diff --git a/research/cv/ReIDStrongBaseline/README.md b/research/cv/ReIDStrongBaseline/README.md
index f13e1e3e96f5c83abaa8ed05e48d65b1cf61eef7..363c9c8b48a1f20c6f652de99658aefce4af1b85 100644
--- a/research/cv/ReIDStrongBaseline/README.md
+++ b/research/cv/ReIDStrongBaseline/README.md
@@ -84,8 +84,8 @@ DukeMTMC-reID
## [Environment Requirements](#contents)
-- Hardware（GPU）
- - Prepare hardware environment with GPU processor.
+- Hardware（Ascend/GPU）
+ - Prepare hardware environment with Ascend or GPU processor.
- Framework
- [MindSpore](https://gitee.com/mindspore/mindspore)
- For more information, please check the resources below：
@@ -94,14 +94,24 @@ DukeMTMC-reID
## [Quick Start](#contents)
-### [Running scripts](#contents)
-
Model uses pre-trained backbone ResNet50 trained on ImageNet2012. [Link](https://download.mindspore.cn/model_zoo/r1.3/resnet50_ascend_v130_imagenet2012_official_cv_bs256_top1acc76.97__top5acc_93.44/)
After dataset preparation, you can start training and evaluation as follows:
(Note that you must specify dataset path in `configs/market1501_config.yml`)
+### [Running on Ascend](#contents)
+
+```bash
+# run distributed training example
+bash scripts/run_distribute_train_ascend.sh ./configs/market1501_config.yml /path/to/dataset/ /path/to/output/ /path/to/pretrained_resnet50.ckpt rank_table_8pcs.json 8
+
+# run evaluation example
+bash scripts/run_eval_ascend.sh ./configs/market1501_config.yml /your/path/checkpoint_file /path/to/dataset/
+```
+
+### [Running on GPU](#contents)
+
```bash
# run training example
bash scripts/run_standalone_train_gpu.sh ./configs/market1501_config.yml 0 /path/to/dataset/ /path/to/output/ /path/to/pretrained_resnet50.pth
@@ -119,6 +129,7 @@ bash scripts/run_eval_gpu.sh ./configs/market1501_config.yml /your/path/checkpoi
```text
ReIDStrongBaseline
+├── ascend310_infer # application for 310 inference
├── configs
│ ├── dukemtmc_config.yml # Training/evaluation config on DukeMTMC dataset
│ └── market1501_config.yml # Training/evaluation config on Market1501 dataset
@@ -129,8 +140,11 @@ ReIDStrongBaseline
│ ├── local_adapter.py # Environment variables parser
│ └── moxing_adapter.py # Moxing adapter for ModelArts
├── scripts
+│ ├── run_distribute_train_Ascend.sh # Start multi Ascend training
│ ├── run_distribute_train_gpu.sh # Start multi GPU training
+│ ├── run_eval_Ascend.sh # Start single Ascend evaluation
│ ├── run_eval_gpu.sh # Start single GPU evaluation
+│ ├── run_infer_310.sh # Start 310 inference
│ └── run_standalone_train_gpu.sh # Start single GPU training
├── src
│ ├── callbacks.py # Logging to file callbacks
@@ -154,6 +168,7 @@ ReIDStrongBaseline
│ └── triplet_loss.py # Triplet Loss definition
├── eval.py # Evaluate the network
├── export.py # Export the network
+├── postprogress.py # post process for 310 inference
├── train.py # Train the network
├── requirements.txt # Required libraries
└── README.md
@@ -176,7 +191,7 @@ usage: train.py --config_path CONFIG_PATH [--distribute DISTRIBUTE] [--device_t
options:
--config_path path to .yml config file
--distribute pre_training by several devices: "true"(training by more than 1 device) | "false", default is "false"
- --device_target target device ("GPU" | "CPU")
+ --device_target target device ("Ascend" | "GPU" | "CPU")
--max_epoch epoch size: N, default is 120
--start_decay_epoch epoch to decay, default is '40,70'
--ids_per_batch number of person in batch, default is 16 (8 for distributed)
@@ -225,6 +240,19 @@ Parameters for learning rate:
- Set options in `configs/market1501_config.yaml` or `configs/dukemtmc_config.yaml`,
including paths, learning rate and network hyperparameters.
+### Usage
+
+#### on Ascend
+
+- Run `run_distribute_train_Ascend.sh` for distributed training of ReID Strong Baseline model.
+- The `RANK_TABLE_FILE` is placed under `scripts/`
+
+ ```bash
+ bash scripts/run_distribute_train_Ascend.sh CONFIG_PATH DATA_DIR OUTPUT_PATH PRETRAINED_RESNET50 RANK_TABLE_FILE RANK_SIZE
+ ```
+
+#### on GPU
+
- Run `run_standalone_train_gpu.sh` for non-distributed training of model.
```bash
@@ -241,6 +269,18 @@ Parameters for learning rate:
- Set options in `market1501_config.yaml`.
+### Usage
+
+#### on Ascend
+
+- Run `bash scripts/run_eval_Ascend.sh` for evaluation of ReID Strong Baseline model.
+
+ ```bash
+ bash scripts/run_eval_Ascend.sh CONFIG_PATH CKPT_PATH DATA_DIR
+ ```
+
+#### on GPU
+
- Run `bash scripts/run_eval_gpu.sh` for evaluation of ReID Strong Baseline model.
