diff --git a/official/cv/retinaface_resnet50/README.md b/official/cv/retinaface_resnet50/README.md
index d4564b4f2c88121595a333c5a40cecd7060e9c18..28c18d01fe1197d812cbb5dc7953e675eef58131 100644
--- a/official/cv/retinaface_resnet50/README.md
+++ b/official/cv/retinaface_resnet50/README.md
@@ -79,11 +79,19 @@ After installing MindSpore via the official website and download the dataset, yo
# run distributed training example
bash scripts/run_distribute_gpu_train.sh 4 0,1,2,3
+ # run exporting ONNX model example
+ python export.py
+
# run evaluation example
export CUDA_VISIBLE_DEVICES=0
python eval.py > eval.log 2>&1 &
OR
bash run_standalone_gpu_eval.sh 0
+
+ # run evaluation of ONNX model example
+ python eval_onnx.py
+ OR
+ bash scripts/run_onnx_eval.sh
```
# [Script Description](#contents)
@@ -98,6 +106,7 @@ After installing MindSpore via the official website and download the dataset, yo
鈹溾攢鈹€ scripts
鈹� 鈹溾攢鈹€run_distribute_gpu_train.sh // shell script for distributed on GPU
鈹� 鈹溾攢鈹€run_standalone_gpu_eval.sh // shell script for evaluation on GPU
+ 鈹� 鈹溾攢鈹€run_onnx_eval.sh // shell script for ONNX model evaluation on GPU or CPU
鈹溾攢鈹€ src
鈹� 鈹溾攢鈹€dataset.py // creating dataset
鈹� 鈹溾攢鈹€network.py // retinaface architecture
@@ -112,6 +121,8 @@ After installing MindSpore via the official website and download the dataset, yo
鈹� 鈹溾攢鈹€ground_truth // eval label
鈹溾攢鈹€ train.py // training script
鈹溾攢鈹€ eval.py // evaluation script
+ 鈹溾攢鈹€ export.py // ONNX model exporting script
+ 鈹溾攢鈹€ eval_onnx.py // ONNX model evaluation script
```
## [Script Parameters](#contents)
@@ -160,6 +171,10 @@ Parameters for both training and evaluation can be set in config.py
'val_save_result': False, # Whether save the resultss
'val_predict_save_folder': './widerface_result', # Result save path
'val_gt_dir': './data/ground_truth/', # Path of val set ground_truth
+ # onnx
+ 'ckpt_model': '../ckpt/retinaface.ckpt', # path of ckpt file to be exported
+ 'onnx_model': '../ckpt/retinaface.onnx', # path of the ONNX model to be evaluated
+ 'device': 'CPU', # device type: CPU or GPU
```
## [Training Process](#contents)
@@ -189,6 +204,16 @@ Parameters for both training and evaluation can be set in config.py
After training, you'll get some checkpoint files under the folder `./checkpoint/ckpt_0/` by default.
+## ONNX EXPORTING
+
+- **preparation**锛歁odify the parameter `device` in the `src/config.py` file to select the type of device: `CPU` or `GPU`; then modify the parameter `ckpt_model` to specify the path of ckpt file that is uesd to export to onnx model.
+
+- **run script**锛歊un the following command to export the `ONNX` model and it will be saved in the current directory.
+
+ ```SHELL
+ python export.py
+ ```
+
## [Evaluation Process](#contents)
### Evaluation
@@ -226,6 +251,32 @@ Parameters for both training and evaluation can be set in config.py
Hard Val AP : 0.8900
```
+## Evaluation for ONNX MODEL
+
+- **preparation**锛歁odify the following parameters in the `src/config.py` file according to the actual situation:
+
+ - `device`锛歵ype of device `CPU` 鎴� `GPU`锛�
+ - `onnx_model`锛歱ath of onnx model锛�
+ - `val_dataset_folder`锛歱ath of validation dataset;
+ - `val_gt_dir`锛歱ath of validation dataset ground_truth label.
