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import torch
from torchvision.datasets import ImageFolder
from torch.utils.data import Dataset
import random
import numpy as np
from tqdm.auto import tqdm
from PIL import Image
from datasets import load_dataset
class SortDataset(Dataset):
def __init__(self, N):
self.N = N
def __len__(self):
return 10000000
def __getitem__(self, idx):
data = torch.zeros(self.N).normal_()
ordering = torch.argsort(data)
inputs = data
return (inputs), (ordering)
class QAMNISTDataset(Dataset):
"""A QAMNIST dataset that includes plus and minus operations on MNIST digits."""
def __init__(self, base_dataset, num_images, num_images_delta, num_repeats_per_input, num_operations, num_operations_delta):
self.base_dataset = base_dataset
self.num_images = num_images
self.num_images_delta = num_images_delta
self.num_images_range = self._calculate_num_images_range()
self.operators = ["+", "-"]
self.num_operations = num_operations
self.num_operations_delta = num_operations_delta
self.num_operations_range = self._calculate_num_operations_range()
self.num_repeats_per_input = num_repeats_per_input
self.current_num_digits = num_images
self.current_num_operations = num_operations
self.modulo_base = 10
self.output_range = [0, 9]
def _calculate_num_images_range(self):
min_val = self.num_images - self.num_images_delta
max_val = self.num_images + self.num_images_delta
assert min_val >= 1, f"Minimum number of images must be at least 1, got {min_val}"
return [min_val, max_val]
def _calculate_num_operations_range(self):
min_val = self.num_operations - self.num_operations_delta
max_val = self.num_operations + self.num_operations_delta
assert min_val >= 1, f"Minimum number of operations must be at least 1, got {min_val}"
return [min_val, max_val]
def set_num_digits(self, num_digits):
self.current_num_digits = num_digits
def set_num_operations(self, num_operations):
self.current_num_operations = num_operations
def _get_target_and_question(self, targets):
question = []
equations = []
num_digits = self.current_num_digits
num_operations = self.current_num_operations
# Select the initial digit
selection_idx = np.random.randint(num_digits)
first_digit = targets[selection_idx]
question.extend([selection_idx] * self.num_repeats_per_input)
# Set current_value to the initial digit (mod is applied in each operation)
current_value = first_digit % self.modulo_base
# For each operation, build an equation line
for _ in range(num_operations):
# Choose the operator ('+' or '-')
operator_idx = np.random.randint(len(self.operators))
operator = self.operators[operator_idx]
encoded_operator = -(operator_idx + 1) # -1 for '+', -2 for '-'
question.extend([encoded_operator] * self.num_repeats_per_input)
# Choose the next digit
selection_idx = np.random.randint(num_digits)
digit = targets[selection_idx]
question.extend([selection_idx] * self.num_repeats_per_input)
# Compute the new value with immediate modulo reduction
if operator == '+':
new_value = (current_value + digit) % self.modulo_base
else: # operator is '-'
new_value = (current_value - digit) % self.modulo_base
# Build the equation string for this step
equations.append(f"({current_value} {operator} {digit}) mod {self.modulo_base} = {new_value}")
# Update current value for the next operation
current_value = new_value
target = current_value
question_readable = "\n".join(equations)
return target, question, question_readable
def __len__(self):
return len(self.base_dataset)
def __getitem__(self, idx):
images, targets = [],[]
for _ in range(self.current_num_digits):
image, target = self.base_dataset[np.random.randint(self.__len__())]
images.append(image)
targets.append(target)
observations = torch.repeat_interleave(torch.stack(images, 0), repeats=self.num_repeats_per_input, dim=0)
target, question, question_readable = self._get_target_and_question(targets)
return observations, question, question_readable, target
class ImageNet(Dataset):
def __init__(self, which_split, transform):
"""
Most simple form of the custom dataset structure.
Args:
base_dataset (Dataset): The base dataset to sample from.
N (int): The number of images to construct into an observable sequence.
R (int): number of repeats
operators (list): list of operators from which to sample
action to take on observations (str): can be 'global' to compute operator over full observations, or 'select_K', where K=integer.
"""
dataset = load_dataset('imagenet-1k', split=which_split, trust_remote_code=True)
self.transform = transform
self.base_dataset = dataset
def __len__(self):
return len(self.base_dataset)
def __getitem__(self, idx):
data_item = self.base_dataset[idx]
image = self.transform(data_item['image'].convert('RGB'))
target = data_item['label']
return image, target
class MazeImageFolder(ImageFolder):
"""
A custom dataset class that extends the ImageFolder class.
Args:
root (string): Root directory path.
transform (callable, optional): A function/transform that takes in
a sample and returns a transformed version.
