From a6e8d9c3ed79ed83638ae9e7960ac6c074dbe113 Mon Sep 17 00:00:00 2001 From: Louis Dupont Date: Thu, 2 Nov 2023 14:07:51 +0100 Subject: [PATCH 01/12] first working version without test --- .../classification_models/base_classifer.py | 18 +- .../detection_models/customizable_detector.py | 19 +- .../detection_models/pp_yolo_e/pp_yolo_e.py | 18 +- .../models/detection_models/yolo_base.py | 19 +- .../pose_estimation_models/dekr_hrnet.py | 22 +- .../yolo_nas_pose/yolo_nas_pose_variants.py | 26 ++- .../training/pipelines/pipelines.py | 84 ++++++-- .../training/processing/processing.py | 191 +++++++++++++++++- 8 files changed, 339 insertions(+), 58 deletions(-) diff --git a/src/super_gradients/training/models/classification_models/base_classifer.py b/src/super_gradients/training/models/classification_models/base_classifer.py index 63022b7511..bca9b7e40a 100644 --- a/src/super_gradients/training/models/classification_models/base_classifer.py +++ b/src/super_gradients/training/models/classification_models/base_classifer.py @@ -30,9 +30,10 @@ def set_dataset_processing_params(self, class_names: Optional[List[str]] = None, self._image_processor = image_processor or self._image_processor @lru_cache(maxsize=1) - def _get_pipeline(self, fuse_model: bool = True) -> ClassificationPipeline: + def _get_pipeline(self, fuse_model: bool = True, skip_image_resizing: bool = False) -> ClassificationPipeline: """Instantiate the prediction pipeline of this model. - :param fuse_model: If True, create a copy of the model, and fuse some of its layers to increase performance. This increases memory usage. + :param fuse_model: If True, create a copy of the model, and fuse some of its layers to increase performance. This increases memory usage. + :param skip_image_resizing: If True, the image processor will not resize the images. """ if None in (self._class_names, self._image_processor): raise RuntimeError( @@ -44,22 +45,25 @@ def _get_pipeline(self, fuse_model: bool = True) -> ClassificationPipeline: image_processor=self._image_processor, class_names=self._class_names, fuse_model=fuse_model, + skip_image_resizing=skip_image_resizing, ) return pipeline - def predict(self, images: ImageSource, batch_size: int = 32, fuse_model: bool = True) -> ImagesClassificationPrediction: + def predict(self, images: ImageSource, batch_size: int = 32, fuse_model: bool = True, skip_image_resizing: bool = False) -> ImagesClassificationPrediction: """Predict an image or a list of images. :param images: Images to predict. :param batch_size: Maximum number of images to process at the same time. :param fuse_model: If True, create a copy of the model, and fuse some of its layers to increase performance. This increases memory usage. + :param skip_image_resizing: If True, the image processor will not resize the images. """ - pipeline = self._get_pipeline(fuse_model=fuse_model) + pipeline = self._get_pipeline(fuse_model=fuse_model, skip_image_resizing=skip_image_resizing) return pipeline(images, batch_size=batch_size) # type: ignore - def predict_webcam(self, fuse_model: bool = True) -> None: + def predict_webcam(self, fuse_model: bool = True, skip_image_resizing: bool = False) -> None: """Predict using webcam. - :param fuse_model: If True, create a copy of the model, and fuse some of its layers to increase performance. This increases memory usage. + :param fuse_model: If True, create a copy of the model, and fuse some of its layers to increase performance. This increases memory usage. + :param skip_image_resizing: If True, the image processor will not resize the images. """ - pipeline = self._get_pipeline(fuse_model=fuse_model) + pipeline = self._get_pipeline(fuse_model=fuse_model, skip_image_resizing=skip_image_resizing) pipeline.predict_webcam() diff --git a/src/super_gradients/training/models/detection_models/customizable_detector.py b/src/super_gradients/training/models/detection_models/customizable_detector.py index 779ede0c85..a3d3f63aa5 100644 --- a/src/super_gradients/training/models/detection_models/customizable_detector.py +++ b/src/super_gradients/training/models/detection_models/customizable_detector.py @@ -157,13 +157,16 @@ def get_processing_params(self) -> Optional[Processing]: return self._image_processor @lru_cache(maxsize=1) - def _get_pipeline(self, iou: Optional[float] = None, conf: Optional[float] = None, fuse_model: bool = True) -> DetectionPipeline: + def _get_pipeline( + self, iou: Optional[float] = None, conf: Optional[float] = None, fuse_model: bool = True, skip_image_resizing: bool = False + ) -> DetectionPipeline: """Instantiate the prediction pipeline of this model. :param iou: (Optional) IoU threshold for the nms algorithm. If None, the default value associated to the training is used. :param conf: (Optional) Below the confidence threshold, prediction are discarded. If None, the default value associated to the training is used. - :param fuse_model: If True, create a copy of the model, and fuse some of its layers to increase performance. This increases memory usage. + :param fuse_model: If True, create a copy of the model, and fuse some of its layers to increase performance. This increases memory usage. + :param skip_image_resizing: If True, the image processor will not resize the images. """ if None in (self._class_names, self._image_processor, self._default_nms_iou, self._default_nms_conf): raise RuntimeError( @@ -178,6 +181,7 @@ def _get_pipeline(self, iou: Optional[float] = None, conf: Optional[float] = Non post_prediction_callback=self.get_post_prediction_callback(iou=iou, conf=conf), class_names=self._class_names, fuse_model=fuse_model, + skip_image_resizing=skip_image_resizing, ) return pipeline @@ -188,6 +192,7 @@ def predict( conf: Optional[float] = None, batch_size: int = 32, fuse_model: bool = True, + skip_image_resizing: bool = False, ) -> ImagesDetectionPrediction: """Predict an image or a list of images. @@ -197,19 +202,21 @@ def predict( If None, the default value associated to the training is used. :param batch_size: Maximum number of images to process at the same time. :param fuse_model: If True, create a copy of the model, and fuse some of its layers to increase performance. This increases memory usage. + :param skip_image_resizing: If True, the image processor will not resize the images. """ - pipeline = self._get_pipeline(iou=iou, conf=conf, fuse_model=fuse_model) + pipeline = self._get_pipeline(iou=iou, conf=conf, fuse_model=fuse_model, skip_image_resizing=skip_image_resizing) return pipeline(images, batch_size=batch_size) # type: ignore - def predict_webcam(self, iou: Optional[float] = None, conf: Optional[float] = None, fuse_model: bool = True): + def predict_webcam(self, iou: Optional[float] = None, conf: Optional[float] = None, fuse_model: bool = True, skip_image_resizing: bool = False): """Predict using webcam. :param iou: (Optional) IoU threshold for the nms algorithm. If None, the default value associated to the training is used. :param conf: (Optional) Below the confidence threshold, prediction are discarded. If None, the default value associated to the training is used. - :param fuse_model: If True, create a copy of the model, and fuse some of its layers to increase performance. This increases memory usage. + :param fuse_model: If True, create a copy of the model, and fuse some of its layers to increase performance. This increases memory usage. + :param skip_image_resizing: If True, the image processor will not resize the images. """ - pipeline = self._get_pipeline(iou=iou, conf=conf, fuse_model=fuse_model) + pipeline = self._get_pipeline(iou=iou, conf=conf, fuse_model=fuse_model, skip_image_resizing=skip_image_resizing) pipeline.predict_webcam() def train(self, mode: bool = True): diff --git a/src/super_gradients/training/models/detection_models/pp_yolo_e/pp_yolo_e.py b/src/super_gradients/training/models/detection_models/pp_yolo_e/pp_yolo_e.py index 878354db5d..b0ea258f3c 100644 --- a/src/super_gradients/training/models/detection_models/pp_yolo_e/pp_yolo_e.py +++ b/src/super_gradients/training/models/detection_models/pp_yolo_e/pp_yolo_e.py @@ -150,13 +150,16 @@ def set_dataset_processing_params( self._default_nms_conf = conf or self._default_nms_conf @lru_cache(maxsize=1) - def _get_pipeline(self, iou: Optional[float] = None, conf: Optional[float] = None, fuse_model: bool = True) -> DetectionPipeline: + def _get_pipeline( + self, iou: Optional[float] = None, conf: Optional[float] = None, fuse_model: bool = True, skip_image_resizing: bool = False + ) -> DetectionPipeline: """Instantiate the prediction pipeline of this model. :param iou: (Optional) IoU threshold for the nms algorithm. If None, the default value associated to the training is used. :param conf: (Optional) Below the confidence threshold, prediction are discarded. If None, the default value associated to the training is used. - :param fuse_model: If True, create a copy of the model, and fuse some of its layers to increase performance. This increases memory usage. + :param fuse_model: If True, create a copy of the model, and fuse some of its layers to increase performance. This increases memory usage. + :param skip_image_resizing: If True, the image processor will not resize the images. """ if None in (self._class_names, self._image_processor, self._default_nms_iou, self._default_nms_conf): raise RuntimeError( @@ -181,6 +184,7 @@ def predict( conf: Optional[float] = None, batch_size: int = 32, fuse_model: bool = True, + skip_image_resizing: bool = False, ) -> ImagesDetectionPrediction: """Predict an image or a list of images. @@ -190,19 +194,21 @@ def predict( If None, the default value associated to the training is used. :param batch_size: Maximum number of images to process at the same time. :param fuse_model: If True, create a copy of the model, and fuse some of its layers to increase performance. This increases memory usage. + :param skip_image_resizing: If True, the image processor will not resize the images. """ - pipeline = self._get_pipeline(iou=iou, conf=conf, fuse_model=fuse_model) + pipeline = self._get_pipeline(iou=iou, conf=conf, fuse_model=fuse_model, skip_image_resizing=skip_image_resizing) return