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case.py

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+from uuid import uuid1
+
+import pytest
+
+from aidisdk import AIDIClient
+from aidisdk.algo_house.algorithm_module import AlgoConfig, AlgoFieldEnum
+from aidisdk.compute.job_abstract import (
+    JobType,
+    RunningResourceConfig,
+    StartUpConfig,
+)
+from aidisdk.compute.package_abstract import (
+    CodePackageConfig,
+    LocalPackageItem,
+)
+from aidisdk.model import ModelFramework
+
+
+@pytest.mark.skip("unused")
+def test_create_algo_for_eval_detection3d(unittest_client):
+    client: AIDIClient = unittest_client
+    # create a algorithm with a raw config file
+
+    algorithm = client.algo_house.create(
+        algo_name="eval_for_detection3d_" + str(uuid1()).replace("-", "_"),
+        field=AlgoFieldEnum.AUTO,
+        scene="高速",
+        module="感知",
+        task_types=["2D检测"],
+        framework=ModelFramework.pytorch,
+        startup="cd ${WORKING_PATH} && python3 local_example.py ",  # noqa
+        code_package="test/test_data/eval_experiment",
+        docker_image="docker.hobot.cc/auto/eval-traincli:v1.0.36test",
+        desc="算法仓库发起评测使用，请勿删除.",
+        tags=["test", "unittest"],
+        config_files=[
+            AlgoConfig(
+                name="eval_setting",
+                local_path="test/test_data/"
+                + "eval_experiment/setting_example.yaml",
+            ),
+        ],
+    )
+    client.algo_house.__delete__(algorithm.algo_id)
+
+
+@pytest.mark.skip("unused")
+def test_update_algo(unittest_client):
+    client: AIDIClient = unittest_client
+    algo_name = "eval_for_Semantic_Segmentation"
+    algorithm = client.algo_house.update(
+        algo_name=algo_name,
+        field=AlgoFieldEnum.AUTO,
+        scene="高速",
+        module="感知",
+        task_types=["2D检测"],
+        framework=ModelFramework.pytorch,
+        startup="python3 local_example.py --task_type ${TASK_TYPE} "
+        + "--endpoint"
+        + " 'http://aidi-test.hobot.cc' "
+        + "--group_name ${GROUP_NAME} "
+        + "--experiment_name ${EXPERIMENT_NAME} --run_name '${RUN_NAME}' "
+        + "--gt_dataset_id "
+        + "'${GT_DATASET_ID}' "
+        + "--images_dataset_id "  # detection 3d & 分割
+        + "'${IMAGES_DATASET_ID}' "
+        + "--prediction_name '${PREDICTION_NAME}' "
+        + "--predictions_dataset_id '${PREDICTIONS_DATASET_ID}' "  # 分割
+        + "--labels_dataset_id '${LABELS_DATASET_ID}' "  # 分割
+        + "--setting_file_name ${EVAL_SETTING}",
+
+
+        code_package="test/test_data/eval_experiment",
+        docker_image="docker.hobot.cc/auto/eval-traincli:v1.0.36test",
+        desc="算法仓库发起评测使用，请勿删除.",
+        tags=["test", "unittest"],
+        config_files=[
+            AlgoConfig(
+                name="eval_setting",
+                local_path="test/test_data/eval_experiment/wk_setting.yaml",  # 分割
+                # local_path="test/test_data/eval_experiment/setting_example.yaml",
+                placeholder="${EVAL_SETTING}",
+            ),
+        ],
+    )
+    print(algorithm)
+
+
+# @pytest.mark.skip("unused")
+def test_create_eval_task_env_test(unittest_client):
+    client: AIDIClient = unittest_client
+    # algo_name = "eval_for_detection3d"
+    algo_name = "eval_for_Semantic_Segmentation"
+    algo = client.algo_house.get(
+        algo_name=algo_name, download_config=True, download_package=True
+    )
+    # experiment group name + experiment name + prediction in experiment
+    # 参数会替换cmd命令中的占位符，默认cmd_args_dict的key大写为占位符，如 task_type -> ${TASK_TYPE}
+    cmd_args_dict = {
+        "task_type": "Semantic_Segmentation",
+        "predictions_dataset_id": "dataset://25616",
+        "labels_dataset_id": "dataset://25615",
+
+        # "gt_dataset": "dataset://25575",
+        "gt_dataset": "",
+        "images_dataset_id": "dataset://25613",
+        "group_name": "train-withBN",
+        "experiment_name": "wjx_test_095",
+        "run_name": "test_run_name_wjx_003",
+        # "prediction_name": "wjx_test_023/prediction.json",
+        "prediction_name": "",
+    }
+
+    config_files = [
+        AlgoConfig(
+            name="eval_setting",
+            local_path="test/test_data/eval_experiment/wk_setting.yaml",  # 分割
+            # local_path="test/test_data/eval_experiment/setting_example.yaml",
+            placeholder="${EVAL_SETTING}",
+        ),
+    ]
+
+    algo.update_config(config_files)
+    algo.update_cmd(cmd_args_dict)
+
+    # TODO gen job obj
+    # job = algo.gen_job()
+    cpu_count = 6
+    cpu_mem_ratio = 6
+    queue = "svc-aip-cpu"
+    project = "PD20210425"
+    job = client.single_job.create(
+        job_name="eval_from_algo_%s_%s"
+        % (algo.name, str(uuid1()).replace("-", "_")),
+        job_type=JobType.APP_EVAL,
+        ipd_number=project,
+        queue_name=queue,
+        running_resource=RunningResourceConfig(
+            docker_image=algo.docker_image,
+            instance=1,
+            cpu=cpu_count,
+            gpu=0,
+            cpu_mem_ratio=cpu_mem_ratio,
+        ),
+        mount=[],
+        startup=StartUpConfig(
+            command=algo.startup_command,  # noqa
+        ),
+        code_package=CodePackageConfig(
+            raw_package=LocalPackageItem(
+                lpath=algo.package_path,
+                encrypt_passwd="12345",
+                follow_softlink=True,
+            ).set_as_startup_dir(),
+        ),
+        # subscribers=["dan.song", "shulan.shen"],
+    )
+    print(job)

