diff --git a/projects/perception/activity_recognition/benchmark/README.md b/projects/perception/activity_recognition/benchmark/README.md new file mode 100644 index 0000000000..8e8dcef68e --- /dev/null +++ b/projects/perception/activity_recognition/benchmark/README.md @@ -0,0 +1,28 @@ +# Human Activity Recognition Benchmark +This folder contains a script for benchmarking the inference of Human Activity Recognition learners found at +```python +from opendr.perception.activity_recognition import X3DLearner +from opendr.perception.activity_recognition import CoX3DLearner +``` + +The script include logging of FLOPS and params for `learner.model`, inference timing, and energy-consumption (NVIDIA Jetson only). + +The benchmarking runs twice; Once using `learner.infer` and once using `learner.model.forward`. The results of each are printed accordingly. + + +## Setup +Please install [`pytorch-benchmark`](https://github.com/LukasHedegaard/pytorch-benchmark): +```bash +pip install pytorch-benchmark +``` + +## Running the benchmark +X3D +```bash +./benchmark_x3d.py +``` + +CoX3D +```bash +./benchmark_cox3d.py +``` \ No newline at end of file diff --git a/projects/perception/activity_recognition/benchmark/benchmark_cox3d.py b/projects/perception/activity_recognition/benchmark/benchmark_cox3d.py new file mode 100644 index 0000000000..fb63294bac --- /dev/null +++ b/projects/perception/activity_recognition/benchmark/benchmark_cox3d.py @@ -0,0 +1,127 @@ +# Copyright 2020-2022 OpenDR European Project +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +import torch +import yaml +from opendr.perception.activity_recognition import CoX3DLearner + +from pytorch_benchmark import benchmark +import logging +from typing import List, Union +from opendr.engine.target import Category +from opendr.engine.data import Image + +logger = logging.getLogger("benchmark") +logging.basicConfig() +logger.setLevel("DEBUG") + + +def benchmark_cox3d(): + temp_dir = "./projects/perception/activity_recognition/benchmark/tmp" + + num_runs = 100 + + # As found in src/opendr/perception/activity_recognition/x3d/hparams + input_shape = { + "xs": (3, 160, 160), + "s": (3, 160, 160), + "m": (3, 224, 224), + "l": (3, 312, 312), + } + + # Max power of 2 + # batch_size = { # RTX2080Ti + # "xs": 128, + # "s": 64, + # "m": 64, + # "l": 8, + # } + # batch_size = { # TX2 + # "xs": 32, + # "s": 16, + # "m": 8, + # "l": 4, + # } + # batch_size = { # Xavier + # "xs": 64, + # "s": 32, + # "m": 16, + # "l": 8, + # } + batch_size = { # CPU - larger batch sizes don't increase throughput + "xs": 1, + "s": 1, + "m": 1, + "l": 1, + } + + for backbone in ["s", "m", "l"]: + print(f"==== Benchmarking CoX3DLearner ({backbone}) ====") + + learner = CoX3DLearner( + device="cuda" if torch.cuda.is_available() else "cpu", + temp_path=temp_dir, + backbone=backbone, + ) + + sample = torch.randn( + batch_size[backbone], *input_shape[backbone] + ) # (B, C, T, H, W) + image_samples = [Image(v) for v in sample] + image_sample = [Image(sample[0])] + + def get_device_fn(*args): + nonlocal learner + return next(learner.model.parameters()).device + + def transfer_to_device_fn( + sample: Union[torch.Tensor, List[Category], List[Image]], + device: torch.device, + ): + if isinstance(sample, torch.Tensor): + return sample.to(device=device) + + assert isinstance(sample, list) + + if isinstance(sample[0], Image): + # Image.data i a numpy array, which is always on CPU + return sample + + assert isinstance(sample[0], Category) + return [ + Category(prediction=s.data, confidence=s.confidence.to(device=device),) + for s in sample + ] + + print("== Benchmarking learner.infer ==") + results1 = benchmark( + model=learner.infer, + sample=image_samples, + sample_with_batch_size1=image_sample, + num_runs=num_runs, + get_device_fn=get_device_fn, + transfer_to_device_fn=transfer_to_device_fn, + batch_size=batch_size[backbone], + print_fn=print, + ) + print(yaml.dump({"learner.infer": results1})) + + print("== Benchmarking model directly ==") + results2 = benchmark(learner.model, sample, num_runs=num_runs, print_fn=print) + print(yaml.dump({"learner.model.forward": results2})) + + +if __name__ == "__main__": + benchmark_cox3d() diff --git a/projects/perception/activity_recognition/benchmark/benchmark_x3d.py b/projects/perception/activity_recognition/benchmark/benchmark_x3d.py new file mode 100644 index 0000000000..5256cf308d --- /dev/null +++ b/projects/perception/activity_recognition/benchmark/benchmark_x3d.py @@ -0,0 +1,128 @@ +# Copyright 2020-2022 OpenDR European Project +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +import torch +import yaml +from opendr.perception.activity_recognition import X3DLearner + +from pytorch_benchmark import benchmark +import logging +from typing import List, Union +from opendr.engine.target import Category +from opendr.engine.data import Video + +logger = logging.getLogger("benchmark") +logging.basicConfig() +logger.setLevel("DEBUG") + + +def benchmark_x3d(): + temp_dir = "./projects/perception/activity_recognition/benchmark/tmp" + + num_runs = 100 + + # As found in src/opendr/perception/activity_recognition/x3d/hparams + input_shape = { + "xs": (3, 4, 160, 160), + "s": (3, 13, 160, 160), + "m": (3, 16, 224, 224), + "l": (3, 16, 312, 312), + } + + # Max power of 2 + # batch_size = { # RTX2080Ti + # "xs": 32, + # "s": 16, + # "m": 8, + # "l": 2, + # } + # batch_size = { # TX2 + # "xs": 16, + # "s": 8, + # "m": 4, + # "l": 2, + # } + # batch_size = { # Xavier + # "xs": 32, + # "s": 16, + # "m": 8, + # "l": 2, + # } + batch_size = { # CPU - larger batch sizes don't increase throughput + "xs": 1, + "s": 1, + "m": 1, + "l": 1, + } + + for backbone in ["xs", "s", "m", "l"]: + print(f"==== Benchmarking X3DLearner ({backbone}) ====") + + learner = X3DLearner( + device="cuda" if torch.cuda.is_available() else "cpu", + temp_path=temp_dir, + backbone=backbone, + ) + learner.model.eval() + + sample = torch.randn( + batch_size[backbone], *input_shape[backbone] + ) # (B, C, T, H, W) + video_samples = [Video(v) for v in sample] + video_sample = [Video(sample[0])] + + def get_device_fn(*args): + nonlocal learner + return next(learner.model.parameters()).device + + def transfer_to_device_fn( + sample: Union[torch.Tensor, List[Category], List[Video]], + device: torch.device, + ): + if isinstance(sample, torch.Tensor): + return sample.to(device=device) + + assert isinstance(sample, list) + + if isinstance(sample[0], Video): + # Video.data i a numpy array, which is always on CPU + return sample + + assert isinstance(sample[0], Category) + return [ + Category(prediction=s.data, confidence=s.confidence.to(device=device),) + for s in sample + ] + + print("== Benchmarking learner.infer ==") + results1 = benchmark( + model=learner.infer, + sample=video_samples, + sample_with_batch_size1=video_sample, + num_runs=num_runs, + get_device_fn=get_device_fn, + transfer_to_device_fn=transfer_to_device_fn, + batch_size=batch_size[backbone], + print_fn=print, + ) + print(yaml.dump({"learner.infer": results1})) + + print("== Benchmarking model directly ==") + results2 = benchmark(learner.model, sample, num_runs=num_runs, print_fn=print) + print(yaml.dump({"learner.model.forward": results2})) + + +if __name__ == "__main__": + benchmark_x3d() diff --git a/projects/perception/activity_recognition/benchmark/install_on_server.sh b/projects/perception/activity_recognition/benchmark/install_on_server.sh new file mode 100755 index 0000000000..21b21ccdd2 --- /dev/null +++ b/projects/perception/activity_recognition/benchmark/install_on_server.sh @@ -0,0 +1,13 @@ +#!/bin/bash + +conda create --name opendr python=3.8 -y +conda activate opendr +conda env config vars set OPENDR_HOME=$PWD +conda env config vars set PYTHONPATH=$PWD/src:$PYTHONPATH + +# Reactivate env to let env vars take effect +conda deactivate +conda activate opendr + +pip install torch==1.7.1+cu110 torchvision==0.8.2+cu110 -f https://download.pytorch.org/whl/torch_stable.html +pip install pytorch_lightning==1.2.3 onnxruntime==1.3.0 joblib>=1.0.1 pytorch_benchmark diff --git a/projects/perception/activity_recognition/benchmark/requirements.txt b/projects/perception/activity_recognition/benchmark/requirements.txt new file mode 100644 index 0000000000..0e0189f337 --- /dev/null +++ b/projects/perception/activity_recognition/benchmark/requirements.txt @@ -0,0 +1 @@ +pytorch-benchmark >= 0.2 diff --git a/projects/perception/facial_expression_recognition/landmark_based_facial_expression_recognition/benchmark/benchmark_pstbln.py b/projects/perception/facial_expression_recognition/landmark_based_facial_expression_recognition/benchmark/benchmark_pstbln.py new file mode 100644 index 0000000000..9828f50e06 --- /dev/null +++ b/projects/perception/facial_expression_recognition/landmark_based_facial_expression_recognition/benchmark/benchmark_pstbln.py @@ -0,0 +1,103 @@ +# Copyright 2020-2022 OpenDR European Project +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import os +import torch +import yaml +from pytorch_benchmark import benchmark +import logging + +# opendr imports +import argparse +from opendr.perception.facial_expression_recognition import ProgressiveSpatioTemporalBLNLearner + + +logger = logging.getLogger("benchmark") +logging.basicConfig() +logger.setLevel("DEBUG") + + +def benchmark_pstbln(args): + results_dir = "./results" + if not os.path.exists(results_dir): + os.makedirs(results_dir) + device = args.device + if args.method == 'pstbln_ck+': + num_point = 303 + num_class = 7 + num_frames = 5 + elif args.method == 'pstbln_casia': + num_point = 309 + num_class = 6 + num_frames = 5 + elif args.method == 'pstbln_afew': + num_point = 312 + num_class = 7 + num_frames = 150 + print(f"==== Benchmarking {args.method} ({args.dataset_name}) ====") + learner = ProgressiveSpatioTemporalBLNLearner(device=device, dataset_name='afew', + num_class=num_class, + num_point=num_point, num_person=1, in_channels=2, + blocksize=5, topology=[15, 