diff --git a/research/cv/siamRPN/README_CN.md b/research/cv/siamRPN/README_CN.md
index eb47ad00789b3f1506ca9d714dcc16ecc2f15038..20d5a6f68e79837bc72f406192502626ba766c0a 100644
--- a/research/cv/siamRPN/README_CN.md
+++ b/research/cv/siamRPN/README_CN.md
@@ -117,6 +117,7 @@ Siam-RPN鎻愬嚭浜嗕竴绉嶅熀浜嶳PN鐨勫鐢熺綉缁滅粨鏋勩€傜敱瀛敓瀛愮綉缁滃拰RPN
| |鈹€鈹€run_distribute_train_gpu.sh // 鏈湴GPU澶氬崱璁粌鑴氭湰
| |鈹€鈹€run_infer_310.sh //310鎺ㄧ悊璇勪及鑴氭湰
| |鈹€鈹€run_gpu.sh //GPU鍗曞崱璁粌鑴氭湰
+ | |鈹€鈹€run_evalonnx.sh //onnx鎺ㄧ悊鏁版嵁闆嗚剼鏈�
鈹溾攢鈹€ src
鈹� 鈹溾攢鈹€ data_loader.py // 鏁版嵁闆嗗姞杞藉鐞嗚剼鏈�
鈹� 鈹溾攢鈹€ net.py // siamRPN鏋舵瀯
@@ -146,8 +147,9 @@ Siam-RPN鎻愬嚭浜嗕竴绉嶅熀浜嶳PN鐨勫鐢熺綉缁滅粨鏋勩€傜敱瀛敓瀛愮綉缁滃拰RPN
鈹� 鈹溾攢鈹€ 路路路路路路
鈹� 鈹斺攢鈹€ list.txt
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_mindir.py // 灏哻heckpoint鏂囦欢瀵煎嚭鍒癮ir/mindir
+ 鈹溾攢鈹€ evalonnx.py // onnx璇勪及鑴氭湰
+ 鈹溾攢鈹€ eval.py // ckpt璇勪及鑴氭湰
+ 鈹溾攢鈹€ export.py // 灏哻heckpoint鏂囦欢瀵煎嚭鍒皁nnx鎴杕indir
```
## 鑴氭湰鍙傛暟
@@ -331,6 +333,30 @@ cat acc.log
鍦╰rain.py涓紝鎴戜滑璁剧疆浜嗛殢鏈虹瀛愩€�
+# ONNX鎺ㄧ悊
+
+```bash
+# 鐢熸垚onnx鏂囦欢
+ python export.py --ckpt_file=/path/ckpt/siamRPN-xx_xxxx.ckpt
+```
+
+# onnx鎺ㄧ悊,鏍规嵁vot2015鏁版嵁闆嗗拰vot2016鏁版嵁闆嗗垎鍒€夋嫨瀵瑰簲鐨刼nnx鎺ㄧ悊浠ｇ爜
+
+# 渚嬪閫夋嫨vot2015鏁版嵁闆嗘帹鐞�
+
+```bash
+python evalonnx.py --checkpoint_path=/path/siamrpn.onnx
+鎴栬€� sh run_evalonnx.sh [dataset_path] [model_path] [filename]
+#vot2016鍚岀悊
+```
+
+-缁撴灉淇濆瓨鍦╢ilename褰撲腑锛屼緥濡�
+
+```bash
+{"all_videos": {"accuracy": 0.5890433443656077, "robustness": 0.3868562106735027, "eao": 0.30735406482761557}}
+}
+```
+
# ModelZoo涓婚〉
璇锋祻瑙堝畼缃慬涓婚〉](https://gitee.com/mindspore/models)銆�
diff --git a/research/cv/siamRPN/evalonnx.py b/research/cv/siamRPN/evalonnx.py
new file mode 100644
index 0000000000000000000000000000000000000000..d52d4bda41aa130296846a388edaed98a053cc32
--- /dev/null
+++ b/research/cv/siamRPN/evalonnx.py
@@ -0,0 +1,284 @@
+# 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 vot"""
+
+import argparse
+import os
+import json
+import sys
+import time
+import numpy as np
+import onnxruntime as ort
+import mindspore as ms
+from mindspore import context, ops
+from mindspore import Tensor
+from src import evaluation as eval_
+from src.config import config
+from src.util import get_exemplar_image, get_instance_image, box_transform_inv
+from src.generate_anchors import generate_anchors
+from tqdm import tqdm
+import cv2
+
+sys.path.append(os.getcwd())
+
+
+def create_session(checkpoint_path, target_device):
+ if target_device == 'GPU':
+ providers = ['CUDAExecutionProvider']
+ elif target_device == 'CPU':
+ providers = ['CPUExecutionProvider']
+ else:
+ raise ValueError(
+ f'Unsupported target device {target_device}, '
+ f'Expected one of: "CPU", "GPU"'
+ )
+ session = ort.InferenceSession(checkpoint_path, providers=providers)
+ return session
+
+
+def change(r):
+ return np.maximum(r, 1. / r)
+
+
+def sz(w, h):
+ pad = (w + h) * 0.5
+ sz2 = (w + pad) * (h + pad)
+ return np.sqrt(sz2)
+
+
+def sz_wh(wh):
