# Copyright (c) 2021, NVIDIA CORPORATION. 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.
from maskrcnn_benchmark.data import datasets

from .coco import coco_evaluation
from .voc import voc_evaluation


def evaluate(dataset, predictions, output_folder, **kwargs):
    """evaluate dataset using different methods based on dataset type.
    Args:
        dataset: Dataset object
        predictions(list[BoxList]): each item in the list represents the
            prediction results for one image.
        output_folder: output folder, to save evaluation files or results.
        **kwargs: other args.
    Returns:
        evaluation result
    """
    args = dict(
        dataset=dataset, predictions=predictions, output_folder=output_folder, **kwargs
    )
    if isinstance(dataset, datasets.COCODataset):
        return coco_evaluation(**args)
    elif isinstance(dataset, datasets.PascalVOCDataset):
        return voc_evaluation(**args)
    else:
        dataset_name = dataset.__class__.__name__
        raise NotImplementedError("Unsupported dataset type {}.".format(dataset_name))

# Copyright (c) 2021, NVIDIA CORPORATION. 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.
from maskrcnn_benchmark.data import datasets

from .coco import coco_evaluation
from .voc import voc_evaluation


def evaluate(dataset, predictions, output_folder, **kwargs):
    """evaluate dataset using different methods based on dataset type.
    Args:
        dataset: Dataset object
        predictions(list[BoxList]): each item in the list represents the
            prediction results for one image.
        output_folder: output folder, to save evaluation files or results.
        **kwargs: other args.
    Returns:
        evaluation result
    """
    args = dict(
        dataset=dataset, predictions=predictions, output_folder=output_folder, **kwargs
    )
    if isinstance(dataset, datasets.COCODataset):
        return coco_evaluation(**args)
    elif isinstance(dataset, datasets.PascalVOCDataset):
        return voc_evaluation(**args)
    else:
        dataset_name = dataset.__class__.__name__
        raise NotImplementedError("Unsupported dataset type {}.".format(dataset_name))

# Copyright (c) 2021, NVIDIA CORPORATION. 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. evaluate dataset using different methods based on dataset type.     Args:         dataset: Dataset object         predictions(list[BoxList]): each item in the list represents the             prediction results for one image.         output_folder: output folder, to save evaluation files or results.         **kwargs: other args.     Returns:         evaluation result