Repository: uzh-rpg/dagr Branch: master Commit: 0b1d3170bb47 Files: 62 Total size: 263.1 KB Directory structure: gitextract_6_j6wcbc/ ├── .gitignore ├── LICENSE ├── config/ │ ├── dagr-l-dsec.yaml │ ├── dagr-l-ncaltech.yaml │ ├── dagr-m-dsec.yaml │ ├── dagr-n-dsec.yaml │ └── dagr-s-dsec.yaml ├── download_and_install_dependencies.sh ├── download_example_data.sh ├── install_env.sh ├── readme.md ├── scripts/ │ ├── check_dataset.py │ ├── count_flops.py │ ├── downsample_all_events.sh │ ├── downsample_events.py │ ├── run_test.py │ ├── run_test_interframe.py │ ├── train_dsec.py │ ├── train_ncaltech101.py │ └── visualize_detections.py ├── setup.py └── src/ └── dagr/ ├── asynchronous/ │ ├── __init__.py │ ├── asy_tools/ │ │ └── main.cu │ ├── base/ │ │ ├── __init__.py │ │ ├── base.py │ │ └── utils.py │ ├── batch_norm.py │ ├── cartesian.py │ ├── conv.py │ ├── evaluate_flops.py │ ├── flops/ │ │ ├── __init__.py │ │ └── conv.py │ ├── linear.py │ └── max_pool.py ├── data/ │ ├── augment.py │ ├── dsec_data.py │ ├── dsec_split.yaml │ ├── dsec_utils.py │ ├── ncaltech101_data.py │ └── utils.py ├── graph/ │ ├── ev_graph.cu │ ├── ev_graph.py │ ├── spiral.h │ └── utils.py ├── model/ │ ├── layers/ │ │ ├── components.py │ │ ├── conv.py │ │ ├── ev_tgn.py │ │ ├── pooling.py │ │ └── spline_conv.py │ ├── networks/ │ │ ├── dagr.py │ │ ├── ema.py │ │ ├── net.py │ │ └── net_img.py │ └── utils.py ├── utils/ │ ├── args.py │ ├── buffers.py │ ├── coco_eval.py │ ├── learning_rate_scheduler.py │ ├── logging.py │ └── testing.py └── visualization/ ├── bbox_viz.py └── event_viz.py ================================================ FILE CONTENTS ================================================ ================================================ FILE: .gitignore ================================================ *.so *.egg-info/ *.pyc build/ libs/ .idea/ ================================================ FILE: LICENSE ================================================ GNU GENERAL PUBLIC LICENSE Version 3, 29 June 2007 Copyright (C) 2007 Free Software Foundation, Inc. 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But first, please read . ================================================ FILE: config/dagr-l-dsec.yaml ================================================ dataset_directory: "/data/storage/daniel/aegnn/" output_directory: "/data/storage/daniel/aegnn/logs" task: detection dataset: dsec # network radius: 0.01 time_window_us: 1000000 max_neighbors: 16 n_nodes: 50000 batch_size: 64 activation: relu edge_attr_dim: 2 aggr: sum kernel_size: 5 pooling_aggr: max base_width: 0.5 after_pool_width: 1 net_stem_width: 1 yolo_stem_width: 1 num_scales: 2 # learning weight_decay: 0.00001 clip: 0.1 pooling_dim_at_output: 5x7 aug_trans: 0.1 aug_zoom: 1.5 aug_p_flip: 0.5 img_net: resnet18 l_r: 0.0002 tot_num_epochs: 801 ================================================ FILE: config/dagr-l-ncaltech.yaml ================================================ path: "/data/storage/daniel/aegnn" output_directory: "/data/storage/daniel/aegnn/logs" pooling_dim_at_output: 5x7 task: detection dataset: ncaltech101 # network radius: 0.01 time_window_us: 1000000 max_neighbors: 16 n_nodes: 50000 batch_size: 64 activation: relu edge_attr_dim: 2 aggr: sum kernel_size: 5 pooling_aggr: max base_width: 0.5 after_pool_width: 1 net_stem_width: 1 yolo_stem_width: 1 num_scales: 1 # learning weight_decay: 0.00001 clip: 0.1 aug_trans: 0.1 aug_p_flip: 0 aug_zoom: 1 l_r: 0.001 tot_num_epochs: 801 ================================================ FILE: config/dagr-m-dsec.yaml ================================================ dataset_directory: "/data/storage/daniel/aegnn/" output_directory: "/data/storage/daniel/aegnn/logs" task: detection dataset: dsec # network radius: 0.01 time_window_us: 1000000 max_neighbors: 16 n_nodes: 50000 batch_size: 64 activation: relu edge_attr_dim: 2 aggr: sum kernel_size: 5 pooling_aggr: max base_width: 0.5 after_pool_width: 1 net_stem_width: 0.75 yolo_stem_width: 0.75 num_scales: 2 # learning weight_decay: 0.00001 clip: 0.1 pooling_dim_at_output: 5x7 aug_trans: 0.1 aug_zoom: 1.5 aug_p_flip: 0.5 img_net: resnet18 l_r: 0.0002 tot_num_epochs: 801 ================================================ FILE: config/dagr-n-dsec.yaml ================================================ dataset_directory: "/data/storage/daniel/aegnn/" output_directory: "/data/storage/daniel/aegnn/logs" task: detection dataset: dsec # network radius: 0.01 time_window_us: 1000000 max_neighbors: 16 n_nodes: 50000 batch_size: 64 activation: relu edge_attr_dim: 2 aggr: sum kernel_size: 5 pooling_aggr: max base_width: 0.5 after_pool_width: 1 net_stem_width: 0.25 yolo_stem_width: 0.25 num_scales: 2 # learning weight_decay: 0.00001 clip: 0.1 pooling_dim_at_output: 5x7 aug_trans: 0.1 aug_zoom: 1.5 aug_p_flip: 0.5 img_net: resnet18 l_r: 0.0002 tot_num_epochs: 801 ================================================ FILE: config/dagr-s-dsec.yaml ================================================ dataset_directory: "/data/storage/daniel/aegnn/" output_directory: "/data/storage/daniel/aegnn/logs" task: detection dataset: dsec # network radius: 0.01 time_window_us: 1000000 max_neighbors: 16 n_nodes: 50000 batch_size: 64 activation: relu edge_attr_dim: 2 aggr: sum kernel_size: 5 pooling_aggr: max base_width: 0.5 after_pool_width: 1 net_stem_width: 0.5 yolo_stem_width: 0.5 num_scales: 2 # learning weight_decay: 0.00001 clip: 0.1 pooling_dim_at_output: 5x7 aug_trans: 0.1 aug_zoom: 1.5 aug_p_flip: 0.5 img_net: resnet18 l_r: 0.0002 tot_num_epochs: 801 ================================================ FILE: download_and_install_dependencies.sh ================================================ #! /usr/bin/env bash DAGR_DIR=$(pwd) # Download detectron2 for its fast mAP calculation function mkdir $DAGR_DIR/libs cd $DAGR_DIR/libs git clone --no-checkout git@github.com:facebookresearch/detectron2.git cd $DAGR_DIR/libs/detectron2/ git checkout 32bd159d7263683e39bf4e87e5c4ac88bad2fd73 # Download YOLOX cd $DAGR_DIR/libs git clone --no-checkout git@github.com:Megvii-BaseDetection/YOLOX.git cd $DAGR_DIR/libs/YOLOX git checkout 618fd8c08b2bc5fac9ffbb19a3b7e039ea0d5b9a # Download dsec-det cd $DAGR_DIR/libs git clone git@github.com:uzh-rpg/dsec-det.git cd $DAGR_DIR/libs/dsec-det git checkout 81e381dc0fc1b1a540a604a970a37de038abb83b pip install -e $DAGR_DIR/libs/dsec-det pip install -e $DAGR_DIR/libs/detectron2 pip install -e $DAGR_DIR/libs/YOLOX pip install seaborn ================================================ FILE: download_example_data.sh ================================================ #! /usr/bin/env bash DAGR_DIR=$(pwd) DATA_DIR=$DAGR_DIR/data mkdir $DATA_DIR wget https://download.ifi.uzh.ch/rpg/dagr/data/dagr_s_50.pth -O $DATA_DIR/dagr_s_50.pth wget https://download.ifi.uzh.ch/rpg/dagr/data/DSEC_fragment.zip -O $DATA_DIR/DSEC_fragment.zip unzip $DATA_DIR/DSEC_fragment.zip -d $DATA_DIR rm -rf $DATA_DIR/DSEC_fragment.zip ================================================ FILE: install_env.sh ================================================ #! /usr/bin/env bash TORCH=$(python -c "import torch; print(torch.__version__)") CUDA=$(python -c "import torch; print(torch.version.cuda)") URL=https://data.pyg.org/whl/torch-${TORCH}+${CUDA}.html pip install --no-cache-dir torch-scatter -f $URL; pip install --no-cache-dir torch-cluster -f $URL; pip install --no-cache-dir torch-spline-conv -f $URL; pip install --no-cache-dir torch-sparse -f $URL; pip install torch-geometric; pip install wandb numba hdf5plugin plotly matplotlib pycocotools opencv-python scikit-video pandas ruamel.yaml ================================================ FILE: readme.md ================================================ # Low Latency Automotive Vision with Event Cameras

DAGR

This repository contains code from our 2024 Nature paper which can be accessed for free here [PDF Open Access](https://www.nature.com/articles/s41586-024-07409-w). **_Low Latency Automotive Vision with Event Cameras_** by [Daniel Gehrig](https://danielgehrig18.github.io/) and [Davide Scaramuzza](http://rpg.ifi.uzh.ch/people_scaramuzza.html). If you use our code or refer to this project, please cite it using ```bibtex @Article{Gehrig24nature, author = {Gehrig, Daniel and Scaramuzza, Davide}, title = {Low Latency Automotive Vision with Event Cameras}, booktitle = {Nature}, year = {2024} } ``` ## Updates * Training code for N-Caltech101 and DSEC-DET have been open sourced. To train your model jump to the [training section](#training) ## Installation First, download the github repository and its dependencies ```bash WORK_DIR=/path/to/work/directory/ cd $WORK_DIR git clone git@github.com:uzh-rpg/dagr.git DAGR_DIR=$WORK_DIR/dagr cd $DAGR_DIR ``` Then start by installing the main libraries. Make sure Anaconda (or better Mamba), PyTorch, and CUDA is installed. ```bash cd $DAGR_DIR conda create -y -n dagr python=3.8 conda activate dagr conda install -y setuptools==69.5.1 mkl==2024.0 pytorch==1.11.0 torchvision==0.12.0 torchaudio==0.11.0 cudatoolkit=11.3 -c pytorch ``` Then install the pytorch-geometric libraries. This may take a while. ```bash bash install_env.sh ``` The above bash file will figure out the CUDA and Torch version, and install the appropriate pytorch-geometric packages. Then, download and install additional dependencies locally ```bash bash download_and_install_dependencies.sh conda install -y h5py blosc-hdf5-plugin ``` Finally, install the dagr package ```bash pip install -e . ``` ## Run Example After installing, you can download a data fragment, and checkpoint with ```bash bash download_example_data.sh ``` This will download a checkpoint and data fragment of DSEC-Detection on which you can test the code. Once downloaded, run the following command ```bash LOG_DIR=/path/to/log DEVICE=1 CUDA_VISIBLE_DEVICES=$DEVICE python scripts/run_test_interframe.py --config config/dagr-s-dsec.yaml \ --use_image \ --img_net resnet50 \ --checkpoint data/dagr_s_50.pth \ --batch_size 8 \ --dataset_directory data/DSEC_fragment \ --no_eval \ --output_directory $LOG_DIR ``` note the wandb directory as `$WANDB_DIR` and then visualize the detections with ```bash python scripts/visualize_detections.py --detections_folder $LOG_DIR/$WANDB_DIR \ --dataset_directory data/DSEC_fragment/test \ --vis_time_step_us 1000 \ --event_time_window_us 5000 \ --sequence zurich_city_13_b ``` ## Test on DSEC Start by downloading the DSEC dataset and the additional labelled data introduced in this work. To do so, follow [these instructions](https://github.com/uzh-rpg/dsec-det?tab=readme-ov-file#download-dsec). They are based on the scripts of [dsec-det](https://github.com/uzh-rpg/dsec-det), which can be found in `libs/dsec-det/scripts`. To continue, complete sections [Download DSEC](https://github.com/uzh-rpg/dsec-det?tab=readme-ov-file#download-dsec) until [Test Alignment](https://github.com/uzh-rpg/dsec-det?tab=readme-ov-file#test-alignment). If you already downloaded DSEC, make sure `$DSEC_ROOT` points to it, and instead start at section [Download DSEC-extra ](https://github.com/uzh-rpg/dsec-det?tab=readme-ov-file#download-dsec-extra). After downloading all the data, change back to $DAGR_DIR, and start by downsampling the events ```bash cd $DAGR_DIR bash scripts/downsample_all_events.sh $DSEC_ROOT ``` ### Running Evaluation This repository implements three scripts for running evaluation of the model on DSEC-Det. The first, evaluates the detection performance of the model after seeing one image, and the subsequent 50 milliseconds of events. To run it, specify a device, and logging directory with type ```bash LOG_DIR=/path/to/log DEVICE=1 CUDA_VISIBLE_DEVICES=$DEVICE python scripts/run_test.py --config config/dagr-s-dsec.yaml \ --use_image \ --img_net resnet50 \ --checkpoint data/dagr_s_50.pth \ --batch_size 8 \ --dataset_directory $DSEC_ROOT \ --output_directory $LOG_DIR ``` Then, to evaluate the number of FLOPS generated in asynchronous mode, run ```bash LOG_DIR=/path/to/log DEVICE=1 CUDA_VISIBLE_DEVICES=$DEVICE python scripts/count_flops.py --config config/eagr-s-dsec.yaml \ --use_image \ --img_net resnet50 \ --checkpoint data/dagr_s_50.pth \ --batch_size 8 \ --dataset_directory $DSEC_ROOT \ --output_directory $LOG_DIR ``` Finally, to evaluate the interframe detection performance of our method run ```bash LOG_DIR=/path/to/log DEVICE=1 CUDA_VISIBLE_DEVICES=$DEVICE python scripts/run_test_interframe.py --config config/eagr-s-dsec.yaml \ --use_image \ --img_net resnet50 \ --checkpoint data/dagr_s_50.pth \ --batch_size 8 \ --dataset_directory $DSEC_ROOT \ --output_directory $LOG_DIR \ --num_interframe_steps 10 ``` This last script will write the high-rate detections from our method into the folder `$LOG_DIR/$WANDB_DIR`, where `$WANDB_DIR` is the automatically generated folder created by wandb. To visualize the detections, use the following script: ```bash python scripts/visualize_detections.py --detections_folder $LOG_DIR/$WANDB_DIR \ --dataset_directory $DSEC_ROOT/test/ \ --vis_time_step_us 1000 \ --event_time_window_us 5000 \ --sequence zurich_city_13_b ``` This will start a visualization window showing the detections over a given sequence. If you want to save the detections to a video, use the `--write_to_output` flag, which will create a video in the folder `$LOG_DIR/$WANDB_DIR/visualization}`. ## Training To train on N-Caltech101, download the files with ```bash wget https://download.ifi.uzh.ch/rpg/dagr/data/ncaltech101.zip -P $DAGR_DIR/data/ cd $DAGR_DIR/data/ unzip ncaltech101.zip rm -rf ncaltech101.zip ``` Then run training with ```bash python scripts/train_ncaltech101.py --config config/dagr-l-ncaltech.yaml \ --exp_name ncaltech_l \ --dataset_directory $DAGR_DIR/data/ \ --output_directory $DAGR_DIR/logs/ ``` To train on DSEC, make a symlink to the data directory via ```bash ln -s $DSEC_ROOT $DAGR_DIR/data/dsec ``` Then run training with ```bash python scripts/train_dsec.py --config config/dagr-s-dsec.yaml \ --exp_name dsec_s_50 \ --dataset_directory $DAGR_DIR/data/ \ --output_directory $DAGR_DIR/logs/ \ --use_image --img_net resnet50 --batch_size 32 ``` ================================================ FILE: scripts/check_dataset.py ================================================ ================================================ FILE: scripts/count_flops.py ================================================ import os import tqdm import torch os.environ['CUDA_DEVICE_ORDER'] = 'PCI_BUS_ID' from torch_geometric.data import DataLoader from dagr.utils.args import FLOPS_FLAGS from dagr.utils.buffers import DictBuffer, format_data from dagr.data.augment import Augmentations from dagr.data.dsec_data import DSEC from dagr.model.networks.dagr import DAGR from dagr.asynchronous.evaluate_flops import evaluate_flops if __name__ == '__main__': import torch_geometric seed = 42 torch_geometric.seed.seed_everything(seed) args = FLOPS_FLAGS() assert "checkpoint" in args project = f"flops-{args.dataset}-{args.task}" pbar = tqdm.tqdm(total=4) pbar.set_description("Loading dataset") dataset_path = args.dataset_directory / args.dataset print("init datasets") dataset = DSEC(args.dataset_directory, "test", Augmentations.transform_testing, debug=True, min_bbox_diag=15, min_bbox_height=10) loader = DataLoader(dataset, follow_batch=['bbox', "bbox0"], batch_size=args.batch_size, shuffle=False, num_workers=16) pbar.update(1) pbar.set_description("Initializing net") model = DAGR(args, height=dataset.height, width=dataset.width) model = model.cuda() model.eval() pbar.update(1) assert "checkpoint" in args checkpoint = torch.load(args.checkpoint) model.load_state_dict(checkpoint['ema']) pbar.update(1) model.cache_luts(radius=args.radius, height=dataset.height, width=dataset.width) pbar.set_description("Computing FLOPS") buffer = DictBuffer() args.output_directory.mkdir(parents=True, exist_ok=True) pbar_flops = tqdm.tqdm(total=len(loader.dataset), desc="Computing FLOPS") for i, data in enumerate(loader): data = data.cuda(non_blocking=True) data = format_data(data) flops_evaluation = evaluate_flops(model, data, check_consistency=args.check_consistency, return_all_samples=True, dense=args.dense) if flops_evaluation is None: continue buffer.update(flops_evaluation['flops_per_layer']) buffer.save(args.output_directory / "flops_per_layer.pth") tot_flops = sum(buffer.compute().values()) pbar_flops.set_description(f"Total FLOPS {tot_flops}") pbar_flops.update(1) print(sum(buffer.compute().values())) pbar.update(1) ================================================ FILE: scripts/downsample_all_events.sh ================================================ #!/bin/bash DSEC_ROOT=$1 for split in train test; do for sequence in $DSEC_ROOT/$split/*/; do infile=$sequence/events/left/events.h5 outfile=$sequence/events/left/events_2x.h5 python scripts/downsample_events.py --input_path $infile --output_path $outfile done done ================================================ FILE: scripts/downsample_events.py ================================================ import argparse import tqdm import hdf5plugin import h5py import weakref import numba import numpy as np from pathlib import Path from dsec_det.io import extract_from_h5_by_index, get_num_events def _compression_opts(): compression_level = 1 # {0, ..., 9} shuffle = 2 # {0: none, 1: byte, 2: bit} # From https://github.com/Blosc/c-blosc/blob/7435f28dd08606bd51ab42b49b0e654547becac4/blosc/blosc.h#L66-L71 # define BLOSC_BLOSCLZ 0 # define BLOSC_LZ4 1 # define BLOSC_LZ4HC 2 # define BLOSC_SNAPPY 3 # define BLOSC_ZLIB 4 # define BLOSC_ZSTD 5 compressor_type = 5 compression_opts = (0, 0, 0, 0, compression_level, shuffle, compressor_type) return compression_opts H5_BLOSC_COMPRESSION_FLAGS = dict( compression=32001, compression_opts=_compression_opts(), # Blosc chunks=True ) def create_ms_to_idx(t_us): t_ms = t_us // 1000 x, counts = np.unique(t_ms, return_counts=True) ms_to_idx = np.zeros(shape=(t_ms[-1] + 2,), dtype="uint64") ms_to_idx[x + 1] = counts ms_to_idx = ms_to_idx[:-1].cumsum() return ms_to_idx class H5Writer: def __init__(self, outfile): assert not outfile.exists() self.h5f = h5py.File(outfile, 'a') self._finalizer = weakref.finalize(self, self.close_callback, self.h5f) self.t_offset = None self.num_events = 0 # create hdf5 datasets shape = (2 ** 16,) maxshape = (None,) self.h5f.create_dataset(f'events/x', shape=shape, dtype='u2', maxshape=maxshape, **H5_BLOSC_COMPRESSION_FLAGS) self.h5f.create_dataset(f'events/y', shape=shape, dtype='u2', maxshape=maxshape, **H5_BLOSC_COMPRESSION_FLAGS) self.h5f.create_dataset(f'events/p', shape=shape, dtype='u1', maxshape=maxshape, **H5_BLOSC_COMPRESSION_FLAGS) self.h5f.create_dataset(f'events/t', shape=shape, dtype='u4', maxshape=maxshape, **H5_BLOSC_COMPRESSION_FLAGS) def create_ms_to_idx(self): t_us = self.h5f['events/t'][()] self.h5f.create_dataset(f'ms_to_idx', data=create_ms_to_idx(t_us), dtype='u8', **H5_BLOSC_COMPRESSION_FLAGS) @staticmethod def close_callback(h5f: h5py.File): h5f.close() def add_data(self, events): if self.t_offset is None: self.t_offset = events['t'][0] self.h5f.create_dataset(f't_offset', data=self.t_offset, dtype='i8') events['t'] -= self.t_offset size = len(events['t']) self.num_events += size self.h5f[f'events/x'].resize(self.num_events, axis=0) self.h5f[f'events/y'].resize(self.num_events, axis=0) self.h5f[f'events/p'].resize(self.num_events, axis=0) self.h5f[f'events/t'].resize(self.num_events, axis=0) self.h5f[f'events/x'][self.num_events-size:self.num_events] = events['x'] self.h5f[f'events/y'][self.num_events-size:self.num_events] = events['y'] self.h5f[f'events/p'][self.num_events-size:self.num_events] = events['p'] self.h5f[f'events/t'][self.num_events-size:self.num_events] = events['t'] def downsample_events(events, input_height, input_width, output_height, output_width, change_map=None): # this subsamples events if they were generated with cv2.INTER_AREA if change_map is None: change_map = np.zeros((output_height, output_width), dtype="float32") fx = int(input_width / output_width) fy = int(input_height / output_height) mask = np.zeros(shape=(len(events['t']),), dtype="bool") mask, change_map = _filter_events_resize(events['x'], events['y'], events['p'], mask, change_map, fx, fy) events = {k: v[mask] for k, v in events.items()} events['x'] = (events['x'] / fx).astype("uint16") events['y'] = (events['y'] / fy).astype("uint16") return events, change_map @numba.jit(nopython=True, cache=True) def _filter_events_resize(x, y, p, mask, change_map, fx, fy): # iterates through x,y,p of events, and increments cells of size fx x fy by 1/(fx*fy) # if one of these cells reaches +-1, then reset the cell, and pass through that event. # for memory reasons, this only returns the True/False for every event, indicating if # the event was skipped or passed through. for i in range(len(x)): x_l = x[i] // fx y_l = y[i] // fy change_map[y_l, x_l] += p[i] * 1.0 / (fx * fy) if np.abs(change_map[y_l, x_l]) >= 1: mask[i] = True change_map[y_l, x_l] -= p[i] return mask, change_map if __name__ == '__main__': parser = argparse.ArgumentParser("""Downsample events""") parser.add_argument("--input_path", type=Path, required=True, help="Path to input events.h5. ") parser.add_argument("--output_path", type=Path, required=True, help="Path where output events.h5 will be written.") parser.add_argument("--input_height", type=int, default=480, help="Height of the input events resolution.") parser.add_argument("--input_width", type=int, default=640, help="Width of the input events resolution") parser.add_argument("--output_height", type=int, default=240, help="Height of the output events resolution.") parser.add_argument("--output_width", type=int, default=320, help="Width of the output events resolution.") args = parser.parse_args() num_events = get_num_events(args.input_path) num_events_per_chunk = 100000 num_iterations = num_events // num_events_per_chunk writer = H5Writer(args.output_path) change_map = None pbar = tqdm.tqdm(total=num_iterations+1) for i in range(num_iterations): events = extract_from_h5_by_index(args.input_path, i * num_events_per_chunk, (i+1) * num_events_per_chunk) events['p'] = 2 * events['p'].astype("int8") - 1 downsampled_events, change_map = downsample_events(events, change_map=change_map, input_height=args.input_height, input_width=args.input_width, output_height=args.output_height, output_width=args.output_width) events['p'] = ((events['p'] + 1)//2).astype("int8") writer.add_data(downsampled_events) pbar.update(1) events = extract_from_h5_by_index(args.input_path, num_iterations * num_events_per_chunk, num_events) downsampled_events, change_map = downsample_events(events, change_map=change_map, input_height=args.input_height, input_width=args.input_width, output_height=args.output_height, output_width=args.output_width) writer.add_data(downsampled_events) pbar.update(1) writer.create_ms_to_idx() ================================================ FILE: scripts/run_test.py ================================================ # avoid matlab error on server import os import torch import wandb os.environ['CUDA_DEVICE_ORDER'] = 'PCI_BUS_ID' from torch_geometric.data import DataLoader from dagr.utils.args import FLAGS from dagr.data.dsec_data import DSEC from dagr.data.augment import Augmentations from dagr.model.networks.dagr import DAGR from dagr.model.networks.ema import ModelEMA from dagr.utils.logging import set_up_logging_directory, log_hparams from dagr.utils.testing import run_test_with_visualization if __name__ == '__main__': import torch_geometric import random import numpy as np seed = 42 torch_geometric.seed.seed_everything(seed) torch.random.manual_seed(seed) torch.manual_seed(seed) np.random.seed(seed) random.seed(seed) args = FLAGS() output_directory = set_up_logging_directory(args.dataset, args.task, args.output_directory) project = f"low_latency-{args.dataset}-{args.task}" print(f"PROJECT: {project}") log_hparams(args) print("init datasets") dataset_path = args.dataset_directory.parent / args.dataset test_dataset = DSEC(args.dataset_directory, "test", Augmentations.transform_testing, debug=False, min_bbox_diag=15, min_bbox_height=10) num_iters_per_epoch = 1 sampler = np.random.permutation(np.arange(len(test_dataset))) test_loader = DataLoader(test_dataset, sampler=sampler, follow_batch=['bbox', 'bbox0'], batch_size=args.batch_size, shuffle=False, num_workers=4, drop_last=True) print("init net") # load a dummy sample to get height, width model = DAGR(args, height=test_dataset.height, width=test_dataset.width) model = model.cuda() ema = ModelEMA(model) assert "checkpoint" in args checkpoint = torch.load(args.checkpoint) ema.ema.load_state_dict(checkpoint['ema']) ema.ema.cache_luts(radius=args.radius, height=test_dataset.height, width=test_dataset.width) with torch.no_grad(): metrics = run_test_with_visualization(test_loader, ema.ema, dataset=args.dataset) log_data = {f"testing/metric/{k}": v for k, v in metrics.items()} wandb.log(log_data) print(metrics['mAP']) ================================================ FILE: scripts/run_test_interframe.py ================================================ import torch import tqdm import wandb import os os.environ['CUDA_DEVICE_ORDER'] = 'PCI_BUS_ID' from torch_geometric.data import DataLoader from pprint import pprint from dagr.utils.logging import set_up_logging_directory, log_hparams from dagr.utils.args import FLAGS from dagr.utils.testing import run_test_with_visualization from dagr.data.augment import Augmentations from dagr.data.dsec_data import DSEC from dagr.model.networks.dagr import DAGR from dagr.model.networks.ema import ModelEMA def to_npy(detections): n_boxes = len(detections['boxes']) dtype = np.dtype([('t', ' 0 and i == dry_run_steps: break torch.cuda.empty_cache() return mapcalc if __name__ == '__main__': import torch_geometric import random import numpy as np seed = 42 torch_geometric.seed.seed_everything(seed) torch.random.manual_seed(seed) torch.manual_seed(seed) np.random.seed(seed) random.seed(seed) args = FLAGS() output_directory = set_up_logging_directory(args.dataset, args.task, args.output_directory, exp_name=args.exp_name) log_hparams(args) augmentations = Augmentations(args) print("init datasets") dataset_path = args.dataset_directory / args.dataset train_dataset = DSEC(root=dataset_path, split="train", transform=augmentations.transform_training, debug=False, min_bbox_diag=15, min_bbox_height=10) test_dataset = DSEC(root=dataset_path, split="val", transform=augmentations.transform_testing, debug=False, min_bbox_diag=15, min_bbox_height=10) train_loader = DataLoader(train_dataset, follow_batch=['bbox', 'bbox0'], batch_size=args.batch_size, shuffle=True, num_workers=4, drop_last=True) num_iters_per_epoch = len(train_loader) sampler = np.random.permutation(np.arange(len(test_dataset))) test_loader = DataLoader(test_dataset, sampler=sampler, follow_batch=['bbox', 'bbox0'], batch_size=args.batch_size, shuffle=False, num_workers=4, drop_last=True) print("init net") # load a dummy sample to get height, width model = DAGR(args, height=test_dataset.height, width=test_dataset.width) num_params = sum([np.prod(p.size()) for p in model.parameters()]) print(f"Training with {num_params} number of parameters.") model = model.cuda() ema = ModelEMA(model) nominal_batch_size = 64 lr = args.l_r * np.sqrt(args.batch_size) / np.sqrt(nominal_batch_size) optimizer = torch.optim.AdamW(list(model.parameters()), lr=lr, weight_decay=args.weight_decay) lr_func = LRSchedule(warmup_epochs=.3, num_iters_per_epoch=num_iters_per_epoch, tot_num_epochs=args.tot_num_epochs) lr_scheduler = torch.optim.lr_scheduler.LambdaLR(optimizer=optimizer, lr_lambda=lr_func) checkpointer = Checkpointer(output_directory=output_directory, model=model, optimizer=optimizer, scheduler=lr_scheduler, ema=ema, args=args) start_epoch = checkpointer.restore_if_existing(output_directory, resume_from_best=False) start_epoch = 0 if "resume_checkpoint" in args: start_epoch = checkpointer.restore_checkpoint(args.resume_checkpoint, best=False) print(f"Resume from checkpoint at epoch {start_epoch}") with torch.no_grad(): mapcalc = run_test(test_loader, ema.ema, dry_run_steps=2, dataset=args.dataset) mapcalc.compute() print("starting to train") for epoch in range(start_epoch, args.tot_num_epochs): train(train_loader, model, ema, lr_scheduler, optimizer, args, run_name=wandb.run.name) checkpointer.checkpoint(epoch, name=f"last_model") if epoch % 3 > 0: continue with torch.no_grad(): mapcalc = run_test(test_loader, ema.ema, dataset=args.dataset) metrics = mapcalc.compute() checkpointer.process(metrics, epoch) ================================================ FILE: scripts/train_ncaltech101.py ================================================ # avoid matlab error on server import os os.environ['CUDA_DEVICE_ORDER'] = 'PCI_BUS_ID' import torch import tqdm import wandb from pathlib import Path import argparse from torch_geometric.data import DataLoader from dagr.utils.logging import Checkpointer, set_up_logging_directory, log_hparams from dagr.utils.buffers import DetectionBuffer from dagr.utils.args import FLAGS from dagr.utils.learning_rate_scheduler import LRSchedule from dagr.data.augment import Augmentations from dagr.utils.buffers import format_data from dagr.data.ncaltech101_data import NCaltech101 from dagr.model.networks.dagr import DAGR from dagr.model.networks.ema import ModelEMA def gradients_broken(model): valid_gradients = True for name, param in model.named_parameters(): if param.grad is not None: # valid_gradients = not (torch.isnan(param.grad).any() or torch.isinf(param.grad).any()) valid_gradients = not (torch.isnan(param.grad).any()) if not valid_gradients: break return not valid_gradients def fix_gradients(model): for name, param in model.named_parameters(): if param.grad is not None: param.grad = torch.nan_to_num(param.grad, nan=0.0) def train(loader: DataLoader, model: torch.nn.Module, ema: ModelEMA, scheduler: torch.optim.lr_scheduler.LambdaLR, optimizer: torch.optim.Optimizer, args: argparse.ArgumentParser, run_name=""): model.train() for i, data in enumerate(tqdm.tqdm(loader, desc=f"Training {run_name}")): data = data.cuda(non_blocking=True) data = format_data(data) optimizer.zero_grad(set_to_none=True) model_outputs = model(data) loss_dict = {k: v for k, v in model_outputs.items() if "loss" in k} loss = loss_dict.pop("total_loss") loss.backward() torch.nn.utils.clip_grad_value_(model.parameters(), args.clip) fix_gradients(model) optimizer.step() scheduler.step() ema.update(model) training_logs = {f"training/loss/{k}": v for k, v in loss_dict.items()} wandb.log({"training/loss": loss.item(), "training/lr": scheduler.get_last_lr()[-1], **training_logs}) def run_test(loader: DataLoader, model: torch.nn.Module, dry_run_steps: int=-1, dataset="gen1"): model.eval() mapcalc = DetectionBuffer(height=loader.dataset.height, width=loader.dataset.width, classes=loader.dataset.classes) for i, data in enumerate(tqdm.tqdm(loader)): data = data.cuda() data = format_data(data) detections, targets = model(data) if i % 10 == 0: torch.cuda.empty_cache() mapcalc.update(detections, targets, dataset, data.height[0], data.width[0]) if dry_run_steps > 0 and i == dry_run_steps: break torch.cuda.empty_cache() return mapcalc if __name__ == '__main__': import torch_geometric import random import numpy as np seed = 42 torch_geometric.seed.seed_everything(seed) torch.random.manual_seed(seed) torch.manual_seed(seed) np.random.seed(seed) random.seed(seed) args = FLAGS() output_directory = set_up_logging_directory(args.dataset, args.task, args.output_directory, exp_name=args.exp_name) log_hparams(args) augmentations = Augmentations(args) print("init datasets") dataset_path = args.dataset_directory / args.dataset train_dataset = NCaltech101(dataset_path, "training", augmentations.transform_training, num_events=args.n_nodes) test_dataset = NCaltech101(dataset_path, "validation", augmentations.transform_testing, num_events=args.n_nodes) train_loader = DataLoader(train_dataset, follow_batch=['bbox', 'bbox0'], batch_size=args.batch_size, shuffle=True, num_workers=4, drop_last=True) num_iters_per_epoch = len(train_loader) sampler = np.random.permutation(np.arange(len(test_dataset))) test_loader = DataLoader(test_dataset, sampler=sampler, follow_batch=['bbox', 'bbox0'], batch_size=args.batch_size, shuffle=False, num_workers=4, drop_last=True) print("init net") # load a dummy sample to get height, width model = DAGR(args, height=test_dataset.height, width=test_dataset.width) num_params = sum([np.prod(p.size()) for p in model.parameters()]) print(f"Training with {num_params} number of parameters.") model = model.cuda() ema = ModelEMA(model) nominal_batch_size = 64 lr = args.l_r * np.sqrt(args.batch_size) / np.sqrt(nominal_batch_size) optimizer = torch.optim.AdamW(list(model.parameters()), lr=lr, weight_decay=args.weight_decay) lr_func = LRSchedule(warmup_epochs=.3, num_iters_per_epoch=num_iters_per_epoch, tot_num_epochs=args.tot_num_epochs) lr_scheduler = torch.optim.lr_scheduler.LambdaLR(optimizer=optimizer, lr_lambda=lr_func) checkpointer = Checkpointer(output_directory=output_directory, model=model, optimizer=optimizer, scheduler=lr_scheduler, ema=ema, args=args) start_epoch = checkpointer.restore_if_existing(output_directory, resume_from_best=False) start_epoch = 0 if "resume_checkpoint" in args: start_epoch = checkpointer.restore_checkpoint(args.resume_checkpoint, best=False) print(f"Resume from checkpoint at epoch {start_epoch}") with torch.no_grad(): mapcalc = run_test(test_loader, ema.ema, dry_run_steps=2, dataset=args.dataset) mapcalc.compute() print("starting to train") for epoch in range(start_epoch, args.tot_num_epochs): train(train_loader, model, ema, lr_scheduler, optimizer, args, run_name=wandb.run.name) checkpointer.checkpoint(epoch, name=f"last_model") if epoch % 3 > 0: continue with torch.no_grad(): mapcalc = run_test(test_loader, ema.ema, dataset=args.dataset) metrics = mapcalc.compute() checkpointer.process(metrics, epoch) ================================================ FILE: scripts/visualize_detections.py ================================================ import cv2 import argparse from pathlib import Path import numpy as np from dsec_det.directory import DSECDirectory from dsec_det.io import extract_from_h5_by_timewindow, extract_image_by_index, load_start_and_end_time from dsec_det.preprocessing import compute_index from dagr.visualization.bbox_viz import draw_bbox_on_img from dagr.visualization.event_viz import draw_events_on_image if __name__ == '__main__': parser = argparse.ArgumentParser("""Visualization script to show bounding boxes""") parser.add_argument("--detections_folder", help="Path to folder with detections.", type=Path) parser.add_argument("--dataset_directory", help="Path to DSEC folder including which split.", type=Path, default="/data/scratch1/daniel/datasets/DSEC_fragment/test") parser.add_argument("--vis_time_step_us", help="Number of microseconds to step each iteration.", type=int, default=1000) parser.add_argument("--event_time_window_us", help="Length of sliding event time window for visualization.", type=int, default=5000) parser.add_argument("--sequence", help="Sequence to visualize. Must be an official DSEC sequence e.g. zurich_city_13_b", default="zurich_city_13_b", type=str) parser.add_argument("--write_to_output", help="Whether to save images in folder ${detections_folder}/visualization. Otherwise, just cv2.imshow is used.", action="store_true") args = parser.parse_args() assert args.dataset_directory.exists() assert args.vis_time_step_us > 0 assert args.event_time_window_us > 0 if args.write_to_output: assert (args.detections_folder / f"detections_{args.sequence}.npy").exists() assert args.detections_folder.exists() output_path = args.detections_folder / "visualization" output_path.mkdir(parents=True, exist_ok=True) dsec_directory = DSECDirectory(args.dataset_directory / args.sequence) t0, t1 = load_start_and_end_time(dsec_directory) vis_timestamps = np.arange(t0, t1, step=args.vis_time_step_us) step_index_to_image_index = compute_index(dsec_directory.images.timestamps, vis_timestamps) show_detections = args.detections_folder is not None if not show_detections: print("Did not specifiy detections. Just showing events and images.") if show_detections: detections_file = args.detections_folder / f"detections_{args.sequence}.npy" detections = np.load(detections_file) detection_timestamps = np.unique(detections['t']) step_index_to_boxes_index = compute_index(detection_timestamps, vis_timestamps) scale = 2 for step, t in enumerate(vis_timestamps): # find most recent image image_index = step_index_to_image_index[step] image = extract_image_by_index(dsec_directory.images.image_files_distorted, image_index) # find events within time window [image_timestamps, t] events = extract_from_h5_by_timewindow(dsec_directory.events.event_file, t-args.event_time_window_us, t) image = draw_events_on_image(image, events['x'], events['y'], events['p']) if show_detections: # find most recent bounding boxes boxes_index = step_index_to_boxes_index[step] boxes_timestamp = detection_timestamps[boxes_index] boxes = detections[detections['t'] == boxes_timestamp] # draw them on one image scale = 2 image = draw_bbox_on_img(image, scale*boxes['x'], scale*boxes['y'], scale*boxes['w'], scale*boxes["h"], boxes["class_id"], boxes['class_confidence'], conf=0.3, nms=0.65) if args.write_to_output: cv2.imwrite(str(output_path / ("%06d.png" % step)), image) else: cv2.imshow("DSEC Det: Visualization", image) cv2.waitKey(3) ================================================ FILE: setup.py ================================================ from distutils.core import setup from torch.utils.cpp_extension import BuildExtension, CUDAExtension setup( name='dagr', packages=['dagr'], package_dir={'':'src'}, ext_modules=[ CUDAExtension(name='asy_tools', sources=['src/dagr/asynchronous/asy_tools/main.cu']), CUDAExtension(name="ev_graph_cuda", sources=['src/dagr/graph/ev_graph.cu']) ], cmdclass={ 'build_ext': BuildExtension } ) ================================================ FILE: src/dagr/asynchronous/__init__.py ================================================ import logging import torch.nn import torch_geometric import inspect from torch.nn import ModuleList from .conv import make_conv_asynchronous from .batch_norm import make_batch_norm_asynchronous from .linear import make_linear_asynchronous from .max_pool import make_max_pool_asynchronous from .cartesian import make_cartesian_asynchronous from .flops import compute_flops_from_module from dagr.model.layers.spline_conv import MySplineConv from dagr.model.layers.pooling import Pooling from dagr.model.layers.components import BatchNormData, Cartesian, Linear from torch_geometric.data import Data, Batch from typing import List def is_data_or_data_list(ann): return ann is Data or ann is Batch or ann is List[Data] def make_model_synchronous(module: torch.nn.Module): module.forward = module.sync_forward module.asy_flops_log = [] for key, nn in module.named_modules(): if hasattr(nn, "sync_forward"): nn.forward = nn.sync_forward nn.asy_flops_log = [] return module def make_model_asynchronous(module, log_flops: bool = False): """Module converter from synchronous to asynchronous & sparse processing for graph convolutional layers. By overwriting parts of the module asynchronous processing can be enabled without the need of re-learning and moving its weights and configuration. So, a convolutional layer can be converted by, for example: ``` module = GCNConv(1, 2) module = make_conv_asynchronous(module) ``` :param module: convolutional module to transform. :param grid_size: grid size (grid starting at 0, spanning to `grid_size`), >= `size` for pooling operations, e.g. the image size. :param r: update radius around new events. :param edge_attributes: function for computing edge attributes (default = None), assumed to be the same over all convolutional layers. :param log_flops: log flops of asynchronous update. """ assert isinstance(module, torch.nn.Module), "module must be a `torch.nn.Module`" model_forward = module.forward module.sync_forward = module.forward module.asy_flops_log = [] if log_flops else None # Make all layers asynchronous that have an implemented asynchronous function. Otherwise use # the synchronous forward function. for key, nn in module._modules.items(): nn_class_name = nn.__class__.__name__ logging.debug(f"Making layer {key} of type {nn_class_name} asynchronous") if isinstance(nn, MySplineConv): module._modules[key] = make_conv_asynchronous(nn, log_flops=log_flops) elif isinstance(nn, Pooling): module._modules[key] = make_max_pool_asynchronous(nn, log_flops=log_flops) elif isinstance(nn, BatchNormData): module._modules[key] = make_batch_norm_asynchronous(nn, log_flops=log_flops) elif isinstance(nn, Cartesian): module._modules[key] = make_cartesian_asynchronous(nn, log_flops=log_flops) elif isinstance(nn, Linear): module._modules[key] = make_linear_asynchronous(nn, log_flops=log_flops) elif isinstance(nn, ModuleList): module._modules[key] = make_model_asynchronous(nn, log_flops=log_flops) else: sign = inspect.signature(nn.forward) first_arg = list(sign.parameters.values())[0] if not is_data_or_data_list(first_arg.annotation): continue module._modules[key] = make_model_asynchronous(nn, log_flops=log_flops) logging.debug(f"Asynchronous module for {nn_class_name} is being made asynchronous recursively.") def async_forward(data: torch_geometric.data.Data, *args, **kwargs): out = model_forward(data, *args, **kwargs) if module.asy_flops_log is not None: flops_count = [compute_flops_from_module(layer) for layer in module._modules.values()] module.asy_flops_log.append(sum(flops_count)) logging.debug(f"Model's modules update with overall {sum(flops_count)} flops") return out module.forward = async_forward return module __all__ = [ "make_conv_asynchronous", "make_linear_asynchronous", "make_max_pool_asynchronous", "make_model_asynchronous" ] ================================================ FILE: src/dagr/asynchronous/asy_tools/main.cu ================================================ #include #include #include #include #define CHECK_CUDA(x) AT_ASSERTM(x.type().is_cuda(), #x " must be a CUDA tensor") #define CHECK_CONTIGUOUS(x) AT_ASSERTM(x.is_contiguous(), #x " must be contiguous") #define CHECK_INPUT(x) CHECK_CUDA(x); CHECK_CONTIGUOUS(x) #define CHECK_DEVICE(x, y) AT_ASSERTM(x.device().index() == y.device().index(), #x " and " #y " must be in same CUDA device") template __global__ void masked_isdiff_kernel( int64_t* __restrict__ indices, const scalar_t* __restrict__ x_old, const scalar_t* __restrict__ x_new, int K, int C, float atol, float rtol ) { // linear index const int lin_idx = blockIdx.x * blockDim.x + threadIdx.x; // check that thread is not out of valid range if (lin_idx >= K) return; // find out how many events to write, and what is the offset int64_t temp = indices[lin_idx]; indices[lin_idx] = -1; int offset = temp*C; for (int i=0; i atol + rtol * other) { indices[lin_idx] = temp; break; } } } template __global__ void masked_inplace_BN_kernel( const int64_t* __restrict__ indices, const scalar_t* __restrict__ x, scalar_t* __restrict__ x_out, const scalar_t* __restrict__ running_mean, const scalar_t* __restrict__ running_var, const scalar_t* __restrict__ weight, const scalar_t* __restrict__ bias, int K, int C, float eps ) { // linear index const int lin_idx = blockIdx.x * blockDim.x + threadIdx.x; // check that thread is not out of valid range if (lin_idx >= K*C) return; int i = lin_idx / C; int c = lin_idx % C; int x_lin_idx = C * indices[i] + c; x_out[x_lin_idx] = (x[x_lin_idx] - running_mean[c]) / (sqrt(running_var[c] + eps)) * weight[c] + bias[c]; } void masked_inplace_BN( const torch::Tensor& indices, const torch::Tensor& x, torch::Tensor& x_out, const torch::Tensor& running_mean, const torch::Tensor& running_var, const torch::Tensor& weight, const torch::Tensor& bias, float eps ) { unsigned K = indices.size(0); unsigned C = x.size(1); unsigned threads = 256; dim3 blocks((K*C + threads - 1) / threads, 1); masked_inplace_BN_kernel<<>>( indices.data(), x.data(), x_out.data(), running_mean.data(), running_var.data(), weight.data(), bias.data(), K, C, eps ); } torch::Tensor masked_isdiff( const torch::Tensor& indices, // N -> num events const torch::Tensor& x_old, // K -> num active pixels const torch::Tensor& x_new, // K -> num active pixels float atol, float rtol ) { CHECK_INPUT(indices); CHECK_INPUT(x_old); CHECK_INPUT(x_new); CHECK_DEVICE(indices, x_old); CHECK_DEVICE(indices, x_new); unsigned K = indices.size(0); unsigned C = x_old.size(1); unsigned threads = 256; dim3 blocks((K + threads - 1) / threads, 1); masked_isdiff_kernel<<>>( indices.data(), x_old.data(), x_new.data(), K, C, atol, rtol ); return indices.index({indices > -1}); } template __global__ void masked_lin_kernel( int64_t* __restrict__ indices, const scalar_t* __restrict__ x_in, scalar_t* __restrict__ x_out, const scalar_t* __restrict__ weight, const scalar_t* __restrict__ bias, int K, int Cin, int Cout, bool add ) { // linear index const int lin_idx = blockIdx.x * blockDim.x + threadIdx.x; // check that thread is not out of valid range if (lin_idx >= K*Cout) return; int i = lin_idx / Cout; int cout = lin_idx % Cout; int x_out_lin_idx = Cout * indices[i] + cout; int x_int_lin_idx = Cin * indices[i]; if (!add) x_out[x_out_lin_idx] = 0; for (int cin=0; cin __global__ void masked_lin_no_bias_kernel( int64_t* __restrict__ indices, const scalar_t* __restrict__ x_in, scalar_t* __restrict__ x_out, const scalar_t* __restrict__ weight, int K, int Cin, int Cout, bool add ) { // linear index const int lin_idx = blockIdx.x * blockDim.x + threadIdx.x; // check that thread is not out of valid range if (lin_idx >= K*Cout) return; int i = lin_idx / Cout; int cout = lin_idx % Cout; int x_out_lin_idx = Cout * indices[i] + cout; int x_int_lin_idx = Cin * indices[i]; if (!add) x_out[x_out_lin_idx] = 0; for (int cin=0; cin<<>>( indices.data(), x_in.data(), x_out.data(), weight.data(), K, Cin, Cout, add); } void masked_lin( const torch::Tensor& indices, const torch::Tensor& x_in, torch::Tensor& x_out, const torch::Tensor& weight, const torch::Tensor& bias, bool add ) { unsigned K = indices.size(0); unsigned Cin = weight.size(1); unsigned Cout = weight.size(0); unsigned threads = 256; dim3 blocks((K*Cout + threads - 1) / threads, 1); masked_lin_kernel<<>>( indices.data(), x_in.data(), x_out.data(), weight.data(), bias.data(), K, Cin, Cout, add); } PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) { m.def("masked_lin", &masked_lin, "Find edges from a queue of events."); m.def("masked_lin_no_bias", &masked_lin_no_bias, "Find edges from a queue of events."); m.def("masked_isdiff", &masked_isdiff, "Find edges from a queue of events."); m.def("masked_inplace_BN", &masked_inplace_BN, "Find edges from a queue of events."); } ================================================ FILE: src/dagr/asynchronous/base/__init__.py ================================================ ================================================ FILE: src/dagr/asynchronous/base/base.py ================================================ from contextlib import contextmanager import logging def add_async_graph(module, log_flops: bool = False): module.asy_graph = None module.asy_flops_log = [] if log_flops else None return module def make_asynchronous(module, initialization_func, processing_func): module.sync_forward = module.forward def async_forward(*args, **kwargs): with async_context(module, initialization_func, processing_func) as func: output = func(module, *args, **kwargs) return output module.forward = async_forward return module @contextmanager def async_context(module, initialization_func, processing_func): if module.asy_graph is None: logging.debug(f"Graph initialization of module {module}") yield initialization_func else: logging.debug(f"Calling processing of module {module}") yield processing_func ================================================ FILE: src/dagr/asynchronous/base/utils.py ================================================ import torch from typing import Tuple import asy_tools def _efficient_cat(data_list): data_list = [d for d in data_list if len(d) > 0] if len(data_list) == 1: return data_list[0] return torch.cat(data_list) def _efficient_cat_unique(data_list): # first only keep elements that have len > 0 data_list_filt = [data for data in data_list if data.shape[0] > 0] if len(data_list_filt) == 1: return data_list_filt[0] elif len(data_list_filt) == 0: return data_list[0] else: return torch.cat(data_list_filt).unique() def _to_hom(x, ones=None): if ones is None or len(ones) < len(x): ones = torch.ones_like(x[:,-1:]) else: ones = ones[:len(x)] return torch.cat([x, ones], dim=-1) def _from_hom(x): return x[:,:-1] / (x[:,-1:] + 1e-9) def graph_new_nodes(old_data, new_data): return torch.arange(old_data.x.shape[0], new_data.x.shape[0], device=new_data.x.device, dtype=torch.long) def graph_changed_nodes(old_data, new_data) -> Tuple[torch.Tensor, torch.Tensor]: len_x_old = old_data.x.shape[0] len_pos_old = old_data.pos.shape[0] x_new = new_data.x[:len_x_old] if len_x_old < new_data.x.shape[0] else new_data.x pos_new = new_data.pos[:len_pos_old] if len_pos_old < new_data.pos.shape[0] else new_data.pos diff_idx = asy_tools.masked_isdiff(new_data.diff_idx, x_new, old_data.x, 1e-8, 1e-5) if new_data.diff_idx.numel() > 0 else new_data.diff_idx diff_pos_idx = asy_tools.masked_isdiff(new_data.diff_pos_idx, pos_new, old_data.pos, 1e-8, 1e-5) if new_data.diff_pos_idx.numel() > 0 else new_data.diff_pos_idx