Stable v0.3 merge master (#723)

* fix some failures in X86 UnitTest * opt openvino binary layer impl * add openvino argmax layer builder * opt openvino unary layer builder impl * [DOC] add MatConvertParam description * [DOC] fix link error * [CONVERTER2TNN][UPD] 1. default do not clean build directory; (#694) Co-authored-by: tiankai <tiankai@tencent.com> * [ARM][BUG] fix upsample cubic openmp too many args (#692) * [ARM][BUG] fix upsample cubic openmp too many args * [ARM] fix blob converter unit test on a32 * [ARM] modify arm82 compile option Co-authored-by: seanxcwang <seanxcwang@tencent.com> Co-authored-by: seanxcwang <66675860+seanxcwang@users.noreply.github.com> * [CONVERTER2TNN][BUG]1.fix bug for convert2tnn build.sh (#700) Co-authored-by: tiankai <tiankai@tencent.com> * [TOOLS][BUG] fix model checker bug when only check output * [ARM][BUG] fp16 reformat support multi-inputs * [DOC] update model check doc and model_check cmd help info * [TOOLS][CHG] fix model check info error and modify some return value * [ANDROID] object ssd demo fix force to use fp32 on huawei npu (#712) * [BENCHMARK][FIX] support android 64bit force run 32bit (#716) * Patch benchmark tool java (#719) * [BENCHMARK][FIX] support android 64bit force run 32bit * add java error msg deal Co-authored-by: neiltian <neiltian@tencent.com> * [SCRIPTS][FIX] 发布代码关闭unit test，unit test会暴露符号，并且会引入generate resource 逻辑 (#703) * [EXAMPLE][FIX] remove unused cl include * [UPD]update model type error message (#725) * 修改了 Tensorflow 模型转换时 "-in" 参数的格式 (#717) * [CONVERT2TNN][UPD] 1.refactor convert tensorflow model(input_name[1,128,128,3] -> input_name:1,128,128,3); * [CONVERT2TNN][UPD] 1. update help message; Co-authored-by: Dandiding <Dandiding@tencent.com> Co-authored-by: quinnrong94 <quinnrong@tencent.com> Co-authored-by: lucasktian <lucasktian@tencent.com> Co-authored-by: tiankai <tiankai@tencent.com> Co-authored-by: quinnrong94 <67782915+quinnrong94@users.noreply.github.com> Co-authored-by: seanxcwang <seanxcwang@tencent.com> Co-authored-by: seanxcwang <66675860+seanxcwang@users.noreply.github.com> Co-authored-by: shaundai <shaundai@tencent.com> Co-authored-by: ShaunDai <66760945+shaundai-tencent@users.noreply.github.com> Co-authored-by: lnmdlong <lnmdlong@hotmail.com> Co-authored-by: darrenyao87 <62542779+darrenyao87@users.noreply.github.com>
Tencent · Jan 15, 2021 · 08c9213 · 08c9213
1 parent 38b14ea
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diff --git a/...android/benchmark_app/benchmark/src/main/java/com/tencent/tnn/benchmark/MainActivity.java b/...android/benchmark_app/benchmark/src/main/java/com/tencent/tnn/benchmark/MainActivity.java
@@ -38,11 +38,11 @@ private void init() {
             Bundle bundle = intent.getExtras();
             String benchmark_dir = bundle.getString(ARGS_INTENT_KEY_BENCHMARK_DIR, "/data/local/tmp/tnn-benchmark/");
             String[] load_list = bundle.getStringArray(ARGS_INTENT_KEY_LOAD_LIST);
+            model = bundle.getString(ARGS_INTENT_KEY_MODEL);
             for(String element : load_list) {
                 FileUtils.copyFile(benchmark_dir + "/" + element, getFilesDir().getAbsolutePath() + "/" + element);
                 System.load(getFilesDir().getAbsolutePath() + "/" + element);
             }
-            model = bundle.getString(ARGS_INTENT_KEY_MODEL);
             final String args = bundle.getString(ARGS_INTENT_KEY_ARGS_0, bundle.getString(ARGS_INTENT_KEY_ARGS_1));
             final String file_dir  = this.getFilesDir().getAbsolutePath();
             String output_path = file_dir + "/" + model;
@@ -57,8 +57,8 @@ private void init() {
             if(result != 0) {
                 Log.i("tnn", String.format(" %s TNN Benchmark time cost failed error code: %d \n", model , result));
             }
-        } catch(Exception e) {
-            Log.i("tnn", String.format(" %s TNN Benchmark time cost failed exception: %s \n", model, e.getMessage()));
+        } catch(Error | Exception e) {
+            Log.i("tnn", String.format(" %s TNN Benchmark time cost failed error/exception: %s \n", model, e.getMessage()));
         }
     }
 

