misc.txt

# # convert list of images into tensors
# infected_samples = torch.Tensor(ds_infected)
# not_infected_samples = torch.Tensor(ds_not_infected)
# # create datasets
# inf_samples_dataset = data.TensorDataset(infected_samples)
# ninf_samples_dataset = data.TensorDataset(not_infected_samples)
# # create data loader
# inf_samples_loader = data.DataLoader(inf_samples_dataset)
# ninf_samples_loader = data.DataLoader(ninf_samples_dataset)

#
# labels = np.concatenate([np.repeat(1, len(ds_covid19_yes)),np.repeat(0, len(ds_covid19_not))])
# images = ds_covid19_yes + ds_covid19_not
# #transform = transforms.Compose([ transforms.Grayscale(num_output_channels=1), transforms.Resize(INPUT_SIZE), transforms.ToTensor()])
# transform = transforms.Compose([
#     transforms.Resize(INPUT_SIZE),
#     transforms.ToTensor()
# ])
# dataset = CustomDataset(images, labels, transform=transform)
# dataloader = data.DataLoader(dataset, batch_size=5) # create the data loader
# img, label = dataset.__getitem__(2)  # For 99th item
# plt.imshow(img.numpy()[0])
# plt.show()

#%% md
class CustomDataset(Dataset):
    def __init__(self, images: [np.ndarray], labels: np.ndarray, transform=None):
        self.images = images
        self.labels = torch.Tensor(labels)
        self.transform = transform

    def __getitem__(self, index):
        x =  self.images[index]
        y =  self.labels[index]
        if self.transform:
            x = Image.fromarray(x)
            x = self.transform(x)
        return x, y

    def __len__(self):
        return len(self.images) # return the number of elements in the dataset


https://git-lfs.github.com/ #manage .pt files