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Source code for flash.image.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
#
# 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, Type, Union

from torch import nn, Tensor
from torch.nn import functional as F

from flash.core.classification import ClassificationTask
from flash.core.data.io.input import DataKeys, ServeInput
from flash.core.data.io.output import Output
from flash.core.data.io.output_transform import OutputTransform
from flash.core.model import Task
from flash.core.registry import FlashRegistry
from flash.core.serve import Composition
from flash.core.utilities.imports import _KORNIA_AVAILABLE, _TM_GREATER_EQUAL_0_7_0, requires
from flash.core.utilities.isinstance import _isinstance
from flash.core.utilities.types import (
    INPUT_TRANSFORM_TYPE,
    LOSS_FN_TYPE,
    LR_SCHEDULER_TYPE,
    METRICS_TYPE,
    OPTIMIZER_TYPE,
    OUTPUT_TRANSFORM_TYPE,
)
from flash.image.segmentation.backbones import SEMANTIC_SEGMENTATION_BACKBONES
from flash.image.segmentation.heads import SEMANTIC_SEGMENTATION_HEADS
from flash.image.segmentation.input import SemanticSegmentationDeserializer
from flash.image.segmentation.input_transform import SemanticSegmentationInputTransform
from flash.image.segmentation.output import SEMANTIC_SEGMENTATION_OUTPUTS

if _KORNIA_AVAILABLE:
    import kornia as K

if _TM_GREATER_EQUAL_0_7_0:
    from torchmetrics import JaccardIndex
else:
    from torchmetrics import IoU as JaccardIndex


[docs]class SemanticSegmentationOutputTransform(OutputTransform): def per_sample_transform(self, sample: Any) -> Any: resize = K.geometry.Resize(sample[DataKeys.METADATA]["size"], interpolation="bilinear") sample[DataKeys.PREDS] = resize(sample[DataKeys.PREDS]) sample[DataKeys.INPUT] = resize(sample[DataKeys.INPUT]) return super().per_sample_transform(sample)
[docs]class SemanticSegmentation(ClassificationTask): """``SemanticSegmentation`` is a :class:`~flash.Task` for semantic segmentation of images. For more details, see :ref:`semantic_segmentation`. Args: num_classes: Number of classes to classify. backbone: A string or model to use to compute image features. backbone_kwargs: Additional arguments for the backbone configuration. head: A string or (model, num_features) tuple to use to compute image features. head_kwargs: Additional arguments for the head configuration. pretrained: Use a pretrained backbone. loss_fn: Loss function for training. optimizer: Optimizer to use for training. lr_scheduler: The LR scheduler to use during training. metrics: Metrics to compute for training and evaluation. Can either be an metric from the `torchmetrics` package, a custom metric inherenting from `torchmetrics.Metric`, a callable function or a list/dict containing a combination of the aforementioned. In all cases, each metric needs to have the signature `metric(preds,target)` and return a single scalar tensor. Defaults to :class:`torchmetrics.IOU`. learning_rate: Learning rate to use for training. If ``None`` (the default) then the default LR for your chosen optimizer will be used. multi_label: Whether the targets are multi-label or not. output: The :class:`~flash.core.data.io.output.Output` to use when formatting prediction outputs. output_transform: :class:`~flash.core.data.io.output_transform.OutputTransform` use for post processing samples. """ output_transform_cls = SemanticSegmentationOutputTransform backbones: FlashRegistry = SEMANTIC_SEGMENTATION_BACKBONES heads: FlashRegistry = SEMANTIC_SEGMENTATION_HEADS outputs: FlashRegistry = Task.outputs + SEMANTIC_SEGMENTATION_OUTPUTS required_extras: str = "image" def __init__( self, num_classes: int, backbone: Union[str, nn.Module] = "resnet50", backbone_kwargs: Optional[Dict] = None, head: str = "fpn", head_kwargs: Optional[Dict] = None, pretrained: Union[bool, str] = True, loss_fn: LOSS_FN_TYPE = None, optimizer: OPTIMIZER_TYPE = "Adam", lr_scheduler: LR_SCHEDULER_TYPE = None, metrics: METRICS_TYPE = None, learning_rate: Optional[float] = None, multi_label: bool = False, output_transform: OUTPUT_TRANSFORM_TYPE = None, ) -> None: if metrics is None: metrics = JaccardIndex(num_classes=num_classes) if loss_fn is None: loss_fn = F.cross_entropy # TODO: need to check for multi_label if multi_label: raise NotImplementedError("Multi-label not supported yet.") super().__init__( model=None, loss_fn=loss_fn, optimizer=optimizer, lr_scheduler=lr_scheduler, metrics=metrics, learning_rate=learning_rate, output_transform=output_transform or self.output_transform_cls(), ) self.save_hyperparameters() if not backbone_kwargs: backbone_kwargs = {} if not head_kwargs: head_kwargs = {} if isinstance(backbone, nn.Module): self.backbone = backbone else: self.backbone = self.backbones.get(backbone)(**backbone_kwargs) self.head: nn.Module = self.heads.get(head)( backbone=self.backbone, num_classes=num_classes, pretrained=pretrained, **head_kwargs ) self.backbone = self.head.encoder def training_step(self, batch: Any, batch_idx: int) -> Any: batch = (batch[DataKeys.INPUT], batch[DataKeys.TARGET]) return super().training_step(batch, batch_idx) def validation_step(self, batch: Any, batch_idx: int) -> Any: batch = (batch[DataKeys.INPUT], batch[DataKeys.TARGET]) return super().validation_step(batch, batch_idx) def test_step(self, batch: Any, batch_idx: int) -> Any: batch = (batch[DataKeys.INPUT], batch[DataKeys.TARGET]) return super().test_step(batch, batch_idx) def predict_step(self, batch: Any, batch_idx: int, dataloader_idx: int = 0) -> Any: batch_input = batch[DataKeys.INPUT] batch[DataKeys.PREDS] = super().predict_step(batch_input, batch_idx, dataloader_idx=dataloader_idx) return batch def forward(self, x) -> Tensor: res = self.head(x) # some frameworks like torchvision return a dict. # In particular, torchvision segmentation models return the output logits # in the key `out`. if _isinstance(res, Dict[str, Tensor]): res = res["out"] return res @classmethod def available_pretrained_weights(cls, backbone: str): result = cls.backbones.get(backbone, with_metadata=True) pretrained_weights = None if "weights_paths" in result["metadata"]: pretrained_weights = list(result["metadata"]["weights_paths"]) return pretrained_weights @requires("serve") def serve( self, host: str = "127.0.0.1", port: int = 8000, sanity_check: bool = True, input_cls: Optional[Type[ServeInput]] = SemanticSegmentationDeserializer, transform: INPUT_TRANSFORM_TYPE = SemanticSegmentationInputTransform, transform_kwargs: Optional[Dict] = None, output: Optional[Union[str, Output]] = None, ) -> Composition: return super().serve(host, port, sanity_check, input_cls, transform, transform_kwargs, output) @staticmethod def _ci_benchmark_fn(history: List[Dict[str, Any]]): """This function is used only for debugging usage with CI.""" assert history[-1]["val_jaccardindex"] > 0.2

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