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android / platform / external / pytorch / 12b2f70a89494b2ad374aaa16b8fdbf16da66e57 / . / caffe2 / onnx / onnx_exporter.cc
blob: 16cdd622707124c905be4ce9641ca573eb2eb977 [file] [log] [blame]
#include "caffe2/onnx/onnx_exporter.h"
#include "caffe2/core/logging.h"
#include "caffe2/core/memonger.h"
#include "caffe2/core/tensor_impl.h"
#include "caffe2/onnx/helper.h"
#include "caffe2/proto/caffe2_legacy.pb.h"
#include "caffe2/utils/map_utils.h"
#include "caffe2/utils/proto_utils.h"
#include "caffe2/utils/string_utils.h"
#include <numeric>
#include <unordered_set>
namespace caffe2 {
namespace onnx {
namespace {
// rewrite padding attributes
void ApplyTrans(
std::unordered_map<std::string, AttributeProto>* attrs,
bool global,
const std::string& k,
int dim = 2,
const std::string& ks = "") {
std::string ks2 = ks.empty() ? (k + "s") : ks;
std::string k_h, k_w, k_t, k_l, k_b, k_r;
if (dim == 2) {
k_h = k + "_h";
k_w = k + "_w";
} else {
k_t = k + "_t";
k_l = k + "_l";
k_b = k + "_b";
k_r = k + "_r";
}
std::vector<int64_t> vals;
if (dim == 2 && attrs->count(k_h) && attrs->count(k_w)) {
auto it = attrs->find(k_h);
vals.push_back(it->second.i());
attrs->erase(it);
it = attrs->find(k_w);
vals.push_back(it->second.i());
attrs->erase(it);
} else if (
dim == 4 && attrs->count(k_t) && attrs->count(k_b) && attrs->count(k_l) &&
attrs->count(k_r)) {
auto it = attrs->find(k_t);
vals.push_back(it->second.i());
attrs->erase(it);
it = attrs->find(k_l);
vals.push_back(it->second.i());
attrs->erase(it);
it = attrs->find(k_b);
vals.push_back(it->second.i());
attrs->erase(it);
it = attrs->find(k_r);
vals.push_back(it->second.i());
attrs->erase(it);
} else if (attrs->count(k)) {
auto it = attrs->find(k);
auto tmp = it->second.i();
for (int i = 0; i < dim; ++i) {
vals.push_back(tmp);
}
attrs->erase(it);
}
if (!vals.empty() && !global) {
attrs->emplace(ks2, MakeAttribute(ks2, vals));
}
}
int64_t DimProd(const caffe2::TensorShape& shape, int start, int end) {
int64_t acc = 1;
for (int i = start; i < end; ++i) {
acc *= shape.dims(i);
}
return acc;
}
TensorProto CreateOnnxShapeTensor(
std::shared_ptr<DummyName> dummy,
const std::vector<int64_t>& shape) {
TensorProto tensor;
tensor.set_name(dummy->NewDummyName());
tensor.set_data_type(TensorProto::INT64);
tensor.add_dims(shape.size());
tensor.mutable_raw_data()->assign(
reinterpret_cast<const char*>(shape.data()),
sizeof(int64_t) * shape.size());
return tensor;
}
std::string SsaName(const std::string& n, int version) {
return c10::str(n, "_", version);
}
NodeProto AddShapeNode(const std::string& input, const std::string& output) {
NodeProto shape_node;
shape_node.set_op_type("Shape");
shape_node.add_input(input);
shape_node.add_output(output);
return shape_node;
}
void collectExternalsFromIfOpSubnet(
const NetDef* net,
std::vector<std::string>* input,
std::vector<std::string>* output) {
std::set<std::string> in_input, in_output;
for (const auto& op : net->op()) {
for (const auto& blob : op.input()) {
in_input.emplace(blob);
}
for (const auto& blob : op.output()) {
in_output.emplace(blob);
}
}
for (const auto& blob : in_input) {
if (!in_output.count(blob)) {
input->push_back(blob);
}
}
for (const auto& blob : in_output) {
if (!in_input.count(blob)) {
output->push_back(blob);
}
}
}
void ssaRewriteForIfOp(
OperatorDef* op,
std::unordered_map<std::string, int>* blob_versions,
std::set<std::string>* is_initialized_tensor) {
// Get all the "external" inputs and outputs of the subnet
// Since then_net and else_net has same external input/output, we only collect
// external input/output from one of its subnet And perform the rewrite to
// both then_net and else_net
std::vector<std::string> if_external_input;
std::vector<std::string> if_external_output;
std::unordered_set<std::string> if_inputs, if_outputs;
for (const auto& input : op->input()) {
if_inputs.insert(input);
}
for (const auto& output : op->output()) {
if_outputs.insert(output);
}
ArgumentHelper helper(*op);
Argument *then_arg = nullptr, *else_arg = nullptr;
NetDef* target_net = nullptr;
bool has_then = false, has_else = false;
if (helper.HasSingleArgumentOfType<NetDef>("then_net")) {
then_arg = GetMutableArgument("then_net", false, op);
target_net = then_arg->mutable_n();
has_then = true;
}
if (helper.HasSingleArgumentOfType<NetDef>("else_net")) {
else_arg = GetMutableArgument("else_net", false, op);
if (!has_then) {
target_net = else_arg->mutable_n();
}
has_else = true;
}
if (has_then || has_else) {
collectExternalsFromIfOpSubnet(
target_net, &if_external_input, &if_external_output);
// Add inputs/outputs of the sub_net to the inputs/outputs of the op
for (const auto& input : if_external_input) {
if (if_inputs.count(input) == 0) {
op->add_input(input);
}
}
for (const auto& output : if_external_output) {
if (if_outputs.count(output) == 0) {
op->add_output(output);
}
}
std::map<string, string> oldname_to_newname;
// Build oldname_to_newname map
for (auto& input : if_external_input) {
const auto it = blob_versions->find(input);
if (it != blob_versions->end()) {
oldname_to_newname[input] = SsaName(input, it->second);
}
}
for (auto& output : if_external_output) {
auto it = blob_versions->find(output);
if (it != blob_versions->end()) {
if (is_initialized_tensor->count(output) == 0) {
it->second += 1;
} else {
is_initialized_tensor->erase(output);
}
oldname_to_newname[output] = SsaName(output, it->second);
} else {
blob_versions->emplace(output, 0);
oldname_to_newname[output] = SsaName(output, 0);
}
}
if (has_then) {
rewriteSubnet(then_arg, oldname_to_newname);
}
if (has_else) {
rewriteSubnet(else_arg, oldname_to_newname);
}
}
}
void revertRenamedExternalOutput(
OperatorDef* op,
const std::unordered_map<std::string, std::string>&
renamed_external_outputs) {
for (auto& input : *(op->mutable_input())) {
const auto it = renamed_external_outputs.find(input);