return diff_idx, diff_pos_idx def torch_isin(query, database): if hasattr(torch, "isin"): return torch.isin(query, database) else: return (query.view(1, -1) == database.view(-1, 1)).any(0) def __remove_duplicate_from_A(a, b): a_in_b = (a.view(2,1,-1) == b.view(2,-1,1)).all(0).any(0) return a[:,~a_in_b] ================================================ FILE: src/dagr/asynchronous/batch_norm.py ================================================ import torch import asy_tools from torch_geometric.nn.norm import BatchNorm import torch.nn.functional as F from .base.base import make_asynchronous, add_async_graph from .base.utils import graph_changed_nodes, graph_new_nodes def __sync_forward(m, x): return F.batch_norm(x, m.running_mean, m.running_var, m.weight, m.bias, False, m.momentum, m.eps) def __graph_initialization(module: BatchNorm, data) -> torch.Tensor: module.asy_graph = data.clone() module.graph_out = data.clone() module.graph_out.x = __sync_forward(module.module, data.x) # flops are not counted since BN can be fused with previous conv operator. if module.asy_flops_log is not None: flops = 0 module.asy_flops_log.append(flops) return module.graph_out.clone() def __graph_processing(module: BatchNorm, data) -> torch.Tensor: """Batch norms only execute simple normalization operation, which already is very efficient. The overhead for looking for diff nodes would be much larger than computing the dense update. However, a new node slightly changes the feature distribution and therefore all activations, when calling the dense implementation. Therefore, we approximate the distribution with the initial distribution as num_new_events << num_initial_events. """ if len(module.asy_graph.x) < len(data.x): diff_idx = graph_new_nodes(module.asy_graph, data) module.graph_out.x = torch.cat([module.graph_out.x, torch.zeros_like(data.x[:len(diff_idx)])]) else: diff_idx, _ = graph_changed_nodes(module.asy_graph, data) if data.diff_idx.numel()>0: asy_tools.masked_inplace_BN(data.diff_idx, data.x, module.graph_out.x, module.module.running_mean, module.module.running_var, module.module.weight, module.module.bias, module.module.eps) # If required, compute the flops of the asynchronous update operation. if module.asy_flops_log is not None: flops = 0 module.asy_flops_log.append(flops) data.x = module.graph_out.x return data def __check_support(module): return True def make_batch_norm_asynchronous(module: BatchNorm, log_flops: bool = False): """Module converter from synchronous to asynchronous & sparse processing for batch norm (1d) layers. By overwriting parts of the module asynchronous processing can be enabled without the need of re-learning and moving its weights and configuration. So, a layer can be converted by, for example: ``` module = BatchNorm(4) module = make_batch_norm_asynchronous(module) ``` :param module: batch norm module to transform. :param log_flops: log flops of asynchronous update. """ assert __check_support(module) module = add_async_graph(module, log_flops=log_flops) return make_asynchronous(module, __graph_initialization, __graph_processing) ================================================ FILE: src/dagr/asynchronous/cartesian.py ================================================ import torch from torch_geometric.nn.norm import BatchNorm from .base.base import make_asynchronous, add_async_graph def __edge_attr(pos, edge_index, norm, max): (row, col), pos = edge_index, pos cart = pos[row] - pos[col] cart = cart.view(-1, 1) if cart.dim() == 1 else cart if norm and cart.numel() > 0: max_value = cart.abs().max() if max is None else max cart = cart / (2 * max_value) + 0.5 return cart def __graph_initialization(module: BatchNorm, data) -> torch.Tensor: module.asy_graph = data.clone() module.graph_out = data.clone() module.graph_out.edge_attr = __edge_attr(data.pos, data.edge_index, module.norm, module.max) # flops are not counted since BN can be fused with previous conv operator. if module.asy_flops_log is not None: flops = 2 * len(module.graph_out.edge_attr) module.asy_flops_log.append(flops) return module.graph_out.clone() def __graph_processing(module: BatchNorm, data) -> torch.Tensor: """Batch norms only execute simple normalization operation, which already is very efficient. The overhead for looking for diff nodes would be much larger than computing the dense update. However, a new node slightly changes the feature distribution and therefore all activations, when calling the dense implementation. Therefore, we approximate the distribution with the initial distribution as num_new_events << num_initial_events. """ module.graph_out.pos = torch.cat([module.asy_graph.pos, data.pos]) module.graph_out.x = torch.cat([module.asy_graph.x, data.x]) module.graph_out.edge_attr = __edge_attr(module.graph_out.pos, data.edge_index, module.norm, module.max) module.graph_out.edge_index = data.edge_index # flops are not counted since BN can be fused with previous conv operator. if module.asy_flops_log is not None: flops = 2 * len(module.graph_out.edge_attr) module.asy_flops_log.append(flops) if hasattr(data, "diff_idx"): module.graph_out.diff_idx = data.diff_idx module.graph_out.diff_pos_idx = data.diff_pos_idx return module.graph_out def __check_support(module): return True def make_cartesian_asynchronous(module: BatchNorm, log_flops: bool = False): """Module converter from synchronous to asynchronous & sparse processing for cartesian layers. By overwriting parts of the module asynchronous processing can be enabled without the need of re-learning and moving its weights and configuration. So, a layer can be converted by, for example: ``` module = Cartesian() module = make_cartesian_asynchronous(module) ``` :param module: cartesian module to transform. :param log_flops: log flops of asynchronous update. """ assert __check_support(module) module = add_async_graph(module, log_flops=log_flops) return make_asynchronous(module, __graph_initialization, __graph_processing) ================================================ FILE: src/dagr/asynchronous/conv.py ================================================ import asy_tools import torch import torch_geometric.nn.conv from .base.base import make_asynchronous, add_async_graph from .base.utils import graph_new_nodes, graph_changed_nodes, _efficient_cat_unique, torch_isin from .flops import compute_flops_conv, compute_flops_cat from torch_scatter import scatter_sum def __conv(x, edge_index, edge_attr, mask, nn): if edge_index.numel() > 0: x_j = x[edge_index[0, :], :] phi = nn.message(x_j, edge_attr=edge_attr[:, :nn.dim]) y = nn.aggregate(phi, index=edge_index[1, :], ptr=None, dim_size=x.size()[0]) else: y = torch.zeros(size=(x.shape[0], nn.out_channels), dtype=x.dtype, device=x.device) if hasattr(nn, "root_weight") and nn.root_weight: nn.lin_act = nn.lin(x) y[mask] += nn.lin_act[mask] if hasattr(nn, "bias") and nn.bias is not None: y[mask] += nn.bias return y def __graph_initialization(module, data, *args, **kwargs): module.asy_graph = data.clone() module.graph_out = data.clone() # Concat old and updated feature for output feature vector. if hasattr(module.asy_graph, "active_clusters"): mask = module.asy_graph.active_clusters num_updated_elements = len(mask) else: mask = slice(None) num_updated_elements = len(data.x) module.graph_out.x = __conv(data.x, data.edge_index, data.edge_attr, mask, module) # If required, compute the flops of the asynchronous update operation. Therefore, sum the flops for each node # update, as they highly depend on the number of neighbors of this node. if module.asy_flops_log is not None: flops = compute_flops_conv(module, num_times_apply_bias_and_root=num_updated_elements, num_edges=data.edge_index.shape[1]) module.asy_flops_log.append(flops) if hasattr(module, "to_dense"): mask = module.graph_out.active_clusters batch = module.graph_out.batch if module.graph_out.batch is None else module.graph_out.batch[mask] if batch is None: batch = torch.zeros(len(module.graph_out.pos[mask]), dtype=torch.long, device=data.x.device) return module.to_dense(module.graph_out.x[mask], module.graph_out.pos[mask], module.graph_out.pooling, batch) return module.graph_out.clone() def __edges_with_src_node(node_idx, edge_index, edge_attr=None, node_idx_type="src", return_changed_edges=False, return_mask=False): if node_idx.numel() == 0: outputs = [torch.empty(size=(2,0), dtype=torch.long, device=node_idx.device)] if edge_attr is not None: outputs.append(torch.empty(size=(0,3), dtype=edge_attr.dtype, device=edge_attr.device)) if return_mask: outputs.append(torch.empty(size=(0,), dtype=torch.bool, device=node_idx.device)) if len(outputs) == 1: outputs = outputs[0] return outputs if node_idx_type == "src": mask = torch_isin(edge_index[0], node_idx) elif node_idx_type == "dst": mask = torch_isin(edge_index[1], node_idx) elif node_idx_type == "both": mask = torch_isin(edge_index[0], node_idx) | torch_isin(edge_index[1], node_idx) else: raise ValueError output = [edge_index[:,mask]] if edge_attr is not None: output.append(edge_attr[mask]) if return_changed_edges: output.append(mask.nonzero().ravel()) if return_mask: output.append(mask.nonzero().ravel()) if len(output) == 1: output = output[0] return output def find_only_x(idx_new_comp, idx_diff, pos_idx_diff, edge): return idx_new_comp[torch_isin(idx_new_comp, idx_diff) & ~torch_isin(idx_new_comp, pos_idx_diff) & ~torch_isin(idx_new_comp, edge)] def __graph_processing(module, data, *args, **kwargs): """Asynchronous graph update for graph convolutional layer. After the initialization of the graph, only the nodes (and their receptive field) have to updated which either have changed (different features) or have been added. Therefore, for updating the graph we have to first compute the set of "diff" and "new" nodes to then do the convolutional message passing on this subgraph, and add the resulting residuals to the graph. :param x: graph nodes features. """ num_edges_image_feat = 0 num_edges = 0 num_times_apply_bias_and_root = 0 new_nodes = len(data.x) > len(module.asy_graph.x) # first update the input graph if new_nodes: idx_new = graph_new_nodes(module.asy_graph, data) module.asy_graph.x = torch.cat([module.asy_graph.x, data.x[idx_new]]) idx_new_comp = idx_new # when new edges are added through added events, make sure to add them, otherwise only update the edge attributes module.asy_graph.edge_index = torch.cat([module.asy_graph.edge_index, data.edge_index], dim=-1) module.asy_graph.edge_attr = torch.cat([module.asy_graph.edge_attr, data.edge_attr], dim=0) zero_row = torch.zeros(len(idx_new), module.out_channels, device=data.x.device) module.graph_out.x = torch.cat([module.graph_out.x, zero_row]) data.diff_idx = idx_new_comp pos_idx_diff = torch.zeros(size=(0,), dtype=torch.long, device=data.x.device) if idx_new_comp.numel() > 0: edge_index_new, edge_attr_new = data.edge_index, data.edge_attr num_edges += edge_index_new.shape[1] else: idx_diff, pos_idx_diff = graph_changed_nodes(module.asy_graph, data) idx_new_comp = _efficient_cat_unique([pos_idx_diff, idx_diff, data.edge_index[1].unique()]) data.diff_idx = idx_new_comp if idx_new_comp.numel() > 0: # find out dests of idx new, idx diff and pos_idx_diff edge_index_update_message, mask = __edges_with_src_node(idx_new_comp, module.asy_graph.edge_index, return_mask=True) edge_attr_update_message = module.asy_graph.edge_attr[mask] num_edges += edge_index_update_message.shape[1] if hasattr(module.asy_graph, "active_clusters") and hasattr(data, "_changed_attr"): module.asy_graph.edge_attr[data._changed_attr_indices] = data._changed_attr edge_attr_update_message_new = module.asy_graph.edge_attr[mask] else: edge_attr_update_message_new = edge_attr_update_message # when new edges are added through added events, make sure to add them, otherwise only update the edge attributes if data.edge_index.numel() > 0: module.asy_graph.edge_index = torch.cat([module.asy_graph.edge_index, data.edge_index], dim=-1) module.asy_graph.edge_attr = torch.cat([module.asy_graph.edge_attr, data.edge_attr], dim=0) if idx_new_comp.numel() > 0 and edge_index_update_message.numel() > 0: # first compute update to y x_old = module.asy_graph.x[edge_index_update_message[0], :] phi_old = module.message(x_old, edge_attr=edge_attr_update_message) # new messages x_new = data.x[edge_index_update_message[0], :] phi_new = module.message(x_new, edge_attr=edge_attr_update_message_new) scatter_sum(phi_new-phi_old, index=edge_index_update_message[1],out=module.graph_out.x, dim=0, dim_size=len(module.graph_out.x)) data.diff_idx = _efficient_cat_unique([data.diff_idx, edge_index_update_message[1]]) num_edges += edge_index_update_message.shape[1] only_x = find_only_x(idx_new_comp, idx_diff, pos_idx_diff, data.edge_index[1]) if only_x is not None and len(only_x) > 0: idx_new_comp = idx_new_comp[~torch_isin(idx_new_comp, only_x)] generalized_lin(module, data.x - module.asy_graph.x, module.graph_out.x, only_x) num_times_apply_bias_and_root += len(only_x) if idx_new_comp.numel() > 0: # edge and attrs for newly computed edge_index_new, edge_attr_new = __edges_with_src_node(idx_new_comp, edge_index=module.asy_graph.edge_index, edge_attr=module.asy_graph.edge_attr, node_idx_type="dst") edge_index_pos, _ = __edges_with_src_node(pos_idx_diff, edge_index=module.asy_graph.edge_index, edge_attr=module.asy_graph.edge_attr, node_idx_type="dst") num_edges_image_feat = edge_index_pos.shape[1] num_edges += edge_index_new.shape[1] module.graph_out.x[idx_new_comp] = 0 if idx_new_comp.numel() > 0: if edge_index_new.shape[1] > 0: num_edges += edge_index_new.shape[1] # next compute all messages for computing new index x_j = data.x[edge_index_new[0, :], :] phi = module.message(x_j, edge_attr=edge_attr_new[:,:module.dim]) scatter_sum(phi, out=module.graph_out.x, index=edge_index_new[1], dim=0, dim_size=len(module.graph_out.x)) num_times_apply_bias_and_root += len(idx_new_comp) generalized_lin(module, data.x, module.graph_out.x, idx_new_comp) data.x = module.graph_out.x data.diff_pos_idx = pos_idx_diff # If required, compute the flops of the asynchronous update operation. Therefore, sum the flops for each node # update, as they highly depend on the number of neighbors of this node. if module.asy_flops_log is not None: cat = hasattr(data, "skipped") and data.skipped data.skipped = False flops = compute_flops_conv(module, num_times_apply_bias_and_root=len(idx_new_comp), num_edges=num_edges, concatenation=cat, num_image_channels=getattr(data, "num_image_channels", -1)) if cat: flops += compute_flops_cat(module, num_edges=num_edges_image_feat, num_times_apply_bias_and_root=num_times_apply_bias_and_root, num_image_channels=getattr(data, "num_image_channels", -1)) module.asy_flops_log.append(flops) if hasattr(module, "to_dense"): if pos_idx_diff.numel() > 0 or idx_new_comp.numel() > 0: mask = data.active_clusters batch = data.batch if data.batch is None else data.batch[mask] if batch is None: batch = torch.zeros(len(module.graph_out.pos[mask]), dtype=torch.long, device=data.x.device) return module.to_dense(data.x[mask], data.pos[mask], data.pooling, batch) else: return module.dense[:1] return data def generalized_lin(module, input, output, idx): uses_bias = hasattr(module, "bias") and module.bias is not None uses_weight = hasattr(module, "root_weight") and module.root_weight if not uses_weight: return if uses_bias: asy_tools.masked_lin(idx, input, output, module.lin.weight.data, module.bias.data, True) else: asy_tools.masked_lin_no_bias(idx, input, output, module.lin.weight.data, True) def __check_support(module) -> bool: if isinstance(module, torch_geometric.nn.conv.GCNConv): if module.normalize is True: raise NotImplementedError("GCNConvs with normalization are not yet supported!") return True def make_conv_asynchronous(module, log_flops: bool = False): """Module converter from synchronous to asynchronous & sparse processing for graph convolutional layers. By overwriting parts of the module asynchronous processing can be enabled without the need of re-learning and moving its weights and configuration. So, a convolutional layer can be converted by, for example: ``` module = GCNConv(1, 2) module = make_conv_asynchronous(module) ``` :param module: convolutional module to transform. :param r: update radius around new events. :param edge_attributes: function for computing edge attributes (default = None). :param is_initial: layer initial layer of sequential or deeper (default = False). :param log_flops: log flops of asynchronous update. """ assert __check_support(module) module = add_async_graph(module, log_flops=log_flops) return make_asynchronous(module, __graph_initialization, __graph_processing) ================================================ FILE: src/dagr/asynchronous/evaluate_flops.py ================================================ import torch from torch_geometric.data import Batch, Data from typing import List, Tuple from collections import OrderedDict from . import make_model_asynchronous, make_model_synchronous def split_data(data: Data, index: int)->Tuple[Data, Data]: kwargs = dict(time_window=data.time_window, width=data.width, height=data.height) if hasattr(data, "image"): kwargs['image'] = data.image data1 = Data(pos=data.pos[:index], x=data.x[:index], **kwargs) data2 = Data(pos=data.pos[index:], x=data.x[index:], **kwargs) if hasattr(data, "pos_denorm"): data1.pos_denorm = data.pos_denorm[:index] data2.pos_denorm = data.pos_denorm[index:] return data1, data2 def forward_hook(inst, inp, out): inp = inp[0] if type(inp) is list: inp = inp[0].clone() elif type(inp) is tuple or type(inp) is dict: return else: inp = inp.clone() if type(out) is list: out = out[0].clone() elif type(out) is tuple or type(out) is dict: return else: out = out.clone() if not hasattr(inst, "activations"): inst.activations = [] if type(inp) is torch.Tensor: inp = inp if len(inp.shape) == 2 else inp[0] inp = Data(x=inp) if type(out) is torch.Tensor: out = out if len(out.shape) == 2 else out[0] out = Data(x=out) if hasattr(inp, "active_clusters") and not hasattr(out, "active_clusters"): out.active_clusters = inp.active_clusters elif hasattr(out, "active_clusters") and not hasattr(inp, "active_clusters"): inp.active_clusters = out.active_clusters inp = _mask_if_possible(inp) out = _mask_if_possible(out) inst.activations.append((inp, out)) def _mask_if_possible(data): mask = slice(None, None, None) if hasattr(data,"active_clusters") and len(data.x) > data.active_clusters.max(): mask = data.active_clusters masked = Data() if hasattr(data, "x"): masked.x = data.x[mask] if hasattr(data, "pos") and data.pos is not None: masked.pos = data.pos[mask] if hasattr(data, "edge_index"): masked.edge_index = data.edge_index masked.edge_attr = data.edge_attr return masked def denorm(data): denorm = torch.tensor([int(data.width), int(data.height), int(data.time_window)], device=data.pos.device) data.pos_denorm = (denorm.view(1,-1) * data.pos + 1e-3).int() data.batch = data.batch.int() return data def evaluate_flops(model: torch.nn.Module, batch: Data, dense=False, check_consistency=False, return_all_samples=False) -> OrderedDict: flops_per_layer_batch = [] # for loop over batch for i, data in enumerate(batch.to_data_list()): events_initial, events_new = split_data(data, -1) events_initial = Batch.from_data_list([events_initial]) events_new = Batch.from_data_list([events_new]) data = Batch.from_data_list([data]) # prepare data for fast inference data = denorm(data) events_new = denorm(events_new) events_initial = denorm(events_initial) # make a deep copy asynchronous version handles = [] if check_consistency: for m in model.modules(): handle = m.register_forward_hook(forward_hook) handles.append(handle) with torch.no_grad(): model.forward(data, reset=True, return_targets=False) model = make_model_asynchronous(model, log_flops=True) try: with torch.no_grad(): model.forward(events_initial, reset=True, return_targets=False) model.forward(events_new, reset=False, return_targets=False) except Exception as e: print(f"Crashed at index {i} with message {e}") raise e index = 0 if dense else 1 flops_per_layer = OrderedDict( [ (name, module.asy_flops_log[index]) for name, module in model.named_modules() \ if hasattr(module, "asy_flops_log") and module.asy_flops_log is not None and len( module.asy_flops_log) > 0 ] ) flops_per_layer = _filter_non_leaf_nodes(flops_per_layer) flops_per_layer = _merge_to_level_flops(flops_per_layer, level=3) if not check_consistency: flops_per_layer_batch.append(flops_per_layer) model = make_model_synchronous(model) if check_consistency: # tests if outputs from 0th and 2nd run are equal max_mistake_x_layer, max_mistake_pos_layer, global_summary = test_and_compare_activations(model, runs=[0,2]) if max_mistake_x_layer[0] > 1e-3 or max_mistake_pos_layer[1] > 1e-3: print(global_summary) print(f"AssertionError(Failed at index {i}.)") else: flops_per_layer_batch.append(flops_per_layer) print(global_summary) for handle in handles: handle.remove() for m in model.modules(): if hasattr(m, "activations"): del m.activations if len(flops_per_layer_batch) == 0: return None # global average flops_per_layer = _merge_list_flops(flops_per_layer_batch) output = {"flops_per_layer": flops_per_layer, "total_flops": sum(flops_per_layer.values())} if return_all_samples: output['flops_per_layer_batch'] = flops_per_layer_batch return output def _filter_non_leaf_nodes(flops_per_layer: OrderedDict)->OrderedDict: filter_keys = [] for q_name in flops_per_layer: for name in flops_per_layer: if q_name in name and q_name != name: filter_keys.append(q_name) break for f in filter_keys: flops_per_layer.pop(f) return flops_per_layer def _merge_to_level_flops(flops_per_layer: OrderedDict, level=2)->OrderedDict: known_flops = [] known_keys = [] for name, flops in flops_per_layer.items(): layers = name.split(".") layers_up_to_level = ".".join(layers[:level]) if layers_up_to_level not in known_keys: known_keys.append(layers_up_to_level) known_flops.append(0) index = known_keys.index(layers_up_to_level) known_flops[index] += flops return OrderedDict(zip(known_keys, known_flops)) def _merge_list_flops(flops_per_layer_batch: List[OrderedDict])->OrderedDict: return OrderedDict([(key, sum([f[key] for f in flops_per_layer_batch]) / len(flops_per_layer_batch)) for key in flops_per_layer_batch[0]]) def _summary(est, gt, prefix): if len(est) != len(gt): return "\tCannot compare since x do not have same length\n", None max_diff, max_rel_diff, ind, max_ind = max_abs_diff(gt, est, threshold=1e-6) summary = f"\t{prefix} MAX DIFF: {max_diff} MAX REL DIFF: {max_rel_diff}\n" if ind.numel() > 0: summary += f"\t{prefix} IND: {max_ind.cpu().numpy().ravel().tolist()}\n" return summary, max_diff def max_rel_diff(x, y, threshold=None): return error_above_threshold((x-y).abs() / (x.abs()+1e-6), threshold) def error_above_threshold(error, mag, threshold): if threshold is None: return error.max() else: error_ravel = error.ravel() arg = error_ravel.argmax() return error_ravel[arg], error_ravel[arg] / mag.ravel()[arg], (error > threshold).nonzero()[:,0].unique(), error.max(-1).values.argmax() def max_abs_diff(x, y, threshold=None, alpha=0): error = (x-y).abs()-x.abs()*alpha return error_above_threshold(error, x.abs(), threshold) def _print_summary_for_one(target, estimate, prefix=""): max_diff_pos = None if type(target) is torch.Tensor: summary, max_diff_x = _summary(target, estimate, prefix) else: summary = "" if target.pos is not None and estimate.pos is not None: sub_summary, max_diff_pos = _summary(target.pos[:,:2], estimate.pos[:,:2], f"{prefix} POS") summary += sub_summary sub_summary, max_diff_x = _summary(target.x, estimate.x, prefix=f"{prefix} X") summary += sub_summary return summary, max_diff_x, max_diff_pos def print_summary_of_module(activations, runs=[0,2]): target, estimate = [activations[i][1] for i in runs] return _print_summary_for_one(target, estimate, "OUT") def test_and_compare_activations(model, runs=[0,2]): num_mistakes = [] global_summary = "" for name, module in model.named_modules(): if not hasattr(module, "activations"): continue else: if len(module.activations) <= max(runs): continue summary, max_diff_x, max_diff_pos = print_summary_of_module(module.activations, runs) if max_diff_x is not None and max_diff_pos is not None: num_mistakes.append([max_diff_x, max_diff_pos, name]) global_summary += f"Inspecting {name}\n{summary}\n\n" max_mistake_x_layer = max(num_mistakes, key=lambda x: x[0]) max_mistake_pos_layer = max(num_mistakes, key=lambda x: x[1]) global_summary += f"Maximum mistakes: \n" \ f"\t{max_mistake_x_layer}\n" \ f"\t{max_mistake_pos_layer}" return max_mistake_x_layer, max_mistake_pos_layer, global_summary ================================================ FILE: src/dagr/asynchronous/flops/__init__.py ================================================ import logging from torch.nn import ModuleList from .conv import compute_flops_conv, compute_flops_cat def compute_flops_from_module(module) -> int: """Compute flops from a GNN module (after the forward pass). Generally, there are two cases. Either the module is an asynchronous module, then it should have an `flops_log`, which contains the flops used for the last forward pass. Otherwise, the layer's flops are computed from to the synchronous, dense update. :param module: module to infer the flops from. """ module_name = module.