diff --git a/.github/workflows/manual.yml b/.github/workflows/manual.yml new file mode 100644 index 00000000..d2e010d9 --- /dev/null +++ b/.github/workflows/manual.yml @@ -0,0 +1,35 @@ +name: Run C++ tests + +on: + pull_request: + +jobs: + build-and-test: + + runs-on: ubuntu-latest + defaults: + run: + shell: bash -l {0} + steps: + - name: Checkout code + uses: actions/checkout@v2 + + - name: Install system dependencies + run: | + sudo apt update + sudo apt install -y libx11-dev libxrandr-dev libxinerama-dev libxcursor-dev libxi-dev mesa-common-dev libc++1 + + - name: Setup Conda environment. + uses: conda-incubator/setup-miniconda@v2 + with: + activate-environment: gpudrive + environment-file: environment.yml + + - name: Install dependencies and build the project with Poetry + run: | + poetry install + + - name: Run tests + run: | + cd build/ + ctest --rerun-failed --output-on-failure diff --git a/README.md b/README.md index 653e7cbe..086f1541 100644 --- a/README.md +++ b/README.md @@ -1,36 +1,45 @@ -Madrona Escape Room +GPUDrive ============================ -This is an example RL environment simulator built on the [Madrona Engine](https://madrona-engine.github.io). -The goal of this repository is to provide a simple reference that demonstrates how to use Madrona's ECS APIs and -how to interface with the engine's rigid body physics and rendering functionality. -This example also demonstrates how to integrate the simulator with python code for evaluating agent polices and/or policy learning. Specifically, this codebase includes a simple PyTorch PPO training loop integrated with the simulator that can train agents in under an hour on a high end GPU. - -If you're interested in using Madrona to implement a high-performance batch simulator for a new environment or RL training task, we highly recommend forking this repo and adding/removing code as needed, rather than starting from scratch. This will ensure the build system and backends are setup correctly. +This is an Batch RL environment simulator of Nocturne built on the [Madrona Engine](https://madrona-engine.github.io). It supports loading multiple worlds with multi agent support. Python bindings are exposed for easy integration with RL algorithms. The Environment and Learning Task -------------- -https://github.com/shacklettbp/madrona_escape_room/assets/1111429/ec6231c8-a74b-4f0a-8a1a-b1bcdc7111cd +The codebase trains a shared policy that controls agents individually with direct engine inputs rather than pixel observations. Agents interact with the simulator as follows: -As shown above, the simulator implements a 3D environment consisting of two agents and a row of three rooms. All agents start in the first room, and must navigate to as many new rooms as possible. The agents must step on buttons or push movable blocks over buttons to trigger the opening of doors that lead to new rooms. Agents are rewarded based on their progress along the length of the level. +### Action Space: + * Acceleration: Continuous float values for acceleration applied to the agents. + * Steering angle: Continuous float values for steering angle applied according to the bicycle kinematic model. + * Heading angle (currently unused): Continuous float values for heading angle. This controls where the agent is looking. -The codebase trains a shared policy that controls agents individually with direct engine inputs rather than pixel observations. Agents interact with the simulator as follows: +### Observation Space: + +**SelfObservation** + +The `SelfObservation` tensor of shape `(5,)` for each agent provides information about the agent's own state. The respective values are + +- `SelfObservation[0]`: Represents the current *speed* of the agent. +- `SelfObservation[1:3]` : *length* and *width* of the agent. +- `SelfObservation[3:5]`: *Coordinates (x,y)* of the goal relative to the agent. + +**PartnerObservation** + +The `PartnerObservation` tensor of shape `(num_agents-1,7)` for each agent provides information about other agents in the range `params.observationRadius`. All the values in this tensor are *relative to the ego agent*. The respective values for each `PartnerObservation` are + +- `PartnerObservation[0]`: The *speed* of the observed neighboring agent. +- `PartnerObservation[1:3]`: The *position (x,y)* of the observed neighbouring agent. +- `PartnerObservation[3]`: The *orientation* of the neighboring agent. +- `PartnerObservation[4:6]`: The *length* and *width* of the neighbouring agent. +- `PartnerObservation[6]`: The type of agent. + +**AgentMapObservations** -**Action Space:** - * Movement amount: Egocentric polar coordinates for the direction and amount to move, translated to XY forces in the physics engine. - * Rotation amount: Torque applied to the agent to turn. - * Grab: Boolean, true to grab if possible or release if already holding an object. - -**Observation Space:** - * Global position. - * Position within the current room. - * Distance and direction to all the buttons and cubes in the current room (egocentric polar coordinates). - * 30 Lidar samples arrayed in a circle around the agent, giving distance to the nearest object along a direction. - * Whether the current room's door is open (boolean). - * Whether an object is currently grabbed (boolean). - * The max distance achieved so far in the level. - * The number of steps remaining in the episode. +The `AgentMapObservations` tensor of shape (num_road_objs, 4) for each agent provides information about the road objects in the range `params.observationRadius`. All the values in this tensor are *relative to the ego agent*. The respective values for each `AgentMapObservations` are + +- `AgentMapObservations[0:2]`: The position coordinates for the road object. +- `AgentMapObservations[2]`: The relative orientation of the road object. +- `AgentMapObservations[3]`: The road object type. **Rewards:** Agents are rewarded for the max distance achieved along the Y axis (the length of the level). Each step, new reward is assigned if the agents have progressed further in the level, or a small penalty reward is assigned if not. @@ -57,10 +66,12 @@ The built-in training functionality requires [PyTorch 2.0](https://pytorch.org/g Now that you have the required dependencies, fetch the repo (don't forget `--recursive`!): ```bash -git clone --recursive https://github.com/shacklettbp/madrona_escape_room.git -cd madrona_escape_room +git clone --recursive https://github.com/Emerge-Lab/gpudrive.git +cd gpudrive ``` +# Manual Install + Next, for Linux and MacOS: Run `cmake` and then `make` to build the simulator: ```bash mkdir build @@ -89,6 +100,19 @@ Or test the PyTorch training integration: python scripts/train.py --num-worlds 1024 --num-updates 100 --ckpt-dir build/ckpts ``` +# Poetry install + +### Conda + +Create a conda environment using `environment.yml` and