From dff3ee915802d88b9b10b512089a47fc7197c5eb Mon Sep 17 00:00:00 2001 From: Eugene Date: Tue, 29 Aug 2023 12:07:41 +0300 Subject: [PATCH 1/2] Moving changes from master_320 to master --- documentation/source/models_export.md | 20 ++--- .../models_export_28_0.png | Bin 1044082 -> 1041690 bytes .../models_export_30_1.png | Bin 1047982 -> 1047170 bytes .../examples/model_export/models_export.ipynb | 10 +-- .../module_interfaces/exportable_detector.py | 82 +++++++++++++----- .../module_interfaces/module_interfaces.py | 19 ++-- .../detection_datasets/detection_dataset.py | 3 +- .../base_keypoints.py | 5 +- .../coco_keypoints.py | 2 +- .../detection_models/pp_yolo_e/pp_yolo_e.py | 19 +++- .../pp_yolo_e/pp_yolo_head.py | 4 +- .../models/detection_models/yolo_base.py | 33 +++++-- .../detection_models/yolo_nas/dfl_heads.py | 8 +- .../yolo_nas/yolo_nas_variants.py | 23 ++++- .../unit_tests/export_detection_model_test.py | 19 ++++ tests/unit_tests/preprocessing_unit_test.py | 18 +++- tests/unit_tests/replace_head_test.py | 17 ++-- 17 files changed, 211 insertions(+), 71 deletions(-) diff --git a/documentation/source/models_export.md b/documentation/source/models_export.md index f81590032e..33de990a3b 100644 --- a/documentation/source/models_export.md +++ b/documentation/source/models_export.md @@ -85,10 +85,10 @@ export_result import onnxruntime import numpy as np - session = onnxruntime.InferenceSession("yolo_nas_s.onnx") + session = onnxruntime.InferenceSession("yolo_nas_s.onnx", providers=["CUDAExecutionProvider", "CPUExecutionProvider"]) inputs = [o.name for o in session.get_inputs()] outputs = [o.name for o in session.get_outputs()] - example_input_image = np.zeros(1, 3, 640, 640).astype(np.uint8) + example_input_image = np.zeros((1, 3, 640, 640)).astype(np.uint8) predictions = session.run(outputs, {inputs[0]: example_input_image}) Exported model has predictions in batch format: @@ -117,7 +117,7 @@ image = load_image("https://deci-pretrained-models.s3.amazonaws.com/sample_image image = cv2.resize(image, (export_result.input_image_shape[1], export_result.input_image_shape[0])) image_bchw = np.transpose(np.expand_dims(image, 0), (0, 3, 1, 2)) -session = onnxruntime.InferenceSession(export_result.output) +session = onnxruntime.InferenceSession(export_result.output, providers=["CUDAExecutionProvider", "CPUExecutionProvider"]) inputs = [o.name for o in session.get_inputs()] outputs = [o.name for o in session.get_outputs()] result = session.run(outputs, {inputs[0]: image_bchw}) @@ -337,10 +337,10 @@ export_result import onnxruntime import numpy as np - session = onnxruntime.InferenceSession("yolo_nas_s.onnx") + session = onnxruntime.InferenceSession("yolo_nas_s.onnx", providers=["CUDAExecutionProvider", "CPUExecutionProvider"]) inputs = [o.name for o in session.get_inputs()] outputs = [o.name for o in session.get_outputs()] - example_input_image = np.zeros(1, 3, 640, 640).astype(np.uint8) + example_input_image = np.zeros((1, 3, 640, 640)).astype(np.uint8) predictions = session.run(outputs, {inputs[0]: example_input_image}) Exported model has predictions in flat format: @@ -359,7 +359,7 @@ Now we exported a model that produces predictions in `flat` format. Let's run th ```python -session = onnxruntime.InferenceSession(export_result.output) +session = onnxruntime.InferenceSession(export_result.output, providers=["CUDAExecutionProvider", "CPUExecutionProvider"]) inputs = [o.name for o in session.get_inputs()] outputs = [o.name for o in session.get_outputs()] result = session.run(outputs, {inputs[0]: image_bchw}) @@ -437,7 +437,7 @@ export_result = model.export( output_predictions_format = DetectionOutputFormatMode.FLAT_FORMAT ) -session = onnxruntime.InferenceSession(export_result.output) +session = onnxruntime.InferenceSession(export_result.output, providers=["CUDAExecutionProvider", "CPUExecutionProvider"]) inputs = [o.name for o in session.get_inputs()] outputs = [o.name for o in session.get_outputs()] result = session.run(outputs, {inputs[0]: image_bchw}) @@ -471,7 +471,7 @@ export_result = model.export( quantization_mode=ExportQuantizationMode.INT8 # or ExportQuantizationMode.FP16 ) -session = onnxruntime.InferenceSession(export_result.output) +session = onnxruntime.InferenceSession(export_result.output, providers=["CUDAExecutionProvider", "CPUExecutionProvider"]) inputs = [o.name for o in session.get_inputs()] outputs = [o.name for o in session.get_outputs()] result = session.run(outputs, {inputs[0]: image_bchw}) @@ -514,7 +514,7 @@ export_result = model.export( calibration_loader=dummy_calibration_loader ) -session = onnxruntime.InferenceSession(export_result.output) +session = onnxruntime.InferenceSession(export_result.output, providers=["CUDAExecutionProvider", "CPUExecutionProvider"]) inputs = [o.name for o in session.get_inputs()] outputs = [o.name for o in session.get_outputs()] result = session.run(outputs, {inputs[0]: image_bchw}) @@ -522,7 +522,7 @@ result = session.run(outputs, {inputs[0]: image_bchw}) show_predictions_from_flat_format(image, result) ``` - 25%|█████████████████████████████████████████████████ | 4/16 [00:11<00:34, 2.87s/it] + 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z3om&mk7Tc-%JkUpT3#1D5$B&{)P|}rDxo9P^2SEtftMk8p)@f790oh04h#%L2Z92a zS+ha>p!u82)Yg61WqU!