diff --git a/benchmark/benchmark_android/benchmark_models.sh b/benchmark/benchmark_android/benchmark_models.sh
@@ -203,7 +203,11 @@ function bench_android_app() {
     build_android_bench
     build_android_bench_app
 
-    adb install -r benchmark-release.apk 
+    if [ "$ABI" = "armeabi-v7a with NEON" ];then
+        adb install -r --abi armeabi-v7a benchmark-release.apk 
+    else
+        adb install -r --abi $ABI benchmark-release.apk
+    fi
 
     $ADB shell "mkdir -p $ANDROID_DIR/benchmark-model"
     $ADB push ${BENCHMARK_MODEL_DIR} $ANDROID_DIR

diff --git a/doc/cn/user/convert.md b/doc/cn/user/convert.md
@@ -103,36 +103,34 @@ docker run  -it tnn-convert:latest  python3 ./converter.py tf2tnn -h
 ```
 得到的输出信息如下：
 ``` text
-usage: convert tf2tnn [-h] -tp TF_PATH -in input_name -on output_name
-                      [-o OUTPUT_DIR] [-v v1.0] [-optimize] [-half]
+usage: convert tf2tnn [-h] -tp TF_PATH -in input_info [input_info ...] -on output_name [output_name ...] [-o OUTPUT_DIR] [-v v1.0] [-optimize] [-half] [-align] [-input_file INPUT_FILE_PATH]
+                      [-ref_file REFER_FILE_PATH]
 
