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FCN.py
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# -*- coding: utf-8 -*-
import torch
import torch.nn as nn
import torch.optim as optim
from torchvision import models
from torchvision.models.vgg import VGG
from BagData import dataloader
import pdb
import numpy as np
import time
# import visdom
import numpy as np
from matplotlib import pyplot as plt
class FCN32s(nn.Module):
def __init__(self, pretrained_net, n_class):
super().__init__()
self.n_class = n_class
self.pretrained_net = pretrained_net
self.relu = nn.ReLU(inplace=True)
self.deconv1 = nn.ConvTranspose2d(512, 512, kernel_size=3, stride=2, padding=1, dilation=1, output_padding=1)
self.bn1 = nn.BatchNorm2d(512)
self.deconv2 = nn.ConvTranspose2d(512, 256, kernel_size=3, stride=2, padding=1, dilation=1, output_padding=1)
self.bn2 = nn.BatchNorm2d(256)
self.deconv3 = nn.ConvTranspose2d(256, 128, kernel_size=3, stride=2, padding=1, dilation=1, output_padding=1)
self.bn3 = nn.BatchNorm2d(128)
self.deconv4 = nn.ConvTranspose2d(128, 64, kernel_size=3, stride=2, padding=1, dilation=1, output_padding=1)
self.bn4 = nn.BatchNorm2d(64)
self.deconv5 = nn.ConvTranspose2d(64, 32, kernel_size=3, stride=2, padding=1, dilation=1, output_padding=1)
self.bn5 = nn.BatchNorm2d(32)
self.classifier = nn.Conv2d(32, n_class, kernel_size=1)
def forward(self, x):
output = self.pretrained_net(x)
x5 = output['x5'] # size=(N, 512, x.H/32, x.W/32)
score = self.bn1(self.relu(self.deconv1(x5))) # size=(N, 512, x.H/16, x.W/16)
score = self.bn2(self.relu(self.deconv2(score))) # size=(N, 256, x.H/8, x.W/8)
score = self.bn3(self.relu(self.deconv3(score))) # size=(N, 128, x.H/4, x.W/4)
score = self.bn4(self.relu(self.deconv4(score))) # size=(N, 64, x.H/2, x.W/2)
score = self.bn5(self.relu(self.deconv5(score))) # size=(N, 32, x.H, x.W)
score = self.classifier(score) # size=(N, n_class, x.H/1, x.W/1)
return score # size=(N, n_class, x.H/1, x.W/1)
class FCN16s(nn.Module):
def __init__(self, pretrained_net, n_class):
super().__init__()
self.n_class = n_class
self.pretrained_net = pretrained_net
self.relu = nn.ReLU(inplace=True)
self.deconv1 = nn.ConvTranspose2d(512, 512, kernel_size=3, stride=2, padding=1, dilation=1, output_padding=1)
self.bn1 = nn.BatchNorm2d(512)
self.deconv2 = nn.ConvTranspose2d(512, 256, kernel_size=3, stride=2, padding=1, dilation=1, output_padding=1)
self.bn2 = nn.BatchNorm2d(256)
self.deconv3 = nn.ConvTranspose2d(256, 128, kernel_size=3, stride=2, padding=1, dilation=1, output_padding=1)
self.bn3 = nn.BatchNorm2d(128)
self.deconv4 = nn.ConvTranspose2d(128, 64, kernel_size=3, stride=2, padding=1, dilation=1, output_padding=1)
self.bn4 = nn.BatchNorm2d(64)
self.deconv5 = nn.ConvTranspose2d(64, 32, kernel_size=3, stride=2, padding=1, dilation=1, output_padding=1)
self.bn5 = nn.BatchNorm2d(32)
self.classifier = nn.Conv2d(32, n_class, kernel_size=1)
def forward(self, x):
output = self.pretrained_net(x)
x5 = output['x5'] # size=(N, 512, x.H/32, x.W/32)
x4 = output['x4'] # size=(N, 512, x.H/16, x.W/16)
score = self.relu(self.deconv1(x5)) # size=(N, 512, x.H/16, x.W/16)
score = self.bn1(score + x4) # element-wise add, size=(N, 512, x.H/16, x.W/16)
score = self.bn2(self.relu(self.deconv2(score))) # size=(N, 256, x.H/8, x.W/8)
score = self.bn3(self.relu(self.deconv3(score))) # size=(N, 128, x.H/4, x.W/4)
score = self.bn4(self.relu(self.deconv4(score))) # size=(N, 64, x.H/2, x.W/2)
score = self.bn5(self.relu(self.deconv5(score))) # size=(N, 32, x.H, x.W)
score = self.classifier(score) # size=(N, n_class, x.H/1, x.W/1)
return score # size=(N, n_class, x.H/1, x.W/1)
class FCN8s(nn.Module):
def __init__(self, pretrained_net, n_class):