```bash
@@ -274,61 +314,61 @@ Inference result will be shown in the terminal
#### Market1501 Training Performance
-| Parameters | GPU |
-| -------------------------- | -------------------------------------------------------------- |
-| Resource | 8x Tesla V100-PCIE 32G |
-| uploaded Date | 03/11/2022 (month/day/year) |
-| MindSpore Version | 1.5.0 |
-| Dataset | Market1501 |
-| Training Parameters | max_epoch=120, ids_per_batch=8, start_decay_epoch=151, lr_init=0.0014, lr_cri=1.0, decay_epochs='40,70' |
-| Optimizer | Adam, SGD |
-| Loss Function | Triplet, Smooth Identity, Center |
-| Speed | 182ms/step (8pcs) |
-| Loss | 0.24 |
-| Params (M) | 25.1 |
-| Checkpoint for inference | 319Mb (.ckpt file) |
-| Scripts | [ReID Strong Baseline scripts](scripts) |
+| Parameters | Ascend | GPU |
+| -------------------------- | --------------------------- |---------------------------------------------------------- |
+| Resource | 8x Ascend 910 32G |8x Tesla V100-PCIE 32G |
+| uploaded Date | 04/21/2022 (month/day/year) |03/11/2022 (month/day/year) |
+| MindSpore Version | 1.3.0 |1.5.0 |
+| Dataset | Market1501 |Market1501 |
+| Training Parameters | max_epoch=120, ids_per_batch=8, start_decay_epoch=151, lr_init=0.0014, lr_cri=1.0, decay_epochs='40,70' |max_epoch=120, ids_per_batch=8, start_decay_epoch=151, lr_init=0.0014, lr_cri=1.0, decay_epochs='40,70' |
+| Optimizer | Adam, SGD |Adam, SGD |
+| Loss Function | Triplet, Smooth Identity, Center |Triplet, Smooth Identity, Center |
+| Speed | 536ms/step (8pcs) |182ms/step (8pcs) |
+| Loss | 0.28 |0.24 |
+| Params (M) | 24.8 |25.1 |
+| Checkpoint for inference | 305Mb (.ckpt file) |319Mb (.ckpt file) |
+| Scripts | [ReID Strong Baseline scripts](scripts) |[ReID Strong Baseline scripts](scripts) |
#### Market1501 Evaluation Performance
-| Parameters | GPU |
-| ------------------- | --------------------------- |
-| Resource | 1x Tesla V100-PCIE 32G |
-| Uploaded Date | 03/11/2022 (month/day/year) |
-| MindSpore Version | 1.5.0 |
-| Dataset | Market1501 |
-| batch_size | 32 |
-| outputs | mAP, Rank-1 |
-| Accuracy | mAP: 86.99%, rank-1: 94.48% |
+| Parameters | Ascend | GPU |
+| ------------------- | ----------------------------- | --------------------------- |
+| Resource | 1x Ascend 910 32G | 1x Tesla V100-PCIE 32G |
+| Uploaded Date | 04/21/2022 (month/day/year) | 03/11/2022 (month/day/year) |
+| MindSpore Version | 1.3.0 | 1.5.0 |
+| Dataset | Market1501 | Market1501 |
+| batch_size | 32 | 32 |
+| outputs | mAP, Rank-1 | mAP, Rank-1 |
+| Accuracy | mAP: 86.85%, rank-1: 94.36% | mAP: 86.99%, rank-1: 94.48% |
#### DukeMTMC-reID Training Performance
-| Parameters | GPU |
-| -------------------------- | -------------------------------------------------------------- |
-| Resource | 8x Tesla V100-PCIE 32G |
-| uploaded Date | 03/11/2022 (month/day/year) |
-| MindSpore Version | 1.5.0 |
-| Dataset | DukeMTMC-reID |
-| Training Parameters | max_epoch=120, ids_per_batch=8, start_decay_epoch=151, lr_init=0.0014, lr_cri=1.0, decay_epochs='40,70' |
-| Optimizer | Adam, SGD |
-| Loss Function | Triplet, Smooth Identity, Center |
-| Speed | 180ms/step (8pcs) |
-| Loss | 0.24 |
-| Params (M) | 25.1 |
-| Checkpoint for inference | 319Mb (.ckpt file) |
-| Scripts | [ReID Strong Baseline scripts](scripts) |
+| Parameters | Ascend | GPU |
+| -------------------------- |---------------------------- | ------------------------------------------- |
+| Resource | 8x Ascend 910 32G | 8x Tesla V100-PCIE 32G |
+| uploaded Date | 04/21/2022 (month/day/year) | 03/11/2022 (month/day/year) |
+| MindSpore Version | 1.3.0 | 1.5.0 |
+| Dataset | DukeMTMC-reID | DukeMTMC-reID |
+| Training Parameters | max_epoch=120, ids_per_batch=8, start_decay_epoch=151, lr_init=0.0014, lr_cri=1.0, decay_epochs='40,70'| max_epoch=120, ids_per_batch=8, start_decay_epoch=151, lr_init=0.0014, lr_cri=1.0, decay_epochs='40,70' |
+| Optimizer | Adam, SGD | Adam, SGD |
+| Loss Function | Triplet, Smooth Identity, Center | Triplet, Smooth Identity, Center |
+| Speed | 524ms/step (8pcs) | 180ms/step (8pcs) |
+| Loss | 0.27 | 0.24 |
+| Params (M) | 24.8 |25.1 |
+| Checkpoint for inference | 302Mb (.ckpt file) | 319Mb (.ckpt file) |
+| Scripts | [ReID Strong Baseline scripts](scripts)| [ReID Strong Baseline scripts](scripts) |
#### DukeMTMC-reID Evaluation Performance
-| Parameters | GPU |
-| ------------------- | --------------------------- |
-| Resource | 1x Tesla V100-PCIE 32G |
-| Uploaded Date | 03/11/2022 (month/day/year) |
-| MindSpore Version | 1.5.0 |
-| Dataset | DukeMTMC-reID |
-| batch_size | 32 |
-| outputs | mAP, Rank-1 |
-| Accuracy | mAP: 76.68%, rank-1: 87.34% |
+| Parameters | Ascend | GPU |
