0.2.1.5

# integration
- add travis CI
- fix building parameters for python

# converter
- add half storage option for MNN converter
- fix op name lost in converter
- fix converter bug for print input output, identity remove output

# ops
- add quantized Convolution & Deconvolution support on OpenCL
- add more expression supports
- add DetectionPostProcess Op for TensorFlow Lite (ssd is supported directly now)
- add supports for LSTM & ELU for ONNX
- add support for Convolution that weights is not constant for ONNX
- fix Unary Op compile error on Linux
- fix Metal backend buffer reuse after resize
- fix Metal raw memory access after model releasing
- fix redundant transpose in Winograd generater

CMakeLists.txt

@@ -190,9 +190,10 @@ set(MNN.Source_DIR
     ${MNN.Path}/shape
 )
 include_directories(
-    "include/"
-    "schema/current"
+    "include/" 
+    "schema/current" 
     "3rd_party/flatbuffers/include"
+    "3rd_party/half"
 )
 
 if(MNN_METAL)

MNN.podspec

@@ -32,9 +32,10 @@ Pod::Spec.new do |s|
   s.ios.deployment_target = '8.0'
   s.requires_arc = true
 
-  s.source =  { :git => "git@github.com:alibaba/MNN.git", :branch => 'master' } 
+  s.source =  { :git => "https://github.com/alibaba/MNN.git", :branch => 'master' }
   s.frameworks = 'Metal', 'Accelerate'
   s.library = 'c++'
+  s.prepare_command = 'schema/generate.sh'
 
   s.subspec 'core' do |a|
     a.source_files = \
@@ -52,15 +53,17 @@ Pod::Spec.new do |s|
   end
   s.subspec 'armv7' do |a|
     a.source_files = 'source/backend/cpu/arm/arm32/*.{h,c,m,mm,cc,S,hpp,cpp}'
+    a.pod_target_xcconfig = {'HEADER_SEARCH_PATHS' => '"$(PODS_TARGET_SRCROOT)/source/backend/cpu/arm/"'}
   end
   s.subspec 'aarch64' do |a|
     a.source_files = 'source/backend/cpu/arm/arm64/*.{h,c,m,mm,cc,S,hpp,cpp}'
+    a.pod_target_xcconfig = {'HEADER_SEARCH_PATHS' => '"$(PODS_TARGET_SRCROOT)/source/backend/cpu/arm/"'}
   end
   s.subspec 'metal' do |a|
     a.source_files = 'source/backend/metal/**/*.{h,c,m,mm,cc,hpp,cpp,metal}'
   end
 
   s.default_subspecs = 'core', 'armv7', 'aarch64', 'metal'
-  s.pod_target_xcconfig = {'METAL_LIBRARY_FILE_BASE' => 'mnn', 'HEADER_SEARCH_PATHS' => '"$(PODS_TARGET_SRCROOT)" "$(PODS_TARGET_SRCROOT)/3rd_party/flatbuffers/include" ', 'GCC_PREPROCESSOR_DEFINITIONS' => '$(inherited) MNN_CODEGEN_REGISTER=1 MNN_SUPPORT_TFLITE_QUAN=1'}
-  s.user_target_xcconfig = { 'OTHER_LDFLAGS' => '-force_load $(BUILD_DIR)/$(CONFIGURATION)$(EFFECTIVE_PLATFORM_NAME)/MNN/libMNN.a'}
+  s.pod_target_xcconfig = {'METAL_LIBRARY_FILE_BASE' => 'mnn', 'HEADER_SEARCH_PATHS' => '$(PODS_TARGET_SRCROOT)/include $(PODS_TARGET_SRCROOT)/3rd_party/flatbuffers/include $(PODS_TARGET_SRCROOT)/schema/current $(PODS_TARGET_SRCROOT)/source/core/ $(PODS_TARGET_SRCROOT)/source/backend/cpu/ $(PODS_TARGET_SRCROOT)/source/backend/cpu/compute/ $(PODS_TARGET_SRCROOT)/source/math/ $(PODS_TARGET_SRCROOT)/3rd_party/half', 'GCC_PREPROCESSOR_DEFINITIONS' => '$(inherited) MNN_CODEGEN_REGISTER=1 MNN_SUPPORT_TFLITE_QUAN=1'}
+  s.user_target_xcconfig = {'OTHER_LDFLAGS' => '-force_load $(BUILD_DIR)/$(CONFIGURATION)$(EFFECTIVE_PLATFORM_NAME)/MNN/libMNN.a'}
 end

