From: Efimov Alexander/AI Tools Lab/./Samsung Electronics <a.efimov@samsung.com>
Date: Fri, 7 Dec 2018 14:28:20 +0000 (+0300)
Subject: [nnc] Add acl backend tests for ops in mobilenet and inception (#2570)
X-Git-Tag: nncc_backup~1150
X-Git-Url: http://review.tizen.org/git/?a=commitdiff_plain;h=7b6a61fe121a3a68a1ca72f1a6fe97b40ac5485a;p=platform%2Fcore%2Fml%2Fnnfw.git

[nnc] Add acl backend tests for ops in mobilenet and inception (#2570)

Added tests for operations:
- scale
- bias
- concat
- reshape
- dropout
- conv2d
- depthwise2d
- softmax
- pool
- fc
- relu

Signed-off-by: Efimov Alexander <a.efimov@samsung.com>
---

diff --git a/contrib/nnc/passes/acl_soft_backend/AclCppOpGenerator.cpp b/contrib/nnc/passes/acl_soft_backend/AclCppOpGenerator.cpp
index c6625bf..ec9661c 100644
--- a/contrib/nnc/passes/acl_soft_backend/AclCppOpGenerator.cpp
+++ b/contrib/nnc/passes/acl_soft_backend/AclCppOpGenerator.cpp
@@ -356,7 +356,12 @@ void AclCppOpGenerator::visit(ops::BiasAddOp& op) {
 }
 
 void AclCppOpGenerator::visit(ops::VariableOp& op) {
-  auto tensor = genTensor(op, transposeShape<2, 1, 3, 0>(op.getOutputShape(0)));
+  shared_ptr<ArtifactId> tensor;
+  if (op.getOutputShape(0).rank() == 2) {
+    tensor = genTensor(op, transposeShape<1, 0>(op.getOutputShape(0)));
+  } else {
+    tensor = genTensor(op, transposeShape<2, 1, 3, 0>(op.getOutputShape(0)));
+  }
   allocate(tensor);
 }
 
diff --git a/contrib/nnc/unittests/acl_backend/MIRToDOM.cpp b/contrib/nnc/unittests/acl_backend/MIRToDOM.cpp
index a4ee6bb..bb6e018 100644
--- a/contrib/nnc/unittests/acl_backend/MIRToDOM.cpp
+++ b/contrib/nnc/unittests/acl_backend/MIRToDOM.cpp
@@ -63,20 +63,20 @@ const char* artifactName = "nnmodel";
 /**
  * @brief Creates graph with one operation generated by opGen function and returns this operation node
  * @param g reference to graph which should be filled with operations
- * @param opConstr functor which creates main operations of graph
- * @param inputShapes vector of network input shapes
+ * @param op_constr functor which creates main operations of graph
+ * @param input_shapes vector of network input shapes
  * */
-void fillGraph(Graph& g, OpConstructor opConstr, const vector<Shape>& inputShapes) {
+void fillGraph(Graph& g, OpConstructor op_constr, const vector<Shape>& input_shapes) {
   // Create inputs
   vector<mir::IODescriptor> inputs;
-  int numInputs = inputShapes.size();
-  for (int i = 0; i < numInputs; ++i) {
-    auto inputOp = g.create<ops::VariableOp>("x" + to_string(i), inputShapes[i]);
+  int num_inputs = input_shapes.size();
+  for (int i = 0; i < num_inputs; ++i) {
+    auto inputOp = g.create<ops::VariableOp>("x" + to_string(i), input_shapes[i]);
     inputs.push_back(inputOp->getOutput(0));
   }
 
   // Create operation
-  Operation* op = opConstr(g, inputs);
+  Operation* op = op_constr(g, inputs);
 
