mmdet3d_plugin/datasets/eval_utils/nuplan_eval.py

import argparse
import copy
import json
import os
import random
import time
from typing import Any, Dict, List, Tuple

import cv2
import numpy as np
from matplotlib import pyplot as plt
from nuscenes.eval.common.data_classes import EvalBox, EvalBoxes
from nuscenes.eval.common.render import setup_axis
from nuscenes.eval.detection.algo import accumulate, calc_ap, calc_tp
from nuscenes.eval.detection.constants import (DETECTION_COLORS,
                                               DETECTION_NAMES,
                                               PRETTY_DETECTION_NAMES,
                                               PRETTY_TP_METRICS, TP_METRICS,
                                               TP_METRICS_UNITS)
from nuscenes.eval.detection.data_classes import (DetectionBox,
                                                  DetectionConfig,
                                                  DetectionMetricData,
                                                  DetectionMetricDataList,
                                                  DetectionMetrics)
from nuscenes.eval.detection.evaluate import NuScenesEval
from nuscenes.eval.detection.render import (class_pr_curve, dist_pr_curve,
                                            summary_plot, visualize_sample)

from pyquaternion import Quaternion


Axis = Any

class CustomDetectionBox(DetectionBox):
    """ Data class used during detection evaluation. Can be a prediction or ground truth."""

    def __init__(self,
                 sample_token: str = "",
                 translation: Tuple[float, float, float] = (0, 0, 0),
                 size: Tuple[float, float, float] = (0, 0, 0),
                 rotation: Tuple[float, float, float, float] = (0, 0, 0, 0),
                 velocity: Tuple[float, float] = (0, 0),
                 ego_translation: Tuple[float, float, float] = (0, 0, 0),  # Translation to ego vehicle in meters.
                 num_pts: int = -1,  # Nbr. LIDAR or RADAR inside the box. Only for gt boxes.
                 detection_name: str = 'car',  # The class name used in the detection challenge.
                 detection_score: float = -1.0,  # GT samples do not have a score.
                 attribute_name: str = ''):  # Box attribute. Each box can have at most 1 attribute.

        super(DetectionBox, self).__init__(sample_token, translation, size, rotation, velocity, ego_translation, num_pts)

        assert type(detection_score) == float, 'Error: detection_score must be a float!'
        assert not np.any(np.isnan(detection_score)), 'Error: detection_score may not be NaN!'

        # Assign.
        self.detection_name = detection_name
        self.detection_score = detection_score
        self.attribute_name = attribute_name


class CustomDetectionConfig(DetectionConfig):

    def __init__(self,
                 class_range: Dict[str, int],
                 dist_fcn: str,
                 dist_ths: List[float],
                 dist_th_tp: float,
                 min_recall: float,
                 min_precision: float,
                 max_boxes_per_sample: int,
                 mean_ap_weight: int):

        # assert set(class_range.keys()) == set(DETECTION_NAMES), "Class count mismatch."
        assert dist_th_tp in dist_ths, "dist_th_tp must be in set of dist_ths."

        self.class_range = class_range
        self.dist_fcn = dist_fcn
        self.dist_ths = dist_ths
        self.dist_th_tp = dist_th_tp
        self.min_recall = min_recall
        self.min_precision = min_precision
        self.max_boxes_per_sample = max_boxes_per_sample
        self.mean_ap_weight = mean_ap_weight

        self.class_names = list(self.class_range.keys())

def class_tp_curve(md_list: DetectionMetricDataList,
                   metrics: DetectionMetrics,
                   detection_name: str,
                   min_recall: float,
                   dist_th_tp: float,
                   savepath: str = None,
                   ax: Axis = None) -> None:
    """
    Plot the true positive curve for the specified class.
    :param md_list: DetectionMetricDataList instance.
    :param metrics: DetectionMetrics instance.
    :param detection_name:
    :param min_recall: Minimum recall value.
    :param dist_th_tp: The distance threshold used to determine matches.
    :param savepath: If given, saves the the rendering here instead of displaying.
    :param ax: Axes onto which to render.
    """
    # Get metric data for given detection class with tp distance threshold.

    md = md_list[(detection_name, dist_th_tp)]
    min_recall_ind = round(100 * min_recall)
    if min_recall_ind <= md.max_recall_ind:
        # For traffic_cone and barrier only a subset of the metrics are plotted.
        rel_metrics = [m for m in TP_METRICS if not np.isnan(metrics.get_label_tp(detection_name, m))]
        ylimit = max([max(getattr(md, metric)[min_recall_ind:md.max_recall_ind + 1]) for metric in rel_metrics]) * 1.1
    else:
        ylimit = 1.0

