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import os
import numpy as np
import pandas as pd
import torch
import torchvision
import torchvision.transforms.functional as F
class ShapesData(torch.utils.data.Dataset):
def __init__(self, train, transform=None, target_transform=None):
self.train = train
self.transform = transform
self.target_transform = target_transform
labels_file = './data/labels.csv'
self.labels = pd.read_csv(labels_file)
def __len__(self):
if self.train:
return 9000
else:
return 1000
def get_index(self, i):
if self.train:
assert i < 9000, 'Train dataset index out of bounds'
return i
else:
assert i < 1000, 'Test dataset index out of bounds'
return i + 9000
def __getitem__(self, i):
i = self.get_index(i)
filename = os.path.join('./data', self.labels.loc[i, 'name'])
image = torchvision.io.read_image(filename)
# Images as we read them have four channels, each with values in (0,
# 255) Take the first channel and rescale to (-1, 1)
image = (image[0] / 255 - 0.5) * 2
if self.transform:
image = self.transform(image)
labels = self.labels.loc[i, 'squares':'left']
labels = torch.tensor(labels.values.astype(np.float32))
if self.target_transform:
labels = self.target_transform(labels)
sample = [
image,
labels
]
return sample
def rotation_transform(image, rotation):
return F.rotate(image.unsqueeze(0), angle=(rotation * 90))
def rotation_target_transform(labels, rotation):
labels[2:6] = labels[2:6][[*range(rotation, 4), *range(0, rotation)]]
return labels
def make_rotation(rotation):
return torchvision.transforms.Lambda(
lambda image: rotation_transform(image, rotation)
), torchvision.transforms.Lambda(
lambda labels: rotation_target_transform(labels, rotation)
)
def vertical_flip_target_transform(labels):
labels[[2, 4]] = labels[[4, 2]]
return labels
def make_vertical_flip():
return torchvision.transforms.Lambda(
F.vflip
), torchvision.transforms.Lambda(
vertical_flip_target_transform
)
def horizontal_flip_target_transform(labels):
labels[[3, 5]] = labels[[5, 3]]
return labels
def make_horizontal_flip():
return torchvision.transforms.Lambda(
F.hflip
), torchvision.transforms.Lambda(
horizontal_flip_target_transform
)
def make_transforms(rotation, flip_vertical, flip_horizontal):
"""
Returns an image and label transform for rotating and/or flipping images.
- rotation: 0-3, indicating number of counter clockwise 90° turns
- flip_vertical, flip_horizontal: booleans
"""
transforms = []
target_transforms = []
if rotation > 0:
rotation, target_rotation = make_rotation(rotation)
transforms.append(rotation)
target_transforms.append(target_rotation)
if flip_vertical:
flip, target_flip = make_vertical_flip()
transforms.append(flip)
target_transforms.append(target_flip)
if flip_horizontal:
flip, target_flip = make_horizontal_flip()
transforms.append(flip)
target_transforms.append(target_flip)
if transforms:
# Transformations add another dimension, which screws up the net
transforms.append(torchvision.transforms.Lambda(lambda image:
image.squeeze()
))
return (torchvision.transforms.Compose(transforms),
torchvision.transforms.Compose(target_transforms))
def make_augmented_data(augmentations, after_target_transform=None):
"""
Returns concatanated ShapesData train datasets, each modified by an
augmentation.
- augmentations: list of triples (rotation, vertical flip, horizontal flip).
- after_target_transform: an additional transform to apply after the
augmentation ones.
"""
datasets = []
for rotation, vertical_flip, horizontal_flip in augmentations:
transform, target_transform = make_transforms(
rotation, vertical_flip, horizontal_flip
)
if after_target_transform:
target_transform = torchvision.transforms.Compose([
target_transform,
after_target_transform
])
dataset = ShapesData(
True, transform=transform, target_transform=target_transform
)
datasets.append(dataset)
return torch.utils.data.ConcatDataset(datasets)
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