Merge pull request #1387 from hjchen2/ocr_attention

Add/Refine unit tests

test/CMakeLists.txt

@@ -235,6 +235,15 @@ if (NOT FOUND_MATCH)
     ADD_EXECUTABLE(test-relu-op operators/test_relu_op.cpp test_helper.h test_include.h)
     target_link_libraries(test-relu-op paddle-mobile)
 
+    ADD_EXECUTABLE(test-relu6-op operators/test_relu6_op.cpp test_helper.h test_include.h)
+    target_link_libraries(test-relu6-op paddle-mobile)
+
+    ADD_EXECUTABLE(test-topk-op operators/test_topk_op.cpp test_helper.h test_include.h)
+    target_link_libraries(test-topk-op paddle-mobile)
+
+    ADD_EXECUTABLE(test-cast-op operators/test_cast_op.cpp test_helper.h test_include.h)
+    target_link_libraries(test-cast-op paddle-mobile)
+
     # gen test
     ADD_EXECUTABLE(test-fc-op operators/test_fusion_fc_op.cpp test_helper.h test_include.h)
     target_link_libraries(test-fc-op paddle-mobile)

test/operators/test_batchnorm_op.cpp

@@ -17,157 +17,106 @@ limitations under the License. */
 #include "operators/batchnorm_op.h"
 
 namespace paddle_mobile {
-namespace framework {
-
-template <typename Dtype>
-class TestBatchNormOp {
- public:
-  explicit TestBatchNormOp(const Program<Dtype> p) : program_(p) {
-    if (use_optimize_) {
-      to_predict_program_ = program_.optimizeProgram;
-    } else {
-      to_predict_program_ = program_.originProgram;
-    }
 
-    const std::vector<std::shared_ptr<BlockDesc>> blocks =
-        to_predict_program_->Blocks();
-    //  DLOG << " **block size " << blocks.size();
-    for (int i = 0; i < blocks.size(); ++i) {
-      std::shared_ptr<BlockDesc> block_desc = blocks[i];
-      std::vector<std::shared_ptr<OpDesc>> ops = block_desc->Ops();
-      //    DLOG << " ops " << ops.size();
-      for (int j = 0; j < ops.size(); ++j) {
-        std::shared_ptr<OpDesc> op = ops[j];
-        if (op->Type() == "batch_norm" &&
-            op->Input("X")[0] == "conv2d_5.tmp_0") {
-          DLOG << " mul attr size: " << op->GetAttrMap().size();
-          DLOG << " inputs size: " << op->GetInputs().size();
-          DLOG << " outputs size: " << op->GetOutputs().size();
-          DLOG << " Input X is : " << op->Input("X")[0];
-          DLOG << " Input Mean is : " << op->Input("Mean")[0];
-          DLOG << " Input Variance is : " << op->Input("Variance")[0];
-          DLOG << " Input Scale is : " << op->Input("Scale")[0];
-          DLOG << " Input Bias is : " << op->Input("Bias")[0];
-          DLOG << " Output Y is : " << op->Output("Y")[0];
-          DLOG << " epsilon : " << op->GetAttrMap().at("epsilon").Get<float>();
-          std::shared_ptr<operators::BatchNormOp<Dtype, float>> lrn =
-              std::make_shared<operators::BatchNormOp<Dtype, float>>(
-                  op->Type(), op->GetInputs(), op->GetOutputs(),
-                  op->GetAttrMap(), program_.scope);
-          ops_of_block_[*block_desc.get()].push_back(lrn);
-        }
+void BatchNorm(const framework::Tensor *X, const framework::Tensor *Mean,
+               const framework::Tensor *Var, const framework::Tensor *Scale,
+               const framework::Tensor *Bias, const float eps,
+               framework::Tensor *Y) {
+  const float *x = X->data<float>();
+  const float *m = Mean->data<float>();
+  const float *v = Var->data<float>();
+  const float *s = Scale->data<float>();
+  const float *b = Bias->data<float>();
+  float *y = Y->mutable_data<float>();
+
+  int batch_size = X->dims()[0];
+  int channel = X->dims()[1];
+  int hw = X->dims()[2] * X->dims()[3];
+
+  for (int batch = 0; batch < batch_size; ++batch) {
+    for (int c = 0; c < channel; ++c) {
+      float mean = m[c];
+      float inv_var = 1.f / std::sqrt(v[c] + eps);
+      float scale = s[c];
+      float bias = b[c];
+      const float *input = x + (batch * channel + c) * hw;
+      float *output = y + (batch * channel + c) * hw;
+      for (int j = 0; j < hw; ++j) {
+        output[j] = scale * ((input[j] - mean) * inv_var) + bias;
       }
     }
   }
+}
 
