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job (2)/code/base.py

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+import os
+from dataclasses import dataclass
+from typing import List, Union
+
+import yaml
+from dataclasses_json import DataClassJsonMixin
+
+from aidisdk import AIDIClient
+from aidisdk.experiment import Image, Table
+
+
+@dataclass
+class RunConfig(DataClassJsonMixin):
+    """Dataclass for config of run."""
+
+    endpoint: str
+    token: str
+    group_name: str
+    images_dataset_id: str
+    gt_dataset_id: str
+    labels_dataset_id: str
+    predictions_dataset_id: str
+    prediction_name: str
+    setting_file_name: str
+
+
+@dataclass
+class DetectionEvalConfig(DataClassJsonMixin):
+    """Dataclass for config of evaluation."""
+
+    images_dir: str  # 评测集图片文件夹路径
+    gt: str  # gt数据文件路径
+    prediction: str  # 评测预测结果文件路径
+    setting: str  # 评测配置
+
+
+@dataclass
+class SemanticSegmentationEvalConfig(DataClassJsonMixin):
+    """Dataclass for config of evaluation."""
+
+    images_dir: str  # 评测原图文件夹
+    labels_dir: str  # gt图片文件夹
+    prediction_dir: str  # 评测预测图片文件夹
+    setting: str  # 评测配置
+    images_json: str  # 评测原图json文件
+
+
+@dataclass
+class EvalResult(DataClassJsonMixin):
+    """Dataclass for result of evaluation."""
+
+    summary: dict  # 评测结果summary类型数据 ap ar
+    tables: List[Table]  # 评测结果table类型数据
+    plots: List[dict]  # 评测结果plot类型数据
+    images: List[Image]  # 评测结果images类型数据
+
+
+class BaseEvaluation:
+    # 初始化
+    def __init__(self, run_config: RunConfig):
+        self.run_config = run_config
+        self.client = AIDIClient(endpoint=run_config.endpoint)
+
+    def get_data(self, dataset_id_info: str, file_type: str) -> str:
+        dataset_version = dataset_id_info.split("://")[0]
+        dataset_id = int(dataset_id_info.split("://")[1])
+        if dataset_version == "dataset":
+            dataset_interface = self.client.dataset.load(dataset_id)
+            data_path_list = dataset_interface.file_list(download=True)
+            if file_type == "gt" and len(data_path_list):
+                gt_path = data_path_list[0]
+                return gt_path
+            elif file_type == "images_dir" and len(data_path_list):
+                dir_name = os.path.dirname(data_path_list[0])
+                return dir_name
+            else:
+                raise NotImplementedError
+        else:
+            raise ValueError("dataset version not supported")
+
+    # detection前处理操作，从data获取评测集、预测结果、评测配置等信息
+    def detection_preprocess(self) -> DetectionEvalConfig:
+        # 从aidi-data获取gt
+        images_dir = self.get_data(
+            self.run_config.images_dataset_id, "images_dir"
+        )
+        gt_path = self.get_data(self.run_config.gt_dataset_id, "gt")
+
+        self.client.experiment.init_group(self.run_config.group_name)
+
+        # 从实验管理artifact获取pr结果
+        pr_name = self.run_config.prediction_name.split("/")[0]
+        file_name = self.run_config.prediction_name.split("/")[1]
+        pr_file = self.client.experiment.use_artifact(name=pr_name).get_file(
+            name=file_name
+        )
+
+        # 评测setting数据上传
+        with open(self.run_config.setting_file_name, "r") as fid:
+            cfg_dict = yaml.load(fid, Loader=yaml.Loader)
+        self.client.experiment.log_config(cfg_dict)
+
+        return DetectionEvalConfig(
+            images_dir=images_dir,
+            gt=gt_path,
+            prediction=pr_file,
+            setting=self.run_config.setting_file_name,
+        )
+
+    # semantic_segmentation前处理操作，从data获取评测集、预测结果、评测配置等信息
+    def semantic_segmentation_preprocess(
+        self,
+    ) -> SemanticSegmentationEvalConfig:
+        # 从aidi-data获取images、labels、prediction
+        images_dir = self.get_data(
+            self.run_config.images_dataset_id, "images_dir"
+        )
+        labels_dir = self.get_data(
+            self.run_config.labels_dataset_id, "images_dir"
+        )
+        prediction_dir = self.get_data(
+            self.run_config.predictions_dataset_id, "images_dir"
+        )
+        if self.run_config.gt_dataset_id:
+            images_json_file = self.get_data(
+                self.run_config.gt_dataset_id, "images_dir"
+            )
+        else:
+            images_json_file = None
+
+        self.client.experiment.init_group(self.run_config.group_name)
+
+        # 评测setting数据上传
+        with open(self.run_config.setting_file_name, "r") as fid:
+            cfg_dict = yaml.load(fid, Loader=yaml.Loader)
+        self.client.experiment.log_config(cfg_dict)
+
+        return SemanticSegmentationEvalConfig(
+            images_dir=images_dir,
+            labels_dir=labels_dir,
+            prediction_dir=prediction_dir,
+            setting=self.run_config.setting_file_name,
+            images_json=images_json_file,
+        )
+
+    # 评测操作���从preprocess获取的信息进行评测
+    # 该方法需要具体实现类根据自身情况进行重写
+    def evaluate(
+        self,
+        eval_config: Union[
+            DetectionEvalConfig, SemanticSegmentationEvalConfig
+        ],
+    ) -> EvalResult:
+        # 评测操作
+        raise NotImplementedError
+
+    # 后处理操作，从evaluate获取的信息进行后处理
+    def postprocess(self, eval_result: EvalResult) -> None:
+        self.client.experiment.log_summary(eval_result.summary)
+        if eval_result.tables is not None:
+            for table in eval_result.tables:
+                self.client.experiment.log_table(table)
+        if eval_result.plots is not None:
+            for plot in eval_result.plots:
+                self.client.experiment.log_plot(
+                    plot["Table"].name, plot["Table"], plot["Line"]
+                )
+        if eval_result.images is not None:
+            for image in eval_result.images:
+                self.client.experiment.log_image(image)

