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Trained CIFAR-10 CNN, 60% accuracy, switching to DLA

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# -*- coding: utf-8 -*-
"""
Training a Classifier
=====================
This is it. You have seen how to define neural networks, compute loss and make
updates to the weights of the network.
Now you might be thinking,
What about data?
----------------
Generally, when you have to deal with image, text, audio or video data,
you can use standard python packages that load data into a numpy array.
Then you can convert this array into a ``torch.*Tensor``.
- For images, packages such as Pillow, OpenCV are useful
- For audio, packages such as scipy and librosa
- For text, either raw Python or Cython based loading, or NLTK and
SpaCy are useful
Specifically for vision, we have created a package called
``torchvision``, that has data loaders for common datasets such as
ImageNet, CIFAR10, MNIST, etc. and data transformers for images, viz.,
``torchvision.datasets`` and ``torch.utils.data.DataLoader``.
This provides a huge convenience and avoids writing boilerplate code.
For this tutorial, we will use the CIFAR10 dataset.
It has the classes: ‘airplane’, ‘automobile’, ‘bird’, ‘cat’, ‘deer’,
‘dog’, ‘frog’, ‘horse’, ‘ship’, ‘truck’. The images in CIFAR-10 are of
size 3x32x32, i.e. 3-channel color images of 32x32 pixels in size.
.. figure:: /_static/img/cifar10.png
:alt: cifar10
cifar10
Training an image classifier
----------------------------
We will do the following steps in order:
1. Load and normalize the CIFAR10 training and test datasets using
``torchvision``
2. Define a Convolutional Neural Network
3. Define a loss function
4. Train the network on the training data
5. Test the network on the test data
1. Load and normalize CIFAR10
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Using ``torchvision``, it’s extremely easy to load CIFAR10.
"""
import torch import torch
import torchvision import torchvision
import torchvision.transforms as transforms import torchvision.transforms as transforms
########################################################################
# The output of torchvision datasets are PILImage images of range [0, 1].
# We transform them to Tensors of normalized range [-1, 1].
########################################################################
# .. note::
# If running on Windows and you get a BrokenPipeError, try setting
# the num_worker of torch.utils.data.DataLoader() to 0.
transform = transforms.Compose(
[transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
batch_size = 4
trainset = torchvision.datasets.CIFAR10(root='./data', train=True,
download=True, transform=transform)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=batch_size,
shuffle=True, num_workers=2)
testset = torchvision.datasets.CIFAR10(root='./data', train=False,
download=True, transform=transform)
testloader = torch.utils.data.DataLoader(testset, batch_size=batch_size,
shuffle=False, num_workers=2)
classes = ('plane', 'car', 'bird', 'cat',
'deer', 'dog', 'frog', 'horse', 'ship', 'truck')
########################################################################
# Let us show some of the training images, for fun.
import matplotlib.pyplot as plt import matplotlib.pyplot as plt
import numpy as np import numpy as np
# functions to show an image import torch.optim as optim
def imshow(img):
img = img / 2 + 0.5 # unnormalize
npimg = img.numpy()
plt.imshow(np.transpose(npimg, (1, 2, 0)))
plt.show()
# get some random training images
dataiter = iter(trainloader)
images, labels = next(dataiter)
# show images
imshow(torchvision.utils.make_grid(images))
# print labels
print(' '.join(f'{classes[labels[j]]:5s}' for j in range(batch_size)))
