Rename davinci to ascend in ops st test module

tests/st/ops/ascend/test_apply_momentum.py

@@ -1,44 +1,44 @@
-# 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.
-# ============================================================================
-from mindspore import Tensor
-from mindspore.ops import operations as P
-import mindspore.nn as nn
-from mindspore.common.api import ms_function
-import numpy as np
-import mindspore.context as context
-from mindspore.common.initializer import initializer
-from mindspore.common.parameter import Parameter
-context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
-class Net(nn.Cell):
-    def __init__(self):
-        super(Net, self).__init__()
-        self.apply_momentum = P.ApplyMomentum(gradient_scale=1024.0)
-        self.variable = Parameter(initializer(
-                                'normal', [2, 3, 3, 4]), name='variable')
-        self.accumulation = Parameter(initializer(
-                                'normal', [2, 3, 3, 4]), name='accumulation')
-        self.learning_rate = Parameter(initializer(
-                                'normal', [1, ]), name='learning_rate')
-        self.gradient = Parameter(initializer(
-                                'normal', [2, 3, 3, 4]), name='gradient')
-        self.momentum = Parameter(initializer(
-                                'normal', [1, ]), name='momentum')
-    def construct(self):
-        return self.apply_momentum(self.variable, self.accumulation, self.learning_rate, self.gradient, self.momentum)
-
-def test_net():
-    apply_momentum = Net()
-    output = apply_momentum()
-    print(output.asnumpy())
+# 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.
+# ============================================================================
+from mindspore import Tensor
+from mindspore.ops import operations as P
+import mindspore.nn as nn
+from mindspore.common.api import ms_function
+import numpy as np
+import mindspore.context as context
+from mindspore.common.initializer import initializer
+from mindspore.common.parameter import Parameter
+context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
+class Net(nn.Cell):
+    def __init__(self):
+        super(Net, self).__init__()
+        self.apply_momentum = P.ApplyMomentum(gradient_scale=1024.0)
+        self.variable = Parameter(initializer(
+                                'normal', [2, 3, 3, 4]), name='variable')
+        self.accumulation = Parameter(initializer(
+                                'normal', [2, 3, 3, 4]), name='accumulation')
+        self.learning_rate = Parameter(initializer(
+                                'normal', [1, ]), name='learning_rate')
+        self.gradient = Parameter(initializer(
+                                'normal', [2, 3, 3, 4]), name='gradient')
+        self.momentum = Parameter(initializer(
+                                'normal', [1, ]), name='momentum')
+    def construct(self):
+        return self.apply_momentum(self.variable, self.accumulation, self.learning_rate, self.gradient, self.momentum)
+
+def test_net():
+    apply_momentum = Net()
+    output = apply_momentum()
+    print(output.asnumpy())

tests/st/ops/ascend/test_biasAddGrad.py

@@ -1,42 +1,42 @@
-# 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.
-# ============================================================================
-from mindspore import Tensor
-from mindspore.ops import operations as P
-from mindspore.ops.operations import _grad_ops as G
-import mindspore.nn as nn
-from mindspore.common.api import ms_function
-import numpy as np
-import mindspore.context as context
-from mindspore.common.initializer import initializer
-from mindspore.common.parameter import Parameter
-context.set_context(device_target="Ascend")
-class Net(nn.Cell):
-  def __init__(self):
-    super(Net, self).__init__()
-    self.bias_add_grad = G.BiasAddGrad()
-    #self.dout = Parameter(initializer(
-                #'normal', [2, 3, 3, 4]), name='dout')
-
-
-  @ms_function
-  def construct(self, dout):
-    return self.bias_add_grad(dout)
-
-dout = np.ones([2,3,4,4]).astype(np.float32)
-bias_add_grad = Net()
-output = bias_add_grad(Tensor(dout))
-expect_output = np.array([32.,32.,32.]).astype(np.float32)
-assert np.all(output.asnumpy()==expect_output), "bias_add_grad execute failed, please check current code commit"
-print(output.asnumpy())
+# 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.
+# ============================================================================
+from mindspore import Tensor
+from mindspore.ops import operations as P
+from mindspore.ops.operations import _grad_ops as G
+import mindspore.nn as nn
+from mindspore.common.api import ms_function
+import numpy as np
+import mindspore.context as context
+from mindspore.common.initializer import initializer
+from mindspore.common.parameter import Parameter
+context.set_context(device_target="Ascend")
+class Net(nn.Cell):
+  def __init__(self):
+    super(Net, self).__init__()
+    self.bias_add_grad = G.BiasAddGrad()
+    #self.dout = Parameter(initializer(
+                #'normal', [2, 3, 3, 4]), name='dout')
+
+
+  @ms_function
+  def construct(self, dout):
+    return self.bias_add_grad(dout)
+
+dout = np.ones([2,3,4,4]).astype(np.float32)
+bias_add_grad = Net()
+output = bias_add_grad(Tensor(dout))
+expect_output = np.array([32.,32.,32.]).astype(np.float32)
+assert np.all(output.asnumpy()==expect_output), "bias_add_grad execute failed, please check current code commit"
+print(output.asnumpy())

