diff --git a/research/cv/PAMTRI/MultiTaskNet/README_CN.md b/research/cv/PAMTRI/MultiTaskNet/README_CN.md
index c5535a52ae7107966fc9754ab872315a3c0ccb94..8b4db28f179c6c499dabc4d68f77098259da9adc 100644
--- a/research/cv/PAMTRI/MultiTaskNet/README_CN.md
+++ b/research/cv/PAMTRI/MultiTaskNet/README_CN.md
@@ -181,6 +181,7 @@ bash scripts/run_eval_gpu.sh [DATASET_NAME] [CKPT_PATH] [DEVICE_ID] [HEATMAP_SEG
| 鈹溾攢鈹€ run_single_train_gpu.sh // 鍗曞崱鍒癎PU鐨剆hell鑴氭湰
| 鈹溾攢鈹€ run_eval_ascend.sh // Ascend璇勪及鐨剆hell鑴氭湰
| 鈹溾攢鈹€ run_eval_gpu.sh // GPU璇勪及鐨剆hell鑴氭湰
+ | 鈹溾€斺€� run_onnx_eval_gpu.sh // ONNX鎺ㄧ悊shell鑴氭湰
| 鈹溾攢鈹€ run_infer_310.sh // Ascend鎺ㄧ悊shell鑴氭湰
鈹溾攢鈹€ src
| 鈹溾攢鈹€ dataset
@@ -201,6 +202,7 @@ bash scripts/run_eval_gpu.sh [DATASET_NAME] [CKPT_PATH] [DEVICE_ID] [HEATMAP_SEG
| | 鈹溾攢鈹€ save_callback.py // 杈硅缁冭竟鎺ㄧ悊鐨勫疄鐜�
| | 鈹溾攢鈹€ pthtockpt.py // pth鏍煎紡鐨勯璁粌妯″瀷杞崲涓篶kpt
鈹溾攢鈹€ eval.py // 绮惧害楠岃瘉鑴氭湰
+ 鈹溾€斺€� eval_onnx.py // ONNX绮惧害楠岃瘉鑴氭湰
鈹溾攢鈹€ train.py // 璁粌鑴氭湰
鈹溾攢鈹€ export.py // 鎺ㄧ悊妯″瀷瀵煎嚭鑴氭湰
鈹溾攢鈹€ preprocess.py // 310鎺ㄧ悊鍓嶅鐞嗚剼鏈�
@@ -440,6 +442,18 @@ python export.py --root /path/dataset --ckpt_path /path/ckpt --segmentaware --he
bash scripts/run_infer_310.sh [MINDIR_PATH] [DATASET_PATH] [NEED_PREPROCESS] [DEVICE_TARGET] [DEVICE_ID] [NEED_HEATMAP] [NEED_SEGMENT]
```
+### ONNX鎺ㄧ悊
+
+鍦ㄨ繘琛屾帹鐞嗕箣鍓嶆垜浠渶瑕佸厛瀵煎嚭妯″瀷銆�
+
+- 鍦℅PU鐜涓婁娇鐢╒eRi鏁版嵁闆嗚繘琛屾帹鐞�
+
+ 鎵ц鎺ㄧ悊鐨勫懡浠ゅ涓嬫墍绀猴紝鍏朵腑`ONNX_PATH`鏄痮nnx鏂囦欢璺緞锛沗DATASET_PATH`鏄帹鐞嗘暟鎹泦璺緞锛沗DEVICE_ID`鍙€夛紝榛樿鍊间负0锛沗HEATMAP_SEGMEN` 琛ㄧず閫夋嫨浣跨敤鐑浘杩樻槸鍒嗘銆�
+
+ ```bash
+ bash run_onnx_eval_gpu.sh DATASET_NAME ONNX_PATH DEVICE_ID HEATMAP_SEGMENT
+ ```
+
# 妯″瀷鎻忚堪
## 鎬ц兘
diff --git a/research/cv/PAMTRI/MultiTaskNet/eval_onnx.py b/research/cv/PAMTRI/MultiTaskNet/eval_onnx.py
new file mode 100644
index 0000000000000000000000000000000000000000..8fc5b959f452a1aea67bc41614d1821881ad7bfa
--- /dev/null
+++ b/research/cv/PAMTRI/MultiTaskNet/eval_onnx.py
@@ -0,0 +1,68 @@
+#!/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.