if (it != renamed_external_outputs.end()) {
input = it->second;
}
}
for (auto& output : *(op->mutable_output())) {
const auto it = renamed_external_outputs.find(output);
if (it != renamed_external_outputs.end()) {
output = it->second;
}
}
}
void revertRenamedExternalOutputForIfOp(
OperatorDef* if_op,
const std::unordered_map<std::string, std::string>&
renamed_external_outputs) {
ArgumentHelper helper(*if_op);
Argument *then_arg = nullptr, *else_arg = nullptr;
revertRenamedExternalOutput(if_op, renamed_external_outputs);
if (helper.HasSingleArgumentOfType<NetDef>("then_net")) {
then_arg = GetMutableArgument("then_net", false, if_op);
NetDef* net = then_arg->mutable_n();
for (auto& op : *(net->mutable_op())) {
revertRenamedExternalOutput(&op, renamed_external_outputs);
}
}
if (helper.HasSingleArgumentOfType<NetDef>("else_net")) {
else_arg = GetMutableArgument("else_net", false, if_op);
NetDef* net = else_arg->mutable_n();
for (auto& op : *(net->mutable_op())) {
revertRenamedExternalOutput(&op, renamed_external_outputs);
}
}
}
} // namespace
::ONNX_NAMESPACE::TensorProto::DataType Caffe2TypeToOnnxType(
caffe2::TensorProto::DataType t) {
#define CAFFE2_TO_ONNX_TYPE(x) \
case (caffe2::TensorProto::x): \
return ::ONNX_NAMESPACE::TensorProto::x
switch (t) {
CAFFE2_TO_ONNX_TYPE(FLOAT);
CAFFE2_TO_ONNX_TYPE(BOOL);
CAFFE2_TO_ONNX_TYPE(INT8);
CAFFE2_TO_ONNX_TYPE(UINT8);
CAFFE2_TO_ONNX_TYPE(UINT16);
CAFFE2_TO_ONNX_TYPE(INT16);
CAFFE2_TO_ONNX_TYPE(INT32);
CAFFE2_TO_ONNX_TYPE(INT64);
CAFFE2_TO_ONNX_TYPE(FLOAT16);
default:
LOG(WARNING) << "Unsupported Caffe2 tensor type: " << t
<< ", fallback to FLOAT";
return ::ONNX_NAMESPACE::TensorProto::FLOAT;
}
#undef CAFFE2_TO_ONNX_TYPE
}
void rewriteSubnet(
Argument* arg,
std::map<std::string, std::string> oldname_to_newname) {
NetDef* net = arg->mutable_n();
// clear external inputs and outputs since they're no longer valid
net->mutable_external_input()->Clear();
net->mutable_external_output()->Clear();
for (auto& op : *(net->mutable_op())) {
for (auto& input : *(op.mutable_input())) {
if (oldname_to_newname.find(input) != oldname_to_newname.end()) {
input = oldname_to_newname[input];
}
}
for (auto& output : *(op.mutable_output())) {
if (oldname_to_newname.find(output) != oldname_to_newname.end()) {
output = oldname_to_newname[output];
}
}
}
}
std::unordered_map<std::string, std::string> SsaRewrite(
caffe2::NetDef* init_net,
caffe2::NetDef* pred_net,
bool PreserveInPlaceOps) {
std::unordered_map<std::string, std::string> input_mapping;
std::unordered_map<std::string, int> blob_versions;
if (init_net) {
// No ssa rewrite is done for init net. The reason being that the output
// blobs of init net are what becomes the input blobs of pred_net. Since
// inputs of pred_net are not renamed we are not renaming the output of
// init_net. Furthermore, the assumption made is that init_net is simple net
// with each operator producing the one output and thus not renaming
// translates to not renaming the outputs of the init_net. Create identical
// mapping for now. This shall be removed eventually.
for (const auto& name : init_net->external_input()) {
input_mapping.emplace(name, name);
}
blob_versions.clear();
}
std::set<std::string> is_initialized_tensor;
if (pred_net) {
// Ssa rewriting modifies the net, check if the net passes schema check
run_schema_check(*pred_net);
std::unordered_set<std::string> external_outputs;
for (const auto& input : pred_net->external_input()) {
// Create identical mapping for now. This shall be removed eventually.
input_mapping.emplace(input, input);
}
for (const auto& output : pred_net->external_output()) {
external_outputs.emplace(output);
}
for (auto& op : *pred_net->mutable_op()) {
// Special SSA Rewrite for subnet of If Operator
// This needs to happen first because the inputs/outputs of If/AsyncIf
// may get modified inside ssaRewriteForIfOp
if (op.type() == "If" || op.type() == "AsyncIf") {
ssaRewriteForIfOp(&op, &blob_versions, &is_initialized_tensor);
}
for (auto& input : *op.mutable_input()) {
const auto it = blob_versions.find(input);
if (it != blob_versions.end()) {
input = SsaName(input, it->second);
} else {
// Input blob is not versioned yet.
// If it is not versioned yet, it is assumed to be primary input,
// Thus skip renaming it.
continue;
}
}
for (int out_idx = 0; out_idx < op.output_size(); out_idx++) {
auto& output = *op.mutable_output(out_idx);
// restore in-place settings
bool is_inplace = false;
if (PreserveInPlaceOps) {
for (int in_idx = 0; in_idx < op.input_size(); in_idx++) {
auto* schema = OpSchemaRegistry::Schema(op.type());
if (schema && schema->inplace_enforced(in_idx, out_idx)) {
output = op.input(in_idx);
is_inplace = true;
break;
}
}
}
if (is_inplace) {
continue;
}
auto it = blob_versions.find(output);
if (it != blob_versions.end()) {
if (op.type() != "If" && op.type() != "AsyncIf") {
if (is_initialized_tensor.count(output) == 0) {
it->second += 1;
} else {
is_initialized_tensor.erase(output);
}
}
output = SsaName(output, it->second);
} else {
blob_versions.emplace(output, 0);
// These filling ops are designed for a by-default value for the
// tensors generated by ops like If. For example, if an If op's
// condition is not satisfied, and it does not have else_net, then it
// will not generate any output blob, which may cause some error in
// the future. Here we would like to ensure these tensors only been
// ssa re-write once but not twice. (One in the filling operator, one
// in If op)
if ((caffe2::StartsWith(op.type(), "GivenTensor") &&
caffe2::EndsWith(op.type(), "Fill")) ||
op.type() == "ConstantFill" ||
op.type() == "Int8GivenTensorFill" ||
op.type() == "Int8GivenIntTensorFill") {
is_initialized_tensor.insert(output);
}
output = SsaName(output, 0);
}
}
}
// For all the renamed blobs find if the blob is one of the external
// output. If so add a mapping from it's latest renamed version to its
// original name.