-  std::shared_ptr<Tensor> predict_bn(const Tensor &t1, const Tensor &t2,
-                                     const Tensor &t3, const Tensor &t4,
-                                     const Tensor &t5) {
-    // feed
-    auto scope = program_.scope;
-    Variable *x1_feed_value = scope->Var("conv2d_5.tmp_0");
-    auto tensor_x1 = x1_feed_value->GetMutable<LoDTensor>();
-    tensor_x1->ShareDataWith(t1);
-
-    Variable *mean_feed_value = scope->Var("batch_norm_10.w_1");
-    auto tensor_mean = mean_feed_value->GetMutable<LoDTensor>();
-    tensor_mean->ShareDataWith(t2);
-
-    Variable *scale_feed_value = scope->Var("batch_norm_10.w_0");
-    auto tensor_scale = scale_feed_value->GetMutable<LoDTensor>();
-    tensor_scale->ShareDataWith(t3);
-
-    Variable *variance_feed_value = scope->Var("batch_norm_10.w_2");
-    auto tensor_variance = variance_feed_value->GetMutable<LoDTensor>();
-    tensor_variance->ShareDataWith(t4);
-
-    Variable *bias_feed_value = scope->Var("batch_norm_10.b_0");
-    auto tensor_bias = bias_feed_value->GetMutable<LoDTensor>();
-    tensor_bias->ShareDataWith(t5);
-
-    Variable *output = scope->Var("batch_norm_10.tmp_2");
-    auto *output_tensor = output->GetMutable<LoDTensor>();
-    output_tensor->mutable_data<float>({1, 256, 38, 38});
-    //  DLOG << typeid(output_tensor).name();
-    //  DLOG << "output_tensor dims: " << output_tensor->dims();
-
-    std::shared_ptr<Tensor> out_tensor = std::make_shared<LoDTensor>();
-    out_tensor.reset(output_tensor);
-
-    predict_bn(t1, t2, t3, t4, t5, 0);
-    return out_tensor;
-  }
-
- private:
-  const framework::Program<Dtype> program_;
-  std::shared_ptr<ProgramDesc> to_predict_program_;
-  std::map<framework::BlockDesc,
-           std::vector<std::shared_ptr<OperatorBase<Dtype>>>>
-      ops_of_block_;
-  bool use_optimize_ = false;
-
-  void predict_bn(const Tensor &t1, const Tensor &t2, const Tensor &t3,
-                  const Tensor &t4, const Tensor &t5, int block_id) {
-    std::shared_ptr<BlockDesc> to_predict_block =
-        to_predict_program_->Block(block_id);
-    for (int j = 0; j < ops_of_block_[*to_predict_block.get()].size(); ++j) {
-      auto op = ops_of_block_[*to_predict_block.get()][j];
-      DLOG << "op -> run()";
-      op->Run();
+int TestBatchNormOp(const std::vector<int> input_shape) {
+  framework::DDim dims = framework::make_ddim(input_shape);
+  VariableNameMap inputs;
+  VariableNameMap outputs;
+  auto scope = std::make_shared<framework::Scope>();
+  inputs["X"] = std::vector<std::string>({"input"});
+  inputs["Mean"] = std::vector<std::string>({"mean"});
+  inputs["Variance"] = std::vector<std::string>({"variance"});
+  inputs["Scale"] = std::vector<std::string>({"scale"});
+  inputs["Bias"] = std::vector<std::string>({"bias"});
+  outputs["Y"] = std::vector<std::string>({"output"});
+
+  auto input_var = scope.get()->Var("input");
+  auto input = input_var->template GetMutable<framework::LoDTensor>();
+  SetupTensor<float>(input, dims, -100.0, 100.0);
+
+  auto mean_var = scope.get()->Var("mean");
+  auto mean = mean_var->template GetMutable<framework::LoDTensor>();
+  SetupTensor<float>(mean, framework::make_ddim({input_shape[1]}), -10.0, 10.0);
+
+  auto vari_var = scope.get()->Var("variance");
+  auto vari = vari_var->template GetMutable<framework::LoDTensor>();
+  SetupTensor<float>(vari, framework::make_ddim({input_shape[1]}), -10.0, 10.0);
+
+  auto scale_var = scope.get()->Var("scale");
+  auto scale = scale_var->template GetMutable<framework::LoDTensor>();
+  SetupTensor<float>(scale, framework::make_ddim({input_shape[1]}), -10.0,
+                     10.0);
+
+  auto bias_var = scope.get()->Var("bias");
+  auto bias = bias_var->template GetMutable<framework::LoDTensor>();
+  SetupTensor<float>(bias, framework::make_ddim({input_shape[1]}), -10.0, 10.0);
+
+  auto output_var = scope.get()->Var("output");
+
+  float eps = 1e-6;
+  framework::AttributeMap attrs;
+  attrs["epsilon"].Set<float>(eps);
+  attrs["momentum"].Set<float>(0.f);
+
+  auto *op = new operators::BatchNormOp<CPU, float>("batch_norm", inputs,
+                                                    outputs, attrs, scope);
+  op->InferShape();
+  op->Init();
+  op->Run();
+
+  auto output = output_var->template Get<framework::LoDTensor>();
+
+  framework::Tensor output_cmp;
+  float *output_cmp_data = output_cmp.mutable_data<float>(output->dims());
+  BatchNorm(input, mean, vari, scale, bias, eps, &output_cmp);
+
+  const float *output_data = output->data<float>();
+  for (int i = 0; i < output->numel(); ++i) {
+    float gap = output_data[i] - output_cmp_data[i];
+    if (std::abs(gap / (output_data[i] + 1e-5)) > 1e-3) {
+      LOG(kLOG_INFO) << "output_data[" << i << "] = " << output_data[i]
+                     << ", output_cmp_data[" << i
+                     << "] = " << output_cmp_data[i];
+      delete op;
+      exit(1);
     }
   }
-};
+}
 