-+oFTDHD1LI*vr>_E~g2QQZdI@mvix{O47g=gBia%HH-(o zW#sG{Anu{hT6WG%iLf25AS>C+1LdnKg^>fwbE}r>tsT%&*ib1#PeoAC{r&xDUz&5H zhk7zXG|0xuS!$yJBzn7?<}{NgYIAdQcpp|@UQP%72}H;k)H~*QdV%pM;sQ+7HJ%4U z(m6Y4=(gK5G1ub?(xen(!-z`AvCJj@?)~?&4Eom+&5pmZ=;ME1n_ZOJ?%y!^*BbcW ztG4mM|Gx`Bgx8D>xU2uR%xsv1egDr@`09~!rTeGn|9e&buPfTxH2;6S!iy`cRKtVP UBE1*>H^491*GiHl5=K7%1OCL4vH$=8 diff --git a/src/super_gradients/examples/model_export/models_export.ipynb b/src/super_gradients/examples/model_export/models_export.ipynb index 92da026b35..131dd6e399 100644 --- a/src/super_gradients/examples/model_export/models_export.ipynb +++ b/src/super_gradients/examples/model_export/models_export.ipynb @@ -158,7 +158,7 @@ "image = cv2.resize(image, (export_result.input_image_shape[1], export_result.input_image_shape[0]))\n", "image_bchw = np.transpose(np.expand_dims(image, 0), (0, 3, 1, 2))\n", "\n", - "session = onnxruntime.InferenceSession(export_result.output)\n", + "session = onnxruntime.InferenceSession(export_result.output, providers=[\"CUDAExecutionProvider\", \"CPUExecutionProvider\"])\n", "inputs = [o.name for o in session.get_inputs()]\n", "outputs = [o.name for o in session.get_outputs()]\n", "result = session.run(outputs, {inputs[0]: image_bchw})\n", @@ -486,7 +486,7 @@ } ], "source": [ - "session = onnxruntime.InferenceSession(export_result.output)\n", + "session = onnxruntime.InferenceSession(export_result.output, providers=[\"CUDAExecutionProvider\", \"CPUExecutionProvider\"])\n", "inputs = [o.name for o in session.get_inputs()]\n", "outputs = [o.name for o in session.get_outputs()]\n", "result = session.run(outputs, {inputs[0]: image_bchw})\n", @@ -605,7 +605,7 @@ " output_predictions_format = DetectionOutputFormatMode.FLAT_FORMAT\n", ")\n", "\n", - "session = onnxruntime.InferenceSession(export_result.output)\n", + "session = onnxruntime.InferenceSession(export_result.output, providers=[\"CUDAExecutionProvider\", \"CPUExecutionProvider\"])\n", "inputs = [o.name for o in session.get_inputs()]\n", "outputs = [o.name for o in session.get_outputs()]\n", "result = session.run(outputs, {inputs[0]: image_bchw})\n", @@ -659,7 +659,7 @@ " quantization_mode=ExportQuantizationMode.INT8 # or ExportQuantizationMode.FP16\n", ")\n", "\n", - "session = onnxruntime.InferenceSession(export_result.output)\n", + "session = onnxruntime.InferenceSession(export_result.output, providers=[\"CUDAExecutionProvider\", \"CPUExecutionProvider\"])\n", "inputs = [o.name for o in session.get_inputs()]\n", "outputs = [o.name for o in session.get_outputs()]\n", "result = session.run(outputs, {inputs[0]: image_bchw})\n", @@ -729,7 +729,7 @@ " calibration_loader=dummy_calibration_loader\n", ")\n", "\n", - "session = onnxruntime.InferenceSession(export_result.output)\n", + "session = onnxruntime.InferenceSession(export_result.output, providers=[\"CUDAExecutionProvider\", \"CPUExecutionProvider\"])\n", "inputs = [o.name for o in session.get_inputs()]\n", "outputs = [o.name for o in session.get_outputs()]\n", "result = session.run(outputs, {inputs[0]: image_bchw})\n", diff --git a/src/super_gradients/module_interfaces/exportable_detector.py b/src/super_gradients/module_interfaces/exportable_detector.py index 336789577d..bddde3266f 100644 --- a/src/super_gradients/module_interfaces/exportable_detector.py +++ b/src/super_gradients/module_interfaces/exportable_detector.py @@ -16,12 +16,19 @@ from super_gradients.conversion.gs_utils import import_onnx_graphsurgeon_or_install from super_gradients.training.utils.export_utils import infer_format_from_file_name, infer_image_shape_from_model, infer_image_input_channels from super_gradients.training.utils.quantization.fix_pytorch_quantization_modules import patch_pytorch_quantization_modules_if_needed -from super_gradients.training.utils.utils import infer_model_device, check_model_contains_quantized_modules - +from super_gradients.training.utils.utils import infer_model_device, check_model_contains_quantized_modules, infer_model_dtype logger = get_logger(__name__) -__all__ = ["ExportableObjectDetectionModel", "AbstractObjectDetectionDecodingModule", "ModelExportResult"] +__all__ = ["ExportableObjectDetectionModel", "AbstractObjectDetectionDecodingModule", "ModelExportResult", "ModelHasNoPreprocessingParamsException"] + + +class ModelHasNoPreprocessingParamsException(Exception): + """ + Exception that is raised when model does not have preprocessing parameters. + """ + + pass class AbstractObjectDetectionDecodingModule(nn.Module): @@ -50,6 +57,19 @@ def forward(self, predictions: Any) -> Tuple[Tensor, Tensor]: """ raise NotImplementedError(f"forward() method is not implemented for class {self.__class__.