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data.py
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data.py
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import os
from os.path import join, isfile, basename
from time import time
import numpy as np
import matplotlib.pyplot as plt
import torch
from torch.utils.data import DataLoader
import torchvision.transforms as T
import torchvision.datasets
import pandas as pd
class StandardTransform(object):
def __init__(self, transform=None, target_transform=None):
self.transform = transform
self.target_transform = target_transform
def __call__(self, input, target):
if self.transform is not None:
input = self.transform(input)
if self.target_transform is not None:
target = self.target_transform(target)
return input, target
def _format_transform_repr(self, transform, head):
lines = transform.__repr__().splitlines()
return (["{}{}".format(head, lines[0])] +
["{}{}".format(" " * len(head), line) for line in lines[1:]])
def __repr__(self):
body = [self.__class__.__name__]
if self.transform is not None:
body += self._format_transform_repr(self.transform,
"Transform: ")
if self.target_transform is not None:
body += self._format_transform_repr(self.target_transform,
"Target transform: ")
return '\n'.join(body)
class LabelAugmentor():
def __init__(self, mapping=list(range(10))):
self.mapping = mapping
def __call__(self, l):
return int(self.mapping[l])
class Augmentor():
def __init__(self, deterministic, noise_amplitde, uniform_dequantize, beta, gamma, tanh, ch_pad=0, ch_pad_sig=0):
self.deterministic = deterministic
self.sigma_noise = noise_amplitde
self.uniform_dequantize = uniform_dequantize
self.beta = beta
self.gamma = gamma
self.tanh = tanh
self.ch_pad = ch_pad
self.ch_pad_sig = ch_pad_sig
assert ch_pad_sig <= 1., 'Padding sigma must be between 0 and 1.'
def __call__(self, x):
if not self.deterministic:
if self.uniform_dequantize:
x += torch.rand_like(x) / 256.
if self.sigma_noise > 0.:
x += self.sigma_noise * torch.randn_like(x)
x = self.gamma * (x - self.beta)
if self.tanh:
x.clamp_(min=-(1 - 1e-7), max=(1 - 1e-7))
x = 0.5 * torch.log((1+x) / (1-x))
if self.ch_pad:
padding = torch.cat([x] * int(np.ceil(float(self.ch_pad) / x.shape[0])), dim=0)[:self.ch_pad]
padding *= np.sqrt(1. - self.ch_pad_sig**2)
padding += self.ch_pad_sig * torch.randn(self.ch_pad, x.shape[1], x.shape[2])
x = torch.cat([x, padding], dim=0)
return x
def de_augment(self, x):
if self.ch_pad:
x = x[:, :-self.ch_pad]
if self.tanh:
x = torch.tanh(x)
if isinstance(self.gamma, float):
return x / self.gamma + self.beta
else:
return x / self.gamma.to(x.device) + self.beta.to(x.device)
class HandwritingDataset(torch.utils.data.Dataset):
def __init__(self, csv_path, transform = None, target_transform = None):
self.df = pd.read_csv(csv_path, header = None)
self.transform = transform
self.target_transform = target_transform
self.x = np.asarray(self.df.iloc[:len(self.df),1:]).reshape([len(self.df),28,28,1]) # taking all columns expect column 0
self.x = self.x.astype('uint8')
self.y = np.asarray(self.df.iloc[:len(self.df),0]).reshape([len(self.df)]) # taking column 0
def __len__(self):
return len(self.df)
def __getitem__(self, index):
target = self.y[index]
image = self.x[index]
if self.transform is not None:
image = self.transform(image)
if self.target_transform is not None:
target = self.target_transform(target)
return image, target
class Dataset():
def __init__(self, args):
self.dataset = args['data']['dataset']
self.batch_size = eval(args['data']['batch_size'])
tanh = eval(args['data']['tanh_augmentation'])
self.sigma = eval(args['data']['noise_amplitde'])
unif = eval(args['data']['dequantize_uniform'])
label_smoothing = eval(args['data']['label_smoothing'])
channel_pad = eval(args['data']['pad_noise_channels'])
channel_pad_sigma = eval(args['data']['pad_noise_std'])
self.handwriting_type = 'None'
if args['data'].get('handwriting_type'):
self.handwriting_type = args['data']['handwriting_type']
if self.dataset == 'MNIST':
beta = 0.5
gamma = 2.
