Source code for flash.image.segmentation.input_transform
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# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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# http://www.apache.org/licenses/LICENSE-2.0
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from dataclasses import dataclass
from typing import Any, Callable, Dict, Tuple
import torch
from flash.core.data.io.input import DataKeys
from flash.core.data.io.input_transform import InputTransform
from flash.core.utilities.imports import _KORNIA_AVAILABLE, _TORCHVISION_AVAILABLE
if _KORNIA_AVAILABLE:
import kornia as K
if _TORCHVISION_AVAILABLE:
from flash.core.data.transforms import ApplyToKeys, kornia_collate, KorniaParallelTransforms
[docs]def prepare_target(tensor: torch.Tensor) -> torch.Tensor:
"""Convert the target mask to long and remove the channel dimension."""
return tensor.long().squeeze(1)
def remove_extra_dimensions(batch: Dict[str, Any]):
if isinstance(batch[DataKeys.INPUT], list):
assert len(batch[DataKeys.INPUT]) == 1
batch[DataKeys.INPUT] = batch[DataKeys.INPUT][0]
return batch
[docs]@dataclass
class SemanticSegmentationInputTransform(InputTransform):
image_size: Tuple[int, int] = (128, 128)
def train_per_sample_transform(self) -> Callable:
return ApplyToKeys(
[DataKeys.INPUT, DataKeys.TARGET],
KorniaParallelTransforms(
K.geometry.Resize(self.image_size, interpolation="nearest"), K.augmentation.RandomHorizontalFlip(p=0.5)
),
)
def per_sample_transform(self) -> Callable:
return ApplyToKeys(
[DataKeys.INPUT, DataKeys.TARGET],
KorniaParallelTransforms(K.geometry.Resize(self.image_size, interpolation="nearest")),
)
def predict_input_per_sample_transform(self) -> Callable:
return K.geometry.Resize(self.image_size, interpolation="nearest")
def collate(self) -> Callable:
return kornia_collate
def target_per_batch_transform(self) -> Callable:
return prepare_target
def predict_per_batch_transform(self) -> Callable:
return remove_extra_dimensions
def serve_per_batch_transform(self) -> Callable:
return remove_extra_dimensions