+
+- **run script**锛歊un the following command to evaluate the `ONNX` model:
+
+ ```bash
+ export CUDA_VISIBLE_DEVICES="$1"
+ python eval_onnx.py
+ or
+ bash scripts/run_onnx_eval.sh 0
+ ```
+
+The evaluation results can be viewed in the window锛�
+
+ ```python
+ Easy Val AP : 0.9390
+ Medium Val AP : 0.9306
+ Hard Val AP : 0.8886
+ ```
+
# [Model Description](#contents)
## [Performance](#contents)
diff --git a/official/cv/retinaface_resnet50/README_CN.md b/official/cv/retinaface_resnet50/README_CN.md
index cf4335ba2cf6ffcc394f1e0cc5739c34ae0f2e71..37bc2230672ff2e1ceb3bea108dfd3b418dbc5ce 100644
--- a/official/cv/retinaface_resnet50/README_CN.md
+++ b/official/cv/retinaface_resnet50/README_CN.md
@@ -84,11 +84,19 @@ RetinaFace浣跨敤ResNet50楠ㄥ共鎻愬彇鍥惧儚鐗瑰緛杩涜妫€娴嬨€備粠ModelZoo鑾峰彇
# 鍒嗗竷寮忚缁冪ず渚�
bash scripts/run_distribute_gpu_train.sh 3 0,1,2
+ # ONNX妯″瀷瀵煎嚭绀轰緥
+ python export.py
+
# 璇勪及绀轰緥
export CUDA_VISIBLE_DEVICES=0
python eval.py > eval.log 2>&1 &
OR
bash run_standalone_gpu_eval.sh 0
+
+ # ONNX妯″瀷璇勪及绀轰緥
+ python eval_onnx.py
+ OR
+ bash scripts/run_onnx_eval.sh
```
# 鑴氭湰璇存槑
@@ -103,6 +111,7 @@ RetinaFace浣跨敤ResNet50楠ㄥ共鎻愬彇鍥惧儚鐗瑰緛杩涜妫€娴嬨€備粠ModelZoo鑾峰彇
鈹溾攢鈹€ scripts
鈹� 鈹溾攢鈹€run_distribute_gpu_train.sh // GPU鍒嗗竷寮弒hell鑴氭湰
鈹� 鈹溾攢鈹€run_standalone_gpu_eval.sh // GPU璇勪及shell鑴氭湰
+ 鈹� 鈹溾攢鈹€run_onnx_eval.sh // ONNX妯″瀷璇勪及shell鑴氭湰
鈹溾攢鈹€ src
鈹� 鈹溾攢鈹€dataset.py // 鍒涘缓鏁版嵁闆�
鈹� 鈹溾攢鈹€network.py // RetinaFace鏋舵瀯
@@ -116,6 +125,8 @@ RetinaFace浣跨敤ResNet50楠ㄥ共鎻愬彇鍥惧儚鐗瑰緛杩涜妫€娴嬨€備粠ModelZoo鑾峰彇
鈹� 鈹溾攢鈹€ground_truth // 璇勪及鏍囩
鈹溾攢鈹€ train.py // 璁粌鑴氭湰
鈹溾攢鈹€ eval.py // 璇勪及鑴氭湰
+ 鈹溾攢鈹€ export.py // ONNX妯″瀷瀵煎嚭鑴氭湰
+ 鈹溾攢鈹€ eval_onnx.py // ONNX妯″瀷璇勪及鑴氭湰
```
## 鑴氭湰鍙傛暟
@@ -169,6 +180,10 @@ RetinaFace浣跨敤ResNet50楠ㄥ共鎻愬彇鍥惧儚鐗瑰緛杩涜妫€娴嬨€備粠ModelZoo鑾峰彇
'val_save_result': False, # 鏄惁淇濆瓨缁撴灉
