diff --git a/evaluation/ablation/test-stream.ipynb b/evaluation/ablation/test-stream.ipynb index c84d0a9..2bae143 100644 --- a/evaluation/ablation/test-stream.ipynb +++ b/evaluation/ablation/test-stream.ipynb @@ -2,58 +2,12 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "id": "569e4726-2856-4e5f-a220-e3bef1c110e1", "metadata": { "tags": [] }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Tue Oct 17 09:28:05 2023 \n", - "+---------------------------------------------------------------------------------------+\n", - "| NVIDIA-SMI 530.30.02 Driver Version: 530.30.02 CUDA Version: 12.1 |\n", - "|-----------------------------------------+----------------------+----------------------+\n", - "| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |\n", - "| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |\n", - "| | | MIG M. |\n", - "|=========================================+======================+======================|\n", - "| 0 NVIDIA TITAN Xp On | 00000000:1A:00.0 Off | N/A |\n", - "| 31% 52C P2 61W / 250W| 1916MiB / 12288MiB | 0% Default |\n", - "| | | N/A |\n", - "+-----------------------------------------+----------------------+----------------------+\n", - "| 1 NVIDIA TITAN Xp On | 00000000:1B:00.0 Off | N/A |\n", - "| 23% 32C P8 9W / 250W| 1MiB / 12288MiB | 0% Default |\n", - "| | | N/A |\n", - "+-----------------------------------------+----------------------+----------------------+\n", - "| 2 NVIDIA TITAN Xp On | 00000000:3D:00.0 Off | N/A |\n", - "| 23% 26C P8 9W / 250W| 1MiB / 12288MiB | 0% Default |\n", - "| | | N/A |\n", - "+-----------------------------------------+----------------------+----------------------+\n", - "| 3 NVIDIA TITAN Xp On | 00000000:3E:00.0 Off | N/A |\n", - "| 23% 33C P8 10W / 250W| 1MiB / 12288MiB | 0% Default |\n", - "| | | N/A |\n", - "+-----------------------------------------+----------------------+----------------------+\n", - "| 4 NVIDIA TITAN Xp On | 00000000:88:00.0 Off | N/A |\n", - "| 23% 28C P8 9W / 250W| 1MiB / 12288MiB | 0% Default |\n", - "| | | N/A |\n", - "+-----------------------------------------+----------------------+----------------------+\n", - "| 5 NVIDIA TITAN Xp On | 00000000:89:00.0 Off | N/A |\n", - "| 28% 50C P0 67W / 250W| 1MiB / 12288MiB | 0% Default |\n", - "| | | N/A |\n", - "+-----------------------------------------+----------------------+----------------------+\n", - " \n", - "+---------------------------------------------------------------------------------------+\n", - "| Processes: |\n", - "| GPU GI CI PID Type Process name GPU Memory |\n", - "| ID ID Usage |\n", - "|=======================================================================================|\n", - "+---------------------------------------------------------------------------------------+\n" - ] - } - ], + "outputs": [], "source": [ "import subprocess\n", "import json\n", @@ -79,20 +33,12 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "id": "7bcaf7b9-8b3f-4f2f-ad00-08b197795820", "metadata": { "tags": [] }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "test freddie\n" - ] - } - ], + "outputs": [], "source": [ "hostname = socket.gethostname()\n", "test = hostname.split(\"-\")[-1]\n", @@ -101,20 +47,12 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "id": "df967c40-baae-4473-8ab4-0ddfd630eb59", "metadata": { "tags": [] }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "/data/chanwutk/projects/spatialyze-ablation\n" - ] - } - ], + "outputs": [], "source": [ "def is_notebook() -> bool:\n", " try:\n", @@ -144,55 +82,24 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "id": "edb4d993-d0a4-49d5-8b4d-1b3a62f66da4", "metadata": { "tags": [] }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "mobilitydb_chanwutk\n" - ] - }, - { - "data": { - "text/plain": [ - "0" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "process.wait()" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "id": "ddcd5926-911d-437d-b67f-6814d5eb7c0b", "metadata": { "tags": [] }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CUDA is available.