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!1044 clean pylint warning in test dir

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Merge pull request !1044 from jinyaohui/clean_pylint_test
pull/1044/MERGE
mindspore-ci-bot 5 years ago committed by Gitee
parent 19ecc99718 d40e89b1bc
commit 2bc3fcb1c1
103 changed files with 942 additions and 576 deletions
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11
tests/st/ops/ascend/test_add.py
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@ -20,18 +20,23 @@ 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")
context.set_context(enable_task_sink=True)
class Net(nn.Cell):
def __init__(self):
super(Net, self).__init__()
self.add = P.TensorAdd()
def construct(self, x, y):
return self.add(x, y)
x = np.ones([1,3,3,4]).astype(np.float32)
y = np.ones([1,3,3,4]).astype(np.float32)
x = np.ones([1, 3, 3, 4]).astype(np.float32)
y = np.ones([1, 3, 3, 4]).astype(np.float32)
def test_net():
add = Net()

6
tests/st/ops/ascend/test_addn.py
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@ -20,15 +20,19 @@ 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.add = P.AddN()
def construct(self, x, y):
return self.add((x, y))
def test_net():
x = np.random.randn(1, 3, 3, 4).astype(np.float32)
y = np.random.randn(1, 3, 3, 4).astype(np.float32)

147
tests/st/ops/ascend/test_aicpu_ops/test_expand_dims.py
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@ -18,97 +18,110 @@ import mindspore.nn as nn
from mindspore.common.api import ms_function
import numpy as np
import mindspore.context as context
context.set_context(mode=context.PYNATIVE_MODE, device_target="Ascend")
class Net(nn.Cell):
def __init__(self):
super(Net, self).__init__()
self.expand_dims = P.ExpandDims()
def __init__(self):
super(Net, self).__init__()
self.expand_dims = P.ExpandDims()
def construct(self, tensor, dim):
return self.expand_dims(tensor, dim)
def construct(self, tensor, dim):
return self.expand_dims(tensor, dim)
def test_net_bool():
x = np.random.randn(1, 16, 1, 1).astype(np.bool)
net = Net()
output = net(Tensor(x), -1)
print(output.asnumpy())
assert(np.all(output.asnumpy() == np.expand_dims(x, -1)))
x = np.random.randn(1, 16, 1, 1).astype(np.bool)
net = Net()
output = net(Tensor(x), -1)
print(output.asnumpy())
assert (np.all(output.asnumpy() == np.expand_dims(x, -1)))
def test_net_int8():
x = np.random.randn(1, 16, 1, 1).astype(np.int8)
net = Net()
output = net(Tensor(x), -1)
print(output.asnumpy())
assert(np.all(output.asnumpy() == np.expand_dims(x, -1)))
x = np.random.randn(1, 16, 1, 1).astype(np.int8)
net = Net()
output = net(Tensor(x), -1)
print(output.asnumpy())
assert (np.all(output.asnumpy() == np.expand_dims(x, -1)))
def test_net_uint8():
x = np.random.randn(1, 16, 1, 1).astype(np.uint8)
net = Net()
output = net(Tensor(x), -1)
print(output.asnumpy())
assert(np.all(output.asnumpy() == np.expand_dims(x, -1)))
x = np.random.randn(1, 16, 1, 1).astype(np.uint8)
net = Net()
output = net(Tensor(x), -1)
print(output.asnumpy())
assert (np.all(output.asnumpy() == np.expand_dims(x, -1)))
def test_net_int16():
x = np.random.randn(1, 16, 1, 1).astype(np.int16)
net = Net()
output = net(Tensor(x), -1)
print(output.asnumpy())
assert(np.all(output.asnumpy() == np.expand_dims(x, -1)))
x = np.random.randn(1, 16, 1, 1).astype(np.int16)
net = Net()
output = net(Tensor(x), -1)
print(output.asnumpy())
assert (np.all(output.asnumpy() == np.expand_dims(x, -1)))
def test_net_uint16():
x = np.random.randn(1, 16, 1, 1).astype(np.uint16)
net = Net()
output = net(Tensor(x), -1)
print(output.asnumpy())
assert(np.all(output.asnumpy() == np.expand_dims(x, -1)))
x = np.random.randn(1, 16, 1, 1).astype(np.uint16)
net = Net()
output = net(Tensor(x), -1)
print(output.asnumpy())
assert (np.all(output.asnumpy() == np.expand_dims(x, -1)))
def test_net_int32():
x = np.random.randn(1, 16, 1, 1).astype(np.int32)
net = Net()
output = net(Tensor(x), -1)
print(output.asnumpy())
assert(np.all(output.asnumpy() == np.expand_dims(x, -1)))
x = np.random.randn(1, 16, 1, 1).astype(np.int32)
net = Net()
output = net(Tensor(x), -1)
print(output.asnumpy())
assert (np.all(output.asnumpy() == np.expand_dims(x, -1)))
def test_net_uint32():
x = np.random.randn(1, 16, 1, 1).astype(np.uint32)
net = Net()
output = net(Tensor(x), -1)
print(output.asnumpy())
assert(np.all(output.asnumpy() == np.expand_dims(x, -1)))
x = np.random.randn(1, 16, 1, 1).astype(np.uint32)
net = Net()
output = net(Tensor(x), -1)
print(output.asnumpy())
assert (np.all(output.asnumpy() == np.expand_dims(x, -1)))
def test_net_int64():
x = np.random.randn(1, 16, 1, 1).astype(np.int64)
net = Net()
output = net(Tensor(x), -1)
print(output.asnumpy())
assert(np.all(output.asnumpy() == np.expand_dims(x, -1)))
x = np.random.randn(1, 16, 1, 1).astype(np.int64)
net = Net()
output = net(Tensor(x), -1)
print(output.asnumpy())
assert (np.all(output.asnumpy() == np.expand_dims(x, -1)))
def test_net_uint64():
x = np.random.randn(1, 16, 1, 1).astype(np.uint64)
net = Net()
output = net(Tensor(x), -1)
print(output.asnumpy())
assert(np.all(output.asnumpy() == np.expand_dims(x, -1)))
x = np.random.randn(1, 16, 1, 1).astype(np.uint64)
net = Net()
output = net(Tensor(x), -1)
print(output.asnumpy())
assert (np.all(output.asnumpy() == np.expand_dims(x, -1)))
def test_net_float16():
x = np.random.randn(1, 16, 1, 1).astype(np.float16)
net = Net()
output = net(Tensor(x), -1)
print(output.asnumpy())
assert(np.all(output.asnumpy() == np.expand_dims(x, -1)))
x = np.random.randn(1, 16, 1, 1).astype(np.float16)
net = Net()
output = net(Tensor(x), -1)
print(output.asnumpy())
assert (np.all(output.asnumpy() == np.expand_dims(x, -1)))
def test_net_float32():
x = np.random.randn(1, 16, 1, 1).astype(np.float32)
net = Net()
output = net(Tensor(x), -1)
print(output.asnumpy())
assert(np.all(output.asnumpy() == np.expand_dims(x, -1)))
x = np.random.randn(1, 16, 1, 1).astype(np.float32)
net = Net()
output = net(Tensor(x), -1)
print(output.asnumpy())
assert (np.all(output.asnumpy() == np.expand_dims(x, -1)))
def test_net_float64():
x = np.random.randn(1, 16, 1, 1).astype(np.float64)
net = Net()
output = net(Tensor(x), -1)
print(output.asnumpy())
assert(np.all(output.asnumpy() == np.expand_dims(x, -1)))
x = np.random.randn(1, 16, 1, 1).astype(np.float64)
net = Net()
output = net(Tensor(x), -1)
print(output.asnumpy())
assert (np.all(output.asnumpy() == np.expand_dims(x, -1)))