-template class TestBatchNormOp<CPU>;
-}  // namespace framework
 }  // namespace paddle_mobile
 
 int main() {
-  DLOG << "----------**********----------";
-  DLOG << "begin to run BatchNormOp Test";
-  paddle_mobile::framework::Loader<paddle_mobile::CPU> loader;
-  auto program = loader.Load(std::string(g_mobilenet_ssd));
-
-  /// input x (4,10,2,2)
-  paddle_mobile::framework::Tensor inputx1;
-  SetupTensor<float>(&inputx1, {1, 256, 38, 38}, static_cast<float>(0),
-                     static_cast<float>(1));
-  auto *inputx1_ptr = inputx1.data<float>();
-
-  paddle_mobile::framework::Tensor mean;
-  SetupTensor<float>(&mean, {256}, static_cast<float>(0),
-                     static_cast<float>(1));
-  auto *mean_ptr = mean.data<float>();
-
-  paddle_mobile::framework::Tensor scale;
-  SetupTensor<float>(&scale, {256}, static_cast<float>(0),
-                     static_cast<float>(1));
-  auto *scale_ptr = scale.data<float>();
-
-  paddle_mobile::framework::Tensor variance;
-  SetupTensor<float>(&variance, {256}, static_cast<float>(0),
-                     static_cast<float>(1));
-  auto *variance_ptr = variance.data<float>();
-
-  paddle_mobile::framework::Tensor bias;
-  SetupTensor<float>(&bias, {256}, static_cast<float>(0),
-                     static_cast<float>(1));
-  auto *bias_ptr = bias.data<float>();
-
-  paddle_mobile::framework::TestBatchNormOp<paddle_mobile::CPU> testBatchNormOp(
-      program);
-
-  auto output_bn =
-      testBatchNormOp.predict_bn(inputx1, mean, scale, variance, bias);
-  auto *output_bn_ptr = output_bn->data<float>();
-
-  DLOG << " (" << inputx1_ptr[0] << " - " << mean_ptr[0] << ")/(("
-       << variance_ptr[0] << " + 0.00001"
-       << ")^0.5)* " << scale_ptr[0] << " + " << bias_ptr[0] << " = ";
-  DLOG << output_bn_ptr[0];
-
-  DLOG << "input_ptr 0 : " << inputx1_ptr[0];
-  DLOG << "output_ptr 0 : " << output_bn_ptr[0];
-
+  TestBatchNormOp({1, 1, 10, 10});
+  TestBatchNormOp({1, 32, 100, 100});
   return 0;
 }

