add_st

tests/st/ops/gpu/test_batchtospace_op.py

@@ -0,0 +1,84 @@
+# Copyright 2021 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
+import mindspore.nn as nn
+import mindspore.ops.operations.array_ops as P
+from mindspore import Tensor
+from mindspore.common.api import ms_function
+from mindspore.common.initializer import initializer
+from mindspore.common.parameter import Parameter
+
+class BatchToSpaceNet(nn.Cell):
+    def __init__(self, nptype, block_size=2, input_shape=(4,1,2,2)):
+        super(BatchToSpaceNet, self).__init__()
+        self.BatchToSpace = P.BatchToSpace(block_size=block_size, crops=[[0,0],[0,0]])
+        input_size = 1
+        for i in input_shape:
+            input_size = input_size*i
+        data_np = np.arange(input_size).reshape(input_shape).astype(nptype)
+        self.x1 = Parameter(initializer(Tensor(data_np), input_shape), name='x1')
+
+
+    @ms_function
+    def construct(self):
+        y1 = self.BatchToSpace(self.x1)
+        return y1
+
+
+def BatchToSpace(nptype, block_size=2, input_shape=(4,1,2,2)):
+    context.set_context(mode=context.GRAPH_MODE, device_target='GPU')
+    input_size = 1
+    for i in input_shape:
+        input_size = input_size*i
+    expect = np.array([[[[0,  4,   1,  5],
+                         [8,  12,  9, 13],
+                         [2,   6,  3,  7],
+                         [10, 14, 11, 15]]]]).astype(nptype)
+
+    dts = BatchToSpaceNet(nptype, block_size, input_shape)
+    output = dts()
+
+    assert (output.asnumpy() == expect).all()
+
+def BatchToSpace_pynative(nptype, block_size=2, input_shape=(4,1,2,2)):
+    context.set_context(mode=context.PYNATIVE_MODE, device_target='GPU')
+    input_size = 1
+    for i in input_shape:
+        input_size = input_size*i
+    expect = np.array([[[[0,  4,   1,  5],
+                         [8,  12,  9, 13],
+                         [2,   6,  3,  7],
+                         [10, 14, 11, 15]]]]).astype(nptype)
+
+    dts = P.BatchToSpace(block_size=block_size, crops=[[0,0],[0,0]])
+    arr_input = Tensor(np.arange(input_size).reshape(input_shape).astype(nptype))
+    output = dts(arr_input)
+
+    assert (output.asnumpy() == expect).all()
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.env_onecard
+def test_batchtospace_graph_float32():
+    BatchToSpace(np.float32)
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.env_onecard
+def test_batchtospace_graph_float16():
+    BatchToSpace(np.float16)

tests/st/ops/gpu/test_spacetobatch_op.py

@@ -0,0 +1,92 @@
+# Copyright 2021 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
+import mindspore.nn as nn
+import mindspore.ops.operations.array_ops as P
+from mindspore import Tensor
+from mindspore.common.api import ms_function
+from mindspore.common.initializer import initializer
+from mindspore.common.parameter import Parameter
+
+class SpaceToBatchNet(nn.Cell):
+    def __init__(self, nptype, block_size=2, input_shape=(1, 1, 4, 4)):
+        super(SpaceToBatchNet, self).__init__()
+        self.SpaceToBatch = P.SpaceToBatch(block_size=block_size, paddings=[[0,0],[0,0]])
+        input_size = 1
+        for i in input_shape:
+            input_size = input_size*i
+        data_np = np.arange(input_size).reshape(input_shape).astype(nptype)
+        self.x1 = Parameter(initializer(Tensor(data_np), input_shape), name='x1')
+
+
+    @ms_function
+    def construct(self):
+        y1 = self.BatchToSpace(self.x1)
+        return y1
+
+
+def SpaceToBatch(nptype, block_size=2, input_shape=(1, 1, 4, 4)):
+    context.set_context(mode=context.GRAPH_MODE, device_target='GPU')
+    input_size = 1
+    for i in input_shape:
+        input_size = input_size*i
+    expect = np.array([[[[0, 2],
+                         [8, 10]]],
+                       [[[1, 3],
+                         [9, 11]]],
+                       [[[4, 6],
+                         [12, 14]]],
+                       [[[5, 7],
+                         [13, 15]]]]).astype(nptype)
+
+    dts = SpaceToBatchNet(nptype, block_size, input_shape)
+    output = dts()
+
+    assert (output.asnumpy() == expect).all()
+
+def SpaceToBatch_pynative(nptype, block_size=2, input_shape=(1, 1, 4, 4)):
+    context.set_context(mode=context.PYNATIVE_MODE, device_target='GPU')
+    input_size = 1
+    for i in input_shape:
+        input_size = input_size*i
+    expect = np.array([[[[0, 2],
+                         [8, 10]]],
+                       [[[1, 3],
+                         [9, 11]]],
+                       [[[4, 6],
+                         [12, 14]]],
+                       [[[5, 7],
+                         [13, 15]]]]).astype(nptype)
+
+    dts = P.SpaceToBatch(block_size=block_size, paddings=[[0,0],[0,0]])
+    arr_input = Tensor(np.arange(input_size).reshape(input_shape).astype(nptype))
+    output = dts(arr_input)
+
+    assert (output.asnumpy() == expect).all()
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.env_onecard
+def test_spacetobatch_graph_float32():
+    SpaceToBatch(np.float32)
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.env_onecard
+def test_spacetobatch_graph_float16():
+    SpaceToBatch(np.float16)