+| ------------------- | --------------------------- | --------------------------- |
+| Resource | 1x Ascend 910 32G | 1x Tesla V100-PCIE 32G |
+| Uploaded Date | 04/21/2022 (month/day/year) | 03/11/2022 (month/day/year) |
+| MindSpore Version | 1.3.0 | 1.5.0 |
+| Dataset | DukeMTMC-reID | DukeMTMC-reID |
+| batch_size | 32 | 32 |
+| outputs | mAP, Rank-1 | mAP, Rank-1 |
+| Accuracy | mAP: 76.58%, rank-1: 87.43% | mAP: 76.68%, rank-1: 87.34% |
## [Description of Random Situation](#contents)
diff --git a/research/cv/ReIDStrongBaseline/ascend310_infer/CMakeLists.txt b/research/cv/ReIDStrongBaseline/ascend310_infer/CMakeLists.txt
new file mode 100644
index 0000000000000000000000000000000000000000..ee3c85447340e0449ff2b70ed24f60a17e07b2b6
--- /dev/null
+++ b/research/cv/ReIDStrongBaseline/ascend310_infer/CMakeLists.txt
@@ -0,0 +1,14 @@
+cmake_minimum_required(VERSION 3.14.1)
+project(Ascend310Infer)
+add_compile_definitions(_GLIBCXX_USE_CXX11_ABI=0)
+set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -O0 -g -std=c++17 -Werror -Wall -fPIE -Wl,--allow-shlib-undefined")
+set(PROJECT_SRC_ROOT ${CMAKE_CURRENT_LIST_DIR}/)
+option(MINDSPORE_PATH "mindspore install path" "")
+include_directories(${MINDSPORE_PATH})
+include_directories(${MINDSPORE_PATH}/include)
+include_directories(${PROJECT_SRC_ROOT})
+find_library(MS_LIB libmindspore.so ${MINDSPORE_PATH}/lib)
+file(GLOB_RECURSE MD_LIB ${MINDSPORE_PATH}/_c_dataengine*)
+
+add_executable(main src/main.cc src/utils.cc)
+target_link_libraries(main ${MS_LIB} ${MD_LIB} gflags)
diff --git a/research/cv/ReIDStrongBaseline/ascend310_infer/build.sh b/research/cv/ReIDStrongBaseline/ascend310_infer/build.sh
new file mode 100644
index 0000000000000000000000000000000000000000..a6a9ed6f01913ebb27018273a2412fece3dc3d3c
--- /dev/null
+++ b/research/cv/ReIDStrongBaseline/ascend310_infer/build.sh
@@ -0,0 +1,26 @@
+#!/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 [ -d out ]; then
+ rm -rf out
+fi
+
+mkdir out
+cd out || exit
+
+cmake .. \
+ -DMINDSPORE_PATH="`pip show mindspore-ascend | grep Location | awk '{print $2"/mindspore"}' | xargs realpath`"
+make
diff --git a/research/cv/ReIDStrongBaseline/ascend310_infer/inc/utils.h b/research/cv/ReIDStrongBaseline/ascend310_infer/inc/utils.h
new file mode 100644
index 0000000000000000000000000000000000000000..a128cdfec8f6438f3b00a5be0a826cbd86f1c365
--- /dev/null
+++ b/research/cv/ReIDStrongBaseline/ascend310_infer/inc/utils.h
@@ -0,0 +1,32 @@
+/**
+ * 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.
+ */
+
+#ifndef MINDSPORE_INFERENCE_UTILS_H_
+#define MINDSPORE_INFERENCE_UTILS_H_
+
+#include <sys/stat.h>
+#include <dirent.h>
+#include <vector>
+#include <string>
+#include <memory>
+#include "include/api/types.h"
+
+std::vector<std::string> GetAllFiles(std::string_view dirName, const std::string& inputtype);
+DIR *OpenDir(std::string_view dirName);
+std::string RealPath(std::string_view path);
+mindspore::MSTensor ReadFileToTensor(const std::string &file);
+int WriteResult(const std::string& imageFile, const std::vector<mindspore::MSTensor> &outputs);
+#endif
diff --git a/research/cv/ReIDStrongBaseline/ascend310_infer/src/main.cc b/research/cv/ReIDStrongBaseline/ascend310_infer/src/main.cc
new file mode 100644
index 0000000000000000000000000000000000000000..b6e42d60cb815267d8383900f372e08ea232e494
--- /dev/null
+++ b/research/cv/ReIDStrongBaseline/ascend310_infer/src/main.cc
@@ -0,0 +1,183 @@
+/**
+ * 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.
+ */
+#include <sys/time.h>
+#include <gflags/gflags.h>
+#include <dirent.h>
+#include <iostream>
+#include <string>
+#include <algorithm>
+#include <iosfwd>
+#include <vector>
+#include <fstream>
+#include <sstream>
+
+#include "../inc/utils.h"
+#include "include/dataset/execute.h"
+#include "include/dataset/transforms.h"
+#include "include/dataset/vision.h"
+#include "include/dataset/vision_ascend.h"
+#include "include/api/types.h"
+#include "include/api/model.h"
+#include "include/api/serialization.h"
+#include "include/api/context.h"
+
+using mindspore::Serialization;
+using mindspore::Model;
+using mindspore::Context;
+using mindspore::Status;
+using mindspore::ModelType;
+using mindspore::Graph;
+using mindspore::GraphCell;
+using mindspore::kSuccess;
+using mindspore::MSTensor;
+using mindspore::DataType;
+using mindspore::dataset::Execute;
+using mindspore::dataset::TensorTransform;
+using mindspore::dataset::vision::Decode;
+using mindspore::dataset::vision::Resize;
+using mindspore::dataset::vision::Normalize;
+using mindspore::dataset::vision::HWC2CHW;
+using mindspore::dataset::transforms::TypeCast;
+
+DEFINE_string(model_path, "", "model path");
