# Copyright 2020 MONAI Consortium
# 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 monai.utils.module import export


@export("monai.data")
class Dataset(torch.utils.data.Dataset):
    """
    Generic dataset to handle dictionary format data, it can operate transforms for specific fields.
    For example, typical input data can be a list of dictionaries::

        [{                            {                            {
             'img': 'image1.nii.gz',      'img': 'image2.nii.gz',      'img': 'image3.nii.gz',
             'seg': 'label1.nii.gz',      'seg': 'label2.nii.gz',      'seg': 'label3.nii.gz',
             'extra': 123                 'extra': 456                 'extra': 789
         },                           },                           }]
    """

    def __init__(self, data, transform=None):
        """
        Args:
            data (Iterable): input data to load and transform to generate dataset for model.
            transform (Callable, optional): transforms to excute operations on input data.
        """
        self.data = data
        self.transform = transform

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

    def __getitem__(self, index):
        data = self.data[index]
        if self.transform is not None:
            data = self.transform(data)

        return data

# Copyright 2020 MONAI Consortium
# 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 monai.utils.module import export


@export("monai.data")
class Dataset(torch.utils.data.Dataset):
    """
    Generic dataset to handle dictionary format data, it can operate transforms for specific fields.
    For example, typical input data can be a list of dictionaries::

        [{                            {                            {
             'img': 'image1.nii.gz',      'img': 'image2.nii.gz',      'img': 'image3.nii.gz',
             'seg': 'label1.nii.gz',      'seg': 'label2.nii.gz',      'seg': 'label3.nii.gz',
             'extra': 123                 'extra': 456                 'extra': 789
         },                           },                           }]
    """

    def __init__(self, data, transform=None):
        """
        Args:
            data (Iterable): input data to load and transform to generate dataset for model.
            transform (Callable, optional): transforms to excute operations on input data.
        """
        self.data = data
        self.transform = transform

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

    def __getitem__(self, index):
        data = self.data[index]
        if self.transform is not None:
            data = self.transform(data)

        return data

# Copyright 2020 MONAI Consortium # 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. Generic dataset to handle dictionary format data, it can operate transforms for specific fields.     For example, typical input data can be a list of dictionaries::          [{                            {                            {              'img': 'image1.nii.gz',      'img': 'image2.nii.gz',      'img': 'image3.nii.gz',              'seg': 'label1.nii.gz',      'seg': 'label2.nii.gz',      'seg': 'label3.nii.gz',              'extra': 123                 'extra': 456                 'extra': 789          },                           },                           }] Args:             data (Iterable): input data to load and transform to generate dataset for model.             transform (Callable, optional): transforms to excute operations on input data.