########################################################################
# 2. Define a Convolutional Neural Network
# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
# Copy the neural network from the Neural Networks section before and modify it to
# take 3-channel images (instead of 1-channel images as it was defined).
import torch.nn as nn import torch.nn as nn
import torch.nn.functional as F import torch.nn.functional as F
import dla
class Net(nn.Module):
def __init__(self):
super().__init__()
self.conv1 = nn.Conv2d(3, 6, 5)
self.pool = nn.MaxPool2d(2, 2)
self.conv2 = nn.Conv2d(6, 16, 5)
self.fc1 = nn.Linear(16 * 5 * 5, 120)
self.fc2 = nn.Linear(120, 84)
self.fc3 = nn.Linear(84, 10)
def forward(self, x):
x = self.pool(F.relu(self.conv1(x)))
x = self.pool(F.relu(self.conv2(x)))
x = torch.flatten(x, 1) # flatten all dimensions except batch
x = F.relu(self.fc1(x))
x = F.relu(self.fc2(x))
x = self.fc3(x)
return x
net = Net() def train(model, trainloader, optimizer, epoch):
running_loss = 0.0
for i, [data, target] in enumerate(trainloader, 0):
######################################################################## # zero the parameter gradients
# 3. Define a Loss function and optimizer optimizer.zero_grad()
# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
# Let's use a Classification Cross-Entropy loss and SGD with momentum.
import torch.optim as optim # forward + backward + optimize
outputs = model(data)
criterion = nn.CrossEntropyLoss()
loss = criterion(outputs, target)
loss.backward()
optimizer.step()
criterion = nn.CrossEntropyLoss() # print statistics
optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9) running_loss += loss.item()
if i % 2000 == 1999: # print every 2000 mini-batches
######################################################################## print(f'[{epoch + 1}, {i + 1:5d}] loss: {running_loss / 2000:.3f}')
# 4. Train the network running_loss = 0.0
# ^^^^^^^^^^^^^^^^^^^^
#
# This is when things start to get interesting.
# We simply have to loop over our data iterator, and feed the inputs to the
# network and optimize.
for epoch in range(2): # loop over the dataset multiple times
running_loss = 0.0
for i, data in enumerate(trainloader, 0):
# get the inputs; data is a list of [inputs, labels]
inputs, labels = data
# zero the parameter gradients
optimizer.zero_grad()
# forward + backward + optimize
outputs = net(inputs)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
# print statistics
running_loss += loss.item()
if i % 2000 == 1999: # print every 2000 mini-batches
print(f'[{epoch + 1}, {i + 1:5d}] loss: {running_loss / 2000:.3f}')
running_loss = 0.0
print('Finished Training')
########################################################################
# Let's quickly save our trained model:
PATH = './cifar_net.pth'
torch.save(net.state_dict(), PATH)
########################################################################
# See `here <https://pytorch.org/docs/stable/notes/serialization.html>`_
# for more details on saving PyTorch models.
#
# 5. Test the network on the test data
# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
#
# We have trained the network for 2 passes over the training dataset.
# But we need to check if the network has learnt anything at all.
#
# We will check this by predicting the class label that the neural network
# outputs, and checking it against the ground-truth. If the prediction is
# correct, we add the sample to the list of correct predictions.
#
# Okay, first step. Let us display an image from the test set to get familiar.
dataiter = iter(testloader)
images, labels = next(dataiter)
# print images
imshow(torchvision.utils.make_grid(images))
print('GroundTruth: ', ' '.join(f'{classes[labels[j]]:5s}' for j in range(4)))
########################################################################
# Next, let's load back in our saved model (note: saving and re-loading the model
# wasn't necessary here, we only did it to illustrate how to do so):
net = Net()
net.load_state_dict(torch.load(PATH))
########################################################################
# Okay, now let us see what the neural network thinks these examples above are:
outputs = net(images)
########################################################################
# The outputs are energies for the 10 classes.
# The higher the energy for a class, the more the network
# thinks that the image is of the particular class.
# So, let's get the index of the highest energy:
_, predicted = torch.max(outputs, 1)
print('Predicted: ', ' '.join(f'{classes[predicted[j]]:5s}'
for j in range(4)))