125
tests/st/ops/ascend/test_aicpu_ops/test_flatten.py
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@ -17,83 +17,94 @@ 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.PYNATIVE_MODE, device_target="Ascend")
class Net(nn.Cell):
def __init__(self):
super(Net, self).__init__()
self.flatten = P.Flatten()
def __init__(self):
super(Net, self).__init__()
self.flatten = P.Flatten()
def construct(self, tensor):
return self.flatten(tensor)
def construct(self, tensor):
return self.flatten(tensor)
def test_net_int8():
x = np.random.randn(1, 16, 1, 1).astype(np.int8)
net = Net()
output = net(Tensor(x))
print(output.asnumpy())
assert(np.all(output.asnumpy() == x.flatten()))
x = np.random.randn(1, 16, 1, 1).astype(np.int8)
net = Net()
output = net(Tensor(x))
print(output.asnumpy())
assert (np.all(output.asnumpy() == x.flatten()))
def test_net_uint8():
x = np.random.randn(1, 16, 1, 1).astype(np.uint8)
net = Net()
output = net(Tensor(x))
print(output.asnumpy())
assert(np.all(output.asnumpy() == x.flatten()))
x = np.random.randn(1, 16, 1, 1).astype(np.uint8)
net = Net()
output = net(Tensor(x))
print(output.asnumpy())
assert (np.all(output.asnumpy() == x.flatten()))
def test_net_int16():
x = np.random.randn(1, 16, 1, 1).astype(np.int16)
net = Net()
output = net(Tensor(x))
print(output.asnumpy())
assert(np.all(output.asnumpy() == x.flatten()))
x = np.random.randn(1, 16, 1, 1).astype(np.int16)
net = Net()
output = net(Tensor(x))
print(output.asnumpy())
assert (np.all(output.asnumpy() == x.flatten()))
def test_net_uint16():
x = np.random.randn(1, 16, 1, 1).astype(np.uint16)
net = Net()
output = net(Tensor(x))
print(output.asnumpy())
assert(np.all(output.asnumpy() == x.flatten()))
x = np.random.randn(1, 16, 1, 1).astype(np.uint16)
net = Net()
output = net(Tensor(x))
print(output.asnumpy())
assert (np.all(output.asnumpy() == x.flatten()))
def test_net_int32():
x = np.random.randn(1, 16, 1, 1).astype(np.int32)
net = Net()
output = net(Tensor(x))
print(output.asnumpy())
assert(np.all(output.asnumpy() == x.flatten()))
x = np.random.randn(1, 16, 1, 1).astype(np.int32)
net = Net()
output = net(Tensor(x))
print(output.asnumpy())
assert (np.all(output.asnumpy() == x.flatten()))
def test_net_uint32():
x = np.random.randn(1, 16, 1, 1).astype(np.uint32)
net = Net()
output = net(Tensor(x))
print(output.asnumpy())
assert(np.all(output.asnumpy() == x.flatten()))
x = np.random.randn(1, 16, 1, 1).astype(np.uint32)
net = Net()
output = net(Tensor(x))
print(output.asnumpy())
assert (np.all(output.asnumpy() == x.flatten()))
def test_net_int64():
x = np.random.randn(1, 16, 1, 1).astype(np.int64)
net = Net()
output = net(Tensor(x))
print(output.asnumpy())
assert(np.all(output.asnumpy() == x.flatten()))
x = np.random.randn(1, 16, 1, 1).astype(np.int64)
net = Net()
output = net(Tensor(x))
print(output.asnumpy())
assert (np.all(output.asnumpy() == x.flatten()))
def test_net_uint64():
x = np.random.randn(1, 16, 1, 1).astype(np.uint64)
net = Net()
output = net(Tensor(x))
print(output.asnumpy())
assert(np.all(output.asnumpy() == x.flatten()))
x = np.random.randn(1, 16, 1, 1).astype(np.uint64)
net = Net()
output = net(Tensor(x))
print(output.asnumpy())
assert (np.all(output.asnumpy() == x.flatten()))
def test_net_float16():
x = np.random.randn(1, 16, 1, 1).astype(np.float16)
net = Net()
output = net(Tensor(x))
print(output.asnumpy())
assert(np.all(output.asnumpy() == x.flatten()))
x = np.random.randn(1, 16, 1, 1).astype(np.float16)
net = Net()
output = net(Tensor(x))
print(output.asnumpy())
assert (np.all(output.asnumpy() == x.flatten()))
def test_net_float32():
x = np.random.randn(1, 16, 1, 1).astype(np.float32)
net = Net()
output = net(Tensor(x))
print(output.asnumpy())
assert(np.all(output.asnumpy() == x.flatten()))
x = np.random.randn(1, 16, 1, 1).astype(np.float32)
net = Net()
output = net(Tensor(x))
print(output.asnumpy())
assert (np.all(output.asnumpy() == x.flatten()))

