diff --git a/experiments/bulk_running_experiments.ipynb b/experiments/bulk_running_experiments.ipynb
index fc7b4fbcc92968c685e95ef143d67da9132a07b8..bc04eb94af5f90b05816ba3a234db781e29eb6df 100644
--- a/experiments/bulk_running_experiments.ipynb
+++ b/experiments/bulk_running_experiments.ipynb
@@ -127,7 +127,7 @@
"output_type": "stream",
"text": [
"default_simulation\n",
- "output_folder = \"../../data/nuplan/exp/exp/simulation/open_loop_boxes/2023.12.20.02.52.52\"\n"
+ "output_folder = \"../../data/nuplan/exp/exp/simulation/open_loop_boxes/2023.12.20.23.07.26\"\n"
]
}
],
@@ -151,12 +151,12 @@
" # \"on_stopline_crosswalk\",\n",
" # \"on_stopline_stop_sign\",\n",
" # \"on_stopline_traffic_light\",\n",
- " # \"on_traffic_light_intersection\",\n",
+ " \"on_traffic_light_intersection\",\n",
" # \"starting_protected_cross_turn\",\n",
" # \"starting_protected_noncross_turn\",\n",
" # \"starting_right_turn\",\n",
" # \"starting_straight_stop_sign_intersection_traversal\",\n",
- " # \"starting_straight_traffic_light_intersection_traversal\",\n",
+ " \"starting_straight_traffic_light_intersection_traversal\",\n",
" # \"starting_u_turn\",\n",
" # \"starting_unprotected_cross_turn\",\n",
" # \"starting_unprotected_noncross_turn\",\n",
@@ -165,7 +165,7 @@
" # \"stationary_at_traffic_light_without_lead\",\n",
" # \"traversing_crosswalk\",\n",
" # \"traversing_intersection\",\n",
- " \"traversing_traffic_light_intersection\",\n",
+ " # \"traversing_traffic_light_intersection\",\n",
"]\n",
"\n",
"# scenario_types = ['stationary_at_traffic_light_without_lead']\n",
@@ -174,14 +174,14 @@
"DATASET_PARAMS = [\n",
" f\"scenario_builder={scenario_builder}\",\n",
" \"scenario_filter=all_scenarios\", # [all_scenarios, val14_split]\n",
- " f\"scenario_filter.scenario_types={scenario_types}\", # there are 70 scenario types in the trainingset and 58 in the validation set including \"unknown\" which make up the majority\n",
+ " # f\"scenario_filter.scenario_types={scenario_types}\", # there are 70 scenario types in the trainingset and 58 in the validation set including \"unknown\" which make up the majority\n",
" # \"scenario_filter.ego_displacement_minimum_m=10\", # use scenarios where the ego vehicle moves at least 10m\n",
" # 'scenario_filter.remove_invalid_goals=true', # remove scenarios where the goal is not invalid\n",
" # 'scenario_filter.ego_start_speed_threshold=5', # Exclusive threshold that the ego's speed must rise above (meters per second) for scenario to be kept\n",
" # 'scenario_filter.stop_speed_threshold=10', # Inclusive threshold that the ego's speed must fall below (meters per second) for scenario to be kept:\n",
- " \"scenario_filter.map_names=[us-pa-pittsburgh-hazelwood]\", # [sg-one-north, us-ma-boston, us-pa-pittsburgh-hazelwood, us-nv-las-vegas-strip]\n",
+ " # \"scenario_filter.map_names=[us-pa-pittsburgh-hazelwood]\", # [sg-one-north, us-ma-boston, us-pa-pittsburgh-hazelwood, us-nv-las-vegas-strip]\n",
" # \"scenario_filter.num_scenarios_per_type=200\", # use 10 scenarios per scenario type\n",
- " \"scenario_filter.scenario_tokens=['60e03a4199bd5fbd']\", # List of scenarios to include (token)\n",
+ " \"scenario_filter.scenario_tokens=['be26059ef668586e']\", # List of scenarios to include (token)\n",
" # 'scenario_filter.log_names=[\"2021.08.24.18.07.48_veh-45_01504_01722\"]', # specific scenrios to simulate\n",
" \"scenario_filter.limit_total_scenarios=0.05\", # use n total scenarios if int, or if float smaller than 1, use n as a fraction of total scenarios (changes sampling frequency, unchanged leaves the frequency at 20Hz)\n",
"]\n",
@@ -281,98 +281,98 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "2023-12-20 02:52:52,286 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/script/builders/worker_pool_builder.py:19} Building WorkerPool...\n",
- "2023-12-20 02:52:52,337 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/utils/multithreading/worker_ray.py:78} Starting ray local!\n"
+ "2023-12-20 23:07:27,079 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/script/builders/worker_pool_builder.py:19} Building WorkerPool...\n",
+ "2023-12-20 23:07:27,131 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/utils/multithreading/worker_ray.py:78} Starting ray local!\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
- "2023-12-20 02:52:53,944\tINFO worker.py:1636 -- Started a local Ray instance.\n"