########################################################################
# The results seem pretty good.
#
# Let us look at how the network performs on the whole dataset.
correct = 0
total = 0
# since we're not training, we don't need to calculate the gradients for our outputs
with torch.no_grad():
for data in testloader:
images, labels = data
# calculate outputs by running images through the network
outputs = net(images)
# the class with the highest energy is what we choose as prediction
_, predicted = torch.max(outputs.data, 1)
total += labels.size(0)
correct += (predicted == labels).sum().item()
print(f'Accuracy of the network on the 10000 test images: {100 * correct // total} %')
########################################################################
# That looks way better than chance, which is 10% accuracy (randomly picking
# a class out of 10 classes).
# Seems like the network learnt something.
#
# Hmmm, what are the classes that performed well, and the classes that did
# not perform well:
# prepare to count predictions for each class
correct_pred = {classname: 0 for classname in classes}
total_pred = {classname: 0 for classname in classes}
# again no gradients needed
with torch.no_grad():
for data in testloader:
images, labels = data
outputs = net(images)
_, predictions = torch.max(outputs, 1)
# collect the correct predictions for each class
for label, prediction in zip(labels, predictions):
if label == prediction:
correct_pred[classes[label]] += 1
total_pred[classes[label]] += 1
# print accuracy for each class def test(model, testloader, classes):
for classname, correct_count in correct_pred.items(): correct = 0
accuracy = 100 * float(correct_count) / total_pred[classname] total = 0
print(f'Accuracy for class: {classname:5s} is {accuracy:.1f} %')
######################################################################## # since we're not training, we don't need to calculate the gradients for our outputs
# Okay, so what next? with torch.no_grad():
# for data, target in testloader:
# How do we run these neural networks on the GPU? # calculate outputs by running images through the network
# outputs = model(data)
# Training on GPU # the class with the highest energy is what we choose as prediction
# ---------------- _, predicted = torch.max(outputs.data, 1)
# Just like how you transfer a Tensor onto the GPU, you transfer the neural total += target.size(0)
# net onto the GPU. correct += (predicted == target).sum().item()
#
# Let's first define our device as the first visible cuda device if we have
# CUDA available:
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu') print(f'Accuracy of the network on the 10000 test images: {100 * correct // total} %')
# Assuming that we are on a CUDA machine, this should print a CUDA device:
print(device) # prepare to count predictions for each class
correct_pred = {classname: 0 for classname in classes}
total_pred = {classname: 0 for classname in classes}
######################################################################## # again no gradients needed
# The rest of this section assumes that ``device`` is a CUDA device. with torch.no_grad():
# for data, target in testloader:
# Then these methods will recursively go over all modules and convert their outputs = model(data)
# parameters and buffers to CUDA tensors: _, predictions = torch.max(outputs, 1)
# # collect the correct predictions for each class
# .. code:: python for label, prediction in zip(target, predictions):
# if label == prediction:
net.to(device) correct_pred[classes[label]] += 1
# total_pred[classes[label]] += 1
#
# Remember that you will have to send the inputs and targets at every step
# to the GPU too:
#
# .. code:: python
#
inputs, labels = data[0].to(device), data[1].to(device)