'val_predict_save_folder': './widerface_result', # 缁撴灉淇濆瓨璺緞
'val_gt_dir': './data/ground_truth/', # 楠岃瘉闆唃round_truth璺緞
+ # onnx
+ 'ckpt_model': '../ckpt/retinaface.ckpt', # 寰呰浆鎴怬NNX妯″瀷鐨刢kpt鏂囦欢鐨勮矾寰�
+ 'onnx_model': '../ckpt/retinaface.onnx', # 璇勪及鎵€浣跨敤鐨凮NNX妯″瀷鐨勮矾寰�
+ 'device': 'CPU', # 璇勪及ONNX妯″瀷鍜屽鍑篛NNX妯″瀷鏃剁殑璁惧绫诲瀷锛欳PU or GPU
```
## 璁粌杩囩▼
@@ -198,6 +213,16 @@ RetinaFace浣跨敤ResNet50楠ㄥ共鎻愬彇鍥惧儚鐗瑰緛杩涜妫€娴嬨€備粠ModelZoo鑾峰彇
璁粌缁撴潫鍚庯紝鍙湪榛樿鏂囦欢澶筦./checkpoint/ckpt_0/`涓壘鍒版鏌ョ偣鏂囦欢銆�
+## 瀵煎嚭ONNX妯″瀷
+
+- **鍑嗗宸ヤ綔**锛氫慨鏀� `src/config.py` 鏂囦欢涓殑 `device` 鍙傛暟锛岄€夋嫨璁惧鐨勭被鍨嬶細`CPU` 鎴� `GPU`锛涚劧鍚庡啀淇敼 `ckpt_model` 鍙傛暟锛岀敤浜庣粰瀹氬鍑� `ONNX` 妯″瀷鎵€浣跨敤鐨� `CKPT` 鏂囦欢鐨勮矾寰勩€�
+
+- **杩愯妯″瀷瀵煎嚭鑴氭湰**锛氳繍琛屼互涓嬪懡浠ゅ嵆鍙鍑� `ONNX` 妯″瀷锛宍ONNX` 妯″瀷淇濆瓨鍦ㄥ綋鍓嶇洰褰曚笅銆�
+
+ ```bash
+ python export.py
+ ```
+
## 璇勪及杩囩▼
### 璇勪及
@@ -235,6 +260,32 @@ RetinaFace浣跨敤ResNet50楠ㄥ共鎻愬彇鍥惧儚鐗瑰緛杩涜妫€娴嬨€備粠ModelZoo鑾峰彇
Hard Val AP : 0.8904
```
+## ONNX妯″瀷璇勪及杩囩▼
+
+- **鍑嗗宸ヤ綔**锛氭牴鎹疄闄呮儏鍐典慨鏀� `src/config.py` 鏂囦欢涓互涓嬪弬鏁帮細
+
+ - `device` 鍙傛暟锛氳澶囩被鍨� `CPU` 鎴� `GPU`锛�
+ - `onnx_model` 鍙傛暟锛氳瘎浼版墍浣跨敤鐨� `ONNX` 妯″瀷鐨勮矾寰勶紱
+ - `val_dataset_folder` 鍙傛暟锛氶獙璇侀泦鍥剧墖鎵€鍦ㄦ牴鐩綍锛�
+ - `val_gt_dir` 鍙傛暟锛歚ground truth` 鏍囩鎵€鍦ㄦ牴鐩綍銆�
+
+- **杩愯ONNX鎺ㄧ悊鑴氭湰**锛氳繍琛屼互涓嬪懡浠ゅ嵆鍙 `ONNX` 妯″瀷杩涜璇勪及锛�
+
+ ```bash
+ export CUDA_VISIBLE_DEVICES="$1"
+ python eval_onnx.py
+ or
+ bash scripts/run_onnx_eval.sh 0
+ ```
+
+鍙湪绐楀彛涓煡鐪嬭瘎浼扮粨鏋滐細
+
+ ```python
+Easy Val AP : 0.9390
+Medium Val AP : 0.9306
+Hard Val AP : 0.8886
+ ```
+
# 妯″瀷鎻忚堪
## 鎬ц兘
diff --git a/official/cv/retinaface_resnet50/eval_onnx.py b/official/cv/retinaface_resnet50/eval_onnx.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69244609508d0cfaf850ee2f005ed4643618790
--- /dev/null
+++ b/official/cv/retinaface_resnet50/eval_onnx.py
@@ -0,0 +1,407 @@
+# Copyright 2022 Huawei Technologies Co., Ltd
+#
+# 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.