 optional arguments:
   -h, --help            show this help message and exit
   -tp TF_PATH           the path for tensorflow graphdef file
-  -in input_name        the tensorflow model's input names. If batch is not
-                        specified, you can add input shape after the input
-                        name, e.g. -in "name[1,28,28,3]"
-  -on output_name       the tensorflow model's output name
+  -in input_info [input_info ...]
+                        specify the input name and shape of the model. e.g., -in input1_name:1,128,128,3 input2_name:1,256,256,3
+  -on output_name [output_name ...]
+                        the tensorflow model's output name. e.g. -on output_name1 output_name2
   -o OUTPUT_DIR         the output tnn directory
   -v v1.0               the version for model
   -optimize             optimize the model
   -half                 optimize the model
   -align                align the onnx model with tnn model
   -input_file INPUT_FILE_PATH
-                        the input file path which contains the input data for
-                        the inference model.
+                        the input file path which contains the input data for the inference model.
   -ref_file REFER_FILE_PATH
-                        the reference file path which contains the reference
-                        data to compare the results.
+                        the reference file path which contains the reference data to compare the results.
 ```
 通过上面的输出，可以发现针对 TF 模型的转换，convert2tnn 工具提供了很多参数，我们一次对下面的参数进行解释：
 
 - tp 参数（必须）
     通过 “-tp” 参数指定需要转换的模型的路径。目前只支持单个 TF模型的转换，不支持多个 TF 模型的一起转换。
 - in 参数（必须）
-    通过 “-in” 参数指定模型输入的名称，输入的名称需要放到“”中，例如，-in "name"。如果模型有多个输入，请使用 “;”进行分割。有的 TensorFlow 模型没有指定 batch 导致无法成功转换为 ONNX 模型，进而无法成功转换为 TNN 模型。你可以通过在名称后添加输入 shape 进行指定。shape 信息需要放在 [] 中。例如：-in "name[1,28,28,3]"。
+    通过 “-in” 参数指定模型输入，例如：-in input_name_1:1,128,128,3 input_name_2:1,256,256,3。
 - on 参数（必须）
-    通过 “-on” 参数指定模型输出的名称，如果模型有多个输出，请使用 “;”进行分割
+    通过 “-on” 参数指定模型输出的名称，例如: -on output_name1 output_name2
 - output_dir 参数：
     可以通过 “-o <path>” 参数指定输出路径，但是在 docker 中我们一般不使用这个参数，默认会将生成的 TNN 模型放在当前和 TF 模型相同的路径下。
 - optimize 参数（可选）
@@ -156,8 +154,8 @@ optional arguments:
 ``` shell script
 docker run --volume=$(pwd):/workspace -it tnn-convert:latest  python3 ./converter.py tf2tnn \
     -tp /workspace/test.pb \
-    -in "input0[1,32,32,3];input1[1,32,32,3]" \
-    -on output0 \
+    -in "input0:1,32,32,3 input2:1,32,32,3" \
+    -on output0 output1 \
     -v v2.0 \
     -optimize \
     -align \
@@ -332,31 +330,26 @@ python3 converter.py onnx2tnn -h
 ```
 usage 信息如下：
 ```text
-usage: convert onnx2tnn [-h] [-in input_name [input_name ...]] [-optimize]
-                        [-half] [-v v1.0.0] [-o OUTPUT_DIR] [-align]
-                        [-input_file INPUT_FILE_PATH]
-                        [-ref_file REFER_FILE_PATH]
+usage: convert onnx2tnn [-h] [-in input_info [input_info ...]] [-optimize] [-half] [-v v1.0.0] [-o OUTPUT_DIR] [-align]
+                        [-input_file INPUT_FILE_PATH] [-ref_file REFER_FILE_PATH]
                         onnx_path
 
 positional arguments:
   onnx_path             the path for onnx file
 