else:
beta = torch.Tensor((0.4914, 0.4822, 0.4465)).view(-1, 1, 1)
gamma = 1. / torch.Tensor((0.247, 0.243, 0.261)).view(-1, 1, 1)
self.train_augmentor = Augmentor(False, self.sigma, unif, beta, gamma, tanh, channel_pad, channel_pad_sigma)
self.test_augmentor = Augmentor(True, 0., unif, beta, gamma, tanh, channel_pad, channel_pad_sigma)
self.transform = T.Compose([T.ToTensor(), self.test_augmentor])
if self.handwriting_type == 'OPERATOR':
print("Dataset used is operators")
self.dims = (28, 28)
if channel_pad:
raise ValueError('needs to be fixed, channel padding does not work with mnist')
self.channels = 1
self.n_classes = 12
self.label_mapping = list(range(self.n_classes))
self.label_augment = LabelAugmentor(self.label_mapping)
train_csv_path = '/home/kaushikdas/aashish/pytorch_datasets/OPERATOR/handwriting_operators_train_temp.csv'
test_csv_path = '/home/kaushikdas/aashish/pytorch_datasets/OPERATOR/handwriting_operators_test_temp.csv'
self.test_data = HandwritingDataset(test_csv_path,
transform=T.Compose([T.ToTensor(), self.test_augmentor]),
target_transform = self.label_augment)
self.train_data = HandwritingDataset(train_csv_path,
transform=T.Compose([T.ToTensor(), self.train_augmentor]),
target_transform = self.label_augment)
elif self.handwriting_type == 'LETTER':
print("Dataset used is letters")
self.dims = (28, 28)
if channel_pad:
raise ValueError('needs to be fixed, channel padding does not work with mnist')
self.channels = 1
self.n_classes = 26
self.label_mapping = list(range(self.n_classes))
self.label_augment = LabelAugmentor(self.label_mapping)
train_csv_path = '/home/kaushikdas/aashish/pytorch_datasets/LETTER/handwriting_letters_train.csv'
test_csv_path = '/home/kaushikdas/aashish/pytorch_datasets/LETTER/handwriting_letters_test.csv'
self.test_data = HandwritingDataset(test_csv_path,
transform=T.Compose([T.ToTensor(), self.test_augmentor]),
target_transform = self.label_augment)
self.train_data = HandwritingDataset(train_csv_path,
transform=T.Compose([T.ToTensor(), self.train_augmentor]),
target_transform = self.label_augment)
elif self.handwriting_type == 'EMNIST_LETTER':
print("Dataset used is emnist letters")
self.dims = (28, 28)
if channel_pad:
raise ValueError('needs to be fixed, channel padding does not work with mnist')
self.channels = 1
self.n_classes = 26
self.label_mapping = list(range(self.n_classes))
self.label_augment = LabelAugmentor(self.label_mapping)
train_csv_path = '/home/kaushikdas/aashish/pytorch_datasets/EMNIST_LETTER/emnist_train.csv'
test_csv_path = '/home/kaushikdas/aashish/pytorch_datasets/EMNIST_LETTER/emnist_test.csv'
self.test_data = HandwritingDataset(test_csv_path,
transform=T.Compose([T.ToTensor(), self.test_augmentor]),
target_transform = self.label_augment)
self.train_data = HandwritingDataset(train_csv_path,
transform=T.Compose([T.ToTensor(), self.train_augmentor]),
target_transform = self.label_augment)
elif self.handwriting_type == 'EMNIST_UPPERCASE_LETTER':
print("Dataset used is emnist uppercase letters")
self.dims = (28, 28)
if channel_pad:
raise ValueError('needs to be fixed, channel padding does not work with mnist')
self.channels = 1
self.n_classes = 26
self.label_mapping = list(range(self.n_classes))
self.label_augment = LabelAugmentor(self.label_mapping)
train_csv_path = '/home/kaushikdas/aashish/pytorch_datasets/EMNIST_UPPERCASE_LETTER/emnist_uppercase_train_4th_May_2021.csv'
test_csv_path = '/home/kaushikdas/aashish/pytorch_datasets/EMNIST_UPPERCASE_LETTER/emnist_uppercase_test_3rd_May_2021.csv'
self.test_data = HandwritingDataset(test_csv_path,
transform=T.Compose([T.ToTensor(), self.test_augmentor]),
target_transform = self.label_augment)
self.train_data = HandwritingDataset(train_csv_path,
