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Fix pool shaper bug, fix fused add/mul bug, support batchnorm, suppor… #38

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merged 1 commit into from
Feb 18, 2019
Merged

Fix pool shaper bug, fix fused add/mul bug, support batchnorm, suppor… #38

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22 changes: 22 additions & 0 deletions common/Shaper.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -159,6 +159,28 @@ void Shaper::Pool(const std::string &input_name, int32_t strideX,
shape_map_[output_name] = outputDimen;
}

void Shaper::PoolNew(const std::string &input_name, int32_t padding_left,
int32_t padding_right, int32_t padding_top,
int32_t padding_bottom, int32_t stride_x, int32_t stride_y,
int32_t width, int32_t height,
const std::string &output_name) {
auto inputDimen = shape_map_.at(input_name);

Shape outputDimen;
if (height == -1 && width == -1) {
outputDimen = {inputDimen[0], 1, 1, inputDimen[3]};
} else {
outputDimen = {
inputDimen[0],
(inputDimen[1] - height + padding_top + padding_bottom) / stride_y +
1,
(inputDimen[2] - width + padding_left + padding_right) / stride_x +
1,
inputDimen[3]};
}
shape_map_[output_name] = outputDimen;
}

void Shaper::Softmax(const std::string &input_name,
const std::string &output_name) {
shape_map_[output_name] = shape_map_.at(input_name);
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4 changes: 4 additions & 0 deletions common/Shaper.h
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Expand Up @@ -66,6 +66,10 @@ class Shaper {
int32_t paddingLeft, int32_t paddingRight, int32_t paddingTop,
int32_t paddingBottom, int32_t height, int32_t width,
const std::string &output_name);
void PoolNew(const std::string &input_name, int32_t strideX,
int32_t strideY, int32_t paddingLeft, int32_t paddingRight,
int32_t paddingTop, int32_t paddingBottom, int32_t height,
int32_t width, const std::string &output_name);
void Softmax(const std::string &input_name, const std::string &output_name);
void Relu(const std::string &input_name, const std::string &output_name);
void Concat(const std::vector<std::string> &input_names, uint32_t axis,
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4 changes: 4 additions & 0 deletions dnnlibrary/include/flatbuffers_helper.h
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Expand Up @@ -60,6 +60,10 @@ inline uint32_t unpack_fbs(const DNN::FuseCode fbs) {
throw std::invalid_argument("Invalid fuse_code");
}