147
tests/st/ops/ascend/test_aicpu_ops/test_is_finite.py
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@ -18,97 +18,110 @@ import mindspore.nn as nn
from mindspore.common.api import ms_function
import numpy as np
import mindspore.context as context
context.set_context(mode=context.PYNATIVE_MODE, device_target="Ascend")
class Net(nn.Cell):
def __init__(self):
super(Net, self).__init__()
self.isfinite = P.IsFinite()
def __init__(self):
super(Net, self).__init__()
self.isfinite = P.IsFinite()
def construct(self, tensor):
return self.isfinite(tensor)
def construct(self, tensor):
return self.isfinite(tensor)
def test_net_bool():
x = np.random.randn(1, 16, 1, 1).astype(np.bool)
net = Net()
output = net(Tensor(x))
print(output.asnumpy())
assert(np.all(output.asnumpy() == np.isfinite(x)))
x = np.random.randn(1, 16, 1, 1).astype(np.bool)
net = Net()
output = net(Tensor(x))
print(output.asnumpy())
assert (np.all(output.asnumpy() == np.isfinite(x)))
def test_net_int8():
x = np.random.randn(1, 16, 1, 1).astype(np.int8)
net = Net()
output = net(Tensor(x))
print(output.asnumpy())
assert(np.all(output.asnumpy() == np.isfinite(x)))
x = np.random.randn(1, 16, 1, 1).astype(np.int8)
net = Net()
output = net(Tensor(x))
print(output.asnumpy())
assert (np.all(output.asnumpy() == np.isfinite(x)))
def test_net_uint8():
x = np.random.randn(1, 16, 1, 1).astype(np.uint8)
net = Net()
output = net(Tensor(x))
print(output.asnumpy())
assert(np.all(output.asnumpy() == np.isfinite(x)))
x = np.random.randn(1, 16, 1, 1).astype(np.uint8)
net = Net()
output = net(Tensor(x))
print(output.asnumpy())
assert (np.all(output.asnumpy() == np.isfinite(x)))
def test_net_int16():
x = np.random.randn(1, 16, 1, 1).astype(np.int16)
net = Net()
output = net(Tensor(x))
print(output.asnumpy())
assert(np.all(output.asnumpy() == np.isfinite(x)))
x = np.random.randn(1, 16, 1, 1).astype(np.int16)
net = Net()
output = net(Tensor(x))
print(output.asnumpy())
assert (np.all(output.asnumpy() == np.isfinite(x)))
def test_net_uint16():
x = np.random.randn(1, 16, 1, 1).astype(np.uint16)
net = Net()
output = net(Tensor(x))
print(output.asnumpy())
assert(np.all(output.asnumpy() == np.isfinite(x)))
x = np.random.randn(1, 16, 1, 1).astype(np.uint16)
net = Net()
output = net(Tensor(x))
print(output.asnumpy())
assert (np.all(output.asnumpy() == np.isfinite(x)))
def test_net_int32():
x = np.random.randn(1, 16, 1, 1).astype(np.int32)
net = Net()
output = net(Tensor(x))
print(output.asnumpy())
assert(np.all(output.asnumpy() == np.isfinite(x)))
x = np.random.randn(1, 16, 1, 1).astype(np.int32)
net = Net()
output = net(Tensor(x))
print(output.asnumpy())
assert (np.all(output.asnumpy() == np.isfinite(x)))
def test_net_uint32():
x = np.random.randn(1, 16, 1, 1).astype(np.uint32)
net = Net()
output = net(Tensor(x))
print(output.asnumpy())
assert(np.all(output.asnumpy() == np.isfinite(x)))
x = np.random.randn(1, 16, 1, 1).astype(np.uint32)
net = Net()
output = net(Tensor(x))
print(output.asnumpy())
assert (np.all(output.asnumpy() == np.isfinite(x)))
def test_net_int64():
x = np.random.randn(1, 16, 1, 1).astype(np.int64)
net = Net()
output = net(Tensor(x))
print(output.asnumpy())
assert(np.all(output.asnumpy() == np.isfinite(x)))
x = np.random.randn(1, 16, 1, 1).astype(np.int64)
net = Net()
output = net(Tensor(x))
print(output.asnumpy())
assert (np.all(output.asnumpy() == np.isfinite(x)))
def test_net_uint64():
x = np.random.randn(1, 16, 1, 1).astype(np.uint64)
net = Net()
output = net(Tensor(x))
print(output.asnumpy())
assert(np.all(output.asnumpy() == np.isfinite(x)))
x = np.random.randn(1, 16, 1, 1).astype(np.uint64)
net = Net()
output = net(Tensor(x))
print(output.asnumpy())
assert (np.all(output.asnumpy() == np.isfinite(x)))
def test_net_float16():
x = np.random.randn(1, 16, 1, 1).astype(np.float16)
net = Net()
output = net(Tensor(x))
print(output.asnumpy())
assert(np.all(output.asnumpy() == np.isfinite(x)))
x = np.random.randn(1, 16, 1, 1).astype(np.float16)
net = Net()
output = net(Tensor(x))
print(output.asnumpy())
assert (np.all(output.asnumpy() == np.isfinite(x)))
def test_net_float32():
x = np.random.randn(1, 16, 1, 1).astype(np.float32)
net = Net()
output = net(Tensor(x))
print(output.asnumpy())
assert(np.all(output.asnumpy() == np.isfinite(x)))
x = np.random.randn(1, 16, 1, 1).astype(np.float32)
net = Net()
output = net(Tensor(x))
print(output.asnumpy())
assert (np.all(output.asnumpy() == np.isfinite(x)))
def test_net_float64():
x = np.random.randn(1, 16, 1, 1).astype(np.float64)
net = Net()
output = net(Tensor(x))
print(output.asnumpy())
assert(np.all(output.asnumpy() == np.isfinite(x)))
x = np.random.randn(1, 16, 1, 1).astype(np.float64)
net = Net()
output = net(Tensor(x))
print(output.asnumpy())
assert (np.all(output.asnumpy() == np.isfinite(x)))

