Source code for flash.image.classification.input
# 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.
import os
from typing import Any, Callable, Dict, List, Optional, Union
import pandas as pd
from flash.core.data.io.classification_input import ClassificationInputMixin
from flash.core.data.io.input import DataKeys
from flash.core.data.utilities.classification import MultiBinaryTargetFormatter, TargetFormatter
from flash.core.data.utilities.data_frame import resolve_files, resolve_targets
from flash.core.data.utilities.loading import load_data_frame
from flash.core.data.utilities.paths import PATH_TYPE, filter_valid_files, make_dataset
from flash.core.data.utilities.samples import to_samples
from flash.core.integrations.fiftyone.utils import FiftyOneLabelUtilities
from flash.core.utilities.imports import _FIFTYONE_AVAILABLE, lazy_import, requires
from flash.image.data import (
IMG_EXTENSIONS,
NP_EXTENSIONS,
ImageFilesInput,
ImageInput,
ImageNumpyInput,
ImageTensorInput,
)
if _FIFTYONE_AVAILABLE:
fol = lazy_import("fiftyone.core.labels")
SampleCollection = "fiftyone.core.collections.SampleCollection"
else:
fol = None
SampleCollection = None
class ImageClassificationFilesInput(ClassificationInputMixin, ImageFilesInput):
def load_data(
self,
files: List[PATH_TYPE],
targets: Optional[List[Any]] = None,
target_formatter: Optional[TargetFormatter] = None,
) -> List[Dict[str, Any]]:
if targets is None:
return super().load_data(files)
files, targets = filter_valid_files(files, targets, valid_extensions=IMG_EXTENSIONS + NP_EXTENSIONS)
self.load_target_metadata(targets, target_formatter=target_formatter)
return to_samples(files, targets)
def load_sample(self, sample: Dict[str, Any]) -> Dict[str, Any]:
sample = super().load_sample(sample)
if DataKeys.TARGET in sample:
sample[DataKeys.TARGET] = self.format_target(sample[DataKeys.TARGET])
return sample
class ImageClassificationFolderInput(ImageClassificationFilesInput):
def load_data(self, folder: PATH_TYPE, target_formatter: Optional[TargetFormatter] = None) -> List[Dict[str, Any]]:
files, targets = make_dataset(folder, extensions=IMG_EXTENSIONS + NP_EXTENSIONS)
return super().load_data(files, targets, target_formatter=target_formatter)
[docs]class ImageClassificationFiftyOneInput(ImageClassificationFilesInput):
@requires("fiftyone")
def load_data(
self,
sample_collection: SampleCollection,
label_field: str = "ground_truth",
target_formatter: Optional[TargetFormatter] = None,
) -> List[Dict[str, Any]]:
label_utilities = FiftyOneLabelUtilities(label_field, fol.Label)
label_utilities.validate(sample_collection)
label_path = sample_collection._get_label_field_path(label_field, "label")[1]
filepaths = sample_collection.values("filepath")
targets = sample_collection.values(label_path)
return super().load_data(filepaths, targets, target_formatter=target_formatter)
@requires("fiftyone")
def predict_load_data(
self, data: SampleCollection, target_formatter: Optional[TargetFormatter] = None
) -> List[Dict[str, Any]]:
return super().load_data(data.values("filepath"), target_formatter=target_formatter)
class ImageClassificationTensorInput(ClassificationInputMixin, ImageTensorInput):
def load_data(
self, tensor: Any, targets: Optional[List[Any]] = None, target_formatter: Optional[TargetFormatter] = None
) -> List[Dict[str, Any]]:
if targets is not None:
self.load_target_metadata(targets, target_formatter=target_formatter)
return to_samples(tensor, targets)
def load_sample(self, sample: Dict[str, Any]) -> Dict[str, Any]:
sample = super().load_sample(sample)
if DataKeys.TARGET in sample:
sample[DataKeys.TARGET] = self.format_target(sample[DataKeys.TARGET])
return sample
class ImageClassificationNumpyInput(ClassificationInputMixin, ImageNumpyInput):
def load_data(
self, array: Any, targets: Optional[List[Any]] = None, target_formatter: Optional[TargetFormatter] = None
) -> List[Dict[str, Any]]:
if targets is not None:
self.load_target_metadata(targets, target_formatter=target_formatter)
return to_samples(array, targets)
def load_sample(self, sample: Dict[str, Any]) -> Dict[str, Any]:
sample = super().load_sample(sample)
if DataKeys.TARGET in sample:
sample[DataKeys.TARGET] = self.format_target(sample[DataKeys.TARGET])
return sample
class ImageClassificationImageInput(ClassificationInputMixin, ImageInput):
def load_data(
self, images: Any, targets: Optional[List[Any]] = None, target_formatter: Optional[TargetFormatter] = None
) -> List[Dict[str, Any]]:
if targets is not None:
self.load_target_metadata(targets, target_formatter=target_formatter)
return to_samples(images, targets)
def load_sample(self, sample: Dict[str, Any]) -> Dict[str, Any]:
sample = super().load_sample(sample)
if DataKeys.TARGET in sample:
sample[DataKeys.TARGET] = self.format_target(sample[DataKeys.TARGET])
return sample
class ImageClassificationDataFrameInput(ImageClassificationFilesInput):
labels: list
def load_data(
self,
data_frame: pd.DataFrame,
input_key: str,
target_keys: Optional[Union[str, List[str]]] = None,
root: Optional[PATH_TYPE] = None,
resolver: Optional[Callable[[Optional[PATH_TYPE], Any], PATH_TYPE]] = None,
target_formatter: Optional[TargetFormatter] = None,
) -> List[Dict[str, Any]]:
files = resolve_files(data_frame, input_key, root, resolver)
targets = resolve_targets(data_frame, target_keys) if target_keys is not None else None
result = super().load_data(files, targets, target_formatter=target_formatter)
# If we had binary multi-class targets then we also know the labels (column names)
if (
self.training
and hasattr(self, "target_formatter")
and isinstance(self.target_formatter, MultiBinaryTargetFormatter)
and isinstance(target_keys, List)
):
self.labels = target_keys
return result
class ImageClassificationCSVInput(ImageClassificationDataFrameInput):
def load_data(
self,
csv_file: PATH_TYPE,
input_key: str,
target_keys: Optional[Union[str, List[str]]] = None,
root: Optional[PATH_TYPE] = None,
resolver: Optional[Callable[[Optional[PATH_TYPE], Any], PATH_TYPE]] = None,
target_formatter: Optional[TargetFormatter] = None,
) -> List[Dict[str, Any]]:
data_frame = load_data_frame(csv_file)
if root is None:
root = os.path.dirname(csv_file)
return super().load_data(data_frame, input_key, target_keys, root, resolver, target_formatter=target_formatter)