zhaoting
5 years ago
parent
cc0ba93d17
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# Copyright 2020 Huawei Technologies Co., Ltd
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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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# less 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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# ============================================================================
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"""Evaluation for yolo_v3"""
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import os
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import argparse
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import time
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from mindspore import context, Tensor
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from mindspore.train.serialization import load_checkpoint, load_param_into_net
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from mindspore.model_zoo.yolov3 import yolov3_resnet18, YoloWithEval
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from dataset import create_yolo_dataset, data_to_mindrecord_byte_image
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from config import ConfigYOLOV3ResNet18
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from util import metrics
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def yolo_eval(dataset_path, ckpt_path):
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"""Yolov3 evaluation."""
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ds = create_yolo_dataset(dataset_path, is_training=False)
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config = ConfigYOLOV3ResNet18()
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net = yolov3_resnet18(config)
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eval_net = YoloWithEval(net, config)
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print("Load Checkpoint!")
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param_dict = load_checkpoint(ckpt_path)
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load_param_into_net(net, param_dict)
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eval_net.set_train(False)
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i = 1.
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total = ds.get_dataset_size()
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start = time.time()
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pred_data = []
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print("\n========================================\n")
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print("total images num: ", total)
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print("Processing, please wait a moment.")
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for data in ds.create_dict_iterator():
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img_np = data['image']
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image_shape = data['image_shape']
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annotation = data['annotation']
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eval_net.set_train(False)
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output = eval_net(Tensor(img_np), Tensor(image_shape))
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for batch_idx in range(img_np.shape[0]):
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pred_data.append({"boxes": output[0].asnumpy()[batch_idx],
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"box_scores": output[1].asnumpy()[batch_idx],
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"annotation": annotation})
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percent = round(i / total * 100, 2)
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print(' %s [%d/%d]' % (str(percent) + '%', i, total), end='\r')
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i += 1
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print(' %s [%d/%d] cost %d ms' % (str(100.0) + '%', total, total, int((time.time() - start) * 1000)), end='\n')
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precisions, recalls = metrics(pred_data)
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print("\n========================================\n")
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for i in range(config.num_classes):
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print("class {} precision is {:.2f}%, recall is {:.2f}%".format(i, precisions[i] * 100, recalls[i] * 100))
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if __name__ == '__main__':
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parser = argparse.ArgumentParser(description='Yolov3 evaluation')
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parser.add_argument("--device_id", type=int, default=0, help="Device id, default is 0.")
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parser.add_argument("--mindrecord_dir", type=str, default="./Mindrecord_eval",
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help="Mindrecord directory. If the mindrecord_dir is empty, it wil generate mindrecord file by"
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"image_dir and anno_path. Note if mindrecord_dir isn't empty, it will use mindrecord_dir "
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"rather than image_dir and anno_path. Default is ./Mindrecord_eval")
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parser.add_argument("--image_dir", type=str, default="", help="Dataset directory, "
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"the absolute image path is joined by the image_dir "
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"and the relative path in anno_path.")
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parser.add_argument("--anno_path", type=str, default="", help="Annotation path.")
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parser.add_argument("--ckpt_path", type=str, required=True, help="Checkpoint path.")
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args_opt = parser.parse_args()
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context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", device_id=args_opt.device_id)
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context.set_context(enable_task_sink=True, enable_loop_sink=True, enable_mem_reuse=True,
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enable_auto_mixed_precision=False)
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# It will generate mindrecord file in args_opt.mindrecord_dir,
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# and the file name is yolo.mindrecord0, 1, ... file_num.
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if not os.path.isdir(args_opt.mindrecord_dir):
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os.makedirs(args_opt.mindrecord_dir)
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prefix = "yolo.mindrecord"
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mindrecord_file = os.path.join(args_opt.mindrecord_dir, prefix + "0")
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if not os.path.exists(mindrecord_file):
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if os.path.isdir(args_opt.image_dir) and os.path.exists(args_opt.anno_path):
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print("Create Mindrecord")
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data_to_mindrecord_byte_image(args_opt.image_dir,
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args_opt.anno_path,
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args_opt.mindrecord_dir,
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prefix=prefix,
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file_num=8)
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print("Create Mindrecord Done, at {}".format(args_opt.mindrecord_dir))
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else:
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print("image_dir or anno_path not exits")
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print("Start Eval!")