+ "2023-12-20 23:07:28,731\tINFO worker.py:1636 -- Started a local Ray instance.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "2023-12-20 02:52:54,600 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/utils/multithreading/worker_pool.py:101} Worker: RayDistributed\n",
- "2023-12-20 02:52:54,600 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/utils/multithreading/worker_pool.py:102} Number of nodes: 1\n",
+ "2023-12-20 23:07:29,382 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/utils/multithreading/worker_pool.py:101} Worker: RayDistributed\n",
+ "2023-12-20 23:07:29,382 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/utils/multithreading/worker_pool.py:102} Number of nodes: 1\n",
"Number of CPUs per node: 32\n",
"Number of GPUs per node: 4\n",
"Number of threads across all nodes: 32\n",
- "2023-12-20 02:52:54,600 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/script/builders/worker_pool_builder.py:27} Building WorkerPool...DONE!\n",
- "2023-12-20 02:52:54,600 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/script/builders/folder_builder.py:32} Building experiment folders...\n",
- "2023-12-20 02:52:54,600 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/script/builders/folder_builder.py:35} \n",
+ "2023-12-20 23:07:29,382 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/script/builders/worker_pool_builder.py:27} Building WorkerPool...DONE!\n",
+ "2023-12-20 23:07:29,382 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/script/builders/folder_builder.py:32} Building experiment folders...\n",
+ "2023-12-20 23:07:29,382 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/script/builders/folder_builder.py:35} \n",
"\n",
- "\tFolder where all results are stored: ../../data/nuplan/exp/exp/simulation/open_loop_boxes/2023.12.20.02.52.52\n",
+ "\tFolder where all results are stored: ../../data/nuplan/exp/exp/simulation/open_loop_boxes/2023.12.20.23.07.26\n",
"\n",
- "2023-12-20 02:52:54,613 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/script/builders/folder_builder.py:70} Building experiment folders...DONE!\n",
- "2023-12-20 02:52:54,614 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/script/builders/simulation_callback_builder.py:52} Building AbstractCallback...\n",
- "2023-12-20 02:52:54,614 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/script/builders/simulation_callback_builder.py:68} Building AbstractCallback: 0...DONE!\n",
- "2023-12-20 02:52:54,614 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/script/builders/simulation_builder.py:49} Building simulations...\n",
- "2023-12-20 02:52:54,614 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/script/builders/simulation_builder.py:55} Extracting scenarios...\n",
- "2023-12-20 02:52:54,614 INFO {/home/ehdykhne/nuplan-devkit/nuplan/common/utils/distributed_scenario_filter.py:83} Building Scenarios in mode DistributedMode.SINGLE_NODE\n",
- "2023-12-20 02:52:54,614 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/script/builders/scenario_building_builder.py:18} Building AbstractScenarioBuilder...\n",
- "2023-12-20 02:52:54,632 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/script/builders/scenario_building_builder.py:21} Building AbstractScenarioBuilder...DONE!\n",
- "2023-12-20 02:52:54,632 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/script/builders/scenario_filter_builder.py:35} Building ScenarioFilter...\n",
- "2023-12-20 02:52:54,633 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/script/builders/scenario_filter_builder.py:44} Building ScenarioFilter...DONE!\n"
+ "2023-12-20 23:07:29,392 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/script/builders/folder_builder.py:70} Building experiment folders...DONE!\n",
+ "2023-12-20 23:07:29,393 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/script/builders/simulation_callback_builder.py:52} Building AbstractCallback...\n",
+ "2023-12-20 23:07:29,393 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/script/builders/simulation_callback_builder.py:68} Building AbstractCallback: 0...DONE!\n",
+ "2023-12-20 23:07:29,393 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/script/builders/simulation_builder.py:49} Building simulations...\n",
+ "2023-12-20 23:07:29,393 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/script/builders/simulation_builder.py:55} Extracting scenarios...\n",
+ "2023-12-20 23:07:29,393 INFO {/home/ehdykhne/nuplan-devkit/nuplan/common/utils/distributed_scenario_filter.py:83} Building Scenarios in mode DistributedMode.SINGLE_NODE\n",
