1 files changed, 121 insertions, 0 deletions
diff --git a/compiler/locomotiv/src/Node/EltwiseSub.test.cpp b/compiler/locomotiv/src/Node/EltwiseSub.test.cpp
new file mode 100644
index 000000000..7eff90f9e
--- /dev/null
+++ b/compiler/locomotiv/src/Node/EltwiseSub.test.cpp
@@ -0,0 +1,121 @@
+/*
+ * Copyright (c) 2019 Samsung Electronics Co., Ltd. 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 "NodeExecution.h"
+
+#include "locomotiv/NodeData.h"
+#include "NodeDataImpl.h"
+#include "NodeDomain.h"
+
+#include <nncc/core/ADT/tensor/Shape.h>
+#include <nncc/core/ADT/tensor/Buffer.h>
+#include <nncc/core/ADT/tensor/LexicalLayout.h>
+#include <nncc/core/ADT/tensor/Index.h>
+#include <nncc/core/ADT/tensor/IndexEnumerator.h>
+
+#include <gtest/gtest.h>
+
+using nncc::core::ADT::tensor::Shape;
+using nncc::core::ADT::tensor::LexicalLayout;
+using nncc::core::ADT::tensor::make_buffer;
+using nncc::core::ADT::tensor::IndexEnumerator;
+
+/*
+test case generated from the following:
+
+x = tf.constant([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18],
+                shape=[1, 3, 3, 2], dtype=tf.float32)
+y = tf.constant([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18],
+                shape=[1, 3, 3, 2], dtype=tf.float32)
+out = tf.math.subtract(x, y)
+
+with tf.Session() as sess:
+    print(sess.run(out))
+*/
+TEST(NodeExecution_EltwiseSub, f32)
+{
+  float x_val[] = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18};
+  float y_val[] = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18};
+  float out_val[] = {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0};
+
+  // make EltwiseSub(Pull, Pull)
+  auto g = loco::make_graph();
+  Shape input_shape{1, 3, 3, 2}; // NHWC
+
+  auto inp_lhs = g->nodes()->create<loco::Pull>();
+  {
+    inp_lhs->dtype(loco::DataType::FLOAT32);
+    inp_lhs->shape({1, 3, 3, 2});
+  }
+
+  auto inp_rhs = g->nodes()->create<loco::Pull>();
+  {
+    inp_rhs->dtype(loco::DataType::FLOAT32);
+    inp_rhs->shape({1, 3, 3, 2});
+  }
+
+  auto eltwise_sub = g->nodes()->create<loco::EltwiseSub>();
+  {
+    eltwise_sub->lhs(inp_lhs);
+    eltwise_sub->rhs(inp_rhs);
+  }
+
+  // Make and assign data to two pull nodes
+  auto inp_lhs_buf = make_buffer<float, LexicalLayout>(input_shape);
+  {
+    int n = 0;
+    for (IndexEnumerator e{inp_lhs_buf.shape()}; e.valid(); e.advance())
+    {
+      inp_lhs_buf.at(e.current()) = x_val[n++];
+    }
+  }
+
+  auto inp_rhs_buf = make_buffer<float, LexicalLayout>(input_shape);
+  {
+    int n = 0;
+    for (IndexEnumerator e{inp_rhs_buf.shape()}; e.valid(); e.advance())
+    {
+      inp_rhs_buf.at(e.current()) = y_val[n++];
+    }
+  }
+
+  auto inp_lhs_data = locomotiv::make_data(inp_lhs_buf);
+  locomotiv::annot_data(inp_lhs, std::move(inp_lhs_data));
+  locomotiv::annot_domain(inp_lhs, loco::Domain::Tensor);
+
+  auto inp_rhs_data = locomotiv::make_data(inp_rhs_buf);
+  locomotiv::annot_data(inp_rhs, std::move(inp_rhs_data));
+  locomotiv::annot_domain(inp_rhs, loco::Domain::Tensor);
+
+  // run the network
+  locomotiv::NodeExecution::get().run(eltwise_sub);
+
+  // get result
+  auto eltwise_sub_data = locomotiv::annot_data(eltwise_sub);
+
+  // comparing the result
+  ASSERT_NE(eltwise_sub_data, nullptr);
+  ASSERT_EQ(eltwise_sub_data->dtype(), loco::DataType::FLOAT32);
+  ASSERT_EQ(*(eltwise_sub_data->shape()), Shape({1, 3, 3, 2}));
+
+  uint32_t n = 0;
+  for (IndexEnumerator e{*(eltwise_sub_data->shape())}; e.valid(); e.advance())
+  {
+    ASSERT_FLOAT_EQ(eltwise_sub_data->as_f32_bufptr()->at(e.current()), out_val[n++]);
+  }
+
+  ASSERT_EQ(locomotiv::annot_domain(eltwise_sub), loco::Domain::Tensor);
+}