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data_loaders.py
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data_loaders.py
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import os
import ipdb
import numpy as np
import pandas as pd
from glob import glob
from sklearn.model_selection import train_test_split
from torch.utils.data import DataLoader, Subset
from torchvision import datasets, transforms
# Local imports
from HAM_dataset import HAM_dataset
def getSubset(data, size):
return Subset(data, np.random.randint(0, high=len(data) - 1, size=size))
def load_boston(batch_size, val_split=True, subset=[-1, -1, -1], num_train=50000):
from sklearn.datasets import load_boston
from sklearn.model_selection import train_test_split
import torch
boston = load_boston()
X, y = (boston.data, boston.target)
X_use, X_test, y_use, y_test = train_test_split(X, y, test_size=0.1, random_state=0)
X_train, X_val, y_train, y_val = train_test_split(X_use, y_use, test_size=0.1, random_state=0)
from torch.utils.data import TensorDataset, DataLoader
traindataset = TensorDataset(torch.from_numpy(X_train).clone().float(), torch.from_numpy(y_train).clone().float())
valdataset = TensorDataset(torch.from_numpy(X_val).clone().float(), torch.from_numpy(y_val).clone().float())
testdataset = TensorDataset(torch.from_numpy(X_test).clone().float(), torch.from_numpy(y_test).clone().float())
train_dataloader = DataLoader(dataset=traindataset, batch_size=batch_size, shuffle=True)
val_dataloader = DataLoader(dataset=valdataset, batch_size=batch_size, shuffle=True)
test_dataloader = DataLoader(dataset=testdataset, batch_size=batch_size, shuffle=True)
return train_dataloader, val_dataloader, test_dataloader
def load_mnist(batch_size, val_split=True, subset=[-1, -1, -1], num_train=50000, only_split_train=False):
transformations = [transforms.ToTensor()]
transformations.append(transforms.Normalize((0.1307,), (0.3081,)))
transform = transforms.Compose(transformations)
if val_split:
# num_train = 50000 # Will split training set into 50,000 training and 10,000 validation images
# Train set
original_trainset = datasets.MNIST(root='./data/mnist', train=True, download=True, transform=transform)
trainset = original_trainset
trainset.train_data = trainset.train_data[:num_train, :, :]
trainset.train_labels = trainset.train_labels[:num_train]
# Validation set
valset = datasets.MNIST(root='./data/mnist', train=True, download=True, transform=transform)
valset.train_data = valset.train_data[num_train:, :, :]
valset.train_labels = valset.train_labels[num_train:]
# Test set
testset = datasets.MNIST(root='./data/mnist', train=False, download=True, transform=transform)
testset.test_data = testset.test_data
if only_split_train:
rand_ind = np.random.randint(0, high=len(original_trainset) - 1, size=subset[0] + subset[1])
if subset[0] != -1:
trainset = Subset(original_trainset, rand_ind[:subset[0]])
if subset[2] != -1:
testset = getSubset(testset, subset[2])
if subset[1] != -1:
valset = Subset(original_trainset, rand_ind[subset[0]:subset[0] + subset[1]])
else:
if subset[0] != -1:
trainset = getSubset(trainset, subset[0])
if subset[2] != -1:
testset = getSubset(testset, subset[2])
if subset[1] != -1:
valset = getSubset(valset, subset[1])
train_dataloader = DataLoader(trainset, batch_size=batch_size, shuffle=True, pin_memory=True,
num_workers=0) # 50,000 images
val_dataloader = DataLoader(valset, batch_size=batch_size, shuffle=True, pin_memory=True,
num_workers=0) # 10,000 images
test_dataloader = DataLoader(testset, batch_size=batch_size, shuffle=True, pin_memory=False,
num_workers=0) # 10,000 images
return train_dataloader, val_dataloader, test_dataloader
else:
trainset = datasets.MNIST(root='./data/mnist', train=True, download=True, transform=transform)
testset = datasets.MNIST(root='./data/mnist', train=False, download=True, transform=transform)
if subset[0] != -1:
trainset = getSubset(trainset, subset[0])
if subset[2] != -1:
testset = getSubset(testset, subset[2])
train_dataloader = DataLoader(trainset, batch_size=batch_size, pin_memory=True, shuffle=True,
num_workers=0) # 50,000 images
test_dataloader = DataLoader(testset, batch_size=batch_size, pin_memory=True, shuffle=False,
num_workers=0) # 10,000 images
return train_dataloader, None, test_dataloader
def load_fashion_mnist(batch_size, val_split=True, subset=[-1, -1, -1]):
transformations = [transforms.ToTensor()]
transformations.append(transforms.Normalize((0.1307,), (0.3081,))) # This makes a huge difference!
