Support nlp model and ops

docs/getting_started/create_a_model_deployment.rst

@@ -65,6 +65,8 @@ Configurations
       - The shapes of the input tensors, in NHWC order.
     * - output_shapes
       - The shapes of the output tensors, in NHWC order.
+    * - input_ranges
+      - The numerical range of the input tensors, default [-1, 1]. It is only for test.
     * - validation_inputs_data
       - [optional] Specify Numpy validation inputs. When not provided, [-1, 1] random values will be used.
     * - runtime

mace/core/mace.cc

@@ -217,9 +217,6 @@ MaceStatus MaceEngine::Impl::Run(
                  << "' is not belong to model's inputs: "
                  << MakeString(MapKeys(input_info_map_));
     }
-    MACE_CHECK(input.second.shape().size() == 4,
-               "The Inputs' shape must be 4-dimension with NHWC format,"
-                   " please use 1 to fill missing dimensions");
     Tensor *input_tensor =
         ws_->GetTensor(MakeString("mace_input_node_", input.first));
     MACE_RETURN_IF_ERROR(input_tensor->Resize(input.second.shape()));

mace/core/net.cc

@@ -63,7 +63,9 @@ MaceStatus SerialNet::Run(RunMetadata *run_metadata) {
   for (auto iter = operators_.begin(); iter != operators_.end(); ++iter) {
     auto &op = *iter;
     MACE_LATENCY_LOGGER(2, "Running operator ", op->debug_def().name(), "(",
-                        op->debug_def().type(), ")");
+                        op->debug_def().type(), "), mem_id: ",
+                        MakeListString(op->debug_def().mem_id().data(),
+                                       op->debug_def().mem_id().size()));
     bool future_wait = (device_type_ == DeviceType::GPU &&
                         (run_metadata != nullptr ||
                          std::distance(iter, operators_.end()) == 1));

mace/core/operator.cc

@@ -79,6 +79,7 @@ extern void Register_AddN(OperatorRegistry *op_registry);
 extern void Register_BatchNorm(OperatorRegistry *op_registry);
 extern void Register_BatchToSpaceND(OperatorRegistry *op_registry);
 extern void Register_BiasAdd(OperatorRegistry *op_registry);
+extern void Register_Cast(OperatorRegistry *op_registry);
 extern void Register_ChannelShuffle(OperatorRegistry *op_registry);
 extern void Register_Concat(OperatorRegistry *op_registry);
 extern void Register_Conv2D(OperatorRegistry *op_registry);
@@ -127,6 +128,7 @@ OperatorRegistry::OperatorRegistry() {
   ops::Register_BatchNorm(this);
   ops::Register_BatchToSpaceND(this);
   ops::Register_BiasAdd(this);
+  ops::Register_Cast(this);
   ops::Register_ChannelShuffle(this);
   ops::Register_Concat(this);
   ops::Register_Conv2D(this);

mace/kernels/bias_add.h

@@ -15,6 +15,7 @@
 #ifndef MACE_KERNELS_BIAS_ADD_H_
 #define MACE_KERNELS_BIAS_ADD_H_
 
+#include <functional>
 #include <memory>
 #include <vector>
 
@@ -29,20 +30,27 @@
 namespace mace {
 namespace kernels {
 
-template<DeviceType D, typename T>
+struct BiasAddFunctorBase {
+  explicit BiasAddFunctorBase(const DataFormat data_format) {
+    data_format_ = data_format;
+  }
+
+  DataFormat data_format_;
+};
+
+template <DeviceType D, typename T>
 struct BiasAddFunctor;
 
-template<>
-struct BiasAddFunctor<DeviceType::CPU, float> {
+template <>
+struct BiasAddFunctor<DeviceType::CPU, float> : BiasAddFunctorBase {
+  explicit BiasAddFunctor(const DataFormat data_format)
+      : BiasAddFunctorBase(data_format) {}
+
   MaceStatus operator()(const Tensor *input,
                         const Tensor *bias,
                         Tensor *output,
                         StatsFuture *future) {
     MACE_UNUSED(future);
-    const index_t batch = input->dim(0);
-    const index_t channels = input->dim(1);
-    const index_t height = input->dim(2);
-    const index_t width = input->dim(3);
 
     Tensor::MappingGuard input_mapper(input);
     Tensor::MappingGuard bias_mapper(bias);
@@ -52,12 +60,31 @@ struct BiasAddFunctor<DeviceType::CPU, float> {
     const float *bias_ptr = bias->data<float>();
     float *output_ptr = output->mutable_data<float>();
 
+    if (input->dim_size() == 4 && data_format_ == NCHW) {
+      const index_t batch = input->dim(0);
+      const index_t channels = input->dim(1);
+      const index_t height_width = input->dim(2) * input->dim(3);
+
 #pragma omp parallel for collapse(2)
       for (index_t n = 0; n < batch; ++n) {
         for (index_t c = 0; c < channels; ++c) {
-        for (index_t hw = 0; hw < height * width; ++hw) {
-          index_t pos = (n * channels + c) * height * width + hw;
+          for (index_t hw = 0; hw < height_width; ++hw) {
+            index_t pos = (n * channels + c) * height_width + hw;
+            output_ptr[pos] = input_ptr[pos] + bias_ptr[c];
+          }
+        }
+      }
+    } else {
+      const std::vector<index_t> &shape = input->shape();
+      const index_t fused_batch = std::accumulate(
+          shape.begin(), shape.end() - 1, 1, std::multiplies<index_t>());
+      const index_t channels = *shape.rbegin();
+#pragma omp parallel for
+      for (index_t n = 0; n < fused_batch; ++n) {
+        index_t pos = n * channels;
+        for (index_t c = 0; c < channels; ++c) {
           output_ptr[pos] = input_ptr[pos] + bias_ptr[c];
+          ++pos;
         }
       }
     }
@@ -67,8 +94,10 @@ struct BiasAddFunctor<DeviceType::CPU, float> {
 };
 
 #ifdef MACE_ENABLE_OPENCL
-template<typename T>
-struct BiasAddFunctor<DeviceType::GPU, T> {
+template <typename T>
+struct BiasAddFunctor<DeviceType::GPU, T> : BiasAddFunctorBase {
+  explicit BiasAddFunctor(const DataFormat data_format)
+      : BiasAddFunctorBase(data_format) {}
   MaceStatus operator()(const Tensor *input,
                         const Tensor *bias,
                         Tensor *output,

mace/kernels/eltwise.h

@@ -19,6 +19,7 @@
 #include <memory>
 #include <utility>
 #include <vector>
+#include <functional>
 
