-
Notifications
You must be signed in to change notification settings - Fork 3
/
3dcvae_pytotrch.py
233 lines (193 loc) · 8.31 KB
/
3dcvae_pytotrch.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
@author: desiree lussier
3D convolutional varational autoencoder.
takes 3D nifti images as input.
"""
import os
import numpy as np
import torch
import torchmed
import torch.utils.data
from torch import nn, optim
torch.set_default_tensor_type(torch.cuda.DoubleTensor) #comment out for cpu
from glob import glob
from torch.autograd import Variable
from torch.nn import functional as F
from torch.utils.data import DataLoader, Dataset
#set parameters here for convenience
CUDA=True #for cpu set to 'False' and for gpu set 'True'
SEED=1
BATCH_SIZE=1
LOG_INTERVAL=10
EPOCHS=1
ZDIMS=500 #bottleneck connections
CLASSES=13
OPT_LEARN_RATE=0.001
STEP_SIZE=1
GAMMA=0.9
HDIM=1152
IMG=1 #set to 1 for grayscale and 3 for rgb
torch.manual_seed(SEED)
if CUDA:
torch.cuda.manual_seed(SEED)
# load tensors directly into GPU memory
kwargs = {'num_workers': 1, 'pin_memory': True} if CUDA else {}
train_dir = "../data/train/"
test_dir = "../data/test/"
logfile = './cvae_log.txt'
#create customized dataset
class CustomDataset(Dataset):
def __init__(self,data_root):
self.samples = []
for name in glob(os.path.join(data_root,'*.nii.gz')):
self.samples.append(name)
print('data root: %s' % data_root)
def __len__(self):
return len(self.samples)
def __getitem__(self, idx):
name = self.samples[idx]
print('name is %s' % name)
image = torchmed.readers.SitkReader(name) #read nifti using torchmed
npimage = image.to_numpy() #convert to numpy array
expanded = np.expand_dims(npimage, axis=0) #add image dimension
img = torch.from_numpy(expanded) #transform image to tensor
return img
#define flatten and unflatten
class Flatten(nn.Module):
def forward(self, input):
return input.view(input.size(0), -1)
class UnFlatten(nn.Module):
def forward(self, input, size=HDIM):
return input.view(input.size(0), size, 1, 1, 1)
#build variational autoencoder model
class VAE(nn.Module):
def __init__(self, image_channels=IMG, h_dim=HDIM, z_dim=ZDIMS, n_classes=CLASSES):
super(VAE, self).__init__()
channels = (16,32,96)
print("VAE")
#encoder layers
self.conv1 = nn.Conv3d(image_channels, channels[0], kernel_size=2)
self.conv2 = nn.Conv3d(channels[0], channels[1], kernel_size=2)
self.conv3 = nn.Conv3d(channels[1], channels[2], kernel_size=2)
self.conv4 = nn.Conv3d(channels[2], channels[2], kernel_size=2)
self.maxpool = nn.MaxPool3d(kernel_size=2, return_indices=True) #pooling layers return indices
self.flatten = Flatten() #flattens dims into tensor
self.mu = nn.Linear(h_dim, z_dim) #mu layer
self.logvar = nn.Linear(h_dim, z_dim) #logvariance layer
#decoder layers
self.linear = nn.Linear(z_dim, h_dim) #pulls from bottleneck to hidden
self.unflatten = UnFlatten() #unflattens tensor to dims
self.maxunpool = nn.MaxUnpool3d(kernel_size=2) #unpooling layers require indices from pooling layers
self.conv_tran4 = nn.ConvTranspose3d(h_dim, channels[2], kernel_size=(2,3,2))
self.conv_tran3 = nn.ConvTranspose3d(channels[2], channels[2], kernel_size=(2,2,2))
self.conv_tran2 = nn.ConvTranspose3d(channels[2], channels[1], kernel_size=(3,2,3))
self.conv_tran1 = nn.ConvTranspose3d(channels[1], channels[0], kernel_size=(2,2,3))
self.conv_tran0 = nn.ConvTranspose3d(channels[0], image_channels, kernel_size=(3,3,2))
self.sigmoid = nn.Sigmoid()
def encode(self, x):
h = F.relu(self.conv1(x))
h, indices1 = self.maxpool(h)
h = F.relu(self.conv2(h))
h, indices2 = self.maxpool(h)
h = F.relu(self.conv3(h))
h, indices3 = self.maxpool(h)
h = F.relu(self.conv4(h))
