caffe2
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20 hours ago •
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My changes for Windows
These are my changes for building on Windows (I have Windows 10 and Visaul studio 2015) Strange formating, it is only diff log. I don't know how attach the file
diff --git a/caffe2/CMakeLists.txt b/caffe2/CMakeLists.txt
index 3a7688c7..1469300e 100644
--- a/caffe2/CMakeLists.txt
+++ b/caffe2/CMakeLists.txt
@@ -218,6 +218,9 @@ if (BUILD_PYTHON)
elseif (MSVC)
set_target_properties(caffe2_pybind11_state_gpu PROPERTIES SUFFIX ".pyd")
endif()
+ if (MSVC)
+ set(CMAKE_MODULE_LINKER_FLAGS "${CMAKE_MODULE_LINKER_FLAGS} /FORCE:MULTIPLE")
+ endif()
set_target_properties(
caffe2_pybind11_state_gpu PROPERTIES LIBRARY_OUTPUT_DIRECTORY
${CMAKE_BINARY_DIR}/caffe2/python)
diff --git a/caffe2/binaries/CMakeLists.txt b/caffe2/binaries/CMakeLists.txt
index 3ec61dbd..b9170d92 100644
--- a/caffe2/binaries/CMakeLists.txt
+++ b/caffe2/binaries/CMakeLists.txt
@@ -1,3 +1,7 @@
+if(MSVC)
+ set(CMAKE_EXE_LINKER_FLAGS "${CMAKE_EXE_LINKER_FLAGS} /FORCE:MULTIPLE")
+ENDIF()
+
caffe2_binary_target("convert_caffe_image_db.cc")
caffe2_binary_target("convert_db.cc")
caffe2_binary_target("db_throughput.cc")
diff --git a/caffe2/binaries/make_image_db.cc b/caffe2/binaries/make_image_db.cc
index 2bdbb53d..7e8a8a34 100644
--- a/caffe2/binaries/make_image_db.cc
+++ b/caffe2/binaries/make_image_db.cc
@@ -127,7 +127,7 @@ class Converter {
// Add raw file contents to DB if !raw
if (!caffe2::FLAGS_raw) {
- std::ifstream image_file_stream(input_folder + pair.first);
+ std::ifstream image_file_stream(input_folder + pair.first, std::ios::in | std::ios::binary);
if (!image_file_stream) {
LOG(ERROR) << "Cannot open " << input_folder << pair.first
<< ". Skipping.";
diff --git a/caffe2/core/blob_serialization.h b/caffe2/core/blob_serialization.h
index 66a1284d..48a36427 100644
--- a/caffe2/core/blob_serialization.h
+++ b/caffe2/core/blob_serialization.h
@@ -584,7 +584,7 @@ void TensorDeserializer<Context>::Deserialize(
for (int i = 0; i < chunkSize; ++i) {
temp_blob.Deserialize(proto.string_data(i));
if (i == 0) {
- raw_ptr = tensor->template raw_mutable_data(temp_blob.meta());
+ raw_ptr = tensor->raw_mutable_data(temp_blob.meta());
}
temp_blob.meta().copy()(
temp_blob.GetRaw(),
diff --git a/caffe2/core/operator_schema.cc b/caffe2/core/operator_schema.cc
index f6723cb6..77e10caf 100644
--- a/caffe2/core/operator_schema.cc
+++ b/caffe2/core/operator_schema.cc
@@ -248,7 +248,7 @@ OpSchema& OpSchema::ScalarType(::caffe2::TensorProto_DataType dt) {
}
OpSchema& OpSchema::CostInferenceFunction(
- CostInferenceFunctionType&& function) {
+ CostInferenceFunctionType function) {
cost_inference_function_ =
caffe2::make_unique<CostInferenceFunctionType>(function);
return *this;
diff --git a/caffe2/core/operator_schema.h b/caffe2/core/operator_schema.h
index 29c2ca0b..78796e3c 100644
--- a/caffe2/core/operator_schema.h
+++ b/caffe2/core/operator_schema.h
@@ -199,7 +199,7 @@ class OpSchema {
/**
* @brief Register the Cost inference function.