#
# Why don't I notice MASSIVE speedup compared to CPU? Because your network
# is really small.
#
# **Exercise:** Try increasing the width of your network (argument 2 of
# the first ``nn.Conv2d``, and argument 1 of the second ``nn.Conv2d`` –
# they need to be the same number), see what kind of speedup you get.
#
# **Goals achieved**:
#
# - Understanding PyTorch's Tensor library and neural networks at a high level.
# - Train a small neural network to classify images
#
# Training on multiple GPUs
# -------------------------
# If you want to see even more MASSIVE speedup using all of your GPUs,
# please check out :doc:`data_parallel_tutorial`.
#
# Where do I go next?
# -------------------
#
# - :doc:`Train neural nets to play video games </intermediate/reinforcement_q_learning>`
# - `Train a state-of-the-art ResNet network on imagenet`_
# - `Train a face generator using Generative Adversarial Networks`_
# - `Train a word-level language model using Recurrent LSTM networks`_
# - `More examples`_
# - `More tutorials`_
# - `Discuss PyTorch on the Forums`_
# - `Chat with other users on Slack`_
#
# .. _Train a state-of-the-art ResNet network on imagenet: https://github.com/pytorch/examples/tree/master/imagenet
# .. _Train a face generator using Generative Adversarial Networks: https://github.com/pytorch/examples/tree/master/dcgan
# .. _Train a word-level language model using Recurrent LSTM networks: https://github.com/pytorch/examples/tree/master/word_language_model
# .. _More examples: https://github.com/pytorch/examples
# .. _More tutorials: https://github.com/pytorch/tutorials
# .. _Discuss PyTorch on the Forums: https://discuss.pytorch.org/
# .. _Chat with other users on Slack: https://pytorch.slack.com/messages/beginner/
# %%%%%%INVISIBLE_CODE_BLOCK%%%%%%
del dataiter # print accuracy for each class
# %%%%%%INVISIBLE_CODE_BLOCK%%%%%% for classname, correct_count in correct_pred.items():
accuracy = 100 * float(correct_count) / total_pred[classname]
print(f'Accuracy for class: {classname:5s} is {accuracy:.1f} %')
def main():
transform = transforms.Compose(
[transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
batch_size = 4
trainset = torchvision.datasets.CIFAR10(root='./data', train=True,
download=True, transform=transform)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=batch_size,
shuffle=True, num_workers=2)
testset = torchvision.datasets.CIFAR10(root='./data', train=False,
download=True, transform=transform)
testloader = torch.utils.data.DataLoader(testset, batch_size=batch_size,
shuffle=False, num_workers=2)
classes = ('plane', 'car', 'bird', 'cat',
'deer', 'dog', 'frog', 'horse', 'ship', 'truck')
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
model = dla.DLA().to(device)
optimizer = optim.SGD(model.parameters(), lr=0.001, momentum=0.9)
for epoch in range(14):
train(model, trainloader, optimizer, epoch)
test(model, testloader, classes)
PATH = './cifar_net.pth'
torch.save(model.state_dict(), PATH)
if __name__ == "__main__":
main()

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@ -20,33 +20,33 @@ def pttensor_to_images(data):
return images return images
def gaussian_kuwahara(data, batch_size=64, radius=5): def gaussian_kuwahara(data, dimensions, radius=5):
images = pttensor_to_images(data) images = pttensor_to_images(data)
filtered_images = np.ndarray((batch_size,28,28,1)) filtered_images = np.ndarray(dimensions)
for i in range(batch_size): for i in range(dimensions[0]):
filtered_images[i] = kuwahara(images[i], method='gaussian', radius=radius, image_2d=images[i]) filtered_images[i] = kuwahara(images[i], method='gaussian', radius=radius, image_2d=images[i])
