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Pretrainedencoder.py
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Pretrainedencoder.py
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import torch
from torch.autograd import Variable
import torchvision
import torch.nn as nn
import torch.nn.functional as F
from torchvision import datasets, models,transforms
import torch.optim as optim
from torch.optim import lr_scheduler
import numpy as np
import os
import matplotlib.pyplot as plt
from torch.autograd import Function
zsize = 128
batch_size = 250
iterations = 500
learningRate=0.0001
'''Encoder = models.alexnet(pretrained=True)
new_classifier = nn.Sequential(*list(Encoder.classifier.children())[:-1])
new_classifier.add_module('fc',nn.Linear(4096,zsize))
#new_classifier.add_module('softmax',nn.LogSoftmax())
Encoder.classifier = new_classifier
'''
"""
class Encoder(nn.Module):
def __init__(self):
super(Encoder, self).__init__()
self.features = nn.Sequential(
nn.Conv2d(3, 64, kernel_size=11, stride=4, padding=2),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=3, stride=2, return_indices=True),
nn.Conv2d(64, 192, kernel_size=5, padding=2),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=3, stride=2, return_indices=True),
nn.Conv2d(192, 384, kernel_size=3, padding=1),
nn.ReLU(inplace=True),
nn.Conv2d(384, 256, kernel_size=3, padding=1),
nn.ReLU(inplace=True),
nn.Conv2d(256, 256, kernel_size=3, padding=1),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=3, stride=2, return_indices=True),
)
self.classifier = nn.Sequential(
nn.Dropout(),
nn.Linear(256 * 6 * 6, 4096),
nn.ReLU(inplace=True),
nn.Dropout(),
nn.Linear(4096, 4096),
nn.ReLU(inplace=True),
nn.Linear(4096, 1000),
)
def forward(self, x):
x = self.features(x)
x = x.view(x.size(0), 256 * 6 * 6)
x = self.classifier(x)
return x
"""
#new_classifier = nn.Sequential(*list(net.classifier.children())[:-1])
#autoencoder network class Encoder(nn.Module):
class Encoder(nn.Module):
def __init__(self):
super(Encoder,self).__init__()
self.conv1 = nn.Conv2d(3, 64, 11, stride = 4, padding = 2)
self.conv2 = nn.Conv2d(64, 192, 5, padding = 2)
self.conv3 = nn.Conv2d(192, 384, 3, padding = 1)
self.conv4 = nn.Conv2d(384, 256, 3, padding = 1)
self.conv5 = nn.Conv2d(256, 256, 3, padding = 1)
self.fc1 = nn.Linear(256 * 6 * 6, 4096)
self.fc2 = nn.Linear(4096, 4096)
self.fc3 = nn.Linear(4096, zsize)
#[u'features.0.weight', u'features.3.weight', u'features.6.weight', u'features.8.weight', u'features.10.bias', u'classifier.1.bias', u'classifier.4.bias', u'classifier.6.bias']
def forward(self,x):
x = F.relu(self.conv1(x))
x,indices1 = F.max_pool2d(x,(3,3),(2,2),return_indices = True)
x = F.relu(self.conv2(x))
x,indices2 = F.max_pool2d(x,(3,3),(2,2),return_indices = True)
x = F.relu(self.conv3(x))
x = F.relu(self.conv4(x))
x = F.relu(self.conv5(x))
x,indices3 = F.max_pool2d(x,(3,3),(2,2),return_indices = True)
x = x.view(x.size(0), 256 * 6 * 6)
x = F.dropout(x)
x = F.relu(self.fc1(x))
x = F.dropout(x)
x = F.relu(self.fc2(x))
x = self.fc3(x)
return x,indices1,indices2,indices3
encoder = Encoder()
#encoder_keys = encoder.state_dict().keys()
#print "Before adding weights", encoder.conv1.weight.data[0,0,0,0]#state_dict()[encoder_keys[0]][63][0]
#print "Before adding bias", encoder.conv1.bias.data[0]
#encoder.load_state_dict(torch.load('/home/siplab/Saket/alexnet-owt-4df8aa71.pth'),strict=False)#,map_location=lambda storage, loc: storage.cuda(1)),strict=False)
#print "After adding weights", encoder.conv1.weight.data[0,0,0,0]#encoder.state_dict()[0]#[encoder_keys[0]][63][0]