 optional arguments:
   -h, --help            show this help message and exit
-  -in input_name [input_name ...]
-                        specify the input name and shape of the model. e.g.,
-                        -in in1:1,3,8,8 in2:1,8
+  -in input_info [input_info ...]
+                        specify the input name and shape of the model. e.g., -in input1_name:1,3,8,8 input2_name:1,8
   -optimize             optimize the model
   -half                 save model using half
   -v v1.0.0             the version for model
   -o OUTPUT_DIR         the output tnn directory
   -align                align the onnx model with tnn model
   -input_file INPUT_FILE_PATH
-                        the input file path which contains the input data for
-                        the inference model.
+                        the input file path which contains the input data for the inference model.
   -ref_file REFER_FILE_PATH
-                        the reference file path which contains the reference
-                        data to compare the results.
+                        the reference file path which contains the reference data to compare the results.
 ```
 示例：
 ```shell script
@@ -439,28 +432,26 @@ python3 converter.py caffe2tnn \
 python3 converter.py tf2tnn -h
 ```
 usage 信息如下：
-```
-usage: convert tf2tnn [-h] -tp TF_PATH -in input_name -on output_name
-                      [-o OUTPUT_DIR] [-v v1.0] [-optimize] [-half]
+```text
+usage: convert tf2tnn [-h] -tp TF_PATH -in input_info [input_info ...] -on output_name [output_name ...] [-o OUTPUT_DIR] [-v v1.0] [-optimize] [-half] [-align] [-input_file INPUT_FILE_PATH]
+                      [-ref_file REFER_FILE_PATH]
 
 optional arguments:
   -h, --help            show this help message and exit
   -tp TF_PATH           the path for tensorflow graphdef file
-  -in input_name        the tensorflow model's input names. If batch is not
-                        specified, you can add input shape after the input
-                        name, e.g. -in "name[1,28,28,3]"
-  -on output_name       the tensorflow model's output name
+  -in input_info [input_info ...]
+                        specify the input name and shape of the model. e.g., -in input1_name:1,128,128,3 input2_name:1,256,256,3
+  -on output_name [output_name ...]
+                        the tensorflow model's output name. e.g. -on output_name1 output_name2
   -o OUTPUT_DIR         the output tnn directory
   -v v1.0               the version for model
   -optimize             optimize the model
   -half                 optimize the model
   -align                align the onnx model with tnn model
   -input_file INPUT_FILE_PATH
-                        the input file path which contains the input data for
-                        the inference model.
+                        the input file path which contains the input data for the inference model.
   -ref_file REFER_FILE_PATH
-                        the reference file path which contains the reference
-                        data to compare the results.
+                        the reference file path which contains the reference data to compare the results.
 ```
 - tensorflow-lite2tnn
 