__name__}. ") + @torch.jit.ignore + def infer_total_number_of_predictions(self, predictions: Any) -> int: + """ + This method is used to infer the total number of predictions for a given input resolution. + The function takes raw predictions from the model and returns the total number of predictions. + It is needed to check whether max_predictions_per_image and num_pre_nms_predictions are not greater than + the total number of predictions for a given resolution. + + :param predictions: Predictions from the model itself. + :return: A total number of predictions for a given resolution + """ + raise NotImplementedError(f"forward() method is not implemented for class {self.__class__.__name__}. ") + def get_output_names(self) -> List[str]: """ Returns the names of the outputs of the module. @@ -122,7 +142,7 @@ def export( confidence_threshold: Optional[float] = None, nms_threshold: Optional[float] = None, engine: Optional[ExportTargetBackend] = None, - quantization_mode: ExportQuantizationMode = Optional[None], + quantization_mode: Optional[ExportQuantizationMode] = None, selective_quantizer: Optional["SelectiveQuantizer"] = None, # noqa calibration_loader: Optional[DataLoader] = None, calibration_method: str = "percentile", @@ -295,7 +315,18 @@ def export( if isinstance(preprocessing, nn.Module): preprocessing_module = preprocessing elif preprocessing is True: - preprocessing_module = model.get_preprocessing_callback() + try: + preprocessing_module = model.get_preprocessing_callback() + except ModelHasNoPreprocessingParamsException: + raise ValueError( + "It looks like your model does not have dataset preprocessing params properly set.\n" + "This may happen if you instantiated model from scratch and not trained it yet. \n" + "Here are what you can do to fix this:\n" + "1. Manually fill up dataset processing params via model.set_dataset_processing_params(...).\n" + "2. Train your model first and then export it. Trainer will set_dataset_processing_params(...) for you.\n" + '3. Instantiate a model using pretrained weights: models.get(..., pretrained_weights="coco") \n' + "4. Disable preprocessing by passing model.export(..., preprocessing=False). \n" + ) if isinstance(preprocessing_module, nn.Sequential): preprocessing_module = nn.Sequential(CastTensorTo(model_type), *iter(preprocessing_module)) else: @@ -325,6 +356,27 @@ def export( num_pre_nms_predictions = postprocessing_module.num_pre_nms_predictions max_predictions_per_image = max_predictions_per_image or num_pre_nms_predictions + dummy_input = torch.randn(input_shape).to(device=infer_model_device(model), dtype=infer_model_dtype(model)) + with torch.no_grad(): + number_of_predictions = postprocessing_module.infer_total_number_of_predictions(model.eval()(dummy_input)) + + if num_pre_nms_predictions > number_of_predictions: + logger.warning( + f"num_pre_nms_predictions ({num_pre_nms_predictions}) is greater than the total number of predictions ({number_of_predictions}) for input" + f"shape {input_shape}. Setting num_pre_nms_predictions to {number_of_predictions}" + ) + num_pre_nms_predictions = number_of_predictions + # We have to re-created the postprocessing_module with the new value of num_pre_nms_predictions + postprocessing_kwargs["num_pre_nms_predictions"] = num_pre_nms_predictions + postprocessing_module: AbstractObjectDetectionDecodingModule = model.get_decoding_module(**postprocessing_kwargs) + + if max_predictions_per_image > num_pre_nms_predictions: + logger.warning( + f"max_predictions_per_image ({max_predictions_per_image}) is greater than num_pre_nms_predictions ({num_pre_nms_predictions}). " + f"Setting max_predictions_per_image to {num_pre_nms_predictions}" + ) + max_predictions_per_image = num_pre_nms_predictions + nms_threshold = nms_threshold or getattr(model, "_default_nms_iou", None) if nms_threshold is None: raise ValueError( @@ -339,12 +391,6 @@ def export( "Please specify the confidence_threshold explicitly: model.export(..., confidence_threshold=0.5)" ) - if max_predictions_per_image > num_pre_nms_predictions: - raise ValueError( - f"max_predictions_per_image={max_predictions_per_image} is greater than " - f"num_pre_nms_predictions={num_pre_nms_predictions}. " - f"Please specify max_predictions_per_image <= {num_pre_nms_predictions}." - ) else: attach_nms_postprocessing = False postprocessing_module = None @@ -523,19 +569,15 @@ def export( usage_instructions.append("") usage_instructions.append(" import onnxruntime") usage_instructions.append(" import numpy as np") - usage_instructions.append(f' session = onnxruntime.InferenceSession("{output}")') + usage_instructions.append(f' session = onnxruntime.InferenceSession("{output}", providers=["CUDAExecutionProvider", "CPUExecutionProvider"])') usage_instructions.append(" inputs = [o.name for o in session.get_inputs()]") usage_instructions.append(" outputs = [o.name for o in session.get_outputs()]") dtype_name = np.dtype(torch_dtype_to_numpy_dtype(input_image_dtype)).name - if preprocessing: - usage_instructions.append( - f" example_input_image = np.zeros({batch_size}, {input_image_channels}, {input_image_shape[0]}, {input_image_shape[1]}).astype(np.