Add tile

Signed-off-by: zhao.xia <zhao.xia@verisilicon.com>

include/tim/vx/ops/tile.h

@@ -0,0 +1,51 @@
+/****************************************************************************
+*
+*    Copyright (c) 2021 Vivante Corporation
+*
+*    Permission is hereby granted, free of charge, to any person obtaining a
+*    copy of this software and associated documentation files (the "Software"),
+*    to deal in the Software without restriction, including without limitation
+*    the rights to use, copy, modify, merge, publish, distribute, sublicense,
+*    and/or sell copies of the Software, and to permit persons to whom the
+*    Software is furnished to do so, subject to the following conditions:
+*
+*    The above copyright notice and this permission notice shall be included in
+*    all copies or substantial portions of the Software.
+*
+*    THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+*    IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+*    FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+*    AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+*    LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
+*    FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
+*    DEALINGS IN THE SOFTWARE.
+*
+*****************************************************************************/
+#ifndef TIM_VX_OPS_TILE_H_
+#define TIM_VX_OPS_TILE_H_
+#include "tim/vx/operation.h"
+
+namespace tim {
+namespace vx {
+namespace ops {
+
+/**
+ * ## Tile
+ *
+ * Constructs a tensor by tiling a given tensor.
+ * - multiples :  Must be one of the following types: int32, int64.
+ *                Length must be the same as the number of dimensions in input.
+ */
+
+class Tile : public Operation {
+ public:
+  Tile(Graph* graph, const std::vector<int32_t>& multiples);
+ protected:
+  const std::vector<int32_t> multiples_;
+};
+
+}  // namespace ops
+}  // namespace vx
+}  // namespace tim
+
+#endif /* TIM_VX_OPS_TILE_H_ */

src/tim/vx/ops/README.md

@@ -93,7 +93,7 @@ ScatterND|SCATTER_ND|Mapped|[tf.scatter_nd](https://tensorflow.google.cn/api_doc
 ||MOMENTS|Planned 21Q2|[tf.moments](https://tensorflow.google.cn/api_docs/python/tf/nn/moments)
 ||MATRIXMUL|Planned 21Q2|[tf.experimental.numpy.matmul](https://www.tensorflow.org/api_docs/python/tf/experimental/numpy/matmul)
 Unstack|UNSTACK|Mapped|[tf.unstack](https://tensorflow.google.cn/api_docs/python/tf/unstack)
-||TILE|Planned 21Q2|[tf.tile](https://tensorflow.google.cn/api_docs/python/tf/tile)
+Tile|TILE|Mapped|[tf.tile](https://tensorflow.google.cn/api_docs/python/tf/tile)
 ||TOPK|Planned 21Q2|[tf.math.top_k](https://tensorflow.google.cn/api_docs/python/tf/math/top_k)
 ||GROUPED_CONV2D|Planned 21Q2|[ANEURALNETWORKS_GROUPED_CONV_2D](https://developer.android.com/ndk/reference/group/neural-networks#group___neural_networks_1ggaabbe492c60331b13038e39d4207940e0a847acf8d9f3d2343328c3dbe6d447c50)
 ||PROPOSAL|Planned 21Q3|[Faster-RCNN Proposal Layer](https://github.com/intel/caffe/blob/master/examples/faster-rcnn/lib/rpn/proposal_layer.py)

src/tim/vx/ops/tile.cc

@@ -0,0 +1,41 @@
+/****************************************************************************
+*
+*    Copyright (c) 2021 Vivante Corporation
+*
+*    Permission is hereby granted, free of charge, to any person obtaining a
+*    copy of this software and associated documentation files (the "Software"),
+*    to deal in the Software without restriction, including without limitation
+*    the rights to use, copy, modify, merge, publish, distribute, sublicense,
+*    and/or sell copies of the Software, and to permit persons to whom the
+*    Software is furnished to do so, subject to the following conditions:
+*
+*    The above copyright notice and this permission notice shall be included in
+*    all copies or substantial portions of the Software.
+*
+*    THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+*    IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+*    FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+*    AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+*    LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
+*    FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
+*    DEALINGS IN THE SOFTWARE.
+*
+*****************************************************************************/
+#include "tim/vx/ops/tile.h"
+
+#include "operation_private.h"
+#include "vsi_nn_pub.h"
+
+namespace tim {
+namespace vx {
+namespace ops {
+
+Tile::Tile(Graph* graph, const std::vector<int32_t>& multiples)
+    : Operation(graph, VSI_NN_OP_TILE, 1, 1), multiples_(multiples) {
+  this->impl()->node()->nn_param.tile.multiples = multiples_.data();
+  this->impl()->node()->nn_param.tile.multiples_num = multiples_.size();
+}
+
+}  // namespace ops
+}  // namespace vx
+}  // namespace tim