+# ============================================================================
+"""Eval Retinaface_resnet50 on ONNXRUNTIME"""
+from __future__ import print_function
+import os
+import time
+import datetime
+import numpy as np
+import cv2
+from src.config import cfg_res50
+from src.utils import decode_bbox, prior_box
+import onnxruntime
+
+
+class Timer():
+ def __init__(self):
+ self.start_time = 0.
+ self.diff = 0.
+
+ def start(self):
+ self.start_time = time.time()
+
+ def end(self):
+ self.diff = time.time() - self.start_time
+
+class DetectionEngine:
+ def __init__(self, cfg):
+ self.results = {}
+ self.nms_thresh = cfg['val_nms_threshold']
+ self.conf_thresh = cfg['val_confidence_threshold']
+ self.iou_thresh = cfg['val_iou_threshold']
+ self.var = cfg['variance']
+ self.save_prefix = cfg['val_predict_save_folder']
+ self.gt_dir = cfg['val_gt_dir']
+
+ def _iou(self, a, b):
+ A = a.shape[0]
+ B = b.shape[0]
+ max_xy = np.minimum(
+ np.broadcast_to(np.expand_dims(a[:, 2:4], 1), [A, B, 2]),
+ np.broadcast_to(np.expand_dims(b[:, 2:4], 0), [A, B, 2]))
+ min_xy = np.maximum(
+ np.broadcast_to(np.expand_dims(a[:, 0:2], 1), [A, B, 2]),
+ np.broadcast_to(np.expand_dims(b[:, 0:2], 0), [A, B, 2]))
+ inter = np.maximum((max_xy - min_xy + 1), np.zeros_like(max_xy - min_xy))
+ inter = inter[:, :, 0] * inter[:, :, 1]
+
+ area_a = np.broadcast_to(
+ np.expand_dims(
+ (a[:, 2] - a[:, 0] + 1) * (a[:, 3] - a[:, 1] + 1), 1),
+ np.shape(inter))
+ area_b = np.broadcast_to(
+ np.expand_dims(
+ (b[:, 2] - b[:, 0] + 1) * (b[:, 3] - b[:, 1] + 1), 0),
+ np.shape(inter))
+ union = area_a + area_b - inter
+ return inter / union
+
+ def _nms(self, boxes, threshold=0.5):
+ x1 = boxes[:, 0]
+ y1 = boxes[:, 1]
+ x2 = boxes[:, 2]
+ y2 = boxes[:, 3]
+ scores = boxes[:, 4]
+
+ areas = (x2 - x1 + 1) * (y2 - y1 + 1)
+ order = scores.argsort()[::-1]
+
+ reserved_boxes = []
+ while order.size > 0:
+ i = order[0]
+ reserved_boxes.append(i)
+ max_x1 = np.maximum(x1[i], x1[order[1:]])
+ max_y1 = np.maximum(y1[i], y1[order[1:]])
+ min_x2 = np.minimum(x2[i], x2[order[1:]])
+ min_y2 = np.minimum(y2[i], y2[order[1:]])
+
+ intersect_w = np.maximum(0.0, min_x2 - max_x1 + 1)
+ intersect_h = np.maximum(0.0, min_y2 - max_y1 + 1)
+ intersect_area = intersect_w * intersect_h
+ ovr = intersect_area / (areas[i] + areas[order[1:]] - intersect_area)
+
+ indices = np.where(ovr <= threshold)[0]
+ order = order[indices + 1]
+
+ return reserved_boxes
+
+ def write_result(self):
+ # save result to file.