job (2)/code/detection3d.py

@@ -0,0 +1,191 @@
+import json
+import math
+import os
+import shutil
+from code.base import BaseEvaluation, DetectionEvalConfig, EvalResult
+from typing import List
+
+import cv2
+import numpy as np
+from hat.evaluation.detection3d import evaluate
+from hat.visualize.detection3d.draw_samples import (
+    draw_sample,
+    list_failure_samples,
+)
+
+from aidisdk.experiment import Image, Line, Table
+
+
+def generate_plot(file_name: str) -> List[dict]:
+    plots = []
+    results = json.load(open(file_name, "rb"))
+    recall = results["recall"]
+    precision = results["precision"]
+    fppi = results["fppi"]
+    tab1_data = []
+    for idx, _item in enumerate(recall):
+        data_dict = {"recall": recall[idx], "precision": precision[idx]}
+        tab1_data.append(data_dict)
+    tab2_data = []
+    for idx, _item in enumerate(fppi):
+        data_dict = {"fppi": fppi[idx], "recall": recall[idx]}
+        tab2_data.append(data_dict)
+    table1 = Table(
+        name="recall_vs_precision-{}".format(
+            file_name.split("/")[-1].split(".")[0]
+        ),
+        columns=["recall", "precision"],
+        data=tab1_data,
+    )
+    table2 = Table(
+        name="fppi_vs_recall-{}".format(
+            file_name.split("/")[-1].split(".")[0]
+        ),
+        columns=["fppi", "recall"],
+        data=tab2_data,
+    )
+    plot1 = {
+        "Table": table1,
+        "Line": Line(x="recall", y="precision", stroke="recall-precision"),
+    }
+    plot2 = {
+        "Table": table2,
+        "Line": Line(x="fppi", y="recall", stroke="fppi-recall"),
+    }
+    plots.append(plot1)
+    plots.append(plot2)
+    return plots
+
+
+class Detection3dEval(BaseEvaluation):
+    def __init__(self, run_config):
+        super().__init__(run_config)
+
+    def preprocess(self) -> DetectionEvalConfig:
+        return super().detection_preprocess()
+
+    def evaluate(self, eval_config: DetectionEvalConfig) -> EvalResult:
+        if os.path.exists("outputs"):
+            shutil.rmtree("outputs")
+        os.makedirs("outputs", exist_ok=True)
+        results = evaluate(
+            eval_config.gt,
+            eval_config.prediction,
+            eval_config.setting,
+            "outputs",
+        )
+        summary = {}
+        for result in results:
+            for key, item in result.items():
+                val = item
+                summary[key] = val
+        tables = []
+        results = json.load(open("outputs/tables.json", "rb"))
+        for result in results:
+            data = []
+            for dict_data in result["data"]:
+                new_dict_data = {}
+                for k, v in dict_data.items():
+                    if type(v) == float and math.isnan(v):
+                        v = "nan"
+                    if type(v) in [list, tuple, set]:
+                        v = str(v)
+                    new_dict_data[k] = v
+                data.append(new_dict_data)
+            table = Table(
+                name=result["name"],
+                columns=result["header"],
+                data=data,
+            )
+            tables.append(table)
+        plots = []
+        if os.path.exists("outputs/result.json"):
+            plots_1 = generate_plot("outputs/result.json")
+            plots.extend(plots_1)
+        if os.path.exists("outputs/result_auto.json"):
+            plots_2 = generate_plot("outputs/result_auto.json")
+            plots.extend(plots_2)
+        images = []
+        samples = list_failure_samples(open("outputs/all.json", "rb"))
+        if os.path.exists("outputs/samples"):
+            shutil.rmtree("outputs/samples")
+        os.makedirs("outputs/samples", exist_ok=True)
+        for sample in samples:
+            fp_score = max(
+                [
+                    det["score"]
+                    for det in list(