   // Mark outputs
   g.markOutput(op);
@@ -93,7 +93,7 @@ void checkDomIncludes(const ArtifactModule& m) {
 /**
  * @brief Check that artifact DOM contains appropriate getters
  * @param c Main artifact class
- * @param names List of values accessible via getters
+ * @param tensors List of values accessible via getters
  */
 void checkDomArtifactGetters(const ArtifactClass& c, const vector<string>& tensors) {
   // TODO
@@ -102,7 +102,7 @@ void checkDomArtifactGetters(const ArtifactClass& c, const vector<string>& tenso
 /**
  * @brief Check that artifact class constructor initializes all layers
  * @param c Main artifact class
- * @param layers List of NN layers
+ * @param tensors List of NN layers
  */
 void checkDomArtifactConstructor(const ArtifactClass& c, const vector<string>& tensors) {
   // TODO
@@ -120,6 +120,8 @@ void checkDomInference(const ArtifactFunction& f, const vector<string>& layers)
 /**
  * @brief Check that artifact DOM contains appropriate class
  * @param m Root module of DOM
+ * @param layers Names of NN layers in inference sequence
+ * @param tensors Names of tensors in artifact
  */
 void checkArtifactClass(const ArtifactClass& c,
                         const vector<string>& layers,
@@ -140,6 +142,8 @@ void checkArtifactClass(const ArtifactClass& c,
 /**
  * @brief Root of check functions
  * @param m Main artifact module
+ * @param layers Names of NN layers in inference sequence
+ * @param tensors Names of tensors in artifact
  */
 void checkDomStructure(const ArtifactModule& m,
                        const vector<string>& layers,
@@ -152,22 +156,27 @@ void checkDomStructure(const ArtifactModule& m,
   checkArtifactClass(*cls, layers, tensors);
 }
 
+TensorVariant createTensorVariant(const Shape& shape) {
+  size_t data_size = shape.numElements();
+  shared_ptr<float> data(new float[data_size], default_delete<float[]>());
+  return TensorVariant(shape, data, DTYPE::FLOAT32);
+}
+
 }
 
 // Actual tests
 
 TEST(acl_backend_mir_to_dom, bias) {
-  const int channels = 2;
-  shared_ptr<float> data(new float[channels], default_delete<float[]>());
-  TensorVariant w({channels}, data, DTYPE::FLOAT32);
+  const int32_t channels = 2;
+  TensorVariant w = createTensorVariant({channels});
 
   Graph g;
-  OpConstructor opGenerator = [&w](Graph& g, const vector<IODescriptor>& inputs) {
+  OpConstructor op_generator = [&w](Graph& g, const vector<IODescriptor>& inputs) {
     return g.create<mir::ops::BiasAddOp>("bias", inputs[0], w);
   };
-  vector<Shape> inputShapes{{1, 10, 10, channels}};
+  vector<Shape> input_shapes{{1, 10, 10, channels}};
 
-  fillGraph(g, opGenerator, inputShapes);
+  fillGraph(g, op_generator, input_shapes);
 
   stringstream params_out;
   AclCppOpGenerator dom_gen(artifactName, params_out);
@@ -177,16 +186,51 @@ TEST(acl_backend_mir_to_dom, bias) {
   checkDomStructure(m, {}, {});
 }
 
-TEST(acl_backend_mir_to_dom, DISABLED_scale) {
-  // TODO
+TEST(acl_backend_mir_to_dom, scale) {
+  const int32_t channels = 2;
+  TensorVariant w = createTensorVariant({channels});
+
+  Graph g;
+  OpConstructor op_generator = [&w](Graph& g, const vector<IODescriptor>& inputs) {
+      return g.create<mir::ops::ScaleOp>("scale", inputs[0], w);
+  };
+  vector<Shape> input_shapes{{1, 10, 10, channels}};
+
+  fillGraph(g, op_generator, input_shapes);
+
+  stringstream params_out;
+  AclCppOpGenerator dom_gen(artifactName, params_out);
+
+  const ArtifactModule& m = dom_gen.generate(&g);
+
+  checkDomStructure(m, {}, {});
+
+  stringstream code_out;
+  ArtifactGeneratorCppCode code_gen(code_out);
 }
 
 TEST(acl_backend_mir_to_dom, DISABLED_capped_relu) {
   // TODO
 }
 
-TEST(acl_backend_mir_to_dom, DISABLED_concat) {
-  // TODO
+TEST(acl_backend_mir_to_dom, concat) {
+  Graph g;
+  OpConstructor op_generator = [](Graph& g, const vector<IODescriptor>& inputs) {
+      return g.create<mir::ops::ConcatOp>("concat", inputs, 3);
+  };
+  vector<Shape> input_shapes{{2, 3, 5, 1}, {2, 3, 5, 3}};
+
+  fillGraph(g, op_generator, input_shapes);
+
+  stringstream params_out;
+  AclCppOpGenerator dom_gen(artifactName, params_out);
+
+  const ArtifactModule& m = dom_gen.generate(&g);
+
+  checkDomStructure(m, {}, {});
+
+  stringstream code_out;
+  ArtifactGeneratorCppCode code_gen(code_out);
 }
 
