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runner.py
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from generate_confusion_matrix import GenerateConfusionMatrix
from confusion_matrix import ConfusionMatrix
import os
import numpy as np
from typing import Tuple, Dict, Any, List
from itertools import chain, combinations
from pdb import set_trace as st
import pickle as pkl
from pathlib import Path
from custom_env import dataset_root as dataroot
from mmdet3d.evaluation.metrics import nuscenes_metric as nus_metric
from custom_env import home_dir, cm_dir, repo_dir, output_dir, preds_dir, model_dir, is_set_to_mini
from nuscenes import NuScenes
from nuscenes.eval.common.config import config_factory
from nuscenes.eval.common.data_classes import EvalBoxes
# parameters to setup nuScenes
eval_set_map = {
'v1.0-mini': 'mini_val',
'v1.0-trainval': 'val',
'v1.0-test': 'test'
}
dataset_version = 'v1.0-mini' if is_set_to_mini() else 'v1.0-trainval'
try:
eval_version = 'detection_cvpr_2019'
eval_config = config_factory(eval_version)
except:
eval_version = 'cvpr_2019'
eval_config = config_factory(eval_version)
nusc = NuScenes(version=dataset_version, dataroot = dataroot)
list_of_classes = ["ped", "obs"]
PED = 0
OBS = 1
EMPTY = 2
labels = {0: "ped", 1: "obs", 2:"empty"}
conf_mat_mapping = {
"pedestrian": PED,
"bus": OBS,
"car" : OBS,
"truck": OBS,
"bicycle": OBS,
"motorcycle": OBS,
"traffic_cone": OBS
}
# conf_mat_mapping = {
# "pedestrian": PED,
# "bus": OBS,
# "car" : OBS,
# "truck": OBS,
# }
generator = GenerateConfusionMatrix(nusc=nusc,
config=eval_config,
result_path=f'{model_dir}/results_nusc.json',
eval_set=eval_set_map[dataset_version],
output_dir=os.getcwd(),
verbose=True,
conf_mat_mapping=conf_mat_mapping,
list_of_classes=list_of_classes,
distance_parametrized=True,
max_dist=100,
distance_bin=10
)
generator.set_debug(False)
# generator.set_list_of_classes(list_of_classes)
# generator.set_list_of_propositions()
compute_prop_cm = False
compute_class_cm = True
compute_prop_segmented_cm = False
save_prop_dict = False
list_of_propositions, prop_dict = generator.get_list_of_propositions()
confusion_matrix = ConfusionMatrix(generator, list_of_classes, list_of_propositions, labels, prop_dict)
if compute_prop_cm:
print("===================================")
print("Constructing Proposition labeled CM:")
cm_prop = generator.get_prop_cm()
cm_prop_full = sum(cm_prop_k for cm_prop_k in cm_prop.values())
list_of_propositions, prop_dict = generator.get_list_of_propositions()
confusion_matrix = ConfusionMatrix(generator, list_of_classes, list_of_propositions, labels, prop_dict)
# Saving prop cm:
prop_cm_file = f"{cm_dir}/low_thresh_prop_cm.pkl"
confusion_matrix.set_confusion_matrix(cm_prop, label_type="prop")
confusion_matrix.save_confusion_matrix(prop_cm_file, label_type="prop")
# Printing old prop_cm:
old_prop_cm_pkl_file = Path(f"{repo_dir}/saved_cms/lidar/mini/prop_cm.pkl")
old_prop_cm_pkl_file = f"{repo_dir}/saved_cms/lidar/mini/prop_cm.pkl"
with open(old_prop_cm_pkl_file, "rb") as f:
old_prop_cm = pkl.load(f)
f.close()
old_prop_cm_full = sum(cm_prop_k for cm_prop_k in old_prop_cm.values())
print("===================================")
print("Old Prop-Labeled CM:")
print(old_prop_cm_full)
print("New Prop-Labeled CM:")
print(cm_prop_full)
print("===================================")
if compute_class_cm:
print("===================================")
print("Constructing Class Labeled CM:")
cm = generator.get_class_cm()
cm_full = sum(cm_k for cm_k in cm.values())
confusion_matrix.set_confusion_matrix(cm, label_type="class")
cm_file = f"{cm_dir}/low_thresh_cm.pkl"
confusion_matrix.save_confusion_matrix(cm_file, label_type="class")
# Printing old class_cm:
old_cm_pkl_file = Path(f"{repo_dir}/saved_cms/lidar/mini/cm.pkl")
with open(old_cm_pkl_file, "rb") as f:
old_cm = pkl.load(f)
f.close()
old_cm_full = sum(cm_k for cm_k in old_cm.values())
print("===================================")
print("Old Class Labeled CM:")
print(old_cm_full)
print("New Class Labeled CM:")
print(cm_full)
print("===================================")
if compute_prop_segmented_cm:
cm_prop_w_clusters = generator.get_prop_segmented_cm()
print("Generated clustered conf mat")
cm_prop_w_clusters_full = sum(cm_k for cm_k in cm_prop_w_clusters.values())
print("===================================")
print("Clustered CM:")
print(cm_prop_w_clusters_full)
# Saving clustered confusion matrix:
# Todo: Integrate the cluster saving into confusion matrix
prop_cm_file_w_clusters = f"{cm_dir}/low_thresh_prop_cm_cluster.pkl"
with open(prop_cm_file_w_clusters, "wb") as f:
pkl.dump(cm_prop_w_clusters, f)
f.close()
print("Completed Run")