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###########################################################################
# Computer vision - Binary neural networks demo software by HyperbeeAI. #
# Copyrights © 2023 Hyperbee.AI Inc. All rights reserved. hello@hyperbee.ai #
###########################################################################
import os
import torch
import torchvision
from torchvision import transforms
import random
import numpy as np
class ai85_normalize:
def __init__(self, act_8b_mode):
self.act_8b_mode = act_8b_mode
def __call__(self, img):
if(self.act_8b_mode):
return img.sub(0.5).mul(256.).round().clamp(min=-128, max=127)
return img.sub(0.5).mul(256.).round().clamp(min=-128, max=127).div(128.)
def load_cifar100(batch_size=128, num_workers=1, shuffle=True, act_8b_mode=False):
"""
Maxim's data augmentation: 4 pixels are padded on each side, and a 32x32 crop is randomly sampled
from the padded image or its horizontal flip.
"""
train_transform = transforms.Compose([
transforms.RandomCrop(32, padding=4),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
ai85_normalize(act_8b_mode=act_8b_mode)
])
train_dataset = torchvision.datasets.CIFAR100(root='data', train=True, download=True, transform=train_transform)
test_transform = transforms.Compose([
transforms.ToTensor(),
ai85_normalize(act_8b_mode=act_8b_mode)
])
test_dataset = torchvision.datasets.CIFAR100(root='data', train=False, download=True, transform=test_transform)
train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=batch_size, num_workers=num_workers, shuffle=shuffle)
test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=batch_size, num_workers=num_workers, shuffle=False)
return train_loader, test_loader
def load_cifar100_p(batch_size=128, num_workers=1, shuffle=True, act_8b_mode=False, partial=20.0):
"""
Maxim's data augmentation: 4 pixels are padded on each side, and a 32x32 crop is randomly sampled
from the padded image or its horizontal flip.
"""
dataset_size=50000
# Do an error check since we added the parameter, it's not relayed to torch or something
if((partial > 100.0) or (partial < 0.0)):
print('')
print('Argument partial can only be between 0 and 100')
print('Exiting.')
print('')
sys.exit()
# Train dataset transform # disabled augmentation to compare optimization performance
train_transform = transforms.Compose([
# transforms.RandomCrop(32, padding=4),
# transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
ai85_normalize(act_8b_mode=act_8b_mode)
])
# Load complete training dataset to use as a base for partial dataset
train_dataset = torchvision.datasets.CIFAR100(root='data', train=True, download=True, transform=train_transform)
# Get subset of training dataset
num_elements_to_load = np.round(50000*partial/100.0)
indices_from_dataset = createRandomSortedList(int(num_elements_to_load), 0, dataset_size)
partial_dataset = torch.utils.data.Subset(train_dataset, indices_from_dataset)
print('Loaded',partial,'% of the training dataset, corresponding to', len(indices_from_dataset) ,'image/label tuples')
batch_loader = torch.utils.data.DataLoader(partial_dataset, batch_size=batch_size, num_workers=num_workers, shuffle=shuffle)
return batch_loader
def createRandomSortedList(num, start = 1, end = 100):
arr = []
tmp = random.randint(start, end)
for x in range(num):
while tmp in arr:
tmp = random.randint(start, end)
arr.append(tmp)
arr.sort()
return arr
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