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cifar_training.py
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cifar_training.py
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# -*- coding: utf-8 -*-
"""CIFAR_training.ipynb
Automatically generated by Colaboratory.
Original file is located at
https://colab.research.google.com/drive/1XcUVE2Bt2CVgmD9uHwATsBJ40MC9w0PC
"""
datasetName = "CIFAR-100"
# datasetName = "CIFAR-10"
if (datasetName == "CIFAR-100"):
tc = 100
if (datasetName == "CIFAR-10"):
tc = 10
import torch
import torchvision
import torchvision.transforms as transforms
import pickle
normalize = transforms.Normalize(mean=[0.4914, 0.4824, 0.4467],
std=[0.2471, 0.2435, 0.2616])
test_transforms = transforms.Compose([
transforms.ToTensor(),
normalize,
])
train_transforms = transforms.Compose([
transforms.RandomCrop(32, padding=4),
transforms.RandomHorizontalFlip(),
test_transforms,
])
batch_size = 64
torch.manual_seed(0)
torch.cuda.manual_seed_all(0)
#random.seed(0)
import math
import torch
from torch import nn
from torchvision.models.resnet import conv3x3
class BasicBlock(nn.Module):
expansion = 1
def __init__(self, inplanes, planes, stride=1, downsample=None):
super(BasicBlock, self).__init__()
self.conv1 = conv3x3(inplanes, planes, stride)
self.bn1 = nn.BatchNorm2d(planes)
self.relu1 = nn.ReLU(inplace=True)
self.conv2 = conv3x3(planes, planes)
self.bn2 = nn.BatchNorm2d(planes)
self.relu2 = nn.ReLU(inplace=True)
self.downsample = downsample
self.stride = stride
def forward(self, x):
residual = x
if self.downsample is not None:
x = self.downsample(x)
residual = self.conv1(residual)
residual = self.bn1(residual)
residual = self.relu1(residual)
residual = self.conv2(residual)
residual = self.bn2(residual)
x = x + residual
x = self.relu2(x)
return x
class DownsampleB(nn.Module):
def __init__(self, nIn, nOut, stride):
super(DownsampleB, self).__init__()
self.avg = nn.AvgPool2d(stride)
self.expand_ratio = nOut // nIn
def forward(self, x):
x = self.avg(x)
return torch.cat([x] + [x.mul(0)] * (self.expand_ratio - 1), 1)
class ResNet(nn.Module):
'''Small ResNet for CIFAR & SVHN '''
def __init__(self, depth=32, block=BasicBlock, initial_stride=1, num_classes=tc):
assert (depth - 2) % 6 == 0, 'depth should be one of 6N+2'
super(ResNet, self).__init__()
n = (depth - 2) // 6
self.inplanes = 16
self.conv1 = nn.Conv2d(3, 16, kernel_size=3, stride=initial_stride, padding=1, bias=False)
self.bn1 = nn.BatchNorm2d(16)
self.relu = nn.ReLU(inplace=True)
self.layer1 = self._make_layer(block, 16, n)
self.layer2 = self._make_layer(block, 32, n, stride=2)
self.layer3 = self._make_layer(block, 64, n, stride=2)
self.avgpool = nn.AvgPool2d(8)
self.fc = nn.Linear(64 * block.expansion, num_classes)
for m in self.modules():
if isinstance(m, nn.Conv2d):
n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
m.weight.data.normal_(0, math.sqrt(2. / n))
elif isinstance(m, nn.BatchNorm2d):
m.weight.data.fill_(1)
m.bias.data.zero_()
def _make_layer(self, block, planes, num_blocks, stride=1):
downsample = None
if stride != 1 or self.inplanes != planes * block.expansion:
downsample = DownsampleB(self.inplanes, planes * block.expansion, stride)
layers = [block(self.inplanes, planes, stride, downsample=downsample)]
self.inplanes = planes * block.expansion
for _ in range(1, num_blocks):
layers.append(block(self.inplanes, planes))
return nn.Sequential(*layers)
@property
def classifier(self):
return self.fc
@property
def num_classes(self):
return self.fc.weight.size(-2)
@property
def num_features(self):
return self.fc.weight.size(-1)
def extract_features(self, x):
x = self.conv1(x)
x = self.bn1(x)
x = self.relu(x)
x = self.layer1(x)
x = self.layer2(x)
x = self.layer3(x)
x = self.avgpool(x)
x = x.view(x.size(0), -1)
return x
def forward(self, x):
return self.fc(self.extract_features(x))
import torch.optim as optim
def train(model, trainloader, num_epochs=2):
if torch.cuda.is_available():
model = model.cuda()
# if torch.cuda.device_count() > 1:
# model = torch.nn.DataParallel(model).cuda()
lr=0.1
wd=1e-4
momentum=0.9
lr_drops=[0.5, 0.75]
criterion = nn.CrossEntropyLoss()
milestones = [int(lr_drop * num_epochs) for lr_drop in (lr_drops or [])]
optimizer = torch.optim.SGD(model.parameters(),
lr=lr,
weight_decay=wd,
momentum=momentum,
nesterov=True)
scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer,
milestones=milestones,
gamma=0.1)
for epoch in range(num_epochs):
model.train()
running_loss = 0.0
for i, data in enumerate(trainloader, 0):
inputs, targets = data
optimizer.zero_grad()
if torch.cuda.is_available():
inputs = inputs.cuda()
targets = targets.cuda()
# Compute output and losses
outputs = model(inputs)
loss = criterion(outputs, targets)
preds = outputs.argmax(dim=-1)
# Backward through model
error = torch.ne(targets, preds).float().mean()
loss.backward()
running_loss += loss.item()
# Update the model
optimizer.step()
print('[%d] loss: %.3f' % (epoch + 1, running_loss))
running_loss = 0.0
scheduler.step()
from torch.distributions import Categorical
from torch.nn.functional import cross_entropy
def getFilterIdx(filteredPrediction):
return [i for i in range(len(filteredPrediction)) if filteredPrediction[i] == 1]
def get_test_acc(model, testloader):
correct = 0
total = 0
loss_tot = 0.0
error_tot = 0.0
top5_error_tot = 0.0
count = 0
with torch.no_grad():
for data in testloader:
images, labels = data
if torch.cuda.is_available():
images = images.cuda()
labels = labels.cuda()
outputs = model(images)
_, predicted = torch.max(outputs.data, 1)
total += labels.size(0)
correct += (predicted == labels).sum().item()
losses = cross_entropy(outputs, labels, reduction="none")
confs, preds = outputs.topk(5, dim=-1, largest=True, sorted=True)
is_correct = preds.eq(labels.unsqueeze(-1)).float()
loss = losses.mean()
error = 1 - is_correct[:, 0].mean()
top5_error = 1 - is_correct.sum(dim=-1).mean()
loss_tot += loss.item()
error_tot += error.item()
top5_error_tot += top5_error.item()
count += 1
accuracy = correct / total
loss_tot /= count
error_tot /= count
top5_error_tot /= count
return accuracy, loss_tot, error_tot, top5_error_tot
import pickle
import random
torch.cuda.set_device(0)
szlen = []
noisePerc = "20" #percentage corruption
# print("Solving for:", noisePerc, const)
noisy_labels = np.load(str(noisePerc) + "_NoisyLabels_" + datasetName + ".npy") #load noisy labels
noisy_lvl = np.load(str(noisePerc) + "_NoiseLevels_" + datasetName +".npy") # load noise levels
filteredPrediction = np.load(str(noisePerc) + "_NoiseLevelPrediction_" + datasetName +".npy") #load filtered predictions
grn_truth = np.array(noisy_labels == trainset.targets, dtype=int)
print("Number of mislabelled: ", len(grn_truth) - sum(grn_truth), "out of", len(grn_truth))
trainset = torchvision.datasets.CIFAR100(root='./data', train=True,
download=True, transform=train_transforms)
testset = torchvision.datasets.CIFAR100(root='./data', train=False,
download=True, transform=test_transforms)
testloader = torch.utils.data.DataLoader(testset, batch_size=batch_size,
shuffle=False, num_workers=2)
filterIdx = getFilterIdx(filteredPrediction)
train_now = torch.utils.data.dataset.Subset(trainset, filterIdx)
szlen.append(len(train_now.indices))
model = ResNet()
trainloader = torch.utils.data.DataLoader(train_now, batch_size=batch_size,shuffle=True, num_workers=2)
train(model, trainloader, 300)
accuracy, loss_tot, error_tot, top5_error_tot = get_test_acc(model, testloader)
print(accuracy, loss_tot, error_tot, top5_error_tot)
print()
torch.save(model.state_dict(), noisePerc + '_' + datasetName + '_' + 'Filter')