!4778 Added type support for Reshape gpu kernel for faster RCNN

Merge pull request !4778 from Peilin/reshape-gpu-kernel-type-support

mindspore/ccsrc/backend/kernel_compiler/gpu/nn/flatten_gpu_kernel.cc

@@ -26,10 +26,14 @@ MS_REG_GPU_KERNEL_ONE(Flatten, KernelAttr().AddInputAttr(kNumberTypeFloat16).Add
                       FlattenGpuFwdKernel, half)
 MS_REG_GPU_KERNEL_ONE(Reshape, KernelAttr().AddInputAttr(kNumberTypeFloat32).AddOutputAttr(kNumberTypeFloat32),
                       FlattenGpuFwdKernel, float)
-MS_REG_GPU_KERNEL_ONE(Reshape, KernelAttr().AddInputAttr(kNumberTypeInt32).AddOutputAttr(kNumberTypeInt32),
-                      FlattenGpuFwdKernel, int)
 MS_REG_GPU_KERNEL_ONE(Reshape, KernelAttr().AddInputAttr(kNumberTypeFloat16).AddOutputAttr(kNumberTypeFloat16),
                       FlattenGpuFwdKernel, half)
+MS_REG_GPU_KERNEL_ONE(Reshape, KernelAttr().AddInputAttr(kNumberTypeInt32).AddOutputAttr(kNumberTypeInt32),
+                      FlattenGpuFwdKernel, int)
+MS_REG_GPU_KERNEL_ONE(Reshape, KernelAttr().AddInputAttr(kNumberTypeUInt8).AddOutputAttr(kNumberTypeUInt8),
+                      FlattenGpuFwdKernel, uchar)
+MS_REG_GPU_KERNEL_ONE(Reshape, KernelAttr().AddInputAttr(kNumberTypeBool).AddOutputAttr(kNumberTypeBool),
+                      FlattenGpuFwdKernel, bool)
 MS_REG_GPU_KERNEL_ONE(ExpandDims, KernelAttr().AddInputAttr(kNumberTypeFloat32).AddOutputAttr(kNumberTypeFloat32),
                       FlattenGpuFwdKernel, float)
 MS_REG_GPU_KERNEL_ONE(ExpandDims, KernelAttr().AddInputAttr(kNumberTypeFloat16).AddOutputAttr(kNumberTypeFloat16),

tests/st/ops/gpu/test_reshape_op.py

@@ -0,0 +1,126 @@
+# Copyright 2019 Huawei Technologies Co., Ltd
+#
+# 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.
+# ============================================================================
+
+import numpy as np
+import pytest
+
+import mindspore.context as context
+from mindspore import Tensor
+from mindspore.ops import operations as P
+
+def reshape(nptype):
+    context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
+
+    reshape_op = P.Reshape()
+    data = np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11]).astype(nptype)
+    input_tensor = Tensor(np.array(data))
+
+    new_shape = (2, 6)
+    output_tensor = reshape_op(input_tensor, new_shape)
+    assert new_shape == output_tensor.shape
+    np.testing.assert_array_equal(output_tensor.asnumpy().flatten(), data)
+
+    new_shape = (6, 2)
+    output_tensor = reshape_op(input_tensor, new_shape)
+    assert new_shape == output_tensor.shape
+    np.testing.assert_array_equal(output_tensor.asnumpy().flatten(), data)
+
+    new_shape = (3, 4)
+    output_tensor = reshape_op(input_tensor, new_shape)
+    assert new_shape == output_tensor.shape
+    np.testing.assert_array_equal(output_tensor.asnumpy().flatten(), data)
+
+    new_shape = (4, 3)
+    output_tensor = reshape_op(input_tensor, new_shape)
+    assert new_shape == output_tensor.shape
+    np.testing.assert_array_equal(output_tensor.asnumpy().flatten(), data)
+
+    new_shape = (1, 12)
+    output_tensor = reshape_op(input_tensor, new_shape)
+    assert new_shape == output_tensor.shape
+    np.testing.assert_array_equal(output_tensor.asnumpy().flatten(), data)
+
+    new_shape = (12, 1)
+    output_tensor = reshape_op(input_tensor, new_shape)
+    assert new_shape == output_tensor.shape
+    np.testing.assert_array_equal(output_tensor.asnumpy().flatten(), data)
+
+def reshape_bool():
+    context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
+
+    reshape_op = P.Reshape()
+    data = np.array([True, True, False, True, False, False, True, False, False, False, False, False])
+    input_tensor = Tensor(np.array(data))
+
+    new_shape = (2, 6)
+    output_tensor = reshape_op(input_tensor, new_shape)
+    assert new_shape == output_tensor.shape
+    np.testing.assert_array_equal(output_tensor.asnumpy().flatten(), data)
+
+    new_shape = (6, 2)
+    output_tensor = reshape_op(input_tensor, new_shape)
+    assert new_shape == output_tensor.shape
+    np.testing.assert_array_equal(output_tensor.asnumpy().flatten(), data)
+
+    new_shape = (3, 4)
+    output_tensor = reshape_op(input_tensor, new_shape)
+    assert new_shape == output_tensor.shape
+    np.testing.assert_array_equal(output_tensor.asnumpy().flatten(), data)
+
+    new_shape = (4, 3)
+    output_tensor = reshape_op(input_tensor, new_shape)
+    assert new_shape == output_tensor.shape
+    np.testing.assert_array_equal(output_tensor.asnumpy().flatten(), data)
+
+    new_shape = (1, 12)
+    output_tensor = reshape_op(input_tensor, new_shape)
+    assert new_shape == output_tensor.shape
+    np.testing.assert_array_equal(output_tensor.asnumpy().flatten(), data)
+
+    new_shape = (12, 1)
+    output_tensor = reshape_op(input_tensor, new_shape)
+    assert new_shape == output_tensor.shape
+    np.testing.assert_array_equal(output_tensor.asnumpy().flatten(), data)
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.env_onecard
+def test_reshape_float():
+    reshape(np.float32)
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.env_onecard
+def test_reshape_float16():
+    reshape(np.float16)
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.env_onecard
+def test_reshape_int32():
+    reshape(np.int32)
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.env_onecard
+def test_reshape_uint8():
+    reshape(np.uint8)
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.env_onecard
+def test_reshape_bool():
+    reshape_bool()