+# ============================================================================
+"""MultiTaskNet onnx_eval"""
+import ast
+import argparse
+from src.utils.evaluate import onnx_test
+from src.dataset.dataset import eval_create_dataset
+import onnxruntime as ort
+parser = argparse.ArgumentParser(description='eval MultiTaskNet')
+
+parser.add_argument('--device_target', type=str, default="GPU")
+parser.add_argument('--device_id', type=int, default=0)
+parser.add_argument('--root', type=str, default='./data', help="root path to data directory")
+parser.add_argument('-d', '--dataset', type=str, default='veri', help="name of the dataset")
+parser.add_argument('--height', type=int, default=256, help="height of an image (default: 256)")
+parser.add_argument('--width', type=int, default=256, help="width of an image (default: 256)")
+parser.add_argument('--test-batch', default=1, type=int, help="test batch size")
+parser.add_argument('--heatmapaware', type=ast.literal_eval, default=False, help="embed heatmaps to images")
+parser.add_argument('--segmentaware', type=ast.literal_eval, default=False, help="embed segments to images")
+parser.add_argument('--onnx_path', type=str, default='')
+args = parser.parse_args()
+
+def create_session(onnx_path, target_device):
+ if target_device == 'GPU':
+ providers = ['CUDAExecutionProvider']
+ elif target_device == 'CPU':
+ providers = ['CPUExecutionProvider']
+ else:
+ raise ValueError(f'Unsupported target device {target_device!r}. Expected one of: "CPU", "GPU"')
+
+ sessions = ort.InferenceSession(onnx_path, providers=providers)
+ input_names1 = sessions.get_inputs()[0].name
+ input_names2 = sessions.get_inputs()[1].name
+ input_names = [input_names1, input_names2]
+ return sessions, input_names
+
+if __name__ == '__main__':
+ target = args.device_target
+ train_dataset_path = args.root
+
+ query_dataloader, gallery_dataloader, num_train_vids, \
+ num_train_vcolors, num_train_vtypes, _vcolor2label, \
+ _vtype2label = eval_create_dataset(dataset_dir=args.dataset,
+ root=train_dataset_path,
+ width=args.width,
+ height=args.height,
+ keyptaware=True,
+ heatmapaware=args.heatmapaware,
+ segmentaware=args.segmentaware,
+ train_batch=args.test_batch)
+
+ session, input_name = create_session(args.onnx_path, 'GPU')
+
+ _distmat = onnx_test(session, input_name, True, True, query_dataloader, gallery_dataloader, _vcolor2label,
+ _vtype2label, return_distmat=True)
diff --git a/research/cv/PAMTRI/MultiTaskNet/requirements.txt b/research/cv/PAMTRI/MultiTaskNet/requirements.txt
index 88ef814d79e6464f4dd1007e8d85083501e2b6dd..44584dbe8e914a74b928330af44883afe954b17f 100644
--- a/research/cv/PAMTRI/MultiTaskNet/requirements.txt
+++ b/research/cv/PAMTRI/MultiTaskNet/requirements.txt
@@ -4,4 +4,5 @@ numpy==1.14.5
Pillow==5.1.0
scipy==1.2.0
opencv-python
-matplotlib
\ No newline at end of file
+matplotlib
+onnxruntime-gpu
\ No newline at end of file
diff --git a/research/cv/PAMTRI/MultiTaskNet/scripts/run_onnx_eval_gpu.sh b/research/cv/PAMTRI/MultiTaskNet/scripts/run_onnx_eval_gpu.sh
new file mode 100644
index 0000000000000000000000000000000000000000..c1d6c14bdd778bfcfe399ee8fe0396c02977617a
--- /dev/null
+++ b/research/cv/PAMTRI/MultiTaskNet/scripts/run_onnx_eval_gpu.sh
@@ -0,0 +1,65 @@
+#!/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 "Please run the script as: "
+echo "bash run_eval_gpu.sh DATASET_NAME ONNX_PATH DEVICE_ID HEATMAP_SEGMENT"
+echo "For example: bash run_eval_gpu.sh ../data/ ./*.onnx 0 h"
+echo "It is better to use the absolute path."