+ pad = (wh[0] + wh[1]) * 0.5
+ sz2 = (wh[0] + pad) * (wh[1] + pad)
+ return np.sqrt(sz2)
+
+
+def get_axis_aligned_bbox(region):
+ """ convert region to (cx, cy, w, h) that represent by axis aligned box
+ """
+ nv = len(region)
+ region = np.array(region)
+ if nv == 8:
+ x1 = min(region[0::2])
+ x2 = max(region[0::2])
+ y1 = min(region[1::2])
+ y2 = max(region[1::2])
+ A1 = np.linalg.norm(region[0:2] - region[2:4]) * \
+ np.linalg.norm(region[2:4] - region[4:6])
+ A2 = (x2 - x1) * (y2 - y1)
+ s = np.sqrt(A1 / A2)
+ w = s * (x2 - x1) + 1
+ h = s * (y2 - y1) + 1
+ x = x1
+ y = y1
+ else:
+ x = region[0]
+ y = region[1]
+ w = region[2]
+ h = region[3]
+
+ return x, y, w, h
+
+
+def reshapeimg1(img):
+ img = Tensor(img, ms.float32)
+ img = img.transpose((2, 0, 1))
+ img = img.asnumpy()
+ img = img.reshape(1, 3, 127, 127)
+ return img
+
+
+def reshapeimg2(img):
+ img = Tensor(img, ms.float32)
+ img = img.transpose((2, 0, 1))
+ img = img.asnumpy()
+ img = img.reshape(1, 3, 255, 255)
+ return img
+
+
+def calculate(bbox):
+ gbox = np.array(bbox)
+ gbox = list((gbox[0] - gbox[2] / 2 + 1 / 2, gbox[1] - gbox[3] / 2 + 1 / 2,
+ gbox[0] + gbox[2] / 2 - 1 / 2, gbox[1] + gbox[3] / 2 - 1 / 2))
+ return gbox
+
+
+def show(accuracy, video_paths, robustness, eao):
+ print('accuracy is ', accuracy / float(len(video_paths)))
+ print('robustness is ', robustness)
+ print('eao is ', eao)
+
+
+def predscore(pred_score):
+ pred_score = Tensor(pred_score)
+ softmax = ops.Softmax(axis=2)
+ pred_score = softmax(pred_score)[0, :, 1]
+ pred_score = pred_score.asnumpy()
+ return pred_score
+
+
+def resshow(target, pos, frame, origin_target_sz, lr, target_sz):
+ res_x = np.clip(target[0] + pos[0], 0, frame.shape[1])
+ res_y = np.clip(target[1] + pos[1], 0, frame.shape[0])
+ res_w = np.clip(target_sz[0] * (1 - lr) + target[2] * lr,
+ config.min_scale * origin_target_sz[0],
+ config.max_scale * origin_target_sz[0])
+ res_h = np.clip(target_sz[1] * (1 - lr) + target[3] * lr,
+ config.min_scale * origin_target_sz[1],
+ config.max_scale * origin_target_sz[1])
+ return res_x, res_y, res_w, res_h
+
+
+def bboxshow(bbox, frame):
+ bbox = (
+ np.clip(bbox[0], 0, frame.shape[1]).astype(np.float64),
+ np.clip(bbox[1], 0, frame.shape[0]).astype(np.float64),
+ np.clip(bbox[2], 10, frame.shape[1]).astype(np.float64),
+ np.clip(bbox[3], 10, frame.shape[0]).astype(np.float64))
+ return bbox
+
+
+def result1show(acc, num_failures, frames, duration):
+ result1 = {}
+ result1['acc'] = acc
+ result1['num_failures'] = num_failures
+ result1['fps'] = round(len(frames) / duration, 3)
+ return result1
+
+
+def test(model_path, data_path, save_name):
+ session = create_session(model_path, "GPU")
+ inname = [input.name for input in session.get_inputs()]
+ outname = [output.name for output in session.get_outputs()]
+ direct_file = os.path.join(data_path, 'list.txt')
+ with open(direct_file, 'r') as f:
+ direct_lines = f.readlines()
+ video_names = np.sort([x.split('\n')[0] for x in direct_lines])
+ video_paths = [os.path.join(data_path, x) for x in video_names]
+ results = {}
+ accuracy = 0
+ all_overlaps = []
+ all_failures = []
+ gt_lenth = []
+ for video_path in tqdm(video_paths, total=len(video_paths)):
+ groundtruth_path = os.path.join(video_path, 'groundtruth.txt')
+ with open(groundtruth_path, 'r') as f:
+ boxes = f.readlines()
+ if ',' in boxes[0]:
+ boxes = [list(map(float, box.split(','))) for box in boxes]
+ else:
+ boxes = [list(map(int, box.split())) for box in boxes]
+ gt = boxes.copy()
+ gt[:][2] = gt[:][0] + gt[:][2]
+ gt[:][3] = gt[:][1] + gt[:][3]
+ frames = [os.path.join(video_path, 'color', x) for x in np.sort(os.listdir(os.path.join(video_path, 'color')))]
+ frames = [x for x in frames if '.jpg' in x]
+ tic = time.perf_counter()
+ template_idx = 0
+ valid_scope = 2 * config.valid_scope + 1
+ anchors = generate_anchors(config.total_stride, config.anchor_base_size, config.anchor_scales,
+ config.anchor_ratios,
+ valid_scope)
+ window = np.tile(np.outer(np.hanning(config.score_size), np.hanning(config.score_size))[None, :],
+ [config.anchor_num, 1, 1]).flatten()
+ res = []
+ for idx, frame in tqdm(enumerate(frames), total=len(frames)):
+ frame = cv2.imdecode(np.fromfile(frame, dtype=np.uint8), cv2.IMREAD_UNCHANGED)
+ h, w = frame.shape[0], frame.shape[1]
+ if idx == template_idx:
+ bbox = get_axis_aligned_bbox(boxes[idx])
+ pos = np.array(
+ [bbox[0] + bbox[2] / 2 - 1 / 2, bbox[1] + bbox[3] / 2 - 1 / 2]) # center x, center y, zero based
+ target_sz = np.array([bbox[2], bbox[3]])
+ bbox = np.array([bbox[0] + bbox[2] / 2 - 1 / 2, bbox[1] + bbox[3] / 2 - 1 / 2, bbox[2], bbox[3]])
+ origin_target_sz = np.array([bbox[2], bbox[3]])
+ img_mean = np.mean(frame, axis=(0, 1))
+ exemplar_img, _, _ = get_exemplar_image(frame, bbox,
+ config.exemplar_size, config.context_amount, img_mean)
+ exemplar_img = reshapeimg1(exemplar_img)
+ res.append([1])
+ elif idx < template_idx:
+ res.append([0])
+ else:
+ instance_img_np, _, _, scale_x = get_instance_image(frame, bbox, config.exemplar_size,
+ config.instance_size,
+ config.context_amount, img_mean)
+ instance_img_np = reshapeimg2(instance_img_np)
+ pred_score, pred_regress = session.run(outname, {inname[0]: exemplar_img, inname[1]: instance_img_np})
+ pred_score = predscore(pred_score)
+ delta = pred_regress[0]
+ box_pred = box_transform_inv(anchors, delta)
+ s_c = change(sz(box_pred[:, 2], box_pred[:, 3]) / (sz_wh(target_sz * scale_x))) # scale penalty
+ r_c = change(
+ (target_sz[0] / target_sz[1]) / (box_pred[:, 2] / box_pred[:, 3])) # ratio penalty
+ penalty = np.exp(-(r_c * s_c - 1.) * config.penalty_k)
+ pscore = penalty * pred_score
+ pscore = pscore * (1 - config.window_influence) + window * config.window_influence
+ best_pscore_id = np.argmax(pscore)
+ target = box_pred[best_pscore_id, :] / scale_x