std::unordered_map<std::string, std::string> renamed_external_outputs;
for (const auto& it : blob_versions) {
if (external_outputs.count(it.first)) {
renamed_external_outputs.emplace(
SsaName(it.first, it.second), it.first);
}
}
// Use the mapping to find if the input or output of an op was a renamed
// external output. If so replace it with its original name.
for (auto& op : *pred_net->mutable_op()) {
// If/AsyncIf needs special handling
if (op.type() == "If" || op.type() == "AsyncIf") {
revertRenamedExternalOutputForIfOp(&op, renamed_external_outputs);
} else {
revertRenamedExternalOutput(&op, renamed_external_outputs);
}
}
}
// run schema check again
// NOLINTNEXTLINE(clang-analyzer-core.NonNullParamChecker)
run_schema_check(*pred_net);
return input_mapping;
}
const std::unordered_map<std::string, std::string>&
OnnxExporter::get_renamed_operators() const {
const static std::unordered_map<std::string, std::string> kRenamedOperators{
{"SpatialBN", "BatchNormalization"},
{"Conv1D", "Conv"},
{"Conv2D", "Conv"},
{"Conv3D", "Conv"},
{"ConvTranspose1D", "ConvTranspose"},
{"ConvTranspose2D", "ConvTranspose"},
{"ConvTranspose3D", "ConvTranspose"},
{"MaxPool1D", "MaxPool"},
{"MaxPool2D", "MaxPool"},
{"MaxPool3D", "MaxPool"},
{"AveragePool1D", "AveragePool"},
{"AveragePool2D", "AveragePool"},
{"AveragePool3D", "AveragePool"},
{"Copy", "Identity"}};
return kRenamedOperators;
}
const std::unordered_map<std::string, std::string>&
OnnxExporter::get_renamed_attrs() const {
const static std::unordered_map<std::string, std::string> kRenamedAttrs{
{"kernels", "kernel_shape"}};
return kRenamedAttrs;
}
const std::
unordered_map<std::string, std::unordered_map<std::string, std::string>>&
OnnxExporter::get_per_op_renamed_attrs() const {
const static std::
unordered_map<std::string, std::unordered_map<std::string, std::string>>
kPerOpRenamedAttrs = {
{"Squeeze", {{"dims", "axes"}}},
{"Unsqueeze", {{"dims", "axes"}}},
{"Transpose", {{"axes", "perm"}}},
{"ConvTranspose", {{"adjs", "output_padding"}}},
{"Selu", {{"scale", "gamma"}}}};
return kPerOpRenamedAttrs;
}
const std::unordered_map<std::string, OnnxExporter::SpecialOpConverter>&
OnnxExporter::get_special_operators() const {
const static std::unordered_map<std::string, OnnxExporter::SpecialOpConverter>
kSpecialOperators = {
{"ArgMax", &OnnxExporter::CreateArgMaxMinOpNodes},
{"ArgMin", &OnnxExporter::CreateArgMaxMinOpNodes},
{"Add", &OnnxExporter::CreateBinaryElementwiseOpNodes},
{"Sub", &OnnxExporter::CreateBinaryElementwiseOpNodes},
{"Mul", &OnnxExporter::CreateBinaryElementwiseOpNodes},
{"Div", &OnnxExporter::CreateBinaryElementwiseOpNodes},
{"Pow", &OnnxExporter::CreateBinaryElementwiseOpNodes},
{"And", &OnnxExporter::CreateBinaryElementwiseOpNodes},
{"Or", &OnnxExporter::CreateBinaryElementwiseOpNodes},
{"Xor", &OnnxExporter::CreateBinaryElementwiseOpNodes},
{"Equal", &OnnxExporter::CreateBinaryElementwiseOpNodes},
{"Greater", &OnnxExporter::CreateBinaryElementwiseOpNodes},
{"Less", &OnnxExporter::CreateBinaryElementwiseOpNodes},
{"Cast", &OnnxExporter::CreateCastNodes},
{"ElementwiseLinear", &OnnxExporter::CreateElementwiseLinearNodes},
{"Conv", &OnnxExporter::CreateConvPoolNodes},
{"ConvTranspose", &OnnxExporter::CreateConvPoolNodes},
{"MaxPool", &OnnxExporter::CreateConvPoolNodes},
{"AveragePool", &OnnxExporter::CreateConvPoolNodes},
{"FC", &OnnxExporter::CreateGemmNodes},
{"Concat", &OnnxExporter::CreateConcatNodes},
{"MergeDim", &OnnxExporter::CreateMergeDimNodes},
{"LRN", &OnnxExporter::CreateLrnNodes},
{"Reshape", &OnnxExporter::CreateReshapeNodes},
{"Slice", &OnnxExporter::CreateSliceNodes},
{"ChannelShuffle", &OnnxExporter::CreateChannelShuffleNodes},
{"ReduceMean", &OnnxExporter::CreateReduceMeanNodes},
{"ReduceFrontMean", &OnnxExporter::CreateReduceMeanNodes},
{"ReduceBackMean", &OnnxExporter::CreateReduceMeanNodes},
{"ResizeNearest", &OnnxExporter::CreateUpsampleNodes}};
return kSpecialOperators;
}
void OnnxExporter::CopyCaffe2ArgToOnnxAttr(
AttributeProto* attr,
const std::string& op_type,
const caffe2::Argument& arg) {
std::string name =
caffe2::get_default(get_renamed_attrs(), arg.name(), arg.name());
const auto& per_op_renamed_attr_lut = get_per_op_renamed_attrs();
const auto it = per_op_renamed_attr_lut.find(op_type);
if (it != per_op_renamed_attr_lut.end()) {
// Per-op attribute renames override the global attribute renames
name = caffe2::get_default(it->second, arg.name(), name);
}
attr->set_name(name);
if (arg.has_f()) {
attr->set_f(arg.f());
attr->set_type(AttributeProto::FLOAT);
} else if (arg.has_i()) {
attr->set_i(arg.i());
attr->set_type(AttributeProto::INT);
} else if (arg.has_s()) {
attr->set_s(arg.s());
attr->set_type(AttributeProto::STRING);
} else if (arg.floats_size()) {
attr->mutable_floats()->CopyFrom(arg.floats());
attr->set_type(AttributeProto::STRINGS);
} else if (arg.ints_size()) {
attr->mutable_ints()->CopyFrom(arg.ints());
attr->set_type(AttributeProto::INTS);
} else if (arg.strings_size()) {
attr->mutable_strings()->CopyFrom(arg.strings());
attr->set_type(AttributeProto::STRINGS);
} else {
CAFFE_THROW(c10::str("Unsupported Caffe2 argument: ", arg.name()));
}
}
bool OnnxExporter::IsBlockListed(const caffe2::Argument& arg) {
const static std::unordered_map<std::string, std::unordered_set<std::string>>