transform = transforms.Compose(transformations)
if val_split:
num_train = 50000 # Will split training set into 50,000 training and 10,000 validation images
# Train set
trainset = datasets.FashionMNIST(root='./data/fashion', train=True, download=True, transform=transform)
trainset.train_data = trainset.train_data[:num_train, :, :]
trainset.train_labels = trainset.train_labels[:num_train]
# Validation set
valset = datasets.FashionMNIST(root='./data/fashion', train=True, download=True, transform=transform)
valset.train_data = valset.train_data[num_train:, :, :]
valset.train_labels = valset.train_labels[num_train:]
# Test set
testset = datasets.FashionMNIST(root='./data/fashion', train=False, download=True, transform=transform)
if subset[0] != -1:
trainset = getSubset(trainset, subset[0])
if subset[2] != -1:
testset = getSubset(testset, subset[2])
if subset[1] != -1:
valset = getSubset(valset, subset[1])
train_dataloader = DataLoader(trainset, batch_size=batch_size, shuffle=True, num_workers=0) # 50,000 images
val_dataloader = DataLoader(valset, batch_size=batch_size, shuffle=True, num_workers=0) # 10,000 images
test_dataloader = DataLoader(testset, batch_size=batch_size, shuffle=False, num_workers=0) # 10,000 images
return train_dataloader, val_dataloader, test_dataloader
else:
trainset = datasets.FashionMNIST(root='./data/fashion', train=True, download=True, transform=transform)
testset = datasets.FashionMNIST(root='./data/fashion', train=False, download=True, transform=transform)
if subset[0] != -1:
trainset = getSubset(trainset, subset[0])
if subset[2] != -1:
testset = getSubset(testset, subset[2])
train_dataloader = DataLoader(trainset, batch_size=batch_size, shuffle=True, num_workers=0) # 60,000 images
test_dataloader = DataLoader(testset, batch_size=batch_size, shuffle=False, num_workers=0) # 10,000 images
return train_dataloader, None, test_dataloader
def load_cifar10(batch_size, num_train=45000, val_split=True, augmentation=False, subset=[-1, -1, -1],
only_split_train=False):
train_transforms = []
test_transforms = []
if augmentation:
train_transforms.append(transforms.RandomCrop(32, padding=4))
train_transforms.append(transforms.RandomHorizontalFlip())
train_transforms.append(transforms.ToTensor())
test_transforms.append(transforms.ToTensor())
normalize = transforms.Normalize(mean=[x / 255.0 for x in [125.3, 123.0, 113.9]],