pipeline(images, batch_size=batch_size) # type: ignore - def predict_webcam(self, iou: Optional[float] = None, conf: Optional[float] = None, fuse_model: bool = True): + def predict_webcam(self, iou: Optional[float] = None, conf: Optional[float] = None, fuse_model: bool = True, skip_image_resizing: bool = False): """Predict using webcam. :param iou: (Optional) IoU threshold for the nms algorithm. If None, the default value associated to the training is used. :param conf: (Optional) Below the confidence threshold, prediction are discarded. If None, the default value associated to the training is used. - :param fuse_model: If True, create a copy of the model, and fuse some of its layers to increase performance. This increases memory usage. + :param fuse_model: If True, create a copy of the model, and fuse some of its layers to increase performance. This increases memory usage. + :param skip_image_resizing: If True, the image processor will not resize the images. """ - pipeline = self._get_pipeline(iou=iou, conf=conf, fuse_model=fuse_model) + pipeline = self._get_pipeline(iou=iou, conf=conf, fuse_model=fuse_model, skip_image_resizing=skip_image_resizing) pipeline.predict_webcam() def train(self, mode: bool = True): diff --git a/src/super_gradients/training/models/detection_models/yolo_base.py b/src/super_gradients/training/models/detection_models/yolo_base.py index 6b359a4a95..26e9819617 100755 --- a/src/super_gradients/training/models/detection_models/yolo_base.py +++ b/src/super_gradients/training/models/detection_models/yolo_base.py @@ -536,13 +536,16 @@ def set_dataset_processing_params( self._default_nms_conf = conf or self._default_nms_conf @lru_cache(maxsize=1) - def _get_pipeline(self, iou: Optional[float] = None, conf: Optional[float] = None, fuse_model: bool = True) -> DetectionPipeline: + def _get_pipeline( + self, iou: Optional[float] = None, conf: Optional[float] = None, fuse_model: bool = True, skip_image_resizing: bool = False + ) -> DetectionPipeline: """Instantiate the prediction pipeline of this model. :param iou: (Optional) IoU threshold for the nms algorithm. If None, the default value associated to the training is used. :param conf: (Optional) Below the confidence threshold, prediction are discarded. If None, the default value associated to the training is used. - :param fuse_model: If True, create a copy of the model, and fuse some of its layers to increase performance. This increases memory usage. + :param fuse_model: If True, create a copy of the model, and fuse some of its layers to increase performance. This increases memory usage. + :param skip_image_resizing: If True, the image processor will not resize the images. """ if None in (self._class_names, self._image_processor, self._default_nms_iou, self._default_nms_conf): raise RuntimeError( @@ -558,6 +561,7 @@ def _get_pipeline(self, iou: Optional[float] = None, conf: Optional[float] = Non post_prediction_callback=self.get_post_prediction_callback(iou=iou, conf=conf), class_names=self._class_names, fuse_model=fuse_model, + skip_image_resizing=skip_image_resizing, ) return pipeline @@ -568,6 +572,7 @@ def predict( conf: Optional[float] = None, batch_size: int = 32, fuse_model: bool = True, + skip_image_resizing: bool = False, ) -> ImagesDetectionPrediction: """Predict an image or a list of images. @@ -577,19 +582,21 @@ def predict( If None, the default value associated to the training is used. :param batch_size: Maximum number of images to process at the same time. :param fuse_model: If True, create a copy of the model, and fuse some of its layers to increase performance. This increases memory usage. + :param skip_image_resizing: If True, the image processor will not resize the images. """ - pipeline = self._get_pipeline(iou=iou, conf=conf, fuse_model=fuse_model) + pipeline = self._get_pipeline(iou=iou, conf=conf, fuse_model=fuse_model, skip_image_resizing=skip_image_resizing) return pipeline(images, batch_size=batch_size) # type: ignore - def predict_webcam(self, iou: Optional[float] = None, conf: Optional[float] = None, fuse_model: bool = True): + def predict_webcam(self, iou: Optional[float] = None, conf: Optional[float] = None, fuse_model: bool = True, skip_image_resizing: bool = False): """Predict using webcam. :param iou: (Optional) IoU threshold for the nms algorithm. If None, the default value associated to the training is used. :param conf: (Optional) Below the confidence threshold, prediction are discarded. If None, the default value associated to the training is used. - :param fuse_model: If True, create a copy of the model, and fuse some of its layers to increase performance. This increases memory usage. + :param fuse_model: If True, create a copy of the model, and fuse some of its layers to increase performance. This increases memory usage. + :param skip_image_resizing: If True, the image processor will not resize the images. """ - pipeline = self._get_pipeline(iou=iou, conf=conf, fuse_model=fuse_model) + pipeline = self._get_pipeline(iou=iou, conf=conf, fuse_model=fuse_model, skip_image_resizing=skip_image_resizing) pipeline.predict_webcam() def train(self, mode: bool = True): diff --git a/src/super_gradients/training/models/pose_estimation_models/dekr_hrnet.py b/src/super_gradients/training/models/pose_estimation_models/dekr_hrnet.py index 4b991a1281..c634247317 100644 --- a/src/super_gradients/training/models/pose_estimation_models/dekr_hrnet.py +++ b/src/super_gradients/training/models/pose_estimation_models/dekr_hrnet.py @@ -583,12 +583,13 @@ def set_dataset_processing_params( self._default_nms_conf = conf or self._default_nms_conf @lru_cache(maxsize=1) - def _get_pipeline(self, conf: Optional[float] = None, fuse_model: bool = True) -> PoseEstimationPipeline: + def _get_pipeline(self, conf: Optional[float] = None, fuse_model: bool = True, skip_image_resizing: bool = False) -> PoseEstimationPipeline: """Instantiate the prediction pipeline of this model. :param conf: (Optional) Below the confidence threshold, prediction are discarded. If None, the default value associated to the training is used. - :param fuse_model: If True, create a copy of the model, and fuse some of its layers to increase performance. This increases memory usage. + :param fuse_model: If True, create a copy of the model, and fuse some of its layers to increase performance. This increases memory usage. + :param skip_image_resizing: If True, the image processor will not resize the images. """ if None in (self._edge_links, self._image_processor, self._default_nms_conf): raise RuntimeError( @@ -614,29 +615,34 @@ def _get_pipeline(self, conf: Optional[float] = None, fuse_model: bool = True) - keypoint_colors=self._keypoint_colors, post_prediction_callback=self.get_post_prediction_callback(conf=conf), fuse_model=fuse_model, + skip_image_resizing=skip_image_resizing, ) return pipeline - def predict(self, images: ImageSource, conf: Optional[float] = None, batch_size: int = 32, fuse_model: bool = True) -> ImagesPoseEstimationPrediction: + def predict( + self, images: ImageSource, conf: Optional[float] = None, batch_size: int = 32, fuse_model: bool = True, skip_image_resizing: bool = False + ) -> ImagesPoseEstimationPrediction: """Predict an image or a list of images. :param images: Images to predict. :param conf: (Optional) Below the confidence threshold, prediction are discarded. If None, the default value associated to the training is used. :param batch_size: Maximum number of images to process at the same time. - :param fuse_model: If True, create a copy of the model, and fuse some of its layers to increase performance. This increases memory usage. + :param fuse_model: If True, create a copy of the model, and fuse some of its layers to increase performance. This increases memory usage. + :param skip_image_resizing: If True, the image processor will not resize the images. """ - pipeline = self._get_pipeline(conf=conf, fuse_model=fuse_model) + pipeline = self._get_pipeline(conf=conf, fuse_model=fuse_model, skip_image_resizing=skip_image_resizing) return pipeline(images, batch_size=batch_size) # type: ignore - def predict_webcam(self, conf: Optional[float] = None, fuse_model: bool = True): + def predict_webcam(self, conf: Optional[float] = None, fuse_model: bool = True, skip_image_resizing: bool = False): """Predict using webcam. :param conf: (Optional) Below the confidence threshold, prediction are discarded. If None, the default value associated to the training is used. - :param fuse_model: If True, create a copy of the model, and fuse some of its layers to increase performance. This increases memory usage. + :param fuse_model: If True, create a copy of the model, and fuse some of its layers to increase performance. This increases memory usage. + :param skip_image_resizing: If True, the image processor will not resize the images. """ - pipeline = self._get_pipeline(conf=conf, fuse_model=fuse_model) + pipeline = self._get_pipeline(conf=conf, fuse_model=fuse_model, skip_image_resizing=skip_image_resizing) pipeline.predict_webcam() def train(self, mode: bool = True): diff --git a/src/super_gradients/training/models/pose_estimation_models/yolo_nas_pose/yolo_nas_pose_variants.py b/src/super_gradients/training/models/pose_estimation_models/yolo_nas_pose/yolo_nas_pose_variants.py index 95e5ee7fac..76cec922c0 100644 --- a/src/super_gradients/training/models/pose_estimation_models/yolo_nas_pose/yolo_nas_pose_variants.py +++ b/src/super_gradients/training/models/pose_estimation_models/yolo_nas_pose/yolo_nas_pose_variants.py @@ -148,15 +148,17 @@ def predict( post_nms_max_predictions: Optional[int] = None, batch_size: int = 32, fuse_model: bool = True, + skip_image_resizing: bool = False, ) -> PoseEstimationPrediction: """Predict an image or a list of images. - :param images: Images to predict. - :param iou: (Optional) IoU threshold for the nms algorithm. If None, the default value associated to the training is used. - :param conf: (Optional) Below the confidence threshold, prediction are discarded. - If None, the default value associated to the training is used. - :param batch_size: Maximum number of