__class__.__name__ if hasattr(module, "asy_flops_log") and module.asy_flops_log is not None: assert type(module.asy_flops_log) == list, "asyc. flops log must be a list" if type(module) is ModuleList: flops = sum([compute_flops_from_module(layer) for layer in module._modules.values()]) else: assert len(module.asy_flops_log) > 0, f"asynchronous flops log is empty for module {module.__class__.__name__}" flops = module.asy_flops_log[-1] else: logging.debug(f"Module {module_name} is not asynchronous, using flops = 0") return 0 logging.debug(f"Module {module_name} adds {flops} flops") return flops __all__ = [ "compute_flops_conv", "compute_flops_from_module" ] ================================================ FILE: src/dagr/asynchronous/flops/conv.py ================================================ import torch def compute_flops_conv(module: torch.nn.Module, num_times_apply_bias_and_root: int, num_edges: int, concatenation=False, num_image_channels=-1) -> int: # Iterate over every different and every new node, and add the number of flops introduced # by the node to the overall flops count of the layer. ni = num_edges m_in = module.in_channels if concatenation: m_in -= num_image_channels m_out = module.out_channels flops = ni * (2*m_in-1) * m_out if hasattr(module, "root_weight") and module.root_weight: flops += num_times_apply_bias_and_root * module.lin.weight.shape[0] * (2*module.lin.weight.shape[1]-1) if hasattr(module, "bias") and module.bias is not None: flops += num_times_apply_bias_and_root * module.lin.weight.shape[0] return flops def compute_flops_cat(module, num_edges, num_times_apply_bias_and_root, num_image_channels): ni = num_edges m_in = num_image_channels m_out = module.out_channels flops = ni * (2 * m_in - 1) * m_out if hasattr(module, "root_weight") and module.root_weight: flops += num_times_apply_bias_and_root * module.lin.weight.shape[0] * (2*m_in-1) return flops ================================================ FILE: src/dagr/asynchronous/linear.py ================================================ import numpy as np import torch import torch_geometric import asy_tools from torch.nn import Linear import torch.nn.functional as F from .base.base import make_asynchronous, add_async_graph from .base.utils import graph_new_nodes, graph_changed_nodes def __graph_initialization(module: Linear, data) -> torch.Tensor: mask = data.active_clusters if hasattr(data, "active_clusters") else slice(None, None, None) x = data.x[mask] weight = module.mlp.weight bias = module.mlp.bias y = torch.zeros(size=(len(data.x), weight.shape[0]), dtype=torch.float32, device=data.pos.device) y[mask] = F.linear(x, weight, bias) module.asy_graph = data.clone() module.graph_out = torch_geometric.data.Data(x=y, pos=data.pos) if hasattr(data, "active_clusters"): module.graph_out.active_clusters = data.active_clusters if module.asy_flops_log is not None: flops = int(np.prod(x.size()) * y.size()[-1]) module.asy_flops_log.append(flops) return module.graph_out.clone() def __graph_processing(module: Linear, data) -> torch.Tensor: if len(module.asy_graph.x) < len(data.x): diff_idx = graph_new_nodes(module.asy_graph, data) diff_pos_idx = diff_idx.clone() module.graph_out.x = torch.cat([module.graph_out.x, torch.zeros_like(module.graph_out.x[:len(diff_idx)])]) else: diff_idx, diff_pos_idx = graph_changed_nodes(module.asy_graph, data) weight = module.mlp.weight bias = module.mlp.bias # Update the graph with the new values (only there where it has changed). if diff_idx.numel() > 0: if bias is not None: asy_tools.masked_lin(diff_idx, data.x, module.graph_out.x, weight.data, bias.data, False) else: asy_tools.masked_lin_no_bias(diff_idx, data.x, module.graph_out.x, weight.data, False) # If required, compute the flops of the asynchronous update operation. if module.asy_flops_log is not None: cin = weight.shape[1] cat = hasattr(data, "skipped") and data.skipped data.skipped = False if cat: cin -= data.num_image_channels flops = diff_idx.numel() * int(weight.shape[0] * (2*cin-1)) flops += diff_idx.numel() * weight.shape[0] module.asy_flops_log.append(flops) data.diff_idx = diff_idx data.diff_pos_idx = diff_pos_idx data.x = module.graph_out.x return data def __check_support(module: Linear): return True def make_linear_asynchronous(module: Linear, log_flops: bool = False): """Module converter from synchronous to asynchronous & sparse processing for linear layers. By overwriting parts of the module asynchronous processing can be enabled without the need of re-learning and moving its weights and configuration. So, a linear layer can be converted by, for example: ``` module = Linear(4, 2) module = make_linear_asynchronous(module) ``` :param module: linear module to transform. :param log_flops: log flops of asynchronous update. """ assert __check_support(module) module = add_async_graph(module, log_flops=log_flops) return make_asynchronous(module, __graph_initialization, __graph_processing) ================================================ FILE: src/dagr/asynchronous/max_pool.py ================================================ import logging import torch from torch_geometric.data import Data from torch_scatter import scatter_max, scatter_sum from .base.base import add_async_graph, make_asynchronous from .base.utils import graph_changed_nodes, graph_new_nodes, _efficient_cat_unique, torch_isin, _efficient_cat from .conv import __edges_with_src_node from .base.utils import _to_hom, _from_hom, __remove_duplicate_from_A def pool_edge(cluster, edge_index, self_loop): edge_index = cluster[edge_index] if self_loop: edge_index = edge_index.unique(dim=-1) else: edge_index = edge_index[:,edge_index[0]!=edge_index[1]].unique(dim=-1) if len(edge_index) > 0: return edge_index return torch.zeros((2,0), dtype=torch.long, device=cluster.device) def compute_attrs(transform, edge_index, pos): return (pos[edge_index[0]] - pos[edge_index[1]]) / (2 * transform.max) + 0.5 def __dense_process(module, data: Data, *args, **kwargs) -> Data: # compute the cache to compute the output graph. This contains # 1. the cluster assignment for each input feature -> dim num_input_nodes # 2. the sum of positions for each feature in each cluster -> max_num_clusters # 3. the count of positions for each feature -> max_num_clusters # 4. which input nodes went to the computation of which output_node -> max_num_clusters x num_output cluster_index = __get_global_cluster_index(module, pos=data.pos[:,:module.dim]) x, pos = data.x, data.pos edge_index = pool_edge(cluster_index, data.edge_index, module.self_loop) if hasattr(module.asy_graph, "active_clusters"): active_cluster_index = cluster_index[module.asy_graph.active_clusters] new_cluster_index = torch.full_like(cluster_index, fill_value=-1) new_cluster_index[module.asy_graph.active_clusters] = active_cluster_index cluster_index = new_cluster_index x = x[module.asy_graph.active_clusters] pos = pos[module.asy_graph.active_clusters] else: active_cluster_index = cluster_index pos_hom = scatter_sum(_to_hom(pos[:,:module.dim]), active_cluster_index, dim=0, dim_size=module.num_grid_cells) output_pos = _from_hom(pos_hom) module.wh_inv = 1/ torch.Tensor([data.width[0], data.height[0]]).to(output_pos.device).view(1,-1) output_pos[:,:2] = module.round_to_pixel(output_pos[:,:2], wh_inv=module.wh_inv) active_clusters = torch.unique(active_cluster_index) cache = Data(cluster_index=cluster_index, pos_hom=pos_hom) if module.aggr == 'max': output_x = torch.full(size=(module.num_grid_cells, x.shape[1]), fill_value=-torch.inf, device=x.device) _, output_argmax = scatter_max(x, active_cluster_index, dim=0, out=output_x, dim_size=module.num_grid_cells) cache.output_argmax = output_argmax else: x_hom = _to_hom(x) cache.output_x_hom = scatter_sum(x_hom, active_cluster_index, dim=0, dim_size=module.num_grid_cells) output_x = _from_hom(cache.output_x_hom) module.ones = torch.ones_like(output_x[:,:1]) # construct output. This contains: # the output graph -> has num_unique_clusters nodes if module.keep_temporal_ordering: t = pos[:, -1] if pos.shape[-1] > 2 else data.t_max[active_cluster_index] output_t = torch.full(size=(module.num_grid_cells,), fill_value=-torch.inf, device=x.device) t_max, _ = scatter_max(t, active_cluster_index, dim=0, out=output_t, dim_size=module.num_grid_cells) if edge_index.shape[1] > 0: t_src, t_dst = t_max[edge_index] edge_index = edge_index[:, t_dst > t_src] output_graph = Data(x=output_x, pos=output_pos, edge_index=edge_index, active_clusters=active_clusters, width=data.width, height=data.height) if module.keep_temporal_ordering: output_graph.t_max = output_t if module.transform is not None: output_graph = module.transform(output_graph) return output_graph, cache def __graph_initialization(module, data: Data, *args, **kwargs) -> Data: """Graph initialization for asynchronous update. Both the input as well as the output graph have to be stored, in order to avoid repeated computation. The input graph is used for spotting changed or new nodes (as for other asyn. layers), while the output graph is compared to the set of diff & new nodes, in order to be updated. Depending on the type of pooling (max, mean, average, etc) not only the output voxel feature have to be stored but also aggregations over all nodes in one output voxel such as the sum or count. Next to the features the node positions are averaged over all nodes in the voxel, as well. To do so, position aggregations (count, sum) are stored and updated, too. """ module.asy_graph = data.clone() module.graph_out, module.cache = __dense_process(module, data) module.graph_out.pooling = module.voxel_size logging.debug(f"Resulting in coarse graph {module.graph_out}") # Compute number of floating point operations (no cat, flatten, etc.). if module.asy_flops_log is not None: unique_clusters = len(module.graph_out.active_clusters) flops = 6 * unique_clusters # pos and scatter with index flops += module.graph_out.x.shape[1] * unique_clusters + module.graph_out.edge_index.numel() # every edge has to be re-assigned module.asy_flops_log.append(flops) return module.graph_out.clone() #@profile def __graph_process(module, data, *args, **kwargs) -> Data: new_nodes = len(data.x) > len(module.asy_graph.x) if new_nodes: new_idx = graph_new_nodes(module.asy_graph, data) module.asy_graph.x = torch.cat([module.asy_graph.x, data.x[new_idx]]) module.asy_graph.pos = torch.cat([module.asy_graph.pos, data.pos[new_idx]]) new_cluster_idx = __get_global_cluster_index(module, data.pos[new_idx, :module.dim]) # add to active clusters if new_idx.numel() > 0: module.graph_out.active_clusters = torch.cat([new_cluster_idx, module.graph_out.active_clusters]).sort().values.unique() module.cache.cluster_index = torch.cat([module.cache.cluster_index, new_cluster_idx]) diff_pos_idx = new_idx new_pos_hom = _to_hom(data.pos[new_idx, :module.dim], module.ones) recomp_pos_new = new_cluster_idx recomp_x_new = new_cluster_idx if recomp_x_new.numel() > 0: recomp_x_new = recomp_x_new#.clone() num_diff_x = 0#len(diff_idx) num_new = len(new_idx) scatter_sum(new_pos_hom, new_cluster_idx, out=module.cache.pos_hom, dim=0) if recomp_x_new.numel() > 0: if module.aggr == "max": mask = torch.cat([module.cache.output_argmax[recomp_x_new].ravel(), new_idx]).unique() else: mask = torch_isin(module.cache.cluster_index, recomp_x_new) else: num_new = 0 diff_idx, diff_pos_idx = graph_changed_nodes(module.asy_graph, data) num_diff_x = len(diff_idx) recomp_x_new = None recomp_pos_new = None if diff_pos_idx.numel()> 0: inactive = torch_isin(diff_pos_idx, module.asy_graph.active_clusters) old_pos = module.asy_graph.pos[diff_pos_idx[inactive], :module.dim] module.asy_graph.pos[diff_pos_idx] = data.pos[diff_pos_idx] old_pos_hom = _to_hom(old_pos, module.ones) old_cluster_idx_pos = __get_global_cluster_index(module, old_pos) new_pos_hom = _to_hom(data.pos[diff_pos_idx, :module.dim], module.ones) all_pos = torch.cat([-old_pos_hom, new_pos_hom]) new_cluster_idx_pos = __get_global_cluster_index(module, data.pos[diff_pos_idx, :module.dim]) module.cache.cluster_index[diff_pos_idx] = new_cluster_idx_pos recomp_x_new = new_cluster_idx_pos recomp_pos_new = _efficient_cat([old_cluster_idx_pos, new_cluster_idx_pos]) # todo stupid bug, shallow copy could occur if recomp_pos_new.numel()>0 and recomp_pos_new.data_ptr() == recomp_x_new.data_ptr(): recomp_pos_new = recomp_pos_new#.clone() scatter_sum(all_pos, recomp_pos_new, out=module.cache.pos_hom, dim=0) if diff_idx.numel() > 0: cluster_idx_x = __get_global_cluster_index(module, module.asy_graph.pos[diff_idx, :module.dim]) recomp_x_new = cluster_idx_x if recomp_x_new is None else _efficient_cat_unique([recomp_x_new, cluster_idx_x]) if recomp_x_new is not None and recomp_x_new.numel() > 0: mask = torch_isin(module.cache.cluster_index, recomp_x_new) if module.aggr == "max": module.graph_out.x[recomp_x_new] = -torch.inf if recomp_x_new is not None and recomp_x_new.numel() > 0: if module.aggr == "max": scatter_max(data.x[mask], module.cache.cluster_index[mask], out=module.graph_out.x, dim=0) else: delta_x_hom = _to_hom(data.x[mask], module.ones) # valid = ~torch.isinf(module.asy_graph.x[mask][:,0]) delta_x_hom[valid] -= _to_hom(module.asy_graph.x[mask][valid], module.ones) scatter_sum(delta_x_hom, module.cache.cluster_index[mask], out=module.cache.output_x_hom, dim=0) module.graph_out.x[recomp_x_new] = _from_hom(module.cache.output_x_hom[recomp_x_new]) # find the edges which are associated with changed positions since these need their attrs updated # however, here we can only look at the x,y values. If only the third attr changes, then we don't need to do anything if recomp_pos_new is not None and recomp_pos_new.numel() > 0: # update pos with the updated positions new_pos = _from_hom(module.cache.pos_hom[recomp_pos_new]) new_pos[:,:2] = module.round_to_pixel(new_pos[:,:2], wh_inv=module.wh_inv) module.graph_out.pos[recomp_pos_new,:module.dim] = new_pos update_edge_index, changed_edges = __edges_with_src_node(recomp_pos_new, module.graph_out.edge_index, node_idx_type="both", return_changed_edges=True) if module.transform is not None: module.graph_out._changed_attr = compute_attrs(module.transform, update_edge_index, module.graph_out.pos) module.graph_out._changed_attr_indices = changed_edges # also handle edges which come from new connections at the input. These first need to be pooled # then check if they are actually new. if data.edge_index.numel() > 0: coarse_edge_index = pool_edge(module.cache.cluster_index, data.edge_index, module.self_loop) module.graph_out.edge_index = __remove_duplicate_from_A(coarse_edge_index, module.graph_out.edge_index) else: module.graph_out.edge_index = data.edge_index#torch.empty((2, 0), dtype=torch.long, device=data.x.device) if module.transform is not None: if module.graph_out.edge_index.numel() > 0: module.graph_out.edge_attr = compute_attrs(module.transform, module.graph_out.edge_index, module.graph_out.pos) else: module.graph_out.edge_attr = data.edge_attr module.graph_out.diff_idx = recomp_x_new.unique() if recomp_x_new is not None else diff_idx module.graph_out.diff_pos_idx = recomp_pos_new.unique() if recomp_pos_new is not None else diff_pos_idx if module.asy_flops_log is not None: num_recomp_x = 0 if recomp_x_new is None else len(recomp_x_new) num_recomp_pos = 0 if recomp_pos_new is None else len(recomp_pos_new) flops = 0 flops += num_recomp_x * module.graph_out.x.shape[1] # perform max flops += num_recomp_pos # recompute pos flops += 4 * len(diff_pos_idx) # subtract and add pos twice flops += len(diff_pos_idx) + num_diff_x # get cluster center for each index flops += num_new * 2 # add twice, also compute cluster center module.asy_flops_log.append(flops) return module.graph_out def __get_global_cluster_index(module, pos) -> torch.LongTensor: n_pos_dim = 2#pos.shape[1] voxel_size = module.voxel_size[:n_pos_dim]#, device=pos.device) pos_vertex = (pos[:,:2] / voxel_size).long() x_v, y_v = pos_vertex.t() grid_size = (1 / voxel_size + 1e-3).long() cluster_idx = x_v + grid_size[0] * y_v return cluster_idx def make_max_pool_asynchronous(module, log_flops: bool = False): """Module converter from synchronous to asynchronous & sparse processing for graph max pooling layer. By overwriting parts of the module asynchronous processing can be enabled without the need re-creating the object. So, a max pooling layer can be converted by, for example: ``` module = MaxPool([4, 4]) module = make_max_pool_asynchronous(module) ``` :param module: standard max pooling module. :param grid_size: grid size (grid starting at 0, spanning to `grid_size`), >= `size`. :param r: update radius around new events. :param log_flops: log flops of asynchronous update. """ module = add_async_graph(module, log_flops=log_flops) module = make_asynchronous(module, __graph_initialization, __graph_process) return module ================================================ FILE: src/dagr/data/augment.py ================================================ import torch from torch_geometric.transforms import BaseTransform from torch_geometric.data import Data from typing import List import cv2 import numpy as np import numba import torch_geometric.transforms as T @numba.njit def _add_event(x, y, xlim, ylim, p, i, count, pos, mask, threshold=1): count[ylim, xlim] += float(p * (1 - abs(x - xlim)) * (1 - abs(y - ylim))) pol = 1 if count[ylim, xlim] > 0 else -1 if pol * count[ylim, xlim] > threshold: count[ylim, xlim] -= pol * threshold mask[i] = True pos[i, 0] = xlim pos[i, 1] = ylim @numba.njit def _subsample(pos: np.ndarray, polarity: np.ndarray, mask: np.ndarray, count: np.ndarray, threshold=1): for i in range(len(pos)): x, y = pos[i] x0, x1 = int(x), int(x+1) y0, y1 = int(y), int(y+1) _add_event(x, y, x0, y0, polarity[i,0], i=i, count=count, pos=pos, mask=mask, threshold=threshold) _add_event(x, y, x1, y0, polarity[i,0], i=i, count=count, pos=pos, mask=mask, threshold=threshold) _add_event(x, y, x0, y1, polarity[i,0], i=i, count=count, pos=pos, mask=mask, threshold=threshold) _add_event(x, y, x1, y1, polarity[i,0], i=i, count=count, pos=pos, mask=mask, threshold=threshold) def _crop_events(data, left, right, not_crop_idx=None): if not_crop_idx is None: not_crop_idx = torch.all((data.pos >= left) & (data.pos <= right), dim=1) data.x = data.x[not_crop_idx] data.pos = data.pos[not_crop_idx] if hasattr(data, "t"): data.t = data.t[not_crop_idx] return data def _crop_image(image, left, right): xmin, ymin = left xmax, ymax = right image[:ymin, :] = 0 image[ymax:, :] = 0 image[:, :xmin] = 0 image[:, xmax:] = 0 return image def _resize_image(image, height, width, bg=None): image = image[0].permute(1, 2, 0).numpy() new_image = cv2.resize(image, (width, height), interpolation=cv2.INTER_NEAREST) px = (new_image.shape[1] - image.shape[1])//2 py = (new_image.shape[0] - image.shape[0])//2 if px >= 0: bg = new_image[py:py+image.shape[0], px:px+image.shape[1]] else: assert bg is not None bg[-py:-py+new_image.shape[0], -px:-px+new_image.shape[1]] = new_image bg = torch.from_numpy(bg).permute(2, 0, 1)[None] return bg def _crop_bbox(bbox: torch.Tensor, left: torch.Tensor, right: torch.Tensor): bbox = bbox.clone() bbox[:,2:4] += bbox[:,:2] bbox[:,:2] = torch.clamp(bbox[:,:2], min=left, max=right) bbox[:,2:4] = torch.clamp(bbox[:,2:4], min=left, max=right) bbox[:,2:4] -= bbox[:,:2] return bbox def _scale_and_clip(x, scale): return int(torch.clamp(x * scale, min=0, max=scale-1)) class RandomHFlip(BaseTransform): def __init__(self, p: float): self.p = p def __call__(self, data: Data): if torch.rand(1) > self.p: return data data.pos[:,0] = data.width - 1 - data.pos[:,0] if hasattr(data, "image"): image = data.image[0].permute(1,2,0).numpy() image = np.ascontiguousarray(image[:,::-1]) image = torch.from_numpy(image).permute(2, 0, 1)[None] data.image = image if hasattr(data, "bbox"): data.bbox[:, 0] = data.width - 1 - (data.bbox[:, 0] + data.bbox[:, 2]) if hasattr(data, "bbox0"): data.bbox0[:, 0] = data.width - 1 - (data.bbox0[:, 0] + data.bbox0[:, 2]) return data class Crop(BaseTransform): r"""Crop with max and min values, has to be called before a graph is generated. Args: min (List[float]): min value per dimension max (List[float]): max value per dimension """ def __init__(self, min: List[float], max: List[float]): self.min = torch.as_tensor(min) self.max = torch.as_tensor(max) def init(self, height, width): size = [width, height] self.max = torch.IntTensor([_scale_and_clip(m, s) for m, s in zip(self.max, size)]) self.min = torch.IntTensor([_scale_and_clip(m, s) for m, s in zip(self.min, size)]) def __call__(self, data: Data): data = _crop_events(data, self.min, self.max) if hasattr(data, "image"): data.image = _crop_image(data.image, self.min, self.max) # crop bbox to dimension if hasattr(data, "bbox"): data.bbox = _crop_bbox(data.bbox, self.min, self.max) if hasattr(data, "bbox0"): data.bbox0 = _crop_bbox(data.bbox0, self.min, self.max) return data class RandomZoom(BaseTransform): def __init__(self, zoom, subsample=False): self.zoom = zoom self.subsample = subsample self.image = None if subsample: self._count = None def _subsample(self, data, zoom, count): pos_zoom = data.pos.numpy() mask = np.zeros(len(data.pos), dtype="bool") _subsample(pos_zoom, data.x.numpy(), mask, count, threshold=1/(float(zoom)**2)) data.pos = torch.from_numpy(pos_zoom[mask].astype("int16")) # implicit cast to int data.x = data.x[mask] if hasattr(data, "t"): data.t = data.t[mask] return data def init(self, height, width): self.image = np.zeros((height, width, 3), dtype="uint8") self._count = np.zeros((height + 1, width + 1), dtype="float32") def __call__(self, data): zoom = torch.rand(1) * (self.zoom[1] - self.zoom[0]) + self.zoom[0] width, height = int(np.ceil(data.width * zoom)), int(np.ceil(data.height * zoom)) H, W = self.image.shape[:2] data.pos[:, 0] = ((data.pos[:, 0] - W // 2) * zoom + W // 2).to(torch.int16) data.pos[:, 1] = ((data.pos[:, 1] - H // 2) * zoom + H // 2).to(torch.int16) if self.subsample and zoom < 1: data = self._subsample(data, float(zoom), count=self._count.copy()) if hasattr(data, "image"): data.image = _resize_image(data.image, width=width, height=height, bg=self.image.copy() if zoom < 1 else None) if hasattr(data, "bbox"): data.bbox[:,2:4] *= zoom data.bbox[:,0] = ((data.bbox[:,0] - W//2) * zoom + W//2) data.bbox[:,1] = ((data.bbox[:,1] - H//2) * zoom + H//2) if hasattr(data, "bbox0"): data.bbox0[:,2:4] *= zoom data.bbox0[:,0] = ((data.bbox0[:,0] - W//2) * zoom + W//2) data.bbox0[:,1] = ((data.bbox0[:,1] - H//2) * zoom + H//2) return data class RandomCrop(BaseTransform): r"""Random crop, assumes all pos values are in [0,1] Args: size (List[float]): crop size per dimension dim (List[int]): dimension of the crop, default = [0,1] p float: only to random crop with a probability of p """ def __init__(self, size: List[float] = [0.75, 0.75], dim: List[int]=[0,1], p=0.5): self.size = torch.as_tensor(size) self.dim = dim self.p = p def init(self, height, width): size = torch.IntTensor([width, height]) self.size = torch.IntTensor([_scale_and_clip(s, ss) for s, ss in zip(self.size, size)]) self.left_max = size - self.size def __call__(self, data: Data): if torch.rand(1) > self.p: return data left = (torch.rand(len(self.dim)) * self.left_max).to(torch.int16) right = left + self.size data = _crop_events(data, left, right) if hasattr(data, "image"): data.image = _crop_image(data.image, left, right) # crop bbox to new crop dimension if hasattr(data, "bbox"): data.bbox = _crop_bbox(data.bbox, left, right) if hasattr(data, "bbox0"): data.bbox0 = _crop_bbox(data.bbox0, left, right) return data class RandomTranslate(BaseTransform): r"""Random crop, assumes all pos values are in [0,1] Args: size (float): crop size per dimension dim (int): dimension of the crop, default = [0,1] """ def __init__(self, size: List[float]): self.size = torch.as_tensor(size).float() self.image = None def init(self, height, width): size = [width, height] self.size = torch.IntTensor([_scale_and_clip(s, ss) for s, ss in zip(self.size, size)]) self.image = np.zeros((height + 2 * self.size[1], width + 2 * self.size[0], 3), dtype="uint8") def pad(self, image, bg): px = (bg.shape[1] - image.shape[1])//2 py = (bg.shape[0] - image.shape[0])//2 bg[py:py + image.shape[0], px:px + image.shape[1]] = image return bg def __call__(self, data: Data): move_px = (self.size * (torch.rand(len(self.size)) * 2 - 1)).to(torch.int16) data.pos = data.pos + move_px if hasattr(data, "image"): image = data.image[0].permute(1, 2, 0).numpy() image = self.pad(image, self.image.copy()) image = image[self.size[1]-move_px[1]:self.size[1]-move_px[1]+data.height, \ self.size[0]-move_px[0]:self.size[0]-move_px[0]+data.width] data.image = torch.from_numpy(image).permute(2, 0, 1)[None] if hasattr(data, "bbox"): data.bbox[:,:2] += move_px if hasattr(data, "bbox0"): data.bbox0[:,:2] += move_px return data class Augmentations: transform_testing = T.Compose([ Crop([0, 0], [1, 1]), ]) def __init__(self, args): self.transform_training = T.Compose([ RandomHFlip(p=args.aug_p_flip), RandomCrop([0.75, 0.75], p=0.2), RandomZoom(zoom=[1, args.aug_zoom], subsample=True), RandomTranslate([args.aug_trans, args.aug_trans, 0]), Crop([0, 0], [1, 1]), ]) def init_transforms(transforms, height, width): for t in transforms: if hasattr(t, "init"): t.init(height=height, width=width) ================================================ FILE: src/dagr/data/dsec_data.py ================================================ from pathlib import Path from typing import Optional, Callable from torch_geometric.data import Dataset import numpy as np import cv2 import torch from functools import lru_cache from dsec_det.dataset import DSECDet from dsec_det.io import yaml_file_to_dict from dagr.data.dsec_utils import filter_tracks, crop_tracks, rescale_tracks, compute_class_mapping, map_classes, filter_small_bboxes from dsec_det.directory import BaseDirectory from dagr.data.augment import init_transforms from dagr.data.utils import to_data from dagr.visualization.bbox_viz import draw_bbox_on_img from dagr.visualization.event_viz import draw_events_on_image def tracks_to_array(tracks): return np.stack([tracks['x'], tracks['y'], tracks['w'], tracks['h'], tracks['class_id']], axis=1) def interpolate_tracks(detections_0, detections_1, t): assert len(detections_1) == len(detections_0) if len(detections_0) == 0: return detections_1 t0 = detections_0['t'][0] t1 = detections_1['t'][0] assert t0 < t1 # need to sort detections detections_0 = detections_0[detections_0['track_id'].argsort()] detections_1 = detections_1[detections_1['track_id'].argsort()] r = ( t - t0 ) / ( t1 - t0 ) detections_out = detections_0.copy() for k in 'xywh': detections_out[k] = detections_0[k] * (1 - r) + detections_1[k] * r return detections_out class EventDirectory(BaseDirectory): @property @lru_cache def event_file(self): return self.root / "left/events_2x.h5" class DSEC(Dataset): MAPPING = dict(pedestrian="pedestrian", rider=None, car="car", bus="car", truck="car", bicycle=None, motorcycle=None, train=None) def __init__(self, root: Path, split: str, transform: Optional[Callable]=None, debug=False, min_bbox_diag=0, min_bbox_height=0, scale=2, cropped_height=430, only_perfect_tracks=False, demo=False, no_eval=False): Dataset.