# How about we compute these dynamically, not hard-coded? Then we can handle CIFAR-10 and CIFAR-100 etc.
std=[x / 255.0 for x in [63.0, 62.1, 66.7]])
train_transforms.append(normalize)
test_transforms.append(normalize)
train_transform = transforms.Compose(train_transforms)
test_transform = transforms.Compose(test_transforms)
if val_split:
# num_train = 45000 # Will split training set into 45,000 training and 5,000 validation images
# Train set
original_trainset = datasets.CIFAR10(root='./data/cifar10', train=True, download=True,
transform=train_transform)
trainset = original_trainset
trainset.train_data = trainset.train_data[:num_train, :, :, :]
trainset.train_labels = trainset.train_labels[:num_train]
# Validation set
valset = datasets.CIFAR10(root='./data/cifar10', train=True, download=True, transform=test_transform)
valset.train_data = valset.train_data[num_train:, :, :, :]
valset.train_labels = valset.train_labels[num_train:]
# Test set
testset = datasets.CIFAR10(root='./data/cifar10', train=False, download=True, transform=test_transform)
if only_split_train:
rand_ind = np.random.randint(0, high=len(original_trainset) - 1, size=subset[0] + subset[1])
if subset[0] != -1:
trainset = Subset(original_trainset, rand_ind[:subset[0]])
if subset[2] != -1:
testset = getSubset(testset, subset[2])
if subset[1] != -1:
valset = Subset(original_trainset, rand_ind[subset[0]:subset[0] + subset[1]])
else:
if subset[0] != -1:
trainset = getSubset(trainset, subset[0])
if subset[2] != -1:
testset = getSubset(testset, subset[2])
if subset[1] != -1:
valset = getSubset(valset, subset[1])
train_dataloader = DataLoader(trainset, batch_size=batch_size, shuffle=True, pin_memory=True,
num_workers=0) # 45,000 images
val_dataloader = DataLoader(valset, batch_size=batch_size, shuffle=True, pin_memory=True,
num_workers=0) # 5,000 images
test_dataloader = DataLoader(testset, batch_size=batch_size, shuffle=False, pin_memory=True,
num_workers=0) # 10,000 images
return train_dataloader, val_dataloader, test_dataloader
else:
trainset = datasets.CIFAR10(root='./data/cifar10', train=True, download=True, transform=train_transform)
testset = datasets.CIFAR10(root='./data/cifar10', train=False, download=True, transform=test_transform)
if subset[0] != -1:
trainset = getSubset(trainset, subset[0])
if subset[2] != -1:
testset = getSubset(testset, subset[2])
train_dataloader = DataLoader(trainset, batch_size=batch_size, shuffle=True, pin_memory=True,
num_workers=2) # 50,000 images
test_dataloader = DataLoader(testset, batch_size=batch_size, shuffle=False, pin_memory=True,
num_workers=2) # 10,000 images
return train_dataloader, None, test_dataloader
def load_cifar100(batch_size, num_train=45000, val_split=True, augmentation=False, subset=[-1, -1, -1]):
train_transforms = []
test_transforms = []
if augmentation:
train_transforms.append(transforms.RandomCrop(32, padding=4))
train_transforms.append(transforms.RandomHorizontalFlip())
train_transforms.append(transforms.ToTensor())
test_transforms.append(transforms.ToTensor())
normalize = transforms.Normalize(mean=[x / 255.0 for x in [125.3, 123.0, 113.9]],
std=[x / 255.0 for x in [63.0, 62.1, 66.7]])
train_transforms.append(normalize)
test_transforms.append(normalize)
train_transform = transforms.Compose(train_transforms)
test_transform = transforms.Compose(test_transforms)
if val_split:
# Train set
trainset = datasets.CIFAR100(root='./data/cifar100', train=True, download=True, transform=train_transform)
trainset.train_data = trainset.train_data[:num_train, :, :, :]
trainset.train_labels = trainset.train_labels[:num_train]
# Validation set
valset = datasets.CIFAR100(root='./data/cifar100', train=True, download=True, transform=test_transform)
valset.train_data = valset.train_data[num_train:, :, :, :]
valset.train_labels = valset.train_labels[num_train:]
# Test set
testset = datasets.CIFAR100(root='./data/cifar100', train=False, download=True, transform=test_transform)
if subset[0] != -1:
trainset = getSubset(trainset, subset[0])
