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| from __future__ import annotations |
|
|
| from typing import TYPE_CHECKING, Any, Callable, Dict, Iterable, List, Optional, Sequence, Tuple, Union |
|
|
| import torch |
| from monai.apps.detection.networks.retinanet_detector import RetinaNetDetector |
| from monai.config import IgniteInfo |
| from monai.engines.evaluator import SupervisedEvaluator |
| from monai.engines.utils import IterationEvents, default_metric_cmp_fn |
| from monai.transforms import Transform |
| from monai.utils import ForwardMode, min_version, optional_import |
| from monai.utils.enums import CommonKeys as Keys |
| from torch.utils.data import DataLoader |
|
|
| from .detection_inferer import RetinaNetInferer |
|
|
| if TYPE_CHECKING: |
| from ignite.engine import Engine, EventEnum |
| from ignite.metrics import Metric |
| else: |
| Engine, _ = optional_import("ignite.engine", IgniteInfo.OPT_IMPORT_VERSION, min_version, "Engine") |
| Metric, _ = optional_import("ignite.metrics", IgniteInfo.OPT_IMPORT_VERSION, min_version, "Metric") |
| EventEnum, _ = optional_import("ignite.engine", IgniteInfo.OPT_IMPORT_VERSION, min_version, "EventEnum") |
|
|
| __all__ = ["DetectionEvaluator"] |
|
|
|
|
| def detection_prepare_val_batch( |
| batchdata: List[Dict[str, torch.Tensor]], |
| device: Optional[Union[str, torch.device]] = None, |
| non_blocking: bool = False, |
| **kwargs, |
| ) -> Union[Tuple[torch.Tensor, Optional[torch.Tensor]], torch.Tensor]: |
| """ |
| Default function to prepare the data for current iteration. |
| Args `batchdata`, `device`, `non_blocking` refer to the ignite API: |
| https://pytorch.org/ignite/v0.4.8/generated/ignite.engine.create_supervised_trainer.html. |
| `kwargs` supports other args for `Tensor.to()` API. |
| Returns: |
| image, label(optional). |
| """ |
| inputs = [ |
| batch_data_i["image"].to(device=device, non_blocking=non_blocking, **kwargs) for batch_data_i in batchdata |
| ] |
|
|
| if isinstance(batchdata[0].get(Keys.LABEL), torch.Tensor): |
| targets = [ |
| dict( |
| label=batch_data_i["label"].to(device=device, non_blocking=non_blocking, **kwargs), |
| box=batch_data_i["box"].to(device=device, non_blocking=non_blocking, **kwargs), |
| ) |
| for batch_data_i in batchdata |
| ] |
| return (inputs, targets) |
| return inputs, None |
|
|
|
|
| class DetectionEvaluator(SupervisedEvaluator): |
| """ |
| Supervised detection evaluation method with image and label, inherits from ``SupervisedEvaluator`` and ``Workflow``. |
| Args: |
| device: an object representing the device on which to run. |
| val_data_loader: Ignite engine use data_loader to run, must be Iterable, typically be torch.DataLoader. |
| network: detector to evaluate in the evaluator, should be regular PyTorch `torch.nn.Module`. |
| epoch_length: number of iterations for one epoch, default to `len(val_data_loader)`. |
| non_blocking: if True and this copy is between CPU and GPU, the copy may occur asynchronously |
| with respect to the host. For other cases, this argument has no effect. |
| prepare_batch: function to parse expected data (usually `image`, `label` and other network args) |
| from `engine.state.batch` for every iteration, for more details please refer to: |
| https://pytorch.org/ignite/generated/ignite.engine.create_supervised_trainer.html. |
| iteration_update: the callable function for every iteration, expect to accept `engine` |
| and `engine.state.batch` as inputs, return data will be stored in `engine.state.output`. |
| if not provided, use `self._iteration()` instead. for more details please refer to: |
| https://pytorch.org/ignite/generated/ignite.engine.engine.Engine.html. |
| inferer: inference method that execute model forward on input data, like: SlidingWindow, etc. |
| postprocessing: execute additional transformation for the model output data. |
| Typically, several Tensor based transforms composed by `Compose`. |