*/
- OpSchema& CostInferenceFunction(CostInferenceFunctionType&& function);
+ OpSchema& CostInferenceFunction(CostInferenceFunctionType function);
bool HasCostInferenceFunction() const {
return !!cost_inference_function_;
}
diff --git a/caffe2/image/image_input_op.h b/caffe2/image/image_input_op.h
index ac295c68..a38aec1d 100644
--- a/caffe2/image/image_input_op.h
+++ b/caffe2/image/image_input_op.h
@@ -256,13 +256,13 @@ ImageInputOp<Context>::ImageInputOp(
// hard-coded PCA eigenvectors and eigenvalues, based on RBG channel order
color_lighting_eigvecs_.push_back(
- std::vector<float>{-144.7125, 183.396, 102.2295});
+ std::vector<float>{-144.7125f, 183.396f, 102.2295f});
color_lighting_eigvecs_.push_back(
- std::vector<float>{-148.104, -1.1475, -207.57});
+ std::vector<float>{-148.104f, -1.1475f, -207.57f});
color_lighting_eigvecs_.push_back(
- std::vector<float>{-148.818, -177.174, 107.1765});
+ std::vector<float>{-148.818f, -177.174f, 107.1765f});
- color_lighting_eigvals_ = std::vector<float>{0.2175, 0.0188, 0.0045};
+ color_lighting_eigvals_ = std::vector<float>{0.2175f, 0.0188f, 0.0045f};
CAFFE_ENFORCE_GT(batch_size_, 0, "Batch size should be nonnegative.");
if (use_caffe_datum_) {
diff --git a/caffe2/operators/conv_op_cudnn.cc b/caffe2/operators/conv_op_cudnn.cc
index e8996415..775789a8 100644
--- a/caffe2/operators/conv_op_cudnn.cc
+++ b/caffe2/operators/conv_op_cudnn.cc
@@ -149,22 +149,32 @@ class CudnnConvOpBase : public ConvPoolOpBase<CUDAContext> {
switch (order_) {
case StorageOrder::NHWC:
if (size == 4) {
+#if (CUDNN_VERSION_MIN(7,0,0))
CUDNN_ENFORCE(cudnnSetTensor4dDescriptorEx(
desc_,
cudnnTypeWrapper<T>::type,
N,
-#if CUDNN_VERSION_MIN(7,0,0)
C,
-#else
- C / group_,
-#endif
H,
W,
H * W * C,
1,
W * C,
C));
- } else {
+#else
+ CUDNN_ENFORCE(cudnnSetTensor4dDescriptorEx(
+ desc_,
+ cudnnTypeWrapper<T>::type,
+ N,
+ C / group_,
+ H,
+ W,
+ H * W * C,
+ 1,
+ W * C,
+ C));
+#endif
+ } else {
#if !CUDNN_VERSION_MIN(7,0,0)
C = C / group_;
#endif
@@ -180,22 +190,32 @@ class CudnnConvOpBase : public ConvPoolOpBase<CUDAContext> {
break;
case StorageOrder::NCHW:
if (size == 4) {
+#if CUDNN_VERSION_MIN(7,0,0)
CUDNN_ENFORCE(cudnnSetTensor4dDescriptorEx(
desc_,
cudnnTypeWrapper<T>::type,
N,
-#if CUDNN_VERSION_MIN(7,0,0)
C,
-#else
- C / group_,
-#endif
H,
W,
C * H * W,
H * W,
W,
1));
- } else {
+#else
+ CUDNN_ENFORCE(cudnnSetTensor4dDescriptorEx(
+ desc_,
+ cudnnTypeWrapper<T>::type,
+ N,
+ C / group_,
+ H,
+ W,
+ C * H * W,
+ H * W,
+ W,
+ 1));
+#endif
+ } else {
#if !CUDNN_VERSION_MIN(7,0,0)
C = C / group_;
#endif
@@ -365,19 +385,26 @@ bool CudnnConvOp::DoRunWithType() {
if (filter_changed) {
cudnn_filter_dims_ = filter.dims();
if (kernel_.size() == 2) {
- CUDNN_ENFORCE(cudnnSetFilter4dDescriptor(
+#if CUDNN_VERSION_MIN(7,0,0)