filtered_images = filtered_images.transpose(0,3,1,2) filtered_images = filtered_images.transpose(0,3,1,2)
return torch.tensor(filtered_images).float() return torch.tensor(filtered_images).float()
def mean_kuwahara(data, batch_size=64, radius=5): def mean_kuwahara(data, dimensions, radius=5):
images = pttensor_to_images(data) images = pttensor_to_images(data)
filtered_images = np.ndarray((batch_size,28,28,1)) filtered_images = np.ndarray(dimensions)
for i in range(batch_size): for i in range(dimensions[0]):
filtered_images[i] = kuwahara(images[i], method='mean', radius=radius, image_2d=images[i]) filtered_images[i] = kuwahara(images[i], method='mean', radius=radius, image_2d=images[i])
filtered_images = filtered_images.transpose(0,3,1,2) filtered_images = filtered_images.transpose(0,3,1,2)
return torch.tensor(filtered_images).float() return torch.tensor(filtered_images).float()
def random_noise(data, batch_size=64, intensity=0.001): def random_noise(data, dimensions, intensity=0.001):
images = pttensor_to_images(data) images = pttensor_to_images(data)
filtered_images = np.ndarray((batch_size,28,28,1)) filtered_images = np.ndarray(dimensions)
for i in range(batch_size): for i in range(dimensions[0]):
mean = 0 mean = 0
stddev = 180 stddev = 180
noise = np.zeros(images[i].shape, images[i].dtype) noise = np.zeros(images[i].shape, images[i].dtype)
@ -57,33 +57,33 @@ def random_noise(data, batch_size=64, intensity=0.001):
return torch.tensor(filtered_images).float() return torch.tensor(filtered_images).float()
def gaussian_blur(data, batch_size=64, ksize=(5,5)): def gaussian_blur(data, dimensions, ksize=(5,5)):
images = pttensor_to_images(data) images = pttensor_to_images(data)
filtered_images = np.ndarray((batch_size,28,28,1)) filtered_images = np.ndarray(dimensions)
for i in range(batch_size): for i in range(dimensions[0]):
filtered_images[i] = cv2.GaussianBlur(images[i], ksize=ksize, sigmaX=0).reshape(filtered_images[i].shape) filtered_images[i] = cv2.GaussianBlur(images[i], ksize=ksize, sigmaX=0).reshape(filtered_images[i].shape)
filtered_images = filtered_images.transpose(0,3,1,2) filtered_images = filtered_images.transpose(0,3,1,2)
return torch.tensor(filtered_images).float() return torch.tensor(filtered_images).float()
def bilateral_filter(data, batch_size=64, d=5, sigma=50): def bilateral_filter(data, dimensions, d=5, sigma=50):
images = pttensor_to_images(data) images = pttensor_to_images(data)
filtered_images = np.ndarray((batch_size,28,28,1)) filtered_images = np.ndarray(dimensions)
for i in range(batch_size): for i in range(dimensions[0]):
filtered_images[i] = cv2.bilateralFilter(images[i], d, sigma, sigma).reshape(filtered_images[i].shape) filtered_images[i] = cv2.bilateralFilter(images[i], d, sigma, sigma).reshape(filtered_images[i].shape)
filtered_images = filtered_images.transpose(0,3,1,2) filtered_images = filtered_images.transpose(0,3,1,2)
return torch.tensor(filtered_images).float() return torch.tensor(filtered_images).float()
def threshold_filter(data, batch_size=64, threshold=0.5): def threshold_filter(data, dimensions, threshold=0.5):
images = pttensor_to_images(data) images = pttensor_to_images(data)
filtered_images = np.ndarray((batch_size,28,28,1)) filtered_images = np.ndarray(dimensions)
for i in range(batch_size): for i in range(dimensions[0]):
# If the channel contains any negative values, define the lowest negative value as black # If the channel contains any negative values, define the lowest negative value as black
min_value = np.min(images[i]) min_value = np.min(images[i])