+echo "=============================================================================================================="
+
+if [ $# != 4 ]; then
+ echo "bash run_eval_gpu.sh DATASET_NAME ONNX_PATH DEVICE_ID HEATMAP_SEGMENT"
+ exit 1
+fi
+
+set -e
+get_real_path(){
+ if [ "${1:0:1}" == "/" ]; then
+ echo "$1"
+ else
+ echo "$(realpath -m $PWD/$1)"
+ fi
+}
+DATA_PATH=$(get_real_path $1)
+ONNX_PATH=$(get_real_path $2)
+PROJECT_DIR=$(cd "$(dirname "$0")" || exit; pwd)
+if [ "$4" == "h" ] || [ "$4" == "s" ];then
+ if [ "$4" == "h" ];then
+ need_heatmap=True
+ need_segment=False
+ else
+ need_heatmap=False
+ need_segment=True
+ fi
+else
+ echo "heatmap_segment must be h or s"
+ exit 1
+fi
+
+EXEC_PATH=$(pwd)
+echo "$EXEC_PATH"
+
+export PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION=python
+
+export RANK_SIZE=1
+
+python ${PROJECT_DIR}/../eval_onnx.py --root ${DATA_PATH} \
+ --onnx_path ${ONNX_PATH} \
+ --device_id $3 \
+ --device_target GPU \
+ --heatmapaware ${need_heatmap} \
+ --segmentaware ${need_segment} > ${PROJECT_DIR}/../eval_onnx_gpu.log 2>&1 &
diff --git a/research/cv/PAMTRI/MultiTaskNet/src/utils/evaluate.py b/research/cv/PAMTRI/MultiTaskNet/src/utils/evaluate.py
index c059ae6fdbbc59443506301a3d9c8e2dd1f44434..5e218d6eedd17cfceca36b23a705ce6dc5bf768e 100644
--- a/research/cv/PAMTRI/MultiTaskNet/src/utils/evaluate.py
+++ b/research/cv/PAMTRI/MultiTaskNet/src/utils/evaluate.py
@@ -188,3 +188,120 @@ def test(model, keyptaware, multitask, queryloader, galleryloader,
if return_distmat:
return distmat
return cmc[0]
+
+def onnx_test(InferenceSession, input_name, keyptaware, multitask, queryloader, galleryloader,
+ vcolor2label, vtype2label, ranks=range(1, 51), return_distmat=False):
+ """function eval"""
+
+ qf = []
+ q_vids = []
+ q_camids = []
+ q_vcolors = []
+ q_vtypes = []
+ pred_q_vcolors = []
+ pred_q_vtypes = []
+ for _, data in enumerate(queryloader.create_dict_iterator()):
+ imgs = data["img"]
+ vids = data["vid"]
+ camids = data["camid"]
+ vcolors = data["vcolor"]
+ vtypes = data["vtype"]
+ vkeypts = data["vkeypt"]
+
+ _, output_vcolors, output_vtypes, features = InferenceSession.run(None, {input_name[0]: imgs.asnumpy(),
+ input_name[1]: vkeypts.asnumpy()})
+
+ qf.append(features)
+ q_vids.extend(vids.asnumpy())
+ q_camids.extend(camids.asnumpy())
+ q_vcolors.extend(vcolors.asnumpy())
+ q_vtypes.extend(vtypes.asnumpy())
+ pred_q_vcolors.extend(output_vcolors)
+ pred_q_vtypes.extend(output_vtypes)
+
+ qf = np.concatenate(qf, axis=0)
+ q_vids = np.asarray(q_vids)
+ q_camids = np.asarray(q_camids)
+ q_vcolors = np.asarray(q_vcolors)