{dtype_name})" # noqa - ) - else: - usage_instructions.append( - f" example_input_image = np.zeros({batch_size}, {input_image_channels}, {input_image_shape[0]}, {input_image_shape[1]}).astype(np.{dtype_name})" # noqa - ) + usage_instructions.append( + f" example_input_image = np.zeros(({batch_size}, {input_image_channels}, {input_image_shape[0]}, {input_image_shape[1]})).astype(np.{dtype_name})" # noqa + ) + usage_instructions.append(" predictions = session.run(outputs, {inputs[0]: example_input_image})") usage_instructions.append("") diff --git a/src/super_gradients/module_interfaces/module_interfaces.py b/src/super_gradients/module_interfaces/module_interfaces.py index 6b9544dab4..a2d00ab1ba 100644 --- a/src/super_gradients/module_interfaces/module_interfaces.py +++ b/src/super_gradients/module_interfaces/module_interfaces.py @@ -1,13 +1,13 @@ -from typing import Callable, Optional +from typing import Callable, Optional, TYPE_CHECKING from torch import nn -from typing_extensions import Protocol, runtime_checkable -from super_gradients.training.processing.processing import Processing +if TYPE_CHECKING: + # This is a hack to avoid circular imports while still having type hints. + from super_gradients.training.processing.processing import Processing -@runtime_checkable -class HasPreprocessingParams(Protocol): +class HasPreprocessingParams: """ Protocol interface for torch datasets that support getting preprocessing params, later to be passed to a model that obeys NeedsPreprocessingParams. This interface class serves a purpose of explicitly indicating whether a torch dataset has @@ -16,7 +16,7 @@ class HasPreprocessingParams(Protocol): """ def get_dataset_preprocessing_params(self): - ... + raise NotImplementedError(f"get_dataset_preprocessing_params is not implemented in the derived class {self.__class__.__name__}") class HasPredict: @@ -43,12 +43,11 @@ def get_input_channels(self) -> int: """ raise NotImplementedError(f"get_input_channels is not implemented in the derived class {self.__class__.__name__}") - def get_processing_params(self) -> Optional[Processing]: + def get_processing_params(self) -> Optional["Processing"]: raise NotImplementedError(f"get_processing_params is not implemented in the derived class {self.__class__.__name__}") -@runtime_checkable -class SupportsReplaceNumClasses(Protocol): +class SupportsReplaceNumClasses: """ Protocol interface for modules that support replacing the number of classes. Derived classes should implement the `replace_num_classes` method. @@ -69,4 +68,4 @@ def replace_num_classes(self, num_classes: int, compute_new_weights_fn: Callable It takes existing nn.Module and returns a new one. :return: None """ - ... + raise NotImplementedError(f"replace_num_classes is not implemented in the derived class {self.__class__.__name__}") diff --git a/src/super_gradients/training/datasets/detection_datasets/detection_dataset.py b/src/super_gradients/training/datasets/detection_datasets/detection_dataset.py index 75f2764b50..352a34247b 100644 --- a/src/super_gradients/training/datasets/detection_datasets/detection_dataset.py +++ b/src/super_gradients/training/datasets/detection_datasets/detection_dataset.py @@ -16,6 +16,7 @@ from super_gradients.common.object_names import Datasets, Processings from super_gradients.common.registry.registry import register_dataset from super_gradients.common.decorators.factory_decorator import resolve_param +from super_gradients.module_interfaces import HasPreprocessingParams from super_gradients.training.utils.detection_utils import get_class_index_in_target from super_gradients.common.abstractions.abstract_logger import get_logger from super_gradients.training.transforms.transforms import DetectionTransform, DetectionTargetsFormatTransform, DetectionTargetsFormat @@ -30,7 +31,7 @@ @register_dataset(Datasets.DETECTION_DATASET) -class DetectionDataset(Dataset): +class DetectionDataset(Dataset, HasPreprocessingParams): """Detection dataset. This is a boilerplate class to facilitate the implementation of datasets. diff --git a/src/super_gradients/training/datasets/pose_estimation_datasets/base_keypoints.py b/src/super_gradients/training/datasets/pose_estimation_datasets/base_keypoints.py index 062f0b57d4..8054641428 100644 --- a/src/super_gradients/training/datasets/pose_estimation_datasets/base_keypoints.py +++ b/src/super_gradients/training/datasets/pose_estimation_datasets/base_keypoints.py @@ -8,6 +8,7 @@ from super_gradients.common.abstractions.abstract_logger import get_logger from super_gradients.common.object_names import Processings from super_gradients.common.registry.registry import register_collate_function +from super_gradients.module_interfaces import HasPreprocessingParams from super_gradients.training.datasets.pose_estimation_datasets.target_generators