__init__(self) split_config = None if not demo: split_config = yaml_file_to_dict(Path(__file__).parent / "dsec_split.yaml") assert split in split_config.keys(), f"'{split}' not in {list(split_config.keys())}" self.dataset = DSECDet(root=root, split=split, sync="back", debug=debug, split_config=split_config) for directory in self.dataset.directories.values(): directory.events = EventDirectory(directory.events.root) self.scale = scale self.width = self.dataset.width // scale self.height = cropped_height // scale self.classes = ("car", "pedestrian") self.time_window = 1000000 self.min_bbox_height = min_bbox_height self.min_bbox_diag = min_bbox_diag self.debug = debug self.num_us = -1 self.class_remapping = compute_class_mapping(self.classes, self.dataset.classes, self.MAPPING) if transform is not None and hasattr(transform, "transforms"): init_transforms(transform.transforms, self.height, self.width) self.transform = transform self.no_eval = no_eval if self.no_eval: only_perfect_tracks = False self.image_index_pairs, self.track_masks = filter_tracks(dataset=self.dataset, image_width=self.width, image_height=self.height, class_remapping=self.class_remapping, min_bbox_height=min_bbox_height, min_bbox_diag=min_bbox_diag, only_perfect_tracks=only_perfect_tracks, scale=scale) def set_num_us(self, num_us): self.num_us = num_us def visualize_debug(self, index): data = self.__getitem__(index) image = data.image[0].permute(1,2,0).numpy() p = data.x[:,0].numpy() x, y = data.pos.t().numpy() b_x, b_y, b_w, b_h, b_c = data.bbox.t().numpy() image = draw_events_on_image(image, x, y, p) image = draw_bbox_on_img(image, b_x, b_y, b_w, b_h, b_c, np.ones_like(b_c), conf=0.3, nms=0.65) cv2.imshow(f"Debug {index}", image) cv2.waitKey(0) def __len__(self): return sum(len(d) for d in self.image_index_pairs.values()) def preprocess_detections(self, detections): detections = rescale_tracks(detections, self.scale) detections = crop_tracks(detections, self.width, self.height) detections['class_id'], _ = map_classes(detections['class_id'], self.class_remapping) return detections def preprocess_events(self, events): mask = events['y'] < self.height events = {k: v[mask] for k, v in events.items()} if len(events['t']) > 0: events['t'] = self.time_window + events['t'] - events['t'][-1] events['p'] = 2 * events['p'].reshape((-1,1)).astype("int8") - 1 return events def preprocess_image(self, image): image = image[:self.scale * self.height] image = cv2.resize(image, (self.width, self.height), interpolation=cv2.INTER_CUBIC) image = torch.from_numpy(image).permute(2, 0, 1) image = image.unsqueeze(0) return image def __getitem__(self, idx): dataset, image_index_pairs, track_masks, idx = self.rel_index(idx) image_index_0, image_index_1 = image_index_pairs[idx] image_ts_0, image_ts_1 = dataset.images.timestamps[[image_index_0, image_index_1]] detections_0 = self.dataset.get_tracks(image_index_0, mask=track_masks, directory_name=dataset.root.name) detections_1 = self.dataset.get_tracks(image_index_1, mask=track_masks, directory_name=dataset.root.name) detections_0 = self.preprocess_detections(detections_0) detections_1 = self.preprocess_detections(detections_1) image_0 = self.dataset.get_image(image_index_0, directory_name=dataset.root.name) image_0 = self.preprocess_image(image_0) events = self.dataset.get_events(image_index_0, directory_name=dataset.root.name) if self.num_us >= 0: image_ts_1 = image_ts_0 + self.num_us events = {k: v[events['t'] < image_ts_1] for k, v in events.items()} if not self.no_eval: detections_1 = interpolate_tracks(detections_0, detections_1, image_ts_1) # here, the timestamp of the events is no longer absolute events = self.preprocess_events(events) # convert to torch geometric data data = to_data(**events, bbox=tracks_to_array(detections_1), bbox0=tracks_to_array(detections_0), t0=image_ts_0, t1=image_ts_1, width=self.width, height=self.height, time_window=self.time_window, image=image_0, sequence=str(dataset.root.name)) if self.transform is not None: data = self.transform(data) # remove bboxes if they have 0 width or height mask = filter_small_bboxes(data.bbox[:, 2], data.bbox[:, 3], self.min_bbox_height, self.min_bbox_diag) data.bbox = data.bbox[mask] mask = filter_small_bboxes(data.bbox0[:, 2], data.bbox0[:, 3], self.min_bbox_height, self.min_bbox_diag) data.bbox0 = data.bbox0[mask] return data def rel_index(self, idx): for folder in self.dataset.subsequence_directories: name = folder.name image_index_pairs = self.image_index_pairs[name] directory = self.dataset.directories[name] track_mask = self.track_masks[name] if idx < len(image_index_pairs): return directory, image_index_pairs, track_mask, idx idx -= len(image_index_pairs) raise IndexError ================================================ FILE: src/dagr/data/dsec_split.yaml ================================================ train: - thun_00_a - interlaken_00_c - interlaken_00_d - interlaken_00_e - interlaken_00_f - interlaken_00_g - zurich_city_00_a - zurich_city_00_b - zurich_city_01_a - zurich_city_01_b - zurich_city_01_c - zurich_city_01_d - zurich_city_01_e - zurich_city_01_f - zurich_city_02_a - zurich_city_02_b - zurich_city_02_c - zurich_city_02_d - zurich_city_02_e - zurich_city_03_a - zurich_city_04_a - zurich_city_04_b - zurich_city_04_c - zurich_city_04_d - zurich_city_04_e - zurich_city_04_f - zurich_city_05_a - zurich_city_05_b - zurich_city_06_a - zurich_city_07_a - zurich_city_08_a - zurich_city_09_a - zurich_city_09_b - zurich_city_09_c - zurich_city_09_d - zurich_city_09_e - zurich_city_10_a - zurich_city_10_b - zurich_city_11_a - zurich_city_11_b - zurich_city_11_c val: - zurich_city_16_a - zurich_city_17_a - zurich_city_18_a - zurich_city_19_a - zurich_city_20_a - zurich_city_21_a test: - thun_01_a - thun_01_b - thun_02_a - interlaken_00_a - interlaken_00_b - interlaken_01_a - zurich_city_12_a - zurich_city_13_a - zurich_city_13_b - zurich_city_14_a - zurich_city_14_b - zurich_city_14_c - zurich_city_15_a ================================================ FILE: src/dagr/data/dsec_utils.py ================================================ import numpy as np import h5py def construct_pairs(indices, n=2): indices = np.sort(indices) indices = np.stack([indices[i:i+1-n] for i in range(n-1)] + [indices[n-1:]]) mask = np.ones_like(indices[0]) > 0 for i, row in enumerate(indices): mask = mask & (indices[0] + i == row) indices = indices[...,mask].T return indices def rescale_tracks(tracks, scale): tracks = tracks.copy() for k in "xywh": tracks[k] /= scale return tracks def crop_tracks(tracks, width, height): tracks = tracks.copy() x1, y1 = tracks['x'], tracks['y'] x2, y2 = x1 + tracks['w'], y1 + tracks['h'] x1 = np.clip(x1, 0, width-1) x2 = np.clip(x2, 0, width-1) y1 = np.clip(y1, 0, height-1) y2 = np.clip(y2, 0, height-1) tracks['x'] = x1 tracks['y'] = y1 tracks['w'] = x2-x1 tracks['h'] = y2-y1 return tracks def map_classes(class_ids, old_to_new_mapping): new_class_ids = old_to_new_mapping[class_ids] mask = new_class_ids > -1 return new_class_ids, mask def filter_small_bboxes(w, h, bbox_height=20, bbox_diag=30): """ Filter out tracks that are too small. """ diag = np.sqrt(h ** 2 + w ** 2) return (diag > bbox_diag) & (w > bbox_height) & (h > bbox_height) def filter_tracks(dataset, image_width, image_height, class_remapping, min_bbox_height=0, min_bbox_diag=0, scale=1, only_perfect_tracks=False): image_index_pairs = {} track_masks = {} for directory_path in dataset.subsequence_directories: tracks = dataset.directories[directory_path.name].tracks.tracks image_timestamps = dataset.directories[directory_path.name].images.timestamps tracks_rescaled = rescale_tracks(tracks, scale) tracks_rescaled = crop_tracks(tracks_rescaled, image_width, image_height) _, class_mask = map_classes(tracks_rescaled['class_id'], class_remapping) size_mask = filter_small_bboxes(tracks_rescaled['w'], tracks_rescaled['h'], min_bbox_height, min_bbox_diag) final_mask = size_mask & class_mask # 1. stores indices of images which are valid, i.e. survived all filters above valid_image_indices = np.unique(np.nonzero(np.isin(image_timestamps, tracks_rescaled[final_mask]['t']))[0]) valid_image_index_pairs = construct_pairs(valid_image_indices, 2) if only_perfect_tracks: valid_image_timestamp_brackets = image_timestamps[valid_image_index_pairs] img_idx_to_track_idx = compute_img_idx_to_track_idx(tracks['t'], valid_image_timestamp_brackets) mask = filter_by_only_perfect_tracks(tracks_rescaled, img_idx_to_track_idx, tracks_mask=final_mask) valid_image_index_pairs = valid_image_index_pairs[mask] image_index_pairs[directory_path.name] = valid_image_index_pairs track_masks[directory_path.name] = final_mask return image_index_pairs, track_masks def _load_events(file, t0, num_events=None, num_us=None, height=None, time_window=None): with h5py.File(file, 'r') as f: ms = int((t0 - f['t_offset'][()]) / 1e3) idx0 = int(f['ms_to_idx'][ms]) if num_events is not None: idx1 = idx0 + num_events if num_us is not None: idx1 = int(f['ms_to_idx'][ms + int(num_us / 1e3)]) idx0, idx1 = sorted([idx0, idx1]) idx0 = idx0 if idx0 >= 0 else 0 idx1 = idx1 if idx1 >= 0 else 0 # load all events events = {k: f[f'events/{k}'][idx0:idx1] for k in "xytp"} tq = events['t'][-1] if idx1 > idx0 else f[f'events/t'][max([idx1 - 1, idx0])] # cast to desired types p = 2 * events["p"][..., None].astype("int8") - 1 t_ev = events['t'][..., None] xy = np.stack([events['x'], events['y']], axis=-1).astype("int16") if time_window is not None: t = (time_window - tq + t_ev).astype('int32') else: t = tq.copy() # we have to add the offset here tq += f['t_offset'][()] tq = tq.astype("int64") # crop events to crop height mask = (t[:, 0] > 0) if height is not None: mask &= (xy[:, 1] < height) events = (xy[mask], t[mask], p[mask]) return events, tq def filter_by_only_perfect_tracks(tracks, img_idx_to_track_idx, tracks_mask=None): i0, i1 = img_idx_to_track_idx mask = np.ones_like(i0[0]) > 0 for i in range(i0.shape[1]): track = [tracks[i0[j][i]:i1[j][i]] for j in range(len(i0))] if tracks_mask is not None: track_mask = [tracks_mask[i0[j][i]:i1[j][i]] for j in range(len(i0))] track = [t[m] for t, m in zip(track, track_mask)] mask[i] = not is_invalid_track(track) return mask def is_invalid_track(track): track = [tr[tr['track_id'].argsort()] for tr in track] i_tr = track[0] for c_tr in track[1:]: if len(i_tr) != len(c_tr): return True if not (c_tr['track_id'] == i_tr['track_id']).all(): return True iou = compute_iou(i_tr, c_tr) min_iou = np.min(iou) if min_iou < 0.10: return True else: return False def compute_iou(track0, track1): x1, x2 = track0['x'], track0['x'] + track0['w'] y1, y2 = track0['y'], track0['y'] + track0['h'] x1g, x2g = track1['x'], track1['x'] + track1['w'] y1g, y2g = track1['y'], track1['y'] + track1['h'] # Intersection keypoints xkis1 = np.max(np.stack([x1, x1g]), axis=0) ykis1 = np.max(np.stack([y1, y1g]), axis=0) xkis2 = np.min(np.stack([x2, x2g]), axis=0) ykis2 = np.min(np.stack([y2, y2g]), axis=0) intsct = np.zeros_like(x1) mask = (ykis2 > ykis1) & (xkis2 > xkis1) intsct[mask] = (xkis2[mask] - xkis1[mask]) * (ykis2[mask] - ykis1[mask]) union = (x2 - x1) * (y2 - y1) + (x2g - x1g) * (y2g - y1g) - intsct + 1e-9 iou = intsct / union return iou def compute_indices_for_contiguous_parts(x): x, counts = np.unique(x, return_counts=True) idx = np.concatenate([np.array([0]), counts]).cumsum() return np.stack([idx[:-1], idx[1:]], axis=-1) def _compute_img_idx_to_track_idx(t, t_query): new_img_idx = compute_indices_for_contiguous_parts(t) mask = np.isin(np.unique(t), t_query) new_img_idx = new_img_idx[mask].T return new_img_idx def compute_img_idx_to_track_idx(t, t_query): return np.stack([_compute_img_idx_to_track_idx(t, t_q) for t_q in t_query.T]) def compute_class_mapping(classes, all_classes, mapping): output_mapping = [] for i, c in enumerate(all_classes): mapped_class = mapping[c] output_mapping.append(classes.index(mapped_class) if mapped_class in classes else -1) return np.array(output_mapping) ================================================ FILE: src/dagr/data/ncaltech101_data.py ================================================ import numpy as np import torch import hdf5plugin import h5py from pathlib import Path from typing import Optional, Callable from torch.utils.data import Dataset from torch_geometric.data import Data from dagr.data.augment import init_transforms from dagr.data.utils import to_data class NCaltech101(Dataset): def __init__(self, root: Path, split, transform=Optional[Callable[[Data,], Data]], num_events: int=50000): super().__init__() self.load_dir = root / split self.classes = sorted([d.name for d in self.load_dir.glob("*")]) self.num_classes = len(self.classes) self.files = sorted(list(self.load_dir.rglob("*.h5"))) self.height = 180 self.width = 240 if transform is not None and hasattr(transform, "transforms"): init_transforms(transform.transforms, self.height, self.width) self.transform = transform self.time_window = 1000000 self.num_events = num_events def __len__(self): return len(self.files) def preprocess(self, data): data.t -= (data.t[-1] - self.time_window + 1) return data def load_events(self, f_path): return _load_events(f_path, self.num_events) def __getitem__(self, idx): f_path = self.files[idx] target = self.classes.index(str(f_path.parent.name)) events = self.load_events(f_path) data = to_data(**events, bbox=self.load_bboxes(f_path, target), t0=events['t'][0], t1=events['t'], width=self.width, height=self.height, time_window=self.time_window) data = self.preprocess(data) data = self.transform(data) if self.transform is not None else data if not hasattr(data, "t"): data.t = data.pos[:, -1:] data.pos = data.pos[:, :2].type(torch.int16) return data def load_bboxes(self, raw_file: Path, class_id): rel_path = str(raw_file.relative_to(self.load_dir)) rel_path = rel_path.replace("image_", "annotation_").replace(".h5", ".bin") annotation_file = self.load_dir / "../annotations" / rel_path with annotation_file.open() as fh: annotations = np.fromfile(fh, dtype=np.int16) annotations = np.array(annotations[2:10]) return np.array([ annotations[0], annotations[1], # upper-left corner annotations[2] - annotations[0], # width annotations[5] - annotations[1], # height class_id, 1 ]).astype("float32").reshape((1,-1)) def _load_events(f_path, num_events): with h5py.File(str(f_path)) as fh: fh = fh['events'] x = fh["x"][-num_events:] y = fh["y"][-num_events:] t = fh["t"][-num_events:] p = fh["p"][-num_events:] return dict(x=x, y=y, t=t, p=p) ================================================ FILE: src/dagr/data/utils.py ================================================ import numpy as np import torch from torch_geometric.data import Data def to_data(**kwargs): # convert all tracks to correct format for k, v in kwargs.items(): if k.startswith("bbox"): kwargs[k] = torch.from_numpy(v) xy = np.stack([kwargs['x'], kwargs['y']], axis=-1).astype("int16") t = kwargs['t'].astype("int32") p = kwargs['p'].reshape((-1,1)) kwargs['x'] = torch.from_numpy(p) kwargs['pos'] = torch.from_numpy(xy) kwargs['t'] = torch.from_numpy(t) return Data(**kwargs) ================================================ FILE: src/dagr/graph/ev_graph.cu ================================================ #include #include #include #include "spiral.h" #include #define CHECK_CUDA(x) AT_ASSERTM(x.type().is_cuda(), #x " must be a CUDA tensor") #define CHECK_CONTIGUOUS(x) AT_ASSERTM(x.is_contiguous(), #x " must be contiguous") #define CHECK_INPUT(x) CHECK_CUDA(x); CHECK_CONTIGUOUS(x) #define CHECK_DEVICE(x, y) AT_ASSERTM(x.device().index() == y.device().index(), #x " and " #y " must be in same CUDA device") __global__ void fill_edges_cuda_kernel( const int32_t* __restrict__ batch, const int32_t* __restrict__ pos, const int32_t* __restrict__ all_timestamps, const int32_t* __restrict__ indices, const int32_t* __restrict__ event_queue, int64_t* __restrict__ edges, // int64_t* __restrict__ num_neighbors_array, int B, int Q, int H, int W, int N, int K, float radius, float delta_t_us, int max_num_neighbors, int min_index ) { // linear index const int event_idx = blockIdx.x * blockDim.x + threadIdx.x; // check that thread is not out of valid range if (event_idx >= N) return; int radius_int = radius; int num_neighbors = 0; int offset = event_idx * max_num_neighbors; int b = batch[event_idx]; int x = pos[3 * event_idx + 0]; int y = pos[3 * event_idx + 1]; int ts_event = pos[3 * event_idx + 2]; // first add self edge edges[offset + num_neighbors + K * 0] = indices[event_idx]-min_index; edges[offset + num_neighbors + K * 1] = indices[event_idx]-min_index; num_neighbors++; SpiralOut spiral; for (int i=0; i= max_num_neighbors) break; for (int q=0; q= 0) && (y_neighbor >= 0) && (x_neighbor < W) && (y_neighbor < H))) break; int64_t queue_idx = x_neighbor + W * y_neighbor + H * W * q + H * W * Q * b; int idx = event_queue[queue_idx]; // break if exceeded max num neighbors or no more events in queue if (idx < min_index) break; if (indices[event_idx] > idx) { int32_t ts_neighbor = all_timestamps[idx-min_index]; int32_t dt_us = ts_event - ts_neighbor; // if delta t is too large, no edge is added if (dt_us > delta_t_us) continue; edges[offset + num_neighbors + K * 0] = idx-min_index; edges[offset + num_neighbors + K * 1] = indices[event_idx]-min_index; num_neighbors++; if (num_neighbors >= max_num_neighbors) break; } } spiral.goNext(); } //num_neighbors_array[event_idx] = num_neighbors; } void fill_edges_cuda( const torch::Tensor& batch, // N const torch::Tensor& pos, // N x 3 const torch::Tensor& all_timestamps, // N const torch::Tensor& event_queue, // B x Q x H x W const torch::Tensor& indices, // N const int max_num_neighbors, const float radius, const float delta_t_us, torch::Tensor& edges, // 2 x E const int min_index ) { CHECK_INPUT(batch); CHECK_INPUT(pos); CHECK_INPUT(event_queue); CHECK_INPUT(all_timestamps); CHECK_INPUT(edges); CHECK_INPUT(indices); CHECK_DEVICE(batch, event_queue); CHECK_DEVICE(batch, pos); CHECK_DEVICE(batch, edges); CHECK_DEVICE(batch, indices); CHECK_DEVICE(batch, all_timestamps); unsigned N = batch.size(0); unsigned B = event_queue.size(0); unsigned Q = event_queue.size(1); unsigned H = event_queue.size(2); unsigned W = event_queue.size(3); unsigned K = edges.size(1); unsigned threads = 256; dim3 blocks((N + threads - 1) / threads, 1); fill_edges_cuda_kernel<<>>( batch.data(), pos.data(), all_timestamps.data(), indices.data(), event_queue.data(), edges.data(), //num_neighbors.data(), B, Q, H, W, N, K, radius, delta_t_us, max_num_neighbors, min_index ); } template __global__ void insert_in_queue_single_cuda_kernel( const scalar_t* __restrict__ indices, const scalar_t* __restrict__ events, scalar_t* __restrict__ queue, int B, int Q, int H, int W, int K ) { // linear index const int lin_idx = blockIdx.x * blockDim.x + threadIdx.x; // check that thread is not out of valid range if (lin_idx >= K) return; // find out how many events to write, and what is the offset int counts = 1; int offset = 0; // find out the x, y coords where to write the indices int x = events[0]; int y = events[1]; int b = 0; // write indices. break if queue size or counter is exceeded for (int q=Q-1; q>=0; q--) { int index = b * H * W * Q + q * H * W + y * W + x; // for the current position, get the one at q - shift. // if q - shift goes in the negative, take from indices instead if (q >= counts) { int shifted_index = b * H * W * Q + (q-counts) * H * W + y * W + x; queue[index] = queue[shifted_index]; } else { queue[index] = indices[offset + counts - 1 - q]; } } } template __global__ void insert_in_queue_cuda_kernel( const scalar_t* __restrict__ indices, const scalar_t* __restrict__ unique_coords, const scalar_t* __restrict__ cumsum_counts, scalar_t* __restrict__ queue, int B, int Q, int H, int W, int K ) { // linear index const int lin_idx = blockIdx.x * blockDim.x + threadIdx.x; // check that thread is not out of valid range if (lin_idx >= K) return; // find out how many events to write, and what is the offset int counts, offset; if (lin_idx > 0) { offset = cumsum_counts[lin_idx-1]; counts = cumsum_counts[lin_idx] - offset; } else { offset = 0; counts = cumsum_counts[lin_idx]; } // find out the x, y coords where to write the indices int x = unique_coords[lin_idx] % W; int y = ((unique_coords[lin_idx] - x)/ W) % H; int b = unique_coords[lin_idx] / (W*H); // write indices. break if queue size or counter is exceeded for (int q=Q-1; q>=0; q--) { int index = b * H * W * Q + q * H * W + y * W + x; // for the current position, get the one at q - shift. // if q - shift goes in the negative, take from indices instead if (q >= counts) { int shifted_index = b * H * W * Q + (q-counts) * H * W + y * W + x; queue[index] = queue[shifted_index]; } else { queue[index] = indices[offset + counts - 1 - q]; } } } torch::Tensor insert_in_queue_single_cuda( const torch::Tensor& indices, // 1 const torch::Tensor& events, // 4 x 1 const torch::Tensor& queue // B x Q x H x W ) { unsigned W = queue.size(3); unsigned H = queue.size(2); unsigned Q = queue.size(1); unsigned B = queue.size(0); unsigned K = 1; unsigned threads = 256; dim3 blocks((K + threads - 1) / threads, 1); insert_in_queue_single_cuda_kernel<<>>( indices.data(), events.data(), queue.data(), B, Q, H, W, K ); return queue; } torch::Tensor insert_in_queue_cuda( const torch::Tensor& indices, // N -> num events const torch::Tensor& unique_coords, // K -> num active pixels const torch::Tensor& cumsum_counts, // K -> num active pixels const torch::Tensor& queue // B x Q x H x W ) { CHECK_INPUT(indices); CHECK_INPUT(unique_coords); CHECK_INPUT(cumsum_counts); CHECK_INPUT(queue); CHECK_DEVICE(indices, queue); CHECK_DEVICE(indices, unique_coords); CHECK_DEVICE(indices, cumsum_counts); CHECK_DEVICE(indices, queue); unsigned W = queue.size(3); unsigned H = queue.size(2); unsigned Q = queue.size(1); unsigned B = queue.size(0); unsigned K = unique_coords.size(0); unsigned threads = 256; dim3 blocks((K + threads - 1) / threads, 1); insert_in_queue_cuda_kernel<<>>( indices.data(), unique_coords.data(), cumsum_counts.data(), queue.data(), B, Q, H, W, K ); return queue; } PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) { m.def("fill_edges_cuda", &fill_edges_cuda, "Find edges from a queue of events."); m.def("insert_in_queue_cuda", &insert_in_queue_cuda, "Insert events into queue."); m.def("insert_in_queue_single_cuda", &insert_in_queue_single_cuda, "Insert single events into queue."); } ================================================ FILE: src/dagr/graph/ev_graph.py ================================================ import torch from .utils import _insert_events_into_queue, _search_for_edges def move_to_cuda(func): def wrapper(self, x, *args, **kwargs): device = x.device on_cpu = device == "cpu" if on_cpu: x = x.to("cuda") ret = func(self, x, *args, **kwargs) if on_cpu: ret = ret.cpu() return ret return wrapper class AsyncGraph: def __init__(self, width=640, height=480, batch_size=1, max_num_neighbors=16, max_queue_size=512, radius=7, delta_t_us=600000): self.radius = radius self.delta_t_us = delta_t_us self.event_queue = None self.max_index = 0 self.min_index = 0 self.max_queue_size = max_queue_size self.max_num_neighbors = max_num_neighbors self.width = width self.height = height self.batch_size = batch_size self.device = None self.edges = torch.zeros((2,0), dtype=torch.long) self.all_timestamps = torch.zeros((0,), dtype=torch.int32) self.new_indices = None self.edge_buffer = None self.event_queue = None def initialize(self, n_ev, device): self.edges = torch.zeros((2,0), dtype=torch.long, device=device) self.all_timestamps = torch.zeros((0,), dtype=torch.int32, device=device) self.new_indices = torch.arange(n_ev, dtype=torch.int32, device=device) self.edge_buffer = torch.full((2, self.max_num_neighbors * n_ev), dtype=torch.int64, fill_value=-1, device=device) self.event_queue = torch.full((self.batch_size, self.max_queue_size, self.height, self.width), fill_value=-1, device=device, dtype=torch.int32) def reset(self): self.edges = torch.zeros((2,0), dtype=torch.long, device=self.device) self.all_timestamps = torch.zeros((0,), dtype=torch.int32, device=self.device) self.max_index = 0 self.min_index = 0 if self.edge_buffer is not None: self.edge_buffer.fill_(-1) if self.event_queue is not None: self.event_queue.fill_(-1) @move_to_cuda def forward(self, batch, pos, collect_edges=True): n_ev = len(batch) if self.device is None: self.device = batch.device self.initialize(n_ev, self.device) if len(batch) == 0: return torch.zeros((2,0), device=self.device, dtype=torch.int32) assert type(batch) is torch.Tensor and batch.dtype == torch.int32, [type(batch), batch.dtype] self.all_timestamps = torch.cat([self.all_timestamps, pos[:,2]]) # insert events into queue, they have an ever growing index if n_ev > len(self.new_indices): self.new_indices = torch.arange(0, n_ev, dtype=torch.int32, device=self.device) self.edge_buffer = torch.full((2, self.max_num_neighbors * n_ev), dtype=torch.int64, fill_value=-1, device=self.device) indices = self.max_index + self.new_indices[:n_ev] self.max_index += n_ev self.event_queue = _insert_events_into_queue(batch, pos, indices=indices, queue=self.event_queue) # read out edges from event queue, they need to correspond to indices # from the current nodes self.edge_buffer.fill_(-1) edge_indices = _search_for_edges(batch, pos, all_timestamps=self.all_timestamps.contiguous(), indices=indices, queue=self.event_queue, max_num_neighbors=self.max_num_neighbors, radius=self.radius, delta_t_us=self.delta_t_us, edges=self.edge_buffer, min_index=self.min_index) if collect_edges: self.edges = torch.cat([self.edges, edge_indices], dim=-1) return edge_indices class SlidingWindowGraph(AsyncGraph): def __init__(self, width=640, height=480, batch_size=1, max_num_neighbors=16, max_queue_size=1024, radius=7, delta_t_us=600000): AsyncGraph.__init__(self, width, height, batch_size, max_num_neighbors, max_queue_size, radius, delta_t_us) @property def init(self): return len(self.all_timestamps) > 0 def delete_nodes(self, n_delete, delete_edges=True, return_edges=True): # delete nodes self.all_timestamps = self.all_timestamps[n_delete:] self.min_index += n_delete # the current edges do not correspond to # the nodes anymore, so they need to be decremented if delete_edges: mask = (self.edges[0] < n_delete) | (self.edges[1] < n_delete) deleted_edges = self.edges[:,mask].clone() self.edges = self.edges[:,~mask] self.edges.add_(-n_delete) if delete_edges and return_edges: return deleted_edges @move_to_cuda def forward(self, batch, pos, return_node_counts=False, return_total_edges=False, delete_nodes=True, collect_edges=True): n_delete = len(batch) if self.init else 0 # first find the interactions edges = AsyncGraph.forward(self, batch, pos, collect_edges=collect_edges) if return_total_edges: total_edges = self.edges.clone() if return_node_counts: tot_nodes = len(self.all_timestamps) ret = [edges] if delete_nodes: deleted_edges = self.delete_nodes(n_delete) ret.append(deleted_edges) if return_total_edges: ret.append(total_edges) if return_node_counts: ret.append([n_delete, len(batch), tot_nodes]) if len(ret) == 1: ret = ret[0] return ret ================================================ FILE: src/dagr/graph/spiral.h ================================================ class SpiralOut{ protected: unsigned layer; unsigned leg; public: int x, y; //read these as output from next, do not modify. __device__ SpiralOut():layer(1),leg(0),x(0),y(0){} __device__ void goNext(){ switch(leg){ case 0: ++x; if(x == layer) ++leg; break; case 1: ++y; if(y == layer) ++leg; break; case 2: --x; if(-x == layer) ++leg; break; case 3: --y; if(-y == layer){ leg = 0; ++layer; } break; } } }; ================================================ FILE: src/dagr/graph/utils.py ================================================ import torch import ev_graph_cuda from typing import Union def _insert_events_into_queue(batch, pos, indices, queue: torch.LongTensor): if len(batch) > 1: height, width = queue.shape[-2:] lin_coords = pos[:,0] + width * pos[:,1] + width*height*batch sorted_lin_coords, sort_index = torch.sort(lin_coords, stable=True, descending=False) sorted_indices = indices[sort_index].int() unique_coords, unique_counter = torch.unique_consecutive(sorted_lin_coords, return_counts=True) cumsum_counter = torch.cumsum(unique_counter, dim=0).int() queue = ev_graph_cuda.insert_in_queue_cuda(sorted_indices, unique_coords, cumsum_counter, queue) else: queue = ev_graph_cuda.insert_in_queue_single_cuda(indices, pos, queue) return queue def _search_for_edges(batch, pos, all_timestamps, queue, indices, max_num_neighbors, radius, delta_t_us, edges, min_index): ev_graph_cuda.fill_edges_cuda(batch, pos, all_timestamps, queue, indices, max_num_neighbors, radius, delta_t_us, edges, min_index) edges = edges[:,(edges[1]>=0)] return edges ================================================ FILE: src/dagr/model/layers/components.py ================================================ import torch from torch_geometric.nn import BatchNorm from torch_geometric.data import Data import torch_geometric.transforms as T class BatchNormData(BatchNorm): def forward(self, data: Data): data.x = BatchNorm.forward(self, data.x) return data class Linear(torch.nn.Module): def __init__(self, ic, oc, bias=True): torch.nn.Module.__init__(self) self.mlp = torch.nn.Linear(ic, oc, bias=bias) def forward(self, data: Data): data.x = self.mlp(data.x) return data class Cartesian(torch.nn.Module): def __init__(self, *args, **kwargs): super().__init__() T.Cartesian.__init__(self, *args, **kwargs) def forward(self, data): if data.edge_index.shape[1] > 0: return T.Cartesian.__call__(self, data) else: data.edge_attr = torch.zeros((0, 3), dtype=data.x.dtype, device=data.x.device) return data ================================================ FILE: src/dagr/model/layers/conv.py ================================================ import torch from torch_geometric.data import Data from dagr.model.layers.components import BatchNormData, Linear from dagr.model.layers.spline_conv import MySplineConv from dagr.model.utils import shallow_copy class ConvBlock(torch.nn.Module): def __init__(self, in_channels: int, out_channels: int, args, degree=1) -> None: super(ConvBlock, self).__init__() self.dim = args.edge_attr_dim self.activation = getattr(torch.nn.functional, args.activation, torch.nn.functional.elu) self.conv = MySplineConv(in_channels=in_channels, out_channels=out_channels, args=args, bias=False, degree=degree) self.norm = BatchNormData(in_channels=out_channels) def forward(self, data: Data) -> torch.Tensor: data = self.conv(data) data = self.norm(data) data.x = self.activation(data.x) return data class ConvBlockWithSkip(torch.nn.Module): def __init__(self, in_channel: int, out_channel: int, skip_in_channel: int, args) -> None: super(ConvBlockWithSkip, self).__init__() self.dim = args.edge_attr_dim self.conv = MySplineConv(in_channels=in_channel, out_channels=out_channel, args=args, bias=False) self.activation = getattr(torch.nn.functional, args.activation, torch.nn.functional.elu) self.norm = BatchNormData(in_channels=out_channel) self.lin = Linear(skip_in_channel, out_channel, bias=False) self.norm_skip = BatchNormData(in_channels=out_channel) def forward(self, data: Data, data_skip: Data): data = self.conv(data) data_skip = self.lin(data_skip) data_skip = self.norm_skip(data_skip) data = self.norm(data) data.x = self.activation(data.x + data_skip.x) return data class Layer(torch.nn.Module): def __init__(self, in_channels: int, out_channels: int, args) -> None: super(Layer, self).__init__() self.in_channel = in_channels self.out_channel = out_channels self.conv_block1 = ConvBlock(in_channels, out_channels, args) self.conv_block2 = ConvBlockWithSkip(out_channels, out_channels, in_channels, args=args) def forward(self, data: Data) -> torch.Tensor: data_skip = shallow_copy(data) data = self.conv_block1(data) output = self.conv_block2(data, data_skip) return output ================================================ FILE: src/dagr/model/layers/ev_tgn.py ================================================ import torch from torch_geometric.data import Batch, Data from dagr.graph.ev_graph import SlidingWindowGraph def _get_value_as_int(obj, key): val = getattr(obj, key) return val if type(val) is int else val[0] def denormalize_pos(events): if hasattr(events, "pos_denorm"): return events.pos_denorm denorm = torch.tensor([int(events.width[0]), int(events.height[0]), int(events.time_window[0])], device=events.pos.device) return (denorm.view(1,-1) * events.pos + 1e-3).int() class EV_TGN(torch.nn.Module): def __init__(self, args): torch.nn.Module.__init__(self) self.radius = args.radius self.max_neighbors = args.max_neighbors self.max_queue_size = 128 self.graph_creators = None def init_graph_creator(self, data): delta_t_us = int(self.radius * _get_value_as_int(data, "time_window")) radius = int(self.radius * _get_value_as_int(data, "width")+1) batch_size = data.num_graphs width = int(_get_value_as_int(data, "width")) height = int(_get_value_as_int(data, "height")) self.graph_creators = SlidingWindowGraph(width=width, height=height, max_num_neighbors=self.max_neighbors, max_queue_size=self.max_queue_size, batch_size=batch_size, radius=radius, delta_t_us=delta_t_us) def forward(self, events: Data, reset=True): if events.batch is None: events = Batch.from_data_list([events]) # before we start, are the new events used to generate the graph, or are the new nodes attached to the network? # if the first, then don't delete old events, if the second, delete as many events as are coming in. if self.graph_creators is None: self.init_graph_creator(events) else: if reset: self.graph_creators.reset() pos = denormalize_pos(events) #pos = torch.cat([events.batch.view(-1,1), pos, events.x.int()], dim=1).int() # properties of the edges # src_i <= dst_i # dst_i <= dst_j if i 0: self.bn = BatchNormData(in_channels) @property def num_grid_cells(self): return (1/self.voxel_size+1e-3).int().prod() def round_to_pixel(self, pos, wh_inv): torch.div(pos+1e-5, wh_inv, out=pos, rounding_mode='floor') return pos * wh_inv def forward(self, data: Data): if data.x.shape[0] == 0: return data pos = torch.cat([data.pos, data.batch.float().view(-1,1)], dim=-1) cluster = grid_cluster(pos, size=self.voxel_size, start=self.start, end=self.end) unique_clusters, cluster, perm, _ = consecutive_cluster(cluster) edge_index = cluster[data.edge_index] if self.self_loop: edge_index = edge_index.unique(dim=-1) else: edge_index = edge_index[:, edge_index[0]!=edge_index[1]] if edge_index.shape[1] > 0: edge_index = edge_index.unique(dim=-1) batch = None if data.batch is None else data.batch[perm] pos = None if data.pos is None else pool_pos(cluster, data.pos) if self.keep_temporal_ordering: t_max, _ = torch_scatter.scatter_max(data.pos[:,-1], cluster, dim=0) t_src, t_dst = t_max[edge_index] edge_index = edge_index[:, t_dst > t_src] if self.aggr == 'max': x, argmax = torch_scatter.scatter_max(data.x, cluster, dim=0) else: x = _avg_pool_x(cluster, data.x) new_data = Batch(batch=batch, x=x, edge_index=edge_index, pos=pos) if hasattr(data, "height"): new_data.height = data.height new_data.width = data.width # round x and y coordinates to the center of the voxel grid new_data.pos[:,:2] = self.round_to_pixel(new_data.pos[:,:2], wh_inv=self.wh_inv) if self.transform is not None: if new_data.edge_index.numel() > 0: new_data = self.transform(new_data) else: new_data.edge_attr = torch.zeros(size=(0,pos.shape[1]), dtype=pos.dtype, device=pos.device) if self.bn is not None: new_data = self.bn(new_data) return new_data ================================================ FILE: src/dagr/model/layers/spline_conv.py ================================================ import torch from torch_geometric.nn.conv import SplineConv from torch_geometric.data import Data from torch_geometric.transforms.to_sparse_tensor import ToSparseTensor from torch_spline_conv import spline_basis class MySplineConv(SplineConv): def __init__(self, in_channels, out_channels, args, bias=False, degree=1, **kwargs): self.reproducible = True self.to_sparse_tensor = ToSparseTensor(attr="edge_attr", remove_edge_index=False) super().__init__(in_channels=in_channels, out_channels=out_channels, bias=bias, degree=degree, dim=args.edge_attr_dim, aggr=args.aggr, kernel_size=args.kernel_size) def init_lut(self, height, width, rx=None, Mx=None, ry=None, My=None): # attr is assumed to be computed as attr = (x_i - x_j)/(2M) + 0.5 # where -r <= x_i - x_j <= r. So remapping to integers gives # lut_index = 2M*attr - M + r. and 0 <= lut_index <= 2r ry = ry or rx My = My or Mx self.attr_remapping_matrix = torch.Tensor([[2 * Mx * width, 0, - Mx * width + rx], [ 0, 2 * My * height, - My * height + ry]]) # generate all possible dx, dy dxy = torch.stack(torch.meshgrid(torch.arange(-rx, rx+1), torch.arange(-ry, ry+1))).float() dxy[0] = dxy[0] / (2 * Mx * width) + 0.5 dxy[1] = dxy[1] / (2 * My * height) + 0.5 edge_attr = dxy.view((2,-1)).t() bil_w, indices = spline_basis(edge_attr.to(self.weight.data.device), self.kernel_size, self.is_open_spline, self.degree) lut_weights = (bil_w[...,None,None] * self.weight[indices]).sum(1) _, cin, cout = lut_weights.shape self.lut_weights = lut_weights.view((2 * rx + 1, 2 * ry + 1, cin, cout)) self.message = self.message_lut def message_lut(self, x_j, edge_attr): # index = (attr - 0.5) * 2 * M + r dx_index = (edge_attr[:,0] * self.attr_remapping_matrix[0,0] + self.attr_remapping_matrix[0,-1]+1e-3).long() dy_index = (edge_attr[:,1] * self.attr_remapping_matrix[1,1] + self.attr_remapping_matrix[1,-1]+1e-3).long() weights = self.lut_weights[dx_index, dy_index] # N x C_out x C_in x_out = torch.einsum("nio,ni->no", weights, x_j) return x_out def forward(self, data: Data)->Data: if self.reproducible: # first check we already computed the adjacency matrix if not hasattr(data, "adj_t"): data.edge_attr = data.edge_attr[:,:self.dim] data = self.to_sparse_tensor(data) data.x = self._forward(data.x, edge_index=data.adj_t) else: data.x = self._forward(data.x, edge_index=data.edge_index, edge_attr=data.edge_attr[:, :self.dim], size=(data.x.shape[0], data.x.shape[0])) return data def _forward(self, x, edge_index, edge_attr=None, size=None): """""" # propagate_type: (x: OptPairTensor, edge_attr: OptTensor) if edge_index.numel() > 0: out = self.propagate(edge_index, x=(x, x), edge_attr=edge_attr, size=size) else: out = torch.zeros((x.size(0), self.out_channels), dtype=x.dtype, device=x.device) if x is not None and self.root_weight: out += self.lin(x) if self.bias is not None: out += self.bias return out def to_dense(self, x, pos, pooling, batch=None, batch_size=None): if hasattr(self, "batch_size"): B = self.batch_size elif batch_size is not None: self.batch_size = batch_size B = batch_size elif batch is None: batch = torch.zeros(size=(len(x),), dtype=torch.long, device=x.device) B = 1 self.batch_size = B else: B = batch.max().item() + 1 self.batch_size = B if not hasattr(self, "dense"): W, H = (1 / pooling[:2] + 1e-3).long() C = x.shape[-1] self.dense = torch.zeros(size=(B, C, H, W), dtype=x.dtype, device=x.device) est_x, est_y = (pos[:, :2] / pooling[:2]).t().long() self.dense = self.dense.detach() self.dense.zero_() dense = self.dense[:B] if B < self.dense.shape[0] else self.dense dense[batch.long(), :, est_y, est_x] = x return dense class SplineConvToDense(MySplineConv): def forward(self, data: Data, batch_size: int=None)->torch.Tensor: data = super().forward(data) if data.batch is None: data.batch = torch.zeros(len(data.x), dtype=torch.long, device=data.x.device) return self.to_dense(data.x, data.pos, data.pooling, data.batch, batch_size=batch_size) def to_dense(self, x, pos, pooling, batch=None, batch_size=None): return to_dense(self, x, pos, pooling, batch, batch_size=batch_size) ================================================ FILE: src/dagr/model/networks/dagr.py ================================================ import torch import torch.nn.functional as F from torch_geometric.data import Data from yolox.models import YOLOX, YOLOXHead, IOUloss from dagr.model.networks.net import Net from dagr.model.layers.spline_conv import SplineConvToDense from dagr.model.layers.conv import ConvBlock from dagr.model.utils import shallow_copy, init_subnetwork, voxel_size_to_params, postprocess_network_output, convert_to_evaluation_format, init_grid_and_stride, convert_to_training_format class DAGR(YOLOX): def __init__(self, args, height, width): self.conf_threshold = 0.001 self.nms_threshold = 0.65 self.height = height self.width = width backbone = Net(args, height=height, width=width) head = GNNHead(num_classes=backbone.num_classes, in_channels=backbone.out_channels, in_channels_cnn=backbone.out_channels_cnn, strides=backbone.strides, pretrain_cnn=args.pretrain_cnn, args=args) super().__init__(backbone=backbone, head=head) if "img_net_checkpoint" in args: state_dict = torch.load(args.img_net_checkpoint) init_subnetwork(self, state_dict['ema'], "backbone.net.", freeze=True) init_subnetwork(self, state_dict['ema'], "head.cnn_head.") def cache_luts(self, width, height, radius): M = 2 * float(int(radius * width + 2) / width) r = int(radius * width+1) self.backbone.conv_block1.conv_block1.conv.init_lut(height=height, width=width, Mx=M, rx=r) self.backbone.conv_block1.conv_block2.conv.init_lut(height=height, width=width, Mx=M, rx=r) rx, ry, M = voxel_size_to_params(self.backbone.pool1, height, width) self.backbone.layer2.conv_block1.conv.init_lut(height=height, width=width, Mx=M, rx=rx, ry=ry) self.backbone.layer2.conv_block2.conv.init_lut(height=height, width=width, Mx=M, rx=rx, ry=ry) rx, ry, M = voxel_size_to_params(self.backbone.pool2, height, width) self.backbone.layer3.conv_block1.conv.init_lut(height=height, width=width, Mx=M, rx=rx, ry=ry) self.backbone.layer3.conv_block2.conv.init_lut(height=height, width=width, Mx=M, rx=rx, ry=ry) rx, ry, M = voxel_size_to_params(self.backbone.pool3, height, width) self.backbone.layer4.conv_block1.conv.init_lut(height=height, width=width, Mx=M, rx=rx, ry=ry) self.backbone.layer4.conv_block2.conv.init_lut(height=height, width=width, Mx=M, rx=rx, ry=ry) self.head.stem1.conv.init_lut(height=height, width=width, Mx=M, rx=rx, ry=ry) self.head.cls_conv1.conv.init_lut(height=height, width=width, Mx=M, rx=rx, ry=ry) self.head.reg_conv1.conv.init_lut(height=height, width=width, Mx=M, rx=rx, ry=ry) self.head.cls_pred1.init_lut(height=height, width=width, Mx=M, rx=rx, ry=ry) self.head.reg_pred1.init_lut(height=height, width=width, Mx=M, rx=rx, ry=ry) self.head.obj_pred1.init_lut(height=height, width=width, Mx=M, rx=rx, ry=ry) rx, ry, M = voxel_size_to_params(self.backbone.pool4, height, width) self.backbone.layer5.conv_block1.conv.init_lut(height=height, width=width, Mx=M, rx=rx, ry=ry) self.backbone.layer5.conv_block2.conv.init_lut(height=height, width=width, Mx=M, rx=rx, ry=ry) if self.head.num_scales > 1: self.head.stem2.conv.init_lut(height=height, width=width, Mx=M, rx=rx, ry=ry) self.head.cls_conv2.conv.init_lut(height=height, width=width, Mx=M, rx=rx, ry=ry) self.head.reg_conv2.conv.init_lut(height=height, width=width, Mx=M, rx=rx, ry=ry) self.head.cls_pred2.init_lut(height=height, width=width, Mx=M, rx=rx, ry=ry) self.head.reg_pred2.init_lut(height=height, width=width, Mx=M, rx=rx, ry=ry) self.head.obj_pred2.init_lut(height=height, width=width, Mx=M, rx=rx, ry=ry) def forward(self, x: Data, reset=True, return_targets=True, filtering=True): if not hasattr(self.head, "output_sizes"): self.head.output_sizes = self.backbone.get_output_sizes() if self.training: targets = convert_to_training_format(x.bbox, x.bbox_batch, x.num_graphs) if self.backbone.use_image: targets0 = convert_to_training_format(x.bbox0, x.bbox0_batch, x.num_graphs) targets = (targets, targets0) # gt_target inputs need to be [l cx cy w h] in pixels outputs = YOLOX.forward(self, x, targets) return outputs x.reset = reset outputs = YOLOX.forward(self, x) detections = postprocess_network_output(outputs, self.backbone.num_classes, self.conf_threshold, self.nms_threshold, filtering=filtering, height=self.height, width=self.width) ret = [detections] if return_targets and hasattr(x, 'bbox'): targets = convert_to_evaluation_format(x) ret.append(targets) return ret class CNNHead(YOLOXHead): def forward(self, xin): outputs = dict(cls_output=[], reg_output=[], obj_output=[]) for k, (cls_conv, reg_conv, x) in enumerate(zip(self.cls_convs, self.reg_convs, xin)): x = self.stems[k](x) cls_x = x reg_x = x cls_feat = cls_conv(cls_x) reg_feat = reg_conv(reg_x) outputs["cls_output"].append(self.cls_preds[k](cls_feat)) outputs["reg_output"].append(self.reg_preds[k](reg_feat)) outputs["obj_output"].append(self.obj_preds[k](reg_feat)) return outputs class GNNHead(YOLOXHead): def __init__( self, num_classes, strides=[8, 16, 32], in_channels=[256, 512, 1024], in_channels_cnn=[256, 512, 1024], act="silu", depthwise=False, pretrain_cnn=False, args=None ): YOLOXHead.