unpaired_dataset.py

#   Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
#
# 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 cv2
import random
import os.path
from .base_dataset import BaseDataset, get_transform
from .image_folder import make_dataset

from .builder import DATASETS
from .transforms.builder import build_transforms


@DATASETS.register()
class UnpairedDataset(BaseDataset):
    """
    """
    def __init__(self, cfg):
        """Initialize this dataset class.

        Args:
            cfg (dict) -- stores all the experiment flags
        """
        BaseDataset.__init__(self, cfg)
        self.dir_A = os.path.join(cfg.dataroot, cfg.phase +
                                  'A')  # create a path '/path/to/data/trainA'
        self.dir_B = os.path.join(cfg.dataroot, cfg.phase +
                                  'B')  # create a path '/path/to/data/trainB'

        self.A_paths = sorted(make_dataset(
            self.dir_A,
            cfg.max_dataset_size))  # load images from '/path/to/data/trainA'
        self.B_paths = sorted(make_dataset(
            self.dir_B,
            cfg.max_dataset_size))  # load images from '/path/to/data/trainB'
        self.A_size = len(self.A_paths)  # get the size of dataset A
        self.B_size = len(self.B_paths)  # get the size of dataset B
        btoA = self.cfg.direction == 'BtoA'
        input_nc = self.cfg.output_nc if btoA else self.cfg.input_nc  # get the number of channels of input image
        output_nc = self.cfg.input_nc if btoA else self.cfg.output_nc  # get the number of channels of output image

        self.transform_A = build_transforms(self.cfg.transforms)
        self.transform_B = build_transforms(self.cfg.transforms)

        self.reset_paths()

    def reset_paths(self):
        self.path_dict = {}

    def __getitem__(self, index):
        """Return a data point and its metadata information.

        Parameters:
            index (int)      -- a random integer for data indexing

        Returns a dictionary that contains A, B, A_paths and B_paths
            A (tensor)       -- an image in the input domain
            B (tensor)       -- its corresponding image in the target domain
            A_paths (str)    -- image paths
            B_paths (str)    -- image paths
        """
        A_path = self.A_paths[
            index % self.A_size]  # make sure index is within then range
        if self.cfg.serial_batches:  # make sure index is within then range
            index_B = index % self.B_size
        else:  # randomize the index for domain B to avoid fixed pairs.
            index_B = random.randint(0, self.B_size - 1)
        B_path = self.B_paths[index_B]

        A_img = cv2.cvtColor(cv2.imread(A_path), cv2.COLOR_BGR2RGB)
        B_img = cv2.cvtColor(cv2.imread(B_path), cv2.COLOR_BGR2RGB)
        # apply image transformation
        A = self.transform_A(A_img)
        B = self.transform_B(B_img)

        # return A, B
        return {'A': A, 'B': B, 'A_paths': A_path, 'B_paths': B_path}

    def __len__(self):
        """Return the total number of images in the dataset.

        As we have two datasets with potentially different number of images,
        we take a maximum of
        """
        return max(self.A_size, self.B_size)