k.yaml

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+class:
+- color: [128,64,128]
+  ignore: false
+  label: 0
+  name: road
+- color: [70,70,70]
+  ignore: false
+  label: 1
+  name: background
+- color: [153,153,190]
+  ignore: false
+  label: 2
+  name: fence
+- color: [153,153,153]
+  ignore: false
+  label: 3
+  name: pole
+- color: [30,170,250]
+  ignore: false
+  label: 4
+  name: traffic
+- color: [60,20,220]
+  ignore: false
+  label: 5
+  name: person
+- color: [142,0,0]
+  ignore: false
+  label: 6
+  name: vehicle
+- color: [70,0,0]
+  ignore: false
+  label: 7
+  name: two-wheel
+- color: [200,200,200]
+  ignore: false
+  label: 8
+  name: lane_marking
+- color: [0,192,64]
+  ignore: false
+  label: 9
+  name: crosswalk
+- color: [192,0,128]
+  ignore: false
+  label: 10
+  name: traffic_arrow
+- color: [128,200,200]
+  ignore: false
+  label: 11
+  name: sign_line
+- color: [192,192,0]
+  ignore: false
+  label: 12
+  name: guide_line
+- color: [64,64,0]
+  ignore: false
+  label: 13
+  name: cone
+- color: [0,0,255]
+  ignore: false
+  label: 14
+  name: stop_line
+- color: [0,220,220]
+  ignore: false
+  label: 15
+  name: speed_bump
+
+freespace:
+- freespace_id: [0,8,9,10,11,12,14,15]
+  margin: [5,10]
+  thredshold: [0.5,0.7]