__init__(self, num_classes, args.yolo_stem_width, strides, in_channels, act, depthwise) self.pretrain_cnn = pretrain_cnn self.num_scales = args.num_scales self.use_image = args.use_image self.batch_size = args.batch_size self.no_events = args.no_events self.in_channels = in_channels self.n_anchors = 1 self.num_classes = num_classes n_reg = max(in_channels) self.stem1 = ConvBlock(in_channels=in_channels[0], out_channels=n_reg, args=args) self.cls_conv1 = ConvBlock(in_channels=n_reg, out_channels=n_reg, args=args) self.cls_pred1 = SplineConvToDense(in_channels=n_reg, out_channels=self.n_anchors * self.num_classes, bias=True, args=args) self.reg_conv1 = ConvBlock(in_channels=n_reg, out_channels=n_reg, args=args) self.reg_pred1 = SplineConvToDense(in_channels=n_reg, out_channels=4, bias=True, args=args) self.obj_pred1 = SplineConvToDense(in_channels=n_reg, out_channels=self.n_anchors, bias=True, args=args) if self.num_scales > 1: self.stem2 = ConvBlock(in_channels=in_channels[1], out_channels=n_reg, args=args) self.cls_conv2 = ConvBlock(in_channels=n_reg, out_channels=n_reg, args=args) self.cls_pred2 = SplineConvToDense(in_channels=n_reg, out_channels=self.n_anchors * self.num_classes, bias=True, args=args) self.reg_conv2 = ConvBlock(in_channels=n_reg, out_channels=n_reg, args=args) self.reg_pred2 = SplineConvToDense(in_channels=n_reg, out_channels=4, bias=True, args=args) self.obj_pred2 = SplineConvToDense(in_channels=n_reg, out_channels=self.n_anchors, bias=True, args=args) if self.use_image: self.cnn_head = CNNHead(num_classes=num_classes, strides=strides, in_channels=in_channels_cnn) self.use_l1 = False self.l1_loss = torch.nn.L1Loss(reduction="none") self.bcewithlog_loss = torch.nn.BCEWithLogitsLoss(reduction="none") self.iou_loss = IOUloss(reduction="none") self.strides = strides self.grids = [torch.zeros(1)] * len(in_channels) self.grid_cache = None self.stride_cache = None self.cache = [] def process_feature(self, x, stem, cls_conv, reg_conv, cls_pred, reg_pred, obj_pred, batch_size, cache): x = stem(x) cls_feat = cls_conv(shallow_copy(x)) reg_feat = reg_conv(x) # we need to provide the batchsize, since sometimes it cannot be foudn from the data, especially when nodes=0 cls_output = cls_pred(cls_feat, batch_size=batch_size) reg_output = reg_pred(shallow_copy(reg_feat), batch_size=batch_size) obj_output = obj_pred(reg_feat, batch_size=batch_size) return cls_output, reg_output, obj_output def forward(self, xin: Data, labels=None, imgs=None): # for events + image outputs hybrid_out = dict(outputs=[], origin_preds=[], x_shifts=[], y_shifts=[], expanded_strides=[]) image_out = dict(outputs=[], origin_preds=[], x_shifts=[], y_shifts=[], expanded_strides=[]) if self.use_image: xin, image_feat = xin if labels is not None: if self.use_image: labels, image_labels = labels # resize image, and process with CNN image_feat = [torch.nn.functional.interpolate(f, o) for f, o in zip(image_feat, self.output_sizes)] out_cnn = self.cnn_head(image_feat) # collect outputs from image alone, so the image network also learns to detect on its own. for k in [0, 1]: self.collect_outputs(out_cnn["cls_output"][k], out_cnn["reg_output"][k], out_cnn["obj_output"][k], k, self.strides[k], ret=image_out) batch_size = len(out_cnn["cls_output"][0]) if self.use_image else self.batch_size cls_output, reg_output, obj_output = self.process_feature(xin[0], self.stem1, self.cls_conv1, self.reg_conv1, self.cls_pred1, self.reg_pred1, self.obj_pred1, batch_size=batch_size, cache=self.cache) if self.use_image: cls_output[:batch_size] += out_cnn["cls_output"][0].detach() reg_output[:batch_size] += out_cnn["reg_output"][0].detach() obj_output[:batch_size] += out_cnn["obj_output"][0].detach() self.collect_outputs(cls_output, reg_output, obj_output, 0, self.strides[0], ret=hybrid_out) if self.num_scales > 1: cls_output, reg_output, obj_output = self.process_feature(xin[1], self.stem2, self.cls_conv2, self.reg_conv2, self.cls_pred2, self.reg_pred2, self.obj_pred2, batch_size=batch_size, cache=self.cache) if self.use_image: batch_size = out_cnn["cls_output"][0].shape[0] cls_output[:batch_size] += out_cnn["cls_output"][1].detach() reg_output[:batch_size] += out_cnn["reg_output"][1].detach() obj_output[:batch_size] += out_cnn["obj_output"][1].detach() self.collect_outputs(cls_output, reg_output, obj_output, 1, self.strides[1], ret=hybrid_out) if self.training: # if we are only training the image detectors (pretraining), # we only need to minimize the loss at detections from the image branch. if self.use_image: losses_image = self.get_losses( imgs, image_out['x_shifts'], image_out['y_shifts'], image_out['expanded_strides'], image_labels, torch.cat(image_out['outputs'], 1), image_out['origin_preds'], dtype=image_out['x_shifts'][0].dtype, ) if not self.pretrain_cnn: losses_events = self.get_losses( imgs, hybrid_out['x_shifts'], hybrid_out['y_shifts'], hybrid_out['expanded_strides'], labels, torch.cat(hybrid_out['outputs'], 1), hybrid_out['origin_preds'], dtype=xin[0].x.dtype, ) losses_image = list(losses_image) losses_events = list(losses_events) for i in range(5): losses_image[i] = losses_image[i] + losses_events[i] return losses_image else: return self.get_losses( imgs, hybrid_out['x_shifts'], hybrid_out['y_shifts'], hybrid_out['expanded_strides'], labels, torch.cat(hybrid_out['outputs'], 1), hybrid_out['origin_preds'], dtype=xin[0].x.dtype, ) else: out = image_out['outputs'] if self.no_events else hybrid_out['outputs'] self.hw = [x.shape[-2:] for x in out] # [batch, n_anchors_all, 85] outputs = torch.cat([x.flatten(start_dim=2) for x in out], dim=2).permute(0, 2, 1) return self.decode_outputs(outputs, dtype=out[0].type()) def collect_outputs(self, cls_output, reg_output, obj_output, k, stride_this_level, ret=None): if self.training: output = torch.cat([reg_output, obj_output, cls_output], 1) output, grid = self.get_output_and_grid(output, k, stride_this_level, output.type()) ret['x_shifts'].append(grid[:, :, 0]) ret['y_shifts'].append(grid[:, :, 1]) ret['expanded_strides'].append(torch.zeros(1, grid.shape[1]).fill_(stride_this_level).type_as(output)) else: output = torch.cat( [reg_output, obj_output.sigmoid(), cls_output.sigmoid()], 1 ) ret['outputs'].append(output) def decode_outputs(self, outputs, dtype): if self.grid_cache is None: self.grid_cache, self.stride_cache = init_grid_and_stride(self.hw, self.strides, dtype) outputs[..., :2] = (outputs[..., :2] + self.grid_cache) * self.stride_cache outputs[..., 2:4] = torch.exp(outputs[..., 2:4]) * self.stride_cache return outputs ================================================ FILE: src/dagr/model/networks/ema.py ================================================ import torch import math from copy import deepcopy class ModelEMA: """ Model Exponential Moving Average from https://github.com/rwightman/pytorch-image-models Keep a moving average of everything in the model state_dict (parameters and buffers). This is intended to allow functionality like https://www.tensorflow.org/api_docs/python/tf/train/ExponentialMovingAverage A smoothed version of the weights is necessary for some training schemes to perform well. This class is sensitive where it is initialized in the sequence of model init, GPU assignment and distributed training wrappers. """ def __init__(self, model, decay=0.9999, updates=0): """ Args: model (nn.Module): model to apply EMA. decay (float): ema decay reate. updates (int): counter of EMA updates. """ # Create EMA(FP32) self.ema = deepcopy(model).eval() try: # if we do not do this, all the hooks will be activated for the other model, which will create # a lot of memory usage self.ema.backbone.net.remove_hooks() self.ema.backbone.net.register_hooks() except: pass self.updates = updates # decay exponential ramp (to help early epochs) self.decay = lambda x: decay * (1 - math.exp(-x / 2000)) for p in self.ema.parameters(): p.requires_grad_(False) def update(self, model): # Update EMA parameters with torch.no_grad(): self.updates += 1 d = self.decay(self.updates) msd = model.state_dict() for k, v in self.ema.state_dict().items(): if v.dtype.is_floating_point: v *= d v += (1.0 - d) * msd[k].detach() ================================================ FILE: src/dagr/model/networks/net.py ================================================ import torch import torch_geometric.transforms as T from torch_geometric.data import Data from dagr.model.layers.ev_tgn import EV_TGN from dagr.model.layers.pooling import Pooling from dagr.model.layers.conv import Layer from dagr.model.layers.components import Cartesian from dagr.model.networks.net_img import HookModule from dagr.model.utils import shallow_copy from torchvision.models import resnet18, resnet34, resnet50 def sampling_skip(data, image_feat): image_feat_at_nodes = sample_features(data, image_feat) return torch.cat((data.x, image_feat_at_nodes), dim=1) def compute_pooling_at_each_layer(pooling_dim_at_output, num_layers): py, px = map(int, pooling_dim_at_output.split("x")) pooling_base = torch.tensor([1.0 / px, 1.0 / py, 1.0 / 1]) poolings = [] for i in range(num_layers): pooling = pooling_base / 2 ** (3 - i) pooling[-1] = 1 poolings.append(pooling) poolings = torch.stack(poolings) return poolings class Net(torch.nn.Module): def __init__(self, args, height, width): super().__init__() channels = [1, int(args.base_width*32), int(args.after_pool_width*64), int(args.net_stem_width*128), int(args.net_stem_width*128), int(args.net_stem_width*128)] self.out_channels_cnn = [] if args.use_image: img_net = eval(args.img_net) self.out_channels_cnn = [256, 256] self.net = HookModule(img_net(pretrained=True), input_channels=3, height=height, width=width, feature_layers=["conv1", "layer1", "layer2", "layer3", "layer4"], output_layers=["layer3", "layer4"], feature_channels=channels[1:], output_channels=self.out_channels_cnn) self.use_image = args.use_image self.num_scales = args.num_scales self.num_classes = dict(dsec=2, ncaltech101=100).get(args.dataset, 2) self.events_to_graph = EV_TGN(args) output_channels = channels[1:] self.out_channels = output_channels[-2:] input_channels = channels[:-1] if self.use_image: input_channels = [input_channels[i] + self.net.feature_channels[i] for i in range(len(input_channels))] # parse x and y pooling dimensions at output poolings = compute_pooling_at_each_layer(args.pooling_dim_at_output, num_layers=4) max_vals_for_cartesian = 2*poolings[:,:2].max(-1).values self.strides = torch.ceil(poolings[-2:,1] * height).numpy().astype("int32").tolist() self.strides = self.strides[-self.num_scales:] effective_radius = 2*float(int(args.radius * width + 2) / width) self.edge_attrs = Cartesian(norm=True, cat=False, max_value=effective_radius) self.conv_block1 = Layer(2+input_channels[0], output_channels[0], args=args) cart1 = T.Cartesian(norm=True, cat=False, max_value=2*effective_radius) self.pool1 = Pooling(poolings[0], width=width, height=height, batch_size=args.batch_size, transform=cart1, aggr=args.pooling_aggr, keep_temporal_ordering=args.keep_temporal_ordering) self.layer2 = Layer(input_channels[1]+2, output_channels[1], args=args) cart2 = T.Cartesian(norm=True, cat=False, max_value=max_vals_for_cartesian[1]) self.pool2 = Pooling(poolings[1], width=width, height=height, batch_size=args.batch_size, transform=cart2, aggr=args.pooling_aggr, keep_temporal_ordering=args.keep_temporal_ordering) self.layer3 = Layer(input_channels[2]+2, output_channels[2], args=args) cart3 = T.Cartesian(norm=True, cat=False, max_value=max_vals_for_cartesian[2]) self.pool3 = Pooling(poolings[2], width=width, height=height, batch_size=args.batch_size, transform=cart3, aggr=args.pooling_aggr, keep_temporal_ordering=args.keep_temporal_ordering) self.layer4 = Layer(input_channels[3]+2, output_channels[3], args=args) cart4 = T.Cartesian(norm=True, cat=False, max_value=max_vals_for_cartesian[3]) self.pool4 = Pooling(poolings[3], width=width, height=height, batch_size=args.batch_size, transform=cart4, aggr='mean', keep_temporal_ordering=args.keep_temporal_ordering) self.layer5 = Layer(input_channels[4]+2, output_channels[4], args=args) self.cache = [] def get_output_sizes(self): poolings = [self.pool3.voxel_size[:2], self.pool4.voxel_size[:2]] output_sizes = [(1 / p + 1e-3).cpu().int().numpy().tolist()[::-1] for p in poolings] return output_sizes def forward(self, data: Data, reset=True): if self.use_image: image_feat, image_outputs = self.net(data.image) if hasattr(data, 'reset'): reset = data.reset data = self.events_to_graph(data, reset=reset) if self.use_image: data.x = sampling_skip(data, image_feat[0].detach()) data.skipped = True data.num_image_channels = image_feat[0].shape[1] data = self.edge_attrs(data) data.edge_attr = torch.clamp(data.edge_attr, min=0, max=1) rel_delta = data.pos[:, :2] data.x = torch.cat((data.x, rel_delta), dim=1) data = self.conv_block1(data) if self.use_image: data.x = sampling_skip(data, image_feat[1].detach()) data = self.pool1(data) if self.use_image: data.skipped = True data.num_image_channels = image_feat[1].shape[1] rel_delta = data.pos[:,:2] data.x = torch.cat((data.x, rel_delta), dim=1) data = self.layer2(data) if self.use_image: data.x = sampling_skip(data, image_feat[2].detach()) data = self.pool2(data) if self.use_image: data.skipped = True data.num_image_channels = image_feat[2].shape[1] rel_delta = data.pos[:,:2] data.x = torch.cat((data.x, rel_delta), dim=1) data = self.layer3(data) if self.use_image: data.x = sampling_skip(data, image_feat[3].detach()) data = self.pool3(data) if self.use_image: data.skipped = True data.num_image_channels = image_feat[3].shape[1] rel_delta = data.pos[:,:2] data.x = torch.cat((data.x, rel_delta), dim=1) data = self.layer4(data) out3 = shallow_copy(data) out3.pooling = self.pool3.voxel_size[:3] if self.use_image: data.x = sampling_skip(data, image_feat[4].detach()) data = self.pool4(data) if self.use_image: data.skipped = True data.num_image_channels = image_feat[4].shape[1] rel_delta = data.pos[:,:2] data.x = torch.cat((data.x, rel_delta), dim=1) data = self.layer5(data) out4 = data out4.pooling = self.pool4.voxel_size[:3] output = [out3, out4] if self.use_image: return output[-self.num_scales:], image_outputs[-self.num_scales:] return output[-self.num_scales:] def sample_features(data, image_feat, image_sample_mode="bilinear"): if data.batch is None or len(data.batch) != len(data.pos): data.batch = torch.zeros(len(data.pos), dtype=torch.long, device=data.x.device) return _sample_features(data.pos[:,0] * data.width[0], data.pos[:,1] * data.height[0], data.batch.float(), image_feat, data.width[0], data.height[0], image_feat.shape[0], image_sample_mode) def _sample_features(x, y, b, image_feat, width, height, batch_size, image_sample_mode): x = 2 * x / (width - 1) - 1 y = 2 * y / (height - 1) - 1 batch_size = batch_size if batch_size > 1 else 2 b = 2 * b / (batch_size - 1) - 1 grid = torch.stack((x, y, b), dim=-1).view(1, 1, 1,-1, 3) # N x D_out x H_out x W_out x 3 (N=1, D_out=1, H_out=1) image_feat = image_feat.permute(1,0,2,3).unsqueeze(0) # N x C x D x H x W (N=1) image_feat_sampled = torch.nn.functional.grid_sample(image_feat, grid=grid, mode=image_sample_mode, align_corners=True) # N x C x H_out x W_out (H_out=1, N=1) image_feat_sampled = image_feat_sampled.view(image_feat.shape[1], -1).t() return image_feat_sampled ================================================ FILE: src/dagr/model/networks/net_img.py ================================================ import torch class Layer(torch.nn.Module): def __init__(self, input_channels, output_channels): super(Layer, self).__init__() self.conv1 = torch.nn.Conv2d(input_channels, output_channels, kernel_size=3, stride=1, padding=1) self.bn1 = torch.nn.BatchNorm2d(output_channels) self.conv2 = torch.nn.Conv2d(output_channels, output_channels, kernel_size=3, stride=1, padding=1) self.bn2 = torch.nn.BatchNorm2d(output_channels) self.dwc = torch.nn.Conv2d(input_channels, output_channels, kernel_size=1, stride=1, padding=0) self.bn_skip = torch.nn.BatchNorm2d(output_channels) self.act = torch.nn.ReLU() def forward(self, x): x_skip = x.clone() x = self.act(self.bn1(self.conv1(x))) x = self.bn2(self.conv2(x)) x = x + self.bn_skip(self.dwc(x_skip)) return self.act(x) class ConvBlockDense(torch.nn.Module): def __init__(self, in_channels, out_channels, bias=False, act=torch.nn.ReLU(), bn=True): super(ConvBlockDense, self).__init__() self.conv = torch.nn.Conv2d(in_channels, out_channels, bias=bias, kernel_size=3, stride=1, padding=1) self.bn = torch.nn.BatchNorm2d(out_channels) self.act = act self.use_bn = bn def forward(self, x): x = self.conv(x) if self.use_bn: x = self.bn(x) if self.act is not None: x = self.act(x) return x class HookModule(torch.nn.Module): """ Define the module, then you can determine which features are extracted, and which outputs are extracted. For each you can decide if they are mapped to a lower dimension or not. """ def __init__(self, module, height, width, input_channels=3, feature_layers=(), output_layers=(), feature_channels=None, output_channels=None): torch.nn.Module.__init__(self) self.module = module.cpu() if input_channels != 3: self.module.conv1 = torch.nn.Conv2d(in_channels=input_channels, out_channels=self.module.conv1.out_channels, kernel_size=self.module.conv1.kernel_size, padding=self.module.conv1.padding, bias=False) self.feature_layers = feature_layers self.output_layers = output_layers self.hooks = [] self.features = [] self.outputs = [] self.register_hooks() self.feature_channels = [] self.output_channels = [] self.compute_channels_with_dummy(shape=(1, input_channels, height, width)) self.feature_dconv = torch.nn.ModuleList() if feature_channels is not None: assert len(feature_channels) == len(self.feature_channels) self.feature_dconv = torch.nn.ModuleList( [ torch.nn.Conv2d(in_channels=cin, out_channels=cout, kernel_size=1, stride=1, padding=0) for cin, cout in zip(self.feature_channels, feature_channels) ] ) self.feature_channels = feature_channels self.output_dconv = torch.nn.ModuleList() if output_channels is not None: assert len(output_channels) == len(self.output_channels) self.output_dconv = torch.nn.ModuleList( [ torch.nn.Conv2d(in_channels=cin, out_channels=cout, kernel_size=1, stride=1, padding=0) for cin, cout in zip(self.output_channels, output_channels) ] ) self.output_channels = output_channels def extract_layer(self, module, layer): if len(layer) == 0: return module else: return self.extract_layer(module._modules[layer[0]], layer[1:]) def compute_channels_with_dummy(self, shape): dummy_input = torch.zeros(shape) self.module.forward(dummy_input) self.feature_channels = [f.shape[1] for f in self.features] self.output_channels = [o.shape[1] for o in self.outputs] self.features = [] self.outputs = [] def remove_hooks(self): for h in self.hooks: h.remove() def register_hooks(self): self.features = [] self.outputs = [] features_hook = lambda m, i, o: self.features.append(o) outputs_hook = lambda m, i, o: self.outputs.append(o) for l in self.feature_layers: hook_id = self.extract_layer(self.module, l.split(".")).register_forward_hook(features_hook) self.hooks.append(hook_id) for l in self.output_layers: hook_id = self.extract_layer(self.module, l.split(".")).register_forward_hook(outputs_hook) self.hooks.append(hook_id) def forward(self, x): self.features = [] self.outputs = [] self.module(x) features = self.features if len(self.feature_dconv) > 0: features = [dconv(f) for f, dconv in zip(self.features, self.feature_dconv)] outputs = self.outputs if len(self.output_dconv) > 0: outputs = [dconv(o) for o, dconv in zip(self.outputs, self.output_dconv)] return features, outputs ================================================ FILE: src/dagr/model/utils.py ================================================ import torchvision import torch import numpy as np from torch_geometric.data import Data def init_subnetwork(net, state_dict, name="backbone.net.", freeze=False): assert name.endswith(".") # get submodule attrs = name.split(".")[:-1] for attr in attrs: net = getattr(net, attr) # load weights and freeze sub_state_dict = {k.replace(name, ""): v for k, v in state_dict.items() if name in k} net.load_state_dict(sub_state_dict) if freeze: for param in net.parameters(): param.requires_grad = False def batched_nms_coordinate_trick(boxes, scores, idxs, iou_threshold, width, height): # adopted from torchvision nms, but faster if boxes.numel() == 0: return torch.empty((0,), dtype=torch.int64, device=boxes.device) max_dim = max([width, height]) offsets = idxs * float(max_dim + 1) boxes_for_nms = boxes + offsets[:, None] keep = torchvision.ops.nms(boxes_for_nms, scores, iou_threshold) return keep def convert_to_evaluation_format(data): targets = [] for d in data.to_data_list(): bbox = d.bbox.clone() bbox[:,2:4] += bbox[:,:2] targets.append({ "boxes": bbox[:,:4], "labels": bbox[:, 4].long() # class 0 is background class }) return targets def convert_to_training_format(bbox, batch, batch_size): max_detections = 100 targets = torch.zeros(size=(batch_size, max_detections, 5), dtype=torch.float32, device=bbox.device) unique, counts = torch.unique(batch, return_counts=True) counter = _sequential_counter(counts) bbox = bbox.clone() # xywhlc pix -> lcxcywh pix bbox[:, :2] += bbox[:, 2:4] * .5 bbox = torch.roll(bbox[:, :5], dims=1, shifts=1) targets[batch, counter] = bbox return targets def postprocess_network_output(prediction, num_classes, conf_thre=0.01, nms_thre=0.65, height=640, width=640, filtering=True): prediction[..., :2] -= prediction[...,2:4] / 2 # cxcywh->xywh prediction[..., 2:4] += prediction[...,:2] output = [] for i, image_pred in enumerate(prediction): # If none are remaining => process next image if len(image_pred) == 0: device = prediction.device output.append({ "boxes": torch.zeros(0, 4, dtype=torch.float32, device=device), "scores": torch.zeros(0, dtype=torch.float, device=device), "labels": torch.zeros(0, dtype=torch.long, device=device) }) continue # Get score and class with highest confidence class_conf, class_pred = torch.max(image_pred[:, 5: 5 + num_classes], 1, keepdim=True) image_pred[:, 4:5] *= class_conf conf_mask = (image_pred[:, 4] * class_conf.squeeze() >= conf_thre).squeeze() # Detections ordered as (x1, y1, x2, y2, obj_conf, class_conf, class_pred) detections = torch.cat((image_pred[:, :5], class_pred), 1) if filtering: detections = detections[conf_mask] if len(detections) == 0: device = prediction.device output.append({ "boxes": torch.zeros(0, 4, dtype=torch.float32, device=device), "scores": torch.zeros(0, dtype=torch.float, device=device), "labels": torch.zeros(0, dtype=torch.long, device=device) }) continue nms_out_index = batched_nms_coordinate_trick(detections[:, :4], detections[:, 4], detections[:, 5], nms_thre, width=width, height=height) if filtering: detections = detections[nms_out_index] output.append({ "boxes": detections[:, :4], "scores": detections[:, 4], "labels": detections[:, -1].long() }) return output def voxel_size_to_params(pooling_layer, height, width): rx = int(np.ceil(2*pooling_layer.voxel_size[0].cpu().numpy() * width)) ry = int(np.ceil(2*pooling_layer.voxel_size[1].cpu().numpy() * height)) M = pooling_layer.transform.max return rx, ry, M def init_grid_and_stride(hw, strides, dtype): grids = [] all_strides = [] for (hsize, wsize), stride in zip(hw, strides): yv, xv = torch.meshgrid(torch.arange(hsize), torch.arange(wsize), indexing="ij") grid = torch.stack((xv, yv), 2).view(1, -1, 2) grids.append(grid) shape = grid.shape[:2] all_strides.append(torch.full((*shape, 1), stride)) grid_cache = torch.cat(grids, dim=1).type(dtype) stride_cache = torch.cat(all_strides, dim=1).type(dtype) return grid_cache, stride_cache def _sequential_counter(counts: torch.LongTensor): """ Returns a torch tensor which counts up for each count Example: counts = [2,4,6,2,4] then the output will be output = [0,1,0,1,2,3,0,1,2,3,4,5,0,1,0,1,2,3] """ assert counts.dtype == torch.long assert len(counts.shape) > 0 assert (counts >= 0).all() len_counter = counts.sum() tensors_kwargs = dict(device=counts.device, dtype=torch.long) # first construct delta function, which has value c_N at position sum_k=0^N c_k delta = torch.zeros(size=(len_counter,), **tensors_kwargs) x_coord = counts.cumsum(dim=0) delta[x_coord[:-1]] = counts[:-1] # next construct step function, and the result it a linear function minus this step function step = delta.cumsum(dim=0) counter = torch.arange(len_counter, **tensors_kwargs) - step return counter def shallow_copy(data): out = Data(x=data.x.clone(), edge_index=data.edge_index, edge_attr=data.edge_attr, pos=data.pos, batch=data.batch) for key in ["active_clusters", "_changed_attr", "_changed_attr_indices","diff_idx", "diff_pos_idx", "pooling", "num_image_channels", "skipped", "pooled"]: if hasattr(data, key): setattr(out, key, getattr(data, key)) for key in ["diff_idx", "diff_pos_idx"]: if hasattr(data, key): setattr(out, key, getattr(data, key).clone()) return out ================================================ FILE: src/dagr/utils/args.py ================================================ import argparse import yaml from pathlib import Path def BASE_FLAGS(): parser = argparse.ArgumentParser("") parser.add_argument('--dataset_directory', type=Path, default=argparse.SUPPRESS, help="Path to the directory containing the dataset.") parser.add_argument('--output_directory', type=Path, default=argparse.SUPPRESS, help="Path to the logging directory.") parser.add_argument("--checkpoint", type=Path, default=argparse.SUPPRESS, help="Path to the directory containing the checkpoint.") parser.add_argument("--img_net", default=argparse.SUPPRESS, type=str) parser.add_argument("--img_net_checkpoint", type=Path, default=argparse.SUPPRESS) parser.add_argument("--config", type=Path, default="../config/detection.yaml") parser.add_argument("--use_image", action="store_true") parser.add_argument("--no_events", action="store_true") parser.add_argument("--pretrain_cnn", action="store_true") parser.add_argument("--keep_temporal_ordering", action="store_true") # task params parser.add_argument("--task", default=argparse.SUPPRESS, type=str) parser.add_argument("--dataset", default=argparse.SUPPRESS, type=str) # graph params parser.add_argument('--radius', default=argparse.SUPPRESS, type=float) parser.add_argument('--time_window_us', default=argparse.SUPPRESS, type=int) parser.add_argument('--max_neighbors', default=argparse.SUPPRESS, type=int) parser.add_argument('--n_nodes', default=argparse.SUPPRESS, type=int) # learning