# copyright (c) 2021 PaddlePaddle Authors. All Rights Reserve.
#
# 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.
import csv
import heapq
import logging
import time
from collections import defaultdict
from .ava_evaluation import object_detection_evaluation as det_eval
from .ava_evaluation import standard_fields
from .recall import eval_recalls
import shutil
import pickle
import time
import os
import os.path as osp
from paddlevideo.utils import get_logger, get_dist_info
import paddle.distributed as dist
import sys
import numpy as np
from pathlib import Path
from datetime import datetime
import paddle
def det2csv(info, dataset_len, results, custom_classes):
csv_results = []
for idx in range(dataset_len):
video_id = info[idx]['video_id']
timestamp = info[idx]['timestamp']
result = results[idx]
for label, _ in enumerate(result):
for bbox in result[label]:
if type(bbox) == paddle.Tensor:
bbox = bbox.numpy()
bbox_ = tuple(bbox.tolist())
if custom_classes is not None:
actual_label = custom_classes[label + 1]
else:
actual_label = label + 1
csv_results.append((
video_id,
timestamp,
) + bbox_[:4] + (actual_label, ) + bbox_[4:])
return csv_results
# results is organized by class
def results2csv(info, dataset_len, results, out_file, custom_classes=None):
if isinstance(results[0], list):
csv_results = det2csv(info, dataset_len, results, custom_classes)
# save space for float
def tostr(item):
if isinstance(item, float):
return f'{item:.3f}'
return str(item)
with open(out_file, 'w') as f:
for csv_result in csv_results:
f.write(','.join(map(lambda x: tostr(x), csv_result)))
f.write('\n')
def print_time(message, start):
print('==> %g seconds to %s' % (time.time() - start, message))
def make_image_key(video_id, timestamp):
"""Returns a unique identifier for a video id & timestamp."""
return f'{video_id},{int(timestamp):04d}'
def read_csv(csv_file, class_whitelist=None, capacity=0):
"""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.
capacity: Maximum number of labeled boxes allowed for each example.
Default is 0 where there is no limit.
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.
"""
start = time.time()
entries = defaultdict(list)
boxes = defaultdict(list)
labels = defaultdict(list)
scores = defaultdict(list)
reader = csv.reader(csv_file)
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 len(row) == 8:
score = float(row[7])
if capacity < 1 or len(entries[image_key]) < capacity:
heapq.heappush(entries[image_key],
(score, action_id, y1, x1, y2, x2))
elif score > entries[image_key][0][0]:
heapq.heapreplace(entries[image_key],
(score, action_id, y1, x1, y2, x2))
for image_key in entries:
# Evaluation API assumes boxes with descending scores
entry = sorted(entries[image_key], key=lambda tup: -tup[0])
for item in entry:
score, action_id, y1, x1, y2, x2 = item
boxes[image_key].append([y1, x1, y2, x2])
labels[image_key].append(action_id)
scores[image_key].append(score)
print_time('read file ' + csv_file.name, start)
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:
reader = csv.reader(exclusions_file)
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):
"""Reads a labelmap without the dependency on protocol buffers.
Args:
labelmap_file: A file object containing a label map protocol buffer.
Returns:
labelmap: The label map in the form used by the
object_detection_evaluation
module - a list of {"id": integer, "name": classname } dicts.
class_ids: A set containing all of the valid class id integers.
"""
labelmap = []
class_ids = set()
name = ''
class_id = ''
for line in labelmap_file:
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
# Seems there is at most 100 detections for each image
def ava_eval(result_file,
result_type,
label_file,
ann_file,
exclude_file,
max_dets=(100, ),
verbose=True,
custom_classes=None):
assert result_type in ['mAP']
start = time.time()
categories, class_whitelist = read_labelmap(open(label_file))
if custom_classes is not None:
custom_classes = custom_classes[1:]
assert set(custom_classes).issubset(set(class_whitelist))
class_whitelist = custom_classes
categories = [cat for cat in categories if cat['id'] in custom_classes]
# loading gt, do not need gt score
gt_boxes, gt_labels, _ = read_csv(open(ann_file), class_whitelist, 0)
if verbose:
print_time('Reading detection results', start)
if exclude_file is not None:
excluded_keys = read_exclusions(open(exclude_file))
else:
excluded_keys = list()
start = time.time()
boxes, labels, scores = read_csv(open(result_file), class_whitelist, 0)
if verbose:
print_time('Reading detection results', start)
if result_type == 'proposal':
gts = [
np.array(gt_boxes[image_key], dtype=float) for image_key in gt_boxes
]
proposals = []
for image_key in gt_boxes:
if image_key in boxes:
proposals.append(
np.concatenate(
(np.array(boxes[image_key], dtype=float),
np.array(scores[image_key], dtype=float)[:, None]),
axis=1))
else:
# if no corresponding proposal, add a fake one
proposals.append(np.array([0, 0, 1, 1, 1]))
# Proposals used here are with scores
recalls = eval_recalls(gts, proposals, np.array(max_dets),
np.arange(0.5, 0.96, 0.05))
ar = recalls.mean(axis=1)
ret = {}
for i, num in enumerate(max_dets):
print(f'Recall@0.5@{num}\t={recalls[i, 0]:.4f}')
print(f'AR@{num}\t={ar[i]:.4f}')
ret[f'Recall@0.5@{num}'] = recalls[i, 0]
ret[f'AR@{num}'] = ar[i]
return ret
if result_type == 'mAP':
pascal_evaluator = det_eval.PascalDetectionEvaluator(categories)
start = time.time()
for image_key in gt_boxes:
if verbose and image_key in excluded_keys:
logging.info(
'Found excluded timestamp in detections: %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(gt_boxes[image_key], dtype=float),
standard_fields.InputDataFields.groundtruth_classes:
np.array(gt_labels[image_key], dtype=int),
standard_fields.InputDataFields.groundtruth_difficult:
np.zeros(len(gt_boxes[image_key]), dtype=bool)
})
if verbose:
print_time('Convert groundtruth', start)
start = time.time()
for image_key in boxes:
if verbose and 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)
})
if verbose:
print_time('convert detections', start)
start = time.time()
metrics = pascal_evaluator.evaluate()
if verbose:
print_time('run_evaluator', start)
for display_name in metrics:
print(f'{display_name}=\t{metrics[display_name]}')
ret = {
display_name: metrics[display_name]
for display_name in metrics if 'ByCategory' not in display_name
}
return ret
def mkdir_or_exist(dir_name, mode=0o777):
if dir_name == '':
return
dir_name = osp.expanduser(dir_name)
os.makedirs(dir_name, mode=mode, exist_ok=True)
def dump_to_fileobj(obj, file, **kwargs):
kwargs.setdefault('protocol', 2)
pickle.dump(obj, file, **kwargs)
def dump_to_path(obj, filepath, mode='wb'):
with open(filepath, mode) as f:
dump_to_fileobj(obj, f)
def load_from_fileobj(file, **kwargs):
return pickle.load(file, **kwargs)
def load_from_path(filepath, mode='rb'):
with open(filepath, mode) as f:
return load_from_fileobj(f)
def collect_results_cpu(result_part, size):
"""Collect results in cpu mode.
It saves the results on different gpus to 'tmpdir' and collects
them by the rank 0 worker.
"""
tmpdir = osp.join('./', 'collect_results_cpu')
#1. load results of all parts from tmp dir
mkdir_or_exist(tmpdir)
rank, world_size = get_dist_info()
dump_to_path(result_part, osp.join(tmpdir, f'part_{rank}.pkl'))
dist.barrier()
if rank != 0:
return None
#2. collect all parts
while 1:
all_exist = True
for i in range(world_size):
part_file = osp.join(tmpdir, f'part_{i}.pkl')
if not Path(part_file).exists():
all_exist = False
if all_exist:
break
else:
time.sleep(60)
time.sleep(120)
#3. load results of all parts from tmp dir
part_list = []
for i in range(world_size):
part_file = osp.join(tmpdir, f'part_{i}.pkl')
part_list.append(load_from_path(part_file))
#4. sort the results
ordered_results = []
for res in zip(*part_list):
ordered_results.extend(list(res))
ordered_results = ordered_results[:
size] #the dataloader may pad some samples
#5. remove results of all parts from tmp dir, avoid dump_file fail to tmp dir when dir not exists.
for i in range(world_size):
part_file = osp.join(tmpdir, f'part_{i}.pkl')
os.remove(part_file)
return ordered_results
def ava_evaluate_results(info, dataset_len, results, custom_classes, label_file,
file_path, exclude_file):
# need to create a temp result file
time_now = datetime.now().strftime('%Y%m%d_%H%M%S')
temp_file = f'AVA_{time_now}_result.csv'
results2csv(info, dataset_len, results, temp_file)
ret = {}
eval_result = ava_eval(
temp_file,
'mAP',
label_file,
file_path, #ann_file,
exclude_file,
custom_classes=custom_classes)
ret.update(eval_result)
os.remove(temp_file)
return ret