+DEFINE_string(dataset_path, "", "dataset path");
+DEFINE_string(input_type, "", "gallery or query");
+
+DEFINE_int32(input_width, 128, "input width");
+DEFINE_int32(input_height, 256, "input height");
+DEFINE_int32(device_id, 0, "device id");
+DEFINE_string(precision_mode, "allow_fp32_to_fp16", "precision mode");
+DEFINE_string(op_select_impl_mode, "", "op select impl mode");
+DEFINE_string(aipp_path, "./aipp.cfg", "aipp path");
+DEFINE_string(device_target, "Ascend310", "device target");
+
+size_t GetFeature(MSTensor data);
+
+size_t GetFeature(MSTensor data) {
+ std::string homePath = "./" + FLAGS_input_type + "_result_Files";
+ std::string outFileName = homePath + "/feature_data.txt";
+ float max_value = -1;
+ size_t max_idx = 0;
+ const float *p = reinterpret_cast<const float *>(data.MutableData());
+ std::ofstream outfile;
+ outfile.open(outFileName, std::ios::app);
+
+ for (size_t i = 0; i < data.DataSize() / sizeof(float); ++i) {
+ outfile << p[i] << std::endl;
+ if (p[i] > max_value) {
+ max_value = p[i];
+ max_idx = i;
+ }
+ }
+ return max_idx;
+}
+
+int main(int argc, char **argv) {
+ gflags::ParseCommandLineFlags(&argc, &argv, true);
+ if (RealPath(FLAGS_model_path).empty()) {
+ std::cout << "Invalid model" << std::endl;
+ return 1;
+ }
+
+ auto context = std::make_shared<Context>();
+ auto ascend310_info = std::make_shared<mindspore::Ascend310DeviceInfo>();
+ ascend310_info->SetDeviceID(FLAGS_device_id);
+ context->MutableDeviceInfo().push_back(ascend310_info);
+
+ Graph graph;
+ Status ret = Serialization::Load(FLAGS_model_path, ModelType::kMindIR, &graph);
+ if (ret != kSuccess) {
+ std::cout << "Load model failed." << std::endl;
+ return 1;
+ }
+
+ Model model;
+ ret = model.Build(GraphCell(graph), context);
+ if (ret != kSuccess) {
+ std::cout << "ERROR: Build failed." << std::endl;
+ return 1;
+ }
+
+ std::vector<MSTensor> modelInputs = model.GetInputs();
+
+ auto all_files = GetAllFiles(FLAGS_dataset_path, FLAGS_input_type);
+ if (all_files.empty()) {
+ std::cout << "ERROR: no input data." << std::endl;
+ return 1;
+ }
+
+ auto decode = Decode();
+ auto resize = Resize({256, 128});
+ auto normalize = Normalize({123.675, 116.28, 103.53}, {58.395, 57.12, 57.375});
+ auto hwc2chw = HWC2CHW();
+ auto typeCast = TypeCast(DataType::kNumberTypeFloat16);
+
+ mindspore::dataset::Execute transformDecode(decode);
+ mindspore::dataset::Execute transform({resize, normalize, hwc2chw});
+ mindspore::dataset::Execute transformCast(typeCast);
+
+ std::map<double, double> costTime_map;
+
+ size_t size = all_files.size();
+ for (size_t i = 0; i < size; ++i) {
+ struct timeval start;
+ struct timeval end;
+ double startTime_ms;
+ double endTime_ms;
+ std::vector<MSTensor> inputs;
+ std::vector<MSTensor> outputs;
+
+ std::cout << "Start predict input files:" << all_files[i] << std::endl;
+ mindspore::MSTensor image = ReadFileToTensor(all_files[i]);
+
+ transformDecode(image, &image);
+ std::vector<int64_t> shape = image.Shape();
+ transform(image, &image);
+
+ inputs.emplace_back(modelInputs[0].Name(), modelInputs[0].DataType(), modelInputs[0].Shape(),
+ image.Data().get(), image.DataSize());
+ gettimeofday(&start, NULL);
+ model.Predict(inputs, &outputs);
+ gettimeofday(&end, NULL);
+
+ GetFeature(outputs[0]);
+
+ startTime_ms = (1.0 * start.tv_sec * 1000000 + start.tv_usec) / 1000;
+ endTime_ms = (1.0 * end.tv_sec * 1000000 + end.tv_usec) / 1000;
+ costTime_map.insert(std::pair<double, double>(startTime_ms, endTime_ms));
+ }
+ double average = 0.0;
+ int infer_cnt = 0;
+
+ for (auto iter = costTime_map.begin(); iter != costTime_map.end(); iter++) {
+ double diff = 0.0;
+ diff = iter->second - iter->first;
+ average += diff;
+ infer_cnt++;
+ }
+
+ average = average / infer_cnt;
+
+ std::stringstream timeCost;
+ timeCost << "NN inference cost average time: "<< average << " ms of infer_count " << infer_cnt << std::endl;
+ std::cout << "NN inference cost average time: "<< average << "ms of infer_count " << infer_cnt << std::endl;
+ std::string file_name = "./time_Result" + std::string("/test_perform_static.txt");
+ std::ofstream file_stream(file_name.c_str(), std::ios::trunc);
+ file_stream << timeCost.str();
+ file_stream.close();
+ costTime_map.clear();
+ return 0;
+}
diff --git a/research/cv/ReIDStrongBaseline/ascend310_infer/src/utils.cc b/research/cv/ReIDStrongBaseline/ascend310_infer/src/utils.cc
new file mode 100644
index 0000000000000000000000000000000000000000..60154d7e34ff2aee5d6edde4e428889cfab74868
--- /dev/null
+++ b/research/cv/ReIDStrongBaseline/ascend310_infer/src/utils.cc
@@ -0,0 +1,199 @@
+/**
+ * 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.