| key_val_metric: compute metric when every iteration completed, and save average value to |
| engine.state.metrics when epoch completed. key_val_metric is the main metric to compare and save the |
| checkpoint into files. |
| additional_metrics: more Ignite metrics that also attach to Ignite Engine. |
| metric_cmp_fn: function to compare current key metric with previous best key metric value, |
| it must accept 2 args (current_metric, previous_best) and return a bool result: if `True`, will update |
| `best_metric` and `best_metric_epoch` with current metric and epoch, default to `greater than`. |
| val_handlers: every handler is a set of Ignite Event-Handlers, must have `attach` function, like: |
| CheckpointHandler, StatsHandler, etc. |
| amp: whether to enable auto-mixed-precision evaluation, default is False. |
| mode: model forward mode during evaluation, should be 'eval' or 'train', |
| which maps to `model.eval()` or `model.train()`, default to 'eval'. |
| event_names: additional custom ignite events that will register to the engine. |
| new events can be a list of str or `ignite.engine.events.EventEnum`. |
| event_to_attr: a dictionary to map an event to a state attribute, then add to `engine.state`. |
| for more details, check: https://pytorch.org/ignite/generated/ignite.engine.engine.Engine.html |
| #ignite.engine.engine.Engine.register_events. |
| decollate: whether to decollate the batch-first data to a list of data after model computation, |
| recommend `decollate=True` when `postprocessing` uses components from `monai.transforms`. |
| default to `True`. |
| to_kwargs: dict of other args for `prepare_batch` API when converting the input data, except for |
| `device`, `non_blocking`. |
| amp_kwargs: dict of the args for `torch.cuda.amp.autocast()` API, for more details: |
| https://pytorch.org/docs/stable/amp.html#torch.cuda.amp.autocast. |
| """ |
|
|
| def __init__( |
| self, |
| device: torch.device, |
| val_data_loader: Iterable | DataLoader, |
| network: RetinaNetDetector, |
| epoch_length: int | None = None, |
| non_blocking: bool = False, |
| prepare_batch: Callable = detection_prepare_val_batch, |
| iteration_update: Callable[[Engine, Any], Any] | None = None, |
| inferer: RetinaNetInferer | None = None, |
| postprocessing: Transform | None = None, |
| key_val_metric: dict[str, Metric] | None = None, |
| additional_metrics: dict[str, Metric] | None = None, |
| metric_cmp_fn: Callable = default_metric_cmp_fn, |
| val_handlers: Sequence | None = None, |
| amp: bool = False, |
| mode: ForwardMode | str = ForwardMode.EVAL, |
| event_names: list[str | EventEnum] | None = None, |
| event_to_attr: dict | None = None, |
| decollate: bool = True, |
| to_kwargs: dict | None = None, |
| amp_kwargs: dict | None = None, |
| ) -> None: |
| super().__init__( |
| device=device, |
| val_data_loader=val_data_loader, |
| network=network, |
| epoch_length=epoch_length, |
| non_blocking=non_blocking, |
| prepare_batch=prepare_batch, |
| iteration_update=iteration_update, |
| inferer=inferer, |
| postprocessing=postprocessing, |
| key_val_metric=key_val_metric, |
| additional_metrics=additional_metrics, |
| metric_cmp_fn=metric_cmp_fn, |
| val_handlers=val_handlers, |
| amp=amp, |
| mode=mode, |
| event_names=event_names, |
| event_to_attr=event_to_attr, |
| decollate=decollate, |
| to_kwargs=to_kwargs, |
| amp_kwargs=amp_kwargs, |
| ) |
|
|
| def _register_decollate(self): |
| """ |
| Register the decollate operation for batch data, will execute after model forward and loss forward. |
| """ |
|
|
| @self.on(IterationEvents.MODEL_COMPLETED) |
| def _decollate_data(engine: Engine) -> None: |
| output_list = [] |
| for i in range(len(engine.state.output[Keys.IMAGE])): |
| output_list.append({}) |
| for k in engine.state.output.keys(): |
| if engine.state.output[k] is not None: |
| output_list[i][k] = engine.state.output[k][i] |
| engine.state.output = output_list |
|
|
