fix deconv bias add

mace/kernels/deconv_2d.h

@@ -34,19 +34,24 @@
 namespace mace {
 namespace kernels {
 
+enum FrameworkType {
+  TENSORFLOW = 0,
+  CAFFE = 1,
+};
+
 struct Deconv2dFunctorBase : OpKernel {
   Deconv2dFunctorBase(OpKernelContext *context,
                       const std::vector<int> &strides,
                       const Padding &padding_type,
                       const std::vector<int> &paddings,
-                      const std::vector<index_t> &output_shape,
+                      const FrameworkType model_type,
                       const ActivationType activation,
                       const float relux_max_limit)
       : OpKernel(context),
         strides_(strides),
         padding_type_(padding_type),
         paddings_(paddings),
-        output_shape_(output_shape),
+        model_type_(model_type),
         activation_(activation),
         relux_max_limit_(relux_max_limit) {}
 
@@ -156,7 +161,7 @@ struct Deconv2dFunctorBase : OpKernel {
   std::vector<int> strides_;  // [stride_h, stride_w]
   const Padding padding_type_;
   std::vector<int> paddings_;
-  std::vector<index_t> output_shape_;
+  const FrameworkType model_type_;
   const ActivationType activation_;
   const float relux_max_limit_;
 };
@@ -171,14 +176,14 @@ struct Deconv2dFunctor<DeviceType::CPU, float>: Deconv2dFunctorBase {
                   const std::vector<int> &strides,
                   const Padding &padding_type,
                   const std::vector<int> &paddings,
-                  const std::vector<index_t> &output_shape,
+                  const FrameworkType model_type,
                   const ActivationType activation,
                   const float relux_max_limit)
       : Deconv2dFunctorBase(context,
                             strides,
                             padding_type,
                             paddings,
-                            output_shape,
+                            model_type,
                             activation,
                             relux_max_limit) {}
 
@@ -277,19 +282,16 @@ struct Deconv2dFunctor<DeviceType::CPU, float>: Deconv2dFunctorBase {
     std::vector<int> paddings(2);
     std::vector<int> out_paddings(2);
     std::vector<index_t> output_shape(4);
-    if (paddings_.empty()) {  // tensorflow
+    if (model_type_ == FrameworkType::TENSORFLOW) {  // tensorflow
       paddings = std::vector<int>(2, 0);
-      if (output_shape_.size() == 4) {
-        output_shape = output_shape_;
-      } else {
-        MACE_CHECK_NOTNULL(output_shape_tensor);
-        MACE_CHECK(output_shape_tensor->size() == 4);
-        Tensor::MappingGuard output_shape_mapper(output_shape_tensor);
-        auto output_shape_data =
-            output_shape_tensor->data<int32_t>();
-        output_shape =
-            std::vector<index_t>(output_shape_data, output_shape_data + 4);
-      }
+      MACE_CHECK_NOTNULL(output_shape_tensor);
+      MACE_CHECK(output_shape_tensor->size() == 4);
+      Tensor::MappingGuard output_shape_mapper(output_shape_tensor);
+      auto output_shape_data =
+          output_shape_tensor->data<int32_t>();
+      output_shape =
+          std::vector<index_t>(output_shape_data, output_shape_data + 4);
+
       const index_t t = output_shape[1];
       output_shape[1] = output_shape[3];
       output_shape[3] = output_shape[2];
@@ -437,21 +439,6 @@ struct Deconv2dFunctor<DeviceType::CPU, float>: Deconv2dFunctorBase {
         padded_out_h == output_shape[2] && padded_out_w == output_shape[3];
     float *out_data = no_pad ? output_data : padded_out_data;
 
-    if (bias_data != nullptr) {
-      const index_t batch = output_shape[0];
-      const index_t channels = output_shape[1];
-      const index_t img_size = output_shape[2] * output_shape[3];
-#pragma omp parallel for collapse(3)
-      for (index_t b = 0; b < batch; ++b) {
-        for (index_t c = 0; c < channels; ++c) {
-          for (index_t i = 0; i < img_size; ++i) {
-            output_data[(b * channels + c) * img_size + i] +=
-                bias_data[c];
-          }
-        }
-      }
-    }
-
     deconv_func(input_data,
                 filter_data,
                 in_shape,
@@ -466,7 +453,20 @@ struct Deconv2dFunctor<DeviceType::CPU, float>: Deconv2dFunctorBase {
                  output_data);
     }
 