+                        filter(
+                            lambda det: det["eval_type"] == "FP"
+                            or det["eval_type"] == "ignore",
+                            sample["det_bboxes"],
+                        )
+                    )
+                ]
+                + [-1]
+            )
+            tp_score = max(
+                [
+                    det["score"]
+                    for det in list(
+                        filter(
+                            lambda det: det["eval_type"] == "TP",
+                            sample["det_bboxes"],
+                        )
+                    )
+                ]
+                + [-1]
+            )
+            tp_drot = max(
+                [
+                    det["metrics"]["drot"]
+                    for det in list(
+                        filter(
+                            lambda det: det["eval_type"] == "TP",
+                            sample["det_bboxes"],
+                        )
+                    )
+                ]
+                + [-1]
+            )
+            tp_dxy = max(
+                [
+                    det["metrics"]["dxy"]
+                    for det in list(
+                        filter(
+                            lambda det: det["eval_type"] == "TP",
+                            sample["det_bboxes"],
+                        )
+                    )
+                ]
+                + [-1]
+            )
+            image_name = sample["image_key"]
+            image_file_path = os.path.join(eval_config.images_dir, image_name)
+            output_file_path = os.path.join("outputs/samples/", image_name)
+            if os.path.exists(image_file_path):
+                with open(image_file_path, "rb") as image_file:
+                    image_content = image_file.read()
+                npar = np.fromstring(image_content, dtype="uint8")
+                image = cv2.imdecode(npar, 1)
+                image = draw_sample(image, sample)
+                cv2.imwrite(output_file_path, image)
+                image = Image(
+                    image_name,
+                    attrs={
+                        "fp_score": fp_score,
+                        "tp_score": tp_score,
+                        "tp_drot": tp_drot,
+                        "tp_dxy": tp_dxy,
+                    },
+                )
+                image.add_slice(data_or_path=output_file_path)
+                images.append(image)
+
+        eval_result = EvalResult(
+            summary=summary,
+            tables=tables,
+            plots=plots,
+            images=images,
+        )
+        return eval_result

job (2)/code/eval_handler.py

@@ -0,0 +1,35 @@
+from code.base import BaseEvaluation, RunConfig
+
+
+class EvaluationHandler:
+    def __init__(
+        self,
+        endpoint: str,
+        token: str,
+        group_name: str,
+        images_dataset_id: str,
+        gt_dataset_id: str,
+        labels_dataset_id: str,
+        predictions_dataset_id: str,
+        prediction_name: str,
+        setting_file_name: str,
+        eval_class: BaseEvaluation,
+    ):
+        self.run_config = RunConfig(
+            endpoint=endpoint,
+            token=token,
+            group_name=group_name,
+            images_dataset_id=images_dataset_id,
+            gt_dataset_id=gt_dataset_id,
+            labels_dataset_id=labels_dataset_id,
+            predictions_dataset_id=predictions_dataset_id,
+            prediction_name=prediction_name,
+            setting_file_name=setting_file_name,
+        )
+        self.eval_class = eval_class
+
+    def execute(self):
+        evaluation = self.eval_class(self.run_config)
+        eval_config = evaluation.preprocess()
+        eval_result = evaluation.evaluate(eval_config)
+        evaluation.postprocess(eval_result)

