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329 lines
13 KiB
329 lines
13 KiB
# 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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"""YoloV3 eval."""
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import os
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import argparse
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import datetime
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import time
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import sys
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from collections import defaultdict
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import numpy as np
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from pycocotools.coco import COCO
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from pycocotools.cocoeval import COCOeval
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from mindspore import Tensor
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from mindspore.train import ParallelMode
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from mindspore import context
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from mindspore.train.serialization import load_checkpoint, load_param_into_net
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import mindspore as ms
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from src.yolo import YOLOV3DarkNet53
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from src.logger import get_logger
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from src.yolo_dataset import create_yolo_dataset
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from src.config import ConfigYOLOV3DarkNet53
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devid = int(os.getenv('DEVICE_ID'))
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context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", save_graphs=True, device_id=devid)
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class Redirct:
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def __init__(self):
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self.content = ""
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def write(self, content):
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self.content += content
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def flush(self):
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self.content = ""
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class DetectionEngine:
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"""Detection engine."""
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def __init__(self, args):
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self.ignore_threshold = args.ignore_threshold
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self.labels = ['person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat',
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'traffic light', 'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird', 'cat',
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'dog', 'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra', 'giraffe', 'backpack',
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'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee', 'skis', 'snowboard', 'sports ball',
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'kite', 'baseball bat', 'baseball glove', 'skateboard', 'surfboard', 'tennis racket',
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'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple',
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'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair',
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'couch', 'potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote',
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'keyboard', 'cell phone', 'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book',
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'clock', 'vase', 'scissors', 'teddy bear', 'hair drier', 'toothbrush']
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self.num_classes = len(self.labels)
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self.results = {}
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self.file_path = ''
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self.save_prefix = args.outputs_dir
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self.annFile = args.annFile
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self._coco = COCO(self.annFile)
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self._img_ids = list(sorted(self._coco.imgs.keys()))
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self.det_boxes = []
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self.nms_thresh = args.nms_thresh
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self.coco_catIds = self._coco.getCatIds()
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def do_nms_for_results(self):
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"""Get result boxes."""
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for img_id in self.results:
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for clsi in self.results[img_id]:
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dets = self.results[img_id][clsi]
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dets = np.array(dets)
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keep_index = self._nms(dets, self.nms_thresh)
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keep_box = [{'image_id': int(img_id),
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'category_id': int(clsi),
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'bbox': list(dets[i][:4].astype(float)),
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'score': dets[i][4].astype(float)}
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for i in keep_index]
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self.det_boxes.extend(keep_box)
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def _nms(self, dets, thresh):
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"""Calculate NMS."""
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# conver xywh -> xmin ymin xmax ymax
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x1 = dets[:, 0]
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y1 = dets[:, 1]
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x2 = x1 + dets[:, 2]
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y2 = y1 + dets[:, 3]
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scores = dets[:, 4]
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areas = (x2 - x1 + 1) * (y2 - y1 + 1)
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order = 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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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 <= thresh)[0]
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order = order[inds + 1]
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return keep
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def write_result(self):
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"""Save result to file."""
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import json
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t = datetime.datetime.now().strftime('_%Y_%m_%d_%H_%M_%S')
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try:
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self.file_path = self.save_prefix + '/predict' + t + '.json'
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f = open(self.file_path, 'w')
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json.dump(self.det_boxes, f)
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except IOError as e:
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raise RuntimeError("Unable to open json file to dump. What(): {}".format(str(e)))
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else:
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f.close()
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return self.file_path
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def get_eval_result(self):
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"""Get eval result."""
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cocoGt = COCO(self.annFile)
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cocoDt = cocoGt.loadRes(self.file_path)
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cocoEval = COCOeval(cocoGt, cocoDt, 'bbox')
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cocoEval.evaluate()
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cocoEval.accumulate()
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rdct = Redirct()
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stdout = sys.stdout
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sys.stdout = rdct
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cocoEval.summarize()
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sys.stdout = stdout
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return rdct.content
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def detect(self, outputs, batch, image_shape, image_id):
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"""Detect boxes."""
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outputs_num = len(outputs)
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# output [|32, 52, 52, 3, 85| ]
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for batch_id in range(batch):
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for out_id in range(outputs_num):
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# 32, 52, 52, 3, 85
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out_item = outputs[out_id]
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# 52, 52, 3, 85
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out_item_single = out_item[batch_id, :]
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# get number of items in one head, [B, gx, gy, anchors, 5+80]
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dimensions = out_item_single.shape[:-1]
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out_num = 1
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for d in dimensions:
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out_num *= d
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ori_w, ori_h = image_shape[batch_id]
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img_id = int(image_id[batch_id])
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x = out_item_single[..., 0] * ori_w
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y = out_item_single[..., 1] * ori_h
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w = out_item_single[..., 2] * ori_w
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h = out_item_single[..., 3] * ori_h
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conf = out_item_single[..., 4:5]
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cls_emb = out_item_single[..., 5:]
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cls_argmax = np.expand_dims(np.argmax(cls_emb, axis=-1), axis=-1)
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x = x.reshape(-1)
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y = y.reshape(-1)
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w = w.reshape(-1)
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h = h.reshape(-1)
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cls_emb = cls_emb.reshape(-1, 80)
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conf = conf.reshape(-1)
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cls_argmax = cls_argmax.reshape(-1)
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x_top_left = x - w / 2.
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y_top_left = y - h / 2.