images to process at the same time. - :param fuse_model: If True, create a copy of the model, and fuse some of its layers to increase performance. This increases memory usage. + :param images: Images to predict. + :param iou: (Optional) IoU threshold for the nms algorithm. If None, the default value associated to the training is used. + :param conf: (Optional) Below the confidence threshold, prediction are discarded. + If None, the default value associated to the training is used. + :param batch_size: Maximum number of images to process at the same time. + :param fuse_model: If True, create a copy of the model, and fuse some of its layers to increase performance. This increases memory usage. + :param skip_image_resizing: If True, the image processor will not resize the images. """ pipeline = self._get_pipeline( iou=iou, @@ -164,6 +166,7 @@ def predict( pre_nms_max_predictions=pre_nms_max_predictions, post_nms_max_predictions=post_nms_max_predictions, fuse_model=fuse_model, + skip_image_resizing=skip_image_resizing, ) return pipeline(images, batch_size=batch_size) # type: ignore @@ -175,13 +178,15 @@ def _get_pipeline( pre_nms_max_predictions: Optional[int] = None, post_nms_max_predictions: Optional[int] = None, fuse_model: bool = True, + skip_image_resizing: bool = False, ) -> PoseEstimationPipeline: """Instantiate the prediction pipeline of this model. - :param iou: (Optional) IoU threshold for the nms algorithm. If None, the default value associated to the training is used. - :param conf: (Optional) Below the confidence threshold, prediction are discarded. - If None, the default value associated to the training is used. - :param fuse_model: If True, create a copy of the model, and fuse some of its layers to increase performance. This increases memory usage. + :param iou: (Optional) IoU threshold for the nms algorithm. If None, the default value associated to the training is used. + :param conf: (Optional) Below the confidence threshold, prediction are discarded. + If None, the default value associated to the training is used. + :param fuse_model: If True, create a copy of the model, and fuse some of its layers to increase performance. This increases memory usage. + :param skip_image_resizing: If True, the image processor will not resize the images. """ if None in (self._image_processor, self._default_nms_iou, self._default_nms_conf, self._edge_links): raise RuntimeError( @@ -206,6 +211,7 @@ def _get_pipeline( edge_links=self._edge_links, edge_colors=self._edge_colors, keypoint_colors=self._keypoint_colors, + skip_image_resizing=skip_image_resizing, ) return pipeline diff --git a/src/super_gradients/training/pipelines/pipelines.py b/src/super_gradients/training/pipelines/pipelines.py index b056f980a4..6bd3fc83bf 100644 --- a/src/super_gradients/training/pipelines/pipelines.py +++ b/src/super_gradients/training/pipelines/pipelines.py @@ -30,7 +30,7 @@ from super_gradients.training.utils.media.stream import WebcamStreaming from super_gradients.training.utils.detection_utils import DetectionPostPredictionCallback from super_gradients.training.models.sg_module import SgModule -from super_gradients.training.processing.processing import Processing, ComposeProcessing +from super_gradients.training.processing.processing import Processing, ComposeProcessing, DetectionAutoPadding, KeypointsAutoPadding from super_gradients.common.abstractions.abstract_logger import get_logger logger = get_logger(__name__) @@ -52,11 +52,12 @@ class Pipeline(ABC): """An abstract base class representing a processing pipeline for a specific task. The pipeline includes loading images, preprocessing, prediction, and postprocessing. - :param model: The model used for making predictions. - :param image_processor: A single image processor or a list of image processors for preprocessing and postprocessing the images. - :param device: The device on which the model will be run. If None, will run on current model device. Use "cuda" for GPU support. - :param dtype: Specify the dtype of the inputs. If None, will use the dtype of the model's parameters. - :param fuse_model: If True, create a copy of the model, and fuse some of its layers to increase performance. This increases memory usage. + :param model: The model used for making predictions. + :param image_processor: A single image processor or a list of image processors for preprocessing and postprocessing the images. + :param device: The device on which the model will be run. If None, will run on current model device. Use "cuda" for GPU support. + :param dtype: Specify the dtype of the inputs. If None, will use the dtype of the model's parameters. + :param fuse_model: If True, create a copy of the model, and fuse some of its layers to increase performance. This increases memory usage. + :param skip_image_resizing: If True, the image processor will not resize the images. """ def __init__( @@ -67,6 +68,7 @@ def __init__( device: Optional[str] = None, fuse_model: bool = True, dtype: Optional[torch.dtype] = None, + skip_image_resizing: bool = False, ): model_device: torch.device = infer_model_device(model=model) if device: @@ -79,6 +81,14 @@ def __init__( if isinstance(image_processor, list): image_processor = ComposeProcessing(image_processor) + + self.skip_image_resizing = skip_image_resizing + if skip_image_resizing: + if isinstance(image_processor, ComposeProcessing): + image_processor.disable_image_resizing() + else: + logger.warning("`skip_image_resizing` only works when working with multiple image processing steps") + self.image_processor = image_processor self.fuse_model = fuse_model # If True, the model will be fused in the first forward pass, to make sure it gets the right input_size @@ -181,6 +191,15 @@ def _generate_prediction_result_single_batch(self, images: Iterable[np.ndarray]) images = list(images) # We need to load all the images into memory, and to reuse it afterwards. + if self.skip_image_resizing: + reference_shape = images[0].shape + for img in images: + if img.shape != reference_shape: + raise ValueError( + f"Images have different shapes ({img.shape} != {reference_shape})!\n" + f"Either resize the images to the same size, set `skip_image_resizing=False` or pass one image at a time." + ) + # Preprocess preprocessed_images, processing_metadatas = [], [] for image in images: @@ -261,6 +280,7 @@ class DetectionPipeline(Pipeline): :param image_processor: Single image processor or a list of image processors for preprocessing and postprocessing the images. :param device: The device on which the model will be run. If None, will run on current model device. Use "cuda" for GPU support. :param fuse_model: If True, create a copy of the model, and fuse some of its layers to increase performance. This increases memory usage. + :param skip_image_resizing: If True, the image processor will not resize the images. """ def __init__( @@ -269,10 +289,25 @@ def __init__( class_names: List[str], post_prediction_callback: DetectionPostPredictionCallback, device: Optional[str] = None, - image_processor: Optional[Processing] = None, + image_processor: Union[Processing, List[Processing]] = None, fuse_model: bool = True, + skip_image_resizing: bool = False, ): - super().__init__(model=model, device=device, image_processor=image_processor, class_names=class_names, fuse_model=fuse_model) + if isinstance(image_processor, list): + image_processor = ComposeProcessing(image_processor) + + # Ensure that the image size is divisible by 32. + if isinstance(image_processor, ComposeProcessing) and skip_image_resizing: + image_processor.processings.append(DetectionAutoPadding(shape_multiple=(32, 32), pad_value=0)) + + super().__init__( + model=model, + device=device, + image_processor=image_processor, + class_names=class_names, + fuse_model=fuse_model, + skip_image_resizing=skip_image_resizing, + ) self.post_prediction_callback = post_prediction_callback def _decode_model_output(self, model_output: Union[List, Tuple, torch.Tensor], model_input: np.ndarray) -> List[DetectionPrediction]: @@ -330,6 +365,7 @@ class PoseEstimationPipeline(Pipeline): :param image_processor: Single image processor or a list of image processors for preprocessing and postprocessing the images. :param device: The device on which the model will be run. If None, will run on current model device. Use "cuda" for GPU support. :param fuse_model: If True, create a copy of the model, and fuse some of its layers to increase performance. This increases memory usage. + :param skip_image_resizing: If True, the image processor will not resize the images. """ def __init__( @@ -340,10 +376,25 @@ def __init__( keypoint_colors: Union[np.ndarray, List[Tuple[int, int, int]]], post_prediction_callback, device: Optional[str] = None, - image_processor: Optional[Processing] = None, + image_processor: Union[Processing, List[Processing]] = None, fuse_model: bool = True, + skip_image_resizing: bool = False, ): - super().__init__(model=model, device=device, image_processor=image_processor, class_names=None, fuse_model=fuse_model) + if isinstance(image_processor, list): + image_processor = ComposeProcessing(image_processor) + + # Ensure that the image size is divisible by 32. + if isinstance(image_processor, ComposeProcessing) and skip_image_resizing: + image_processor.processings.append(KeypointsAutoPadding(shape_multiple=(32, 32), pad_value=0)) + + super().__init__( + model=model, + device=device, + image_processor=image_processor, + class_names=None, + fuse_model=fuse_model, + skip_image_resizing=skip_image_resizing, + ) self.post_prediction_callback = post_prediction_callback self.edge_links = np.asarray(edge_links, dtype=int) self.edge_colors = np.asarray(edge_colors, dtype=int) @@ -405,6 +456,7 @@ class ClassificationPipeline(Pipeline): :param image_processor: Single image processor or a list of image processors for preprocessing and postprocessing the images. :param device: The device on which the model will be run. If None, will run on current model device. Use "cuda" for GPU support. :param fuse_model: If True, create a copy of the model, and fuse some of its layers to increase performance. This increases memory usage. + :param skip_image_resizing: If True, the image processor will not resize the images. """ def __init__( @@ -412,10 +464,18 @@ def __init__( model: SgModule, class_names: List[str], device: Optional[str] = None, - image_processor: Optional[Processing] = None, + image_processor: Union[Processing, List[Processing]] = None, fuse_model: bool = True, + skip_image_resizing: bool = False, ): - super().