!13830 deeplabv3 & ssd & lstm new interface

From: @zhangxiaoxiao16
Reviewed-by: @c_34,@wuxuejian
Signed-off-by: @c_34

model_zoo/official/cv/deeplabv3/ascend310_infer/build.sh

@@ -13,11 +13,11 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ============================================================================
-if [ ! -d out ]; then
-    mkdir out
+if [ -d out ]; then
+    rm -rf out
 fi
 
-cd out
+mkdir out && cd out
 
 if [ -f "Makefile" ]; then
   make clean

model_zoo/official/cv/deeplabv3/ascend310_infer/src/main.cc

@@ -1,12 +1,12 @@
 /**
  * Copyright 2021 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.
@@ -31,10 +31,9 @@
 #include "include/minddata/dataset/include/execute.h"
 #include "../inc/utils.h"
 
-using mindspore::GlobalContext;
+using mindspore::Context;
 using mindspore::Serialization;
 using mindspore::Model;
-using mindspore::ModelContext;
 using mindspore::Status;
 using mindspore::ModelType;
 using mindspore::GraphCell;
@@ -127,15 +126,18 @@ int main(int argc, char **argv) {
     std::cout << "Invalid fusion switch path" << std::endl;
     return 1;
   }
-  GlobalContext::SetGlobalDeviceTarget(mindspore::kDeviceTypeAscend310);
-  GlobalContext::SetGlobalDeviceID(FLAGS_device_id);
-  auto graph = Serialization::LoadModel(FLAGS_mindir_path, ModelType::kMindIR);
-  auto model_context = std::make_shared<mindspore::Context>();
+  auto context = std::make_shared<Context>();
+  auto ascend310 = std::make_shared<mindspore::Ascend310DeviceInfo>();
+  ascend310->SetDeviceID(FLAGS_device_id);
+  context->MutableDeviceInfo().push_back(ascend310);
+  mindspore::Graph graph;
+  Serialization::Load(FLAGS_mindir_path, ModelType::kMindIR, &graph);
+
   if (!FLAGS_fusion_switch_path.empty()) {
-    ModelContext::SetFusionSwitchConfigPath(model_context, FLAGS_fusion_switch_path);
+    ascend310->SetFusionSwitchConfigPath(FLAGS_fusion_switch_path);
   }
-  Model model(GraphCell(graph), model_context);
-  Status ret = model.Build();
+  Model model;
+  Status ret = model.Build(GraphCell(graph), context);
   if (ret != kSuccess) {
     std::cout << "ERROR: Build failed." << std::endl;
     return 1;

model_zoo/official/cv/deeplabv3/scripts/run_infer_310.sh

@@ -15,7 +15,7 @@
 # ============================================================================
 
 if [[ $# -lt 4 || $# -gt 5 ]]; then
-    echo "Usage: sh run_infer_310.sh [MINDIR_PATH] [DATA_PATH] [DATA_ROOT] [DATA_LIST] [DEVICE_ID]
+    echo "Usage: bash run_infer_310.sh [MINDIR_PATH] [DATA_PATH] [DATA_ROOT] [DATA_LIST] [DEVICE_ID]
     DEVICE_ID is optional, it can be set by environment variable device_id, otherwise the value is zero"
 exit 1
 fi

model_zoo/official/cv/ssd/README.md

@@ -406,7 +406,7 @@ The ckpt_file parameter is required,
 
 ### Infer on Ascend310
 
-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.
+Before performing inference, the mindir file must bu exported by `export.py` script. We only provide an example of inference using MINDIR model.
 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.
 
 ```shell

model_zoo/official/cv/ssd/README_CN.md

@@ -17,6 +17,10 @@
     - [评估过程](#评估过程)
         - [Ascend处理器环境评估](#ascend处理器环境评估)
         - [GPU处理器环境评估](#gpu处理器环境评估)
+    - [推理过程](#推理过程)
+        - [导出MindIR](#导出mindir)
+        - [在Ascend310执行推理](#在ascend310执行推理)
+        - [结果](#结果)
 - [模型描述](#模型描述)
     - [性能](#性能)
         - [评估性能](#评估性能)
@@ -312,6 +316,49 @@ Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.686
 mAP: 0.2244936111705981
 ```
 