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yolo_eval(mindrecord_file, args_opt.ckpt_path)
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#!/bin/bash
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# Copyright 2020 Huawei Technologies Co., Ltd
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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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# ============================================================================
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echo "=============================================================================================================="
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echo "Please run the scipt as: "
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echo "sh run_eval.sh DEVICE_ID CKPT_PATH MINDRECORD_DIR IMAGE_DIR ANNO_PATH"
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echo "for example: sh run_eval.sh 0 yolo.ckpt ./Mindrecord_eval ./dataset ./dataset/eval.txt"
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echo "=============================================================================================================="
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python eval.py --device_id=$1 --ckpt_path=$2 --mindrecord_dir=$3 --image_dir=$4 --anno_path=$5
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# Copyright 2020 Huawei Technologies Co., Ltd
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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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# ============================================================================
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"""metrics utils"""
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import numpy as np
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from config import ConfigYOLOV3ResNet18
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def calc_iou(bbox_pred, bbox_ground):
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"""Calculate iou of predicted bbox and ground truth."""
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x1 = bbox_pred[0]
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y1 = bbox_pred[1]
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width1 = bbox_pred[2] - bbox_pred[0]
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height1 = bbox_pred[3] - bbox_pred[1]
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x2 = bbox_ground[0]
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y2 = bbox_ground[1]
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width2 = bbox_ground[2] - bbox_ground[0]
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height2 = bbox_ground[3] - bbox_ground[1]
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endx = max(x1 + width1, x2 + width2)
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startx = min(x1, x2)
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width = width1 + width2 - (endx - startx)
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endy = max(y1 + height1, y2 + height2)
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starty = min(y1, y2)
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height = height1 + height2 - (endy - starty)
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if width <= 0 or height <= 0:
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iou = 0
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else:
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area = width * height
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area1 = width1 * height1
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area2 = width2 * height2
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iou = area * 1. / (area1 + area2 - area)
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return iou
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def apply_nms(all_boxes, all_scores, thres, max_boxes):
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"""Apply NMS to bboxes."""
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x1 = all_boxes[:, 0]
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y1 = all_boxes[:, 1]
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x2 = all_boxes[:, 2]
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y2 = all_boxes[:, 3]
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areas = (x2 - x1 + 1) * (y2 - y1 + 1)
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order = all_scores.argsort()[::-1]
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keep = []
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while order.size > 0:
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i = order[0]
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keep.append(i)
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if len(keep) >= max_boxes:
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break
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xx1 = np.maximum(x1[i], x1[order[1:]])
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yy1 = np.maximum(y1[i], y1[order[1:]])
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xx2 = np.minimum(x2[i], x2[order[1:]])
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yy2 = np.minimum(y2[i], y2[order[1:]])
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w = np.maximum(0.0, xx2 - xx1 + 1)
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h = np.maximum(0.0, yy2 - yy1 + 1)
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inter = w * h
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ovr = inter / (areas[i] + areas[order[1:]] - inter)
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inds = np.where(ovr <= thres)[0]
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order = order[inds + 1]
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return keep
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def metrics(pred_data):
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"""Calculate precision and recall of predicted bboxes."""