+ lr = penalty[best_pscore_id] * pred_score[best_pscore_id] * config.lr_box
+ res_x, res_y, res_w, res_h = resshow(target, pos, frame, origin_target_sz, lr, target_sz)
+ pos = np.array([res_x, res_y])
+ target_sz = np.array([res_w, res_h])
+ bbox = np.array([res_x, res_y, res_w, res_h])
+ bbox = bboxshow(bbox, frame)
+ gbox = calculate(bbox)
+ if eval_.judge_failures(gbox, boxes[idx], 0):
+ res.append([2])
+ template_idx = min(idx + 5, len(frames) - 1)
+ else:
+ res.append(gbox)
+ duration = time.perf_counter() - tic
+ acc, overlaps, failures, num_failures = eval_.calculate_accuracy_failures(res, gt, [w, h])
+ accuracy += acc
+ result1 = result1show(acc, num_failures, frames, duration)
+ results[video_path.split('/')[-1]] = result1
+ all_overlaps.append(overlaps)
+ all_failures.append(failures)
+ gt_lenth.append(len(frames))
+ all_length = sum([len(x) for x in all_overlaps])
+ robustness = sum([len(x) for x in all_failures]) / all_length * 100
+ eao = eval_.calculate_eao("VOT2015", all_failures, all_overlaps, gt_lenth)
+ result1 = {}
+ result1['accuracy'] = accuracy / float(len(video_paths))
+ result1['robustness'] = robustness
+ result1['eao'] = eao
+ results['all_videos'] = result1
+ show(accuracy, video_paths, robustness, eao)
+ json.dump(results, open(save_name, 'w'))
+
+
+def parse_args():
+ '''parse_args'''
+ parser = argparse.ArgumentParser(description='Mindspore SiameseRPN Infering')
+ parser.add_argument('--device_target', type=str, default='GPU', choices=('Ascend', 'GPU'), help='run platform')
+ parser.add_argument('--device_id', type=int, default=0, help='DEVICE_ID')
+ # choose dataset_path vot2016 or vot vot2015
+ parser.add_argument('--dataset_path', type=str, default='vot2015', help='Dataset path')
+ parser.add_argument('--checkpoint_path', type=str, default='siamrpn.onnx', help='checkpoint of siamRPN')
+ parser.add_argument('--filename', type=str, default='onnx2015', help='save result file')
+ args_opt = parser.parse_args()
+ return args_opt
+
+
+if __name__ == '__main__':
+ args = parse_args()
+ if args.device_target == 'GPU':
+ device_id = args.device_id
+ context.set_context(device_id=device_id)
+ context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target)
+ model_file_path = args.checkpoint_path
+ data_file_path = args.dataset_path
+ save_file_name = args.filename
+ test(model_path=model_file_path, data_path=data_file_path, save_name=save_file_name)
diff --git a/research/cv/siamRPN/export_mindir.py b/research/cv/siamRPN/export.py
similarity index 93%
rename from research/cv/siamRPN/export_mindir.py
rename to research/cv/siamRPN/export.py
index 1706b521723105be03585a59c72b568b240f362d..47ebc23b996d7d73d07d480651e13dbe0c4d557b 100644
--- a/research/cv/siamRPN/export_mindir.py
+++ b/research/cv/siamRPN/export.py
@@ -1,4 +1,4 @@
-# Copyright 2021 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.