-
+    if (bias_data != nullptr) {
+      const index_t batch = output_shape[0];
+      const index_t channels = output_shape[1];
+      const index_t img_size = output_shape[2] * output_shape[3];
+#pragma omp parallel for collapse(3)
+      for (index_t b = 0; b < batch; ++b) {
+        for (index_t c = 0; c < channels; ++c) {
+          for (index_t i = 0; i < img_size; ++i) {
+            output_data[(b * channels + c) * img_size + i] +=
+                bias_data[c];
+          }
+        }
+      }
+    }
 
     DoActivation<float>(output_data,
                  output_data,
@@ -501,7 +501,7 @@ struct Deconv2dFunctor<DeviceType::GPU, T> : Deconv2dFunctorBase {
                   const std::vector<int> &strides,
                   const Padding &padding_type,
                   const std::vector<int> &paddings,
-                  const std::vector<index_t> &output_shape,
+                  const FrameworkType model_type,
                   const ActivationType activation,
                   const float relux_max_limit);
 

mace/kernels/opencl/deconv_2d.cc

@@ -24,14 +24,14 @@ Deconv2dFunctor<DeviceType::GPU, T>::Deconv2dFunctor(
     const std::vector<int> &strides,
     const Padding &padding_type,
     const std::vector<int> &paddings,
-    const std::vector<index_t> &output_shape,
+    const FrameworkType model_type,
     const ActivationType activation,
     const float relux_max_limit)
     : Deconv2dFunctorBase(context,
                           strides,
                           padding_type,
                           paddings,
-                          output_shape,
+                          model_type,
                           activation,
                           relux_max_limit) {
   if (context->device()->opencl_runtime()->UseImageMemory()) {
@@ -55,19 +55,15 @@ MaceStatus Deconv2dFunctor<DeviceType::GPU, T>::operator()(
   std::vector<int> paddings(2);
   std::vector<int> out_paddings(2);
   std::vector<index_t> output_shape(4);
-  if (paddings_.empty()) {
+  if (model_type_ == FrameworkType::TENSORFLOW) {
     paddings = std::vector<int>(2, 0);
-    if (output_shape_.size() != 4) {
-      MACE_CHECK_NOTNULL(output_shape_tensor);
-      MACE_CHECK(output_shape_tensor->size() == 4);
-      Tensor::MappingGuard output_shape_mapper(output_shape_tensor);
-      auto output_shape_data =
-          output_shape_tensor->data<int32_t>();
-      output_shape =
-          std::vector<index_t>(output_shape_data, output_shape_data + 4);
-    } else {
-      output_shape = output_shape_;
-    }
+    MACE_CHECK_NOTNULL(output_shape_tensor);
+    MACE_CHECK(output_shape_tensor->size() == 4);
+    Tensor::MappingGuard output_shape_mapper(output_shape_tensor);
+    auto output_shape_data =
+        output_shape_tensor->data<int32_t>();
+    output_shape =
+        std::vector<index_t>(output_shape_data, output_shape_data + 4);
     CalcDeconvPaddingAndInputSize(input->shape().data(),
                                   filter->shape().data(),
                                   strides_.data(),

mace/ops/deconv_2d.h

@@ -34,28 +34,39 @@ class Deconv2dOp : public Operator<D, T> {
                  static_cast<Padding>(OperatorBase::GetOptionalArg<int>(
                      "padding", static_cast<int>(SAME))),
                  OperatorBase::GetRepeatedArgs<int>("padding_values"),
-                 OperatorBase::GetRepeatedArgs<index_t>("output_shape"),
+                 static_cast<kernels::FrameworkType>(
+                     OperatorBase::GetOptionalArg<int>("framework_type", 0)),
                  kernels::StringToActivationType(
                      OperatorBase::GetOptionalArg<std::string>("activation",
                                                                "NOOP")),
                  OperatorBase::GetOptionalArg<float>("max_limit", 0.0f)) {}
 
   MaceStatus Run(StatsFuture *future) override {
-    const Tensor *input = this->Input(INPUT);
-    const Tensor *filter = this->Input(FILTER);
-    const Tensor *output_shape =
-        this->InputSize() >= 3 ? this->Input(OUTPUT_SHAPE) : nullptr;
-    const Tensor *bias = this->InputSize() >= 4 ? this->Input(BIAS) : nullptr;
-    Tensor *output = this->Output(OUTPUT);
+    MACE_CHECK(this->InputSize() >= 2, "deconv needs >= 2 inputs.");
+    const Tensor *input = this->Input(0);
+    const Tensor *filter = this->Input(1);
+    const kernels::FrameworkType model_type =
+        static_cast<kernels::FrameworkType>(
+            OperatorBase::GetOptionalArg<int>("framework_type", 0));
+    if (model_type == kernels::CAFFE) {
+      const Tensor *bias = this->InputSize() >= 3 ? this->Input(2) : nullptr;
+      Tensor *output = this->Output(OUTPUT);
 
