piiswrong closed pull request #11114: [MXNET-502] Fixing broken feature_extract
cpp example
URL: https://github.com/apache/incubator-mxnet/pull/11114
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diff --git a/cpp-package/example/feature_extract/Makefile
b/cpp-package/example/feature_extract/Makefile
index cc76d0506aa..f598183bd16 100644
--- a/cpp-package/example/feature_extract/Makefile
+++ b/cpp-package/example/feature_extract/Makefile
@@ -16,15 +16,13 @@
# under the License.
CXX=g++
-BLAS=-L /opt/openblas/lib -lopenblas -DMSHADOW_USE_CBLAS=1 -DMSHADOW_USE_MKL=0
+BLAS=-L /opt/openblas/lib -lopenblas -DMSHADOW_USE_CBLAS=1 -DMSHADOW_USE_MKL=0
CUDA=-DMSHADOW_USE_CUDA=1
OPENCV_CFLAGS=`pkg-config --cflags opencv`
OPENCV_LDFLAGS=`pkg-config --libs opencv`
-#COMMFLAGS=-static -static-libgcc -static-libstdc++
-
-CFLAGS=$(COMMFLAGS) -I ../../include -Wall -O3 -msse3 -funroll-loops
-Wno-unused-parameter -Wno-unknown-pragmas -fopenmp
-LDFLAGS=$(COMMFLAGS) -L ../../lib/linux -lmxnet $(BLAS) $(CUDA) -lgomp -pthread
+CFLAGS=$(COMMFLAGS) -I../../../3rdparty/nnvm/include
-I../../../3rdparty/dmlc-core/include -I ../../include -I ../../../include
-Wall -O3 -msse3 -funroll-loops -Wno-unused-parameter -Wno-unknown-pragmas
-fopenmp
+LDFLAGS=$(COMMFLAGS) -L ../../../lib -lmxnet $(BLAS) $(CUDA) -lgomp -pthread
all: feature_extract prepare_data_with_opencv
@@ -34,7 +32,7 @@ feature_extract: ./feature_extract.cpp
-rm -f $(basename $@).o
prepare_data_with_opencv: ./prepare_data_with_opencv.cpp
- $(CXX) -c -std=c++0x $(OPENCV_CFLAGS) $^
+ $(CXX) -c -std=c++0x $(OPENCV_CFLAGS) $^
$(CXX) $(basename $@).o -o $@ $(OPENCV_LDFLAGS)
-rm -f $(basename $@).o
diff --git a/cpp-package/example/feature_extract/README.md
b/cpp-package/example/feature_extract/README.md
index 4367a0c2efe..87cfcb4021f 100644
--- a/cpp-package/example/feature_extract/README.md
+++ b/cpp-package/example/feature_extract/README.md
@@ -1,8 +1,12 @@
This example shows how to extract features with a pretrained model.
-You can first get a pretrained model from
<https://github.com/dmlc/mxnet-model-gallery/blob/master/imagenet-1k-inception-bn.md>,
-then prepare 2 pictures 1.jpg and 2.jpg to extract by executing `run.sh`.
+Execute `run.sh` to:
+- Download a pretrained model
+- Download sample pictures (`dog.jpg` and `cat.jpg`)
+- Compile the files
+- Execute the featurization on `dog.jpg` and `cat.jpg`
+
Note:
1. The filename of network parameters may vary, line 67 in
`feature_extract.cpp` should be updated accordingly.
-2. As the build system has changed a lot, to build this example, you need to
put the compiled library `libmxnet.so` in `../lib/linux`.