inline float unpack_fbs(const float fbs) {
return fbs;
}

inline uint32_t unpack_fbs(const uint32_t fbs) {
return fbs;
}
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8 changes: 4 additions & 4 deletions dnnlibrary/src/DaqReader.cpp
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Expand Up @@ -219,19 +219,19 @@ void AddLayers(const DNN::Model &model, ModelBuilder &builder) {
break;
}
case DNN::LayerType::Add: {
ADD_LAYER_QUANT(add, OperationAdd, input1, input2, output);
ADD_LAYER_QUANT(add, Add, input1, input2, fuse, output);
break;
}
case DNN::LayerType::AddScalar: {
ADD_LAYER(add, OperationAdd, input1, input2, output);
ADD_LAYER(add_scalar, Add, input1, input2, fuse, output);
break;
}
case DNN::LayerType::Mul: {
ADD_LAYER_QUANT(mul, Mul, input1, input2, output);
ADD_LAYER_QUANT(mul, Mul, input1, input2, fuse, output);
break;
}
case DNN::LayerType::MulScalar: {
ADD_LAYER(mul, Mul, input1, input2, output);
ADD_LAYER(mul_scalar, Mul, input1, input2, fuse, output);
break;
}
case DNN::LayerType::FC: {
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12 changes: 6 additions & 6 deletions dnnlibrary/src/ModelBuilder.cpp
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Expand Up @@ -132,9 +132,9 @@ ModelBuilder::Index ModelBuilder::AddAvePool(
AddScalarOperands(input_indexes, padding_left, padding_right, padding_top,
padding_bottom, stride_x, stride_y, kernel_width,
kernel_height, fuse_code);
shaper_.Pool(input, padding_left, padding_right, padding_top,
padding_bottom, stride_x, stride_y, kernel_width,
kernel_height, output);
shaper_.PoolNew(input, padding_left, padding_right, padding_top,
padding_bottom, stride_x, stride_y, kernel_width,
kernel_height, output);
const OperandType operand_type = GetOperandType(
operand_types_.at(input).type, shaper_[output], output_quant_info);
const auto output_idx = AddOperation(ANEURALNETWORKS_AVERAGE_POOL_2D,
Expand All @@ -156,9 +156,9 @@ ModelBuilder::Index ModelBuilder::AddMaxPool(
AddScalarOperands(input_indexes, padding_left, padding_right, padding_top,
padding_bottom, stride_x, stride_y, kernel_width,
kernel_height, fuse_code);
shaper_.Pool(input, padding_left, padding_right, padding_top,
padding_bottom, stride_x, stride_y, kernel_width,
kernel_height, output);
shaper_.PoolNew(input, padding_left, padding_right, padding_top,
padding_bottom, stride_x, stride_y, kernel_width,
kernel_height, output);
const OperandType operand_type = GetOperandType(
operand_types_.at(input).type, shaper_[output], output_quant_info);
const auto output_idx = AddOperation(ANEURALNETWORKS_MAX_POOL_2D,
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4 changes: 2 additions & 2 deletions ops.yml
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Expand Up @@ -87,7 +87,7 @@
cpp_type: int32_t
needed_by_shaper: true
nnapi: AVERAGE_POOL_2D
shaper: Pool
shaper: PoolNew
fused: true
support_quant_asymm: true
api: 27
Expand Down Expand Up @@ -135,7 +135,7 @@
cpp_type: int32_t
needed_by_shaper: true
nnapi: MAX_POOL_2D
shaper: Pool
shaper: PoolNew
fused: true
support_quant_asymm: true
api: 27
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67 changes: 60 additions & 7 deletions tools/onnx2daq/OnnxConverter.cpp
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Expand Up @@ -387,7 +387,7 @@ void OnnxConverter::AddLayerDequantize(css &input_name, css &output_name) {
layers_.push_back(layer);
}

void OnnxConverter::AddLayerDropout(css &input_name, css &output_name) {
void OnnxConverter::SetIdentity(css &input_name, css &output_name) {
// Dropout does nothing, so the output is the same as the input
shaper_.Eltwise(input_name, output_name);
name_map_[output_name] = m(input_name);
Expand Down Expand Up @@ -430,8 +430,12 @@ void OnnxConverter::HandleInitializer() {
vector<char>(ptr, ptr + Product(shape) * sizeof(int32_t));
onnx_tensors_[name] = {name, data_vec, shape,
Tensor::DataType::INT32};
} else if (tensor.data_type() ==
ONNX_NAMESPACE::TensorProto_DataType_INT64) {
// TODO: shape of reshape layer
} else {
LOG(INFO) << "invalid " << tensor.data_type();
PNT(tensor.name(), tensor.data_type());
DNN_ASSERT(false, "");
}
operands_.push_back(name);
}
Expand Down Expand Up @@ -560,10 +564,6 @@ void OnnxConverter::Convert(const ONNX_NAMESPACE::ModelProto &model_proto,