if min_value < 0: if min_value < 0:
@ -104,11 +104,11 @@ def threshold_filter(data, batch_size=64, threshold=0.5):
return torch.tensor(filtered_images).float() return torch.tensor(filtered_images).float()
def bit_depth(data, batch_size=64, bits=16): def bit_depth(data, dimensions, bits=16):
images = pttensor_to_images(data) images = pttensor_to_images(data)
filtered_images = np.ndarray((batch_size,28,28,1)) filtered_images = np.ndarray(dimensions)
for i in range(batch_size): for i in range(dimensions[0]):
filtered_images[i] = (images[i]*(2**bits)).astype(int).astype(float)/(2**bits) filtered_images[i] = (images[i]*(2**bits)).astype(int).astype(float)/(2**bits)
filtered_images = filtered_images.transpose(0,3,1,2) filtered_images = filtered_images.transpose(0,3,1,2)
@ -125,23 +125,23 @@ Filter Options:
- bilateral_filter - bilateral_filter
- bit_depth - bit_depth
''' '''
def filtered(data, batch_size=64, strength=0, filter="gaussian_blur"): def filtered(data, dimensions, strength=0, filter="gaussian_blur"):
if filter == "threshold_filter": if filter == "threshold_filter":
threshold = (2*strength + 1) / 10 threshold = (2*strength + 1) / 10
return threshold_filter(data, batch_size, threshold) return threshold_filter(data, dimensions, threshold)
elif filter == "bit_depth": elif filter == "bit_depth":
bits = 2**strength bits = 2**strength
return bit_depth(data, batch_size, bits) return bit_depth(data, dimensions, bits)
elif filter == "random_noise": elif filter == "random_noise":
intensity = 0.0005*(2*strength + 1) intensity = 0.0005*(2*strength + 1)
return random_noise(data, batch_size, intensity) return random_noise(data, dimensions, intensity)
else: else:
strength = (2*strength + 1) strength = (2*strength + 1)
if filter == "gaussian_blur": if filter == "gaussian_blur":
return gaussian_blur(data, batch_size, ksize=(strength, strength)) return gaussian_blur(data, dimensions, ksize=(strength, strength))
elif filter == "bilateral_filter": elif filter == "bilateral_filter":
return bilateral_filter(data, batch_size, d=strength) return bilateral_filter(data, dimensions, d=strength)
elif filter == "gaussian_kuwahara": elif filter == "gaussian_kuwahara":
return gaussian_kuwahara(data, batch_size, strength) return gaussian_kuwahara(data, dimensions, strength)
elif filter == "mean_kuwahara": elif filter == "mean_kuwahara":
return mean_kuwahara(data, batch_size, strength) return mean_kuwahara(data, dimensions, strength)

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@ -0,0 +1,135 @@
'''DLA in PyTorch.
Reference:
Deep Layer Aggregation. https://arxiv.org/abs/1707.06484
'''
import torch
import torch.nn as nn
import torch.nn.functional as F
class BasicBlock(nn.Module):
expansion = 1
def __init__(self, in_planes, planes, stride=1):
super(BasicBlock, self).__init__()
self.conv1 = nn.Conv2d(
in_planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
self.bn1 = nn.BatchNorm2d(planes)
self.conv2 = nn.Conv2d(planes, planes, kernel_size=3,
stride=1, padding=1, bias=False)
self.bn2 = nn.BatchNorm2d(planes)
self.shortcut = nn.Sequential()
if stride != 1 or in_planes != self.expansion*planes:
self.shortcut = nn.Sequential(
nn.Conv2d(in_planes, self.expansion*planes,
kernel_size=1, stride=stride, bias=False),
nn.BatchNorm2d(self.expansion*planes)
)
def forward(self, x):
out = F.relu(self.bn1(self.conv1(x)))
out = self.bn2(self.conv2(out))
out += self.shortcut(x)
out = F.relu(out)
return out
class Root(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size=1):
super(Root, self).__init__()
self.conv = nn.Conv2d(
in_channels, out_channels, kernel_size,
