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
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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"])
[docs]@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),
]
)