+ import json
+ t = datetime.datetime.now().strftime('_%Y_%m_%d_%H_%M_%S')
+ try:
+ if not os.path.isdir(self.save_prefix):
+ os.makedirs(self.save_prefix)
+
+ self.file_path = self.save_prefix + '/predict' + t + '.json'
+ f = open(self.file_path, 'w')
+ json.dump(self.results, f)
+ except IOError as e:
+ raise RuntimeError("Unable to open json file to dump. What(): {}".format(str(e)))
+ else:
+ f.close()
+ return self.file_path
+
+ def detect(self, boxes, confs, resize, scale, image_path, priors):
+ if boxes.shape[0] == 0:
+ # add to result
+ event_name, img_name = image_path.split('/')
+ self.results[event_name][img_name[:-4]] = {'img_path': image_path,
+ 'bboxes': []}
+ return
+
+ boxes = decode_bbox(np.squeeze(boxes, 0), priors, self.var)
+ boxes = boxes * scale / resize
+
+ scores = np.squeeze(confs, 0)[:, 1]
+ # ignore low scores
+ inds = np.where(scores > self.conf_thresh)[0]
+ boxes = boxes[inds]
+ scores = scores[inds]
+
+ # keep top-K before NMS
+ order = scores.argsort()[::-1]
+ boxes = boxes[order]
+ scores = scores[order]
+
+ # do NMS
+ dets = np.hstack((boxes, scores[:, np.newaxis])).astype(np.float32, copy=False)
+ keep = self._nms(dets, self.nms_thresh)
+ dets = dets[keep, :]
+
+ dets[:, 2:4] = (dets[:, 2:4].astype(np.int32) - dets[:, 0:2].astype(np.int32)).astype(np.float64) # int
+ dets[:, 0:4] = dets[:, 0:4].astype(np.int32).astype(np.float64) # int
+
+
+ # add to result
+ event_name, img_name = image_path.split('/')
+ if event_name not in self.results.keys():
+ self.results[event_name] = {}
+ self.results[event_name][img_name[:-4]] = {'img_path': image_path,
+ 'bboxes': dets[:, :5].astype(np.float64).tolist()}
+
+ def _get_gt_boxes(self):
+ from scipy.io import loadmat
+ gt = loadmat(os.path.join(self.gt_dir, 'wider_face_val.mat'))
+ hard = loadmat(os.path.join(self.gt_dir, 'wider_hard_val.mat'))
+ medium = loadmat(os.path.join(self.gt_dir, 'wider_medium_val.mat'))
+ easy = loadmat(os.path.join(self.gt_dir, 'wider_easy_val.mat'))
+
+ faceboxes = gt['face_bbx_list']
+ events = gt['event_list']
+ files = gt['file_list']
+
+ hard_gt_list = hard['gt_list']
+ medium_gt_list = medium['gt_list']
+ easy_gt_list = easy['gt_list']
+
+ return faceboxes, events, files, hard_gt_list, medium_gt_list, easy_gt_list
+
+ def _norm_pre_score(self):
+ max_score = 0
+ min_score = 1
+
+ for event in self.results:
+ for name in self.results[event].keys():
+ bbox = np.array(self.results[event][name]['bboxes']).astype(np.float64)
+ if bbox.shape[0] <= 0:
+ continue
+ max_score = max(max_score, np.max(bbox[:, -1]))
+ min_score = min(min_score, np.min(bbox[:, -1]))
+
+ length = max_score - min_score
+ for event in self.results:
+ for name in self.results[event].keys():
+ bbox = np.array(self.results[event][name]['bboxes']).astype(np.float64)
+ if bbox.shape[0] <= 0:
+ continue
+ bbox[:, -1] -= min_score
+ bbox[:, -1] /= length
