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lacuna.py
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lacuna.py
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import numpy as np
import os
from PIL import Image
from torchvision.datasets import VisionDataset
class Lacuna100(VisionDataset):
base_folder = ''
def __init__(self, root, train=True, transform=None, target_transform=None):
super(Lacuna100, self).__init__(root, transform=transform,
target_transform=target_transform)
self.train = train
split = 'train' if train else 'test'
self.targets = np.load(os.path.join(self.root, self.base_folder, split, 'label.npy'))
self.data = np.load(os.path.join(self.root, self.base_folder, split, 'data.npy'))
# The data is saved in BGR format. Convert to RGB.
#self.data = self.data[...,::-1]
def __len__(self):
return len(self.data)
def __getitem__(self, index):
"""
Args:
index (int): Index
Returns:
tuple: (image, target) where target is index of the target class.
"""
img, target = self.data[index], self.targets[index]
# doing this so that it is consistent with all other datasets
# to return a PIL Image
img = Image.fromarray(img)
if self.transform is not None:
img = self.transform(img)
if self.target_transform is not None:
target = self.target_transform(target)
return img, target
def extra_repr(self):
return "Split: {}".format("Train" if self.train is True else "Test")
class Lacuna10(Lacuna100):
base_folder = ''
class Small_Lacuna6(VisionDataset):
base_folder = ''
def __init__(self, root, train=True, transform=None, target_transform=None):
super(Small_Lacuna6, self).__init__(root, transform=transform,
target_transform=target_transform)
self.train = train
split = 'train' if train else 'test'
self.targets = np.load(os.path.join(self.root, self.base_folder, split, 'label.npy'))
self.data = np.load(os.path.join(self.root, self.base_folder, split, 'data.npy'))
# The data is saved in BGR format. Convert to RGB.
#self.data = self.data[...,::-1]
targets=np.array(self.targets)
data=np.array(self.data)
sub_ds_data_list=[]
sub_ds_target_list=[]
for i in range(6):
if self.train:
sub_cls_id = np.random.choice(np.where(targets==i)[0],125,replace=False)
else:
sub_cls_id = np.random.choice(np.where(targets==i)[0],100,replace=False)
#np.where(ds.targets==i)[0]
sub_ds_data_list.append(data[sub_cls_id,:,:,:])
sub_ds_target_list.append(targets[sub_cls_id])
self.data=np.concatenate(sub_ds_data_list)
self.targets=np.concatenate(sub_ds_target_list)
def __len__(self):
return len(self.data)
def __getitem__(self, index):
"""
Args:
index (int): Index
Returns:
tuple: (image, target) where target is index of the target class.
"""
img, target = self.data[index], self.targets[index]
# doing this so that it is consistent with all other datasets
# to return a PIL Image
img = Image.fromarray(img)
if self.transform is not None:
img = self.transform(img)
if self.target_transform is not None:
target = self.target_transform(target)
return img, target
def extra_repr(self):
return "Split: {}".format("Train" if self.train is True else "Test")
class Small_Lacuna5(VisionDataset):
base_folder = ''
def __init__(self, root, train=True, transform=None, target_transform=None):
super(Small_Lacuna5, self).__init__(root, transform=transform,
target_transform=target_transform)
self.train = train
split = 'train' if train else 'test'
self.targets = np.load(os.path.join(self.root, self.base_folder, split, 'label.npy'))
self.data = np.load(os.path.join(self.root, self.base_folder, split, 'data.npy'))
# The data is saved in BGR format. Convert to RGB.
#self.data = self.data[...,::-1]
targets=np.array(self.targets)
data=np.array(self.data)
sub_ds_data_list=[]
sub_ds_target_list=[]
for i in range(5):
if self.train:
sub_cls_id = np.random.choice(np.where(targets==i)[0],125,replace=False)
else:
sub_cls_id = np.random.choice(np.where(targets==i)[0],100,replace=False)
#np.where(ds.targets==i)[0]
sub_ds_data_list.append(data[sub_cls_id,:,:,:])
sub_ds_target_list.append(targets[sub_cls_id])
self.data=np.concatenate(sub_ds_data_list)
self.targets=np.concatenate(sub_ds_target_list)
def __len__(self):
return len(self.data)
def __getitem__(self, index):
"""
Args:
index (int): Index
Returns:
tuple: (image, target) where target is index of the target class.