job (2)/code/semantic_segmentation.py

@@ -0,0 +1,404 @@
+import json
+import math
+import os
+import shutil
+from code.base import (
+    BaseEvaluation,
+    EvalResult,
+    SemanticSegmentationEvalConfig,
+)
+
+import cv2
+import numpy as np
+import yaml
+from hat.evaluation.semantic_segmentation import evaluate
+from hat.visualize.semantic_segmentation import (
+    base64_to_img,
+    plot_cm,
+    render_image,
+)
+from PIL import Image as Img
+
+from aidisdk.experiment import Image, Table
+
+
+class SemanticSegmentationEval(BaseEvaluation):
+    def __init__(self, run_config):
+        super().__init__(run_config)
+
+    def preprocess(self) -> SemanticSegmentationEvalConfig:
+        return super().semantic_segmentation_preprocess()
+
+    def evaluate(
+        self, eval_config: SemanticSegmentationEvalConfig
+    ) -> EvalResult:
+        if os.path.exists("outputs"):
+            shutil.rmtree("outputs")
+        os.makedirs("outputs", exist_ok=True)
+        if eval_config.images_json:
+            results = evaluate(
+                images_dir=eval_config.images_dir,
+                gt_dir=eval_config.labels_dir,
+                pred_dir=eval_config.prediction_dir,
+                config_file=eval_config.setting,
+                outfile_dir="outputs",
+                images_json=eval_config.images_json,
+            )
+        else:
+            results = evaluate(
+                images_dir=eval_config.images_dir,
+                gt_dir=eval_config.labels_dir,
+                pred_dir=eval_config.prediction_dir,
+                config_file=eval_config.setting,
+                outfile_dir="outputs",
+            )
+
+        summary = {}
+        for result in results:
+            summary[result["name"]] = result["value"]
+
+        tables = []
+        analysis_images = []
+        result = json.load(open("outputs/all.json", "rb"))
+        if result.get("number") is None:
+            table1 = Table(
+                name="all",
+                columns=["mean_iou", "mean_acc"],
+                data=[
+                    {
+                        "mean_iou": "%.4f" % result["mean_iou"],
+                        "mean_acc": "%.4f" % result["mean_acc"],
+                    }
+                ],
+            )
+            tables.append(table1)
+            data = []
+            for classinfo in result["classes"]:
+                new_dict_data = {
+                    "name": "%s" % classinfo["name"],
+                    "iou": "%.4f" % classinfo["iou"],
+                    "acc": "%.4f" % classinfo["acc"],
+                }
+                data.append(new_dict_data)
+            table2 = Table(
+                name="classes",
+                columns=["name", "iou", "acc"],
+                data=data,
+            )
+            tables.append(table2)
+        else:
+            table1 = Table(
+                name="all",
+                columns=["mean_iou", "mean_acc", "number"],
+                data=[
+                    {
+                        "mean_iou": "%.4f" % result["mean_iou"],
+                        "mean_acc": "%.4f" % result["mean_acc"],
+                        "number": "%s" % result["number"],
+                    }
+                ],
+            )
+            tables.append(table1)
+            data = []
+            for classinfo in result["classes"]:
+                new_dict_data = {
+                    "name": "%s" % classinfo["name"],
+                    "number": "%s" % classinfo["number"],
+                    "iou": "%.4f" % classinfo["iou"],
+                    "acc": "%.4f" % classinfo["acc"],
+                }
+                data.append(new_dict_data)
+            table2 = Table(
+                name="classes",
+                columns=["name", "number", "iou", "acc"],
+                data=data,
+            )
+            tables.append(table2)
+        if "freespace" in result.keys():
+            table3 = Table(
+                name="freespace",
+                columns=["mean_iou_freespace", "mean_acc_freespace"],
+                data=[
+                    {
+                        "mean_iou_freespace": "%.4f"
+                        % result["freespace"]["mean_iou_freespace"]
+                        if "mean_iou_freespace" in result["freespace"].keys()
+                        else result["freespace"]["freespace_iou"],
+                        "mean_acc_freespace": "%.4f"
+                        % result["freespace"]["mean_acc_freespace"]
+                        if "mean_acc_freespace" in result["freespace"].keys()
+                        else result["freespace"]["freespace_acc"],
+                    }
+                ],
+            )
+            tables.append(table3)
+            if "margin_res" in result["freespace"].keys():
+                arr_acc = list()
+                arr_precison_05 = list()
+                arr_precison_07 = list()
+                margin_classes = [" "]
+                for idx, margin in enumerate(
+                    result["freespace"]["margin_res"]
+                ):
+                    margin_class = list(margin.keys())[0]
+                    margin_classes = margin_classes + [margin_class]
+                    arr_acc.append(
+                        result["freespace"]["margin_res"][idx][margin_class][
+                            "acc"
+                        ]
+                    )
+                    arr_precison_05.append(