__init__(model=model, device=device, image_processor=image_processor, class_names=class_names, fuse_model=fuse_model) + super().__init__( + model=model, + device=device, + image_processor=image_processor, + class_names=class_names, + fuse_model=fuse_model, + skip_image_resizing=skip_image_resizing, + ) def _decode_model_output(self, model_output: Union[List, Tuple, torch.Tensor], model_input: np.ndarray) -> List[ClassificationPrediction]: """Decode the model output diff --git a/src/super_gradients/training/processing/processing.py b/src/super_gradients/training/processing/processing.py index 625a48822c..97120d497f 100644 --- a/src/super_gradients/training/processing/processing.py +++ b/src/super_gradients/training/processing/processing.py @@ -21,6 +21,9 @@ _shift_keypoints, ) from super_gradients.training.utils.predict import Prediction, DetectionPrediction, PoseEstimationPrediction +from super_gradients.common.abstractions.abstract_logger import get_logger + +logger = get_logger(__name__) @dataclass @@ -84,20 +87,44 @@ def infer_image_input_shape(self) -> Optional[Tuple[int, int]]: """ return None + @property + @abstractmethod + def resizes_image(self) -> bool: + """Return True if the processing resizes the image, False otherwise.""" + pass + @register_processing(Processings.ComposeProcessing) class ComposeProcessing(Processing): """Compose a list of Processing objects into a single Processing object.""" - def __init__(self, processings: List[Processing]): + def __init__(self, processings: List[Processing], skip_image_resizing: bool = False): + """ + :param processings: List of Processing objects to compose. + :param skip_image_resizing: If True, skip the image resizing processings. + """ self.processings = processings + self.include_image_resizing = not skip_image_resizing + + def disable_image_resizing(self): + """Disable the image resizing for all the processings.""" + if self.include_image_resizing: + self.include_image_resizing = False + for processing in self.processings: + if processing.resizes_image: + logger.info(f"Skipping processing `{processing.__class__.__name__}` because it resizes the image.") + + def enable_image_resizing(self): + """Enable image resizing for all the processings.""" + self.include_image_resizing = True def preprocess_image(self, image: np.ndarray) -> Tuple[np.ndarray, ComposeProcessingMetadata]: """Processing an image, before feeding it to the network.""" processed_image, metadata_lst = image.copy(), [] for processing in self.processings: - processed_image, metadata = processing.preprocess_image(image=processed_image) - metadata_lst.append(metadata) + if self.include_image_resizing or not processing.resizes_image: + processed_image, metadata = processing.preprocess_image(image=processed_image) + metadata_lst.append(metadata) return processed_image, ComposeProcessingMetadata(metadata_lst=metadata_lst) def postprocess_predictions(self, predictions: Prediction, metadata: ComposeProcessingMetadata) -> Prediction: @@ -130,6 +157,10 @@ def infer_image_input_shape(self) -> Optional[Tuple[int, int]]: return output_shape + @property + def resizes_image(self) -> bool: + return any(processing.resizes_image for processing in self.processings) + @register_processing(Processings.ImagePermute) class ImagePermute(Processing): @@ -151,6 +182,10 @@ def postprocess_predictions(self, predictions: Prediction, metadata: None) -> Pr def get_equivalent_photometric_module(self) -> Optional[nn.Module]: return None + @property + def resizes_image(self) -> bool: + return False + @register_processing(Processings.ReverseImageChannels) class ReverseImageChannels(Processing): @@ -177,6 +212,10 @@ def get_equivalent_photometric_module(self) -> nn.Module: return ChannelSelect(channels=np.array([2, 1, 0], dtype=int)) + @property + def resizes_image(self) -> bool: + return False + @register_processing(Processings.StandardizeImage) class StandardizeImage(Processing): @@ -211,6 +250,10 @@ def get_equivalent_photometric_module(self) -> nn.Module: return ApplyMeanStd(mean=np.array([0], dtype=np.float32), std=np.array([self.max_value], dtype=np.float32)) + @property + def resizes_image(self) -> bool: + return False + @register_processing(Processings.NormalizeImage) class NormalizeImage(Processing): @@ -235,6 +278,10 @@ def get_equivalent_photometric_module(self) -> nn.Module: return ApplyMeanStd(mean=self.mean, std=self.std) + @property + def resizes_image(self) -> bool: + return False + class _DetectionPadding(Processing, ABC): """Base class for detection padding methods. One should implement the `_get_padding_params` method to work with a custom padding method. @@ -277,6 +324,10 @@ def infer_image_input_shape(self) -> Optional[Tuple[int, int]]: """ return self.output_shape + @property + def resizes_image(self) -> bool: + return True + class _KeypointsPadding(Processing, ABC): """Base class for keypoints padding methods. One should implement the `_get_padding_params` method to work with a custom padding method. @@ -325,6 +376,10 @@ def infer_image_input_shape(self) -> Optional[Tuple[int, int]]: """ return self.output_shape + @property + def resizes_image(self) -> bool: + return True + @register_processing(Processings.DetectionCenterPadding) class DetectionCenterPadding(_DetectionPadding): @@ -344,6 +399,120 @@ def _get_padding_params(self, input_shape: Tuple[int, int]) -> PaddingCoordinate return _get_bottom_right_padding_coordinates(input_shape=input_shape, output_shape=self.output_shape) +@register_processing() +class DetectionAutoPadding(Processing): + def __init__(self, shape_multiple: Tuple[int, int], pad_value: int): + """ + :param shape_multiple: Tuple of (H, W) indicating the height and width multiples to which the input image dimensions will be padded. + For instance, with a value of (32, 40), an input image of size (45, 67) will be padded to (64, 80). + :param pad_value: Value to pad the image with. + """ + self.shape_multiple = shape_multiple + self.pad_value = pad_value + + def preprocess_image(self, image: np.ndarray) -> Tuple[np.ndarray, DetectionPadToSizeMetadata]: + is_input_channel_first = image.ndim == 3 and image.shape[0] < image.shape[2] + if is_input_channel_first: # Channel first + image = image.transpose((1, 2, 0)) # CHW -> HWC + padding_coordinates = self._get_padding_params(input_shape=image.shape[:2]) # HWC -> (H, W) + processed_image = _pad_image(image=image, padding_coordinates=padding_coordinates, pad_value=self.pad_value) + + if is_input_channel_first: + processed_image = processed_image.transpose((2, 0, 1)) # HWC -> CHW + return processed_image, DetectionPadToSizeMetadata(padding_coordinates=padding_coordinates) + + def _get_padding_params(self, input_shape: Tuple[int, int]) -> PaddingCoordinates: + input_height, input_width = input_shape + height_modulo, width_modulo = self.shape_multiple + + # Calculate necessary padding to reach the modulo + padded_height = ((input_height + height_modulo - 1) // height_modulo) * height_modulo + padded_width = ((input_width + width_modulo - 1) // width_modulo) * width_modulo + + padding_top = 0 # No padding at the top + padding_left = 0 # No padding on the left + padding_bottom = padded_height - input_height + padding_right = padded_width - input_width + + return PaddingCoordinates(top=padding_top, left=padding_left, bottom=padding_bottom, right=padding_right) + + def postprocess_predictions(self, predictions: DetectionPrediction, metadata: DetectionPadToSizeMetadata) -> DetectionPrediction: + predictions.bboxes_xyxy = _shift_bboxes( + targets=predictions.bboxes_xyxy, + shift_h=-metadata.padding_coordinates.top, + shift_w=-metadata.padding_coordinates.left, + ) + return predictions + + def get_equivalent_photometric_module(self) -> Optional[nn.Module]: + return None + + @property + def resizes_image(self) -> bool: + # This implementation only pads the image, doesn't resize it. + return False + + +@register_processing() +class KeypointsAutoPadding(Processing): + def __init__(self, shape_multiple: Tuple[int, int], pad_value: int): + """ + :param shape_multiple: Tuple of (H, W) indicating the height and width multiples to which the input image dimensions will be padded. + For instance, with a value of (32, 40), an input image of size (45, 67) will be padded to (64, 80). + :param pad_value: Value to pad the image with. + """ + self.shape_multiple = shape_multiple + self.pad_value = pad_value + + def preprocess_image(self, image: np.ndarray) -> Tuple[np.ndarray, DetectionPadToSizeMetadata]: + is_input_channel_first = image.ndim == 3 and image.shape[0] < image.shape[2] + if is_input_channel_first: # Channel first + image = image.transpose((1, 2, 0)) # CHW -> HWC + padding_coordinates = self._get_padding_params(input_shape=image.shape[:2]) # HWC -> (H, W) + processed_image = _pad_image(image=image, padding_coordinates=padding_coordinates, pad_value=self.pad_value) + + if is_input_channel_first: + processed_image = processed_image.transpose((2, 0, 1)) # HWC -> CHW + return processed_image, DetectionPadToSizeMetadata(padding_coordinates=padding_coordinates) + + def _get_padding_params(self, input_shape: Tuple[int, int]) -> PaddingCoordinates: + input_height, input_width = input_shape + height_modulo, width_modulo = self.shape_multiple + + # Calculate necessary padding to reach the modulo + padded_height = ((input_height + height_modulo - 1) // height_modulo) * height_modulo + padded_width = ((input_width + width_modulo - 1) // width_modulo) * width_modulo + + padding_top = 0 # No padding at the top + padding_left = 0 # No padding on the left + padding_bottom = padded_height - input_height + padding_right = padded_width - input_width + + return PaddingCoordinates(top=padding_top, left=padding_left, bottom=padding_bottom, right=padding_right) + + def