 #include "mace/core/future.h"
 #include "mace/core/tensor.h"
@@ -44,6 +45,157 @@ enum EltwiseType {
   NONE = 10,
 };
 
+inline index_t GetIndex(const std::vector<index_t> &shape,
+                        const std::vector<index_t> &index) {
+  index_t idx = 0;
+  for (size_t i = 0; i < shape.size(); ++i) {
+    if (shape[i] > 1) {
+      idx = idx * shape[i] + index[i];
+    }
+  }
+  return idx;
+}
+
+inline void IncreaseIndex(const std::vector<index_t> &shape,
+                          std::vector<index_t> *index) {
+  for (index_t i = static_cast<index_t>(shape.size()) - 1; i >= 0; --i) {
+    ++(*index)[i];
+    if ((*index)[i] >= shape[i]) {
+      (*index)[i] -= shape[i];
+    } else {
+      break;
+    }
+  }
+}
+
+inline void TensorGeneralBroadcastEltwise(const EltwiseType type,
+                                          const float *input0,
+                                          const float *input1,
+                                          const std::vector<float> &coeff,
+                                          const bool swapped,
+                                          const std::vector<index_t>
+                                          &input0_shape,
+                                          const std::vector<index_t>
+                                          &input1_shape,
+                                          const std::vector<index_t>
+                                          &output_shape,
+                                          float *output) {
+  const index_t output_size = std::accumulate(output_shape.begin(),
+                                              output_shape.end(),
+                                              1,
+                                              std::multiplies<index_t>());
+  std::vector<index_t> out_index(output_shape.size(), 0);
+  switch (type) {
+    case SUM:
+      if (coeff.empty()) {
+        for (index_t i = 0; i < output_size; ++i) {
+          const index_t idx0 = GetIndex(input0_shape, out_index);
+          const index_t idx1 = GetIndex(input1_shape, out_index);
+          output[i] = input0[idx0] + input1[idx1];
+          IncreaseIndex(output_shape, &out_index);
+        }
+      } else {
+        std::vector<float> coeff_copy = coeff;
+        if (swapped) {
+          std::swap(coeff_copy[0], coeff_copy[1]);
+        }
+        for (index_t i = 0; i < output_size; ++i) {
+          const index_t idx0 = GetIndex(input0_shape, out_index);
+          const index_t idx1 = GetIndex(input1_shape, out_index);
+          output[i] =
+              input0[idx0] * coeff_copy[0] + input1[idx1] * coeff_copy[1];
+          IncreaseIndex(output_shape, &out_index);
+        }
+      }
+      break;
+    case SUB:
+      if (!swapped) {
+        for (index_t i = 0; i < output_size; ++i) {
+          const index_t idx0 = GetIndex(input0_shape, out_index);
+          const index_t idx1 = GetIndex(input1_shape, out_index);
+          output[i] = input0[idx0] - input1[idx1];
+          IncreaseIndex(output_shape, &out_index);
+        }
+      } else {
+        for (index_t i = 0; i < output_size; ++i) {
+          const index_t idx0 = GetIndex(input0_shape, out_index);
+          const index_t idx1 = GetIndex(input1_shape, out_index);
+          output[i] = input1[idx1] - input0[idx0];
+          IncreaseIndex(output_shape, &out_index);
+        }
+      }
+      break;
+    case PROD:
+      for (index_t i = 0; i < output_size; ++i) {
+        const index_t idx0 = GetIndex(input0_shape, out_index);
+        const index_t idx1 = GetIndex(input1_shape, out_index);
+        output[i] = input0[idx0] * input1[idx1];
+        IncreaseIndex(output_shape, &out_index);
+      }
+      break;
+    case DIV:
+      if (!swapped) {
+        for (index_t i = 0; i < output_size; ++i) {
+          const index_t idx0 = GetIndex(input0_shape, out_index);
+          const index_t idx1 = GetIndex(input1_shape, out_index);
+          output[i] = input0[idx0] / input1[idx1];
+          IncreaseIndex(output_shape, &out_index);
+        }
+      } else {
+        for (index_t i = 0; i < output_size; ++i) {
+          const index_t idx0 = GetIndex(input0_shape, out_index);
+          const index_t idx1 = GetIndex(input1_shape, out_index);
+          output[i] = input1[idx1] / input0[idx0];
+          IncreaseIndex(output_shape, &out_index);
+        }
+      }
+      break;
+    case MIN:
+      for (index_t i = 0; i < output_size; ++i) {
+        const index_t idx0 = GetIndex(input0_shape, out_index);
+        const index_t idx1 = GetIndex(input1_shape, out_index);
+        output[i] = std::min(input1[idx1], input0[idx0]);
+        IncreaseIndex(output_shape, &out_index);
+      }
+      break;
+    case MAX:
+      for (index_t i = 0; i < output_size; ++i) {
+        const index_t idx0 = GetIndex(input0_shape, out_index);
+        const index_t idx1 = GetIndex(input1_shape, out_index);
+        output[i] = std::max(input1[idx1], input0[idx0]);
+        IncreaseIndex(output_shape, &out_index);
+      }
+      break;
+    case SQR_DIFF:
+      for (index_t i = 0; i < output_size; ++i) {
+        const index_t idx0 = GetIndex(input0_shape, out_index);
+        const index_t idx1 = GetIndex(input1_shape, out_index);
+        output[i] = std::pow(input1[idx1] - input0[idx0], 2.f);
+        IncreaseIndex(output_shape, &out_index);
+      }
+      break;
+    case POW:
+      if (!swapped) {
+        for (index_t i = 0; i < output_size; ++i) {
+          const index_t idx0 = GetIndex(input0_shape, out_index);
+          const index_t idx1 = GetIndex(input1_shape, out_index);
+          output[i] = std::pow(input0[idx0], input1[idx1]);
+          IncreaseIndex(output_shape, &out_index);
+        }
+      } else {
+        for (index_t i = 0; i < output_size; ++i) {
+          const index_t idx0 = GetIndex(input0_shape, out_index);
+          const index_t idx1 = GetIndex(input1_shape, out_index);
+          output[i] = std::pow(input1[idx1], input0[idx0]);
+          IncreaseIndex(output_shape, &out_index);
+        }
+      }
+      break;
+    default:
+      LOG(FATAL) << "Eltwise op not support type " << type;
+  }
+}
+
 inline void TensorBroadcastEltwise(const EltwiseType type,
                                    const float *input0,
                                    const float *input1,
@@ -662,40 +814,71 @@ struct EltwiseFunctor<DeviceType::CPU, float> : EltwiseFunctorBase {
                       && input1->dim(0) == input0->dim(1))),
           "only support broadcast channel dimension");
     } else {
-      if (rank_diff > 0 && rank_diff < input0->dim_size()) {
       for (uint32_t i = 0; i < input1->dim_size(); ++i) {
-          MACE_CHECK(input0->dim(rank_diff + i) == input1->dim(i),
+        MACE_CHECK(input0->dim(rank_diff + i) == 1
+                       || input1->dim(i) == 1
+                       || input0->dim(rank_diff + i) == input1->dim(i),
                    "Element-Wise op only support tail dimensions broadcast");
       }
     }
-    }
-
-    index_t common_size = input1->size();
-    index_t diff_size = input0->size() / common_size;
-
-    MACE_RETURN_IF_ERROR(output->ResizeLike(input0));
 
