Add test cases for uniform ops on GPU

tests/st/ops/ascend/test_aicpu_ops/test_uniform_int.py

@@ -41,15 +41,3 @@ def test_net_1D():
     tminval, tmaxval = Tensor(minval, mstype.int32), Tensor(maxval, mstype.int32)
     output = net(tminval, tmaxval)
     assert output.shape == (3, 2, 4)
-
-
-def test_net_ND():
-    seed = 10
-    shape = (3, 2, 1)
-    minval = np.array([[[1, 2]], [[3, 4]], [[5, 6]]]).astype(np.int32)
-    maxval = np.array([10]).astype(np.int32)
-    net = Net(shape, seed)
-    tminval, tmaxval = Tensor(minval), Tensor(maxval)
-    output = net(tminval, tmaxval)
-    print(output.asnumpy())
-    assert output.shape == (3, 2, 2)

tests/st/ops/ascend/test_aicpu_ops/test_uniform_real.py

@@ -36,15 +36,3 @@ def test_net():
     net = Net(shape, seed=seed)
     output = net()
     assert output.shape == (3, 2, 4)
-
-
-def test_net_ND():
-    seed = 10
-    shape = (3, 2, 1)
-    a = np.array([[[1, 2]], [[3, 4]], [[5, 6]]]).astype(np.float32)
-    b = np.array([10]).astype(np.float32)
-    net = Net(shape, seed)
-    ta, tb = Tensor(a), Tensor(b)
-    output = net(ta, tb)
-    print(output.asnumpy())
-    assert output.shape == (3, 2, 2)

tests/st/ops/gpu/test_standard_normal.py

@@ -13,6 +13,7 @@
 # limitations under the License.
 # ============================================================================
 
+import pytest
 import mindspore.context as context
 import mindspore.nn as nn
 from mindspore.ops import operations as P
@@ -31,7 +32,9 @@ class Net(nn.Cell):
     def construct(self):
         return self.stdnormal(self.shape)
 
-
+@pytest.mark.level0
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.env_onecard
 def test_net():
     seed = 10
     seed2 = 10

tests/st/ops/gpu/test_uniform_int.py

@@ -0,0 +1,46 @@
+# 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.
+# ============================================================================
+
+import pytest
+import mindspore.context as context
+import mindspore.nn as nn
+from mindspore import Tensor
+from mindspore.ops import operations as P
+from mindspore.common import dtype as mstype
+
+context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
+
+
+class Net(nn.Cell):
+    def __init__(self, shape, seed=0, seed2=0):
+        super(Net, self).__init__()
+        self.uniformint = P.UniformInt(seed=seed)
+        self.shape = shape
+
+    def construct(self, a, b):
+        return self.uniformint(self.shape, a, b)
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.env_onecard
+def test_net_1D():
+    seed = 10
+    shape = (3, 2, 4)
+    a = 1
+    b = 5
+    net = Net(shape, seed=seed)
+    ta, tb = Tensor(a, mstype.int32), Tensor(b, mstype.int32)
+    output = net(ta, tb)
+    assert output.shape == (3, 2, 4)

tests/st/ops/gpu/test_uniform_real.py

@@ -12,32 +12,30 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ============================================================================
+
+import pytest
 import mindspore.context as context
 import mindspore.nn as nn
-from mindspore import Tensor
 from mindspore.ops import operations as P
-from mindspore.common import dtype as mstype
 
 context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
 
 
 class Net(nn.Cell):
-    def __init__(self, shape, seed=0):
+    def __init__(self, shape, seed=0, seed2=0):
         super(Net, self).__init__()
         self.uniformreal = P.UniformReal(seed=seed)
         self.shape = shape
 
-    def construct(self, minval, maxval):
+    def construct(self):
-        return self.uniformreal(self.shape, minval, maxval)
+        return self.uniformreal(self.shape)
 
-
+@pytest.mark.level0
-def test_net_1D():
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.env_onecard
+def test_net():
     seed = 10
     shape = (3, 2, 4)
-    minval = 0.0
+    net = Net(shape, seed=seed)
-    maxval = 1.0
+    output = net()
-    net = Net(shape, seed)
-    tminval, tmaxval = Tensor(minval, mstype.float32), Tensor(maxval, mstype.float32)
-    output = net(tminval, tmaxval)
-    print(output.asnumpy())
     assert output.shape == (3, 2, 4)