Add Unstack

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

include/tim/vx/ops/unstack.h

@@ -0,0 +1,52 @@
+/****************************************************************************
+*
+*    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_UNSTACK_H_
+#define TIM_VX_OPS_UNSTACK_H_
+#include "tim/vx/operation.h"
+
+namespace tim {
+namespace vx {
+namespace ops {
+
+/**
+ * ## Unstack
+ *
+ * Unpacks the given dimension of a rank-R tensor into rank-(R-1) tensors.
+ * - axis : An int. The axis to unstack along. Defaults to the first dimension.
+ * Negative values wrap around, so the valid range is [-R, R).
+ */
+
+class Unstack : public Operation {
+ public:
+  Unstack(Graph* graph, int32_t axis, uint32_t output_num);
+
+ protected:
+  int32_t axis_;
+};
+
+}  // namespace ops
+}  // namespace vx
+}  // namespace tim
+
+#endif /* TIM_VX_OPS_UNSTACK_H_ */

src/tim/vx/ops/README.md

@@ -92,7 +92,7 @@ Linear|LINEAR|Mapped|[tf.keras.activations.linear](https://www.tensorflow.org/ap
 ScatterND|SCATTER_ND|Mapped|[tf.scatter_nd](https://tensorflow.google.cn/api_docs/python/tf/scatter_nd)
 ||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|Planned 21Q2|[tf.unstack](https://tensorflow.google.cn/api_docs/python/tf/unstack)
+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)
 ||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)

src/tim/vx/ops/unstack.cc

@@ -0,0 +1,40 @@
+/****************************************************************************
+*
+*    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/unstack.h"
+
+#include "operation_private.h"
+#include "vsi_nn_pub.h"
+
+namespace tim {
+namespace vx {
+namespace ops {
+
+Unstack::Unstack(Graph* graph, int32_t axis, uint32_t output_num)
+    : Operation(graph, VSI_NN_OP_UNSTACK, 1, output_num), axis_(axis) {
+  this->impl()->node()->nn_param.unstack.axis = axis_;
+}
+
+}  // namespace ops
+}  // namespace vx
+}  // namespace tim

src/tim/vx/ops/unstack_test.cc

@@ -0,0 +1,140 @@
+/****************************************************************************
+*
+*    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/unstack.h"
+
+#include "gtest/gtest.h"
+
+TEST(Unstack, shape_4_3_axis_0) {
+    auto ctx = tim::vx::Context::Create();
+    auto graph = ctx->CreateGraph();
+
+    tim::vx::ShapeType input_shape({4,3});
+    tim::vx::ShapeType output_shape({3});
+    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 output1_tensor = graph->CreateTensor(output_spec);
+    auto output2_tensor = graph->CreateTensor(output_spec);
+    auto output3_tensor = graph->CreateTensor(output_spec);
+    auto output4_tensor = graph->CreateTensor(output_spec);
+
+    std::vector<float> in_data = {
+        1,2,3,4,
+        5,6,7,8,
+        9,10,11,12,
+        };
+    std::vector<float> golden1 = {
+        1,5,9
+    };
+    std::vector<float> golden2 = {
+        2,6,10
+    };
+    std::vector<float> golden3 = {
+        3,7,11
+    };
+    std::vector<float> golden4 = {
+        4,8,12
+    };
+
+    EXPECT_TRUE(input_tensor->CopyDataToTensor(
+        in_data.data(), in_data.size() * sizeof(float)));
+
+    auto op = graph->CreateOperation<tim::vx::ops::Unstack>(0, 4);
+    (*op).BindInputs({input_tensor}).BindOutputs(
+        {output1_tensor, output2_tensor, output3_tensor, output4_tensor});
+
+    EXPECT_TRUE(graph->Compile());
+    EXPECT_TRUE(graph->Run());
+
+    std::vector<float> output1(golden1.size());
+    std::vector<float> output2(golden2.size());
+    std::vector<float> output3(golden3.size());
+    std::vector<float> output4(golden4.size());
+    EXPECT_TRUE(output1_tensor->CopyDataFromTensor(output1.data()));
+    EXPECT_TRUE(output2_tensor->CopyDataFromTensor(output2.data()));
+    EXPECT_TRUE(output3_tensor->CopyDataFromTensor(output3.data()));
+    EXPECT_TRUE(output4_tensor->CopyDataFromTensor(output4.data()));
+    EXPECT_EQ(golden1, output1);
+    EXPECT_EQ(golden2, output2);
+    EXPECT_EQ(golden3, output3);
+    EXPECT_EQ(golden4, output4);
+}
+
+TEST(Unstack, shape_4_3_axis_1) {
+    auto ctx = tim::vx::Context::Create();
+    auto graph = ctx->CreateGraph();
+
+    tim::vx::ShapeType input_shape({4,3});
+    tim::vx::ShapeType output_shape({4});
+    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 output1_tensor = graph->CreateTensor(output_spec);
+    auto output2_tensor = graph->CreateTensor(output_spec);
+    auto output3_tensor = graph->CreateTensor(output_spec);
+
+    std::vector<uint8_t> in_data = {
+        2,4,6,8,
+        10,12,14,16,
+        18,20,22,24,
+        };
+    std::vector<uint8_t> golden1 = {
+        2,4,6,8
+    };
+    std::vector<uint8_t> golden2 = {
+        10,12,14,16,
+    };
+    std::vector<uint8_t> golden3 = {
+        18,20,22,24,
+    };
+
+    EXPECT_TRUE(input_tensor->CopyDataToTensor(
+        in_data.data(), in_data.size() * sizeof(float)));
+
+    auto op = graph->CreateOperation<tim::vx::ops::Unstack>(1, 3);
+    (*op).BindInputs({input_tensor}).BindOutputs(
+        {output1_tensor, output2_tensor, output3_tensor});
+
+    EXPECT_TRUE(graph->Compile());
+    EXPECT_TRUE(graph->Run());
+
+    std::vector<uint8_t> output1(golden1.size());
+    std::vector<uint8_t> output2(golden2.size());
+    std::vector<uint8_t> output3(golden3.size());
+    EXPECT_TRUE(output1_tensor->CopyDataFromTensor(output1.data()));
+    EXPECT_TRUE(output2_tensor->CopyDataFromTensor(output2.data()));
+    EXPECT_TRUE(output3_tensor->CopyDataFromTensor(output3.data()));
+    EXPECT_EQ(golden1, output1);
+    EXPECT_EQ(golden2, output2);
+    EXPECT_EQ(golden3, output3);
+}