     Tensor::MappingGuard input0_guard(input0);
     Tensor::MappingGuard input1_guard(input1);
-    Tensor::MappingGuard output_guard(output);
 
     const float *input0_ptr = input0->data<float>();
     const float *input1_ptr = input1->data<float>();
-    float *output_ptr = output->mutable_data<float>();
 
     if (data_format_ == NCHW && input1->dim_size() > 0 &&
         input1->size() < input0->size()) {
+      MACE_RETURN_IF_ERROR(output->ResizeLike(input0));
+      Tensor::MappingGuard output_guard(output);
+      float *output_ptr = output->mutable_data<float>();
       TensorEltwisePerChannel(
           type_, input0_ptr, input1_ptr, coeff_, input0->dim(0),
           input1->dim_size() == 1 ? 1 : input1->dim(0), input0->dim(1),
           input0->dim(2) * input0->dim(3), swapped, output_ptr);
 
     } else {
-      if (input1->size() == input0->size()) {
+      const std::vector<index_t> &input0_shape = input0->shape();
+      std::vector<index_t> input1_shape(rank_diff, 1);
+      input1_shape.insert(input1_shape.end(),
+                          input1->shape().begin(),
+                          input1->shape().end());
+
+      std::vector<index_t> output_shape(input0->dim_size(), 0);
+      for (unsigned int i = 0; i < input0_shape.size(); ++i) {
+        output_shape[i] = std::max(input0_shape[i], input1_shape[i]);
+      }
+      MACE_RETURN_IF_ERROR(output->Resize(output_shape));
+      Tensor::MappingGuard output_guard(output);
+      float *output_ptr = output->mutable_data<float>();
+
+      bool need_general_broadcast = false;
+      for (uint32_t i = 0; i < input1->dim_size(); ++i) {
+        if ((input0->dim(rank_diff + i) == 1 && input1->dim(i) > 1)
+            || (input0->dim(rank_diff + i) > 1 && input1->dim(i) == 1)) {
+          need_general_broadcast = true;
+          break;
+        }
+      }
+
+      if (need_general_broadcast) {
+        TensorGeneralBroadcastEltwise(type_,
+                                      input0_ptr,
+                                      input1_ptr,
+                                      coeff_,
+                                      swapped,
+                                      input0_shape,
+                                      input1_shape,
+                                      output_shape,
+                                      output_ptr);
+      } else if (input1->size() == input0->size()) {
         TensorEltwise(type_, input0_ptr, input1_ptr, coeff_, input0->size(),
                       swapped, output_ptr);
       } else if (input1->size() < input0->size()) {
         if (input1->size() > 1) {
+          index_t common_size = input1->size();
+          index_t diff_size = input0->size() / common_size;
           TensorBroadcastEltwise(type_, input0_ptr, input1_ptr, coeff_,
                                  diff_size, common_size, swapped, output_ptr);
         } else {

mace/kernels/opencl/bias_add.cc

@@ -26,6 +26,9 @@ MaceStatus BiasAddFunctor<DeviceType::GPU, T>::operator()(const Tensor *input,
                                                           const Tensor *bias,
                                                           Tensor *output,
                                                           StatsFuture *future) {
+  MACE_CHECK(input->dim_size() == 4 && data_format_ == NHWC,
+             "gpu only support biasadd for 4-dimensional NHWC format tensor");
+
   const index_t batch = input->dim(0);
   const index_t height = input->dim(1);
   const index_t width = input->dim(2);

mace/ops/bias_add.h

@@ -25,14 +25,14 @@ template <DeviceType D, class T>
 class BiasAddOp : public Operator<D, T> {
  public:
   BiasAddOp(const OperatorDef &operator_def, Workspace *ws)
-      : Operator<D, T>(operator_def, ws), functor_() {}
+      : Operator<D, T>(operator_def, ws),
+        functor_(static_cast<DataFormat>(OperatorBase::GetOptionalArg<int>(
+            "data_format", NHWC))) {}
 
   MaceStatus Run(StatsFuture *future) override {
     const Tensor *input = this->Input(INPUT);
     const Tensor *bias = this->Input(BIAS);
 
-    MACE_CHECK(input->dim_size() == 4, "input must be 4-dimensional. ",
-               input->dim_size());
     MACE_CHECK(bias->dim_size() == 1, "bias must be 1-dimensional. ",
                bias->dim_size());
 

mace/ops/bias_add_benchmark.cc

@@ -42,6 +42,7 @@ void BiasAdd(int iters, int batch, int channels, int height, int width) {
     OpDefBuilder("BiasAdd", "BiasAddBM")
       .Input("Input")
       .Input("Bias")
+      .AddIntArg("data_format", NCHW)
       .Output("Output")
       .Finalize(net.NewOperatorDef());
   } else if (D == DeviceType::GPU) {

mace/ops/bias_add_test.cc

@@ -37,6 +37,7 @@ void BiasAddSimple() {
     OpDefBuilder("BiasAdd", "BiasAddTest")
         .Input("InputNCHW")
         .Input("Bias")
+        .AddIntArg("data_format", NCHW)
         .Output("OutputNCHW")
         .Finalize(net.NewOperatorDef());
     // Run
@@ -99,6 +100,7 @@ TEST_F(BiasAddOpTest, SimpleRandomOPENCL) {
   OpDefBuilder("BiasAdd", "BiasAddTest")
       .Input("InputNCHW")
       .Input("Bias")
+      .AddIntArg("data_format", NCHW)
       .Output("OutputNCHW")
       .Finalize(net.NewOperatorDef());
 