+ self.results[event][name]['bboxes'] = bbox.tolist()
+
+ def _image_eval(self, predict, gt, keep, iou_thresh, section_num):
+
+ _predict = predict.copy()
+ _gt = gt.copy()
+
+ image_p_right = np.zeros(_predict.shape[0])
+ image_gt_right = np.zeros(_gt.shape[0])
+ proposal = np.ones(_predict.shape[0])
+
+ # x1y1wh -> x1y1x2y2
+ _predict[:, 2:4] = _predict[:, 0:2] + _predict[:, 2:4]
+ _gt[:, 2:4] = _gt[:, 0:2] + _gt[:, 2:4]
+
+ ious = self._iou(_predict[:, 0:4], _gt[:, 0:4])
+ for i in range(_predict.shape[0]):
+ gt_ious = ious[i, :]
+ max_iou, max_index = gt_ious.max(), gt_ious.argmax()
+ if max_iou >= iou_thresh:
+ if keep[max_index] == 0:
+ image_gt_right[max_index] = -1
+ proposal[i] = -1
+ elif image_gt_right[max_index] == 0:
+ image_gt_right[max_index] = 1
+
+ right_index = np.where(image_gt_right == 1)[0]
+ image_p_right[i] = len(right_index)
+
+
+
+ image_pr = np.zeros((section_num, 2), dtype=np.float64)
+ for section in range(section_num):
+ _thresh = 1 - (section + 1)/section_num
+ over_score_index = np.where(predict[:, 4] >= _thresh)[0]
+ if over_score_index.shape[0] <= 0:
+ image_pr[section, 0] = 0
+ image_pr[section, 1] = 0
+ else:
+ index = over_score_index[-1]
+ p_num = len(np.where(proposal[0:(index+1)] == 1)[0])
+ image_pr[section, 0] = p_num
+ image_pr[section, 1] = image_p_right[index]
+
+ return image_pr
+
+
+ def get_eval_result(self):
+ self._norm_pre_score()
+ facebox_list, event_list, file_list, hard_gt_list, medium_gt_list, easy_gt_list = self._get_gt_boxes()
+ section_num = 1000
+ sets = ['easy', 'medium', 'hard']
+ set_gts = [easy_gt_list, medium_gt_list, hard_gt_list]
+ ap_key_dict = {0: "Easy Val AP : ", 1: "Medium Val AP : ", 2: "Hard Val AP : ",}
+ ap_dict = {}
+ for _set in range(len(sets)):
+ gt_list = set_gts[_set]
+ count_gt = 0
+ pr_curve = np.zeros((section_num, 2), dtype=np.float64)
+ for i, _ in enumerate(event_list):
+ event = str(event_list[i][0][0])
+ image_list = file_list[i][0]
+ event_predict_dict = self.results[event]
+ event_gt_index_list = gt_list[i][0]
+ event_gt_box_list = facebox_list[i][0]
+
+ for j, _ in enumerate(image_list):
+ predict = np.array(event_predict_dict[str(image_list[j][0][0])]['bboxes']).astype(np.float64)
+ gt_boxes = event_gt_box_list[j][0].astype('float')
+ keep_index = event_gt_index_list[j][0]
+ count_gt += len(keep_index)
+
+ if gt_boxes.shape[0] <= 0 or predict.shape[0] <= 0:
+ continue
+ keep = np.zeros(gt_boxes.shape[0])
+ if keep_index.shape[0] > 0:
+ keep[keep_index-1] = 1
+
+ image_pr = self._image_eval(predict, gt_boxes, keep,
+ iou_thresh=self.iou_thresh,
+ section_num=section_num)
+ pr_curve += image_pr
+
+ precision = pr_curve[:, 1] / pr_curve[:, 0]
+ recall = pr_curve[:, 1] / count_gt
+
+ precision = np.concatenate((np.array([0.]), precision, np.array([0.])))
+ recall = np.concatenate((np.array([0.]), recall, np.array([1.])))