+                        result["freespace"]["margin_res"][idx][margin_class][
+                            "precison_0.5"
+                        ]
+                    )
+                    arr_precison_07.append(
+                        result["freespace"]["margin_res"][idx][margin_class][
+                            "precison_0.7"
+                        ]
+                    )
+                acc_list = ["acc"] + arr_acc
+                p_05_list = ["precison_0.5"] + arr_precison_05
+                p_07_list = ["precison_0.7"] + arr_precison_07
+
+                data = []
+                new_dict_data1 = {}
+                for idx, value in enumerate(acc_list):
+                    new_dict_data1[margin_classes[idx]] = value
+                data.append(new_dict_data1)
+                new_dict_data2 = {}
+                for idx, value in enumerate(p_05_list):
+                    new_dict_data2[margin_classes[idx]] = value
+                data.append(new_dict_data2)
+                new_dict_data3 = {}
+                for idx, value in enumerate(p_07_list):
+                    new_dict_data3[margin_classes[idx]] = value
+                data.append(new_dict_data3)
+                table4 = Table(
+                    name="freespace margin res",
+                    columns=margin_classes,
+                    data=data,
+                )
+                tables.append(table4)
+            if "margin_result" in result["freespace"].keys():
+                arr_acc = list()
+                arr_precison_05 = list()
+                arr_precison_07 = list()
+                margin_classes = [" "]
+                for idx, margin in enumerate(
+                    result["freespace"]["margin_result"]
+                ):
+                    margin_class = list(margin.keys())[0]
+                    margin_classes = margin_classes + [margin_class]
+                    arr_acc.append(
+                        result["freespace"]["margin_result"][idx][
+                            margin_class
+                        ]["acc_point"]
+                    )
+                    arr_precison_05.append(
+                        result["freespace"]["margin_result"][idx][
+                            margin_class
+                        ]["precison_0.5_image"]
+                    )
+                    arr_precison_07.append(
+                        result["freespace"]["margin_result"][idx][
+                            margin_class
+                        ]["precison_0.7_image"]
+                    )
+                acc_list = ["acc_point"] + arr_acc
+                p_05_list = ["precison_0.5_image"] + arr_precison_05
+                p_07_list = ["precison_0.7_image"] + arr_precison_07
+
+                data = []
+                new_dict_data1 = {}
+                for idx, value in enumerate(acc_list):
+                    new_dict_data1[margin_classes[idx]] = value
+                data.append(new_dict_data1)
+                new_dict_data2 = {}
+                for idx, value in enumerate(p_05_list):
+                    new_dict_data2[margin_classes[idx]] = value
+                data.append(new_dict_data2)
+                new_dict_data3 = {}
+                for idx, value in enumerate(p_07_list):
+                    new_dict_data3[margin_classes[idx]] = value
+                data.append(new_dict_data3)
+                table4 = Table(
+                    name="freespace margin res",
+                    columns=margin_classes,
+                    data=data,
+                )
+                tables.append(table4)
+            for key in result["freespace"].keys():
+                if key.startswith("categories_result_vertical"):
+                    titles = [
+                        " ",
+                        "precision",
+                        "recall",
+                        "F1",
+                        "num_gt",
+                        "num_pred",
+                    ]
+                    data = []
+                    for item in result["freespace"][key]:
+                        new_dict_data = {}
+                        for idx, _value in enumerate(titles):
+                            if idx == 0:
+                                new_dict_data[titles[idx]] = list(item.keys())[
+                                    0
+                                ]
+                            else:
+                                v = item[list(item.keys())[0]][titles[idx]]
+                                if type(v) == float and math.isnan(v):
+                                    v = "nan"
+                                new_dict_data[titles[idx]] = v
+                        data.append(new_dict_data)
+                    table5 = Table(
+                        name=key,
+                        columns=titles,
+                        data=data,
+                    )
+                    tables.append(table5)
+            if "freespace_distance_statistic" in result["freespace"].keys():
+                titles = [
+                    " ",
+                    "max",
+                    "min",
+                    "mean",
+                    "var",
+                    "std",
+                    "90 percentile",
+                    "95 percentile",
+                    "98 percentile",
+                ]
+                data = []
+                for item in result["freespace"][
+                    "freespace_distance_statistic"