import KeypointsTargetsGenerator from super_gradients.training.transforms.keypoint_transforms import KeypointsCompose, KeypointTransform from super_gradients.training.utils.visualization.utils import generate_color_mapping @@ -15,7 +16,7 @@ logger = get_logger(__name__) -class BaseKeypointsDataset(Dataset): +class BaseKeypointsDataset(Dataset, HasPreprocessingParams): """ Base class for pose estimation datasets. Descendants should implement the load_sample method to read a sample from the disk and return (image, mask, joints, extras) tuple. @@ -116,7 +117,7 @@ def get_dataset_preprocessing_params(self): """ pipeline = self.transforms.get_equivalent_preprocessing() params = dict( - conf=0.25, + conf=0.05, image_processor={Processings.ComposeProcessing: {"processings": pipeline}}, edge_links=self.edge_links, edge_colors=self.edge_colors, diff --git a/src/super_gradients/training/datasets/pose_estimation_datasets/coco_keypoints.py b/src/super_gradients/training/datasets/pose_estimation_datasets/coco_keypoints.py index 77ee826dc8..7f71fe07af 100644 --- a/src/super_gradients/training/datasets/pose_estimation_datasets/coco_keypoints.py +++ b/src/super_gradients/training/datasets/pose_estimation_datasets/coco_keypoints.py @@ -216,7 +216,7 @@ def get_dataset_preprocessing_params(self): # to match with the expected input of the model. pipeline = [Processings.ReverseImageChannels] + self.transforms.get_equivalent_preprocessing() params = dict( - conf=0.25, + conf=0.05, image_processor={Processings.ComposeProcessing: {"processings": pipeline}}, edge_links=self.edge_links, edge_colors=self.edge_colors, 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 a96665c7ae..5932dfa7c9 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 @@ -1,5 +1,5 @@ from functools import lru_cache -from typing import Union, Optional, List, Tuple +from typing import Union, Optional, List, Tuple, Any import torch from torch import Tensor @@ -10,6 +10,7 @@ from super_gradients.common.object_names import Models from super_gradients.common.registry.registry import register_model from super_gradients.module_interfaces import AbstractObjectDetectionDecodingModule, ExportableObjectDetectionModel, HasPredict +from super_gradients.module_interfaces.exportable_detector import ModelHasNoPreprocessingParamsException from super_gradients.modules import RepVGGBlock from super_gradients.training.models.arch_params_factory import get_arch_params from super_gradients.training.models.detection_models.csp_resnet import CSPResNetBackbone @@ -81,6 +82,20 @@ def forward(self, inputs: Tuple[Tuple[Tensor, Tensor], Tuple[Tensor, ...]]) -> T return output_pred_bboxes, output_pred_scores + @torch.jit.ignore + def infer_total_number_of_predictions(self, predictions: Any) -> int: + """ + + :param inputs: + :return: + """ + if torch.jit.is_tracing(): + pred_bboxes, pred_scores = predictions + else: + pred_bboxes, pred_scores = predictions[0] + + return pred_bboxes.size(1) + class PPYoloE(SgModule, ExportableObjectDetectionModel, HasPredict): def __init__(self, arch_params): @@ -104,6 +119,8 @@ def get_post_prediction_callback(conf: float, iou: float) -> DetectionPostPredic def get_preprocessing_callback(self, **kwargs): processing = self.get_processing_params() + if processing is None: + raise ModelHasNoPreprocessingParamsException() preprocessing_module = processing.get_equivalent_photometric_module() return preprocessing_module diff --git a/src/super_gradients/training/models/detection_models/pp_yolo_e/pp_yolo_head.py b/src/super_gradients/training/models/detection_models/pp_yolo_e/pp_yolo_head.py index 2275ae7245..c82e5a652f 100644 --- a/src/super_gradients/training/models/detection_models/pp_yolo_e/pp_yolo_head.py +++ b/src/super_gradients/training/models/detection_models/pp_yolo_e/pp_yolo_head.py @@ -270,8 +270,8 @@ def _generate_anchors(self, feats=None, dtype=None, device=None): else: h = int(self.eval_size[0] / stride) w = int(self.eval_size[1] / stride) - shift_x = torch.arange(end=w) + self.grid_cell_offset - shift_y = torch.arange(end=h) + self.grid_cell_offset + shift_x = torch.arange(end=w, dtype=dtype) + self.grid_cell_offset + shift_y = torch.arange(end=h, dtype=dtype) + self.grid_cell_offset if torch_version_is_greater_or_equal(1, 10): shift_y, shift_x = torch.meshgrid(shift_y, shift_x, indexing="ij") else: 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 955bcd62a9..a81854a953 100755 --- a/src/super_gradients/training/models/detection_models/yolo_base.py +++ b/src/super_gradients/training/models/detection_models/yolo_base.py @@ -1,7 +1,7 @@ import collections import math import warnings -from typing import Union, Type, List, Tuple, Optional +from typing import Union, Type, List, Tuple, Optional, Any from functools import lru_cache import numpy as np @@ -11,6 +11,7 @@ from super_gradients.common.decorators.factory_decorator import resolve_param from super_gradients.common.factories.processing_factory import ProcessingFactory