then run `poetry install` + +```bash +conda env create -f environment.yml` +poetry install +``` + + + Simulator Code Walkthrough (Learning the Madrona ECS APIs) ----------------------------------------------------------- diff --git a/build.py b/build.py new file mode 100644 index 00000000..3151bec7 --- /dev/null +++ b/build.py @@ -0,0 +1,28 @@ +import subprocess +import os +import sys +import logging +import shutil + +logging.basicConfig(level=logging.INFO) + +def build(): + # Cloning the repository, although typically you would not do this in the build step + # as the code should already be present. Including it just for completeness. + subprocess.check_call(['git', 'submodule', 'update', '--init', '--recursive', '--force']) + + # Create and enter the build directory + if not os.path.exists('build'): + os.mkdir('build') + os.chdir('build') + + # Run CMake and Make + subprocess.check_call(['cmake', '..', '-DCMAKE_BUILD_TYPE=Release']) + subprocess.check_call(['make', f"-j{os.cpu_count()}"]) # Utilize all available cores + + # Going back to the root directory + os.chdir('..') + +if __name__ == '__main__': + logging.info("Building the C++ code and installing the Python package") + build() \ No newline at end of file diff --git a/config.py b/config.py new file mode 100644 index 00000000..4e430c3b --- /dev/null +++ b/config.py @@ -0,0 +1,45 @@ +import re +import os +import yaml +from scripts.sim_utils.create import SimCreator +import build +import torch + +def get_constants(): + fullpath = os.path.join(os.path.dirname(__file__), "config.yml") + with open(fullpath, 'r') as file: + config = yaml.safe_load(file) + sim = SimCreator(config) + consts = sim.shape_tensor().to_torch() + max_agents = torch.max(consts[:, 0], 0).values.item() + max_roads = torch.max(consts[:, 1], 0).values.item() + print(consts) + print(max_agents, max_roads) + return max_agents, max_roads + +def update_constants(filepath, new_max_agent_count, new_max_road_entity_count): + with open(filepath, 'r') as file: + content = file.read() + + content = re.sub( + r"(inline constexpr madrona::CountT kMaxAgentCount = )\d+;", + r"\g<1>{};".format(new_max_agent_count), + content) + + content = re.sub( + r"(inline constexpr madrona::CountT kMaxRoadEntityCount = )\d+;", + r"\g<1>{};".format(new_max_road_entity_count), + content) + + # Write the updated content back to the file + with open(filepath, 'w') as file: + file.write(content) + + build.build() + +def main(): + new_max_agent_count, new_max_road_entity_count = get_constants() + update_constants("src/consts.hpp", new_max_agent_count, new_max_road_entity_count+1) + +if __name__ == "__main__": + update_constants("src/consts.hpp", 200, 2000) \ No newline at end of file diff --git a/config.yml b/config.yml new file mode 100644 index 00000000..3738e94c --- /dev/null +++ b/config.yml @@ -0,0 +1,15 @@ +parameters: + datasetInitOptions: FirstN + observationRadius: 1000.0 + polylineReductionThreshold: 1.0 + rewardParams: reward_params +reward_params: + distanceToExpertThreshold: 1.0 + distanceToGoalThreshold: 1.0 + rewardType: DistanceBased +sim_manager: + auto_reset: true + exec_mode: CUDA + gpu_id: 0 + json_path: /home/aarav/nocturne_data/binned_jsons/bin_0 + num_worlds: 100 diff --git a/environment.yml b/environment.yml new file mode 100644 index 00000000..913858eb --- /dev/null +++ b/environment.yml @@ -0,0 +1,67 @@ +name: gpudrive +channels: + - defaults +dependencies: + - _libgcc_mutex=0.1=main + - _openmp_mutex=5.1=1_gnu + - bzip2=1.0.8=h5eee18b_5 + - ca-certificates=2023.12.12=h06a4308_0 + - 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+version = "0.1.0" +description = "" +authors = ["Sébastien Eustace "] +readme = "README.md" +packages = [ + {include = "gpudrive.cpython-311-x86_64-linux-gnu.so", from = "build", to = "gpudrive"}, + {include = "gpudrive_learn", from = "train_src", to = "gpudrive_learn"}, +] \ No newline at end of file diff --git a/run_bench.sh b/run_bench.sh new file mode 100755 index 00000000..61da9726 --- /dev/null +++ b/run_bench.sh @@ -0,0 +1,28 @@ +#!/bin/bash + +# FOR FIRSTN +# Loop from 1 to 30 to set the numEnvs value +for numEnvs in {1..150}; do + echo "Running benchmarks for numEnvs = $numEnvs" + + MADRONA_MWGPU_KERNEL_CACHE=./gpudrive_cache python scripts/benchmark.py --numEnvs "$numEnvs" +done + + +#For RANDOMN +# echo "1..$numEnvsRange" | pv -p -s $numEnvsRange | while read numEnvs; do +# echo "Running benchmarks for numEnvs = $numEnvs" +# for i in $(seq 1 100); do +# MADRONA_MWGPU_KERNEL_CACHE=./gpudrive_cache python scripts/benchmark.py --numEnvs "$numEnvs" +# done +# done + +#!/bin/bash + +# Use seq to generate a range and pipe it through tqdm for the progress bar +# seq 1 150 | tqdm --total=150 | while read numEnvs; do +# echo "Running benchmarks for numEnvs = $numEnvs" +# for i in {1..100}; do +# MADRONA_MWGPU_KERNEL_CACHE=./gpudrive_cache python scripts/benchmark.py --numEnvs "$numEnvs" +# done +# done diff --git a/scripts/benchmark.py b/scripts/benchmark.py new file mode 100644 index 00000000..734fef1b --- /dev/null +++ b/scripts/benchmark.py @@ -0,0 +1,58 @@ +from gpudrive import SimManager +from sim_utils.create import SimCreator + +from time import perf_counter +import argparse +import csv +import yaml + +def run_benchmark(sim: SimManager, config: dict): + + shape = sim.shape_tensor().to_torch() + useful_num_agents, useful_num_roads = shape[0].flatten().tolist() + num_envs = shape.shape[0] + + actual_num_agents = sim.self_observation_tensor().to_torch().shape[1] + actual_num_roads = sim.map_observation_tensor().to_torch().shape[1] + + start = perf_counter() + for i in range(0): + sim.reset(i) + time_to_reset = perf_counter() - start + + start = perf_counter() + for i in range(91): + sim.step() + time_to_step = perf_counter() - start + + fps = (91 / time_to_step) * num_envs + afps = fps * actual_num_agents + + + print(f"num_agents: {actual_num_agents}, num_roads: {actual_num_roads}, num_envs: {num_envs}, time_to_reset: {time_to_reset}, time_to_step: {time_to_step}, fps: {fps}, afps: {afps}, exec_mode: {config['sim_manager']['exec_mode']}, datasetInitOptions: {config['parameters']['datasetInitOptions']}") + # check if benchmark_results.csv exists + try: + with open('benchmark_results.csv', mode='r') as file: + pass + except FileNotFoundError: + with open('benchmark_results.csv', mode='w') as file: + writer = csv.writer(file) + writer.writerow(['actual_num_agents', 'actual_num_roads', 'useful_num_agents', 'useful_num_roads', 'num_envs', 'time_to_reset', 'time_to_step', 'fps', 'afps', 'exec_mode', 'datasetInitOptions']) + + with open('benchmark_results.csv', mode='a') as file: + writer = csv.writer(file) + writer.writerow([actual_num_agents, actual_num_roads, useful_num_agents, useful_num_roads, num_envs, time_to_reset, time_to_step, fps, afps, config['sim_manager']['exec_mode'], config['parameters']['datasetInitOptions']]) + + +if __name__ == "__main__": + # Export the + parser = argparse.ArgumentParser(description='GPUDrive Benchmarking Tool') + parser.add_argument('--datasetPath', type=str, help='Path to the config file', default='/home/aarav/gpudrive/config.yml', required=False) + parser.add_argument('--numEnvs', type=int, help='Number of environments', default=2, required=False) + parser.add_argument('--randomized', type=bool, help='Randomize the dataset', default=False, required=False) + args = parser.parse_args() + with open(args.datasetPath, 'r') as file: + config = yaml.safe_load(file) + config['sim_manager']['num_worlds'] = args.numEnvs + sim = SimCreator(config) + run_benchmark(sim, config) \ No newline at end of file diff --git a/scripts/dataset_binning.py b/scripts/dataset_binning.py new file mode 100644 index 00000000..65a4d1c3 --- /dev/null +++ b/scripts/dataset_binning.py @@ -0,0 +1,110 @@ +from gpudrive import SimManager +from sim_utils.create import SimCreator + +import json +import os +import shutil +import yaml +import csv +from tqdm import tqdm +import pandas as pd + +VALID_FILES_PATH = "/home/aarav/nocturne_data/formatted_json_v2_no_tl_valid" +BINNING_TEMP_PATH = "/home/aarav/nocturne_data/binning_temp" +FINAL_BINNED_JSON_PATHS = "/home/aarav/nocturne_data/binned_jsons" +CSV_PATH = "/home/aarav/nocturne_data/datacsv.csv" + +def modify_valid_files_json(valid_files_path: str, file_path: str): + if(os.path.exists(valid_files_path + "/valid_files.json") == False): + with open(valid_files_path + "/valid_files.json", 'w') as file: + json.dump({}, file) + with open(valid_files_path + "/valid_files.json", 'r') as file: + valid_files = json.load(file) + valid_files.clear() + valid_files[file_path] = [] + with open(valid_files_path + "/valid_files.json", 'w') as file: + json.dump(valid_files, file) + +def delete_file_from_dest(file_path: str): + os.remove(file_path) + +def copy_file_to_dest(file_path: str, dest_path: str): + shutil.copy(file_path, dest_path) + return os.path.join(dest_path, os.path.basename(file_path)) + +def return_list_of_files(valid_files_path: str): + with open(valid_files_path + "/valid_files.json", 'r') as file: + valid_files = json.load(file) + file_list = [] + for file in valid_files: + file_list.append(os.path.join(valid_files_path, file)) + return file_list + +# def return_agent_numbers(sim: SimManager): +# shape = sim.shape_tensor().to_torch() +# num_agents, num_roads = shape[0].flatten().tolist() +# return num_agents, num_roads + +def return_agent_numbers(file_path: str): + with open(file_path, 'r') as file: + data = json.load(file) + num_agents = len(data['objects']) + num_roads = len(data['roads']) + num_road_segments = 0 + for road in data['roads']: + if(road['type'] == "road_edge" or road['type'] == "road_line" or road['type'] == "lane"): + num_road_segments += len(road['geometry']) - 1 + else: + num_road_segments += 1 + return num_agents, num_road_segments + +if __name__ == "__main__": + # with open("config.yml", 'r') as file: + # config = yaml.safe_load(file) + # config['sim_manager']['num_worlds'] = 1 + # config['sim_manager']['exec_mode'] = "CPU" + # config['sim_manager']['json_path'] = BINNING_TEMP_PATH + # file_list = return_list_of_files(VALID_FILES_PATH) + # file_meta_data = [] + # file_meta_data.append(["File Path", "Number of Agents", "Number of Roads"]) + # for file in tqdm(file_list): + # # currfile = copy_file_to_dest(file, BINNING_TEMP_PATH) + # # modify_valid_files_json(BINNING_TEMP_PATH, file) + # num_entities = return_agent_numbers(file) + # file_meta_data.append([file, num_entities[0], num_entities[1]]) + # # delete_file_from_dest(currfile) + + # with open(CSV_PATH, 'w') as file: + # writer = csv.writer(file) + # writer.writerows(file_meta_data) + + data = pd.read_csv(CSV_PATH) + sorted_data = data.sort_values('Number of Agents') + + bins = [] + bin_size = 100 + number_of_bins = len(sorted_data) // bin_size + (1 if len(sorted_data) % bin_size > 0 else 0) + + # Create bins of 100 files each + for i in range(number_of_bins): + bin_start = i * bin_size + bin_end = min((i + 1) * bin_size, len(sorted_data)) + bins.append(sorted_data.iloc[bin_start:bin_end]) + + if not os.path.exists(FINAL_BINNED_JSON_PATHS): + os.makedirs(FINAL_BINNED_JSON_PATHS) + + for i, bin in enumerate(bins): + if not os.path.exists(FINAL_BINNED_JSON_PATHS + f"/bin_{i}"): + os.makedirs(FINAL_BINNED_JSON_PATHS + f"/bin_{i}") + bin_folder = FINAL_BINNED_JSON_PATHS + f"/bin_{i}" + print(bin_folder) + d = {} + for index, row in bin.iterrows(): + file_path = row['File Path'] + d[file_path] = [row['Number of Agents'], row['Number of Roads']] + filepath = os.path.join(bin_folder, f"valid_files.json") + print(filepath) + with open(filepath, 'w') as file: + json.dump(d, file) + print("Binning complete") \ No newline at end of file diff --git a/scripts/run_bench.py b/scripts/run_bench.py new file mode 100644 index 00000000..8a1cd742 --- /dev/null +++ b/scripts/run_bench.py @@ -0,0 +1,70 @@ +import os +import argparse +import subprocess +from tqdm import tqdm +import yaml + +# def run_bench(total_num_envs: int): +# for numEnvs in tqdm(range(1, total_num_envs + 1), desc="Overall progress", unit="env"): +# if(args.randomized): +# for _ in range(1,100): +# command = f"MADRONA_MWGPU_KERNEL_CACHE=./gpudrive_cache python scripts/benchmark.py --numEnvs {numEnvs} --datasetPath config.yml" +# subprocess.run(command, shell=True, check=True) +# else: +# command = f"MADRONA_MWGPU_KERNEL_CACHE=./gpudrive_cache python scripts/benchmark.py --numEnvs {numEnvs} --datasetPath config.yml" +# subprocess.run(command, shell=True, check=True) + +def run_bench(total_num_envs: int, args): + if args.binned: + num_bins = len(os.listdir("/home/aarav/nocturne_data/binned_jsons")) + for bin in range(1, num_bins + 1): + modifyConfigToBinned(bin) + command = f"MADRONA_MWGPU_KERNEL_CACHE=./gpudrive_cache python scripts/benchmark.py --numEnvs {total_num_envs} --datasetPath config.yml" + subprocess.run(command, shell=True, check=True) + for numEnvs in tqdm(range(1, total_num_envs + 1), desc="Overall progress", unit="env", position=0): + if args.randomized: + with tqdm(total=99, desc=f"Inner progress for numEnvs={numEnvs}", unit="run", leave=False, position=1) as pbar: + for _ in range(1, 100): + command = f"MADRONA_MWGPU_KERNEL_CACHE=./gpudrive_cache python scripts/benchmark.py --numEnvs {numEnvs} --datasetPath config.yml" + try: + subprocess.run(command, shell=True, check=True) + except subprocess.CalledProcessError: + pass + pbar.update(1) + else: + command = f"MADRONA_MWGPU_KERNEL_CACHE=./gpudrive_cache python scripts/benchmark.py --numEnvs {numEnvs} --datasetPath config.yml" + subprocess.run(command, shell=True, check=True) + + +def modifyConfigToRandomize(randomize: bool): + config_path = "config.yml" + if(randomize): + with open(config_path, 'r') as file: + config = yaml.safe_load(file) + config['parameters']['datasetInitOptions'] = "RandomN" + with open(config_path, 'w') as file: + yaml.dump(config, file) + else: + with open(config_path, 'r') as file: + config = yaml.safe_load(file) + config['parameters']['datasetInitOptions'] = "FirstN" + with open(config_path, 'w') as file: + yaml.dump(config, file) + +def modifyConfigToBinned(bin: int): + config_path = "config.yml" + with open(config_path, 'r') as file: + config = yaml.safe_load(file) + config['sim_manager']['json_path'] = f"/home/aarav/nocturne_data/binned_jsons/bin_{bin}" + with open(config_path, 'w') as file: + yaml.dump(config, file) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description='GPUDrive Benchmarking Tool') + parser.add_argument('--totalNumEnvs', type=int, help='Number of environments', default=150, required=False) + parser.add_argument('--randomized', help='Randomize the dataset', action='store_true', required=False) + parser.add_argument('--binned', help='Use binned dataset', action='store_true', required=False) + args = parser.parse_args() + modifyConfigToRandomize(args.randomized) + run_bench(args.totalNumEnvs, args) \ No newline at end of file diff --git a/scripts/sim_utils/create.py b/scripts/sim_utils/create.py new file mode 100644 index 00000000..6c9be810 --- /dev/null +++ b/scripts/sim_utils/create.py @@ -0,0 +1,35 @@ +import yaml +import gpudrive + +def SimCreator(config: dict) -> gpudrive.SimManager: + # Initialize RewardParams + reward_params_config = config['reward_params'] + reward_params = gpudrive.RewardParams() + reward_params.rewardType = getattr(gpudrive.RewardType, reward_params_config['rewardType']) + reward_params.distanceToGoalThreshold = reward_params_config['distanceToGoalThreshold'] + reward_params.distanceToExpertThreshold = reward_params_config['distanceToExpertThreshold'] + + # Initialize Parameters + params_config = config['parameters'] + params = gpudrive.Parameters() + params.polylineReductionThreshold = params_config['polylineReductionThreshold'] + params.observationRadius = params_config['observationRadius'] + params.datasetInitOptions = getattr(gpudrive.DatasetInitOptions, params_config['datasetInitOptions']) + params.rewardParams = reward_params + + # Initialize SimManager with parameters from the config + sim_manager_config = config['sim_manager'] + sim = gpudrive.SimManager( + exec_mode=getattr(gpudrive.madrona.ExecMode, sim_manager_config['exec_mode']), + gpu_id=sim_manager_config['gpu_id'], + num_worlds=sim_manager_config['num_worlds'], + auto_reset=sim_manager_config['auto_reset'], + json_path=sim_manager_config['json_path'], + params=params + ) + return sim + +if __name__ == "__main__": + with open("/home/aarav/gpudrive/config.yml", 'r') as file: + config = yaml.safe_load(file) + sim = SimCreator(config) diff --git a/scripts/test.py b/scripts/test.py index 31ddc741..91ba7288 100644 --- a/scripts/test.py +++ b/scripts/test.py @@ -15,11 +15,11 @@ # Now use the 'params' instance when creating SimManager sim = gpudrive.SimManager( - exec_mode=gpudrive.madrona.ExecMode.CUDA, + exec_mode=gpudrive.madrona.ExecMode.CPU, gpu_id=0, num_worlds=1, auto_reset=True, - json_path="waymo_data", + json_path="build/tests/testJsons", params=params ) diff --git a/src/MapReader.hpp b/src/MapReader.hpp index 6341495a..5c7b13bf 100644 --- a/src/MapReader.hpp +++ b/src/MapReader.hpp @@ -4,7 +4,7 @@ #include #include -#include "init.hpp" +#include "types.hpp" namespace gpudrive { diff --git a/src/bindings.cpp b/src/bindings.cpp index 40d1f2a8..a5154d66 100644 --- a/src/bindings.cpp +++ b/src/bindings.cpp @@ -18,6 +18,12 @@ namespace gpudrive // like madrona::py::Tensor and madrona::py::PyExecMode. madrona::py::setupMadronaSubmodule(m); + nb::enum_(m, "DatasetInitOptions") + .value("FirstN", DatasetInitOptions::FirstN) + .value("RandomN", DatasetInitOptions::RandomN) + .value("PadN", DatasetInitOptions::PadN) + .value("ExactN", DatasetInitOptions::ExactN); + // Define RewardType enum nb::enum_(m, "RewardType") .value("DistanceBased", RewardType::DistanceBased) @@ -36,9 +42,16 @@ namespace gpudrive .def(nb::init<>()) // Default constructor .def_rw("polylineReductionThreshold", &Parameters::polylineReductionThreshold) .def_rw("observationRadius", &Parameters::observationRadius) + .def_rw("datasetInitOptions", &Parameters::datasetInitOptions) .def_rw("rewardParams", &Parameters::rewardParams) + .def_rw("collisionBehaviour", &Parameters::collisionBehaviour) .def_rw("maxNumControlledVehicles", &Parameters::maxNumControlledVehicles); + // Define CollisionBehaviour enum + nb::enum_(m, "CollisionBehaviour") + .value("AgentStop", CollisionBehaviour::AgentStop) + .value("AgentRemoved", CollisionBehaviour::AgentRemoved) + .value("Ignore", CollisionBehaviour::Ignore); // Bindings for Manager class nb::class_(m, "SimManager") @@ -70,8 +83,7 @@ namespace gpudrive .def("lidar_tensor", &Manager::lidarTensor) .def("steps_remaining_tensor", &Manager::stepsRemainingTensor) .def("shape_tensor", &Manager::shapeTensor) - .def("controlled_state_tensor", &Manager::controlledStateTensor) - .def("agent_roadmap_tensor", &Manager::agentMapObservationsTensor); + .def("controlled_state_tensor", &Manager::controlledStateTensor); } } diff --git a/src/consts.hpp b/src/consts.hpp index d0f8611f..5d422275 100644 --- a/src/consts.hpp +++ b/src/consts.hpp @@ -8,8 +8,8 @@ namespace gpudrive { namespace consts { -inline constexpr madrona::CountT kMaxAgentCount = 100; -inline constexpr madrona::CountT kMaxRoadEntityCount = 5000; +inline constexpr madrona::CountT kMaxAgentCount = 360; +inline constexpr madrona::CountT kMaxRoadEntityCount = 2400; // Various world / entity size parameters inline constexpr float worldLength = 40.f; diff --git a/src/headless.cpp b/src/headless.cpp index 5eb36762..18095ec5 100755 --- a/src/headless.cpp +++ b/src/headless.cpp @@ -62,7 +62,7 @@ int main(int argc, char *argv[]) .gpuID = 0, .numWorlds = (uint32_t)num_worlds, .autoReset = false, - .jsonPath = "/home/samk/gpudrive/maps.small", + .jsonPath = "/home/aarav/gpudrive/valid_nocturne", .params = { .polylineReductionThreshold = 1.0, .observationRadius = 100.0, @@ -70,7 +70,8 @@ int main(int argc, char *argv[]) .rewardType = RewardType::DistanceBased, .distanceToGoalThreshold = 0.5, .distanceToExpertThreshold = 0.5 - } + }, + .datasetInitOptions = DatasetInitOptions::FirstN } }); diff --git a/src/init.hpp b/src/init.hpp index d69277de..24325bdc 100755 --- a/src/init.hpp +++ b/src/init.hpp @@ -1,94 +1,21 @@ #pragma once #include -#include -#include -#include -#include - -namespace madrona::viz { -struct VizECSBridge; -} +#include "types.hpp" namespace gpudrive { - // Constants computed from train files. - constexpr size_t MAX_OBJECTS = 515; - constexpr size_t MAX_ROADS = 956; - constexpr