@@ -155,6 +157,7 @@ TEST_F(BiasAddOpTest, ComplexRandomOPENCL) {
   OpDefBuilder("BiasAdd", "BiasAddTest")
       .Input("InputNCHW")
       .Input("Bias")
+      .AddIntArg("data_format", NCHW)
       .Output("OutputNCHW")
       .Finalize(net.NewOperatorDef());
 

mace/ops/cast.cc

+// Copyright 2018 Xiaomi, Inc.  All rights reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "mace/ops/cast.h"
+
+namespace mace {
+namespace ops {
+
+void Register_Cast(OperatorRegistry *op_registry) {
+  MACE_REGISTER_OPERATOR(op_registry, OpKeyBuilder("Cast")
+                                          .Device(DeviceType::CPU)
+                                          .TypeConstraint<float>("T")
+                                          .Build(),
+                         CastOp<DeviceType::CPU, float>);
+  MACE_REGISTER_OPERATOR(op_registry, OpKeyBuilder("Cast")
+                                          .Device(DeviceType::CPU)
+                                          .TypeConstraint<int32_t>("T")
+                                          .Build(),
+                         CastOp<DeviceType::CPU, int32_t>);
+}
+
+}  // namespace ops
+}  // namespace mace

mace/ops/cast.h

+// Copyright 2018 Xiaomi, Inc.  All rights reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#ifndef MACE_OPS_CAST_H_
+#define MACE_OPS_CAST_H_
+
+#include <vector>
+
+#include "mace/core/operator.h"
+
+namespace mace {
+namespace ops {
+
+template <DeviceType D, typename DT>
+class CastOp : public Operator<D, DT> {
+ public:
+  CastOp(const OperatorDef &op_def, Workspace *ws)
+      : Operator<D, DT>(op_def, ws) {}
+
+  MaceStatus Run(StatsFuture *future) override {
+    MACE_UNUSED(future);
+    const Tensor *input = this->Input(INPUT);
+    Tensor *output = this->Output(OUTPUT);
+    MACE_RETURN_IF_ERROR(output->ResizeLike(input))
+
+    Tensor::MappingGuard input_guard(input);
+    Tensor::MappingGuard output_guard(output);
+    auto src_dtype = input->dtype();
+
+    auto output_data = output->mutable_data<DT>();
+
+#define MACE_CAST_COPY \
+    auto input_data = input->data<T>();                                 \
+    for (index_t i = 0; i < output->size(); ++i) {                      \
+      output_data[i] = static_cast<DT>(input_data[i]);                  \
+    }
+
+    MACE_RUN_WITH_TYPE_ENUM(src_dtype, MACE_CAST_COPY);
+
+    return MACE_SUCCESS;
+  }
+
+ private:
+  MACE_OP_INPUT_TAGS(INPUT);
+  MACE_OP_OUTPUT_TAGS(OUTPUT);
+};
+
+}  // namespace ops
+}  // namespace mace
+
+#endif  // MACE_OPS_CAST_H_

mace/ops/cast_test.cc

+// Copyright 2018 Xiaomi, Inc.  All rights reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "gmock/gmock.h"
+#include "mace/core/operator.h"
+#include "mace/ops/ops_test_util.h"
+
+namespace mace {
+namespace ops {
+namespace test {
+
+class CastOpTest : public OpsTestBase {};
+
+namespace {
+template <typename SrcType, typename DstType>
+void TestCast(const std::vector<index_t> &input_shape,
+              const std::vector<SrcType> &input) {
+  // Construct graph
+  OpsTestNet net;
+  OpDefBuilder("Cast", "CastTest")
+      .Input("Input")
+      .Output("Output")
+      .AddIntArg("T", DataTypeToEnum<DstType>::v())
+      .Finalize(net.NewOperatorDef());
+
+  // Add input data
+  net.AddInputFromArray<DeviceType::CPU, SrcType>("Input", input_shape, input);
+
+  // Run
+  net.RunOp();
+
+  auto input_tensor = net.GetTensor("Input");
+  auto output_tensor = net.GetTensor("Output");
+
+  EXPECT_THAT(output_tensor->shape(), ::testing::ContainerEq(input_shape));
+
+  const int size = output_tensor->size();
+  for (int i = 0; i < size; ++i) {
+    Expector<SrcType, DstType, true>::Near(
+        *input_tensor, *output_tensor, 1e-5, 1.f);
+  }
+}
+}  // namespace
+
+TEST_F(CastOpTest, TestCastFromFloatToInt32) {
+  TestCast<float, int32_t>({1, 2, 3}, {1.1, 2.2, 3.3, 4.4, 5.5, 6.6});
+}
+
+TEST_F(CastOpTest, TestCastFromInt32ToFloat) {
+  TestCast<int32_t, float>({1, 2, 3}, {1, 2, 3, 4, 5, 6});
+}
+
+}  // namespace test
+}  // namespace ops
+}  // namespace mace

mace/ops/concat.cc

@@ -23,7 +23,11 @@ void Register_Concat(OperatorRegistry *op_registry) {
                                           .TypeConstraint<float>("T")
                                           .Build(),
                          ConcatOp<DeviceType::CPU, float>);
-
+  MACE_REGISTER_OPERATOR(op_registry, OpKeyBuilder("Concat")
+                                          .Device(DeviceType::CPU)
+                                          .TypeConstraint<int32_t>("T")
+                                          .Build(),
+                         ConcatOp<DeviceType::CPU, int32_t>);
 #ifdef MACE_ENABLE_OPENCL
   MACE_REGISTER_OPERATOR(op_registry, OpKeyBuilder("Concat")
                                           .Device(DeviceType::GPU)

mace/ops/eltwise_test.cc

@@ -135,6 +135,41 @@ void SimpleTensorEltwise(const kernels::EltwiseType type,
 
   ExpectTensorNear<float>(*expected, *net.GetOutput("Output"), 1e-5);
 }
+
+template<DeviceType D, typename T>
+void TensorGeneralBroadcastEltwise(const kernels::EltwiseType type,
+                                   const std::vector<index_t> &shape0,
+                                   const std::vector<float> &input0,
+                                   const std::vector<index_t> &shape1,
+                                   const std::vector<float> &input1,
+                                   const std::vector<index_t> &output_shape,
+                                   const std::vector<float> &output,
+                                   const std::vector<float> &coeff = {}) {
+  // Construct graph
+  OpsTestNet net;
+
+  // Add input data
+  net.AddInputFromArray<D, float>("Input0", shape0, input0);
+  net.AddInputFromArray<D, float>("Input1", shape1, input1);
+
+  if (D == DeviceType::CPU) {
+    auto op_builder = OpDefBuilder("Eltwise", "EltwiseTest")
+        .Input("Input0")
+        .Input("Input1")
+        .AddIntArg("type", static_cast<int>(type))
+        .AddFloatsArg("coeff", coeff)
+        .Output("Output");
+    op_builder.Finalize(net.NewOperatorDef());
+
+    // Run
+    net.RunOp(D);
+  } else {
+    MACE_NOT_IMPLEMENTED;
+  }
+
+  auto expected = CreateTensor<float>(output_shape, output);
+  ExpectTensorNear<float>(*expected, *net.GetOutput("Output"), 1e-5);
+}
 }  // namespace
 