+ "2023-12-20 23:07:29,393 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/script/builders/scenario_building_builder.py:18} Building AbstractScenarioBuilder...\n",
+ "2023-12-20 23:07:29,410 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/script/builders/scenario_building_builder.py:21} Building AbstractScenarioBuilder...DONE!\n",
+ "2023-12-20 23:07:29,411 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/script/builders/scenario_filter_builder.py:35} Building ScenarioFilter...\n",
+ "2023-12-20 23:07:29,412 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/script/builders/scenario_filter_builder.py:44} Building ScenarioFilter...DONE!\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
- "Ray objects: 100%|██████████| 32/32 [00:01<00:00, 26.04it/s]\n"
+ "Ray objects: 100%|██████████| 32/32 [00:01<00:00, 26.00it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "2023-12-20 02:52:55,890 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/script/builders/simulation_builder.py:76} Building metric engines...\n",
- "2023-12-20 02:52:55,905 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/script/builders/simulation_builder.py:78} Building metric engines...DONE\n",
- "2023-12-20 02:52:55,905 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/script/builders/simulation_builder.py:82} Building simulations from 1 scenarios...\n",
- "2023-12-20 02:52:56,174 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/script/builders/simulation_builder.py:142} Building simulations...DONE!\n",
- "2023-12-20 02:52:56,174 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/script/run_simulation.py:114} Running simulation...\n",
- "2023-12-20 02:52:56,174 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/script/utils.py:138} Executing runners...\n",
- "2023-12-20 02:52:56,174 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/simulation/runner/executor.py:82} Starting 1 simulations using RayDistributed!\n"
+ "2023-12-20 23:07:30,671 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/script/builders/simulation_builder.py:76} Building metric engines...\n",
+ "2023-12-20 23:07:30,686 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/script/builders/simulation_builder.py:78} Building metric engines...DONE\n",
+ "2023-12-20 23:07:30,686 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/script/builders/simulation_builder.py:82} Building simulations from 1 scenarios...\n",
+ "2023-12-20 23:07:30,951 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/script/builders/simulation_builder.py:142} Building simulations...DONE!\n",
+ "2023-12-20 23:07:30,951 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/script/run_simulation.py:114} Running simulation...\n",
+ "2023-12-20 23:07:30,952 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/script/utils.py:138} Executing runners...\n",
+ "2023-12-20 23:07:30,952 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/simulation/runner/executor.py:82} Starting 1 simulations using RayDistributed!\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
- "Ray objects: 100%|██████████| 1/1 [00:06<00:00, 6.42s/it]\n"
+ "Ray objects: 100%|██████████| 1/1 [00:21<00:00, 21.51s/it]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "2023-12-20 02:53:02,597 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/simulation/runner/executor.py:127} Number of successful simulations: 1\n",
- "2023-12-20 02:53:02,598 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/simulation/runner/executor.py:128} Number of failed simulations: 0\n",
- "2023-12-20 02:53:02,598 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/script/utils.py:147} Finished executing runners!\n",
- "2023-12-20 02:53:02,604 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/script/utils.py:74} Saved runner reports to ../../data/nuplan/exp/exp/simulation/open_loop_boxes/2023.12.20.02.52.52/runner_report.parquet\n",
- "2023-12-20 02:53:02,604 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/simulation/main_callback/time_callback.py:27} Simulation duration: 00:00:10 [HH:MM:SS]\n",
- "2023-12-20 02:53:02,632 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/simulation/main_callback/metric_file_callback.py:79} Metric files integration: 00:00:00 [HH:MM:SS]\n",
- "2023-12-20 02:53:02,663 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/simulation/main_callback/metric_aggregator_callback.py:58} Running metric aggregator: open_loop_boxes_weighted_average\n",
