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Paddle-Lite
提交 1aa706ae 编写于 作者: Z zhangwen31
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Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle-Lite into matrix_nms_host

上级 5c265189 b2c14f01
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cmake/external/flatbuffers.cmake
浏览文件 @ 1aa706ae
......@@ -45,7 +45,7 @@ SET(OPTIONAL_ARGS "-DCMAKE_CXX_COMPILER=${HOST_CXX_COMPILER}"
ExternalProject_Add(
extern_flatbuffers
${EXTERNAL_PROJECT_LOG_ARGS}
GIT_REPOSITORY "https://github.com/google/flatbuffers.git"
GIT_REPOSITORY "https://github.com/Shixiaowei02/flatbuffers.git"
GIT_TAG "v1.12.0"
SOURCE_DIR ${FLATBUFFERS_SOURCES_DIR}
PREFIX ${FLATBUFFERS_PREFIX_DIR}
......
docs/index.rst
浏览文件 @ 1aa706ae
......@@ -46,6 +46,7 @@ Welcome to Paddle-Lite's documentation!
user_guides/post_quant_with_data
user_guides/post_quant_no_data
user_guides/model_quantization
user_guides/model_visualization
user_guides/debug
.. toctree::
......
docs/user_guides/model_visualization.md 0 → 100644
浏览文件 @ 1aa706ae
# 模型可视化方法
Paddle Lite框架中主要使用到的模型结构有2种：(1) 为[PaddlePaddle](https://github.com/PaddlePaddle/Paddle)深度学习框架产出的模型格式; (2) 使用[Lite模型优化工具opt](model_optimize_tool)优化后的模型格式。因此本章节包含内容如下：
1. [Paddle推理模型可视化](model_visualization.html#paddle)
2. [Lite优化模型可视化](model_visualization.html#lite)
3. [Lite子图方式下模型可视化](model_visualization.html#id2)
## Paddle推理模式可视化
Paddle用于推理的模型是通过[save_inference_model](https://www.paddlepaddle.org.cn/documentation/docs/zh/api_cn/io_cn/save_inference_model_cn.html#save-inference-model)这个API保存下来的，存储格式有两种，由save_inference_model接口中的 `model_filename` 和 `params_filename` 变量控制：
- **non-combined形式**：参数保存到独立的文件，如设置 `model_filename` 为 `None` , `params_filename` 为 `None`
```bash
$ ls -l recognize_digits_model_non-combined/
total 192K
-rw-r--r-- 1 root root 28K Sep 24 09:39 __model__ # 模型文件
-rw-r--r-- 1 root root 104 Sep 24 09:39 conv2d_0.b_0 # 独立权重文件
-rw-r--r-- 1 root root 2.0K Sep 24 09:39 conv2d_0.w_0 # 独立权重文件
-rw-r--r-- 1 root root 224 Sep 24 09:39 conv2d_1.b_0 # ...
-rw-r--r-- 1 root root 98K Sep 24 09:39 conv2d_1.w_0
-rw-r--r-- 1 root root 64 Sep 24 09:39 fc_0.b_0
-rw-r--r-- 1 root root 32K Sep 24 09:39 fc_0.w_0
```
- **combined形式**：参数保存到同一个文件，如设置 `model_filename` 为 `model` , `params_filename` 为 `params`
```bash
$ ls -l recognize_digits_model_combined/
total 160K
-rw-r--r-- 1 root root 28K Sep 24 09:42 model # 模型文件
-rw-r--r-- 1 root root 132K Sep 24 09:42 params # 权重文件
```
通过以上方式保存下来的模型文件都可以通过[Netron](https://lutzroeder.github.io/netron/)工具来打开查看模型的网络结构。
**注意：**[Netron](https://github.com/lutzroeder/netron)当前要求PaddlePaddle的保存模型文件名必须为`__model__`，否则无法识别。如果是通过第二种方式保存下来的combined形式的模型文件，需要将文件重命名为`__model__`。
## Lite优化模型可视化
Paddle Lite在执行模型推理之前需要使用[模型优化工具opt](model_optimize_tool)来对模型进行优化，优化后的模型结构同样可以使用[Netron](https://lutzroeder.github.io/netron/)工具进行查看，但是必须保存为`protobuf`格式，而不是`naive_buffer`格式。
**注意**: 为了减少第三方库的依赖、提高Lite预测框架的通用性，在移动端使用Lite API您需要准备Naive Buffer存储格式的模型(该模型格式是以`.nb`为后缀的单个文件)。但是Naive Buffer格式的模型为序列化模型，不支持可视化。
这里以[paddle_lite_opt](opt/opt_python)工具为例：
- 当模型输入为`non-combined`格式的Paddle模型时，需要通过`--model_dir`来指定模型文件夹
```bash
$ paddle_lite_opt \
--model_dir=./recognize_digits_model_non-combined/ \
--valid_targets=arm \
--optimize_out_type=protobuf \ # 注意：这里必须输出为protobuf格式
--optimize_out=model_opt_dir_non-combined
```
优化后的模型文件会存储在由`--optimize_out`指定的输出文件夹下，格式如下
```bash
$ ls -l model_opt_dir_non-combined/
total 152K
-rw-r--r-- 1 root root 17K Sep 24 09:51 model # 优化后的模型文件
-rw-r--r-- 1 root root 132K Sep 24 09:51 params # 优化后的权重文件
```
- 当模式输入为`combined`格式的Paddle模型时，需要同时输入`--model_file`和`--param_file`来分别指定Paddle模型的模型文件和权重文件
```bash
$ paddle_lite_opt \
--model_file=./recognize_digits_model_combined/model \
--param_file=./recognize_digits_model_combined/params \
--valid_targets=arm \
--optimize_out_type=protobuf \ # 注意：这里必须输出为protobuf格式
--optimize_out=model_opt_dir_combined
```
优化后的模型文件同样存储在由`--optimize_out`指定的输出文件夹下，格式相同
```bash
ls -l model_opt_dir_combined/
total 152K
-rw-r--r-- 1 root root 17K Sep 24 09:56 model # 优化后的模型文件
-rw-r--r-- 1 root root 132K Sep 24 09:56 params # 优化后的权重文件
```
将通过以上步骤输出的优化后的模型文件`model`重命名为`__model__`，然后用[Netron](https://lutzroeder.github.io/netron/)工具打开即可查看优化后的模型结构。将优化前后的模型进行对比，即可发现优化后的模型比优化前的模型更轻量级，在推理任务中耗费资源更少且执行速度也更快。
<p align="center"><img width="600" src="https://paddlelite-data.bj.bcebos.com/doc_images/model_visualization/model_visualization.png"/></p>
## Lite子图方式下模型可视化
当模型优化的目标硬件平台为 [华为NPU](../demo_guides/huawei_kirin_npu), [百度XPU](../demo_guides/baidu_xpu), [瑞芯微NPU](../demo_guides/rockchip_npu), [联发科APU](../demo_guides/mediatek_apu) 等通过子图方式接入的硬件平台时，得到的优化后的`protobuf`格式模型中运行在这些硬件平台上的算子都由`subgraph`算子包含，无法查看具体的网络结构。
以[华为NPU](../demo_guides/huawei_kirin_npu)为例，运行以下命令进行模型优化，得到输出文件夹下的`model, params`两个文件。
```bash
$ paddle_lite_opt \
--model_dir=./recognize_digits_model_non-combined/ \
--valid_targets=npu,arm \ # 注意：这里的目标硬件平台为NPU,ARM
--optimize_out_type=protobuf \
--optimize_out=model_opt_dir_npu
```
将优化后的模型文件`model`重命名为`__model__`，然后用[Netron](https://lutzroeder.github.io/netron/)工具打开，只看到单个的subgraph算子，如下图所示：
<p align="center"><img width="200" src="https://paddlelite-data.bj.bcebos.com/doc_images/model_visualization/subgraph.png"/></p>
如果想要查看subgraph中的具体模型结构和算子信息需要打开Lite Debug Log，Lite在优化过程中会以.dot文本形式输出模型的拓扑结构，将.dot的文本内容复制到[webgraphviz](http://www.webgraphviz.com/)即可查看模型结构。
```bash
$ export GLOG_v=5 # 注意：这里打开Lite中Level为5及以下的的Debug Log信息
$ paddle_lite_opt \
--model_dir=./recognize_digits_model_non-combined/ \
--valid_targets=npu,arm \
--optimize_out_type=protobuf \
--optimize_out=model_opt_dir_npu > debug_log.txt 2>&1
# 以上命令会将所有的debug log存储在debug_log.txt文件中
```
打开debug_log.txt文件，将会看到多个由以下格式构成的拓扑图定义，由于recognize_digits模型在优化后仅存在一个subgraph，所以在文本搜索`subgraphs`的关键词，即可得到子图拓扑如下：
```shell
I0924 10:50:12.715279 122828 optimizer.h:202] == Running pass: npu_subgraph_pass
I0924 10:50:12.715335 122828 ssa_graph.cc:27] node count 33
I0924 10:50:12.715412 122828 ssa_graph.cc:27] node count 33
I0924 10:50:12.715438 122828 ssa_graph.cc:27] node count 33
subgraphs: 1 # 注意：搜索subgraphs:这个关键词，
digraph G {
node_30[label="fetch"]
node_29[label="fetch0" shape="box" style="filled" color="black" fillcolor="white"]
node_28[label="save_infer_model/scale_0.tmp_0"]
node_26[label="fc_0.tmp_1"]
node_24[label="fc_0.w_0"]
node_23[label="fc0_subgraph_0" shape="box" style="filled" color="black" fillcolor="red"]
...
node_15[label="batch_norm_0.tmp_1"]
node_17[label="conv2d1_subgraph_0" shape="box" style="filled" color="black" fillcolor="red"]
node_19[label="conv2d_1.b_0"]
node_1->node_0
node_0->node_2
node_2->node_3
...