test/operators/test_cast_op.cpp

+/* Copyright (c) 2018 PaddlePaddle Authors. 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 "../test_include.h"
+#include "operators/cast_op.h"
+
+namespace paddle_mobile {
+
+template <typename Itype, typename Otype>
+void Cast(const framework::Tensor *X, framework::Tensor *Y) {
+  const Itype *x = X->data<Itype>();
+  Otype *y = Y->mutable_data<Otype>();
+
+  for (int i = 0; i < X->numel(); ++i) {
+    y[i] = static_cast<Otype>(x[i]);
+  }
+}
+
+template <typename T>
+int TypeInt() {}
+template <>
+int TypeInt<bool>() {
+  return 0;
+}
+template <>
+int TypeInt<int>() {
+  return 2;
+}
+template <>
+int TypeInt<int64_t>() {
+  return 3;
+}
+template <>
+int TypeInt<float>() {
+  return 5;
+}
+template <>
+int TypeInt<double>() {
+  return 6;
+}
+template <>
+int TypeInt<size_t>() {
+  return 19;
+}
+template <>
+int TypeInt<uint8_t>() {
+  return 20;
+}
+template <>
+int TypeInt<int8_t>() {
+  return 21;
+}
+
+template <typename Itype, typename Otype>
+int TestCastOp(const std::vector<int> input_shape) {
+  framework::DDim dims = framework::make_ddim(input_shape);
+  VariableNameMap inputs;
+  VariableNameMap outputs;
+  auto scope = std::make_shared<framework::Scope>();
+  inputs["X"] = std::vector<std::string>({"input"});
+  outputs["Out"] = std::vector<std::string>({"output"});
+
+  auto input_var = scope.get()->Var("input");
+  auto input = input_var->template GetMutable<framework::LoDTensor>();
+  SetupTensor<Itype>(input, dims, static_cast<Itype>(-100),
+                     static_cast<Itype>(100));
+
+  auto output_var = scope.get()->Var("output");
+
+  framework::AttributeMap attrs;
+  attrs["in_dtype"].Set<int>(TypeInt<Itype>());
+  attrs["out_dtype"].Set<int>(TypeInt<Otype>());
+  auto *op =
+      new operators::CastOp<CPU, float>("cast", inputs, outputs, attrs, scope);
+  op->InferShape();
+  op->Init();
+  op->Run();
+
+  auto output = output_var->template Get<framework::LoDTensor>();
+
+  framework::Tensor output_cmp;
+  Otype *output_cmp_data = output_cmp.mutable_data<Otype>(output->dims());
+  Cast<Itype, Otype>(input, &output_cmp);
+
+  const Otype *output_data = output->data<Otype>();
+  for (int i = 0; i < output->numel(); ++i) {
+    float gap = output_data[i] - output_cmp_data[i];
+    if (std::abs(gap / (output_data[i] + 1e-5)) > 1e-3) {
+      LOG(kLOG_INFO) << "output_data[" << i << "] = " << output_data[i]
+                     << ", output_cmp_data[" << i
+                     << "] = " << output_cmp_data[i];
+      delete op;
+      exit(1);
+    }
+  }
+  delete op;
+  return 0;
+}
+
+}  // namespace paddle_mobile
+
+int main(int argc, char *argv[]) {
+  TestCastOp<float, int>({1, 100});
+  TestCastOp<float, int>({128, 100});
+
+  TestCastOp<float, int64_t>({1, 100});
+  TestCastOp<float, int64_t>({128, 100});
+
+  TestCastOp<int, float>({1, 100});
+  TestCastOp<int, float>({128, 100});
+
+  TestCastOp<int64_t, float>({1, 100});
+  TestCastOp<int64_t, float>({128, 100});
+  return 0;
+}

test/operators/test_pool_op.cpp

@@ -103,20 +103,7 @@ int TestPoolOp(int in_channels, int in_height, int in_width) {
 }
 }  // namespace paddle_mobile
 
-int main(int argc, char *argv[]) {
-  if (argc < 4) {
-    LOG(paddle_mobile::kLOG_INFO)
-        << "Usage:\n"
-        << "  ./test-pool-op in_channels in_height in_width \n"
-        << "  params:\n"
-        << "   -in_channels: int, input image's channels\n"
-        << "   -in_height: int, input image's height\n"
-        << "   -in_width: int, input image's width\n";
-    return 1;
-  }
-  int in_channels = atoi(argv[1]);
-  int in_height = atoi(argv[2]);
-  int in_width = atoi(argv[3]);
+int Test(const int in_channels, const int in_height, const int in_width) {
   LOG(paddle_mobile::kLOG_INFO)
       << "float, pooling_type=max, kernel=3, pad=0, stride=1";
   paddle_mobile::TestPoolOp<0, 3, 0, 1>(in_channels, in_height, in_width);
@@ -169,55 +156,75 @@ int main(int argc, char *argv[]) {
       << "float, pooling_type=avg, kernel=3, pad=5, stride=2";
   paddle_mobile::TestPoolOp<1, 3, 5, 2>(in_channels, in_height, in_width);
 