 TEST(acl_backend_mir_to_dom, DISABLED_add) {
@@ -205,28 +249,142 @@ TEST(acl_backend_mir_to_dom, DISABLED_conv_transposed2d) {
   // TODO
 }
 
-TEST(acl_backend_mir_to_dom, DISABLED_conv2d) {
-  // TODO
+TEST(acl_backend_mir_to_dom, conv2d) {
+  const int32_t channels = 3;
+  mir::Shape kernel_shape{3, 3, channels, 1}; // Height, Width, input Channels, output Channel
+  mir::Shape strides{1, 1};
+  mir::TensorVariant kernel = createTensorVariant(kernel_shape);
+
+  Graph g;
+  OpConstructor op_generator =
+      [kernel, strides](mir::Graph& g,
+                        const std::vector<mir::IODescriptor>& inputs) {
+        std::vector<int32_t> padding{0, 0};
+        return g.create<mir::ops::Conv2DOp>("conv2d", inputs[0], kernel, strides, padding, padding);
+      };
+
+  vector<Shape> input_shapes{{1, 10, 10, channels}};
+
+  fillGraph(g, op_generator, input_shapes);
+
+  stringstream params_out;
+  AclCppOpGenerator dom_gen(artifactName, params_out);
+
+  const ArtifactModule& m = dom_gen.generate(&g);
+
+  checkDomStructure(m, {}, {});
+
+  stringstream code_out;
+  ArtifactGeneratorCppCode code_gen(code_out);
 }
 
-TEST(acl_backend_mir_to_dom, DISABLED_depthwise_conv) {
-  // TODO
+TEST(acl_backend_mir_to_dom, depthwise_conv) {
+  const int32_t channels = 3;
+  mir::Shape kernel_shape{3, 3, channels, 1}; // Height, Width, Channels, Channel multiplier
+  mir::Shape strides{1, 1};
+  mir::TensorVariant kernel = createTensorVariant(kernel_shape);
+
+  Graph g;
+  OpConstructor op_generator =
+      [kernel, strides](mir::Graph& g,
+                        const std::vector<mir::IODescriptor>& inputs) {
+          std::vector<int32_t> padding{0, 0};
+          return g.create<mir::ops::DepthwiseConv2DOp>("depthwiseConv2d",
+                                                       inputs[0], kernel,
+                                                       strides, padding, padding);
+      };
+
+  vector<Shape> input_shapes{{1, 10, 10, channels}};
+
+  fillGraph(g, op_generator, input_shapes);
+
+  stringstream params_out;
+  AclCppOpGenerator dom_gen(artifactName, params_out);
+
+  const ArtifactModule& m = dom_gen.generate(&g);
+
+  checkDomStructure(m, {}, {});
+
+  stringstream code_out;
+  ArtifactGeneratorCppCode code_gen(code_out);
 }
 
-TEST(acl_backend_mir_to_dom, DISABLED_fully_connected) {
-  // TODO
+TEST(acl_backend_mir_to_dom, fully_connected) {
+  const int32_t in_size = 13;
+  const int32_t out_size = 7;
+  Shape input_shape_data{1, in_size};
+  Shape weights_shape{in_size, out_size};
+  TensorVariant weights = createTensorVariant(weights_shape);
+
+  Graph g;
+  OpConstructor opGenerator = [weights](Graph& g, const vector<IODescriptor>& inputs) {
+      return g.create<mir::ops::FullyConnectedOp>("fc", inputs[0], weights);
+  };
+
+  fillGraph(g, opGenerator, {input_shape_data});
+
+  stringstream params_out;
+  AclCppOpGenerator dom_gen(artifactName, params_out);
+
+  const ArtifactModule& m = dom_gen.generate(&g);
+
+  checkDomStructure(m, {}, {});
+
+  stringstream code_out;
+  ArtifactGeneratorCppCode code_gen(code_out);
 }
 
