diff --git a/research/cv/FaceDetection/README.md b/research/cv/FaceDetection/README.md
index 330739f9e6b908bf656798b0fc757ea465fb6b20..c9389e1fe4d87446f5e009ca6796b94fa631063a 100644
--- a/research/cv/FaceDetection/README.md
+++ b/research/cv/FaceDetection/README.md
@@ -406,6 +406,8 @@ Saving ../../results/0-2441_61000/.._.._results_0-2441_61000_face_AP_0.7575.png
| Accuracy | 8pcs: 76.0% | 4pcs: 77.8% |
| Model for inference | 37M (.ckpt file) | -- |
+Note there is ±2 deviation of accuracy.
+
### Inference Performance
| Parameters | Ascend |
diff --git a/research/cv/FaceDetection/eval.py b/research/cv/FaceDetection/eval.py
index e367653c7960a7eebe0a56c68e0cbf58800167eb..3ded3b96e41e740253688fb3cbfb0a04eebac6c8 100644
--- a/research/cv/FaceDetection/eval.py
+++ b/research/cv/FaceDetection/eval.py
@@ -1,4 +1,4 @@
-# Copyright 2020-2021 Huawei Technologies Co., Ltd
+# Copyright 2020-2022 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.
@@ -37,10 +37,16 @@ from model_utils.device_adapter import get_device_id, get_device_num, get_rank_i
plt.switch_backend('agg')
+
def load_pretrain(net, cfg):
'''load pretrain model'''
- if os.path.isfile(cfg.pretrained):
- param_dict = load_checkpoint(cfg.pretrained)
+ return load_ckpt(net, cfg.pretrained)
+
+
+def load_ckpt(net, ckpt):
+ '''load ckpt'''
+ if os.path.isfile(ckpt):
+ param_dict = load_checkpoint(ckpt)
param_dict_new = {}
for key, values in param_dict.items():
if key.startswith('moments.'):
@@ -50,13 +56,30 @@ def load_pretrain(net, cfg):
else:
param_dict_new[key] = values
load_param_into_net(net, param_dict_new)
- print('load model {} success'.format(cfg.pretrained))
+ print('load model {} success'.format(ckpt))
else:
- print('load model {} failed, please check the path of model, evaluating end'.format(cfg.pretrained))
+ print('load model {} failed, please check the path of model, evaluating end'.format(ckpt))
exit(0)
return net
+def get_ckpt_list(cfg, max_ckpt_nums):
+ # get ckpt list
+ ckpt_url_list = []
+ if os.path.isfile(cfg.pretrained):
+ ckpt_url_list.append(cfg.pretrained)
+ elif os.path.isdir(cfg.pretrained):
+ lists = os.listdir(cfg.pretrained)
+ lists.sort(key=lambda fn: os.path.getmtime(
+ cfg.pretrained+'/'+fn), reverse=True)
+ for ckpt_name in lists:
+ if "ckpt" in ckpt_name:
+ ckpt_url = os.path.join(cfg.pretrained, ckpt_name)
+ ckpt_url_list.append(ckpt_url)
+ if len(ckpt_url_list) >= max_ckpt_nums:
+ break
+ return ckpt_url_list
+
def modelarts_pre_process():
'''modelarts pre process function.'''
def unzip(zip_file, save_dir):
@@ -73,7 +96,8 @@ def modelarts_pre_process():
i = 0
for file in fz.namelist():
if i % data_print == 0:
- print("unzip percent: {}%".format(int(i * 100 / data_num)), flush=True)
+ print("unzip percent: {}%".format(
+ int(i * 100 / data_num)), flush=True)
i += 1
fz.extract(file, save_dir)
print("cost time: {}min:{}s.".format(int((time.time() - s_time) / 60),
@@ -85,7 +109,8 @@ def modelarts_pre_process():
print("Zip has been extracted.")