express/include/Expr.hpp

@@ -100,6 +100,11 @@ class MNN_EXPRESS_PUBLIC Variable {
     size_t linkNumber() const {
         return mTo.size();
     }
+
+    const std::list< std::pair<int, WeakEXPRP> >& toExprs() const{
+        return mTo;
+    }
+
 private:
     Variable(EXPRP expr, int index) {
         mFrom      = expr;

express/include/MathOp.hpp

@@ -14,6 +14,8 @@ MNN_EXPRESS_PUBLIC VARP _Mul(VARP x, VARP y);
 MNN_EXPRESS_PUBLIC VARP _Sub(VARP x, VARP y);
 MNN_EXPRESS_PUBLIC VARP _Add(VARP x, VARP y);
 MNN_EXPRESS_PUBLIC VARP _Div(VARP x, VARP y);
+MNN_EXPRESS_PUBLIC VARP _Min(VARP x, VARP y);
+MNN_EXPRESS_PUBLIC VARP _Max(VARP x, VARP y);
 MNN_EXPRESS_PUBLIC VARP _Log(VARP x);
 MNN_EXPRESS_PUBLIC VARP _Neg(VARP x);
 MNN_EXPRESS_PUBLIC VARP _Rsqrt(VARP x);

express/include/NeuralNetWorkOp.hpp

@@ -33,8 +33,10 @@ MNN_EXPRESS_PUBLIC VARP _Scale(VARP x, int channels, std::vector<float>&& scales
 
 MNN_EXPRESS_PUBLIC VARP _Relu(VARP x, float slope = 0.0f);
 MNN_EXPRESS_PUBLIC VARP _Relu6(VARP x);
+MNN_EXPRESS_PUBLIC VARP _PRelu(VARP x, std::vector<float> &&slopes);
 MNN_EXPRESS_PUBLIC VARP _Softmax(VARP x, int axis);
-MNN_EXPRESS_PUBLIC std::vector<VARP> _Slice(VARP x, INTS points, int axis);
+MNN_EXPRESS_PUBLIC std::vector<VARP> _Split(VARP x, INTS points, int axis);
+MNN_EXPRESS_PUBLIC VARP _Slice(VARP x, VARP starts, VARP sizes);
 MNN_EXPRESS_PUBLIC VARP _Concat(VARPS xs, int axis);
 MNN_EXPRESS_PUBLIC VARP _Convert(VARP x, Dimensionformat dest);
 MNN_EXPRESS_PUBLIC VARP _Transpose(VARP x, INTS perm);
@@ -51,13 +53,17 @@ MNN_EXPRESS_PUBLIC VARP _Resize(VARP x, float xScale, float yScale);
 MNN_EXPRESS_PUBLIC VARP _Pad(VARP x, VARP pads);
 MNN_EXPRESS_PUBLIC VARP _ExpandDims(VARP x, int axis);
 MNN_EXPRESS_PUBLIC VARP _ExpandDims(VARP x, VARP axis);
-
+
+MNN_EXPRESS_PUBLIC VARP _Shape(VARP x);
 MNN_EXPRESS_PUBLIC VARP _Pack(VARPS xs, halide_type_t dtype, int axis);
 enum InterpolationMethod {BILINEAR, NEAREST};
 MNN_EXPRESS_PUBLIC VARP _CropAndResize(VARP image, VARP boxes, VARP indexes, VARP sizes, float extrapolation, InterpolationMethod method);
 MNN_EXPRESS_PUBLIC VARP _Fill(VARP s, VARP v);
 MNN_EXPRESS_PUBLIC VARP _Tile(VARP x, VARP mul);
+MNN_EXPRESS_PUBLIC VARP _Gather(VARP embedding, VARP indices);
 MNN_EXPRESS_PUBLIC VARP _GatherV2(VARP params, VARP indices, VARP axis = nullptr);
-
+MNN_EXPRESS_PUBLIC VARP _Squeeze(VARP x, INTS axes = {});
+MNN_EXPRESS_PUBLIC VARP _Unsqueeze(VARP x, INTS axes = {});
+
 } // namespace Express
 } // namespace MNN