 TEST_F(EltwiseOpTest, CPUSimpleTensorScalar) {
@@ -559,6 +594,30 @@ TEST_F(EltwiseOpTest, RandomTensorTensorHalf) {
                             {3, 31, 37, 17});
 }
 
+TEST_F(EltwiseOpTest, TensorGeneralBroadcast) {
+  TensorGeneralBroadcastEltwise<DeviceType::CPU, float>(
+      kernels::EltwiseType::SUM, {1, 1, 2, 3}, {1, 2, 3, 4, 5, 6}, {1, 1, 2, 1},
+      {1, 2}, {1, 1, 2, 3}, {2, 3, 4, 6, 7, 8});
+  TensorGeneralBroadcastEltwise<DeviceType::CPU, float>(
+      kernels::EltwiseType::SUB, {1, 1, 2, 3}, {1, 2, 3, 4, 5, 6}, {1, 1, 2, 1},
+      {1, 2}, {1, 1, 2, 3}, {0, 1, 2, 2, 3, 4});
+  TensorGeneralBroadcastEltwise<DeviceType::CPU, float>(
+      kernels::EltwiseType::PROD, {1, 1, 2, 3}, {1, 2, 3, 4, 5, 6},
+      {1, 1, 2, 1}, {1, 2}, {1, 1, 2, 3}, {1, 2, 3, 8, 10, 12});
+  TensorGeneralBroadcastEltwise<DeviceType::CPU, float>(
+      kernels::EltwiseType::DIV, {1, 1, 2, 3}, {1, 2, 3, 4, 5, 6}, {1, 1, 2, 1},
+      {1, 2}, {1, 1, 2, 3}, {1, 2, 3, 2, 2.5, 3});
+  TensorGeneralBroadcastEltwise<DeviceType::CPU, float>(
+      kernels::EltwiseType::MIN, {1, 1, 2, 3}, {1, 2, 3, 4, 5, 6}, {1, 1, 2, 1},
+      {1, 2}, {1, 1, 2, 3}, {1, 1, 1, 2, 2, 2});
+  TensorGeneralBroadcastEltwise<DeviceType::CPU, float>(
+      kernels::EltwiseType::MAX, {1, 1, 2, 3}, {1, 2, 3, 4, 5, 6}, {1, 1, 2, 1},
+      {1, 2}, {1, 1, 2, 3}, {1, 2, 3, 4, 5, 6});
+  TensorGeneralBroadcastEltwise<DeviceType::CPU, float>(
+      kernels::EltwiseType::SQR_DIFF, {1, 1, 2, 3}, {1, 2, 3, 4, 5, 6},
+      {1, 1, 2, 1}, {1, 2}, {1, 1, 2, 3}, {0, 1, 4, 4, 9, 16});
+}
+
 }  // namespace test
 }  // namespace ops
 }  // namespace mace

mace/ops/reshape.cc

@@ -23,6 +23,12 @@ void Register_Reshape(OperatorRegistry *op_registry) {
                                           .TypeConstraint<float>("T")
                                           .Build(),
                          ReshapeOp<DeviceType::CPU, float>);
+  MACE_REGISTER_OPERATOR(op_registry, OpKeyBuilder("Reshape")
+                                          .Device(DeviceType::CPU)
+                                          .TypeConstraint<int32_t>("T")
+                                          .Build(),
+                         ReshapeOp<DeviceType::CPU, int32_t>);
+
 
 #ifdef MACE_ENABLE_OPENCL
   MACE_REGISTER_OPERATOR(op_registry, OpKeyBuilder("Reshape")

mace/ops/stack.cc

@@ -23,6 +23,11 @@ void Register_Stack(OperatorRegistry *op_registry) {
                                           .TypeConstraint<float>("T")
                                           .Build(),
                          StackOp<DeviceType::CPU, float>);
+  MACE_REGISTER_OPERATOR(op_registry, OpKeyBuilder("Stack")
+                                          .Device(DeviceType::CPU)
+                                          .TypeConstraint<int32_t>("T")
+                                          .Build(),
+                         StackOp<DeviceType::CPU, int32_t>);
 }
 
 }  // namespace ops

mace/ops/strided_slice.cc

@@ -23,6 +23,11 @@ void Register_StridedSlice(OperatorRegistry *op_registry) {
                                           .TypeConstraint<float>("T")
                                           .Build(),
                          StridedSliceOp<DeviceType::CPU, float>);
+  MACE_REGISTER_OPERATOR(op_registry, OpKeyBuilder("StridedSlice")
+                                          .Device(DeviceType::CPU)
+                                          .TypeConstraint<int32_t>("T")
+                                          .Build(),
+                         StridedSliceOp<DeviceType::CPU, int32_t>);
 }
 
 }  // namespace ops

mace/python/tools/converter.py

@@ -107,6 +107,9 @@ def main(unused_args):
         else:
             print("%s does not support dsp runtime yet." % FLAGS.platform)
             sys.exit(-1)
+    else:
+        if FLAGS.transformers:
+            option = cvt.ConverterOption(FLAGS.transformers.split(','))
         else:
             option = cvt.ConverterOption()
         option.winograd_enabled = bool(FLAGS.winograd)
@@ -285,6 +288,11 @@ def parse_args():
         type=str,
         default="fp16_fp32",
         help="fp16_fp32/fp32_fp32")
+    parser.add_argument(
+        "--transformers",
+        type=str,
+        default="",
+        help="model transformers")
     return parser.parse_known_args()
 
 

mace/python/tools/converter_tool/base_converter.py

@@ -74,6 +74,7 @@ MaceSupportedOps = [
     'BatchNorm',
     'BatchToSpaceND',
     'BiasAdd',
+    'Cast',
     'ChannelShuffle',
     'Concat',
     'Conv2D',
@@ -177,9 +178,10 @@ class TransformerRule(Enum):
     TRANSPOSE_DATA_FORMAT = 15
     TRANSFORM_GLOBAL_CONV_TO_FC = 16
     TRANSFORM_BUFFER_IMAGE = 17
-    ADD_DEVICE_AND_DATA_TYPE = 18
+    ADD_DEVICE = 18
     SORT_BY_EXECUTION = 19
     ADD_IN_OUT_TENSOR_INFO = 20
+    ADD_MACE_INPUT_AND_OUTPUT_NODES = 21
 
 
 class ConverterInterface(object):
@@ -219,12 +221,16 @@ class NodeInfo(object):
 class ConverterOption(object):
     """A class for specifying options passed to converter tool"""
 
-    def __init__(self):
+    def __init__(self, transformers=None):
         self._input_nodes = {}
         self._output_nodes = {}
         self._data_type = mace_pb2.DT_FLOAT
         self._device = DeviceType.CPU.value
         self._winograd_enabled = False
+        if transformers:
+            self._transformer_option = [TransformerRule[transformer]
+                                        for transformer in transformers]
+        else:
             self._transformer_option = [
                 TransformerRule.REMOVE_IDENTITY_OP,
                 TransformerRule.TRANSFORM_GLOBAL_POOLING,
@@ -244,7 +250,8 @@ class ConverterOption(object):
                 TransformerRule.TRANSFORM_GLOBAL_CONV_TO_FC,
                 TransformerRule.RESHAPE_FC_WEIGHT,
                 TransformerRule.TRANSFORM_BUFFER_IMAGE,
-            TransformerRule.ADD_DEVICE_AND_DATA_TYPE,
+                TransformerRule.ADD_DEVICE,
+                TransformerRule.ADD_MACE_INPUT_AND_OUTPUT_NODES,
                 TransformerRule.SORT_BY_EXECUTION,
             ]
 

mace/python/tools/converter_tool/caffe_converter.py

@@ -341,6 +341,10 @@ class CaffeConverter(base_converter.ConverterInterface):
         op.input.extend(caffe_op.layer.bottom)
         op.output.extend(caffe_op.layer.top)
 
+        data_type_arg = op.arg.add()
+        data_type_arg.name = 'T'
+        data_type_arg.i = self._option.data_type
+
         ConverterUtil.add_data_format_arg(op, DataFormat.NCHW)
 
         return op

mace/python/tools/converter_tool/tensorflow_converter.py

@@ -92,6 +92,7 @@ TFSupportedOps = [
     'Slice',
     'Stack',
     'Pack',
+    'Cast',
 ]
 
 TFOpType = Enum('TFOpType', [(op, op) for op in TFSupportedOps], type=str)
@@ -181,7 +182,8 @@ class TensorflowConverter(base_converter.ConverterInterface):
             TFOpType.StridedSlice.name: self.convert_stridedslice,
             TFOpType.Slice.name: self.convert_slice,
             TFOpType.Pack.name: self.convert_stack,
-            TFOpType.Stack.name: self.convert_stack
+            TFOpType.Stack.name: self.convert_stack,
+            TFOpType.Cast.name: self.convert_cast
         }
         self._option = option
         self._mace_net_def = mace_pb2.NetDef()
@@ -300,6 +302,19 @@ class TensorflowConverter(base_converter.ConverterInterface):
             output_shape = op.output_shape.add()
             output_shape.dims.extend(self.infer_tensor_shape(tf_output))
 
+        data_type_arg = op.arg.add()
+        data_type_arg.name = 'T'
+        try:
+            dtype = tf_op.get_attr('T')
+            if dtype == tf.int32:
+                data_type_arg.i = mace_pb2.DT_INT32
+            elif dtype == tf.float32:
+                data_type_arg.i = self._option.data_type
+            else:
+                mace_check(False, "data type %s not supported" % dtype)
+        except ValueError:
+            data_type_arg.i = self._option.data_type
+
         ConverterUtil.add_data_format_arg(op, DataFormat.NHWC)
 
         return op
@@ -367,7 +382,7 @@ class TensorflowConverter(base_converter.ConverterInterface):
                 value_arg.f = tf_op.inputs[0].eval().astype(np.float32)
                 self._skip_tensor.add(tf_op.inputs[0].name)
                 del op.input[0]
-            elif len(tf_op.inputs[1].shape) == 0:
+            elif len(tf_op.inputs) > 1 and len(tf_op.inputs[1].shape) == 0:
                 value_arg = op.arg.add()
                 value_arg.name = MaceKeyword.mace_value_str
                 value_arg.f = tf_op.inputs[1].eval().astype(np.float32)
@@ -655,6 +670,9 @@ class TensorflowConverter(base_converter.ConverterInterface):
     def convert_slice(self, tf_op):
         op = self.convert_general_op(tf_op)
         op.type = MaceOp.StridedSlice.name
+        arg = op.arg.add()
+        arg.name = 'slice'
+        arg.i = 1
 
     def convert_stack(self, tf_op):
         op = self.convert_general_op(tf_op)
@@ -666,3 +684,19 @@ class TensorflowConverter(base_converter.ConverterInterface):
             axis_arg.i = tf_op.get_attr(MaceKeyword.mace_axis_str)
         except ValueError:
             axis_arg.i = 0
+
+    def convert_cast(self, tf_op):
+        op = self.convert_general_op(tf_op)
+        op.type = MaceOp.Cast.name
+
+        data_type_arg = ConverterUtil.get_arg(op, 'T')
+        try:
+            dtype = tf_op.get_attr('DstT')
+            if dtype == tf.int32:
+                data_type_arg.i = mace_pb2.DT_INT32
+            elif dtype == tf.float32:
+                data_type_arg.i = self._option.data_type
+            else:
+                mace_check(False, "data type %s not supported" % dtype)
+        except ValueError:
+            data_type_arg.i = self._option.data_type

mace/python/tools/converter_tool/transformer.py

@@ -53,30 +53,6 @@ class Transformer(base_converter.ConverterInterface):
     """
 
     def __init__(self, option, model):
-        # DO NOT reorder the following transformers' order
-        self._registered_transformers_order = [
-            TransformerRule.REMOVE_IDENTITY_OP,
-            TransformerRule.TRANSFORM_GLOBAL_POOLING,
-            TransformerRule.FOLD_RESHAPE,
-            TransformerRule.TRANSFORM_MATMUL_TO_FC,
-            TransformerRule.FOLD_BATCHNORM,
-            TransformerRule.FOLD_CONV_AND_BN,
-            TransformerRule.FOLD_DEPTHWISE_CONV_AND_BN,
-            TransformerRule.TRANSFORM_GPU_WINOGRAD,
-            TransformerRule.TRANSFORM_ADD_TO_BIASADD,
-            TransformerRule.FOLD_BIASADD,
-            TransformerRule.FOLD_ACTIVATION,
-            TransformerRule.FLATTEN_ATROUS_CONV,
-            TransformerRule.FOLD_ACTIVATION,
-            TransformerRule.TRANSPOSE_FILTERS,
-            TransformerRule.TRANSPOSE_DATA_FORMAT,
-            TransformerRule.ADD_IN_OUT_TENSOR_INFO,
-            TransformerRule.TRANSFORM_GLOBAL_CONV_TO_FC,
-            TransformerRule.RESHAPE_FC_WEIGHT,
-            TransformerRule.TRANSFORM_BUFFER_IMAGE,
-            TransformerRule.ADD_DEVICE_AND_DATA_TYPE,
-            TransformerRule.SORT_BY_EXECUTION,
-        ]
         self._registered_transformers = {
             TransformerRule.REMOVE_IDENTITY_OP: self.remove_identity_op,
             TransformerRule.TRANSFORM_GLOBAL_POOLING:
@@ -105,8 +81,10 @@ class Transformer(base_converter.ConverterInterface):
             TransformerRule.RESHAPE_FC_WEIGHT: self.reshape_fc_weight,
             TransformerRule.TRANSFORM_BUFFER_IMAGE:
                 self.transform_buffer_image,
-            TransformerRule.ADD_DEVICE_AND_DATA_TYPE:
-                self.add_device_and_data_type,
+            TransformerRule.ADD_DEVICE:
+                self.add_device,
+            TransformerRule.ADD_MACE_INPUT_AND_OUTPUT_NODES:
+                self.add_mace_input_and_output_nodes,
             TransformerRule.SORT_BY_EXECUTION: self.sort_by_execution,
         }
 
@@ -119,13 +97,13 @@ class Transformer(base_converter.ConverterInterface):
         self._consumers = {}
         self._producer = {}
         self._target_data_format = DataFormat.NHWC
+        self._input_output_added = False
 
         if self._option.device == DeviceType.CPU.value:
             self._target_data_format = DataFormat.NCHW
 
     def run(self):
-        for key in self._registered_transformers_order:
-            if key in self._option.transformer_option:
+        for key in self._option.transformer_option:
             transformer = self._registered_transformers[key]
             while True:
                 self.construct_ops_and_consumers()
@@ -900,6 +878,8 @@ class Transformer(base_converter.ConverterInterface):
                 else:
                     op.type = MaceOp.Identity.name
 
+            self._input_output_added = True
+
         return False
 
     def transpose_filters(self):
@@ -1060,6 +1040,8 @@ class Transformer(base_converter.ConverterInterface):
 
             ConverterUtil.add_data_format_arg(op_def, DataFormat.NHWC)
 
+        self._input_output_added = True
+
         return False
 
     def fold_reshape(self):
@@ -1164,16 +1146,13 @@ class Transformer(base_converter.ConverterInterface):
                                       in_channels * filter_width
                                       * filter_height][:]
 
-    def add_device_and_data_type(self):
+    def add_device(self):
         # TODO(liuqi) add device definition in OperatorDef
         net = self._model
         for op in net.op:
             arg = op.arg.add()
             arg.name = MaceKeyword.mace_device
             arg.i = self._option.device
-            data_type_arg = op.arg.add()
-            data_type_arg.name = 'T'
-            data_type_arg.i = self._option.data_type
 
         return False
 
@@ -1188,6 +1167,37 @@ class Transformer(base_converter.ConverterInterface):
                     self.sort_dfs(producer_op, visited, sorted_nodes)
         sorted_nodes.append(op)
 
+    def add_mace_input_and_output_nodes(self):
+        if self._input_output_added:
+            return
+
+        print("add mace input and output nodes")
+
+        for input_node in self._option.input_nodes.values():
+            new_input_name = MaceKeyword.mace_input_node_name \
+                             + '_' + input_node.name
+            op_def = self._model.op.add()
+            op_def.name = self.normalize_op_name(input_node.name)
+            op_def.type = MaceOp.Identity.name
+            op_def.input.extend([new_input_name])
+            op_def.output.extend([input_node.name])
+            output_shape = op_def.output_shape.add()
+            output_shape.dims.extend(input_node.shape)
+
+            ConverterUtil.add_data_format_arg(op_def, DataFormat.NHWC)
+
+        for output_node in self._option.output_nodes.values():
+            output_name = MaceKeyword.mace_output_node_name \
+                          + '_' + output_node.name
+            op_def = self._model.op.add()
+            op_def.name = self.normalize_op_name(output_name)
+            op_def.type = MaceOp.Identity.name
+            op_def.input.extend([output_node.name])
+            op_def.output.extend([output_name])
+            output_shape = op_def.output_shape.add()
+            output_shape.dims.extend(
+                self._producer[output_node.name].output_shape[0].dims)
+
     def sort_by_execution(self):
         print("Sort by execution")
         net = self._model

mace/python/tools/memory_optimizer.py

@@ -24,7 +24,8 @@ class MemoryOptimizer(object):
         self.op_mem = {}  # op_name->mem_id
         self.mem_block = {}  # mem_id->[size] or mem_id->[x, y]
         self.total_mem_count = 0
-        self.ref_counter = {}
+        self.input_ref_counter = {}
+        self.mem_ref_counter = {}
 
         consumers = {}
         for op in net_def.op:
@@ -41,9 +42,10 @@ class MemoryOptimizer(object):
             for output in op.output:
                 tensor_name = output
                 if tensor_name in consumers:
-                    self.ref_counter[tensor_name] = len(consumers[tensor_name])
+                    self.input_ref_counter[tensor_name] = \
+                        len(consumers[tensor_name])
                 else:
-                    self.ref_counter[tensor_name] = 0
+                    self.input_ref_counter[tensor_name] = 0
 
     def op_need_optimize_memory(self, op):
         return True
@@ -93,8 +95,8 @@ class MemoryOptimizer(object):
             if not self.op_need_optimize_memory(op):
                 continue
             if not op.output_shape:
-                print('WARNING: There is no output shape information to '
-                      'do memory optimization.')
+                print("WARNING: There is no output shape information to "
+                      "do memory optimization. %s (%s)" % (op.name, op.type))
                 return
             if len(op.output_shape) != len(op.output):
                 print('WARNING: the number of output shape is not equal to '
@@ -146,16 +148,23 @@ class MemoryOptimizer(object):
                 if mem_id != -1:
                     op.mem_id.extend([mem_id])
                     self.op_mem[op.output[i]] = mem_id
+                    if mem_id not in self.mem_ref_counter:
+                        self.mem_ref_counter[mem_id] = 1
+                    else:
+                        self.mem_ref_counter[mem_id] += 1
 