- "2023-12-20 02:53:02,670 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/simulation/main_callback/metric_aggregator_callback.py:69} Metric aggregator: 00:00:00 [HH:MM:SS]\n"
+ "2023-12-20 23:07:52,468 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/simulation/runner/executor.py:127} Number of successful simulations: 1\n",
+ "2023-12-20 23:07:52,468 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/simulation/runner/executor.py:128} Number of failed simulations: 0\n",
+ "2023-12-20 23:07:52,468 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/script/utils.py:147} Finished executing runners!\n",
+ "2023-12-20 23:07:52,474 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/script/utils.py:74} Saved runner reports to ../../data/nuplan/exp/exp/simulation/open_loop_boxes/2023.12.20.23.07.26/runner_report.parquet\n",
+ "2023-12-20 23:07:52,474 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/simulation/main_callback/time_callback.py:27} Simulation duration: 00:00:25 [HH:MM:SS]\n",
+ "2023-12-20 23:07:52,501 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/simulation/main_callback/metric_file_callback.py:79} Metric files integration: 00:00:00 [HH:MM:SS]\n",
+ "2023-12-20 23:07:52,535 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/simulation/main_callback/metric_aggregator_callback.py:58} Running metric aggregator: open_loop_boxes_weighted_average\n",
+ "2023-12-20 23:07:52,541 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/simulation/main_callback/metric_aggregator_callback.py:69} Metric aggregator: 00:00:00 [HH:MM:SS]\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
- "Rendering histograms: 100%|██████████| 11/11 [00:01<00:00, 7.93it/s]\n"
+ "Rendering histograms: 100%|██████████| 11/11 [00:01<00:00, 8.01it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "2023-12-20 02:53:05,417 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/simulation/main_callback/metric_summary_callback.py:344} Metric summary: 00:00:02 [HH:MM:SS]\n",
- "2023-12-20 02:53:05,417 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/script/run_simulation.py:116} Finished running simulation!\n"
+ "2023-12-20 23:07:55,266 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/simulation/main_callback/metric_summary_callback.py:344} Metric summary: 00:00:02 [HH:MM:SS]\n",
+ "2023-12-20 23:07:55,266 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/script/run_simulation.py:116} Finished running simulation!\n"
]
},
{
@@ -387,7 +387,7 @@
},
{
"data": {
- "image/png": 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",
+ "image/png": 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",
"text/plain": [
"<Figure size 600x600 with 2 Axes>"
]
@@ -427,7 +427,7 @@
},
{
"data": {
- "image/png": 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",
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",
"text/plain": [
"<Figure size 600x600 with 4 Axes>"
]
@@ -437,7 +437,7 @@
},
{
"data": {
- "image/png": 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",
+ "image/png": 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",
"text/plain": [
"<Figure size 600x600 with 2 Axes>"
]
@@ -447,7 +447,7 @@
},
{
"data": {
- "image/png": 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",
+ "image/png": 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",
"text/plain": [
"<Figure size 600x600 with 4 Axes>"
]
@@ -533,6 +533,10 @@
},
"outputs": [],
"source": [
+ "scenario_builder = \"val\" # [nuplan (uses trainval), nuplan_mini, test, val, train_boston, train_pittsburgh, train_singapore]\n",
+ "output_folder = (\n",
+ " \"../../data/nuplan/exp/exp/simulation/open_loop_boxes/2023.12.20.13.59.34\"\n",
+ ")\n",
"# output_folder = '../../data/nuplan/exp/exp/simulation/open_loop_boxes/2023.12.09.21.19.06'\n",
"# output_folder = \"../../data/nuplan/exp/exp/simulation/closed_loop_reactive_agents/2023.12.09.21.59.48\"\n",
"# Location of path with all nuBoard configs\n",
@@ -573,63 +577,123 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "2023-12-20 02:53:08,274 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/script/builders/scenario_building_builder.py:18} Building AbstractScenarioBuilder...\n",
- "2023-12-20 02:53:08,291 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/script/builders/scenario_building_builder.py:21} Building AbstractScenarioBuilder...DONE!\n",
- "2023-12-20 02:53:08,292 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/nuboard/nuboard.py:84} Opening Bokeh application on http://localhost:5006/\n",