"""
img, target = self.data[index], self.targets[index]
# doing this so that it is consistent with all other datasets
# to return a PIL Image
img = Image.fromarray(img)
if self.transform is not None:
img = self.transform(img)
if self.target_transform is not None:
target = self.target_transform(target)
return img, target
def extra_repr(self):
return "Split: {}".format("Train" if self.train is True else "Test")
class Small_Lacuna10(VisionDataset):
base_folder = ''
def __init__(self, root, train=True, transform=None, target_transform=None):
super(Small_Lacuna10, self).__init__(root, transform=transform,
target_transform=target_transform)
self.train = train
split = 'train' if train else 'test'
self.targets = np.load(os.path.join(self.root, self.base_folder, split, 'label.npy'))
self.data = np.load(os.path.join(self.root, self.base_folder, split, 'data.npy'))
# The data is saved in BGR format. Convert to RGB.
#self.data = self.data[...,::-1]
targets=np.array(self.targets)
data=np.array(self.data)
sub_ds_data_list=[]
sub_ds_target_list=[]
for i in range(10):
if self.train:
sub_cls_id = np.random.choice(np.where(targets==i)[0],125,replace=False)
else:
sub_cls_id = np.random.choice(np.where(targets==i)[0],100,replace=False)
#np.where(ds.targets==i)[0]
sub_ds_data_list.append(data[sub_cls_id,:,:,:])
sub_ds_target_list.append(targets[sub_cls_id])
self.data=np.concatenate(sub_ds_data_list)
self.targets=np.concatenate(sub_ds_target_list)
def __len__(self):
return len(self.data)
def __getitem__(self, index):
"""
Args:
index (int): Index
Returns:
tuple: (image, target) where target is index of the target class.
"""
img, target = self.data[index], self.targets[index]
# doing this so that it is consistent with all other datasets
# to return a PIL Image
img = Image.fromarray(img)
if self.transform is not None:
img = self.transform(img)
if self.target_transform is not None:
target = self.target_transform(target)
return img, target
def extra_repr(self):
return "Split: {}".format("Train" if self.train is True else "Test")
class Small_Binary_Lacuna10(VisionDataset):
base_folder = ''
def __init__(self, root, train=True, transform=None, target_transform=None):
super(Small_Binary_Lacuna10, self).__init__(root, transform=transform,
target_transform=target_transform)
self.train = train
split = 'train' if train else 'test'
self.targets = np.load(os.path.join(self.root, self.base_folder, split, 'label.npy'))
self.data = np.load(os.path.join(self.root, self.base_folder, split, 'data.npy'))
# The data is saved in BGR format. Convert to RGB.
#self.data = self.data[...,::-1]
targets=np.array(self.targets)
data=np.array(self.data)
sub_ds_data_list=[]
sub_ds_target_list=[]
for i in range(3):
if self.train:
sub_cls_id = np.random.choice(np.where(targets==i)[0],200,replace=False)
else:
sub_cls_id = np.random.choice(np.where(targets==i)[0],100,replace=False)
#np.where(ds.targets==i)[0]
sub_ds_data_list.append(data[sub_cls_id,:,:,:])
sub_ds_target_list.append(targets[sub_cls_id])
self.data=np.concatenate(sub_ds_data_list)
self.targets=np.concatenate(sub_ds_target_list)
def __len__(self):
return len(self.data)
def __getitem__(self, index):
"""
Args:
index (int): Index
Returns:
tuple: (image, target) where target is index of the target class.
"""
img, target = self.data[index], self.targets[index]
# doing this so that it is consistent with all other datasets
# to return a PIL Image
img = Image.fromarray(img)
if self.transform is not None:
img = self.transform(img)
if self.target_transform is not None:
target = self.target_transform(target)
return img, target
def extra_repr(self):
return "Split: {}".format("Train" if self.train is True else "Test")