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Source code for flash.image.instance_segmentation.model

# 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
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# 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 typing import Any, Dict, List, Optional

from flash.core.adapter import AdapterTask
from flash.core.registry import FlashRegistry
from flash.core.utilities.types import LR_SCHEDULER_TYPE, OPTIMIZER_TYPE, OUTPUT_TRANSFORM_TYPE
from flash.image.instance_segmentation.backbones import INSTANCE_SEGMENTATION_HEADS
from flash.image.instance_segmentation.data import InstanceSegmentationOutputTransform


[docs]class InstanceSegmentation(AdapterTask): """The ``InstanceSegmentation`` is a :class:`~flash.Task` for detecting objects in images. For more details, see :ref:`object_detection`. Args: num_classes: The number of object classes. backbone: String indicating the backbone CNN architecture to use. head: String indicating the head module to use on top of the backbone. pretrained: Whether the model should be loaded with it's pretrained weights. optimizer: Optimizer to use for training. lr_scheduler: The LR scheduler to use during training. learning_rate: The learning rate to use for training. predict_kwargs: dictionary containing parameters that will be used during the prediction phase. **kwargs: additional kwargs used for initializing the task """ heads: FlashRegistry = INSTANCE_SEGMENTATION_HEADS required_extras: List[str] = ["image", "icevision"] def __init__( self, num_classes: int, backbone: Optional[str] = "resnet18_fpn", head: Optional[str] = "mask_rcnn", pretrained: bool = True, optimizer: OPTIMIZER_TYPE = "Adam", lr_scheduler: LR_SCHEDULER_TYPE = None, learning_rate: Optional[float] = None, output_transform: OUTPUT_TRANSFORM_TYPE = InstanceSegmentationOutputTransform(), predict_kwargs: Dict = None, **kwargs: Any, ): self.save_hyperparameters() predict_kwargs = predict_kwargs if predict_kwargs else {} metadata = self.heads.get(head, with_metadata=True) adapter = metadata["metadata"]["adapter"].from_task( self, num_classes=num_classes, backbone=backbone, head=head, pretrained=pretrained, predict_kwargs=predict_kwargs, **kwargs, ) super().__init__( adapter, learning_rate=learning_rate, optimizer=optimizer, lr_scheduler=lr_scheduler, output_transform=output_transform, ) def _ci_benchmark_fn(self, history: List[Dict[str, Any]]) -> None: """This function is used only for debugging usage with CI.""" # todo @property def predict_kwargs(self) -> Dict[str, Any]: """The kwargs used for the prediction step.""" return self.adapter.predict_kwargs @predict_kwargs.setter def predict_kwargs(self, predict_kwargs: Dict[str, Any]): self.adapter.predict_kwargs = predict_kwargs

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