+                ]:
+                    new_dict_data = {}
+                    for idx, _value in enumerate(titles):
+                        if idx == 0:
+                            new_dict_data[titles[idx]] = list(item.keys())[0]
+                        else:
+                            v = item[list(item.keys())[0]][titles[idx]]
+                            if type(v) == float and math.isnan(v):
+                                v = "nan"
+                            new_dict_data[titles[idx]] = v
+                    data.append(new_dict_data)
+                table6 = Table(
+                    name="freespace_distance_statistic",
+                    columns=titles,
+                    data=data,
+                )
+                tables.append(table6)
+
+        classnames = [classinfo["name"] for classinfo in result["classes"]]
+        confusion_matrix = np.array(result["confusion_matrix"])
+        confusion_matrix_img_io = plot_cm(classnames, confusion_matrix)
+        confusion_matrix_img = Img.open(confusion_matrix_img_io)
+        image_name = "outputs/confusion_matrix.png"
+        confusion_matrix_img.save(image_name)
+        image = Image(image_name)
+        image.add_slice(data_or_path=image_name)
+        analysis_images.append(image)
+
+        samples = []
+        if os.path.exists("outputs/samples"):
+            shutil.rmtree("outputs/samples")
+        os.makedirs("outputs/samples", exist_ok=True)
+        with open(self.run_config.setting_file_name, "r") as fid:
+            config_dict = yaml.load(fid, Loader=yaml.FullLoader)
+        add_vis_info = config_dict.get("add_vis", None)
+        labels_info = json.load(open("outputs/labels.json", "rb"))["labels"]
+        eval_file_io = open("outputs/images.json", "rb")
+        for line in eval_file_io:
+            sample_eval = json.loads(line)
+            image_name = sample_eval["image_name"]
+            image_file_path = os.path.join(eval_config.images_dir, image_name)
+            with open(image_file_path.encode("utf-8"), "rb") as f:
+                image_content = f.read()
+            npar = np.frombuffer(image_content, dtype="uint8")
+            image = cv2.imdecode(npar, 1)
+            if "b64_diff" in sample_eval:
+                image_map = (
+                    base64_to_img(sample_eval["b64_image"])
+                    if sample_eval.get("b64_image", None) is not None
+                    else None
+                )
+                diff_map = base64_to_img(sample_eval["b64_diff"])
+                gt_label_map = base64_to_img(sample_eval["b64_gt_label"])
+                pred_label_map = base64_to_img(sample_eval["b64_pred_label"])
+            else:
+                image_npar = np.frombuffer(
+                    open(
+                        os.path.join("outputs/res_image", image_name), "rb"
+                    ).read(),
+                    dtype=np.uint8,
+                )
+                diff_npar = np.frombuffer(
+                    open(
+                        os.path.join(
+                            "outputs/res_diff",
+                            os.path.splitext(image_name)[0] + ".png",
+                        ),
+                        "rb",
+                    ).read(),
+                    dtype=np.uint8,
+                )
+                gt_label_npar = np.frombuffer(
+                    open(
+                        os.path.join(
+                            "outputs/res_gt_label",
+                            os.path.splitext(image_name)[0] + ".png",
+                        ),
+                        "rb",
+                    ).read(),
+                    dtype=np.uint8,
+                )
+                pred_label_npar = np.frombuffer(
+                    open(
+                        os.path.join(
+                            "outputs/res_pred_label",
+                            os.path.splitext(image_name)[0] + ".png",
+                        ),
+                        "rb",
+                    ).read(),
+                    dtype=np.uint8,
+                )
+                image_map = cv2.imdecode(image_npar, cv2.IMREAD_UNCHANGED)
+                diff_map = cv2.imdecode(diff_npar, cv2.IMREAD_UNCHANGED)
+                gt_label_map = cv2.imdecode(
+                    gt_label_npar, cv2.IMREAD_UNCHANGED
+                )
+                pred_label_map = cv2.imdecode(
+                    pred_label_npar, cv2.IMREAD_UNCHANGED
+                )
+
+            gt_freespace_line, pred_freespace_line = sample_eval.get(
+                "gt_freespace_line"
+            ), sample_eval.get("pred_freespace_line")
+
+            rendered_image = render_image(
+                image,
+                diff_map,
+                gt_label_map,
+                pred_label_map,
+                gt_freespace_line,
+                pred_freespace_line,
+                labels_info,
+                image_map,
+                add_vis_info=add_vis_info,
+            )
+
+            output_file_path = os.path.join("outputs/samples/", image_name)
+            cv2.imwrite(output_file_path, rendered_image)
+            image = Image(image_name)
+            image.add_slice(data_or_path=output_file_path)
+            samples.append(image)
+        images = samples + analysis_images
+
+        return EvalResult(
+            summary=summary,
+            tables=tables,
+            plots=None,
+            images=images,
+        )