express/source/Expr.cpp

@@ -116,7 +116,7 @@ bool Expr::requireInfo() {
             return false;
         }
         mInside->mInputInfos[i] = mInputs[i]->getInfo();
-        if (nullptr == mInside->mInputInfos[i] && OpType_Concat != mOp->type()) {
+        if (nullptr == mInside->mInputInfos[i] && (!mInside->mReq.supportError[i])) {
 #ifdef MNN_EXPRESS_ERROR_REPORT
             MNN_ERROR("%s, %d input not ready\n", mName.c_str(), i);
 #endif

express/source/MathOp.cpp

@@ -70,6 +70,12 @@ VARP _Sub(VARP x, VARP y) {
 VARP _Add(VARP x, VARP y) {
     return _Binary(x, y, BinaryOpOperation_ADD);
 }
+VARP _Min(VARP x, VARP y) {
+    return _Binary(x, y, BinaryOpOperation_MINIMUM);
+}
+VARP _Max(VARP x, VARP y) {
+    return _Binary(x, y, BinaryOpOperation_MAXIMUM);
+}
 VARP _Neg(VARP x) {
     return _Unary(x, UnaryOpOperation_NEG);
 }

express/source/NeuralNetWorkOp.cpp

@@ -300,6 +300,15 @@ VARP _Relu6(VARP x) {
     relu->type = OpType_ReLU6;
     return (Variable::create(Expr::create(relu.get(), {x})));
 }
+VARP _PRelu(VARP x, std::vector<float> &&slopes) {
+    std::unique_ptr<OpT> prelu(new OpT);
+    prelu->type                       = OpType_PReLU;
+    prelu->main.type                  = OpParameter_PRelu;
+    prelu->main.value                 = new PReluT;
+    prelu->main.AsPRelu()->slope      = slopes;
+    prelu->main.AsPRelu()->slopeCount = slopes.size();
+    return (Variable::create(Expr::create(prelu.get(), {x})));
+}
 
 VARP _Softmax(VARP x, int axis) {
     std::unique_ptr<OpT> softmax(new OpT);
@@ -335,7 +344,7 @@ VARP _Convert(VARP x, Dimensionformat dest) {
     return (Variable::create(Expr::create(convert.get(), {x})));
 }
 
-std::vector<VARP> _Slice(VARP x, INTS points, int axis) {
+std::vector<VARP> _Split(VARP x, INTS points, int axis) {
     MNN_ASSERT(points.size() >= 1);
     std::unique_ptr<OpT> op(new OpT);
     op->type                        = OpType_Slice;
@@ -354,6 +363,12 @@ std::vector<VARP> _Slice(VARP x, INTS points, int axis) {
     return res;
 }
 
+VARP _Slice(VARP x, VARP starts, VARP sizes) {
+    std::unique_ptr<OpT> slice(new OpT);
+    slice->type = OpType_SliceTf;
+    return (Variable::create(Expr::create(slice.get(), {x, starts, sizes})));
+}
+
 VARP _Transpose(VARP x, INTS perm) {
     auto permVar = _Const((const void*)perm.data(), {static_cast<int>(perm.size())}, NHWC, halide_type_of<int>());
     return _Transpose(x, permVar);
@@ -508,6 +523,11 @@ VARP _ExpandDims(VARP x, VARP axis) {
     return (Variable::create(Expr::create(std::move(expand), {x, axis})));
 }
 
