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# fmt: off
# flake8: noqa
import copy
import os
import pickle
import time
import traceback
from functools import partial
from multiprocessing.pool import Pool
import numpy as np
from . import _timing, utils
from .config import get_default_eval_config, init_config
from .utils import TrackEvalException
class Evaluator:
"""Evaluator class for evaluating different metrics for each datasets."""
def __init__(self, config=None):
"""Initialize the evaluator with a config file."""
self.config = init_config(config, get_default_eval_config(), "Eval")
# Only run timing analysis if not run in parallel.
if self.config["TIME_PROGRESS"] and not self.config["USE_PARALLEL"]:
_timing.DO_TIMING = True
if self.config["DISPLAY_LESS_PROGRESS"]:
_timing.DISPLAY_LESS_PROGRESS = True
@_timing.time
def evaluate(self, dataset_list, metrics_list):
"""Evaluate a set of metrics on a set of datasets."""
config = self.config
metrics_list = metrics_list
metric_names = utils.validate_metrics_list(metrics_list)
dataset_names = [dataset.get_name() for dataset in dataset_list]
output_res = {}
output_msg = {}
for dataset, dname in zip(dataset_list, dataset_names):
# Get dataset info about what to evaluate
output_res[dname] = {}
output_msg[dname] = {}
tracker_list, seq_list, class_list = dataset.get_eval_info()
print(
f"\nEvaluating {len(tracker_list)} tracker(s) on "
f"{len(seq_list)} sequence(s) for {len(class_list)} class(es)"
f" on {dname} dataset using the following "
f'metrics: {", ".join(metric_names)}\n'
)
# Evaluate each tracker
for tracker in tracker_list:
try:
output_res, output_msg = self.evaluate_tracker(
tracker,
dataset,
dname,
class_list,
metrics_list,
metric_names,
seq_list,
output_res,
output_msg,
)
except Exception as err:
output_res[dname][tracker] = None
if type(err) == TrackEvalException:
output_msg[dname][tracker] = str(err)
else:
output_msg[dname][tracker] = "Unknown error occurred."
print("Tracker %s was unable to be evaluated." % tracker)
print(err)
traceback.print_exc()
if config["LOG_ON_ERROR"] is not None:
with open(config["LOG_ON_ERROR"], "a") as f:
print(dname, file=f)
print(tracker, file=f)
print(traceback.format_exc(), file=f)
print("\n\n\n", file=f)
if config["BREAK_ON_ERROR"]:
raise err
elif config["RETURN_ON_ERROR"]:
return output_res, output_msg
return output_res, output_msg
def evaluate_tracker(
self,
tracker,
dataset,
dname,
class_list,
metrics_list,
metric_names,
seq_list,
output_res,
output_msg,
):
"""Evaluate each sequence in parallel or in series."""
print("\nEvaluating %s\n" % tracker)
time_start = time.time()
config = self.config
if config["USE_PARALLEL"]:
with Pool(config["NUM_PARALLEL_CORES"]) as pool:
_eval_sequence = partial(
eval_sequence,
dataset=dataset,
tracker=tracker,
class_list=class_list,
metrics_list=metrics_list,
metric_names=metric_names,
)
results = pool.map(_eval_sequence, seq_list)
res = dict(zip(seq_list, results))
else:
res = {}
for curr_seq in sorted(seq_list):
res[curr_seq] = eval_sequence(
curr_seq, dataset, tracker, class_list, metrics_list, metric_names
)
# collecting combined cls keys (cls averaged, det averaged, super classes)
cls_keys = []
res["COMBINED_SEQ"] = {}
# combine sequences for each class
for c_cls in class_list:
res["COMBINED_SEQ"][c_cls] = {}
for metric, mname in zip(metrics_list, metric_names):
curr_res = {
seq_key: seq_value[c_cls][mname]
for seq_key, seq_value in res.items()
if seq_key != "COMBINED_SEQ"
}
# combine results over all sequences and then over all classes
res["COMBINED_SEQ"][c_cls][mname] = metric.combine_sequences(curr_res)
# combine classes
if dataset.should_classes_combine:
if config["OUTPUT_PER_SEQ_RES"]:
video_keys = res.keys()
else:
video_keys = ["COMBINED_SEQ"]
for v_key in video_keys:
cls_keys += ["average"]
res[v_key]["average"] = {}
for metric, mname in zip(metrics_list, metric_names):
cls_res = {
cls_key: cls_value[mname]
for cls_key, cls_value in res[v_key].items()
if cls_key not in cls_keys
}
res[v_key]["average"][
mname
] = metric.combine_classes_class_averaged(
cls_res, ignore_empty=True
)
# combine classes to super classes
if dataset.use_super_categories:
for cat, sub_cats in dataset.super_categories.items():
cls_keys.append(cat)
res["COMBINED_SEQ"][cat] = {}
for metric, mname in zip(metrics_list, metric_names):
cat_res = {
cls_key: cls_value[mname]
for cls_key, cls_value in res["COMBINED_SEQ"].items()
if cls_key in sub_cats
}
res["COMBINED_SEQ"][cat][
mname
] = metric.combine_classes_det_averaged(cat_res)
# Print and output results in various formats
if config["TIME_PROGRESS"]:
print(
f"\nAll sequences for {tracker} finished in"
f" {time.time() - time_start} seconds"
)
output_fol = dataset.get_output_fol(tracker)
os.makedirs(output_fol, exist_ok=True)
# take a mean of each field of each thr
if config["OUTPUT_PER_SEQ_RES"]:
all_res = copy.deepcopy(res)
summary_keys = res.keys()
else:
all_res = copy.deepcopy(res["COMBINED_SEQ"])
summary_keys = ["COMBINED_SEQ"]
thr_key_list = [50]
for s_key in summary_keys:
for metric, mname in zip(metrics_list, metric_names):
if mname != "TETA":
if s_key == "COMBINED_SEQ":
metric.print_table(
{"COMBINED_SEQ": res["COMBINED_SEQ"][cls_keys[0]][mname]},
tracker,
cls_keys[0],
)
continue
for c_cls in res[s_key].keys():
for thr in thr_key_list:
all_res[s_key][c_cls][mname][thr] = metric._summary_row(
res[s_key][c_cls][mname][thr]
)
x = (
np.array(list(all_res[s_key][c_cls]["TETA"].values()))
.astype("float")
.mean(axis=0)
)
all_res_summary = list(x.round(decimals=2).astype("str"))
all_res[s_key][c_cls][mname]["ALL"] = all_res_summary
if config["OUTPUT_SUMMARY"] and s_key == "COMBINED_SEQ":
for t in thr_key_list:
metric.print_summary_table(
all_res[s_key][cls_keys[0]][mname][t],
t,
tracker,
cls_keys[0],
)
if config["OUTPUT_TEM_RAW_DATA"]:
out_file = os.path.join(output_fol, "teta_summary_results.pth")
pickle.dump(all_res, open(out_file, "wb"))
print("Saved the TETA summary results.")
# output
output_res[dname][mname] = all_res[s_key][cls_keys[0]][mname][t]
output_msg[dname][tracker] = "Success"
return output_res, output_msg
@_timing.time
def eval_sequence(seq, dataset, tracker, class_list, metrics_list, metric_names):
"""Function for evaluating a single sequence."""
raw_data = dataset.get_raw_seq_data(tracker, seq)
seq_res = {}
if "TETA" in metric_names:
thresholds = [50]
data_all_class = dataset.get_preprocessed_seq_data(
raw_data, "all", thresholds=thresholds
)
teta = metrics_list[metric_names.index("TETA")]
assignment = teta.compute_global_assignment(data_all_class)
# create a dict to save Cls_FP for each class in different thr.
cls_fp = {
key: {
cls: np.zeros((len(np.arange(0.5, 0.99, 0.05)))) for cls in class_list
}
for key in thresholds
}
for cls in class_list:
seq_res[cls] = {}
data = dataset.get_preprocessed_seq_data(raw_data, cls, assignment, thresholds)
for metric, mname in zip(metrics_list, metric_names):
if mname == "TETA":
seq_res[cls][mname], cls_fp, _ = metric.eval_sequence(
data, cls, dataset.clsid2cls_name, cls_fp
)
else:
seq_res[cls][mname] = metric.eval_sequence(data)
if "TETA" in metric_names:
for thr in thresholds:
for cls in class_list:
seq_res[cls]["TETA"][thr]["Cls_FP"] += cls_fp[thr][cls]
return seq_res
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