job (2)/inputs/3d_prediction.json

@@ -0,0 +1,96 @@
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+{"image_key": "1668164613735__rear_left__1.jpg", "vehicle": [{"attrs": {}, "depth": 7.666119575500488, "dimensions": [3.855005979537964, 2.7959840297698975, 15.889264106750488], "rotation_y": -2.4040806048438155, "location": [-3.4325008232537324, -0.04175521777828317, 7.666119575500488], "score": 0.8529712909059839, "bbox_2d": [77.35858154296875, 24.257049560546875, 1696.725830078125, 869.9302978515625]}, {"attrs": {}, "depth": 28.775474548339844, "dimensions": [1.6749573945999146, 1.8495125770568848, 4.310815811157227], "rotation_y": 0.7447214962361721, "location": [-26.369316292019743, -1.6358232755869753, 28.775474548339844], "score": 0.20119890257525608, "bbox_2d": [95.54200744628906, 538.8551025390625, 155.69517517089844, 593.1007690429688]}, {"attrs": {}, "depth": 34.31995391845703, "dimensions": [1.6938191652297974, 1.8464328050613403, 4.380648136138916], "rotation_y": 0.7535004393760282, "location": [-33.53325607995741, -2.3173898712026286, 34.31995391845703], "score": 0.015424738997777965, "bbox_2d": [69.15289306640625, 538.6925659179688, 107.64305114746094, 581.5032958984375]}]}
+{"image_key": "1666864333329__rear_left__1.jpg", "vehicle": [{"attrs": {}, "depth": 7.228184223175049, "dimensions": [1.5465408563613892, 1.7306747436523438, 4.313336372375488], "rotation_y": 2.24484062259956, "location": [4.23914373090878, 0.31027206923797107, 7.228184223175049], "score": 0.15411970126171326, "bbox_2d": [1404.070556640625, 463.92901611328125, 1746.820068359375, 712.5178833007812]}]}

job (2)/inputs/3d_setting.yaml

@@ -0,0 +1,62 @@
+eval_type:
+- NuScenes: '~'
+  iou_threshold: 0.5
+  enable_ignore: true
+  eval_class: vehicle
+  target_recalls:
+  - 0.5
+  - 0.6
+  - 0.7
+  target_precisions:
+  - 0.7
+  - 0.8
+  - 0.9
+  eval_bbox_type: all
+  horizon_truncate_offset: 1
+  max_depth: 30
+  image_size:
+  - 1920
+  - 1280
+  eval_cameras:
+  - __front_right__
+  - __rear_right__
+  - __front_left__
+  - __rear_left__
+- Auto: '~'
+  eval_class: vehicle
+  iou_thresh: 0.5
+  target_recalls:
+  - 0.8
+  - 0.85
+  - 0.9
+  - 0.95
+  eval_bbox_type: all
+  enable_ignore: true
+  apply_dist_coeff: true
+  proj_z_thresh: 1.5
+  lidar_error: true
+  y_thresh:
+  - -9
+  - -6
+  - -3
+  - 0
+  - 3
+  - 6
+  - 9
+  gt_dist_thresh: 30
+  eval_cameras:
+  - __front_right__
+  - __rear_right__
+  - __front_left__
+  - __rear_left__
+  clip_bbox_with_img_size:
+  - 1920
+  - 1280
+  dep_thresh:
+  - -30
+  - -20
+  - -10
+  - 0
+  - 10
+  - 20
+  - 30

job (2)/inputs/ss_setting.yaml

@@ -0,0 +1,70 @@
+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]