+ for i in range(precision.shape[0]-1, 0, -1):
+ precision[i-1] = np.maximum(precision[i-1], precision[i])
+ index = np.where(recall[1:] != recall[:-1])[0]
+ ap = np.sum((recall[index + 1] - recall[index]) * precision[index + 1])
+
+
+ print(ap_key_dict[_set] + '{:.4f}'.format(ap))
+
+ return ap_dict
+
+
+def val():
+ cfg = cfg_res50
+
+ # testing dataset
+ testset_folder = cfg['val_dataset_folder']
+ testset_label_path = cfg['val_dataset_folder'] + "label.txt"
+ with open(testset_label_path, 'r') as f:
+ _test_dataset = f.readlines()
+ test_dataset = []
+ for im_path in _test_dataset:
+ if im_path.startswith('# '):
+ test_dataset.append(im_path[2:-1]) # delete '# ...\n'
+
+ num_images = len(test_dataset)
+
+ timers = {'forward_time': Timer(), 'misc': Timer()}
+
+ if cfg['val_origin_size']:
+ h_max, w_max = 0, 0
+ for img_name in test_dataset:
+ image_path = os.path.join(testset_folder, 'images', img_name)
+ _img = cv2.imread(image_path, cv2.IMREAD_COLOR)
+ if _img.shape[0] > h_max:
+ h_max = _img.shape[0]
+ if _img.shape[1] > w_max:
+ w_max = _img.shape[1]
+
+ h_max = (int(h_max / 32) + 1) * 32
+ w_max = (int(w_max / 32) + 1) * 32
+
+ priors = prior_box(image_sizes=(h_max, w_max),
+ min_sizes=[[16, 32], [64, 128], [256, 512]],
+ steps=[8, 16, 32],
+ clip=False)
+ else:
+ target_size = 1600
+ max_size = 2176
+ priors = prior_box(image_sizes=(max_size, max_size),
+ min_sizes=[[16, 32], [64, 128], [256, 512]],
+ steps=[8, 16, 32],
+ clip=False)
+
+ # init detection engine
+ detection = DetectionEngine(cfg)
+
+ # testing begin
+ print('Predict box starting')
+ for i, img_name in enumerate(test_dataset):
+ image_path = os.path.join(testset_folder, 'images/', img_name)
+
+ img_raw = cv2.imread(image_path, cv2.IMREAD_COLOR)
+
+ img = np.float32(img_raw)
+
+ # testing scale
+ if cfg['val_origin_size']:
+ resize = 1
+ assert img.shape[0] <= h_max and img.shape[1] <= w_max
+ image_t = np.empty((h_max, w_max, 3), dtype=img.dtype)
+ image_t[:, :] = (104.0, 117.0, 123.0)
+ image_t[0:img.shape[0], 0:img.shape[1]] = img
+ img = image_t
+ else:
+ im_size_min = np.min(img.shape[0:2])
+ im_size_max = np.max(img.shape[0:2])
+ resize = float(target_size) / float(im_size_min)
+ # prevent bigger axis from being more than max_size:
+ if np.round(resize * im_size_max) > max_size:
+ resize = float(max_size) / float(im_size_max)
+
+ img = cv2.resize(img, None, None, fx=resize, fy=resize, interpolation=cv2.INTER_LINEAR)
+
+ assert img.shape[0] <= max_size and img.shape[1] <= max_size
+ image_t = np.empty((max_size, max_size, 3), dtype=img.dtype)
+ image_t[:, :] = (104.0, 117.0, 123.0)
+ image_t[0:img.shape[0], 0:img.shape[1]] = img
+ img = image_t
+
+ scale = np.array([img.shape[1], img.shape[0], img.shape[1], img.shape[0]], dtype=img.dtype)
+ img -= (104, 117, 123)
+ img = img.transpose(2, 0, 1)
+ img = np.expand_dims(img, 0)
+
+ timers['forward_time'].start()
+ if cfg['device'] == 'GPU':
+ providers = ['CUDAExecutionProvider']
+ elif cfg['device'] == 'CPU':
+ providers = ['CPUExecutionProvider']
+ else:
+ raise ValueError(
+ f'Please Select CPU or GPU'
+ )
+ session = onnxruntime.InferenceSession(cfg['onnx_model'], providers=providers)
+ inputs = {session.get_inputs()[0].name: img}
+ boxes, confs, _ = session.run(None, inputs)
+ timers['forward_time'].end()
+ timers['misc'].start()
+ detection.detect(boxes, confs, resize, scale, img_name, priors)
+ timers['misc'].end()
+
+ print('im_detect: {:d}/{:d} forward_pass_time: {:.4f}s misc: {:.4f}s'.format(i + 1, num_images,
+ timers['forward_time'].diff,
+ timers['misc'].diff))
+ print('Predict box done.')
+ print('Eval starting')
+
+ if cfg['val_save_result']:
+ # Save the predict result if you want.
+ predict_result_path = detection.write_result()
+ print('predict result path is {}'.format(predict_result_path))
+
+ detection.get_eval_result()
+ print('Eval done.')