node_28->node_29
node_29->node_30
} // end G
I0924 10:50:12.715745 122828 op_lite.h:62] valid places 0
I0924 10:50:12.715764 122828 op_registry.cc:32] creating subgraph kernel for host/float/NCHW
I0924 10:50:12.715770 122828 op_lite.cc:89] pick kernel for subgraph host/float/NCHW get 0 kernels
```
将以上文本中以`digraph G {`开头和以`} // end G`结尾的这段文本复制粘贴到[webgraphviz](http://www.webgraphviz.com/)，即可看到子图中的具体模型结构，如下图。其中高亮的方形节点为算子，椭圆形节点为变量或张量。
<p align="center"><img width="600" src="https://paddlelite-data.bj.bcebos.com/doc_images/model_visualization/subgraph1.png"/></p>
若模型中存在多个子图，以上方法同样可以得到所有子图的具体模型结构。
同样以[华为NPU](../demo_guides/huawei_kirin_npu)和ARM平台混合调度为例，子图的产生往往是由于模型中存在部分算子无法运行在NPU平台上(比如NPU不支持的算子)，这会导致整个模型被切分为多个子图，子图中包含的算子会运行在NPU平台上，而子图与子图之间的一个或多个算子则只能运行在ARM平台上。这里可以通过[华为NPU](../demo_guides/huawei_kirin_npu)的[自定义子图分割](../demo_guides/huawei_kirin_npu.html#npuarm-cpu)功能，将recognize_digits模型中的`batch_norm`设置为禁用NPU的算子，从而将模型分割为具有两个子图的模型：
```bash
# 此txt配置文件文件中的内容为 batch_norm
$ export SUBGRAPH_CUSTOM_PARTITION_CONFIG_FILE=./subgraph_custom_partition_config_file.txt
$ export GLOG_v=5 # 继续打开Lite的Debug Log信息
$ paddle_lite_opt \
--model_dir=./recognize_digits_model_non-combined/ \
--valid_targets=npu,arm \
--optimize_out_type=protobuf \
--optimize_out=model_opt_dir_npu > debug_log.txt 2>&1 #
```
将执行以上命令之后，得到的优化后模型文件`model`重命名为`__model__`，然后用[Netron](https://lutzroeder.github.io/netron/)工具打开，就可以看到优化后的模型中存在2个subgraph算子，如左图所示，两个子图中间即为通过环境变量和配置文件指定的禁用NPU的`batch_norm`算子。
打开新保存的debug_log.txt文件，搜索`final program`关键字，拷贝在这之后的以`digraph G {`开头和以`} // end G`结尾的文本用[webgraphviz](http://www.webgraphviz.com/)查看，也是同样的模型拓扑结构，存在`subgraph1`和`subgraph3`两个子图，两个子图中间同样是被禁用NPU的`batch_norm`算子，如右图所示。
<p align="center"><img src="https://paddlelite-data.bj.bcebos.com/doc_images/model_visualization/final_program.png"/></p>
之后继续在debug_log.txt文件中，搜索`subgraphs`关键字，可以得到所有子图的.dot格式内容如下：
```bash
digraph G {
node_30[label="fetch"]
node_29[label="fetch0" shape="box" style="filled" color="black" fillcolor="white"]
node_28[label="save_infer_model/scale_0.tmp_0"]
node_26[label="fc_0.tmp_1"]
node_24[label="fc_0.w_0"]
...
node_17[label="conv2d1_subgraph_0" shape="box" style="filled" color="black" fillcolor="red"]
node_19[label="conv2d_1.b_0"]
node_0[label="feed0" shape="box" style="filled" color="black" fillcolor="white"]
node_5[label="conv2d_0.b_0"]
node_1[label="feed"]
node_23[label="fc0_subgraph_0" shape="box" style="filled" color="black" fillcolor="red"]
node_7[label="pool2d0_subgraph_1" shape="box" style="filled" color="black" fillcolor="green"]
node_21[label="pool2d1_subgraph_0" shape="box" style="filled" color="black" fillcolor="red"]
...
node_18[label="conv2d_1.w_0"]
node_1->node_0
node_0->node_2
...
node_28->node_29
node_29->node_30
} // end G
```
将以上文本复制到[webgraphviz](http://www.webgraphviz.com/)查看，即可显示两个子图分别在整个模型中的结构，如下图所示。可以看到图中绿色高亮的方形节点的为`subgraph1`中的算子，红色高亮的方形节点为`subgraph2`中的算子，两个子图中间白色不高亮的方形节点即为被禁用NPU的`batch_norm`算子。
<p align="center"><img src="https://paddlelite-data.bj.bcebos.com/doc_images/model_visualization/subgraph2.png"/></p>
**注意：** 本章节用到的recognize_digits模型代码位于[PaddlePaddle/book](https://github.com/PaddlePaddle/book/tree/develop/02.recognize_digits)
lite/CMakeLists.txt
浏览文件 @ 1aa706ae
......@@ -38,34 +38,31 @@ if (LITE_WITH_LIGHT_WEIGHT_FRAMEWORK AND NOT LITE_ON_TINY_PUBLISH)
endif()
if (WITH_TESTING)
set(LITE_URL_FOR_UNITTESTS "http://paddle-inference-dist.bj.bcebos.com/PaddleLite/models_and_data_for_unittests")
# models
lite_download_and_uncompress(${LITE_MODEL_DIR} ${LITE_URL} "lite_naive_model.tar.gz")
lite_download_and_uncompress(${LITE_MODEL_DIR} ${LITE_URL} "mobilenet_v1.tar.gz")
lite_download_and_uncompress(${LITE_MODEL_DIR} ${LITE_URL} "mobilenet_v2_relu.tar.gz")
lite_download_and_uncompress(${LITE_MODEL_DIR} ${LITE_URL} "inception_v4_simple.tar.gz")
if(LITE_WITH_LIGHT_WEIGHT_FRAMEWORK)
lite_download_and_uncompress(${LITE_MODEL_DIR} ${LITE_URL} "mobilenet_v1.tar.gz")
lite_download_and_uncompress(${LITE_MODEL_DIR} ${LITE_URL} "mobilenet_v1_int16.tar.gz")
lite_download_and_uncompress(${LITE_MODEL_DIR} ${LITE_URL} "mobilenet_v2_relu.tar.gz")
lite_download_and_uncompress(${LITE_MODEL_DIR} ${LITE_URL} "resnet50.tar.gz")
lite_download_and_uncompress(${LITE_MODEL_DIR} ${LITE_URL} "inception_v4_simple.tar.gz")
lite_download_and_uncompress(${LITE_MODEL_DIR} ${LITE_URL} "MobileNetV1_quant.tar.gz")
lite_download_and_uncompress(${LITE_MODEL_DIR} ${LITE_URL} "transformer_with_mask_fp32.tar.gz")
endif()
if(NOT LITE_WITH_LIGHT_WEIGHT_FRAMEWORK)
lite_download_and_uncompress(${LITE_MODEL_DIR} ${LITE_URL_FOR_UNITTESTS} "mobilenet_v1_int8_for_mediatek_apu.tar.gz")
lite_download_and_uncompress(${LITE_MODEL_DIR} ${LITE_URL_FOR_UNITTESTS} "mobilenet_v1_int8_for_rockchip_npu.tar.gz")
else()
lite_download_and_uncompress(${LITE_MODEL_DIR} ${LITE_URL} "GoogleNet_inference.tar.gz")
lite_download_and_uncompress(${LITE_MODEL_DIR} ${LITE_URL} "mobilenet_v1.tar.gz")
lite_download_and_uncompress(${LITE_MODEL_DIR} ${LITE_URL} "mobilenet_v2_relu.tar.gz")
lite_download_and_uncompress(${LITE_MODEL_DIR} ${LITE_URL} "inception_v4_simple.tar.gz")
lite_download_and_uncompress(${LITE_MODEL_DIR} ${LITE_URL} "step_rnn.tar.gz")
set(LITE_URL_FOR_UNITTESTS "http://paddle-inference-dist.bj.bcebos.com/PaddleLite/models_and_data_for_unittests")
# models
lite_download_and_uncompress(${LITE_MODEL_DIR} ${LITE_URL_FOR_UNITTESTS} "resnet50.tar.gz")
lite_download_and_uncompress(${LITE_MODEL_DIR} ${LITE_URL_FOR_UNITTESTS} "bert.tar.gz")
lite_download_and_uncompress(${LITE_MODEL_DIR} ${LITE_URL_FOR_UNITTESTS} "ernie.tar.gz")
lite_download_and_uncompress(${LITE_MODEL_DIR} ${LITE_URL_FOR_UNITTESTS} "GoogLeNet.tar.gz")
lite_download_and_uncompress(${LITE_MODEL_DIR} ${LITE_URL_FOR_UNITTESTS} "VGG19.tar.gz")
# data
lite_download_and_uncompress(${LITE_MODEL_DIR} ${LITE_URL_FOR_UNITTESTS} "ILSVRC2012_small.tar.gz")
lite_download_and_uncompress(${LITE_MODEL_DIR} ${LITE_URL_FOR_UNITTESTS} "bert_data.tar.gz")
endif()
# data
lite_download_and_uncompress(${LITE_MODEL_DIR} ${LITE_URL_FOR_UNITTESTS} "ILSVRC2012_small.tar.gz")
lite_download_and_uncompress(${LITE_MODEL_DIR} ${LITE_URL_FOR_UNITTESTS} "bert_data.tar.gz")
endif()
# ----------------------------- PUBLISH -----------------------------
......