- "2023-12-20 02:53:08,292 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/nuboard/nuboard.py:85} Async rendering is set to: True\n",
- "2023-12-20 02:53:08,292 INFO {/home/ehdykhne/miniconda3/envs/nuplan/lib/python3.9/site-packages/bokeh/server/server.py:403} Starting Bokeh server version 2.4.3 (running on Tornado 6.3.3)\n",
- "2023-12-20 02:53:08,292 WARNING {/home/ehdykhne/miniconda3/envs/nuplan/lib/python3.9/site-packages/bokeh/server/util.py:145} Host wildcard '*' will allow connections originating from multiple (or possibly all) hostnames or IPs. Use non-wildcard values to restrict access explicitly\n",
- "2023-12-20 02:53:08,293 INFO {/home/ehdykhne/miniconda3/envs/nuplan/lib/python3.9/site-packages/bokeh/server/tornado.py:360} User authentication hooks NOT provided (default user enabled)\n",
- "2023-12-20 02:53:09,039 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/nuboard/base/simulation_tile.py:172} Minimum frame time=0.017 s\n",
- "2023-12-20 02:53:09,044 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/nuboard/tabs/scenario_tab.py:485} Rending scenario plot takes 0.0005 seconds.\n",
- "2023-12-20 02:53:09,142 INFO {/home/ehdykhne/miniconda3/envs/nuplan/lib/python3.9/site-packages/tornado/web.py:2344} 200 GET / (127.0.0.1) 166.67ms\n"
+ "2023-12-20 23:07:58,120 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/script/builders/scenario_building_builder.py:18} Building AbstractScenarioBuilder...\n",
+ "2023-12-20 23:07:58,137 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/script/builders/scenario_building_builder.py:21} Building AbstractScenarioBuilder...DONE!\n",
+ "2023-12-20 23:07:58,137 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/nuboard/nuboard.py:84} Opening Bokeh application on http://localhost:5006/\n",
+ "2023-12-20 23:07:58,137 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/nuboard/nuboard.py:85} Async rendering is set to: True\n",
+ "2023-12-20 23:07:58,137 INFO {/home/ehdykhne/miniconda3/envs/nuplan/lib/python3.9/site-packages/bokeh/server/server.py:403} Starting Bokeh server version 2.4.3 (running on Tornado 6.3.3)\n",
+ "2023-12-20 23:07:58,138 WARNING {/home/ehdykhne/miniconda3/envs/nuplan/lib/python3.9/site-packages/bokeh/server/util.py:145} Host wildcard '*' will allow connections originating from multiple (or possibly all) hostnames or IPs. Use non-wildcard values to restrict access explicitly\n",
+ "2023-12-20 23:07:58,138 INFO {/home/ehdykhne/miniconda3/envs/nuplan/lib/python3.9/site-packages/bokeh/server/tornado.py:360} User authentication hooks NOT provided (default user enabled)\n",
+ "2023-12-20 23:08:15,323 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/nuboard/base/simulation_tile.py:172} Minimum frame time=0.017 s\n",
+ "2023-12-20 23:08:15,571 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/nuboard/tabs/scenario_tab.py:485} Rending scenario plot takes 0.0072 seconds.\n",
+ "2023-12-20 23:08:15,669 INFO {/home/ehdykhne/miniconda3/envs/nuplan/lib/python3.9/site-packages/tornado/web.py:2344} 200 GET / (10.40.112.28) 468.69ms\n",
+ "2023-12-20 23:08:15,717 INFO {/home/ehdykhne/miniconda3/envs/nuplan/lib/python3.9/site-packages/tornado/web.py:2344} 200 GET /resource/spectre.min.css (10.40.112.28) 2.65ms\n",
+ "2023-12-20 23:08:15,724 INFO {/home/ehdykhne/miniconda3/envs/nuplan/lib/python3.9/site-packages/tornado/web.py:2344} 200 GET /resource/spectre-exp.min.css (10.40.112.28) 0.51ms\n",
+ "2023-12-20 23:08:15,724 INFO {/home/ehdykhne/miniconda3/envs/nuplan/lib/python3.9/site-packages/tornado/web.py:2344} 200 GET /resource/spectre-icons.min.css (10.40.112.28) 0.36ms\n",
+ "2023-12-20 23:08:15,725 INFO {/home/ehdykhne/miniconda3/envs/nuplan/lib/python3.9/site-packages/tornado/web.py:2344} 200 GET /resource/style.css (10.40.112.28) 0.40ms\n",
+ "2023-12-20 23:08:15,726 INFO {/home/ehdykhne/miniconda3/envs/nuplan/lib/python3.9/site-packages/tornado/web.py:2344} 200 GET /resource/css/overview.css (10.40.112.28) 0.31ms\n",
+ "2023-12-20 23:08:15,726 INFO {/home/ehdykhne/miniconda3/envs/nuplan/lib/python3.9/site-packages/tornado/web.py:2344} 200 GET /resource/css/cloud.css (10.40.112.28) 0.46ms\n",
+ "2023-12-20 23:08:15,732 INFO {/home/ehdykhne/miniconda3/envs/nuplan/lib/python3.9/site-packages/tornado/web.py:2344} 200 GET /resource/css/scenario.css (10.40.112.28) 0.27ms\n",
+ "2023-12-20 23:08:15,736 INFO {/home/ehdykhne/miniconda3/envs/nuplan/lib/python3.9/site-packages/tornado/web.py:2344} 200 GET /resource/css/histogram.css (10.40.112.28) 0.28ms\n",
