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# Copyright (c) Facebook, Inc. and its affiliates.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
##############################################################################
#
# Based on:
# --------------------------------------------------------
# ActivityNet
# Copyright (c) 2015 ActivityNet
# Licensed under The MIT License
# [see https://github.com/activitynet/ActivityNet/blob/master/LICENSE for details]
# --------------------------------------------------------
"""Helper functions for AVA evaluation."""
from __future__ import (
absolute_import,
division,
print_function,
unicode_literals,
)
import csv
import logging
import numpy as np
import pprint
import time
from collections import defaultdict
from fvcore.common.file_io import PathManager
import timesformer.utils.distributed as du
from timesformer.utils.ava_evaluation import (
object_detection_evaluation,
standard_fields,
)
logger = logging.getLogger(__name__)
def make_image_key(video_id, timestamp):
"""Returns a unique identifier for a video id & timestamp."""
return "%s,%04d" % (video_id, int(timestamp))
def read_csv(csv_file, class_whitelist=None, load_score=False):
"""Loads boxes and class labels from a CSV file in the AVA format.
CSV file format described at https://research.google.com/ava/download.html.
Args:
csv_file: A file object.
class_whitelist: If provided, boxes corresponding to (integer) class labels
not in this set are skipped.
Returns:
boxes: A dictionary mapping each unique image key (string) to a list of
boxes, given as coordinates [y1, x1, y2, x2].
labels: A dictionary mapping each unique image key (string) to a list of
integer class lables, matching the corresponding box in `boxes`.
scores: A dictionary mapping each unique image key (string) to a list of
score values lables, matching the corresponding label in `labels`. If
scores are not provided in the csv, then they will default to 1.0.
"""
boxes = defaultdict(list)
labels = defaultdict(list)
scores = defaultdict(list)
with PathManager.open(csv_file, "r") as f:
reader = csv.reader(f)
for row in reader:
assert len(row) in [7, 8], "Wrong number of columns: " + row
image_key = make_image_key(row[0], row[1])
x1, y1, x2, y2 = [float(n) for n in row[2:6]]
action_id = int(row[6])
if class_whitelist and action_id not in class_whitelist:
continue
score = 1.0
if load_score:
score = float(row[7])
boxes[image_key].append([y1, x1, y2, x2])
labels[image_key].append(action_id)
scores[image_key].append(score)
return boxes, labels, scores
def read_exclusions(exclusions_file):
"""Reads a CSV file of excluded timestamps.
Args:
exclusions_file: A file object containing a csv of video-id,timestamp.
Returns:
A set of strings containing excluded image keys, e.g. "aaaaaaaaaaa,0904",
or an empty set if exclusions file is None.
"""
excluded = set()
if exclusions_file:
with PathManager.open(exclusions_file, "r") as f:
reader = csv.reader(f)
for row in reader:
assert len(row) == 2, "Expected only 2 columns, got: " + row
excluded.add(make_image_key(row[0], row[1]))
return excluded
def read_labelmap(labelmap_file):
"""Read label map and class ids."""
labelmap = []
class_ids = set()
name = ""
class_id = ""
with PathManager.open(labelmap_file, "r") as f:
for line in f:
if line.startswith(" name:"):
name = line.split('"')[1]
elif line.startswith(" id:") or line.startswith(" label_id:"):
class_id = int(line.strip().split(" ")[-1])
labelmap.append({"id": class_id, "name": name})
class_ids.add(class_id)
return labelmap, class_ids
def evaluate_ava_from_files(labelmap, groundtruth, detections, exclusions):
"""Run AVA evaluation given annotation/prediction files."""
categories, class_whitelist = read_labelmap(labelmap)
excluded_keys = read_exclusions(exclusions)
groundtruth = read_csv(groundtruth, class_whitelist, load_score=False)
detections = read_csv(detections, class_whitelist, load_score=True)
run_evaluation(categories, groundtruth, detections, excluded_keys)
def evaluate_ava(
preds,
original_boxes,
metadata,
excluded_keys,
class_whitelist,
categories,
groundtruth=None,
video_idx_to_name=None,
name="latest",
):
"""Run AVA evaluation given numpy arrays."""