@@ -16,19 +16,17 @@
import argparse
import numpy as np
-
import mindspore
from mindspore import context, Tensor, export
from mindspore.train.serialization import load_checkpoint
from src.net import SiameseRPN
-
def siamrpn_export():
""" export function """
context.set_context(
mode=context.GRAPH_MODE,
- device_target="Ascend",
+ device_target="GPU",
save_graphs=False,
device_id=args.device_id)
net = SiameseRPN(groups=1, is_310infer=True)
@@ -37,6 +35,7 @@ def siamrpn_export():
input_data1 = Tensor(np.zeros([1, 3, 127, 127]), mindspore.float32)
input_data2 = Tensor(np.zeros([1, 3, 255, 255]), mindspore.float32)
input_data = [input_data1, input_data2]
+ # choose file_format= mindir or onnx
export(net, *input_data, file_name='siamrpn', file_format="MINDIR")
diff --git a/research/cv/siamRPN/requirement.txt b/research/cv/siamRPN/requirement.txt
index 2faf9a2c64d64e1de59c58f2d3844e393dc45bf6..924a77634bae2bf77a9d30848df9c7f1fd643bcd 100644
--- a/research/cv/siamRPN/requirement.txt
+++ b/research/cv/siamRPN/requirement.txt
@@ -2,4 +2,5 @@ lmdb
fire
opencv-python
tqdm
-Shapely
\ No newline at end of file
+Shapely
+onnxruntime-gpu
\ No newline at end of file
diff --git a/research/cv/siamRPN/scripts/run_evalonnx.sh b/research/cv/siamRPN/scripts/run_evalonnx.sh
new file mode 100644
index 0000000000000000000000000000000000000000..9e008bc3ddc9a1d554c16502e8253db21a6496dd
--- /dev/null
+++ b/research/cv/siamRPN/scripts/run_evalonnx.sh
@@ -0,0 +1,27 @@
+#!/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.
+# ============================================================================
+
+if [ $# != 3 ]
+then
+ echo "Usage: bash run_eval_onnx.sh [DATA_ROOT] [DATA_LST] [FILE_NAME]"
+exit 1
+fi
+
+export DATA_NAME=$1
+export MODEL_PATH=$2
+export FILENAME=$3
+python evalonnx.py --dataset_path=$DATA_NAME --checkpoint_path=$MODEL_PATH --filename=$FILENAME &> evalonnx2015.log &
+
diff --git a/research/cv/siamRPN/src/config.py b/research/cv/siamRPN/src/config.py
index 43cfc9c33175506398ccbeda0dc8775b8c48dabd..3d7627fe14ca3eefb325ddfa092b469c116d2e14 100644
--- a/research/cv/siamRPN/src/config.py
+++ b/research/cv/siamRPN/src/config.py
@@ -63,7 +63,7 @@ class Config:
gray_ratio = 0.25
score_size = int((instance_size - exemplar_size) / 8 + 1)
penalty_k = 0.22
- window_influence = 0.40
+ window_influence = 0.20
lr_box = 0.30
min_scale = 0.1
max_scale = 10
diff --git a/research/cv/siamRPN/src/net.py b/research/cv/siamRPN/src/net.py
index 7d059899eea30e2c00294eb6f7135023d1c70de2..3294c5e2956321d0cb9c5e4531563ccb3acb06c2 100644
--- a/research/cv/siamRPN/src/net.py
+++ b/research/cv/siamRPN/src/net.py
@@ -74,8 +74,8 @@ class SiameseRPN(nn.Cell):
self.op_split_input = ops.Split(axis=1, output_num=self.groups)
self.op_split_krenal = ops.Split(axis=0, output_num=self.groups)
self.op_concat = ops.Concat(axis=1)
- self.conv2d_cout = ops.Conv2D(out_channel=10, kernel_size=4)
- self.conv2d_rout = ops.Conv2D(out_channel=20, kernel_size=4)
+ self.conv2d_cout = ops.Conv2D(out_channel=10, kernel_size=(4, 4))
+ self.conv2d_rout = ops.Conv2D(out_channel=20, kernel_size=(4, 4))
self.regress_adjust = nn.Conv2d(4 * self.k, 4 * self.k, 1, pad_mode='valid', has_bias=True)
self.reshape = ops.Reshape()
self.transpose = ops.Transpose()