+ "2023-12-20 23:08:15,745 INFO {/home/ehdykhne/miniconda3/envs/nuplan/lib/python3.9/site-packages/tornado/web.py:2344} 200 GET /resource/scripts/utils.js (10.40.112.28) 0.26ms\n",
+ "2023-12-20 23:08:15,759 INFO {/home/ehdykhne/miniconda3/envs/nuplan/lib/python3.9/site-packages/tornado/web.py:2344} 200 GET /resource/motional_logo.png (10.40.112.28) 0.41ms\n",
+ "2023-12-20 23:08:15,768 INFO {/home/ehdykhne/miniconda3/envs/nuplan/lib/python3.9/site-packages/tornado/web.py:2344} 200 GET /static/js/bokeh-widgets.min.js?v=8a1ff6f5aa0d967f4998d275803bbb111d928fd9f605ef9e1f30cfd021df0e77224ee3d13f83edb3a942f6e4ccc569ee5dd8951a8aa6cb600602463b90c65a87 (10.40.112.28) 30.90ms\n",
+ "2023-12-20 23:08:15,768 INFO {/home/ehdykhne/miniconda3/envs/nuplan/lib/python3.9/site-packages/tornado/web.py:2344} 200 GET /static/js/bokeh-gl.min.js?v=e5df31fd9010eacff0aa72d315264604b5e34972ba445acea6fce98080eecf33acf2d2986126360faaa5852813cffa16f6f6f4889923318300f062497c02da4e (10.40.112.28) 31.17ms\n"
+ ]
+ },
+ {
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+ "output_type": "stream",
+ "text": [
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+ "INFO:tornado.access:200 GET /resource/css/scenario.css (10.40.112.28) 0.27ms\n",
+ "INFO:tornado.access:200 GET /resource/css/histogram.css (10.40.112.28) 0.28ms\n",
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+ "INFO:tornado.access:200 GET /static/js/bokeh-tables.min.js?v=ae2903e57cf57f52819fdf4d938c648982b51c34f73b6e653a0f3bb3c8ab44f338505931ace43eafc1636e215492e2314acf54c54baffb47813b86b4923a7fe0 (10.40.112.28) 82.73ms\n"
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+ {
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+ "text": [
+ "2023-12-20 23:08:15,823 INFO {/home/ehdykhne/miniconda3/envs/nuplan/lib/python3.9/site-packages/tornado/web.py:2344} 200 GET /static/js/bokeh-tables.min.js?v=ae2903e57cf57f52819fdf4d938c648982b51c34f73b6e653a0f3bb3c8ab44f338505931ace43eafc1636e215492e2314acf54c54baffb47813b86b4923a7fe0 (10.40.112.28) 82.73ms\n",
+ "2023-12-20 23:08:15,966 INFO {/home/ehdykhne/miniconda3/envs/nuplan/lib/python3.9/site-packages/tornado/web.py:2344} 200 GET /static/js/bokeh-mathjax.min.js?v=176c36fdbcd8fc1019fc828101a2804081a35baf4018d7f2633cd263156b593aa73112f400112b662daa0590138b74851bc91f1f2a5fbf5416ee8c876c3e0d0c (10.40.112.28) 223.66ms\n",
+ "2023-12-20 23:08:16,015 INFO {/home/ehdykhne/miniconda3/envs/nuplan/lib/python3.9/site-packages/tornado/web.py:2344} 200 GET /static/js/bokeh.min.js?v=3c61e952b808bb7e346ce828a565a5f23aaf7708d034fa9d0906403813355d45bb4e8d8b0b23a93f032c76831d4f0221846f28699c7f5147caa62e0d31668314 (10.40.112.28) 278.15ms\n"
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+ "INFO:tornado.access:200 GET /static/js/bokeh-mathjax.min.js?v=176c36fdbcd8fc1019fc828101a2804081a35baf4018d7f2633cd263156b593aa73112f400112b662daa0590138b74851bc91f1f2a5fbf5416ee8c876c3e0d0c (10.40.112.28) 223.66ms\n",
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- "2023-12-20 02:53:09,832 INFO {/home/ehdykhne/miniconda3/envs/nuplan/lib/python3.9/site-packages/tornado/web.py:2344} 101 GET /ws (127.0.0.1) 0.47ms\n",
- "2023-12-20 02:53:09,832 INFO {/home/ehdykhne/miniconda3/envs/nuplan/lib/python3.9/site-packages/bokeh/server/views/ws.py:132} WebSocket connection opened\n",
- "2023-12-20 02:53:09,832 INFO {/home/ehdykhne/miniconda3/envs/nuplan/lib/python3.9/site-packages/bokeh/server/views/ws.py:213} ServerConnection created\n"
+ "2023-12-20 23:08:16,316 INFO {/home/ehdykhne/miniconda3/envs/nuplan/lib/python3.9/site-packages/tornado/web.py:2344} 101 GET /ws (10.40.112.28) 0.45ms\n",
+ "2023-12-20 23:08:16,317 INFO {/home/ehdykhne/miniconda3/envs/nuplan/lib/python3.9/site-packages/bokeh/server/views/ws.py:132} WebSocket connection opened\n",
+ "2023-12-20 23:08:16,317 INFO {/home/ehdykhne/miniconda3/envs/nuplan/lib/python3.9/site-packages/bokeh/server/views/ws.py:213} ServerConnection created\n"
]
},
{
"name": "stderr",
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"text": [
- "INFO:tornado.access:101 GET /ws (127.0.0.1) 0.47ms\n"
+ "INFO:tornado.access:101 GET /ws (10.40.112.28) 0.45ms\n"
]
},
{
"name": "stdout",
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"text": [
- "2023-12-20 02:53:10,335 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/nuboard/base/simulation_tile.py:172} Minimum frame time=0.017 s\n",
- "2023-12-20 02:53:10,339 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/nuboard/tabs/scenario_tab.py:485} Rending scenario plot takes 0.0005 seconds.\n",
