update tools

autonomous_driving/openlane-v2/tools/deployment/test_torchserver.py

+from argparse import ArgumentParser
+
+import numpy as np
+import requests
+
+from mmdet3d.apis import inference_detector, init_model
+
+
+def parse_args():
+    parser = ArgumentParser()
+    parser.add_argument('pcd', help='Point cloud file')
+    parser.add_argument('config', help='Config file')
+    parser.add_argument('checkpoint', help='Checkpoint file')
+    parser.add_argument('model_name', help='The model name in the server')
+    parser.add_argument(
+        '--inference-addr',
+        default='127.0.0.1:8080',
+        help='Address and port of the inference server')
+    parser.add_argument(
+        '--device', default='cuda:0', help='Device used for inference')
+    parser.add_argument(
+        '--score-thr', type=float, default=0.5, help='3d bbox score threshold')
+    args = parser.parse_args()
+    return args
+
+
+def parse_result(input):
+    bbox = input[0]['3dbbox']
+    result = np.array(bbox)
+    return result
+
+
+def main(args):
+    # build the model from a config file and a checkpoint file
+    model = init_model(args.config, args.checkpoint, device=args.device)
+    # test a single point cloud file
+    model_result, _ = inference_detector(model, args.pcd)
+    # filter the 3d bboxes whose scores > 0.5
+    if 'pts_bbox' in model_result[0].keys():
+        pred_bboxes = model_result[0]['pts_bbox']['boxes_3d'].tensor.numpy()
+        pred_scores = model_result[0]['pts_bbox']['scores_3d'].numpy()
+    else:
+        pred_bboxes = model_result[0]['boxes_3d'].tensor.numpy()
+        pred_scores = model_result[0]['scores_3d'].numpy()
+    model_result = pred_bboxes[pred_scores > 0.5]
+
+    url = 'http://' + args.inference_addr + '/predictions/' + args.model_name
+    with open(args.pcd, 'rb') as points:
+        response = requests.post(url, points)
+    server_result = parse_result(response.json())
+    assert np.allclose(model_result, server_result)
+
+
+if __name__ == '__main__':
+    args = parse_args()
+    main(args)

autonomous_driving/openlane-v2/tools/dist_test.sh

+#!/usr/bin/env bash
+
+CONFIG=$1
+CHECKPOINT=$2
+GPUS=$3
+NNODES=${NNODES:-1}
+NODE_RANK=${NODE_RANK:-0}
+PORT=${PORT:-29500}
+MASTER_ADDR=${MASTER_ADDR:-"127.0.0.1"}
+
+PYTHONPATH="$(dirname $0)/..":$PYTHONPATH \
+python -m torch.distributed.launch \
+    --nnodes=$NNODES \
+    --node_rank=$NODE_RANK \
+    --master_addr=$MASTER_ADDR \
+    --nproc_per_node=$GPUS \
+    --master_port=$PORT \
+    $(dirname "$0")/test.py \
+    $CONFIG \
+    $CHECKPOINT \
+    --launcher pytorch \
+    ${@:4}

autonomous_driving/openlane-v2/tools/dist_train.sh

+#!/usr/bin/env bash
+
+CONFIG=$1
+GPUS=$2
+NNODES=${NNODES:-1}
+NODE_RANK=${NODE_RANK:-0}
+PORT=${PORT:-29500}
+MASTER_ADDR=${MASTER_ADDR:-"127.0.0.1"}
+
+PYTHONPATH="$(dirname $0)/..":$PYTHONPATH \
+python -m torch.distributed.launch \
+    --nnodes=$NNODES \
+    --node_rank=$NODE_RANK \
+    --master_addr=$MASTER_ADDR \
+    --nproc_per_node=$GPUS \
+    --master_port=$PORT \
+    $(dirname "$0")/train.py \
+    $CONFIG \
+    --seed 0 \
+    --launcher pytorch ${@:3}

autonomous_driving/openlane-v2/tools/slurm_test.sh

+#!/usr/bin/env bash
+
+set -x
+
+PARTITION=$1
+JOB_NAME=$2
+CONFIG=$3
+CHECKPOINT=$4
+GPUS=${GPUS:-8}
+GPUS_PER_NODE=${GPUS_PER_NODE:-8}
+CPUS_PER_TASK=${CPUS_PER_TASK:-5}
+PY_ARGS=${@:5}
+SRUN_ARGS=${SRUN_ARGS:-""}
+
+PYTHONPATH="$(dirname $0)/..":$PYTHONPATH \
+srun -p ${PARTITION} \
+    --job-name=${JOB_NAME} \
+    --gres=gpu:${GPUS_PER_NODE} \
+    --ntasks=${GPUS} \
+    --ntasks-per-node=${GPUS_PER_NODE} \
+    --cpus-per-task=${CPUS_PER_TASK} \
+    --kill-on-bad-exit=1 \
+    ${SRUN_ARGS} \
+    python -u tools/test.py ${CONFIG} ${CHECKPOINT} --launcher="slurm" ${PY_ARGS}