size_t MAX_POSITIONS = 91; - constexpr size_t MAX_GEOMETRY = 1746; - - enum class MapObjectType : uint32_t - { - Vehicle, - Pedestrian, - Cyclist, - Invalid - }; - - enum class MapRoadType : uint32_t - { - RoadEdge, - RoadLine, - Lane, - CrossWalk, - SpeedBump, - StopSign, - Invalid - }; - - // Cannot use Madrona::math::Vector2 because it is not a POD type. - // Getting all zeros if using any madrona types. - struct MapVector2 - { - float x; - float y; - }; - - struct MapObject - { - MapVector2 position[MAX_POSITIONS]; - float width; - float length; - float heading[MAX_POSITIONS]; - MapVector2 velocity[MAX_POSITIONS]; - bool valid[MAX_POSITIONS]; - MapVector2 goalPosition; - MapObjectType type; - - uint32_t numPositions; - uint32_t numHeadings; - uint32_t numVelocities; - uint32_t numValid; - MapVector2 mean; - }; - - struct MapRoad - { - // std::array geometry; - MapVector2 geometry[MAX_GEOMETRY]; - MapRoadType type; - uint32_t numPoints; - MapVector2 mean; - }; - - struct Map + struct EpisodeManager { - MapObject objects[MAX_OBJECTS]; - MapRoad roads[MAX_ROADS]; - - uint32_t numObjects; - uint32_t numRoads; - uint32_t numRoadSegments; - MapVector2 mean; - - // Constructor - Map() = default; + madrona::AtomicU32 curEpisode; }; - struct EpisodeManager + enum class DatasetInitOptions : uint32_t { - madrona::AtomicU32 curEpisode; + FirstN, + RandomN, + PadN, // Pad the worlds by repeating the first world. + ExactN, // Will fail if N != NumWorlds }; enum class RewardType : uint32_t @@ -98,6 +25,13 @@ namespace gpudrive Dense // negative distance to expert trajectory }; + enum class CollisionBehaviour: uint32_t + { + AgentStop, + AgentRemoved, + Ignore + }; + struct RewardParams { RewardType rewardType; @@ -110,6 +44,8 @@ namespace gpudrive float polylineReductionThreshold; float observationRadius; RewardParams rewardParams; + CollisionBehaviour collisionBehaviour = CollisionBehaviour::AgentStop; // Default: AgentStop + DatasetInitOptions datasetInitOptions; uint32_t maxNumControlledVehicles = 10000; // Arbitrary high number to by default control all vehicles }; @@ -118,8 +54,7 @@ namespace gpudrive EpisodeManager *episodeMgr; madrona::phys::ObjectManager *rigidBodyObjMgr; const madrona::viz::VizECSBridge *vizBridge; - Map *map; - madrona::ExecMode mode; + gpudrive::Map *map; const Parameters *params; }; diff --git a/src/json_serialization.hpp b/src/json_serialization.hpp index 39e262a5..5f380ed3 100644 --- a/src/json_serialization.hpp +++ b/src/json_serialization.hpp @@ -1,6 +1,7 @@ #pragma once #include "init.hpp" +#include "types.hpp" #include #include @@ -85,13 +86,13 @@ namespace gpudrive from_json(j.at("goalPosition"), obj.goalPosition); std::string type = j.at("type"); if(type == "vehicle") - obj.type = MapObjectType::Vehicle; + obj.type = EntityType::Vehicle; else if(type == "pedestrian") - obj.type = MapObjectType::Pedestrian; + obj.type = EntityType::Pedestrian; else if(type == "cyclist") - obj.type = MapObjectType::Cyclist; + obj.type = EntityType::Cyclist; else - obj.type = MapObjectType::Invalid; + obj.type = EntityType::None; } void from_json(const nlohmann::json &j, MapRoad &road, float polylineReductionThreshold = 0.0) @@ -99,19 +100,19 @@ namespace gpudrive road.mean = {0,0}; std::string type = j.at("type"); if(type == "road_edge") - road.type = MapRoadType::RoadEdge; + road.type = EntityType::RoadEdge; else if(type == "road_line") - road.type = MapRoadType::RoadLine; + road.type = EntityType::RoadLine; else if(type == "lane") - road.type = MapRoadType::Lane; + road.type = EntityType::RoadLane; else if(type == "crosswalk") - road.type = MapRoadType::CrossWalk; + road.type = EntityType::CrossWalk; else if(type == "speed_bump") - road.type = MapRoadType::SpeedBump; + road.type = EntityType::SpeedBump; else if(type == "stop_sign") - road.type = MapRoadType::StopSign; + road.type = EntityType::StopSign; else - road.type = MapRoadType::Invalid; + road.type = EntityType::None; std::vector geometry_points_; for(const auto &point: j.at("geometry")) @@ -124,7 +125,7 @@ namespace gpudrive const int64_t num_segments = j["geometry"].size() - 1; const int64_t sample_every_n_ = 1; const int64_t num_sampled_points = (num_segments + sample_every_n_ - 1) / sample_every_n_ + 1; - if (num_segments >= 10 && (road.type == MapRoadType::Lane || road.type == MapRoadType::RoadEdge || road.type == MapRoadType::RoadLine)) + if (num_segments >= 10 && (road.type == EntityType::RoadLane || road.type == EntityType::RoadEdge || road.type == EntityType::RoadLine)) { std::vector skip(num_sampled_points, false); // This list tracks the points that are skipped int64_t k = 0; @@ -240,9 +241,9 @@ namespace gpudrive map.mean.x = ((map.mean.x * totalPoints) + (map.roads[i].mean.x * roadPoints)) / (totalPoints + roadPoints); map.mean.y = ((map.mean.y * totalPoints) + (map.roads[i].mean.y * roadPoints)) / (totalPoints + roadPoints); totalPoints += roadPoints; - if(map.roads[i].type <= MapRoadType::Lane) + if(map.roads[i].type <= EntityType::RoadLane) count_road_points += roadPoints - 1; - else if(map.roads[i].type > MapRoadType::Lane) + else if(map.roads[i].type > EntityType::RoadLane) count_road_points += 1; ++i; } diff --git a/src/level_gen.cpp b/src/level_gen.cpp index d839ef38..21a6ba23 100755 --- a/src/level_gen.cpp +++ b/src/level_gen.cpp @@ -54,24 +54,10 @@ static inline Entity createAgent(Engine &ctx, const MapObject &agentInit) { ctx.get(agent) = Diag3x3{.d0 = agentInit.length/2, .d1 = agentInit.width/2, .d2 = 1}; ctx.get(agent) = ObjectID{(int32_t)SimObject::Agent}; ctx.get(agent) = ResponseType::Dynamic; - if(agentInit.type == MapObjectType::Vehicle) - { - ctx.get(agent) = EntityType::Vehicle; - } - else if(agentInit.type == MapObjectType::Pedestrian) - { - ctx.get(agent) = EntityType::Pedestrian; - } - else if(agentInit.type == MapObjectType::Cyclist) - { - ctx.get(agent) = EntityType::Cyclist; - } - else - { - MADRONA_UNREACHABLE(); - } + assert(agentInit.type >= EntityType::Vehicle || agentInit.type == EntityType::None); + ctx.get(agent) = agentInit.type; ctx.get(agent)= Goal{.position = Vector2{.x = agentInit.goalPosition.x - ctx.data().mean.x, .y = agentInit.goalPosition.y - ctx.data().mean.y}}; - if(ctx.data().numControlledVehicles < ctx.data().params.maxNumControlledVehicles && agentInit.type == MapObjectType::Vehicle && agentInit.valid[0]) + if(ctx.data().numControlledVehicles < ctx.data().params.maxNumControlledVehicles && agentInit.type == EntityType::Vehicle && agentInit.valid[0]) { ctx.get(agent) = ControlledState{.controlledState = ControlMode::BICYCLE}; ctx.data().numControlledVehicles++; @@ -102,7 +88,7 @@ static inline Entity createAgent(Engine &ctx, const MapObject &agentInit) { } static Entity makeRoadEdge(Engine &ctx, const MapVector2 &p1, - const MapVector2 &p2, const MapRoadType &type) { + const MapVector2 &p2, const EntityType &type) { float x1 = p1.x; float y1 = p1.y; float x2 = p2.x; @@ -115,7 +101,7 @@ static Entity makeRoadEdge(Engine &ctx, const MapVector2 &p1, ctx.get(road_edge) = Vector3{.x = (start.x + end.x)/2, .y = (start.y + end.y)/2, .z = 1}; ctx.get(road_edge) = Quat::angleAxis(atan2(end.y - start.y, end.x - start.x), madrona::math::up); ctx.get(road_edge) = Diag3x3{.d0 = distance/2, .d1 = 0.1, .d2 = 0.1}; - ctx.get(road_edge) = EntityType::Cube; + ctx.get(road_edge) = type; ctx.get(road_edge) = ObjectID{(int32_t)SimObject::Cube}; registerRigidBodyEntity(ctx, road_edge, SimObject::Cube); ctx.get(road_edge) = ResponseType::Static; @@ -127,8 +113,8 @@ float calculateDistance(float x1, float y1, float x2, float y2) { return sqrt(pow(x2 - x1, 2) + pow(y2 - y1, 2)); } -static Entity makeSpeedBump(Engine &ctx, const MapVector2 &p1, const MapVector2 &p2, const MapVector2 &p3, - const MapVector2 &p4) { +static Entity makeCube(Engine &ctx, const MapVector2 &p1, const MapVector2 &p2, const MapVector2 &p3, + const MapVector2 &p4, const EntityType &type) { float x1 = p1.x; float y1 = p1.y; float x2 = p2.x; @@ -185,11 +171,11 @@ static Entity makeSpeedBump(Engine &ctx, const MapVector2 &p1, const MapVector2 ctx.get(speed_bump) = Vector3{.x = (x1 + x2 + x3 + x4)/4 - ctx.data().mean.x, .y = (y1 + y2 + y3 + y4)/4 - ctx.data().mean.y, .z = 1}; ctx.get(speed_bump) = Quat::angleAxis(angle, madrona::math::up); ctx.get(speed_bump) = Diag3x3{.d0 = lengths[maxLength_i]/2, .d1 = lengths[minLength_i]/2, .d2 = 0.1}; - ctx.get(speed_bump) = EntityType::Cube; + ctx.get(speed_bump) = type; ctx.get(speed_bump) = ObjectID{(int32_t)SimObject::SpeedBump}; registerRigidBodyEntity(ctx, speed_bump, SimObject::SpeedBump); ctx.get(speed_bump) = ResponseType::Static; - ctx.get(speed_bump) = MapObservation{.position = Vector2{.x = (x1 + x2 + x3 + x4)/4 - ctx.data().mean.x, .y = (y1 + y2 + y3 + y4)/4 - ctx.data().mean.y}, .heading = angle, .type = (float)MapRoadType::SpeedBump}; + ctx.get(speed_bump) = MapObservation{.position = Vector2{.x = (x1 + x2 + x3 + x4)/4 - ctx.data().mean.x, .y = (y1 + y2 + y3 + y4)/4 - ctx.data().mean.y}, .heading = angle, .type = (float)type}; return speed_bump; } @@ -201,16 +187,16 @@ static Entity makeStopSign(Engine &ctx, const MapVector2 &p1) { ctx.get(stop_sign) = Vector3{.x = x1 - ctx.data().mean.x, .y = y1 - ctx.data().mean.y, .z = 0.5}; ctx.get(stop_sign) = Quat::angleAxis(0, madrona::math::up); ctx.get(stop_sign) = Diag3x3{.d0 = 0.2, .d1 = 0.2, .d2 = 0.5}; - ctx.get(stop_sign) = EntityType::Cube; + ctx.get(stop_sign) = EntityType::StopSign; ctx.get(stop_sign) = ObjectID{(int32_t)SimObject::StopSign}; registerRigidBodyEntity(ctx, stop_sign, SimObject::StopSign); ctx.get(stop_sign) = ResponseType::Static; - ctx.get(stop_sign) = MapObservation{.position = Vector2{.x = x1 - ctx.data().mean.x, .y = y1 - ctx.data().mean.y}, .heading = 0, .type = (float)MapRoadType::StopSign}; + ctx.get(stop_sign) = MapObservation{.position = Vector2{.x = x1 - ctx.data().mean.x, .y = y1 - ctx.data().mean.y}, .heading = 0, .type = (float)EntityType::StopSign}; return stop_sign; } static inline void createRoadEntities(Engine &ctx, const MapRoad &roadInit, CountT &idx) { - if (roadInit.type == MapRoadType::RoadEdge || roadInit.type == MapRoadType::RoadLine || roadInit.type == MapRoadType::Lane) + if (roadInit.type == EntityType::RoadEdge || roadInit.type == EntityType::RoadLine || roadInit.type == EntityType::RoadLane) { size_t numPoints = roadInit.numPoints; for(size_t j = 1; j <= numPoints - 1; j++) @@ -219,13 +205,13 @@ static inline void createRoadEntities(Engine &ctx, const MapRoad &roadInit, Coun return; ctx.data().roads[idx++] = makeRoadEdge(ctx, roadInit.geometry[j-1], roadInit.geometry[j], roadInit.type); } - } else if (roadInit.type == MapRoadType::SpeedBump) { + } else if (roadInit.type == EntityType::SpeedBump || roadInit.type == EntityType::CrossWalk) { assert(roadInit.numPoints >= 4); // TODO: Speed Bump are not guranteed to have 4 points. Need to handle this case. if(idx >= ctx.data().MaxRoadEntityCount) return; - ctx.data().roads[idx++] = makeSpeedBump(ctx, roadInit.geometry[0], roadInit.geometry[1], roadInit.geometry[2], roadInit.geometry[3]); - } else if (roadInit.type == MapRoadType::StopSign) { + ctx.data().roads[idx++] = makeCube(ctx, roadInit.geometry[0], roadInit.geometry[1], roadInit.geometry[2], roadInit.geometry[3], roadInit.type); + } else if (roadInit.type == EntityType::StopSign) { assert(roadInit.numPoints >= 1); // TODO: Stop Sign are not guranteed to have 1 point. Need to handle this case. if(idx >= ctx.data().MaxRoadEntityCount) diff --git a/src/mgr.cpp b/src/mgr.cpp index f93b270a..e6291548 100755 --- a/src/mgr.cpp +++ b/src/mgr.cpp @@ -8,6 +8,8 @@ #include #include +#include + #include #include #include @@ -16,6 +18,8 @@ #include #include #include +#include + #ifdef MADRONA_CUDA_SUPPORT #include @@ -255,6 +259,59 @@ static void loadPhysicsObjects(PhysicsLoader &loader) free(rigid_body_data); } +static std::vector getMapFiles(const Manager::Config &cfg) +{ + std::filesystem::path path(cfg.jsonPath); + auto validFilesJsonPath = path / "valid_files.json"; + assert(std::filesystem::exists(validFilesJsonPath)); + // check if validFiles.json exists + + std::ifstream validFilesJson(validFilesJsonPath); + assert(validFilesJson.good()); + + nlohmann::json validFiles; + validFilesJson >> validFiles; + + std::vector mapFiles; + for (auto& [key, value] : validFiles.items()) { + std::filesystem::path fullPath = path / key; + mapFiles.emplace_back(fullPath.string()); + } + assert(mapFiles.size() != 0); + + if(cfg.params.datasetInitOptions == DatasetInitOptions::FirstN) + { + assert(cfg.numWorlds <= mapFiles.size()); + mapFiles.resize(cfg.numWorlds); + } + else if(cfg.params.datasetInitOptions == DatasetInitOptions::RandomN) + { + assert(cfg.numWorlds <= mapFiles.size()); + std::random_device rd; + std::mt19937 g(rd()); + std::shuffle(mapFiles.begin(), mapFiles.end(), g); + mapFiles.resize(cfg.numWorlds); + } + else if(cfg.params.datasetInitOptions == DatasetInitOptions::PadN) + { + assert(cfg.numWorlds >= mapFiles.size()); + for(int i = 0; i < cfg.numWorlds; i++) + { + mapFiles.push_back(mapFiles[0]); + } + } + else if(cfg.params.datasetInitOptions == DatasetInitOptions::ExactN) + { + // Do nothing + } + else + { + FATAL("Invalid datasetInitOptions"); + } + + return mapFiles; +} + Manager::Impl * Manager::Impl::init( const Manager::Config &mgr_cfg, const viz::VizECSBridge *viz_bridge) @@ -266,6 +323,8 @@ Manager::Impl * Manager::Impl::init( 0 // kMaxRoadEntityCount }; + std::vector mapFiles = getMapFiles(mgr_cfg); + switch (mgr_cfg.execMode) { case ExecMode::CUDA: { #ifdef MADRONA_CUDA_SUPPORT @@ -280,21 +339,21 @@ Manager::Impl * Manager::Impl::init( HeapArray world_inits(mgr_cfg.numWorlds); - Parameters* paramsDevicePtr = (Parameters*)cu::allocGPU(sizeof(Parameters)); REQ_CUDA(cudaMemcpy(paramsDevicePtr, &(mgr_cfg.params), sizeof(Parameters), cudaMemcpyHostToDevice)); int64_t worldIdx{0}; - for (auto const &mapFile : std::filesystem::directory_iterator(mgr_cfg.jsonPath)) + for (auto const &mapFile : mapFiles) { - auto [map_, mapCounts] = MapReader::parseAndWriteOut(mapFile.path(), mgr_cfg.execMode, mgr_cfg.params.polylineReductionThreshold); + auto [map_, mapCounts] = MapReader::parseAndWriteOut(mapFile, mgr_cfg.execMode, mgr_cfg.params.polylineReductionThreshold); world_inits[worldIdx++] = WorldInit{episode_mgr, phys_obj_mgr, - viz_bridge, map_, mgr_cfg.execMode, paramsDevicePtr}; + viz_bridge, map_, paramsDevicePtr}; sim_cfg.kMaxAgentCount = std::max(mapCounts.first, sim_cfg.kMaxAgentCount); sim_cfg.kMaxRoadEntityCount = std::max(mapCounts.second, sim_cfg.kMaxRoadEntityCount); } + assert(worldIdx == static_cast(mgr_cfg.numWorlds)); // Bounds on the maxagent and maxroadentity counts. assert(sim_cfg.kMaxAgentCount <= consts::kMaxAgentCount); @@ -356,11 +415,11 @@ Manager::Impl * Manager::Impl::init( int64_t worldIdx{0}; - for (auto const &mapFile : std::filesystem::directory_iterator(mgr_cfg.jsonPath)) + for (auto const &mapFile : mapFiles) { - auto [map_, mapCounts] = MapReader::parseAndWriteOut(mapFile.path(), mgr_cfg.execMode, mgr_cfg.params.polylineReductionThreshold); + auto [map_, mapCounts] = MapReader::parseAndWriteOut(mapFile, mgr_cfg.execMode, mgr_cfg.params.polylineReductionThreshold); world_inits[worldIdx++] = WorldInit{episode_mgr, phys_obj_mgr, - viz_bridge, map_, mgr_cfg.execMode, &(mgr_cfg.params)}; + viz_bridge, map_, &(mgr_cfg.params)}; sim_cfg.kMaxAgentCount = std::max(mapCounts.first, sim_cfg.kMaxAgentCount); sim_cfg.kMaxRoadEntityCount = std::max(mapCounts.second, sim_cfg.kMaxRoadEntityCount); } @@ -461,7 +520,7 @@ Tensor Manager::bicycleModelTensor() const { impl_->cfg.numWorlds, impl_->agentRoadCounts.first, - 4, // Number of states for the bicycle model + BicycleModelExportSize, // Number of states for the bicycle model }); } @@ -493,7 +552,7 @@ Tensor Manager::selfObservationTensor() const { impl_->cfg.numWorlds, impl_->agentRoadCounts.first, - 6 + SelfObservationExportSize }); } @@ -504,7 +563,7 @@ Tensor Manager::mapObservationTensor() const { impl_->cfg.numWorlds, impl_->agentRoadCounts.second, - 4 + MapObservationExportSize }); } @@ -516,7 +575,7 @@ Tensor Manager::partnerObservationsTensor() const impl_->cfg.numWorlds, impl_->agentRoadCounts.first, consts::kMaxAgentCount-1, - 7, + PartnerObservationExportSize, }); } @@ -528,7 +587,7 @@ Tensor Manager::agentMapObservationsTensor() const impl_->cfg.numWorlds, impl_->agentRoadCounts.first, consts::kMaxRoadEntityCount, - 4, + AgentMapObservationExportSize, }); } diff --git a/src/sim.cpp b/src/sim.cpp index eebfae9f..6a2aa85d 100755 --- a/src/sim.cpp +++ b/src/sim.cpp @@ -231,7 +231,22 @@ inline void movementSystem(Engine &e, if (collisionEvent.hasCollided.load_relaxed()) { - return; + if(e.data().params.collisionBehaviour == CollisionBehaviour::AgentStop) + return; + else if(e.data().params.collisionBehaviour == CollisionBehaviour::AgentRemoved) + { + position = consts::kPaddingPosition; + velocity.linear.x = 0; + velocity.linear.y = 0; + velocity.linear.z = fminf(velocity.linear.z, 0); + velocity.angular = Vector3::zero(); + external_force = Vector3::zero(); + external_torque = Vector3::zero(); + } + else if(e.data().params.collisionBehaviour == CollisionBehaviour::Ignore) + { + // Do nothing. + } } if (type == EntityType::Vehicle && controlledState.controlledState == ControlMode::BICYCLE) @@ -499,6 +514,24 @@ void collisionDetectionSystem(Engine &ctx, if (not hasCollided) { return; } + + EntityType aEntitytype = ctx.get(candidateCollision.aEntity); + EntityType bEntitytype = ctx.get(candidateCollision.bEntity); + + // Ignore collisions between certain entity types + if(aEntitytype == EntityType::Padding || bEntitytype == EntityType::Padding) + { + return; + } + + for(auto &pair : ctx.data().collisionPairs) + { + if((pair.first == aEntitytype && pair.second == bEntitytype) || + (pair.first == bEntitytype && pair.second == aEntitytype)) + { + return; + } + } auto maybeCollisionEventA = ctx.getSafe(candidateCollision.aEntity); @@ -680,7 +713,8 @@ Sim::Sim(Engine &ctx, episodeMgr(init.episodeMgr), params(*init.params), MaxAgentCount(cfg.kMaxAgentCount), - MaxRoadEntityCount(cfg.kMaxRoadEntityCount) + MaxRoadEntityCount(cfg.kMaxRoadEntityCount), + collisionPairs(initializeCollisionPairs()) { // Below check is used to ensure that the map is not empty due to incorrect WorldInit copy to GPU assert(init.map->numObjects); diff --git a/src/sim.hpp b/src/sim.hpp index f49ef4d4..b02aa7a5 100755 --- a/src/sim.hpp +++ b/src/sim.hpp @@ -68,6 +68,35 @@ struct Sim : public madrona::WorldBase { static void setupTasks(madrona::TaskGraphBuilder &builder, const Config &cfg); + // Function to initialize your collision pairs array + static madrona::InlineArray, 64> initializeCollisionPairs() { + madrona::InlineArray, 64> collisionPairs; + collisionPairs.push_back({EntityType::Pedestrian, EntityType::Pedestrian}); + collisionPairs.push_back({EntityType::Pedestrian, EntityType::RoadEdge}); + collisionPairs.push_back({EntityType::Pedestrian, EntityType::Cyclist}); + collisionPairs.push_back({EntityType::Pedestrian, EntityType::RoadLine}); + collisionPairs.push_back({EntityType::Pedestrian, EntityType::RoadLane}); + collisionPairs.push_back({EntityType::Pedestrian, EntityType::CrossWalk}); + collisionPairs.push_back({EntityType::Pedestrian, EntityType::SpeedBump}); + collisionPairs.push_back({EntityType::Pedestrian, EntityType::StopSign}); + collisionPairs.push_back({EntityType::Cyclist, EntityType::Pedestrian}); + collisionPairs.push_back({EntityType::Cyclist, EntityType::RoadEdge}); + collisionPairs.push_back({EntityType::Cyclist, EntityType::Cyclist}); + collisionPairs.push_back({EntityType::Cyclist, EntityType::RoadLine}); + collisionPairs.push_back({EntityType::Cyclist, EntityType::RoadLane}); + collisionPairs.push_back({EntityType::Cyclist, EntityType::CrossWalk}); + collisionPairs.push_back({EntityType::Cyclist, EntityType::SpeedBump}); + collisionPairs.push_back({EntityType::Cyclist, EntityType::StopSign}); + collisionPairs.push_back({EntityType::Vehicle, EntityType::CrossWalk}); + collisionPairs.push_back({EntityType::Vehicle, EntityType::SpeedBump}); + collisionPairs.push_back({EntityType::Vehicle, EntityType::RoadLine}); + collisionPairs.push_back({EntityType::Vehicle, EntityType::RoadLane}); + // TODO: Break Cube into road types for better control over collisions + return collisionPairs; + } + + + const madrona::InlineArray, 64> collisionPairs; // The constructor is called for each world during initialization. // Config is global across all worlds, while WorldInit (src/init.hpp) // can contain per-world