+ q_vtypes = np.asarray(q_vtypes)
+ pred_q_vcolors = np.asarray(pred_q_vcolors)
+ pred_q_vtypes = np.asarray(pred_q_vtypes)
+
+ print("Extracted features for query set, obtained {}-by-{} matrix".format(qf.shape[0], qf.shape[1]))
+
+ gf = []
+ g_vids = []
+ g_camids = []
+ g_vcolors = []
+ g_vtypes = []
+ pred_g_vcolors = []
+ pred_g_vtypes = []
+ for _, data in enumerate(galleryloader.create_dict_iterator()):
+ imgs = data["img"]
+ vids = data["vid"]
+ camids = data["camid"]
+ vcolors = data["vcolor"]
+ vtypes = data["vtype"]
+ vkeypts = data["vkeypt"]
+
+ _, output_vcolors, output_vtypes, features = InferenceSession.run(None, {input_name[0]: imgs.asnumpy(),
+ input_name[1]: vkeypts.asnumpy()})
+ gf.append(features)
+ g_vids.extend(vids.asnumpy())
+ g_camids.extend(camids.asnumpy())
+ g_vcolors.extend(vcolors.asnumpy())
+ g_vtypes.extend(vtypes.asnumpy())
+ pred_g_vcolors.extend(output_vcolors)
+ pred_g_vtypes.extend(output_vtypes)
+
+ gf = np.concatenate(gf, axis=0)
+ g_vids = np.asarray(g_vids)
+ g_camids = np.asarray(g_camids)
+ g_vcolors = np.asarray(g_vcolors)
+ g_vtypes = np.asarray(g_vtypes)
+ pred_g_vcolors = np.asarray(pred_g_vcolors)
+ pred_g_vtypes = np.asarray(pred_g_vtypes)
+
+ print("Extracted features for gallery set, obtained {}-by-{} matrix".format(gf.shape[0], gf.shape[1]))
+
+ m, n = qf.shape[0], gf.shape[0]
+ distmat = np.broadcast_to(np.power(qf, 2).sum(axis=1, keepdims=True), (m, n)) + \
+ np.broadcast_to(np.power(gf, 2).sum(axis=1, keepdims=True), (n, m)).T
+
+ distmat = distmat * 1 + (-2) * (np.matmul(qf, gf.T))
+
+ print("Computing CMC and mAP")
+ cmc, mAP = evaluate(distmat, q_vids, g_vids, q_camids, g_camids)
+
+ print("Results ----------")
+ print("mAP: {:.2%}".format(mAP))
+ print("CMC curve")
+ for r in ranks:
+ print("Rank-{:<3}: {:.2%}".format(r, cmc[r-1]))
+ print("------------------")
+
+ print("Compute attribute classification accuracy")
+
+ for q in range(q_vcolors.size):
+ q_vcolors[q] = vcolor2label[q_vcolors[q]]
+ for g in range(g_vcolors.size):
+ g_vcolors[g] = vcolor2label[g_vcolors[g]]
+ q_vcolor_errors = np.argmax(pred_q_vcolors, axis=1) - q_vcolors
+ g_vcolor_errors = np.argmax(pred_g_vcolors, axis=1) - g_vcolors
+ vcolor_error_num = np.count_nonzero(q_vcolor_errors) + np.count_nonzero(g_vcolor_errors)
+ vcolor_accuracy = 1.0 - (float(vcolor_error_num) / float(distmat.shape[0] + distmat.shape[1]))
+ print("Color classification accuracy: {:.2%}".format(vcolor_accuracy))
+
+ for q in range(q_vtypes.size):
+ q_vtypes[q] = vcolor2label[q_vtypes[q]]
+ for g in range(g_vtypes.size):
