From c7a3017270ec0bb71da5f59ab44b35198804c85c Mon Sep 17 00:00:00 2001 From: LukasHedegaard Date: Fri, 11 Feb 2022 15:31:43 +0100 Subject: [PATCH 01/60] Add Human Activity Recognition benchmark scripts --- .../activity_recognition/benchmark/README.md | 28 +++++ .../benchmark/benchmark_cox3d.py | 102 ++++++++++++++++++ .../benchmark/benchmark_x3d.py | 102 ++++++++++++++++++ .../benchmark/requirements.txt | 1 + 4 files changed, 233 insertions(+) create mode 100644 projects/perception/activity_recognition/benchmark/README.md create mode 100644 projects/perception/activity_recognition/benchmark/benchmark_cox3d.py create mode 100644 projects/perception/activity_recognition/benchmark/benchmark_x3d.py create mode 100644 projects/perception/activity_recognition/benchmark/requirements.txt 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..bc31c800a5 --- /dev/null +++ b/projects/perception/activity_recognition/benchmark/benchmark_cox3d.py @@ -0,0 +1,102 @@ +# 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" + + batch_size = 8 + 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), + } + + for backbone in ["xs", "s", "m", "l"]: + logger.info(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, *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 + ] + + logger.info("== 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, + ) + logger.info(yaml.dump({"learner.infer": results1})) + + logger.info("== Benchmarking model directly ==") + results2 = benchmark(learner.model, sample, num_runs=num_runs) + logger.info(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..4f590c234c --- /dev/null +++ b/projects/perception/activity_recognition/benchmark/benchmark_x3d.py @@ -0,0 +1,102 @@ +# 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" + + batch_size = 8 + 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), + } + + for backbone in ["xs", "s", "m", "l"]: + logger.info(f"==== Benchmarking X3DLearner ({backbone}) ====") + + learner = X3DLearner( + device="cuda" if torch.cuda.is_available() else "cpu", + temp_path=temp_dir, + backbone=backbone, + ) + + sample = torch.randn(batch_size, *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 + ] + + logger.info("== 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, + ) + logger.info(yaml.dump({"learner.infer": results1})) + + logger.info("== Benchmarking model directly ==") + results2 = benchmark(learner.model, sample, num_runs=num_runs) + logger.info(yaml.dump({"learner.model.forward": results2})) + + +if __name__ == "__main__": + benchmark_x3d() 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 From c7bc50a63e21e156112fa705d09cfdda9496f3db Mon Sep 17 00:00:00 2001 From: LukasHedegaard Date: Mon, 14 Feb 2022 09:12:47 +0000 Subject: [PATCH 02/60] Remove pandas dependency in activity_recognition --- .../perception/activity_recognition/datasets/kinetics.py | 8 ++++---- .../perception/activity_recognition/dependencies.ini | 1 - 2 files changed, 4 insertions(+), 5 deletions(-) diff --git a/src/opendr/perception/activity_recognition/datasets/kinetics.py b/src/opendr/perception/activity_recognition/datasets/kinetics.py index 91301d059d..e4e037325e 100644 --- a/src/opendr/perception/activity_recognition/datasets/kinetics.py +++ b/src/opendr/perception/activity_recognition/datasets/kinetics.py @@ -13,6 +13,7 @@ # limitations under the License. import av +import csv import json import random import torch @@ -29,13 +30,12 @@ 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 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): diff --git a/src/opendr/perception/activity_recognition/dependencies.ini b/src/opendr/perception/activity_recognition/dependencies.ini index a4b8e669e5..a811aa3688 100644 --- a/src/opendr/perception/activity_recognition/dependencies.ini +++ b/src/opendr/perception/activity_recognition/dependencies.ini @@ -10,6 +10,5 @@ python=torch==1.7.1 av==8.0.1 joblib>=1.0.1 pyyaml==5.3 - pandas>=1.2 opendr=opendr-toolkit-engine From 6e85dd78c70b691cb4d54ff9542b30968e8e7e64 Mon Sep 17 00:00:00 2001 From: LukasHedegaard Date: Mon, 14 Feb 2022 09:21:17 +0000 Subject: [PATCH 03/60] Remove pyav dependency --- .../perception/activity_recognition/datasets/kinetics.py | 7 ++++--- .../perception/activity_recognition/dependencies.ini | 1 - 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/src/opendr/perception/activity_recognition/datasets/kinetics.py b/src/opendr/perception/activity_recognition/datasets/kinetics.py index e4e037325e..e8b4208cb9 100644 --- a/src/opendr/perception/activity_recognition/datasets/kinetics.py +++ b/src/opendr/perception/activity_recognition/datasets/kinetics.py @@ -12,7 +12,6 @@ # See the License for the specific language governing permissions and # limitations under the License. -import av import csv import json import random @@ -145,7 +144,7 @@ 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="torchvision", ) except Exception as e: logger.info( @@ -172,7 +171,7 @@ def __getitem__(self, idx): num_clips=1, video_meta=self.video_meta[idx], target_fps=self.target_fps, - backend="pyav", + backend="torchvision", ) # If decoding failed (wrong format, video is too short, and etc), @@ -386,6 +385,8 @@ def _get_video_container(path_to_vid, multi_thread_decode=False, backend="pyav") container = fp.read() return container elif backend == "pyav": + import av + container = av.open(path_to_vid) if multi_thread_decode: # Enable multiple threads for decoding. diff --git a/src/opendr/perception/activity_recognition/dependencies.ini b/src/opendr/perception/activity_recognition/dependencies.ini index a811aa3688..dfd49904f8 100644 --- a/src/opendr/perception/activity_recognition/dependencies.ini +++ b/src/opendr/perception/activity_recognition/dependencies.ini @@ -7,7 +7,6 @@ python=torch==1.7.1 onnx==1.8.0 onnxruntime==1.3.0 pytorch_lightning==1.2.3 - av==8.0.1 joblib>=1.0.1 pyyaml==5.3 From 71960abcea5805c16cc7ad66c241e4b6f132aaca Mon Sep 17 00:00:00 2001 From: LukasHedegaard Date: Mon, 14 Feb 2022 10:09:17 +0000 Subject: [PATCH 04/60] Add decoder option to kinetics loader --- .../activity_recognition/datasets/kinetics.py | 21 +++++++++++++------ 1 file changed, 15 insertions(+), 6 deletions(-) diff --git a/src/opendr/perception/activity_recognition/datasets/kinetics.py b/src/opendr/perception/activity_recognition/datasets/kinetics.py index e8b4208cb9..cfa204753b 100644 --- a/src/opendr/perception/activity_recognition/datasets/kinetics.py +++ b/src/opendr/perception/activity_recognition/datasets/kinetics.py @@ -30,6 +30,14 @@ from opendr.engine.constants import OPENDR_SERVER_URL from urllib.request import urlretrieve +try: + import av +except ImportError: + try: + import torchvision + except ImportError: + raise ImportError("Either pyav (`pip install av`) or torchvision must be installed for the Kinetics loader to work") + logger = getLogger(__file__) with open(Path(__file__).parent / "kinetics400_classes.csv", "r") as file: @@ -58,7 +66,8 @@ def __init__( temporal_downsampling=1, split="train", video_transform=None, - use_caching=False + use_caching=False, + decoder_backend="pyav" ): """ Kinetics dataset @@ -76,7 +85,7 @@ def __init__( 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. 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) @@ -98,6 +107,8 @@ 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 @@ -144,7 +155,7 @@ 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="torchvision", + self.file_paths[idx], multi_thread_decode=False, backend=self.decoder_backend, ) except Exception as e: logger.info( @@ -171,7 +182,7 @@ def __getitem__(self, idx): num_clips=1, video_meta=self.video_meta[idx], target_fps=self.target_fps, - backend="torchvision", + backend=self.decoder_backend, ) # If decoding failed (wrong format, video is too short, and etc), @@ -385,8 +396,6 @@ def _get_video_container(path_to_vid, multi_thread_decode=False, backend="pyav") container = fp.read() return container elif backend == "pyav": - import av - container = av.open(path_to_vid) if multi_thread_decode: # Enable multiple threads for decoding. From dc59eb415996e6972396bd1ed9e8701b08445be4 Mon Sep 17 00:00:00 2001 From: LukasHedegaard Date: Mon, 14 Feb 2022 10:35:46 +0000 Subject: [PATCH 05/60] Add script for har benchmark install --- .../benchmark/install_on_server.sh | 13 +++++++++++++ 1 file changed, 13 insertions(+) create mode 100644 projects/perception/activity_recognition/benchmark/install_on_server.sh 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 100644 index 0000000000..facf3f33d2 --- /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 +conda activate opendr +conda env config vars set OPENDR_HOME=$PWD +conda env config vars set PYTHONPATH=$OPENDR_HOME/src:$PYTHONPATH + +# Reactivate env to let env vars take effect +conda deactivate opendr +conda activate opendr + +pip install torch==1.7.1+cu110 torchvision==0.8.2+cu110 -f https://download.pytorch.org/whl/torch_stable.html +pip install pytorch_lightning==1.2.3 onnxruntime==1.3.0 opencv-python==4.5.1.48 joblib>=1.0.1 pytorch_benchmark From 1efac6fd0c37301555523cfe13de4525536193b0 Mon Sep 17 00:00:00 2001 From: LukasHedegaard Date: Mon, 14 Feb 2022 11:49:56 +0000 Subject: [PATCH 06/60] Update har-benchmark install script --- .../activity_recognition/benchmark/install_on_server.sh | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) mode change 100644 => 100755 projects/perception/activity_recognition/benchmark/install_on_server.sh diff --git a/projects/perception/activity_recognition/benchmark/install_on_server.sh b/projects/perception/activity_recognition/benchmark/install_on_server.sh old mode 100644 new mode 100755 index facf3f33d2..f550fc7bd6 --- a/projects/perception/activity_recognition/benchmark/install_on_server.sh +++ b/projects/perception/activity_recognition/benchmark/install_on_server.sh @@ -1,12 +1,12 @@ #!/bin/bash -conda create --name opendr python=3.8 +conda create --name opendr python=3.8 -y conda activate opendr conda env config vars set OPENDR_HOME=$PWD conda env config vars set PYTHONPATH=$OPENDR_HOME/src:$PYTHONPATH # Reactivate env to let env vars take effect -conda deactivate opendr +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 From 73a786e54242f02a8dbbc2c5b00250507c959616 Mon Sep 17 00:00:00 2001 From: LukasHedegaard Date: Mon, 14 Feb 2022 11:50:21 +0000 Subject: [PATCH 07/60] Add variable batch_sizes to har benchmark script --- .../benchmark/benchmark_cox3d.py | 11 +++++++++-- .../activity_recognition/benchmark/benchmark_x3d.py | 12 +++++++++--- 2 files changed, 18 insertions(+), 5 deletions(-) diff --git a/projects/perception/activity_recognition/benchmark/benchmark_cox3d.py b/projects/perception/activity_recognition/benchmark/benchmark_cox3d.py index bc31c800a5..4b3326da1d 100644 --- a/projects/perception/activity_recognition/benchmark/benchmark_cox3d.py +++ b/projects/perception/activity_recognition/benchmark/benchmark_cox3d.py @@ -42,6 +42,13 @@ def benchmark_cox3d(): "l": (3, 312, 312), } + batch_size = { # RTX2080Ti max power of 2 + "xs": 128, + "s": 128, + "m": 64, + "l": 8, + } + for backbone in ["xs", "s", "m", "l"]: logger.info(f"==== Benchmarking CoX3DLearner ({backbone}) ====") @@ -51,7 +58,7 @@ def benchmark_cox3d(): backbone=backbone, ) - sample = torch.randn(batch_size, *input_shape[backbone]) # (B, C, T, H, W) + 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])] @@ -89,7 +96,7 @@ def transfer_to_device_fn( num_runs=num_runs, get_device_fn=get_device_fn, transfer_to_device_fn=transfer_to_device_fn, - batch_size=batch_size, + batch_size=batch_size[backbone], ) logger.info(yaml.dump({"learner.infer": results1})) diff --git a/projects/perception/activity_recognition/benchmark/benchmark_x3d.py b/projects/perception/activity_recognition/benchmark/benchmark_x3d.py index 4f590c234c..3559551b86 100644 --- a/projects/perception/activity_recognition/benchmark/benchmark_x3d.py +++ b/projects/perception/activity_recognition/benchmark/benchmark_x3d.py @@ -31,7 +31,6 @@ def benchmark_x3d(): temp_dir = "./projects/perception/activity_recognition/benchmark/tmp" - batch_size = 8 num_runs = 100 # As found in src/opendr/perception/activity_recognition/x3d/hparams @@ -42,6 +41,12 @@ def benchmark_x3d(): "l": (3, 16, 312, 312), } + batch_size = { # RTX2080Ti max power of 2 + "xs": 32, + "s": 16, + "m": 8, + "l": 2, + } for backbone in ["xs", "s", "m", "l"]: logger.info(f"==== Benchmarking X3DLearner ({backbone}) ====") @@ -50,8 +55,9 @@ def benchmark_x3d(): temp_path=temp_dir, backbone=backbone, ) + learner.model.eval() - sample = torch.randn(batch_size, *input_shape[backbone]) # (B, C, T, H, W) + 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])] @@ -89,7 +95,7 @@ def transfer_to_device_fn( num_runs=num_runs, get_device_fn=get_device_fn, transfer_to_device_fn=transfer_to_device_fn, - batch_size=batch_size, + batch_size=batch_size[backbone], ) logger.info(yaml.dump({"learner.infer": results1})) From 78688859c97af51a41d36dcb92da5db938650940 Mon Sep 17 00:00:00 2001 From: LukasHedegaard Date: Mon, 14 Feb 2022 11:50:31 +0000 Subject: [PATCH 08/60] Add har benchmark RTX2080Ti results --- .../results/benchmark_cox3d_RTX2080Ti.txt | 797 ++++++++++ .../results/benchmark_x3d_RTX2080Ti.txt | 1293 +++++++++++++++++ 2 files changed, 2090 insertions(+) create mode 100644 projects/perception/activity_recognition/benchmark/results/benchmark_cox3d_RTX2080Ti.txt create mode 100644 projects/perception/activity_recognition/benchmark/results/benchmark_x3d_RTX2080Ti.txt diff --git a/projects/perception/activity_recognition/benchmark/results/benchmark_cox3d_RTX2080Ti.txt b/projects/perception/activity_recognition/benchmark/results/benchmark_cox3d_RTX2080Ti.txt new file mode 100644 index 0000000000..679e3b31ed --- /dev/null +++ b/projects/perception/activity_recognition/benchmark/results/benchmark_cox3d_RTX2080Ti.txt @@ -0,0 +1,797 @@ +INFO:benchmark:==== Benchmarking CoX3DLearner (xs) ==== +WARNING:opendr.perception.activity_recognition.cox3d.algorithm.utils:Padding along the temporal dimension only affects the computation in `forward3d`. In `forward` it is omitted. +INFO:benchmark:== Benchmarking learner.infer == +ERROR:torch-benchmark:Unable to measure model params due to error: 'function' object has no attribute 'parameters' +ERROR:torch-benchmark:Unable to measure model FLOPs due to error: 'list' object has no attribute 'shape' + +Warming up with batch_size=128: 0%| | 0/10 [00:00 + benchmark_cox3d() + File "benchmark_cox3d.py", line 92, in benchmark_cox3d + results1 = benchmark( + File "/opt/anaconda/envs/opendr/lib/python3.8/site-packages/pytorch_benchmark/benchmark.py", line 327, in benchmark + pre_mem, post_mem, max_mem = measure_allocated_memory( + File "/opt/anaconda/envs/opendr/lib/python3.8/site-packages/pytorch_benchmark/benchmark.py", line 84, in measure_allocated_memory + model(transfer_to_device_fn(sample, model_device)), + File "/home/lh/projects/opendr/src/opendr/perception/activity_recognition/cox3d/cox3d_learner.py", line 140, in infer + results = self.model.forward(batch) + File "/home/lh/projects/opendr/src/opendr/perception/activity_recognition/cox3d/algorithm/cox3d.py", line 1067, in forward + x = module(x) + File "/opt/anaconda/envs/opendr/lib/python3.8/site-packages/torch/nn/modules/module.py", line 727, in _call_impl + result = self.forward(*input, **kwargs) + File "/home/lh/projects/opendr/src/opendr/perception/activity_recognition/cox3d/algorithm/cox3d.py", line 494, in forward + x = m(x) + File "/opt/anaconda/envs/opendr/lib/python3.8/site-packages/torch/nn/modules/module.py", line 727, in _call_impl + result = self.forward(*input, **kwargs) + File "/home/lh/projects/opendr/src/opendr/perception/activity_recognition/cox3d/algorithm/cox3d.py", line 301, in forward + output = delayed_x + f_x +RuntimeError: CUDA out of memory. Tried to allocate 20.00 MiB (GPU 0; 10.76 GiB total capacity; 9.43 GiB already allocated; 13.44 MiB free; 9.65 GiB reserved in total by PyTorch) diff --git a/projects/perception/activity_recognition/benchmark/results/benchmark_x3d_RTX2080Ti.txt b/projects/perception/activity_recognition/benchmark/results/benchmark_x3d_RTX2080Ti.txt new file mode 100644 index 0000000000..e522b8821a --- /dev/null +++ b/projects/perception/activity_recognition/benchmark/results/benchmark_x3d_RTX2080Ti.txt @@ -0,0 +1,1293 @@ +INFO:benchmark:==== Benchmarking X3DLearner (xs) ==== +INFO:benchmark:== Benchmarking learner.infer == +ERROR:torch-benchmark:Unable to measure model params due to error: 'function' object has no attribute 'parameters' +ERROR:torch-benchmark:Unable to measure model FLOPs due to error: 'list' object has no attribute 'shape' + +Warming up with batch_size=32: 100%|██████████| 10/10 [00:01<00:00, 8.75it/s] + +Measuring inference with batch_size=32: 100%|██████████| 100/100 [00:12<00:00, 7.86it/s] +ERROR:torch-benchmark:Unable to measure energy consumption. Device must be a NVIDIA Jetson. + +Warming up with batch_size=32: 100%|██████████| 10/10 [00:04<00:00, 2.10it/s] + +Measuring inference with batch_size=32: 100%|██████████| 100/100 [00:42<00:00, 2.33it/s] +ERROR:torch-benchmark:Unable to measure energy consumption. Device must be a NVIDIA Jetson. +INFO:benchmark:learner.infer: + device: cuda + machine_info: + cpu: + architecture: x86_64 + cores: + physical: 52 + total: 104 + frequency: 2.10 GHz + model: Intel(R) Xeon(R) Gold 6230R CPU @ 2.10GHz + gpus: + - memory: 11019.0 MB + name: NVIDIA GeForce RTX 2080 Ti + - memory: 11019.0 MB + name: NVIDIA GeForce RTX 2080 Ti + - memory: 11019.0 MB + name: NVIDIA GeForce RTX 2080 Ti + - memory: 11019.0 MB + name: NVIDIA GeForce RTX 2080 Ti + - memory: 11019.0 MB + name: NVIDIA GeForce RTX 2080 Ti + - memory: 11019.0 MB + name: NVIDIA GeForce RTX 2080 Ti + - memory: 11019.0 MB + name: NVIDIA GeForce RTX 2080 Ti + - memory: 11019.0 MB + name: NVIDIA GeForce RTX 2080 Ti + memory: + available: 156.86 GB + total: 187.56 GB + used: 29.02 GB + system: + node: charybdis + release: 4.15.0-167-generic + system: Linux + max_inference_memory: 3897715712 + post_inference_memory: 15445504 + pre_inference_memory: 15445504 + timing: + batch_size_1: + cpu_to_gpu: + human_readable: + batch_latency: "3.161 \xB5s +/- 0.475 \xB5s [2.146 \xB5s, 5.007 \xB5s]" + batches_per_second: 323.12 K +/- 46.51 K [199.73 K, 466.03 K] + metrics: + batches_per_second_max: 466033.77777777775 + batches_per_second_mean: 323119.55398953985 + batches_per_second_min: 199728.7619047619 + batches_per_second_std: 46514.899386665966 + seconds_per_batch_max: 5.0067901611328125e-06 + seconds_per_batch_mean: 3.1614303588867187e-06 + seconds_per_batch_min: 2.1457672119140625e-06 + seconds_per_batch_std: 4.7518922239742955e-07 + gpu_to_cpu: + human_readable: + batch_latency: "91.345 \xB5s +/- 10.070 \xB5s [75.579 \xB5s, 133.991 \xB5\ + s]" + batches_per_second: 11.06 K +/- 1.04 K [7.46 K, 13.23 K] + metrics: + batches_per_second_max: 13231.242902208201 + batches_per_second_mean: 11060.70445891393 + batches_per_second_min: 7463.174377224199 + batches_per_second_std: 1040.632434127901 + seconds_per_batch_max: 0.00013399124145507812 + seconds_per_batch_mean: 9.134531021118164e-05 + seconds_per_batch_min: 7.557868957519531e-05 + seconds_per_batch_std: 1.0070223626934436e-05 + on_device_inference: + human_readable: + batch_latency: 126.841 ms +/- 16.983 ms [109.998 ms, 191.978 ms] + batches_per_second: 