147
tests/st/ops/ascend/test_aicpu_ops/test_reshape.py
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@ -18,97 +18,110 @@ import mindspore.nn as nn
from mindspore.common.api import ms_function
import numpy as np
import mindspore.context as context
context.set_context(mode=context.PYNATIVE_MODE, device_target="Ascend")
class Net(nn.Cell):
def __init__(self):
super(Net, self).__init__()
self.reshape = P.Reshape()
def __init__(self):
super(Net, self).__init__()
self.reshape = P.Reshape()
def construct(self, tensor):
return self.reshape(tensor, (4,4))
def construct(self, tensor):
return self.reshape(tensor, (4, 4))
def test_net_bool():
x = np.random.randn(1, 16, 1, 1).astype(np.bool)
net = Net()
output = net(Tensor(x))
print(output.asnumpy())
assert(np.all(output.asnumpy() == np.reshape(x, (4,4))))
x = np.random.randn(1, 16, 1, 1).astype(np.bool)
net = Net()
output = net(Tensor(x))
print(output.asnumpy())
assert (np.all(output.asnumpy() == np.reshape(x, (4, 4))))
def test_net_int8():
x = np.random.randn(1, 16, 1, 1).astype(np.int8)
net = Net()
output = net(Tensor(x))
print(output.asnumpy())
assert(np.all(output.asnumpy() == np.reshape(x, (4,4))))
x = np.random.randn(1, 16, 1, 1).astype(np.int8)
net = Net()
output = net(Tensor(x))
print(output.asnumpy())
assert (np.all(output.asnumpy() == np.reshape(x, (4, 4))))
def test_net_uint8():
x = np.random.randn(1, 16, 1, 1).astype(np.uint8)
net = Net()
output = net(Tensor(x))
print(output.asnumpy())
assert(np.all(output.asnumpy() == np.reshape(x, (4,4))))
x = np.random.randn(1, 16, 1, 1).astype(np.uint8)
net = Net()
output = net(Tensor(x))
print(output.asnumpy())
assert (np.all(output.asnumpy() == np.reshape(x, (4, 4))))
def test_net_int16():
x = np.random.randn(1, 16, 1, 1).astype(np.int16)
net = Net()
output = net(Tensor(x))
print(output.asnumpy())
assert(np.all(output.asnumpy() == np.reshape(x, (4,4))))
x = np.random.randn(1, 16, 1, 1).astype(np.int16)
net = Net()
output = net(Tensor(x))
print(output.asnumpy())
assert (np.all(output.asnumpy() == np.reshape(x, (4, 4))))
def test_net_uint16():
x = np.random.randn(1, 16, 1, 1).astype(np.uint16)
net = Net()
output = net(Tensor(x))
print(output.asnumpy())
assert(np.all(output.asnumpy() == np.reshape(x, (4,4))))
x = np.random.randn(1, 16, 1, 1).astype(np.uint16)
net = Net()
output = net(Tensor(x))
print(output.asnumpy())
assert (np.all(output.asnumpy() == np.reshape(x, (4, 4))))
def test_net_int32():
x = np.random.randn(1, 16, 1, 1).astype(np.int32)
net = Net()
output = net(Tensor(x))
print(output.asnumpy())
assert(np.all(output.asnumpy() == np.reshape(x, (4,4))))
x = np.random.randn(1, 16, 1, 1).astype(np.int32)
net = Net()
output = net(Tensor(x))
print(output.asnumpy())
assert (np.all(output.asnumpy() == np.reshape(x, (4, 4))))
def test_net_uint32():
x = np.random.randn(1, 16, 1, 1).astype(np.uint32)
net = Net()
output = net(Tensor(x))
print(output.asnumpy())
assert(np.all(output.asnumpy() == np.reshape(x, (4,4))))
x = np.random.randn(1, 16, 1, 1).astype(np.uint32)
net = Net()
output = net(Tensor(x))
print(output.asnumpy())
assert (np.all(output.asnumpy() == np.reshape(x, (4, 4))))
def test_net_int64():
x = np.random.randn(1, 16, 1, 1).astype(np.int64)
net = Net()
output = net(Tensor(x))
print(output.asnumpy())
assert(np.all(output.asnumpy() == np.reshape(x, (4,4))))
x = np.random.randn(1, 16, 1, 1).astype(np.int64)
net = Net()
output = net(Tensor(x))
print(output.asnumpy())
assert (np.all(output.asnumpy() == np.reshape(x, (4, 4))))
def test_net_uint64():
x = np.random.randn(1, 16, 1, 1).astype(np.uint64)
net = Net()
output = net(Tensor(x))
print(output.asnumpy())
assert(np.all(output.asnumpy() == np.reshape(x, (4,4))))
x = np.random.randn(1, 16, 1, 1).astype(np.uint64)
net = Net()
output = net(Tensor(x))
print(output.asnumpy())
assert (np.all(output.asnumpy() == np.reshape(x, (4, 4))))
def test_net_float16():
x = np.random.randn(1, 16, 1, 1).astype(np.float16)
net = Net()
output = net(Tensor(x))
print(output.asnumpy())
assert(np.all(output.asnumpy() == np.reshape(x, (4,4))))
x = np.random.randn(1, 16, 1, 1).astype(np.float16)
net = Net()
output = net(Tensor(x))
print(output.asnumpy())
assert (np.all(output.asnumpy() == np.reshape(x, (4, 4))))
def test_net_float32():
x = np.random.randn(1, 16, 1, 1).astype(np.float32)
net = Net()
output = net(Tensor(x))
print(output.asnumpy())
assert(np.all(output.asnumpy() == np.reshape(x, (4,4))))
x = np.random.randn(1, 16, 1, 1).astype(np.float32)
net = Net()
output = net(Tensor(x))
print(output.asnumpy())
assert (np.all(output.asnumpy() == np.reshape(x, (4, 4))))
def test_net_float64():
x = np.random.randn(1, 16, 1, 1).astype(np.float64)
net = Net()
output = net(Tensor(x))
print(output.asnumpy())
assert(np.all(output.asnumpy() == np.reshape(x, (4,4))))
x = np.random.randn(1, 16, 1, 1).astype(np.float64)
net = Net()
output = net(Tensor(x))
print(output.asnumpy())
assert (np.all(output.asnumpy() == np.reshape(x, (4, 4))))