stride=1, padding=(kernel_size - 1) // 2, bias=False)
self.bn = nn.BatchNorm2d(out_channels)
def forward(self, xs):
x = torch.cat(xs, 1)
out = F.relu(self.bn(self.conv(x)))
return out
class Tree(nn.Module):
def __init__(self, block, in_channels, out_channels, level=1, stride=1):
super(Tree, self).__init__()
self.level = level
if level == 1:
self.root = Root(2*out_channels, out_channels)
self.left_node = block(in_channels, out_channels, stride=stride)
self.right_node = block(out_channels, out_channels, stride=1)
else:
self.root = Root((level+2)*out_channels, out_channels)
for i in reversed(range(1, level)):
subtree = Tree(block, in_channels, out_channels,
level=i, stride=stride)
self.__setattr__('level_%d' % i, subtree)
self.prev_root = block(in_channels, out_channels, stride=stride)
self.left_node = block(out_channels, out_channels, stride=1)
self.right_node = block(out_channels, out_channels, stride=1)
def forward(self, x):
xs = [self.prev_root(x)] if self.level > 1 else []
for i in reversed(range(1, self.level)):
level_i = self.__getattr__('level_%d' % i)
x = level_i(x)
xs.append(x)
x = self.left_node(x)
xs.append(x)
x = self.right_node(x)
xs.append(x)
out = self.root(xs)
return out
class DLA(nn.Module):
def __init__(self, block=BasicBlock, num_classes=10):
super(DLA, self).__init__()
self.base = nn.Sequential(
nn.Conv2d(3, 16, kernel_size=3, stride=1, padding=1, bias=False),
nn.BatchNorm2d(16),
nn.ReLU(True)
)
self.layer1 = nn.Sequential(
nn.Conv2d(16, 16, kernel_size=3, stride=1, padding=1, bias=False),
nn.BatchNorm2d(16),
nn.ReLU(True)
)
self.layer2 = nn.Sequential(
nn.Conv2d(16, 32, kernel_size=3, stride=1, padding=1, bias=False),
nn.BatchNorm2d(32),
nn.ReLU(True)
)
self.layer3 = Tree(block, 32, 64, level=1, stride=1)
self.layer4 = Tree(block, 64, 128, level=2, stride=2)
self.layer5 = Tree(block, 128, 256, level=2, stride=2)
self.layer6 = Tree(block, 256, 512, level=1, stride=2)
self.linear = nn.Linear(512, num_classes)
def forward(self, x):
out = self.base(x)
out = self.layer1(out)
out = self.layer2(out)
out = self.layer3(out)
out = self.layer4(out)
out = self.layer5(out)
out = self.layer6(out)
out = F.avg_pool2d(out, 4)
out = out.view(out.size(0), -1)
out = self.linear(out)
return out
def test():
net = DLA()
print(net)
x = torch.randn(1, 3, 32, 32)
y = net(x)
print(y.size())
if __name__ == '__main__':
test()

79
Filter_Analysis/results/cifar10_fgsm.json Normal file
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@ -0,0 +1,79 @@
{
"attack": "FGSM",
"dataset": "CIFAR-10",
"epsilons": [
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],
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]
]
}
}

645
Filter_Analysis/results/mnist_fgsm.json
View File

@ -1,6 +1,6 @@
{ {
"attack": "FGSM",
"dataset": "MNIST", "dataset": "MNIST",
"attack": "FGSM",
"epsilons": [ "epsilons": [
0.0, 0.0,
0.025, 0.025,
@ -19,12 +19,653 @@
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672
Filter_Analysis/results/mnist_fgsm_reformatted.json
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@ -1,672 +0,0 @@
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0.3992,
0.2414,
0.2168,
0.2149
],
[
0.2624,
0.2091,
0.1874,
0.161,
0.1599
]
],
"threshold_filter": [
[
0.982,
0.9817,
0.9799,
0.9713,
0.9502
],
[
0.978,
0.9755,
0.9751,
0.9655,
0.9334
],
[
0.9728,
0.9713,
0.9696,
0.9578,
0.9077
],
[
0.9678,
0.9668,
0.9645,
0.9508,
0.1837
],
[
0.9644,
0.9604,
0.9583,
0.9407,
0.1818
],
[
0.9586,
0.9537,
0.9477,
0.9238,
0.1817
],
[
0.9522,
0.9458,
0.9343,
0.9032,
0.1845
],
[
0.9418,
0.9387,
0.9236,
0.8766,
0.1849
],
[
0.9331,
0.9297,