8.01 +/- 0.92 [5.21, 9.09] + metrics: + batches_per_second_max: 9.091075395836269 + batches_per_second_mean: 8.005090792837397 + batches_per_second_min: 5.2089307935530185 + batches_per_second_std: 0.9178220491439262 + seconds_per_batch_max: 0.19197797775268555 + seconds_per_batch_mean: 0.1268414807319641 + seconds_per_batch_min: 0.10999798774719238 + seconds_per_batch_std: 0.016983356267859594 + total: + human_readable: + batch_latency: 126.936 ms +/- 16.988 ms [110.080 ms, 192.090 ms] + batches_per_second: 8.00 +/- 0.92 [5.21, 9.08] + metrics: + batches_per_second_max: 9.084341368010449 + batches_per_second_mean: 7.999018793847885 + batches_per_second_min: 5.205898597835625 + batches_per_second_std: 0.9167117577795798 + seconds_per_batch_max: 0.19208979606628418 + seconds_per_batch_mean: 0.12693598747253418 + seconds_per_batch_min: 0.11007952690124512 + seconds_per_batch_std: 0.016988001896867437 + batch_size_32: + cpu_to_gpu: + human_readable: + batch_latency: "2.923 \xB5s +/- 0.367 \xB5s [2.384 \xB5s, 4.053 \xB5s]" + batches_per_second: 347.21 K +/- 41.14 K [246.72 K, 419.43 K] + metrics: + batches_per_second_max: 419430.4 + batches_per_second_mean: 347209.16572368814 + batches_per_second_min: 246723.76470588235 + batches_per_second_std: 41138.82580468164 + seconds_per_batch_max: 4.0531158447265625e-06 + seconds_per_batch_mean: 2.923011779785156e-06 + seconds_per_batch_min: 2.384185791015625e-06 + seconds_per_batch_std: 3.672265334985762e-07 + gpu_to_cpu: + human_readable: + batch_latency: "789.857 \xB5s +/- 75.882 \xB5s [706.911 \xB5s, 1.043 ms]" + batches_per_second: 1.28 K +/- 106.95 [958.70, 1.41 K] + metrics: + batches_per_second_max: 1414.6050590219224 + batches_per_second_mean: 1276.2802559106174 + batches_per_second_min: 958.6980571428571 + batches_per_second_std: 106.95337111849891 + seconds_per_batch_max: 0.001043081283569336 + seconds_per_batch_mean: 0.0007898569107055664 + seconds_per_batch_min: 0.0007069110870361328 + seconds_per_batch_std: 7.58818321643605e-05 + on_device_inference: + human_readable: + batch_latency: 428.383 ms +/- 161.575 ms [363.476 ms, 1.308 s] + batches_per_second: 2.47 +/- 0.36 [0.76, 2.75] + metrics: + batches_per_second_max: 2.751214802220753 + batches_per_second_mean: 2.4659891888314247 + batches_per_second_min: 0.7646781492556688 + batches_per_second_std: 0.35984308870298215 + seconds_per_batch_max: 1.3077397346496582 + seconds_per_batch_mean: 0.428383424282074 + seconds_per_batch_min: 0.3634757995605469 + seconds_per_batch_std: 0.1615749489382343 + total: + human_readable: + batch_latency: 429.176 ms +/- 161.597 ms [364.260 ms, 1.309 s] + batches_per_second: 2.46 +/- 0.36 [0.76, 2.75] + metrics: + batches_per_second_max: 2.7452921390454486 + batches_per_second_mean: 2.4611072608243596 + batches_per_second_min: 0.7642190653553078 + batches_per_second_std: 0.35887149027735976 + seconds_per_batch_max: 1.3085253238677979 + seconds_per_batch_mean: 0.4291762042045593 + seconds_per_batch_min: 0.3642599582672119 + seconds_per_batch_std: 0.16159653847373043 + +INFO:benchmark:== Benchmarking model directly == + +Warming up with batch_size=32: 100%|██████████| 10/10 [00:01<00:00, 8.36it/s] + +Measuring inference with batch_size=32: 100%|██████████| 100/100 [00:10<00:00, 9.29it/s] +ERROR:torch-benchmark:Unable to measure energy consumption. Device must be a NVIDIA Jetson. + +Warming up with batch_size=32: 100%|██████████| 10/10 [00:03<00:00, 2.79it/s] + +Measuring inference with batch_size=32: 100%|██████████| 100/100 [00:37<00:00, 2.69it/s] +ERROR:torch-benchmark:Unable to measure energy consumption. Device must be a NVIDIA Jetson. +INFO:benchmark:learner.model.forward: + device: cuda + flops: 635560544 + machine_info: + cpu: + architecture: x86_64 + cores: + physical: 52 + total: 104 + frequency: 2.10 GHz + model: Intel(R) Xeon(R) Gold 6230R CPU @ 2.10GHz + gpus: + - memory: 11019.0 MB + name: NVIDIA GeForce RTX 2080 Ti + - memory: 11019.0 MB + name: NVIDIA GeForce RTX 2080 Ti + - memory: 11019.0 MB + name: NVIDIA GeForce RTX 2080 Ti + - memory: 11019.0 MB + name: NVIDIA GeForce RTX 2080 Ti + - memory: 11019.0 MB + name: NVIDIA GeForce RTX 2080 Ti + - memory: 11019.0 MB + name: NVIDIA GeForce RTX 2080 Ti + - memory: 11019.0 MB + name: NVIDIA GeForce RTX 2080 Ti + - memory: 11019.0 MB + name: NVIDIA GeForce RTX 2080 Ti + memory: + available: 156.80 GB + total: 187.56 GB + used: 29.08 GB + system: + node: charybdis + release: 4.15.0-167-generic + system: Linux + max_inference_memory: 3901368832 + params: 3794322 + post_inference_memory: 15445504 + pre_inference_memory: 15445504 + timing: + batch_size_1: + cpu_to_gpu: + human_readable: + batch_latency: "331.297 \xB5s +/- 48.587 \xB5s [305.653 \xB5s, 678.539 \xB5\ + s]" + batches_per_second: 3.05 K +/- 235.76 [1.47 K, 3.27 K] + metrics: + batches_per_second_max: 3271.687987519501 + batches_per_second_mean: 3052.4986785057995 + batches_per_second_min: 1473.75404075896 + batches_per_second_std: 235.75547517724394 + seconds_per_batch_max: 0.0006785392761230469 + seconds_per_batch_mean: 0.0003312969207763672 + seconds_per_batch_min: 0.0003056526184082031 + seconds_per_batch_std: 4.858726508434223e-05 + gpu_to_cpu: + human_readable: + batch_latency: "66.762 \xB5s +/- 8.078 \xB5s [56.267 \xB5s, 113.964 \xB5\ + s]" + batches_per_second: 15.14 K +/- 1.37 K [8.77 K, 17.77 K] + metrics: + batches_per_second_max: 17772.474576271186 + batches_per_second_mean: 15139.557922140795 + batches_per_second_min: 8774.694560669455 + batches_per_second_std: 1365.696695881183 + seconds_per_batch_max: 0.00011396408081054688 + seconds_per_batch_mean: 6.676197052001953e-05 + seconds_per_batch_min: 5.626678466796875e-05 + seconds_per_batch_std: 8.07813234076046e-06 + on_device_inference: + human_readable: + batch_latency: 107.042 ms +/- 3.186 ms [103.451 ms, 131.227 ms] + batches_per_second: 9.35 +/- 0.24 [7.62, 9.67] + metrics: + batches_per_second_max: 9.666433128065194 + batches_per_second_mean: 9.349349053322747 + batches_per_second_min: 7.620395890298962 + batches_per_second_std: 0.2436737793721103 + seconds_per_batch_max: 0.1312267780303955 + seconds_per_batch_mean: 0.10704208850860596 + seconds_per_batch_min: 0.10345077514648438 + seconds_per_batch_std: 0.0031864838809737062 + total: + human_readable: + batch_latency: 107.440 ms +/- 3.187 ms [103.838 ms, 131.617 ms] + batches_per_second: 9.31 +/- 0.24 [7.60, 9.63] + metrics: + batches_per_second_max: 9.63041097337249 + batches_per_second_mean: 9.3146611852705 + batches_per_second_min: 7.597798718213469 + batches_per_second_std: 0.24199316317952704 + seconds_per_batch_max: 0.13161706924438477 + seconds_per_batch_mean: 0.10744014739990235 + seconds_per_batch_min: 0.10383772850036621 + seconds_per_batch_std: 0.0031866436449242094 + batch_size_32: + cpu_to_gpu: + human_readable: + batch_latency: 6.582 ms +/- 1.186 ms [6.109 ms, 17.791 ms] + batches_per_second: 154.03 +/- 12.39 [56.21, 163.69] + metrics: + batches_per_second_max: 163.6929321312883 + batches_per_second_mean: 154.02829578118505 + batches_per_second_min: 56.20959809163886 + batches_per_second_std: 12.391863599077443 + seconds_per_batch_max: 0.017790555953979492 + seconds_per_batch_mean: 0.00658217191696167 + seconds_per_batch_min: 0.006108999252319336 + seconds_per_batch_std: 0.0011855239158629642 + gpu_to_cpu: + human_readable: + batch_latency: 30.019 ms +/- 2.540 ms [13.320 ms, 34.144 ms] + batches_per_second: 33.69 +/- 4.79 [29.29, 75.08] + metrics: + batches_per_second_max: 75.07524880074462 + batches_per_second_mean: 33.69331619731284 + batches_per_second_min: 29.287582657756737 + batches_per_second_std: 4.788462609380826 + seconds_per_batch_max: 0.03414416313171387 + seconds_per_batch_mean: 0.030019392967224123 + seconds_per_batch_min: 0.013319969177246094 + seconds_per_batch_std: 0.002539988732493175 + on_device_inference: + human_readable: + batch_latency: 335.229 ms +/- 3.062 ms [331.311 ms, 351.121 ms] + batches_per_second: 2.98 +/- 0.03 [2.85, 3.02] + metrics: + batches_per_second_max: 3.0183143856800934 + batches_per_second_mean: 2.9832838586717214 + batches_per_second_min: 2.8480195340695276 + batches_per_second_std: 0.026788245290522473 + seconds_per_batch_max: 0.351121187210083 + seconds_per_batch_mean: 0.33522858142852785 + seconds_per_batch_min: 0.3313107490539551 + seconds_per_batch_std: 0.0030621233873875733 + total: + human_readable: + batch_latency: 371.830 ms +/- 2.491 ms [368.759 ms, 382.377 ms] + batches_per_second: 2.69 +/- 0.02 [2.62, 2.71] + metrics: + batches_per_second_max: 2.711800585251305 + batches_per_second_mean: 2.689519551052322 + batches_per_second_min: 2.6152206783243597 + batches_per_second_std: 0.017846717414215663 + seconds_per_batch_max: 0.38237690925598145 + seconds_per_batch_mean: 0.3718301463127136 + seconds_per_batch_min: 0.3687586784362793 + seconds_per_batch_std: 0.002490665374346185 + +INFO:benchmark:==== Benchmarking X3DLearner (s) ==== +INFO:benchmark:== Benchmarking learner.infer == +ERROR:torch-benchmark:Unable to measure model params due to error: 'function' object has no attribute 'parameters' +ERROR:torch-benchmark:Unable to measure model FLOPs due to error: 'list' object has no attribute 'shape' + +Warming up with batch_size=16: 100%|██████████| 10/10 [00:01<00:00, 7.51it/s] + +Measuring inference with batch_size=16: 100%|██████████| 100/100 [00:14<00:00, 7.01it/s] +ERROR:torch-benchmark:Unable to measure energy consumption. Device must be a NVIDIA Jetson. + +Warming up with batch_size=16: 100%|██████████| 10/10 [00:05<00:00, 1.69it/s] + +Measuring inference with batch_size=16: 100%|██████████| 100/100 [00:54<00:00, 1.83it/s] +ERROR:torch-benchmark:Unable to measure energy consumption. Device must be a NVIDIA Jetson. +INFO:benchmark:learner.infer: + device: cuda + machine_info: + cpu: + architecture: x86_64 + cores: + physical: 52 + total: 104 + frequency: 2.10 GHz + model: Intel(R) Xeon(R) Gold 6230R CPU @ 2.10GHz + gpus: + - memory: 11019.0 MB + name: NVIDIA GeForce RTX 2080 Ti + - memory: 11019.0 MB + name: NVIDIA GeForce RTX 2080 Ti + - memory: 11019.0 MB + name: NVIDIA GeForce RTX 2080 Ti + - memory: 11019.0 MB + name: NVIDIA GeForce RTX 2080 Ti + - memory: 11019.0 MB + name: NVIDIA GeForce RTX 2080 Ti + - memory: 11019.0 MB + name: NVIDIA GeForce RTX 2080 Ti + - memory: 11019.0 MB + name: NVIDIA GeForce RTX 2080 Ti + - memory: 11019.0 MB + name: NVIDIA GeForce RTX 2080 Ti + memory: + available: 156.61 GB + total: 187.56 GB + used: 29.25 GB + system: + node: charybdis + release: 4.15.0-167-generic + system: Linux + max_inference_memory: 6298952704 + post_inference_memory: 30891008 + pre_inference_memory: 30891008 + timing: + batch_size_1: + cpu_to_gpu: + human_readable: + batch_latency: "2.866 \xB5s +/- 0.434 \xB5s [2.146 \xB5s, 4.292 \xB5s]" + batches_per_second: 356.23 K +/- 49.01 K [233.02 K, 466.03 K] + metrics: + batches_per_second_max: 466033.77777777775 + batches_per_second_mean: 356225.14400255954 + batches_per_second_min: 233016.88888888888 + batches_per_second_std: 49008.40713090392 + seconds_per_batch_max: 4.291534423828125e-06 + seconds_per_batch_mean: 2.865791320800781e-06 + seconds_per_batch_min: 2.1457672119140625e-06 + seconds_per_batch_std: 4.343931551605507e-07 + gpu_to_cpu: + human_readable: + batch_latency: "97.222 \xB5s +/- 13.327 \xB5s [75.817 \xB5s, 141.859 \xB5\ + s]" + batches_per_second: 10.46 K +/- 1.32 K [7.05 K, 13.19 K] + metrics: + batches_per_second_max: 13189.635220125787 + batches_per_second_mean: 10463.342312615388 + batches_per_second_min: 7049.250420168068 + batches_per_second_std: 1318.03451463311 + seconds_per_batch_max: 0.0001418590545654297 + seconds_per_batch_mean: 9.722232818603515e-05 + seconds_per_batch_min: 7.581710815429688e-05 + seconds_per_batch_std: 1.332666363106734e-05 + on_device_inference: + human_readable: + batch_latency: 142.304 ms +/- 17.070 ms [111.439 ms, 173.499 ms] + batches_per_second: 7.13 +/- 0.86 [5.76, 8.97] + metrics: + batches_per_second_max: 8.973558376318712 + batches_per_second_mean: 7.129296976930103 + batches_per_second_min: 5.763734255317394 + batches_per_second_std: 0.8555113047014313 + seconds_per_batch_max: 0.17349863052368164 + seconds_per_batch_mean: 0.14230358362197876 + seconds_per_batch_min: 0.11143851280212402 + seconds_per_batch_std: 0.017070221249596576 + total: + human_readable: + batch_latency: 142.404 ms +/- 17.077 ms [111.535 ms, 173.636 ms] + batches_per_second: 7.12 +/- 0.85 [5.76, 8.97] + metrics: + batches_per_second_max: 8.965808841604836 + batches_per_second_mean: 7.124219931814314 + batches_per_second_min: 5.759175703916889 + batches_per_second_std: 0.8546122016337631 + seconds_per_batch_max: 0.17363595962524414 + seconds_per_batch_mean: 0.1424036717414856 + seconds_per_batch_min: 0.11153483390808105 + seconds_per_batch_std: 0.017077019315624362 + batch_size_16: + cpu_to_gpu: + human_readable: + batch_latency: "3.173 \xB5s +/- 1.247 \xB5s [2.384 \xB5s, 14.782 \xB5s]" + batches_per_second: 331.04 K +/- 52.38 K [67.65 K, 419.43 K] + metrics: + batches_per_second_max: 419430.4 + batches_per_second_mean: 331043.7124246498 + batches_per_second_min: 67650.06451612903 + batches_per_second_std: 52381.97288702972 + seconds_per_batch_max: 1.4781951904296875e-05 + seconds_per_batch_mean: 3.173351287841797e-06 + seconds_per_batch_min: 2.384185791015625e-06 + seconds_per_batch_std: 1.2469519618560517e-06 + gpu_to_cpu: + human_readable: + batch_latency: "421.257 \xB5s +/- 66.942 \xB5s [352.859 \xB5s, 717.640 \xB5\ + s]" + batches_per_second: 2.42 K +/- 316.70 [1.39 K, 2.83 K] + metrics: + batches_per_second_max: 2833.9891891891893 + batches_per_second_mean: 2423.353438527375 + batches_per_second_min: 1393.4564784053157 + batches_per_second_std: 316.70009560904043 + seconds_per_batch_max: 0.0007176399230957031 + seconds_per_batch_mean: 0.0004212570190429688 + seconds_per_batch_min: 0.0003528594970703125 + seconds_per_batch_std: 6.694248814430214e-05 + on_device_inference: + human_readable: + batch_latency: 545.564 ms +/- 55.635 ms [507.165 ms, 994.301 ms] + batches_per_second: 1.85 +/- 0.13 [1.01, 1.97] + metrics: + batches_per_second_max: 1.9717450700544001 + batches_per_second_mean: 1.8460330765713748 + batches_per_second_min: 1.005732065445057 + batches_per_second_std: 0.13260749096370014 + seconds_per_batch_max: 0.9943006038665771 + seconds_per_batch_mean: 0.5455638265609741 + seconds_per_batch_min: 0.5071649551391602 + seconds_per_batch_std: 0.05563453802437276 + total: + human_readable: + batch_latency: 545.988 ms +/- 55.648 ms [507.557 ms, 994.807 ms] + batches_per_second: 1.84 +/- 0.13 [1.01, 1.97] + metrics: + batches_per_second_max: 1.970220541606971 + batches_per_second_mean: 1.844586976530122 + batches_per_second_min: 1.005219621212575 + batches_per_second_std: 0.13246022044563008 + seconds_per_batch_max: 0.9948074817657471 + seconds_per_batch_mean: 0.5459882569313049 + seconds_per_batch_min: 0.5075573921203613 + seconds_per_batch_std: 0.05564797919488597 + +INFO:benchmark:== Benchmarking model directly == + + +Warming up with batch_size=16: 100%|██████████| 10/10 [00:01<00:00, 8.08it/s] + + +Measuring inference with batch_size=16: 100%|██████████| 100/100 [00:11<00:00, 8.87it/s] +ERROR:torch-benchmark:Unable to measure energy consumption. Device must be a NVIDIA Jetson. + + +Warming up with batch_size=16: 100%|██████████| 10/10 [00:04<00:00, 2.02it/s] + + +Measuring inference with batch_size=16: 100%|██████████| 100/100 [00:49<00:00, 2.02it/s] +ERROR:torch-benchmark:Unable to measure energy consumption. Device must be a NVIDIA Jetson. +INFO:benchmark:learner.model.forward: + device: cuda + flops: 2061365744 + machine_info: + cpu: + architecture: x86_64 + cores: + physical: 52 + total: 104 + frequency: 2.10 GHz + model: Intel(R) Xeon(R) Gold 6230R CPU @ 2.10GHz + gpus: + - memory: 11019.0 MB + name: NVIDIA GeForce RTX 2080 Ti + - memory: 11019.0 MB + name: NVIDIA GeForce RTX 2080 Ti + - memory: 11019.0 MB + name: NVIDIA GeForce RTX 2080 Ti + - memory: 11019.0 MB + name: NVIDIA GeForce RTX 2080 Ti + - memory: 11019.0 MB + name: NVIDIA GeForce RTX 2080 Ti + - memory: 11019.0 MB + name: NVIDIA GeForce RTX 2080 Ti + - memory: 11019.0 MB + name: NVIDIA GeForce RTX 2080 Ti + - memory: 11019.0 MB + name: NVIDIA GeForce RTX 2080 Ti + memory: + available: 158.99 GB + total: 187.56 GB + used: 26.89 GB + system: + node: charybdis + release: 4.15.0-167-generic + system: Linux + max_inference_memory: 6286550016 + params: 3794322 + post_inference_memory: 30891008 + pre_inference_memory: 30891008 + timing: + batch_size_1: + cpu_to_gpu: + human_readable: + batch_latency: "743.034 \xB5s +/- 27.973 \xB5s [695.705 \xB5s, 832.558 \xB5\ + s]" + batches_per_second: 1.35 K +/- 48.44 [1.20 K, 1.44 K] + metrics: + batches_per_second_max: 1437.3899931459905 + batches_per_second_mean: 1347.655220309263 + batches_per_second_min: 1201.1179839633448 + batches_per_second_std: 48.44349526214127 + seconds_per_batch_max: 0.0008325576782226562 + seconds_per_batch_mean: 0.0007430338859558106 + seconds_per_batch_min: 0.0006957054138183594 + seconds_per_batch_std: 2.7972596224271895e-05 + gpu_to_cpu: + human_readable: + batch_latency: "68.896 \xB5s +/- 9.211 \xB5s [61.035 \xB5s, 140.429 \xB5\ + s]" + batches_per_second: 14.68 K +/- 1.30 K [7.12 K, 16.38 K] + metrics: + batches_per_second_max: 16384.0 + batches_per_second_mean: 14681.480093473909 + batches_per_second_min: 7121.059422750424 + batches_per_second_std: 1302.1559663048208 + seconds_per_batch_max: 0.0001404285430908203 + seconds_per_batch_mean: 6.889581680297852e-05 + seconds_per_batch_min: 6.103515625e-05 + seconds_per_batch_std: 9.210826234647333e-06 + on_device_inference: + human_readable: + batch_latency: 111.690 ms +/- 8.392 ms [104.807 ms, 141.541 ms] + batches_per_second: 9.00 +/- 0.59 [7.07, 9.54] + metrics: + batches_per_second_max: 9.541334825622792 + batches_per_second_mean: 8.997413586822784 + batches_per_second_min: 7.065078570983396 + batches_per_second_std: 0.5878110971583906 + seconds_per_batch_max: 0.1415412425994873 + seconds_per_batch_mean: 0.1116902470588684 + seconds_per_batch_min: 0.10480713844299316 + seconds_per_batch_std: 0.008391664812002214 + total: + human_readable: + batch_latency: 112.502 ms +/- 8.411 ms [105.586 ms, 142.398 ms] + batches_per_second: 8.93 +/- 0.58 [7.02, 9.47] + metrics: + batches_per_second_max: 9.47099070130832 + batches_per_second_mean: 8.932078130062095 + batches_per_second_min: 7.02257643237451 + batches_per_second_std: 0.5810503964925592 + seconds_per_batch_max: 0.14239788055419922 + seconds_per_batch_mean: 0.1125021767616272 + seconds_per_batch_min: 0.10558557510375977 + seconds_per_batch_std: 0.00841136946259066 + batch_size_16: + cpu_to_gpu: + human_readable: + batch_latency: 10.096 ms +/- 1.619 ms [9.363 ms, 24.650 ms] + batches_per_second: 100.26 +/- 7.80 [40.57, 106.81] + metrics: + batches_per_second_max: 106.80682454800102 + batches_per_second_mean: 100.25732156414092 + batches_per_second_min: 40.568576623979574 + batches_per_second_std: 7.8019492046038295 + seconds_per_batch_max: 0.024649620056152344 + seconds_per_batch_mean: 0.01009577989578247 + seconds_per_batch_min: 0.00936269760131836 + seconds_per_batch_std: 0.0016185695677532251 + gpu_to_cpu: + human_readable: + batch_latency: "14.857 ms +/- 3.188 ms [84.162 \xB5s, 17.632 ms]" + batches_per_second: 198.69 +/- 1.18 K [56.71, 11.88 K] + metrics: + batches_per_second_max: 11881.881019830029 + batches_per_second_mean: 198.69453205248703 + batches_per_second_min: 56.71350532749202 + batches_per_second_std: 1176.9513832700977 + seconds_per_batch_max: 0.017632484436035156 + seconds_per_batch_mean: 0.014857473373413086 + seconds_per_batch_min: 8.416175842285156e-05 + seconds_per_batch_std: 0.003187945104627096 + on_device_inference: + human_readable: + batch_latency: 469.821 ms +/- 3.228 ms [466.189 ms, 484.202 ms] + batches_per_second: 2.13 +/- 0.01 [2.07, 2.15] + metrics: + batches_per_second_max: 2.145050988574877 + batches_per_second_mean: 2.128569288870713 + batches_per_second_min: 2.065252115819059 + batches_per_second_std: 0.01430644505438133 + seconds_per_batch_max: 0.48420238494873047 + seconds_per_batch_mean: 0.4698208260536194 + seconds_per_batch_min: 0.4661893844604492 + seconds_per_batch_std: 0.003228157563939141 + total: + human_readable: + batch_latency: 494.774 ms +/- 1.787 ms [492.717 ms, 509.117 ms] + batches_per_second: 2.02 +/- 0.01 [1.96, 2.03] + metrics: + batches_per_second_max: 2.0295644591631774 + batches_per_second_mean: 2.021150313015857 + batches_per_second_min: 1.9641864015472583 + batches_per_second_std: 0.0071550422295123695 + seconds_per_batch_max: 0.5091166496276855 + seconds_per_batch_mean: 0.4947740793228149 + seconds_per_batch_min: 0.49271655082702637 + seconds_per_batch_std: 0.0017870149445324412 + +INFO:benchmark:==== Benchmarking X3DLearner (m) ==== +INFO:benchmark:== Benchmarking learner.infer == +ERROR:torch-benchmark:Unable to measure model params due to error: 'function' object has no attribute 'parameters' +ERROR:torch-benchmark:Unable to measure model FLOPs due to error: 'list' object has no attribute 'shape' + +Warming up with batch_size=8: 100%|██████████| 10/10 [00:01<00:00, 6.67it/s] + +Measuring inference with batch_size=8: 100%|██████████| 100/100 [00:14<00:00, 6.82it/s] +ERROR:torch-benchmark:Unable to measure energy consumption. Device must be a NVIDIA Jetson. + +Warming up with batch_size=8: 100%|██████████| 10/10 [00:07<00:00, 1.41it/s] + +Measuring inference with batch_size=8: 100%|██████████| 100/100 [01:11<00:00, 1.40it/s] +ERROR:torch-benchmark:Unable to measure energy consumption. Device must be a NVIDIA Jetson. +INFO:benchmark:learner.infer: + device: cuda + machine_info: + cpu: + architecture: x86_64 + cores: + physical: 52 + total: 104 + frequency: 2.10 GHz + model: Intel(R) Xeon(R) Gold 6230R CPU @ 2.10GHz + gpus: + - memory: 11019.0 MB + name: NVIDIA GeForce RTX 2080 Ti + - memory: 11019.0 MB + name: NVIDIA GeForce RTX 2080 Ti + - memory: 11019.0 MB + name: NVIDIA GeForce RTX 2080 Ti + - memory: 11019.0 MB + name: NVIDIA GeForce RTX 2080 Ti + - memory: 11019.0 MB + name: NVIDIA GeForce RTX 2080 Ti + - memory: 11019.0 MB + name: NVIDIA GeForce RTX 2080 Ti + - memory: 11019.0 MB + name: NVIDIA GeForce RTX 2080 Ti + - memory: 11019.0 MB + name: NVIDIA GeForce RTX 2080 Ti + memory: + available: 155.49 GB + total: 187.56 GB + used: 30.38 GB + system: + node: charybdis + release: 4.15.0-167-generic + system: Linux + max_inference_memory: 7570050048 + post_inference_memory: 30891008 + pre_inference_memory: 30891008 + timing: + batch_size_1: + cpu_to_gpu: + human_readable: + batch_latency: "2.980 \xB5s +/- 0.766 \xB5s [2.146 \xB5s, 6.437 \xB5s]" + batches_per_second: 352.34 K +/- 68.77 K [155.34 K, 466.03 K] + metrics: + batches_per_second_max: 466033.77777777775 + batches_per_second_mean: 352342.6388734473 + batches_per_second_min: 155344.59259259258 + batches_per_second_std: 68773.99062859628 + seconds_per_batch_max: 6.4373016357421875e-06 + seconds_per_batch_mean: 2.9802322387695312e-06 + seconds_per_batch_min: 2.1457672119140625e-06 + seconds_per_batch_std: 7.655427149773752e-07 + gpu_to_cpu: + human_readable: + batch_latency: "85.592 \xB5s +/- 11.833 \xB5s [72.002 \xB5s, 137.806 \xB5\ + s]" + batches_per_second: 11.87 K +/- 1.41 K [7.26 K, 13.89 K] + metrics: + batches_per_second_max: 13888.423841059603 + batches_per_second_mean: 11874.878410288326 + batches_per_second_min: 7256.581314878893 + batches_per_second_std: 1409.2139146900663 + seconds_per_batch_max: 0.00013780593872070312 + seconds_per_batch_mean: 8.559226989746094e-05 + seconds_per_batch_min: 7.200241088867188e-05 + seconds_per_batch_std: 1.1833009758428916e-05 + on_device_inference: + human_readable: + batch_latency: 146.343 ms +/- 18.591 ms [113.679 ms, 206.796 ms] + batches_per_second: 6.94 +/- 0.88 [4.84, 8.80] + metrics: + batches_per_second_max: 8.796686276360356 + batches_per_second_mean: 6.944163568320094 + batches_per_second_min: 4.8356850922045505 + batches_per_second_std: 0.8824478111017662 + seconds_per_batch_max: 0.20679593086242676 + seconds_per_batch_mean: 0.14634258270263673 + seconds_per_batch_min: 0.11367917060852051 + seconds_per_batch_std: 0.01859126225999914 + total: + human_readable: + batch_latency: 146.431 ms +/- 18.599 ms [113.761 ms, 206.902 ms] + batches_per_second: 6.94 +/- 0.88 [4.83, 8.79] + metrics: + batches_per_second_max: 8.790344315926472 + batches_per_second_mean: 6.939921261564116 + batches_per_second_min: 4.833210994585214 + batches_per_second_std: 0.8817296297200394 + seconds_per_batch_max: 0.20690178871154785 + seconds_per_batch_mean: 0.14643115520477296 + seconds_per_batch_min: 0.11376118659973145 + seconds_per_batch_std: 0.01859924782201525 + batch_size_8: + cpu_to_gpu: + human_readable: + batch_latency: "2.573 \xB5s +/- 0.468 \xB5s [1.907 \xB5s, 4.530 \xB5s]" + batches_per_second: 399.72 K +/- 62.69 K [220.75 K, 524.29 K] + metrics: + batches_per_second_max: 524288.0 + batches_per_second_mean: 399723.30073699815 + batches_per_second_min: 220752.84210526315 + batches_per_second_std: 62686.846076198475 + seconds_per_batch_max: 4.5299530029296875e-06 + seconds_per_batch_mean: 2.572536468505859e-06 + seconds_per_batch_min: 1.9073486328125e-06 + seconds_per_batch_std: 4.675618724797623e-07 + gpu_to_cpu: + human_readable: + batch_latency: "249.622 \xB5s +/- 45.881 \xB5s [206.232 \xB5s, 418.425 \xB5\ + s]" + batches_per_second: 4.11 K +/- 584.76 [2.39 K, 4.85 K] + metrics: + batches_per_second_max: 4848.906358381503 + batches_per_second_mean: 4111.950889508322 + batches_per_second_min: 2389.916809116809 + batches_per_second_std: 584.7639076791254 + seconds_per_batch_max: 0.0004184246063232422 + seconds_per_batch_mean: 0.0002496218681335449 + seconds_per_batch_min: 0.00020623207092285156 + seconds_per_batch_std: 4.588068677218183e-05 + on_device_inference: + human_readable: + batch_latency: 714.472 ms +/- 33.942 ms [670.206 ms, 829.748 ms] + batches_per_second: 1.40 +/- 0.06 [1.21, 1.49] + metrics: + batches_per_second_max: 1.4920794608128078 + batches_per_second_mean: 1.4026602176065648 + batches_per_second_min: 1.2051853118269993 + batches_per_second_std: 0.06378130769681416 + seconds_per_batch_max: 0.8297479152679443 + seconds_per_batch_mean: 0.7144717001914977 + seconds_per_batch_min: 0.6702055931091309 + seconds_per_batch_std: 0.03394229342138034 + total: + human_readable: + batch_latency: 714.724 ms +/- 33.940 ms [670.430 ms, 830.023 ms] + batches_per_second: 1.40 +/- 0.06 [1.20, 1.49] + metrics: + batches_per_second_max: 1.491580153108816 + batches_per_second_mean: 1.4021627815593558 + batches_per_second_min: 1.204785472385871 + batches_per_second_std: 0.06373262610002604 + seconds_per_batch_max: 0.8300232887268066 + seconds_per_batch_mean: 0.7147238945960999 + seconds_per_batch_min: 0.6704299449920654 + seconds_per_batch_std: 0.03394011058977354 + +INFO:benchmark:== Benchmarking model directly == + +Warming up with batch_size=8: 100%|██████████| 10/10 [00:01<00:00, 9.04it/s] + +Measuring inference with batch_size=8: 100%|██████████| 100/100 [00:11<00:00, 8.92it/s] +ERROR:torch-benchmark:Unable to measure energy consumption. Device must be a NVIDIA Jetson. + +Warming up with batch_size=8: 100%|██████████| 10/10 [00:06<00:00, 1.53it/s] + +Measuring inference with batch_size=8: 100%|██████████| 100/100 [01:05<00:00, 1.54it/s] +ERROR:torch-benchmark:Unable to measure energy consumption. Device must be a NVIDIA Jetson. +INFO:benchmark:learner.model.forward: + device: cuda + flops: 4970008352 + machine_info: + cpu: + architecture: x86_64 + cores: + physical: 52 + total: 104 + frequency: 2.10 GHz + model: Intel(R) Xeon(R) Gold 6230R CPU @ 2.10GHz + gpus: + - memory: 11019.0 MB + name: NVIDIA GeForce RTX 2080 Ti + - memory: 11019.0 MB + name: NVIDIA GeForce RTX 2080 Ti + - memory: 11019.0 MB + name: NVIDIA GeForce RTX 2080 Ti + - memory: 11019.0 MB + name: NVIDIA GeForce RTX 2080 Ti + - memory: 11019.0 MB + name: NVIDIA GeForce RTX 2080 Ti + - memory: 11019.0 MB + name: NVIDIA GeForce RTX 2080 Ti + - memory: 11019.0 MB + name: NVIDIA GeForce RTX 2080 Ti + - memory: 11019.0 MB + name: NVIDIA GeForce RTX 2080 Ti + memory: + available: 155.99 GB + total: 187.56 GB + used: 29.87 GB + system: + node: charybdis + release: 4.15.0-167-generic + system: Linux + max_inference_memory: 7569689600 + params: 3794322 + post_inference_memory: 30891008 + pre_inference_memory: 30891008 + timing: + batch_size_1: + cpu_to_gpu: + human_readable: + batch_latency: "1.629 ms +/- 609.968 \xB5s [1.275 ms, 7.606 ms]" + batches_per_second: 635.35 +/- 68.41 [131.48, 784.28] + metrics: + batches_per_second_max: 784.2752430815258 + batches_per_second_mean: 635.3514998497731 + batches_per_second_min: 131.4787624212407 + batches_per_second_std: 68.40710943415525 + seconds_per_batch_max: 0.007605791091918945 + seconds_per_batch_mean: 0.0016294384002685546 + seconds_per_batch_min: 0.0012750625610351562 + seconds_per_batch_std: 0.0006099682313089673 + gpu_to_cpu: + human_readable: + batch_latency: "67.887 \xB5s +/- 7.838 \xB5s [60.797 \xB5s, 112.534 \xB5\ + s]" + batches_per_second: 14.88 K +/- 1.31 K [8.89 K, 16.45 K] + metrics: + batches_per_second_max: 16448.250980392157 + batches_per_second_mean: 14878.688147498113 + batches_per_second_min: 8886.237288135593 + batches_per_second_std: 1305.7936440015405 + seconds_per_batch_max: 0.0001125335693359375 + seconds_per_batch_mean: 6.788730621337891e-05 + seconds_per_batch_min: 6.079673767089844e-05 + seconds_per_batch_std: 7.837527680828964e-06 + on_device_inference: + human_readable: + batch_latency: 110.185 ms +/- 2.631 ms [105.058 ms, 119.190 ms] + batches_per_second: 9.08 +/- 0.21 [8.39, 9.52] + metrics: + batches_per_second_max: 9.51851255086271 + batches_per_second_mean: 9.080705464014008 + batches_per_second_min: 8.389983957369008 + batches_per_second_std: 0.2130314444140438 + seconds_per_batch_max: 0.11918973922729492 + seconds_per_batch_mean: 0.11018528699874879 + seconds_per_batch_min: 0.10505843162536621 + seconds_per_batch_std: 0.0026310194813892874 + total: + human_readable: + batch_latency: 111.883 ms +/- 2.666 ms [106.695 ms, 121.078 ms] + batches_per_second: 8.94 +/- 0.21 [8.26, 9.37] + metrics: + batches_per_second_max: 9.37249503923917 + batches_per_second_mean: 8.942929502365097 + batches_per_second_min: 8.259170283319811 + batches_per_second_std: 0.20954949579374973 + seconds_per_batch_max: 0.1210775375366211 + seconds_per_batch_mean: 0.11188261270523071 + seconds_per_batch_min: 0.10669517517089844 + seconds_per_batch_std: 0.0026660009863460838 + batch_size_8: + cpu_to_gpu: + human_readable: + batch_latency: 10.610 ms +/- 3.455 ms [9.228 ms, 28.271 ms] + batches_per_second: 98.80 +/- 14.42 [35.37, 108.36] + metrics: + batches_per_second_max: 108.36034825742114 + batches_per_second_mean: 98.80079616634268 + batches_per_second_min: 35.37168783416823 + batches_per_second_std: 14.418761787725009 + seconds_per_batch_max: 0.028271198272705078 + seconds_per_batch_mean: 0.010610449314117431 + seconds_per_batch_min: 0.00922846794128418 + seconds_per_batch_std: 0.003455423978759472 + gpu_to_cpu: + human_readable: + batch_latency: 17.198 ms +/- 3.772 ms [2.995 ms, 20.231 ms] + batches_per_second: 66.85 +/- 42.49 [49.43, 333.94] + metrics: + batches_per_second_max: 333.94140127388533 + batches_per_second_mean: 66.84715576732079 + batches_per_second_min: 49.42790812779146 + batches_per_second_std: 42.4948825150087 + seconds_per_batch_max: 0.02023148536682129 + seconds_per_batch_mean: 0.017197630405426025 + seconds_per_batch_min: 0.002994537353515625 + seconds_per_batch_std: 0.0037724224888370933 + on_device_inference: + human_readable: + batch_latency: 622.048 ms +/- 3.808 ms [618.302 ms, 636.279 ms] + batches_per_second: 1.61 +/- 0.01 [1.57, 1.62] + metrics: + batches_per_second_max: 1.6173317271727512 + batches_per_second_mean: 1.6076536054510004 + batches_per_second_min: 1.571637925963008 + batches_per_second_std: 0.009698354784753149 + seconds_per_batch_max: 0.6362788677215576 + seconds_per_batch_mean: 0.6220475149154663 + seconds_per_batch_min: 0.6183023452758789 + seconds_per_batch_std: 0.0038079459692814894 + total: + human_readable: + batch_latency: 649.856 ms +/- 3.516 ms [646.941 ms, 668.032 ms] + batches_per_second: 1.54 +/- 0.01 [1.50, 1.55] + metrics: + batches_per_second_max: 1.5457355679153024 + batches_per_second_mean: 1.5388475091610627 + batches_per_second_min: 1.4969349676365702 + batches_per_second_std: 0.008151678976143757 + seconds_per_batch_max: 0.6680316925048828 + seconds_per_batch_mean: 0.6498555946350097 + seconds_per_batch_min: 0.6469411849975586 + seconds_per_batch_std: 0.003516330596620928 + +INFO:benchmark:==== Benchmarking X3DLearner (l) ==== +INFO:benchmark:== Benchmarking learner.infer == +ERROR:torch-benchmark:Unable to measure model params due to error: 'function' object has no attribute 'parameters' +ERROR:torch-benchmark:Unable to measure model FLOPs due to error: 'list' object has no attribute 'shape' + +Warming up with batch_size=2: 100%|██████████| 10/10 [00:02<00:00, 3.42it/s] + +Measuring inference with batch_size=2: 100%|██████████| 100/100 [00:28<00:00, 3.51it/s] +ERROR:torch-benchmark:Unable to measure energy consumption. Device must be a NVIDIA Jetson. + +Warming up with batch_size=2: 100%|██████████| 10/10 [00:06<00:00, 1.62it/s] + +Measuring inference with batch_size=2: 100%|██████████| 100/100 [01:01<00:00, 1.62it/s] +ERROR:torch-benchmark:Unable to measure energy consumption. Device must be a NVIDIA Jetson. +INFO:benchmark:learner.infer: + device: cuda + machine_info: + cpu: + architecture: x86_64 + cores: + physical: 52 + total: 104 + frequency: 2.10 GHz + model: Intel(R) Xeon(R) Gold 6230R CPU @ 2.10GHz + gpus: + - memory: 11019.0 MB + name: NVIDIA GeForce RTX 2080 Ti + - memory: 11019.0 MB + name: NVIDIA GeForce RTX 2080 Ti + - memory: 11019.0 MB + name: NVIDIA GeForce RTX 2080 Ti + - memory: 11019.0 MB + name: NVIDIA GeForce RTX 2080 Ti + - memory: 11019.0 MB + name: NVIDIA GeForce RTX 2080 Ti + - memory: 11019.0 MB + name: NVIDIA GeForce RTX 2080 Ti + - memory: 11019.0 MB + name: NVIDIA GeForce RTX 2080 Ti + - memory: 11019.0 MB + name: NVIDIA GeForce RTX 2080 Ti + memory: + available: 156.12 GB + total: 187.56 GB + used: 29.74 GB + system: + node: charybdis + release: 4.15.0-167-generic + system: Linux + max_inference_memory: 6667727360 + post_inference_memory: 56050688 + pre_inference_memory: 56050688 + timing: + batch_size_1: + cpu_to_gpu: + human_readable: + batch_latency: "2.453 \xB5s +/- 0.468 \xB5s [1.669 \xB5s, 4.530 \xB5s]" + batches_per_second: 420.76 K +/- 71.88 K [220.75 K, 599.19 K] + metrics: + batches_per_second_max: 599186.2857142857 + batches_per_second_mean: 420758.23728907347 + batches_per_second_min: 220752.84210526315 + batches_per_second_std: 71881.1871544786 + seconds_per_batch_max: 4.5299530029296875e-06 + seconds_per_batch_mean: 2.453327178955078e-06 + seconds_per_batch_min: 1.6689300537109375e-06 + seconds_per_batch_std: 4.6756187247976237e-07 + gpu_to_cpu: + human_readable: + batch_latency: "82.507 \xB5s +/- 9.914 \xB5s [72.479 \xB5s, 117.779 \xB5\ + s]" + batches_per_second: 12.27 K +/- 1.28 K [8.49 K, 13.80 K] + metrics: + batches_per_second_max: 13797.052631578947 + batches_per_second_mean: 12272.496501418853 + batches_per_second_min: 8490.493927125506 + batches_per_second_std: 1280.9997524378953 + seconds_per_batch_max: 0.00011777877807617188 + seconds_per_batch_mean: 8.250713348388672e-05 + seconds_per_batch_min: 7.2479248046875e-05 + seconds_per_batch_std: 9.913960563142161e-06 + on_device_inference: + human_readable: + batch_latency: 284.979 ms +/- 30.387 ms [246.973 ms, 371.621 ms] + batches_per_second: 3.55 +/- 0.36 [2.69, 4.05] + metrics: + batches_per_second_max: 4.049024983588833 + batches_per_second_mean: 3.5469557673202536 + batches_per_second_min: 2.690914363398518 + batches_per_second_std: 0.3567746285405532 + seconds_per_batch_max: 0.37162089347839355 + seconds_per_batch_mean: 0.28497867345809935 + seconds_per_batch_min: 0.24697303771972656 + seconds_per_batch_std: 0.030386612464472953 + total: + human_readable: + batch_latency: 285.064 ms +/- 30.392 ms [247.049 ms, 371.702 ms] + batches_per_second: 3.55 +/- 0.36 [2.69, 4.05] + metrics: + batches_per_second_max: 4.047782373639015 + batches_per_second_mean: 3.5458890476774054 + batches_per_second_min: 2.690327517541345 + batches_per_second_std: 0.3566233247877221 + seconds_per_batch_max: 0.3717019557952881 + seconds_per_batch_mean: 0.2850636339187622 + seconds_per_batch_min: 0.24704885482788086 + seconds_per_batch_std: 0.030391786506924815 + batch_size_2: + cpu_to_gpu: + human_readable: + batch_latency: "2.460 \xB5s +/- 0.393 \xB5s [1.669 \xB5s, 3.815 \xB5s]" + batches_per_second: 416.15 K +/- 62.78 K [262.14 K, 599.19 K] + metrics: + batches_per_second_max: 599186.2857142857 + batches_per_second_mean: 416154.89486158296 + batches_per_second_min: 262144.0 + batches_per_second_std: 62782.37312829757 + seconds_per_batch_max: 3.814697265625e-06 + seconds_per_batch_mean: 2.460479736328125e-06 + seconds_per_batch_min: 1.6689300537109375e-06 + seconds_per_batch_std: 3.9303648061742547e-07 + gpu_to_cpu: + human_readable: + batch_latency: "101.564 \xB5s +/- 12.288 \xB5s [88.692 \xB5s, 170.708 \xB5\ + s]" + batches_per_second: 9.96 K +/- 962.79 [5.86 K, 11.28 K] + metrics: + batches_per_second_max: 11275.010752688173 + batches_per_second_mean: 9960.28032041261 + batches_per_second_min: 5857.966480446928 + batches_per_second_std: 962.793941307588 + seconds_per_batch_max: 0.00017070770263671875 + seconds_per_batch_mean: 0.00010156393051147461 + seconds_per_batch_min: 8.869171142578125e-05 + seconds_per_batch_std: 1.228781207801777e-05 + on_device_inference: + human_readable: + batch_latency: 618.023 ms +/- 30.161 ms [589.648 ms, 783.198 ms] + batches_per_second: 1.62 +/- 0.07 [1.28, 1.70] + metrics: + batches_per_second_max: 1.6959270375628295 + batches_per_second_mean: 1.6215612473353076 + batches_per_second_min: 1.276816919733489 + batches_per_second_std: 0.07200033847927961 + seconds_per_batch_max: 0.7831976413726807 + seconds_per_batch_mean: 0.6180234742164612 + seconds_per_batch_min: 0.5896480083465576 + seconds_per_batch_std: 0.030161362656271902 + total: + human_readable: + batch_latency: 618.127 ms +/- 30.167 ms [589.746 ms, 783.310 ms] + batches_per_second: 1.62 +/- 0.07 [1.28, 1.70] + metrics: + batches_per_second_max: 1.6956466195445292 + batches_per_second_mean: 1.621288442578485 + batches_per_second_min: 1.276633486950458 + batches_per_second_std: 0.07198943798066947 + seconds_per_batch_max: 0.7833101749420166 + seconds_per_batch_mean: 0.618127498626709 + seconds_per_batch_min: 0.5897455215454102 + seconds_per_batch_std: 0.030166632314954284 + +INFO:benchmark:== Benchmarking model directly == + +Warming up with batch_size=2: 100%|██████████| 10/10 [00:02<00:00, 4.11it/s] + +Measuring inference with batch_size=2: 100%|██████████| 100/100 [00:24<00:00, 4.07it/s] +ERROR:torch-benchmark:Unable to measure energy consumption. Device must be a NVIDIA Jetson. + +Warming up with batch_size=2: 100%|██████████| 10/10 [00:05<00:00, 1.70it/s] + +Measuring inference with batch_size=2: 100%|██████████| 100/100 [00:58<00:00, 1.70it/s] +ERROR:torch-benchmark:Unable to measure energy consumption. Device must be a NVIDIA Jetson. +INFO:benchmark:learner.model.forward: + device: cuda + flops: 19166052038 + machine_info: + cpu: + architecture: x86_64 + cores: + physical: 52 + total: 104 + frequency: 2.10 GHz + model: Intel(R) Xeon(R) Gold 6230R CPU @ 2.10GHz + gpus: + - memory: 11019.0 MB + name: NVIDIA GeForce RTX 2080 Ti + - memory: 11019.0 MB + name: NVIDIA GeForce RTX 2080 Ti + - memory: 11019.0 MB + name: NVIDIA GeForce RTX 2080 Ti + - memory: 11019.0 MB + name: NVIDIA GeForce RTX 2080 Ti + - memory: 11019.0 MB + name: NVIDIA GeForce RTX 2080 Ti + - memory: 11019.0 MB + name: NVIDIA GeForce RTX 2080 Ti + - memory: 11019.0 MB + name: NVIDIA GeForce RTX 2080 Ti + - memory: 11019.0 MB + name: NVIDIA GeForce RTX 2080 Ti + memory: + available: 156.46 GB + total: 187.56 GB + used: 29.43 GB + system: + node: charybdis + release: 4.15.0-167-generic + system: Linux + max_inference_memory: 6667727360 + params: 6153432 + post_inference_memory: 56050688 + pre_inference_memory: 56050688 + timing: + batch_size_1: + cpu_to_gpu: + human_readable: + batch_latency: "2.913 ms +/- 625.667 \xB5s [2.650 ms, 8.968 ms]" + batches_per_second: 349.09 +/- 28.96 [111.51, 377.36] + metrics: + batches_per_second_max: 377.3552856500225 + batches_per_second_mean: 349.0949005677601 + batches_per_second_min: 111.51208358812113 + batches_per_second_std: 28.95824834596472 + seconds_per_batch_max: 0.00896763801574707 + seconds_per_batch_mean: 0.0029129600524902344 + seconds_per_batch_min: 0.002650022506713867 + seconds_per_batch_std: 0.0006256671067115095 + gpu_to_cpu: + human_readable: + batch_latency: "67.136 \xB5s +/- 5.811 \xB5s [60.558 \xB5s, 88.692 \xB5\ + s]" + batches_per_second: 14.99 K +/- 1.12 K [11.28 K, 16.51 K] + metrics: + batches_per_second_max: 16513.007874015748 + batches_per_second_mean: 14991.362082263971 + batches_per_second_min: 11275.010752688173 + batches_per_second_std: 1118.5831066913304 + seconds_per_batch_max: 8.869171142578125e-05 + seconds_per_batch_mean: 6.713628768920898e-05 + seconds_per_batch_min: 6.0558319091796875e-05 + seconds_per_batch_std: 5.8107743723032125e-06 + on_device_inference: + human_readable: + batch_latency: 242.677 ms +/- 10.362 ms [230.077 ms, 290.871 ms] + batches_per_second: 4.13 +/- 0.16 [3.44, 4.35] + metrics: + batches_per_second_max: 4.346379471322089 + batches_per_second_mean: 4.127608299760917 + batches_per_second_min: 3.437951280367803 + batches_per_second_std: 0.16233020406949533 + seconds_per_batch_max: 0.29087090492248535 + seconds_per_batch_mean: 0.24267747402191162 + seconds_per_batch_min: 0.23007655143737793 + seconds_per_batch_std: 0.01036162960858265 + total: + human_readable: + batch_latency: 245.658 ms +/- 10.404 ms [232.790 ms, 293.987 ms] + batches_per_second: 4.08 +/- 0.16 [3.40, 4.30] + metrics: + batches_per_second_max: 4.295721693460919 + batches_per_second_mean: 4.077438230838669 + batches_per_second_min: 3.4015160546571197 + batches_per_second_std: 0.1593573651102482 + seconds_per_batch_max: 0.29398655891418457 + seconds_per_batch_mean: 0.24565757036209107 + seconds_per_batch_min: 0.2327897548675537 + seconds_per_batch_std: 0.010403947292159281 + batch_size_2: + cpu_to_gpu: + human_readable: + batch_latency: 5.263 ms +/- 1.685 ms [4.229 ms, 18.686 ms] + batches_per_second: 197.96 +/- 28.51 [53.52, 236.49] + metrics: + batches_per_second_max: 236.4853405502932 + batches_per_second_mean: 197.95973495509486 + batches_per_second_min: 53.515157701337145 + batches_per_second_std: 28.505000336758208 + seconds_per_batch_max: 0.018686294555664062 + seconds_per_batch_mean: 0.005262765884399414 + seconds_per_batch_min: 0.0042285919189453125 + seconds_per_batch_std: 0.0016845920801753951 + gpu_to_cpu: + human_readable: + batch_latency: "311.687 \xB5s +/- 700.964 \xB5s [64.373 \xB5s, 3.946 ms]" + batches_per_second: 12.14 K +/- 4.92 K [253.40, 15.53 K] + metrics: + batches_per_second_max: 15534.45925925926 + batches_per_second_mean: 12140.469769746756 + batches_per_second_min: 253.4016433059449 + batches_per_second_std: 4915.688632166892 + seconds_per_batch_max: 0.0039463043212890625 + seconds_per_batch_mean: 0.00031168699264526366 + seconds_per_batch_min: 6.437301635742188e-05 + seconds_per_batch_std: 0.0007009637790008035 + on_device_inference: + human_readable: + batch_latency: 580.896 ms +/- 7.701 ms [573.048 ms, 629.689 ms] + batches_per_second: 1.72 +/- 0.02 [1.59, 1.75] + metrics: + batches_per_second_max: 1.7450548920195528 + batches_per_second_mean: 1.721767315222335 + batches_per_second_min: 1.5880856019843195 + batches_per_second_std: 0.021753979704037665 + seconds_per_batch_max: 0.6296889781951904 + seconds_per_batch_mean: 0.5808958339691163 + seconds_per_batch_min: 0.5730478763580322 + seconds_per_batch_std: 0.007701065941076332 + total: + human_readable: + batch_latency: 586.470 ms +/- 7.612 ms [580.213 ms, 634.180 ms] + batches_per_second: 1.71 +/- 0.02 [1.58, 1.72] + metrics: + batches_per_second_max: 1.723504072787909 + batches_per_second_mean: 1.7053903026793207 + batches_per_second_min: 1.5768392116525762 + batches_per_second_std: 0.021124736632684677 + seconds_per_batch_max: 0.6341800689697266 + seconds_per_batch_mean: 0.5864702868461609 + seconds_per_batch_min: 0.5802133083343506 + seconds_per_batch_std: 0.007611839281326458 + From 7b09bfebfc492db322c033ed3e69af6c05507ce5 Mon Sep 17 00:00:00 2001 From: LukasHedegaard Date: Mon, 14 Feb 2022 15:33:51 +0100 Subject: [PATCH 09/60] Update har benchmark scripts --- .../benchmark/benchmark_cox3d.py | 23 +++++++++++++------ .../benchmark/benchmark_x3d.py | 11 ++++++++- 2 files changed, 26 insertions(+), 8 deletions(-) diff --git a/projects/perception/activity_recognition/benchmark/benchmark_cox3d.py b/projects/perception/activity_recognition/benchmark/benchmark_cox3d.py index 4b3326da1d..16da442f6d 100644 --- a/projects/perception/activity_recognition/benchmark/benchmark_cox3d.py +++ b/projects/perception/activity_recognition/benchmark/benchmark_cox3d.py @@ -42,7 +42,15 @@ def benchmark_cox3d(): "l": (3, 312, 312), } - batch_size = { # RTX2080Ti max power of 2 + # Max power of 2 + # batch_size = { # RTX2080Ti + # "xs": 128, + # "s": 128, + # "m": 64, + # "l": 8, + # } + + batch_size = { # Xavier "xs": 128, "s": 128, "m": 64, @@ -50,7 +58,7 @@ def benchmark_cox3d(): } for backbone in ["xs", "s", "m", "l"]: - logger.info(f"==== Benchmarking CoX3DLearner ({backbone}) ====") + print(f"==== Benchmarking CoX3DLearner ({backbone}) ====") learner = CoX3DLearner( device="cuda" if torch.cuda.is_available() else "cpu", @@ -88,7 +96,7 @@ def transfer_to_device_fn( for s in sample ] - logger.info("== Benchmarking learner.infer ==") + print("== Benchmarking learner.infer ==") results1 = benchmark( model=learner.infer, sample=image_samples, @@ -97,12 +105,13 @@ def transfer_to_device_fn( get_device_fn=get_device_fn, transfer_to_device_fn=transfer_to_device_fn, batch_size=batch_size[backbone], + print_fn=print, ) - logger.info(yaml.dump({"learner.infer": results1})) + print(yaml.dump({"learner.infer": results1})) - logger.info("== Benchmarking model directly ==") - results2 = benchmark(learner.model, sample, num_runs=num_runs) - logger.info(yaml.dump({"learner.model.forward": results2})) + 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__": diff --git a/projects/perception/activity_recognition/benchmark/benchmark_x3d.py b/projects/perception/activity_recognition/benchmark/benchmark_x3d.py index 3559551b86..db4c340664 100644 --- a/projects/perception/activity_recognition/benchmark/benchmark_x3d.py +++ b/projects/perception/activity_recognition/benchmark/benchmark_x3d.py @@ -41,12 +41,21 @@ def benchmark_x3d(): "l": (3, 16, 312, 312), } - batch_size = { # RTX2080Ti max power of 2 + # Max power of 2 + # batch_size = { # RTX2080Ti + # "xs": 32, + # "s": 16, + # "m": 8, + # "l": 2, + # } + batch_size = { # Xavier "xs": 32, "s": 16, "m": 8, "l": 2, } + + for backbone in ["xs", "s", "m", "l"]: logger.info(f"==== Benchmarking X3DLearner ({backbone}) ====") From d3168ed3f9e04cce9315a4b5dd69d840ddd8d794 Mon Sep 17 00:00:00 2001 From: LukasHedegaard Date: Mon, 14 Feb 2022 16:10:55 +0100 Subject: [PATCH 10/60] Add xavier benchmark har results --- .../results/benchmark_cox3d_Xavier.txt | 2295 ++++++++++++++++ .../results/benchmark_x3d_Xavier.txt | 2303 +++++++++++++++++ 2 files changed, 4598 insertions(+) create mode 100644 projects/perception/activity_recognition/benchmark/results/benchmark_cox3d_Xavier.txt create mode 100644 projects/perception/activity_recognition/benchmark/results/benchmark_x3d_Xavier.txt diff --git a/projects/perception/activity_recognition/benchmark/results/benchmark_cox3d_Xavier.txt b/projects/perception/activity_recognition/benchmark/results/benchmark_cox3d_Xavier.txt new file mode 100644 index 0000000000..68e19e9831 --- /dev/null +++ b/projects/perception/activity_recognition/benchmark/results/benchmark_cox3d_Xavier.txt @@ -0,0 +1,2295 @@ +/home/maleci/.local/lib/python3.6/site-packages/pkg_resources/__init__.py:119: PkgResourcesDeprecationWarning: 0.18ubuntu0.18.04.1 is an invalid version and will not be supported in a future release + PkgResourcesDeprecationWarning, +/home/maleci/.local/lib/python3.6/site-packages/torchvision-0.10.0-py3.6-linux-aarch64.egg/torchvision/transforms/_functional_video.py:6: UserWarning: The _functional_video module is deprecated. Please use the functional module instead. + "The _functional_video module is deprecated. Please use the functional module instead." +/home/maleci/.local/lib/python3.6/site-packages/torchvision-0.10.0-py3.6-linux-aarch64.egg/torchvision/transforms/_transforms_video.py:26: UserWarning: The _transforms_video module is deprecated. Please use the transforms module instead. + "The _transforms_video module is deprecated. Please use the transforms module instead." +WARNING:opendr.perception.activity_recognition.cox3d.algorithm.utils:Padding along the temporal dimension only affects the computation in `forward3d`. In `forward` it is omitted. +ERROR:torch-benchmark:Unable to measure model params due to error: 'function' object has no attribute 'parameters' +ERROR:torch-benchmark:Unable to measure model FLOPs due to error: 'list' object has no attribute 'shape' +==== Benchmarking CoX3DLearner (xs) ==== +== Benchmarking learner.infer == +Machine info: + cpu: + architecture: aarch64 + cores: + physical: 8 + total: 8 + frequency: 2.27 GHz + model: ARMv8 Processor rev 0 (v8l) + gpus: null + memory: + available: 25.27 GB + total: 31.17 GB + used: 8.88 GB + system: + node: xavier + release: 4.9.140-tegra + system: Linux + +Model device: cuda:0 +Allocated GPU memory prior to inference: 15445504 (14.73 MB) +Allocated GPU memory after to inference: 7504768512 (6.99 GB) +Max allocated GPU memory during inference: 7553511424 (7.03 GB) + Warming up with batch_size=128: 0%| | 0/10 [00:00 Date: Mon, 14 Feb 2022 16:14:20 +0100 Subject: [PATCH 11/60] Update har benchmark scripts with CPU batch_sizes --- .../benchmark/benchmark_cox3d.py | 19 +++++++----- .../benchmark/benchmark_x3d.py | 29 +++++++++++-------- 2 files changed, 29 insertions(+), 19 deletions(-) diff --git a/projects/perception/activity_recognition/benchmark/benchmark_cox3d.py b/projects/perception/activity_recognition/benchmark/benchmark_cox3d.py index 16da442f6d..6ead92fbcb 100644 --- a/projects/perception/activity_recognition/benchmark/benchmark_cox3d.py +++ b/projects/perception/activity_recognition/benchmark/benchmark_cox3d.py @@ -43,18 +43,23 @@ def benchmark_cox3d(): } # Max power of 2 - # batch_size = { # RTX2080Ti + # batch_size = { # RTX2080Ti # "xs": 128, # "s": 128, # "m": 64, # "l": 8, # } - - batch_size = { # Xavier - "xs": 128, - "s": 128, - "m": 64, - "l": 8, + # batch_size = { # Xavier + # "xs": 128, + # "s": 128, + # "m": 64, + # "l": 8, + # } + batch_size = { # CPU - larger batch sizes don't help + "xs": 1, + "s": 1, + "m": 1, + "l": 1, } for backbone in ["xs", "s", "m", "l"]: diff --git a/projects/perception/activity_recognition/benchmark/benchmark_x3d.py b/projects/perception/activity_recognition/benchmark/benchmark_x3d.py index db4c340664..5ade93d475 100644 --- a/projects/perception/activity_recognition/benchmark/benchmark_x3d.py +++ b/projects/perception/activity_recognition/benchmark/benchmark_x3d.py @@ -42,22 +42,27 @@ def benchmark_x3d(): } # Max power of 2 - # batch_size = { # RTX2080Ti + # batch_size = { # RTX2080Ti # "xs": 32, # "s": 16, # "m": 8, # "l": 2, # } - batch_size = { # Xavier - "xs": 32, - "s": 16, - "m": 8, - "l": 2, + # batch_size = { # Xavier + # "xs": 32, + # "s": 16, + # "m": 8, + # "l": 2, + # } + batch_size = { # CPU - larger batch sizes don't help + "xs": 1, + "s": 1, + "m": 1, + "l": 1, } - for backbone in ["xs", "s", "m", "l"]: - logger.info(f"==== Benchmarking X3DLearner ({backbone}) ====") + print(f"==== Benchmarking X3DLearner ({backbone}) ====") learner = X3DLearner( device="cuda" if torch.cuda.is_available() else "cpu", @@ -96,7 +101,7 @@ def transfer_to_device_fn( for s in sample ] - logger.info("== Benchmarking learner.infer ==") + print("== Benchmarking learner.infer ==") results1 = benchmark( model=learner.infer, sample=video_samples, @@ -106,11 +111,11 @@ def transfer_to_device_fn( transfer_to_device_fn=transfer_to_device_fn, batch_size=batch_size[backbone], ) - logger.info(yaml.dump({"learner.infer": results1})) + print(yaml.dump({"learner.infer": results1})) - logger.info("== Benchmarking model directly ==") + print("== Benchmarking model directly ==") results2 = benchmark(learner.model, sample, num_runs=num_runs) - logger.info(yaml.dump({"learner.model.forward": results2})) + print(yaml.dump({"learner.model.forward": results2})) if __name__ == "__main__": From 3ddad9e0676a861f7b3456769c7f0d14b882436f Mon Sep 17 00:00:00 2001 From: LukasHedegaard Date: Mon, 14 Feb 2022 16:14:36 +0100 Subject: [PATCH 12/60] Add har benchmark CPU results --- .../benchmark/results/benchmark_cox3d_CPU.txt | 621 ++++++++++++++++++ .../benchmark/results/benchmark_x3d_CPU.txt | 340 ++++++++++ 2 files changed, 961 insertions(+) create mode 100644 projects/perception/activity_recognition/benchmark/results/benchmark_cox3d_CPU.txt create mode 100644 projects/perception/activity_recognition/benchmark/results/benchmark_x3d_CPU.txt diff --git a/projects/perception/activity_recognition/benchmark/results/benchmark_cox3d_CPU.txt b/projects/perception/activity_recognition/benchmark/results/benchmark_cox3d_CPU.txt new file mode 100644 index 0000000000..f5f7ece776 --- /dev/null +++ b/projects/perception/activity_recognition/benchmark/results/benchmark_cox3d_CPU.txt @@ -0,0 +1,621 @@ +WARNING:opendr.perception.activity_recognition.cox3d.algorithm.utils:Padding along the temporal dimension only affects the computation in `forward3d`. In `forward` it is omitted. +ERROR:torch-benchmark:Unable to measure model params due to error: 'function' object has no attribute 'parameters' +ERROR:torch-benchmark:Unable to measure model FLOPs due to error: 'list' object has no attribute 'shape' +WARNING:torch-benchmark:Measurement of allocated memory is only available on CUDA devices +==== Benchmarking CoX3DLearner (xs) ==== +== Benchmarking learner.infer == +Machine info: + cpu: + architecture: x86_64 + cores: + physical: 6 + total: 12 + frequency: 2.60 GHz + model: Intel(R) Core(TM) i7-9750H CPU @ 2.60GHz + gpus: null + memory: + available: 4.84 GB + total: 16.00 GB + used: 8.69 GB + system: + node: d40049 + release: 21.2.0 + system: Darwin + +Model device: cpu + Warming up with batch_size=1: 0%| | 0/10 [00:00 Date: Mon, 14 Feb 2022 16:30:50 +0100 Subject: [PATCH 13/60] Fix black lint issue --- .../activity_recognition/benchmark/benchmark_cox3d.py | 6 +++--- .../activity_recognition/benchmark/benchmark_x3d.py | 6 +++--- 2 files changed, 6 insertions(+), 6 deletions(-) diff --git a/projects/perception/activity_recognition/benchmark/benchmark_cox3d.py b/projects/perception/activity_recognition/benchmark/benchmark_cox3d.py index 6ead92fbcb..d99d5dc161 100644 --- a/projects/perception/activity_recognition/benchmark/benchmark_cox3d.py +++ b/projects/perception/activity_recognition/benchmark/benchmark_cox3d.py @@ -43,19 +43,19 @@ def benchmark_cox3d(): } # Max power of 2 - # batch_size = { # RTX2080Ti + # batch_size = { # RTX2080Ti # "xs": 128, # "s": 128, # "m": 64, # "l": 8, # } - # batch_size = { # Xavier + # batch_size = { # Xavier # "xs": 128, # "s": 128, # "m": 64, # "l": 8, # } - batch_size = { # CPU - larger batch sizes don't help + batch_size = { # CPU - larger batch sizes don't help "xs": 1, "s": 1, "m": 1, diff --git a/projects/perception/activity_recognition/benchmark/benchmark_x3d.py b/projects/perception/activity_recognition/benchmark/benchmark_x3d.py index 5ade93d475..46abe78d50 100644 --- a/projects/perception/activity_recognition/benchmark/benchmark_x3d.py +++ b/projects/perception/activity_recognition/benchmark/benchmark_x3d.py @@ -42,19 +42,19 @@ def benchmark_x3d(): } # Max power of 2 - # batch_size = { # RTX2080Ti + # batch_size = { # RTX2080Ti # "xs": 32, # "s": 16, # "m": 8, # "l": 2, # } - # batch_size = { # Xavier + # batch_size = { # Xavier # "xs": 32, # "s": 16, # "m": 8, # "l": 2, # } - batch_size = { # CPU - larger batch sizes don't help + batch_size = { # CPU - larger batch sizes don't help "xs": 1, "s": 1, "m": 1, From ff6b82e5e5de353f0a56d05fb80a5d2da36761ec Mon Sep 17 00:00:00 2001 From: LukasHedegaard Date: Mon, 14 Feb 2022 15:47:01 +0000 Subject: [PATCH 14/60] Remove obsolete dependency --- .../activity_recognition/benchmark/install_on_server.sh | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/projects/perception/activity_recognition/benchmark/install_on_server.sh b/projects/perception/activity_recognition/benchmark/install_on_server.sh index f550fc7bd6..3ddf58a47c 100755 --- a/projects/perception/activity_recognition/benchmark/install_on_server.sh +++ b/projects/perception/activity_recognition/benchmark/install_on_server.sh @@ -10,4 +10,4 @@ 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 opencv-python==4.5.1.48 joblib>=1.0.1 pytorch_benchmark +pip install pytorch_lightning==1.2.3 onnxruntime==1.3.0 joblib>=1.0.1 pytorch_benchmark From 653da79163a5b7816db6f2ba2337e28599ac118c Mon Sep 17 00:00:00 2001 From: LukasHedegaard Date: Tue, 15 Feb 2022 11:01:24 +0000 Subject: [PATCH 15/60] Redo cox3d RTX2080Ti benchmark --- .../results/benchmark_cox3d_RTX2080Ti.txt | 3234 +++++++++++++---- 1 file changed, 2437 insertions(+), 797 deletions(-) diff --git a/projects/perception/activity_recognition/benchmark/results/benchmark_cox3d_RTX2080Ti.txt b/projects/perception/activity_recognition/benchmark/results/benchmark_cox3d_RTX2080Ti.txt index 679e3b31ed..e7d3282fb8 100644 --- a/projects/perception/activity_recognition/benchmark/results/benchmark_cox3d_RTX2080Ti.txt +++ b/projects/perception/activity_recognition/benchmark/results/benchmark_cox3d_RTX2080Ti.txt @@ -1,797 +1,2437 @@ -INFO:benchmark:==== Benchmarking CoX3DLearner (xs) ==== -WARNING:opendr.perception.activity_recognition.cox3d.algorithm.utils:Padding along the temporal dimension only affects the computation in `forward3d`. In `forward` it is omitted. -INFO:benchmark:== Benchmarking learner.infer == -ERROR:torch-benchmark:Unable to measure model params due to error: 'function' object has no attribute 'parameters' -ERROR:torch-benchmark:Unable to measure model FLOPs due to error: 'list' object has no attribute 'shape' - -Warming up with batch_size=128: 0%| | 0/10 [00:00 - benchmark_cox3d() - File "benchmark_cox3d.py", line 92, in benchmark_cox3d - results1 = benchmark( - File "/opt/anaconda/envs/opendr/lib/python3.8/site-packages/pytorch_benchmark/benchmark.py", line 327, in benchmark - pre_mem, post_mem, max_mem = measure_allocated_memory( - File "/opt/anaconda/envs/opendr/lib/python3.8/site-packages/pytorch_benchmark/benchmark.py", line 84, in measure_allocated_memory - model(transfer_to_device_fn(sample, model_device)), - File "/home/lh/projects/opendr/src/opendr/perception/activity_recognition/cox3d/cox3d_learner.py", line 140, in infer - results = self.model.forward(batch) - File "/home/lh/projects/opendr/src/opendr/perception/activity_recognition/cox3d/algorithm/cox3d.py", line 1067, in forward - x = module(x) - File "/opt/anaconda/envs/opendr/lib/python3.8/site-packages/torch/nn/modules/module.py", line 727, in _call_impl - result = self.forward(*input, **kwargs) - File "/home/lh/projects/opendr/src/opendr/perception/activity_recognition/cox3d/algorithm/cox3d.py", line 494, in forward - x = m(x) - File "/opt/anaconda/envs/opendr/lib/python3.8/site-packages/torch/nn/modules/module.py", line 727, in _call_impl - result = self.forward(*input, **kwargs) - File "/home/lh/projects/opendr/src/opendr/perception/activity_recognition/cox3d/algorithm/cox3d.py", line 301, in forward - output = delayed_x + f_x -RuntimeError: CUDA out of memory. Tried to allocate 20.00 MiB (GPU 0; 10.76 GiB total capacity; 9.43 GiB already allocated; 13.44 MiB free; 9.65 GiB reserved in total by PyTorch) +WARNING:opendr.perception.activity_recognition.cox3d.algorithm.utils:Padding along the temporal dimension only affects the computation in `forward3d`. In `forward` it is omitted. +ERROR:torch-benchmark:Unable to measure model params due to error: 'function' object has no attribute 'parameters' +ERROR:torch-benchmark:Unable to measure model FLOPs due to error: 'list' object has no attribute 'shape' +==== Benchmarking CoX3DLearner (xs) ==== +== Benchmarking learner.infer == +Machine info: + cpu: + architecture: x86_64 + cores: + physical: 52 + total: 104 + frequency: 2.10 GHz + model: Intel(R) Xeon(R) Gold 6230R CPU @ 2.10GHz + gpus: + - memory: 11019.0 MB + name: NVIDIA GeForce RTX 2080 Ti + - memory: 11019.0 MB + name: NVIDIA GeForce RTX 2080 Ti + - memory: 11019.0 MB + name: NVIDIA GeForce RTX 2080 Ti + - memory: 11019.0 MB + name: NVIDIA GeForce RTX 2080 Ti + - memory: 11019.0 MB + name: NVIDIA GeForce RTX 2080 Ti + - memory: 11019.0 MB + name: NVIDIA GeForce RTX 2080 Ti + - memory: 11019.0 MB + name: NVIDIA GeForce RTX 2080 Ti + - memory: 11019.0 MB + name: NVIDIA GeForce RTX 2080 Ti + memory: + available: 177.76 GB + total: 187.56 GB + used: 8.43 GB + system: + node: charybdis + release: 4.15.0-167-generic + system: Linux + +Model device: cuda:0 +Allocated GPU memory prior to inference: 15445504 (14.73 MB) +Allocated GPU memory after to inference: 7505677824 (6.99 GB) +Max allocated GPU memory during inference: 7554420736 (7.04 GB) + Warming up with batch_size=128: 0%| | 0/10 [00:00 Date: Tue, 15 Feb 2022 11:01:38 +0000 Subject: [PATCH 16/60] Update har benchmark batch sizes --- .../benchmark/benchmark_cox3d.py | 13 ++++++------- .../activity_recognition/benchmark/benchmark_x3d.py | 11 +++++------ 2 files changed, 11 insertions(+), 13 deletions(-) diff --git a/projects/perception/activity_recognition/benchmark/benchmark_cox3d.py b/projects/perception/activity_recognition/benchmark/benchmark_cox3d.py index d99d5dc161..6ba2e292fa 100644 --- a/projects/perception/activity_recognition/benchmark/benchmark_cox3d.py +++ b/projects/perception/activity_recognition/benchmark/benchmark_cox3d.py @@ -45,7 +45,7 @@ def benchmark_cox3d(): # Max power of 2 # batch_size = { # RTX2080Ti # "xs": 128, - # "s": 128, + # "s": 64, # "m": 64, # "l": 8, # } @@ -55,7 +55,7 @@ def benchmark_cox3d(): # "m": 64, # "l": 8, # } - batch_size = { # CPU - larger batch sizes don't help + batch_size = { # CPU - larger batch sizes don't increase throughput "xs": 1, "s": 1, "m": 1, @@ -71,7 +71,9 @@ def benchmark_cox3d(): backbone=backbone, ) - sample = torch.randn(batch_size[backbone], *input_shape[backbone]) # (B, C, T, H, W) + 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])] @@ -94,10 +96,7 @@ def transfer_to_device_fn( assert isinstance(sample[0], Category) return [ - Category( - prediction=s.data, - confidence=s.confidence.to(device=device), - ) + Category(prediction=s.data, confidence=s.confidence.to(device=device),) for s in sample ] diff --git a/projects/perception/activity_recognition/benchmark/benchmark_x3d.py b/projects/perception/activity_recognition/benchmark/benchmark_x3d.py index 46abe78d50..e627d26b6a 100644 --- a/projects/perception/activity_recognition/benchmark/benchmark_x3d.py +++ b/projects/perception/activity_recognition/benchmark/benchmark_x3d.py @@ -54,7 +54,7 @@ def benchmark_x3d(): # "m": 8, # "l": 2, # } - batch_size = { # CPU - larger batch sizes don't help + batch_size = { # CPU - larger batch sizes don't increase throughput "xs": 1, "s": 1, "m": 1, @@ -71,7 +71,9 @@ def benchmark_x3d(): ) learner.model.eval() - sample = torch.randn(batch_size[backbone], *input_shape[backbone]) # (B, C, T, H, W) + 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])] @@ -94,10 +96,7 @@ def transfer_to_device_fn( assert isinstance(sample[0], Category) return [ - Category( - prediction=s.data, - confidence=s.confidence.to(device=device), - ) + Category(prediction=s.data, confidence=s.confidence.to(device=device),) for s in sample ] From d556d848d8f9d536c3b82e008b4bd58a0ad7818c Mon Sep 17 00:00:00 2001 From: LukasHedegaard Date: Fri, 18 Feb 2022 09:35:02 +0100 Subject: [PATCH 17/60] Fix install script env vars --- .../activity_recognition/benchmark/install_on_server.sh | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/projects/perception/activity_recognition/benchmark/install_on_server.sh b/projects/perception/activity_recognition/benchmark/install_on_server.sh index 3ddf58a47c..21b21ccdd2 100755 --- a/projects/perception/activity_recognition/benchmark/install_on_server.sh +++ b/projects/perception/activity_recognition/benchmark/install_on_server.sh @@ -3,7 +3,7 @@ conda create --name opendr python=3.8 -y conda activate opendr conda env config vars set OPENDR_HOME=$PWD -conda env config vars set PYTHONPATH=$OPENDR_HOME/src:$PYTHONPATH +conda env config vars set PYTHONPATH=$PWD/src:$PYTHONPATH # Reactivate env to let env vars take effect conda deactivate From b3f58273b1468e98faf18bafce056e9097e68d98 Mon Sep 17 00:00:00 2001 From: LukasHedegaard Date: Thu, 17 Feb 2022 10:15:35 +0100 Subject: [PATCH 18/60] Fix exception handling in kinetics --- .../perception/activity_recognition/datasets/kinetics.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/src/opendr/perception/activity_recognition/datasets/kinetics.py b/src/opendr/perception/activity_recognition/datasets/kinetics.py index cfa204753b..278552862e 100644 --- a/src/opendr/perception/activity_recognition/datasets/kinetics.py +++ b/src/opendr/perception/activity_recognition/datasets/kinetics.py @@ -32,11 +32,11 @@ try: import av -except ImportError: +except ModuleNotFoundError: try: import torchvision - except ImportError: - raise ImportError("Either pyav (`pip install av`) or torchvision must be installed for the Kinetics loader to work") + except ModuleNotFoundError: + raise ModuleNotFoundError("Either pyav (`pip install av`) or torchvision must be installed for the Kinetics loader to work") logger = getLogger(__file__) From 707acbf11acc49c4145bc86b4ee29e1216a39014 Mon Sep 17 00:00:00 2001 From: LukasHedegaard Date: Fri, 18 Feb 2022 11:04:47 +0100 Subject: [PATCH 19/60] Update Xavier har benchmark --- .../benchmark/benchmark_cox3d.py | 24 +- .../benchmark/benchmark_x3d.py | 25 +- .../results/benchmark_cox3d_Xavier.txt | 2982 ++++++----------- .../results/benchmark_x3d_Xavier.txt | 2793 +++++---------- 4 files changed, 1909 insertions(+), 3915 deletions(-) diff --git a/projects/perception/activity_recognition/benchmark/benchmark_cox3d.py b/projects/perception/activity_recognition/benchmark/benchmark_cox3d.py index 6ba2e292fa..f5600f45cc 100644 --- a/projects/perception/activity_recognition/benchmark/benchmark_cox3d.py +++ b/projects/perception/activity_recognition/benchmark/benchmark_cox3d.py @@ -49,20 +49,20 @@ def benchmark_cox3d(): # "m": 64, # "l": 8, # } - # batch_size = { # Xavier - # "xs": 128, - # "s": 128, - # "m": 64, - # "l": 8, - # } - batch_size = { # CPU - larger batch sizes don't increase throughput - "xs": 1, - "s": 1, - "m": 1, - "l": 1, + 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 ["xs", "s", "m", "l"]: + for backbone in ["s", "m", "l"]: print(f"==== Benchmarking CoX3DLearner ({backbone}) ====") learner = CoX3DLearner( diff --git a/projects/perception/activity_recognition/benchmark/benchmark_x3d.py b/projects/perception/activity_recognition/benchmark/benchmark_x3d.py index e627d26b6a..5d98b0ba21 100644 --- a/projects/perception/activity_recognition/benchmark/benchmark_x3d.py +++ b/projects/perception/activity_recognition/benchmark/benchmark_x3d.py @@ -48,18 +48,18 @@ def benchmark_x3d(): # "m": 8, # "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, + 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}) ====") @@ -109,11 +109,12 @@ def transfer_to_device_fn( 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) + results2 = benchmark(learner.model, sample, num_runs=num_runs, print_fn=print) print(yaml.dump({"learner.model.forward": results2})) diff --git a/projects/perception/activity_recognition/benchmark/results/benchmark_cox3d_Xavier.txt b/projects/perception/activity_recognition/benchmark/results/benchmark_cox3d_Xavier.txt index 68e19e9831..f2121a8853 100644 --- a/projects/perception/activity_recognition/benchmark/results/benchmark_cox3d_Xavier.txt +++ b/projects/perception/activity_recognition/benchmark/results/benchmark_cox3d_Xavier.txt @@ -7,7 +7,7 @@ WARNING:opendr.perception.activity_recognition.cox3d.algorithm.utils:Padding along the temporal dimension only affects the computation in `forward3d`. In `forward` it is omitted. ERROR:torch-benchmark:Unable to measure model params due to error: 'function' object has no attribute 'parameters' ERROR:torch-benchmark:Unable to measure model FLOPs due to error: 'list' object has no attribute 'shape' -==== Benchmarking CoX3DLearner (xs) ==== +==== Benchmarking CoX3DLearner (s) ==== == Benchmarking learner.infer == Machine info: cpu: @@ -19,720 +19,176 @@ Machine info: model: ARMv8 Processor rev 0 (v8l) gpus: null memory: - available: 25.27 GB + available: 26.98 GB total: 31.17 GB - used: 8.88 GB + used: 7.18 GB system: node: xavier release: 4.9.140-tegra system: Linux Model device: cuda:0 -Allocated GPU memory prior to inference: 15445504 (14.73 MB) -Allocated GPU memory after to inference: 7504768512 (6.99 GB) -Max allocated GPU memory during inference: 7553511424 (7.03 GB) - Warming up with batch_size=128: 0%| | 0/10 [00:00 + benchmark_cox3d() + File "benchmark_cox3d.py", line 113, in benchmark_cox3d + print_fn=print, + File "/home/maleci/.local/lib/python3.6/site-packages/pytorch_benchmark/benchmark.py", line 400, in benchmark + File "/home/maleci/.local/lib/python3.6/site-packages/pytorch_benchmark/benchmark.py", line 262, in measure_energy + meas.append(p_est.estimate_fn_power(test_fn)[0] / 1000) + File "/home/maleci/.local/lib/python3.6/site-packages/pytorch_benchmark/jetson_power.py", line 119, in estimate_fn_power + sleep(self.sampling_interval) +KeyboardInterrupt +Exception ignored in: +Traceback (most recent call last): + File "/usr/lib/python3.6/threading.py", line 1294, in _shutdown + t.join() + File "/usr/lib/python3.6/threading.py", line 1056, in join + self._wait_for_tstate_lock() + File "/usr/lib/python3.6/threading.py", line 1072, in _wait_for_tstate_lock +KeyboardInterrupt +Error in atexit._run_exitfuncs: diff --git a/projects/perception/activity_recognition/benchmark/results/benchmark_x3d_Xavier.txt b/projects/perception/activity_recognition/benchmark/results/benchmark_x3d_Xavier.txt index 72221bc4ae..0cad510d07 100644 --- a/projects/perception/activity_recognition/benchmark/results/benchmark_x3d_Xavier.txt +++ b/projects/perception/activity_recognition/benchmark/results/benchmark_x3d_Xavier.txt @@ -4,105 +4,25 @@ "The _functional_video module is deprecated. Please use the functional module instead." /home/maleci/.local/lib/python3.6/site-packages/torchvision-0.10.0-py3.6-linux-aarch64.egg/torchvision/transforms/_transforms_video.py:26: UserWarning: The _transforms_video module is deprecated. Please use the transforms module instead. "The _transforms_video module is deprecated. Please use the transforms module instead." -INFO:benchmark:==== Benchmarking X3DLearner (xs) ==== -INFO:benchmark:== Benchmarking learner.infer == -ERROR:pytorch-benchmark:Unable to measure model params due to error: 'function' object has no attribute 'parameters' -ERROR:pytorch-benchmark:Unable to measure model FLOPs due to error: 'list' object has no attribute 'shape' -Machine info: - cpu: - architecture: aarch64 - cores: - physical: 8 - total: 8 - frequency: 2.27 GHz - model: ARMv8 Processor rev 0 (v8l) - gpus: null - memory: - available: 25.35 GB - total: 31.17 GB - used: 8.80 GB - system: - node: xavier - release: 4.9.140-tegra - system: Linux - -Model device: cuda:0 -Allocated GPU memory prior to inference: 15445504 (14.73 MB) -Allocated GPU memory after to inference: 15445504 (14.73 MB) -Max allocated GPU memory during inference: 3897797632 (3.63 GB) - Warming up with batch_size=32: 0%| | 0/10 [00:00 Date: Mon, 21 Feb 2022 10:11:40 +0100 Subject: [PATCH 20/60] Add har results on TX2 --- .../benchmark/results/benchmark_cox3d_TX2.txt | 1981 +++++++++++++++ .../benchmark/results/benchmark_x3d_TX2.txt | 2164 +++++++++++++++++ 2 files changed, 4145 insertions(+) create mode 100644 projects/perception/activity_recognition/benchmark/results/benchmark_cox3d_TX2.txt create mode 100644 projects/perception/activity_recognition/benchmark/results/benchmark_x3d_TX2.txt diff --git a/projects/perception/activity_recognition/benchmark/results/benchmark_cox3d_TX2.txt b/projects/perception/activity_recognition/benchmark/results/benchmark_cox3d_TX2.txt new file mode 100644 index 0000000000..fa7ae99e7e --- /dev/null +++ b/projects/perception/activity_recognition/benchmark/results/benchmark_cox3d_TX2.txt @@ -0,0 +1,1981 @@ +WARNING:opendr.perception.activity_recognition.cox3d.algorithm.utils:Padding along the temporal dimension only affects the computation in `forward3d`. In `forward` it is omitted. +ERROR:torch-benchmark:Unable to measure model params due to error: 'function' object has no attribute 'parameters' +==== Benchmarking CoX3DLearner (s) ==== +== Benchmarking learner.infer == +Machine info: + cpu: + architecture: aarch64 + cores: + physical: 4 + total: 4 + frequency: 2.04 GHz + model: ARMv8 Processor rev 3 (v8l) + gpus: null + memory: + available: 5.42 GB + total: 7.67 GB + used: 2.69 GB + system: + node: tx2 + release: 4.9.140-tegra + system: Linux + +Model device: cuda:0 + Warming up with batch_size=1: 0%| | 0/1 [00:00 Date: Mon, 21 Feb 2022 10:20:36 +0100 Subject: [PATCH 21/60] Add batch size for tx2 in har benchmark scripts --- .../benchmark/benchmark_cox3d.py | 29 +++++++++++-------- .../benchmark/benchmark_x3d.py | 28 +++++++++++------- 2 files changed, 34 insertions(+), 23 deletions(-) diff --git a/projects/perception/activity_recognition/benchmark/benchmark_cox3d.py b/projects/perception/activity_recognition/benchmark/benchmark_cox3d.py index f5600f45cc..fb63294bac 100644 --- a/projects/perception/activity_recognition/benchmark/benchmark_cox3d.py +++ b/projects/perception/activity_recognition/benchmark/benchmark_cox3d.py @@ -31,7 +31,6 @@ def benchmark_cox3d(): temp_dir = "./projects/perception/activity_recognition/benchmark/tmp" - batch_size = 8 num_runs = 100 # As found in src/opendr/perception/activity_recognition/x3d/hparams @@ -49,18 +48,24 @@ def benchmark_cox3d(): # "m": 64, # "l": 8, # } - 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, + # 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}) ====") diff --git a/projects/perception/activity_recognition/benchmark/benchmark_x3d.py b/projects/perception/activity_recognition/benchmark/benchmark_x3d.py index 5d98b0ba21..5256cf308d 100644 --- a/projects/perception/activity_recognition/benchmark/benchmark_x3d.py +++ b/projects/perception/activity_recognition/benchmark/benchmark_x3d.py @@ -48,18 +48,24 @@ def benchmark_x3d(): # "m": 8, # "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, + # 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}) ====") From 76708ca925a772fccea0a38e79f525ddb35428d1 Mon Sep 17 00:00:00 2001 From: Illia Oleksiienko Date: Thu, 17 Feb 2022 13:41:32 +0000 Subject: [PATCH 22/60] Add 3D object detection benchmark --- .../object_detection_3d/benchmark/.gitignore | 1 + .../benchmark/benchmark.py | 109 +++++++++++ .../configs/pointpillars_car_xyres_16.proto | 173 +++++++++++++++++ .../pointpillars_ped_cycle_xyres_16.proto | 183 ++++++++++++++++++ .../configs/tanet_car_xyres_16.proto | 173 +++++++++++++++++ .../tanet_car_xyres_16_near_0.24.proto | 173 +++++++++++++++++ .../tanet_car_xyres_16_near_0.24_2.proto | 173 +++++++++++++++++ .../tanet_car_xyres_16_near_0.33.proto | 173 +++++++++++++++++ .../configs/tanet_car_xyres_16_near_0.5.proto | 173 +++++++++++++++++ .../configs/tanet_ped_cycle_xyres_16.proto | 183 ++++++++++++++++++ .../benchmark/media/000000.bin | Bin 0 -> 2010160 bytes .../dependencies.ini | 2 +- 12 files changed, 1515 insertions(+), 1 deletion(-) create mode 100644 projects/perception/object_detection_3d/benchmark/.gitignore create mode 100644 projects/perception/object_detection_3d/benchmark/benchmark.py create mode 100644 projects/perception/object_detection_3d/benchmark/configs/pointpillars_car_xyres_16.proto create mode 100644 projects/perception/object_detection_3d/benchmark/configs/pointpillars_ped_cycle_xyres_16.proto create mode 100644 projects/perception/object_detection_3d/benchmark/configs/tanet_car_xyres_16.proto create mode 100644 projects/perception/object_detection_3d/benchmark/configs/tanet_car_xyres_16_near_0.24.proto create mode 100644 projects/perception/object_detection_3d/benchmark/configs/tanet_car_xyres_16_near_0.24_2.proto create mode 100644 projects/perception/object_detection_3d/benchmark/configs/tanet_car_xyres_16_near_0.33.proto create mode 100644 projects/perception/object_detection_3d/benchmark/configs/tanet_car_xyres_16_near_0.5.proto create mode 100644 projects/perception/object_detection_3d/benchmark/configs/tanet_ped_cycle_xyres_16.proto create mode 100755 projects/perception/object_detection_3d/benchmark/media/000000.bin diff --git a/projects/perception/object_detection_3d/benchmark/.gitignore b/projects/perception/object_detection_3d/benchmark/.gitignore new file mode 100644 index 0000000000..965f0a706d --- /dev/null +++ b/projects/perception/object_detection_3d/benchmark/.gitignore @@ -0,0 +1 @@ +results.txt \ No newline at end of file diff --git a/projects/perception/object_detection_3d/benchmark/benchmark.py b/projects/perception/object_detection_3d/benchmark/benchmark.py new file mode 100644 index 0000000000..ec2aef01c5 --- /dev/null +++ b/projects/perception/object_detection_3d/benchmark/benchmark.py @@ -0,0 +1,109 @@ +# 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_detection(): + 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.txt"): + os.remove(root_dir + "/results.