+ CUDNN_ENFORCE(cudnnSetFilter4dDescriptor(
filter_desc_,
cudnnTypeWrapper<T_W>::type,
GetCudnnTensorFormat(order_),
-#if CUDNN_VERSION_MIN(7,0,0)
M,
-#else
- M / group_,
-#endif
C / group_,
kernel_h(),
kernel_w()));
- } else {
+#else
+ CUDNN_ENFORCE(cudnnSetFilter4dDescriptor(
+ filter_desc_,
+ cudnnTypeWrapper<T_W>::type,
+ GetCudnnTensorFormat(order_),
+ M / group_,
+ C / group_,
+ kernel_h(),
+ kernel_w()));
+#endif
+ } else {
vector<int> dims(filter.dims().begin(), filter.dims().end());
dims[0] /= group_;
#if !CUDNN_VERSION_MIN(7,0,0)
@@ -729,19 +756,26 @@ bool CudnnConvGradientOp::DoRunWithType() {
if (filter_changed) {
cudnn_filter_dims_ = filter.dims();
if (kernel_.size() == 2) {
+#if CUDNN_VERSION_MIN(7,0,0)
CUDNN_ENFORCE(cudnnSetFilter4dDescriptor(
filter_desc_,
cudnnTypeWrapper<T_W>::type,
GetCudnnTensorFormat(order_),
-#if CUDNN_VERSION_MIN(7,0,0)
M,
-#else
- M / group_,
-#endif
C / group_,
kernel_h(),
kernel_w()));
- } else {
+#else
+ CUDNN_ENFORCE(cudnnSetFilter4dDescriptor(
+ filter_desc_,
+ cudnnTypeWrapper<T_W>::type,
+ GetCudnnTensorFormat(order_),
+ M / group_,
+ C / group_,
+ kernel_h(),
+ kernel_w()));
+#endif
+ } else {
vector<int> dims(filter.dims().begin(), filter.dims().end());
#if !CUDNN_VERSION_MIN(7,0,0)
dims[0] /= group_;
diff --git a/caffe2/operators/layer_norm_op.cu b/caffe2/operators/layer_norm_op.cu
index 23ced9c8..84bf0161 100644
--- a/caffe2/operators/layer_norm_op.cu
+++ b/caffe2/operators/layer_norm_op.cu
@@ -277,7 +277,7 @@ bool LayerNormGradientOp<CUDAContext>::DoRunWithType<float>() {
stats_dims.push_back(1);
dmean_.Resize(stats_dims);
dstdev_.Resize(stats_dims);
- gscratch_.Resize(std::vector<size_t>{left, right});
+ gscratch_.Resize(std::vector<TIndex>{left, right});
std::vector<int> segs(left + 1);
std::iota(segs.begin(), segs.end(), 0);
@@ -307,7 +307,7 @@ bool LayerNormGradientOp<CUDAContext>::DoRunWithType<float>() {
dout.data<float>(),
gscratch_.mutable_data<float>());
- dstdev_.Resize(vector<size_t>{left, 1});
+ dstdev_.Resize(vector<TIndex>{left, 1});
// dstdev = reduce(temp1)
allocScratchAndReduce(
gscratch_.data<float>(),
diff --git a/caffe2/operators/recurrent_op_cudnn.cc b/caffe2/operators/recurrent_op_cudnn.cc
index 21b3ed3d..c9a19f11 100644
--- a/caffe2/operators/recurrent_op_cudnn.cc
+++ b/caffe2/operators/recurrent_op_cudnn.cc
@@ -131,10 +131,9 @@ void RecurrentBaseOp<T>::initialize(
// RNN setup
{
- CUDNN_ENFORCE(cudnnSetRNNDescriptor(
#if CUDNN_MAJOR >= 7
+ CUDNN_ENFORCE(cudnnSetRNNDescriptor(
cudnn_wrapper_.inline_cudnn_handle(),
-#endif
rnnDesc_,
hiddenSize,
numLayers,
@@ -142,10 +141,19 @@ void RecurrentBaseOp<T>::initialize(
rnnInput,
rnnDirection,
rnnMode,
-#if CUDNN_MAJOR >= 7
CUDNN_RNN_ALGO_STANDARD, // TODO: verify correctness / efficiency.