kBlockListString = {{"order", {"NCHW"}}};
const static std::unordered_map<std::string, std::unordered_set<int64_t>>
kBlockListInt = {
{"cudnn_exhaustive_search", {0, 1}},
{"use_cudnn", {0, 1}},
{"exhaustive_search", {0, 1}},
{"is_test", {0, 1}},
{"broadcast", {0, 1}}};
if (arg.has_i()) {
const auto it = kBlockListInt.find(arg.name());
if (it != kBlockListInt.end()) {
return it->second.count(arg.i());
}
} else if (arg.has_s()) {
const auto it = kBlockListString.find(arg.name());
if (it != kBlockListString.end()) {
return it->second.count(arg.s());
}
}
return false;
}
ConvertedResult OnnxExporter::Caffe2OpToOnnxNodes(
const caffe2::OperatorDef& def,
const std::unordered_map<std::string, caffe2::TensorShape>& shapes) {
std::string type = def.type();
const auto& renamed_op_lut = get_renamed_operators();
const auto it = renamed_op_lut.find(type);
if (it != renamed_op_lut.end()) {
type = it->second;
}
const auto& special_op_lut = get_special_operators();
const auto it_op = get_special_operators().find(type);
if (it_op != special_op_lut.end()) {
return (this->*(it_op->second))(def, shapes);
} else {
return CommonCaffe2OpToOnnxNodes(def);
}
}
ConvertedResult OnnxExporter::CommonCaffe2OpToOnnxNodes(
const caffe2::OperatorDef& def) {
ConvertedResult result;
auto& nodes = result.first;
nodes.emplace_back();
NodeProto& node = nodes.back();
node.set_name(def.name());
node.set_op_type(
caffe2::get_default(get_renamed_operators(), def.type(), def.type()));
for (const auto& i : def.input()) {
node.add_input(i);
}
for (const auto& o : def.output()) {
node.add_output(o);
}
for (const auto& a : def.arg()) {
if (!IsBlockListed(a)) {
auto* attr = node.add_attribute();
CopyCaffe2ArgToOnnxAttr(attr, def.type(), a);
}
}
return result;
}
ConvertedResult OnnxExporter::CreateArgMaxMinOpNodes(
const caffe2::OperatorDef& def,
const std::unordered_map<std::string, caffe2::TensorShape>& shapes) {
auto result = CommonCaffe2OpToOnnxNodes(def);
auto& nodes = result.first;
CAFFE_ENFORCE_EQ(nodes.size(), 1);
auto& node = nodes.back();
if (!ArgumentHelper::HasArgument(def, "axis")) {
const auto& x = def.input(0);
const auto& x_shape = shapes.at(x);
node.add_attribute()->CopyFrom(
MakeAttribute("axis", x_shape.dims().size() - 1));
}
return result;
}
ConvertedResult OnnxExporter::CreateBinaryElementwiseOpNodes(
const caffe2::OperatorDef& def,
const std::unordered_map<std::string, caffe2::TensorShape>& shapes) {
caffe2::OperatorDef mdef(def); // The modified def without broadcast and axis
const auto& x = mdef.input(0);
const auto& y = def.input(1); // Refer to the old def, later won't change it.
const auto& x_shape = shapes.at(x);
const auto& y_shape = shapes.at(y);
for (int i = 0; i < mdef.arg_size(); ++i) {
const auto& arg = mdef.arg(i);
if (arg.name() == "broadcast") {
ArgumentHelper::RemoveArgument(mdef, i);
break;
}
}
std::vector<int64_t> axes;
for (int i = 0; i < mdef.arg_size(); ++i) {
const auto& arg = mdef.arg(i);
if (arg.name() == "axis") {
int64_t axis = arg.i();
if (x_shape.dims().size() - axis != y_shape.dims().size()) {
// The upper bound (excluded) of expanded y.
int64_t end_dim =
y_shape.dims().size() - 1 - axis + x_shape.dims().size();
axes.resize(end_dim - y_shape.dims().size());
std::iota(axes.begin(), axes.end(), y_shape.dims().size());
mdef.set_input(1, dummy_->NewDummyName());
}
ArgumentHelper::RemoveArgument(mdef, i);
break;
}
}
auto result = CommonCaffe2OpToOnnxNodes(mdef);
if (axes.size() != 0) {
result.first.insert(
result.first.begin(),
MakeNode(
"Unsqueeze", {y}, {mdef.input(1)}, {MakeAttribute("axes", axes)}));
}
return result;
}
ConvertedResult OnnxExporter::CreateCastNodes(
const caffe2::OperatorDef& def,
const std::unordered_map<std::string, caffe2::TensorShape>& shapes) {
auto result = CommonCaffe2OpToOnnxNodes(def);
auto* attr = result.first[0].mutable_attribute(0);
auto onnx_dtype = ::ONNX_NAMESPACE::TensorProto::UNDEFINED;
const auto& arg = def.arg(0);
if (arg.has_s()) {
auto c2_dtype = arg.s();
std::transform(
c2_dtype.begin(), c2_dtype.end(), c2_dtype.begin(), ::toupper);
if (c2_dtype == "FLOAT") {
onnx_dtype = ::ONNX_NAMESPACE::TensorProto::FLOAT;
} else if (c2_dtype == "INT32") {
onnx_dtype = ::ONNX_NAMESPACE::TensorProto::INT32;
} else if (c2_dtype == "BOOL") {
onnx_dtype = ::ONNX_NAMESPACE::TensorProto::BOOL;
} else if (c2_dtype == "UINT8") {
onnx_dtype = ::ONNX_NAMESPACE::TensorProto::UINT8;
} else if (c2_dtype == "INT8") {
onnx_dtype = ::ONNX_NAMESPACE::TensorProto::INT8;
} else if (c2_dtype == "UINT16") {
onnx_dtype = ::ONNX_NAMESPACE::TensorProto::UINT16;
} else if (c2_dtype == "INT16") {
onnx_dtype = ::ONNX_NAMESPACE::TensorProto::INT16;
} else if (c2_dtype == "INT64") {
onnx_dtype = ::ONNX_NAMESPACE::TensorProto::INT64;
} else if (c2_dtype == "FLOAT16") {
onnx_dtype = ::ONNX_NAMESPACE::TensorProto::FLOAT16;
} else if (c2_dtype == "DOUBLE") {
onnx_dtype = ::ONNX_NAMESPACE::TensorProto::DOUBLE;
} else {
onnx_dtype = ::ONNX_NAMESPACE::TensorProto::UNDEFINED;
}
CAFFE_ENFORCE_NE(
onnx_dtype,
::ONNX_NAMESPACE::TensorProto::UNDEFINED,
"Casting to '",
c2_dtype,
"' dtype is not supported");
attr->clear_s();
attr->set_type(AttributeProto::INT);
} else if (arg.has_i()) {
const auto& c2_dtype = arg.i();
switch (c2_dtype) {
case caffe2::TensorProto::FLOAT:
onnx_dtype = ::ONNX_NAMESPACE::TensorProto::FLOAT;
break;
case caffe2::TensorProto::INT32:
onnx_dtype = ::ONNX_NAMESPACE::TensorProto::INT32;
break;
case caffe2::TensorProto::BOOL:
onnx_dtype = ::ONNX_NAMESPACE::TensorProto::BOOL;
break;
case caffe2::TensorProto::UINT8:
onnx_dtype = ::ONNX_NAMESPACE::TensorProto::UINT8;
break;
case caffe2::TensorProto::INT8:
onnx_dtype = ::ONNX_NAMESPACE::TensorProto::INT8;
break;
case caffe2::TensorProto::UINT16:
onnx_dtype = ::ONNX_NAMESPACE::TensorProto::UINT16;
break;
case caffe2::TensorProto::INT16:
onnx_dtype = ::ONNX_NAMESPACE::TensorProto::INT16;
break;
case caffe2::TensorProto::INT64:
onnx_dtype = ::ONNX_NAMESPACE::TensorProto::INT64;
break;
case caffe2::TensorProto::FLOAT16:
onnx_dtype = ::ONNX_NAMESPACE::TensorProto::FLOAT16;
break;
case caffe2::TensorProto::DOUBLE:
onnx_dtype = ::ONNX_NAMESPACE::TensorProto::DOUBLE;
break;
case caffe2::TensorProto::STRING:
case caffe2::TensorProto::BYTE:
case caffe2::TensorProto::UNDEFINED:
onnx_dtype = ::ONNX_NAMESPACE::TensorProto::UNDEFINED;
break;
}
CAFFE_ENFORCE_NE(
onnx_dtype,
::ONNX_NAMESPACE::TensorProto::UNDEFINED,
"Casting to '",
c2_dtype,
"' dtype is not supported");
}
attr->set_i(onnx_dtype);
return result;
}
ConvertedResult OnnxExporter::CreateElementwiseLinearNodes(
const caffe2::OperatorDef& def,
const std::unordered_map<std::string, caffe2::TensorShape>& shapes) {
CAFFE_ENFORCE_EQ(def.input_size(), 3);
CAFFE_ENFORCE_GE(def.output_size(), 1);
const auto& x = def.input(0);
const auto& w = def.input(1);
const auto& b = def.input(2);
const auto& y = def.output(0);
CAFFE_ENFORCE_EQ(shapes.at(w).dims().size(), 1);
CAFFE_ENFORCE_EQ(shapes.at(b).dims().size(), 1);
ConvertedResult result;
auto& nodes = result.first;
auto& const_tensors = result.second;
std::unordered_map<std::string, const caffe2::Argument*> args;
for (const auto& a : def.arg()) {
args.emplace(a.name(), &a);
}
const auto& x_shape = shapes.at(x);
const auto it = args.find("axis");
const int64_t axis = it == args.end() ? 1 : it->second->i();
const bool need_reshape = axis + 1 != x_shape.dims().size();
auto fma_x_input = x;
if (need_reshape) {
const auto inner = DimProd(x_shape, axis, x_shape.dims().size());
CAFFE_ENFORCE_EQ(shapes.at(w).dims(0), inner);
CAFFE_ENFORCE_EQ(shapes.at(b).dims(0), inner);
fma_x_input = dummy_->NewDummyName();
const_tensors.emplace_back(CreateOnnxShapeTensor(
dummy_, std::vector<int64_t>{-1, shapes.at(w).dims(0)}));
nodes.emplace_back(
MakeNode("Reshape", {x, const_tensors.back().name()}, {fma_x_input}));
}
const auto& mul_output = dummy_->NewDummyName();
nodes.emplace_back(
MakeNode("Mul", {fma_x_input, w}, {mul_output}, def.name()));
const auto& fma_y_output = need_reshape ? dummy_->NewDummyName() : y;
nodes.emplace_back(
MakeNode("Add", {mul_output, b}, {fma_y_output}, def.name()));
if (need_reshape) {
const auto shape = dummy_->NewDummyName();
nodes.emplace_back(MakeNode("Shape", {x}, {shape}));
nodes.emplace_back(MakeNode("Reshape", {fma_y_output, shape}, {y}));
}
return result;
}
ConvertedResult OnnxExporter::CreateConvPoolNodes(
const caffe2::OperatorDef& def,
const std::unordered_map<std::string, caffe2::TensorShape>& shapes) {
auto result = CommonCaffe2OpToOnnxNodes(def);
auto& nodes = result.first;
auto& node = nodes.back();
std::unordered_map<std::string, AttributeProto> attrs;
for (const auto& attr : node.attribute()) {
attrs.emplace(attr.name(), attr);
}
// Handle global pooling
bool global = false;
if (node.op_type() == "MaxPool" || node.op_type() == "AveragePool") {
auto it = attrs.find("global_pooling");
if (it != attrs.end() && it->second.has_i() && it->second.i()) {
node.set_op_type("Global" + node.op_type());
global = true;
attrs.erase(it);
}
}
ApplyTrans(&attrs, global, "kernel", 2, "kernel_shape");
ApplyTrans(&attrs, global, "stride");
ApplyTrans(&attrs, global, "dilation");
ApplyTrans(&attrs, global, "adj");
ApplyTrans(&attrs, global, "pad", 4);
// Fix legacy pad attr
auto it = attrs.find("legacy_pad");
if (it != attrs.end()) {
auto legacy_pad_attr = it->second;
attrs.erase(it);
CAFFE_ENFORCE(
node.op_type().size() >= 4 &&
(node.op_type().rfind("Pool") == node.op_type().size() - 4));
const auto& input_size = shapes.at(node.input(0));
const auto& output_size = shapes.at(node.output(0));
CAFFE_ENFORCE_EQ(output_size.dims().size(), 4);
if (!global && // global pool does not care about legacy pad
legacy_pad_attr.i() !=
static_cast<int64_t>(caffe2::LegacyPadding::NOTSET)) {
if (legacy_pad_attr.i() ==
static_cast<int64_t>(caffe2::LegacyPadding::VALID)) {
CAFFE_ENFORCE(!attrs.count("pads"));
attrs.emplace("auto_pad", MakeAttribute("auto_pad", "VALID"));
} else if (
legacy_pad_attr.i() ==
static_cast<int64_t>(caffe2::LegacyPadding::SAME)) {
CAFFE_ENFORCE(!attrs.count("pads"));
// default behavior in Caffe2 is SAME_UPPER
// https://github.com/caffe2/caffe2/blob/master/caffe2/operators/conv_pool_op_base.h#L39
attrs.emplace("auto_pad", MakeAttribute("auto_pad", "SAME_UPPER"));
} else if (
legacy_pad_attr.i() ==
static_cast<int64_t>(caffe2::LegacyPadding::CAFFE_LEGACY_POOLING)) {
// The problem here is that, Pool op in Caffe may add an additional
// pixel, if the last part is smaller than stride. So we use the
// explicit padding to replace legacy_pad. pad[end] = output_size[start
