Why is RandomCrop with size 84 and padding 8 returning an image size of 84 and not 100 in pytorch? - deep-learning

Question

I was using the mini-imagenet data set and noticed this line of code:
elif data_augmentation == 'lee2019:
normalize = Normalize(
mean=[120.39586422 / 255.0, 115.59361427 / 255.0, 104.54012653 / 255.0],
std=[70.68188272 / 255.0, 68.27635443 / 255.0, 72.54505529 / 255.0],
)
train_data_transforms = Compose([
ToPILImage(),
RandomCrop(84, padding=8),
ColorJitter(brightness=0.4, contrast=0.4, saturation=0.4),
RandomHorizontalFlip(),
ToTensor(),
normalize,
])
test_data_transforms = Compose([
normalize,
])
but when I checked the image size it was 84 instead of 100 (after adding padding):
X.size()=torch.Size([50, 3, 84, 84])
what is going on with this? Shouldn't it be 100?
reproduction:
import random
from typing import Callable
import learn2learn as l2l
import numpy as np
import torch
from learn2learn.data import TaskDataset, MetaDataset, DataDescription
from learn2learn.data.transforms import TaskTransform
from torch.utils.data import Dataset
class IndexableDataSet(Dataset):
def __init__(self, datasets):
self.datasets = datasets
def __len__(self) -> int:
return len(self.datasets)
def __getitem__(self, idx: int):
return self.datasets[idx]
class SingleDatasetPerTaskTransform(Callable):
"""
Transform that samples a data set first, then creates a task (e.g. n-way, k-shot) and finally
applies the remaining task transforms.
"""
def __init__(self, indexable_dataset: IndexableDataSet, cons_remaining_task_transforms: Callable):
"""
:param: cons_remaining_task_transforms; constructor that builds the remaining task transforms. Cannot be a list
of transforms because we don't know apriori which is the data set we will use. So this function should be of
type MetaDataset -> list[TaskTransforms] i.e. given the dataset it returns the transforms for it.
"""
self.indexable_dataset = MetaDataset(indexable_dataset)
self.cons_remaining_task_transforms = cons_remaining_task_transforms
def __call__(self, task_description: list):
"""
idea:
- receives the index of the dataset to use
- then use the normal NWays l2l function
"""
# - this is what I wish could have gone in a seperate callable transform, but idk how since the transforms take apriori (not dynamically) which data set to use.
i = random.randint(0, len(self.indexable_dataset) - 1)
task_description = [DataDescription(index=i)] # using this to follow the l2l convention
# - get the sampled data set
dataset_index = task_description[0].index
dataset = self.indexable_dataset[dataset_index]
dataset = MetaDataset(dataset)
# - use the sampled data set to create task
remaining_task_transforms: list[TaskTransform] = self.cons_remaining_task_transforms(dataset)
description = None
for transform in remaining_task_transforms:
description = transform(description)
return description
def sample_dataset(dataset):
def sample_random_dataset(x):
print(f'{x=}')
i = random.randint(0, len(dataset) - 1)
return [DataDescription(index=i)]
# return dataset[i]
return sample_random_dataset
def get_task_transforms(dataset: IndexableDataSet) -> list[TaskTransform]:
"""
:param dataset:
:return:
"""
transforms = [
sample_dataset(dataset),
l2l.data.transforms.NWays(dataset, n=5),
l2l.data.transforms.KShots(dataset, k=5),
l2l.data.transforms.LoadData(dataset),
l2l.data.transforms.RemapLabels(dataset),
l2l.data.transforms.ConsecutiveLabels(dataset),
]
return transforms
def print_datasets(dataset_lst: list):
for dataset in dataset_lst:
print(f'\n{dataset=}\n')
def get_indexable_list_of_datasets_mi_and_cifarfs(root: str = '~/data/l2l_data/') -> IndexableDataSet:
from learn2learn.vision.benchmarks import mini_imagenet_tasksets
datasets, transforms = mini_imagenet_tasksets(root=root)
mi = datasets[0].dataset
from learn2learn.vision.benchmarks import cifarfs_tasksets
datasets, transforms = cifarfs_tasksets(root=root)
cifarfs = datasets[0].dataset
dataset_list = [mi, cifarfs]
dataset_list = [l2l.data.MetaDataset(dataset) for dataset in dataset_list]
dataset = IndexableDataSet(dataset_list)
return dataset
# -- tests
def loop_through_l2l_indexable_datasets_test():
"""
:return:
"""
# - for determinism
random.seed(0)
torch.manual_seed(0)
np.random.seed(0)
# - options for number of tasks/meta-batch size
batch_size: int = 10
# - create indexable data set
indexable_dataset: IndexableDataSet = get_indexable_list_of_datasets_mi_and_cifarfs()
# - get task transforms
def get_remaining_transforms(dataset: MetaDataset) -> list[TaskTransform]:
remaining_task_transforms = [
l2l.data.transforms.NWays(dataset, n=5),
l2l.data.transforms.KShots(dataset, k=5),
l2l.data.transforms.LoadData(dataset),
l2l.data.transforms.RemapLabels(dataset),
l2l.data.transforms.ConsecutiveLabels(dataset),
]
return remaining_task_transforms
task_transforms: TaskTransform = SingleDatasetPerTaskTransform(indexable_dataset, get_remaining_transforms)
# -
taskset: TaskDataset = TaskDataset(dataset=indexable_dataset, task_transforms=task_transforms)
# - loop through tasks
for task_num in range(batch_size):
print(f'{task_num=}')
X, y = taskset.sample()
print(f'{X.size()=}')
print(f'{y.size()=}')
print(f'{y=}')
print()
print('-- end of test --')
# -- Run experiment
if __name__ == "__main__":
import time
from uutils import report_times
start = time.time()
# - run experiment
loop_through_l2l_indexable_datasets_test()
# - Done
print(f"\nSuccess Done!: {report_times(start)}\a")
context: https://github.com/learnables/learn2learn/issues/333
crossposted:
https://discuss.pytorch.org/t/why-is-randomcrop-with-size-84-and-padding-8-returning-an-image-size-of-84-and-not-100-in-pytorch/151463
https://www.reddit.com/r/pytorch/comments/uno1ih/why_is_randomcrop_with_size_84_and_padding_8/

Answer 1

The padding is applied to the input image or tensor before applying the random crop. Ultimately, the output image has a spatial size equal to that of the provided size(s) given to the T.RandomCrop function since the operation is performed after.
After all, it makes more sense to pad the input image rather than the cropped image, doesn't it?