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visual.py
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visual.py
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import os
import numpy as np
import torch
import torch.nn as nn
from torch.autograd import Variable
import torchvision
import torchvision.transforms as transforms
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
from model import Discriminator
os.environ["CUDA_VISIBLE_DEVICES"] = "1"
# This function is used to plot a 10 by 10 grid of images scaled between 0 and 1
def plot(samples):
fig = plt.figure(figsize=(10, 10))
gs = gridspec.GridSpec(10, 10)
gs.update(wspace=0.02, hspace=0.02)
for i, sample in enumerate(samples):
ax = plt.subplot(gs[i])
plt.axis('off')
ax.set_xticklabels([])
ax.set_yticklabels([])
ax.set_aspect('equal')
plt.imshow(sample)
return fig
transform_test = transforms.Compose([
transforms.CenterCrop(32),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
])
batch_size = 100
testset = torchvision.datasets.CIFAR10(root='./', train=False, download=False, transform=transform_test)
testloader = torch.utils.data.DataLoader(testset, batch_size=batch_size, shuffle=False, num_workers=8)
testloader = enumerate(testloader)
# discriminator trained without the generator
model = Discriminator()
checkpoint = torch.load('./checkpoint/discriminator-run-20181025021504/discriminator.model')
model.load_state_dict(checkpoint['state_dict'])
model.cuda()
model.eval()
# discriminator trained with the generator
model2 = torch.load('./checkpoint/gan-run-20181025172252/discriminator.model')
model2.cuda()
model2.eval()
############## Perturb Real Images ##############
# Grab a sample batch from the test dataset
batch_idx, (X_batch, Y_batch) = testloader.__next__()
X_batch = Variable(X_batch,requires_grad=True).cuda()
# Create an alternative label which is simply +1 to the true label
Y_batch_alternate = (Y_batch + 1)%10
Y_batch_alternate = Variable(Y_batch_alternate).cuda()
Y_batch = Variable(Y_batch).cuda()
samples = X_batch.data.cpu().numpy()
samples += 1.0
samples /= 2.0
samples = samples.transpose(0,2,3,1)
# Save the first 100 real images
fig = plot(samples[0:100])
plt.savefig('visualization/real_images.png', bbox_inches='tight')
plt.close(fig)
_, output = model(X_batch)
prediction = output.data.max(1)[1] # first column has actual prob.
accuracy = ( float( prediction.eq(Y_batch.data).sum() ) /float(batch_size))*100.0
print("Classification accuracy of discriminator without generator is: {}".format(accuracy))
# slightly jitter all input images
# Calculate the loss based on the alternative classes instead of the real classes
criterion = nn.CrossEntropyLoss(reduce=False)
loss = criterion(output, Y_batch_alternate)
gradients = torch.autograd.grad(outputs=loss, inputs=X_batch,
grad_outputs=torch.ones(loss.size()).cuda(),
create_graph=True, retain_graph=False, only_inputs=True)[0]
# save gradient jitter
gradient_image = gradients.data.cpu().numpy()
# Scale the gradient image between 0 and 1 and save it
gradient_image = (gradient_image - np.min(gradient_image))/(np.max(gradient_image)-np.min(gradient_image))
gradient_image = gradient_image.transpose(0, 2, 3, 1)
fig = plot(gradient_image[0:100])
plt.savefig('visualization/gradient_image.png', bbox_inches='tight')
plt.close(fig)
# jitter input image
gradients[gradients>0.0] = 1.0
gradients[gradients<0.0] = -1.0
gain = 8.0
X_batch_modified = X_batch - gain*0.007843137*gradients
X_batch_modified[X_batch_modified>1.0] = 1.0
X_batch_modified[X_batch_modified<-1.0] = -1.0
## evaluate new fake images
_, output = model(X_batch_modified)
prediction = output.data.max(1)[1] # first column has actual prob.
accuracy = ( float( prediction.eq(Y_batch.data).sum() ) /float(batch_size))*100.0
print(accuracy)
## save fake images
samples = X_batch_modified.data.cpu().numpy()
samples += 1.0
samples /= 2.0
samples = samples.transpose(0,2,3,1)
fig = plot(samples[0:100])
plt.savefig('visualization/jittered_images.png', bbox_inches='tight')
plt.close(fig)
############## Synthetic Images Maximizing Classification Output ##############
X = X_batch.mean(dim=0)
X = X.repeat(10,1,1,1)
Y = torch.arange(10).type(torch.int64)
Y = Variable(Y).cuda()
lr = 0.1
weight_decay = 0.001
for i in range(200):
_, output = model(X)
loss = -output[torch.arange(10).type(torch.int64),torch.arange(10).type(torch.int64)]
gradients = torch.autograd.grad(outputs=loss, inputs=X,
grad_outputs=torch.ones(loss.size()).cuda(),
create_graph=True, retain_graph=False, only_inputs=True)[0]
prediction = output.data.max(1)[1] # first column has actual prob.
accuracy = ( float( prediction.eq(Y.data).sum() ) /float(10.0))*100.0
print(i,accuracy,-loss)
X = X - lr*gradients.data - weight_decay*X.data*torch.abs(X.data)
X[X>1.0] = 1.0
X[X<-1.0] = -1.0
## save new images
samples = X.data.cpu().numpy()
samples += 1.0
samples /= 2.0
samples = samples.transpose(0,2,3,1)
fig = plot(samples)
plt.savefig('visualization/discri_max_class.png', bbox_inches='tight')
plt.close(fig)
for i in range(200):
_, output = model2(X)
loss = -output[torch.arange(10).type(torch.int64),torch.arange(10).type(torch.int64)]
gradients = torch.autograd.grad(outputs=loss, inputs=X,
grad_outputs=torch.ones(loss.size()).cuda(),
create_graph=True, retain_graph=False, only_inputs=True)[0]
prediction = output.data.max(1)[1] # first column has actual prob.
accuracy = ( float( prediction.eq(Y.data).sum() ) /float(10.0))*100.0
print(i,accuracy,-loss)
X = X - lr*gradients.data - weight_decay*X.data*torch.abs(X.data)
X[X>1.0] = 1.0
X[X<-1.0] = -1.0
## save new images
samples = X.data.cpu().numpy()
samples += 1.0
samples /= 2.0
samples = samples.transpose(0,2,3,1)
fig = plot(samples)
plt.savefig('visualization/gan_max_class.png', bbox_inches='tight')
plt.close(fig)