\n", - " > 0: NVIDIA TITAN Xp\n", - " 1: NVIDIA TITAN Xp\n", - " 2: NVIDIA TITAN Xp\n", - " 3: NVIDIA TITAN Xp\n", - " 4: NVIDIA TITAN Xp\n" - ] - } - ], + "outputs": [], "source": [ "from spatialyze.video_processor.camera_config import camera_config\n", "from spatialyze.video_processor.payload import Payload\n", @@ -203,7 +110,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "id": "863b7bd6-7bc4-4658-bb06-043ba955aef3", "metadata": { "tags": [] @@ -224,7 +131,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "id": "6ee9880e-27d0-47f3-a3dd-36b70d199d8a", "metadata": { "tags": [] @@ -242,7 +149,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "id": "3c4bddca-a1ae-4806-be85-8cf397d01ec9", "metadata": { "tags": [] @@ -255,7 +162,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "id": "6737b34b-d928-45aa-940e-b23a7a6e5eb0", "metadata": { "tags": [] @@ -274,7 +181,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "id": "e9087777-3c98-414a-8f00-eda64e8f8ffd", "metadata": {}, "outputs": [], @@ -290,7 +197,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "id": "59907886", "metadata": { "tags": [] @@ -303,7 +210,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "id": "e650e6ca-9d7c-41e9-98ee-f682b399040f", "metadata": { "tags": [] @@ -317,7 +224,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "id": "264c8190-89ee-472a-bd73-d553eb3e3278", "metadata": { "tags": [] @@ -335,21 +242,12 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "id": "355b8977", "metadata": { "tags": [] }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "True\n", - "/data/apperception-data/processed/nuscenes/full-dataset-v1.0/Trainval\n" - ] - } - ], + "outputs": [], "source": [ "NUSCENES_PROCESSED_DATA = \"NUSCENES_PROCESSED_DATA\"\n", "print(NUSCENES_PROCESSED_DATA in os.environ)\n", @@ -358,7 +256,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "id": "00c1dd1e", "metadata": { "tags": [] @@ -374,20 +272,12 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "id": "1f6981ac-60b4-43f4-9c3b-32a4e84e4aa1", "metadata": { "tags": [] }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0523 0778 467\n" - ] - } - ], + "outputs": [], "source": [ "with open('./data/evaluation/video-samples/boston-seaport.txt', 'r') as f:\n", " sampled_scenes = f.read().split('\\n')\n", @@ -396,7 +286,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "id": "fcb3599c-3807-4044-9636-45b2d94fe7e8", "metadata": { "tags": [] @@ -412,7 +302,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "id": "ad97a25a-0356-4d7e-9096-f26c00d2d9d4", "metadata": { "tags": [] @@ -445,7 +335,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": null, "id": "5d4149a3-43b5-4531-90dd-31dd795bdaa1", "metadata": { "tags": [] @@ -467,7 +357,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": null, "id": "275836d5", "metadata": { "tags": [] @@ -670,7 +560,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": null, "id": "98283938-b68f-4925-a5ef-eee7c6c46c65", "metadata": { "tags": [] @@ -742,7 +632,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": null, "id": "f4adca3d-7963-4dc6-bde1-d0ce107ae959", "metadata": { "tags": [] @@ -879,7 +769,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": null, "id": "653e586a-a98c-4c15-ac5a-17551b3155db", "metadata": { "tags": [] @@ -892,7 +782,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": null, "id": "48f7c558-dc6e-4b86-b447-a3ffac74c966", "metadata": { "tags": [] @@ -1032,143 +922,12 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": null, "id": "811c4351-f2a7-478b-b264-69a3e8d75c69", "metadata": { "tags": [] }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "types []\n", - "----------- 0 / 4 --- de -----------\n", - "Pipeline P2:\n", - " - DecodeFrame.ParallelDecodeFrame\n", - " - Detection2D.YoloDetection\n", - " - DepthEstimation\n", - " - Detection3D.FromDetection2DAndDepth\n", - " - DetectionEstimation\n", - " - Tracking2D.StrongSORT\n", - " - Tracking3D.FromTracking2DAndDetection3D\n", - "q2-de\n", - "# of total videos: 5100\n", - "# of filtered videos: 60\n", - "# of sliced videos: 60\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "56bd9a06db2740e8822ec73063eda095", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - " 0%| | 0/60 [00:00 14\u001b[0m \u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[43m_test\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 15\u001b[0m done \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[1;32m 16\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n", - "Cell \u001b[0;32mIn[24], line 118\u001b[0m, in \u001b[0;36mrun\u001b[0;34m(__test)\u001b[0m\n\u001b[1;32m 116\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m s \u001b[38;5;129;01min\u001b[39;00m p2\u001b[38;5;241m.