lite/backends/arm/math/conv3x3s1p01_depthwise_fp32_relu.cc
浏览文件 @ 1aa706ae
......@@ -2307,12 +2307,10 @@ void conv_depthwise_3x3s1p0_bias_no_relu(float *dout,
//! process bottom pad
if (i + 3 >= h_in) {
switch (i + 3 - h_in) {
case 3:
din_ptr1 = zero_ptr;
case 2:
din_ptr2 = zero_ptr;
din_ptr1 = zero_ptr;
case 1:
din_ptr3 = zero_ptr;
din_ptr2 = zero_ptr;
case 0:
din_ptr3 = zero_ptr;
default:
......@@ -2591,12 +2589,10 @@ void conv_depthwise_3x3s1p0_bias_relu(float *dout,
//! process bottom pad
if (i + 3 >= h_in) {
switch (i + 3 - h_in) {
case 3:
din_ptr1 = zero_ptr;
case 2:
din_ptr2 = zero_ptr;
din_ptr1 = zero_ptr;
case 1:
din_ptr3 = zero_ptr;
din_ptr2 = zero_ptr;
case 0:
din_ptr3 = zero_ptr;
default:
......@@ -2730,12 +2726,10 @@ void conv_depthwise_3x3s1p0_bias_s_no_relu(float *dout,
if (j + 3 >= h_in) {
switch (j + 3 - h_in) {
case 3:
dr1 = zero_ptr;
case 2:
dr2 = zero_ptr;
dr1 = zero_ptr;
case 1:
dr3 = zero_ptr;
dr2 = zero_ptr;
doutr1 = trash_buf;
case 0:
dr3 = zero_ptr;
......@@ -2889,12 +2883,10 @@ void conv_depthwise_3x3s1p0_bias_s_relu(float *dout,
if (j + 3 >= h_in) {
switch (j + 3 - h_in) {
case 3:
dr1 = zero_ptr;
case 2:
dr2 = zero_ptr;
dr1 = zero_ptr;
case 1:
dr3 = zero_ptr;
dr2 = zero_ptr;
doutr1 = trash_buf;
case 0:
dr3 = zero_ptr;
......
lite/backends/arm/math/conv3x3s1px_depthwise_fp32.cc
浏览文件 @ 1aa706ae
......@@ -645,7 +645,6 @@ void conv_3x3s1_depthwise_fp32_bias(const float* i_data,
bool flag_bias = param.bias != nullptr;
/// get workspace
LOG(INFO) << "conv_3x3s1_depthwise_fp32_bias: ";
float* ptr_zero = ctx->workspace_data<float>();
memset(ptr_zero, 0, sizeof(float) * win_round);
float* ptr_write = ptr_zero + win_round;
......
lite/backends/arm/math/conv3x3s2p01_depthwise_fp32_relu.cc
浏览文件 @ 1aa706ae
......@@ -713,7 +713,7 @@ void conv_depthwise_3x3s2p1_bias_relu(float* dout,
cnt_col++;
size_right_remain -= 8;
}
int cnt_remain = (size_right_remain == 8) ? 4 : (w_out % 4); //
int cnt_remain = (size_right_remain == 8 && w_out % 4 == 0) ? 4 : (w_out % 4);
int size_right_pad = w_out * 2 - w_in;
......@@ -966,7 +966,7 @@ void conv_depthwise_3x3s2p1_bias_no_relu(float* dout,
cnt_col++;
size_right_remain -= 8;
}
int cnt_remain = (size_right_remain == 8) ? 4 : (w_out % 4); //
int cnt_remain = (size_right_remain == 8 && w_out % 4 == 0) ? 4 : (w_out % 4);
int size_right_pad = w_out * 2 - w_in;
......
lite/backends/arm/math/conv_impl.cc
浏览文件 @ 1aa706ae
......@@ -620,10 +620,8 @@ void conv_depthwise_3x3_fp32(const void* din,
int pad = pad_w;
bool flag_bias = param.bias != nullptr;
bool pads_less = ((paddings[1] < 2) && (paddings[3] < 2));
bool ch_four = ch_in <= 4 * w_in;
if (stride == 1) {
if (ch_four && pads_less && (pad_h == pad_w) &&
(pad < 2)) { // support pad = [0, 1]
if (pads_less && (pad_h == pad_w) && (pad < 2)) { // support pad = [0, 1]
conv_depthwise_3x3s1_fp32(reinterpret_cast<const float*>(din),
reinterpret_cast<float*>(dout),
num,
......@@ -656,8 +654,7 @@ void conv_depthwise_3x3_fp32(const void* din,
ctx);
}
} else if (stride == 2) {
if (ch_four && pads_less && pad_h == pad_w &&
(pad < 2)) { // support pad = [0, 1]
if (pads_less && pad_h == pad_w && (pad < 2)) { // support pad = [0, 1]
conv_depthwise_3x3s2_fp32(reinterpret_cast<const float*>(din),
reinterpret_cast<float*>(dout),
num,
......
lite/backends/arm/math/interpolate.cc
浏览文件 @ 1aa706ae
......@@ -70,8 +70,7 @@ void bilinear_interp(const float* src,
int h_out,
float scale_x,
float scale_y,
bool align_corners,
bool align_mode) {
bool with_align) {
int* buf = new int[w_out + h_out + w_out * 2 + h_out * 2];
int* xofs = buf;
......@@ -79,13 +78,14 @@ void bilinear_interp(const float* src,
float* alpha = reinterpret_cast<float*>(buf + w_out + h_out);
float* beta = reinterpret_cast<float*>(buf + w_out + h_out + w_out * 2);
bool with_align = (align_mode == 0 && !align_corners);
float fx = 0.0f;
float fy = 0.0f;
int sx = 0;
int sy = 0;
if (!with_align) {
if (with_align) {
scale_x = static_cast<float>(w_in - 1) / (w_out - 1);
scale_y = static_cast<float>(h_in - 1) / (h_out - 1);
// calculate x axis coordinate
for (int dx = 0; dx < w_out; dx++) {
fx = dx * scale_x;
......@@ -105,6 +105,8 @@ void bilinear_interp(const float* src,
beta[dy * 2 + 1] = fy;
}
} else {
scale_x = static_cast<float>(w_in) / w_out;
scale_y = static_cast<float>(h_in) / h_out;
// calculate x axis coordinate
for (int dx = 0; dx < w_out; dx++) {
fx = scale_x * (dx + 0.5f) - 0.5f;
......@@ -466,9 +468,15 @@ void nearest_interp(const float* src,
float* dst,
int w_out,
int h_out,
float scale_w_new,
float scale_h_new,
float scale_x,
float scale_y,
bool with_align) {
float scale_w_new = (with_align)
? (static_cast<float>(w_in - 1) / (w_out - 1))
: (static_cast<float>(w_in) / (w_out));
float scale_h_new = (with_align)
? (static_cast<float>(h_in - 1) / (h_out - 1))
: (static_cast<float>(h_in) / (h_out));
if (with_align) {
for (int h = 0; h < h_out; ++h) {
float* dst_p = dst + h * w_out;
......@@ -498,8 +506,7 @@ void interpolate(lite::Tensor* X,
int out_height,
int out_width,
float scale,
bool align_corners,
bool align_mode,
bool with_align,
std::string interpolate_type) {
int in_h = X->dims()[2];
int in_w = X->dims()[3];
......@@ -524,12 +531,12 @@ void interpolate(lite::Tensor* X,
out_width = out_size_data[1];
}
}
// float height_scale = scale;
// float width_scale = scale;
// if (out_width > 0 && out_height > 0) {
// height_scale = static_cast<float>(out_height / X->dims()[2]);
// width_scale = static_cast<float>(out_width / X->dims()[3]);
// }
float height_scale = scale;
float width_scale = scale;
if (out_width > 0 && out_height > 0) {
height_scale = static_cast<float>(out_height / X->dims()[2]);
width_scale = static_cast<float>(out_width / X->dims()[3]);
}
int num_cout = X->dims()[0];
int c_cout = X->dims()[1];
Out->Resize({num_cout, c_cout, out_height, out_width});
......@@ -544,10 +551,6 @@ void interpolate(lite::Tensor* X,
int spatial_in = in_h * in_w;
int spatial_out = out_h * out_w;
float scale_x = (align_corners) ? (static_cast<float>(in_w - 1) / (out_w - 1))
: (static_cast<float>(in_w) / (out_w));
float scale_y = (align_corners) ? (static_cast<float>(in_h - 1) / (out_h - 1))
: (static_cast<float>(in_h) / (out_h));
if ("Bilinear" == interpolate_type) {
#pragma omp parallel for
for (int i = 0; i < count; ++i) {
......@@ -557,10 +560,9 @@ void interpolate(lite::Tensor* X,
dout + spatial_out * i,
out_w,
out_h,
scale_x,
scale_y,
align_corners,
align_mode);
1.f / width_scale,
1.f / height_scale,
with_align);
}
} else if ("Nearest" == interpolate_type) {
#pragma omp parallel for
......@@ -571,9 +573,9 @@ void interpolate(lite::Tensor* X,
dout + spatial_out * i,
out_w,
out_h,
scale_x,
scale_y,
align_corners);
1.f / width_scale,
1.f / height_scale,
with_align);
}
}
}
......
lite/backends/arm/math/interpolate.h
浏览文件 @ 1aa706ae
......@@ -30,8 +30,7 @@ void bilinear_interp(const float* src,
int h_out,
float scale_x,
float scale_y,
bool align_corners,
bool align_mode);
bool with_align);
void nearest_interp(const float* src,
int w_in,
......@@ -41,7 +40,7 @@ void nearest_interp(const float* src,
int h_out,
float scale_x,
float scale_y,
bool align_corners);
bool with_align);
void interpolate(lite::Tensor* X,
lite::Tensor* OutSize,
......@@ -51,8 +50,7 @@ void interpolate(lite::Tensor* X,
int out_height,
int out_width,
float scale,
bool align_corners,
bool align_mode,
bool with_align,
std::string interpolate_type);
} /* namespace math */
......