    # Prepare axis.
    if ax is None:
        ax = setup_axis(title=PRETTY_DETECTION_NAMES[detection_name], xlabel='Recall', ylabel='Error', xlim=1,
                        min_recall=min_recall)
    ax.set_ylim(0, ylimit)

    # Plot the recall vs. error curve for each tp metric.
    for metric in TP_METRICS:
        tp = metrics.get_label_tp(detection_name, metric)

        # Plot only if we have valid data.
        if tp is not np.nan and min_recall_ind <= md.max_recall_ind:
            recall, error = md.recall[:md.max_recall_ind + 1], getattr(md, metric)[:md.max_recall_ind + 1]
        else:
            recall, error = [], []

        # Change legend based on tp value
        if tp is np.nan:
            label = '{}: n/a'.format(PRETTY_TP_METRICS[metric])
        elif min_recall_ind > md.max_recall_ind:
            label = '{}: nan'.format(PRETTY_TP_METRICS[metric])
        else:
            label = '{}: {:.2f} ({})'.format(PRETTY_TP_METRICS[metric], tp, TP_METRICS_UNITS[metric])
        if metric == 'trans_err':
            label += f' ({md.max_recall_ind})'  # add recall
            print(f'Recall: {detection_name}: {md.max_recall_ind/100}')
        ax.plot(recall, error, label=label)
    ax.axvline(x=md.max_recall, linestyle='-.', color=(0, 0, 0, 0.3))
    ax.legend(loc='best')

    if savepath is not None:
        plt.savefig(savepath)
        plt.close()


class NuScenesEval_NuPlan(NuScenesEval):
    """
    Dummy class for backward-compatibility. Same as DetectionEval.
    """

    def __init__(self,
                 gt_boxes,
                 result_boxes,
                 config: DetectionConfig,
                 output_dir: str = None,
                 verbose: bool = True,
                 ):

        self.output_dir = output_dir
        self.verbose = verbose
        self.cfg = config

        # Make dirs.
        self.plot_dir = os.path.join(self.output_dir, 'plots')
        if not os.path.isdir(self.output_dir):
            os.makedirs(self.output_dir)
        if not os.path.isdir(self.plot_dir):
            os.makedirs(self.plot_dir)

        # Load data.
        if verbose:
            print('Initializing nuScenes detection evaluation')
        self.pred_boxes = result_boxes
        self.gt_boxes = gt_boxes

        assert set(self.pred_boxes.sample_tokens) == set(self.gt_boxes.sample_tokens), \
            "Samples in split doesn't match samples in predictions."

        self.all_gt = copy.deepcopy(self.gt_boxes)
        self.all_preds = copy.deepcopy(self.pred_boxes)
        self.sample_tokens = self.gt_boxes.sample_tokens

    def evaluate(self) -> Tuple[DetectionMetrics, DetectionMetricDataList]:
        """
        Performs the actual evaluation.
        :return: A tuple of high-level and the raw metric data.
        """
        start_time = time.time()

        # -----------------------------------
        # Step 1: Accumulate metric data for all classes and distance thresholds.
        # -----------------------------------
        if self.verbose:
            print('Accumulating metric data...')
        metric_data_list = DetectionMetricDataList()

        # print(self.cfg.dist_fcn_callable, self.cfg.dist_ths)
        # self.cfg.dist_ths = [0.3]
        # self.cfg.dist_fcn_callable
        for class_name in self.cfg.class_names:
            for dist_th in self.cfg.dist_ths:
                md = accumulate(self.gt_boxes, self.pred_boxes, class_name, self.cfg.dist_fcn_callable, dist_th)
                metric_data_list.set(class_name, dist_th, md)

        # -----------------------------------
        # Step 2: Calculate metrics from the data.
        # -----------------------------------
        if self.verbose:
            print('Calculating metrics...')
        metrics = DetectionMetrics(self.cfg)
        for class_name in self.cfg.class_names:
            # Compute APs.
            for dist_th in self.cfg.dist_ths:
                metric_data = metric_data_list[(class_name, dist_th)]
                ap = calc_ap(metric_data, self.cfg.min_recall, self.cfg.min_precision)
                metrics.add_label_ap(class_name, dist_th, ap)
            # Compute TP metrics.
            for metric_name in TP_METRICS:
                metric_data = metric_data_list[(class_name, self.cfg.dist_th_tp)]
                if class_name in ['traffic_cone'] and metric_name in ['attr_err', 'vel_err', 'orient_err']:
                    tp = np.nan
                elif class_name in ['barrier', 'czone_sign'] and metric_name in ['attr_err', 'vel_err']:
                    tp = np.nan
                else:
                    tp = calc_tp(metric_data, self.cfg.min_recall, metric_name)
                metrics.add_label_tp(class_name, metric_name, tp)