if subset[2] != -1:
testset = getSubset(testset, subset[2])
if subset[1] != -1:
valset = getSubset(valset, subset[1])
train_dataloader = DataLoader(trainset, batch_size=batch_size, shuffle=True, num_workers=0) # 45,000 images
val_dataloader = DataLoader(valset, batch_size=batch_size, shuffle=True, num_workers=0) # 5,000 images
test_dataloader = DataLoader(testset, batch_size=batch_size, shuffle=False, num_workers=0) # 10,000 images
return train_dataloader, val_dataloader, test_dataloader
else:
trainset = datasets.CIFAR100(root='./data/cifar100', train=True, download=True, transform=train_transform)
testset = datasets.CIFAR100(root='./data/cifar100', train=False, download=True, transform=test_transform)
if subset[0] != -1:
trainset = getSubset(trainset, subset[0])
if subset[2] != -1:
testset = getSubset(testset, subset[2])
train_dataloader = DataLoader(trainset, batch_size=batch_size, shuffle=True, num_workers=0) # 50,000 images
test_dataloader = DataLoader(testset, batch_size=batch_size, shuffle=False, num_workers=0) # 10,000 images
return train_dataloader, None, test_dataloader
def load_ham(batch_size, val_split=True, augmentation=False, subset=[-1, -1, -1]):
train_transforms = []
test_transforms = []
if augmentation:
train_transforms.append(transforms.RandomHorizontalFlip())
train_transforms.append(transforms.RandomVerticalFlip())
train_transforms.append(transforms.CenterCrop(256))
train_transforms.append(transforms.RandomCrop(224))
train_transforms.append(transforms.ToTensor())
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
train_transforms.append(normalize)
train_transform = transforms.Compose(train_transforms)
base_skin_dir = os.path.join('..', 'data/HAM')
imageid_path_dict = {os.path.splitext(os.path.basename(x))[0]: x
for x in glob(os.path.join(base_skin_dir, '*', '*.jpg'))}
lesion_type_dict = {
'nv': 'Melanocytic nevi',
'mel': 'dermatofibroma',
'bkl': 'Benign keratosis-like lesions ',
'bcc': 'Basal cell carcinoma',
'akiec': 'Actinic keratoses',
'vasc': 'Vascular lesions',
'df': 'Dermatofibroma'
}
tile_df = pd.read_csv(os.path.join(base_skin_dir, 'HAM10000_metadata.csv'))
tile_df['path'] = tile_df['image_id'].map(imageid_path_dict.get)
tile_df['cell_type'] = tile_df['dx'].map(lesion_type_dict.get)
tile_df['cell_type_idx'] = pd.Categorical(tile_df['cell_type']).codes
train_df, test_df = train_test_split(tile_df, test_size=0.1)
train_df = train_df.reset_index()
if val_split:
validation_df, test_df = train_test_split(test_df, test_size=0.5)
validation_df = validation_df.reset_index()
test_df = test_df.reset_index()
# Train set
trainset = HAM_dataset(train_df, transform=train_transform)
# Validation set
valset = HAM_dataset(validation_df, transform=train_transform)
# Test set
testset = HAM_dataset(test_df, transform=train_transform)
if subset[0] != -1:
trainset = getSubset(trainset, subset[0])
if subset[2] != -1:
testset = getSubset(testset, subset[2])
if subset[1] != -1:
valset = getSubset(valset, subset[1])
train_dataloader = DataLoader(trainset, batch_size=batch_size, shuffle=True, pin_memory=True,
num_workers=0) # 45,000 images
val_dataloader = DataLoader(valset, batch_size=batch_size, shuffle=True, pin_memory=True,
num_workers=0) # 5,000 images
test_dataloader = DataLoader(testset, batch_size=batch_size, shuffle=False, pin_memory=True,
num_workers=0) # 10,000 images
return train_dataloader, val_dataloader, test_dataloader
else:
test_df = test_df.reset_index()
# Train set
trainset = HAM_dataset(train_df, transform=train_transform)
# Validation set
testset = HAM_dataset(test_df, transform=train_transform)
if subset[0] != -1:
trainset = getSubset(trainset, subset[0])
if subset[2] != -1:
testset = getSubset(testset, subset[2])
train_dataloader = DataLoader(trainset, batch_size=batch_size, shuffle=True, pin_memory=True,
num_workers=2) # 50,000 images
test_dataloader = DataLoader(testset, batch_size=batch_size, shuffle=False, pin_memory=True,
num_workers=2) # 10,000 images
return train_dataloader, None, test_dataloader