147
tests/st/ops/ascend/test_aicpu_ops/test_squeeze.py
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@ -17,97 +17,110 @@ 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.PYNATIVE_MODE, device_target="Ascend")
class Net(nn.Cell):
def __init__(self):
super(Net, self).__init__()
self.squeeze = P.Squeeze()
def __init__(self):
super(Net, self).__init__()
self.squeeze = P.Squeeze()
def construct(self, tensor):
return self.squeeze(tensor)
def construct(self, tensor):
return self.squeeze(tensor)
def test_net_bool():
x = np.random.randn(1, 16, 1, 1).astype(np.bool)
net = Net()
output = net(Tensor(x))
print(output.asnumpy())
assert(np.all(output.asnumpy() == x.squeeze()))
x = np.random.randn(1, 16, 1, 1).astype(np.bool)
net = Net()
output = net(Tensor(x))
print(output.asnumpy())
assert (np.all(output.asnumpy() == x.squeeze()))
def test_net_int8():
x = np.random.randn(1, 16, 1, 1).astype(np.int8)
net = Net()
output = net(Tensor(x))
print(output.asnumpy())
assert(np.all(output.asnumpy() == x.squeeze()))
x = np.random.randn(1, 16, 1, 1).astype(np.int8)
net = Net()
output = net(Tensor(x))
print(output.asnumpy())
assert (np.all(output.asnumpy() == x.squeeze()))
def test_net_uint8():
x = np.random.randn(1, 16, 1, 1).astype(np.uint8)
net = Net()
output = net(Tensor(x))
print(output.asnumpy())
assert(np.all(output.asnumpy() == x.squeeze()))
x = np.random.randn(1, 16, 1, 1).astype(np.uint8)
net = Net()
output = net(Tensor(x))
print(output.asnumpy())
assert (np.all(output.asnumpy() == x.squeeze()))
def test_net_int16():
x = np.random.randn(1, 16, 1, 1).astype(np.int16)
net = Net()
output = net(Tensor(x))
print(output.asnumpy())
assert(np.all(output.asnumpy() == x.squeeze()))
x = np.random.randn(1, 16, 1, 1).astype(np.int16)
net = Net()
output = net(Tensor(x))
print(output.asnumpy())
assert (np.all(output.asnumpy() == x.squeeze()))
def test_net_uint16():
x = np.random.randn(1, 16, 1, 1).astype(np.uint16)
net = Net()
output = net(Tensor(x))
print(output.asnumpy())
assert(np.all(output.asnumpy() == x.squeeze()))
x = np.random.randn(1, 16, 1, 1).astype(np.uint16)
net = Net()
output = net(Tensor(x))
print(output.asnumpy())
assert (np.all(output.asnumpy() == x.squeeze()))
def test_net_int32():
x = np.random.randn(1, 16, 1, 1).astype(np.int32)
net = Net()
output = net(Tensor(x))
print(output.asnumpy())
assert(np.all(output.asnumpy() == x.squeeze()))
x = np.random.randn(1, 16, 1, 1).astype(np.int32)
net = Net()
output = net(Tensor(x))
print(output.asnumpy())
assert (np.all(output.asnumpy() == x.squeeze()))
def test_net_uint32():
x = np.random.randn(1, 16, 1, 1).astype(np.uint32)
net = Net()
output = net(Tensor(x))
print(output.asnumpy())
assert(np.all(output.asnumpy() == x.squeeze()))
x = np.random.randn(1, 16, 1, 1).astype(np.uint32)
net = Net()
output = net(Tensor(x))
print(output.asnumpy())
assert (np.all(output.asnumpy() == x.squeeze()))
def test_net_int64():
x = np.random.randn(1, 16, 1, 1).astype(np.int64)
net = Net()
output = net(Tensor(x))
print(output.asnumpy())
assert(np.all(output.asnumpy() == x.squeeze()))
x = np.random.randn(1, 16, 1, 1).astype(np.int64)
net = Net()
output = net(Tensor(x))
print(output.asnumpy())
assert (np.all(output.asnumpy() == x.squeeze()))
def test_net_uint64():
x = np.random.randn(1, 16, 1, 1).astype(np.uint64)
net = Net()
output = net(Tensor(x))
print(output.asnumpy())
assert(np.all(output.asnumpy() == x.squeeze()))
x = np.random.randn(1, 16, 1, 1).astype(np.uint64)
net = Net()
output = net(Tensor(x))
print(output.asnumpy())
assert (np.all(output.asnumpy() == x.squeeze()))
def test_net_float16():
x = np.random.randn(1, 16, 1, 1).astype(np.float16)
net = Net()
output = net(Tensor(x))
print(output.asnumpy())
assert(np.all(output.asnumpy() == x.squeeze()))
x = np.random.randn(1, 16, 1, 1).astype(np.float16)
net = Net()
output = net(Tensor(x))
print(output.asnumpy())
assert (np.all(output.asnumpy() == x.squeeze()))
def test_net_float32():
x = np.random.randn(1, 16, 1, 1).astype(np.float32)
net = Net()
output = net(Tensor(x))
print(output.asnumpy())
assert(np.all(output.asnumpy() == x.squeeze()))
x = np.random.randn(1, 16, 1, 1).astype(np.float32)
net = Net()
output = net(Tensor(x))
print(output.asnumpy())
assert (np.all(output.asnumpy() == x.squeeze()))
def test_net_float64():
x = np.random.randn(1, 16, 1, 1).astype(np.float64)
net = Net()
output = net(Tensor(x))
print(output.asnumpy())
assert(np.all(output.asnumpy() == x.squeeze()))
x = np.random.randn(1, 16, 1, 1).astype(np.float64)
net = Net()
output = net(Tensor(x))
print(output.asnumpy())
assert (np.all(output.asnumpy() == x.squeeze()))