h, indices4 = self.maxpool(h)
h = self.flatten(h)
mu, logvar = self.mu(h), self.logvar(h)
std = logvar.mul(0.5).exp_() #reparametization
esp = torch.randn(*mu.size())
z = mu + std * esp
return z, mu, logvar, indices1, indices2, indices3, indices4
def decode(self, z, indices1, indices2, indices3, indices4):
z = self.linear(z)
z = self.unflatten(z)
z = F.relu(self.conv_tran4(z))
z = self.maxunpool(z, indices4)
z = F.relu(self.conv_tran3(z))
z = self.maxunpool(z, indices3)
z = F.relu(self.conv_tran2(z))
z = self.maxunpool(z, indices2)
z = F.relu(self.conv_tran1(z))
z = self.maxunpool(z, indices1)
z = F.relu(self.conv_tran0(z))
z = self.sigmoid(z)
return z
def forward(self, x):
z, mu, logvar, indices1, indices2, indices3, indices4 = self.encode(x)
z = self.decode(z, indices1, indices2, indices3, indices4)
return z, mu, logvar, indices1, indices2, indices3, indices4
model = VAE()
if CUDA:
model.cuda()
#load previous state
model.load_state_dict(torch.load('3dcvae.torch', map_location='cpu'))
#loss function is reconstruction + KL divergence losses summed over all elements and batch
# Kingma and Welling. Auto-Encoding Variational Bayes. ICLR, 2014; https://arxiv.org/abs/1312.6114
def loss_function(recon_x, x, mu, logvar):
BCE = F.binary_cross_entropy(recon_x, x, reduction='sum')
KLD = -0.5 * torch.mean(1 + logvar - mu.pow(2) - logvar.exp())
return BCE + KLD
#optimizer and scheduler
optimizer = optim.Adam(model.parameters(), lr=OPT_LEARN_RATE)
scheduler = optim.lr_scheduler.StepLR(optimizer, step_size = STEP_SIZE, gamma = GAMMA)
#load dataset using custom dataset and parameters from above
trainset = CustomDataset(train_dir)
testset = CustomDataset(test_dir)
train_loader = DataLoader(dataset=trainset, batch_size=BATCH_SIZE, shuffle=True, **kwargs)
test_loader = DataLoader(dataset=testset, batch_size=BATCH_SIZE, shuffle=False, **kwargs)
#train and test model
def train(epoch):
model.train()
train_loss = 0
for batch_idx, (data,_) in enumerate(train_loader):
data = Variable(data)
if CUDA:
data = data.cuda()
scheduler.step()
optimizer.zero_grad()
recon_batch, mu, logvar, indices1, indices2, indices3, indices4 = model(data)
loss = loss_function(recon_batch, data, mu, logvar)
loss.backward()
train_loss += loss.data
optimizer.step()
if batch_idx % LOG_INTERVAL == 0:
print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
epoch, batch_idx * len(data), len(train_loader.dataset),
100. * batch_idx / len(train_loader),
loss.data / len(data)))
#print loss
print('====> Epoch: {} Average loss: {:.4f}'.format(
epoch, train_loss / len(train_loader.dataset)))
def test(epoch):
model.eval()
test_loss = 0
for i, (data,_) in enumerate(test_loader):
if CUDA:
data = data.cuda()
data = Variable(data, requires_grad=False)
recon_batch, mu, logvar, indices1, indices2, indices3, indices4 = model(data)
test_loss += loss_function(recon_batch, data, mu, logvar).data
#print footer and header of target and recontructed voxel values to log file for numerical comparison
if i == 0:
n = min(data.size(0), 8)
comparison = torch.cat([data[:n],recon_batch.view(BATCH_SIZE, IMG, 52, 64, 53)[:n]])
comparison = comparison.data.cpu()
comparison = comparison.detach().numpy()
comparison = np.squeeze(comparison)
with open(logfile, 'a') as log:
print(comparison.shape, file=log)
print(comparison.size, file=log)
print(comparison, file=log)
#print loss
test_loss /= len(test_loader.dataset)
print('====> Test set loss: {:.4f}'.format(test_loss))
#run train and test
if __name__ == "__main__":
for epoch in range(1, EPOCHS + 1):
train(epoch)
test(epoch)
#save model state
torch.save(model.state_dict(), '3dcvae.torch')