!13830 deeplabv3 & ssd & lstm new interface
From: @zhangxiaoxiao16 Reviewed-by: @c_34,@wuxuejian Signed-off-by: @c_34
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d03eef497b
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@ -13,11 +13,11 @@
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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if [ ! -d out ]; then
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mkdir out
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if [ -d out ]; then
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rm -rf out
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fi
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cd out
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mkdir out && cd out
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if [ -f "Makefile" ]; then
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make clean
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@ -1,12 +1,12 @@
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/**
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* Copyright 2021 Huawei Technologies Co., Ltd
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*
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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@ -31,10 +31,9 @@
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#include "include/minddata/dataset/include/execute.h"
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#include "../inc/utils.h"
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using mindspore::GlobalContext;
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using mindspore::Context;
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using mindspore::Serialization;
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using mindspore::Model;
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using mindspore::ModelContext;
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using mindspore::Status;
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using mindspore::ModelType;
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using mindspore::GraphCell;
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@ -127,15 +126,18 @@ int main(int argc, char **argv) {
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std::cout << "Invalid fusion switch path" << std::endl;
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return 1;
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}
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GlobalContext::SetGlobalDeviceTarget(mindspore::kDeviceTypeAscend310);
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GlobalContext::SetGlobalDeviceID(FLAGS_device_id);
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auto graph = Serialization::LoadModel(FLAGS_mindir_path, ModelType::kMindIR);
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auto model_context = std::make_shared<mindspore::Context>();
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auto context = std::make_shared<Context>();
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auto ascend310 = std::make_shared<mindspore::Ascend310DeviceInfo>();
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ascend310->SetDeviceID(FLAGS_device_id);
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context->MutableDeviceInfo().push_back(ascend310);
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mindspore::Graph graph;
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Serialization::Load(FLAGS_mindir_path, ModelType::kMindIR, &graph);
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if (!FLAGS_fusion_switch_path.empty()) {
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ModelContext::SetFusionSwitchConfigPath(model_context, FLAGS_fusion_switch_path);
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ascend310->SetFusionSwitchConfigPath(FLAGS_fusion_switch_path);
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}
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Model model(GraphCell(graph), model_context);
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Status ret = model.Build();
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Model model;
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Status ret = model.Build(GraphCell(graph), context);
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if (ret != kSuccess) {
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std::cout << "ERROR: Build failed." << std::endl;
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return 1;
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@ -15,7 +15,7 @@
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# ============================================================================
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if [[ $# -lt 4 || $# -gt 5 ]]; then
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echo "Usage: sh run_infer_310.sh [MINDIR_PATH] [DATA_PATH] [DATA_ROOT] [DATA_LIST] [DEVICE_ID]
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echo "Usage: bash run_infer_310.sh [MINDIR_PATH] [DATA_PATH] [DATA_ROOT] [DATA_LIST] [DEVICE_ID]
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DEVICE_ID is optional, it can be set by environment variable device_id, otherwise the value is zero"
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exit 1
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fi
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@ -406,7 +406,7 @@ The ckpt_file parameter is required,
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### Infer on Ascend310
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Before performing inference, the mindir file must bu exported by export script on the 910 environment. We only provide an example of inference using MINDIR model.
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Before performing inference, the mindir file must bu exported by `export.py` script. We only provide an example of inference using MINDIR model.
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Current batch_Size can only be set to 1. The precision calculation process needs about 70G+ memory space, otherwise the process will be killed for execeeding memory limits.
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```shell
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@ -17,6 +17,10 @@
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- [评估过程](#评估过程)
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- [Ascend处理器环境评估](#ascend处理器环境评估)
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- [GPU处理器环境评估](#gpu处理器环境评估)
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- [推理过程](#推理过程)
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- [导出MindIR](#导出mindir)
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- [在Ascend310执行推理](#在ascend310执行推理)
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- [结果](#结果)
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- [模型描述](#模型描述)
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- [性能](#性能)
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- [评估性能](#评估性能)
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@ -312,6 +316,49 @@ Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.686
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mAP: 0.2244936111705981
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```
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## 推理过程
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### [导出MindIR](#contents)
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```shell
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python export.py --ckpt_file [CKPT_PATH] --file_name [FILE_NAME] --file_format [FILE_FORMAT]
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```
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参数ckpt_file为必填项，
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`EXPORT_FORMAT` 必须在 ["AIR", "MINDIR"]中选择。
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### 在Ascend310执行推理
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在执行推理前，mindir文件必须通过`export.py`脚本导出。以下展示了使用minir模型执行推理的示例。
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目前仅支持batch_Size为1的推理。精度计算过程需要70G+的内存，否则进程将会因为超出内存被系统终止。
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```shell
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# Ascend310 inference
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bash run_infer_310.sh [MINDIR_PATH] [DATA_PATH] [DVPP] [DEVICE_ID]
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```
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- `DVPP` 为必填项，需要在["DVPP", "CPU"]选择，大小写均可。需要注意的是ssd_vgg16执行推理的图片尺寸为[300, 300]，由于DVPP硬件限制宽为16整除，高为2整除，因此，这个网络需要通过CPU算子对图像进行前处理。
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- `DEVICE_ID` 可选，默认值为0。
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### 结果
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推理结果保存在脚本执行的当前路径，你可以在acc.log中看到以下精度计算结果。
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```bash
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Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.339
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Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.521
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Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.370
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Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.168
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Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.386
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Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.461
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Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.310
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Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.481
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Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.515
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Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.293
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Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.659