+ */
+
+#include "inc/utils.h"
+
+#include <fstream>
+#include <algorithm>
+#include <iostream>
+#include <string>
+#include <vector>
+using mindspore::MSTensor;
+using mindspore::DataType;
+
+std::vector<std::string> split(const std::string &s, const std::string &separator) {
+ std::vector<std::string> result;
+ std::size_t i = 0;
+
+ while (i != s.size()) {
+ int flag = 0;
+ while (i != s.size() && flag == 0) {
+ flag = 1;
+ for (std::size_t x = 0; x < separator.size(); ++x) {
+ if (s[i] == separator[x]) {
+ ++i;
+ flag = 0;
+ break;
+ }
+ }
+ }
+ flag = 0;
+ std::size_t j = i;
+ while (j != s.size() && flag == 0) {
+ for (std::size_t x = 0; x < separator.size(); ++x) {
+ if (s[j] == separator[x]) {
+ flag = 1;
+ break;
+ }
+ }
+ if (flag == 0) {
+ ++j;
+ }
+ }
+ if (i != j) {
+ result.push_back(s.substr(i, j-i));
+ i = j;
+ }
+ }
+ return result;
+}
+
+std::vector<std::string> GetAllFiles(std::string_view dirName, const std::string& inputtype) {
+ struct dirent *filename;
+ DIR *dir = OpenDir(dirName);
+ if (dir == nullptr) {
+ return {};
+ }
+ std::vector<std::string> res;
+ std::string homePath = "./" + inputtype + "_result_Files";
+ std::string pidfilename = homePath + "/savepid.txt";
+ std::string camidfilename = homePath + "/savecamid.txt";
+ std::ofstream outfilepid;
+ std::ofstream outfilecmd;
+ outfilepid.open(pidfilename, std::ios::app);
+ outfilecmd.open(camidfilename, std::ios::app);
+ std::string pid;
+ std::string camid;
+
+ while ((filename = readdir(dir)) != nullptr) {
+ std::string dName = std::string(filename->d_name);
+ if (dName == "." || dName == ".." || filename->d_type != DT_REG) {
+ continue;
+ }
+ if (dName == "Thumbs.db") {
+ continue;
+ }
+ std::vector<std::string> v = split(dName, "_");
+ pid = v[0];
+ camid = v[1].substr(1, 1);
+ if (pid == "-1") {
+ continue;
+ }
+ outfilepid << pid << std::endl;
+ outfilecmd << camid << std::endl;
+ res.emplace_back(std::string(dirName) + "/" + filename->d_name);
+ }
+ for (auto &f : res) {
+ std::cout << "image file: " << f << std::endl;
+ }
+ return res;
+}
+
+int WriteResult(const std::string& imageFile, const std::vector<MSTensor> &outputs) {
+ std::string homePath = "./result_Files";
+ const int INVALID_POINTER = -1;
+ const int ERROR = -2;
+ for (size_t i = 0; i < outputs.size(); ++i) {
+ size_t outputSize;
+ std::shared_ptr<const void> netOutput = outputs[i].Data();
+ outputSize = outputs[i].DataSize();
+
+ int pos = imageFile.rfind('/');
+ std::string fileName(imageFile, pos + 1);
+ fileName.replace(fileName.find('.'), fileName.size() - fileName.find('.'), '_' + std::to_string(i) + ".txt");
+ std::string outFileName = homePath + "/" + fileName;
+ FILE *outputFile = fopen(outFileName.c_str(), "wb");
+ if (outputFile == nullptr) {
+ std::cout << "open result file " << outFileName << " failed" << std::endl;
+ return INVALID_POINTER;
+ }
+ size_t size = fwrite(netOutput.get(), sizeof(char), outputSize, outputFile);
+
+ if (size != outputSize) {
+ fclose(outputFile);
+ outputFile = nullptr;
+ std::cout << "write result file " << outFileName << " failed, write size[" << size <<
+ "] is smaller than output size[" << outputSize << "], maybe the disk is full." << std::endl;
+ return ERROR;
+ }
+ fclose(outputFile);
+ outputFile = nullptr;
+ }
+ return 0;
+}
+
+mindspore::MSTensor ReadFileToTensor(const std::string &file) {
+ if (file.empty()) {
+ std::cout << "Pointer file is nullptr" << std::endl;
+ return mindspore::MSTensor();
+ }
+
+ std::ifstream ifs(file);
+ if (!ifs.good()) {
+ std::cout << "File: " << file << " is not exist" << std::endl;
+ return mindspore::MSTensor();
+ }
+
+ if (!ifs.is_open()) {
+ std::cout << "File: " << file << "open failed" << std::endl;
+ return mindspore::MSTensor();
+ }
+
+ ifs.seekg(0, std::ios::end);
+ size_t size = ifs.tellg();
+ mindspore::MSTensor buffer(file, mindspore::DataType::kNumberTypeUInt8, {static_cast<int64_t>(size)}, nullptr, size);
+
+ ifs.seekg(0, std::ios::beg);
+ ifs.read(reinterpret_cast<char *>(buffer.MutableData()), size);
+ ifs.close();
+
+ return buffer;
+}
+
+DIR *OpenDir(std::string_view dirName) {
+ if (dirName.empty()) {
+ std::cout << " dirName is null ! " << std::endl;
+ return nullptr;
+ }
+ std::string realPath = RealPath(dirName);
+ struct stat s;
+ lstat(realPath.c_str(), &s);
+ if (!S_ISDIR(s.st_mode)) {
+ std::cout << "dirName is not a valid directory !" << std::endl;
+ return nullptr;
+ }
+ DIR *dir = opendir(realPath.c_str());
+ if (dir == nullptr) {
+ std::cout << "Can not open dir " << dirName << std::endl;
+ return nullptr;
+ }
+ std::cout << "Successfully opened the dir " << dirName << std::endl;
+ return dir;
+}
+
+std::string RealPath(std::string_view path) {
+ char realPathMem[PATH_MAX] = {0};
+ char *realPathRet = nullptr;
+ realPathRet = realpath(path.data(), realPathMem);
+ if (realPathRet == nullptr) {
+ std::cout << "File: " << path << " is not exist.";
+ return "";
+ }
+
+ std::string realPath(realPathMem);
+ std::cout << path << " realpath is: " << realPath << std::endl;
+ return realPath;
+}
diff --git a/research/cv/ReIDStrongBaseline/configs/dukemtmc_config.yml b/research/cv/ReIDStrongBaseline/configs/dukemtmc_config.yml
index 6c417f66d4862d9ddc6222ff8b112949266c3538..aac10450eaaf8c5093c28299dcda8b55ff246514 100644
--- a/research/cv/ReIDStrongBaseline/configs/dukemtmc_config.yml
+++ b/research/cv/ReIDStrongBaseline/configs/dukemtmc_config.yml
@@ -8,9 +8,9 @@ checkpoint_url: ""
data_path: "/cache/data"
output_path: "/cache/train"
load_path: "/cache/checkpoint_path"
-device_target: "GPU"
+device_target: "Ascend" # ['Ascend', 'GPU']
need_modelarts_dataset_unzip: False
-modelarts_dataset_unzip_name: "market1501"
+modelarts_dataset_unzip_name: "DukeMTMC-reID"
# ==============================================================================
# options
diff --git a/research/cv/ReIDStrongBaseline/configs/market1501_config.yml b/research/cv/ReIDStrongBaseline/configs/market1501_config.yml
index cb61f20a7b531efee8ac4ee0bbd8c5c94d2c4f7e..dc98208d45fa67777d8265f4d806b655fd32490a 100644
--- a/research/cv/ReIDStrongBaseline/configs/market1501_config.yml
+++ b/research/cv/ReIDStrongBaseline/configs/market1501_config.yml
@@ -8,7 +8,7 @@ checkpoint_url: ""
data_path: "/cache/data"
output_path: "/cache/train"
load_path: "/cache/checkpoint_path"
-device_target: "GPU"
+device_target: "Ascend" # ['Ascend', 'GPU']
need_modelarts_dataset_unzip: False
modelarts_dataset_unzip_name: "market1501"
diff --git a/research/cv/ReIDStrongBaseline/postprocess.py b/research/cv/ReIDStrongBaseline/postprocess.py
new file mode 100644
index 0000000000000000000000000000000000000000..ec79f032cd33b51e6705d28a5b6b5eb4c6db2fe2
--- /dev/null
+++ b/research/cv/ReIDStrongBaseline/postprocess.py
@@ -0,0 +1,157 @@
+# 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.