diff --git a/doc/en/user/convert_en.md b/doc/en/user/convert_en.md
@@ -103,31 +103,34 @@ docker run  -it tnn-convert:latest  python3 ./converter.py tf2tnn -h
 ```
 The output shows below：
 ``` text
-usage: convert tf2tnn [-h] -tp TF_PATH -in input_name -on output_name
-                      [-o OUTPUT_DIR] [-v v1.0] [-optimize] [-half]
+usage: convert tf2tnn [-h] -tp TF_PATH -in input_info [input_info ...] -on output_name [output_name ...] [-o OUTPUT_DIR] [-v v1.0] [-optimize] [-half] [-align] [-input_file INPUT_FILE_PATH]
+                      [-ref_file REFER_FILE_PATH]
 
 optional arguments:
-  -h, --help       show this help message and exit
-  -tp TF_PATH      the path for tensorflow graphdef file
-  -in input_name   the tensorflow model's input names
-  -on output_name  the tensorflow model's output name
-  -o OUTPUT_DIR    the output tnn directory
-  -v v1.0          the version for model
-  -optimize        optimize the model
-  -half            optimize the model
-  -align           align the onnx model with tnn model
-  -fold_const      enable tf constant_folding transformation before conversion
-  -input_file      the input file path which contains the input data for the inference model
-  -ref_file        the reference file path which contains the reference data to compare the results
+  -h, --help            show this help message and exit
+  -tp TF_PATH           the path for tensorflow graphdef file
+  -in input_info [input_info ...]
+                        specify the input name and shape of the model. e.g., -in input1_name:1,128,128,3 input2_name:1,256,256,3
+  -on output_name [output_name ...]
+                        the tensorflow model's output name. e.g. -on output_name1 output_name2
+  -o OUTPUT_DIR         the output tnn directory
+  -v v1.0               the version for model
+  -optimize             optimize the model
+  -half                 optimize the model
+  -align                align the onnx model with tnn model
+  -input_file INPUT_FILE_PATH
+                        the input file path which contains the input data for the inference model.
+  -ref_file REFER_FILE_PATH
+                        the reference file path which contains the reference data to compare the results.
 ```
 Here are the explanations for each parameter:
 
 - tp parameter (required)
     Use the "-tp" parameter to specify the path of the model to be converted. Currently only supports the conversion of a single TF model, does not support the conversion of multiple TF models together.
 - in parameter (required)
-    Specify the name of the model input through the "-in" parameter. If the model has multiple inputs, use ";" to split. Some models specify placeholders with unknown ranks and dims which can not be mapped to onnx. In those cases one can add the shape after the input name inside [], for example -in name[1,28,28,3]
+    Specify the name of the model input through the "-in" parameter, for example "-in input1_name:1,128,128,3 input2_name:1,256,256,3"
 - on parameter (required)
-    Specify the name of the model output through the "-on" parameter. If the model has multiple outputs, use ";" to split
+    Specify the name of the model output through the "-on" parameter, for example "-on output_name1 output_name2"
 - output_dir parameter:
     You can specify the output path through the "-o <path>" parameter, but we generally do not apply this parameter in docker. By default, the generated TNN model will be placed in the same path as the TF model.
 - optimize parameter (optional)
@@ -317,21 +320,26 @@ python3 converter.py onnx2tnn -h
 ```
 usage information：
 ```text
-usage: convert onnx2tnn [-h] [-optimize] [-half] [-v v1.0.0] [-o OUTPUT_DIR]
+usage: convert onnx2tnn [-h] [-in input_info [input_info ...]] [-optimize] [-half] [-v v1.0.0] [-o OUTPUT_DIR] [-align]
+                        [-input_file INPUT_FILE_PATH] [-ref_file REFER_FILE_PATH]
                         onnx_path
 
 positional arguments:
-  onnx_path      the path for onnx file
+  onnx_path             the path for onnx file
 
 optional arguments:
   -h, --help            show this help message and exit
+  -in input_info [input_info ...]
+                        specify the input name and shape of the model. e.g., -in input1_name:1,3,8,8 input2_name:1,8
   -optimize             optimize the model
   -half                 save model using half
   -v v1.0.0             the version for model
   -o OUTPUT_DIR         the output tnn directory
   -align                align the onnx model with tnn model
-  -input_file in.txt    the input file path which contains the input data for the inference model
-  -ref_file   ref.txt   the reference file path which contains the reference data to compare the results
+  -input_file INPUT_FILE_PATH
+                        the input file path which contains the input data for the inference model.
+  -ref_file REFER_FILE_PATH
+                        the reference file path which contains the reference data to compare the results.
 ```
 Example:
 ```shell script
@@ -397,23 +405,26 @@ The current convert2tnn model only supports the graphdef model, but does not sup
 python3 converter.py tf2tnn -h
 ```
 usage information：
-```
-usage: convert tf2tnn [-h] -tp TF_PATH -in input_name -on output_name
-                      [-o OUTPUT_DIR] [-v v1.0] [-optimize] [-half]
+```text
+usage: convert tf2tnn [-h] -tp TF_PATH -in input_info [input_info ...] -on output_name [output_name ...] [-o OUTPUT_DIR] [-v v1.0] [-optimize] [-half] [-align] [-input_file INPUT_FILE_PATH]
+                      [-ref_file REFER_FILE_PATH]
 
 optional arguments:
   -h, --help            show this help message and exit
   -tp TF_PATH           the path for tensorflow graphdef file
-  -in input_name        the tensorflow model's input names
-  -on output_name       the tensorflow model's output name
+  -in input_info [input_info ...]
+                        specify the input name and shape of the model. e.g., -in input1_name:1,128,128,3 input2_name:1,256,256,3
+  -on output_name [output_name ...]
+                        the tensorflow model's output name. e.g. -on output_name1 output_name2
   -o OUTPUT_DIR         the output tnn directory
   -v v1.0               the version for model
   -optimize             optimize the model
   -half                 optimize the model
   -align                align the onnx model with tnn model
-  -fold_const           enable tf constant_folding transformation before conversion
-  -input_file in.txt    the input file path which contains the input data for the inference model
-  -ref_file   out.txt   the reference file path which contains the reference data to compare the results
+  -input_file INPUT_FILE_PATH
+                        the input file path which contains the input data for the inference model.
+  -ref_file REFER_FILE_PATH
+                        the reference file path which contains the reference data to compare the results.
 ```
 Example：
 ```shell script