postprocess_predictions(self, predictions: PoseEstimationPrediction, metadata: DetectionPadToSizeMetadata) -> PoseEstimationPrediction: + predictions.poses = _shift_keypoints( + targets=predictions.poses, + shift_h=-metadata.padding_coordinates.top, + shift_w=-metadata.padding_coordinates.left, + ) + if predictions.bboxes_xyxy is not None: + predictions.bboxes_xyxy = _shift_bboxes( + targets=predictions.bboxes_xyxy, + shift_h=-metadata.padding_coordinates.top, + shift_w=-metadata.padding_coordinates.left, + ) + return predictions + + def get_equivalent_photometric_module(self) -> Optional[nn.Module]: + return None + + @property + def resizes_image(self) -> bool: + # This implementation only pads the image, doesn't resize it. + return False + + class _Rescale(Processing, ABC): """Resize image to given image dimensions WITHOUT preserving aspect ratio. @@ -370,6 +539,10 @@ def infer_image_input_shape(self) -> Optional[Tuple[int, int]]: """ return self.output_shape + @property + def resizes_image(self) -> bool: + return True + class _LongestMaxSizeRescale(Processing, ABC): """Resize image to given image dimensions WITH preserving aspect ratio. @@ -401,6 +574,10 @@ def infer_image_input_shape(self) -> Optional[Tuple[int, int]]: """ return None + @property + def resizes_image(self) -> bool: + return True + @register_processing(Processings.DetectionRescale) class DetectionRescale(_Rescale): @@ -468,6 +645,10 @@ def infer_image_input_shape(self) -> Optional[Tuple[int, int]]: """ return (self.size, self.size) + @property + def resizes_image(self) -> bool: + return True + @register_processing(Processings.CenterCrop) class CenterCrop(ClassificationProcess): @@ -507,6 +688,10 @@ def infer_image_input_shape(self) -> Optional[Tuple[int, int]]: """ return (self.size, self.size) + @property + def resizes_image(self) -> bool: + return True + def default_yolox_coco_processing_params() -> dict: """Processing parameters commonly used for training YoloX on COCO dataset. From 6cf8cc9e3446e8b27179fb75e4aa550569a4d182 Mon Sep 17 00:00:00 2001 From: Louis Dupont Date: Mon, 6 Nov 2023 10:49:52 +0100 Subject: [PATCH 02/12] add --- .../training/pipelines/pipelines.py | 2 ++ .../training/processing/processing.py | 32 ++++++++----------- 2 files changed, 16 insertions(+), 18 deletions(-) diff --git a/src/super_gradients/training/pipelines/pipelines.py b/src/super_gradients/training/pipelines/pipelines.py index 6bd3fc83bf..3172d21649 100644 --- a/src/super_gradients/training/pipelines/pipelines.py +++ b/src/super_gradients/training/pipelines/pipelines.py @@ -298,6 +298,7 @@ def __init__( # Ensure that the image size is divisible by 32. if isinstance(image_processor, ComposeProcessing) and skip_image_resizing: + image_processor = image_processor.get_equivalent_compose_without_resizing() image_processor.processings.append(DetectionAutoPadding(shape_multiple=(32, 32), pad_value=0)) super().__init__( @@ -385,6 +386,7 @@ def __init__( # Ensure that the image size is divisible by 32. if isinstance(image_processor, ComposeProcessing) and skip_image_resizing: + image_processor = image_processor.get_equivalent_compose_without_resizing() image_processor.processings.append(KeypointsAutoPadding(shape_multiple=(32, 32), pad_value=0)) super().__init__( diff --git a/src/super_gradients/training/processing/processing.py b/src/super_gradients/training/processing/processing.py index d1720f975d..2b154a1d73 100644 --- a/src/super_gradients/training/processing/processing.py +++ b/src/super_gradients/training/processing/processing.py @@ -98,33 +98,18 @@ def resizes_image(self) -> bool: class ComposeProcessing(Processing): """Compose a list of Processing objects into a single Processing object.""" - def __init__(self, processings: List[Processing], skip_image_resizing: bool = False): + def __init__(self, processings: List[Processing]): """ :param processings: List of Processing objects to compose. - :param skip_image_resizing: If True, skip the image resizing processings. """ self.processings = processings - self.include_image_resizing = not skip_image_resizing - - def disable_image_resizing(self): - """Disable the image resizing for all the processings.""" - if self.include_image_resizing: - self.include_image_resizing = False - for processing in self.processings: - if processing.resizes_image: - logger.info(f"Skipping processing `{processing.__class__.__name__}` because it resizes the image.") - - def enable_image_resizing(self): - """Enable image resizing for all the processings.""" - self.include_image_resizing = True def preprocess_image(self, image: np.ndarray) -> Tuple[np.ndarray, ComposeProcessingMetadata]: """Processing an image, before feeding it to the network.""" processed_image, metadata_lst = image.copy(), [] for processing in self.processings: - if self.include_image_resizing or not processing.resizes_image: - processed_image, metadata = processing.preprocess_image(image=processed_image) - metadata_lst.append(metadata) + processed_image, metadata = processing.preprocess_image(image=processed_image) + metadata_lst.append(metadata) return processed_image, ComposeProcessingMetadata(metadata_lst=metadata_lst) def postprocess_predictions(self, predictions: Prediction, metadata: ComposeProcessingMetadata) -> Prediction: @@ -161,6 +146,17 @@ def infer_image_input_shape(self) -> Optional[Tuple[int, int]]: def resizes_image(self) -> bool: return any(processing.resizes_image for processing in self.processings) + def get_equivalent_compose_without_resizing(self) -> "ComposeProcessing": + processings = [] + for processing in self.processings: + if isinstance(processing, ComposeProcessing): + processings.append(processing.get_equivalent_compose_without_resizing()) + elif not processing.resizes_image: + processings.append(processing) + else: + logger.info(f"Skipping processing `{processing.__class__.__name__}` because it resizes the image.") + return ComposeProcessing(processings) + @register_processing(Processings.ImagePermute) class ImagePermute(Processing): From 9117d588c182e915c5087703911ea8cbb3660874 Mon Sep 17 00:00:00 2001 From: Louis Dupont Date: Mon, 6 Nov 2023 13:49:05 +0100 Subject: [PATCH 03/12] add tests --- .../classification_models/base_classifer.py | 4 ++- .../detection_models/pp_yolo_e/pp_yolo_e.py | 2 ++ .../training/pipelines/pipelines.py | 28 +++++-------------- .../training/transforms/utils.py | 3 ++ tests/unit_tests/test_predict.py | 16 +++++++++++ 5 files changed, 31 insertions(+), 22 deletions(-) diff --git a/src/super_gradients/training/models/classification_models/base_classifer.py b/src/super_gradients/training/models/classification_models/base_classifer.py index bca9b7e40a..56ea93dbed 100644 --- a/src/super_gradients/training/models/classification_models/base_classifer.py +++ b/src/super_gradients/training/models/classification_models/base_classifer.py @@ -40,12 +40,14 @@ def _get_pipeline(self, fuse_model: bool = True, skip_image_resizing: bool = Fal "You must set the dataset processing parameters before calling predict.\n" "Please call `model.set_dataset_processing_params(...)` first." ) + if skip_image_resizing: + raise ValueError("`skip_image_resizing` is not supported for classification models.") + pipeline = ClassificationPipeline( model=self, image_processor=self._image_processor, class_names=self._class_names, fuse_model=fuse_model, - skip_image_resizing=skip_image_resizing, ) return pipeline diff --git a/src/super_gradients/training/models/detection_models/pp_yolo_e/pp_yolo_e.py b/src/super_gradients/training/models/detection_models/pp_yolo_e/pp_yolo_e.py index b0ea258f3c..eff6ebd5ee 100644 --- a/src/super_gradients/training/models/detection_models/pp_yolo_e/pp_yolo_e.py +++ b/src/super_gradients/training/models/detection_models/pp_yolo_e/pp_yolo_e.py @@ -174,6 +174,8 @@ def _get_pipeline( image_processor=self._image_processor, post_prediction_callback=self.get_post_prediction_callback(iou=iou, conf=conf), class_names=self._class_names, + fuse_model=fuse_model, + skip_image_resizing=skip_image_resizing, ) return pipeline diff --git a/src/super_gradients/training/pipelines/pipelines.py b/src/super_gradients/training/pipelines/pipelines.py index 3172d21649..f3d0128218 100644 --- a/src/super_gradients/training/pipelines/pipelines.py +++ b/src/super_gradients/training/pipelines/pipelines.py @@ -68,7 +68,6 @@ def __init__( device: Optional[str] = None, fuse_model: bool = True, dtype: Optional[torch.dtype] = None, - skip_image_resizing: bool = False, ): model_device: torch.device = infer_model_device(model=model) if device: @@ -82,13 +81,6 @@ def __init__( if isinstance(image_processor, list): image_processor = ComposeProcessing(image_processor) - self.skip_image_resizing = skip_image_resizing - if skip_image_resizing: - if isinstance(image_processor, ComposeProcessing): - image_processor.disable_image_resizing() - else: - logger.warning("`skip_image_resizing` only works when working with multiple image processing steps") - self.image_processor = image_processor self.fuse_model = fuse_model # If True, the model will be fused in the first forward pass, to make sure it gets the right input_size @@ -191,14 +183,13 @@ def _generate_prediction_result_single_batch(self, images: Iterable[np.ndarray]) images = list(images) # We need to load all the images into memory, and to reuse it afterwards. - if self.skip_image_resizing: - reference_shape = images[0].shape - for img in images: - if img.shape != reference_shape: - raise ValueError( - f"Images have different shapes ({img.shape} != {reference_shape})!\n" - f"Either resize the images to the same size, set `skip_image_resizing=False` or pass one image at a time." - ) + reference_shape = images[0].shape + for img in images: + if img.shape != reference_shape: + raise ValueError( + f"Images have different shapes ({img.shape} != {reference_shape})!