lite/core/arena/CMakeLists.txt
浏览文件 @ 1aa706ae
......@@ -6,5 +6,5 @@ endif()
lite_cc_library(arena_framework SRCS framework.cc DEPS program gtest)
if((NOT LITE_WITH_OPENCL) AND (LITE_WITH_X86 OR LITE_WITH_ARM))
lite_cc_test(test_arena_framework SRCS framework_test.cc DEPS arena_framework ${rknpu_kernels} ${mlu_kernels} ${bm_kernels} ${npu_kernels} ${huawei_ascend_npu_kernels} ${xpu_kernels} ${x86_kernels} ${cuda_kernels} ${fpga_kernels} ${arm_kernels} ${lite_ops} ${host_kernels})
lite_cc_test(test_arena_framework SRCS framework_test.cc DEPS arena_framework ${rknpu_kernels} ${mlu_kernels} ${bm_kernels} ${npu_kernels} ${apu_kernels} ${huawei_ascend_npu_kernels} ${xpu_kernels} ${x86_kernels} ${cuda_kernels} ${fpga_kernels} ${arm_kernels} ${lite_ops} ${host_kernels})
endif()
lite/core/mir/fusion/conv_conv_fuse_pass.cc
浏览文件 @ 1aa706ae
......@@ -27,7 +27,7 @@ namespace mir {
void ConvConvFusePass::Apply(const std::unique_ptr<SSAGraph>& graph) {
// initialze fuser params
std::vector<bool> conv_has_bias_cases{true, false};
std::vector<std::string> conv_type_cases{"conv2d", "depthwise_conv2d"};
std::vector<std::string> conv_type_cases{"conv2d"};
bool has_int8 = false;
bool has_weight_quant = false;
for (auto& place : graph->valid_places()) {
......
lite/core/mir/fusion/conv_conv_fuser.cc
浏览文件 @ 1aa706ae
......@@ -132,8 +132,8 @@ void ConvConvFuser::BuildPattern() {
VLOG(5) << "The kernel size of the second conv must be 1x1";
continue;
}
if (groups1 != 1) {
VLOG(5) << "The groups of weight1_dim must be 1";
if (groups0 != 1 || groups1 != 1) {
VLOG(5) << "The all groups of weight_dim must be 1";
continue;
}
if (ch_out_0 != ch_in_1) {
......
lite/kernels/arm/conv_depthwise.cc
浏览文件 @ 1aa706ae
......@@ -32,11 +32,10 @@ void DepthwiseConv<PRECISION(kFloat), PRECISION(kFloat)>::PrepareForRun() {
auto hin = param.x->dims()[2];
auto win = param.x->dims()[3];
auto paddings = *param.paddings;
bool ch_four = channel <= 4 * win;
// select dw conv kernel
if (kw == 3) {
bool pads_less = ((paddings[1] < 2) && (paddings[3] < 2));
if (ch_four && pads_less && paddings[0] == paddings[2] &&
if (pads_less && paddings[0] == paddings[2] &&
(paddings[0] == 0 || paddings[0] == 1)) {
flag_trans_weights_ = false;
} else {
......
lite/kernels/arm/interpolate_compute.cc
浏览文件 @ 1aa706ae
......@@ -35,7 +35,6 @@ void BilinearInterpCompute::Run() {
int out_w = param.out_w;
int out_h = param.out_h;
bool align_corners = param.align_corners;
bool align_mode = param.align_mode;
std::string interp_method = "Bilinear";
lite::arm::math::interpolate(X,
OutSize,
......@@ -46,7 +45,6 @@ void BilinearInterpCompute::Run() {
out_w,
scale,
align_corners,
align_mode,
interp_method);
}
......@@ -61,7 +59,6 @@ void NearestInterpCompute::Run() {
int out_w = param.out_w;
int out_h = param.out_h;
bool align_corners = param.align_corners;
bool align_mode = param.align_mode;
std::string interp_method = "Nearest";
lite::arm::math::interpolate(X,
OutSize,
......@@ -72,7 +69,6 @@ void NearestInterpCompute::Run() {
out_w,
scale,
align_corners,
align_mode,
interp_method);
}
......
lite/kernels/x86/activation_compute.cc
浏览文件 @ 1aa706ae
......@@ -88,3 +88,14 @@ REGISTER_LITE_KERNEL(sigmoid,
.BindInput("X", {LiteType::GetTensorTy(TARGET(kX86))})
.BindOutput("Out", {LiteType::GetTensorTy(TARGET(kX86))})
.Finalize();
// float
REGISTER_LITE_KERNEL(relu6,
kX86,
kFloat,
kNCHW,
paddle::lite::kernels::x86::Relu6Compute<float>,
def)
.BindInput("X", {LiteType::GetTensorTy(TARGET(kX86))})
.BindOutput("Out", {LiteType::GetTensorTy(TARGET(kX86))})
.Finalize();
lite/kernels/x86/activation_compute.h
浏览文件 @ 1aa706ae
......@@ -248,6 +248,42 @@ class SoftsignCompute : public KernelLite<TARGET(kX86), PRECISION(kFloat)> {
virtual ~SoftsignCompute() = default;
};
// relu6(x) = min(max(0, x), 6)
template <typename T>
struct Relu6Functor {
float threshold;
explicit Relu6Functor(float threshold_) : threshold(threshold_) {}
template <typename Device, typename X, typename Out>
void operator()(Device d, X x, Out out) const {
out.device(d) =
x.cwiseMax(static_cast<T>(0)).cwiseMin(static_cast<T>(threshold));
}
};
template <typename T>
class Relu6Compute : public KernelLite<TARGET(kX86), PRECISION(kFloat)> {
public:
using param_t = operators::ActivationParam;
void Run() override {
auto& param = *param_.get_mutable<operators::ActivationParam>();
param.Out->template mutable_data<T>();
auto X = param.X;
auto Out = param.Out;
auto place = lite::fluid::EigenDeviceType<TARGET(kX86)>();
CHECK(X);
CHECK(Out);
auto x = lite::fluid::EigenVector<T>::Flatten(*X);
auto out = lite::fluid::EigenVector<T>::Flatten(*Out);
Relu6Functor<T> functor(param.threshold);
functor(place, x, out);
}
virtual ~Relu6Compute() = default;
};
} // namespace x86
} // namespace kernels
} // namespace lite
......
lite/kernels/x86/reduce_compute.cc
浏览文件 @ 1aa706ae
......@@ -23,3 +23,13 @@ REGISTER_LITE_KERNEL(reduce_sum,
.BindInput("X", {LiteType::GetTensorTy(TARGET(kX86))})
.BindOutput("Out", {LiteType::GetTensorTy(TARGET(kX86))})
.Finalize();
REGISTER_LITE_KERNEL(reduce_mean,
kX86,
kFloat,
kNCHW,
paddle::lite::kernels::x86::ReduceMeanCompute<float>,
def)
.BindInput("X", {LiteType::GetTensorTy(TARGET(kX86))})
.BindOutput("Out", {LiteType::GetTensorTy(TARGET(kX86))})
.Finalize();
lite/kernels/x86/reduce_compute.h
浏览文件 @ 1aa706ae
......@@ -31,11 +31,18 @@ struct SumFunctor {
}
};
#define HANDLE_DIM(NDIM, RDIM) \
if (ndim == NDIM && rdim == RDIM) { \
paddle::lite::kernels::x86:: \
ReduceFunctor<lite::TargetType::kX86, T, NDIM, RDIM, SumFunctor>( \
*input, output, dims, keep_dim); \
struct MeanFunctor {
template <typename X, typename Y, typename Dim>
void operator()(X* x, Y* y, const Dim& dim) {
y->device(lite::fluid::EigenDeviceType<TARGET(kX86)>()) = x->mean(dim);
}
};
#define HANDLE_DIM(NDIM, RDIM, FUNCTOR) \
if (ndim == NDIM && rdim == RDIM) { \
paddle::lite::kernels::x86:: \
ReduceFunctor<lite::TargetType::kX86, T, NDIM, RDIM, FUNCTOR>( \
*input, output, dims, keep_dim); \
}
template <typename T>
......@@ -64,19 +71,58 @@ class ReduceSumCompute : public KernelLite<TARGET(kX86), PRECISION(kFloat)> {
} else {
int ndim = input->dims().size();
int rdim = dims.size();
HANDLE_DIM(4, 3);
HANDLE_DIM(4, 2);
HANDLE_DIM(4, 1);
HANDLE_DIM(3, 2);
HANDLE_DIM(3, 1);
HANDLE_DIM(2, 1);
HANDLE_DIM(1, 1);
HANDLE_DIM(4, 3, SumFunctor);
HANDLE_DIM(4, 2, SumFunctor);
HANDLE_DIM(4, 1, SumFunctor);
HANDLE_DIM(3, 2, SumFunctor);
HANDLE_DIM(3, 1, SumFunctor);
HANDLE_DIM(2, 1, SumFunctor);
HANDLE_DIM(1, 1, SumFunctor);
}
}
virtual ~ReduceSumCompute() = default;
};
template <typename T>
class ReduceMeanCompute : public KernelLite<TARGET(kX86), PRECISION(kFloat)> {
public:
using param_t = operators::ReduceParam;
void Run() override {
auto& param = *param_.get_mutable<operators::ReduceParam>();
// auto& context = ctx_->As<X86Context>();
auto* input = param.x;
auto* output = param.output;
param.output->template mutable_data<T>();
const auto& dims = param.dim;
bool keep_dim = param.keep_dim;
if (dims.size() == 0) {
// Flatten and reduce 1-D tensor
auto x = lite::fluid::EigenVector<T>::Flatten(*input);
auto out = lite::fluid::EigenScalar<T>::From(output);
// auto& place = *platform::CPUDeviceContext().eigen_device();
auto reduce_dim = Eigen::array<int, 1>({{0}});
MeanFunctor functor;
functor(&x, &out, reduce_dim);
} else {
int ndim = input->dims().size();
int rdim = dims.size();
HANDLE_DIM(4, 3, MeanFunctor);
HANDLE_DIM(4, 2, MeanFunctor);
HANDLE_DIM(4, 1, MeanFunctor);
HANDLE_DIM(3, 2, MeanFunctor);
HANDLE_DIM(3, 1, MeanFunctor);
HANDLE_DIM(2, 1, MeanFunctor);
HANDLE_DIM(1, 1, MeanFunctor);
}
}
virtual ~ReduceMeanCompute() = default;
};
} // namespace x86
} // namespace kernels
} // namespace lite
......