+ g_vtypes[g] = vcolor2label[g_vtypes[g]]
+ q_vtype_errors = np.argmax(pred_q_vtypes, axis=1) - q_vtypes
+ g_vtype_errors = np.argmax(pred_g_vtypes, axis=1) - g_vtypes
+ vtype_error_num = np.count_nonzero(q_vtype_errors) + np.count_nonzero(g_vtype_errors)
+ vtype_accuracy = 1.0 - (float(vtype_error_num) / float(distmat.shape[0] + distmat.shape[1]))
+ print("Type classification accuracy: {:.2%}".format(vtype_accuracy))
+
+ if return_distmat:
+ return distmat
+ return cmc[0]
diff --git a/research/cv/PAMTRI/PoseEstNet/README_CN.md b/research/cv/PAMTRI/PoseEstNet/README_CN.md
index 236091997764f9946cfe6992b8fd0fd9e92b3dc7..754f286297e926cc008326c4f7c10bf180856cb2 100644
--- a/research/cv/PAMTRI/PoseEstNet/README_CN.md
+++ b/research/cv/PAMTRI/PoseEstNet/README_CN.md
@@ -153,6 +153,7 @@ bash run_trans_gpu.sh [DATA_PATH] [CKPT_PATH] [DEVICE_ID]
| 鈹溾攢鈹€ run_distribute_train_gpu.sh // 鍒嗗竷寮忓埌GPU鐨剆hell鑴氭湰
| 鈹溾攢鈹€ run_eval_ascend.sh // Ascend璇勪及鐨剆hell鑴氭湰
| 鈹溾攢鈹€ run_eval_gpu.sh // GPU璇勪及鐨剆hell鑴氭湰
+ | 鈹溾€斺€� run_onnx_eval_gpu.sh // ONNX鎺ㄧ悊shell鑴氭湰
| 鈹溾攢鈹€ run_trans_ascend.sh // Ascend鐜涓嬬敓鎴愭暟鎹泦鑴氭湰
| 鈹溾攢鈹€ run_trans_gpu.sh // GPU鐜涓嬬敓鎴愭暟鎹泦鑴氭湰
| 鈹溾攢鈹€ run_infer_310.sh // Ascend鎺ㄧ悊shell鑴氭湰
@@ -186,6 +187,7 @@ bash run_trans_gpu.sh [DATA_PATH] [CKPT_PATH] [DEVICE_ID]
鈹溾攢鈹€ config.yaml // 鍥哄畾鍙傛暟
鈹溾攢鈹€ config_gpu.yaml // 鍥哄畾鍙傛暟锛坓pu锛�
鈹溾攢鈹€ eval.py // 绮惧害楠岃瘉鑴氭湰
+ 鈹溾€斺€� eval_onnx.py // ONNX绮惧害楠岃瘉鑴氭湰
鈹溾攢鈹€ train.py // 璁粌鑴氭湰
鈹溾攢鈹€ trans.py // 杞崲鏁版嵁闆嗚剼鏈�
鈹溾攢鈹€ export.py // 鎺ㄧ悊妯″瀷瀵煎嚭鑴氭湰
@@ -329,6 +331,25 @@ epcoh:2 step:, loss is 3.0897788
| pose_hrnet | 85.968 | 81.682 | 70.630 | 76.568 | 86.492 | 89.771 | 82.577 | 16.681 |
```
+### ONNX鎺ㄧ悊
+
+鍦ㄨ繘琛屾帹鐞嗕箣鍓嶆垜浠渶瑕佸厛瀵煎嚭妯″瀷銆侽NNX鍙互鍦℅PU鐜涓嬪鍑恒€�
+
+- 鍦℅PU鐜涓婁娇鐢╒eRi鏁版嵁闆嗚繘琛屾帹鐞�
+ 鎵ц鎺ㄧ悊鐨勫懡浠ゅ涓嬫墍绀猴紝鍏朵腑'ONNX_PATH'鏄痮nnx鏂囦欢璺緞锛�'DATA_PATH'鏄帹鐞嗘暟鎹泦璺緞锛�'DEVICE_TARGET'鏄澶囩被鍨嬶紝榛樿'GPU'銆�
+
+ ```bash
+ bash run_eval_gpu.sh DATA_PATH ONNX_PATH DEVICE_ID
+ ```
+
+ 鎺ㄧ悊鐨勭簿搴︾粨鏋滀繚瀛樺湪scripts鐩綍涓嬶紝鍦╫nnx_eval.log鏃ュ織鏂囦欢涓彲浠ユ壘鍒扮被浼间互涓嬬殑鍒嗙被鍑嗙‘鐜囩粨鏋溿€�
+
+ ```bash
+ | Arch | Wheel | Fender | Back | Front | WindshieldBack | WindshieldFront | Mean | Mean@0.1 |
+ | --- | --- | --- | --- | --- | --- | --- | --- | --- |
+ | pose_hrnet | 85.968 | 81.669 | 70.630 | 77.568 | 86.492 | 89.771 | 82.574 | 16.681 |
+ ```
+
## 瀵煎嚭杩囩▼
### 瀵煎嚭
diff --git a/research/cv/PAMTRI/PoseEstNet/eval_onnx.py b/research/cv/PAMTRI/PoseEstNet/eval_onnx.py
new file mode 100644
index 0000000000000000000000000000000000000000..3a681518d77aca042f5d3d9c046e638f6edc8970
--- /dev/null
+++ b/research/cv/PAMTRI/PoseEstNet/eval_onnx.py
@@ -0,0 +1,67 @@
+#!/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.