params parser.add_argument('--batch_size', default=argparse.SUPPRESS, type=int) # network params parser.add_argument("--activation", default=argparse.SUPPRESS, type=str, help="Can be one of ['Hardshrink', 'Hardsigmoid', 'Hardswish', 'ReLU', 'ReLU6', 'SoftShrink', 'HardTanh']") parser.add_argument("--edge_attr_dim", default=argparse.SUPPRESS, type=int) parser.add_argument("--aggr", default=argparse.SUPPRESS, type=str) parser.add_argument("--kernel_size", default=argparse.SUPPRESS, type=int) parser.add_argument("--pooling_aggr", default=argparse.SUPPRESS, type=str) parser.add_argument("--base_width", default=argparse.SUPPRESS, type=float) parser.add_argument("--after_pool_width", default=argparse.SUPPRESS, type=float) parser.add_argument('--net_stem_width', default=argparse.SUPPRESS, type=float) parser.add_argument("--yolo_stem_width", default=argparse.SUPPRESS, type=float) parser.add_argument("--num_scales", default=argparse.SUPPRESS, type=int) parser.add_argument('--pooling_dim_at_output', default=argparse.SUPPRESS) parser.add_argument('--weight_decay', default=argparse.SUPPRESS, type=float) parser.add_argument('--clip', default=argparse.SUPPRESS, type=float) parser.add_argument('--aug_p_flip', default=argparse.SUPPRESS, type=float) return parser def FLAGS(): parser = BASE_FLAGS() # learning params parser.add_argument('--aug_trans', default=argparse.SUPPRESS, type=float) parser.add_argument('--aug_zoom', default=argparse.SUPPRESS, type=float) parser.add_argument('--exp_name', default=argparse.SUPPRESS, type=str) parser.add_argument('--l_r', default=argparse.SUPPRESS, type=float) parser.add_argument('--no_eval', action="store_true") parser.add_argument('--tot_num_epochs', default=argparse.SUPPRESS, type=int) parser.add_argument('--run_test', action="store_true") parser.add_argument('--num_interframe_steps', type=int, default=10) args = parser.parse_args() if args.config != "": args = parse_config(args, args.config) args.dataset_directory = Path(args.dataset_directory) args.output_directory = Path(args.output_directory) if "checkpoint" in args: args.checkpoint = Path(args.checkpoint) return args def FLOPS_FLAGS(): parser = BASE_FLAGS() # for flop eval parser.add_argument("--check_consistency", action="store_true") parser.add_argument("--dense", action="store_true") # for runtime eval args = parser.parse_args() if args.config != "": args = parse_config(args, args.config) args.dataset_directory = Path(args.dataset_directory) args.output_directory = Path(args.output_directory) if "checkpoint" in args: args.checkpoint = Path(args.checkpoint) return args def parse_config(args: argparse.ArgumentParser, config: Path): with config.open() as f: config = yaml.load(f, Loader=yaml.SafeLoader) for k, v in config.items(): if k not in args: setattr(args, k, v) return args ================================================ FILE: src/dagr/utils/buffers.py ================================================ import numpy as np import torch from typing import List, Dict from pathlib import Path from .coco_eval import evaluate_detection def diag_filter(bbox, height: int, width: int, min_box_diagonal: int = 30, min_box_side: int = 20): bbox[..., 0::2] = torch.clamp(bbox[..., 0::2], 0, width - 1) bbox[..., 1::2] = torch.clamp(bbox[..., 1::2], 0, height - 1) w, h = (bbox[..., 2:] - bbox[..., :2]).t() diag = torch.sqrt(w ** 2 + h ** 2) mask = (diag > min_box_diagonal) & (w > min_box_side) & (h > min_box_side) return mask def filter_bboxes(detections: List[Dict[str, torch.Tensor]], height: int, width: int, min_box_diagonal: int = 30, min_box_side: int = 20): filtered_bboxes = [] for d in detections: bbox = d["boxes"] # first clamp boxes to image mask = diag_filter(bbox, height, width, min_box_diagonal, min_box_side) bbox = {k: v[mask] for k, v in d.items()} filtered_bboxes.append(bbox) return filtered_bboxes def format_data(data, normalizer=None): if normalizer is None: normalizer = torch.stack([data.width[0], data.height[0], data.time_window[0]], dim=-1) if hasattr(data, "image"): data.image = data.image.float() / 255.0 data.pos = torch.cat([data.pos, data.t.view((-1,1))], dim=-1) data.t = None data.x = data.x.float() data.pos = data.pos / normalizer return data def bbox_t_to_ndarray(bbox, t): dtype = [('t', ' 0: output = {k: np.concatenate(v) for k, v in output.items() if len(v) > 0} return output def to_cpu(data_list: List[Dict[str, torch.Tensor]]): return [{k: v.cpu() for k, v in d.items()} for d in data_list] class Buffer: def __init__(self): self.buffer = [] def extend(self, elements: List[Dict[str, torch.Tensor]]): self.buffer.extend(to_cpu(elements)) def clear(self): self.buffer.clear() def __iter__(self): return iter(self.buffer) def __next__(self): return next(self.buffer) class DetectionBuffer: def __init__(self, height: int, width: int, classes: List[str]): self.height = height self.width = width self.classes = classes self.detections = Buffer() self.ground_truth = Buffer() def compile(self, sequences, timestamps): detections = compile(self.detections, sequences, timestamps) groundtruth = compile(self.ground_truth, sequences, timestamps) return detections, groundtruth def update(self, detections: List[Dict[str, torch.Tensor]], groundtruth: List[Dict[str, torch.Tensor]], dataset: str, height=None, width=None): self.detections.extend(detections) self.ground_truth.extend(groundtruth) def compute(self)->Dict[str, float]: output = evaluate_detection(self.ground_truth.buffer, self.detections.buffer, height=self.height, width=self.width, classes=self.classes) output = {k.replace("AP", "mAP"): v for k, v in output.items()} self.detections.clear() self.ground_truth.clear() return output class DictBuffer: def __init__(self): self.running_mean = None self.n = 0 def __recursive_mean(self, mn: float, s: float): return self.n / (self.n + 1) * mn + s / (self.n + 1) def update(self, dictionary: Dict[str, float]): if self.running_mean is None: self.running_mean = {k: 0 for k in dictionary} self.running_mean = {k: self.__recursive_mean(self.running_mean[k], dictionary[k]) for k in dictionary} self.n += 1 def save(self, path): torch.save(self.running_mean, path) def compute(self)->Dict[str, float]: return self.running_mean ================================================ FILE: src/dagr/utils/coco_eval.py ================================================ from __future__ import absolute_import from __future__ import division from __future__ import print_function import os import contextlib from pycocotools.coco import COCO from detectron2.evaluation.fast_eval_api import COCOeval_opt as COCOeval #from detectron2.evaluation.fast_eval_api import COCOeval import numpy as np from typing import List, Dict, Tuple from torch import Tensor BBOX_DTYPE = np.dtype({'names':['t','x','y','w','h','class_id','track_id','class_confidence'], 'formats':[' Tuple[Dict, Dict]: """ Compute detection KPIs on list of boxes in the numpy format, using the COCO python API https://github.com/cocodataset/cocoapi KPIs are only computed on timestamps where there is actual at least one box (fully empty frames are not considered) :param gt_boxes_list: list of numpy array for GT boxes (one per file) :param dt_boxes_list: list of numpy array for detected boxes :param classes: iterable of classes names :param height: int for box size statistics :param width: int for box size statistics :param time_tol: int size of the temporal window in micro seconds to look for a detection around a gt box """ flattened_gt = [] flattened_dt = [] for gt_boxes, dt_boxes in zip(gt_boxes_list, dt_boxes_list): gt_boxes = _to_prophesee(gt_boxes) dt_boxes = _to_prophesee(dt_boxes) assert np.all(gt_boxes['t'][1:] >= gt_boxes['t'][:-1]) assert np.all(dt_boxes['t'][1:] >= dt_boxes['t'][:-1]) all_ts = np.unique(gt_boxes['t']) gt_win, dt_win = _match_times(all_ts, gt_boxes, dt_boxes, time_tol) flattened_gt = flattened_gt + gt_win flattened_dt = flattened_dt + dt_win num_detections = sum([d.size for d in flattened_dt]) if num_detections == 0: # Corner case at the very beginning of the training. print('no detections for evaluation found.') return None categories = [{"id": id + 1, "name": class_name, "supercategory": "none"} for id, class_name in enumerate(classes)] return _to_coco_format(flattened_gt, flattened_dt, categories, height=height, width=width), len(flattened_gt) def evaluate_detection(gt_boxes_list: List[Dict[str, Tensor]], dt_boxes_list: List[Dict[str, Tensor]], classes: str=("car", "pedestrian"), height: int=240, width: int=304, time_tol: int=50000) -> Dict[str, float]: """ Compute detection KPIs on list of boxes in the numpy format, using the COCO python API https://github.com/cocodataset/cocoapi KPIs are only computed on timestamps where there is actual at least one box (fully empty frames are not considered) :param gt_boxes_list: list of numpy array for GT boxes (one per file) :param dt_boxes_list: list of numpy array for detected boxes :param classes: iterable of classes names :param height: int for box size statistics :param width: int for box size statistics :param time_tol: int size of the temporal window in micro seconds to look for a detection around a gt box """ output = _convert_to_coco_format(gt_boxes_list, dt_boxes_list, classes, height, width, time_tol) if output is None: out_keys = ('AP', 'AP_50', 'AP_75', 'AP_S', 'AP_M', 'AP_L') return {k: 0 for k in out_keys} else: (dataset, results), num_gts = output return _coco_eval(dataset, results, num_gts) def _to_prophesee(det: Dict[str, Tensor]): num_bboxes = len(det['boxes']) out = np.zeros(shape=(num_bboxes,), dtype=BBOX_DTYPE) det = {k: v.cpu().numpy() for k, v in det.items()} x1, y1, x2, y2 = det['boxes'].T out["x"] = x1 out["y"] = y1 out["w"] = x2-x1 out["h"] = y2-y1 out["class_id"] = det["labels"] out["class_confidence"] = det.get("scores", np.ones(shape=(num_bboxes,), dtype="float32")) return out def _match_times(all_ts, gt_boxes, dt_boxes, time_tol): """ match ground truth boxes and ground truth detections at all timestamps using a specified tolerance return a list of boxes vectors """ gt_size = len(gt_boxes) dt_size = len(dt_boxes) windowed_gt = [] windowed_dt = [] low_gt, high_gt = 0, 0 low_dt, high_dt = 0, 0 for ts in all_ts: while low_gt < gt_size and gt_boxes[low_gt]['t'] < ts: low_gt += 1 # the high index is at least as big as the low one high_gt = max(low_gt, high_gt) while high_gt < gt_size and gt_boxes[high_gt]['t'] <= ts: high_gt += 1 # detection are allowed to be inside a window around the right detection timestamp low = ts - time_tol high = ts + time_tol while low_dt < dt_size and dt_boxes[low_dt]['t'] < low: low_dt += 1 # the high index is at least as big as the low one high_dt = max(low_dt, high_dt) while high_dt < dt_size and dt_boxes[high_dt]['t'] <= high: high_dt += 1 windowed_gt.append(gt_boxes[low_gt:high_gt]) windowed_dt.append(dt_boxes[low_dt:high_dt]) return windowed_gt, windowed_dt def _coco_eval(dataset, results, num_gts): """simple helper function wrapping around COCO's Python API :params: gts iterable of numpy boxes for the ground truth :params: detections iterable of numpy boxes for the detections :params: height int :params: width int :params: labelmap iterable of class labels """ # Meaning: https://cocodataset.org/#detection-eval out_keys = ('AP', 'AP_50', 'AP_75', 'AP_S', 'AP_M', 'AP_L') out_dict = {k: 0.0 for k in out_keys} coco_gt = COCO() coco_gt.dataset = dataset coco_gt.createIndex() coco_pred = coco_gt.loadRes(results) coco_eval = COCOeval(coco_gt, coco_pred, 'bbox') coco_eval.params.imgIds = np.arange(1, num_gts + 1, dtype=int) coco_eval.evaluate() coco_eval.accumulate() with open(os.devnull, 'w') as f, contextlib.redirect_stdout(f): # info: https://stackoverflow.com/questions/8391411/how-to-block-calls-to-print coco_eval.summarize() for idx, key in enumerate(out_keys): out_dict[key] = coco_eval.stats[idx] return out_dict def _to_coco_format(gts, detections, categories, height=240, width=304): """ utilitary function producing our data in a COCO usable format """ annotations = [] results = [] images = [] # to dictionary for image_id, (gt, pred) in enumerate(zip(gts, detections)): im_id = image_id + 1 images.append( {"date_captured": "2019", "file_name": "n.a", "id": im_id, "license": 1, "url": "", "height": height, "width": width}) for bbox in gt: x1, y1 = bbox['x'], bbox['y'] w, h = bbox['w'], bbox['h'] area = w * h annotation = { "area": float(area), "iscrowd": False, "image_id": im_id, "bbox": [x1, y1, w, h], "category_id": int(bbox['class_id']) + 1, "id": len(annotations) + 1 } annotations.append(annotation) for bbox in pred: image_result = { 'image_id': im_id, 'category_id': int(bbox['class_id']) + 1, 'score': float(bbox['class_confidence']), 'bbox': [bbox['x'], bbox['y'], bbox['w'], bbox['h']], } results.append(image_result) dataset = {"info": {}, "licenses": [], "type": 'instances', "images": images, "annotations": annotations, "categories": categories} return dataset, results ================================================ FILE: src/dagr/utils/learning_rate_scheduler.py ================================================ from functools import partial import math from typing import List import numpy as np class LRSchedule: def __init__(self, warmup_epochs: float, num_iters_per_epoch: int, tot_num_epochs: int, min_lr_ratio: float=0.05, warmup_lr_start: float=0, steps_at_iteration=[50000], reduction_at_step=0.5): warmup_total_iters = num_iters_per_epoch * warmup_epochs total_iters = tot_num_epochs * num_iters_per_epoch no_aug_iters = 0 self.lr_func = partial(_yolox_warm_cos_lr, min_lr_ratio, total_iters, warmup_total_iters, warmup_lr_start, no_aug_iters, steps_at_iteration, reduction_at_step) def __call__(self, *args, **kwargs)->float: return self.lr_func(*args, **kwargs) def _yolox_warm_cos_lr( min_lr_ratio: float, total_iters: int, warmup_total_iters: int, warmup_lr_start: float, no_aug_iter: int, steps_at_iteration: List[int], reduction_at_step: float, iters: int)->float: """Cosine learning rate with warm up.""" min_lr = min_lr_ratio if iters < warmup_total_iters: # lr = (lr - warmup_lr_start) * iters / float(warmup_total_iters) + warmup_lr_start lr = (1 - warmup_lr_start) * pow(iters / float(warmup_total_iters), 2) + warmup_lr_start else: lr = min_lr + 0.5 * (1 - min_lr) * (1.0 + math.cos(math.pi * (iters - warmup_total_iters) / (total_iters - warmup_total_iters - no_aug_iter))) for step in steps_at_iteration: if iters >= step: lr *= reduction_at_step return lr ================================================ FILE: src/dagr/utils/logging.py ================================================ import torch import wandb import os from typing import List, Dict, Optional from torch_geometric.data import Batch from pathlib import PosixPath from pprint import pprint from pathlib import Path from torch_geometric.data import Data class Checkpointer: def __init__(self, output_directory: Optional[Path] = None, args=None, optimizer=None, scheduler=None, ema=None, model=None): self.optimizer = optimizer self.scheduler = scheduler self.ema = ema self.model = model self.mAP_max = 0 self.output_directory = output_directory self.args = args def restore_if_existing(self, folder, resume_from_best=False): checkpoint = self.search_for_checkpoint(folder, best=resume_from_best) if checkpoint is not None: print(f"Found existing checkpoint at {checkpoint}, resuming...") self.restore_checkpoint(folder, best=resume_from_best) def mAP_from_checkpoint_name(self, checkpoint_name: Path): return float(str(checkpoint_name).split("_")[-1].split(".pth")[0]) def search_for_checkpoint(self, resume_checkpoint: Path, best=False): checkpoints = list(resume_checkpoint.glob("*.pth")) if len(checkpoints) == 0: return None if not best: if resume_checkpoint / "last_model.pth" in checkpoints: return resume_checkpoint / "last_model.pth" # remove "last_model.pth" from checkpoints if resume_checkpoint / "last_model.pth" in checkpoints: checkpoints.remove(resume_checkpoint / "last_model.pth") checkpoints = sorted(checkpoints, key=lambda x: self.mAP_from_checkpoint_name(x.name)) return checkpoints[-1] def restore_if_not_none(self, target, source): if target is not None: target.load_state_dict(source) def restore_checkpoint(self, checkpoint_directory, best=False): path = self.search_for_checkpoint(checkpoint_directory, best) assert path is not None, "No checkpoint found in {}".format(checkpoint_directory) print("Restoring checkpoint from {}".format(path)) checkpoint = torch.load(path) checkpoint['model'] = self.fix_checkpoint(checkpoint['model']) checkpoint['ema'] = self.fix_checkpoint(checkpoint['ema']) if self.ema is not None: self.ema.ema.load_state_dict(checkpoint.get('ema', checkpoint['model'])) self.ema.updates = checkpoint.get('ema_updates', 0) self.restore_if_not_none(self.model, checkpoint['model']) self.restore_if_not_none(self.optimizer, checkpoint['optimizer']) self.restore_if_not_none(self.scheduler, checkpoint['scheduler']) return checkpoint['epoch'] def fix_checkpoint(self, state_dict): return state_dict def checkpoint(self, epoch: int, name: str=""): self.output_directory.mkdir(exist_ok=True, parents=True) checkpoint = { "ema": self.ema.ema.state_dict(), "ema_updates": self.ema.updates, "model": self.model.state_dict(), "optimizer": self.optimizer.state_dict(), "scheduler": self.scheduler.state_dict(), "epoch": epoch, "args": self.args } torch.save(checkpoint, self.output_directory / f"{name}.pth") def process(self, data: Dict[str, float], epoch: int): mAP = data['mAP'] data = {f"validation/metric/{k}": v for k, v in data.items()} data['epoch'] = epoch wandb.log(data) if mAP > self.mAP_max: self.checkpoint(epoch, name=f"best_model_mAP_{mAP}") self.mAP_max = mAP def set_up_logging_directory(dataset, task, output_directory, exp_name="temp"): project = f"low_latency-{dataset}-{task}" output_directory = output_directory / dataset / task output_directory.mkdir(parents=True, exist_ok=True) wandb.init(project=project, id=exp_name, entity="danielgehrig18", save_code=True, dir=str(output_directory)) name = wandb.run.id output_directory = output_directory / name output_directory.mkdir(parents=True, exist_ok=True) return output_directory def log_hparams(args): hparams = {k: str(v) if type(v) is PosixPath else v for k, v in vars(args).items()} pprint(hparams) wandb.log(hparams) def log_bboxes(data: Batch, targets: List[Dict[str, torch.Tensor]], detections: List[Dict[str, torch.Tensor]], class_names: List[str], bidx: int, key: str): gt_bbox = [] det_bbox = [] images = [] for b, datum in enumerate(data.to_data_list()): image = visualize_events(datum) image = torch.cat([image, image], dim=1) images.append(image) if len(detections) > 0: det = detections[b] det = torch.cat([det['boxes'], det['labels'].view(-1,1), det['scores'].view(-1,1)], dim=-1) det[:, [0, 2]] += b * datum.width det_bbox.append(det) if len(targets) > 0: tar = targets[b] tar = torch.cat([tar['boxes'], tar['labels'].view(-1, 1), torch.ones_like(tar['labels'].view(-1, 1))], dim=-1) tar[:, [0, 2]] += b * datum.width tar[:, [1, 3]] += datum.height gt_bbox.append(tar) if b == bidx-1: break pred_bbox = torch.cat(det_bbox) gt_bbox = torch.cat(gt_bbox) images = torch.cat(images, dim=-1) bidx = min([bidx, len(data)]) gt_bbox[:,[0,2]] /= (bidx * datum.width) gt_bbox[:,[1,3]] /= (2 * datum.height) pred_bbox[:,[0,2]] /= (bidx * datum.width) pred_bbox[:,[1,3]] /= (2 * datum.height) image = __convert_to_wandb_data(images.detach().float().cpu(), gt_bbox.detach().cpu(), pred_bbox.detach().cpu(), class_names) wandb.log({key: image}) def visualize_events(data: Data)->torch.Tensor: x, y = data.pos[:,:2].long().t() p = data.x[:,0].long() if hasattr(data, "image"): image = data.image[0].clone() else: image = torch.full(size=(3, data.height, data.width), fill_value=255, device=p.device, dtype=torch.uint8) is_pos = p == 1 image[:, y[is_pos], x[is_pos]] = torch.tensor([[0],[0],[255]], dtype=torch.uint8, device=p.device) image[:, y[~is_pos], x[~is_pos]] = torch.tensor([[255],[0],[0]], dtype=torch.uint8, device=p.device) return image def __convert_to_wandb_data(image: torch.Tensor, gt: torch.Tensor, p: torch.Tensor, class_names: List[str])->wandb.Image: return wandb.Image(image, boxes={ "predictions": __parse_bboxes(p, class_names, suffix="P"), "ground_truth": __parse_bboxes(gt, class_names) }) def __parse_bboxes(bboxes: torch.Tensor, class_names: List[str], suffix: str="GT"): # bbox N x 6 -> xyxycs return { "box_data": [__parse_bbox(bbox, class_names, suffix) for bbox in bboxes], "class_labels": dict(enumerate(class_names)) } def __parse_bbox(bbox: torch.Tensor, class_names: List[str], suffix: str="GT"): # bbox xyxycs return { "position": { "minX": float(bbox[0]), "minY": float(bbox[1]), "maxX": float(bbox[2]), "maxY": float(bbox[3]) }, "class_id": int(bbox[-2]), "scores": { "object score": float(bbox[-1]) }, "bbox_caption": f"{suffix} - {class_names[int(bbox[-2])]}" } ================================================ FILE: src/dagr/utils/testing.py ================================================ import torch from dagr.utils.logging import log_bboxes from dagr.utils.buffers import DetectionBuffer, format_data import tqdm def to_npy(detections): return [{k: v.cpu().numpy() for k, v in d.items()} for d in detections] def format_detections(sequences, t, detections): detections = to_npy(detections) for i, det in enumerate(detections): det['sequence'] = sequences[i] det['t'] = t[i] return detections def run_test_with_visualization(loader, model, dataset: str, log_every_n_batch=-1, name="", compile_detections=False, no_eval=False): model.eval() if not no_eval: mapcalc = DetectionBuffer(height=loader.dataset.height, width=loader.dataset.width, classes=loader.dataset.classes) counter = 0 if compile_detections: compiled_detections = [] for i, data in enumerate(tqdm.tqdm(loader, desc=f"Testing {name}")): data = data.cuda(non_blocking=True) data_for_visualization = data.clone() data = format_data(data) detections, targets = model(data.clone()) if compile_detections: compiled_detections.extend(format_detections(data.sequence, data.t1, detections)) if log_every_n_batch > 0 and counter % log_every_n_batch == 0: log_bboxes(data_for_visualization, targets=targets, detections=detections, bidx=4, class_names=loader.dataset.classes, key="testing/evaluated_bboxes") if not no_eval: mapcalc.update(detections, targets, dataset, data.height[0], data.width[0]) if i % 5 == 0: torch.cuda.empty_cache() counter += 1 torch.cuda.empty_cache() data = None if not no_eval: data = mapcalc.compute() return (data, compiled_detections) if compile_detections else data ================================================ FILE: src/dagr/visualization/bbox_viz.py ================================================ import numpy as np import cv2 import torchvision import torch _COLORS = np.array([[0.000, 0.8, 0.1], [1, 0.67, 0.00]]) class_names = ["car", "pedestrian"] def draw_bbox_on_img(img, x, y, w, h, labels, scores=None, conf=0.5, nms=0.45, label="", linewidth=2): if scores is not None: mask = filter_boxes(x, y, w, h, labels, scores, conf, nms) x = x[mask] y = y[mask] w = w[mask] h = h[mask] labels = labels[mask] scores = scores[mask] for i in range(len(x)): if scores is not None and scores[i] < conf: continue x0 = int(x[i]) y0 = int(y[i]) x1 = int(x[i] + w[i]) y1 = int(y[i] + h[i]) cls_id = int(labels[i]) color = (_COLORS[cls_id] * 255).astype(np.uint8).tolist() text = f"{label}-{class_names[cls_id]}" if scores is not None: text += f":{scores[i] * 100: .1f}" txt_color = (0, 0, 0) if np.mean(_COLORS[cls_id]) > 0.5 else (255, 255, 255) font = cv2.FONT_HERSHEY_SIMPLEX txt_size = cv2.getTextSize(text, font, 0.4, 1)[0] cv2.rectangle(img, (x0, y0), (x1, y1), color, linewidth) txt_bk_color = (_COLORS[cls_id] * 255 * 0.7).astype(np.uint8).tolist() txt_height = int(1.5*txt_size[1]) cv2.rectangle( img, (x0, y0 - txt_height), (x0 + txt_size[0] + 1, y0 + 1), txt_bk_color, -1 ) cv2.putText(img, text, (x0, y0 + txt_size[1]-txt_height), font, 0.4, txt_color, thickness=1) return img def filter_boxes(x, y, w, h, labels, scores, conf, nms): mask = scores > conf x1, y1 = x + w, y + h box_coords = np.stack([x, y, x1, y1], axis=-1) nms_out_index = torchvision.ops.batched_nms( torch.from_numpy(box_coords), torch.from_numpy(np.ascontiguousarray(scores)), torch.from_numpy(labels), nms ) nms_mask = np.ones_like(mask) == 0 nms_mask[nms_out_index] = True return mask & nms_mask ================================================ FILE: src/dagr/visualization/event_viz.py ================================================ import numba @numba.jit(nopython=True) def draw_events_on_image(img, x, y, p, alpha=0.5): img_copy = img.copy() for i in range(len(p)): if y[i] < len(img): img[y[i], x[i], :] = alpha * img_copy[y[i], x[i], :] img[y[i], x[i], int(p[i])-1] += 255 * (1-alpha) return img