loaded_weights = torch.load('/home/deepkliv/Desktop/AE/ram/AE_classifier/alexnet-owt-4df8aa71.pth')
#print loaded_weights['features.0.weight'][0,0,0,0]
#print loaded_weights['features.0.bias'][0]
encoder.conv1.weight = loaded_weights['features.0.weight']
encoder.conv1.bias = loaded_weights['features.0.bias']
encoder.conv2.weight = loaded_weights['features.3.weight']
encoder.conv2.bias = loaded_weights['features.3.bias']
encoder.conv3.weight = loaded_weights['features.6.weight']
encoder.conv3.bias = loaded_weights['features.6.bias']
encoder.conv4.weight = loaded_weights['features.8.weight']
encoder.conv4.bias = loaded_weights['features.8.bias']
encoder.conv5.weight = loaded_weights['features.10.weight']
encoder.conv5.bias = loaded_weights['features.10.bias']
encoder.fc1.weight = loaded_weights['classifier.1.weight']
encoder.fc1.bias = loaded_weights['classifier.1.bias']
encoder.fc2.weight = loaded_weights['classifier.4.weight']
encoder.fc2.bias = loaded_weights['classifier.4.bias']
#encoder = torch.nn.DataParallel(encoder, device_ids=[0, 1, 2])
#encoder.fc3.weight = loaded_weights['classifier.6.weight']
#encoder.fc3.bias = loaded_weights['classifier.6.bias']
#print "After adding weights", encoder.conv1.weight.data[0,0,0,0]#encoder.state_dict()[0]#[encoder_keys[0]][63][0]
#print "After adding bias", encoder.conv1.bias.data[0]
'''class Binary(nn.Module):
def __init__(self):
super(Binary,self).__init__()
self.encoder = Encoder()
def forward(self, x):
x,i2,i1 = self.encoder(x)
x = torch.sign(x)
#print "x grad ", x.grad
return x, i2, i1
def backward(self, grad_output):
return grad_output'''
class Binary(Function):
@staticmethod
def forward(ctx, input):
return F.relu(Variable(input.sign())).data
@staticmethod
def backward(ctx, grad_output):
return grad_output
binary = Binary()
#binary = torch.nn.DataParallel(binary, device_ids=[0, 1, 2])
class Decoder(nn.Module):
def __init__(self):
super(Decoder,self).__init__()
self.dfc3 = nn.Linear(zsize, 4096)
self.bn3 = nn.BatchNorm2d(4096)
self.dfc2 = nn.Linear(4096, 4096)
self.bn2 = nn.BatchNorm2d(4096)
self.dfc1 = nn.Linear(4096,256 * 6 * 6)
self.bn1 = nn.BatchNorm2d(256*6*6)
self.dconv5 = nn.ConvTranspose2d(256, 256, 3, padding = 1)
self.dconv4 = nn.ConvTranspose2d(256, 384, 3, padding = 1)
self.dconv3 = nn.ConvTranspose2d(384, 192, 3, padding = 1)
self.dconv2 = nn.ConvTranspose2d(192, 64, 5, padding = 2)
self.dconv1 = nn.ConvTranspose2d(64, 3, 12, stride = 4, padding = 2)
def forward(self,x,i1,i2,i3):
x = self.dfc3(x)
#x = F.relu(x)
x = F.relu(self.bn3(x))
x = self.dfc2(x)
x = F.relu(self.bn2(x))
#x = F.relu(x)
x = self.dfc1(x)
x = F.relu(self.bn1(x))
#x = F.relu(x)
#print(x.size())
x = x.view(batch_size,256,6,6)
#print x
x = self.dconv5(F.max_unpool2d(x,i3,kernel_size =(3,3),stride =(2,2)))
x = F.relu(x)
x = F.relu(self.dconv4(x))
x = F.relu(self.dconv3(x))
x = self.dconv2(F.max_unpool2d(x,i2,kernel_size = (3,3), stride = (2,2)))
x = F.relu(x)
x = self.dconv1(F.max_unpool2d(x,i1,kernel_size = 3 ,stride = 2))
#print x
x = F.sigmoid(x)
#print x
return x
decoder = Decoder()
#decoder = torch.nn.DataParallel(decoder, device_ids=[0, 1, 2])
'''
decoder.dconv1.weight = loaded_weights['features.0.weight']
decoder.dconv1.bias = loaded_weights['features.0.bias']
decoder.dconv2.weight = loaded_weights['features.3.weight']
decoder.dconv2.bias = loaded_weights['features.3.bias']
decoder.dconv3.weight = loaded_weights['features.6.weight']
decoder.dconv3.bias = loaded_weights['features.6.bias']