from super_gradients.module_interfaces import ExportableObjectDetectionModel, AbstractObjectDetectionDecodingModule, HasPredict +from super_gradients.module_interfaces.exportable_detector import ModelHasNoPreprocessingParamsException from super_gradients.modules import CrossModelSkipConnection, Conv from super_gradients.training.models.classification_models.regnet import AnyNetX, Stage from super_gradients.training.models.detection_models.csp_darknet53 import GroupedConvBlock, CSPDarknet53, get_yolo_type_params, SPP @@ -205,7 +206,12 @@ def __init__( self.n_anchors = 1 self.grid = [torch.zeros(1)] * self.detection_layers_num # init grid - self.register_buffer("stride", torch.tensor(stride), persistent=False) + if torch.is_tensor(stride): + stride = stride.clone().detach() + else: + stride = torch.tensor(stride) + + self.register_buffer("stride", stride, persistent=False) self.cls_convs = nn.ModuleList() self.reg_convs = nn.ModuleList() @@ -276,9 +282,9 @@ def forward(self, inputs): def _make_grid(nx: int, ny: int, dtype: torch.dtype): if torch_version_is_greater_or_equal(1, 10): # https://github.com/pytorch/pytorch/issues/50276 - yv, xv = torch.meshgrid([torch.arange(ny), torch.arange(nx)], indexing="ij") + yv, xv = torch.meshgrid([torch.arange(ny, dtype=dtype), torch.arange(nx, dtype=dtype)], indexing="ij") else: - yv, xv = torch.meshgrid([torch.arange(ny), torch.arange(nx)]) + yv, xv = torch.meshgrid([torch.arange(ny, dtype=dtype), torch.arange(nx, dtype=dtype)]) return torch.stack((xv, yv), 2).view((1, 1, ny, nx, 2)).to(dtype) @@ -497,6 +503,8 @@ def get_processing_params(self) -> Optional[Processing]: def get_preprocessing_callback(self, **kwargs): processing = self.get_processing_params() + if processing is None: + raise ModelHasNoPreprocessingParamsException() preprocessing_module = processing.get_equivalent_photometric_module() return preprocessing_module @@ -691,7 +699,7 @@ def replace_head(self, new_num_classes=None, new_head=None): new_last_layer = DetectX( num_classes=new_num_classes, - stride=self._head.anchors.stride, + stride=self.strides, activation_func_type=activation_type, channels=[width_mult(v) for v in (256, 512, 1024)], depthwise=isinstance(old_detectx.cls_convs[0][0], GroupedConvBlock), @@ -740,3 +748,18 @@ def forward(self, predictions): output_pred_scores = pred_scores.reshape(-1, pred_scores.size(2))[flat_indices, :].reshape(pred_scores.size(0), nms_top_k, pred_scores.size(2)) return output_pred_bboxes, output_pred_scores + + def get_num_pre_nms_predictions(self) -> int: + return self.num_pre_nms_predictions + + @torch.jit.ignore + def infer_total_number_of_predictions(self, predictions: Any) -> int: + """ + + :param inputs: + :return: + """ + if isinstance(predictions, (tuple, list)): + predictions = predictions[0] + + return predictions.size(1) diff --git a/src/super_gradients/training/models/detection_models/yolo_nas/dfl_heads.py b/src/super_gradients/training/models/detection_models/yolo_nas/dfl_heads.py index 63b52fceac..8ec7328999 100644 --- a/src/super_gradients/training/models/detection_models/yolo_nas/dfl_heads.py +++ b/src/super_gradients/training/models/detection_models/yolo_nas/dfl_heads.py @@ -281,14 +281,16 @@ def _generate_anchors(self, feats=None, dtype=None, device=None): else: h = int(self.eval_size[0] / stride) w = int(self.eval_size[1] / stride) - shift_x = torch.arange(end=w) + self.grid_cell_offset - shift_y = torch.arange(end=h) + self.grid_cell_offset + + shift_x = torch.arange(end=w, dtype=dtype) + self.grid_cell_offset + shift_y = torch.arange(end=h, dtype=dtype) + self.grid_cell_offset + if torch_version_is_greater_or_equal(1, 10): shift_y, shift_x = torch.meshgrid(shift_y, shift_x, indexing="ij") else: shift_y, shift_x = torch.meshgrid(shift_y, shift_x) - anchor_point = torch.stack([shift_x, shift_y], dim=-1).to(dtype=dtype) + anchor_point = torch.stack([shift_x, shift_y], dim=-1) anchor_points.append(anchor_point.reshape([-1, 2])) stride_tensor.append(torch.full([h * w, 1], stride, dtype=dtype)) anchor_points = torch.cat(anchor_points) diff --git a/src/super_gradients/training/models/detection_models/yolo_nas/yolo_nas_variants.py b/src/super_gradients/training/models/detection_models/yolo_nas/yolo_nas_variants.py index 614893f423..daa0d34b63 100644 --- a/src/super_gradients/training/models/detection_models/yolo_nas/yolo_nas_variants.py +++ b/src/super_gradients/training/models/detection_models/yolo_nas/yolo_nas_variants.py @@ -1,5 +1,5 @@ import copy -from typing import Union, Tuple, Optional +from typing import Union, Tuple, Optional, Any import torch from omegaconf import DictConfig @@ -8,7 +8,7 @@ from super_gradients.common.abstractions.abstract_logger import get_logger from super_gradients.common.object_names import Models from super_gradients.common.registry import register_model -from super_gradients.module_interfaces import ExportableObjectDetectionModel, AbstractObjectDetectionDecodingModule +from super_gradients.module_interfaces import ExportableObjectDetectionModel, AbstractObjectDetectionDecodingModule, ModelHasNoPreprocessingParamsException 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.models.detection_models.pp_yolo_e import PPYoloEPostPredictionCallback @@ -28,6 +28,23 @@ def __init__( super().