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# creat all False
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flag = np.random.random(cls_emb.shape) > sys.maxsize
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for i in range(flag.shape[0]):
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c = cls_argmax[i]
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flag[i, c] = True
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confidence = cls_emb[flag] * conf
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for x_lefti, y_lefti, wi, hi, confi, clsi in zip(x_top_left, y_top_left, w, h, confidence, cls_argmax):
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if confi < self.ignore_threshold:
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continue
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if img_id not in self.results:
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self.results[img_id] = defaultdict(list)
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x_lefti = max(0, x_lefti)
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y_lefti = max(0, y_lefti)
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wi = min(wi, ori_w)
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hi = min(hi, ori_h)
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# transform catId to match coco
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coco_clsi = self.coco_catIds[clsi]
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self.results[img_id][coco_clsi].append([x_lefti, y_lefti, wi, hi, confi])
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def parse_args():
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"""Parse arguments."""
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parser = argparse.ArgumentParser('mindspore coco testing')
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# dataset related
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parser.add_argument('--data_dir', type=str, default='', help='train data dir')
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parser.add_argument('--per_batch_size', default=1, type=int, help='batch size for per gpu')
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# network related
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parser.add_argument('--pretrained', default='', type=str, help='model_path, local pretrained model to load')
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# logging related
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parser.add_argument('--log_path', type=str, default='outputs/', help='checkpoint save location')
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# detect_related
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parser.add_argument('--nms_thresh', type=float, default=0.5, help='threshold for NMS')
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parser.add_argument('--annFile', type=str, default='', help='path to annotation')
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parser.add_argument('--testing_shape', type=str, default='', help='shape for test ')
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parser.add_argument('--ignore_threshold', type=float, default=0.001, help='threshold to throw low quality boxes')
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args, _ = parser.parse_known_args()
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args.data_root = os.path.join(args.data_dir, 'val2014')
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args.annFile = os.path.join(args.data_dir, 'annotations/instances_val2014.json')
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return args
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def conver_testing_shape(args):
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"""Convert testing shape to list."""
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testing_shape = [int(args.testing_shape), int(args.testing_shape)]
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return testing_shape
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def test():
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"""The function of eval."""
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start_time = time.time()
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args = parse_args()
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# logger
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args.outputs_dir = os.path.join(args.log_path,
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datetime.datetime.now().strftime('%Y-%m-%d_time_%H_%M_%S'))
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rank_id = int(os.environ.get('RANK_ID'))
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args.logger = get_logger(args.outputs_dir, rank_id)
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context.reset_auto_parallel_context()
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parallel_mode = ParallelMode.STAND_ALONE
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context.set_auto_parallel_context(parallel_mode=parallel_mode, mirror_mean=True, device_num=1)
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args.logger.info('Creating Network....')
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network = YOLOV3DarkNet53(is_training=False)
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args.logger.info(args.pretrained)
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if os.path.isfile(args.pretrained):
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param_dict = load_checkpoint(args.pretrained)
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param_dict_new = {}
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for key, values in param_dict.items():
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if key.startswith('moments.'):
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continue
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elif key.startswith('yolo_network.'):
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param_dict_new[key[13:]] = values
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else:
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param_dict_new[key] = values
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load_param_into_net(network, param_dict_new)
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args.logger.info('load_model {} success'.format(args.pretrained))
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else:
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args.logger.info('{} not exists or not a pre-trained file'.format(args.pretrained))
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assert FileNotFoundError('{} not exists or not a pre-trained file'.format(args.pretrained))
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exit(1)
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data_root = args.data_root
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ann_file = args.annFile
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config = ConfigYOLOV3DarkNet53()
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if args.testing_shape:
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config.test_img_shape = conver_testing_shape(args)
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ds, data_size = create_yolo_dataset(data_root, ann_file, is_training=False, batch_size=args.per_batch_size,
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max_epoch=1, device_num=1, rank=rank_id, shuffle=False,
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config=config)
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args.logger.info('testing shape : {}'.format(config.test_img_shape))
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args.logger.info('totol {} images to eval'.format(data_size))
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network.set_train(False)
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# init detection engine
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detection = DetectionEngine(args)
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input_shape = Tensor(tuple(config.test_img_shape), ms.float32)
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args.logger.info('Start inference....')
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for i, data in enumerate(ds.create_dict_iterator()):
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image = Tensor(data["image"])
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image_shape = Tensor(data["image_shape"])
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image_id = Tensor(data["img_id"])
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prediction = network(image, input_shape)
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output_big, output_me, output_small = prediction
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output_big = output_big.asnumpy()
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output_me = output_me.asnumpy()
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output_small = output_small.asnumpy()
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image_id = image_id.asnumpy()
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image_shape = image_shape.asnumpy()
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detection.detect([output_small, output_me, output_big], args.per_batch_size, image_shape, image_id)
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if i % 1000 == 0:
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args.logger.info('Processing... {:.2f}% '.format(i * args.per_batch_size / data_size * 100))
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args.logger.info('Calculating mAP...')
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detection.do_nms_for_results()
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result_file_path = detection.write_result()
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args.logger.info('result file path: {}'.format(result_file_path))
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eval_result = detection.get_eval_result()
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cost_time = time.time() - start_time
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args.logger.info('\n=============coco eval reulst=========\n' + eval_result)
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args.logger.info('testing cost time {:.2f}h'.format(cost_time / 3600.))
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if __name__ == "__main__":
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test()
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