- "2023-12-20 02:53:10,430 INFO {/home/ehdykhne/miniconda3/envs/nuplan/lib/python3.9/site-packages/tornado/web.py:2344} 200 GET / (127.0.0.1) 155.88ms\n"
+ "2023-12-20 23:08:16,537 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/nuboard/base/simulation_tile.py:172} Minimum frame time=0.017 s\n",
+ "2023-12-20 23:08:16,785 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/nuboard/tabs/scenario_tab.py:485} Rending scenario plot takes 0.0073 seconds.\n",
+ "2023-12-20 23:08:16,877 INFO {/home/ehdykhne/miniconda3/envs/nuplan/lib/python3.9/site-packages/tornado/web.py:2344} 200 GET / (10.40.112.28) 455.86ms\n"
]
},
{
"name": "stderr",
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"text": [
- "INFO:tornado.access:200 GET / (127.0.0.1) 155.88ms\n"
+ "INFO:tornado.access:200 GET / (10.40.112.28) 455.86ms\n",
+ "Rendering a scenario: 100%|██████████| 1/1 [00:00<00:00, 15.44it/s]\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "2023-12-20 23:09:51,245 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/nuboard/tabs/scenario_tab.py:485} Rending scenario plot takes 1.7209 seconds.\n"
]
},
{
- "ename": "",
+ "ename": "KeyboardInterrupt",
"evalue": "",
"output_type": "error",
"traceback": [
- "\u001b[1;31mThe Kernel crashed while executing code in the the current cell or a previous cell. Please review the code in the cell(s) to identify a possible cause of the failure. Click <a href='https://aka.ms/vscodeJupyterKernelCrash'>here</a> for more info. View Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
+ "\u001b[1;32m/home/ehdykhne/nuplan-devkit/experiments/bulk_running_experiments.ipynb Cell 12\u001b[0m line \u001b[0;36m4\n\u001b[1;32m <a href='vscode-notebook-cell://ssh-remote%2Bwisedave.eng.uwaterloo.ca/home/ehdykhne/nuplan-devkit/experiments/bulk_running_experiments.ipynb#X14sdnNjb2RlLXJlbW90ZQ%3D%3D?line=0'>1</a>\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mnuplan\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mplanning\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mscript\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mrun_nuboard\u001b[39;00m \u001b[39mimport\u001b[39;00m main \u001b[39mas\u001b[39;00m main_nuboard\n\u001b[1;32m <a href='vscode-notebook-cell://ssh-remote%2Bwisedave.eng.uwaterloo.ca/home/ehdykhne/nuplan-devkit/experiments/bulk_running_experiments.ipynb#X14sdnNjb2RlLXJlbW90ZQ%3D%3D?line=2'>3</a>\u001b[0m \u001b[39m# Run nuBoard\u001b[39;00m\n\u001b[0;32m----> <a href='vscode-notebook-cell://ssh-remote%2Bwisedave.eng.uwaterloo.ca/home/ehdykhne/nuplan-devkit/experiments/bulk_running_experiments.ipynb#X14sdnNjb2RlLXJlbW90ZQ%3D%3D?line=3'>4</a>\u001b[0m main_nuboard(cfg)\n",
+ "File \u001b[0;32m~/miniconda3/envs/nuplan/lib/python3.9/site-packages/hydra/main.py:44\u001b[0m, in \u001b[0;36mmain.<locals>.main_decorator.<locals>.decorated_main\u001b[0;34m(cfg_passthrough)\u001b[0m\n\u001b[1;32m 41\u001b[0m \u001b[39m@functools\u001b[39m\u001b[39m.\u001b[39mwraps(task_function)\n\u001b[1;32m 42\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mdecorated_main\u001b[39m(cfg_passthrough: Optional[DictConfig] \u001b[39m=\u001b[39m \u001b[39mNone\u001b[39;00m) \u001b[39m-\u001b[39m\u001b[39m>\u001b[39m Any:\n\u001b[1;32m 43\u001b[0m \u001b[39mif\u001b[39;00m cfg_passthrough \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m---> 44\u001b[0m \u001b[39mreturn\u001b[39;00m task_function(cfg_passthrough)\n\u001b[1;32m 45\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m 46\u001b[0m args \u001b[39m=\u001b[39m get_args_parser()\n",
+ "File \u001b[0;32m~/nuplan-devkit/nuplan/planning/script/run_nuboard.py:74\u001b[0m, in \u001b[0;36mmain\u001b[0;34m(cfg)\u001b[0m\n\u001b[1;32m 69\u001b[0m \u001b[39m\u001b[39m\u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m 70\u001b[0m \u001b[39mExecute all available challenges simultaneously on the same scenario.\u001b[39;00m\n\u001b[1;32m 71\u001b[0m \u001b[39m:param cfg: DictConfig. Configuration that is used to run the experiment.\u001b[39;00m\n\u001b[1;32m 72\u001b[0m \u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m 73\u001b[0m nuboard \u001b[39m=\u001b[39m initialize_nuboard(cfg)\n\u001b[0;32m---> 74\u001b[0m nuboard\u001b[39m.\u001b[39;49mrun()\n",