        # Compute evaluation time.
        metrics.add_runtime(time.time() - start_time)

        return metrics, metric_data_list

    def render(self, metrics: DetectionMetrics, md_list: DetectionMetricDataList) -> None:
        """
        Renders various PR and TP curves.
        :param metrics: DetectionMetrics instance.
        :param md_list: DetectionMetricDataList instance.
        """
        if self.verbose:
            print('Rendering PR and TP curves')

        def savepath(name):
            return os.path.join(self.plot_dir, name + '.pdf')

        summary_plot(md_list, metrics, min_precision=self.cfg.min_precision, min_recall=self.cfg.min_recall,
                     dist_th_tp=self.cfg.dist_th_tp, savepath=savepath('summary'))

        for detection_name in self.cfg.class_names:
            class_pr_curve(md_list, metrics, detection_name, self.cfg.min_precision, self.cfg.min_recall,
                           savepath=savepath(detection_name + '_pr'))

            class_tp_curve(md_list, metrics, detection_name, self.cfg.min_recall, self.cfg.dist_th_tp,
                           savepath=savepath(detection_name + '_tp'))

        for dist_th in self.cfg.dist_ths:
            dist_pr_curve(md_list, metrics, dist_th, self.cfg.min_precision, self.cfg.min_recall,
                          savepath=savepath('dist_pr_' + str(dist_th)))

    def main(self,
             plot_examples: int = 0,
             render_curves: bool = True) -> Dict[str, Any]:
        """
        Main function that loads the evaluation code, visualizes samples, runs the evaluation and renders stat plots.
        :param plot_examples: How many example visualizations to write to disk.
        :param render_curves: Whether to render PR and TP curves to disk.
        :return: A dict that stores the high-level metrics and meta data.
        """
        if plot_examples > 0:
            # Select a random but fixed subset to plot.
            random.seed(42)
            sample_tokens = list(self.sample_tokens)
            random.shuffle(sample_tokens)
            sample_tokens = sample_tokens[:plot_examples]

        # Run evaluation.
        metrics, metric_data_list = self.evaluate()

        # Render PR and TP curves.
        if render_curves:
            self.render(metrics, metric_data_list)

        # Dump the metric data, meta and metrics to disk.
        if self.verbose:
            print('Saving metrics to: %s' % self.output_dir)
        metrics_summary = metrics.serialize()
        metrics_summary['meta'] = {}
        with open(os.path.join(self.output_dir, 'metrics_summary.json'), 'w') as f:
            json.dump(metrics_summary, f, indent=2)
        with open(os.path.join(self.output_dir, 'metrics_details.json'), 'w') as f:
            json.dump(metric_data_list.serialize(), f, indent=2)

        # Print high-level metrics.
        print('mAP: %.4f' % (metrics_summary['mean_ap']))
        err_name_mapping = {
            'trans_err': 'mATE',
            'scale_err': 'mASE',
            'orient_err': 'mAOE',
            'vel_err': 'mAVE',
            'attr_err': 'mAAE'
        }
        for tp_name, tp_val in metrics_summary['tp_errors'].items():
            print('%s: %.4f' % (err_name_mapping[tp_name], tp_val))
        print('NDS: %.4f' % (metrics_summary['nd_score']))
        print('Eval time: %.1fs' % metrics_summary['eval_time'])

        # Print per-class metrics.
        print()
        print('Per-class results:')
        print('Object Class\tAP\tATE\tASE\tAOE\tAVE\tAAE')
        class_aps = metrics_summary['mean_dist_aps']
        class_tps = metrics_summary['label_tp_errors']
        for class_name in class_aps.keys():
            print('%s\t%.3f\t%.3f\t%.3f\t%.3f\t%.3f\t%.3f'
                  % (class_name, class_aps[class_name],
                     class_tps[class_name]['trans_err'],
                     class_tps[class_name]['scale_err'],
                     class_tps[class_name]['orient_err'],
                     class_tps[class_name]['vel_err'],
                     class_tps[class_name]['attr_err']))

        return metrics_summary