lite/operators/activation_ops.cc
浏览文件 @ 1aa706ae
......@@ -89,6 +89,9 @@ bool ActivationOp::AttachImpl(const cpp::OpDesc& opdesc, lite::Scope* scope) {
} else if (opdesc.Type() == "elu") {
param_.active_type = lite_api::ActivationType::kElu;
param_.Elu_alpha = opdesc.GetAttr<float>("alpha");
} else if (opdesc.Type() == "relu6") {
param_.active_type = lite_api::ActivationType::kRelu6;
param_.threshold = opdesc.GetAttr<float>("threshold");
}
VLOG(4) << "opdesc.Type():" << opdesc.Type();
......
lite/operators/op_params.h
浏览文件 @ 1aa706ae
......@@ -403,6 +403,8 @@ struct ActivationParam : ParamBase {
float relu_threshold{1.0f};
// elu
float Elu_alpha{1.0f};
// relu6
float threshold{6.0f};
///////////////////////////////////////////////////////////////////////////////////
// get a vector of input tensors
......
lite/tests/api/CMakeLists.txt
浏览文件 @ 1aa706ae
if(LITE_WITH_ARM)
lite_cc_test(test_transformer_with_mask_fp32_arm SRCS test_transformer_with_mask_fp32_arm.cc
function(lite_cc_test_with_model_and_data TARGET)
if(NOT WITH_TESTING)
return()
endif()
set(options "")
set(oneValueArgs MODEL DATA CONFIG ARGS)
set(multiValueArgs "")
cmake_parse_arguments(args "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN})
set(ARGS "")
if(DEFINED args_MODEL)
set(ARGS "${ARGS} --model_dir=${LITE_MODEL_DIR}/${args_MODEL}")
endif()
if(DEFINED args_DATA)
set(ARGS "${ARGS} --data_dir=${LITE_MODEL_DIR}/${args_DATA}")
endif()
if(DEFINED args_CONFIG)
set(ARGS "${ARGS} --config_dir=${LITE_MODEL_DIR}/${args_CONFIG}")
endif()
if(DEFINED args_ARGS)
set(ARGS "${ARGS} ${args_ARGS}")
endif()
lite_cc_test(${TARGET} SRCS ${TARGET}.cc
DEPS ${lite_model_test_DEPS} paddle_api_full
ARM_DEPS ${arm_kernels}
ARGS --model_dir=${LITE_MODEL_DIR}/transformer_with_mask_fp32 SERIAL)
if(WITH_TESTING)
add_dependencies(test_transformer_with_mask_fp32_arm extern_lite_download_transformer_with_mask_fp32_tar_gz)
X86_DEPS ${x86_kernels}
NPU_DEPS ${npu_kernels} ${npu_bridges}
HUAWEI_ASCEND_NPU_DEPS ${huawei_ascend_npu_kernels} ${huawei_ascend_npu_bridges}
XPU_DEPS ${xpu_kernels} ${xpu_bridges}
APU_DEPS ${apu_kernels} ${apu_bridges}
RKNPU_DEPS ${rknpu_kernels} ${rknpu_bridges}
BM_DEPS ${bm_kernels} ${bm_bridges}
MLU_DEPS ${mlu_kernels} ${mlu_bridges}
ARGS ${ARGS} SERIAL)
if(DEFINED args_MODEL)
add_dependencies(${TARGET} extern_lite_download_${args_MODEL}_tar_gz)
endif()
endif()
function(xpu_x86_without_xtcl_test TARGET MODEL DATA)
if(${DATA} STREQUAL "")
lite_cc_test(${TARGET} SRCS ${TARGET}.cc
DEPS mir_passes lite_api_test_helper paddle_api_full paddle_api_light gflags utils
${ops} ${host_kernels} ${x86_kernels} ${xpu_kernels}
ARGS --model_dir=${LITE_MODEL_DIR}/${MODEL})
else()
lite_cc_test(${TARGET} SRCS ${TARGET}.cc
DEPS mir_passes lite_api_test_helper paddle_api_full paddle_api_light gflags utils
${ops} ${host_kernels} ${x86_kernels} ${xpu_kernels}
ARGS --model_dir=${LITE_MODEL_DIR}/${MODEL} --data_dir=${LITE_MODEL_DIR}/${DATA})
if(DEFINED args_DATA)
add_dependencies(${TARGET} extern_lite_download_${args_DATA}_tar_gz)
endif()
if(WITH_TESTING)
add_dependencies(${TARGET} extern_lite_download_${MODEL}_tar_gz)
if(NOT ${DATA} STREQUAL "")
add_dependencies(${TARGET} extern_lite_download_${DATA}_tar_gz)
endif()
if(DEFINED args_CONFIG)
add_dependencies(${TARGET} extern_lite_download_${args_CONFIG}_tar_gz)
endif()
endfunction()
if(LITE_WITH_ARM)
lite_cc_test_with_model_and_data(test_transformer_with_mask_fp32_arm MODEL transformer_with_mask_fp32 ARGS)
endif()
if(LITE_WITH_NPU)
lite_cc_test_with_model_and_data(test_mobilenetv1_fp32_huawei_kirin_npu MODEL mobilenet_v1 DATA ILSVRC2012_small)
lite_cc_test_with_model_and_data(test_mobilenetv2_fp32_huawei_kirin_npu MODEL mobilenet_v2_relu DATA ILSVRC2012_small)
lite_cc_test_with_model_and_data(test_resnet50_fp32_huawei_kirin_npu MODEL resnet50 DATA ILSVRC2012_small)
endif()
if(LITE_WITH_XPU AND NOT LITE_WITH_XTCL)
xpu_x86_without_xtcl_test(test_resnet50_fp32_xpu resnet50 ILSVRC2012_small)
xpu_x86_without_xtcl_test(test_googlenet_fp32_xpu GoogLeNet ILSVRC2012_small)
xpu_x86_without_xtcl_test(test_vgg19_fp32_xpu VGG19 ILSVRC2012_small)
xpu_x86_without_xtcl_test(test_ernie_fp32_xpu ernie bert_data)
xpu_x86_without_xtcl_test(test_bert_fp32_xpu bert bert_data)
lite_cc_test_with_model_and_data(test_resnet50_fp32_xpu MODEL resnet50 DATA ILSVRC2012_small)
lite_cc_test_with_model_and_data(test_googlenet_fp32_xpu MODEL GoogLeNet DATA ILSVRC2012_small)
lite_cc_test_with_model_and_data(test_vgg19_fp32_xpu MODEL VGG19 DATA ILSVRC2012_small)
lite_cc_test_with_model_and_data(test_ernie_fp32_xpu MODEL ernie DATA bert_data)
lite_cc_test_with_model_and_data(test_bert_fp32_xpu MODEL bert DATA bert_data)
endif()
if(LITE_WITH_RKNPU)
lite_cc_test(test_mobilenetv1_int8_rknpu SRCS test_mobilenetv1_int8_rknpu.cc
DEPS ${lite_model_test_DEPS} paddle_api_full
RKNPU_DEPS ${rknpu_kernels} ${rknpu_bridges}
ARGS --model_dir=${LITE_MODEL_DIR}/MobilenetV1_full_quant SERIAL)
lite_cc_test_with_model_and_data(test_mobilenetv1_int8_rockchip_npu MODEL mobilenet_v1_int8_for_rockchip_npu DATA ILSVRC2012_small)
endif()
if(LITE_WITH_APU)
lite_cc_test(test_mobilenetv1_int8_apu SRCS test_mobilenetv1_int8_apu.cc
DEPS ${lite_model_test_DEPS} paddle_api_full
APU_DEPS ${apu_kernels} ${apu_bridges}
ARGS --model_dir=${LITE_MODEL_DIR}/MobilenetV1_full_quant SERIAL)
lite_cc_test_with_model_and_data(test_mobilenetv1_int8_mediatek_apu MODEL mobilenet_v1_int8_for_mediatek_apu DATA ILSVRC2012_small)
endif()
lite/tests/api/test_mobilenetv1_fp32_huawei_kirin_npu.cc 0 → 100644
浏览文件 @ 1aa706ae
// Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
//
// 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 <gflags/gflags.h>
#include <gtest/gtest.h>
#include <vector>
#include "lite/api/lite_api_test_helper.h"
#include "lite/api/paddle_api.h"
#include "lite/api/paddle_use_kernels.h"
#include "lite/api/paddle_use_ops.h"
#include "lite/api/paddle_use_passes.h"
#include "lite/api/test_helper.h"
#include "lite/tests/api/ILSVRC2012_utility.h"
#include "lite/utils/cp_logging.h"
DEFINE_string(data_dir, "", "data dir");
DEFINE_int32(iteration, 100, "iteration times to run");
DEFINE_int32(batch, 1, "batch of image");
DEFINE_int32(channel, 3, "image channel");
namespace paddle {
namespace lite {
TEST(MobileNetV1, test_mobilenetv1_fp32_huawei_kirin_npu) {
lite_api::CxxConfig config;
config.set_model_dir(FLAGS_model_dir);
config.set_valid_places({lite_api::Place{TARGET(kARM), PRECISION(kFloat)},
lite_api::Place{TARGET(kNPU), PRECISION(kFloat)}});
auto predictor = lite_api::CreatePaddlePredictor(config);
std::string raw_data_dir = FLAGS_data_dir + std::string("/raw_data");
std::vector<int> input_shape{
FLAGS_batch, FLAGS_channel, FLAGS_im_width, FLAGS_im_height};
auto raw_data = ReadRawData(raw_data_dir, input_shape, FLAGS_iteration);
int input_size = 1;
for (auto i : input_shape) {
input_size *= i;
}
for (int i = 0; i < FLAGS_warmup; ++i) {
auto input_tensor = predictor->GetInput(0);
input_tensor->Resize(
std::vector<int64_t>(input_shape.begin(), input_shape.end()));
auto* data = input_tensor->mutable_data<float>();
for (int j = 0; j < input_size; j++) {
data[j] = 0.f;
}
predictor->Run();
}
std::vector<std::vector<float>> out_rets;
out_rets.resize(FLAGS_iteration);
double cost_time = 0;
for (size_t i = 0; i < raw_data.size(); ++i) {
auto input_tensor = predictor->GetInput(0);
input_tensor->Resize(
std::vector<int64_t>(input_shape.begin(), input_shape.end()));
auto* data = input_tensor->mutable_data<float>();
memcpy(data, raw_data[i].data(), sizeof(float) * input_size);
double start = GetCurrentUS();
predictor->Run();
cost_time += GetCurrentUS() - start;
auto output_tensor = predictor->GetOutput(0);
auto output_shape = output_tensor->shape();
auto output_data = output_tensor->data<float>();
ASSERT_EQ(output_shape.size(), 2UL);
ASSERT_EQ(output_shape[0], 1);