diff --git a/examples/android/demo/src/main/jni/cc/objectdetectorssd_jni.cc b/examples/android/demo/src/main/jni/cc/objectdetectorssd_jni.cc
@@ -59,8 +59,6 @@ TNN_OBJECT_DETECTORSSD(init)(JNIEnv *env, jobject thiz, jstring modelPath, jint
         //add for huawei_npu store the om file
         LOGI("the device type  %d device huawei_npu", gComputeUnitType);
         option->compute_units = TNN_NS::TNNComputeUnitsHuaweiNPU;
-        // skip low precision for tmp on npu, need to fix
-        option->precision = TNN_NS::PRECISION_HIGH;
         gDetector->setNpuModelPath(modelPathStr + "/");
         gDetector->setCheckNpuSwitch(false);
         status = gDetector->Init(option);

diff --git a/examples/android/demo/src/main/jni/cc/tnn_lib.h b/examples/android/demo/src/main/jni/cc/tnn_lib.h
@@ -23,8 +23,6 @@
 #define CL_HPP_TARGET_OPENCL_VERSION 110
 #define CL_HPP_MINIMUM_OPENCL_VERSION 110
 
-#include "CL/cl2.hpp"
-
 class TNNLib {
 public:
     TNNLib();

diff --git a/scripts/build_aarch64_macos.sh b/scripts/build_aarch64_macos.sh
@@ -16,7 +16,6 @@ cd build_aarch64_macos
 
 cmake ${TNN_ROOT_PATH} \
     -DTNN_TEST_ENABLE=ON \
-    -DTNN_UNIT_TEST_ENABLE=ON \
     -DCMAKE_BUILD_TYPE=Release \
     -DTNN_ARM_ENABLE:BOOL=$ARM \
     -DTNN_ARM82_ENABLE:BOOL=$ARM82 \

diff --git a/scripts/build_armhf_linux.sh b/scripts/build_armhf_linux.sh
@@ -27,7 +27,6 @@ cmake ${TNN_ROOT_PATH} \
     -DTNN_ARM_ENABLE:BOOL=$ARM \
     -DTNN_OPENMP_ENABLE:BOOL=$OPENMP \
     -DTNN_OPENCL_ENABLE:BOOL=$OPENCL \
-    -DTNN_UNIT_TEST_ENABLE=OFF \
     -DCMAKE_SYSTEM_PROCESSOR=$TARGET_ARCH \
     -DTNN_BUILD_SHARED:BOOL=$SHARED_LIB
 

diff --git a/scripts/build_cuda_linux.sh b/scripts/build_cuda_linux.sh
@@ -24,7 +24,6 @@ cmake ${TNN_ROOT_PATH} \
     -DTNN_OPENMP_ENABLE=OFF \
     -DTNN_OPENCL_ENABLE=OFF \
     -DTNN_QUANTIZATION_ENABLE=OFF \
-    -DTNN_UNIT_TEST_ENABLE=ON \
     -DTNN_COVERAGE=OFF \
     -DTNN_BENCHMARK_MODE=OFF \
     -DTNN_BUILD_SHARED=ON \

diff --git a/source/tnn/core/tnn.cc b/source/tnn/core/tnn.cc
@@ -26,8 +26,8 @@ TNN::~TNN() {
 Status TNN::Init(ModelConfig& config) {
     impl_ = TNNImplManager::GetTNNImpl(config.model_type);
     if (!impl_) {
-        LOGE("Error: not support mode type: %d\n", config.model_type);
-        return Status(TNNERR_NET_ERR, "not support mode type");
+        LOGE("Error: not support mode type: %d. If TNN is a static library, link it with option -Wl,--whole-archive tnn -Wl,--no-whole-archive on android or add -force_load on iOS\n", config.model_type);
+        return Status(TNNERR_NET_ERR, "unsupport mode type, If TNN is a static library, link it with option -Wl,--whole-archive tnn -Wl,--no-whole-archive on android or add -force_load on iOS");
     }
     return impl_->Init(config);
 }