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mAP: 0.33880018942412393
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```
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# 模型描述
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## 性能
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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if [ ! -d out ]; then
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mkdir out
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if [ -d out ]; then
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rm -rf out
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fi
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cd out
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mkdir out && cd out
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if [ -f "Makefile" ]; then
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make clean
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@ -32,10 +32,9 @@
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#include "include/minddata/dataset/include/vision.h"
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#include "inc/utils.h"
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using mindspore::GlobalContext;
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using mindspore::Context;
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using mindspore::Serialization;
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using mindspore::Model;
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using mindspore::ModelContext;
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using mindspore::Status;
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using mindspore::ModelType;
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using mindspore::GraphCell;
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return 1;
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}
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GlobalContext::SetGlobalDeviceTarget(mindspore::kDeviceTypeAscend310);
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GlobalContext::SetGlobalDeviceID(FLAGS_device_id);
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auto graph = Serialization::LoadModel(FLAGS_mindir_path, ModelType::kMindIR);
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auto model_context = std::make_shared<mindspore::ModelContext>();
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auto context = std::make_shared<Context>();
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auto ascend310 = std::make_shared<mindspore::Ascend310DeviceInfo>();
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ascend310->SetDeviceID(FLAGS_device_id);
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context->MutableDeviceInfo().push_back(ascend310);
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mindspore::Graph graph;
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Serialization::Load(FLAGS_mindir_path, ModelType::kMindIR, &graph);
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if (FLAGS_cpu_dvpp == "DVPP") {
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if (RealPath(FLAGS_aipp_path).empty()) {
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std::cout << "Invalid aipp path" << std::endl;
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return 1;
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} else {
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ModelContext::SetInsertOpConfigPath(model_context, FLAGS_aipp_path);
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ascend310->SetInsertOpConfigPath(FLAGS_aipp_path);
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}
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}
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Model model(GraphCell(graph), model_context);
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Status ret = model.Build();
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Model model;
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Status ret = model.Build(GraphCell(graph), context);
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if (ret != kSuccess) {
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std::cout << "ERROR: Build failed." << std::endl;
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return 1;
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# ============================================================================
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if [[ $# -lt 3 || $# -gt 4 ]]; then
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echo "Usage: sh run_infer_310.sh [MINDIR_PATH] [DATA_PATH] [DVPP] [DEVICE_ID]
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echo "Usage: bash run_infer_310.sh [MINDIR_PATH] [DATA_PATH] [DVPP] [DEVICE_ID]
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DVPP is mandatory, and must choose from [DVPP|CPU], it's case-insensitive
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DEVICE_ID is optional, it can be set by environment variable device_id, otherwise the value is zero"
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exit 1
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function compile_app()
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{
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cd ../ascend310_infer
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sh build.sh &> build.log
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bash build.sh &> build.log
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}
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function infer()
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# Copyright 2020 Huawei Technologies Co., Ltd
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# Copyright 2020-2021 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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import numpy as np
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from mindspore import Tensor
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from mindspore import Tensor, context
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from mindspore import export, load_checkpoint, load_param_into_net
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from src.config import lstm_cfg as cfg
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from src.config import lstm_cfg, lstm_cfg_ascend
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from src.lstm import SentimentNet
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if __name__ == '__main__':
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parser.add_argument('--preprocess_path', type=str, default='./preprocess',
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help='path where the pre-process data is stored.')
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parser.add_argument('--ckpt_file', type=str, required=True, help='lstm ckpt file.')
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parser.add_argument("--device_id", type=int, default=0, help="Device id")
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parser.add_argument("--file_name", type=str, default="lstm", help="output file name.")
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parser.add_argument('--file_format', type=str, choices=["AIR", "MINDIR"], default='AIR', help='file format')
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parser.add_argument('--device_target', type=str, default="Ascend", choices=['GPU', 'CPU', 'Ascend'],
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help='the target device to run, support "GPU", "CPU". Default: "Ascend".')
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args = parser.parse_args()
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context.set_context(
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mode=context.GRAPH_MODE,
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save_graphs=False,
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device_target=args.device_target,
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device_id=args.device_id)
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if args.device_target == 'Ascend':
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cfg = lstm_cfg_ascend
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else:
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cfg = lstm_cfg
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embedding_table = np.loadtxt(os.path.join(args.preprocess_path, "weight.txt")).astype(np.float32)
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if args.device_target == 'Ascend':
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pad_num = int(np.ceil(cfg.embed_size / 16) * 16 - cfg.embed_size)
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if pad_num > 0:
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embedding_table = np.pad(embedding_table, [(0, 0), (0, pad_num)], 'constant')
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cfg.embed_size = int(np.ceil(cfg.embed_size / 16) * 16)
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network = SentimentNet(vocab_size=embedding_table.shape[0],
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embed_size=cfg.embed_size,
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num_hiddens=cfg.num_hiddens,
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param_dict = load_checkpoint(args.ckpt_file)
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load_param_into_net(network, param_dict)
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input_arr = Tensor(np.random.uniform(0.0, 1e5, size=[64, 500]).astype(np.int32))
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export(network, input_arr, file_name="lstm", file_format="MINDIR")
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input_arr = Tensor(np.random.uniform(0.0, 1e5, size=[cfg.batch_size, 500]).astype(np.int32))
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export(network, input_arr, file_name=args.file_name, file_format=args.file_format)
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