\n" + f"Either resize the images to the same size, set `skip_image_resizing=False` or pass one image at a time." + ) # Preprocess preprocessed_images, processing_metadatas = [], [] @@ -307,7 +298,6 @@ def __init__( image_processor=image_processor, class_names=class_names, fuse_model=fuse_model, - skip_image_resizing=skip_image_resizing, ) self.post_prediction_callback = post_prediction_callback @@ -395,7 +385,6 @@ def __init__( image_processor=image_processor, class_names=None, fuse_model=fuse_model, - skip_image_resizing=skip_image_resizing, ) self.post_prediction_callback = post_prediction_callback self.edge_links = np.asarray(edge_links, dtype=int) @@ -458,7 +447,6 @@ class ClassificationPipeline(Pipeline): :param image_processor: Single image processor or a list of image processors for preprocessing and postprocessing the images. :param device: The device on which the model will be run. If None, will run on current model device. Use "cuda" for GPU support. :param fuse_model: If True, create a copy of the model, and fuse some of its layers to increase performance. This increases memory usage. - :param skip_image_resizing: If True, the image processor will not resize the images. """ def __init__( @@ -468,7 +456,6 @@ def __init__( device: Optional[str] = None, image_processor: Union[Processing, List[Processing]] = None, fuse_model: bool = True, - skip_image_resizing: bool = False, ): super().__init__( model=model, @@ -476,7 +463,6 @@ def __init__( image_processor=image_processor, class_names=class_names, fuse_model=fuse_model, - skip_image_resizing=skip_image_resizing, ) def _decode_model_output(self, model_output: Union[List, Tuple, torch.Tensor], model_input: np.ndarray) -> List[ClassificationPrediction]: diff --git a/src/super_gradients/training/transforms/utils.py b/src/super_gradients/training/transforms/utils.py index 9adec24ea9..11d5465681 100644 --- a/src/super_gradients/training/transforms/utils.py +++ b/src/super_gradients/training/transforms/utils.py @@ -148,6 +148,9 @@ def _pad_image(image: np.ndarray, padding_coordinates: PaddingCoordinates, pad_v constant_values = pad_value padding_values = (pad_h, pad_w) + print(image.shape) + print(padding_values) + print(constant_values) return np.pad(image, pad_width=padding_values, mode="constant", constant_values=constant_values) diff --git a/tests/unit_tests/test_predict.py b/tests/unit_tests/test_predict.py index 65ed786ea3..886b3f1a84 100644 --- a/tests/unit_tests/test_predict.py +++ b/tests/unit_tests/test_predict.py @@ -3,6 +3,8 @@ import tempfile from pathlib import Path +import numpy as np + from super_gradients.common.object_names import Models from super_gradients.training import models from super_gradients.training.datasets import COCODetectionDataset @@ -84,6 +86,20 @@ def test_predict_class_names(self): with self.assertRaises(ValueError): _ = predictions.show(class_names=["human"]) + def test_predict_detection_skip_resize(self): + for model_name in [Models.YOLO_NAS_S, Models.YOLOX_S, Models.PP_YOLOE_S]: + model = models.get(model_name, pretrained_weights="coco") + pipeline = model._get_pipeline(skip_image_resizing=True) + + dummy_images = [np.random.random((21, 21, 3)), np.random.random((21, 32, 3)), np.random.random((640, 640, 3))] + expected_preprocessing_shape = [(3, 32, 32), (3, 32, 32), (3, 640, 640)] + for image, expected_shape in zip(dummy_images, expected_preprocessing_shape): + pred = model.predict(image, skip_image_resizing=True)[0] + self.assertEqual(image.shape, pred.draw().shape) + + preprocessed_shape = pipeline.image_processor.preprocess_image(image)[0].shape + self.assertEqual(preprocessed_shape, expected_shape) + if __name__ == "__main__": unittest.main() From 292259960ecca72a0d5afdc46149ce624e666d24 Mon Sep 17 00:00:00 2001 From: Louis Dupont Date: Mon, 6 Nov 2023 14:01:57 +0100 Subject: [PATCH 04/12] move to _get_pipeline --- .../detection_models/customizable_detector.py | 13 ++++++++++--- .../detection_models/pp_yolo_e/pp_yolo_e.py | 12 +++++++++--- .../models/detection_models/yolo_base.py | 12 +++++++++--- .../models/pose_estimation_models/dekr_hrnet.py | 11 +++++++++-- .../yolo_nas_pose/yolo_nas_pose_variants.py | 11 +++++++++-- .../training/pipelines/pipelines.py | 17 +---------------- 6 files changed, 47 insertions(+), 29 deletions(-) diff --git a/src/super_gradients/training/models/detection_models/customizable_detector.py b/src/super_gradients/training/models/detection_models/customizable_detector.py index a3d3f63aa5..94aa2f92a4 100644 --- a/src/super_gradients/training/models/detection_models/customizable_detector.py +++ b/src/super_gradients/training/models/detection_models/customizable_detector.py @@ -21,7 +21,7 @@ import super_gradients.common.factories.detection_modules_factory as det_factory from super_gradients.training.utils.predict import ImagesDetectionPrediction from super_gradients.training.pipelines.pipelines import DetectionPipeline -from super_gradients.training.processing.processing import Processing +from super_gradients.training.processing.processing import Processing, ComposeProcessing, DetectionAutoPadding from super_gradients.training.utils.detection_utils import DetectionPostPredictionCallback from super_gradients.training.utils.media.image import ImageSource @@ -175,13 +175,20 @@ def _get_pipeline( iou = iou or self._default_nms_iou conf = conf or self._default_nms_conf + + # Ensure that the image size is divisible by 32. + if isinstance(self._image_processor, ComposeProcessing) and skip_image_resizing: + image_processor = self._image_processor.get_equivalent_compose_without_resizing() + image_processor.processings.append(DetectionAutoPadding(shape_multiple=(32, 32), pad_value=0)) + else: + image_processor = self._image_processor + pipeline = DetectionPipeline( model=self, - image_processor=self._image_processor, + image_processor=image_processor, post_prediction_callback=self.get_post_prediction_callback(iou=iou, conf=conf), class_names=self._class_names, fuse_model=fuse_model, - skip_image_resizing=skip_image_resizing, ) return pipeline diff --git a/src/super_gradients/training/models/detection_models/pp_yolo_e/pp_yolo_e.py b/src/super_gradients/training/models/detection_models/pp_yolo_e/pp_yolo_e.py index eff6ebd5ee..7c539d6eee 100644 --- a/src/super_gradients/training/models/detection_models/pp_yolo_e/pp_yolo_e.py +++ b/src/super_gradients/training/models/detection_models/pp_yolo_e/pp_yolo_e.py @@ -20,7 +20,7 @@ from super_gradients.training.models.detection_models.pp_yolo_e.pp_yolo_head import PPYOLOEHead from super_gradients.training.models.sg_module import SgModule from super_gradients.training.pipelines.pipelines import DetectionPipeline -from super_gradients.training.processing.processing import Processing +from super_gradients.training.processing.processing import Processing, ComposeProcessing, DetectionAutoPadding from super_gradients.training.utils import HpmStruct from super_gradients.training.utils.media.image import ImageSource from super_gradients.training.utils.predict import ImagesDetectionPrediction @@ -169,13 +169,19 @@ def _get_pipeline( iou = iou or self._default_nms_iou conf = conf or self._default_nms_conf + # Ensure that the image size is divisible by 32. + if isinstance(self._image_processor, ComposeProcessing) and skip_image_resizing: + image_processor = self._image_processor.get_equivalent_compose_without_resizing() + image_processor.processings.append(DetectionAutoPadding(shape_multiple=(32, 32), pad_value=0)) + else: + image_processor = self._image_processor + pipeline = DetectionPipeline( model=self, - image_processor=self._image_processor, + image_processor=image_processor, post_prediction_callback=self.get_post_prediction_callback(iou=iou, conf=conf), class_names=self._class_names, fuse_model=fuse_model, - skip_image_resizing=skip_image_resizing, ) return pipeline diff --git a/src/super_gradients/training/models/detection_models/yolo_base.py b/src/super_gradients/training/models/detection_models/yolo_base.py index 26e9819617..55c0d7abb3 100755 --- a/src/super_gradients/training/models/detection_models/yolo_base.py +++ b/src/super_gradients/training/models/detection_models/yolo_base.py @@ -28,7 +28,7 @@ from super_gradients.training.utils.utils import HpmStruct, check_img_size_divisibility, get_param, infer_model_dtype, infer_model_device from super_gradients.training.utils.predict import ImagesDetectionPrediction from super_gradients.training.pipelines.pipelines import DetectionPipeline -from super_gradients.training.processing.processing import Processing +from super_gradients.training.processing.processing import Processing, ComposeProcessing, DetectionAutoPadding from super_gradients.training.utils.media.image import ImageSource from super_gradients.module_interfaces import SupportsReplaceInputChannels @@ -555,13 +555,19 @@ def _get_pipeline( iou = iou or self._default_nms_iou conf = conf or self._default_nms_conf + # Ensure that the image size is divisible by 32. + if isinstance(self._image_processor, ComposeProcessing) and skip_image_resizing: + image_processor = self._image_processor.get_equivalent_compose_without_resizing() + image_processor.processings.append(DetectionAutoPadding(shape_multiple=(32, 32), pad_value=0)) + else: + image_processor = self._image_processor + pipeline = DetectionPipeline( model=self, - image_processor=self._image_processor, + image_processor=image_processor, post_prediction_callback=self.get_post_prediction_callback(iou=iou, conf=conf), class_names=self._class_names, fuse_model=fuse_model, - skip_image_resizing=skip_image_resizing, ) return pipeline diff --git a/src/super_gradients/training/models/pose_estimation_models/dekr_hrnet.py b/src/super_gradients/training/models/pose_estimation_models/dekr_hrnet.py index c634247317..4e3b661003 100644 --- a/src/super_gradients/training/models/pose_estimation_models/dekr_hrnet.py +++ b/src/super_gradients/training/models/pose_estimation_models/dekr_hrnet.py @@ -34,7 +34,7 @@ from super_gradients.training.pipelines.pipelines import PoseEstimationPipeline -from super_gradients.training.processing.processing import Processing +from super_gradients.training.processing.processing import Processing, ComposeProcessing, KeypointsAutoPadding from super_gradients.training.utils import HpmStruct, DEKRPoseEstimationDecodeCallback, get_param from super_gradients.training.utils.media.image import ImageSource @@ -607,9 +607,16 @@ def _get_pipeline(self, conf: Optional[float] = None, fuse_model: bool = True, s "The number of colors for the joints ({}) does not match the number of joint links ({})".format(len(self._edge_colors), len(self._edge_links)) ) + # Ensure that the image size is divisible by 32. + if isinstance(self._image_processor, ComposeProcessing) and skip_image_resizing: + image_processor = self._image_processor.get_equivalent_compose_without_resizing() + image_processor.processings.append(KeypointsAutoPadding(shape_multiple=(32, 32), pad_value=0)) + else: + image_processor = self._image_processor + pipeline = PoseEstimationPipeline( model=self, - image_processor=self._image_processor, + image_processor=image_processor, edge_links=self._edge_links, edge_colors=self._edge_colors, keypoint_colors=self._keypoint_colors, diff --git a/src/super_gradients/training/models/pose_estimation_models/yolo_nas_pose/yolo_nas_pose_variants.py b/src/super_gradients/training/models/pose_estimation_models/yolo_nas_pose/yolo_nas_pose_variants.py index 76cec922c0..ffc75e6254 100644 --- a/src/super_gradients/training/models/pose_estimation_models/yolo_nas_pose/yolo_nas_pose_variants.py +++ b/src/super_gradients/training/models/pose_estimation_models/yolo_nas_pose/yolo_nas_pose_variants.py @@ -13,7 +13,7 @@ from super_gradients.training.models.arch_params_factory import get_arch_params from super_gradients.training.models.detection_models.customizable_detector import CustomizableDetector from super_gradients.training.pipelines.pipelines import PoseEstimationPipeline -from super_gradients.training.processing.processing import Processing +from super_gradients.training.processing.processing import Processing, ComposeProcessing, KeypointsAutoPadding from super_gradients.training.utils import get_param from super_gradients.training.utils.media.image import ImageSource from super_gradients.training.utils.predict import PoseEstimationPrediction @@ -198,9 +198,16 @@ def _get_pipeline( pre_nms_max_predictions = pre_nms_max_predictions or self._default_pre_nms_max_predictions post_nms_max_predictions = post_nms_max_predictions or self._default_post_nms_max_predictions + # Ensure that the image size is divisible by 32. + if isinstance(self._image_processor, ComposeProcessing) and skip_image_resizing: + image_processor = self._image_processor.get_equivalent_compose_without_resizing() + image_processor.processings.append(KeypointsAutoPadding(shape_multiple=(32, 32), pad_value=0)) + else: + image_processor = self._image_processor + pipeline = PoseEstimationPipeline( model=self, - image_processor=self._image_processor, + image_processor=image_processor, post_prediction_callback=self.get_post_prediction_callback( iou=iou, conf=conf, diff --git a/src/super_gradients/training/pipelines/pipelines.py b/src/super_gradients/training/pipelines/pipelines.py index f3d0128218..f17183abba 100644 --- a/src/super_gradients/training/pipelines/pipelines.py +++ b/src/super_gradients/training/pipelines/pipelines.py @@ -30,7 +30,7 @@ from super_gradients.training.utils.media.stream import WebcamStreaming from super_gradients.training.utils.detection_utils import DetectionPostPredictionCallback from super_gradients.training.models.sg_module import SgModule -from super_gradients.training.processing.processing import Processing, ComposeProcessing, DetectionAutoPadding, KeypointsAutoPadding +from super_gradients.training.processing.processing import Processing, ComposeProcessing from super_gradients.common.abstractions.abstract_logger import get_logger logger = get_logger(__name__) @@ -57,7 +57,6 @@ class Pipeline(ABC): :param device: The device on which the model will be run. If None, will run on current model device. Use "cuda" for GPU support. :param dtype: Specify the dtype of the inputs. If None, will use the dtype of the model's parameters. :param fuse_model: If True, create a copy of the model, and fuse some of its layers to increase performance. This increases memory usage. - :param skip_image_resizing: If True, the image processor will not resize the images. """ def __init__( @@ -271,7 +270,6 @@ class DetectionPipeline(Pipeline): :param image_processor: Single image processor or a list of image processors for preprocessing and postprocessing the images. :param device: The device on which the model will be run. If None, will run on current model device. Use "cuda" for GPU support. :param fuse_model: If True, create a copy of the model, and fuse some of its layers to increase performance. This increases memory usage. - :param skip_image_resizing: If True, the image processor will not resize the images. """ def __init__( @@ -282,16 +280,10 @@ def __init__( device: Optional[str] = None, image_processor: Union[Processing, List[Processing]] = None, fuse_model: bool = True, - skip_image_resizing: bool = False, ): if isinstance(image_processor, list): image_processor = ComposeProcessing(image_processor) - # Ensure that the image size is divisible by 32. - if isinstance(image_processor, ComposeProcessing) and skip_image_resizing: - image_processor = image_processor.get_equivalent_compose_without_resizing() - image_processor.processings.append(DetectionAutoPadding(shape_multiple=(32, 32), pad_value=0)) - super().__init__( model=model, device=device, @@ -356,7 +348,6 @@ class PoseEstimationPipeline(Pipeline): :param image_processor: Single image processor or a list of image processors for preprocessing and postprocessing the images. :param device: The device on which the model will be run. If None, will run on current model device. Use "cuda" for GPU support. :param fuse_model: If True, create a copy of the model, and fuse some of its layers to increase performance. This increases memory usage. - :param skip_image_resizing: If True, the image processor will not resize the images. """ def __init__( @@ -369,16 +360,10 @@ def __init__( device: Optional[str] = None, image_processor: Union[Processing, List[Processing]] = None, fuse_model: bool = True, - skip_image_resizing: bool = False, ): if isinstance(image_processor, list): image_processor = ComposeProcessing(image_processor) - # Ensure that the image size is divisible by 32. - if isinstance(image_processor, ComposeProcessing) and skip_image_resizing: - image_processor = image_processor.get_equivalent_compose_without_resizing() - image_processor.processings.append(KeypointsAutoPadding(shape_multiple=(32, 32), pad_value=0)) - super().__init__( model=model, device=device, From 94d5eb25292cbb2576deafc2bf453a8a9b2df2a1 Mon Sep 17 00:00:00 2001 From: Louis Dupont Date: Mon, 6 Nov 2023 15:05:51 +0100 Subject: [PATCH 05/12] fix --- .../training/models/pose_estimation_models/dekr_hrnet.py | 1 - .../yolo_nas_pose/yolo_nas_pose_variants.py | 1 - 2 files changed, 2 deletions(-) diff --git a/src/super_gradients/training/models/pose_estimation_models/dekr_hrnet.py b/src/super_gradients/training/models/pose_estimation_models/dekr_hrnet.py index 4e3b661003..77b253df80 100644 --- a/src/super_gradients/training/models/pose_estimation_models/dekr_hrnet.py +++ b/src/super_gradients/training/models/pose_estimation_models/dekr_hrnet.py @@ -622,7 +622,6 @@ def _get_pipeline(self, conf: Optional[float] = None, fuse_model: bool = True, s keypoint_colors=self._keypoint_colors, post_prediction_callback=self.get_post_prediction_callback(conf=conf), fuse_model=fuse_model, - skip_image_resizing=skip_image_resizing, ) return pipeline diff --git a/src/super_gradients/training/models/pose_estimation_models/yolo_nas_pose/yolo_nas_pose_variants.py b/src/super_gradients/training/models/pose_estimation_models/yolo_nas_pose/yolo_nas_pose_variants.py index ffc75e6254..194ea902df 100644 --- a/src/super_gradients/training/models/pose_estimation_models/yolo_nas_pose/yolo_nas_pose_variants.py +++ b/src/super_gradients/training/models/pose_estimation_models/yolo_nas_pose/yolo_nas_pose_variants.py @@ -218,7 +218,6 @@ def _get_pipeline( edge_links=self._edge_links, edge_colors=self._edge_colors, keypoint_colors=self._keypoint_colors, - skip_image_resizing=skip_image_resizing, ) return pipeline From 8b428da16f48e1c75c63a5c277b77161bd6fbb4e Mon Sep 17 00:00:00 2001 From: Louis Dupont Date: Tue, 7 Nov 2023 14:04:12 +0100 Subject: [PATCH 06/12] fix test --- .../training/pipelines/pipelines.py | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/src/super_gradients/training/pipelines/pipelines.py b/src/super_gradients/training/pipelines/pipelines.py index f17183abba..9653bb2acd 100644 --- a/src/super_gradients/training/pipelines/pipelines.py +++ b/src/super_gradients/training/pipelines/pipelines.py @@ -182,14 +182,6 @@ def _generate_prediction_result_single_batch(self, images: Iterable[np.ndarray]) images = list(images) # We need to load all the images into memory, and to reuse it afterwards. - reference_shape = images[0].shape - for img in images: - if img.shape != reference_shape: - raise ValueError( - f"Images have different shapes ({img.shape} != {reference_shape})!\n" - f"Either resize the images to the same size, set `skip_image_resizing=False` or pass one image at a time." - ) - # Preprocess preprocessed_images, processing_metadatas = [], [] for image in images: @@ -197,6 +189,14 @@ def _generate_prediction_result_single_batch(self, images: Iterable[np.ndarray]) preprocessed_images.append(preprocessed_image) processing_metadatas.append(processing_metadata) + reference_shape = preprocessed_images[0].shape + for img in preprocessed_images: + if img.shape != reference_shape: + raise ValueError( + f"Images have different shapes ({img.shape} != {reference_shape})!