+VARP _Shape(VARP x) {
+    std::unique_ptr<OpT> shape(new OpT);
+    shape->type = OpType_Shape;
+    return (Variable::create(Expr::create(std::move(shape), {x})));
+}
 VARP _Pack(VARPS xs, halide_type_t dtype, int axis) {
     std::unique_ptr<OpT> pack(new OpT);
     pack->type                         = OpType_Pack;
@@ -546,6 +566,12 @@ VARP _Tile(VARP x, VARP mul) {
     tile->type = OpType_Tile;
     return (Variable::create(Expr::create(std::move(tile), {x, mul})));
 }
+VARP _Gather(VARP embedding, VARP indices) {
+    std::unique_ptr<OpT> gather(new OpT);
+    gather->type       = OpType_Gather;
+    gather->main.value = new GatherT;
+    return (Variable::create(Expr::create(std::move(gather), {embedding, indices})));
+}
 VARP _GatherV2(VARP params, VARP indices, VARP axis) {
     std::unique_ptr<OpT> gather(new OpT);
     gather->type       = OpType_GatherV2;
@@ -556,5 +582,26 @@ VARP _GatherV2(VARP params, VARP indices, VARP axis) {
         return (Variable::create(Expr::create(std::move(gather), {params, indices})));
     }
 }
+
+VARP _Squeeze(VARP x, INTS axes){
+    std::unique_ptr<OpT> squeeze(new OpT);
+    squeeze->type = OpType_Squeeze;
+    auto squeezeParam = new SqueezeParamT;
+    squeezeParam->squeezeDims = axes;
+    squeeze->main.type = OpParameter_SqueezeParam;
+    squeeze->main.value = squeezeParam;
+    return Variable::create(Expr::create(std::move(squeeze), {x}));
+}
+
+VARP _Unsqueeze(VARP x, INTS axes){
+    std::unique_ptr<OpT> squeeze(new OpT);
+    squeeze->type = OpType_Unsqueeze;
+    auto squeezeParam = new SqueezeParamT;
+    squeezeParam->squeezeDims = axes;
+    squeeze->main.type = OpParameter_SqueezeParam;
+    squeeze->main.value = squeezeParam;
+    return Variable::create(Expr::create(std::move(squeeze), {x}));
+}
+
 } // namespace Express
 } // namespace MNN

project/ios/MNN.xcodeproj/project.pbxproj

@@ -765,6 +765,9 @@
 		EBB38F3621E748B9005F76D7 /* ShapeSpaceToBatchND.cpp in Sources */ = {isa = PBXBuildFile; fileRef = EBB38EFD21E748B9005F76D7 /* ShapeSpaceToBatchND.cpp */; };
 		EBB38F3721E748B9005F76D7 /* ShapePack.cpp in Sources */ = {isa = PBXBuildFile; fileRef = EBB38EFE21E748B9005F76D7 /* ShapePack.cpp */; };
 		EBB38F3821E748B9005F76D7 /* ShapeDeconvolution.cpp in Sources */ = {isa = PBXBuildFile; fileRef = EBB38EFF21E748B9005F76D7 /* ShapeDeconvolution.cpp */; };
+		EBD9FF12236A939700E188F5 /* ShapeDetectionPostProcess.cpp in Sources */ = {isa = PBXBuildFile; fileRef = EBD9FF11236A939700E188F5 /* ShapeDetectionPostProcess.cpp */; };
+		EBD9FF15236A93AB00E188F5 /* CPUDetectionPostProcess.cpp in Sources */ = {isa = PBXBuildFile; fileRef = EBD9FF13236A93AB00E188F5 /* CPUDetectionPostProcess.cpp */; };
+		EBD9FF16236A93AB00E188F5 /* CPUDetectionPostProcess.hpp in Headers */ = {isa = PBXBuildFile; fileRef = EBD9FF14236A93AB00E188F5 /* CPUDetectionPostProcess.hpp */; };
 /* End PBXBuildFile section */
 
 /* Begin PBXContainerItemProxy section */
@@ -1571,6 +1574,9 @@
 		EBB38EFD21E748B9005F76D7 /* ShapeSpaceToBatchND.cpp */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.cpp.cpp; path = ShapeSpaceToBatchND.cpp; sourceTree = "<group>"; };
 		EBB38EFE21E748B9005F76D7 /* ShapePack.cpp */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.cpp.cpp; path = ShapePack.cpp; sourceTree = "<group>"; };
 		EBB38EFF21E748B9005F76D7 /* ShapeDeconvolution.cpp */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.cpp.cpp; path = ShapeDeconvolution.cpp; sourceTree = "<group>"; };
+		EBD9FF11236A939700E188F5 /* ShapeDetectionPostProcess.cpp */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.cpp.cpp; path = ShapeDetectionPostProcess.cpp; sourceTree = "<group>"; };
+		EBD9FF13236A93AB00E188F5 /* CPUDetectionPostProcess.cpp */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.cpp.cpp; path = CPUDetectionPostProcess.cpp; sourceTree = "<group>"; };
+		EBD9FF14236A93AB00E188F5 /* CPUDetectionPostProcess.hpp */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.cpp.h; path = CPUDetectionPostProcess.hpp; sourceTree = "<group>"; };
 /* End PBXFileReference section */
 