tests/st/ops/ascend/test_bias_add_grad.py

@@ -1,39 +1,39 @@
-# 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.
-# ============================================================================
-from mindspore import Tensor
-from mindspore.ops import operations as P
-from mindspore.ops.operations import _grad_ops as G
-import mindspore.nn as nn
-from mindspore.common.api import ms_function
-import numpy as np
-import mindspore.context as context
-from mindspore.common.initializer import initializer
-from mindspore.common.parameter import Parameter
-context.set_context(device_target="Ascend")
-class Net(nn.Cell):
-    def __init__(self):
-        super(Net, self).__init__()
-        self.bias_add_grad = G.BiasAddGrad()
-    
-
-    @ms_function
-    def construct(self, dout):
-        return self.bias_add_grad(dout)
-
-def test_net():
-    dout = np.random.rand(1, 1001).astype(np.float32)
-    bias_add_grad = Net()
-    output = bias_add_grad(dout)
-    print(output.asnumpy())
+# 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.
+# ============================================================================
+from mindspore import Tensor
+from mindspore.ops import operations as P
+from mindspore.ops.operations import _grad_ops as G
+import mindspore.nn as nn
+from mindspore.common.api import ms_function
+import numpy as np
+import mindspore.context as context
+from mindspore.common.initializer import initializer
+from mindspore.common.parameter import Parameter
+context.set_context(device_target="Ascend")
+class Net(nn.Cell):
+    def __init__(self):
+        super(Net, self).__init__()
+        self.bias_add_grad = G.BiasAddGrad()
+    
+
+    @ms_function
+    def construct(self, dout):
+        return self.bias_add_grad(dout)
+
+def test_net():
+    dout = np.random.rand(1, 1001).astype(np.float32)
+    bias_add_grad = Net()
+    output = bias_add_grad(dout)
+    print(output.asnumpy())

tests/st/ops/ascend/test_drop_out_gen_mask.py

@@ -1,44 +1,44 @@
-# Copyright 2020 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.
-# ============================================================================
-from mindspore import Tensor
-from mindspore.ops import operations as P
-import mindspore.nn as nn
-import numpy as np
-import mindspore.context as context
-context.set_context(mode=context.GRAPH_MODE,
-                    device_target="Ascend")
-
-
-class Net(nn.Cell):
-    def __init__(self):
-        super(Net, self).__init__()
-        self.mask = P.DropoutGenMask(10, 28)
-        self.shape = P.Shape()
-
-    def construct(self, x, y):
-        shape_x = self.shape(x)
-        return self.mask(shape_x, y)
-
-
-x = np.ones([2, 4, 2, 2]).astype(np.int32)
-y = np.array([1.0]).astype(np.float32)
-
-
-def test_net():
-    mask = Net()
-    tx, ty = Tensor(x), Tensor(y)
-    output = mask(tx, ty)
-    print(output.asnumpy())
-    assert ([255, 255, 255, 255] == output.asnumpy()).all()
+# Copyright 2020 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.
+# ============================================================================
+from mindspore import Tensor
+from mindspore.ops import operations as P
+import mindspore.nn as nn
+import numpy as np
+import mindspore.context as context
+context.set_context(mode=context.GRAPH_MODE,
+                    device_target="Ascend")
+
+
+class Net(nn.Cell):
+    def __init__(self):
+        super(Net, self).__init__()
+        self.mask = P.DropoutGenMask(10, 28)
+        self.shape = P.Shape()
+
+    def construct(self, x, y):
+        shape_x = self.shape(x)
+        return self.mask(shape_x, y)
+
+
+x = np.ones([2, 4, 2, 2]).astype(np.int32)
+y = np.array([1.0]).astype(np.float32)
+
+
+def test_net():
+    mask = Net()
+    tx, ty = Tensor(x), Tensor(y)
+    output = mask(tx, ty)
+    print(output.asnumpy())
+    assert ([255, 255, 255, 255] == output.asnumpy()).all()