COMPMID-935 - Implementing Convolution with Winograd on OpenCL (part 2)

Implemented Winograd Filter Transform 3x3 on OpenCL

Change-Id: I8f2b2dd938c5c000ef7ce392a37fb7b8b4202a4e
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/122708
Reviewed-by: Georgios Pinitas <georgios.pinitas@arm.com>
Tested-by: Jenkins <bsgcomp@arm.com>

2 files changed, 160 insertions, 0 deletions

diff --git a/tests/datasets/ShapeDatasets.h b/tests/datasets/ShapeDatasets.h
index 4b563708e1..e939a6f5a7 100644
--- a/tests/datasets/ShapeDatasets.h
+++ b/tests/datasets/ShapeDatasets.h
@@ -238,6 +238,38 @@ public:
     }
 };
 
+/** Data set containing medium 3D tensor shapes. */
+class Medium3DShapes final : public ShapeDataset
+{
+public:
+    Medium3DShapes()
+        : ShapeDataset("Shape",
+    {
+        TensorShape{ 42U, 37U, 8U },
+                     TensorShape{ 57U, 60U, 13U },
+                     TensorShape{ 128U, 64U, 21U },
+                     TensorShape{ 83U, 72U, 14U }
+    })
+    {
+    }
+};
+
+/** Data set containing medium 4D tensor shapes. */
+class Medium4DShapes final : public ShapeDataset
+{
+public:
+    Medium4DShapes()
+        : ShapeDataset("Shape",
+    {
+        TensorShape{ 42U, 37U, 8U, 15U },
+                     TensorShape{ 57U, 60U, 13U, 8U },
+                     TensorShape{ 128U, 64U, 21U, 13U },
+                     TensorShape{ 83U, 72U, 14U, 5U }
+    })
+    {
+    }
+};
+
 /** Data set containing large tensor shapes. */
 class LargeShapes final : public ShapeDataset
 {
diff --git a/tests/datasets/WinogradFilterTransformDataset.h b/tests/datasets/WinogradFilterTransformDataset.h
new file mode 100644
index 0000000000..07d0283b55
--- /dev/null
+++ b/tests/datasets/WinogradFilterTransformDataset.h
@@ -0,0 +1,128 @@
+/*
+ * Copyright (c) 2018 ARM Limited.
+ *
+ * SPDX-License-Identifier: MIT
+ *
+ * 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 ARM_COMPUTE_TEST_WINOGRAD_FILTER_TRANSFORM_DATASET
+#define ARM_COMPUTE_TEST_WINOGRAD_FILTER_TRANSFORM_DATASET
+
+#include "utils/TypePrinter.h"
+
+#include "arm_compute/core/TensorShape.h"
+
+namespace arm_compute
+{
+namespace test
+{
+namespace datasets
+{
+class WinogradFilterTransformDataset
+{
+public:
+    using type = std::tuple<TensorShape, bool>;
+
+    struct iterator
+    {
+        iterator(std::vector<TensorShape>::const_iterator a_it,
+                 std::vector<bool>::const_iterator        is_nchw_it)
+            : _a_it{ std::move(a_it) },
+              _is_nchw_it{ std::move(is_nchw_it) }
+        {
+        }
+
+        std::string description() const
+        {
+            std::stringstream description;
+            description << "Input=" << *_a_it << ":";
+            description << "IsNCHW=" << *_is_nchw_it << ":";
+            return description.str();
+        }
+
+        WinogradFilterTransformDataset::type operator*() const
+        {
+            return std::make_tuple(*_a_it, *_is_nchw_it);
+        }
+
+        iterator &operator++()
+        {
+            ++_a_it;
+            ++_is_nchw_it;
+
+            return *this;
+        }
+
+    private:
+        std::vector<TensorShape>::const_iterator _a_it;
+        std::vector<bool>::const_iterator        _is_nchw_it;
+    };
+
+    iterator begin() const
+    {
+        return iterator(_a_shapes.begin(), _is_nchw.begin());
+    }
+
+    int size() const
+    {
+        return std::min(_a_shapes.size(), _is_nchw.size());
+    }
+
+    void add_config(TensorShape a, bool is_nchw)
+    {
+        _a_shapes.emplace_back(std::move(a));
+        _is_nchw.emplace_back(std::move(is_nchw));
+    }
+
+protected:
+    WinogradFilterTransformDataset()                                  = default;
+    WinogradFilterTransformDataset(WinogradFilterTransformDataset &&) = default;
+
+private:
+    std::vector<TensorShape> _a_shapes{};
+    std::vector<bool>        _is_nchw{};
+};
+
+class SmallWinogradFilterTransformDataset final : public WinogradFilterTransformDataset
+{
+public:
+    SmallWinogradFilterTransformDataset()
+    {
+        add_config(TensorShape(3U, 3U, 7U, 4U), true);
+        add_config(TensorShape(3U, 3U, 4U, 13U), true);
+        add_config(TensorShape(3U, 3U, 9U, 2U), true);
+        add_config(TensorShape(3U, 3U, 3U, 5U), true);
+    }
+};
+
+class LargeWinogradFilterTransformDataset final : public WinogradFilterTransformDataset
+{
+public:
+    LargeWinogradFilterTransformDataset()
+    {
+        add_config(TensorShape(3U, 3U, 32U, 64U), true);
+        add_config(TensorShape(3U, 3U, 51U, 13U), true);
+        add_config(TensorShape(3U, 3U, 53U, 47U), true);
+        add_config(TensorShape(3U, 3U, 128U, 384U), true);
+    }
+};
+} // namespace datasets
+} // namespace test
+} // namespace arm_compute
+#endif /* ARM_COMPUTE_TEST_WINOGRAD_FILTER_TRANSFORM_DATASET */