Dubhe/model_measuring/kamal/vision/datasets/unlabeled.py

"""
 Copyright 2020 Tianshu AI Platform. All Rights Reserved.

 Licensed under the Apache License, Version 2.0 (the "License");
 you may not use this file except in compliance with the License.
 You may obtain a copy of the License at

     http://www.apache.org/licenses/LICENSE-2.0

 Unless required by applicable law or agreed to in writing, software
 distributed under the License is distributed on an "AS IS" BASIS,
 WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 See the License for the specific language governing permissions and
 limitations under the License.
 =============================================================
"""

import torch
from torch.utils.data import Dataset
import os

from PIL import Image
import random
from copy import deepcopy

def _collect_all_images(root, postfix=['png', 'jpg', 'jpeg', 'JPEG']):
    images = []
    if isinstance( postfix, str):
        postfix = [ postfix ]
    for dirpath, dirnames, files in os.walk(root):
        for pos in postfix:
            for f in files:
                if f.endswith( pos ):
                    images.append( os.path.join( dirpath, f ) )
    return images

def get_train_val_set(root, val_size=0.3):
    if not isinstance(root, (list, tuple)):
        root = [root]
    train_set = []
    val_set = []
    for _root in root:
        _part_train_set = _collect_all_images( _root)
        if os.path.isdir( os.path.join(_root, 'test') ):
            _part_val_set = _collect_all_images( os.path.join(_root, 'test') )
        else:
            _val_size = int( len(_part_train_set) * val_size )
            _part_val_set = random.sample( _part_train_set, k=_val_size )
        _part_train_set = [ d for d in _part_train_set if d not in _part_val_set ]
        train_set.extend(_part_train_set)
        val_set.extend(_part_val_set)
    return train_set, val_set

class UnlabeledDataset(Dataset):
    def __init__(self, data, transform=None, postfix=['png', 'jpg', 'jpeg', 'JPEG']):
        self.transform = transform
        self.data = data

    def __getitem__(self, idx):
        data = Image.open( self.data[idx] )
        if self.transform is not None:
            data = self.transform(data)
        return data

    def __len__(self):
        return len(self.data)