0.9108,
0.8358,
0.1869
],
[
0.9215,
0.9188,
0.8927,
0.2164,
0.1904
],
[
0.9079,
0.9053,
0.8758,
0.223,
0.1927
],
[
0.8943,
0.8882,
0.8508,
0.2275,
0.1979
],
[
0.8761,
0.8687,
0.8142,
0.2348,
0.2025
]
]
}
}

34
Filter_Analysis/test_defenses.py
View File

@ -6,7 +6,8 @@ from torchvision import datasets, transforms
import numpy as np import numpy as np
from scipy import stats from scipy import stats
import matplotlib.pyplot as plt import matplotlib.pyplot as plt
from mnist import Net import mnist
import cifar10
import json import json
import sys import sys
@ -16,30 +17,39 @@ import defense_filters
ATTACK = "FGSM" ATTACK = "FGSM"
DATASET = "MNIST" DATASET = "CIFAR-10"
RES_X = 32
RES_Y = 32
CHANNELS = 3
MAX_EPSILON = 0.3 MAX_EPSILON = 0.3
EPSILON_STEP = 0.025 EPSILON_STEP = 0.025
TESTED_STRENGTH_COUNT = 5 TESTED_STRENGTH_COUNT = 5
epsilons = np.arange(0.0, MAX_EPSILON+EPSILON_STEP, EPSILON_STEP) epsilons = np.arange(0.0, MAX_EPSILON+EPSILON_STEP, EPSILON_STEP)
pretrained_model = "mnist_cnn_unfiltered.pt" pretrained_model = "cifar_net.pth"
use_cuda=False use_cuda=False
torch.manual_seed(69) torch.manual_seed(69)
test_loader = torch.utils.data.DataLoader( #test_loader = torch.utils.data.DataLoader(
datasets.MNIST('data/', train=False, download=True, transform=transforms.Compose([ # datasets.MNIST('data/', train=False, download=True, transform=transforms.Compose([
transforms.ToTensor(), # transforms.ToTensor(),
transforms.Normalize((0.1307,), (0.3081,)), # transforms.Normalize((0.1307,), (0.3081,)),
])), # ])),
batch_size=1, shuffle=True) # batch_size=1, shuffle=True)
transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
batch_size = 1
testset = datasets.CIFAR10(root='./data', train=False, download=True, transform=transform)
test_loader = torch.utils.data.DataLoader(testset, batch_size=batch_size, shuffle=True, num_workers=2)
print("CUDA Available: ", torch.cuda.is_available()) print("CUDA Available: ", torch.cuda.is_available())
device = torch.device("cuda" if use_cuda and torch.cuda.is_available() else "cpu") device = torch.device("cuda" if use_cuda and torch.cuda.is_available() else "cpu")
model = Net().to(device) model = cifar10.Net().to(device)
model.load_state_dict(torch.load(pretrained_model, map_location=device)) model.load_state_dict(torch.load(pretrained_model, map_location=device))
@ -126,7 +136,7 @@ def test(model, device, test_loader, epsilon, filter):
# Evaluate performance for # Evaluate performance for
for i in range(TESTED_STRENGTH_COUNT): for i in range(TESTED_STRENGTH_COUNT):
# Apply the filter with the specified strength # Apply the filter with the specified strength
filtered_input = defense_filters.filtered(perturbed_data_normalized, batch_size=len(perturbed_data_normalized), strength=i, filter=filter) filtered_input = defense_filters.filtered(perturbed_data_normalized, dimensions=(len(perturbed_data_normalized), RES_X, RES_Y, CHANNELS), strength=i, filter=filter)
# Evaluate the model on the filtered images # Evaluate the model on the filtered images
filtered_output = model(filtered_input) filtered_output = model(filtered_input)
# Get the predicted classification # Get the predicted classification
@ -186,7 +196,7 @@ for filter in filters:
results["filters"][filter].append(accuracies) results["filters"][filter].append(accuracies)
results_json = json.dumps(results, indent=4) results_json = json.dumps(results, indent=4)
with open("results/mnist_fgsm.json", "w") as outfile: with open("results/cifar10_fgsm.json", "w") as outfile:
outfile.write(results_json) outfile.write(results_json)
# Plot the results # Plot the results