10, 15, 5, 5, 10]) + learner.init_model() + + if args.device == 'cuda': + learner.model.cuda() + + batch_size = 1 + num_runs = 1 + C = 2 + T = num_frames + V = num_point + M = 1 + data = torch.randn(batch_size, C, T, V, M) + samples = data + + def get_device_fn(*args): + nonlocal learner + return next(learner.model.parameters()).device + + def transfer_to_device_fn(sample, device,): + return sample + + print("== Benchmarking learner.infer ==") + results1 = benchmark(model=learner.infer, + sample=samples, + sample_with_batch_size1=None, + num_runs=num_runs, + get_device_fn=get_device_fn, + transfer_to_device_fn=transfer_to_device_fn, + batch_size=batch_size, + print_fn=print, + ) + with open(results_dir + f"/benchmark_{args.method}_{device}.txt", "a") as f: + print("== Benchmarking learner.infer ==", file=f) + print(yaml.dump({"learner.infer": results1}), file=f) + print("\n\n", file=f) + print("== Benchmarking model directly ==", file=f) + results2 = benchmark(learner.model, data, num_runs=num_runs, print_fn=print) + print(yaml.dump({"learner.model.forward": results2})) + + +if __name__ == '__main__': + parser = argparse.ArgumentParser() + parser.add_argument("--device", help="Device to use (cpu, cuda)", type=str, default="cuda") + parser.add_argument('--method', type=str, default='pstbln_afew', + help='action detection method') + parser.add_argument('--dataset_name', type=str, default='afew', + help='action detection method') + + args = parser.parse_args() + + benchmark_pstbln(args) diff --git a/projects/perception/object_detection_3d/benchmark/.gitignore b/projects/perception/object_detection_3d/benchmark/.gitignore new file mode 100644 index 0000000000..314f02b1bc --- /dev/null +++ b/projects/perception/object_detection_3d/benchmark/.gitignore @@ -0,0 +1 @@ +*.txt \ No newline at end of file diff --git a/projects/perception/object_detection_3d/benchmark/benchmark_voxel.py b/projects/perception/object_detection_3d/benchmark/benchmark_voxel.py new file mode 100644 index 0000000000..05690550c4 --- /dev/null +++ b/projects/perception/object_detection_3d/benchmark/benchmark_voxel.py @@ -0,0 +1,112 @@ +# Copyright 2020-2022 OpenDR European Project +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import os +import yaml +import torch +import logging +from pytorch_benchmark import benchmark +from opendr.perception.object_detection_3d import VoxelObjectDetection3DLearner +from opendr.engine.datasets import PointCloudsDatasetIterator + +logger = logging.getLogger("benchmark") +logging.basicConfig() +logger.setLevel("DEBUG") + + +def benchmark_voxel(): + root_dir = "./projects/perception/object_detection_3d/benchmark" + temp_dir = root_dir + "/tmp" + configs_dir = root_dir + "/configs" + models_dir = root_dir + "/models" + media_dir = root_dir + "/media" + num_runs = 100 + + models = [ + ["pointpillars_car_xyres_16", "pointpillars_car_xyres_16.proto"], + ["pointpillars_ped_cycle_xyres_16", "pointpillars_ped_cycle_xyres_16.proto"], + ["tanet_car_xyres_16", "tanet_car_xyres_16.proto"], + ["tanet_car_xyres_16", "tanet_car_xyres_16_near_0.24.proto"], + ["tanet_ped_cycle_xyres_16", "tanet_ped_cycle_xyres_16.proto"], + ] + + batch_size = 2 + + dataset = PointCloudsDatasetIterator(media_dir) + sample = dataset[0] + samples = [dataset[0] for _ in range(batch_size)] + + if os.path.exists(root_dir + "/results_voxel.txt"): + os.remove(root_dir + "/results_voxel.txt") + + for model_name, config in models: + print(f"==== Benchmarking VoxelObjectDetection3DLearner ({config}) ====") + + config_path = configs_dir + "/" + config + + learner = VoxelObjectDetection3DLearner( + config_path, + temp_path=temp_dir, + ) + + if model_name is not None and not os.path.exists( + models_dir + "/" + model_name + ): + learner.download(model_name, models_dir) + learner.load(models_dir + "/" + model_name, verbose=True) + + def get_device_fn(*args): + nonlocal learner + return torch.device(learner.device) + + def transfer_to_device_fn( + sample, + device, + ): + return sample + + print("== Benchmarking learner.infer ==") + results1 = benchmark( + model=learner.infer, + sample=samples, + sample_with_batch_size1=sample, + num_runs=num_runs, + get_device_fn=get_device_fn, + transfer_to_device_fn=transfer_to_device_fn, + batch_size=batch_size, + ) + + inner_fps = ( + learner.model._total_inference_count / + (learner.model._total_forward_time + learner.model._total_postprocess_time) + ) + + print("Inner FPS =", inner_fps) + print(yaml.dump({"learner.infer": results1})) + + with open(root_dir + "/results_voxel.txt", "a") as f: + print(f"==== Benchmarking VoxelObjectDetection3DLearner ({config}) ====", file=f) + print("Inner FPS =", inner_fps, file=f) + print(yaml.dump({"learner.infer": results1}), file=f) + print("\n\n", file=f) + + # print("== Benchmarking model directly ==") + # results2 = benchmark(learner.model, sample, num_runs=num_runs) + # print(yaml.dump({"learner.model.forward": results2})) + + print("===END===") + + +if __name__ == "__main__": + benchmark_voxel() diff --git a/projects/perception/object_detection_3d/benchmark/configs/pointpillars_car_xyres_16.proto b/projects/perception/object_detection_3d/benchmark/configs/pointpillars_car_xyres_16.proto new file mode 100644 index 0000000000..2e09c77864 --- /dev/null +++ b/projects/perception/object_detection_3d/benchmark/configs/pointpillars_car_xyres_16.proto @@ -0,0 +1,173 @@ +model: { + second: { + voxel_generator { + point_cloud_range : [0, -39.68, -3, 69.12, 39.68, 1] + voxel_size : [0.16, 0.16, 4] + max_number_of_points_per_voxel : 100 + } + num_class: 1 + voxel_feature_extractor: { + module_class_name: "PillarFeatureNet" + num_filters: [64] + with_distance: false + } + middle_feature_extractor: { + module_class_name: "PointPillarsScatter" + } + rpn: { + module_class_name: "RPN" + layer_nums: [3, 5, 5] + layer_strides: [2, 2, 2] + num_filters: [64, 128, 256] + upsample_strides: [1, 2, 4] + num_upsample_filters: [128, 128, 128] + use_groupnorm: false + num_groups: 32 + } + loss: { + classification_loss: { + weighted_sigmoid_focal: { + alpha: 0.25 + gamma: 2.0 + anchorwise_output: true + } + } + localization_loss: { + weighted_smooth_l1: { + sigma: 3.0 + code_weight: [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0] + } + } + classification_weight: 1.0 + localization_weight: 2.0 + } + # Outputs + use_sigmoid_score: true + encode_background_as_zeros: true + encode_rad_error_by_sin: true + + use_direction_classifier: true + direction_loss_weight: 0.2 + use_aux_classifier: false + # Loss + pos_class_weight: 1.0 + neg_class_weight: 1.0 + + loss_norm_type: NormByNumPositives + # Postprocess + post_center_limit_range: [0, -39.68, -5, 69.12, 39.68, 5] + use_rotate_nms: false + use_multi_class_nms: false + nms_pre_max_size: 1000 + nms_post_max_size: 300 + nms_score_threshold: 0.05 + nms_iou_threshold: 0.5 + + use_bev: false + num_point_features: 4 + without_reflectivity: false + box_coder: { + ground_box3d_coder: { + linear_dim: false + encode_angle_vector: false + } + } + target_assigner: { + anchor_generators: { + anchor_generator_stride: { + sizes: [1.6, 3.9, 1.56] # wlh + strides: [0.32, 0.32, 0.0] # if generate only 1 z_center, z_stride will be ignored + offsets: [0.16, -39.52, -1.78] # origin_offset + strides / 2 + rotations: [0, 1.57] # 0, pi/2 + matched_threshold : 0.6 + unmatched_threshold : 0.45 + } + } + + sample_positive_fraction : -1 + sample_size : 512 + region_similarity_calculator: { + nearest_iou_similarity: { + } + } + } + } +} + + +train_input_reader: { + record_file_path: "kitti_train.tfrecord" + class_names: ["Car"] + max_num_epochs : 160 + batch_size: 2 + prefetch_size : 25 + max_number_of_voxels: 12000 + shuffle_points: true + num_workers: 2 + groundtruth_localization_noise_std: [0.25, 0.25, 0.25] + groundtruth_rotation_uniform_noise: [-0.15707963267, 0.15707963267] + global_rotation_uniform_noise: [-0.78539816, 0.78539816] + global_scaling_uniform_noise: [0.95, 1.05] + global_random_rotation_range_per_object: [0, 0] + anchor_area_threshold: 1 + remove_points_after_sample: false + groundtruth_points_drop_percentage: 0.0 + groundtruth_drop_max_keep_points: 15 + database_sampler { + database_info_path: "kitti_dbinfos_train.pkl" + sample_groups { + name_to_max_num { + key: "Car" + value: 15 + } + } + database_prep_steps { + filter_by_min_num_points { + min_num_point_pairs { + key: "Car" + value: 5 + } + } + } + database_prep_steps { + filter_by_difficulty { + removed_difficulties: [-1] + } + } + global_random_rotation_range_per_object: [0, 0] + rate: 1.0 + } + + remove_unknown_examples: false + remove_environment: false + kitti_info_path: "kitti_infos_train.pkl" + kitti_root_path: "" +} + +train_config: { + + inter_op_parallelism_threads: 4 + intra_op_parallelism_threads: 4 + steps: 296960 # 1856 steps per epoch * 160 epochs + steps_per_eval: 9280 # 1856 steps per epoch * 5 epochs + save_checkpoints_secs : 1800 # half hour + save_summary_steps : 10 + enable_mixed_precision: false + loss_scale_factor : 512.0 + clear_metrics_every_epoch: false +} + +eval_input_reader: { + record_file_path: "kitti_val.tfrecord" + class_names: ["Car"] + batch_size: 1 + max_num_epochs : 160 + prefetch_size : 25 + max_number_of_voxels: 12000 + shuffle_points: false + num_workers: 3 + anchor_area_threshold: 1 + remove_environment: false + kitti_info_path: "kitti_infos_val.pkl" + kitti_root_path: "" +} diff --git a/projects/perception/object_detection_3d/benchmark/configs/pointpillars_ped_cycle_xyres_16.proto b/projects/perception/object_detection_3d/benchmark/configs/pointpillars_ped_cycle_xyres_16.proto new file mode 100644 index 0000000000..8bdeb20fcd --- /dev/null +++ b/projects/perception/object_detection_3d/benchmark/configs/pointpillars_ped_cycle_xyres_16.proto @@ -0,0 +1,183 @@ +model: { + second: { + voxel_generator { + point_cloud_range : [0, -19.84, -2.5, 47.36, 19.84, 0.5] + voxel_size : [0.16, 0.16, 3] + max_number_of_points_per_voxel : 100 + } + num_class: 2 + voxel_feature_extractor: { + module_class_name: "PillarFeatureNet" + num_filters: [64] + with_distance: false + } + middle_feature_extractor: { + module_class_name: "PointPillarsScatter" + } + rpn: { + module_class_name: "RPN" + layer_nums: [3, 5, 5] + layer_strides: [1, 2, 2] + num_filters: [64, 128, 256] + upsample_strides: [1, 2, 4] + num_upsample_filters: [128, 128, 128] + use_groupnorm: false + num_groups: 32 + } + loss: { + classification_loss: { + weighted_sigmoid_focal: { + alpha: 0.25 + gamma: 2.0 + anchorwise_output: true + } + } + localization_loss: { + weighted_smooth_l1: { + sigma: 3.0 + code_weight: [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0] + } + } + classification_weight: 1.0 + localization_weight: 2.0 + } + # Outputs + use_sigmoid_score: true + encode_background_as_zeros: true + encode_rad_error_by_sin: true + + use_direction_classifier: true + direction_loss_weight: 0.2 + use_aux_classifier: false + # Loss + pos_class_weight: 1.0 + neg_class_weight: 1.0 + + loss_norm_type: NormByNumPositives + # Postprocess + post_center_limit_range: [0, -19.84, -2.5, 47.36, 19.84, 0.5] + use_rotate_nms: false + use_multi_class_nms: false + nms_pre_max_size: 1000 + nms_post_max_size: 300 + nms_score_threshold: 0.05 + nms_iou_threshold: 0.5 + + use_bev: false + num_point_features: 4 + without_reflectivity: false + box_coder: { + ground_box3d_coder: { + linear_dim: false + encode_angle_vector: false + } + } + target_assigner: { + anchor_generators: { + anchor_generator_stride: { + sizes: [0.6, 1.76, 1.73] # wlh + strides: [0.16, 0.16, 0.0] # if generate only 1 z_center, z_stride will be ignored + offsets: [0.08, -19.76, -1.465] # origin_offset + strides / 2 + rotations: [0, 1.57] # 0, pi/2 + matched_threshold : 0.5 + unmatched_threshold : 0.35 + } + } + anchor_generators: { + anchor_generator_stride: { + sizes: [0.6, 0.8, 1.73] # wlh + strides: [0.16, 0.16, 0.0] # if generate only 1 z_center, z_stride will be ignored + offsets: [0.08, -19.76, -1.465] # origin_offset + strides / 2 + rotations: [0, 1.57] # 0, pi/2 + matched_threshold : 0.5 + unmatched_threshold : 0.35 + } + } + + sample_positive_fraction : -1 + sample_size : 512 + region_similarity_calculator: { + nearest_iou_similarity: { + } + } + } + } +} + + +train_input_reader: { + record_file_path: "kitti_train.tfrecord" + class_names: ["Cyclist", "Pedestrian"] + max_num_epochs : 160 + batch_size: 2 + prefetch_size : 25 + max_number_of_voxels: 12000 + shuffle_points: true + num_workers: 2 + groundtruth_localization_noise_std: [0.25, 0.25, 0.25] + groundtruth_rotation_uniform_noise: [-0.15707963267, 0.15707963267] + global_rotation_uniform_noise: [-0.78539816, 0.78539816] + global_scaling_uniform_noise: [0.95, 1.05] + global_random_rotation_range_per_object: [0, 0] + anchor_area_threshold: 1 + remove_points_after_sample: false + groundtruth_points_drop_percentage: 0.0 + groundtruth_drop_max_keep_points: 15 + database_sampler { + database_info_path: "kitti_dbinfos_train.pkl" + sample_groups { + name_to_max_num { + key: "Cyclist" + value: 8 + } + } + database_prep_steps { + filter_by_min_num_points { + min_num_point_pairs { + key: "Cyclist" + value: 5 + } + } + } + database_prep_steps { + filter_by_difficulty { + removed_difficulties: [-1] + } + } + global_random_rotation_range_per_object: [0, 0] + rate: 1.0 + } + + remove_unknown_examples: false + remove_environment: false + kitti_info_path: "kitti_infos_train.pkl" + kitti_root_path: "" +} + +train_config: { + + inter_op_parallelism_threads: 4 + intra_op_parallelism_threads: 4 + steps: 296960 # 1856 steps per epoch * 160 epochs + steps_per_eval: 9280 # 1856 steps per epoch * 5 epochs + save_checkpoints_secs : 1800 # half hour + save_summary_steps : 10 + enable_mixed_precision: false + loss_scale_factor : 512.0 + clear_metrics_every_epoch: false +} + +eval_input_reader: { + record_file_path: "kitti_val.tfrecord" + class_names: ["Cyclist", "Pedestrian"] + batch_size: 1 + max_num_epochs : 160 + prefetch_size : 25 + max_number_of_voxels: 12000 + shuffle_points: false + num_workers: 3 + anchor_area_threshold: 1 + remove_environment: false + kitti_info_path: "kitti_infos_val.pkl" + kitti_root_path: "" +} diff --git a/projects/perception/object_detection_3d/benchmark/configs/tanet_car_xyres_16.proto b/projects/perception/object_detection_3d/benchmark/configs/tanet_car_xyres_16.proto new file mode 100644 index 0000000000..393ca0fc81 --- /dev/null +++ b/projects/perception/object_detection_3d/benchmark/configs/tanet_car_xyres_16.proto @@ -0,0 +1,173 @@ +model: { + second: { + voxel_generator { + point_cloud_range : [0, -39.68, -3, 69.12, 39.68, 1] + voxel_size : [0.16, 0.16, 4] + max_number_of_points_per_voxel : 100 + } + num_class: 1 + voxel_feature_extractor: { + module_class_name: "PillarFeature_TANet" + num_filters: [64] + with_distance: false + } + middle_feature_extractor: { + module_class_name: "PointPillarsScatter" + } + rpn: { + module_class_name: "PSA" + layer_nums: [3, 5, 5] + layer_strides: [2, 2, 2] + num_filters: [64, 128, 256] + upsample_strides: [1, 2, 4] + num_upsample_filters: [128, 128, 128] + use_groupnorm: false + num_groups: 32 + } + loss: { + classification_loss: { + weighted_sigmoid_focal: { + alpha: 0.25 + gamma: 2.0 + anchorwise_output: true + } + } + localization_loss: { + weighted_smooth_l1: { + sigma: 3.0 + code_weight: [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0] + } + } + classification_weight: 1.0 + localization_weight: 2.0 + } + # Outputs + use_sigmoid_score: true + encode_background_as_zeros: true + encode_rad_error_by_sin: true + + use_direction_classifier: true + direction_loss_weight: 0.2 + use_aux_classifier: false + # Loss + pos_class_weight: 1.0 + neg_class_weight: 1.0 + + loss_norm_type: NormByNumPositives + # Postprocess + post_center_limit_range: [0, -39.68, -5, 69.12, 39.68, 5] + use_rotate_nms: false + use_multi_class_nms: false + nms_pre_max_size: 1000 + nms_post_max_size: 300 + nms_score_threshold: 0.3 #0.05 + nms_iou_threshold: 0.1 #0.5 + + use_bev: false + num_point_features: 4 + without_reflectivity: false + box_coder: { + ground_box3d_coder: { + linear_dim: false + encode_angle_vector: false + } + } + target_assigner: { + anchor_generators: { + anchor_generator_stride: { + sizes: [1.6, 3.9, 1.56] # wlh + strides: [0.32, 0.32, 0.0] # if generate only 1 z_center, z_stride will be ignored + offsets: [0.16, -39.52, -1.78] # origin_offset + strides / 2 + rotations: [0, 1.57] # 0, pi/2 + matched_threshold : 0.6 + unmatched_threshold : 0.45 + } + } + + sample_positive_fraction : -1 + sample_size : 512 + region_similarity_calculator: { + nearest_iou_similarity: { + } + } + } + } +} + + +train_input_reader: { + record_file_path: "kitti_train.tfrecord" + class_names: ["Car"] + max_num_epochs : 160 + batch_size: 1 + prefetch_size : 25 + max_number_of_voxels: 12000 + shuffle_points: true + num_workers: 2 + groundtruth_localization_noise_std: [0.25, 0.25, 0.25] + groundtruth_rotation_uniform_noise: [-0.15707963267, 0.15707963267] + global_rotation_uniform_noise: [-0.78539816, 0.78539816] + global_scaling_uniform_noise: [0.95, 1.05] + global_random_rotation_range_per_object: [0, 0] + anchor_area_threshold: 1 + remove_points_after_sample: false + groundtruth_points_drop_percentage: 0.0 + groundtruth_drop_max_keep_points: 15 + database_sampler { + database_info_path: "kitti_dbinfos_train.pkl" + sample_groups { + name_to_max_num { + key: "Car" + value: 15 + } + } + database_prep_steps { + filter_by_min_num_points { + min_num_point_pairs { + key: "Car" + value: 5 + } + } + } + database_prep_steps { + filter_by_difficulty { + removed_difficulties: [-1] + } + } + global_random_rotation_range_per_object: [0, 0] + rate: 1.0 + } + + remove_unknown_examples: false + remove_environment: false + kitti_info_path: "kitti_infos_train.pkl" + kitti_root_path: "" +} + +train_config: { + + inter_op_parallelism_threads: 4 + intra_op_parallelism_threads: 4 + steps: 296960 # 1856 steps per epoch * 160 epochs + steps_per_eval: 9280 # 1856 steps per epoch * 5 epochs + save_checkpoints_secs : 1800 # half hour + save_summary_steps : 10 + enable_mixed_precision: false + loss_scale_factor : 512.0 + clear_metrics_every_epoch: false +} + +eval_input_reader: { + record_file_path: "kitti_val.tfrecord" + class_names: ["Car"] + batch_size: 1 + max_num_epochs : 160 + prefetch_size : 25 + max_number_of_voxels: 12000 + shuffle_points: false + num_workers: 1 + anchor_area_threshold: 1 + remove_environment: false + kitti_info_path: "kitti_infos_val.pkl" + kitti_root_path: "" +} diff --git a/projects/perception/object_detection_3d/benchmark/configs/tanet_car_xyres_16_near_0.24.proto b/projects/perception/object_detection_3d/benchmark/configs/tanet_car_xyres_16_near_0.24.proto new file mode 100644 index 0000000000..833b09c549 --- /dev/null +++ b/projects/perception/object_detection_3d/benchmark/configs/tanet_car_xyres_16_near_0.24.proto @@ -0,0 +1,173 @@ +model: { + second: { + voxel_generator { + point_cloud_range : [0, -39.68, -3, 16.64, 39.68, 1] + voxel_size : [0.16, 0.16, 4] + max_number_of_points_per_voxel : 100 + } + num_class: 1 + voxel_feature_extractor: { + module_class_name: "PillarFeature_TANet" + num_filters: [64] + with_distance: false + } + middle_feature_extractor: { + module_class_name: "PointPillarsScatter" + } + rpn: { + module_class_name: "PSA" + layer_nums: [3, 5, 5] + layer_strides: [2, 2, 2] + num_filters: [64, 128, 256] + upsample_strides: [1, 2, 4] + num_upsample_filters: [128, 128, 128] + use_groupnorm: false + num_groups: 32 + } + loss: { + classification_loss: { + weighted_sigmoid_focal: { + alpha: 0.25 + gamma: 2.0 + anchorwise_output: true + } + } + localization_loss: { + weighted_smooth_l1: { + sigma: 3.0 + code_weight: [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0] + } + } + classification_weight: 1.0 + localization_weight: 2.0 + } + # Outputs + use_sigmoid_score: true + encode_background_as_zeros: true + encode_rad_error_by_sin: true + + use_direction_classifier: true + direction_loss_weight: 0.2 + use_aux_classifier: false + # Loss + pos_class_weight: 1.0 + neg_class_weight: 1.0 + + loss_norm_type: NormByNumPositives + # Postprocess + post_center_limit_range: [0, -39.68, -5, 16.64, 39.68, 5] + use_rotate_nms: false + use_multi_class_nms: false + nms_pre_max_size: 1000 + nms_post_max_size: 300 + nms_score_threshold: 0.3 #0.05 + nms_iou_threshold: 0.1 #0.5 + + use_bev: false + num_point_features: 4 + without_reflectivity: false + box_coder: { + ground_box3d_coder: { + linear_dim: false + encode_angle_vector: false + } + } + target_assigner: { + anchor_generators: { + anchor_generator_stride: { + sizes: [1.6, 3.9, 1.56] # wlh + strides: [0.32, 0.32, 0.0] # if generate only 1 z_center, z_stride will be ignored + offsets: [0.16, -39.52, -1.78] # origin_offset + strides / 2 + rotations: [0, 1.57] # 0, pi/2 + matched_threshold : 0.6 + unmatched_threshold : 0.45 + } + } + + sample_positive_fraction : -1 + sample_size : 512 + region_similarity_calculator: { + nearest_iou_similarity: { + } + } + } + } +} + + +train_input_reader: { + record_file_path: "kitti_train.tfrecord" + class_names: ["Car"] + max_num_epochs : 160 + batch_size: 1 + prefetch_size : 25 + max_number_of_voxels: 12000 + shuffle_points: true + num_workers: 2 + groundtruth_localization_noise_std: [0.25, 0.25, 0.25] + groundtruth_rotation_uniform_noise: [-0.15707963267, 0.15707963267] + global_rotation_uniform_noise: [-0.78539816, 0.78539816] + global_scaling_uniform_noise: [0.95, 1.05] + global_random_rotation_range_per_object: [0, 0] + anchor_area_threshold: 1 + remove_points_after_sample: false + groundtruth_points_drop_percentage: 0.0 + groundtruth_drop_max_keep_points: 15 + database_sampler { + database_info_path: "kitti_dbinfos_train.pkl" + sample_groups { + name_to_max_num { + key: "Car" + value: 15 + } + } + database_prep_steps { + filter_by_min_num_points { + min_num_point_pairs { + key: "Car" + value: 5 + } + } + } + database_prep_steps { + filter_by_difficulty { + removed_difficulties: [-1] + } + } + global_random_rotation_range_per_object: [0, 0] + rate: 1.0 + } + + remove_unknown_examples: false + remove_environment: false + kitti_info_path: "kitti_infos_train.pkl" + kitti_root_path: "" +} + +train_config: { + + inter_op_parallelism_threads: 4 + intra_op_parallelism_threads: 4 + steps: 296960 # 1856 steps per epoch * 160 epochs + steps_per_eval: 9280 # 1856 steps per epoch * 5 epochs + save_checkpoints_secs : 1800 # half hour + save_summary_steps : 10 + enable_mixed_precision: false + loss_scale_factor : 512.0 + clear_metrics_every_epoch: false +} + +eval_input_reader: { + record_file_path: "kitti_val.tfrecord" + class_names: ["Car"] + batch_size: 1 + max_num_epochs : 160 + prefetch_size : 25 + max_number_of_voxels: 12000 + shuffle_points: false + num_workers: 1 + anchor_area_threshold: 1 + remove_environment: false + kitti_info_path: "kitti_infos_val.pkl" + kitti_root_path: "" +} diff --git a/projects/perception/object_detection_3d/benchmark/configs/tanet_car_xyres_16_near_0.24_2.proto b/projects/perception/object_detection_3d/benchmark/configs/tanet_car_xyres_16_near_0.24_2.proto new file mode 100644 index 0000000000..e5658d6248 --- /dev/null +++ b/projects/perception/object_detection_3d/benchmark/configs/tanet_car_xyres_16_near_0.24_2.proto @@ -0,0 +1,173 @@ +model: { + second: { + voxel_generator { + point_cloud_range : [0, -20.48, -3, 16.64, 20.48, 1] + voxel_size : [0.16, 0.16, 4] + max_number_of_points_per_voxel : 100 + } + num_class: 1 + voxel_feature_extractor: { + module_class_name: "PillarFeature_TANet" + num_filters: [64] + with_distance: false + } + middle_feature_extractor: { + module_class_name: "PointPillarsScatter" + } + rpn: { + module_class_name: "PSA" + layer_nums: [3, 5, 5] + layer_strides: [2, 2, 2] + num_filters: [64, 128, 256] + upsample_strides: [1, 2, 4] + num_upsample_filters: [128, 128, 128] + use_groupnorm: false + num_groups: 32 + } + loss: { + classification_loss: { + weighted_sigmoid_focal: { + alpha: 0.25 + gamma: 2.0 + anchorwise_output: true + } + } + localization_loss: { + weighted_smooth_l1: { + sigma: 3.0 + code_weight: [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0] + } + } + classification_weight: 1.0 + localization_weight: 2.0 + } + # Outputs + use_sigmoid_score: true + encode_background_as_zeros: true + encode_rad_error_by_sin: true + + use_direction_classifier: true + direction_loss_weight: 0.2 + use_aux_classifier: false + # Loss + pos_class_weight: 1.0 + neg_class_weight: 1.0 + + loss_norm_type: NormByNumPositives + # Postprocess + post_center_limit_range: [0, -20.48, -5, 16.64, 20.48, 5] + use_rotate_nms: false + use_multi_class_nms: false + nms_pre_max_size: 1000 + nms_post_max_size: 300 + nms_score_threshold: 0.3 #0.05 + nms_iou_threshold: 0.1 #0.5 + + use_bev: false + num_point_features: 4 + without_reflectivity: false + box_coder: { + ground_box3d_coder: { + linear_dim: false + encode_angle_vector: false + } + } + target_assigner: { + anchor_generators: { + anchor_generator_stride: { + sizes: [1.6, 3.9, 1.56] # wlh + strides: [0.32, 0.32, 0.0] # if generate only 1 z_center, z_stride will be ignored + offsets: [0.16, -20.38, -1.78] # origin_offset + strides / 2 + rotations: [0, 1.57] # 0, pi/2 + matched_threshold : 0.6 + unmatched_threshold : 0.45 + } + } + + sample_positive_fraction : -1 + sample_size : 512 + region_similarity_calculator: { + nearest_iou_similarity: { + } + } + } + } +} + + +train_input_reader: { + record_file_path: "kitti_train.tfrecord" + class_names: ["Car"] + max_num_epochs : 160 + batch_size: 1 + prefetch_size : 25 + max_number_of_voxels: 12000 + shuffle_points: true + num_workers: 2 + groundtruth_localization_noise_std: [0.25, 0.25, 0.25] + groundtruth_rotation_uniform_noise: [-0.15707963267, 0.15707963267] + global_rotation_uniform_noise: [-0.78539816, 0.78539816] + global_scaling_uniform_noise: [0.95, 1.05] + global_random_rotation_range_per_object: [0, 0] + anchor_area_threshold: 1 + remove_points_after_sample: false + groundtruth_points_drop_percentage: 0.0 + groundtruth_drop_max_keep_points: 15 + database_sampler { + database_info_path: "kitti_dbinfos_train.pkl" + sample_groups { + name_to_max_num { + key: "Car" + value: 15 + } + } + database_prep_steps { + filter_by_min_num_points { + min_num_point_pairs { + key: "Car" + value: 5 + } + } + } + database_prep_steps { + filter_by_difficulty { + removed_difficulties: [-1] + } + } + global_random_rotation_range_per_object: [0, 0] + rate: 1.0 + } + + remove_unknown_examples: false + remove_environment: false + kitti_info_path: "kitti_infos_train.pkl" + kitti_root_path: "" +} + +train_config: { + + inter_op_parallelism_threads: 4 + intra_op_parallelism_threads: 4 + steps: 296960 # 1856 steps per epoch * 160 epochs + steps_per_eval: 9280 # 1856 steps per epoch * 5 epochs + save_checkpoints_secs : 1800 # half hour + save_summary_steps : 10 + enable_mixed_precision: false + loss_scale_factor : 512.0 + clear_metrics_every_epoch: false +} + +eval_input_reader: { + record_file_path: "kitti_val.tfrecord" + class_names: ["Car"] + batch_size: 1 + max_num_epochs : 160 + prefetch_size : 25 + max_number_of_voxels: 12000 + shuffle_points: false + num_workers: 1 + anchor_area_threshold: 1 + remove_environment: false + kitti_info_path: "kitti_infos_val.pkl" + kitti_root_path: "" +} diff --git a/projects/perception/object_detection_3d/benchmark/configs/tanet_car_xyres_16_near_0.33.proto b/projects/perception/object_detection_3d/benchmark/configs/tanet_car_xyres_16_near_0.33.proto new file mode 100644 index 0000000000..b28ac07909 --- /dev/null +++ b/projects/perception/object_detection_3d/benchmark/configs/tanet_car_xyres_16_near_0.33.proto @@ -0,0 +1,173 @@ +model: { + second: { + voxel_generator { + point_cloud_range : [0, -39.68, -3, 23.04, 39.68, 1] + voxel_size : [0.16, 0.16, 4] + max_number_of_points_per_voxel : 100 + } + num_class: 1 + voxel_feature_extractor: { + module_class_name: "PillarFeature_TANet" + num_filters: [64] + with_distance: false + } + middle_feature_extractor: { + module_class_name: "PointPillarsScatter" + } + rpn: { + module_class_name: "PSA" + layer_nums: [3, 5, 5] + layer_strides: [2, 2, 2] + num_filters: [64, 128, 256] + upsample_strides: [1, 2, 4] + num_upsample_filters: [128, 128, 128] + use_groupnorm: false + num_groups: 32 + } + loss: { + classification_loss: { + weighted_sigmoid_focal: { + alpha: 0.25 + gamma: 2.0 + anchorwise_output: true + } + } + localization_loss: { + weighted_smooth_l1: { + sigma: 3.0 + code_weight: [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0] + } + } + classification_weight: 1.0 + localization_weight: 2.0 + } + # Outputs + use_sigmoid_score: true + encode_background_as_zeros: true + encode_rad_error_by_sin: true + + use_direction_classifier: true + direction_loss_weight: 0.2 + use_aux_classifier: false + # Loss + pos_class_weight: 1.0 + neg_class_weight: 1.0 + + loss_norm_type: NormByNumPositives + # Postprocess + post_center_limit_range: [0, -39.68, -5, 23.04, 39.68, 5] + use_rotate_nms: false + use_multi_class_nms: false + nms_pre_max_size: 1000 + nms_post_max_size: 300 + nms_score_threshold: 0.3 #0.05 + nms_iou_threshold: 0.1 #0.5 + + use_bev: false + num_point_features: 4 + without_reflectivity: false + box_coder: { + ground_box3d_coder: { + linear_dim: false + encode_angle_vector: false + } + } + target_assigner: { + anchor_generators: { + anchor_generator_stride: { + sizes: [1.6, 3.9, 1.56] # wlh + strides: [0.32, 0.32, 0.0] # if generate only 1 z_center, z_stride will be ignored + offsets: [0.16, -39.52, -1.78] # origin_offset + strides / 2 + rotations: [0, 1.57] # 0, pi/2 + matched_threshold : 0.6 + unmatched_threshold : 0.45 + } + } + + sample_positive_fraction : -1 + sample_size : 512 + region_similarity_calculator: { + nearest_iou_similarity: { + } + } + } + } +} + + +train_input_reader: { + record_file_path: "kitti_train.tfrecord" + class_names: ["Car"] + max_num_epochs : 160 + batch_size: 1 + prefetch_size : 25 + max_number_of_voxels: 12000 + shuffle_points: true + num_workers: 2 + groundtruth_localization_noise_std: [0.25, 0.25, 0.25] + groundtruth_rotation_uniform_noise: [-0.15707963267, 0.15707963267] + global_rotation_uniform_noise: [-0.78539816, 0.78539816] + global_scaling_uniform_noise: [0.95, 1.05] + global_random_rotation_range_per_object: [0, 0] + anchor_area_threshold: 1 + remove_points_after_sample: false + groundtruth_points_drop_percentage: 0.0 + groundtruth_drop_max_keep_points: 15 + database_sampler { + database_info_path: "kitti_dbinfos_train.pkl" + sample_groups { + name_to_max_num { + key: "Car" + value: 15 + } + } + database_prep_steps { + filter_by_min_num_points { + min_num_point_pairs { + key: "Car" + value: 5 + } + } + } + database_prep_steps { + filter_by_difficulty { + removed_difficulties: [-1] + } + } + global_random_rotation_range_per_object: [0, 0] + rate: 1.0 + } + + remove_unknown_examples: false + remove_environment: false + kitti_info_path: "kitti_infos_train.pkl" + kitti_root_path: "" +} + +train_config: { + + inter_op_parallelism_threads: 4 + intra_op_parallelism_threads: 4 + steps: 296960 # 1856 steps per epoch * 160 epochs + steps_per_eval: 9280 # 1856 steps per epoch * 5 epochs + save_checkpoints_secs : 1800 # half hour + save_summary_steps : 10 + enable_mixed_precision: false + loss_scale_factor : 512.0 + clear_metrics_every_epoch: false +} + +eval_input_reader: { + record_file_path: "kitti_val.tfrecord" + class_names: ["Car"] + batch_size: 1 + max_num_epochs : 160 + prefetch_size : 25 + max_number_of_voxels: 12000 + shuffle_points: false + num_workers: 1 + anchor_area_threshold: 1 + remove_environment: false + kitti_info_path: "kitti_infos_val.pkl" + kitti_root_path: "" +} diff --git a/projects/perception/object_detection_3d/benchmark/configs/tanet_car_xyres_16_near_0.5.proto b/projects/perception/object_detection_3d/benchmark/configs/tanet_car_xyres_16_near_0.5.proto new file mode 100644 index 0000000000..2f636ff200 --- /dev/null +++ b/projects/perception/object_detection_3d/benchmark/configs/tanet_car_xyres_16_near_0.5.proto @@ -0,0 +1,173 @@ +model: { + second: { + voxel_generator { + point_cloud_range : [0, -39.68, -3, 34.56, 39.68, 1] + voxel_size : [0.16, 0.16, 4] + max_number_of_points_per_voxel : 100 + } + num_class: 1 + voxel_feature_extractor: { + module_class_name: "PillarFeature_TANet" + num_filters: [64] + with_distance: false + } + middle_feature_extractor: { + module_class_name: "PointPillarsScatter" + } + rpn: { + module_class_name: "PSA" + layer_nums: [3, 5, 5] + layer_strides: [2, 2, 2] + num_filters: [64, 128, 256] + upsample_strides: [1, 2, 4] + num_upsample_filters: [128, 128, 128] + use_groupnorm: false + num_groups: 32 + } + loss: { + classification_loss: { + weighted_sigmoid_focal: { + alpha: 0.25 + gamma: 2.0 + anchorwise_output: true + } + } + localization_loss: { + weighted_smooth_l1: { + sigma: 3.0 + code_weight: [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0] + } + } + classification_weight: 1.0 + localization_weight: 2.0 + } + # Outputs + use_sigmoid_score: true + encode_background_as_zeros: true + encode_rad_error_by_sin: true + + use_direction_classifier: true + direction_loss_weight: 0.2 + use_aux_classifier: false + # Loss + pos_class_weight: 1.0 + neg_class_weight: 1.0 + + loss_norm_type: NormByNumPositives + # Postprocess + post_center_limit_range: [0, -39.68, -5, 34.56, 39.68, 5] + use_rotate_nms: false + use_multi_class_nms: false + nms_pre_max_size: 1000 + nms_post_max_size: 300 + nms_score_threshold: 0.3 #0.05 + nms_iou_threshold: 0.1 #0.5 + + use_bev: false + num_point_features: 4 + without_reflectivity: false + box_coder: { + ground_box3d_coder: { + linear_dim: false + encode_angle_vector: false + } + } + target_assigner: { + anchor_generators: { + anchor_generator_stride: { + sizes: [1.6, 3.9, 1.56] # wlh + strides: [0.32, 0.32, 0.0] # if generate only 1 z_center, z_stride will be ignored + offsets: [0.16, -39.52, -1.78] # origin_offset + strides / 2 + rotations: [0, 1.57] # 0, pi/2 + matched_threshold : 0.6 + unmatched_threshold : 0.45 + } + } + + sample_positive_fraction : -1 + sample_size : 512 + region_similarity_calculator: { + nearest_iou_similarity: { + } + } + } + } +} + + +train_input_reader: { + record_file_path: "kitti_train.tfrecord" + class_names: ["Car"] + max_num_epochs : 160 + batch_size: 1 + prefetch_size : 25 + max_number_of_voxels: 12000 + shuffle_points: true + num_workers: 2 + groundtruth_localization_noise_std: [0.25, 0.25, 0.25] + groundtruth_rotation_uniform_noise: [-0.15707963267, 0.15707963267] + global_rotation_uniform_noise: [-0.78539816, 0.78539816] + global_scaling_uniform_noise: [0.95, 1.05] + global_random_rotation_range_per_object: [0, 0] + anchor_area_threshold: 1 + remove_points_after_sample: false + groundtruth_points_drop_percentage: 0.0 + groundtruth_drop_max_keep_points: 15 + database_sampler { + database_info_path: "kitti_dbinfos_train.pkl" + sample_groups { + name_to_max_num { + key: "Car" + value: 15 + } + } + database_prep_steps { + filter_by_min_num_points { + min_num_point_pairs { + key: "Car" + value: 5 + } + } + } + database_prep_steps { + filter_by_difficulty { + removed_difficulties: [-1] + } + } + global_random_rotation_range_per_object: [0, 0] + rate: 1.0 + } + + remove_unknown_examples: false + remove_environment: false + kitti_info_path: "kitti_infos_train.pkl" + kitti_root_path: "" +} + +train_config: { + + inter_op_parallelism_threads: 4 + intra_op_parallelism_threads: 4 + steps: 296960 # 1856 steps per epoch * 160 epochs + steps_per_eval: 9280 # 1856 steps per epoch * 5 epochs + save_checkpoints_secs : 1800 # half hour + save_summary_steps : 10 + enable_mixed_precision: false + loss_scale_factor : 512.0 + clear_metrics_every_epoch: false +} + +eval_input_reader: { + record_file_path: "kitti_val.tfrecord" + class_names: ["Car"] + batch_size: 1 + max_num_epochs : 160 + prefetch_size : 25 + max_number_of_voxels: 12000 + shuffle_points: false + num_workers: 1 + anchor_area_threshold: 1 + remove_environment: false + kitti_info_path: "kitti_infos_val.pkl" + kitti_root_path: "" +} diff --git a/projects/perception/object_detection_3d/benchmark/configs/tanet_ped_cycle_xyres_16.proto b/projects/perception/object_detection_3d/benchmark/configs/tanet_ped_cycle_xyres_16.proto new file mode 100644 index 0000000000..0dd79d933c --- /dev/null +++ b/projects/perception/object_detection_3d/benchmark/configs/tanet_ped_cycle_xyres_16.proto @@ -0,0 +1,183 @@ +model: { + second: { + voxel_generator { + point_cloud_range : [0, -19.84, -2.5, 47.36, 19.84, 0.5] + voxel_size : [0.16, 0.16, 3] + max_number_of_points_per_voxel : 100 + } + num_class: 2 + voxel_feature_extractor: { + module_class_name: "PillarFeature_TANet" + num_filters: [64] + with_distance: false + } + middle_feature_extractor: { + module_class_name: "PointPillarsScatter" + } + rpn: { + module_class_name: "PSA" + layer_nums: [3, 5, 5] + layer_strides: [1, 2, 2] + num_filters: [64, 128, 256] + upsample_strides: [1, 2, 4] + num_upsample_filters: [128, 128, 128] + use_groupnorm: false + num_groups: 32 + } + loss: { + classification_loss: { + weighted_sigmoid_focal: { + alpha: 0.25 + gamma: 2.0 + anchorwise_output: true + } + } + localization_loss: { + weighted_smooth_l1: { + sigma: 3.0 + code_weight: [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0] + } + } + classification_weight: 1.0 + localization_weight: 2.0 + } + # Outputs + use_sigmoid_score: true + encode_background_as_zeros: true + encode_rad_error_by_sin: true + + use_direction_classifier: true + direction_loss_weight: 0.2 + use_aux_classifier: false + # Loss + pos_class_weight: 1.0 + neg_class_weight: 1.0 + + loss_norm_type: NormByNumPositives + # Postprocess + post_center_limit_range: [0, -19.84, -2.5, 47.36, 19.84, 0.5] + use_rotate_nms: false + use_multi_class_nms: false + nms_pre_max_size: 1000 + nms_post_max_size: 300 + nms_score_threshold: 0.05 + nms_iou_threshold: 0.5 + + use_bev: false + num_point_features: 4 + without_reflectivity: false + box_coder: { + ground_box3d_coder: { + linear_dim: false + encode_angle_vector: false + } + } + target_assigner: { + anchor_generators: { + anchor_generator_stride: { + sizes: [0.6, 1.76, 1.73] # wlh + strides: [0.16, 0.16, 0.0] # if generate only 1 z_center, z_stride will be ignored + offsets: [0.08, -19.76, -1.465] # origin_offset + strides / 2 + rotations: [0, 1.57] # 0, pi/2 + matched_threshold : 0.5 + unmatched_threshold : 0.35 + } + } + anchor_generators: { + anchor_generator_stride: { + sizes: [0.6, 0.8, 1.73] # wlh + strides: [0.16, 0.16, 0.0] # if generate only 1 z_center, z_stride will be ignored + offsets: [0.08, -19.76, -1.465] # origin_offset + strides / 2 + rotations: [0, 1.57] # 0, pi/2 + matched_threshold : 0.5 + unmatched_threshold : 0.35 + } + } + + sample_positive_fraction : -1 + sample_size : 512 + region_similarity_calculator: { + nearest_iou_similarity: { + } + } + } + } +} + + +train_input_reader: { + record_file_path: "kitti_train.tfrecord" + class_names: ["Cyclist", "Pedestrian"] + max_num_epochs : 160 + batch_size: 2 + prefetch_size : 25 + max_number_of_voxels: 12000 + shuffle_points: true + num_workers: 2 + groundtruth_localization_noise_std: [0.25, 0.25, 0.25] + groundtruth_rotation_uniform_noise: [-0.15707963267, 0.15707963267] + global_rotation_uniform_noise: [-0.78539816, 0.78539816] + global_scaling_uniform_noise: [0.95, 1.05] + global_random_rotation_range_per_object: [0, 0] + anchor_area_threshold: 