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cifar.py
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cifar.py
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"""
Classification on CIFAR10
=========================
Based on pytorch example for MNIST
"""
import torch.optim
from torchvision import datasets, transforms
import torch.nn.functional as F
from kymatio import Scattering2D
import kymatio.datasets as scattering_datasets
import torch
import argparse
import torch.nn as nn
class Scattering2dCNN(nn.Module):
'''
Simple CNN with 3x3 convs based on VGG
'''
def __init__(self, in_channels, classifier_type='cnn'):
super(Scattering2dCNN, self).__init__()
self.in_channels = in_channels
self.classifier_type = classifier_type
self.build()
def build(self):
cfg = [256, 256, 256, 'M', 512, 512, 512, 1024, 1024]
layers = []
self.K = self.in_channels
self.bn = nn.BatchNorm2d(self.K)
if self.classifier_type == 'cnn':
for v in cfg:
if v == 'M':
layers += [nn.MaxPool2d(kernel_size=2, stride=2)]
else:
conv2d = nn.Conv2d(self.in_channels, v, kernel_size=3, padding=1)
layers += [conv2d, nn.BatchNorm2d(v), nn.ReLU(inplace=True)]
self.in_channels = v
layers += [nn.AdaptiveAvgPool2d(2)]
self.features = nn.Sequential(*layers)
self.classifier = nn.Linear(1024*4, 10)
elif self.classifier_type == 'mlp':
self.classifier = nn.Sequential(
nn.Linear(self.K*8*8, 1024), nn.ReLU(),
nn.Linear(1024, 1024), nn.ReLU(),
nn.Linear(1024, 10))
self.features = None
elif self.classifier_type == 'linear':
self.classifier = nn.Linear(self.K*8*8,10)
self.features = None
def forward(self, x):
x = self.bn(x.view(-1, self.K, 8, 8))
if self.features:
x = self.features(x)
x = x.view(x.size(0), -1)
x = self.classifier(x)
return x
def train(model, device, train_loader, optimizer, epoch, scattering):
model.train()
for batch_idx, (data, target) in enumerate(train_loader):
data, target = data.to(device), target.to(device)
optimizer.zero_grad()
output = model(scattering(data))
loss = F.cross_entropy(output, target)
loss.backward()
optimizer.step()
if batch_idx % 50 == 0:
print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
epoch, batch_idx * len(data), len(train_loader.dataset),
100. * batch_idx / len(train_loader), loss.item()))
def test(model, device, test_loader, scattering):
model.eval()
test_loss = 0
correct = 0
with torch.no_grad():
for data, target in test_loader:
data, target = data.to(device), target.to(device)
output = model(scattering(data))
test_loss += F.cross_entropy(output, target, size_average=False).item() # sum up batch loss
pred = output.max(1, keepdim=True)[1] # get the index of the max log-probability
correct += pred.eq(target.view_as(pred)).sum().item()
test_loss /= len(test_loader.dataset)
print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.2f}%)\n'.format(
test_loss, correct, len(test_loader.dataset),
100. * correct / len(test_loader.dataset)))
def main():
"""Train a simple Hybrid Scattering + CNN model on CIFAR.
Three models are demoed:
'linear' - scattering + linear model
'mlp' - scattering + MLP
'cnn' - scattering + CNN
scattering 1st order can also be set by the mode
Scattering features are normalized by batch normalization.
The model achieves around 88% testing accuracy after 10 epochs.
scatter 1st order + linear achieves 64% in 90 epochs
scatter 2nd order + linear achieves 70.5% in 90 epochs
scatter + cnn achieves 88% in 15 epochs
"""
parser = argparse.ArgumentParser(description='MNIST scattering + hybrid examples')
parser.add_argument('--mode', type=int, default=1,help='scattering 1st or 2nd order')
parser.add_argument('--classifier', type=str, default='cnn',help='classifier model')
args = parser.parse_args()
assert(args.classifier in ['linear','mlp','cnn'])
use_cuda = torch.cuda.is_available()
device = torch.device("cuda" if use_cuda else "cpu")
if args.mode == 1:
scattering = Scattering2D(J=2, shape=(32, 32), max_order=1)
K = 17*3
else:
scattering = Scattering2D(J=2, shape=(32, 32))
K = 81*3
if use_cuda:
scattering = scattering.cuda()
model = Scattering2dCNN(K,args.classifier).to(device)
# DataLoaders
if use_cuda:
num_workers = 4
pin_memory = True
else:
num_workers = None
pin_memory = False
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
train_loader = torch.utils.data.DataLoader(
datasets.CIFAR10(root=scattering_datasets.get_dataset_dir('CIFAR'), train=True, transform=transforms.Compose([
transforms.RandomHorizontalFlip(),
transforms.RandomCrop(32, 4),
transforms.ToTensor(),
normalize,
]), download=True),
batch_size=128, shuffle=True, num_workers=num_workers, pin_memory=pin_memory)
test_loader = torch.utils.data.DataLoader(
datasets.CIFAR10(root=scattering_datasets.get_dataset_dir('CIFAR'), train=False, transform=transforms.Compose([
transforms.ToTensor(),
normalize,
])),
batch_size=128, shuffle=False, num_workers=num_workers, pin_memory=pin_memory)
# Optimizer
lr = 0.1
for epoch in range(0, 90):
if epoch%20==0:
optimizer = torch.optim.SGD(model.parameters(), lr=lr, momentum=0.9,
weight_decay=0.0005)
lr*=0.2
train(model, device, train_loader, optimizer, epoch+1, scattering)
test(model, device, test_loader, scattering)
if __name__ == '__main__':
main()