// + 2] * stride[start] - pad[start] - 1 + kernel[start] - input[start +
// 2]; end = start + len(pad) / 2
LOG(WARNING) << "Converting legacy padding to explicit padding.";
auto* pads_attr = attrs.at("pads").mutable_ints();
auto& strides_attr = attrs.at("strides").ints();
auto& kernel_shape_attr = attrs.at("kernel_shape").ints();
for (int i = 0; i < 2; ++i) {
int64_t tmp_pad = output_size.dims(i + 2) * strides_attr.Get(i) -
pads_attr->Get(i) - 1 + kernel_shape_attr.Get(i) -
input_size.dims(i + 2);
pads_attr->Set(i + 2, tmp_pad);
}
} else {
LOG(ERROR) << "Don't know how to handle the legacy_pad:"
<< legacy_pad_attr.i();
CAFFE_THROW("Failed to handle legacy padding in pool operator!");
}
}
}
node.clear_attribute();
for (const auto& kv : attrs) {
auto* attr = node.add_attribute();
attr->CopyFrom(kv.second);
}
return result;
}
ConvertedResult OnnxExporter::CreateLrnNodes(
const caffe2::OperatorDef& def,
const std::unordered_map<std::string, caffe2::TensorShape>& shapes) {
auto result = CommonCaffe2OpToOnnxNodes(def);
auto& nodes = result.first;
CAFFE_ENFORCE_EQ(nodes.size(), 1);
auto& node = nodes.back();
if (node.output_size() == 2) {
node.mutable_output()->RemoveLast();
}
return result;
}
ConvertedResult OnnxExporter::CreateConcatNodes(
const caffe2::OperatorDef& def,
const std::unordered_map<std::string, caffe2::TensorShape>& shapes) {
caffe2::OperatorDef mdef(def); // The modified def without add_axis
// In caffe2, we can optionally add an axis specified by `add_axis`
int add_axis = 0;
for (int i = 0; i < mdef.arg_size(); ++i) {
const auto& arg = mdef.arg(i);
if (arg.name() == "add_axis") {
add_axis = arg.i();
ArgumentHelper::RemoveArgument(mdef, i);
break;
}
}
auto result = CommonCaffe2OpToOnnxNodes(mdef);
auto& nodes = result.first;
nodes.reserve(nodes.size() + 3);
auto& const_tensors = result.second;
CAFFE_ENFORCE_EQ(nodes.size(), 1);
auto& node = nodes.back();
bool explicit_axis = false;
int axis = -1;
if (ArgumentHelper::HasArgument(mdef, "axis")) {
axis = ArgumentHelper::GetSingleArgument(mdef, "axis", -1);
explicit_axis = true;
}
if (!explicit_axis) {
node.add_attribute()->CopyFrom(MakeAttribute("axis", 1));
}
// If we have add_axis, we need to add a reshape node
auto final_output = node.output(0);
if (add_axis > 0) {
CAFFE_ENFORCE_GE(axis, 0);
std::vector<int64_t> dims;
const auto& shape0 = shapes.at(mdef.input(0));
for (int i = 1; i < mdef.input_size(); ++i) {
const auto& shape = shapes.at(mdef.input(i));
CAFFE_ENFORCE_EQ(shape.dims(axis), shape0.dims(axis));
}
for (const auto d : shape0.dims()) {
dims.push_back(d);
}
dims.insert(dims.begin() + axis, mdef.input_size());
auto concat_output = dummy_->NewDummyName();
*node.mutable_output(0) = concat_output;
const_tensors.emplace_back(CreateOnnxShapeTensor(dummy_, dims));
nodes.emplace_back(MakeNode(
"Reshape",
{concat_output, const_tensors.back().name()},
{final_output}));
}
// If we have two output, we need to output the split_info, which can be
// statically inferred from the input shapes
if (node.output_size() == 2) {
std::string second_output = node.output(1);
node.mutable_output()->RemoveLast();
std::vector<int32_t> split_info;
int adj_size = shapes.at(mdef.input(0)).dims_size() + (add_axis ? 1 : 0);
int canonical_axis = canonical_axis_index_(axis, adj_size);
CAFFE_ENFORCE_LT(canonical_axis, adj_size, "Axis not in input ndim range.");
for (int i = 0; i < mdef.input_size(); ++i) {
// NOLINTNEXTLINE(performance-inefficient-vector-operation)
split_info.push_back(
add_axis ? 1 : shapes.at(mdef.input(i)).dims(canonical_axis));
}
auto split_info_tensor =
MakeTensor("split_info", split_info, TensorProto::INT32);
auto cnode = MakeNode("Constant", {}, {second_output});
cnode.add_attribute()->CopyFrom(MakeAttribute("value", split_info_tensor));
nodes.emplace_back(std::move(cnode));
}
return result;
}
ConvertedResult OnnxExporter::CreateMergeDimNodes(
const caffe2::OperatorDef& def,
const std::unordered_map<std::string, caffe2::TensorShape>& shapes) {
const auto& x = def.input(0);
const auto& y = def.output(0);
ConvertedResult result;
auto& nodes = result.first;
auto& const_tensors = result.second;
{
const auto ndim = shapes.at(x).dims().size();
CAFFE_ENFORCE_GE(ndim, 2, "No enough dims to merge.");
std::vector<int64_t> dims(ndim);
dims[0] = 1;
dims[1] = -1;
const_tensors.emplace_back(CreateOnnxShapeTensor(dummy_, dims));
}
const auto reshaped = dummy_->NewDummyName();
nodes.emplace_back(
MakeNode("Reshape", {x, const_tensors.back().name()}, {reshaped}));
nodes.emplace_back(MakeNode(
"Squeeze",
{reshaped},
{y},
std::vector<AttributeProto>{
MakeAttribute("axes", std::vector<int64_t>{0}),
}));
return result;
}
ConvertedResult OnnxExporter::CreateChannelShuffleNodes(
const caffe2::OperatorDef& def,
const std::unordered_map<std::string, caffe2::TensorShape>& shapes) {
const auto& x = def.input(0);
const auto& y = def.output(0);
const auto& x_shape = shapes.at(x);
CAFFE_ENFORCE_EQ(
x_shape.dims().size(),
4,
"Input shape of ChannelShuffle needs to be in NCHW format");
auto n = x_shape.dims(0);
auto c = x_shape.dims(1);
auto h = x_shape.dims(2);
auto w = x_shape.dims(3);
int64_t g = 0;
for (const auto& arg : def.arg()) {
if (arg.name() == "group") {
g = arg.i();
break;
}
}
CAFFE_ENFORCE(g && c % g == 0);