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(yaml.dump({"learner.infer": results1})) + print(f"Inner FPS =", inner_fps) + + with open(root_dir + "/results.txt", "a") as f: + print(f"==== Benchmarking VoxelObjectDetection3DLearner ({config}) ====", file=f) + print(f"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_detection() 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 0000000000000000000000000000000000000000..744999c1f708a680eef3632de9227a38b1c62d0b GIT binary patch literal 2010160 zcmYIR30RfY)<#4kL_|bpP-c)25fOu)cZMNxf}dk?!fT6hou`I=xH1HNTI2?*l`_lTv-z zY#YMM|2N5CNI9$ziqPQW58E3;vsUXbjwt7NvG2WSNR7Rv-#I}0KJBe!gMFB#5?0=h z-}ldX&!CU8S0+!^+RN@b8TQ|~pnn)#tx|O4f6^tl!w73`ldx+#|^c!HcV@6t3=q8 z=&z5djJvs{jbVIqJH;||562J9YGY_xWTzY*wnx9%sWfh0=Qf6?%N&&h$9D7k!A&pJ zv}#YnK$m`e^Ak1ZKsRN>^qpMK?#<0=rFR!)=YVyempW9DJ~W^A))MS6%GXL#aQr zUO!8>2Iu;#dT~TAtY;n8J1_3;giO-vo9IF+u){_^4~z5lDW zKAWfnMQp(NtpNU7)C0$omC?Df9$Snd^QR~}+x6UU_1rV6W5#GDF*S#9NYW+s?4x&- z-1T4Rlk&`1&qemPivNP+FQ~bwHu_Ii1eX2S)-|c~3RWmWV@->k^t(6zteQ?NR1SCh 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Mon Sep 17 00:00:00 2001 From: Illia Oleksiienko Date: Mon, 21 Feb 2022 15:17:45 +0000 Subject: [PATCH 23/60] Fix bounding box to str --- src/opendr/engine/target.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) 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, From fd9e5c025288269ebabf65efde0692389ba61011 Mon Sep 17 00:00:00 2001 From: Illia Oleksiienko Date: Mon, 21 Feb 2022 15:57:22 +0000 Subject: [PATCH 24/60] Add detection_3d and tracking_2d benchmarks --- .../benchmark/benchmark.py | 4 +- .../object_tracking_2d/benchmark/.gitignore | 1 + .../benchmark/benchmark_deep_sort.py | 129 ++++++++++++++++++ .../benchmark/benchmark_fair_mot.py | 101 ++++++++++++++ .../benchmark/media/000001.jpg | Bin 0 -> 413479 bytes .../builder/dataset_builder.py | 2 +- .../deep_sort/algorithm/deep_sort_tracker.py | 2 +- .../object_tracking_2d_deep_sort_learner.py | 10 ++ .../object_tracking_2d_fair_mot_learner.py | 9 ++ 9 files changed, 254 insertions(+), 4 deletions(-) create mode 100644 projects/perception/object_tracking_2d/benchmark/.gitignore create mode 100644 projects/perception/object_tracking_2d/benchmark/benchmark_deep_sort.py create mode 100644 projects/perception/object_tracking_2d/benchmark/benchmark_fair_mot.py create mode 100644 projects/perception/object_tracking_2d/benchmark/media/000001.jpg diff --git a/projects/perception/object_detection_3d/benchmark/benchmark.py b/projects/perception/object_detection_3d/benchmark/benchmark.py index ec2aef01c5..c10fc5a46b 100644 --- a/projects/perception/object_detection_3d/benchmark/benchmark.py +++ b/projects/perception/object_detection_3d/benchmark/benchmark.py @@ -89,12 +89,12 @@ def transfer_to_device_fn( 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})) - print(f"Inner FPS =", inner_fps) with open(root_dir + "/results.txt", "a") as f: print(f"==== Benchmarking VoxelObjectDetection3DLearner ({config}) ====", file=f) - print(f"Inner FPS =", inner_fps, file=f) + print("Inner FPS =", inner_fps, file=f) print(yaml.dump({"learner.infer": results1}), file=f) print("\n\n", file=f) diff --git a/projects/perception/object_tracking_2d/benchmark/.gitignore b/projects/perception/object_tracking_2d/benchmark/.gitignore new file mode 100644 index 0000000000..965f0a706d --- /dev/null +++ b/projects/perception/object_tracking_2d/benchmark/.gitignore @@ -0,0 +1 @@ +results.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..cf28b9c965 --- /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.engine.data import Image +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.txt"): + os.remove(root_dir + "/results.txt") + + for model_name in models: + print( + f"==== Benchmarking ObjectTracking2DFairMotLearner ({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.txt", "a") as f: + print( + f"==== Benchmarking ObjectTracking2DFairMotLearner ({model_name}) ====", + file=f, + ) + print(f"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..1e1c7a7f34 --- /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.txt"): + os.remove(root_dir + "/results.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.txt", "a") as f: + print(f"==== Benchmarking ObjectTracking2DFairMotLearner ({model_name}) ====", file=f) + # print(f"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 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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_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..1bd77f44c4 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..6febdf17b6 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) @@ -373,6 +376,7 @@ def infer(self, batch, frame_ids=None, img_size=(1088, 608)): results = [] + for image, frame_id in zip(batch, frame_ids): img0 = image.convert("channels_last", "bgr") # BGR @@ -385,6 +389,7 @@ 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 +420,10 @@ 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: From 182235ffffbd057b20a2854bd80d278f0b06faec Mon Sep 17 00:00:00 2001 From: LukasHedegaard Date: Thu, 17 Feb 2022 17:36:15 +0100 Subject: [PATCH 25/60] Fix imports not working in python 3.6 --- .../second_detector/builder/preprocess_builder.py | 2 +- .../voxel_object_detection_3d/second_detector/run.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) 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, ) From 7254a3a823cbc8859370623bc8cc76985be860f3 Mon Sep 17 00:00:00 2001 From: Illia Oleksiienko Date: Mon, 21 Feb 2022 16:58:30 +0000 Subject: [PATCH 26/60] Update file names --- .../object_detection_3d/benchmark/.gitignore | 2 +- .../benchmark/{benchmark.py => benchmark_voxel.py} | 10 +++++----- .../perception/object_tracking_2d/benchmark/.gitignore | 2 +- .../benchmark/benchmark_deep_sort.py | 10 +++++----- 4 files changed, 12 insertions(+), 12 deletions(-) rename projects/perception/object_detection_3d/benchmark/{benchmark.py => benchmark_voxel.py} (94%) diff --git a/projects/perception/object_detection_3d/benchmark/.gitignore b/projects/perception/object_detection_3d/benchmark/.gitignore index 965f0a706d..314f02b1bc 100644 --- a/projects/perception/object_detection_3d/benchmark/.gitignore +++ b/projects/perception/object_detection_3d/benchmark/.gitignore @@ -1 +1 @@ -results.txt \ No newline at end of file +*.txt \ No newline at end of file diff --git a/projects/perception/object_detection_3d/benchmark/benchmark.py b/projects/perception/object_detection_3d/benchmark/benchmark_voxel.py similarity index 94% rename from projects/perception/object_detection_3d/benchmark/benchmark.py rename to projects/perception/object_detection_3d/benchmark/benchmark_voxel.py index c10fc5a46b..44ecfd71c7 100644 --- a/projects/perception/object_detection_3d/benchmark/benchmark.py +++ b/projects/perception/object_detection_3d/benchmark/benchmark_voxel.py @@ -25,7 +25,7 @@ logger.setLevel("DEBUG") -def benchmark_detection(): +def benchmark_voxel(): root_dir = "./projects/perception/object_detection_3d/benchmark" temp_dir = root_dir + "/tmp" configs_dir = root_dir + "/configs" @@ -47,8 +47,8 @@ def benchmark_detection(): sample = dataset[0] samples = [dataset[0] for _ in range(batch_size)] - if os.path.exists(root_dir + "/results.txt"): - os.remove(root_dir + "/results.txt") + 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}) ====") @@ -92,7 +92,7 @@ def transfer_to_device_fn( print("Inner FPS =", inner_fps) print(yaml.dump({"learner.infer": results1})) - with open(root_dir + "/results.txt", "a") as f: + 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) @@ -106,4 +106,4 @@ def transfer_to_device_fn( if __name__ == "__main__": - benchmark_detection() + benchmark_voxel() diff --git a/projects/perception/object_tracking_2d/benchmark/.gitignore b/projects/perception/object_tracking_2d/benchmark/.gitignore index 965f0a706d..314f02b1bc 100644 --- a/projects/perception/object_tracking_2d/benchmark/.gitignore +++ b/projects/perception/object_tracking_2d/benchmark/.gitignore @@ -1 +1 @@ -results.txt \ No newline at end of file +*.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 index cf28b9c965..9a4fddb64a 100644 --- a/projects/perception/object_tracking_2d/benchmark/benchmark_deep_sort.py +++ b/projects/perception/object_tracking_2d/benchmark/benchmark_deep_sort.py @@ -65,12 +65,12 @@ def benchmark_fair_mot(): sample = next(data_generator) samples = [next(data_generator) for _ in range(batch_size)] - if os.path.exists(root_dir + "/results.txt"): - os.remove(root_dir + "/results.txt") + 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 ObjectTracking2DFairMotLearner ({model_name}) ====" + f"==== Benchmarking ObjectTracking2DDeepSortLearner ({model_name}) ====" ) learner = ObjectTracking2DDeepSortLearner( @@ -109,9 +109,9 @@ def transfer_to_device_fn( print("Inner FPS =", inner_fps) print(yaml.dump({"learner.infer": results1})) - with open(root_dir + "/results.txt", "a") as f: + with open(root_dir + "/results_deep_sort.txt", "a") as f: print( - f"==== Benchmarking ObjectTracking2DFairMotLearner ({model_name}) ====", + f"==== Benchmarking ObjectTracking2DDeepSortLearner ({model_name}) ====", file=f, ) print(f"Inner FPS =", inner_fps, file=f) From 1d193e75b59861dfe02455245e8e4bc872815c15 Mon Sep 17 00:00:00 2001 From: Illia Oleksiienko Date: Mon, 21 Feb 2022 17:04:12 +0000 Subject: [PATCH 27/60] Update far mot names --- .../object_tracking_2d/benchmark/benchmark_fair_mot.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/projects/perception/object_tracking_2d/benchmark/benchmark_fair_mot.py b/projects/perception/object_tracking_2d/benchmark/benchmark_fair_mot.py index 1e1c7a7f34..bb10a1d891 100644 --- a/projects/perception/object_tracking_2d/benchmark/benchmark_fair_mot.py +++ b/projects/perception/object_tracking_2d/benchmark/benchmark_fair_mot.py @@ -42,8 +42,8 @@ def benchmark_fair_mot(): sample = Image.open(media_dir + "/000001.jpg") samples = [sample for _ in range(batch_size)] - if os.path.exists(root_dir + "/results.txt"): - os.remove(root_dir + "/results.txt") + 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}) ====") @@ -84,7 +84,7 @@ def transfer_to_device_fn( print("Inner FPS =", inner_fps) print(yaml.dump({"learner.infer": results1})) - with open(root_dir + "/results.txt", "a") as f: + with open(root_dir + "/results_fair_mot.txt", "a") as f: print(f"==== Benchmarking ObjectTracking2DFairMotLearner ({model_name}) ====", file=f) # print(f"Inner FPS =", inner_fps, file=f) print(yaml.dump({"learner.infer": results1}), file=f) From fbff37521ba164bebb5007a89cdb06cdaa93504d Mon Sep 17 00:00:00 2001 From: Illia Oleksiienko Date: Mon, 21 Feb 2022 17:28:09 +0000 Subject: [PATCH 28/60] Add AB3DMOT benchmark --- .../benchmark/benchmark_fair_mot.py | 2 +- .../object_tracking_3d/benchmark/.gitignore | 1 + .../benchmark/benchmark_ab3dmot.py | 91 +++++++++++++++++++ .../object_tracking_3d_ab3dmot_learner.py | 11 +++ 4 files changed, 104 insertions(+), 1 deletion(-) create mode 100644 projects/perception/object_tracking_3d/benchmark/.gitignore create mode 100644 projects/perception/object_tracking_3d/benchmark/benchmark_ab3dmot.py diff --git a/projects/perception/object_tracking_2d/benchmark/benchmark_fair_mot.py b/projects/perception/object_tracking_2d/benchmark/benchmark_fair_mot.py index bb10a1d891..de0417ade4 100644 --- a/projects/perception/object_tracking_2d/benchmark/benchmark_fair_mot.py +++ b/projects/perception/object_tracking_2d/benchmark/benchmark_fair_mot.py @@ -86,7 +86,7 @@ def transfer_to_device_fn( with open(root_dir + "/results_fair_mot.txt", "a") as f: print(f"==== Benchmarking ObjectTracking2DFairMotLearner ({model_name}) ====", file=f) - # print(f"Inner FPS =", inner_fps, file=f) + print(f"Inner FPS =", inner_fps, file=f) print(yaml.dump({"learner.infer": results1}), file=f) print("\n\n", file=f) diff --git a/projects/perception/object_tracking_3d/benchmark/.gitignore b/projects/perception/object_tracking_3d/benchmark/.gitignore new file mode 100644 index 0000000000..314f02b1bc --- /dev/null +++ b/projects/perception/object_tracking_3d/benchmark/.gitignore @@ -0,0 +1 @@ +*.txt \ 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..f6b7dc489e --- /dev/null +++ b/projects/perception/object_tracking_3d/benchmark/benchmark_ab3dmot.py @@ -0,0 +1,91 @@ +# 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 +from opendr.engine.datasets import PointCloudsDatasetIterator + +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(f"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/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..ccc4c6b85a 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 @@ -48,6 +49,9 @@ def __init__( self.covariance_matrix = covariance_matrix self.process_uncertainty_matrix = process_uncertainty_matrix self.iou_threshold = iou_threshold + + self.infers_count = 0 + self.infers_time = 0 self.__create_model() @@ -126,8 +130,15 @@ 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] From 35f7a2a3aec5179d2570348daa953f2ed9e57105 Mon Sep 17 00:00:00 2001 From: Illia Oleksiienko Date: Mon, 21 Feb 2022 17:31:27 +0000 Subject: [PATCH 29/60] Update media tracking 3d --- 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1.534006 +152 13 Car 0 1 1.775673 345.382414 187.303718 429.203781 244.777187 1.422414 1.512803 3.707634 -6.224595 1.855775 20.386035 1.482367 +152 14 Car 0 2 -1.725986 642.929811 181.940903 724.300748 241.805234 1.365956 1.508586 3.485915 1.751786 1.610038 18.504256 -1.633961 +153 -1 DontCare -1 -1 -10.000000 354.930000 155.110000 558.490000 215.090000 -1000.000000 -1000.000000 -1000.000000 -10.000000 -1.000000 -1.000000 -1.000000 +153 0 Van 0 1 -1.574728 567.476659 168.430941 599.830393 203.859657 2.000000 1.823255 4.433886 -1.617146 1.781002 43.341040 -1.612283 +153 1 Cyclist 0 0 -1.582040 576.698660 172.091469 625.534894 273.527842 1.739063 0.824591 1.785241 -0.221780 1.739875 13.330103 -1.599646 +153 8 Van 0 1 2.071620 0.000000 177.812504 319.844246 367.694610 1.773438 1.711916 4.314977 -5.337001 1.852546 8.959424 1.546809 +153 9 Car 0 0 -1.933141 756.900536 167.630718 1086.145857 374.000000 1.596000 1.698089 3.562650 2.693079 1.583962 7.270567 -1.592798 +153 10 Car 0 2 -1.818856 680.294919 177.511028 842.313244 284.070033 1.524000 1.728591 3.894227 2.353367 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 From 7247461b0b82a7ba796050dedda7787dde867689 Mon Sep 17 00:00:00 2001 From: Negar Date: Tue, 22 Feb 2022 14:35:35 +0100 Subject: [PATCH 30/60] benchmark_stgcn added --- .../benchmark/benchmark_stgcn.py | 105 ++++++++++++++++++ 1 file changed, 105 insertions(+) create mode 100644 projects/perception/skeleton_based_action_recognition/benchmark/benchmark_stgcn.py 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..10d2263c53 --- /dev/null +++ b/projects/perception/skeleton_based_action_recognition/benchmark/benchmark_stgcn.py @@ -0,0 +1,105 @@ +# 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 numpy as np +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 = "./projects/perception/skeleton_based_action_recognition/benchmark/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 = 100 + num_runs = 100 + C = 3 + T = 300 + V = 25 + M = 2 + # seq = np.zeros((batch_size, C, T, V, M)) + seq = torch.randn(batch_size, C, T, V, M) + sample = seq[0] + samples = [seq[0] for _ in range(batch_size)] + + 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=sample, + 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(f"== Benchmarking learner.infer ==", file=f) + print(yaml.dump({"learner.infer": results1}), file=f) + print("\n\n", file=f) + print(f"== Benchmarking model directly ==", file=f) + results2 = benchmark(learner.model, sample, 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) + From 1871b12371caa11f590c9f18831f61bd98c52066 Mon Sep 17 00:00:00 2001 From: Negar Date: Tue, 22 Feb 2022 15:25:56 +0100 Subject: [PATCH 31/60] benchmark_stgcn added --- .../benchmark/benchmark_stgcn.py | 8 +++++--- 1 file changed, 5 insertions(+), 3 deletions(-) diff --git a/projects/perception/skeleton_based_action_recognition/benchmark/benchmark_stgcn.py b/projects/perception/skeleton_based_action_recognition/benchmark/benchmark_stgcn.py index 10d2263c53..1fc4d8afa3 100644 --- a/projects/perception/skeleton_based_action_recognition/benchmark/benchmark_stgcn.py +++ b/projects/perception/skeleton_based_action_recognition/benchmark/benchmark_stgcn.py @@ -23,6 +23,8 @@ import argparse from opendr.perception.skeleton_based_action_recognition import ProgressiveSpatioTemporalGCNLearner from opendr.perception.skeleton_based_action_recognition import SpatioTemporalGCNLearner +from opendr.engine.data import SkeletonSequence + logger = logging.getLogger("benchmark") @@ -62,9 +64,9 @@ def benchmark_stgcn(args): V = 25 M = 2 # seq = np.zeros((batch_size, C, T, V, M)) - seq = torch.randn(batch_size, C, T, V, M) - sample = seq[0] - samples = [seq[0] for _ in range(batch_size)] + data = torch.randn(batch_size, C, T, V, M) + sample = SkeletonSequence(data[0]) + samples = [SkeletonSequence(data[v]) for v in range(batch_size)] def get_device_fn(*args): nonlocal learner From 2a7a4aa42ada4edebd1ef4698c94a9f0ae8fc064 Mon Sep 17 00:00:00 2001 From: Negar Date: Tue, 22 Feb 2022 15:26:44 +0100 Subject: [PATCH 32/60] benchmark_stgcn added --- .../benchmark/benchmark_stgcn.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/projects/perception/skeleton_based_action_recognition/benchmark/benchmark_stgcn.py b/projects/perception/skeleton_based_action_recognition/benchmark/benchmark_stgcn.py index 1fc4d8afa3..4a119980d6 100644 --- a/projects/perception/skeleton_based_action_recognition/benchmark/benchmark_stgcn.py +++ b/projects/perception/skeleton_based_action_recognition/benchmark/benchmark_stgcn.py @@ -63,8 +63,8 @@ def benchmark_stgcn(args): T = 300 V = 25 M = 2 - # seq = np.zeros((batch_size, C, T, V, M)) - data = torch.randn(batch_size, C, T, V, M) + data = np.zeros((batch_size, C, T, V, M)) + # data = torch.randn(batch_size, C, T, V, M) sample = SkeletonSequence(data[0]) samples = [SkeletonSequence(data[v]) for v in range(batch_size)] From 28209a6834858582c7bb03ac4a15e207f1b2df75 Mon Sep 17 00:00:00 2001 From: Negar Date: Tue, 22 Feb 2022 15:38:15 +0100 Subject: [PATCH 33/60] benchmark_stgcn added --- .../benchmark/benchmark_stgcn.py | 7 +++++-- 1 file changed, 5 insertions(+), 2 deletions(-) diff --git a/projects/perception/skeleton_based_action_recognition/benchmark/benchmark_stgcn.py b/projects/perception/skeleton_based_action_recognition/benchmark/benchmark_stgcn.py index 4a119980d6..4b99f8ff2b 100644 --- a/projects/perception/skeleton_based_action_recognition/benchmark/benchmark_stgcn.py +++ b/projects/perception/skeleton_based_action_recognition/benchmark/benchmark_stgcn.py @@ -65,8 +65,11 @@ def benchmark_stgcn(args): M = 2 data = np.zeros((batch_size, C, T, V, M)) # data = torch.randn(batch_size, C, T, V, M) - sample = SkeletonSequence(data[0]) - samples = [SkeletonSequence(data[v]) for v in range(batch_size)] + sample = SkeletonSequence(data[0:1]) + sample = torch.from_numpy(sample.numpy()) + samples = SkeletonSequence(data) + samples = torch.from_numpy(samples.numpy()) + # samples = [SkeletonSequence(data[v]) for v in range(batch_size)] def get_device_fn(*args): nonlocal learner From 0d832fd6ac306d762d764cb3f4bf77fb136b4b45 Mon Sep 17 00:00:00 2001 From: Negar Date: Tue, 22 Feb 2022 15:44:30 +0100 Subject: [PATCH 34/60] benchmark_stgcn added --- .../benchmark/benchmark_stgcn.