+## 推理过程
+
+### [导出MindIR](#contents)
+
+```shell
+python export.py --ckpt_file [CKPT_PATH] --file_name [FILE_NAME] --file_format [FILE_FORMAT]
+```
+
+参数ckpt_file为必填项，
+`EXPORT_FORMAT` 必须在 ["AIR", "MINDIR"]中选择。
+
+### 在Ascend310执行推理
+
+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。
+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。
+
+```shell
+# Ascend310 inference
+bash run_infer_310.sh [MINDIR_PATH] [DATA_PATH] [DVPP] [DEVICE_ID]
+```
+
+- `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。
+- `DEVICE_ID` 可选，默认值为0。
+
+### 结果
+
+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。
+
+```bash
+Average Precision (AP) @[ IoU=0.50:0.95 | area= all   | maxDets=100 ] = 0.339
+Average Precision (AP) @[ IoU=0.50      | area= all   | maxDets=100 ] = 0.521
+Average Precision (AP) @[ IoU=0.75      | area= all   | maxDets=100 ] = 0.370
+Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.168
+Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.386
+Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.461
+Average Recall    (AR) @[ IoU=0.50:0.95 | area= all   | maxDets=  1 ] = 0.310
+Average Recall    (AR) @[ IoU=0.50:0.95 | area= all   | maxDets= 10 ] = 0.481
+Average Recall    (AR) @[ IoU=0.50:0.95 | area= all   | maxDets=100 ] = 0.515
+Average Recall    (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.293
+Average Recall    (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.659
+mAP: 0.33880018942412393
+```
+
 # 模型描述
 
 ## 性能

model_zoo/official/cv/ssd/ascend310_infer/build.sh

@@ -13,11 +13,11 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ============================================================================
-if [ ! -d out ]; then
-    mkdir out
+if [ -d out ]; then
+    rm -rf out
 fi
 
-cd out
+mkdir out && cd out
 
 if [ -f "Makefile" ]; then
   make clean

model_zoo/official/cv/ssd/ascend310_infer/src/main.cc

@@ -32,10 +32,9 @@
 #include "include/minddata/dataset/include/vision.h"
 #include "inc/utils.h"
 
-using mindspore::GlobalContext;
+using mindspore::Context;
 using mindspore::Serialization;
 using mindspore::Model;
-using mindspore::ModelContext;
 using mindspore::Status;
 using mindspore::ModelType;
 using mindspore::GraphCell;
@@ -64,21 +63,23 @@ int main(int argc, char **argv) {
     return 1;
   }
 
-  GlobalContext::SetGlobalDeviceTarget(mindspore::kDeviceTypeAscend310);
-  GlobalContext::SetGlobalDeviceID(FLAGS_device_id);
-  auto graph = Serialization::LoadModel(FLAGS_mindir_path, ModelType::kMindIR);
-  auto model_context = std::make_shared<mindspore::ModelContext>();
+  auto context = std::make_shared<Context>();
+  auto ascend310 = std::make_shared<mindspore::Ascend310DeviceInfo>();
+  ascend310->SetDeviceID(FLAGS_device_id);
+  context->MutableDeviceInfo().push_back(ascend310);
+  mindspore::Graph graph;
+  Serialization::Load(FLAGS_mindir_path, ModelType::kMindIR, &graph);
   if (FLAGS_cpu_dvpp == "DVPP") {
     if (RealPath(FLAGS_aipp_path).empty()) {
       std::cout << "Invalid aipp path" << std::endl;
       return 1;
     } else {
-      ModelContext::SetInsertOpConfigPath(model_context, FLAGS_aipp_path);
+      ascend310->SetInsertOpConfigPath(FLAGS_aipp_path);
     }
   }
 