eval_start = time.time()
detections = get_ava_eval_data(
preds,
original_boxes,
metadata,
class_whitelist,
video_idx_to_name=video_idx_to_name,
)
logger.info("Evaluating with %d unique GT frames." % len(groundtruth[0]))
logger.info(
"Evaluating with %d unique detection frames" % len(detections[0])
)
write_results(detections, "detections_%s.csv" % name)
write_results(groundtruth, "groundtruth_%s.csv" % name)
results = run_evaluation(categories, groundtruth, detections, excluded_keys)
logger.info("AVA eval done in %f seconds." % (time.time() - eval_start))
return results["PascalBoxes_Precision/[email protected]"]
def run_evaluation(
categories, groundtruth, detections, excluded_keys, verbose=True
):
"""AVA evaluation main logic."""
pascal_evaluator = object_detection_evaluation.PascalDetectionEvaluator(
categories
)
boxes, labels, _ = groundtruth
gt_keys = []
pred_keys = []
for image_key in boxes:
if image_key in excluded_keys:
logging.info(
(
"Found excluded timestamp in ground truth: %s. "
"It will be ignored."
),
image_key,
)
continue
pascal_evaluator.add_single_ground_truth_image_info(
image_key,
{
standard_fields.InputDataFields.groundtruth_boxes: np.array(
boxes[image_key], dtype=float
),
standard_fields.InputDataFields.groundtruth_classes: np.array(
labels[image_key], dtype=int
),
standard_fields.InputDataFields.groundtruth_difficult: np.zeros(
len(boxes[image_key]), dtype=bool
),
},
)
gt_keys.append(image_key)
boxes, labels, scores = detections
for image_key in boxes:
if image_key in excluded_keys:
logging.info(
(
"Found excluded timestamp in detections: %s. "
"It will be ignored."
),
image_key,
)
continue
pascal_evaluator.add_single_detected_image_info(
image_key,
{
standard_fields.DetectionResultFields.detection_boxes: np.array(
boxes[image_key], dtype=float
),
standard_fields.DetectionResultFields.detection_classes: np.array(
labels[image_key], dtype=int
),
standard_fields.DetectionResultFields.detection_scores: np.array(
scores[image_key], dtype=float
),
},
)
pred_keys.append(image_key)
metrics = pascal_evaluator.evaluate()
if du.is_master_proc():
pprint.pprint(metrics, indent=2)
return metrics
def get_ava_eval_data(
scores,
boxes,
metadata,
class_whitelist,
verbose=False,
video_idx_to_name=None,
):
"""
Convert our data format into the data format used in official AVA
evaluation.
"""
out_scores = defaultdict(list)
out_labels = defaultdict(list)
out_boxes = defaultdict(list)
count = 0
for i in range(scores.shape[0]):
video_idx = int(np.round(metadata[i][0]))
sec = int(np.round(metadata[i][1]))
video = video_idx_to_name[video_idx]
key = video + "," + "%04d" % (sec)
batch_box = boxes[i].tolist()
# The first is batch idx.
batch_box = [batch_box[j] for j in [0, 2, 1, 4, 3]]
one_scores = scores[i].tolist()
for cls_idx, score in enumerate(one_scores):
if cls_idx + 1 in class_whitelist:
out_scores[key].append(score)
out_labels[key].append(cls_idx + 1)
out_boxes[key].append(batch_box[1:])
count += 1
return out_boxes, out_labels, out_scores
def write_results(detections, filename):
"""Write prediction results into official formats."""
start = time.time()
boxes, labels, scores = detections
with PathManager.open(filename, "w") as f:
for key in boxes.keys():
for box, label, score in zip(boxes[key], labels[key], scores[key]):
f.write(
"%s,%.03f,%.03f,%.03f,%.03f,%d,%.04f\n"
% (key, box[1], box[0], box[3], box[2], label, score)
)
logger.info("AVA results wrote to %s" % filename)
logger.info("\ttook %d seconds." % (time.time() - start))
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