-#endif
cudnnTypeWrapper<T>::type));
+#else
+ CUDNN_ENFORCE(cudnnSetRNNDescriptor(
+ rnnDesc_,
+ hiddenSize,
+ numLayers,
+ dropoutDesc_,
+ rnnInput,
+ rnnDirection,
+ rnnMode,
+ cudnnTypeWrapper<T>::type));
+#endif
}
// X setup
{
diff --git a/caffe2/utils/GpuBitonicSort.cuh b/caffe2/utils/GpuBitonicSort.cuh
index f52bb508..668d9d80 100644
--- a/caffe2/utils/GpuBitonicSort.cuh
+++ b/caffe2/utils/GpuBitonicSort.cuh
@@ -28,6 +28,8 @@ __device__ inline void bitonicSwap(K& kA, V& vA,
}
};
+#define INTEGER_IS_POWER_OF_2(v) (v && !(v & (v - 1)))
+
template <typename Comparator, typename K, typename V,
int Power2SortSize,
int ThreadsPerBlock>
@@ -39,9 +41,9 @@ __device__ inline void bitonicSort(K* keys,
// Assume the sort is taking place in shared memory
// static_assert(Power2SortSize * (sizeof(K) + sizeof(V)) < 32768,
// "sort data too large (>32768 bytes)");
- static_assert(math::integerIsPowerOf2(Power2SortSize),
+ static_assert(/*math::integerIsPowerOf2*/INTEGER_IS_POWER_OF_2(Power2SortSize),
"sort size must be power of 2");
- static_assert(math::integerIsPowerOf2(ThreadsPerBlock),
+ static_assert(/*math::integerIsPowerOf2*/INTEGER_IS_POWER_OF_2(ThreadsPerBlock),
"threads in block must be power of 2");
// If what we are sorting is too small, then not all threads
@@ -107,7 +109,7 @@ __device__ inline void warpBitonicSort(K* keys,
// Smaller sorts should use a warp shuffle sort
static_assert(Power2SortSize > kWarpSize,
"sort not large enough");
- static_assert(math::integerIsPowerOf2(Power2SortSize),
+ static_assert(/*math::integerIsPowerOf2*/INTEGER_IS_POWER_OF_2(Power2SortSize),
"sort size must be power of 2");
static_assert(Power2SortSize <= kMaxBitonicSortSize,
"sort size <= 4096 only supported");
diff --git a/caffe2/utils/proto_utils.cc b/caffe2/utils/proto_utils.cc
index 65d53676..5d08c3cb 100644
--- a/caffe2/utils/proto_utils.cc
+++ b/caffe2/utils/proto_utils.cc
@@ -183,7 +183,7 @@ bool ReadProtoFromBinaryFile(const char* filename, MessageLite* proto) {
}
void WriteProtoToBinaryFile(const MessageLite& proto, const char* filename) {
- int fd = open(filename, O_WRONLY | O_CREAT | O_TRUNC, 0644);
+ int fd = open(filename, O_WRONLY | O_CREAT | O_TRUNC | O_BINARY, 0644);
CAFFE_ENFORCE_NE(
fd, -1, "File cannot be created: ", filename, " error number: ", errno);
std::unique_ptr<ZeroCopyOutputStream> raw_output(new FileOutputStream(fd));
