Loading custom dataset of images using PyTorch

Question

I'm using the coil-100 dataset which has images of 100 objects, 72 images per object taken from a fixed camera by turning the object 5 degrees per image. Following is the folder structure I'm using:

data/train/obj1/obj01_0.png, obj01_5.png ... obj01_355.png
.
.
data/train/obj85/obj85_0.png, obj85_5.png ... obj85_355.png
.
.
data/test/obj86/obj86_0.ong, obj86_5.png ... obj86_355.png
.
.
data/test/obj100/obj100_0.ong, obj100_5.png ... obj100_355.png

I have used the imageloader and dataloader classes. The train and test datasets loaded properly and I can print the class names.

train_path = 'data/train/'
test_path = 'data/test/'
data_transforms = {
    transforms.Compose([
    transforms.Resize(224, 224),
    transforms.ToTensor(),
    transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
    ])
}

train_data = torchvision.datasets.ImageFolder(
    root=train_path,
    transform= data_transforms
)
test_data = torchvision.datasets.ImageFolder(
    root = test_path,
    transform = data_transforms
)
train_loader = torch.utils.data.DataLoader(
    train_data,
    batch_size=None,
    num_workers=1,
    shuffle=False
)
test_loader = torch.utils.data.DataLoader(
    test_data,
    batch_size=None,
    num_workers=1,
    shuffle=False
)

print(len(train_data))
print(len(test_data))
classes = train_data.class_to_idx
print("detected classes: ", classes)

In my model I wish to pass every image through pretrained resnet and make a dataset from the output of resnet to feed into a biderectional LSTM. For which I need to access the images by classname and index. for ex. pre_resnet_train_data['obj01'][0] should be obj01_0.png and post_resnet_train_data['obj01'][0] should be the resnet output of obj01_0.png and so on.
I'm a beginner in Pytorch and for the past 2 days, I have read many tutorials and stackoverflow questions about creating a custom dataset class but couldn't figure out how to achieve what I want. please help!

Shai · Accepted Answer · 2019-08-26 05:53:42Z

Assuming you only plan on running resent on the images once and save the output for later use, I suggest you write your own data set, derived from ImageFolder.
Save each resnet output at the same location as the image file with .pth extension.

class MyDataset(torchvision.datasets.ImageFolder):
  def __init__(self, root, transform):
    super(MyDataset, self).__init__(root, transform)

  def __getitem__(self, index):
    # override ImageFolder's method
    """
    Args:
      index (int): Index
    Returns:
      tuple: (sample, resnet, target) where target is class_index of the target class.
    """
    path, target = self.samples[index]
    sample = self.loader(path)
    if self.transform is not None:
      sample = self.transform(sample)
    if self.target_transform is not None:
      target = self.target_transform(target)
    # this is where you load your resnet data
    resnet_path = os.path.join(os.path.splitext(path)[0], '.pth')  # replace image extension with .pth
    resnet = torch.load(resnet_path)  # load the stored features
    return sample, resnet, target

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