initialization data, created in (src/mgr.cpp) diff --git a/src/types.hpp b/src/types.hpp index 455a4a52..d0958cf6 100644 --- a/src/types.hpp +++ b/src/types.hpp @@ -20,12 +20,89 @@ using madrona::phys::ResponseType; using madrona::phys::ExternalForce; using madrona::phys::ExternalTorque; +// This enum is used to track the type of each entity +// The order of the enum is important and should not be changed +// The order is {Road types that can be reduced, Road types that cannot be reduced, agent types, other types} +enum class EntityType : uint32_t { + None, + RoadEdge, + RoadLine, + RoadLane, + CrossWalk, + SpeedBump, + StopSign, + Vehicle, + Pedestrian, + Cyclist, + Padding, + NumTypes, +}; + +// Constants computed from train files. +constexpr size_t MAX_OBJECTS = 515; +constexpr size_t MAX_ROADS = 956; +constexpr size_t MAX_POSITIONS = 91; +constexpr size_t MAX_GEOMETRY = 1746; + +// Cannot use Madrona::math::Vector2 because it is not a POD type. +// Getting all zeros if using any madrona types. +struct MapVector2 +{ + float x; + float y; +}; + +struct MapObject +{ + MapVector2 position[MAX_POSITIONS]; + float width; + float length; + float heading[MAX_POSITIONS]; + MapVector2 velocity[MAX_POSITIONS]; + bool valid[MAX_POSITIONS]; + MapVector2 goalPosition; + EntityType type; + + uint32_t numPositions; + uint32_t numHeadings; + uint32_t numVelocities; + uint32_t numValid; + MapVector2 mean; +}; + +struct MapRoad +{ + // std::array geometry; + MapVector2 geometry[MAX_GEOMETRY]; + EntityType type; + uint32_t numPoints; + MapVector2 mean; +}; + +struct Map +{ + MapObject objects[MAX_OBJECTS]; + MapRoad roads[MAX_ROADS]; + + uint32_t numObjects; + uint32_t numRoads; + uint32_t numRoadSegments; + MapVector2 mean; + + // Constructor + Map() = default; +}; + struct BicycleModel { madrona::math::Vector2 position; float heading; float speed; }; +const int BicycleModelExportSize = 4; + +static_assert(sizeof(BicycleModel) == sizeof(float) * BicycleModelExportSize); + struct VehicleSize { float length; float width; @@ -72,12 +149,24 @@ struct SelfObservation { float collisionState; }; +const int SelfObservationExportSize = 6; + +// SelfObservation is exported as a +// [N, A, 5] float tensor to pytorch +static_assert(sizeof(SelfObservation) == sizeof(float) * SelfObservationExportSize); + struct MapObservation { madrona::math::Vector2 position; float heading; float type; }; +const int MapObservationExportSize = 4; + +// MapObservation is exported as a +// [N, numRoadEntities, 4] float tensor to pytorch +static_assert(sizeof(MapObservation) == sizeof(float) * MapObservationExportSize); + struct PartnerObservation { float speed; madrona::math::Vector2 position; @@ -91,15 +180,24 @@ struct PartnerObservations { PartnerObservation obs[consts::kMaxAgentCount - 1]; }; +const int PartnerObservationExportSize = 7; + // PartnerObservations is exported as a // [N, A, consts::numAgents - 1, 3] // tensor to pytorch static_assert(sizeof(PartnerObservations) == sizeof(float) * - (consts::kMaxAgentCount - 1) * 7); + (consts::kMaxAgentCount - 1) * PartnerObservationExportSize); struct AgentMapObservations { MapObservation obs[consts::kMaxRoadEntityCount]; }; +const int AgentMapObservationExportSize = 4; + +// AgentMapObservations is exported as a +// [N, A, numRoadEntities, 4] float tensor to pytorch + +static_assert(sizeof(AgentMapObservations) == sizeof(float) * + consts::kMaxRoadEntityCount * AgentMapObservationExportSize); struct LidarSample { @@ -129,19 +227,6 @@ struct OtherAgents { madrona::Entity e[consts::kMaxAgentCount - 1]; }; -// This enum is used to track the type of each entity for the purposes of -// classifying the objects hit by each lidar sample. -enum class EntityType : uint32_t { - None, - Button, - Cube, - Vehicle, - Pedestrian, - Cyclist, - Padding, - NumTypes, -}; - struct Trajectory { madrona::math::Vector2 positions[consts::kTrajectoryLength]; madrona::math::Vector2 velocities[consts::kTrajectoryLength]; diff --git a/tests/bicyclemodel.cpp b/tests/bicyclemodel.cpp index 5899c32e..2f041dc6 100644 --- a/tests/bicyclemodel.cpp +++ b/tests/bicyclemodel.cpp @@ -25,7 +25,8 @@ class BicycleKinematicModelTest : public ::testing::Test { .jsonPath = "testJsons", .params = { .polylineReductionThreshold = 0.0, - .observationRadius = 100.0 + .observationRadius = 100.0, + .collisionBehaviour = gpudrive::CollisionBehaviour::Ignore, } }); diff --git a/tests/observationTest.cpp b/tests/observationTest.cpp index 8713a6f5..0b735f6d 100755 --- a/tests/observationTest.cpp +++ b/tests/observationTest.cpp @@ -59,31 +59,31 @@ class ObservationsTest : public ::testing::Test { if(obj["type"] == "road_edge") { - roadTypes.push_back(0); + roadTypes.push_back((float)gpudrive::EntityType::RoadEdge); } else if(obj["type"] == "road_line") { - roadTypes.push_back(1); + roadTypes.push_back((float)gpudrive::EntityType::RoadLine); } else if(obj["type"] == "lane") { - roadTypes.push_back(2); + roadTypes.push_back((float)gpudrive::EntityType::RoadLane); } else if(obj["type"] == "crosswalk") { - roadTypes.push_back(3); + roadTypes.push_back((float)gpudrive::EntityType::CrossWalk); } else if(obj["type"] == "speed_bump") { - roadTypes.push_back(4); + roadTypes.push_back((float)gpudrive::EntityType::SpeedBump); } else if(obj["type"] == "stop_sign") { - roadTypes.push_back(5); + roadTypes.push_back((float)gpudrive::EntityType::StopSign); } else if(obj["type"] == "invalid") { - roadTypes.push_back(6); + roadTypes.push_back(0); } } } @@ -100,14 +100,25 @@ TEST_F(ObservationsTest, TestObservations) { float roadType = roadTypes[i]; for(int64_t j = 0; j < roadGeom.size() - 1; j++) { - if(roadType > 2) + if(roadType > (float)gpudrive::EntityType::RoadLane && roadType < (float)gpudrive::EntityType::StopSign) { float x = (roadGeom[j].first + roadGeom[j+1].first + roadGeom[j+2].first + roadGeom[j+3].first)/4 - mean.first; float y = (roadGeom[j].second + roadGeom[j+1].second + roadGeom[j+2].second + roadGeom[j+3].second)/4 - mean.second; ASSERT_NEAR(flat_obs[idx], x, test_utils::EPSILON); ASSERT_NEAR(flat_obs[idx+1], y, test_utils::EPSILON); + ASSERT_EQ(flat_obs[idx+3], roadType); + idx += 4; + break; + } + else if(roadType == (float)gpudrive::EntityType::StopSign) + { + float x = roadGeom[j].first - mean.first; + float y = roadGeom[j].second - mean.second; + ASSERT_NEAR(flat_obs[idx], x, test_utils::EPSILON); + ASSERT_NEAR(flat_obs[idx+1], y, test_utils::EPSILON); + ASSERT_EQ(flat_obs[idx+3], roadType); idx += 4; break; } diff --git a/tests/test_utils.cpp b/tests/test_utils.cpp index 60e19f38..519d10ce 100644 --- a/tests/test_utils.cpp +++ b/tests/test_utils.cpp @@ -29,10 +29,6 @@ namespace test_utils int64_t numEntities = 0; for (const auto &obj : rawJson["objects"]) { - if (obj["type"] != "vehicle") - { - continue; - } for (const auto &pos : obj["position"]) { numEntities++;