decoder.dconv4.weight = loaded_weights['features.8.weight']
decoder.dconv4.bias = loaded_weights['features.8.bias']
decoder.dconv5.weight = loaded_weights['features.10.weight']
decoder.dconv5.bias = loaded_weights['features.10.bias']
decoder.dfc1.weight = loaded_weights['classifier.1.weight']
decoder.dfc1.bias = loaded_weights['classifier.1.bias']
decoder.dfc2.weight = loaded_weights['classifier.4.weight']
decoder.dfc2.bias = loaded_weights['classifier.4.bias']
'''
class Autoencoder(nn.Module):
def __init__(self):
super(Autoencoder,self).__init__()
self.encoder = Encoder()
self.binary = Binary()
self.decoder = Decoder()
def forward(self,x):
#x=Encoder(x)
x,i1,i2,i3 = self.encoder(x)
x = binary.apply(x)
#print x
#x,i2,i1 = self.binary(x)
#x=Variable(x)
x = self.decoder(x,i1,i2,i3)
return x
print Autoencoder()
autoencoder = Autoencoder()
#autoencoder = torch.nn.DataParallel(autoencoder, device_ids=[0, 1, 2])
class Classifier(nn.Module):
def __init__(self):
super(Classifier,self).__init__()
self.L1 = nn.Linear(zsize,64)
self.L2 = nn.Linear(64,16)
self.L3 = nn.Linear(16,10)
def forward(self,x):
x = F.relu(self.L1(x))
x = F.relu(self.L2(x))
x = F.log_softmax(self.L3(x))
return x
print Classifier()
classifier = Classifier()
#classifier = torch.nn.DataParallel(classifier, device_ids=[0, 1, 2])
class Classification(nn.Module):
def __init__(self):
super(Classification,self).__init__()
self.encoder = Encoder()
#self.binary = Binary()
self.classifier = Classifier()
def forward(self,x):
x,_,_,_= self.encoder(x)
#x,_,_ = self.binary(x)
x = self.classifier(x)
return x
print Classification()
classification = Classification()
if torch.cuda.is_available():
autoencoder.cuda()
classification.cuda()
decoder.cuda()
encoder.cuda()
classifier.cuda()
#binary.cuda()
#data
plt.ion()
transform = transforms.Compose(
[
transforms.Scale((224,224), interpolation=2),
transforms.ToTensor(),
#transforms.Normalize(mean=[0.485, 0.456, 0.406],std=[0.229, 0.224, 0.225]),
#transforms.Normalize((0.5,0.5,0.5),(0.5,0.5,0.5))
])
trainset = torchvision.datasets.CIFAR10('CIFAR10',train = True , download = True , transform = transform)
trainloader = torch.utils.data.DataLoader(trainset, shuffle = True , batch_size = batch_size , num_workers = 2)
testset = torchvision.datasets.CIFAR10('CIFAR10', train = False, download = True, transform = transform)
testloader = torch.utils.data.DataLoader(testset, shuffle = False , batch_size = batch_size ,num_workers = 2)
'''trainset=torchvision.datasets.STL10('home/siplab/Saket/STL10', split = 'train' , download = True , transform = transform)
trainloader = torch.utils.data.DataLoader(trainset, shuffle = True , batch_size = batch_size , num_workers = 2)
testset = torchvision.datasets.STL10('home/siplab/Saket/STL10', split = 'test', download = True, transform = transform)
testloader = torch.utils.data.DataLoader(testset, shuffle = False , batch_size = batch_size ,num_workers = 2)
#classes = ('airplane', 'bird', 'car', 'cat', 'deer', 'dog', 'horse', 'monkey', 'ship', 'truck')'''
classes = ('airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck')
autoencoder_criterion = nn.MSELoss()
classification_criterion = nn.NLLLoss()
autoencoder_optimizer = optim.Adam(autoencoder.parameters(), lr = learningRate)
classification_optimizer = optim.Adam(classification.parameters(), lr = learningRate)
#encoder_optimizer = optim.Adam(Encoder.parameters(), lr = learningRate)
list_a_loss = []
list_c_loss = []
#fig = plt.figure()
for epoch in range(iterations):
run_loss = 0
run_c_loss = 0
for i,data in enumerate(trainloader):
