diff --git a/research/cv/3dcnn/README_CN.md b/research/cv/3dcnn/README_CN.md
index 0bee4fd768d0092cbfbd551937f2d934ebdc1532..9a01da084685c4dd8e2eeb1bcd7d1d7dc8553445 100644
--- a/research/cv/3dcnn/README_CN.md
+++ b/research/cv/3dcnn/README_CN.md
@@ -139,6 +139,7 @@ BraTS 2017原始数据集的文件目录结构如下所示：
│ ├── run_distribute_train_gpu.sh # 启动GPU分布式训练（8卡）
│ ├── run_standalone_train_gpu.sh # 启动GPU单机训练（单卡）
│ ├── run_eval_gpu.sh # 启动GPU评估
+ │ ├── run_onnx_eval.sh # ONNX推理shell脚本
├── src
│ ├── config.py # yaml文件解析
│ ├── dataset.py # 创建数据集
@@ -152,6 +153,7 @@ BraTS 2017原始数据集的文件目录结构如下所示：
│ ├── train.txt # 训练数据集
├── train.py # 训练脚本
├── eval.py # 评估脚本
+ ├── eval_onnx.py # ONNX评估脚本
├── export.py # 推理模型导出脚本
├── preprocess.py # 310推理数据预处理
├── postprocess.py # 310推理数据后处理
@@ -303,7 +305,7 @@ mean dice enhance:
python export.py --ckpt_file [CKPT_PATH] --file_name [FILE_NAME] --file_format [FILE_FORMAT]
```
-参数ckpt_file 是必需的，EXPORT_FORMAT 必须在 ["AIR", "MINDIR"]中进行选择。
+参数ckpt_file 是必需的，EXPORT_FORMAT 必须在 ["AIR", "MINDIR", "ONNX"]中进行选择。
## 推理过程
@@ -322,6 +324,21 @@ python export.py --ckpt_file [CKPT_PATH] --file_name [FILE_NAME] --file_format [
bash run_infer_310.sh [MINDIR_PATH] [DATASET_PATH] [TEST_PATH] [DEVICE_ID]
```
+### ONNX推理
+
+在执行推理之前，需要通过export.py导出onnx文件
+
+- 在GPU上使用修正后的BraST 2017训练数据集进行推理
+
+ 执行推理的命令如下所示，其中`DATA_PATH`是数据集路径；`TEST_PATH`是推理数据路径；`ONNX_PATH`是ONNX文件路径；`DEVICE_ID`可选，默认值为0。
+
+ ```shell
+ # ONNX 推理
+ bash run_onnx_eval.sh [DATA_PATH] [TEST_PATH] [ONNX_PATH] [CONFIG_PATH] [DEVICE_ID]
+ ```
+
+ 上述python命令将在后台运行，您可以通过eval_onnx.log文件查看结果。
+
# 模型描述
## 性能
diff --git a/research/cv/3dcnn/config.yaml b/research/cv/3dcnn/config.yaml
index fb1fc876968d193ce2ed093f055beb1398cdc093..697a1e561fbcf91221fac91332d076f1740a0542 100644
--- a/research/cv/3dcnn/config.yaml
+++ b/research/cv/3dcnn/config.yaml
@@ -51,6 +51,9 @@ image_width: 240
image_height: 240
image_channel: 155
+# onnx eval
+onnx_path: ""
+
---
# Help description for each configuration
data_path: "The directory of data."
diff --git a/research/cv/3dcnn/config_gpu.yaml b/research/cv/3dcnn/config_gpu.yaml
index 0c7f36b36948dcfd87e3f74e922d8f7c36eea953..d074f9de728f5b144c549d3ebe23a3d4dd6e9103 100644
--- a/research/cv/3dcnn/config_gpu.yaml
+++ b/research/cv/3dcnn/config_gpu.yaml
@@ -40,6 +40,9 @@ ckpt_file: "./dense24-5_4200.ckpt"
file_name: "dense24"
file_format: "MINDIR"
+# onnx eval
+onnx_path: ""
+
---
# Help description for each configuration
data_path: "The directory of data."
diff --git a/research/cv/3dcnn/eval_onnx.py b/research/cv/3dcnn/eval_onnx.py
new file mode 100644
index 0000000000000000000000000000000000000000..75b9000f396f76fbe5fe2d3c69d78ee547dd5d8e
--- /dev/null
+++ b/research/cv/3dcnn/eval_onnx.py
@@ -0,0 +1,151 @@
+# 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.
