn.Conv2d(256, 128, 3, stride=1, padding=0)
self.conv15_bn = nn.BatchNorm2d(128)
self.conv16 = nn.Conv2d(128, 128, 3, stride=1, padding=0)
self.conv16_bn = nn.BatchNorm2d(128)
self.upsampconv4 = nn.ConvTranspose2d(128, 64, 2, stride=2, padding=0)
self.conv17 = nn.Conv2d(128, 64, 3, stride=1, padding=0)
self.conv17_bn = nn.BatchNorm2d(64)
self.conv18 = nn.Conv2d(64, 64, 3, stride=1, padding=0)
self.conv18_bn = nn.BatchNorm2d(64)
self.conv19 = nn.Conv2d(64, 2, 1, stride=1, padding=0)
self.conv19_bn = nn.BatchNorm2d(2)
self.softmax = nn.Softmax2d()
# weights can be initialized here:
# for example:
for m in self.modules():
if isinstance(m, nn.Conv2d):
# force float division, therefore use 2.0
# http://andyljones.tumblr.com/post/110998971763/an-explanation-of-xavier-initialization
# https://arxiv.org/abs/1502.01852
# a rectifying linear unit is zero for half of its input,
# so you need to double the size of weight variance to keep the signals variance constant.
# xavier would be: scalefactor * sqrt(2/ (inchannels + outchannels )
std = math.sqrt(2.0/(m.kernel_size[0]*m.kernel_size[0]*m.in_channels))
nn.init.normal_(m.weight, std=std)
nn.init.constant_(m.bias, 0)
# elif isinstance(m, nn.BatchNorm2d):
# print
# # nn.init.constant_(m.weight, 1)
# #nn.init.constant_(m.bias, 0)
# elif isinstance(m, nn.ConvTranspose2d):
# print
# # nn.init.xavier_normal_(m.weight, 1)
# # do max pooling layers have weight? maybe can add bias.
# elif isinstance(m, nn.MaxPool2d):
# print
# #nn.init.xavier_normal_(m.weight)
def forward(self, x, padding=False):
# https://pytorch.org/docs/master/nn.html#torch.nn.ReLU
# https://pytorch.org/docs/master/nn.html#id26 F.relu
# input, inplace
# https://pytorch.org/docs/master/nn.html#torch.nn.functional.pad
# input, pad , mode
padmode = 'reflect'
if padding:
pad = (1, 1, 1, 1)
else:
pad = (0, 0, 0, 0)
x = F.relu(self.conv1_bn(self.conv1(F.pad(x, pad, padmode))))
x = F.relu(self.conv2_bn(self.conv2(F.pad(x, pad, padmode))))
# save result for combination later
x_copy1_2 = x
x = self.maxPool1(x)
x = F.relu(self.conv3_bn(self.conv3(F.pad(x, pad, padmode))))
x = F.relu(self.conv4_bn(self.conv4(F.pad(x, pad, padmode))))
x_copy3_4 = x
x = self.maxPool2(x)
x = F.relu(self.conv5_bn(self.conv5(F.pad(x, pad, padmode))))
x = F.relu(self.conv6_bn(self.conv6(F.pad(x, pad, padmode))))
x_copy5_6 = x
x = self.maxPool3(x)
x = F.relu(self.conv7_bn(self.conv7(F.pad(x, pad, padmode))))
x = F.relu(self.conv8_bn(self.conv8(F.pad(x, pad, padmode))))
# input, probability of an element to be zero-ed
# https://pytorch.org/docs/master/nn.html#dropout
x = F.dropout(x, 0.5)
x_copy7_8 = x
x = self.maxPool4(x)
x = F.relu(self.conv9_bn(self.conv9(F.pad(x, pad, padmode))))
x = F.relu(self.conv10_bn(self.conv10(F.pad(x, pad, padmode))))
x = F.dropout(x, 0.5)
x = F.relu(self.upsampconv1(x))
x = self.crop_and_concat(x, x_copy7_8)
x = F.relu(self.conv11_bn(self.conv11(F.pad(x, pad, padmode))))
x = F.relu(self.conv12_bn(self.conv12(F.pad(x, pad, padmode))))
x = F.relu(self.upsampconv2(x))
x = self.crop_and_concat(x, x_copy5_6)
x = F.relu(self.conv13_bn(self.conv13(F.pad(x, pad, padmode))))
x = F.relu(self.conv14_bn(self.conv14(F.pad(x, pad, padmode))))
x = F.relu(self.upsampconv3(x))
x = self.crop_and_concat(x, x_copy3_4)
x = F.relu(self.conv15_bn(self.conv15(F.pad(x, pad, padmode))))
x = F.relu(self.conv16_bn(self.conv16(F.pad(x, pad, padmode))))
x = F.relu(self.upsampconv4(x))
x = self.crop_and_concat(x, x_copy1_2)
x = F.relu(self.conv17_bn(self.conv17(F.pad(x, pad, padmode))))
x = F.relu(self.conv18_bn(self.conv18(F.pad(x, pad, padmode))))
x = F.relu(self.conv19_bn(self.conv19(x)))
x = self.softmax(x)
return x
# when no padding is used, the upsampled image gets smaller
# to copy a bigger image to the corresponding layer, it needs to be cropped
def crop_and_concat(self, upsampled, bypass):
# Python 2 / Integer division ( if int intputs ), // integer division
c = (bypass.size()[2] - upsampled.size()[2]) // 2
d = c
# checks if bypass.size() is odd
# if input image is 512, at x = self.crop_and_concat(x, x_copy5_6)
# x_copy5_6 is 121*121
# therefore cut one more row and column
if (bypass.size()[2] & 1) == 1:
d = c + 1
# padleft padright padtop padbottom
bypass = F.pad(bypass, (-c, -d, -c, -d))
return torch.cat((bypass, upsampled), 1)