<reponame>abhisharsinha/similarity<gh_stars>0
# Copyright 2021 The TensorFlow Authors
#
# 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 abc
from tensorflow_similarity.types import Tensor

# from tensorflow import Tensor
from typing import List


class Augmenter(abc.ABC):
    @abc.abstractmethod
    def augment(
        self,
        x: Tensor,
        y: Tensor,
        num_augmentations_per_example: int,
        is_warmup: bool,
    ) -> List[Tensor]:
        pass

    def __call__(
        self,
        x: Tensor,
        y: Tensor,
        num_augmentations_per_example: int,
        is_warmup: bool,
    ) -> List[Tensor]:
        return self.augment(x, y, num_augmentations_per_example, is_warmup)

# Copyright 2021 The TensorFlow Authors
#
# 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 abc
from tensorflow_similarity.types import Tensor

# from tensorflow import Tensor
from typing import List


class Augmenter(abc.ABC):
    @abc.abstractmethod
    def augment(
        self,
        x: Tensor,
        y: Tensor,
        num_augmentations_per_example: int,
        is_warmup: bool,
    ) -> List[Tensor]:
        pass

    def __call__(
        self,
        x: Tensor,
        y: Tensor,
        num_augmentations_per_example: int,
        is_warmup: bool,
    ) -> List[Tensor]:
        return self.augment(x, y, num_augmentations_per_example, is_warmup)
# Copyright 2021 The TensorFlow Authors # # 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 tensorflow import Tensor