\u001b[39mstages:\n\u001b[1;32m 117\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m -\u001b[39m\u001b[38;5;124m'\u001b[39m, s)\n\u001b[0;32m--> 118\u001b[0m \u001b[43mrun_benchmark\u001b[49m\u001b[43m(\u001b[49m\u001b[43mp2\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mq2-\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[43m \u001b[49m\u001b[43m__test\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m[\u001b[49m\u001b[43mpred2\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpred2_notrack\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrun\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mignore_error\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[1;32m 120\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mPipeline P3,P4:\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m 121\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m s \u001b[38;5;129;01min\u001b[39;00m p34\u001b[38;5;241m.\u001b[39mstages:\n", - "Cell \u001b[0;32mIn[20], line 66\u001b[0m, in \u001b[0;36mrun_benchmark\u001b[0;34m(pipeline, filename, predicates, run, ignore_error)\u001b[0m\n\u001b[1;32m 64\u001b[0m tracks\u001b[38;5;241m.\u001b[39mchildren_progress\u001b[38;5;241m.\u001b[39mappend(\u001b[38;5;241m0\u001b[39m)\n\u001b[1;32m 65\u001b[0m output \u001b[38;5;241m=\u001b[39m tracks\u001b[38;5;241m.\u001b[39mstream(frames)\n\u001b[0;32m---> 66\u001b[0m output \u001b[38;5;241m=\u001b[39m [\u001b[38;5;241m*\u001b[39moutput]\n\u001b[1;32m 67\u001b[0m \u001b[38;5;66;03m# output = pipeline.run(Payload(frames))\u001b[39;00m\n\u001b[1;32m 68\u001b[0m \n\u001b[1;32m 69\u001b[0m \u001b[38;5;66;03m# metadata_strongsort[name] = output[StrongSORT2D]\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 75\u001b[0m \u001b[38;5;66;03m# json.dump(metadata[name], f, cls=MetadataJSONEncoder,\u001b[39;00m\n\u001b[1;32m 76\u001b[0m \u001b[38;5;66;03m# indent=1)\u001b[39;00m\n\u001b[1;32m 78\u001b[0m times_rquery \u001b[38;5;241m=\u001b[39m []\n", - "File \u001b[0;32m/data/chanwutk/projects/spatialyze-ablation/spatialyze/video_processor/stream/reusable.py:44\u001b[0m, in \u001b[0;36mreusable..ReusableStream.stream\u001b[0;34m(self, video)\u001b[0m\n\u001b[1;32m 42\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;28;01mTrue\u001b[39;00m:\n\u001b[1;32m 43\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mresults) \u001b[38;5;241m<\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mchildren_progress[idx]:\n\u001b[0;32m---> 44\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mresults\u001b[38;5;241m.\u001b[39mappend(\u001b[38;5;28;43mnext\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43miter_stream\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[1;32m 46\u001b[0m \u001b[38;5;28;01myield\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mresults[\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mchildren_progress[idx]]\n\u001b[1;32m 47\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mchildren_progress[idx] \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n", - "File \u001b[0;32m/data/chanwutk/projects/spatialyze-ablation/spatialyze/video_processor/stream/strongsort.py:70\u001b[0m, in \u001b[0;36mStrongSORT.stream\u001b[0;34m(self, video)\u001b[0m\n\u001b[1;32m 68\u001b[0m clss \u001b[38;5;241m=\u001b[39m _classes\n\u001b[1;32m 69\u001b[0m det \u001b[38;5;241m=\u001b[39m det\u001b[38;5;241m.\u001b[39mcpu()\n\u001b[0;32m---> 70\u001b[0m \u001b[43mstrongsort\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mupdate\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdet\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdids\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mim0\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 71\u001b[0m saved_detections\u001b[38;5;241m.