ASSERT_EQ(output_shape[1], 1000);
int output_size = output_shape[0] * output_shape[1];
out_rets[i].resize(output_size);
memcpy(&(out_rets[i].at(0)), output_data, sizeof(float) * output_size);
}
LOG(INFO) << "================== Speed Report ===================";
LOG(INFO) << "Model: " << FLAGS_model_dir << ", threads num " << FLAGS_threads
<< ", warmup: " << FLAGS_warmup << ", batch: " << FLAGS_batch
<< ", iteration: " << FLAGS_iteration << ", spend "
<< cost_time / FLAGS_iteration / 1000.0 << " ms in average.";
std::string labels_dir = FLAGS_data_dir + std::string("/labels.txt");
float out_accuracy = CalOutAccuracy(out_rets, labels_dir);
ASSERT_GE(out_accuracy, 0.57f);
}
} // namespace lite
} // namespace paddle
lite/tests/api/test_mobilenetv1_int8_apu.cc 已删除 100644 → 0
浏览文件 @ 5c265189
// Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
//
// 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 <fstream>
#include <iostream>
#include <numeric>
#include <string>
#include <vector>
#include "lite/api/paddle_api.h"
#include "lite/api/paddle_use_kernels.h"
#include "lite/api/paddle_use_ops.h"
#include "lite/api/paddle_use_passes.h"
using namespace paddle::lite_api; // NOLINT
inline double GetCurrentUS() {
struct timeval time;
gettimeofday(&time, NULL);
return 1e+6 * time.tv_sec + time.tv_usec;
}
inline int64_t ShapeProduction(std::vector<int64_t> shape) {
int64_t s = 1;
for (int64_t dim : shape) {
s *= dim;
}
return s;
}
int main(int argc, char** argv) {
if (argc < 2) {
std::cerr << "[ERROR] usage: ./" << argv[0]
<< " model_dir [thread_num] [warmup_times] [repeat_times] "
"[input_data_path] [output_data_path]"
<< std::endl;
return -1;
}
std::string model_dir = argv[1];
int thread_num = 1;
if (argc > 2) {
thread_num = atoi(argv[2]);
}
int warmup_times = 5;
if (argc > 3) {
warmup_times = atoi(argv[3]);
}
int repeat_times = 10;
if (argc > 4) {
repeat_times = atoi(argv[4]);
}
std::string input_data_path;
if (argc > 5) {
input_data_path = argv[5];
}
std::string output_data_path;
if (argc > 6) {
output_data_path = argv[6];
}
paddle::lite_api::CxxConfig config;
config.set_model_dir(model_dir);
config.set_threads(thread_num);
config.set_power_mode(paddle::lite_api::LITE_POWER_HIGH);
config.set_valid_places(
{paddle::lite_api::Place{
TARGET(kARM), PRECISION(kFloat), DATALAYOUT(kNCHW)},
paddle::lite_api::Place{
TARGET(kARM), PRECISION(kInt8), DATALAYOUT(kNCHW)},
paddle::lite_api::Place{
TARGET(kAPU), PRECISION(kInt8), DATALAYOUT(kNCHW)}});
auto predictor = paddle::lite_api::CreatePaddlePredictor(config);
std::unique_ptr<paddle::lite_api::Tensor> input_tensor(
std::move(predictor->GetInput(0)));
input_tensor->Resize({1, 3, 224, 224});
auto input_data = input_tensor->mutable_data<float>();
auto input_size = ShapeProduction(input_tensor->shape());
// test loop
int total_imgs = 500;
float test_num = 0;
float top1_num = 0;
float top5_num = 0;
int output_len = 1000;
std::vector<int> index(1000);
bool debug = true; // false;
int show_step = 500;
for (int i = 0; i < total_imgs; i++) {
// set input
std::string filename = input_data_path + "/" + std::to_string(i);
std::ifstream fs(filename, std::ifstream::binary);
if (!fs.is_open()) {
std::cout << "open input file fail.";
}
auto input_data_tmp = input_data;
for (int i = 0; i < input_size; ++i) {
fs.read(reinterpret_cast<char*>(input_data_tmp), sizeof(*input_data_tmp));
input_data_tmp++;
}
int label = 0;
fs.read(reinterpret_cast<char*>(&label), sizeof(label));
fs.close();
if (debug && i % show_step == 0) {
std::cout << "input data:" << std::endl;
std::cout << input_data[0] << " " << input_data[10] << " "
<< input_data[input_size - 1] << std::endl;
std::cout << "label:" << label << std::endl;
}
// run
predictor->Run();
auto output0 = predictor->GetOutput(0);
auto output0_data = output0->data<float>();
// get output
std::iota(index.begin(), index.end(), 0);
std::stable_sort(
index.begin(), index.end(), [output0_data](size_t i1, size_t i2) {
return output0_data[i1] > output0_data[i2];
});
test_num++;
if (label == index[0]) {
top1_num++;
}
for (int i = 0; i < 5; i++) {
if (label == index[i]) {
top5_num++;
}
}
if (debug && i % show_step == 0) {
std::cout << index[0] << " " << index[1] << " " << index[2] << " "
<< index[3] << " " << index[4] << std::endl;
std::cout << output0_data[index[0]] << " " << output0_data[index[1]]
<< " " << output0_data[index[2]] << " "
<< output0_data[index[3]] << " " << output0_data[index[4]]
<< std::endl;
std::cout << output0_data[630] << std::endl;
}
if (i % show_step == 0) {
std::cout << "step " << i << "; top1 acc:" << top1_num / test_num
<< "; top5 acc:" << top5_num / test_num << std::endl;
}
}
std::cout << "final result:" << std::endl;
std::cout << "top1 acc:" << top1_num / test_num << std::endl;
std::cout << "top5 acc:" << top5_num / test_num << std::endl;
return 0;
}
lite/tests/api/test_mobilenetv1_int8_mediatek_apu.cc 0 → 100644
浏览文件 @ 1aa706ae
// Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
//
// 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 <gflags/gflags.h>
#include <gtest/gtest.h>
#include <vector>
#include "lite/api/lite_api_test_helper.h"
#include "lite/api/paddle_api.h"
#include "lite/api/paddle_use_kernels.h"
#include "lite/api/paddle_use_ops.h"
#include "lite/api/paddle_use_passes.h"
#include "lite/api/test_helper.h"
#include "lite/tests/api/ILSVRC2012_utility.h"
#include "lite/utils/cp_logging.h"
DEFINE_string(data_dir, "", "data dir");
DEFINE_int32(iteration, 100, "iteration times to run");
DEFINE_int32(batch, 1, "batch of image");
DEFINE_int32(channel, 3, "image channel");
namespace paddle {
namespace lite {
TEST(MobileNetV1, test_mobilenetv1_int8_mediatek_apu) {
lite_api::CxxConfig config;
config.set_model_dir(FLAGS_model_dir);
config.set_valid_places({lite_api::Place{TARGET(kARM), PRECISION(kFloat)},
lite_api::Place{TARGET(kARM), PRECISION(kInt8)},
lite_api::Place{TARGET(kAPU), PRECISION(kInt8)}});
auto predictor = lite_api::CreatePaddlePredictor(config);
std::string raw_data_dir = FLAGS_data_dir + std::string("/raw_data");
std::vector<int> input_shape{
FLAGS_batch, FLAGS_channel, FLAGS_im_width, FLAGS_im_height};
auto raw_data = ReadRawData(raw_data_dir, input_shape, FLAGS_iteration);
int input_size = 1;
for (auto i : input_shape) {
input_size *= i;
}
for (int i = 0; i < FLAGS_warmup; ++i) {
auto input_tensor = predictor->GetInput(0);
input_tensor->Resize(
std::vector<int64_t>(input_shape.begin(), input_shape.end()));
auto* data = input_tensor->mutable_data<float>();
for (int j = 0; j < input_size; j++) {
data[j] = 0.f;
}
predictor->Run();
}
std::vector<std::vector<float>> out_rets;
out_rets.resize(FLAGS_iteration);
double cost_time = 0;
for (size_t i = 0; i < raw_data.size(); ++i) {
auto input_tensor = predictor->GetInput(0);
input_tensor->Resize(
std::vector<int64_t>(input_shape.begin(), input_shape.end()));
auto* data = input_tensor->mutable_data<float>();
memcpy(data, raw_data[i].data(), sizeof(float) * input_size);
double start = GetCurrentUS();
predictor->Run();
cost_time += GetCurrentUS() - start;
auto output_tensor = predictor->GetOutput(0);
auto output_shape = output_tensor->shape();
auto output_data = output_tensor->data<float>();
ASSERT_EQ(output_shape.size(), 2UL);
ASSERT_EQ(output_shape[0], 1);
ASSERT_EQ(output_shape[1], 1000);
int output_size = output_shape[0] * output_shape[1];
out_rets[i].resize(output_size);
memcpy(&(out_rets[i].at(0)), output_data, sizeof(float) * output_size);
}
LOG(INFO) << "================== Speed Report ===================";
LOG(INFO) << "Model: " << FLAGS_model_dir << ", threads num " << FLAGS_threads
<< ", warmup: " << FLAGS_warmup << ", batch: " << FLAGS_batch
<< ", iteration: " << FLAGS_iteration << ", spend "
<< cost_time / FLAGS_iteration / 1000.0 << " ms in average.";
std::string labels_dir = FLAGS_data_dir + std::string("/labels.txt");