if config.need_modelarts_dataset_unzip:
- zip_file_1 = os.path.join(config.data_path, config.modelarts_dataset_unzip_name + ".zip")
+ zip_file_1 = os.path.join(
+ config.data_path, config.modelarts_dataset_unzip_name + ".zip")
save_dir_1 = os.path.join(config.data_path)
sync_lock = "/tmp/unzip_sync.lock"
@@ -106,18 +131,21 @@ def modelarts_pre_process():
break
time.sleep(1)
- print("Device: {}, Finish sync unzip data from {} to {}.".format(get_device_id(), zip_file_1, save_dir_1))
+ print("Device: {}, Finish sync unzip data from {} to {}.".format(
+ get_device_id(), zip_file_1, save_dir_1))
config.result_path = os.path.join(config.output_path, "results")
+
@moxing_wrapper(pre_process=modelarts_pre_process)
def run_eval():
'''run eval'''
config.world_size = get_device_num()
config.local_rank = get_rank_id()
devid = get_device_id() if config.run_platform != 'CPU' else 0
- context.set_context(mode=context.GRAPH_MODE, device_target=config.run_platform, save_graphs=False, device_id=devid)
+ context.set_context(mode=context.GRAPH_MODE,
+ device_target=config.run_platform, save_graphs=False, device_id=devid)
print('=============yolov3 start evaluating==================')
context.set_auto_parallel_context(parallel_mode=ParallelMode.STAND_ALONE, device_num=config.world_size,
@@ -139,7 +167,8 @@ def run_eval():
# dataloader
print('Loading data from {}'.format(config.mindrecord_path))
- ds = de.MindDataset(config.mindrecord_path + "0", columns_list=["image", "annotation", "image_name", "image_size"])
+ ds = de.MindDataset(config.mindrecord_path + "0",
+ columns_list=["image", "annotation", "image_name", "image_size"])
single_scale_trans = SingleScaleTrans(resize=config.input_shape)
ds = ds.batch(config.batch_size, per_batch_map=single_scale_trans,
@@ -147,99 +176,104 @@ def run_eval():
config.steps_per_epoch = ds.get_dataset_size()
- # backbone
- network = backbone_HwYolov3(num_classes, num_anchors_list, config)
- network = load_pretrain(network, config)
-
-
- det = {}
- img_size = {}
- img_anno = {}
-
- model_name = config.pretrained.split('/')[-1].replace('.ckpt', '')
- result_path = os.path.join(config.result_path, model_name)
- if os.path.exists(result_path):
- pass
- if not os.path.isdir(result_path):
- os.makedirs(result_path, exist_ok=True)
-
- # result file
- ret_files_set = {'face': os.path.join(result_path, 'comp4_det_test_face_rm5050.txt'),}
-
- test_net = BuildTestNetwork(network, reduction_0, reduction_1, reduction_2, anchors, anchors_mask, num_classes,
- config)
-
- print('conf_thresh:', config.conf_thresh)
-
- eval_times = 0
-
- for data in ds.create_tuple_iterator(output_numpy=True):
- batch_images, batch_labels, batch_image_name, batch_image_size = data[0:4]
- eval_times += 1
-
- img_tensor = Tensor(batch_images, mstype.float32)
-
- dets = []
- tdets = []
-
- coords_0, cls_scores_0, coords_1, cls_scores_1, coords_2, cls_scores_2 = test_net(img_tensor)
-
- boxes_0, boxes_1, boxes_2 = get_bounding_boxes(coords_0, cls_scores_0, coords_1, cls_scores_1, coords_2,
- cls_scores_2, config.conf_thresh, config.input_shape,
- num_classes)
-
- converted_boxes_0, converted_boxes_1, converted_boxes_2 = tensor_to_brambox(boxes_0, boxes_1, boxes_2,
- config.input_shape, labels)
-
- tdets.append(converted_boxes_0)
- tdets.append(converted_boxes_1)
- tdets.append(converted_boxes_2)
-
- batch = len(tdets[0])
- for b in range(batch):
- single_dets = []
- for op in range(3):
- single_dets.extend(tdets[op][b])
- dets.append(single_dets)
-
- det.update({batch_image_name[k].decode('UTF-8'): v for k, v in enumerate(dets)})
- img_size.update({batch_image_name[k].decode('UTF-8'): v for k, v in enumerate(batch_image_size)})
- img_anno.update({batch_image_name[k].decode('UTF-8'): v for k, v in enumerate(batch_labels)})
-
- print('eval times:', eval_times)
- print('batch size: ', config.batch_size)
-
- netw, neth = config.input_shape
- reorg_dets = voc_wrapper.reorg_detection(det, netw, neth, img_size)
- voc_wrapper.gen_results(reorg_dets, result_path, img_size, config.nms_thresh)
-
- # compute mAP
- ground_truth = parse_gt_from_anno(img_anno, classes)
-
- ret_list = parse_rets(ret_files_set)
- iou_thr = 0.5
- evaluate = calc_recall_precision_ap(ground_truth, ret_list, iou_thr)
-
- aps_str = ''
- for cls in evaluate:
- per_line, = plt.plot(evaluate[cls]['recall'], evaluate[cls]['precision'], 'b-')
- per_line.set_label('%s:AP=%.3f' % (cls, evaluate[cls]['ap']))