bool has_reshape = false;
for (const auto &node : model_proto_.graph().node()) {
if (has_reshape) {
throw std::invalid_argument(
"Reshape can only be the last layer for now");
}
NodeAttrHelper helper(node);
const auto &op = node.op_type();
LOG(INFO) << "Node " << node.name();
Expand All @@ -572,6 +572,11 @@ void OnnxConverter::Convert(const ONNX_NAMESPACE::ModelProto &model_proto,
LOG(INFO) << "Skip layer " << node.name();
continue;
}
if (has_reshape && op != "Gemm") {
throw std::invalid_argument(
"Reshape can only be the last layer or precede a gemm layer "
"for now");
}
if (op == "Conv") {
LOG(INFO) << "Start converting Conv";
const auto strides = helper.get("strides", vector<int>{1, 1});
Expand Down Expand Up @@ -713,6 +718,7 @@ void OnnxConverter::Convert(const ONNX_NAMESPACE::ModelProto &model_proto,
const auto beta = helper.get("beta", 1.0f);
AddLayerGemm(input_name, weight_name, bias_name, transA, transB,
alpha, beta, output_name);
has_reshape = false;
LOG(INFO) << "Converting Gemm completed";
} else if (op == "Softmax") {
LOG(INFO) << "Start converting Softmax";
Expand All @@ -732,11 +738,58 @@ void OnnxConverter::Convert(const ONNX_NAMESPACE::ModelProto &model_proto,
LOG(INFO) << "Converting Concat completed";
} else if (op == "Dropout") {
LOG(INFO) << "Start converting Dropout";
AddLayerDropout(node.input(0), node.output(0));
SetIdentity(node.input(0), node.output(0));
LOG(INFO) << "Converting Dropout completed";

} else if (op == "BatchNormalization") {
LOG(INFO) << "Start converting BatchNormalization";
DNN_ASSERT(node.output_size() == 1,
"Your onnx model may be in training mode, please export "
"it in test mode.")
const auto input_name = m(node.input(0));

const auto scale_name = m(node.input(1));
const auto bias_name = m(node.input(2));
const auto mean_name = m(node.input(3));
const auto var_name = m(node.input(4));

const auto scale_tensor = onnx_tensors_.at(scale_name);
const auto bias_tensor = onnx_tensors_.at(bias_name);
const auto mean_tensor = onnx_tensors_.at(mean_name);
const auto var_tensor = onnx_tensors_.at(var_name);

const auto eps = helper.get("epsilon", 1e-5f);

const auto output_name = m(node.output(0));

vector<float> a, b;
FORZ(i, scale_tensor.shape[0]) {
a.push_back(scale_tensor.float_data()[i] /
sqrt(var_tensor.float_data()[i] + eps));
b.push_back((scale_tensor.float_data()[i] *
-mean_tensor.float_data()[i]) /
sqrt(var_tensor.float_data()[i] + eps) +
bias_tensor.float_data()[i]);
}

const auto flat_tensor_a = DNN::CreateTensorDirect(
builder_, DNN::DataType::Float32, nullptr, &a,
&scale_tensor.shape, (output_name + "_imm_a").c_str());
const auto flat_tensor_b = DNN::CreateTensorDirect(
builder_, DNN::DataType::Float32, nullptr, &b,
&scale_tensor.shape, (output_name + "_imm_b").c_str());
tensors_.push_back(flat_tensor_a);
tensors_.push_back(flat_tensor_b);
AddLayerMul(input_name, output_name + "_imm_a",
output_name + "_imm_mul");
AddLayerAdd(output_name + "_imm_mul", output_name + "_imm_b",
output_name);

LOG(INFO) << "Converting BatchNormalization completed";
} else if (op == "Reshape") {
LOG(INFO) << "Start converting Reshape";
has_reshape = true;
SetIdentity(node.input(0), node.output(0));
LOG(INFO) << "Converting Reshape completed";
} else {
throw std::invalid_argument("Unsupported operator " + op);
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2 changes: 1 addition & 1 deletion tools/onnx2daq/OnnxConverter.h
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Expand Up @@ -101,7 +101,7 @@ class OnnxConverter {
void AddLayerConcat(const std::vector<std::string> &inputs,
css &output_name, const int axis);
void AddLayerDequantize(css &input_name, css &output_name);
void AddLayerDropout(css &input_name, css &output_name);
void SetIdentity(css &input_name, css &output_name);

/**
* onnx: [filter_out_channel, filter_in_channel / group, height, width]
Expand Down