-  //  LOG(paddle_mobile::kLOG_INFO)
-  //      << "float, pooling_type=max, kernel=2, pad=0, stride=1";
-  //  paddle_mobile::TestPoolOp<0, 2, 0, 1>(in_channels, in_height, in_width);
-  //  LOG(paddle_mobile::kLOG_INFO)
-  //      << "float, pooling_type=max, kernel=2, pad=1, stride=1";
-  //  paddle_mobile::TestPoolOp<0, 2, 1, 1>(in_channels, in_height, in_width);
-  //  LOG(paddle_mobile::kLOG_INFO)
-  //      << "float, pooling_type=max, kernel=2, pad=2, stride=1";
-  //  paddle_mobile::TestPoolOp<0, 2, 2, 1>(in_channels, in_height, in_width);
-  //  LOG(paddle_mobile::kLOG_INFO)
-  //      << "float, pooling_type=max, kernel=2, pad=5, stride=1";
-  //  paddle_mobile::TestPoolOp<0, 2, 5, 1>(in_channels, in_height, in_width);
-  //
-  //  LOG(paddle_mobile::kLOG_INFO)
-  //      << "float, pooling_type=avg, kernel=2, pad=0, stride=1";
-  //  paddle_mobile::TestPoolOp<1, 2, 0, 1>(in_channels, in_height, in_width);
-  //  LOG(paddle_mobile::kLOG_INFO)
-  //      << "float, pooling_type=avg, kernel=2, pad=1, stride=1";
-  //  paddle_mobile::TestPoolOp<1, 2, 1, 1>(in_channels, in_height, in_width);
-  //  LOG(paddle_mobile::kLOG_INFO)
-  //      << "float, pooling_type=avg, kernel=2, pad=2, stride=1";
-  //  paddle_mobile::TestPoolOp<1, 2, 2, 1>(in_channels, in_height, in_width);
-  //  LOG(paddle_mobile::kLOG_INFO)
-  //      << "float, pooling_type=avg, kernel=2, pad=5, stride=1";
-  //  paddle_mobile::TestPoolOp<1, 2, 5, 1>(in_channels, in_height, in_width);
-  //
-  //  LOG(paddle_mobile::kLOG_INFO)
-  //      << "float, pooling_type=max, kernel=2, pad=0, stride=2";
-  //  paddle_mobile::TestPoolOp<0, 2, 0, 2>(in_channels, in_height, in_width);
-  //  LOG(paddle_mobile::kLOG_INFO)
-  //      << "float, pooling_type=max, kernel=2, pad=1, stride=2";
-  //  paddle_mobile::TestPoolOp<0, 2, 1, 2>(in_channels, in_height, in_width);
-  //  LOG(paddle_mobile::kLOG_INFO)
-  //      << "float, pooling_type=max, kernel=2, pad=2, stride=2";
-  //  paddle_mobile::TestPoolOp<0, 2, 2, 2>(in_channels, in_height, in_width);
-  //  LOG(paddle_mobile::kLOG_INFO)
-  //      << "float, pooling_type=max, kernel=2, pad=5, stride=2";
-  //  paddle_mobile::TestPoolOp<0, 2, 5, 2>(in_channels, in_height, in_width);
-  //
-  //  LOG(paddle_mobile::kLOG_INFO)
-  //      << "float, pooling_type=avg, kernel=2, pad=0, stride=2";
-  //  paddle_mobile::TestPoolOp<1, 2, 0, 2>(in_channels, in_height, in_width);
-  //  LOG(paddle_mobile::kLOG_INFO)
-  //      << "float, pooling_type=avg, kernel=2, pad=1, stride=2";
-  //  paddle_mobile::TestPoolOp<1, 2, 1, 2>(in_channels, in_height, in_width);
-  //  LOG(paddle_mobile::kLOG_INFO)
-  //      << "float, pooling_type=avg, kernel=2, pad=2, stride=2";
-  //  paddle_mobile::TestPoolOp<1, 2, 2, 2>(in_channels, in_height, in_width);
-  //  LOG(paddle_mobile::kLOG_INFO)
-  //      << "float, pooling_type=avg, kernel=2, pad=5, stride=2";
-  //  paddle_mobile::TestPoolOp<1, 2, 5, 2>(in_channels, in_height, in_width);
+  LOG(paddle_mobile::kLOG_INFO)
+      << "float, pooling_type=max, kernel=2, pad=0, stride=1";
+  paddle_mobile::TestPoolOp<0, 2, 0, 1>(in_channels, in_height, in_width);
+  LOG(paddle_mobile::kLOG_INFO)
+      << "float, pooling_type=max, kernel=2, pad=1, stride=1";
+  paddle_mobile::TestPoolOp<0, 2, 1, 1>(in_channels, in_height, in_width);
+  LOG(paddle_mobile::kLOG_INFO)
+      << "float, pooling_type=max, kernel=2, pad=2, stride=1";
+  paddle_mobile::TestPoolOp<0, 2, 2, 1>(in_channels, in_height, in_width);
+  LOG(paddle_mobile::kLOG_INFO)
+      << "float, pooling_type=max, kernel=2, pad=5, stride=1";
+  paddle_mobile::TestPoolOp<0, 2, 5, 1>(in_channels, in_height, in_width);
+
+  LOG(paddle_mobile::kLOG_INFO)
+      << "float, pooling_type=avg, kernel=2, pad=0, stride=1";
+  paddle_mobile::TestPoolOp<1, 2, 0, 1>(in_channels, in_height, in_width);
+  LOG(paddle_mobile::kLOG_INFO)
+      << "float, pooling_type=avg, kernel=2, pad=1, stride=1";
+  paddle_mobile::TestPoolOp<1, 2, 1, 1>(in_channels, in_height, in_width);
+  LOG(paddle_mobile::kLOG_INFO)
+      << "float, pooling_type=avg, kernel=2, pad=2, stride=1";
+  paddle_mobile::TestPoolOp<1, 2, 2, 1>(in_channels, in_height, in_width);
+  LOG(paddle_mobile::kLOG_INFO)
+      << "float, pooling_type=avg, kernel=2, pad=5, stride=1";
+  paddle_mobile::TestPoolOp<1, 2, 5, 1>(in_channels, in_height, in_width);
+
+  LOG(paddle_mobile::kLOG_INFO)
+      << "float, pooling_type=max, kernel=2, pad=0, stride=2";
+  paddle_mobile::TestPoolOp<0, 2, 0, 2>(in_channels, in_height, in_width);
+  LOG(paddle_mobile::kLOG_INFO)
+      << "float, pooling_type=max, kernel=2, pad=1, stride=2";
+  paddle_mobile::TestPoolOp<0, 2, 1, 2>(in_channels, in_height, in_width);
+  LOG(paddle_mobile::kLOG_INFO)
+      << "float, pooling_type=max, kernel=2, pad=2, stride=2";
+  paddle_mobile::TestPoolOp<0, 2, 2, 2>(in_channels, in_height, in_width);
+  LOG(paddle_mobile::kLOG_INFO)
+      << "float, pooling_type=max, kernel=2, pad=5, stride=2";
+  paddle_mobile::TestPoolOp<0, 2, 5, 2>(in_channels, in_height, in_width);
+
+  LOG(paddle_mobile::kLOG_INFO)
+      << "float, pooling_type=avg, kernel=2, pad=0, stride=2";
+  paddle_mobile::TestPoolOp<1, 2, 0, 2>(in_channels, in_height, in_width);
+  LOG(paddle_mobile::kLOG_INFO)
+      << "float, pooling_type=avg, kernel=2, pad=1, stride=2";
+  paddle_mobile::TestPoolOp<1, 2, 1, 2>(in_channels, in_height, in_width);
+  LOG(paddle_mobile::kLOG_INFO)
+      << "float, pooling_type=avg, kernel=2, pad=2, stride=2";
+  paddle_mobile::TestPoolOp<1, 2, 2, 2>(in_channels, in_height, in_width);
+  LOG(paddle_mobile::kLOG_INFO)
+      << "float, pooling_type=avg, kernel=2, pad=5, stride=2";
+  paddle_mobile::TestPoolOp<1, 2, 5, 2>(in_channels, in_height, in_width);
+}
+
+int main(int argc, char *argv[]) {
+  //  if (argc < 4) {
+  //    LOG(paddle_mobile::kLOG_INFO)
+  //        << "Usage:\n"
+  //        << "  ./test-pool-op in_channels in_height in_width \n"
+  //        << "  params:\n"
+  //        << "   -in_channels: int, input image's channels\n"
+  //        << "   -in_height: int, input image's height\n"
+  //        << "   -in_width: int, input image's width\n";
+  //    return 1;
+  //  }
+  //  int in_channels = atoi(argv[1]);
+  //  int in_height = atoi(argv[2]);
+  //  int in_width = atoi(argv[3]);
+  Test(1, 10, 10);
+  Test(1, 50, 50);
+  Test(32, 10, 10);
+  Test(32, 50, 50);
 }