+# ============================================================================
+"""
+########################## eval_onnx PoseEstNet ##########################
+eval lenet according to model file:
+python eval_onnx.py --cfg config.yaml --data_dir datapath --onnx_path onnxpath
+"""
+import json
+import argparse
+from pathlib import Path
+import onnxruntime as ort
+from src.dataset import create_dataset, get_label
+from src.utils.function import onnx_validate
+from src.config import cfg, update_config
+
+parser = argparse.ArgumentParser(description='Eval PoseEstNet')
+
+parser.add_argument('--cfg', type=str, default='')
+parser.add_argument('--data_dir', type=str, default='')
+parser.add_argument('--onnx_path', type=str, default='')
+parser.add_argument('--device_target', type=str, default="GPU")
+
+args = parser.parse_args()
+
+def create_session(onnx_path, target_device, is_train):
+ if target_device == 'GPU':
+ providers = ['CUDAExecutionProvider']
+ elif target_device == 'CPU':
+ providers = ['CPUExecutionProvider']
+ else:
+ raise ValueError(f'Unsupported target device {target_device!r}. Expected one of: "CPU", "GPU"')
+ sessions = ort.InferenceSession(onnx_path, providers=providers)
+ input_names = sessions.get_inputs()[0].name
+ onnx_train = is_train
+ return sessions, input_names, onnx_train
+
+
+if __name__ == '__main__':
+ update_config(cfg, args)
+ target = args.device_target
+ data, dataset = create_dataset(cfg, args.data_dir, is_train=False)
+ json_path = get_label(cfg, args.data_dir)
+ dst_json_path = Path(json_path)
+
+ with dst_json_path.open('r') as dst_file:
+ allImage = json.load(dst_file)
+
+
+ print(target)
+ session, input_name, train = create_session(args.onnx_path, target, False)
+
+ print("============== Starting Testing ==============")
+
+ perf_indicator = onnx_validate(cfg, dataset, data, session, input_name, train, allImage)
diff --git a/research/cv/PAMTRI/PoseEstNet/requirements.txt b/research/cv/PAMTRI/PoseEstNet/requirements.txt
index 44b48adcd6b23e41f2e7e9fb81623e9571a1ca7e..54f47bf0435ad6aa3db28cfcbc083b76e7f2939e 100644
--- a/research/cv/PAMTRI/PoseEstNet/requirements.txt
+++ b/research/cv/PAMTRI/PoseEstNet/requirements.txt
@@ -8,3 +8,4 @@ pyyaml
json_tricks
scikit-image
yacs
+onnxruntime-gpu
\ No newline at end of file
diff --git a/research/cv/PAMTRI/PoseEstNet/scripts/run_onnx_eval_gpu.sh b/research/cv/PAMTRI/PoseEstNet/scripts/run_onnx_eval_gpu.sh
new file mode 100644
index 0000000000000000000000000000000000000000..688582f246321eccd54ac78002392807ee1e8a9d
--- /dev/null
+++ b/research/cv/PAMTRI/PoseEstNet/scripts/run_onnx_eval_gpu.sh
@@ -0,0 +1,49 @@
+#!/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 "Please run the script as: "
+echo "bash run_eval_gpu.sh DATA_PATH ONNX_PATH DEVICE_ID"
+echo "For example: bash run_eval_gpu.sh /path/dataset /path/onnx 0"
+echo "It is better to use the absolute path."