super().__init__()
self.n_class = n_class
self.pretrained_net = pretrained_net
self.relu = nn.ReLU(inplace=True)
self.deconv1 = nn.ConvTranspose2d(512, 512, kernel_size=3, stride=2, padding=1, dilation=1, output_padding=1)
self.bn1 = nn.BatchNorm2d(512)
self.deconv2 = nn.ConvTranspose2d(512, 256, kernel_size=3, stride=2, padding=1, dilation=1, output_padding=1)
self.bn2 = nn.BatchNorm2d(256)
self.deconv3 = nn.ConvTranspose2d(256, 128, kernel_size=3, stride=2, padding=1, dilation=1, output_padding=1)
self.bn3 = nn.BatchNorm2d(128)
self.deconv4 = nn.ConvTranspose2d(128, 64, kernel_size=3, stride=2, padding=1, dilation=1, output_padding=1)
self.bn4 = nn.BatchNorm2d(64)
self.deconv5 = nn.ConvTranspose2d(64, 32, kernel_size=3, stride=2, padding=1, dilation=1, output_padding=1)
self.bn5 = nn.BatchNorm2d(32)
self.classifier = nn.Conv2d(32, n_class, kernel_size=1)
def forward(self, x):
output = self.pretrained_net(x)
x5 = output['x5'] # size=(N, 512, x.H/32, x.W/32)
x4 = output['x4'] # size=(N, 512, x.H/16, x.W/16)
x3 = output['x3'] # size=(N, 256, x.H/8, x.W/8)
score = self.relu(self.deconv1(x5)) # size=(N, 512, x.H/16, x.W/16)
score = self.bn1(score + x4) # element-wise add, size=(N, 512, x.H/16, x.W/16)
score = self.relu(self.deconv2(score)) # size=(N, 256, x.H/8, x.W/8)
score = self.bn2(score + x3) # element-wise add, size=(N, 256, x.H/8, x.W/8)
score = self.bn3(self.relu(self.deconv3(score))) # size=(N, 128, x.H/4, x.W/4)
score = self.bn4(self.relu(self.deconv4(score))) # size=(N, 64, x.H/2, x.W/2)
score = self.bn5(self.relu(self.deconv5(score))) # size=(N, 32, x.H, x.W)
score = self.classifier(score) # size=(N, n_class, x.H/1, x.W/1)
return score # size=(N, n_class, x.H/1, x.W/1)
class FCNs(nn.Module):
def __init__(self, pretrained_net, n_class):
super().__init__()
self.n_class = n_class
self.pretrained_net = pretrained_net
self.relu = nn.ReLU(inplace=True)
self.deconv1 = nn.ConvTranspose2d(512, 512, kernel_size=3, stride=2, padding=1, dilation=1, output_padding=1)
self.bn1 = nn.BatchNorm2d(512)
self.deconv2 = nn.ConvTranspose2d(512, 256, kernel_size=3, stride=2, padding=1, dilation=1, output_padding=1)
self.bn2 = nn.BatchNorm2d(256)
self.deconv3 = nn.ConvTranspose2d(256, 128, kernel_size=3, stride=2, padding=1, dilation=1, output_padding=1)
self.bn3 = nn.BatchNorm2d(128)
self.deconv4 = nn.ConvTranspose2d(128, 64, kernel_size=3, stride=2, padding=1, dilation=1, output_padding=1)
self.bn4 = nn.BatchNorm2d(64)
self.deconv5 = nn.ConvTranspose2d(64, 32, kernel_size=3, stride=2, padding=1, dilation=1, output_padding=1)
self.bn5 = nn.BatchNorm2d(32)
self.classifier = nn.Conv2d(32, n_class, kernel_size=1)
def forward(self, x):
output = self.pretrained_net(x)
x5 = output['x5'] # size=(N, 512, x.H/32, x.W/32)
x4 = output['x4'] # size=(N, 512, x.H/16, x.W/16)
x3 = output['x3'] # size=(N, 256, x.H/8, x.W/8)
x2 = output['x2'] # size=(N, 128, x.H/4, x.W/4)
x1 = output['x1'] # size=(N, 64, x.H/2, x.W/2)
score = self.bn1(self.relu(self.deconv1(x5))) # size=(N, 512, x.H/16, x.W/16)
score = score + x4 # element-wise add, size=(N, 512, x.H/16, x.W/16)
score = self.bn2(self.relu(self.deconv2(score))) # size=(N, 256, x.H/8, x.W/8)
score = score + x3 # element-wise add, size=(N, 256, x.H/8, x.W/8)
score = self.bn3(self.relu(self.deconv3(score))) # size=(N, 128, x.H/4, x.W/4)
score = score + x2 # element-wise add, size=(N, 128, x.H/4, x.W/4)
score = self.bn4(self.relu(self.deconv4(score))) # size=(N, 64, x.H/2, x.W/2)
score = score + x1 # element-wise add, size=(N, 64, x.H/2, x.W/2)
score = self.bn5(self.relu(self.deconv5(score))) # size=(N, 32, x.H, x.W)
score = self.classifier(score) # size=(N, n_class, x.H/1, x.W/1)
return score # size=(N, n_class, x.H/1, x.W/1)
