def build_train_dataset(self):
transform = transforms.Compose([
transforms.Resize(240),
transforms.RandomCrop(224),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
])
ds = CatDogDataset(self.data_dir, train=True, transform=transform)
return ds
mean = [0.485, 0.456, 0.406]
std = [0.229, 0.224, 0.225]
transform = transforms.Compose([
transforms.Resize(image_size),
# transforms.Grayscale(),
transforms.ToTensor(),
transforms.Normalize(mean, std)
])
path = '/home/aims/Documents/Pytorch/pytorch_exercise/data'
net = Classification()
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)
train_data = CatDogDataset(path + "/" + 'train', transform=transform)
test_data = CatDogDataset(path + "/" + 'val', transform=transform)
trainloader = torch.utils.data.DataLoader(test_data,
batch_size=64,
shuffle=True,
num_workers=4)
testloader = torch.utils.data.DataLoader(test_data,
batch_size=64,
shuffle=True,
num_workers=4)
#Training
for epoch in range(10): # loop over the dataset multiple times
running_loss = 0.0
def build_test_dataset(self):
ds = CatDogDataset(self.data_dir, train=False, transform=self.test_transform)
return ds