#print i
inputs, labels = data
inputs, labels = Variable(inputs).cuda(), Variable(labels).cuda()
autoencoder_optimizer.zero_grad()
classification_optimizer.zero_grad()
#print(inputs.size())
pred = autoencoder(inputs)
#torchvision.utils.save_image(pred.data[0:8], os.path.join('/home/siplab/Saket/AE_Classifier/', 'batch_%d_%d'%((epoch+1)/1,i+1) + '.jpg'))
a_loss = autoencoder_criterion(pred , inputs)
a_loss.backward()
autoencoder_optimizer.step()
#print("efc3", autoencoder.encoder.fc3.bias.grad)
class_pred = classification(inputs)
c_loss = classification_criterion(class_pred , labels)
#_,xxpred = torch.max(class_pred.data, 1)
#print("class_pred")
#print(xxpred.cpu().numpy())
c_loss.backward()
classification_optimizer.step()
#encoder_optimizer.step()
run_loss += a_loss.data[0]
run_c_loss += c_loss.data[0]
#print i
if (i +1) % 2 == 0:
print('[%d, %5d] Autoencoder loss: %.3f Classification loss: %.3f' % (epoch + 1, i + 1 , run_loss/2 , run_c_loss/2))
#print('[%d,%5d] Classification loss: %.3f' % (epoch + 1, i + 1, run_c_loss/10))
run_c_loss = 0.0
run_loss = 0.0
decoder_path = os.path.join('Decoder/', 'decoder-%d.pkl' %(epoch+1))
encoder_path = os.path.join('Encoder/', 'encoder-%d.pkl' %(epoch+1))
autoencoder_path = os.path.join('Autoencoder/', 'autoencoder-%d.pkl' %(epoch+1))
classifier_path = os.path.join('Classifier/', 'classifier-%d.pkl' %(epoch+1))
classification_path = os.path.join('Classification/','classification-%d.pkl' %(epoch+1))
torch.save(decoder.state_dict(), decoder_path)
torch.save(encoder.state_dict(), encoder_path)
torch.save(autoencoder.state_dict(), autoencoder_path)
torch.save(classifier.state_dict(), classifier_path)
torch.save(classification.state_dict(), classification_path)
if ( epoch+1 )% 1 == 0:
list_a_loss.append(run_loss/5000)
list_c_loss.append(run_c_loss/5000)
#plt.plot(range(epoch+1),list_a_loss,'r--',label='autoencoder')
#plt.plot(range(epoch+1),list_c_loss,'b--',label='classifier')
#if epoch==0:
#plt.legend(loc='upper left')
#plt.xlabel('Epochs')
#plt.ylabel('Loss')
#fig.savefig('/home/siplab/Saket/loss_plot.png')
correct = 0
total = 0
print('\n Testing ....')
for t_i,t_data in enumerate(testloader):
if t_i * batch_size >1000:
break
t_inputs,t_labels = t_data
t_inputs = Variable(t_inputs).cuda()
t_labels = t_labels.cuda()
t_outputs = autoencoder(t_inputs)
c_pred = classification(t_inputs)
_, predicted = torch.max(c_pred.data, 1)
#print predicted.type() , t_labels.type()
total += t_labels.size(0)
correct += (predicted == t_labels).sum()
if (epoch + 1)%1 == 0:
print("saving image")
test_result_path = os.path.join('', 'batch_%d_%d'%((epoch+1)/1,t_i+1) + '.jpg')
image_tensor = torch.cat((t_inputs.data[0:8], t_outputs.data[0:8]), 0)
torchvision.utils.save_image(image_tensor, test_result_path)
print('Accuracy of the network on the 8000 test images: %d %%' % (100 * correct / total))
print('Finished Training and Testing')
"""
classification_criterion = nn.NLLLoss()
for i,data in enumerate(testloader):
inputs,labels = data
inputs,labels = Variable(inputs).cuda(), Variable(labels).cuda()
test_model = Autoencoder().cuda()
test_model.load_state_dict(torch.load('/home/siplab/Saket/AE_Classifier/Autoencoder/autoencoder-500.pkl',map_location=lambda storage, loc: storage.cuda(1)))
outputs = test_model(inputs)
test_result_path = os.path.join('/home/siplab/Saket/AE_Classifier/Test_results/', 'batch_%d'%(i+1) + '.jpg')
image_tensor = torch.cat((inputs.data[0:8], outputs.data[0:8]), 0)
torchvision.utils.save_image(image_tensor, test_result_path)
"""