-    return functor_(input, filter, bias, output_shape, output, future);
+      return functor_(input, filter, bias, nullptr, output, future);
+    } else {
+      const Tensor *output_shape =
+          this->InputSize() >= 3 ? this->Input(2) : nullptr;
+      const Tensor *bias = this->InputSize() >= 4 ? this->Input(3) : nullptr;
+      Tensor *output = this->Output(OUTPUT);
+
+      return functor_(input, filter, bias, output_shape, output, future);
+    }
   }
 
  private:
   kernels::Deconv2dFunctor<D, T> functor_;
 
  protected:
-  MACE_OP_INPUT_TAGS(INPUT, FILTER, OUTPUT_SHAPE,  BIAS);
   MACE_OP_OUTPUT_TAGS(OUTPUT);
 };
 

mace/ops/deconv_2d_benchmark.cc

@@ -49,28 +49,35 @@ static void Deconv2d(int iters,
   net.AddRandomInput<D, float>("Filter",
                                {output_channels, channels, kernel_h,
                                 kernel_w});
+  net.AddRandomInput<D, float>("Bias", {output_channels});
+  net.AddInputFromArray<D, int32_t>("OutputShape", {4},
+                                    {batch, out_h, out_w, output_channels});
   if (D == DeviceType::GPU) {
     BufferToImage<D, T>(&net, "Input", "InputImage",
                         kernels::BufferType::IN_OUT_CHANNEL);
     BufferToImage<D, T>(&net, "Filter", "FilterImage",
                         kernels::BufferType::CONV2D_FILTER);
+    BufferToImage<D, T>(&net, "Bias", "BiasImage",
+                        kernels::BufferType::ARGUMENT);
     OpDefBuilder("Deconv2D", "Deconv2dTest")
         .Input("InputImage")
         .Input("FilterImage")
+        .Input("OutputShape")
+        .Input("BiasImage")
         .Output("Output")
         .AddIntsArg("strides", {stride, stride})
         .AddIntArg("padding", padding)
-        .AddIntsArg("output_shape", {batch, out_h, out_w, output_channels})
         .AddIntArg("T", static_cast<int>(DataTypeToEnum<T>::value))
         .Finalize(net.NewOperatorDef());
   } else {
     OpDefBuilder("Deconv2D", "Deconv2dTest")
         .Input("Input")
         .Input("Filter")
+        .Input("OutputShape")
+        .Input("Bias")
         .Output("Output")
         .AddIntsArg("strides", {stride, stride})
         .AddIntArg("padding", padding)
-        .AddIntsArg("output_shape", {batch, out_h, out_w, output_channels})
         .AddIntArg("T", static_cast<int>(DataTypeToEnum<T>::value))
         .Finalize(net.NewOperatorDef());
   }

mace/python/tools/converter_tool/base_converter.py

@@ -70,6 +70,11 @@ class EltwiseType(Enum):
     EQUAL = 10
 
 
+class FrameworkType(Enum):
+    TENSORFLOW = 0
+    CAFFE = 1
+
+
 MaceSupportedOps = [
     'Activation',
     'AddN',
@@ -176,6 +181,7 @@ class MaceKeyword(object):
     mace_seperate_buffer_str = 'seperate_buffer'
     mace_scalar_input_index_str = 'scalar_input_index'
     mace_opencl_mem_type = "opencl_mem_type"
+    mace_framework_type_str = "framework_type"
 
 
 class TransformerRule(Enum):

mace/python/tools/converter_tool/caffe_converter.py

@@ -25,6 +25,7 @@ from mace.python.tools.converter_tool import shape_inference
 from mace.python.tools.converter_tool.base_converter import PoolingType
 from mace.python.tools.converter_tool.base_converter import ActivationType
 from mace.python.tools.converter_tool.base_converter import EltwiseType
+from mace.python.tools.converter_tool.base_converter import FrameworkType
 from mace.python.tools.converter_tool.base_converter import DataFormat
 from mace.python.tools.converter_tool.base_converter import FilterFormat
 from mace.python.tools.converter_tool.base_converter import MaceOp
@@ -351,6 +352,10 @@ class CaffeConverter(base_converter.ConverterInterface):
         data_type_arg.name = 'T'
         data_type_arg.i = self._option.data_type
 