-TEST(acl_backend_mir_to_dom, DISABLED_maxpool) {
-  // TODO
+TEST(acl_backend_mir_to_dom, maxpool) {
+  mir::Shape window_shape{3, 3}; // Height, Width
+  mir::Shape strides{1, 1};
+
+  Graph g;
+  OpConstructor op_generator = [window_shape, strides](mir::Graph& g,
+                                                    const std::vector<mir::IODescriptor>& inputs) {
+      std::vector<int32_t> padding{0, 0};
+      return g.create<mir::ops::PoolOp>("maxPool", inputs[0], ops::PoolOp::PoolingType::MAX,
+                                        window_shape, strides, padding, padding,
+                                        mir::ops::PoolOp::BorderType::EMPTY,
+                                        ops::PoolOp::RoundMode::floor);
+  };
+
+  vector<Shape> input_shapes{{1, 10, 10, 3}};
+
+  fillGraph(g, op_generator, input_shapes);
+
+  stringstream params_out;
+  AclCppOpGenerator dom_gen(artifactName, params_out);
+
+  const ArtifactModule& m = dom_gen.generate(&g);
+
+  checkDomStructure(m, {}, {});
+
+  stringstream code_out;
+  ArtifactGeneratorCppCode code_gen(code_out);
 }
 
 TEST(acl_backend_mir_to_dom, DISABLED_avgpool) {
   // TODO
 }
 
-TEST(acl_backend_mir_to_dom, DISABLED_relu) {
-  // TODO
+TEST(acl_backend_mir_to_dom, relu) {
+  Graph g;
+  OpConstructor op_generator = [](Graph& g, const std::vector<IODescriptor>& inputs) {
+      return g.create<mir::ops::ReluOp>("relu", inputs[0]);
+  };
+  vector<Shape> input_shapes{{1, 10, 10, 3}};
+
+  fillGraph(g, op_generator, input_shapes);
+
+  stringstream params_out;
+  AclCppOpGenerator dom_gen(artifactName, params_out);
+
+  const ArtifactModule& m = dom_gen.generate(&g);
+
+  checkDomStructure(m, {}, {});
+
+  stringstream code_out;
+  ArtifactGeneratorCppCode code_gen(code_out);
 }
 
 TEST(acl_backend_mir_to_dom, DISABLED_elu) {
@@ -241,12 +399,51 @@ TEST(acl_backend_mir_to_dom, DISABLED_reduce_mean) {
   // TODO
 }
 
-TEST(acl_backend_mir_to_dom, DISABLED_softmax) {
-  // TODO
+TEST(acl_backend_mir_to_dom, softmax) {
+  Graph g;
+  OpConstructor op_generator = [](Graph& g, const vector<IODescriptor>& inputs) {
+      return g.create<mir::ops::SoftmaxOp>("softmax", inputs[0], 3);
+  };
+  vector<Shape> input_shapes{{1, 1, 1, 3}};
+
+  fillGraph(g, op_generator, input_shapes);
+
+  stringstream params_out;
+  AclCppOpGenerator dom_gen(artifactName, params_out);
+
+  const ArtifactModule& m = dom_gen.generate(&g);
+
+  checkDomStructure(m, {}, {});
+
+  stringstream code_out;
+  ArtifactGeneratorCppCode code_gen(code_out);
 }
 
-TEST(acl_backend_mir_to_dom, DISABLED_reshape) {
-  // TODO
+TEST(acl_backend_mir_to_dom, reshape) {
+  Graph g;
+
+  const int32_t h = 10;
+  const int32_t w = 10;
+  const int32_t c = 3;
+
+  Shape input_shape{1, h, w, c};
+  Shape output_shape{1, h * w * c};
+
+  OpConstructor op_generator = [output_shape](Graph& g, const vector<IODescriptor>& inputs) {
+    return g.create<mir::ops::ReshapeOp>("reshape", inputs[0], output_shape);
+  };
+
+  fillGraph(g, op_generator, {input_shape});
+
+  stringstream params_out;
+  AclCppOpGenerator dom_gen(artifactName, params_out);
+
+  const ArtifactModule& m = dom_gen.generate(&g);
+
+  checkDomStructure(m, {}, {});
+
+  stringstream code_out;
+  ArtifactGeneratorCppCode code_gen(code_out);
 }
 
 TEST(acl_backend_mir_to_dom, DISABLED_pad) {