15
tests/st/ops/ascend/test_apply_momentum.py
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@ -20,24 +20,29 @@ 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')
'normal', [2, 3, 3, 4]), name='variable')
self.accumulation = Parameter(initializer(
'normal', [2, 3, 3, 4]), name='accumulation')
'normal', [2, 3, 3, 4]), name='accumulation')
self.learning_rate = Parameter(initializer(
'normal', [1, ]), name='learning_rate')
'normal', [1, ]), name='learning_rate')
self.gradient = Parameter(initializer(
'normal', [2, 3, 3, 4]), name='gradient')
'normal', [2, 3, 3, 4]), name='gradient')
self.momentum = Parameter(initializer(
'normal', [1, ]), name='momentum')
'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()

25
tests/st/ops/ascend/test_biasAddGrad.py
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@ -21,22 +21,25 @@ 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')
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)
@ms_function
def construct(self, dout):
return self.bias_add_grad(dout)
dout = np.ones([2,3,4,4]).astype(np.float32)
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"
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())

5
tests/st/ops/ascend/test_bias_add_grad.py
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@ -21,17 +21,20 @@ 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()

23
tests/st/ops/ascend/test_conv.py
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@ -20,32 +20,33 @@ 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__()
out_channel = 64
kernel_size = 7
self.conv = P.Conv2D(out_channel,
kernel_size,
mode=1,
pad_mode="valid",
pad=0,
stride=1,
dilation=1,
group=1)
kernel_size,
mode=1,
pad_mode="valid",
pad=0,
stride=1,
dilation=1,
group=1)
self.w = Parameter(initializer(
'normal', [64, 3, 7, 7]), name='w')
'normal', [64, 3, 7, 7]), name='w')
@ms_function
def construct(self, x):
return self.conv(x, self.w)
def test_net():
x = np.random.randn(32,3,224,224).astype(np.float32)
x = np.random.randn(32, 3, 224, 224).astype(np.float32)
conv = Net()
output = conv(Tensor(x))
print(output.asnumpy())

45
tests/st/ops/ascend/test_conv2dGradFilter.py
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@ -21,37 +21,40 @@ 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.conv2d_grad = G.Conv2DBackpropFilter(4,1)
yt = Tensor(np.array([[[[1, 0, -1], [1, 0, -1], [1, 0, -1]]]]).astype(np.float32))
self.y = Parameter(yt, name='y')
self.get_shape = P.Shape()
def __init__(self):
super(Net, self).__init__()
self.conv2d_grad = G.Conv2DBackpropFilter(4, 1)
yt = Tensor(np.array([[[[1, 0, -1], [1, 0, -1], [1, 0, -1]]]]).astype(np.float32))
self.y = Parameter(yt, name='y')
self.get_shape = P.Shape()
@ms_function
def construct(self, x, out):
return self.conv2d_grad(out, x, self.get_shape(self.y))
@ms_function
def construct(self, x, out):
return self.conv2d_grad(out, x, self.get_shape(self.y))
x = Tensor(np.array([[[
[3, 0, 1, 2, 7, 4],
[1, 5, 8, 9, 3, 1],
[2, 7, 2, 5, 1, 3],
[0, 1, 3, 1, 7, 8],
[4, 2, 1, 6, 2, 8],
[2, 4, 5, 2, 3, 9]]]]).astype(np.float32))
[3, 0, 1, 2, 7, 4],
[1, 5, 8, 9, 3, 1],
[2, 7, 2, 5, 1, 3],
[0, 1, 3, 1, 7, 8],
[4, 2, 1, 6, 2, 8],
[2, 4, 5, 2, 3, 9]]]]).astype(np.float32))
out = Tensor(np.array([[[
[ -5, -4, 0, 8],
[-10, -2, 2, 3],
[ 0, -2, -4, -7],
[ -3, -2, -3, -16]]]]).astype(np.float32))
[-5, -4, 0, 8],
[-10, -2, 2, 3],
[0, -2, -4, -7],
[-3, -2, -3, -16]]]]).astype(np.float32))
operator = Net()
output = operator(x, out)
expect_out = np.array([[[[ -60., -142., -265.],[-104., -211., -322.],[-102., -144., -248.]]]]).astype(np.float32)
expect_out = np.array([[[[-60., -142., -265.], [-104., -211., -322.], [-102., -144., -248.]]]]).astype(np.float32)
print(output.asnumpy())
print(expect_out)
assert np.all(output.asnumpy()==expect_out), "conv2d_grad execute failed, please check current code commit"
assert np.all(output.asnumpy() == expect_out), "conv2d_grad execute failed, please check current code commit"