__init__() self.num_pre_nms_predictions = num_pre_nms_predictions + @torch.jit.ignore + def infer_total_number_of_predictions(self, predictions: Any) -> int: + """ + + :param inputs: + :return: + """ + if torch.jit.is_tracing(): + pred_bboxes, pred_scores = predictions + else: + pred_bboxes, pred_scores = predictions[0] + + return pred_bboxes.size(1) + + def get_num_pre_nms_predictions(self) -> int: + return self.num_pre_nms_predictions + def forward(self, inputs: Tuple[Tuple[Tensor, Tensor], Tuple[Tensor, ...]]): if torch.jit.is_tracing(): pred_bboxes, pred_scores = inputs @@ -72,6 +89,8 @@ def get_decoding_module(self, num_pre_nms_predictions: int, **kwargs) -> Abstrac def get_preprocessing_callback(self, **kwargs): processing = self.get_processing_params() + if processing is None: + raise ModelHasNoPreprocessingParamsException() preprocessing_module = processing.get_equivalent_photometric_module() return preprocessing_module diff --git a/tests/unit_tests/export_detection_model_test.py b/tests/unit_tests/export_detection_model_test.py index 9e8d7dbf1c..9b2d924ac9 100644 --- a/tests/unit_tests/export_detection_model_test.py +++ b/tests/unit_tests/export_detection_model_test.py @@ -38,6 +38,25 @@ def setUp(self) -> None: this_dir = os.path.dirname(__file__) self.test_image_path = os.path.join(this_dir, "../data/tinycoco/images/val2017/000000444010.jpg") + def test_export_model_on_small_size(self): + with tempfile.TemporaryDirectory() as tmpdirname: + for model_type in [ + Models.YOLO_NAS_S, + Models.PP_YOLOE_S, + Models.YOLOX_S, + ]: + out_path = os.path.join(tmpdirname, model_type + ".onnx") + ppyolo_e: ExportableObjectDetectionModel = models.get(model_type, pretrained_weights="coco") + result = ppyolo_e.export( + out_path, + input_image_shape=(64, 64), + num_pre_nms_predictions=2000, + max_predictions_per_image=1000, + output_predictions_format=DetectionOutputFormatMode.BATCH_FORMAT, + ) + assert result.input_image_dtype == torch.uint8 + assert result.input_image_shape == (64, 64) + def test_the_most_common_export_use_case(self): """ Test the most common export use case - export to ONNX with all default parameters diff --git a/tests/unit_tests/preprocessing_unit_test.py b/tests/unit_tests/preprocessing_unit_test.py index 0360c761b5..9416f309ba 100644 --- a/tests/unit_tests/preprocessing_unit_test.py +++ b/tests/unit_tests/preprocessing_unit_test.py @@ -3,10 +3,13 @@ from pathlib import Path import numpy as np +import torch from super_gradients import Trainer from super_gradients.common.factories.list_factory import ListFactory from super_gradients.common.factories.processing_factory import ProcessingFactory +from super_gradients.module_interfaces import HasPreprocessingParams +from super_gradients.training import dataloaders from super_gradients.training import models from super_gradients.training.datasets import COCODetectionDataset from super_gradients.training.metrics import DetectionMetrics @@ -20,7 +23,6 @@ ) from super_gradients.training.transforms import DetectionPaddedRescale, DetectionRGB2BGR from super_gradients.training.utils.detection_utils import DetectionCollateFN, CrowdDetectionCollateFN -from super_gradients.training import dataloaders class PreprocessingUnitTest(unittest.TestCase): @@ -84,10 +86,12 @@ def test_setting_preprocessing_params_from_validation_set(self): ], } trainset = COCODetectionDataset(**train_dataset_params) - train_loader = dataloaders.get(dataset=trainset, dataloader_params={"collate_fn": DetectionCollateFN()}) + self.assertIsInstance(trainset, HasPreprocessingParams) + train_loader = dataloaders.get(dataset=trainset, dataloader_params={"collate_fn": DetectionCollateFN(), "num_workers": 0}) valset = COCODetectionDataset(**val_dataset_params) - valid_loader = dataloaders.get(dataset=valset, dataloader_params={"collate_fn": CrowdDetectionCollateFN()}) + self.assertIsInstance(valset, HasPreprocessingParams) + valid_loader = dataloaders.get(dataset=valset, dataloader_params={"collate_fn": CrowdDetectionCollateFN(), "num_workers": 0}) trainer = Trainer("test_setting_preprocessing_params_from_validation_set") @@ -119,6 +123,10 @@ def test_setting_preprocessing_params_from_validation_set(self): self.assertEqual(model._default_nms_iou, 0.65) self.assertEqual(model._default_nms_conf, 0.5) + checkpoint_path = os.path.join(trainer.checkpoints_dir_path, "ckpt_best.pth") + checkpoint = torch.load(checkpoint_path, map_location="cpu") + self.assertTrue("processing_params" in checkpoint) + def test_setting_preprocessing_params_from_checkpoint(self): model = models.get("yolox_s", num_classes=80) self.assertTrue(model._image_processor is None) @@ -187,6 +195,10 @@ def