test/operators/test_relu6_op.cpp

+/* Copyright (c) 2018 PaddlePaddle Authors. 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 <cmath>
+#include <iostream>
+#include "../test_include.h"
+#include "operators/relu_op.h"
+
+namespace paddle_mobile {
+
+void Relu6(const framework::Tensor *X, framework::Tensor *Y) {
+  const float *x = X->data<float>();
+  float *y = Y->mutable_data<float>();
+
+  for (int i = 0; i < X->numel(); ++i) {
+    float q = x[i];
+    y[i] = std::min(std::max(0.f, q), 6.f);
+  }
+}
+
+int TestRelu6Op(const std::vector<int> input_shape) {
+  framework::DDim dims = framework::make_ddim(input_shape);
+  VariableNameMap inputs;
+  VariableNameMap outputs;
+  auto scope = std::make_shared<framework::Scope>();
+  inputs["X"] = std::vector<std::string>({"input"});
+  outputs["Out"] = std::vector<std::string>({"output"});
+
+  auto input_var = scope.get()->Var("input");
+  auto input = input_var->template GetMutable<framework::LoDTensor>();
+  SetupTensor<float>(input, dims, -100.0, 100.0);
+
+  auto output_var = scope.get()->Var("output");
+
+  framework::AttributeMap attrs;
+  auto *op = new operators::Relu6Op<CPU, float>("relu6", inputs, outputs, attrs,
+                                                scope);
+  op->InferShape();
+  op->Init();
+  op->Run();
+
+  auto output = output_var->template Get<framework::LoDTensor>();
+
+  framework::Tensor output_cmp;
+  float *output_cmp_data = output_cmp.mutable_data<float>(output->dims());
+  Relu6(input, &output_cmp);
+
+  const float *output_data = output->data<float>();
+  for (int i = 0; i < output->numel(); ++i) {
+    float gap = output_data[i] - output_cmp_data[i];
+    if (std::abs(gap / (output_data[i] + 1e-5)) > 1e-3) {
+      LOG(kLOG_INFO) << "output_data[" << i << "] = " << output_data[i]
+                     << ", output_cmp_data[" << i
+                     << "] = " << output_cmp_data[i];
+      delete op;
+      exit(1);
+    }
+  }
+  delete op;
+  return 0;
+}
+
+}  // namespace paddle_mobile
+
+int main() {
+  paddle_mobile::TestRelu6Op({1, 1, 2, 3});
+  paddle_mobile::TestRelu6Op({1, 3, 11, 22});
+  paddle_mobile::TestRelu6Op({1, 32, 112, 112});
+  std::cout << "test relu6 op pass." << std::endl;
+  return 0;
+}