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/projects/perception/skeleton_based_action_recognition/benchmark/benchmark_stgcn.py b/projects/perception/skeleton_based_action_recognition/benchmark/benchmark_stgcn.py index 4b99f8ff2b..9a46de94f8 100644 --- a/projects/perception/skeleton_based_action_recognition/benchmark/benchmark_stgcn.py +++ b/projects/perception/skeleton_based_action_recognition/benchmark/benchmark_stgcn.py @@ -64,11 +64,11 @@ def benchmark_stgcn(args): V = 25 M = 2 data = np.zeros((batch_size, C, T, V, M)) + data = SkeletonSequence(data) + data = torch.from_numpy(data.numpy()) # data = torch.randn(batch_size, C, T, V, M) - sample = SkeletonSequence(data[0:1]) - sample = torch.from_numpy(sample.numpy()) - samples = SkeletonSequence(data) - samples = torch.from_numpy(samples.numpy()) + sample = [data[0:1]] + samples = [data[0:1] for _ in range(batch_size)] # samples = [SkeletonSequence(data[v]) for v in range(batch_size)] def get_device_fn(*args): From e49b8961a0da3d8cb57a49968f90408f4efb4fa1 Mon Sep 17 00:00:00 2001 From: Negar Date: Tue, 22 Feb 2022 16:03:24 +0100 Subject: [PATCH 35/60] benchmark_stgcn added --- .../benchmark/benchmark_stgcn.py | 6 ++---- 1 file changed, 2 insertions(+), 4 deletions(-) diff --git a/projects/perception/skeleton_based_action_recognition/benchmark/benchmark_stgcn.py b/projects/perception/skeleton_based_action_recognition/benchmark/benchmark_stgcn.py index 9a46de94f8..2d1d264770 100644 --- a/projects/perception/skeleton_based_action_recognition/benchmark/benchmark_stgcn.py +++ b/projects/perception/skeleton_based_action_recognition/benchmark/benchmark_stgcn.py @@ -63,10 +63,8 @@ def benchmark_stgcn(args): T = 300 V = 25 M = 2 - data = np.zeros((batch_size, C, T, V, M)) - data = SkeletonSequence(data) - data = torch.from_numpy(data.numpy()) - # data = torch.randn(batch_size, C, T, V, M) + # data = np.zeros((batch_size, C, T, V, M)) + data = torch.randn(batch_size, C, T, V, M) sample = [data[0:1]] samples = [data[0:1] for _ in range(batch_size)] # samples = [SkeletonSequence(data[v]) for v in range(batch_size)] From 96a7b2a2eb24fdbbfee331079238ecb66b219b68 Mon Sep 17 00:00:00 2001 From: Negar Date: Tue, 22 Feb 2022 16:09:58 +0100 Subject: [PATCH 36/60] benchmark_stgcn added --- .../benchmark/benchmark_stgcn.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/projects/perception/skeleton_based_action_recognition/benchmark/benchmark_stgcn.py b/projects/perception/skeleton_based_action_recognition/benchmark/benchmark_stgcn.py index 2d1d264770..9dc61d61a5 100644 --- a/projects/perception/skeleton_based_action_recognition/benchmark/benchmark_stgcn.py +++ b/projects/perception/skeleton_based_action_recognition/benchmark/benchmark_stgcn.py @@ -65,9 +65,9 @@ def benchmark_stgcn(args): M = 2 # data = np.zeros((batch_size, C, T, V, M)) data = torch.randn(batch_size, C, T, V, M) - sample = [data[0:1]] - samples = [data[0:1] for _ in range(batch_size)] - # samples = [SkeletonSequence(data[v]) for v in range(batch_size)] + sample = data[0:1] # [data[0:1]] + # samples = [data[0:1] for _ in range(batch_size)] + samples = data def get_device_fn(*args): nonlocal learner From 2760c278fb22cc6f180a085cd02fdd62af16e3de Mon Sep 17 00:00:00 2001 From: Negar Date: Tue, 22 Feb 2022 16:10:33 +0100 Subject: [PATCH 37/60] benchmark_stgcn added --- .../benchmark/benchmark_stgcn.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/projects/perception/skeleton_based_action_recognition/benchmark/benchmark_stgcn.py b/projects/perception/skeleton_based_action_recognition/benchmark/benchmark_stgcn.py index 9dc61d61a5..26379b594a 100644 --- a/projects/perception/skeleton_based_action_recognition/benchmark/benchmark_stgcn.py +++ b/projects/perception/skeleton_based_action_recognition/benchmark/benchmark_stgcn.py @@ -65,7 +65,7 @@ def benchmark_stgcn(args): M = 2 # data = np.zeros((batch_size, C, T, V, M)) data = torch.randn(batch_size, C, T, V, M) - sample = data[0:1] # [data[0:1]] + sample = None # data[0:1] # [data[0:1]] # samples = [data[0:1] for _ in range(batch_size)] samples = data From d362abc203631d9a5e94823df5a0ee811212c002 Mon Sep 17 00:00:00 2001 From: Negar Date: Tue, 22 Feb 2022 16:15:37 +0100 Subject: [PATCH 38/60] benchmark_stgcn modified --- .../benchmark/benchmark_stgcn.py | 7 ++----- 1 file changed, 2 insertions(+), 5 deletions(-) diff --git a/projects/perception/skeleton_based_action_recognition/benchmark/benchmark_stgcn.py b/projects/perception/skeleton_based_action_recognition/benchmark/benchmark_stgcn.py index 26379b594a..1b9697a32f 100644 --- a/projects/perception/skeleton_based_action_recognition/benchmark/benchmark_stgcn.py +++ b/projects/perception/skeleton_based_action_recognition/benchmark/benchmark_stgcn.py @@ -63,10 +63,7 @@ def benchmark_stgcn(args): T = 300 V = 25 M = 2 - # data = np.zeros((batch_size, C, T, V, M)) data = torch.randn(batch_size, C, T, V, M) - sample = None # data[0:1] # [data[0:1]] - # samples = [data[0:1] for _ in range(batch_size)] samples = data def get_device_fn(*args): @@ -79,7 +76,7 @@ def transfer_to_device_fn(sample, device,): print("== Benchmarking learner.infer ==") results1 = benchmark(model=learner.infer, sample=samples, - sample_with_batch_size1=sample, + sample_with_batch_size1=None, num_runs=num_runs, get_device_fn=get_device_fn, transfer_to_device_fn=transfer_to_device_fn, @@ -91,7 +88,7 @@ def transfer_to_device_fn(sample, device,): print(yaml.dump({"learner.infer": results1}), file=f) print("\n\n", file=f) print(f"== Benchmarking model directly ==", file=f) - results2 = benchmark(learner.model, sample, num_runs=num_runs, print_fn=print) + results2 = benchmark(learner.model, data, num_runs=num_runs, print_fn=print) print(yaml.dump({"learner.model.forward": results2})) From 04d2b04d59c8df20c4378fab1fa09aea0fac3af0 Mon Sep 17 00:00:00 2001 From: Negar Date: Tue, 22 Feb 2022 16:37:30 +0100 Subject: [PATCH 39/60] benchmark_stgcn modified --- .../benchmark/benchmark_stgcn.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/projects/perception/skeleton_based_action_recognition/benchmark/benchmark_stgcn.py b/projects/perception/skeleton_based_action_recognition/benchmark/benchmark_stgcn.py index 1b9697a32f..3a509e79ee 100644 --- a/projects/perception/skeleton_based_action_recognition/benchmark/benchmark_stgcn.py +++ b/projects/perception/skeleton_based_action_recognition/benchmark/benchmark_stgcn.py @@ -33,7 +33,7 @@ def benchmark_stgcn(args): - results_dir = "./projects/perception/skeleton_based_action_recognition/benchmark/results" + results_dir = "./results" if not os.path.exists(results_dir): os.makedirs(results_dir) device = args.device From 3f65ed1478db83c4dfd312ded853903f1b067ef7 Mon Sep 17 00:00:00 2001 From: Negar Date: Tue, 22 Feb 2022 16:47:00 +0100 Subject: [PATCH 40/60] benchmark_stgcn modified --- .../benchmark/benchmark_stgcn.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/projects/perception/skeleton_based_action_recognition/benchmark/benchmark_stgcn.py b/projects/perception/skeleton_based_action_recognition/benchmark/benchmark_stgcn.py index 3a509e79ee..3c08ba4d10 100644 --- a/projects/perception/skeleton_based_action_recognition/benchmark/benchmark_stgcn.py +++ b/projects/perception/skeleton_based_action_recognition/benchmark/benchmark_stgcn.py @@ -57,7 +57,7 @@ def benchmark_stgcn(args): if args.device == 'cuda': learner.model.cuda() - batch_size = 100 + batch_size = 1 num_runs = 100 C = 3 T = 300 From 3d0c59c05d741986796bd34313737d3791e78584 Mon Sep 17 00:00:00 2001 From: Negar Date: Tue, 22 Feb 2022 19:23:41 +0100 Subject: [PATCH 41/60] benchmark_stgcn modified --- .../benchmark/benchmark_pstbln.py | 105 ++++++++++++++++++ .../benchmark/benchmark_stgcn.py | 2 - 2 files changed, 105 insertions(+), 2 deletions(-) create mode 100644 projects/perception/facial_expression_recognition/landmark_based_facial_expression_recognition/benchmark/benchmark_pstbln.py 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..a358afd5cd --- /dev/null +++ b/projects/perception/facial_expression_recognition/landmark_based_facial_expression_recognition/benchmark/benchmark_pstbln.py @@ -0,0 +1,105 @@ +# 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 + 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 = 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(f"== Benchmarking learner.infer ==", file=f) + print(yaml.dump({"learner.infer": results1}), file=f) + print("\n\n", file=f) + print(f"== 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:2") + 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() + 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 + + benchmark_pstbln(args) + diff --git a/projects/perception/skeleton_based_action_recognition/benchmark/benchmark_stgcn.py b/projects/perception/skeleton_based_action_recognition/benchmark/benchmark_stgcn.py index 3c08ba4d10..77ce16f334 100644 --- a/projects/perception/skeleton_based_action_recognition/benchmark/benchmark_stgcn.py +++ b/projects/perception/skeleton_based_action_recognition/benchmark/benchmark_stgcn.py @@ -14,7 +14,6 @@ import os import torch -import numpy as np import yaml from pytorch_benchmark import benchmark import logging @@ -23,7 +22,6 @@ import argparse from opendr.perception.skeleton_based_action_recognition import ProgressiveSpatioTemporalGCNLearner from opendr.perception.skeleton_based_action_recognition import SpatioTemporalGCNLearner -from opendr.engine.data import SkeletonSequence From 2f9352bd336b241a00beddbb663609b9e4bd001d Mon Sep 17 00:00:00 2001 From: Negar Date: Tue, 22 Feb 2022 19:27:18 +0100 Subject: [PATCH 42/60] benchmark_stbln modified --- .../benchmark/benchmark_pstbln.py | 29 +++++++++---------- 1 file changed, 14 insertions(+), 15 deletions(-) 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 index a358afd5cd..aac89ac8eb 100644 --- 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 @@ -33,6 +33,18 @@ def benchmark_pstbln(args): 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, @@ -46,8 +58,8 @@ def benchmark_pstbln(args): batch_size = 1 num_runs = 100 C = 3 - T = 300 - V = 25 + T = num_frames + V = num_point M = 2 data = torch.randn(batch_size, C, T, V, M) samples = data @@ -87,19 +99,6 @@ def transfer_to_device_fn(sample, device,): help='action detection method') args = parser.parse_args() - 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 benchmark_pstbln(args) From de70a0722decad05dd632682c3d35b2b3ef76c25 Mon Sep 17 00:00:00 2001 From: Negar Date: Tue, 22 Feb 2022 19:28:53 +0100 Subject: [PATCH 43/60] benchmark_stbln modified --- .../benchmark/benchmark_pstbln.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) 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 index aac89ac8eb..fc050c01ba 100644 --- 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 @@ -57,10 +57,10 @@ def benchmark_pstbln(args): batch_size = 1 num_runs = 100 - C = 3 + C = 2 T = num_frames V = num_point - M = 2 + M = 1 data = torch.randn(batch_size, C, T, V, M) samples = data @@ -92,7 +92,7 @@ def transfer_to_device_fn(sample, device,): if __name__ == '__main__': parser = argparse.ArgumentParser() - parser.add_argument("--device", help="Device to use (cpu, cuda)", type=str, default="cuda:2") + 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', From 10c021e6d4e1915479bc8cfbcef5a17a6bd57e49 Mon Sep 17 00:00:00 2001 From: Negar Date: Tue, 22 Feb 2022 19:31:06 +0100 Subject: [PATCH 44/60] benchmark_stbln modified --- .../benchmark/benchmark_stgcn.py | 2 -- .../progressive_spatio_temporal_bln_learner.py | 2 +- 2 files changed, 1 insertion(+), 3 deletions(-) diff --git a/projects/perception/skeleton_based_action_recognition/benchmark/benchmark_stgcn.py b/projects/perception/skeleton_based_action_recognition/benchmark/benchmark_stgcn.py index 77ce16f334..b9f7d93890 100644 --- a/projects/perception/skeleton_based_action_recognition/benchmark/benchmark_stgcn.py +++ b/projects/perception/skeleton_based_action_recognition/benchmark/benchmark_stgcn.py @@ -23,8 +23,6 @@ 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") 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 0941f362c4..dd345d6b8b 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=100): """ This method performs inference on the batch provided. :param facial_landmarks_batch: Object that holds a batch of data to run inference on. From 547098f3e20eae928dbe4f565866d4513bcd2e74 Mon Sep 17 00:00:00 2001 From: Negar Date: Tue, 22 Feb 2022 19:33:38 +0100 Subject: [PATCH 45/60] benchmark_stbln modified --- .../progressive_spatio_temporal_bln_learner.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) 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 dd345d6b8b..025e23677c 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 @@ -55,7 +55,7 @@ class ProgressiveSpatioTemporalBLNLearner(Learner): def __init__(self, lr=1e-1, batch_size=128, optimizer_name='sgd', lr_schedule='', checkpoint_after_iter=0, checkpoint_load_iter=0, temp_path='temp', device='cuda', num_workers=32, epochs=400, experiment_name='pstbln_casia', - device_indices=[0], val_batch_size=128, drop_after_epoch=[400], + device_indices=[2], val_batch_size=128, drop_after_epoch=[400], start_epoch=0, dataset_name='CASIA', num_class=6, num_point=309, num_person=1, in_channels=2, blocksize=5, num_blocks=100, num_layers=10, topology=[], layer_threshold=1e-4, block_threshold=1e-4): From 4fd2ec41e008b684e004ac58949f732eea6986e0 Mon Sep 17 00:00:00 2001 From: Negar Date: Tue, 22 Feb 2022 19:34:50 +0100 Subject: [PATCH 46/60] benchmark_stbln modified --- .../benchmark/benchmark_pstbln.py | 2 +- .../progressive_spatio_temporal_bln_learner.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) 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 index fc050c01ba..ad489e8eca 100644 --- 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 @@ -92,7 +92,7 @@ def transfer_to_device_fn(sample, device,): if __name__ == '__main__': parser = argparse.ArgumentParser() - parser.add_argument("--device", help="Device to use (cpu, cuda)", type=str, default="cuda") + parser.add_argument("--device", help="Device to use (cpu, cuda)", type=str, default="cpu") parser.add_argument('--method', type=str, default='pstbln_afew', help='action detection method') parser.add_argument('--dataset_name', type=str, default='afew', 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 025e23677c..dd345d6b8b 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 @@ -55,7 +55,7 @@ class ProgressiveSpatioTemporalBLNLearner(Learner): def __init__(self, lr=1e-1, batch_size=128, optimizer_name='sgd', lr_schedule='', checkpoint_after_iter=0, checkpoint_load_iter=0, temp_path='temp', device='cuda', num_workers=32, epochs=400, experiment_name='pstbln_casia', - device_indices=[2], val_batch_size=128, drop_after_epoch=[400], + device_indices=[0], val_batch_size=128, drop_after_epoch=[400], start_epoch=0, dataset_name='CASIA', num_class=6, num_point=309, num_person=1, in_channels=2, blocksize=5, num_blocks=100, num_layers=10, topology=[], layer_threshold=1e-4, block_threshold=1e-4): From 5b72d5f4006f687224a8801fbb73cd7d347e8597 Mon Sep 17 00:00:00 2001 From: Negar Date: Tue, 22 Feb 2022 20:35:18 +0100 Subject: [PATCH 47/60] mcdo is modified --- .../progressive_spatio_temporal_bln_learner.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) 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 dd345d6b8b..57875979fd 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=False, 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. From 779d60f3a39d62219993cd2d648f43fd9d598cda Mon Sep 17 00:00:00 2001 From: Negar Date: Tue, 22 Feb 2022 20:44:15 +0100 Subject: [PATCH 48/60] mcdo is modified --- .../benchmark/benchmark_pstbln.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) 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 index ad489e8eca..5b474d347d 100644 --- 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 @@ -72,7 +72,7 @@ def transfer_to_device_fn(sample, device,): return sample print("== Benchmarking learner.infer ==") - results1 = benchmark(model=learner.infer, + results1 = benchmark(model=learner.infer(monte_carlo_dropout=False), sample=samples, sample_with_batch_size1=None, num_runs=num_runs, From 5f0138937d401bd71e6c478b400b024ea31367cf Mon Sep 17 00:00:00 2001 From: Negar Date: Tue, 22 Feb 2022 20:46:05 +0100 Subject: [PATCH 49/60] mcdo is modified --- .../benchmark/benchmark_pstbln.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) 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 index 5b474d347d..192426d8c9 100644 --- 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 @@ -72,7 +72,7 @@ def transfer_to_device_fn(sample, device,): return sample print("== Benchmarking learner.infer ==") - results1 = benchmark(model=learner.infer(monte_carlo_dropout=False), + results1 = benchmark(model=learner.infer(samples, monte_carlo_dropout=False), sample=samples, sample_with_batch_size1=None, num_runs=num_runs, From 092d6fb40c97b862eefd465b25f87e98c25004a3 Mon Sep 17 00:00:00 2001 From: Negar Date: Tue, 22 Feb 2022 20:47:07 +0100 Subject: [PATCH 50/60] mcdo is modified --- .../benchmark/benchmark_pstbln.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) 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 index 192426d8c9..ad489e8eca 100644 --- 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 @@ -72,7 +72,7 @@ def transfer_to_device_fn(sample, device,): return sample print("== Benchmarking learner.infer ==") - results1 = benchmark(model=learner.infer(samples, monte_carlo_dropout=False), + results1 = benchmark(model=learner.infer, sample=samples, sample_with_batch_size1=None, num_runs=num_runs, From 6a32fa9d1983b92510b050515e030699aabd8222 Mon Sep 17 00:00:00 2001 From: Negar Date: Tue, 22 Feb 2022 20:57:34 +0100 Subject: [PATCH 51/60] mcdo is modified --- .../benchmark/benchmark_pstbln.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) 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 index ad489e8eca..fc050c01ba 100644 --- 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 @@ -92,7 +92,7 @@ def transfer_to_device_fn(sample, device,): if __name__ == '__main__': parser = argparse.ArgumentParser() - parser.add_argument("--device", help="Device to use (cpu, cuda)", type=str, default="cpu") + 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', From 9b74859a6bd624935de7a967de7ad84f075fff8a Mon Sep 17 00:00:00 2001 From: Negar Date: Tue, 22 Feb 2022 21:22:44 +0100 Subject: [PATCH 52/60] mcdo is modified --- .../benchmark/benchmark_pstbln.