transform=T.Compose([T.ToTensor(), self.train_augmentor]),
target_transform = self.label_augment)
elif self.handwriting_type == 'EMNIST_LOWERCASE_LETTER':
print("Dataset used is emnist lowercase letters")
self.dims = (28, 28)
if channel_pad:
raise ValueError('needs to be fixed, channel padding does not work with mnist')
self.channels = 1
self.n_classes = 26
self.label_mapping = list(range(self.n_classes))
self.label_augment = LabelAugmentor(self.label_mapping)
train_csv_path = '/home/kaushikdas/aashish/pytorch_datasets/EMNIST_LOWERCASE_LETTER/emnist_lowercase_train_13th_May.csv'
test_csv_path = '/home/kaushikdas/aashish/pytorch_datasets/EMNIST_LOWERCASE_LETTER/emnist_lowercase_test_13th_May.csv'
self.test_data = HandwritingDataset(test_csv_path,
transform=T.Compose([T.ToTensor(), self.test_augmentor]),
target_transform = self.label_augment)
self.train_data = HandwritingDataset(train_csv_path,
transform=T.Compose([T.ToTensor(), self.train_augmentor]),
target_transform = self.label_augment)
elif self.dataset == 'MNIST':
self.dims = (28, 28)
if channel_pad:
raise ValueError('needs to be fixed, channel padding does not work with mnist')
self.channels = 1
self.n_classes = 10
self.label_mapping = list(range(self.n_classes))
self.label_augment = LabelAugmentor(self.label_mapping)
data_dir = '/home/kaushikdas/aashish/pytorch_datasets'
self.test_data = torchvision.datasets.MNIST(data_dir, train=False, download=True,
transform=T.Compose([T.ToTensor(), self.test_augmentor]),
target_transform=self.label_augment)
self.train_data = torchvision.datasets.MNIST(data_dir, train=True, download=True,
transform=T.Compose([T.ToTensor(), self.train_augmentor]),
target_transform=self.label_augment)
elif self.dataset in ['CIFAR10', 'CIFAR100']:
self.dims = (3 + channel_pad, 32, 32)
self.channels = 3 + channel_pad
if self.dataset == 'CIFAR10':
data_dir = 'cifar_data'
self.n_classes = 10
dataset_class = torchvision.datasets.CIFAR10
else:
data_dir = 'cifar100_data'
self.n_classes = 100
dataset_class = torchvision.datasets.CIFAR100
self.label_mapping = list(range(self.n_classes))
self.label_augment = LabelAugmentor(self.label_mapping)
self.test_data = dataset_class(data_dir, train=False, download=True,
transform=T.Compose([T.ToTensor(), self.test_augmentor]),
target_transform=self.label_augment)
self.train_data = dataset_class(data_dir, train=True, download=True,
transform=T.Compose([T.RandomHorizontalFlip(),
T.ColorJitter(0.1, 0.1, 0.05),
T.Pad(8, padding_mode='edge'),
T.RandomRotation(12),
T.CenterCrop(36),
T.RandomCrop(32),
T.ToTensor(),
self.train_augmentor]),
target_transform=self.label_augment)
else:
raise ValueError(f"what is this dataset, {args['data']['dataset']}?")
self.train_data, self.val_data = torch.utils.data.random_split(self.train_data, (len(self.train_data) - 1024, 1024))
self.val_x = torch.stack([x[0] for x in self.val_data], dim=0).cuda()
self.val_y = self.onehot(torch.LongTensor([x[1] for x in self.val_data]).cuda(), label_smoothing)
self.train_loader = DataLoader(self.train_data, batch_size=self.batch_size, shuffle=True,
num_workers=6, pin_memory=True, drop_last=True)
self.test_loader = DataLoader(self.test_data, batch_size=self.batch_size, shuffle=False,
num_workers=4, pin_memory=True, drop_last=True)
def show_data_hist(self):
x = self.val_x.cpu().numpy()
plt.hist(x.flatten(), bins=200)
plt.show()
def de_augment(self, x):
return self.test_augmentor.de_augment(x)
def augment(self, x):
return self.test_augmentor(x)
def onehot(self, l, label_smooth=0):
y = torch.cuda.FloatTensor(l.shape[0], self.n_classes).zero_()
y.scatter_(1, l.view(-1, 1), 1.)
if label_smooth:
y = y * (1 - label_smooth) + label_smooth / self.n_classes
return y