Source code for flash.image.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,
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from dataclasses import dataclass
from typing import Tuple, Union

import torch
from torch import nn

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 _ALBUMENTATIONS_AVAILABLE, _TORCHVISION_AVAILABLE, requires

if _TORCHVISION_AVAILABLE:
    from torchvision import transforms as T

if _ALBUMENTATIONS_AVAILABLE:
    import albumentations


class AlbumentationsAdapter(nn.Module):
    @requires("albumentations")
    def __init__(self, transform):
        super().__init__()
        if not isinstance(transform, list):
            transform = [transform]
        self.transform = albumentations.Compose(transform)

    def forward(self, x):
        return torch.from_numpy(self.transform(image=x.numpy())["image"])


@dataclass
class ImageClassificationInputTransform(InputTransform):
    image_size: Tuple[int, int] = (196, 196)
    mean: Union[float, Tuple[float, float, float]] = (0.485, 0.456, 0.406)
    std: Union[float, Tuple[float, float, float]] = (0.229, 0.224, 0.225)

    def per_sample_transform(self):
        return T.Compose(
            [
                ApplyToKeys(
                    DataKeys.INPUT,
                    T.Compose([T.ToTensor(), T.Resize(self.image_size), T.Normalize(self.mean, self.std)]),
                ),
                ApplyToKeys(DataKeys.TARGET, torch.as_tensor),
            ]
        )

    def train_per_sample_transform(self):
        return T.Compose(
            [
                ApplyToKeys(
                    DataKeys.INPUT,
                    T.Compose(
                        [
                            T.ToTensor(),
                            T.Resize(self.image_size),
                            T.Normalize(self.mean, self.std),
                            T.RandomHorizontalFlip(),
                        ]
                    ),
                ),
                ApplyToKeys(DataKeys.TARGET, torch.as_tensor),
            ]
        )