+2. You need to build MXNet from source to get access to the `lib/libmxnet.so`
or point `LD_LIBRARY_PATH` to where it is installed in your system
diff --git a/cpp-package/example/feature_extract/feature_extract.cpp
b/cpp-package/example/feature_extract/feature_extract.cpp
index 1886c576400..c23623e23bd 100644
--- a/cpp-package/example/feature_extract/feature_extract.cpp
+++ b/cpp-package/example/feature_extract/feature_extract.cpp
@@ -58,13 +58,13 @@ class FeatureExtractor {
LG<<layer_name;
}
*/
- net = Symbol::Load("./model/Inception_BN-symbol.json")
+ net = Symbol::Load("./model/Inception-BN-symbol.json")
.GetInternals()["global_pool_output"];
}
/*Fill the trained paramters into the model, a.k.a. net, executor*/
void LoadParameters() {
map<string, NDArray> paramters;
- NDArray::Load("./model/Inception_BN-0039.params", 0, ¶mters);
+ NDArray::Load("./model/Inception-BN-0126.params", 0, ¶mters);
for (const auto &k : paramters) {
if (k.first.substr(0, 4) == "aux:") {
auto name = k.first.substr(4, k.first.size() - 4);
@@ -99,7 +99,7 @@ class FeatureExtractor {
data.Slice(0, 1) -= mean_img;
data.Slice(1, 2) -= mean_img;
args_map["data"] = data;
- /*bind the excutor*/
+ /*bind the executor*/
executor = net.SimpleBind(global_ctx, args_map, map<string, NDArray>(),
map<string, OpReqType>(), aux_map);
executor->Forward(false);
@@ -117,7 +117,7 @@ NDArray Data2NDArray() {
NDArray ret(Shape(2, 3, 224, 224), global_ctx, false);
ifstream inf("./img.dat", ios::binary);
vector<float> data(2 * 3 * 224 * 224);
- inf.read(reinterpret_cast<char *>data.data(), 2 * 3 * 224 * 224 *
sizeof(float));
+ inf.read(reinterpret_cast<char *>(data.data()), 2 * 3 * 224 * 224 *
sizeof(float));
inf.close();
ret.SyncCopyFromCPU(data.data(), 2 * 3 * 224 * 224);
NDArray::WaitAll();
diff --git a/cpp-package/example/feature_extract/prepare_data_with_opencv.cpp
b/cpp-package/example/feature_extract/prepare_data_with_opencv.cpp
index a7b4cba0a64..fe32e896adb 100644
--- a/cpp-package/example/feature_extract/prepare_data_with_opencv.cpp
+++ b/cpp-package/example/feature_extract/prepare_data_with_opencv.cpp
@@ -29,7 +29,7 @@ using namespace std;
/*read images and store them the NDArray format that MXNet.cpp can handle*/
void Mat2Array() {
- string file_name_list[] = {"./1.jpg", "./2.jpg"};
+ string file_name_list[] = {"./dog.jpg", "./cat.jpg"};
std::vector<float> array;
for (auto &t : file_name_list) {
@@ -45,7 +45,7 @@ void Mat2Array() {
}
}
ofstream outf("./img.dat", ios::binary);
- outf.write(reinterpret_cast<char *>array.data(), array.size() *
sizeof(float));
+ outf.write(reinterpret_cast<char *>(array.data()), array.size() *
sizeof(float));
outf.close();
}
diff --git a/cpp-package/example/feature_extract/run.sh
b/cpp-package/example/feature_extract/run.sh
index dc6665604b1..616445dbd67 100755
--- a/cpp-package/example/feature_extract/run.sh
+++ b/cpp-package/example/feature_extract/run.sh
@@ -15,15 +15,19 @@
# specific language governing permissions and limitations
# under the License.
-### To run the this example,
-###
-### 1.
-### Get Inseption-BN model first, from here
-### https://github.com/dmlc/mxnet-model-gallery
-###
-### 2.
-### Then Prepare 2 pictures, 1.jpg 2.jpg to extract
+# Downloading the data and model
+mkdir -p model
+wget -nc http://data.dmlc.ml/mxnet/models/imagenet/inception-bn.tar.gz
+wget -nc -O cat.jpg
https://github.com/dmlc/web-data/blob/master/mxnet/doc/tutorials/python/predict_image/cat.jpg?raw=true
+wget -nc -O dog.jpg
https://github.com/dmlc/web-data/blob/master/mxnet/doc/tutorials/python/predict_image/dog.jpg?raw=true
+wget -nc -O model/mean_224.nd
https://github.com/dmlc/web-data/raw/master/mxnet/example/feature_extract/mean_224.nd
+tar -xvzf inception-bn.tar.gz -C model --skip-old-files
+# Building
make
+
+# Preparing the data
./prepare_data_with_opencv
-LD_LIBRARY_PATH=../../lib/linux ./feature_extract
+
+# Running the featurization
+LD_LIBRARY_PATH=../../../lib ./feature_extract
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