test/operators/test_relu_op.cpp

@@ -12,46 +12,71 @@ 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 <cmath>
+#include <iostream>
 #include "../test_include.h"
 #include "operators/relu_op.h"
 
-int main() {
-  paddle_mobile::framework::Loader<paddle_mobile::CPU> loader;
-  auto program = loader.Load(g_resnet);
-  PADDLE_MOBILE_ENFORCE(program.originProgram != nullptr,
-                        "program file read fail");
+namespace paddle_mobile {
 
-  Executor4Test<paddle_mobile::CPU,
-                paddle_mobile::operators::ReluOp<paddle_mobile::CPU, float>>
-      executor(program, "relu");
+void Relu(const framework::Tensor *X, framework::Tensor *Y) {
+  const float *x = X->data<float>();
+  float *y = Y->mutable_data<float>();
 
-  // 1. input_tensors;
-  vector<Tensor> input_tensors;
+  for (int i = 0; i < X->numel(); ++i) {
+    float q = x[i];
+    y[i] = std::max(0.f, q);
+  }
+}
 
-  Tensor input1;
-  auto input1_data = CreateInput<float>(&input1, {1, 2, 3, 4}, -1, 1);
-  input_tensors.push_back(input1);
+int TestReluOp(const std::vector<int> input_shape) {
+  framework::DDim dims = framework::make_ddim(input_shape);
+  VariableNameMap inputs;
+  VariableNameMap outputs;
+  auto scope = std::make_shared<framework::Scope>();
+  inputs["X"] = std::vector<std::string>({"input"});
+  outputs["Out"] = std::vector<std::string>({"output"});
 
-  // 2. input_names
-  vector<string> input_names({
-      "batch_norm_0.tmp_2",
-  });
+  auto input_var = scope.get()->Var("input");
+  auto input = input_var->template GetMutable<framework::LoDTensor>();
+  SetupTensor<float>(input, dims, -100.0, 100.0);
 
-  // 3. output_names
-  vector<string> output_names({"batch_norm_0.tmp_3"});
+  auto output_var = scope.get()->Var("output");
 
-  // 4. out_dims;
-  vector<DDim> out_ddims;
-  auto out_ddim = paddle_mobile::framework::make_ddim({1, 2, 3, 4});
-  out_ddims.push_back(out_ddim);
+  framework::AttributeMap attrs;
+  auto *op =
+      new operators::ReluOp<CPU, float>("relu", inputs, outputs, attrs, scope);
+  op->InferShape();
+  op->Init();
+  op->Run();
 
-  auto output = executor.Predict<LoDTensor>(input_tensors, input_names,
-                                            output_names, out_ddims);
+  auto output = output_var->template Get<framework::LoDTensor>();
 
-  auto output0_data = output[0]->data<float>();
+  framework::Tensor output_cmp;
+  float *output_cmp_data = output_cmp.mutable_data<float>(output->dims());
+  Relu(input, &output_cmp);
 
-  for (int j = 0; j < output[0]->numel(); ++j) {
-    DLOG << " value of output: " << output0_data[j];
+  const float *output_data = output->data<float>();
+  for (int i = 0; i < output->numel(); ++i) {
+    float gap = output_data[i] - output_cmp_data[i];
+    if (std::abs(gap / (output_data[i] + 1e-5)) > 1e-3) {
+      LOG(kLOG_INFO) << "output_data[" << i << "] = " << output_data[i]
+                     << ", output_cmp_data[" << i
+                     << "] = " << output_cmp_data[i];
+      delete op;
+      exit(1);
+    }
   }
+  delete op;
+  return 0;
+}
+
+}  // namespace paddle_mobile
+
+int main() {
+  paddle_mobile::TestReluOp({1, 1, 2, 3});
+  paddle_mobile::TestReluOp({1, 3, 11, 22});
+  paddle_mobile::TestReluOp({1, 32, 112, 112});
+  std::cout << "test relu op pass." << std::endl;
   return 0;
 }