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) 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 index fc050c01ba..f512594d3b 100644 --- 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 @@ -56,7 +56,7 @@ def benchmark_pstbln(args): learner.model.cuda() batch_size = 1 - num_runs = 100 + num_runs = 1 C = 2 T = num_frames V = num_point From d62bb8933f49e199b38811e9690a6bdf1a41f9c2 Mon Sep 17 00:00:00 2001 From: LukasHedegaard Date: Mon, 28 Feb 2022 16:35:47 +0100 Subject: [PATCH 53/60] Fix style in kinetics --- .../activity_recognition/datasets/kinetics.py | 56 +++++++++++-------- 1 file changed, 33 insertions(+), 23 deletions(-) diff --git a/src/opendr/perception/activity_recognition/datasets/kinetics.py b/src/opendr/perception/activity_recognition/datasets/kinetics.py index 278552862e..bbf8df91b4 100644 --- a/src/opendr/perception/activity_recognition/datasets/kinetics.py +++ b/src/opendr/perception/activity_recognition/datasets/kinetics.py @@ -26,7 +26,9 @@ 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 @@ -36,7 +38,10 @@ try: import torchvision except ModuleNotFoundError: - raise ModuleNotFoundError("Either pyav (`pip install av`) or torchvision must be installed for the Kinetics loader to work") + raise ModuleNotFoundError( + "Either pyav (`pip install av`) or torchvision " + "must be installed for the Kinetics loader to work" + ) logger = getLogger(__file__) @@ -67,7 +72,7 @@ def __init__( split="train", video_transform=None, use_caching=False, - decoder_backend="pyav" + decoder_backend="pyav", ): """ Kinetics dataset @@ -83,9 +88,11 @@ 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". + 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) @@ -114,11 +121,15 @@ def __init__( 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, @@ -128,11 +139,7 @@ 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." @@ -155,7 +162,9 @@ 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=self.decoder_backend, + self.file_paths[idx], + multi_thread_decode=False, + backend=self.decoder_backend, ) except Exception as e: logger.info( @@ -235,7 +244,7 @@ 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)) @@ -244,7 +253,7 @@ 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) @@ -267,7 +276,7 @@ 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)) @@ -276,7 +285,7 @@ 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) @@ -297,7 +306,10 @@ def map_segments(segment_arr: List[float]) -> Tuple[str, str]: if "label" in annotation["annotations"] and annotation["annotations"]["label"]: label = annotation["annotations"]["label"] p = ( - root_path / annotation["subset"] / label / f"{key}_{start}_{stop}.{extention}" + root_path + / annotation["subset"] + / label + / f"{key}_{start}_{stop}.{extention}" ).resolve() else: p = ( @@ -309,9 +321,7 @@ 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() From 637e5a9b0dd04a516ba25b3e02924268dd817ac1 Mon Sep 17 00:00:00 2001 From: LukasHedegaard Date: Mon, 28 Feb 2022 16:38:53 +0100 Subject: [PATCH 54/60] Fix W503 --- .../perception/activity_recognition/datasets/kinetics.py | 5 +---- 1 file changed, 1 insertion(+), 4 deletions(-) diff --git a/src/opendr/perception/activity_recognition/datasets/kinetics.py b/src/opendr/perception/activity_recognition/datasets/kinetics.py index bbf8df91b4..1996714644 100644 --- a/src/opendr/perception/activity_recognition/datasets/kinetics.py +++ b/src/opendr/perception/activity_recognition/datasets/kinetics.py @@ -306,10 +306,7 @@ def map_segments(segment_arr: List[float]) -> Tuple[str, str]: if "label" in annotation["annotations"] and annotation["annotations"]["label"]: label = annotation["annotations"]["label"] p = ( - root_path - / annotation["subset"] - / label - / f"{key}_{start}_{stop}.{extention}" + root_path / annotation["subset"] / label / f"{key}_{start}_{stop}.{extention}" ).resolve() else: p = ( From 41f497d16bfdbdccb83a422cd1dca2066583ba54 Mon Sep 17 00:00:00 2001 From: Negar Date: Tue, 1 Mar 2022 10:32:44 +0100 Subject: [PATCH 55/60] code style fixed --- .../benchmark/benchmark_pstbln.py | 3 +-- .../benchmark/benchmark_stgcn.py | 11 +++++------ 2 files changed, 6 insertions(+), 8 deletions(-) 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 index f512594d3b..29dc11fb19 100644 --- 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 @@ -100,5 +100,4 @@ def transfer_to_device_fn(sample, device,): args = parser.parse_args() - benchmark_pstbln(args) - + benchmark_pstbln(args) \ No newline at end of file diff --git a/projects/perception/skeleton_based_action_recognition/benchmark/benchmark_stgcn.py b/projects/perception/skeleton_based_action_recognition/benchmark/benchmark_stgcn.py index b9f7d93890..d461ba0fe6 100644 --- a/projects/perception/skeleton_based_action_recognition/benchmark/benchmark_stgcn.py +++ b/projects/perception/skeleton_based_action_recognition/benchmark/benchmark_stgcn.py @@ -37,12 +37,12 @@ def benchmark_stgcn(args): 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') + 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') + 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', @@ -97,5 +97,4 @@ def transfer_to_device_fn(sample, device,): help='action detection method') args = parser.parse_args() - benchmark_stgcn(args) - + benchmark_stgcn(args) \ No newline at end of file From 2a72c213bfc1ba133f8d3117d94215a27f393d00 Mon Sep 17 00:00:00 2001 From: Negar Date: Mon, 7 Mar 2022 09:49:23 +0100 Subject: [PATCH 56/60] style fixed --- .../benchmark/benchmark_pstbln.py | 2 +- .../benchmark/benchmark_stgcn.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) 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 index 29dc11fb19..b0c4ada756 100644 --- 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 @@ -100,4 +100,4 @@ def transfer_to_device_fn(sample, device,): args = parser.parse_args() - benchmark_pstbln(args) \ No newline at end of file + benchmark_pstbln(args) diff --git a/projects/perception/skeleton_based_action_recognition/benchmark/benchmark_stgcn.py b/projects/perception/skeleton_based_action_recognition/benchmark/benchmark_stgcn.py index d461ba0fe6..1513c28efc 100644 --- a/projects/perception/skeleton_based_action_recognition/benchmark/benchmark_stgcn.py +++ b/projects/perception/skeleton_based_action_recognition/benchmark/benchmark_stgcn.py @@ -97,4 +97,4 @@ def transfer_to_device_fn(sample, device,): help='action detection method') args = parser.parse_args() - benchmark_stgcn(args) \ No newline at end of file + benchmark_stgcn(args) From e103ac2c9e15909bf7bdcf545e58914ce87c34a9 Mon Sep 17 00:00:00 2001 From: Illia Oleksiienko Date: Mon, 7 Mar 2022 17:42:22 +0100 Subject: [PATCH 57/60] Fix style errors --- .../benchmark/benchmark_pstbln.py | 4 ++-- .../object_detection_3d/benchmark/benchmark_voxel.py | 7 +++++-- .../object_tracking_2d/benchmark/benchmark_deep_sort.py | 6 +++--- .../object_tracking_2d/benchmark/benchmark_fair_mot.py | 4 ++-- .../object_tracking_3d/benchmark/benchmark_ab3dmot.py | 3 +-- .../benchmark/benchmark_stgcn.py | 4 ++-- .../deep_sort/object_tracking_2d_deep_sort_learner.py | 2 +- .../fair_mot/object_tracking_2d_fair_mot_learner.py | 1 - .../ab3dmot/object_tracking_3d_ab3dmot_learner.py | 6 +++--- 9 files changed, 19 insertions(+), 18 deletions(-) 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 index b0c4ada756..9828f50e06 100644 --- 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 @@ -82,10 +82,10 @@ def transfer_to_device_fn(sample, device,): print_fn=print, ) with open(results_dir + f"/benchmark_{args.method}_{device}.txt", "a") as f: - print(f"== Benchmarking learner.infer ==", file=f) + print("== Benchmarking learner.infer ==", file=f) print(yaml.dump({"learner.infer": results1}), file=f) print("\n\n", file=f) - print(f"== Benchmarking model directly ==", 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})) diff --git a/projects/perception/object_detection_3d/benchmark/benchmark_voxel.py b/projects/perception/object_detection_3d/benchmark/benchmark_voxel.py index 44ecfd71c7..05690550c4 100644 --- a/projects/perception/object_detection_3d/benchmark/benchmark_voxel.py +++ b/projects/perception/object_detection_3d/benchmark/benchmark_voxel.py @@ -59,7 +59,7 @@ def benchmark_voxel(): config_path, temp_path=temp_dir, ) - + if model_name is not None and not os.path.exists( models_dir + "/" + model_name ): @@ -87,7 +87,10 @@ def 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) + 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})) diff --git a/projects/perception/object_tracking_2d/benchmark/benchmark_deep_sort.py b/projects/perception/object_tracking_2d/benchmark/benchmark_deep_sort.py index 9a4fddb64a..5cd6ce8e83 100644 --- a/projects/perception/object_tracking_2d/benchmark/benchmark_deep_sort.py +++ b/projects/perception/object_tracking_2d/benchmark/benchmark_deep_sort.py @@ -17,7 +17,6 @@ import torch import logging from pytorch_benchmark import benchmark -from opendr.engine.data import Image 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 ( @@ -60,7 +59,8 @@ def benchmark_fair_mot(): "nano_mot20.train", ) }, - cycle=True) + cycle=True + ) sample = next(data_generator) samples = [next(data_generator) for _ in range(batch_size)] @@ -114,7 +114,7 @@ def transfer_to_device_fn( f"==== Benchmarking ObjectTracking2DDeepSortLearner ({model_name}) ====", file=f, ) - print(f"Inner FPS =", inner_fps, file=f) + print("Inner FPS =", inner_fps, file=f) print(yaml.dump({"learner.infer": results1}), file=f) print("\n\n", file=f) diff --git a/projects/perception/object_tracking_2d/benchmark/benchmark_fair_mot.py b/projects/perception/object_tracking_2d/benchmark/benchmark_fair_mot.py index de0417ade4..23f205fe79 100644 --- a/projects/perception/object_tracking_2d/benchmark/benchmark_fair_mot.py +++ b/projects/perception/object_tracking_2d/benchmark/benchmark_fair_mot.py @@ -51,7 +51,7 @@ def benchmark_fair_mot(): learner = ObjectTracking2DFairMotLearner( temp_path=temp_dir, ) - + if model_name is not None and not os.path.exists( models_dir + "/" + model_name ): @@ -86,7 +86,7 @@ def transfer_to_device_fn( with open(root_dir + "/results_fair_mot.txt", "a") as f: print(f"==== Benchmarking ObjectTracking2DFairMotLearner ({model_name}) ====", file=f) - print(f"Inner FPS =", inner_fps, file=f) + print("Inner FPS =", inner_fps, file=f) print(yaml.dump({"learner.infer": results1}), file=f) print("\n\n", file=f) diff --git a/projects/perception/object_tracking_3d/benchmark/benchmark_ab3dmot.py b/projects/perception/object_tracking_3d/benchmark/benchmark_ab3dmot.py index f6b7dc489e..f86caa334c 100644 --- a/projects/perception/object_tracking_3d/benchmark/benchmark_ab3dmot.py +++ b/projects/perception/object_tracking_3d/benchmark/benchmark_ab3dmot.py @@ -18,7 +18,6 @@ import logging from pytorch_benchmark import benchmark from opendr.perception.object_tracking_3d import ObjectTracking3DAb3dmotLearner, KittiTrackingDatasetIterator -from opendr.engine.datasets import PointCloudsDatasetIterator logger = logging.getLogger("benchmark") logging.basicConfig() @@ -76,7 +75,7 @@ def transfer_to_device_fn( with open(root_dir + "/results_ab3dmot.txt", "a") as f: print(f"==== Benchmarking ObjectTracking3DAb3dmotLearner ({model_name}) ====", file=f) - print(f"Inner FPS =", inner_fps, file=f) + print("Inner FPS =", inner_fps, file=f) print(yaml.dump({"learner.infer": results1}), file=f) print("\n\n", file=f) diff --git a/projects/perception/skeleton_based_action_recognition/benchmark/benchmark_stgcn.py b/projects/perception/skeleton_based_action_recognition/benchmark/benchmark_stgcn.py index 1513c28efc..ae43b60323 100644 --- a/projects/perception/skeleton_based_action_recognition/benchmark/benchmark_stgcn.py +++ b/projects/perception/skeleton_based_action_recognition/benchmark/benchmark_stgcn.py @@ -80,10 +80,10 @@ def transfer_to_device_fn(sample, device,): print_fn=print, ) with open(results_dir + f"/benchmark_{args.method}_{device}.txt", "a") as f: - print(f"== Benchmarking learner.infer ==", file=f) + print("== Benchmarking learner.infer ==", file=f) print(yaml.dump({"learner.infer": results1}), file=f) print("\n\n", file=f) - print(f"== Benchmarking model directly ==", 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})) 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 1bd77f44c4..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 @@ -285,7 +285,7 @@ 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) 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 6febdf17b6..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 @@ -376,7 +376,6 @@ def infer(self, batch, frame_ids=None, img_size=(1088, 608)): results = [] - for image, frame_id in zip(batch, frame_ids): img0 = image.convert("channels_last", "bgr") # BGR 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 ccc4c6b85a..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 @@ -49,7 +49,7 @@ def __init__( self.covariance_matrix = covariance_matrix self.process_uncertainty_matrix = process_uncertainty_matrix self.iou_threshold = iou_threshold - + self.infers_count = 0 self.infers_time = 0 @@ -130,12 +130,12 @@ 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 From 9f99a221caa332d8291e94a139bb3ebb401be8e6 Mon Sep 17 00:00:00 2001 From: Illia Oleksiienko Date: Mon, 7 Mar 2022 17:46:12 +0100 Subject: [PATCH 58/60] Fix kinetics unused torchvision --- src/opendr/perception/activity_recognition/datasets/kinetics.py | 1 + 1 file changed, 1 insertion(+) diff --git a/src/opendr/perception/activity_recognition/datasets/kinetics.py b/src/opendr/perception/activity_recognition/datasets/kinetics.py index 1996714644..3eab1ad19f 100644 --- a/src/opendr/perception/activity_recognition/datasets/kinetics.py +++ b/src/opendr/perception/activity_recognition/datasets/kinetics.py @@ -37,6 +37,7 @@ except ModuleNotFoundError: try: import torchvision + assert len(torchvision.__version__) > 0 except ModuleNotFoundError: raise ModuleNotFoundError( "Either pyav (`pip install av`) or torchvision " From 361becbe70c803a2c03f2164912aea7146f00be7 Mon Sep 17 00:00:00 2001 From: LukasHedegaard Date: Mon, 14 Mar 2022 14:15:35 +0100 Subject: [PATCH 59/60] Remove activity recognition results --- .../benchmark/results/benchmark_cox3d_CPU.txt | 621 ----- .../results/benchmark_cox3d_RTX2080Ti.txt | 2437 ----------------- .../benchmark/results/benchmark_cox3d_TX2.txt | 1981 -------------- .../results/benchmark_cox3d_Xavier.txt | 1437 ---------- .../benchmark/results/benchmark_x3d_CPU.txt | 340 --- .../results/benchmark_x3d_RTX2080Ti.txt | 1293 --------- .../benchmark/results/benchmark_x3d_TX2.txt | 2164 --------------- .../results/benchmark_x3d_Xavier.txt | 1154 -------- 8 files changed, 11427 deletions(-) delete mode 100644 projects/perception/activity_recognition/benchmark/results/benchmark_cox3d_CPU.txt delete mode 100644 projects/perception/activity_recognition/benchmark/results/benchmark_cox3d_RTX2080Ti.txt delete mode 100644 projects/perception/activity_recognition/benchmark/results/benchmark_cox3d_TX2.txt delete mode 100644 projects/perception/activity_recognition/benchmark/results/benchmark_cox3d_Xavier.txt delete mode 100644 projects/perception/activity_recognition/benchmark/results/benchmark_x3d_CPU.txt delete mode 100644 projects/perception/activity_recognition/benchmark/results/benchmark_x3d_RTX2080Ti.txt delete mode 100644 projects/perception/activity_recognition/benchmark/results/benchmark_x3d_TX2.txt delete mode 100644 projects/perception/activity_recognition/benchmark/results/benchmark_x3d_Xavier.txt diff --git a/projects/perception/activity_recognition/benchmark/results/benchmark_cox3d_CPU.txt b/projects/perception/activity_recognition/benchmark/results/benchmark_cox3d_CPU.txt deleted file mode 100644 index f5f7ece776..0000000000 --- a/projects/perception/activity_recognition/benchmark/results/benchmark_cox3d_CPU.txt +++ /dev/null @@ -1,621 +0,0 @@ -WARNING:opendr.perception.activity_recognition.cox3d.algorithm.utils:Padding along the temporal dimension only affects the computation in `forward3d`. In `forward` it is omitted. -ERROR:torch-benchmark:Unable to measure model params due to error: 'function' object has no attribute 'parameters' -ERROR:torch-benchmark:Unable to measure model FLOPs due to error: 'list' object has no attribute 'shape' -WARNING:torch-benchmark:Measurement of allocated memory is only available on CUDA devices -==== Benchmarking CoX3DLearner (xs) ==== -== Benchmarking learner.infer == -Machine info: - cpu: - architecture: x86_64 - cores: - physical: 6 - total: 12 - frequency: 2.60 GHz - model: Intel(R) Core(TM) i7-9750H CPU @ 2.60GHz - gpus: null - memory: - available: 4.84 GB - total: 16.00 GB - used: 8.69 GB - system: - node: d40049 - release: 21.2.0 - system: Darwin - -Model device: cpu - Warming up with batch_size=1: 0%| | 0/10 [00:00 - benchmark_cox3d() - File "benchmark_cox3d.py", line 113, in benchmark_cox3d - print_fn=print, - File "/home/maleci/.local/lib/python3.6/site-packages/pytorch_benchmark/benchmark.py", line 400, in benchmark - File "/home/maleci/.local/lib/python3.6/site-packages/pytorch_benchmark/benchmark.py", line 262, in measure_energy - meas.append(p_est.estimate_fn_power(test_fn)[0] / 1000) - File "/home/maleci/.local/lib/python3.6/site-packages/pytorch_benchmark/jetson_power.py", line 119, in estimate_fn_power - sleep(self.sampling_interval) -KeyboardInterrupt -Exception ignored in: -Traceback (most recent call last): - File "/usr/lib/python3.6/threading.py", line 1294, in _shutdown - t.join() - File "/usr/lib/python3.6/threading.py", line 1056, in join - self._wait_for_tstate_lock() - File "/usr/lib/python3.6/threading.py", line 1072, in _wait_for_tstate_lock -KeyboardInterrupt -Error in atexit._run_exitfuncs: - diff --git a/projects/perception/activity_recognition/benchmark/results/benchmark_x3d_CPU.txt b/projects/perception/activity_recognition/benchmark/results/benchmark_x3d_CPU.txt deleted file mode 100644 index c6f010f24e..0000000000 --- a/projects/perception/activity_recognition/benchmark/results/benchmark_x3d_CPU.txt +++ /dev/null @@ -1,340 +0,0 @@ -INFO:benchmark:==== Benchmarking X3DLearner (xs) ==== -INFO:benchmark:== Benchmarking learner.infer == -ERROR:torch-benchmark:Unable to measure model params due to error: 'function' object has no attribute 'parameters' -ERROR:torch-benchmark:Unable to measure model FLOPs due to error: 'list' object has no attribute 'shape' -WARNING:torch-benchmark:Measurement of allocated memory is only available on CUDA devices - Warming up with batch_size=1: 0%| | 0/10 [00:00 Date: Mon, 28 Mar 2022 14:52:36 +0200 Subject: [PATCH 60/60] Add av dependency back --- src/opendr/perception/activity_recognition/dependencies.ini | 1 + 1 file changed, 1 insertion(+) diff --git a/src/opendr/perception/activity_recognition/dependencies.ini b/src/opendr/perception/activity_recognition/dependencies.ini index 189a755ff0..ea5aaa01db 100644 --- a/src/opendr/perception/activity_recognition/dependencies.ini +++ b/src/opendr/perception/activity_recognition/dependencies.ini @@ -7,6 +7,7 @@ python=torch==1.9.0 onnx==1.8.0 onnxruntime==1.3.0 pytorch_lightning==1.2.3 + av==8.0.1 joblib>=1.0.1 pyyaml>=5.3 pandas>=1.2