+        framework_type_arg = op.arg.add()
+        framework_type_arg.name = MaceKeyword.mace_framework_type_str
+        framework_type_arg.i = FrameworkType.CAFFE.value
+
         ConverterUtil.add_data_format_arg(op, DataFormat.NCHW)
 
         return op

mace/python/tools/converter_tool/tensorflow_converter.py

@@ -25,6 +25,7 @@ from mace.python.tools.converter_tool.base_converter import PoolingType
 from mace.python.tools.converter_tool.base_converter import PaddingMode
 from mace.python.tools.converter_tool.base_converter import ActivationType
 from mace.python.tools.converter_tool.base_converter import EltwiseType
+from mace.python.tools.converter_tool.base_converter import FrameworkType
 from mace.python.tools.converter_tool.base_converter import DataFormat
 from mace.python.tools.converter_tool.base_converter import FilterFormat
 from mace.python.tools.converter_tool.base_converter import MaceOp
@@ -372,6 +373,10 @@ class TensorflowConverter(base_converter.ConverterInterface):
             except ValueError:
                 data_type_arg.i = self._option.data_type
 
+        framework_type_arg = op.arg.add()
+        framework_type_arg.name = MaceKeyword.mace_framework_type_str
+        framework_type_arg.i = FrameworkType.TENSORFLOW.value
+
         ConverterUtil.add_data_format_arg(op, DataFormat.NHWC)
 
         return op
@@ -414,13 +419,13 @@ class TensorflowConverter(base_converter.ConverterInterface):
                        "deconv should have (>=) 3 inputs.")
             output_shape_arg = op.arg.add()
             output_shape_arg.name = MaceKeyword.mace_output_shape_str
-            if tf_op.inputs[0].op.type == TFOpType.Const.name:
-                output_shape_value = \
-                    tf_op.inputs[0].eval().astype(np.int32).flat
-                output_shape_arg.ints.extend(output_shape_value)
-            else:
-                output_shape_value = {}
-                output_shape_arg.ints.extend(output_shape_value)
+            # if tf_op.inputs[0].op.type == TFOpType.Const.name:
+            #     output_shape_value = \
+            #         tf_op.inputs[0].eval().astype(np.int32).flat
+            #     output_shape_arg.ints.extend(output_shape_value)
+            # else:
+            #     output_shape_value = {}
+            #     output_shape_arg.ints.extend(output_shape_value)
             del op.input[:]
             op.input.extend([tf_op.inputs[2].name,
                              tf_op.inputs[1].name,

mace/python/tools/converter_tool/transformer.py

@@ -26,6 +26,7 @@ from mace.python.tools.converter_tool.base_converter import ConverterUtil
 from mace.python.tools.converter_tool.base_converter import DataFormat
 from mace.python.tools.converter_tool.base_converter import DeviceType
 from mace.python.tools.converter_tool.base_converter import EltwiseType
+from mace.python.tools.converter_tool.base_converter import FrameworkType
 from mace.python.tools.converter_tool.base_converter import FilterFormat
 from mace.python.tools.converter_tool.base_converter import MaceKeyword
 from mace.python.tools.converter_tool.base_converter import MaceOp
@@ -810,12 +811,22 @@ class Transformer(base_converter.ConverterInterface):
         net = self._model
         for op in net.op:
             if (((op.type == MaceOp.Conv2D.name
-                  or op.type == MaceOp.Deconv2D.name
                   or op.type == MaceOp.DepthwiseConv2d.name
                   or op.type == MaceOp.FullyConnected.name)
                  and len(op.input) == 2)
                 or (op.type == MaceOp.WinogradInverseTransform.name
-                    and len(op.input) == 1)) \
+                    and len(op.input) == 1)
+                or (op.type == MaceOp.Deconv2D.name
+                    and ((ConverterUtil.get_arg(
+                                op,
+                                MaceKeyword.mace_framework_type_str).i ==
+                          FrameworkType.CAFFE.value
+                          and len(op.input) == 2)
+                         or (ConverterUtil.get_arg(
+                                        op,
+                                        MaceKeyword.mace_framework_type_str).i
+                             == FrameworkType.TENSORFLOW.value
+                             and len(op.input) == 3)))) \
                     and len(self._consumers.get(op.output[0], [])) == 1:
                 consumer_op = self._consumers[op.output[0]][0]
                 if consumer_op.type == MaceOp.BiasAdd.name: