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update code #24

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c84d60f
replace cuda async with non-blocking
juliecious Mar 25, 2021
c7f18aa
update code
juliecious Mar 25, 2021
40f5c20
attribute targets instead of train_labels
juliecious Mar 25, 2021
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5 changes: 1 addition & 4 deletions datasets/cifar.py
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Expand Up @@ -7,10 +7,7 @@ class CIFAR10Instance(datasets.CIFAR10):
"""CIFAR10Instance Dataset.
"""
def __getitem__(self, index):
if self.train:
img, target = self.train_data[index], self.train_labels[index]
else:
img, target = self.test_data[index], self.test_labels[index]
img, target = self.data[index], self.targets[index]

# doing this so that it is consistent with all other datasets
# to return a PIL Image
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2 changes: 1 addition & 1 deletion main.py
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Expand Up @@ -217,7 +217,7 @@ def train(train_loader, model, lemniscate, criterion, optimizer, epoch):
# measure data loading time
data_time.update(time.time() - end)

index = index.cuda(async=True)
index = index.cuda(non_blocking=True)

# compute output
feature = model(input)
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13 changes: 6 additions & 7 deletions test.py
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Expand Up @@ -25,7 +25,7 @@ def NN(epoch, net, lemniscate, trainloader, testloader, recompute_memory=0):
trainloader.dataset.transform = testloader.dataset.transform
temploader = torch.utils.data.DataLoader(trainloader.dataset, batch_size=100, shuffle=False, num_workers=1)
for batch_idx, (inputs, targets, indexes) in enumerate(temploader):
targets = targets.cuda(async=True)
targets = targets.cuda(non_blocking=True)
batchSize = inputs.size(0)
features = net(inputs)
trainFeatures[:, batch_idx*batchSize:batch_idx*batchSize+batchSize] = features.data.t()
Expand All @@ -35,7 +35,7 @@ def NN(epoch, net, lemniscate, trainloader, testloader, recompute_memory=0):
end = time.time()
with torch.no_grad():
for batch_idx, (inputs, targets, indexes) in enumerate(testloader):
targets = targets.cuda(async=True)
targets = targets.cuda(non_blocking=True)
batchSize = inputs.size(0)
features = net(inputs)
net_time.update(time.time() - end)
Expand Down Expand Up @@ -75,19 +75,19 @@ def kNN(epoch, net, lemniscate, trainloader, testloader, K, sigma, recompute_mem
if hasattr(trainloader.dataset, 'imgs'):
trainLabels = torch.LongTensor([y for (p, y) in trainloader.dataset.imgs]).cuda()
else:
trainLabels = torch.LongTensor(trainloader.dataset.train_labels).cuda()
trainLabels = torch.LongTensor(trainloader.dataset.targets).cuda()
C = trainLabels.max() + 1

if recompute_memory:
transform_bak = trainloader.dataset.transform
trainloader.dataset.transform = testloader.dataset.transform
temploader = torch.utils.data.DataLoader(trainloader.dataset, batch_size=100, shuffle=False, num_workers=1)
for batch_idx, (inputs, targets, indexes) in enumerate(temploader):
targets = targets.cuda(async=True)
targets = targets.cuda(non_blocking=True)
batchSize = inputs.size(0)
features = net(inputs)
trainFeatures[:, batch_idx*batchSize:batch_idx*batchSize+batchSize] = features.data.t()
trainLabels = torch.LongTensor(temploader.dataset.train_labels).cuda()
trainLabels = torch.LongTensor(temploader.dataset.targets).cuda()
trainloader.dataset.transform = transform_bak

top1 = 0.
Expand All @@ -97,7 +97,7 @@ def kNN(epoch, net, lemniscate, trainloader, testloader, K, sigma, recompute_mem
retrieval_one_hot = torch.zeros(K, C).cuda()
for batch_idx, (inputs, targets, indexes) in enumerate(testloader):
end = time.time()
targets = targets.cuda(async=True)
targets = targets.cuda(non_blocking=True)
batchSize = inputs.size(0)
features = net(inputs)
net_time.update(time.time() - end)
Expand Down Expand Up @@ -133,4 +133,3 @@ def kNN(epoch, net, lemniscate, trainloader, testloader, K, sigma, recompute_mem
print(top1*100./total)

return top1/total