E.g, ``transforms.RandomCrop`` for images.
target_transform (callable, optional): A function/transform that takes
in the target and transforms it.
loader (callable, optional): A function to load an image given its path.
is_valid_file (callable, optional): A function that takes path of an Image file
and check if the file is a valid file (used to check of corrupt files)
Attributes:
classes (list): List of the class names.
class_to_idx (dict): Dict with items (class_name, class_index).
imgs (list): List of (image path, class_index) tuples
"""
def __init__(self, root, transform=None, target_transform=None,
loader=Image.open,
is_valid_file=None,
which_set='train',
augment_p=0.5,
maze_route_length=10,
trunc=False,
expand_range=True):
super(MazeImageFolder, self).__init__(root, transform, target_transform, loader, is_valid_file)
self.which_set = which_set
self.augment_p = augment_p
self.maze_route_length = maze_route_length
self.all_paths = {}
self.trunc = trunc
self.expand_range = expand_range
self._preload()
print('Solving all mazes...')
for index in range(len(self.preloaded_samples)):
path = self.get_solution(self.preloaded_samples[index])
self.all_paths[index] = path
def _preload(self):
preloaded_samples = []
with tqdm(total=self.__len__(), initial=0, leave=True, position=0, dynamic_ncols=True) as pbar:
for index in range(self.__len__()):
pbar.set_description('Loading mazes')
path, target = self.samples[index]
sample = self.loader(path)
sample = np.array(sample).astype(np.float32)/255
preloaded_samples.append(sample)
pbar.update(1)
if self.trunc and index == 999: break
self.preloaded_samples = preloaded_samples
def __len__(self):
if hasattr(self, 'preloaded_samples') and self.preloaded_samples is not None:
return len(self.preloaded_samples)
else:
return super().__len__()
def get_solution(self, x):
x = np.copy(x)
# Find start (red) and end (green) pixel coordinates
start_coords = np.argwhere((x == [1, 0, 0]).all(axis=2))
end_coords = np.argwhere((x == [0, 1, 0]).all(axis=2))
if len(start_coords) == 0 or len(end_coords) == 0:
print("Start or end point not found.")
return None
start_y, start_x = start_coords[0]
end_y, end_x = end_coords[0]
current_y, current_x = start_y, start_x
path = [4] * self.maze_route_length
pi = 0
while (current_y, current_x) != (end_y, end_x):
next_y, next_x = -1, -1 # Initialize to invalid coordinates
direction = -1 # Initialize to an invalid direction
# Check Up
if current_y > 0 and ((x[current_y - 1, current_x] == [0, 0, 1]).all() or (x[current_y - 1, current_x] == [0, 1, 0]).all()):
next_y, next_x = current_y - 1, current_x
direction = 0
# Check Down
elif current_y < x.shape[0] - 1 and ((x[current_y + 1, current_x] == [0, 0, 1]).all() or (x[current_y + 1, current_x] == [0, 1, 0]).all()):
next_y, next_x = current_y + 1, current_x
direction = 1
# Check Left
elif current_x > 0 and ((x[current_y, current_x - 1] == [0, 0, 1]).all() or (x[current_y, current_x - 1] == [0, 1, 0]).all()):
next_y, next_x = current_y, current_x - 1
direction = 2
# Check Right
elif current_x < x.shape[1] - 1 and ((x[current_y, current_x + 1] == [0, 0, 1]).all() or (x[current_y, current_x + 1] == [0, 1, 0]).all()):
next_y, next_x = current_y, current_x + 1
direction = 3
path[pi] = direction
pi += 1
x[current_y, current_x] = [255,255,255] # mark the current as white to avoid going in circles
current_y, current_x = next_y, next_x
if pi == len(path):
break
return np.array(path)
def __getitem__(self, index):
"""
Args:
index (int): Index
Returns:
tuple: (sample, target) where target is class_index of the target class.
"""
sample = np.copy(self.preloaded_samples[index])
path = np.copy(self.all_paths[index])
if self.which_set == 'train':
# Randomly rotate -90 or +90 degrees
if random.random() < self.augment_p:
which_rot = random.choice([-1, 1])
sample = np.rot90(sample, k=which_rot, axes=(0, 1))
for pi in range(len(path)):
if path[pi] == 0: path[pi] = 3 if which_rot == -1 else 2
elif path[pi] == 1: path[pi] = 2 if which_rot == -1 else 3
elif path[pi] == 2: path[pi] = 0 if which_rot == -1 else 1
elif path[pi] == 3: path[pi] = 1 if which_rot == -1 else 0
# Random horizontal flip
if random.random() < self.augment_p:
sample = np.fliplr(sample)
for pi in range(len(path)):
if path[pi] == 2: path[pi] = 3
elif path[pi] == 3: path[pi] = 2
# Random vertical flip
if random.random() < self.augment_p:
sample = np.flipud(sample)
for pi in range(len(path)):
if path[pi] == 0: path[pi] = 1
elif path[pi] == 1: path[pi] = 0
sample = torch.from_numpy(np.copy(sample)).permute(2,0,1)
blue_mask = (sample[0] == 0) & (sample[1] == 0) & (sample[2] == 1)
sample[:, blue_mask] = 1
target = path
if not self.expand_range:
return sample, target
return (sample*2)-1, (target)
class ParityDataset(Dataset):
def __init__(self, sequence_length=64, length=100000):
self.sequence_length = sequence_length
self.length = length
def __len__(self):
return self.length
def __getitem__(self, idx):
vector = 2 * torch.randint(0, 2, (self.sequence_length,)) - 1
vector = vector.float()
negatives = (vector == -1).to(torch.long)
cumsum = torch.cumsum(negatives, dim=0)
target = (cumsum % 2 != 0).to(torch.long)
return vector, target
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