float out_accuracy = CalOutAccuracy(out_rets, labels_dir);
ASSERT_GE(out_accuracy, 0.55f);
}
} // namespace lite
} // namespace paddle
lite/tests/api/test_mobilenetv1_int8_rknpu.cc 已删除 100644 → 0
浏览文件 @ 5c265189
// Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
//
// 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 <fstream>
#include <iostream>
#include <string>
#include <vector>
#include "lite/api/paddle_api.h"
#include "lite/api/paddle_use_kernels.h"
#include "lite/api/paddle_use_ops.h"
#include "lite/api/paddle_use_passes.h"
inline double GetCurrentUS() {
struct timeval time;
gettimeofday(&time, NULL);
return 1e+6 * time.tv_sec + time.tv_usec;
}
inline int64_t ShapeProduction(std::vector<int64_t> shape) {
int64_t s = 1;
for (int64_t dim : shape) {
s *= dim;
}
return s;
}
int main(int argc, char** argv) {
if (argc < 2) {
std::cerr << "[ERROR] usage: ./" << argv[0]
<< " model_dir [thread_num] [warmup_times] [repeat_times] "
"[input_data_path] [output_data_path]"
<< std::endl;
return -1;
}
std::string model_dir = argv[1];
int thread_num = 1;
if (argc > 2) {
thread_num = atoi(argv[2]);
}
int warmup_times = 5;
if (argc > 3) {
warmup_times = atoi(argv[3]);
}
int repeat_times = 10;
if (argc > 4) {
repeat_times = atoi(argv[4]);
}
std::string input_data_path;
if (argc > 5) {
input_data_path = argv[5];
}
std::string output_data_path;
if (argc > 6) {
output_data_path = argv[6];
}
paddle::lite_api::CxxConfig config;
config.set_model_dir(model_dir);
config.set_threads(thread_num);
config.set_power_mode(paddle::lite_api::LITE_POWER_HIGH);
config.set_valid_places(
{paddle::lite_api::Place{
TARGET(kARM), PRECISION(kFloat), DATALAYOUT(kNCHW)},
paddle::lite_api::Place{
TARGET(kARM), PRECISION(kInt8), DATALAYOUT(kNCHW)},
paddle::lite_api::Place{
TARGET(kARM), PRECISION(kInt8), DATALAYOUT(kNCHW)},
paddle::lite_api::Place{
TARGET(kRKNPU), PRECISION(kInt8), DATALAYOUT(kNCHW)}});
auto predictor = paddle::lite_api::CreatePaddlePredictor(config);
std::unique_ptr<paddle::lite_api::Tensor> input_tensor(
std::move(predictor->GetInput(0)));
input_tensor->Resize({1, 3, 224, 224});
auto input_data = input_tensor->mutable_data<float>();
auto input_size = ShapeProduction(input_tensor->shape());
if (input_data_path.empty()) {
for (int i = 0; i < input_size; i++) {
input_data[i] = 1;
}
} else {
std::fstream fs(input_data_path, std::ios::in);
if (!fs.is_open()) {
std::cerr << "open input data file failed." << std::endl;
return -1;
}
for (int i = 0; i < input_size; i++) {
fs >> input_data[i];
}
}
for (int i = 0; i < warmup_times; ++i) {
predictor->Run();
}
auto start = GetCurrentUS();
for (int i = 0; i < repeat_times; ++i) {
predictor->Run();
}
std::cout << "Model: " << model_dir << ", threads num " << thread_num
<< ", warmup times: " << warmup_times
<< ", repeat times: " << repeat_times << ", spend "
<< (GetCurrentUS() - start) / repeat_times / 1000.0
<< " ms in average." << std::endl;
std::unique_ptr<const paddle::lite_api::Tensor> output_tensor(
std::move(predictor->GetOutput(0)));
auto output_data = output_tensor->data<float>();
auto output_size = ShapeProduction(output_tensor->shape());
std::cout << "output data:";
for (int i = 0; i < output_size; i += 100) {
std::cout << "[" << i << "] " << output_data[i] << std::endl;
}
return 0;
}
lite/tests/api/test_mobilenetv1_int8_rockchip_npu.cc 0 → 100644
浏览文件 @ 1aa706ae
// Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
//
// 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 <gflags/gflags.h>
#include <gtest/gtest.h>
#include <vector>
#include "lite/api/lite_api_test_helper.h"
#include "lite/api/paddle_api.h"
#include "lite/api/paddle_use_kernels.h"
#include "lite/api/paddle_use_ops.h"
#include "lite/api/paddle_use_passes.h"
#include "lite/api/test_helper.h"
#include "lite/tests/api/ILSVRC2012_utility.h"
#include "lite/utils/cp_logging.h"
DEFINE_string(data_dir, "", "data dir");
DEFINE_int32(iteration, 100, "iteration times to run");
DEFINE_int32(batch, 1, "batch of image");
DEFINE_int32(channel, 3, "image channel");
namespace paddle {
namespace lite {
TEST(MobileNetV1, test_mobilenetv1_int8_rockchip_apu) {
lite_api::CxxConfig config;
config.set_model_dir(FLAGS_model_dir);
config.set_valid_places({lite_api::Place{TARGET(kARM), PRECISION(kFloat)},
lite_api::Place{TARGET(kARM), PRECISION(kInt8)},
lite_api::Place{TARGET(kRKNPU), PRECISION(kInt8)}});
auto predictor = lite_api::CreatePaddlePredictor(config);
std::string raw_data_dir = FLAGS_data_dir + std::string("/raw_data");
std::vector<int> input_shape{
FLAGS_batch, FLAGS_channel, FLAGS_im_width, FLAGS_im_height};
auto raw_data = ReadRawData(raw_data_dir, input_shape, FLAGS_iteration);
int input_size = 1;
for (auto i : input_shape) {
input_size *= i;
}
for (int i = 0; i < FLAGS_warmup; ++i) {
auto input_tensor = predictor->GetInput(0);
input_tensor->Resize(
std::vector<int64_t>(input_shape.begin(), input_shape.end()));
auto* data = input_tensor->mutable_data<float>();
for (int j = 0; j < input_size; j++) {
data[j] = 0.f;
}
predictor->Run();
}
std::vector<std::vector<float>> out_rets;
out_rets.resize(FLAGS_iteration);
double cost_time = 0;
for (size_t i = 0; i < raw_data.size(); ++i) {
auto input_tensor = predictor->GetInput(0);
input_tensor->Resize(
std::vector<int64_t>(input_shape.begin(), input_shape.end()));
auto* data = input_tensor->mutable_data<float>();
memcpy(data, raw_data[i].data(), sizeof(float) * input_size);
double start = GetCurrentUS();
predictor->Run();
cost_time += GetCurrentUS() - start;
auto output_tensor = predictor->GetOutput(0);
auto output_shape = output_tensor->shape();
auto output_data = output_tensor->data<float>();
ASSERT_EQ(output_shape.size(), 2UL);
ASSERT_EQ(output_shape[0], 1);
ASSERT_EQ(output_shape[1], 1000);
int output_size = output_shape[0] * output_shape[1];
out_rets[i].resize(output_size);
memcpy(&(out_rets[i].at(0)), output_data, sizeof(float) * output_size);
}
LOG(INFO) << "================== Speed Report ===================";
LOG(INFO) << "Model: " << FLAGS_model_dir << ", threads num " << FLAGS_threads
<< ", warmup: " << FLAGS_warmup << ", batch: " << FLAGS_batch
<< ", iteration: " << FLAGS_iteration << ", spend "
<< cost_time / FLAGS_iteration / 1000.0 << " ms in average.";
std::string labels_dir = FLAGS_data_dir + std::string("/labels.txt");
float out_accuracy = CalOutAccuracy(out_rets, labels_dir);
ASSERT_GE(out_accuracy, 0.52f);
}
} // namespace lite
} // namespace paddle
lite/tests/api/test_mobilenetv2_fp32_huawei_kirin_npu.cc 0 → 100644
浏览文件 @ 1aa706ae
// Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
//
// 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 <gflags/gflags.h>
#include <gtest/gtest.h>
#include <vector>
#include "lite/api/lite_api_test_helper.h"
#include "lite/api/paddle_api.h"
#include "lite/api/paddle_use_kernels.h"
#include "lite/api/paddle_use_ops.h"
#include "lite/api/paddle_use_passes.h"
#include "lite/api/test_helper.h"
#include "lite/tests/api/ILSVRC2012_utility.h"
#include "lite/utils/cp_logging.h"
DEFINE_string(data_dir, "", "data dir");
DEFINE_int32(iteration, 100, "iteration times to run");
DEFINE_int32(batch, 1, "batch of image");
DEFINE_int32(channel, 3, "image channel");
namespace paddle {
namespace lite {
TEST(MobileNetV2, test_mobilenetv2_fp32_huawei_kirin_npu) {
lite_api::CxxConfig config;
config.set_model_dir(FLAGS_model_dir);
config.set_valid_places({lite_api::Place{TARGET(kARM), PRECISION(kFloat)},
lite_api::Place{TARGET(kNPU), PRECISION(kFloat)}});
auto predictor = lite_api::CreatePaddlePredictor(config);
std::string raw_data_dir = FLAGS_data_dir + std::string("/raw_data");
std::vector<int> input_shape{
FLAGS_batch, FLAGS_channel, FLAGS_im_width, FLAGS_im_height};
auto raw_data = ReadRawData(raw_data_dir, input_shape, FLAGS_iteration);
int input_size = 1;
for (auto i : input_shape) {
input_size *= i;
}
for (int i = 0; i < FLAGS_warmup; ++i) {
auto input_tensor = predictor->GetInput(0);
input_tensor->Resize(
std::vector<int64_t>(input_shape.begin(), input_shape.end()));
auto* data = input_tensor->mutable_data<float>();
for (int j = 0; j < input_size; j++) {
data[j] = 0.f;
}
predictor->Run();
}
std::vector<std::vector<float>> out_rets;
out_rets.resize(FLAGS_iteration);
double cost_time = 0;
for (size_t i = 0; i < raw_data.size(); ++i) {
auto input_tensor = predictor->GetInput(0);
input_tensor->Resize(
std::vector<int64_t>(input_shape.begin(), input_shape.end()));
auto* data = input_tensor->mutable_data<float>();
memcpy(data, raw_data[i].data(), sizeof(float) * input_size);
double start = GetCurrentUS();
predictor->Run();
cost_time += GetCurrentUS() - start;
auto output_tensor = predictor->GetOutput(0);
auto output_shape = output_tensor->shape();
auto output_data = output_tensor->data<float>();
ASSERT_EQ(output_shape.size(), 2UL);
ASSERT_EQ(output_shape[0], 1);
ASSERT_EQ(output_shape[1], 1000);
int output_size = output_shape[0] * output_shape[1];
out_rets[i].resize(output_size);
memcpy(&(out_rets[i].at(0)), output_data, sizeof(float) * output_size);
}
LOG(INFO) << "================== Speed Report ===================";
LOG(INFO) << "Model: " << FLAGS_model_dir << ", threads num " << FLAGS_threads
<< ", warmup: " << FLAGS_warmup << ", batch: " << FLAGS_batch
<< ", iteration: " << FLAGS_iteration << ", spend "
<< cost_time / FLAGS_iteration / 1000.0 << " ms in average.";
std::string labels_dir = FLAGS_data_dir + std::string("/labels.txt");
float out_accuracy = CalOutAccuracy(out_rets, labels_dir);
ASSERT_GE(out_accuracy, 0.57f);
}
} // namespace lite
} // namespace paddle
lite/tests/api/test_resnet50_fp32_huawei_kirin_npu.cc 0 → 100644
浏览文件 @ 1aa706ae
// Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
//
// 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 <gflags/gflags.h>
#include <gtest/gtest.h>
#include <vector>
#include "lite/api/lite_api_test_helper.h"
#include "lite/api/paddle_api.h"
#include "lite/api/paddle_use_kernels.h"
#include "lite/api/paddle_use_ops.h"
#include "lite/api/paddle_use_passes.h"
#include "lite/api/test_helper.h"
#include "lite/tests/api/ILSVRC2012_utility.h"
#include "lite/utils/cp_logging.h"
DEFINE_string(data_dir, "", "data dir");
DEFINE_int32(iteration, 100, "iteration times to run");
DEFINE_int32(batch, 1, "batch of image");
DEFINE_int32(channel, 3, "image channel");
namespace paddle {
namespace lite {
TEST(ResNet50, test_resnet50_fp32_huawei_kirin_npu) {
lite_api::CxxConfig config;
config.set_model_dir(FLAGS_model_dir);
config.set_valid_places({lite_api::Place{TARGET(kARM), PRECISION(kFloat)},
lite_api::Place{TARGET(kNPU), PRECISION(kFloat)}});
auto predictor = lite_api::CreatePaddlePredictor(config);
std::string raw_data_dir = FLAGS_data_dir + std::string("/raw_data");
std::vector<int> input_shape{
FLAGS_batch, FLAGS_channel, FLAGS_im_width, FLAGS_im_height};
auto raw_data = ReadRawData(raw_data_dir, input_shape, FLAGS_iteration);
int input_size = 1;
for (auto i : input_shape) {
input_size *= i;
}
for (int i = 0; i < FLAGS_warmup; ++i) {
auto input_tensor = predictor->GetInput(0);
input_tensor->Resize(
std::vector<int64_t>(input_shape.begin(), input_shape.end()));
auto* data = input_tensor->mutable_data<float>();
for (int j = 0; j < input_size; j++) {
data[j] = 0.f;
}
predictor->Run();
}
std::vector<std::vector<float>> out_rets;
out_rets.resize(FLAGS_iteration);
double cost_time = 0;
for (size_t i = 0; i < raw_data.size(); ++i) {
auto input_tensor = predictor->GetInput(0);
input_tensor->Resize(
std::vector<int64_t>(input_shape.begin(), input_shape.end()));
auto* data = input_tensor->mutable_data<float>();
memcpy(data, raw_data[i].data(), sizeof(float) * input_size);
double start = GetCurrentUS();
predictor->Run();
cost_time += GetCurrentUS() - start;
auto output_tensor = predictor->GetOutput(0);
auto output_shape = output_tensor->shape();
auto output_data = output_tensor->data<float>();
ASSERT_EQ(output_shape.size(), 2UL);
ASSERT_EQ(output_shape[0], 1);
ASSERT_EQ(output_shape[1], 1000);
int output_size = output_shape[0] * output_shape[1];
out_rets[i].resize(output_size);
memcpy(&(out_rets[i].at(0)), output_data, sizeof(float) * output_size);
}
LOG(INFO) << "================== Speed Report ===================";
LOG(INFO) << "Model: " << FLAGS_model_dir << ", threads num " << FLAGS_threads
<< ", warmup: " << FLAGS_warmup << ", batch: " << FLAGS_batch
<< ", iteration: " << FLAGS_iteration << ", spend "
<< cost_time / FLAGS_iteration / 1000.0 << " ms in average.";
std::string labels_dir = FLAGS_data_dir + std::string("/labels.txt");
float out_accuracy = CalOutAccuracy(out_rets, labels_dir);
ASSERT_GE(out_accuracy, 0.64f);
}
} // namespace lite
} // namespace paddle
lite/tests/kernels/CMakeLists.txt
浏览文件 @ 1aa706ae
此差异已折叠。
lite/tests/kernels/activation_compute_test.cc
浏览文件 @ 1aa706ae
......@@ -58,6 +58,7 @@ class ActivationComputeTester : public arena::TestCase {
float hard_swish_offset = 3.0;
float relu_threshold_ = 1.0;
float elu_alpha_ = 1.0;
float threshold_ = 6.0;
DDim dims_{{1}};
std::string type_ = "";
activation_type_test act_type_ = RELU;
......@@ -170,7 +171,8 @@ class ActivationComputeTester : public arena::TestCase {
case RELU6: {
for (int i = 0; i < dims_.production(); i++) {
output_data[i] = x_data[i] > 0.f ? x_data[i] : 0.f;
output_data[i] = output_data[i] < 6.0 ? output_data[i] : 6.0;
output_data[i] =
output_data[i] < threshold_ ? output_data[i] : threshold_;
}
break;
}
......@@ -273,6 +275,9 @@ class ActivationComputeTester : public arena::TestCase {
if (act_type_ == ELU) {
op_desc->SetAttr("alpha", elu_alpha_);
}
if (act_type_ == RELU6) {
op_desc->SetAttr("threshold", threshold_);
}
}
void PrepareData() override {
......@@ -510,6 +515,8 @@ TEST(Activation_relu6, precision) {
#elif defined(LITE_WITH_HUAWEI_ASCEND_NPU)
place = TARGET(kHuaweiAscendNPU);
abs_error = 1e-2; // precision_mode default is force_fp16
#elif defined(LITE_WITH_X86)
place = TARGET(kX86);
#else
return;
#endif
......
lite/tests/kernels/interp_compute_test.cc
浏览文件 @ 1aa706ae
......@@ -416,6 +416,10 @@ void TestInterpAlignMode(Place place, float abs_error = 2e-5) {
for (auto x_dims : std::vector<std::vector<int64_t>>{{3, 4, 8, 9}}) {
for (bool align_corners : {true, false}) {
for (int align_mode : {0, 1}) {
// may exist bug in arm kernel
if (place == TARGET(kARM) && align_mode == 1 && !align_corners) {
continue;
}
// Ascend NPU DDK
if (place == TARGET(kHuaweiAscendNPU) && align_mode == 0 &&
!align_corners) {
......
lite/tests/kernels/reduce_mean_compute_test.cc
浏览文件 @ 1aa706ae
......@@ -333,9 +333,10 @@ void test_reduce_mean(Place place) {
}
TEST(ReduceMean, precision) {
// #ifdef LITE_WITH_X86
// Place place(TARGET(kX86));
// #endif
#ifdef LITE_WITH_X86
Place place(TARGET(kX86));
test_reduce_mean(place);
#endif
#ifdef LITE_WITH_ARM
Place place(TARGET(kARM));
test_reduce_mean(place);
......
lite/tests/math/conv_compute_test.cc
浏览文件 @ 1aa706ae
......@@ -306,8 +306,7 @@ void test_conv_fp32(const std::vector<DDim>& input_dims,
const float leakey_relu_scale) {}
#endif // LITE_WITH_ARM
// TODO(chenjiaoAngel): fix multi-threds, diff: 3x3 depthwise conv
#if 0 // 3x3dw
#if 0 // 3x3dw if only run one case. its ok
TEST(TestConv3x3DW, test_conv3x3_depthwise) {
if (FLAGS_basic_test) {
for (auto& stride : {1, 2}) {
......@@ -325,13 +324,6 @@ TEST(TestConv3x3DW, test_conv3x3_depthwise) {
dims.push_back(DDim({batch, c, h, h}));
}
}
#ifdef __aarch64__
#else
if (stride == 1 && (pad_bottom == 2 || pad_right == 2 ||
pad_top == 2 || pad_left == 2)) {
continue;
}
#endif
const float leakey_relu_scale = 8.88;
test_conv_fp32(dims,
weights_dim,
......
lite/tools/ci_build.sh
浏览文件 @ 1aa706ae
此差异已折叠。
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