job (2)/local_example.py

@@ -0,0 +1,92 @@
+import argparse
+from code.detection3d import Detection3dEval
+from code.eval_handler import EvaluationHandler
+from code.semantic_segmentation import SemanticSegmentationEval
+
+from aidisdk import AIDIClient
+
+
+def process(args):
+    task_type = args.task_type
+    endpoint = args.endpoint
+    token = args.token
+    experiment_name = args.experiment_name
+    group_name = args.group_name
+    run_name = args.run_name
+    if args.images_dataset_id:
+        images_dataset_id = args.images_dataset_id
+    else:
+        images_dataset_id = None
+    if args.labels_dataset_id:
+        labels_dataset_id = args.labels_dataset_id
+    else:
+        labels_dataset_id = None
+    if args.predictions_dataset_id:
+        predictions_dataset_id = args.predictions_dataset_id
+    else:
+        predictions_dataset_id = None
+    if args.gt_dataset_id:
+        gt_dataset_id = args.gt_dataset_id
+    else:
+        gt_dataset_id = None
+    prediction_name = args.prediction_name
+    setting_file_name = args.setting_file_name
+
+    if task_type == "Detection_3D":
+        eval_class = Detection3dEval
+    elif task_type == "Semantic_Segmentation":
+        eval_class = SemanticSegmentationEval
+    else:
+        raise NotImplementedError
+
+    client = AIDIClient(token=token, endpoint=endpoint)
+
+    # 任务发起阶段，支持发起一个本地任务和艾迪平台dag任；
+    # 本文档举例本地是如何结合实验管理来发起；
+
+    # 开始初始化一个实验run
+    # run_name是必填的，可以填写已经存在的或者不存在的run name；
+    # 当填写的是不存在的run name时，会自动创建；
+
+    with client.experiment.init(
+        experiment_name=experiment_name, run_name=run_name, enabled=True
+    ) as run:
+        # 进行一次上报，runtime为默认值"local"即可
+        # config_file可以填写当前任务的配置文件，会自动上传并记录
+        # 除了runtime和config_file，其他参数均为用户自定义上报内容
+        # 此处举例上报aidisdk版本
+        run.log_runtime(
+            runtime="local",
+            horizon_hat="1.3.1",
+        )
+
+    EvaluationHandler(
+        endpoint,
+        token,
+        group_name,
+        images_dataset_id,
+        gt_dataset_id,
+        labels_dataset_id,
+        predictions_dataset_id,
+        prediction_name,
+        setting_file_name,
+        eval_class,
+    ).execute()
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--task_type", type=str)
+    parser.add_argument("--endpoint", type=str)
+    parser.add_argument("--token", type=str)
+    parser.add_argument("--group_name", type=str)
+    parser.add_argument("--experiment_name", type=str)
+    parser.add_argument("--run_name", type=str)
+    parser.add_argument("--images_dataset_id", type=str)
+    parser.add_argument("--gt_dataset_id", type=str)
+    parser.add_argument("--labels_dataset_id", type=str)
+    parser.add_argument("--predictions_dataset_id", type=str)
+    parser.add_argument("--prediction_name", type=str)  # pr_name/file_name
+    parser.add_argument("--setting_file_name", type=str)
+    args = parser.parse_args()
+    process(args)

job (2)/setting_example.yaml

@@ -0,0 +1,62 @@
+eval_type:
+- NuScenes: '~'
+  iou_threshold: 0.5
+  enable_ignore: true
+  eval_class: vehicle
+  target_recalls:
+  - 0.5
+  - 0.6
+  - 0.7
+  target_precisions:
+  - 0.7
+  - 0.8
+  - 0.9
+  eval_bbox_type: all
+  horizon_truncate_offset: 1
+  max_depth: 30
+  image_size:
+  - 1920
+  - 1280
+  eval_cameras:
+  - __front_right__
+  - __rear_right__
+  - __front_left__
+  - __rear_left__
+- Auto: '~'
+  eval_class: vehicle
+  iou_thresh: 0.5
+  target_recalls:
+  - 0.8
+  - 0.85
+  - 0.9
+  - 0.95
+  eval_bbox_type: all
+  enable_ignore: true
+  apply_dist_coeff: true
+  proj_z_thresh: 1.5
+  lidar_error: true
+  y_thresh:
+  - -9
+  - -6
+  - -3
+  - 0
+  - 3
+  - 6
+  - 9
+  gt_dist_thresh: 30
+  eval_cameras:
+  - __front_right__
+  - __rear_right__
+  - __front_left__
+  - __rear_left__
+  clip_bbox_with_img_size:
+  - 1920
+  - 1280
+  dep_thresh:
+  - -30
+  - -20
+  - -10
+  - 0
+  - 10
+  - 20
+  - 30