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kmnist.py
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kmnist.py
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from PIL import Image
import os
import os.path
import errno
import copy
import codecs
import numpy as np
import torch
import torch.utils.data as data
import torch.nn.functional as F
from copy import deepcopy
from torchvision.datasets.vision import VisionDataset
import torchvision.datasets as dsets
from sklearn.preprocessing import OneHotEncoder
from augment.cutout import Cutout
from torchvision.transforms import Compose, ToTensor, Normalize, Pad, RandomCrop, RandomHorizontalFlip, RandomErasing, ToPILImage
class MY_KMNIST(VisionDataset):
"""`Kuzushiji-MNIST <https://github.com/rois-codh/kmnist>`_ Dataset.
Args:
root (string): Root directory of dataset where ``processed/training.pt``
and ``processed/test.pt`` exist.
train (bool, optional): If True, creates dataset from ``training.pt``,
otherwise from ``test.pt``.
download (bool, optional): If true, downloads the dataset from the internet and
puts it in root directory. If dataset is already downloaded, it is not
downloaded again.
transform (callable, optional): A function/transform that takes in an PIL image
and returns a transformed version. E.g, ``transforms.RandomCrop``
target_transform (callable, optional): A function/transform that takes in the
target and transforms it.
"""
urls = [
'http://codh.rois.ac.jp/kmnist/dataset/kmnist/train-images-idx3-ubyte.gz',
'http://codh.rois.ac.jp/kmnist/dataset/kmnist/train-labels-idx1-ubyte.gz',
'http://codh.rois.ac.jp/kmnist/dataset/kmnist/t10k-images-idx3-ubyte.gz',
'http://codh.rois.ac.jp/kmnist/dataset/kmnist/t10k-labels-idx1-ubyte.gz'
]
raw_folder = 'raw'
processed_folder = 'processed'
training_file = 'training.pt'
test_file = 'test.pt'
def __init__(self, root, train=True, transform=None, target_transform=None,
download=False, rate_partial=0.3):
super(MY_KMNIST, self).__init__(root, transform=transform,
target_transform=target_transform)
self.train = train # training set or test set
if download:
self.download()
if not self._check_exists():
raise RuntimeError('Dataset not found.' +
' You can use download=True to download it')
if self.train:
downloaded_list = self.training_file
else:
downloaded_list = self.test_file
self.data, self.targets = torch.load(
os.path.join(self.root, self.processed_folder, downloaded_list))
self.transform = Compose([
RandomHorizontalFlip(),
RandomCrop(28, 4, padding_mode='reflect'),
ToTensor(),
Normalize(mean=[0.5], std=[0.5]),
])
self.transform1 = Compose([
RandomHorizontalFlip(),
RandomCrop(28, 4, padding_mode='reflect'),
ToTensor(),
Cutout(n_holes=1, length=16),
ToPILImage(),
ToTensor(),
Normalize(mean=[0.5], std=[0.5]),
])
self.rate_partial = rate_partial
self.partial_labels = self.generate_partial_labels()
def __getitem__(self, index):
"""
Args:
index (int): Index
Returns:
tuple: (image, target) where target is index of the target class.
"""
img, target, partial_label = self.data[index], self.targets[index], self.partial_labels[index]
img = Image.fromarray(img.numpy(), mode='L')
if self.transform is not None:
img_ori = self.transform(img)
img1 = self.transform1(img)
img2 = self.transform1(img)
if self.target_transform is not None:
target = self.target_transform(target)
return img_ori, img1, img2, target, partial_label, index
def __len__(self):
return len(self.data)
def generate_partial_labels(self):
if(self.rate_partial!=-1):
def binarize_class(y):
label = y.reshape(len(y), -1)
enc = OneHotEncoder(categories='auto')
enc.fit(label)
label = enc.transform(label).toarray().astype(np.float32)
label = torch.from_numpy(label)
return label
new_y = binarize_class(self.targets)
n = len(self.targets)
c = max(self.targets) + 1
avgC = 0
partial_rate = self.rate_partial
print(partial_rate)
for i in range(n):
row = new_y[i, :]
row[np.where(np.random.binomial(1, partial_rate, c) == 1)] = 1
while torch.sum(row) == 1:
row[np.random.randint(0, c)] = 1
avgC += torch.sum(row)
new_y[i] = row
avgC = avgC / n
print("Finish Generating Candidate Label Sets:{}!\n".format(avgC))
new_y = new_y.cpu().numpy()
return new_y
else:
def binarize_class(y):