test/operators/test_topk_op.cpp

+/* Copyright (c) 2018 PaddlePaddle Authors. 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 <math.h>
+#include <limits>
+#include "../test_include.h"
+#include "operators/top_k_op.h"
+
+namespace paddle_mobile {
+
+void TopK(const framework::Tensor *X, framework::Tensor *Y,
+          framework::Tensor *Indices, const int K) {
+  const float *x = X->data<float>();
+  float *y = Y->mutable_data<float>();
+  int64_t *indices = Indices->mutable_data<int64_t>();
+
+  int dim_size = X->dims().size();
+  int row = 1;
+  int col = X->dims()[dim_size - 1];
+  for (int i = 0; i < dim_size - 1; ++i) {
+    row *= X->dims()[i];
+  }
+
+  std::vector<float> vec(col);
+  for (int i = 0; i < row; ++i) {
+    for (int j = 0; j < col; ++j) {
+      vec[j] = x[i * col + j];
+    }
+    for (int k = 0; k < K; ++k) {
+      float max = vec[0];
+      int index = 0;
+      for (int j = 1; j < col; ++j) {
+        if (vec[j] > max) {
+          max = vec[j];
+          index = j;
+        }
+      }
+      y[i * K + k] = max;
+      indices[i * K + k] = index;
+      vec[index] = -std::numeric_limits<float>::max();
+    }
+  }
+}
+
+int TestTopKOp(const std::vector<int> input_shape, const int K) {
+  framework::DDim dims = framework::make_ddim(input_shape);
+  VariableNameMap inputs;
+  VariableNameMap outputs;
+  auto scope = std::make_shared<framework::Scope>();
+  inputs["X"] = std::vector<std::string>({"input"});
+  outputs["Out"] = std::vector<std::string>({"output"});
+  outputs["Indices"] = std::vector<std::string>({"indices"});
+
+  auto input_var = scope.get()->Var("input");
+  auto input = input_var->template GetMutable<framework::LoDTensor>();
+  SetupTensor<float>(input, dims, -100.0, 100.0);
+
+  auto output_var = scope.get()->Var("output");
+  auto indices_var = scope.get()->Var("indices");
+
+  framework::AttributeMap attrs;
+  attrs["k"].Set<int>(K);
+  auto *op =
+      new operators::TopKOp<CPU, float>("top_k", inputs, outputs, attrs, scope);
+  op->InferShape();
+  op->Init();
+  op->Run();
+
+  auto output = output_var->template Get<framework::LoDTensor>();
+  auto indices = indices_var->template Get<framework::LoDTensor>();
+
+  framework::Tensor output_cmp, indices_cmp;
+  float *output_cmp_data = output_cmp.mutable_data<float>(output->dims());
+  int64_t *indices_cmp_data =
+      indices_cmp.mutable_data<int64_t>(indices->dims());
+  TopK(input, &output_cmp, &indices_cmp, K);
+
+  // sort output
+  float *output_data = const_cast<float *>(output->data<float>());
+  int64_t *indices_data = const_cast<int64_t *>(indices->data<int64_t>());
+  //  std::vector<std::pair<float, size_t>> vec(K);
+  //  for (int i = 0; i < output->numel() / K; ++i) {
+  //    for (int j = 0; j < K; ++j) {
+  //      vec[j] = std::move(std::make_pair(output_data[i * K + j],
+  //      indices_data[i * K + j]));
+  //    }
+  //    std::sort(vec.begin(), vec.end(),
+  //              [](const std::pair<float, size_t> &l,
+  //                 const std::pair<float, size_t> &r) {
+  //                   return l.first > r.first; });
+  //    for (int j = 0; j < K; ++j) {
+  //      output_data[i * K + j] = vec[j].first;
+  //      indices_data[i * K + j] = vec[j].second;
+  //    }
+  //  }
+
+  for (int i = 0; i < output->numel(); ++i) {
+    float gap = output_data[i] - output_cmp_data[i];
+    if (std::abs(gap / (output_data[i] + 1e-5)) > 1e-3) {
+      LOG(kLOG_INFO) << "output_data[" << i << "] = " << output_data[i]
+                     << ", output_cmp_data[" << i
+                     << "] = " << output_cmp_data[i];
+      delete op;
+      exit(1);
+    }
+  }
+
+  for (int i = 0; i < indices->numel(); ++i) {
+    if (indices_data[i] != indices_cmp_data[i]) {
+      LOG(kLOG_INFO) << "indices_data[" << i << "] = " << indices_data[i]
+                     << ", indices_cmp_data[" << i
+                     << "] = " << indices_cmp_data[i];
+      delete op;
+      exit(1);
+    }
+  }
+  delete op;
+  return 0;
+}
+
+}  // namespace paddle_mobile
+
+int main(int argc, char *argv[]) {
+  TestTopKOp({1, 100}, 1);
+  TestTopKOp({128, 100}, 10);
+  TestTopKOp({128, 2, 100}, 10);
+  return 0;
+}