 /* Begin PBXFrameworksBuildPhase section */
@@ -1945,6 +1951,8 @@
 		48887410215B639D0079B12E /* cpu */ = {
 			isa = PBXGroup;
 			children = (
+				EBD9FF13236A93AB00E188F5 /* CPUDetectionPostProcess.cpp */,
+				EBD9FF14236A93AB00E188F5 /* CPUDetectionPostProcess.hpp */,
 				48057D8B2330E85C00F922BE /* CPUMatrixBandPart.cpp */,
 				48057D8A2330E85C00F922BE /* CPUMatrixBandPart.hpp */,
 				48057D872330A90900F922BE /* CPUGatherND.cpp */,
@@ -2559,6 +2567,7 @@
 		EBB38EC621E748B9005F76D7 /* shape */ = {
 			isa = PBXGroup;
 			children = (
+				EBD9FF11236A939700E188F5 /* ShapeDetectionPostProcess.cpp */,
 				48057D842330A8F900F922BE /* ShapeGatherND.cpp */,
 				C422D73D2326449500FD59D0 /* ShapePool3D.cpp */,
 				C422D737232634DD00FD59D0 /* ShapeConvolution3D.cpp */,
@@ -2683,6 +2692,7 @@
 				4841B60D21EC607E002E5D66 /* CPUQuantizedLogistic.hpp in Headers */,
 				92D765AD2228188700178BE5 /* Session.hpp in Headers */,
 				CE96FE7321707D58004AB400 /* MetalSigmoid.hpp in Headers */,
+				EBD9FF16236A93AB00E188F5 /* CPUDetectionPostProcess.hpp in Headers */,
 				48887612215B639F0079B12E /* CPUSigmoid.hpp in Headers */,
 				4888767D215B639F0079B12E /* MNNAsmGlobal.h in Headers */,
 				4843AA5B22A7E9AB00889A63 /* CPUSoftmaxGrad.hpp in Headers */,
@@ -3365,6 +3375,7 @@
 				925A89122223951200D22428 /* MetalConvolutionActivation.metal in Sources */,
 				EBB38F1821E748B9005F76D7 /* ShapeNonMaxSuppressionV2.cpp in Sources */,
 				48BF21F421CA43AE00AFF78E /* MNNSamplerC4NearestOpt.S in Sources */,
+				EBD9FF15236A93AB00E188F5 /* CPUDetectionPostProcess.cpp in Sources */,
 				488876AA215B639F0079B12E /* MNNGemmInt16to32_4x4_Common.S in Sources */,
 				92369E64222544FE009D3A05 /* MetalConvolutionGEMM.metal in Sources */,
 				EBB38F3021E748B9005F76D7 /* ShapeProposal.cpp in Sources */,
@@ -3475,6 +3486,7 @@
 				924F131921A81C74006D46A4 /* MetalTranspose.mm in Sources */,
 				488876D0215B639F0079B12E /* MNNWinogradMatrixProductRight.S in Sources */,
 				48070742231E512D00528CE5 /* NeuralNetWorkOp.cpp in Sources */,
+				EBD9FF12236A939700E188F5 /* ShapeDetectionPostProcess.cpp in Sources */,
 				4807073F231E512D00528CE5 /* InsideExpr.cpp in Sources */,
 				48A8A63721D8A43D00C2B9A7 /* BufferAllocator.cpp in Sources */,
 				92D765AF2228188700178BE5 /* Interpreter.cpp in Sources */,
@@ -3719,6 +3731,7 @@
 				HEADER_SEARCH_PATHS = (
 					"${inherited}",
 					"${SRCROOT}/../../3rd_party/flatbuffers/include",
+					"${SRCROOT}/../../3rd_party/half",
 				);
 				INFOPLIST_FILE = "$(SRCROOT)/MNN/Info.plist";
 				INSTALL_PATH = "$(LOCAL_LIBRARY_DIR)/Frameworks";
@@ -3763,6 +3776,7 @@
 				HEADER_SEARCH_PATHS = (
 					"${inherited}",
 					"${SRCROOT}/../../3rd_party/flatbuffers/include",
+					"${SRCROOT}/../../3rd_party/half",
 				);
 				INFOPLIST_FILE = "$(SRCROOT)/MNN/Info.plist";
 				INSTALL_PATH = "$(LOCAL_LIBRARY_DIR)/Frameworks";
@@ -3792,7 +3806,7 @@
 				ASSETCATALOG_COMPILER_APPICON_NAME = AppIcon;
 				ASSETCATALOG_COMPILER_LAUNCHIMAGE_NAME = LaunchImage;
 				CODE_SIGN_STYLE = Automatic;
-				DEVELOPMENT_TEAM = 6G7464HHUS;
+				DEVELOPMENT_TEAM = "";
 				HEADER_SEARCH_PATHS = (
 					"${inherited}",
 					"${SRCROOT}/../../3rd_party/flatbuffers/include",
@@ -3813,7 +3827,7 @@
 				ASSETCATALOG_COMPILER_APPICON_NAME = AppIcon;
 				ASSETCATALOG_COMPILER_LAUNCHIMAGE_NAME = LaunchImage;
 				CODE_SIGN_STYLE = Automatic;
-				DEVELOPMENT_TEAM = 6G7464HHUS;
+				DEVELOPMENT_TEAM = "";
 				HEADER_SEARCH_PATHS = (
 					"${inherited}",
 					"${SRCROOT}/../../3rd_party/flatbuffers/include",