src/tim/vx/ops/tile_test.cc

@@ -0,0 +1,106 @@
+/****************************************************************************
+*
+*    Copyright (c) 2021 Vivante Corporation
+*
+*    Permission is hereby granted, free of charge, to any person obtaining a
+*    copy of this software and associated documentation files (the "Software"),
+*    to deal in the Software without restriction, including without limitation
+*    the rights to use, copy, modify, merge, publish, distribute, sublicense,
+*    and/or sell copies of the Software, and to permit persons to whom the
+*    Software is furnished to do so, subject to the following conditions:
+*
+*    The above copyright notice and this permission notice shall be included in
+*    all copies or substantial portions of the Software.
+*
+*    THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+*    IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+*    FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+*    AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+*    LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
+*    FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
+*    DEALINGS IN THE SOFTWARE.
+*
+*****************************************************************************/
+#include "tim/vx/context.h"
+#include "tim/vx/graph.h"
+#include "tim/vx/ops/tile.h"
+
+#include "gtest/gtest.h"
+
+TEST(Tile, shape_3_2_float_multiples_2_1) {
+    auto ctx = tim::vx::Context::Create();
+    auto graph = ctx->CreateGraph();
+
+    tim::vx::ShapeType input_shape({3,2});
+    tim::vx::ShapeType output_shape({6,2});
+    tim::vx::TensorSpec input_spec(tim::vx::DataType::FLOAT32,
+        input_shape, tim::vx::TensorAttribute::INPUT);
+    tim::vx::TensorSpec output_spec(tim::vx::DataType::FLOAT32,
+        output_shape, tim::vx::TensorAttribute::OUTPUT);
+
+    auto input_tensor = graph->CreateTensor(input_spec);
+    auto output_tensor = graph->CreateTensor(output_spec);
+
+    std::vector<float> in_data = {
+        1,2,3,
+        4,5,6,
+        };
+    std::vector<float> golden = {
+        1,2,3,1,2,3,
+        4,5,6,4,5,6,
+        };
+
+    EXPECT_TRUE(input_tensor->CopyDataToTensor(
+        in_data.data(), in_data.size() * sizeof(float)));
+
+    std::vector<int32_t> multiples = {2,1};
+    auto op = graph->CreateOperation<tim::vx::ops::Tile>(multiples);
+    (*op).BindInputs({input_tensor}).BindOutputs({output_tensor});
+
+    EXPECT_TRUE(graph->Compile());
+    EXPECT_TRUE(graph->Run());
+
+    std::vector<float> output(golden.size());
+    EXPECT_TRUE(output_tensor->CopyDataFromTensor(output.data()));
+    EXPECT_EQ(golden, output);
+}
+
+TEST(Tile, shape_3_2_1_int8_multiples_2_2_1) {
+    auto ctx = tim::vx::Context::Create();
+    auto graph = ctx->CreateGraph();
+
+    tim::vx::ShapeType input_shape({3,2,1});
+    tim::vx::ShapeType output_shape({6,4,1});
+    tim::vx::Quantization quant(tim::vx::QuantType::ASYMMETRIC, 0.5, 0);
+    tim::vx::TensorSpec input_spec(tim::vx::DataType::UINT8,
+        input_shape, tim::vx::TensorAttribute::INPUT, quant);
+    tim::vx::TensorSpec output_spec(tim::vx::DataType::UINT8,
+        output_shape, tim::vx::TensorAttribute::OUTPUT, quant);
+
+    auto input_tensor = graph->CreateTensor(input_spec);
+    auto output_tensor = graph->CreateTensor(output_spec);
+
+    std::vector<uint8_t> in_data = {
+        2,4,6,
+        8,10,12,
+        };
+    std::vector<uint8_t> golden = {
+        2,4,6,2,4,6,
+        8,10,12,8,10,12,
+        2,4,6,2,4,6,
+        8,10,12,8,10,12,
+        };
+
+    EXPECT_TRUE(input_tensor->CopyDataToTensor(in_data.data(), in_data.size()));
+
+    std::vector<int32_t> multiples = {2,2,1};
+    auto op = graph->CreateOperation<tim::vx::ops::Tile>(multiples);
+    (*op).BindInputs({input_tensor}).BindOutputs({output_tensor});
+
+    EXPECT_TRUE(graph->Compile());
+    EXPECT_TRUE(graph->Run());
+
+    std::vector<uint8_t> output(golden.size());
+    EXPECT_TRUE(output_tensor->CopyDataFromTensor(output.data()));
+    EXPECT_EQ(golden, output);
+}