+ "File \u001b[0;32m~/nuplan-devkit/nuplan/planning/nuboard/nuboard.py:112\u001b[0m, in \u001b[0;36mNuBoard.run\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 110\u001b[0m \u001b[39m# Catch RuntimeError in jupyter notebook\u001b[39;00m\n\u001b[1;32m 111\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m--> 112\u001b[0m io_loop\u001b[39m.\u001b[39;49mstart()\n\u001b[1;32m 113\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mRuntimeError\u001b[39;00m \u001b[39mas\u001b[39;00m e:\n\u001b[1;32m 114\u001b[0m logger\u001b[39m.\u001b[39mwarning(\u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39m{\u001b[39;00me\u001b[39m}\u001b[39;00m\u001b[39m\"\u001b[39m)\n",
+ "File \u001b[0;32m~/miniconda3/envs/nuplan/lib/python3.9/site-packages/tornado/platform/asyncio.py:195\u001b[0m, in \u001b[0;36mBaseAsyncIOLoop.start\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 194\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mstart\u001b[39m(\u001b[39mself\u001b[39m) \u001b[39m-\u001b[39m\u001b[39m>\u001b[39m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m--> 195\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49masyncio_loop\u001b[39m.\u001b[39;49mrun_forever()\n",
+ "File \u001b[0;32m~/miniconda3/envs/nuplan/lib/python3.9/site-packages/nest_asyncio.py:82\u001b[0m, in \u001b[0;36m_patch_loop.<locals>.run_forever\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 80\u001b[0m \u001b[39mwith\u001b[39;00m manage_run(\u001b[39mself\u001b[39m), manage_asyncgens(\u001b[39mself\u001b[39m):\n\u001b[1;32m 81\u001b[0m \u001b[39mwhile\u001b[39;00m \u001b[39mTrue\u001b[39;00m:\n\u001b[0;32m---> 82\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_run_once()\n\u001b[1;32m 83\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_stopping:\n\u001b[1;32m 84\u001b[0m \u001b[39mbreak\u001b[39;00m\n",
+ "File \u001b[0;32m~/miniconda3/envs/nuplan/lib/python3.9/site-packages/nest_asyncio.py:116\u001b[0m, in \u001b[0;36m_patch_loop.<locals>._run_once\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 109\u001b[0m heappop(scheduled)\n\u001b[1;32m 111\u001b[0m timeout \u001b[39m=\u001b[39m (\n\u001b[1;32m 112\u001b[0m \u001b[39m0\u001b[39m \u001b[39mif\u001b[39;00m ready \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_stopping\n\u001b[1;32m 113\u001b[0m \u001b[39melse\u001b[39;00m \u001b[39mmin\u001b[39m(\u001b[39mmax\u001b[39m(\n\u001b[1;32m 114\u001b[0m scheduled[\u001b[39m0\u001b[39m]\u001b[39m.\u001b[39m_when \u001b[39m-\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mtime(), \u001b[39m0\u001b[39m), \u001b[39m86400\u001b[39m) \u001b[39mif\u001b[39;00m scheduled\n\u001b[1;32m 115\u001b[0m \u001b[39melse\u001b[39;00m \u001b[39mNone\u001b[39;00m)\n\u001b[0;32m--> 116\u001b[0m event_list \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_selector\u001b[39m.\u001b[39;49mselect(timeout)\n\u001b[1;32m 117\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_process_events(event_list)\n\u001b[1;32m 119\u001b[0m end_time \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mtime() \u001b[39m+\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_clock_resolution\n",
+ "File \u001b[0;32m~/miniconda3/envs/nuplan/lib/python3.9/selectors.py:469\u001b[0m, in \u001b[0;36mEpollSelector.select\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m 467\u001b[0m ready \u001b[39m=\u001b[39m []\n\u001b[1;32m 468\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m--> 469\u001b[0m fd_event_list \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_selector\u001b[39m.\u001b[39;49mpoll(timeout, max_ev)\n\u001b[1;32m 470\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mInterruptedError\u001b[39;00m:\n\u001b[1;32m 471\u001b[0m \u001b[39mreturn\u001b[39;00m ready\n",
+ "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
]
}
],
diff --git a/nuplan/planning/metrics/evaluation_metrics/common/can_scenario_be_made_dangerous.py b/nuplan/planning/metrics/evaluation_metrics/common/can_scenario_be_made_dangerous.py
index aa83647e75177bd2fb4200b49bdfefa274cc112d..5039a5c60089855b6b8184375206778b9ccf791a 100644
--- a/nuplan/planning/metrics/evaluation_metrics/common/can_scenario_be_made_dangerous.py
+++ b/nuplan/planning/metrics/evaluation_metrics/common/can_scenario_be_made_dangerous.py
@@ -135,6 +135,8 @@ class CanScenarioBeMadeDangerousStatistics(MetricBase):
d += 1
if connector.has_traffic_lights() and connector.id in traffic_light_status_dict[iteration][TrafficLightStatusType.RED]:
n += 1
+ elif connector.has_traffic_lights() and connector.id in traffic_light_status_dict[iteration][TrafficLightStatusType.GREEN]:
+ return True #if the light is explicitly green at any point, we can assume it is not red for the relavant portion of the scenario
if n / d > threshold:
#print('hi there', n, d, n / d)