-  Model model(GraphCell(graph), model_context);
-  Status ret = model.Build();
+  Model model;
+  Status ret = model.Build(GraphCell(graph), context);
   if (ret != kSuccess) {
     std::cout << "ERROR: Build failed." << std::endl;
     return 1;

model_zoo/official/cv/ssd/scripts/run_infer_310.sh

@@ -15,7 +15,7 @@
 # ============================================================================
 
 if [[ $# -lt 3 || $# -gt 4 ]]; then
-    echo "Usage: sh run_infer_310.sh [MINDIR_PATH] [DATA_PATH] [DVPP] [DEVICE_ID]
+    echo "Usage: bash run_infer_310.sh [MINDIR_PATH] [DATA_PATH] [DVPP] [DEVICE_ID]
     DVPP is mandatory, and must choose from [DVPP|CPU], it's case-insensitive
     DEVICE_ID is optional, it can be set by environment variable device_id, otherwise the value is zero"
 exit 1
@@ -59,7 +59,7 @@ fi
 function compile_app()
 {
     cd ../ascend310_infer
-    sh build.sh &> build.log
+    bash build.sh &> build.log
 }
 
 function infer()

model_zoo/official/nlp/lstm/export.py

@@ -1,4 +1,4 @@
-# Copyright 2020 Huawei Technologies Co., Ltd
+# Copyright 2020-2021 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.
@@ -21,9 +21,9 @@ import os
 
 import numpy as np
 
-from mindspore import Tensor
+from mindspore import Tensor, context
 from mindspore import export, load_checkpoint, load_param_into_net
-from src.config import lstm_cfg as cfg
+from src.config import lstm_cfg, lstm_cfg_ascend
 from src.lstm import SentimentNet
 
 if __name__ == '__main__':
@@ -31,9 +31,32 @@ if __name__ == '__main__':
     parser.add_argument('--preprocess_path', type=str, default='./preprocess',
                         help='path where the pre-process data is stored.')
     parser.add_argument('--ckpt_file', type=str, required=True, help='lstm ckpt file.')
+    parser.add_argument("--device_id", type=int, default=0, help="Device id")
+    parser.add_argument("--file_name", type=str, default="lstm", help="output file name.")
+    parser.add_argument('--file_format', type=str, choices=["AIR", "MINDIR"], default='AIR', help='file format')
+    parser.add_argument('--device_target', type=str, default="Ascend", choices=['GPU', 'CPU', 'Ascend'],
+                        help='the target device to run, support "GPU", "CPU". Default: "Ascend".')
     args = parser.parse_args()
 
+    context.set_context(
+        mode=context.GRAPH_MODE,
+        save_graphs=False,
+        device_target=args.device_target,
+        device_id=args.device_id)
+
+    if args.device_target == 'Ascend':
+        cfg = lstm_cfg_ascend
+    else:
+        cfg = lstm_cfg
+
     embedding_table = np.loadtxt(os.path.join(args.preprocess_path, "weight.txt")).astype(np.float32)
+
+    if args.device_target == 'Ascend':
+        pad_num = int(np.ceil(cfg.embed_size / 16) * 16 - cfg.embed_size)
+        if pad_num > 0:
+            embedding_table = np.pad(embedding_table, [(0, 0), (0, pad_num)], 'constant')
+        cfg.embed_size = int(np.ceil(cfg.embed_size / 16) * 16)
+
     network = SentimentNet(vocab_size=embedding_table.shape[0],
                            embed_size=cfg.embed_size,
                            num_hiddens=cfg.num_hiddens,
@@ -46,5 +69,5 @@ if __name__ == '__main__':
     param_dict = load_checkpoint(args.ckpt_file)
     load_param_into_net(network, param_dict)
 
-    input_arr = Tensor(np.random.uniform(0.0, 1e5, size=[64, 500]).astype(np.int32))
-    export(network, input_arr, file_name="lstm", file_format="MINDIR")
+    input_arr = Tensor(np.random.uniform(0.0, 1e5, size=[cfg.batch_size, 500]).astype(np.int32))
+    export(network, input_arr, file_name=args.file_name, file_format=args.file_format)