+# ============================================================================
+"""
+python eval.py
+"""
+import numpy as np
+import onnxruntime as ort
+
+from src.config import config
+from src.dataset import vox_generator_test
+
+def one_hot(label, num_classes):
+ """ one-hot encode """
+ label_ = np.zeros([len(label), num_classes])
+ label_[np.arange(len(label)), label] = 1
+ return label_
+
+def calculate_dice(true_label, pred_label, num_classes):
+ """
+ calculate dice
+
+ Args:
+ true_label: true sparse labels
+ pred_label: predict sparse labels
+ num_classes: number of classes
+
+ Returns:
+ dice evaluation index
+ """
+ true_label = true_label.astype(int)
+ pred_label = pred_label.astype(int)
+ true_label = true_label.flatten()
+ true_label = one_hot(true_label, num_classes)
+ pred_label = pred_label.flatten()
+ pred_label = one_hot(pred_label, num_classes)
+ intersection = np.sum(true_label * pred_label, axis=0)
+ return (2. * intersection) / (np.sum(true_label, axis=0) + np.sum(pred_label, axis=0))
+
+def create_session(checkpoint_path, target_device):
+ '''
+ create onnx session
+ '''
+ 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"')
+ session = ort.InferenceSession(checkpoint_path, providers=providers)
+ input_name_1 = session.get_inputs()[0].name
+ input_name_2 = session.get_inputs()[1].name
+
+ return session, input_name_1, input_name_2
+
+if __name__ == '__main__':
+ # test dataset
+ test_files = []
+ with open(config.test_path) as f:
+ for line in f:
+ test_files.append(line[:-1])
+
+ data_gen_test = vox_generator_test(config.data_path, test_files, config.correction)
+
+ # network
+ network, flair_t2_node_name, t1_t1ce_node_name = create_session(config.onnx_path, config.device_target)
+
+ OFFSET_H = config.offset_height
+ OFFSET_W = config.offset_width
+ OFFSET_C = config.offset_channel
+ HSIZE = config.height_size
+ WSIZE = config.width_size
+ CSIZE = config.channel_size
+ PSIZE = config.pred_size
+
+ OFFSET_PH = (HSIZE - PSIZE) // 2
+ OFFSET_PW = (WSIZE - PSIZE) // 2
+ OFFSET_PC = (CSIZE - PSIZE) // 2
+
+ batches_w = int(np.ceil((240 - WSIZE) / float(OFFSET_W))) + 1
+ batches_h = int(np.ceil((240 - HSIZE) / float(OFFSET_H))) + 1
+ batches_c = int(np.ceil((155 - CSIZE) / float(OFFSET_C))) + 1
+
+ dice_whole, dice_core, dice_et = [], [], []
+ for i in range(len(test_files)):
+ print('predicting %s' % test_files[i])
+ x, x_n, y = data_gen_test.__next__()
+ pred = np.zeros([240, 240, 155, 5])
+ for hi in range(batches_h):
+ offset_h = min(OFFSET_H * hi, 240 - HSIZE)
+ offset_ph = offset_h + OFFSET_PH
+ for wi in range(batches_w):
+ offset_w = min(OFFSET_W * wi, 240 - WSIZE)
+ offset_pw = offset_w + OFFSET_PW
+ for ci in range(batches_c):
+ offset_c = min(OFFSET_C * ci, 155 - CSIZE)
+ offset_pc = offset_c + OFFSET_PC
+ data = x[offset_h:offset_h + HSIZE, offset_w:offset_w + WSIZE, offset_c:offset_c + CSIZE, :]
+ data_norm = x_n[offset_h:offset_h + HSIZE, offset_w:offset_w + WSIZE, offset_c:offset_c + CSIZE, :]
+ data_norm = np.expand_dims(data_norm, 0)
+ if not np.max(data) == 0 and np.min(data) == 0:
+ flair_t2_node = data_norm[:, :, :, :, :2]
+ t1_t1ce_node = data_norm[:, :, :, :, 2:]
+ flair_t2_node = np.transpose(flair_t2_node, axes=[0, 4, 1, 2, 3])
+ t1_t1ce_node = np.transpose(t1_t1ce_node, axes=[0, 4, 1, 2, 3])
+ flair_t2_score, t1_t1ce_score = network.run(None, {flair_t2_node_name: flair_t2_node,
+ t1_t1ce_node_name: t1_t1ce_node})
+ t1_t1ce_score = np.transpose(t1_t1ce_score, axes=[0, 2, 3, 4, 1])
+ pred[offset_ph:offset_ph + PSIZE, offset_pw:offset_pw + PSIZE, offset_pc:offset_pc + PSIZE, :] \
+ += np.squeeze(t1_t1ce_score)
+
+ pred = np.argmax(pred, axis=-1)
+ pred = pred.astype(int)
+ print('calculating dice...')