+# ============================================================================
+"""post process for 310 inference"""
+
+import argparse
+import numpy as np
+
+parser = argparse.ArgumentParser(description='Train StrongBaseline')
+parser.add_argument('--q_feature', type=str, default='')
+parser.add_argument('--q_pid', type=str, default='')
+parser.add_argument('--q_camid', type=str, default='')
+parser.add_argument('--g_feature', type=str, default='')
+parser.add_argument('--g_pid', type=str, default='')
+parser.add_argument('--g_camid', type=str, default='')
+
+parser.add_argument('--train_url', type=str, default='log')
+parser.add_argument('--reranking', type=lambda x: x.lower() == 'true', default=True, help='re_rank')
+parser.add_argument('--test_distance', type=str, default='global_local', help='test distance type')
+parser.add_argument('--unaligned', action='store_true')
+args = parser.parse_args()
+
+def get_query(feature_file, pid_file, camid_file):
+ """get query data"""
+ qf, q_pids, q_camids = [], [], []
+ openfilef = open(feature_file, 'r')
+ for line in openfilef.readlines():
+ temp = float(line)
+ qf.append(temp)
+ openfilep = open(pid_file, 'r')
+ for line in openfilep.readlines():
+ temp = int(line)
+ q_pids.append(temp)
+ openfilec = open(camid_file, 'r')
+ for line in openfilec.readlines():
+ temp = int(line)
+ q_camids.append(temp)
+ qf = np.array(qf)
+ qf = qf.reshape(-1, 2048)
+ #add norm
+ c = np.linalg.norm(qf, ord=2, axis=1, keepdims=True)
+ qf = qf/c
+ q_pids = np.asarray(q_pids)
+ q_camids = np.asarray(q_camids)
+ print("Extracted features for query set, obtained {}-by-{} matrix".format(qf.shape[0], qf.shape[1]))
+ return qf, q_pids, q_camids
+
+def get_gallery(feature_file, pid_file, camid_file):
+ """get gallery data"""
+ gf, g_pids, g_camids = [], [], []
+ openfilef = open(feature_file, 'r')
+ for line in openfilef.readlines():
+ temp = float(line)
+ gf.append(temp)
+ openfilep = open(pid_file, 'r')
+ for line in openfilep.readlines():
+ temp = int(line)
+ g_pids.append(temp)
+ openfilec = open(camid_file, 'r')
+ for line in openfilec.readlines():
+ temp = int(line)
+ g_camids.append(temp)
+ gf = np.array(gf)
+ gf = gf.reshape(-1, 2048)
+ #add norm
+ c = np.linalg.norm(gf, ord=2, axis=1, keepdims=True)
+ gf = gf/c
+ g_pids = np.asarray(g_pids)
+ g_camids = np.asarray(g_camids)
+ print("Extracted features for gallery set, obtained {}-by-{} matrix".format(gf.shape[0], gf.shape[1]))
+ return gf, g_pids, g_camids
+
+
+def eval_func(dist_mat, q_pids, g_pids, q_camids, g_camids, max_rank=50):
+ """Evaluation with market1501 metric
+ Key: for each query identity, its gallery images from the same camera view are discarded.
+ """
+ num_q, num_g = dist_mat.shape
+ if num_g < max_rank:
+ max_rank = num_g
+ print("Note: number of gallery samples is quite small, got {}".format(num_g))
+ indices = np.argsort(dist_mat, axis=1)
+ matches = (g_pids[indices] == q_pids[:, np.newaxis]).astype(np.int32)
+
+ # compute cmc curve for each query
+ print("==============================compute cmc curve for each query")
+ all_cmc = []
+ all_AP = []
+ num_valid_q = 0. # number of valid query
+ for q_idx in range(num_q):
+ # get query pid and camid
+ q_pid = q_pids[q_idx]
+ q_camid = q_camids[q_idx]
+
+ # remove gallery samples that have the same pid and camid with query
+ order = indices[q_idx]
+ remove = (g_pids[order] == q_pid) & (g_camids[order] == q_camid)
+ keep = np.invert(remove)
+
+ # compute cmc curve
+ # binary vector, positions with value 1 are correct matches
+ orig_cmc = matches[q_idx][keep]
+ if not np.any(orig_cmc):
+ # this condition is true when query identity does not appear in gallery
+ continue
+
+ cmc = orig_cmc.cumsum()
+ cmc[cmc > 1] = 1
+
+ all_cmc.append(cmc[:max_rank])
+ num_valid_q += 1.
+
+ # compute average precision
+ # reference: https://en.wikipedia.org/wiki/Evaluation_measures_(information_retrieval)#Average_precision
+ num_rel = orig_cmc.sum()
+ tmp_cmc = orig_cmc.cumsum()
+ tmp_cmc = [x / (i + 1.) for i, x in enumerate(tmp_cmc)]
+ tmp_cmc = np.asarray(tmp_cmc) * orig_cmc
+ AP = tmp_cmc.sum() / num_rel
+ all_AP.append(AP)
+
+ assert num_valid_q > 0, "Error: all query identities do not appear in gallery"
+
+ all_cmc = np.asarray(all_cmc).astype(np.float32)
+ all_cmc = all_cmc.sum(0) / num_valid_q
+ mAP = np.mean(all_AP)
+
+ return all_cmc, mAP
+
+
+if __name__ == '__main__':
+ sqf, sq_pids, sq_camids = get_query(args.q_feature, args.q_pid, args.q_camid)
+ sgf, sg_pids, sg_camids = get_gallery(args.g_feature, args.g_pid, args.g_camid)
+ m, n = sqf.shape[0], sgf.shape[0]
+ distmat = np.power(sqf, 2).sum(axis=1, keepdims=True).repeat(n, axis=1) + \
+ np.power(sgf, 2).sum(axis=1, keepdims=True).repeat(m, axis=1).T
+ distmat = 1 * distmat - 2 * np.dot(sqf, sgf.transpose())
+ r, m_ap = eval_func(distmat, sq_pids, sg_pids, sq_camids, sg_camids)
+ s = 'After BNNeck'
+ print(f'[INFO] {s}')
+ print(
+ '[INFO] mAP: {:.4f} rank1: {:.4f} rank3: {:.4f} rank5: {:.4f} rank10: {:.4f}'.format(
+ m_ap,
+ r[0], r[2], r[4], r[9],
+ )
+ )
diff --git a/research/cv/ReIDStrongBaseline/scripts/run_distribute_train_ascend.sh b/research/cv/ReIDStrongBaseline/scripts/run_distribute_train_ascend.sh
new file mode 100644
index 0000000000000000000000000000000000000000..f701d720f4cf897ca0c605105dc206054443fed8
--- /dev/null
+++ b/research/cv/ReIDStrongBaseline/scripts/run_distribute_train_ascend.sh
@@ -0,0 +1,58 @@
+#!/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 at the diractory same with train.py: "
+echo "bash scripts/run_distribute_train_ascend.sh CONFIG_PATH DATA_DIR OUTPUT_PATH PRETRAINED_RESNET50 RANK_TABLE_FILE RANK_SIZE"
+echo "for example: bash scripts/run_distribute_train_ascend.sh ./configs/market1501_config.yml /path/to/dataset/ /path/to/output/ /path/to/resnet50_ascend_v130_imagenet2012_official_cv_bs256_top1acc76.97__top5acc_93.44.ckpt rank_table_8pcs.json 8"
+echo "It is better to use the absolute path."
+echo "=============================================================================================================="
+set -e
+
+config_path=$1
+DATA_DIR=$2
+OUTPUT_PATH=$3
+PRETRAINED_RESNET50=$4
+rank_table_8pcs_file=$5
+
+EXEC_PATH=$(pwd)
+
+export RANK_TABLE_FILE=${EXEC_PATH}/scripts/$rank_table_8pcs_file
+export RANK_SIZE=$6
+
+for((i=0;i<${RANK_SIZE};i++))
+do
+ rm -rf device$i
+ mkdir device$i
+ cp ./train.py ./device$i
+ cd ./device$i
+ export DEVICE_ID=$i
+ export RANK_ID=$i
+ echo "start training for device $i"
+ env > env$i.log
+ python ${EXEC_PATH}/train.py \
+ --config_path="$config_path" \
+ --device_target="Ascend" \
+ --data_dir="$DATA_DIR" \
+ --ckpt_path="$OUTPUT_PATH" \
+ --train_log_path="$OUTPUT_PATH" \
+ --pre_trained_backbone="$PRETRAINED_RESNET50" \
+ --lr_init=0.00140 \
+ --lr_cri=1.0 \
+ --ids_per_batch=8 \
+ --is_distributed=1 > output.train.log 2>&1 &
+ cd ../
+done
diff --git a/research/cv/ReIDStrongBaseline/scripts/run_eval_ascend.sh b/research/cv/ReIDStrongBaseline/scripts/run_eval_ascend.sh
new file mode 100644
index 0000000000000000000000000000000000000000..f04532d23bf53072bcf46ac4d91b43c7a2c5796b
--- /dev/null
+++ b/research/cv/ReIDStrongBaseline/scripts/run_eval_ascend.sh
@@ -0,0 +1,44 @@
+#!/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 [ $# != 3 ] ; then
+echo "=============================================================================================================="
+echo "Please run the script as: "
+echo "bash scripts/run_eval_gpu.sh CONFIG_PATH CKPT_PATH DATA_DIR"
+echo "for example: bash scripts/run_eval_gpu.sh ./configs/market1501_config.yml /your/path/checkpoint_file /path/to/dataset/"
+echo "It is better to use absolute path."
+echo "=============================================================================================================="
+exit 1;
+fi
+
+get_real_path(){
+ if [ "${1:0:1}" == "/" ]; then
+ echo "$1"
+ else
+ echo "$(realpath -m $PWD/$1)"
+ fi
+}
+config_path=$(get_real_path "$1")
+
+PATH1=$(get_real_path "$2")
+echo "$PATH1"
+DATA_DIR=$(get_real_path "$3")
+
+python eval.py \
+ --config_path="$config_path" \
+ --device_target="Ascend" \
+ --data_dir="$DATA_DIR" \
+ --eval_model="$PATH1" > output.eval.log 2>&1 &
diff --git a/research/cv/ReIDStrongBaseline/scripts/run_infer_310.sh b/research/cv/ReIDStrongBaseline/scripts/run_infer_310.sh
new file mode 100644
index 0000000000000000000000000000000000000000..85ae7b405548c09162d00ab06e25fa6d852a100c
--- /dev/null
+++ b/research/cv/ReIDStrongBaseline/scripts/run_infer_310.sh
@@ -0,0 +1,107 @@
+#!/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.
+# ============================================================================
+
+model_path=$1
+query_datapath=$2
+gallery_datapath=$3
+echo "-----------------------------------"
+echo "mindir name: "$model_path
+echo "query dataset path: "$query_datapath
+echo "gallery dataset path: "$gallery_datapath
+echo "-----------------------------------"
+
+export ASCEND_HOME=/usr/local/Ascend
+if [ -d ${ASCEND_HOME}/ascend-toolkit ]; then
+ export PATH=$ASCEND_HOME/fwkacllib/bin:$ASCEND_HOME/fwkacllib/ccec_compiler/bin:$ASCEND_HOME/ascend-toolkit/latest/fwkacllib/ccec_compiler/bin:$ASCEND_HOME/ascend-toolkit/latest/atc/bin:$PATH
+ export LD_LIBRARY_PATH=$ASCEND_HOME/fwkacllib/lib64:/usr/local/lib:$ASCEND_HOME/ascend-toolkit/latest/atc/lib64:$ASCEND_HOME/ascend-toolkit/latest/fwkacllib/lib64:$ASCEND_HOME/driver/lib64:$ASCEND_HOME/add-ons:$LD_LIBRARY_PATH
+ export TBE_IMPL_PATH=$ASCEND_HOME/ascend-toolkit/latest/opp/op_impl/built-in/ai_core/tbe
+ export PYTHONPATH=$ASCEND_HOME/fwkacllib/python/site-packages:${TBE_IMPL_PATH}:$ASCEND_HOME/ascend-toolkit/latest/fwkacllib/python/site-packages:$PYTHONPATH
+ export ASCEND_OPP_PATH=$ASCEND_HOME/ascend-toolkit/latest/opp
+else
+ export ASCEND_HOME=/usr/local/Ascend/latest/
+ export PATH=$ASCEND_HOME/fwkacllib/bin:$ASCEND_HOME/fwkacllib/ccec_compiler/bin:$ASCEND_HOME/atc/ccec_compiler/bin:$ASCEND_HOME/atc/bin:$PATH
+ export LD_LIBRARY_PATH=$ASCEND_HOME/fwkacllib/lib64:/usr/local/lib:$ASCEND_HOME/atc/lib64:$ASCEND_HOME/acllib/lib64:$ASCEND_HOME/driver/lib64:$ASCEND_HOME/add-ons:$LD_LIBRARY_PATH
+ export PYTHONPATH=$ASCEND_HOME/fwkacllib/python/site-packages:$ASCEND_HOME/atc/python/site-packages:$PYTHONPATH
+ export ASCEND_OPP_PATH=$ASCEND_HOME/opp
+fi
+
+function compile_app()
+{
+ cd ascend310_infer || exit
+ bash build.sh &> build.log
+}
+
+function query_infer()
+{
+ if [ -d query_result_Files ]; then
+ rm -rf ./query_result_Files
+ fi
+ mkdir query_result_Files
+ input_type="query"
+ ../ascend310_infer/out/main --model_path=$model_path --dataset_path=$query_datapath --input_type=$input_type &> query_infer.log
+}
+
+function gallery_infer()
+{
+ cd ../ascend310_infer || exit
+ if [ -d gallery_result_Files ]; then
+ rm -rf ./gallery_result_Files
+ fi
+ mkdir gallery_result_Files
+ input_type="gallery"
+ ../ascend310_infer/out/main --model_path=$model_path --dataset_path=$gallery_datapath --input_type=$input_type &> gallery_infer.log
+}
+
+function cal_acc()
+{
+ cd ..
+ qf=ascend310_infer/query_result_Files/feature_data.txt
+ qp=ascend310_infer/query_result_Files/savepid.txt
+ qc=ascend310_infer/query_result_Files/savecamid.txt
+ gf=ascend310_infer/gallery_result_Files/feature_data.txt
+ gp=ascend310_infer/gallery_result_Files/savepid.txt
+ gc=ascend310_infer/gallery_result_Files/savecamid.txt
+ python postprocess.py --q_feature=$qf --q_pid=$qp --q_camid=$qc --g_feature=$gf --g_pid=$gp --g_camid=$gc &> acc.log
+}
+
+compile_app
+if [ $? -ne 0 ]; then
+ echo "compile app code failed"
+ exit 1
+fi
+echo "compile app code success"
+
+query_infer
+if [ $? -ne 0 ]; then
+ echo "execute query inference failed"
+ exit 1
+fi
+echo "execute query inference success"
+
+gallery_infer
+if [ $? -ne 0 ]; then
+ echo "execute gallery inference failed"
+ exit 1
+fi
+echo "execute gallery inference success"
+
+cal_acc
+if [ $? -ne 0 ]; then
+ echo "calculate accuracy failed"
+ exit 1
+fi
+echo "calculate accuracy success"
+echo "ascend 310 infer success"
diff --git a/research/cv/ReIDStrongBaseline/src/dataset.py b/research/cv/ReIDStrongBaseline/src/dataset.py
index 6955853c0844210dcbda48e1f118db222d907b79..88fd13504e7a7720e124b2348719cda2dadbf7f4 100644
--- a/research/cv/ReIDStrongBaseline/src/dataset.py
+++ b/research/cv/ReIDStrongBaseline/src/dataset.py
@@ -186,6 +186,7 @@ def create_dataset(
column_names=['image', 'label'],
sampler=sampler,
shuffle=shuffle,
+ num_parallel_workers=num_parallel_workers,
)
dataset = dataset.map(
diff --git a/research/cv/ReIDStrongBaseline/train.py b/research/cv/ReIDStrongBaseline/train.py
index f7bd3d93faa98fc02b2ca0a62bc1f8942a15288a..39c385d7398d64a4459fa89aaebd94a119bc8582 100644
--- a/research/cv/ReIDStrongBaseline/train.py
+++ b/research/cv/ReIDStrongBaseline/train.py
@@ -128,16 +128,23 @@ def _prepare_configuration():
if not config.need_modelarts_dataset_unzip:
init()
- config.group_size = get_group_size()
- config.rank = get_rank()
-
- device_num = config.group_size
- context.reset_auto_parallel_context()
- context.set_auto_parallel_context(
- device_num=device_num,
- parallel_mode=ParallelMode.DATA_PARALLEL,
- gradients_mean=True,
- )
+ config.group_size = get_group_size()
+ config.rank = get_rank()
+
+ device_num = config.group_size
+ context.reset_auto_parallel_context()
+ context.set_auto_parallel_context(
+ device_num=device_num,
+ parallel_mode=ParallelMode.DATA_PARALLEL,
+ gradients_mean=True,
+ )
+ if config.device_target == "Ascend":
+ device_id = int(os.getenv('DEVICE_ID'))
+ context.set_context(device_id=device_id)
+ init()
+ context.set_auto_parallel_context(parallel_mode=ParallelMode.DATA_PARALLEL, gradients_mean=True)
+ config.rank = get_rank()
+ config.group_size = get_group_size()
else:
config.group_size = 1
config.rank = 0