             # de-ref input tensor mem
             for idx in xrange(len(op.input)):
                 ipt = op.input[idx]
-                if ipt in self.ref_counter:
-                    self.ref_counter[ipt] -= 1
-                    if self.ref_counter[ipt] == 0 and \
-                            (idx > 0 or not self.is_memory_reuse_op(op)):
+                if ipt in self.input_ref_counter:
+                    self.input_ref_counter[ipt] -= 1
+                    if self.input_ref_counter[ipt] == 0 \
+                            and ipt in self.op_mem:
+                        mem_id = self.op_mem[ipt]
+                        self.mem_ref_counter[mem_id] -= 1
+                        if self.mem_ref_counter[mem_id] == 0:
                             self.idle_mem.add(self.op_mem[ipt])
-                    elif self.ref_counter[ipt] < 0:
+                    elif self.input_ref_counter[ipt] < 0:
                         raise Exception('ref count is less than 0')
 
         self.add_net_mem_blocks()

mace/utils/string_util.h

@@ -54,10 +54,9 @@ std::string MakeString(const Args &... args) {
 }
 
 template <typename T>
-std::string MakeString(const std::vector<T> &args) {
+std::string MakeListString(const T *args, size_t size) {
   std::stringstream ss;
   ss << "[";
-  const size_t size = args.size();
   for (size_t i = 0; i < size; ++i) {
     ss << args[i];
     if (i < size - 1) {
@@ -68,6 +67,11 @@ std::string MakeString(const std::vector<T> &args) {
   return ss.str();
 }
 
+template <typename T>
+std::string MakeString(const std::vector<T> &args) {
+  return MakeListString(args.data(), args.size());
+}
+
 // Specializations for already-a-string types.
 template <>
 inline std::string MakeString(const std::string &str) {

tools/converter.py

@@ -136,6 +136,7 @@ class YAMLKeyword(object):
     subgraphs = 'subgraphs'
     input_tensors = 'input_tensors'
     input_shapes = 'input_shapes'
+    input_ranges = 'input_ranges'
     output_tensors = 'output_tensors'
     output_shapes = 'output_shapes'
     runtime = 'runtime'
@@ -145,6 +146,7 @@ class YAMLKeyword(object):
     obfuscate = 'obfuscate'
     winograd = 'winograd'
     validation_inputs_data = 'validation_inputs_data'
+    transformers = 'transformers'  # keep it private for now
 
 
 class ModuleName(object):
@@ -640,7 +642,8 @@ def convert_model(configs):
             model_config[YAMLKeyword.winograd],
             model_config[YAMLKeyword.obfuscate],
             configs[YAMLKeyword.build_type],
-            data_type)
+            data_type,
+            ",".join(model_config.get(YAMLKeyword.transformers, [])))
 
         if configs[YAMLKeyword.build_type] == BuildType.proto:
             sh.mv("-f",
@@ -732,7 +735,8 @@ def build_specific_lib(target_abi, target_soc, serial_num,
                 model_output_dir,
                 subgraphs[0][YAMLKeyword.input_tensors],
                 subgraphs[0][YAMLKeyword.input_shapes],
-                subgraphs[0][YAMLKeyword.validation_inputs_data])
+                subgraphs[0][YAMLKeyword.validation_inputs_data],
+                input_ranges=subgraphs[0].get(YAMLKeyword.input_ranges, None))
 
             device_type = parse_device_type(RuntimeType.gpu)
             sh_commands.tuning_run(
@@ -975,7 +979,8 @@ def run_specific_target(flags, configs, target_abi,
             model_output_dir,
             subgraphs[0][YAMLKeyword.input_tensors],
             subgraphs[0][YAMLKeyword.input_shapes],
-            subgraphs[0][YAMLKeyword.validation_inputs_data])
+            subgraphs[0][YAMLKeyword.validation_inputs_data],
+            input_ranges=subgraphs[0].get(YAMLKeyword.input_ranges, None))
         runtime_list = []
         if target_abi == ABIType.host:
             runtime_list.extend([RuntimeType.cpu])
@@ -1123,7 +1128,8 @@ def bm_specific_target(flags, configs, target_abi, target_soc, serial_num):
             model_output_dir,
             subgraphs[0][YAMLKeyword.input_tensors],
             subgraphs[0][YAMLKeyword.input_shapes],
-            subgraphs[0][YAMLKeyword.validation_inputs_data])
+            subgraphs[0][YAMLKeyword.validation_inputs_data],
+            input_ranges=subgraphs[0].get(YAMLKeyword.input_ranges, None))
         runtime_list = []
         if target_abi == ABIType.host:
             runtime_list.extend([RuntimeType.cpu])

tools/generate_data.py

@@ -23,25 +23,34 @@ import common
 #    python generate_data.py \
 #        --input_node input_node \
 #        --input_shape 1,64,64,3 \
-#        --input_file input_file
-#
+#        --input_file input_file \
+#        --input_ranges -1,1
 
 
-def generate_data(name, shape, input_file):
+def generate_data(name, shape, input_file, tensor_range):
     np.random.seed()
-    data = np.random.random(shape) * 2 - 1
+    data = np.random.random(shape) * (tensor_range[1] - tensor_range[0]) \
+        + tensor_range[0]
     input_file_name = common.formatted_file_name(input_file, name)
     print 'Generate input file: ', input_file_name
     data.astype(np.float32).tofile(input_file_name)
 
 
-def generate_input_data(input_file, input_node, input_shape):
+def generate_input_data(input_file, input_node, input_shape, input_ranges):
     input_names = [name for name in input_node.split(',')]
     input_shapes = [shape for shape in input_shape.split(':')]
+    if input_ranges:
+        input_ranges = [r for r in input_ranges.split(':')]
+    else:
+        input_ranges = None
     assert len(input_names) == len(input_shapes)
     for i in range(len(input_names)):
         shape = [int(x) for x in input_shapes[i].split(',')]
-        generate_data(input_names[i], shape, input_file)
+        if input_ranges:
+            input_range = [float(x) for x in input_ranges[i].split(',')]
+        else:
+            input_range = [-1, 1]
+        generate_data(input_names[i], shape, input_file, input_range)
     print "Generate input file done."
 
 
@@ -55,10 +64,13 @@ def parse_args():
         "--input_node", type=str, default="input_node", help="input node")
     parser.add_argument(
         "--input_shape", type=str, default="1,64,64,3", help="input shape.")
+    parser.add_argument(
+        "--input_ranges", type=str, default="-1,1", help="input range.")
 
     return parser.parse_known_args()
 
 
 if __name__ == '__main__':
     FLAGS, unparsed = parse_args()
-    generate_input_data(FLAGS.input_file, FLAGS.input_node, FLAGS.input_shape)
+    generate_input_data(FLAGS.input_file, FLAGS.input_node, FLAGS.input_shape,
+                        FLAGS.input_ranges)