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config = ConfigYOLOV3ResNet18()
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num_classes = config.num_classes
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count_corrects = [1e-6 for _ in range(num_classes)]
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count_grounds = [1e-6 for _ in range(num_classes)]
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count_preds = [1e-6 for _ in range(num_classes)]
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for i, sample in enumerate(pred_data):
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gt_anno = sample["annotation"]
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box_scores = sample['box_scores']
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boxes = sample['boxes']
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mask = box_scores >= config.obj_threshold
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boxes_ = []
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scores_ = []
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classes_ = []
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max_boxes = config.nms_max_num
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for c in range(num_classes):
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class_boxes = np.reshape(boxes, [-1, 4])[np.reshape(mask[:, c], [-1])]
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class_box_scores = np.reshape(box_scores[:, c], [-1])[np.reshape(mask[:, c], [-1])]
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nms_index = apply_nms(class_boxes, class_box_scores, config.nms_threshold, max_boxes)
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class_boxes = class_boxes[nms_index]
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class_box_scores = class_box_scores[nms_index]
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classes = np.ones_like(class_box_scores, 'int32') * c
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boxes_.append(class_boxes)
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scores_.append(class_box_scores)
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classes_.append(classes)
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boxes = np.concatenate(boxes_, axis=0)
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classes = np.concatenate(classes_, axis=0)
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# metric
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count_correct = [1e-6 for _ in range(num_classes)]
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count_ground = [1e-6 for _ in range(num_classes)]
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count_pred = [1e-6 for _ in range(num_classes)]
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for anno in gt_anno:
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count_ground[anno[4]] += 1
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for box_index, box in enumerate(boxes):
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bbox_pred = [box[1], box[0], box[3], box[2]]
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count_pred[classes[box_index]] += 1
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for anno in gt_anno:
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class_ground = anno[4]
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if classes[box_index] == class_ground:
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iou = calc_iou(bbox_pred, anno)
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if iou >= 0.5:
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count_correct[class_ground] += 1
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break
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count_corrects = [count_corrects[i] + count_correct[i] for i in range(num_classes)]
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count_preds = [count_preds[i] + count_pred[i] for i in range(num_classes)]
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count_grounds = [count_grounds[i] + count_ground[i] for i in range(num_classes)]
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precision = np.array([count_corrects[ix] / count_preds[ix] for ix in range(num_classes)])
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recall = np.array([count_corrects[ix] / count_grounds[ix] for ix in range(num_classes)])
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return precision, recall
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@ -0,0 +1,76 @@
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# Copyright 2020 Huawei Technologies Co., Ltd
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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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# ============================================================================
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"""ReduceMin op"""
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from mindspore.ops.op_info_register import op_info_register
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@op_info_register("""{
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"op_name": "ReduceMin",
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"imply_type": "TBE",
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"fusion_type": "OPAQUE",
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"async_flag": false,
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"binfile_name": "reduce_min_d.so",
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"compute_cost": 10,
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"kernel_name": "reduce_min_d",
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"partial_flag": true,
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"attr": [
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{
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"name": "axis",
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"param_type": "required",
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"type": "listInt",
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"value": "all"
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},
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{
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"name": "keep_dims",
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"param_type": "required",
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"type": "bool",
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"value": "all"
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}
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],
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"inputs": [
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{
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"index": 0,
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"dtype": [
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"float16", "float16", "float", "float", "int8", "int8", "uint8", "uint8"
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],
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"format": [
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"DefaultFormat", "FracZ", "DefaultFormat", "FracZ", "DefaultFormat", "FracZ", "DefaultFormat", "FracZ"
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],
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"name": "x",
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"need_compile": false,
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"param_type": "required",
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"shape": "all"
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}
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],
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"outputs": [
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{
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"index": 0,
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"dtype": [
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"float16", "float16", "float", "float", "int8", "int8", "uint8", "uint8"
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],
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"format": [
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"DefaultFormat", "FracZ", "DefaultFormat", "FracZ", "DefaultFormat", "FracZ", "DefaultFormat", "FracZ"
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],
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"name": "y",
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"need_compile": false,
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"param_type": "required",
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"shape": "all"
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}
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]
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}""")
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def _reduce_min_tbe():
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"""ReduceMin TBE register"""
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return
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@ -0,0 +1,65 @@
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# Copyright 2020 Huawei Technologies Co., Ltd
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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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# ============================================================================
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"""Round op"""
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from mindspore.ops.op_info_register import op_info_register
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@op_info_register("""{
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"op_name": "Round",
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"imply_type": "TBE",
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"fusion_type": "ELEMWISE",
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"async_flag": false,
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"binfile_name": "round.so",
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"compute_cost": 10,
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"kernel_name": "round",
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"partial_flag": true,
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"attr": [
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],
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"inputs": [
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{
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"index": 0,
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"dtype": [
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"float16", "float16", "float16", "float", "float", "float"
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],
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"format": [
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"DefaultFormat", "NC1HWC0", "FracZ", "DefaultFormat", "NC1HWC0", "FracZ"
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],
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"name": "x",
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"need_compile": false,
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"param_type": "required",
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"shape": "all"
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}
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],
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"outputs": [
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{
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"index": 0,
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"dtype": [
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"float16", "float16", "float16", "float", "float", "float"
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],
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"format": [
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"DefaultFormat", "NC1HWC0", "FracZ", "DefaultFormat", "NC1HWC0", "FracZ"
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],
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"name": "y",
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"need_compile": false,
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"param_type": "required",
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"shape": "all"
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}
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]
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}""")
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def _round_tbe():
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"""Round TBE register"""
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return
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