label = y.reshape(len(y), -1)
enc = OneHotEncoder(categories='auto')
enc.fit(label)
label = enc.transform(label).toarray().astype(np.float32)
label = torch.from_numpy(label)
return label
def create_model(ds, feature, c):
from partial_models.resnet import resnet
from partial_models.mlp import mlp_phi
if ds in ['kmnist', 'fmnist']:
net = mlp_phi(feature, c)
elif ds in ['cifar10']:
net = resnet(depth=32, n_outputs=c)
else:
pass
return net
with torch.no_grad():
c=max(self.targets)+1
data=self.data
y=binarize_class(torch.tensor(self.targets,dtype=torch.long))
f = np.prod(list(data.shape)[1:])
batch_size = 2000
rate = 0.4
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
weight_path = ('./weights/' + 'kmnist' + '/400.pt')
model = create_model('kmnist', f, c).to(device)
model.load_state_dict(torch.load(weight_path, map_location=device))
train_X, train_Y = data.to(device), y.to(device)
train_X = train_X.view(train_X.shape[0], -1).float()
train_p_Y_list = []
step = train_X.size(0) // batch_size
for i in range(0, step):
_, outputs = model(train_X[i * batch_size:(i + 1) * batch_size])
train_p_Y = train_Y[i * batch_size:(i + 1) * batch_size].clone().detach()
partial_rate_array = F.softmax(outputs, dim=1).clone().detach()
partial_rate_array[torch.where(train_Y[i * batch_size:(i + 1) * batch_size] == 1)] = 0
partial_rate_array = partial_rate_array / torch.max(partial_rate_array, dim=1, keepdim=True)[0]
partial_rate_array = partial_rate_array / partial_rate_array.mean(dim=1, keepdim=True) * rate
partial_rate_array[partial_rate_array > 1.0] = 1.0
m = torch.distributions.binomial.Binomial(total_count=1, probs=partial_rate_array)
z = m.sample()
train_p_Y[torch.where(z == 1)] = 1.0
train_p_Y_list.append(train_p_Y)
train_p_Y = torch.cat(train_p_Y_list, dim=0)
assert train_p_Y.shape[0] == train_X.shape[0]
final_y = train_p_Y.cpu().clone()
pn = final_y.sum() / torch.ones_like(final_y).sum()
print("Partial type: instance dependent, Average Label: " + str(pn * 10))
return final_y.cpu().numpy()
def _check_exists(self):
return os.path.exists(os.path.join(self.root, self.processed_folder, self.training_file)) and \
os.path.exists(os.path.join(self.root, self.processed_folder, self.test_file))
def download(self):
"""Download the Kuzushiji-MNIST data if it doesn't exist in processed_folder already."""
import urllib.request
import requests
import gzip
if self._check_exists():
return
try:
os.makedirs(os.path.join(self.root, self.raw_folder))
os.makedirs(os.path.join(self.root, self.processed_folder))
except OSError as e:
if e.errno == errno.EEXIST:
pass
else:
raise
for url in self.urls:
print('Downloading ' + url)
data = urllib.request.urlopen(url)
filename = url.rpartition('/')[2]
file_path = os.path.join(self.root, self.raw_folder, filename)
with open(file_path, 'wb') as f:
f.write(data.read())
with open(file_path.replace('.gz', ''), 'wb') as out_f, gzip.GzipFile(file_path) as zip_f:
out_f.write(zip_f.read())
os.unlink(file_path)
print('Processing...')
training_set = (
read_image_file(os.path.join(self.root, self.raw_folder, 'train-images-idx3-ubyte')),
read_label_file(os.path.join(self.root, self.raw_folder, 'train-labels-idx1-ubyte'))
)
test_set = (
read_image_file(os.path.join(self.root, self.raw_folder, 't10k-images-idx3-ubyte')),
read_label_file(os.path.join(self.root, self.raw_folder, 't10k-labels-idx1-ubyte'))
)
with open(os.path.join(self.root, self.processed_folder, self.training_file), 'wb') as f:
torch.save(training_set, f)
with open(os.path.join(self.root, self.processed_folder, self.test_file), 'wb') as f:
torch.save(test_set, f)
print('Done!')
def get_int(b):
return int(codecs.encode(b, 'hex'), 16)
def read_label_file(path):
with open(path, 'rb') as f:
data = f.read()
assert get_int(data[:4]) == 2049
length = get_int(data[4:8])
parsed = np.frombuffer(data, dtype=np.uint8, offset=8)
return torch.from_numpy(parsed).view(length).long()
def read_image_file(path):
with open(path, 'rb') as f:
data = f.read()
assert get_int(data[:4]) == 2051
length = get_int(data[4:8])
num_rows = get_int(data[8:12])
num_cols = get_int(data[12:16])
images = []
parsed = np.frombuffer(data, dtype=np.uint8, offset=16)
return torch.from_numpy(parsed).view(length, num_rows, num_cols)