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Source code for flash.video.classification.input_transform

# Copyright The PyTorch Lightning team.
#
# 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 dataclasses import dataclass
from typing import Callable

import torch
from torch import Tensor

from flash.core.data.io.input import DataKeys
from flash.core.data.io.input_transform import InputTransform
from flash.core.data.transforms import ApplyToKeys
from flash.core.utilities.imports import _KORNIA_AVAILABLE, _PYTORCHVIDEO_AVAILABLE, requires

if _KORNIA_AVAILABLE:
    import kornia.augmentation as K

if _PYTORCHVIDEO_AVAILABLE:
    from pytorchvideo.transforms import UniformTemporalSubsample
    from torchvision.transforms import CenterCrop, Compose, RandomCrop
else:
    ClipSampler, LabeledVideoDataset, EncodedVideo, ApplyTransformToKey = None, None, None, None


def normalize(x: Tensor) -> Tensor:
    return x / 255.0


[docs]@requires("video") @dataclass class VideoClassificationInputTransform(InputTransform): image_size: int = 244 temporal_sub_sample: int = 8 mean: Tensor = torch.tensor([0.45, 0.45, 0.45]) std: Tensor = torch.tensor([0.225, 0.225, 0.225]) data_format: str = "BCTHW" same_on_frame: bool = False def per_sample_transform(self) -> Callable: per_sample_transform = [CenterCrop(self.image_size)] return ApplyToKeys( DataKeys.INPUT, Compose([UniformTemporalSubsample(self.temporal_sub_sample), normalize] + per_sample_transform), ) def train_per_sample_transform(self) -> Callable: per_sample_transform = [RandomCrop(self.image_size, pad_if_needed=True)] return ApplyToKeys( DataKeys.INPUT, Compose([UniformTemporalSubsample(self.temporal_sub_sample), normalize] + per_sample_transform), ) def per_batch_transform_on_device(self) -> Callable: return ApplyToKeys( DataKeys.INPUT, K.VideoSequential( K.Normalize(self.mean, self.std), data_format=self.data_format, same_on_frame=self.same_on_frame, ), )

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