1 + remove_points_after_sample: false + groundtruth_points_drop_percentage: 0.0 + groundtruth_drop_max_keep_points: 15 + database_sampler { + database_info_path: "kitti_dbinfos_train.pkl" + sample_groups { + name_to_max_num { + key: "Cyclist" + value: 8 + } + } + database_prep_steps { + filter_by_min_num_points { + min_num_point_pairs { + key: "Cyclist" + value: 5 + } + } + } + database_prep_steps { + filter_by_difficulty { + removed_difficulties: [-1] + } + } + global_random_rotation_range_per_object: [0, 0] + rate: 1.0 + } + + remove_unknown_examples: false + remove_environment: false + kitti_info_path: "kitti_infos_train.pkl" + kitti_root_path: "" +} + +train_config: { + + inter_op_parallelism_threads: 4 + intra_op_parallelism_threads: 4 + steps: 371200 # 296960 = 1856 steps per epoch * 160 epochs + steps_per_eval: 9280 # 1856 steps per epoch * 5 epochs + save_checkpoints_secs : 1800 # half hour + save_summary_steps : 10 + enable_mixed_precision: false + loss_scale_factor : 512.0 + clear_metrics_every_epoch: false +} + +eval_input_reader: { + record_file_path: "kitti_val.tfrecord" + class_names: ["Cyclist", "Pedestrian"] + batch_size: 1 # 2 + max_num_epochs : 160 + prefetch_size : 25 + max_number_of_voxels: 12000 + shuffle_points: false + num_workers: 3 + anchor_area_threshold: 1 + remove_environment: false + kitti_info_path: "kitti_infos_val.pkl" + kitti_root_path: "" +} diff --git a/projects/perception/object_detection_3d/benchmark/media/000000.bin b/projects/perception/object_detection_3d/benchmark/media/000000.bin new file mode 100755 index 0000000000..744999c1f7 Binary files /dev/null and b/projects/perception/object_detection_3d/benchmark/media/000000.bin differ diff --git a/projects/perception/object_tracking_2d/benchmark/.gitignore b/projects/perception/object_tracking_2d/benchmark/.gitignore new file mode 100644 index 0000000000..314f02b1bc --- /dev/null +++ b/projects/perception/object_tracking_2d/benchmark/.gitignore @@ -0,0 +1 @@ +*.txt \ No newline at end of file diff --git a/projects/perception/object_tracking_2d/benchmark/benchmark_deep_sort.py b/projects/perception/object_tracking_2d/benchmark/benchmark_deep_sort.py new file mode 100644 index 0000000000..5cd6ce8e83 --- /dev/null +++ b/projects/perception/object_tracking_2d/benchmark/benchmark_deep_sort.py @@ -0,0 +1,129 @@ +# Copyright 2020-2022 OpenDR European Project +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import os +import yaml +import torch +import logging +from pytorch_benchmark import benchmark +from opendr.perception.object_tracking_2d import ObjectTracking2DDeepSortLearner +from opendr.perception.object_tracking_2d.datasets.mot_dataset import MotDataset +from projects.perception.object_tracking_2d.demos.fair_mot_deep_sort.data_generators import ( + disk_image_with_detections_generator, +) + +logger = logging.getLogger("benchmark") +logging.basicConfig() +logger.setLevel("DEBUG") + + +def benchmark_fair_mot(): + root_dir = "./projects/perception/object_tracking_2d/benchmark" + temp_dir = root_dir + "/tmp" + models_dir = root_dir + "/models" + num_runs = 100 + + models = [ + "deep_sort", + ] + + if not os.path.exists(temp_dir + "/nano_MOT20"): + MotDataset.download_nano_mot20( + os.path.join(temp_dir, "mot_dataset"), True + ).path + + batch_size = 2 + + data_generator = disk_image_with_detections_generator( + temp_dir, + { + "nano_mot20": os.path.join( + ".", + "src", + "opendr", + "perception", + "object_tracking_2d", + "datasets", + "splits", + "nano_mot20.train", + ) + }, + cycle=True + ) + + sample = next(data_generator) + samples = [next(data_generator) for _ in range(batch_size)] + + if os.path.exists(root_dir + "/results_deep_sort.txt"): + os.remove(root_dir + "/results_deep_sort.txt") + + for model_name in models: + print( + f"==== Benchmarking ObjectTracking2DDeepSortLearner ({model_name}) ====" + ) + + learner = ObjectTracking2DDeepSortLearner( + temp_path=temp_dir, + ) + + if model_name is not None and not os.path.exists( + models_dir + "/" + model_name + ): + learner.download(model_name, models_dir) + learner.load(models_dir + "/" + model_name, verbose=True) + + def get_device_fn(*args): + nonlocal learner + return torch.device(learner.device) + + def transfer_to_device_fn( + sample, + device, + ): + return sample + + print("== Benchmarking learner.infer ==") + results1 = benchmark( + model=learner.infer, + sample=samples, + sample_with_batch_size1=sample, + num_runs=num_runs, + get_device_fn=get_device_fn, + transfer_to_device_fn=transfer_to_device_fn, + batch_size=batch_size, + ) + + inner_fps = learner.infers_count / (learner.infers_time) + + print("Inner FPS =", inner_fps) + print(yaml.dump({"learner.infer": results1})) + + with open(root_dir + "/results_deep_sort.txt", "a") as f: + print( + f"==== Benchmarking ObjectTracking2DDeepSortLearner ({model_name}) ====", + file=f, + ) + print("Inner FPS =", inner_fps, file=f) + print(yaml.dump({"learner.infer": results1}), file=f) + print("\n\n", file=f) + + # print("== Benchmarking model directly ==") + # results2 = benchmark(learner.model, sample, num_runs=num_runs) + # print(yaml.dump({"learner.model.forward": results2})) + + print("===END===") + + +if __name__ == "__main__": + benchmark_fair_mot() diff --git a/projects/perception/object_tracking_2d/benchmark/benchmark_fair_mot.py b/projects/perception/object_tracking_2d/benchmark/benchmark_fair_mot.py new file mode 100644 index 0000000000..23f205fe79 --- /dev/null +++ b/projects/perception/object_tracking_2d/benchmark/benchmark_fair_mot.py @@ -0,0 +1,101 @@ +# Copyright 2020-2022 OpenDR European Project +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import os +import yaml +import torch +import logging +from pytorch_benchmark import benchmark +from opendr.perception.object_tracking_2d import ObjectTracking2DFairMotLearner +from opendr.engine.data import Image + +logger = logging.getLogger("benchmark") +logging.basicConfig() +logger.setLevel("DEBUG") + + +def benchmark_fair_mot(): + root_dir = "./projects/perception/object_tracking_2d/benchmark" + temp_dir = root_dir + "/tmp" + models_dir = root_dir + "/models" + media_dir = root_dir + "/media" + num_runs = 100 + + models = [ + "crowdhuman_dla34", + "fairmot_dla34", + ] + + batch_size = 2 + + sample = Image.open(media_dir + "/000001.jpg") + samples = [sample for _ in range(batch_size)] + + if os.path.exists(root_dir + "/results_fair_mot.txt"): + os.remove(root_dir + "/results_fair_mot.txt") + + for model_name in models: + print(f"==== Benchmarking ObjectTracking2DFairMotLearner ({model_name}) ====") + + learner = ObjectTracking2DFairMotLearner( + temp_path=temp_dir, + ) + + if model_name is not None and not os.path.exists( + models_dir + "/" + model_name + ): + learner.download(model_name, models_dir) + learner.load(models_dir + "/" + model_name, verbose=True) + + def get_device_fn(*args): + nonlocal learner + return torch.device(learner.device) + + def transfer_to_device_fn( + sample, + device, + ): + return sample + + print("== Benchmarking learner.infer ==") + results1 = benchmark( + model=learner.infer, + sample=samples, + sample_with_batch_size1=sample, + num_runs=num_runs, + get_device_fn=get_device_fn, + transfer_to_device_fn=transfer_to_device_fn, + batch_size=batch_size, + ) + + inner_fps = learner.infers_count / (learner.infers_time) + + print("Inner FPS =", inner_fps) + print(yaml.dump({"learner.infer": results1})) + + with open(root_dir + "/results_fair_mot.txt", "a") as f: + print(f"==== Benchmarking ObjectTracking2DFairMotLearner ({model_name}) ====", file=f) + print("Inner FPS =", inner_fps, file=f) + print(yaml.dump({"learner.infer": results1}), file=f) + print("\n\n", file=f) + + # print("== Benchmarking model directly ==") + # results2 = benchmark(learner.model, sample, num_runs=num_runs) + # print(yaml.dump({"learner.model.forward": results2})) + + print("===END===") + + +if __name__ == "__main__": + benchmark_fair_mot() diff --git a/projects/perception/object_tracking_2d/benchmark/media/000001.jpg b/projects/perception/object_tracking_2d/benchmark/media/000001.jpg new file mode 100644 index 0000000000..7e0b7898a9 Binary files /dev/null and b/projects/perception/object_tracking_2d/benchmark/media/000001.jpg differ diff --git a/projects/perception/object_tracking_3d/benchmark/.gitignore b/projects/perception/object_tracking_3d/benchmark/.gitignore new file mode 100644 index 0000000000..de180aafdc --- /dev/null +++ b/projects/perception/object_tracking_3d/benchmark/.gitignore @@ -0,0 +1,2 @@ +*.txt +!media/* \ No newline at end of file diff --git a/projects/perception/object_tracking_3d/benchmark/benchmark_ab3dmot.py b/projects/perception/object_tracking_3d/benchmark/benchmark_ab3dmot.py new file mode 100644 index 0000000000..f86caa334c --- /dev/null +++ b/projects/perception/object_tracking_3d/benchmark/benchmark_ab3dmot.py @@ -0,0 +1,90 @@ +# Copyright 2020-2022 OpenDR European Project +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import os +import yaml +import torch +import logging +from pytorch_benchmark import benchmark +from opendr.perception.object_tracking_3d import ObjectTracking3DAb3dmotLearner, KittiTrackingDatasetIterator + +logger = logging.getLogger("benchmark") +logging.basicConfig() +logger.setLevel("DEBUG") + + +def benchmark_ab3dmot(): + root_dir = "./projects/perception/object_tracking_3d/benchmark" + media_dir = root_dir + "/media" + num_runs = 100 + + models = [ + "ab3dmot", + ] + + batch_size = 10 + + dataset = KittiTrackingDatasetIterator(media_dir, media_dir, "tracking") + sample = dataset[0][0][0] + samples = dataset[0][0][:10] + + if os.path.exists(root_dir + "/results_ab3dmot.txt"): + os.remove(root_dir + "/results_ab3dmot.txt") + + for model_name in models: + print(f"==== Benchmarking ObjectTracking3DAb3dmotLearner ({model_name}) ====") + + learner = ObjectTracking3DAb3dmotLearner() + + def