\n" + f"Either resize the images to the same size, set `skip_image_resizing=False` or pass one image at a time." + ) + # Predict with eval_mode(self.model), torch.no_grad(), torch.cuda.amp.autocast(): torch_inputs = torch.from_numpy(np.array(preprocessed_images)).to(self.device) From e68a3a29f935f6df62f1488d91771976e6cf7fbd Mon Sep 17 00:00:00 2001 From: hakuryuu96 Date: Wed, 8 Nov 2023 11:49:40 +0200 Subject: [PATCH 07/12] Way to fix bug with validation frequency (#1601) * Way to fix bug with validation frequency * Fixed test, the state of net was rewritten * Added validating the latest epoch and epochs from save_ckpt_epoch_list * Added one more testcase to check wether latest notdivisible epoch has valid in metrics * Following the SRP recommendation... * Which inference time exactly * Fixed incorrect keyword in writing function * Missing brackets around epoch+1 in valid run check function. * Final fixes hopefully :) * Fixed trainer to add scalars only in main process --------- Co-authored-by: Eugene Khvedchenya --- .../training/sg_trainer/sg_trainer.py | 100 +++++++++++------- tests/end_to_end_tests/trainer_test.py | 31 ++++++ 2 files changed, 91 insertions(+), 40 deletions(-) diff --git a/src/super_gradients/training/sg_trainer/sg_trainer.py b/src/super_gradients/training/sg_trainer/sg_trainer.py index 8ee72916ce..c9efd304c8 100755 --- a/src/super_gradients/training/sg_trainer/sg_trainer.py +++ b/src/super_gradients/training/sg_trainer/sg_trainer.py @@ -756,6 +756,22 @@ def _init_arch_params(self) -> None: if arch_params is not None: self.arch_params.override(**arch_params.to_dict()) + def _should_run_validation_for_epoch(self, epoch: int) -> bool: + """ + Method returns true if the validation should to be calculated on this epoch (starting from 0). + + We need to calculate validation if + 1) the epoch is divisible by #run_validation_freq + 2) if epoch is last + 3) if epoch is in self.save_ckpt_epoch_list + """ + + is_run_val_freq_divisible = ((epoch + 1) % self.run_validation_freq) == 0 + is_last_epoch = (epoch + 1) == self.max_epochs + is_in_checkpoint_list = (epoch + 1) in self.training_params.save_ckpt_epoch_list + + return is_run_val_freq_divisible or is_last_epoch or is_in_checkpoint_list + # FIXME - we need to resolve flake8's 'function is too complex' for this function def train( self, @@ -1262,9 +1278,14 @@ def forward(self, inputs, targets): logger.warning("[Warning] Checkpoint does not include EMA weights, continuing training without EMA.") self.run_validation_freq = self.training_params.run_validation_freq + + if self.max_epochs % self.run_validation_freq != 0: + logger.warning( + "max_epochs is not divisible by run_validation_freq. " + "Please check the training parameters and ensure that run_validation_freq has been set correctly." + ) self.run_test_freq = self.training_params.run_test_freq - inf_time = 0 timer = core_utils.Timer(device_config.device) # IF THE LR MODE IS NOT DEFAULT TAKE IT FROM THE TRAINING PARAMS @@ -1438,6 +1459,7 @@ def forward(self, inputs, targets): for epoch in range(self.start_epoch, self.max_epochs): # broadcast_from_master is necessary here, since in DDP mode, only the master node will # receive the Ctrl-C signal, and we want all nodes to stop training. + timer.start() if broadcast_from_master(context.stop_training): logger.info("Request to stop training has been received, stopping training") break @@ -1485,13 +1507,18 @@ def forward(self, inputs, targets): keep_model = self.net self.net = self.ema_model.ema + train_inf_time = timer.stop() + self._write_scalars_to_logger(metrics=train_metrics_dict, epoch=1 + epoch, inference_time=train_inf_time, tag="Train") + # RUN TEST ON VALIDATION SET EVERY self.run_validation_freq EPOCHS valid_metrics_dict = {} - if (epoch + 1) % self.run_validation_freq == 0: + should_run_validation = self._should_run_validation_for_epoch(epoch) + + if should_run_validation: self.phase_callback_handler.on_validation_loader_start(context) timer.start() valid_metrics_dict = self._validate_epoch(context=context, silent_mode=silent_mode) - inf_time = timer.stop() + val_inf_time = timer.stop() self.valid_monitored_values = sg_trainer_utils.update_monitored_values_dict( monitored_values_dict=self.valid_monitored_values, @@ -1502,11 +1529,16 @@ def forward(self, inputs, targets): context.update_context(metrics_dict=valid_metrics_dict) self.phase_callback_handler.on_validation_loader_end(context) + self._write_scalars_to_logger(metrics=valid_metrics_dict, epoch=1 + epoch, inference_time=val_inf_time, tag="Valid") + test_metrics_dict = {} if (epoch + 1) % self.run_test_freq == 0: self.phase_callback_handler.on_test_loader_start(context) + test_inf_time = 0.0 for dataset_name, dataloader in self.test_loaders.items(): + timer.start() dataset_metrics_dict = self._test_epoch(data_loader=dataloader, context=context, silent_mode=silent_mode, dataset_name=dataset_name) + test_inf_time += timer.stop() dataset_metrics_dict_with_name = { f"{dataset_name}:{metric_name}": metric_value for metric_name, metric_value in dataset_metrics_dict.items() } @@ -1519,20 +1551,23 @@ def forward(self, inputs, targets): context.update_context(metrics_dict=test_metrics_dict) self.phase_callback_handler.on_test_loader_end(context) + self._write_scalars_to_logger(metrics=test_metrics_dict, epoch=1 + epoch, inference_time=test_inf_time, tag="Test") + if self.ema: self.net = keep_model if not self.ddp_silent_mode: + self.sg_logger.add_scalars(tag_scalar_dict=self._epoch_start_logging_values, global_step=1 + epoch) + # SAVING AND LOGGING OCCURS ONLY IN THE MAIN PROCESS (IN CASES THERE ARE SEVERAL PROCESSES - DDP) - self._write_to_disk_operations( - train_metrics_dict=train_metrics_dict, - validation_results_dict=valid_metrics_dict, - test_metrics_dict=test_metrics_dict, - lr_dict=self._epoch_start_logging_values, - inf_time=inf_time, - epoch=epoch, - context=context, - ) + if should_run_validation and self.training_params.save_model: + self._save_checkpoint( + optimizer=self.optimizer, + epoch=epoch + 1, + train_metrics_dict=train_metrics_dict, + validation_results_dict=valid_metrics_dict, + context=context, + ) self.sg_logger.upload() if not silent_mode: @@ -1938,35 +1973,20 @@ def _get_hyper_param_config(self): } return hyper_param_config - def _write_to_disk_operations( - self, - train_metrics_dict: dict, - validation_results_dict: dict, - test_metrics_dict: dict, - lr_dict: dict, - inf_time: float, - epoch: int, - context: PhaseContext, - ): - """Run the various logging operations, e.g.: log file, Tensorboard, save checkpoint etc.""" - result_dict = { - "Inference Time": inf_time, - **{f"Train_{k}": v for k, v in train_metrics_dict.items()}, - **{f"Valid_{k}": v for k, v in validation_results_dict.items()}, - **{f"Test_{k}": v for k, v in test_metrics_dict.items()}, - } - self.sg_logger.add_scalars(tag_scalar_dict=result_dict, global_step=epoch) - self.sg_logger.add_scalars(tag_scalar_dict=lr_dict, global_step=epoch) + def _write_scalars_to_logger(self, metrics: dict, epoch: int, inference_time: float, tag: str) -> None: + """ + Method for writing metrics and LR info to logger. - # SAVE THE CHECKPOINT - if self.training_params.save_model: - self._save_checkpoint( - optimizer=self.optimizer, - epoch=epoch + 1, - train_metrics_dict=train_metrics_dict, - validation_results_dict=validation_results_dict, - context=context, - ) + :param metrics: (dict) dict of metrics.. + :param epoch: (inf) 1-based number of epoch. + :param inference_time: (float) time of inference. + :param tag: (str) tag for writing to logger (rule of thumb: Train/Test/Valid) + """ + + if not self.ddp_silent_mode: + info_dict = {f"{tag} Inference Time": inference_time, **{f"{tag}_{k}": v for k, v in metrics.items()}} + + self.sg_logger.add_scalars(tag_scalar_dict=info_dict, global_step=epoch) def _get_epoch_start_logging_values(self) -> dict: """Get all the values that should be logged at the start of each epoch. diff --git a/tests/end_to_end_tests/trainer_test.py b/tests/end_to_end_tests/trainer_test.py index 0dbcbbad17..9703d19745 100644 --- a/tests/end_to_end_tests/trainer_test.py +++ b/tests/end_to_end_tests/trainer_test.py @@ -93,6 +93,37 @@ def test_checkpoint_content(self): weights_only = torch.load(os.path.join(trainer.checkpoints_dir_path, "ckpt_latest_weights_only.pth")) self.assertListEqual(["net"], list(weights_only.keys())) + def test_validation_frequency_divisible(self): + trainer, model = self.get_classification_trainer(self.experiment_names[0]) + training_params = self.training_params.copy() + training_params["max_epochs"] = 4 + training_params["run_validation_freq"] = 2 + trainer.train( + model=model, training_params=training_params, train_loader=classification_test_dataloader(), valid_loader=classification_test_dataloader() + ) + ckpt_filename = ["ckpt_best.pth", "ckpt_latest.pth"] + ckpt_paths = [os.path.join(trainer.checkpoints_dir_path, suf) for suf in ckpt_filename] + metrics = {} + for ckpt_path in ckpt_paths: + ckpt = torch.load(ckpt_path) + metrics[ckpt_path] = ckpt["metrics"] + self.assertTrue(metrics[ckpt_paths[0]] == metrics[ckpt_paths[1]]) + + def test_validation_frequency_and_save_ckpt_list(self): + trainer, model = self.get_classification_trainer(self.experiment_names[0]) + training_params = self.training_params.copy() + training_params["max_epochs"] = 5 + training_params["run_validation_freq"] = 3 + training_params["save_ckpt_epoch_list"] = [1] + trainer.train( + model=model, training_params=training_params, train_loader=classification_test_dataloader(), valid_loader=classification_test_dataloader() + ) + ckpt_filename = ["ckpt_epoch_1.pth", "ckpt_latest.pth"] + ckpt_paths = [os.path.join(trainer.checkpoints_dir_path, suf) for suf in ckpt_filename] + for ckpt_path in ckpt_paths: + ckpt = torch.load(ckpt_path) + self.assertTrue("valid" in ckpt["metrics"].keys()) + if __name__ == "__main__": unittest.main() From 3874c8672d1af4b11403307c69d367348ccad499 Mon Sep 17 00:00:00 2001 From: Louis Dupont Date: Thu, 9 Nov 2023 14:54:23 +0100 Subject: [PATCH 08/12] add images and update autopadding responsability --- .../detection_example_beach_raw_image.jpeg | Bin 0 -> 1844352 bytes ...ion_example_beach_raw_image_prediction.jpg | Bin 0 -> 438614 bytes ...tion_example_beach_resized_predictions.jpg | Bin 0 -> 430095 bytes .../examples/predict/detection_predict.py | 8 +- .../detection_models/customizable_detector.py | 5 +- .../training/processing/processing.py | 76 ++++++------------ 6 files changed, 31 insertions(+), 58 deletions(-) create mode 100644 documentation/source/images/detection_example_beach_raw_image.jpeg create mode 100644 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