class VGGNet(VGG):
def __init__(self, pretrained=True, model='vgg16', requires_grad=True, remove_fc=True, show_params=False):
super().__init__(make_layers(cfg[model]))
self.ranges = ranges[model]
if pretrained:
exec("self.load_state_dict(models.%s(pretrained=True).state_dict())" % model)
if not requires_grad:
for param in super().parameters():
param.requires_grad = False
if remove_fc: # delete redundant fully-connected layer params, can save memory
del self.classifier
if show_params:
for name, param in self.named_parameters():
print(name, param.size())
def forward(self, x):
output = {}
# get the output of each maxpooling layer (5 maxpool in VGG net)
for idx in range(len(self.ranges)):
for layer in range(self.ranges[idx][0], self.ranges[idx][1]):
x = self.features[layer](x)
output["x%d"%(idx+1)] = x
return output
ranges = {
'vgg11': ((0, 3), (3, 6), (6, 11), (11, 16), (16, 21)),
'vgg13': ((0, 5), (5, 10), (10, 15), (15, 20), (20, 25)),
'vgg16': ((0, 5), (5, 10), (10, 17), (17, 24), (24, 31)),
'vgg19': ((0, 5), (5, 10), (10, 19), (19, 28), (28, 37))
}
# cropped version from https://github.com/pytorch/vision/blob/master/torchvision/models/vgg.py
cfg = {
'vgg11': [64, 'M', 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'],
'vgg13': [64, 64, 'M', 128, 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'],
'vgg16': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 'M', 512, 512, 512, 'M', 512, 512, 512, 'M'],
'vgg19': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 256, 'M', 512, 512, 512, 512, 'M', 512, 512, 512, 512, 'M'],
}
def make_layers(cfg, batch_norm=False):
layers = []
in_channels = 3
for v in cfg:
if v == 'M':
layers += [nn.MaxPool2d(kernel_size=2, stride=2)]
else:
conv2d = nn.Conv2d(in_channels, v, kernel_size=3, padding=1)
if batch_norm:
layers += [conv2d, nn.BatchNorm2d(v), nn.ReLU(inplace=True)]
else:
layers += [conv2d, nn.ReLU(inplace=True)]
in_channels = v
return nn.Sequential(*layers)
if __name__ == "__main__":
# vis = visdom.Visdom()
vgg_model = VGGNet(requires_grad=True)
fcn_model = FCNs(pretrained_net=vgg_model, n_class=2)
fcn_model = fcn_model.cuda()
criterion = nn.BCELoss().cuda()
optimizer = optim.SGD(fcn_model.parameters(), lr=1e-2, momentum=0.7)
#input = torch.autograd.Variable(torch.randn(batch_size, 3, h, w))
#y = torch.autograd.Variable(torch.randn(batch_size, n_class, h, w), requires_grad=False)
saving_index =0
for epo in range(120):
saving_index +=1
index = 0
epo_loss = 0
start = time.time()
for item in dataloader:
index += 1
start = time.time()
input = item['A']
y = item['B']
input = torch.autograd.Variable(input)
y = torch.autograd.Variable(y)
input = input.cuda()
y = y.cuda()
optimizer.zero_grad()
output = fcn_model(input)
output = nn.functional.sigmoid(output)
loss = criterion(output, y)
loss.backward()
iter_loss = loss.item()
epo_loss += iter_loss
optimizer.step()
output_np = output.cpu().data.numpy().copy()
output_np = np.argmin(output_np, axis=1)
y_np = y.cpu().data.numpy().copy()
y_np = np.argmin(y_np, axis=1)
if np.mod(index, 20) ==1:
print('epoch {}, {}/{}, loss is {}'.format(epo, index, len(dataloader), iter_loss))
# vis.close()
# vis.images(output_np[:, None, :, :], opts=dict(title='pred'))
# vis.images(y_np[:, None, :, :], opts=dict(title='label'))
if np.mod(epo, 4) ==1 and np.mod(index, 40) ==1:
plt.subplot(1, 2, 1)
plt.imshow(np.squeeze(y_np[0, :, :]), 'gray')
plt.subplot(1, 2, 2)
plt.imshow(np.squeeze(output_np[0, :, :]), 'gray')
plt.pause(0.5)
print('epoch loss = %f'%(epo_loss/len(dataloader)))
if np.mod(saving_index, 5)==1:
torch.save(fcn_model, 'checkpoints/fcn_model_{}.pt'.format(epo))
print('saveing checkpoints/fcn_model_{}.pt'.format(epo))