get_device_fn(*args): + nonlocal learner + return torch.device(learner.device) + + def transfer_to_device_fn( + sample, + device, + ): + return sample + + print("== Benchmarking learner.infer ==") + results1 = benchmark( + model=learner.infer, + sample=samples, + sample_with_batch_size1=sample, + num_runs=num_runs, + get_device_fn=get_device_fn, + transfer_to_device_fn=transfer_to_device_fn, + batch_size=batch_size, + ) + + inner_fps = learner.infers_count / (learner.infers_time) + + print("Inner FPS =", inner_fps) + print(yaml.dump({"learner.infer": results1})) + + with open(root_dir + "/results_ab3dmot.txt", "a") as f: + print(f"==== Benchmarking ObjectTracking3DAb3dmotLearner ({model_name}) ====", file=f) + print("Inner FPS =", inner_fps, file=f) + print(yaml.dump({"learner.infer": results1}), file=f) + print("\n\n", file=f) + + # print("== Benchmarking model directly ==") + # results2 = benchmark(learner.model, sample, num_runs=num_runs) + # print(yaml.dump({"learner.model.forward": results2})) + + print("===END===") + + +if __name__ == "__main__": + benchmark_ab3dmot() diff --git a/projects/perception/object_tracking_3d/benchmark/media/0000.txt b/projects/perception/object_tracking_3d/benchmark/media/0000.txt new file mode 100644 index 0000000000..14cd54d52e --- /dev/null +++ b/projects/perception/object_tracking_3d/benchmark/media/0000.txt @@ -0,0 +1,1089 @@ +0 -1 DontCare -1 -1 -10.000000 219.310000 188.490000 245.500000 218.560000 -1000.000000 -1000.000000 -1000.000000 -10.000000 -1.000000 -1.000000 -1.000000 +0 -1 DontCare 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1.622590 12.436503 -1.637280 +153 11 Car 0 2 1.864481 245.920800 194.456182 394.817829 286.444967 1.444000 1.595116 3.791789 -5.458963 1.908188 13.979427 1.497916 +153 12 Pedestrian 1 0 0.826456 1185.199080 151.165841 1241.000000 348.552707 1.688000 0.800000 0.884000 5.739732 1.500532 6.279632 1.543272 +153 13 Car 0 0 1.773993 344.361560 188.772369 430.531955 248.482384 1.422414 1.512803 3.707634 -6.033258 1.888008 19.788795 1.481180 +153 14 Car 0 2 -1.728662 652.362288 183.789605 737.478033 246.613864 1.365956 1.508586 3.485915 1.955738 1.651867 17.818612 -1.622048 diff --git a/projects/perception/skeleton_based_action_recognition/benchmark/benchmark_stgcn.py b/projects/perception/skeleton_based_action_recognition/benchmark/benchmark_stgcn.py new file mode 100644 index 0000000000..ae43b60323 --- /dev/null +++ b/projects/perception/skeleton_based_action_recognition/benchmark/benchmark_stgcn.py @@ -0,0 +1,100 @@ +# Copyright 2020-2022 OpenDR European Project +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import os +import torch +import yaml +from pytorch_benchmark import benchmark +import logging + +# opendr imports +import argparse +from opendr.perception.skeleton_based_action_recognition import ProgressiveSpatioTemporalGCNLearner +from opendr.perception.skeleton_based_action_recognition import SpatioTemporalGCNLearner + +logger = logging.getLogger("benchmark") +logging.basicConfig() +logger.setLevel("DEBUG") + + +def benchmark_stgcn(args): + results_dir = "./results" + if not os.path.exists(results_dir): + os.makedirs(results_dir) + device = args.device + if args.method == 'pstgcn': + print(f"==== Benchmarking pstgcn ({args.dataset_name}) ====") + if args.dataset_name == 'nturgbd_cv': + learner = ProgressiveSpatioTemporalGCNLearner(device=args.device, dataset_name='nturgbd_cv', + topology=[5, 4, 5, 2, 3, 4, 3, 4], in_channels=3, + num_point=25, graph_type='ntu') + elif args.dataset_name == 'nturgbd_cs': + learner = ProgressiveSpatioTemporalGCNLearner(device=args.device, dataset_name='nturgbd_cs', + topology=[5, 4, 3, 5, 3, 5, 7, 4], in_channels=3, + num_point=25, graph_type='ntu') + else: + print(f"==== Benchmarking {args.method} ({args.dataset_name}) ====") + learner = SpatioTemporalGCNLearner(device=args.device, dataset_name='nturgbd_cv', + method_name=args.method, in_channels=3, num_point=25, + graph_type='ntu', num_class=60, num_subframes=100) + + learner.init_model() + if args.device == 'cuda': + learner.model.cuda() + + batch_size = 1 + num_runs = 100 + C = 3 + T = 300 + V = 25 + M = 2 + data = torch.randn(batch_size, C, T, V, M) + samples = data + + def get_device_fn(*args): + nonlocal learner + return next(learner.model.parameters()).device + + def transfer_to_device_fn(sample, device,): + return sample + + print("== Benchmarking learner.infer ==") + results1 = benchmark(model=learner.infer, + sample=samples, + sample_with_batch_size1=None, + num_runs=num_runs, + get_device_fn=get_device_fn, + transfer_to_device_fn=transfer_to_device_fn, + batch_size=batch_size, + print_fn=print, + ) + with open(results_dir + f"/benchmark_{args.method}_{device}.txt", "a") as f: + print("== Benchmarking learner.infer ==", file=f) + print(yaml.dump({"learner.infer": results1}), file=f) + print("\n\n", file=f) + print("== Benchmarking model directly ==", file=f) + results2 = benchmark(learner.model, data, num_runs=num_runs, print_fn=print) + print(yaml.dump({"learner.model.forward": results2})) + + +if __name__ == '__main__': + parser = argparse.ArgumentParser() + parser.add_argument("--device", help="Device to use (cpu, cuda)", type=str, default="cuda") + parser.add_argument('--method', type=str, default='stgcn', + help='action detection method') + parser.add_argument('--dataset_name', type=str, default='nturgbd_cv', + help='action detection method') + + args = parser.parse_args() + benchmark_stgcn(args) diff --git a/src/opendr/engine/target.py b/src/opendr/engine/target.py index efb922aa2f..ec822e00f2 100644 --- a/src/opendr/engine/target.py +++ b/src/opendr/engine/target.py @@ -323,7 +323,7 @@ def mot(self, with_confidence=True, frame=-1): if with_confidence: result = np.array([ - self.frame, + frame, self.left, self.top, self.width, diff --git a/src/opendr/perception/activity_recognition/datasets/kinetics.py b/src/opendr/perception/activity_recognition/datasets/kinetics.py index 91301d059d..3eab1ad19f 100644 --- a/src/opendr/perception/activity_recognition/datasets/kinetics.py +++ b/src/opendr/perception/activity_recognition/datasets/kinetics.py @@ -12,7 +12,7 @@ # See the License for the specific language governing permissions and # limitations under the License. -import av +import csv import json import random import torch @@ -26,16 +26,29 @@ from typing import List, Optional, Tuple, Union from joblib import Memory from opendr.perception.activity_recognition.datasets.utils import decoder -from opendr.perception.activity_recognition.datasets.utils.transforms import standard_video_transforms +from opendr.perception.activity_recognition.datasets.utils.transforms import ( + standard_video_transforms, +) from opendr.engine.constants import OPENDR_SERVER_URL from urllib.request import urlretrieve -import pandas as pd + +try: + import av +except ModuleNotFoundError: + try: + import torchvision + assert len(torchvision.__version__) > 0 + except ModuleNotFoundError: + raise ModuleNotFoundError( + "Either pyav (`pip install av`) or torchvision " + "must be installed for the Kinetics loader to work" + ) logger = getLogger(__file__) -CLASSES = pd.read_csv( - Path(__file__).parent / "kinetics400_classes.csv", verbose=True, index_col=0 -).to_dict()["name"] +with open(Path(__file__).parent / "kinetics400_classes.csv", "r") as file: + my_reader = csv.reader(file, delimiter=",") + CLASSES = [row[1] for i, row in enumerate(my_reader) if i != 0] class KineticsDataset(ExternalDataset, DatasetIterator, torch.utils.data.Dataset): @@ -59,7 +72,8 @@ def __init__( temporal_downsampling=1, split="train", video_transform=None, - use_caching=False + use_caching=False, + decoder_backend="pyav", ): """ Kinetics dataset @@ -75,9 +89,11 @@ def __init__( temporal_downsampling (int): rate of downsampling in time. Defaults to 1. split (str, optional): Which split to use (Options are ["train", "val", "test"]). Defaults to "train". video_transform (callable, optional): A function/transform that takes in a TxHxWxC video - and returns a transformed version. If None, a standard video transform will be applied. Defaults to None. + and returns a transformed version. If None, a standard video transform will be applied. + Defaults to None. use_caching (bool): Cache long-running operations. Defaults to False. - + decoder_backend (str): Name of library to use for video decoding + (Options are ["pyav", "torchvision"]). Defaults to "pyav". """ ExternalDataset.__init__(self, path=str(path), dataset_type="kinetics") DatasetIterator.