Add space_to_batch op

inference-engine/src/plaidml_plugin/ops/space_to_batch.cpp

@@ -0,0 +1,99 @@
+// Copyright (C) 2020 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "ngraph/opsets/opset.hpp"
+#include "ngraph/opsets/opset2.hpp"
+#include "plaidml/op/op.h"
+#include "plaidml_ops.hpp"
+#include "plaidml_util.hpp"
+
+using namespace plaidml;          // NOLINT[build/namespaces]
+using namespace InferenceEngine;  // NOLINT[build/namespaces]
+
+namespace PlaidMLPlugin {
+
+static OpRegistration reg("SpaceToBatch", [](const Context& ctx) {
+    IE_ASSERT(ctx.operands.size() == 4);
+    auto I = ctx.operands.at(0);
+    auto block_shape = get_shape_from_constant_operand(1, ctx.layer);
+    if (block_shape.size() != I.rank()) {
+        THROW_IE_EXCEPTION << "block_shape and tensor dims have to equal!";
+    }
+    if (block_shape[0] != 1) {
+        THROW_IE_EXCEPTION << "block_shape[0] is expected to be one.";
+    }
+    auto crops_begin = get_coords_from_constant_operand(2, ctx.layer);
+    auto crops_end = get_coords_from_constant_operand(3, ctx.layer);
+
+    /** According to openvino op spec, follow below operate step:
+     *
+     *  Zero-pad the start and end of dimensions [D_0, ..., D_{N - 1}] of the input according to `pads_begin` and `pads_end`:
+     *  x = [batch + P_0, D_1 + P_1, D_2 + P_2, ..., D_{N - 1} + P_{N - 1}], where P_i = pads_begin[i] + pads_end[i]
+     **/
+    std::vector<int> lo_pads;
+    for (auto pad : crops_begin) {
+        lo_pads.push_back(pad);
+    }
+    std::vector<int> hi_pads;
+    for (auto pad : crops_end) {
+        hi_pads.push_back(pad);
+    }
+    if (lo_pads[0] || hi_pads[0]) {
+        THROW_IE_EXCEPTION << "batch dim pad have to be zero!";
+    }
+    edsl::Tensor padding_I = op::explicit_padding(I, lo_pads, hi_pads).padval(edsl::Constant(0));
+
+    /** reshape padding tensor
+     *  x' = reshape(x, [batch, (D_1 + P_1) / B_1, B_1, (D_2 + P_2) / B_2, B_2, ..., (D_{N - 1} + P_{N - 1}) / B_{N - 1},
+     *                  B_{N - 1}]), where B_i = block_shape[i]
+     */
+    std::vector<edsl::TensorDim> I_dims(padding_I.rank());
+    padding_I.bind_dims(I_dims);
+
+    std::vector<edsl::TensorDim> temp_dims;
+    temp_dims.push_back(I_dims[0]);
+    for (size_t i = 1; i < block_shape.size(); i++) {
+        temp_dims.emplace_back(I_dims[i] / block_shape[i]);
+        temp_dims.emplace_back(block_shape[i]);
+    }
+    auto reshape_I = edsl::reshape(padding_I, temp_dims);
+
+    /** transpose reshape tensor.
+     *  x'' = transpose(x',  [2, 4, ..., (N - 1) + (N - 1), 0, 1, 3, ..., N + (N - 1)])
+     **/
+    // setup the sort dims
+    std::vector<size_t> reshape_dims(reshape_I.rank());
+    auto dims_size = reshape_dims.size();
+    auto mid_dim = dims_size / 2;
+    reshape_dims[mid_dim] = 0;
+    for (size_t i = 0; i < mid_dim; i++) {
+        reshape_dims[i] = 2 * (i + 1);
+        reshape_dims[i + mid_dim + 1] = 2 * i + 1;
+    }
+    std::vector<edsl::Value> dims_wrapper;
+    for (auto dim : reshape_dims) {
+        dims_wrapper.emplace_back(dim);
+    }
+    auto transpose_I = op::transpose(reshape_I, edsl::Value(dims_wrapper));
+
+    /** reshape to the final tensor.
+     *  y = reshape(x'', [batch * B_1 * ... * B_{N - 1}, (D_1 + P_1) / B_1, (D_2 + P_2) / B_2, ... ,
+     *              (D_{N - 1} + P_{N - 1}) / B_{N - 1}])
+     */
+    size_t total_block_size = 1;
+    for (size_t i = 0; i < block_shape.size(); i++) {
+        total_block_size *= block_shape[i];
+    }
+    for (size_t i = 0; i < I_dims.size(); i++) {
+        if (i == 0) {
+            I_dims[i] = I_dims[i] * total_block_size;
+        }
+        I_dims[i] = I_dims[i] / block_shape[i];
+    }
+    edsl::Tensor O = edsl::reshape(transpose_I, I_dims);
+
+    return edsl::make_tuple(O);
+});
+
+}  // namespace PlaidMLPlugin

inference-engine/tests/functional/plugin/plaidml/shared_tests_instances/single_layer_tests/space_to_batch.cpp

@@ -0,0 +1,58 @@
+// Copyright (C) 2020 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include <vector>
+
+#include "common_test_utils/test_constants.hpp"
+#include "single_layer_tests/space_to_batch.hpp"
+
+using LayerTestsDefinitions::SpaceToBatchLayerTest;
+using LayerTestsDefinitions::spaceToBatchParamsTuple;
+
+namespace {
+
+const std::vector<InferenceEngine::Precision> inputPrecisions = {
+    InferenceEngine::Precision::FP32,
+    InferenceEngine::Precision::I32,
+};
+
+const std::vector<std::vector<size_t>> blockShapes = {
+    {1, 2, 2, 1}
+};
+
+const std::vector<std::vector<size_t>> pads_begins = {
+    {0, 0, 0, 0},
+    {0, 2, 2, 0},
+    {0, 4, 0, 0}
+};
+
+const std::vector<std::vector<size_t>> pads_ends = {
+    {0, 0, 0, 0},
+    {0, 2, 2, 0},
+    {0, 0, 4, 0},
+};
+
+const std::vector<std::vector<size_t>> inputShapes = {
+    {1, 8, 8, 1},  //
+    {3, 4, 4, 1},  //
+};
+
+INSTANTIATE_TEST_CASE_P(SpaceToBatchSmokeCheck, SpaceToBatchLayerTest,
+                        ::testing::Combine(::testing::ValuesIn(blockShapes),
+                                           ::testing::ValuesIn(pads_begins),
+                                           ::testing::ValuesIn(pads_ends),
+                                           ::testing::ValuesIn(inputShapes),
+                                           ::testing::ValuesIn(inputPrecisions),
+                                           ::testing::Values(CommonTestUtils::DEVICE_PLAIDML)),
+                        SpaceToBatchLayerTest::getTestCaseName);
+
+INSTANTIATE_TEST_CASE_P(SpaceToBatchTfCaseCheck, SpaceToBatchLayerTest,
+                        ::testing::Combine(::testing::Values(std::vector<size_t>({1, 2, 2, 1})),
+                                           ::testing::Values(std::vector<size_t>({0, 1, 1, 0})),
+                                           ::testing::Values(std::vector<size_t>({0, 1, 1, 0})),
+                                           ::testing::Values(std::vector<size_t>({2, 2, 4, 1})),
+                                           ::testing::ValuesIn(inputPrecisions),
+                                           ::testing::Values(CommonTestUtils::DEVICE_PLAIDML)),
+                        SpaceToBatchLayerTest::getTestCaseName);
+}  // namespace