+ whole_pred = (pred > 0).astype(int)
+ whole_gt = (y > 0).astype(int)
+ core_pred = (pred == 1).astype(int) + (pred == 4).astype(int)
+ core_gt = (y == 1).astype(int) + (y == 4).astype(int)
+ et_pred = (pred == 4).astype(int)
+ et_gt = (y == 4).astype(int)
+ dice_whole_batch = calculate_dice(whole_gt, whole_pred, 2)
+ dice_core_batch = calculate_dice(core_gt, core_pred, 2)
+ dice_et_batch = calculate_dice(et_gt, et_pred, 2)
+ dice_whole.append(dice_whole_batch)
+ dice_core.append(dice_core_batch)
+ dice_et.append(dice_et_batch)
+ print(dice_whole_batch)
+ print(dice_core_batch)
+ print(dice_et_batch)
+
+ dice_whole = np.array(dice_whole)
+ dice_core = np.array(dice_core)
+ dice_et = np.array(dice_et)
+
+ print('mean dice whole:')
+ print(np.mean(dice_whole, axis=0))
+ print('mean dice core:')
+ print(np.mean(dice_core, axis=0))
+ print('mean dice enhance:')
+ print(np.mean(dice_et, axis=0))
diff --git a/research/cv/3dcnn/requirements.txt b/research/cv/3dcnn/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..5feb16c5e2b381ee4b58577d20e0ce82fc096f2d
--- /dev/null
+++ b/research/cv/3dcnn/requirements.txt
@@ -0,0 +1,4 @@
+onnxruntime-gpu
+numpy
+pyyaml
+nibabel
diff --git a/research/cv/3dcnn/scripts/run_onnx_eval.sh b/research/cv/3dcnn/scripts/run_onnx_eval.sh
new file mode 100644
index 0000000000000000000000000000000000000000..26d633e516d1e14e1135102525a68821796e73f9
--- /dev/null
+++ b/research/cv/3dcnn/scripts/run_onnx_eval.sh
@@ -0,0 +1,55 @@
+#!/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.
+# ============================================================================
+
+set -e
+if [ $# -ne 5 ]
+then
+ echo "=============================================================================================================="
+ echo "Please run the script as: "
+ echo "bbash run_onnx_eval.sh DATA_PATH TEST_PATH ONNX_PATH CONFIG_PATH DEVICE_ID"
+ echo "For example: bash run_onnx_eval.sh ./data ./test ./onnx default_config.ymal 0"
+ echo "It is better to use the absolute path."
+ echo "=============================================================================================================="
+exit 1
+fi
+
+DATA_PATH=$1
+TEST_PATH=$2
+ONNX_PATH=$3
+CONFIG_PATH=$4
+DEVICE_ID=$5
+
+export DATA_PATH=${DATA_PATH}
+export TEST_PATH=${TEST_PATH}
+export ONNX_PATH=${ONNX_PATH}
+export CONFIG_PATH=${CONFIG_PATH}
+export CUDA_VISIBLE_DEVICES=${DEVICE_ID}
+
+if [ ! -d "$DATA_PATH" ]; then
+ echo "dataset does not exit"
+ exit
+fi
+
+echo "eval_onnx begin."
+cd ../
+nohup python eval_onnx.py > eval_onnx.log 2>&1 \
+--batch_size=1 \
+--data_path=$DATA_PATH \
+--test_path=$TEST_PATH \
+--onnx_path=$ONNX_PATH \
+--config_path=$CONFIG_PATH \
+--device_id=$DEVICE_ID &
+echo "eval_onnx background..."