test_setting_preprocessing_params_from_checkpoint(self): self.assertEqual(model._default_nms_iou, 0.65) self.assertEqual(model._default_nms_conf, 0.5) + checkpoint_path = os.path.join(trainer.checkpoints_dir_path, "ckpt_best.pth") + checkpoint = torch.load(checkpoint_path, map_location="cpu") + self.assertTrue("processing_params" in checkpoint) + def test_processings_from_dataset_params(self): transforms = [DetectionRGB2BGR(prob=1), DetectionPaddedRescale(input_dim=(512, 512))] diff --git a/tests/unit_tests/replace_head_test.py b/tests/unit_tests/replace_head_test.py index 753dfaa860..12c0c56ff3 100644 --- a/tests/unit_tests/replace_head_test.py +++ b/tests/unit_tests/replace_head_test.py @@ -1,5 +1,3 @@ -import os -import shutil import unittest import torch @@ -14,6 +12,17 @@ def setUp(self) -> None: self.device = "cuda" if torch.cuda.is_available() and torch.cuda.device_count() > 0 else "cpu" super_gradients.init_trainer() + def test_yolox_replace_head(self): + input = torch.randn(1, 3, 640, 640).to(self.device) + for model in [Models.YOLOX_S, Models.YOLOX_M, Models.YOLOX_L, Models.YOLOX_T]: + model = models.get(model, pretrained_weights="coco").to(self.device).eval() + num_classes = 100 + model.replace_head(new_num_classes=num_classes) + outputs = model.forward(input) + self.assertEqual(outputs[0].size(4), num_classes + 5) + self.assertEqual(outputs[1].size(4), num_classes + 5) + self.assertEqual(outputs[2].size(4), num_classes + 5) + def test_ppyolo_replace_head(self): input = torch.randn(1, 3, 640, 640).to(self.device) for model in [Models.PP_YOLOE_S, Models.PP_YOLOE_M, Models.PP_YOLOE_L, Models.PP_YOLOE_X]: @@ -37,10 +46,6 @@ def test_dekr_replace_head(self): self.assertEqual(heatmap.size(1), 20 + 1) self.assertEqual(offsets.size(1), 20 * 2) - def tearDown(self) -> None: - if os.path.exists("~/.cache/torch/hub/"): - shutil.rmtree("~/.cache/torch/hub/") - if __name__ == "__main__": unittest.main() From 82788808aeeb7db9c2db5d53cb1a3664ca499163 Mon Sep 17 00:00:00 2001 From: Eugene Khvedchenya Date: Wed, 30 Aug 2023 15:14:39 +0300 Subject: [PATCH 2/2] Hotfix/sg 000 Fix support of arbitrary number of heads (#1431) * Added missing import * Fix YoloNASDFLHead to respect the number of heads * Revert back dtype --- src/super_gradients/module_interfaces/__init__.py | 11 +++++++++-- .../models/detection_models/yolo_nas/dfl_heads.py | 5 +++-- 2 files changed, 12 insertions(+), 4 deletions(-) diff --git a/src/super_gradients/module_interfaces/__init__.py b/src/super_gradients/module_interfaces/__init__.py index 6cf5060367..1193e4efc9 100644 --- a/src/super_gradients/module_interfaces/__init__.py +++ b/src/super_gradients/module_interfaces/__init__.py @@ -1,4 +1,11 @@ from .module_interfaces import HasPredict, HasPreprocessingParams, SupportsReplaceNumClasses -from .exportable_detector import ExportableObjectDetectionModel, AbstractObjectDetectionDecodingModule +from .exportable_detector import ExportableObjectDetectionModel, AbstractObjectDetectionDecodingModule, ModelHasNoPreprocessingParamsException -__all__ = ["HasPredict", "HasPreprocessingParams", "SupportsReplaceNumClasses", "ExportableObjectDetectionModel", "AbstractObjectDetectionDecodingModule"] +__all__ = [ + "HasPredict", + "HasPreprocessingParams", + "SupportsReplaceNumClasses", + "ExportableObjectDetectionModel", + "AbstractObjectDetectionDecodingModule", + "ModelHasNoPreprocessingParamsException", +] diff --git a/src/super_gradients/training/models/detection_models/yolo_nas/dfl_heads.py b/src/super_gradients/training/models/detection_models/yolo_nas/dfl_heads.py index 8ec7328999..0418f97782 100644 --- a/src/super_gradients/training/models/detection_models/yolo_nas/dfl_heads.py +++ b/src/super_gradients/training/models/detection_models/yolo_nas/dfl_heads.py @@ -198,6 +198,7 @@ def _init_weights(self): @torch.jit.ignore def forward_train(self, feats: Tuple[Tensor, ...]): + feats = feats[: self.num_heads] anchors, anchor_points, num_anchors_list, stride_tensor = generate_anchors_for_grid_cell( feats, self.fpn_strides, self.grid_cell_scale, self.grid_cell_offset ) @@ -215,7 +216,7 @@ def forward_train(self, feats: Tuple[Tensor, ...]): return cls_score_list, reg_distri_list, anchors, anchor_points, num_anchors_list, stride_tensor def forward_eval(self, feats: Tuple[Tensor, ...]) -> Tuple[Tuple[Tensor, Tensor], Tuple[Tensor, ...]]: - + feats = feats[: self.num_heads] cls_score_list, reg_distri_list, reg_dist_reduced_list = [], [], [] for i, feat in enumerate(feats): @@ -290,7 +291,7 @@ def _generate_anchors(self, feats=None, dtype=None, device=None): else: shift_y, shift_x = torch.meshgrid(shift_y, shift_x) - anchor_point = torch.stack([shift_x, shift_y], dim=-1) + anchor_point = torch.stack([shift_x, shift_y], dim=-1).to(dtype=dtype) anchor_points.append(anchor_point.reshape([-1, 2])) stride_tensor.append(torch.full([h * w, 1], stride, dtype=dtype)) anchor_points = torch.cat(anchor_points)

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