20
tests/st/ops/ascend/test_conv_grad.py
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@ -21,8 +21,10 @@ import mindspore.context as context
from mindspore.common.initializer import initializer
from mindspore.common.parameter import Parameter
from mindspore.ops.composite import GradOperation
context.set_context(device_target="Ascend")
class Grad(nn.Cell):
def __init__(self, network):
super(Grad, self).__init__()
@ -33,26 +35,28 @@ class Grad(nn.Cell):
def construct(self, input, output_grad):
return self.grad(self.network)(input, output_grad)
class Net(nn.Cell):
def __init__(self):
super(Net, self).__init__()
out_channel = 512
kernel_size = 2048
self.conv = P.Conv2D(out_channel,
(kernel_size, kernel_size),
mode=1,
pad_mode="same",
pad=3,
stride=2,
dilation=1,
group=1)
(kernel_size, kernel_size),
mode=1,
pad_mode="same",
pad=3,
stride=2,
dilation=1,
group=1)
self.w = Parameter(initializer(
'normal', [512, 2048, 1, 1]), name='w')
'normal', [512, 2048, 1, 1]), name='w')
@ms_function
def construct(self, x):
return self.conv(x, self.w)
def test_net():
x = np.ones([32, 2048, 7, 7]).astype(np.float32)
sens = np.ones([32, 512, 7, 7]).astype(np.float32)

4
tests/st/ops/ascend/test_dense.py
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@ -20,7 +20,10 @@ 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__()
@ -30,6 +33,7 @@ class Net(nn.Cell):
def construct(self, x):
return self.dense(x)
def test_net():
x = np.random.randn(32, 2048).astype(np.float32)
net = Net()

4
tests/st/ops/ascend/test_dense_grad.py
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@ -21,8 +21,10 @@ import mindspore.context as context
from mindspore.common.initializer import initializer
from mindspore.common.parameter import Parameter
from mindspore.ops.composite import GradOperation
context.set_context(device_target="Ascend")
class Grad(nn.Cell):
def __init__(self, network):
super(Grad, self).__init__()
@ -33,6 +35,7 @@ class Grad(nn.Cell):
def construct(self, input, output_grad):
return self.grad(self.network)(input, output_grad)
class Net(nn.Cell):
def __init__(self):
super(Net, self).__init__()
@ -41,6 +44,7 @@ class Net(nn.Cell):
def construct(self, x):
return self.dense(x)
def test_net():
x = np.random.randn(32, 2048).astype(np.float32)
sens = np.random.randn(32, 1001).astype(np.float32)

1
tests/st/ops/ascend/test_drop_out_gen_mask.py
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@ -17,6 +17,7 @@ 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")

1
tests/st/ops/ascend/test_full_connection.py
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@ -21,6 +21,7 @@ import mindspore.context as context
context.set_context(device_target="Ascend")
class Net(nn.Cell):
def __init__(self):
super(Net, self).__init__()

6
tests/st/ops/ascend/test_fused_batchnorm.py
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@ -20,7 +20,10 @@ 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__()
@ -35,7 +38,7 @@ class Net(nn.Cell):
def test_net():
x = np.random.randn(1,64,112,112).astype(np.float32)
x = np.random.randn(1, 64, 112, 112).astype(np.float32)
# mean = np.random.randn(1,16,1,1).astype(np.float32)
# variance = np.random.randn(1,16,1,1).astype(np.float32)
fusedBn = Net()
@ -45,4 +48,3 @@ def test_net():
print("***********output y*********")
print(output.asnumpy())

10
tests/st/ops/ascend/test_fused_batchnorm_grad.py
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@ -21,8 +21,11 @@ import mindspore.context as context
from mindspore.common.initializer import initializer
from mindspore.common.parameter import Parameter
from mindspore.ops.composite import GradOperation
#context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
# context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
context.set_context(device_target="Ascend")
class Grad(nn.Cell):
def __init__(self, network):
super(Grad, self).__init__()
@ -33,6 +36,7 @@ class Grad(nn.Cell):
def construct(self, input, output_grad):
return self.grad(self.network)(input, output_grad)
class Net(nn.Cell):
def __init__(self):
super(Net, self).__init__()
@ -47,8 +51,8 @@ class Net(nn.Cell):
def test_net():
x = np.random.randn(1,64,112,112).astype(np.float32)
sens = np.random.randn(1,64,112,112).astype(np.float32)
x = np.random.randn(1, 64, 112, 112).astype(np.float32)
sens = np.random.randn(1, 64, 112, 112).astype(np.float32)
net = Grad(Net())
output = net(Tensor(x), Tensor(sens))
print("***********x*********")