pymnn/pip_package/build_deps.py

@@ -18,22 +18,10 @@ def build_deps():
     os.makedirs(cmake_build_dir)
     os.chdir(cmake_build_dir)
     if IS_WINDOWS:
-        os.system('cmake -G "Ninja" -DMNN_BUILD_QUANTOOLS=ON\
+        os.system('cmake -G "Ninja" -DMNN_BUILD_QUANTOOLS=ON -DMNN_BUILD_CONVERTER=on\
             -DMNN_BUILD_SHARED_LIBS=OFF -DCMAKE_BUILD_TYPE=Release .. && ninja')
     else:
-        os.system('cmake -DMNN_BUILD_QUANTOOLS=ON -DMNN_BUILD_SHARED_LIBS=OFF .. && make -j4')
-    #build_converter_project
-    converter_dir = os.path.join(root_dir, "tools", "converter")
-    converter_build_dir = os.path.join(converter_dir, BUILD_DIR)
-    if os.path.exists(converter_build_dir):
-        shutil.rmtree(converter_build_dir)
-    os.makedirs(converter_build_dir)
-    os.chdir(converter_build_dir)
-    if IS_WINDOWS:
-        os.system('cmake -G "Ninja" -DMNN_BUILD_SHARED_LIBS=OFF -DCMAKE_BUILD_TYPE=Release .. && ninja')
-    else:
-        os.system('cmake -DMNN_BUILD_SHARED_LIBS=OFF .. && make -j4')
-    os.chdir(root_dir)
+        os.system('cmake -DMNN_BUILD_QUANTOOLS=ON -DMNN_BUILD_CONVERTER=on -DMNN_BUILD_SHARED_LIBS=OFF .. && make -j4')
 ################################################################################
 # Building dependent libraries
 ################################################################################

pymnn/pip_package/build_wheel.py

@@ -3,17 +3,17 @@
 """ build wheel tool """
 from __future__ import print_function
 import os
+import shutil
 import platform
 IS_WINDOWS = (platform.system() == 'Windows')
 IS_DARWIN = (platform.system() == 'Darwin')
 IS_LINUX = (platform.system() == 'Linux')
 if __name__ == '__main__':
+    if os.path.exists('build'):
+        shutil.rmtree('build')
     if IS_DARWIN:
-        os.system('rm -rf build')
         os.system('python setup.py bdist_wheel')
     if IS_LINUX:
-        os.system('rm -rf build')
         os.system('python setup.py bdist_wheel --plat-name=manylinux1_x86_64')
     if IS_WINDOWS:
-        os.system('rm -r -force build')
         os.system('python setup.py bdist_wheel')