+echo "=============================================================================================================="
+
+if [ $# != 3 ]; then
+ echo "bash run_eval_gpu.sh DATA_PATH ONNX_PATH DEVICE_ID"
+ exit 1
+fi
+
+set -e
+get_real_path(){
+ if [ "${1:0:1}" == "/" ]; then
+ echo "$1"
+ else
+ echo "$(realpath -m $PWD/$1)"
+ fi
+}
+DATA_PATH=$(get_real_path $1)
+ONNX_PATH=$(get_real_path $2)
+PROJECT_DIR=$(cd "$(dirname "$0")" || exit; pwd)
+
+
+export PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION=python
+
+export DEVICE_ID=$3
+export RANK_SIZE=1
+
+python ${PROJECT_DIR}/../eval_onnx.py --cfg ${PROJECT_DIR}/../config_gpu.yaml \
+ --data_dir ${DATA_PATH} \
+ --device_target GPU \
+ --onnx_path ${ONNX_PATH} > ${PROJECT_DIR}/../eval_onnx_gpu.log 2>&1 &
diff --git a/research/cv/PAMTRI/PoseEstNet/src/dataset/dataset.py b/research/cv/PAMTRI/PoseEstNet/src/dataset/dataset.py
index b8cf26b86cbdaac24ba4f278fcd1841ffde77eba..2c9aa8d2caf3850dedfc2cb508093a649f41144d 100644
--- a/research/cv/PAMTRI/PoseEstNet/src/dataset/dataset.py
+++ b/research/cv/PAMTRI/PoseEstNet/src/dataset/dataset.py
@@ -18,9 +18,8 @@ import copy
import json
from pathlib import Path
import mindspore.dataset as ds
-
-import mindspore.dataset.vision.py_transforms as py_vision
-from mindspore.dataset.transforms.py_transforms import Compose
+import mindspore.dataset.vision as vision
+from mindspore.dataset.transforms.transforms import Compose
from mindspore.communication.management import get_rank
from .veri import VeRiDataset
@@ -44,8 +43,8 @@ def create_dataset(cfg, data_dir, is_train=True):
"joints", "joints_vis"], num_parallel_workers=1, shuffle=False, num_shards=1, shard_id=0)
trans = Compose([
- py_vision.ToTensor(),
- py_vision.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225), is_hwc=False)
+ vision.ToTensor(),
+ vision.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225), is_hwc=False)
])
dataset = dataset.map(operations=trans, input_columns="input", num_parallel_workers=8)
diff --git a/research/cv/PAMTRI/PoseEstNet/src/dataset/veri.py b/research/cv/PAMTRI/PoseEstNet/src/dataset/veri.py
index 9b67412f9753629a4144a2aea8a24fb3e7585577..26570e7f43f35fbd1fe6c32e61399dc53ab1226d 100644
--- a/research/cv/PAMTRI/PoseEstNet/src/dataset/veri.py
+++ b/research/cv/PAMTRI/PoseEstNet/src/dataset/veri.py
@@ -117,9 +117,9 @@ class VeRiDataset(JointsDataset):
for row in reader:
joints = []
vis = []
- top_lft = btm_rgt = [int(row[3]), int(row[4])]
+ top_lft = btm_rgt = [int(float(row[3])), int(float(row[4]))]
for j in range(36):
- joint = [int(row[j*3+3]), int(row[j*3+4]), int(row[j*3+5])]
+ joint = [int(float(row[j*3+3])), int(float(row[j*3+4])), int(float(row[j*3+5]))]
joints.append(joint)
vis.append(joint[2])
if joint[0] < top_lft[0]:
diff --git a/research/cv/PAMTRI/PoseEstNet/src/utils/function.py b/research/cv/PAMTRI/PoseEstNet/src/utils/function.py
index 0f8c81958b49628226692c939081338a2cf5d776..a443edd9c9713afb3177a7053079848dd0979d52 100644
--- a/research/cv/PAMTRI/PoseEstNet/src/utils/function.py