40
tests/st/ops/ascend/test_image_gradients.py
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@ -20,6 +20,8 @@ from mindspore import Tensor
from mindspore.common.api import ms_function
context.set_context(device_target="Ascend")
class Net(nn.Cell):
def __init__(self):
super(Net, self).__init__()
@ -31,32 +33,32 @@ class Net(nn.Cell):
def test_image_gradients():
image = Tensor(np.array([[[[1,2],[3,4]]]]), dtype=mstype.int32)
expected_dy = np.array([[[[2,2],[0,0]]]]).astype(np.int32)
expected_dx = np.array([[[[1,0],[1,0]]]]).astype(np.int32)
image = Tensor(np.array([[[[1, 2], [3, 4]]]]), dtype=mstype.int32)
expected_dy = np.array([[[[2, 2], [0, 0]]]]).astype(np.int32)
expected_dx = np.array([[[[1, 0], [1, 0]]]]).astype(np.int32)
net = Net()
dy, dx = net(image)
assert np.any(dx.asnumpy()-expected_dx) == False
assert np.any(dy.asnumpy()-expected_dy) == False
assert np.any(dx.asnumpy() - expected_dx) == False
assert np.any(dy.asnumpy() - expected_dy) == False
def test_image_gradients_multi_channel_depth():
# 4 x 2 x 2 x 2
dtype = mstype.int32
image = Tensor(np.array([[[[1,2],[3,4]], [[5,6],[7,8]]],
[[[3,5],[7,9]], [[11,13],[15,17]]],
[[[5,10],[15,20]], [[25,30],[35,40]]],
[[[10,20],[30,40]], [[50,60],[70,80]]]]), dtype=dtype)
expected_dy = Tensor(np.array([[[[2,2],[0,0]], [[2,2],[0,0]]],
[[[4,4],[0,0]], [[4,4],[0,0]]],
[[[10,10],[0,0]], [[10,10],[0,0]]],
[[[20,20],[0,0]], [[20,20],[0,0]]]]), dtype=dtype)
expected_dx = Tensor(np.array([[[[1,0],[1,0]], [[1,0],[1,0]]],
[[[2,0],[2,0]], [[2,0],[2,0]]],
[[[5,0],[5,0]], [[5,0],[5,0]]],
[[[10,0],[10,0]], [[10,0],[10,0]]]]), dtype=dtype)
image = Tensor(np.array([[[[1, 2], [3, 4]], [[5, 6], [7, 8]]],
[[[3, 5], [7, 9]], [[11, 13], [15, 17]]],
[[[5, 10], [15, 20]], [[25, 30], [35, 40]]],
[[[10, 20], [30, 40]], [[50, 60], [70, 80]]]]), dtype=dtype)
expected_dy = Tensor(np.array([[[[2, 2], [0, 0]], [[2, 2], [0, 0]]],
[[[4, 4], [0, 0]], [[4, 4], [0, 0]]],
[[[10, 10], [0, 0]], [[10, 10], [0, 0]]],
[[[20, 20], [0, 0]], [[20, 20], [0, 0]]]]), dtype=dtype)
expected_dx = Tensor(np.array([[[[1, 0], [1, 0]], [[1, 0], [1, 0]]],
[[[2, 0], [2, 0]], [[2, 0], [2, 0]]],
[[[5, 0], [5, 0]], [[5, 0], [5, 0]]],
[[[10, 0], [10, 0]], [[10, 0], [10, 0]]]]), dtype=dtype)
net = Net()
dy, dx = net(image)
assert np.any(dx.asnumpy()-expected_dx.asnumpy()) == False
assert np.any(dy.asnumpy()-expected_dy.asnumpy()) == False
assert np.any(dx.asnumpy() - expected_dx.asnumpy()) == False
assert np.any(dy.asnumpy() - expected_dy.asnumpy()) == False

9
tests/st/ops/ascend/test_matmul.py
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@ -20,7 +20,10 @@ 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__()
@ -30,8 +33,10 @@ class Net(nn.Cell):
def construct(self, x1, x2):
return self.matmul(x1, x2)
x1 = np.random.randn(1,3).astype(np.float32)
x2 = np.random.randn(3,4).astype(np.float32)
x1 = np.random.randn(1, 3).astype(np.float32)
x2 = np.random.randn(3, 4).astype(np.float32)
def test_net():
matmul = Net()

5
tests/st/ops/ascend/test_maxpool.py
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@ -20,12 +20,13 @@ import numpy as np
import mindspore.context as context
context.set_context(device_target="Ascend")
class Net(nn.Cell):
def __init__(self):
super(Net, self).__init__()
self.maxpool = P.MaxPool(pad_mode="SAME", window=3, stride=2)
@ms_function
def construct(self, x):
output = self.maxpool(x)
@ -33,7 +34,7 @@ class Net(nn.Cell):
def test_net():
x = np.random.randn(32,64,112,112).astype(np.float32)
x = np.random.randn(32, 64, 112, 112).astype(np.float32)
maxpool = Net()
output = maxpool(Tensor(x))
print(output.asnumpy())

1
tests/st/ops/ascend/test_maxpool_grad.py
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@ -19,6 +19,7 @@ from mindspore.common.api import ms_function
import numpy as np
import mindspore.context as context
from mindspore.ops.composite import GradOperation
context.set_context(device_target="Ascend")

8
tests/st/ops/ascend/test_maxpool_with_argmax_grad.py
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@ -21,8 +21,10 @@ import mindspore.context as context
from mindspore.common.initializer import initializer
from mindspore.common.parameter import Parameter
from mindspore.ops.composite import GradOperation
context.set_context(device_target="Ascend")
class Grad(nn.Cell):
def __init__(self, network):
super(Grad, self).__init__()
@ -33,6 +35,7 @@ class Grad(nn.Cell):
def construct(self, input, output_grad):
return self.grad(self.network)(input, output_grad)
class Net(nn.Cell):
def __init__(self):
super(Net, self).__init__()
@ -43,8 +46,9 @@ class Net(nn.Cell):
@ms_function
def construct(self, x):
output = self.maxpool(x)
return output[0]
output = self.maxpool(x)
return output[0]
def test_net():
x = np.random.randn(32, 64, 112, 112).astype(np.float32)

6
tests/st/ops/ascend/test_relu.py
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@ -20,7 +20,10 @@ 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__()
@ -30,8 +33,9 @@ class Net(nn.Cell):
def construct(self, x):
return self.relu(x)
def test_net():
x = np.random.randn(2,3,3,4).astype(np.float32)
x = np.random.randn(2, 3, 3, 4).astype(np.float32)
relu = Net()
output = relu(Tensor(x))
print(x)

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