ConvertedResult result;
auto& nodes = result.first;
auto& const_tensors = result.second;
const auto reshape_output = dummy_->NewDummyName();
std::vector<int64_t> dims = {n, g, c / g, h, w};
const_tensors.emplace_back(CreateOnnxShapeTensor(dummy_, dims));
nodes.emplace_back(
MakeNode("Reshape", {x, const_tensors.back().name()}, {reshape_output}));
const auto transpose_output = dummy_->NewDummyName();
dims = {0, 2, 1, 3, 4};
nodes.emplace_back(MakeNode(
"Transpose",
{reshape_output},
{transpose_output},
{MakeAttribute("perm", dims)}));
dims = {n, c, h, w};
const_tensors.emplace_back(CreateOnnxShapeTensor(dummy_, dims));
nodes.emplace_back(MakeNode(
"Reshape", {transpose_output, const_tensors.back().name()}, {y}));
return result;
}
ConvertedResult OnnxExporter::CreateReduceMeanNodes(
const caffe2::OperatorDef& def,
const std::unordered_map<std::string, caffe2::TensorShape>& shapes) {
CAFFE_ENFORCE_GE(def.input_size(), 1);
CAFFE_ENFORCE_LE(def.input_size(), 2);
CAFFE_ENFORCE_EQ(def.input_size(), 1, "Input \"lengths\" is not supported.");
CAFFE_ENFORCE_GE(def.output_size(), 1);
const auto& x = def.input(0);
const auto& y = def.output(0);
const auto& dims = shapes.at(x).dims();
ConvertedResult result;
auto& nodes = result.first;
std::unordered_map<std::string, const caffe2::Argument*> args;
for (const auto& a : def.arg()) {
args.emplace(a.name(), &a);
}
std::vector<int64_t> axes;
int64_t keepdims = 1;
if (def.type() == "ReduceMean") {
// axes
auto it = args.find("axes");
if (it == args.end()) {
axes.resize(dims.size());
std::iota(axes.begin(), axes.end(), 0);
} else {
axes.assign(it->second->ints().begin(), it->second->ints().end());
}
// keepdims
it = args.find("keepdims");
if (it != args.end()) {
keepdims = it->second->i();
}
} else {
// num_reduce_dim
auto it = args.find("num_reduce_dim");
const int64_t num_reduce_dim = it == args.end() ? 1 : it->second->i();
CAFFE_ENFORCE_LE(num_reduce_dim, dims.size());
axes.resize(num_reduce_dim);
int64_t start_dim = 0;
if (def.type() == "ReduceFrontMean") {
start_dim = 0;
} else if (def.type() == "ReduceBackMean") {
start_dim = dims.size() - axes.size();
}
std::iota(axes.begin(), axes.end(), start_dim);
keepdims = 0;
}
nodes.emplace_back(MakeNode(
"ReduceMean",
{x},
{y},
{
MakeAttribute("axes", axes),
MakeAttribute("keepdims", keepdims),
},
def.name()));
return result;
}
ConvertedResult OnnxExporter::CreateUpsampleNodes(
const caffe2::OperatorDef& def,
const std::unordered_map<std::string, caffe2::TensorShape>& shapes) {
ConvertedResult result;
//{H, W} => {1, 1, H, W}
auto& nodes = result.first;
auto resolved_scale = dummy_->NewDummyName();
if (def.input_size() == 1) {
float width_scale = 1.0;
float height_scale = 1.0;
for (const auto& a : def.arg()) {
if (a.name() == "width_scale") {
width_scale = a.f();
} else if (a.name() == "height_scale") {
height_scale = a.f();
}
}
CAFFE_ENFORCE_GT(width_scale, 0);
CAFFE_ENFORCE_GT(height_scale, 0);
std::vector<float> tmp_vector = {1, 1, height_scale, width_scale};
auto resolved_scale_tensor =
MakeTensor("resolved scale tensor", tmp_vector, TensorProto::FLOAT);
auto node = MakeNode("Constant", {}, {resolved_scale});
node.add_attribute()->CopyFrom(
MakeAttribute("value", resolved_scale_tensor));
nodes.emplace_back(node);
} else {
CAFFE_ENFORCE_EQ(def.input_size(), 2);
std::vector<float> tmp_vector = {1, 1};
auto scale_pads_tensor =
MakeTensor("scale pads", tmp_vector, TensorProto::FLOAT);
auto unresolved_scale_pads = dummy_->NewDummyName();
auto node = MakeNode("Constant", {}, {unresolved_scale_pads});
node.add_attribute()->CopyFrom(MakeAttribute("value", scale_pads_tensor));
nodes.emplace_back(node);
node = MakeNode(
"Concat", {unresolved_scale_pads, def.input(1)}, {resolved_scale});
node.add_attribute()->CopyFrom(MakeAttribute("axis", 0));
nodes.emplace_back(node);
}
std::vector<std::string> inputs = {def.input(0), resolved_scale};
std::vector<std::string> outputs(def.output().begin(), def.output().end());
auto node = MakeNode("Upsample", inputs, outputs, def.name());
node.add_attribute()->CopyFrom(MakeAttribute("mode", "nearest"));
nodes.emplace_back(node);
return result;
}
ConvertedResult OnnxExporter::CreateSliceNodes(
const caffe2::OperatorDef& def,
const std::unordered_map<std::string, caffe2::TensorShape>& shapes) {
CAFFE_ENFORCE_EQ(
def.input_size(),
1,
"ONNX Slice operator does not support dynamic slice.");
auto result = CommonCaffe2OpToOnnxNodes(def);
auto& nodes = result.first;
CAFFE_ENFORCE_EQ(nodes.size(), 1);
auto& node = nodes.back();
const auto& shape = shapes.at(node.input(0));
std::vector<int64_t> dims;
for (auto& attr : *node.mutable_attribute()) {
if (attr.name() == "starts") {
auto len = attr.ints_size();
if (len) {
dims.resize(len);
std::iota(dims.begin(), dims.end(), 0);
}
} else if (attr.name() == "ends") {
for (int i = 0; i < attr.ints_size(); ++i) {
auto end = attr.ints(i);
if (end >= 0) {
continue;
}
if (end == -1) {
end = shape.dims(i);
} else {
++end;
}
attr.set_ints(i, end);
}
}
}
if (!dims.empty()) {
node.add_attribute()->CopyFrom(MakeAttribute("axes", dims));
}
return result;
}
ConvertedResult OnnxExporter::CreateReshapeNodes(
const caffe2::OperatorDef& def,
const std::unordered_map<std::string, caffe2::TensorShape>& shapes) {
auto result = CommonCaffe2OpToOnnxNodes(def);