diff --git a/cmake/Cuda.cmake b/cmake/Cuda.cmake
index 2425375e..54605ef1 100644
--- a/cmake/Cuda.cmake
+++ b/cmake/Cuda.cmake
@@ -6,7 +6,7 @@
# Default is set to cuda 9. If we detect the cuda architectores to be less than
# 9, we will lower it to the corresponding known archs.
set(Caffe2_known_gpu_archs "30 35 50 52 60 61 70") # for CUDA 9.x
-set(Caffe2_known_gpu_archs8 "20 21(20) 30 35 50 52 60 61") # for CUDA 8.x
+set(Caffe2_known_gpu_archs8 "20 21(20) 30 35 50 52 60 61 62") # for CUDA 8.x
set(Caffe2_known_gpu_archs7 "20 21(20) 30 35 50 52") # for CUDA 7.x
diff --git a/cmake/Dependencies.cmake b/cmake/Dependencies.cmake
index b18288d1..da53e3d4 100644
--- a/cmake/Dependencies.cmake
+++ b/cmake/Dependencies.cmake
@@ -145,7 +145,7 @@ if(USE_LMDB)
find_package(LMDB)
if(LMDB_FOUND)
caffe2_include_directories(${LMDB_INCLUDE_DIR})
- list(APPEND Caffe2_DEPENDENCY_LIBS ${LMDB_LIBRARIES})
+ list(APPEND Caffe2_DEPENDENCY_LIBS "ntdll.lib" ${LMDB_LIBRARIES})
else()
message(WARNING "Not compiling with LMDB. Suppress this warning with -DUSE_LMDB=OFF")
set(USE_LMDB OFF)
@@ -213,6 +213,7 @@ if(USE_OPENCV)
# OpenCV 2
find_package(OpenCV QUIET COMPONENTS core highgui imgproc)
endif()
+ set(OpenCV_FOUND TRUE)
if(OpenCV_FOUND)
caffe2_include_directories(${OpenCV_INCLUDE_DIRS})
list(APPEND Caffe2_DEPENDENCY_LIBS ${OpenCV_LIBS})
@@ -356,9 +357,9 @@ if(USE_NCCL)
if(NOT USE_CUDA)
message(WARNING "If not using cuda, one should not use NCCL either.")
set(USE_NCCL OFF)
- elseif(NOT ${CMAKE_SYSTEM_NAME} STREQUAL "Linux")
- message(WARNING "NCCL is currently only supported under Linux.")
- set(USE_NCCL OFF)
+ #elseif(NOT ${CMAKE_SYSTEM_NAME} STREQUAL "Linux")
+ # message(WARNING "NCCL is currently only supported under Linux.")
+ # set(USE_NCCL OFF)
else()
include("cmake/External/nccl.cmake")
caffe2_include_directories(${NCCL_INCLUDE_DIRS})
diff --git a/cmake/Utils.cmake b/cmake/Utils.cmake
index b804d9a0..11d89c09 100644
--- a/cmake/Utils.cmake
+++ b/cmake/Utils.cmake
@@ -245,6 +245,9 @@ function(caffe2_binary_target target_name_or_src)
add_executable(${__target} ${__srcs})
add_dependencies(${__target} ${Caffe2_MAIN_LIBS_ORDER})
target_link_libraries(${__target} ${Caffe2_MAIN_LIBS} ${Caffe2_DEPENDENCY_LIBS})
+ if (USE_CUDA)
+ target_link_libraries(${__target} ${Caffe2_CUDA_DEPENDENCY_LIBS})
+ endif()
install(TARGETS ${__target} DESTINATION bin)
endfunction()
(I updated your comment a bit - you can mark it as a code section.)
I assume that the current head is passing Windows building - if not please let us know for sure :) And feel free to send a pull request.