+
+
+if __name__ == '__main__':
+ val()
diff --git a/official/cv/retinaface_resnet50/export.py b/official/cv/retinaface_resnet50/export.py
new file mode 100644
index 0000000000000000000000000000000000000000..9176e06ef4fe5ec1dcaa564bdc1506f11017bb01
--- /dev/null
+++ b/official/cv/retinaface_resnet50/export.py
@@ -0,0 +1,49 @@
+# Copyright 2022 Huawei Technologies Co., Ltd
+#
+# 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.
+# ============================================================================
+"""EXPORT ONNX MODEL WITH CKPT MODEL BASED ON MINDSPORE"""
+from __future__ import print_function
+import numpy as np
+import mindspore as ms
+from mindspore import Tensor, export
+from src.network import RetinaFace, resnet50
+from src.config import cfg_res50
+
+
+def export_ONNX_model():
+ cfg = cfg_res50
+
+ ms.set_context(mode=ms.GRAPH_MODE, device_target=cfg['device'], save_graphs=True)
+
+ # build network
+ backbone = resnet50(1001)
+ network = RetinaFace(phase='predict', backbone=backbone)
+ backbone.set_train(False)
+ network.set_train(False)
+
+ # load checkpoint into network
+ param_dict = ms.load_checkpoint(cfg['ckpt_model'])
+ network.init_parameters_data()
+ ms.load_param_into_net(network, param_dict)
+
+ # build input data
+ input_data = Tensor(np.ones([1, 3, 2176, 2176]).astype(np.float32))
+
+ # export onnx model
+ print("Begin to Export ONNX Model...")
+ export(network, input_data, file_name='retinaface', file_format='ONNX')
+ print("Export ONNX Model Successfully!")
+
+if __name__ == '__main__':
+ export_ONNX_model()
diff --git a/official/cv/retinaface_resnet50/requirements.txt b/official/cv/retinaface_resnet50/requirements.txt
index e7590640de37f69a782424616f0d5ecc836b634a..e9ed1cae243b1cabf1cfc6e15a6ae55fd1c45cbb 100644
--- a/official/cv/retinaface_resnet50/requirements.txt
+++ b/official/cv/retinaface_resnet50/requirements.txt
@@ -1,3 +1,4 @@
numpy
opencv-python
scipy
+onnxruntime-gpu
diff --git a/official/cv/retinaface_resnet50/scripts/run_onnx_eval.sh b/official/cv/retinaface_resnet50/scripts/run_onnx_eval.sh
new file mode 100644
index 0000000000000000000000000000000000000000..0ce748981b7cdb36388b6e01405e6b5170c0e504
--- /dev/null
+++ b/official/cv/retinaface_resnet50/scripts/run_onnx_eval.sh
@@ -0,0 +1,24 @@
+#!/bin/bash
+# Copyright 2022 Huawei Technologies Co., Ltd
+#
+# 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.
+# ============================================================================
+
+echo "=============================================================================================================="
+echo "Before running the script, you should modify 4 params of src/config.py file"
+echo "including val_dataset_folder, val_gt_dir, onnx_model and device."
+echo "And then, you can run the script as: bash run_onnx_eval.sh 0"
+echo "=============================================================================================================="
+
+export CUDA_VISIBLE_DEVICES="$1"
+python eval_onnx.py > eval.log 2>&1 &
diff --git a/official/cv/retinaface_resnet50/src/config.py b/official/cv/retinaface_resnet50/src/config.py
index 473124ccf11eed0cb9fe28861478af77c42c54f7..7765b3eb5fccfe3df930cb85b7e3c1d4dabb813d 100644
--- a/official/cv/retinaface_resnet50/src/config.py
+++ b/official/cv/retinaface_resnet50/src/config.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Huawei Technologies Co., Ltd
+# Copyright 2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -65,4 +65,8 @@ cfg_res50 = {
'val_predict_save_folder': './widerface_result',
'val_gt_dir': './data/ground_truth/',
+ # onnx
+ 'ckpt_model': '../ckpt/retinaface.ckpt',
+ 'onnx_model': './retinaface.onnx',
+ 'device': 'GPU',
}