auto& nodes = result.first;
auto& const_tensors = result.second;
CAFFE_ENFORCE_EQ(nodes.size(), 1);
auto& node = nodes.back();
int i = 0;
int attr_size = node.attribute_size();
for (; i < attr_size; ++i) {
const auto& attr = node.attribute(i);
if (attr.name() == "shape") {
std::vector<int64_t> shape;
for (const auto k : attr.ints()) {
shape.push_back(k);
}
const_tensors.emplace_back(CreateOnnxShapeTensor(dummy_, shape));
node.add_input(const_tensors.back().name());
break;
}
}
if (i != attr_size) {
if (i != attr_size - 1) {
node.mutable_attribute()->SwapElements(i, attr_size - 1);
}
node.mutable_attribute()->RemoveLast();
}
if (node.output_size() == 2) {
std::string shape_input = node.output(0);
std::string shape_output = node.output(1);
node.mutable_output()->RemoveLast();
nodes.emplace_back(AddShapeNode(shape_input, shape_output));
}
return result;
}
ConvertedResult OnnxExporter::CreateGemmNodes(
const caffe2::OperatorDef& def,
const std::unordered_map<std::string, caffe2::TensorShape>& shapes) {
CAFFE_ENFORCE_EQ(def.input_size(), 3);
CAFFE_ENFORCE_GE(def.output_size(), 1);
// NOLINTNEXTLINE(performance-unnecessary-copy-initialization)
auto x = def.input(0);
auto w = def.input(1);
const auto& b = def.input(2);
const auto& y = def.output(0);
const auto& x_shape = shapes.at(x);
const auto& w_shape = shapes.at(w);
CAFFE_ENFORCE_GE(x_shape.dims().size(), 2);
CAFFE_ENFORCE_GE(w_shape.dims().size(), 2);
ConvertedResult result;
auto& nodes = result.first;
auto& const_tensors = result.second;
std::unordered_map<std::string, const caffe2::Argument*> args;
for (const auto& a : def.arg()) {
args.emplace(a.name(), &a);
}
auto it = args.find("axis");
int64_t axis = 1;
bool has_axis = (it != args.end());
if (has_axis) {
axis = it->second->i();
}
auto gemm_x_input = x;
if (x_shape.dims().size() > 2) {
// we need to reshape only when dimension is higher than 2
const auto inner = DimProd(x_shape, axis, x_shape.dims().size());
gemm_x_input = dummy_->NewDummyName();
const_tensors.emplace_back(
CreateOnnxShapeTensor(dummy_, std::vector<int64_t>{-1, inner}));
nodes.emplace_back(
MakeNode("Reshape", {x, const_tensors.back().name()}, {gemm_x_input}));
}
it = args.find("axis_w");
int64_t axis_w = 1;
if (it != args.end()) {
axis_w = it->second->i();
}
if (w_shape.dims().size() > 2) {
// we need to reshape only when dimension is higher than 2
auto outer = DimProd(w_shape, 0, axis_w);
auto inner = DimProd(w_shape, axis_w, w_shape.dims().size());
auto reshaped_w = dummy_->NewDummyName();
const_tensors.emplace_back(
CreateOnnxShapeTensor(dummy_, std::vector<int64_t>{outer, inner}));
nodes.emplace_back(
MakeNode("Reshape", {w, const_tensors.back().name()}, {reshaped_w}));
w = reshaped_w;
}
auto gemm_y_output = axis > 1 ? dummy_->NewDummyName() : y;
nodes.emplace_back(MakeNode(
"Gemm",
{gemm_x_input, w, b},
{gemm_y_output},
{MakeAttribute("transB", 1L)},
def.name()));
// capture the outer shape if needed.
if (axis > 1) {
const auto x_shape = dummy_->NewDummyName();
nodes.emplace_back(MakeNode("Shape", {x}, {x_shape}));
const auto x_shape_outer = dummy_->NewDummyName();
nodes.emplace_back(MakeNode(
"Slice",
{x_shape},
{x_shape_outer},
std::vector<AttributeProto>{
MakeAttribute("starts", std::vector<int64_t>{0}),
MakeAttribute("ends", std::vector<int64_t>{axis}),
}));
const auto y_shape = dummy_->NewDummyName();
const_tensors.emplace_back(CreateOnnxShapeTensor(dummy_, {-1}));
nodes.emplace_back(MakeNode(
"Concat",
{x_shape_outer, const_tensors.back().name()},
{y_shape},
std::vector<AttributeProto>{
MakeAttribute("axis", static_cast<int64_t>(0)),
}));
nodes.emplace_back(MakeNode("Reshape", {gemm_y_output, y_shape}, {y}));
}
return result;
}
void OnnxExporter::InitOpToTensorProto(
const caffe2::OperatorDef& op,
TensorProto* tensor) {
CAFFE_ENFORCE_EQ(op.input_size(), 0);
CAFFE_ENFORCE_EQ(op.output_size(), 1);
// Set name
tensor->set_name(op.output(0));
const Argument* values = nullptr;
const Argument* shape = nullptr;
for (const auto& arg : op.arg()) {
if (arg.name() == "values") {
values = &arg;
} else if (arg.name() == "shape") {
shape = &arg;
}
}
CAFFE_ENFORCE(values);
CAFFE_ENFORCE(shape);
// Set dims
for (const auto i : shape->ints()) {
tensor->add_dims(i);
}
// Set value
if (op.type() == "GivenTensorFill") {
tensor->set_data_type(TensorProto::FLOAT);
for (const auto i : values->floats()) {
tensor->add_float_data(i);
}
} else if (op.type() == "GivenTensorInt64Fill") {
tensor->set_data_type(TensorProto::INT64);
for (const auto i : values->ints()) {
tensor->add_int64_data(i);
}
} else if (op.type() == "GivenTensorIntFill") {
tensor->set_data_type(TensorProto::INT32);
for (const auto i : values->ints()) {
tensor->add_int32_data(i);
}
} else if (op.type() == "GivenTensorBoolFill") {
tensor->set_data_type(TensorProto::INT32);
for (const auto i : values->ints()) {
tensor->add_int32_data(i);
}
} else if (op.type() == "GivenTensorStringFill") {
tensor->set_data_type(TensorProto::STRING);
// TODO: we might need to do two pass to avoid adverse memory allocations
for (const auto& s : values->strings()) {
tensor->mutable_raw_data()->append(s);
}
} else {
CAFFE_THROW(
c10::str("Cannot convert C2 op ", op.type(), "to ONNX TensorProto"));
}
}
} // namespace onnx
} // namespace caffe2