- aps_str += '_%s_AP_%.3f' % (cls, evaluate[cls]['ap'])
- plt.plot([i / 1000.0 for i in range(1, 1001)], [i / 1000.0 for i in range(1, 1001)], 'y--')
- plt.axis([0, 1.2, 0, 1.2])
- plt.xlabel('recall')
- plt.ylabel('precision')
- plt.grid()
-
- plt.legend()
- plt.title('PR')
-
- # save mAP
- ap_save_path = os.path.join(result_path, result_path.replace('/', '_') + aps_str + '.png')
- print('Saving {}'.format(ap_save_path))
- plt.savefig(ap_save_path)
+ # get ckpt list
+ ckpt_url_list = get_ckpt_list(config, 10)
+ max_aps = 0
+ for ckpt_url in ckpt_url_list:
+ det = {}
+ img_size = {}
+ img_anno = {}
+ # backbone
+ network = backbone_HwYolov3(num_classes, num_anchors_list, config)
+ network = load_ckpt(network, ckpt_url)
+ model_name = ckpt_url.split('/')[-1].replace('.ckpt', '')
+ result_path = os.path.join(config.result_path, model_name)
+ if os.path.exists(result_path):
+ pass
+ if not os.path.isdir(result_path):
+ os.makedirs(result_path, exist_ok=True)
+
+ # result file
+ ret_files_set = {'face': os.path.join(result_path, 'comp4_det_test_face_rm5050.txt'),}
+
+ test_net = BuildTestNetwork(network, reduction_0, reduction_1, reduction_2, anchors, anchors_mask, num_classes,
+ config)
+
+ print('conf_thresh:', config.conf_thresh)
+
+ eval_times = 0
+
+ for data in ds.create_tuple_iterator(output_numpy=True):
+ batch_images, batch_labels, batch_image_name, batch_image_size = data[0:4]
+ eval_times += 1
+
+ img_tensor = Tensor(batch_images, mstype.float32)
+
+ dets = []
+ tdets = []
+
+ coords_0, cls_scores_0, coords_1, cls_scores_1, coords_2, cls_scores_2 = test_net(img_tensor)
+
+ boxes_0, boxes_1, boxes_2 = get_bounding_boxes(coords_0, cls_scores_0, coords_1, cls_scores_1, coords_2,
+ cls_scores_2, config.conf_thresh, config.input_shape,
+ num_classes)
+
+ converted_boxes_0, converted_boxes_1, converted_boxes_2 = tensor_to_brambox(boxes_0, boxes_1, boxes_2,
+ config.input_shape, labels)
+
+ tdets.append(converted_boxes_0)
+ tdets.append(converted_boxes_1)
+ tdets.append(converted_boxes_2)
+
+ batch = len(tdets[0])
+ for b in range(batch):
+ single_dets = []
+ for op in range(3):
+ single_dets.extend(tdets[op][b])
+ dets.append(single_dets)
+
+ det.update({batch_image_name[k].decode('UTF-8'): v for k, v in enumerate(dets)})
+ img_size.update({batch_image_name[k].decode('UTF-8'): v for k, v in enumerate(batch_image_size)})
+ img_anno.update({batch_image_name[k].decode('UTF-8'): v for k, v in enumerate(batch_labels)})
+
+ print('eval times:', eval_times)
+ print('batch size: ', config.batch_size)
+
+ netw, neth = config.input_shape
+ reorg_dets = voc_wrapper.reorg_detection(det, netw, neth, img_size)
+ voc_wrapper.gen_results(reorg_dets, result_path, img_size, config.nms_thresh)
+
+ # compute mAP
+ ground_truth = parse_gt_from_anno(img_anno, classes)
+
+ ret_list = parse_rets(ret_files_set)
+ iou_thr = 0.5
+ evaluate = calc_recall_precision_ap(ground_truth, ret_list, iou_thr)
+
+ aps_str = ''
+ aps = 0
+ for cls in evaluate:
+ per_line, = plt.plot(evaluate[cls]['recall'], evaluate[cls]['precision'], 'b-')
+ per_line.set_label('%s:AP=%.3f' % (cls, evaluate[cls]['ap']))
+ aps_str += '_%s_AP_%.3f' % (cls, evaluate[cls]['ap'])
+ aps = evaluate[cls]['ap']
+ plt.plot([i / 1000.0 for i in range(1, 1001)], [i / 1000.0 for i in range(1, 1001)], 'y--')
+ plt.axis([0, 1.2, 0, 1.2])
+ plt.xlabel('recall')
+ plt.ylabel('precision')
+ plt.grid()
+
+ plt.legend()
+ plt.title('PR')
+ if aps >= max_aps:
+ # save mAP
+ max_aps = aps
+ ap_save_path = os.path.join(result_path, result_path.replace('/', '_') + aps_str + '.png')
+ print('Saving {}'.format(ap_save_path))
+ plt.savefig(ap_save_path)
print('=============yolov3 evaluating finished==================')
+
if __name__ == "__main__":
run_eval()
diff --git a/research/cv/FaceDetection/scripts/run_eval.sh b/research/cv/FaceDetection/scripts/run_eval.sh
index 456e4e5f60bd0e393674fb64137785ec5fd2ce10..3a22480caf0bac8c8d03ff1b8b6be78d990a6973 100644
--- a/research/cv/FaceDetection/scripts/run_eval.sh
+++ b/research/cv/FaceDetection/scripts/run_eval.sh
@@ -47,9 +47,9 @@ MINDRECORD_FILE=$(get_real_path $2)
USE_DEVICE_ID=$3
PRETRAINED_BACKBONE=$(get_real_path $4)
-if [ ! -f $PRETRAINED_BACKBONE ]
+if [ ! -f $PRETRAINED_BACKBONE ] && [ ! -d $PRETRAINED_BACKBONE ]
then
- echo "error: PRETRAINED_PATH=$PRETRAINED_BACKBONE is not a file"
+ echo "error: PRETRAINED_PATH=$PRETRAINED_BACKBONE is not a file neither a dir."
exit 1
fi