# -*- coding: utf-8 -*-

# Copyright (C) 2020. Huawei Technologies Co., Ltd. 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.

"""Transforms used in simclr."""

import torchvision


class TransformsSimCLR():
    """Transform for simclr."""

    def __init__(self, size=32):
        color_jitter = torchvision.transforms.ColorJitter(0.8, 0.8, 0.8, 0.2)
        self.train_transform = torchvision.transforms.Compose(
            [
                torchvision.transforms.RandomResizedCrop(size=size),
                torchvision.transforms.RandomHorizontalFlip(),  # with 0.5 probability
                torchvision.transforms.RandomApply([color_jitter], p=0.8),
                torchvision.transforms.RandomGrayscale(p=0.2),
                torchvision.transforms.ToTensor(),
            ]
        )

        self.test_transform = torchvision.transforms.Compose(
            [
                torchvision.transforms.Resize(size=size),
                torchvision.transforms.ToTensor(),
            ]
        )

    def __call__(self, x):
        """Call simclr transforms."""
        return self.train_transform(x), self.train_transform(x)

# -*- coding: utf-8 -*-

# Copyright (C) 2020. Huawei Technologies Co., Ltd. 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.

"""Transforms used in simclr."""

import torchvision


class TransformsSimCLR():
    """Transform for simclr."""

    def __init__(self, size=32):
        color_jitter = torchvision.transforms.ColorJitter(0.8, 0.8, 0.8, 0.2)
        self.train_transform = torchvision.transforms.Compose(
            [
                torchvision.transforms.RandomResizedCrop(size=size),
                torchvision.transforms.RandomHorizontalFlip(),  # with 0.5 probability
                torchvision.transforms.RandomApply([color_jitter], p=0.8),
                torchvision.transforms.RandomGrayscale(p=0.2),
                torchvision.transforms.ToTensor(),
            ]
        )

        self.test_transform = torchvision.transforms.Compose(
            [
                torchvision.transforms.Resize(size=size),
                torchvision.transforms.ToTensor(),
            ]
        )

    def __call__(self, x):
        """Call simclr transforms."""
        return self.train_transform(x), self.train_transform(x)

# -*- coding: utf-8 -*- # Copyright (C) 2020. Huawei Technologies Co., Ltd. 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. Transforms used in simclr. Transform for simclr. # with 0.5 probability Call simclr transforms.