+++ b/research/cv/PAMTRI/PoseEstNet/src/utils/function.py
@@ -136,6 +136,97 @@ def validate(config, val_loader, val_dataset, model, allImage):
return perf_indicator
+def onnx_validate(config, val_loader, val_dataset, InferenceSession, input_name, is_train, allImage):
+ """onnx_validate"""
+
+ num_samples = len(val_dataset)
+ all_preds = np.zeros(
+ (num_samples, config.MODEL.NUM_JOINTS, 3),
+ dtype=np.float32
+ )
+ all_boxes = np.zeros((num_samples, 6))
+ image_path = []
+ filenames = []
+ imgnums = []
+ idx = 0
+ for i, data in enumerate(val_loader.create_dict_iterator()):
+ _input = data["input"]
+ center = data["center"]
+ scale = data["scale"]
+ score = data["score"]
+ image_label = data["image"]
+ joints = data["joints"]
+ joints_vis = data["joints_vis"]
+
+ joints = joints.asnumpy()
+ joints_vis = joints_vis.asnumpy()
+
+ image = []
+ for j in range(config.TEST.BATCH_SIZE):
+ image.append(allImage['{}'.format(image_label[j])])
+
+ outputs = InferenceSession.run(None, {input_name: _input.asnumpy()})[0]
+
+ if isinstance(outputs, list):
+ print("output is tuple")
+ output = outputs[-1]
+ else:
+ output = outputs
+ if config.TEST.FLIP_TEST:
+ # this part is ugly, because pytorch has not supported negative index
+ input_flipped = np.flip(_input.asnumpy(), 3)
+ outputs_flipped = InferenceSession.run(None, {input_name: input_flipped})[0]
+
+ if isinstance(outputs_flipped, list):
+ output_flipped = outputs_flipped[-1]
+ else:
+ output_flipped = outputs_flipped
+
+ output_flipped = flip_back(output_flipped, val_dataset.flip_pairs)
+
+ # feature is not aligned, shift flipped heatmap for higher accuracy
+ if config.TEST.SHIFT_HEATMAP: # true
+ output_flipped_copy = output_flipped
+ output_flipped[:, :, :, 1:] = output_flipped_copy[:, :, :, 0:-1]
+
+ output = (output + output_flipped) * 0.5
+
+ # measure accuracy and record loss
+ num_images = _input.shape[0]
+
+ c = center.asnumpy()
+ s = scale.asnumpy()
+ score = score.asnumpy()
+
+ output_copy = output
+
+ preds, maxvals = get_final_preds(config, output_copy, c, s)
+
+ all_preds[idx:idx + num_images, :, 0:2] = preds[:, :, 0:2]
+ all_preds[idx:idx + num_images, :, 2:3] = maxvals
+ # double check this all_boxes parts
+ all_boxes[idx:idx + num_images, 0:2] = c[:, 0:2]
+ all_boxes[idx:idx + num_images, 2:4] = s[:, 0:2]
+ all_boxes[idx:idx + num_images, 4] = np.prod(s*200, 1)
+ all_boxes[idx:idx + num_images, 5] = score
+ image_path.extend(image)
+
+ idx += num_images
+
+ print('-------- Test: [{0}/{1}] ---------'.format(i, val_loader.get_dataset_size()))
+ name_values, perf_indicator = val_dataset.evaluate(
+ all_preds, '', all_boxes, image_path,
+ filenames, imgnums
+ )
+ model_name = config.MODEL.NAME
+ if isinstance(name_values, list):
+ for name_value in name_values:
+ _print_name_value(name_value, model_name)
+ else:
+ _print_name_value(name_values, model_name)
+
+ return perf_indicator
+
def output_preds(config, val_loader, val_dataset, model, root, test_set, output_dir):
"""output_preds"""
gt_file = os.path.join(root, 'label_{}.csv'.format(test_set))