\u001b[39mappend(det)\n\u001b[1;32m 73\u001b[0m deleted_tracks \u001b[38;5;241m=\u001b[39m strongsort\u001b[38;5;241m.\u001b[39mtracker\u001b[38;5;241m.\u001b[39mdeleted_tracks\n", - "File \u001b[0;32m/data/chanwutk/projects/spatialyze-ablation/spatialyze/video_processor/modules/yolo_tracker/trackers/strong_sort/strong_sort.py:53\u001b[0m, in \u001b[0;36mStrongSORT.update\u001b[0;34m(self, dets, ids, ori_img)\u001b[0m\n\u001b[1;32m 51\u001b[0m features \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_features(xywhs, ori_img)\n\u001b[1;32m 52\u001b[0m bbox_tlwh \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_xywh_to_tlwh(xywhs)\n\u001b[0;32m---> 53\u001b[0m detections \u001b[38;5;241m=\u001b[39m [Detection(bbox_tlwh[i], conf, features[i], ids[i]) \u001b[38;5;28;01mfor\u001b[39;00m i, conf \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(\n\u001b[1;32m 54\u001b[0m confs)]\n\u001b[1;32m 56\u001b[0m \u001b[38;5;66;03m# run on non-maximum supression\u001b[39;00m\n\u001b[1;32m 57\u001b[0m boxes \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39marray([d\u001b[38;5;241m.\u001b[39mtlwh \u001b[38;5;28;01mfor\u001b[39;00m d \u001b[38;5;129;01min\u001b[39;00m detections])\n", - "File \u001b[0;32m/data/chanwutk/projects/spatialyze-ablation/spatialyze/video_processor/modules/yolo_tracker/trackers/strong_sort/strong_sort.py:53\u001b[0m, in \u001b[0;36m\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m 51\u001b[0m features \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_features(xywhs, ori_img)\n\u001b[1;32m 52\u001b[0m bbox_tlwh \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_xywh_to_tlwh(xywhs)\n\u001b[0;32m---> 53\u001b[0m detections \u001b[38;5;241m=\u001b[39m [\u001b[43mDetection\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbbox_tlwh\u001b[49m\u001b[43m[\u001b[49m\u001b[43mi\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mconf\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfeatures\u001b[49m\u001b[43m[\u001b[49m\u001b[43mi\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mids\u001b[49m\u001b[43m[\u001b[49m\u001b[43mi\u001b[49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mfor\u001b[39;00m i, conf \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(\n\u001b[1;32m 54\u001b[0m confs)]\n\u001b[1;32m 56\u001b[0m \u001b[38;5;66;03m# run on non-maximum supression\u001b[39;00m\n\u001b[1;32m 57\u001b[0m boxes \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39marray([d\u001b[38;5;241m.\u001b[39mtlwh \u001b[38;5;28;01mfor\u001b[39;00m d \u001b[38;5;129;01min\u001b[39;00m detections])\n", - "File \u001b[0;32m/data/chanwutk/projects/spatialyze-ablation/spatialyze/video_processor/modules/yolo_tracker/trackers/strong_sort/sort/detection.py:32\u001b[0m, in \u001b[0;36mDetection.__init__\u001b[0;34m(self, tlwh, confidence, feature, id)\u001b[0m\n\u001b[1;32m 30\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtlwh \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39masarray(tlwh, dtype\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mfloat\u001b[39m)\n\u001b[1;32m 31\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconfidence \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mfloat\u001b[39m(confidence)\n\u001b[0;32m---> 32\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfeature \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39masarray(\u001b[43mfeature\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcpu\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m, dtype\u001b[38;5;241m=\u001b[39mnp\u001b[38;5;241m.\u001b[39mfloat32)\n\u001b[1;32m 33\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mid \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mid\u001b[39m\n", - "\u001b[0;31mKeyboardInterrupt\u001b[0m: " - ] - } - ], + "outputs": [], "source": [ "tests = ['optde', 'de', 'noopt', 'inview', 'objectfilter', 'geo', 'opt']\n", "tests = ['de', 'noopt', 'inview', 'objectfilter']\n",