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
import torch
from torch import nn
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) -> torch.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, torch.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