__init__(self) @@ -99,16 +115,22 @@ def __init__( self.step_between_clips = step_between_clips self.target_fps = 30 self.temporal_downsampling = temporal_downsampling + assert decoder_backend in {"pyav", "torchvision"} + self.decoder_backend = decoder_backend if video_transform: self.video_transform = video_transform else: train_transform, eval_transform = standard_video_transforms() - self.video_transform = train_transform if self.split == "train" else eval_transform + self.video_transform = ( + train_transform if self.split == "train" else eval_transform + ) - validate_splits = Memory(Path(os.getcwd()) / ".cache", verbose=1).cache( - _validate_splits - ) if use_caching else _validate_splits + validate_splits = ( + Memory(Path(os.getcwd()) / ".cache", verbose=1).cache(_validate_splits) + if use_caching + else _validate_splits + ) ( self.labels, @@ -118,11 +140,7 @@ def __init__( self.classes, _, # num_not_found, self.video_inds, - ) = validate_splits( - self.root_path, - self.annotation_path, - split - ) + ) = validate_splits(self.root_path, self.annotation_path, split) if len(self.classes) not in {400, 600, 700}: logger.warning( f"Only found {len(self.classes)} classes for {split} set, but expected either 400, 600, or 700 for Kinetics." @@ -145,7 +163,9 @@ def __getitem__(self, idx): for _ in range(self.num_retries): try: video_container = _get_video_container( - self.file_paths[idx], multi_thread_decode=False, backend="pyav", + self.file_paths[idx], + multi_thread_decode=False, + backend=self.decoder_backend, ) except Exception as e: logger.info( @@ -172,7 +192,7 @@ def __getitem__(self, idx): num_clips=1, video_meta=self.video_meta[idx], target_fps=self.target_fps, - backend="pyav", + backend=self.decoder_backend, ) # If decoding failed (wrong format, video is too short, and etc), @@ -225,7 +245,7 @@ def download_mini(path: Union[str, Path]): "perception", "activity_recognition", "datasets", - "kinetics400mini.zip" + "kinetics400mini.zip", ) zip_path = str(Path(path) / "kinetics400mini.zip") unzip_path = str(Path(path)) @@ -234,7 +254,7 @@ def download_mini(path: Union[str, Path]): urlretrieve(url=url, filename=zip_path) logger.info(f"Unzipping Kinetics400 mini to {(unzip_path)}") - with zipfile.ZipFile(zip_path, 'r') as zip_ref: + with zipfile.ZipFile(zip_path, "r") as zip_ref: zip_ref.extractall(unzip_path) os.remove(zip_path) @@ -257,7 +277,7 @@ def download_micro(path: Union[str, Path]): "perception", "activity_recognition", "datasets", - "kinetics3.zip" + "kinetics3.zip", ) zip_path = str(Path(path) / "kinetics3.zip") unzip_path = str(Path(path)) @@ -266,7 +286,7 @@ def download_micro(path: Union[str, Path]): urlretrieve(url=url, filename=zip_path) logger.info(f"Unzipping Kinetics3 to {(unzip_path)}") - with zipfile.ZipFile(zip_path, 'r') as zip_ref: + with zipfile.ZipFile(zip_path, "r") as zip_ref: zip_ref.extractall(unzip_path) os.remove(zip_path) @@ -299,9 +319,7 @@ def map_segments(segment_arr: List[float]) -> Tuple[str, str]: def _validate_splits( - root: str, - annotation_path: str, - split: str, + root: str, annotation_path: str, split: str, ): root_path = Path(root) assert root_path.is_dir() diff --git a/src/opendr/perception/facial_expression_recognition/landmark_based_facial_expression_recognition/progressive_spatio_temporal_bln_learner.py b/src/opendr/perception/facial_expression_recognition/landmark_based_facial_expression_recognition/progressive_spatio_temporal_bln_learner.py index b5b048bb30..0f591adc89 100644 --- a/src/opendr/perception/facial_expression_recognition/landmark_based_facial_expression_recognition/progressive_spatio_temporal_bln_learner.py +++ b/src/opendr/perception/facial_expression_recognition/landmark_based_facial_expression_recognition/progressive_spatio_temporal_bln_learner.py @@ -538,7 +538,7 @@ def network_builder(self, dataset, val_dataset, monte_carlo_dropout=True, mcdo_r np.save(os.path.join(self.parent_dir, 'Topology.npy'), self.topology) return self.topology - def infer(self, facial_landmarks_batch, monte_carlo_dropout=True, mcdo_repeats=100): + def infer(self, facial_landmarks_batch, monte_carlo_dropout=False, mcdo_repeats=1): """ This method performs inference on the batch provided. :param facial_landmarks_batch: Object that holds a batch of data to run inference on. diff --git a/src/opendr/perception/object_detection_3d/voxel_object_detection_3d/second_detector/builder/dataset_builder.py b/src/opendr/perception/object_detection_3d/voxel_object_detection_3d/second_detector/builder/dataset_builder.py index a8d226f8f7..a5237bcf91 100644 --- a/src/opendr/perception/object_detection_3d/voxel_object_detection_3d/second_detector/builder/dataset_builder.py +++ b/src/opendr/perception/object_detection_3d/voxel_object_detection_3d/second_detector/builder/dataset_builder.py @@ -76,7 +76,7 @@ def create_prep_func( remove_outside_points=False, remove_unknown=cfg.remove_unknown_examples, create_targets=training, - shuffle_points=cfg.shuffle_points, + shuffle_points=cfg.shuffle_points if training else False, gt_rotation_noise=list(cfg.groundtruth_rotation_uniform_noise), gt_loc_noise_std=list(cfg.groundtruth_localization_noise_std), global_rotation_noise=list(cfg.global_rotation_uniform_noise), diff --git a/src/opendr/perception/object_detection_3d/voxel_object_detection_3d/second_detector/builder/preprocess_builder.py b/src/opendr/perception/object_detection_3d/voxel_object_detection_3d/second_detector/builder/preprocess_builder.py index 792c2c19eb..77f319e53e 100644 --- a/src/opendr/perception/object_detection_3d/voxel_object_detection_3d/second_detector/builder/preprocess_builder.py +++ b/src/opendr/perception/object_detection_3d/voxel_object_detection_3d/second_detector/builder/preprocess_builder.py @@ -1,4 +1,4 @@ -import opendr.perception.object_detection_3d.voxel_object_detection_3d.second_detector.core.preprocess as prep +from opendr.perception.object_detection_3d.voxel_object_detection_3d.second_detector.core import preprocess as prep def build_db_preprocess(db_prep_config): diff --git a/src/opendr/perception/object_detection_3d/voxel_object_detection_3d/second_detector/run.py b/src/opendr/perception/object_detection_3d/voxel_object_detection_3d/second_detector/run.py index 449131d53a..afd9d01693 100644 --- a/src/opendr/perception/object_detection_3d/voxel_object_detection_3d/second_detector/run.py +++ b/src/opendr/perception/object_detection_3d/voxel_object_detection_3d/second_detector/run.py @@ -16,7 +16,7 @@ import numpy as np import time -import opendr.perception.object_detection_3d.voxel_object_detection_3d.second_detector.data.kitti_common as kitti +from opendr.perception.object_detection_3d.voxel_object_detection_3d.second_detector.data import kitti_common as kitti from opendr.perception.object_detection_3d.voxel_object_detection_3d.second_detector.data.preprocess import ( merge_second_batch, ) diff --git a/src/opendr/perception/object_tracking_2d/deep_sort/algorithm/deep_sort_tracker.py b/src/opendr/perception/object_tracking_2d/deep_sort/algorithm/deep_sort_tracker.py index 7073b1e6ff..edd44325f2 100644 --- a/src/opendr/perception/object_tracking_2d/deep_sort/algorithm/deep_sort_tracker.py +++ b/src/opendr/perception/object_tracking_2d/deep_sort/algorithm/deep_sort_tracker.py @@ -56,7 +56,7 @@ def infer(self, imageWithDetections: ImageWithDetections, frame_id=None): if frame_id is not None: self.frame = frame_id - image = imageWithDetections.numpy().transpose(1, 2, 0) + image = imageWithDetections.numpy().transpose(2, 1, 0) detections = imageWithDetections.boundingBoxList bbox_xywh = [] diff --git a/src/opendr/perception/object_tracking_2d/deep_sort/object_tracking_2d_deep_sort_learner.py b/src/opendr/perception/object_tracking_2d/deep_sort/object_tracking_2d_deep_sort_learner.py index 2cec93e397..d0cd3f36a4 100644 --- a/src/opendr/perception/object_tracking_2d/deep_sort/object_tracking_2d_deep_sort_learner.py +++ b/src/opendr/perception/object_tracking_2d/deep_sort/object_tracking_2d_deep_sort_learner.py @@ -12,6 +12,7 @@ # See the License for the specific language governing permissions and # limitations under the License. +import time import os import json import torch @@ -76,6 +77,9 @@ def __init__( self.tracker.deepsort.extractor.net.parameters(), lr, momentum=0.9, weight_decay=5e-4 ) + self.infers_count = 0 + self.infers_time = 0 + def save(self, path, verbose=False): """ This method is used to save a trained model. @@ -280,9 +284,15 @@ def infer(self, batch, frame_ids=None): for image, frame_id in zip(batch, frame_ids): + t0 = time.time() + result = self.tracker.infer(image, frame_id) results.append(result) + t0 = time.time() - t0 + self.infers_count += 1 + self.infers_time += t0 + if is_single_image: results = results[0] diff --git a/src/opendr/perception/object_tracking_2d/fair_mot/object_tracking_2d_fair_mot_learner.py b/src/opendr/perception/object_tracking_2d/fair_mot/object_tracking_2d_fair_mot_learner.py index d22735a919..a5eae1a456 100644 --- a/src/opendr/perception/object_tracking_2d/fair_mot/object_tracking_2d_fair_mot_learner.py +++ b/src/opendr/perception/object_tracking_2d/fair_mot/object_tracking_2d_fair_mot_learner.py @@ -14,6 +14,7 @@ import os import json +import time import torch import ntpath import shutil @@ -125,6 +126,8 @@ def __init__( main_batch_size = self.batch_size // len(self.gpus) rest_batch_size = (self.batch_size - main_batch_size) self.chunk_sizes = [main_batch_size] + self.infers_count = 0 + self.infers_time = 0 for i in range(len(self.gpus) - 1): worker_chunk_size = rest_batch_size // (len(self.gpus) - 1) @@ -385,6 +388,7 @@ def infer(self, batch, frame_ids=None, img_size=(1088, 608)): blob = torch.from_numpy(img).to(self.device).unsqueeze(0) + t0 = time.time() online_targets = self.tracker.update(blob, img0) online_tlwhs = [] online_ids = [] @@ -415,6 +419,10 @@ def infer(self, batch, frame_ids=None, img_size=(1088, 608)): ) ]) + t0 = time.time() - t0 + self.infers_count += 1 + self.infers_time += t0 + results.append(result) if is_single_image: diff --git a/src/opendr/perception/object_tracking_3d/ab3dmot/object_tracking_3d_ab3dmot_learner.py b/src/opendr/perception/object_tracking_3d/ab3dmot/object_tracking_3d_ab3dmot_learner.py index b542fc510d..067410f992 100644 --- a/src/opendr/perception/object_tracking_3d/ab3dmot/object_tracking_3d_ab3dmot_learner.py +++ b/src/opendr/perception/object_tracking_3d/ab3dmot/object_tracking_3d_ab3dmot_learner.py @@ -12,6 +12,7 @@ # See the License for the specific language governing permissions and # limitations under the License. +import time from opendr.engine.learners import Learner from opendr.engine.datasets import DatasetIterator from opendr.engine.target import BoundingBox3DList @@ -49,6 +50,9 @@ def __init__( self.process_uncertainty_matrix = process_uncertainty_matrix self.iou_threshold = iou_threshold + self.infers_count = 0 + self.infers_time = 0 + self.__create_model() def save(self, path): @@ -126,9 +130,16 @@ def infer(self, bounding_boxes_3d_list): results = [] for box_list in bounding_boxes_3d_list: + + t0 = time.time() + result = self.model.update(box_list) results.append(result) + t0 = time.time() - t0 + self.infers_count += 1 + self.infers_time += t0 + if is_single_input: results = results[0]