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114 lines
4.5 KiB
Python
114 lines
4.5 KiB
Python
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import numpy as np
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import copy
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import collections
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import math
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def eval_detection(model,
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data_dir,
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ann_file,
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conf_threshold=None,
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nms_iou_threshold=None,
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plot=False,
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batch_size=1):
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from .utils import CocoDetection
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from .utils import COCOMetric
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import cv2
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from tqdm import trange
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import time
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if conf_threshold is not None or nms_iou_threshold is not None:
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assert conf_threshold is not None and nms_iou_threshold is not None, "The conf_threshold and nms_iou_threshold should be setted at the same time"
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assert isinstance(conf_threshold, (
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float,
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int)), "The conf_threshold:{} need to be int or float".format(
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conf_threshold)
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assert isinstance(nms_iou_threshold, (
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float,
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int)), "The nms_iou_threshold:{} need to be int or float".format(
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nms_iou_threshold)
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eval_dataset = CocoDetection(
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data_dir=data_dir, ann_file=ann_file, shuffle=False)
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all_image_info = eval_dataset.file_list
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image_num = eval_dataset.num_samples
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eval_dataset.data_fields = {
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'im_id', 'image_shape', 'image', 'gt_bbox', 'gt_class', 'is_crowd'
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}
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eval_metric = COCOMetric(
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coco_gt=copy.deepcopy(eval_dataset.coco_gt), classwise=False)
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scores = collections.OrderedDict()
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twenty_percent_image_num = math.ceil(image_num * 0.2)
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start_time = 0
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end_time = 0
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average_inference_time = 0
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im_list = list()
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im_id_list = list()
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for image_info, i in zip(all_image_info,
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trange(
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image_num, desc="Inference Progress")):
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if i == twenty_percent_image_num:
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start_time = time.time()
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im = cv2.imread(image_info["image"])
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im_id = image_info["im_id"]
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if batch_size == 1:
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if conf_threshold is None and nms_iou_threshold is None:
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result = model.predict(im.copy())
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else:
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result = model.predict(im, conf_threshold, nms_iou_threshold)
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pred = {
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'bbox': [[c] + [s] + b
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for b, s, c in zip(result.boxes, result.scores,
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result.label_ids)],
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'bbox_num': len(result.boxes),
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'im_id': im_id
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}
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eval_metric.update(im_id, pred)
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else:
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im_list.append(im)
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im_id_list.append(im_id)
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# If the batch_size is not satisfied, the remaining pictures are formed into a batch
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if (i + 1) % batch_size != 0 and i != image_num - 1:
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continue
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if conf_threshold is None and nms_iou_threshold is None:
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results = model.batch_predict(im_list)
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else:
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model.postprocessor.conf_threshold = conf_threshold
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model.postprocessor.nms_threshold = nms_iou_threshold
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results = model.batch_predict(im_list)
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for k in range(len(im_list)):
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pred = {
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'bbox': [[c] + [s] + b
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for b, s, c in zip(results[k].boxes, results[
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k].scores, results[k].label_ids)],
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'bbox_num': len(results[k].boxes),
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'im_id': im_id_list[k]
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}
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eval_metric.update(im_id_list[k], pred)
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im_list.clear()
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im_id_list.clear()
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if i == image_num - 1:
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end_time = time.time()
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average_inference_time = round(
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(end_time - start_time) / (image_num - twenty_percent_image_num), 4)
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eval_metric.accumulate()
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eval_details = eval_metric.details
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scores.update(eval_metric.get())
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scores.update({'average_inference_time(s)': average_inference_time})
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eval_metric.reset()
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return scores
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