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Add fp64 as input type fot GPU op of ReduceMin and ReduceMean.

This commit is contained in:
hezhenhao1 2021-11-08 10:23:58 +08:00
parent efc33a8225
commit cb13783059
3 changed files with 89 additions and 416 deletions
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4
mindspore/ccsrc/backend/kernel_compiler/gpu/arrays/array_reduce_gpu_kernel.cc
View File

@ -24,6 +24,8 @@ MS_REG_GPU_KERNEL_ONE(ReduceMax, KernelAttr().AddInputAttr(kNumberTypeFloat32).A
ArrayReduceGpuKernel, float)
MS_REG_GPU_KERNEL_ONE(ReduceMax, KernelAttr().AddInputAttr(kNumberTypeFloat16).AddOutputAttr(kNumberTypeFloat16),
ArrayReduceGpuKernel, half)
MS_REG_GPU_KERNEL_ONE(ReduceMean, KernelAttr().AddInputAttr(kNumberTypeFloat64).AddOutputAttr(kNumberTypeFloat64),
ArrayReduceGpuKernel, double)
MS_REG_GPU_KERNEL_ONE(ReduceMean, KernelAttr().AddInputAttr(kNumberTypeFloat32).AddOutputAttr(kNumberTypeFloat32),
ArrayReduceGpuKernel, float)
MS_REG_GPU_KERNEL_ONE(ReduceMean, KernelAttr().AddInputAttr(kNumberTypeFloat16).AddOutputAttr(kNumberTypeFloat16),
@ -34,6 +36,8 @@ MS_REG_GPU_KERNEL_ONE(ReduceSum, KernelAttr().AddInputAttr(kNumberTypeFloat32).A
ArrayReduceGpuKernel, float)
MS_REG_GPU_KERNEL_ONE(ReduceSum, KernelAttr().AddInputAttr(kNumberTypeFloat16).AddOutputAttr(kNumberTypeFloat16),
ArrayReduceGpuKernel, half)
MS_REG_GPU_KERNEL_ONE(ReduceMin, KernelAttr().AddInputAttr(kNumberTypeFloat64).AddOutputAttr(kNumberTypeFloat64),
ArrayReduceGpuKernel, double)
MS_REG_GPU_KERNEL_ONE(ReduceMin, KernelAttr().AddInputAttr(kNumberTypeFloat32).AddOutputAttr(kNumberTypeFloat32),
ArrayReduceGpuKernel, float)
MS_REG_GPU_KERNEL_ONE(ReduceMin, KernelAttr().AddInputAttr(kNumberTypeFloat16).AddOutputAttr(kNumberTypeFloat16),

298
tests/st/ops/gpu/test_reduce_mean_op.py
View File

@ -19,297 +19,83 @@ import pytest
import mindspore.context as context
import mindspore.nn as nn
from mindspore import Tensor
from mindspore.common.api import ms_function
from mindspore.ops import operations as P
from mindspore.ops.operations import _inner_ops as inner
x0 = np.random.rand(2, 3, 4, 4).astype(np.float32)
axis0 = 3
keep_dims0 = True
x1 = np.random.rand(2, 3, 4, 4).astype(np.float32)
axis1 = 3
keep_dims1 = False
x2 = np.random.rand(2, 3, 1, 4).astype(np.float32)
axis2 = 2
keep_dims2 = True
x3 = np.random.rand(2, 3, 1, 4).astype(np.float32)
axis3 = 2
keep_dims3 = False
x4 = np.random.rand(2, 3, 4, 1).astype(np.float32)
axis4 = 3
keep_dims4 = True
x5 = np.random.rand(2, 3, 4, 1).astype(np.float32)
axis5 = 3
keep_dims5 = False
x6 = np.random.rand(2, 3, 4, 4).astype(np.float32)
axis6 = (1, 2)
keep_dims6 = False
x7 = np.random.rand(2, 3, 4, 4).astype(np.float32)
axis7 = (1, 2)
keep_dims7 = True
x8 = np.random.rand(2, 1, 1, 4).astype(np.float32)
axis8 = (1, 2)
keep_dims8 = True
x9 = np.random.rand(2, 1, 1, 4).astype(np.float32)
axis9 = (1, 2)
keep_dims9 = False
x10 = np.random.rand(2, 3, 4, 4).astype(np.float32)
axis10 = (0, 1, 2, 3)
keep_dims10 = False
x11 = np.random.rand(1, 1, 1, 1).astype(np.float32)
axis11 = (0, 1, 2, 3)
keep_dims11 = False
x12 = np.random.rand(2, 3, 4, 4, 5, 6).astype(np.float32)
axis12 = -2
keep_dims12 = False
x13 = np.random.rand(2, 3, 4, 4).astype(np.float32)
axis13 = (-2, -1)
keep_dims13 = True
x14 = np.random.rand(1, 1, 1, 1).astype(np.float32)
axis14 = ()
np_axis14 = None
keep_dims14 = True
class ReduceMean(nn.Cell):
def __init__(self):
def __init__(self, keep_dims):
super(ReduceMean, self).__init__()
self.reduce_mean = P.ReduceMean(keep_dims=keep_dims)
self.x0 = Tensor(x0)
self.axis0 = axis0
self.keep_dims0 = keep_dims0
self.x1 = Tensor(x1)
self.axis1 = axis1
self.keep_dims1 = keep_dims1
self.x2 = Tensor(x2)
self.axis2 = axis2
self.keep_dims2 = keep_dims2
self.x3 = Tensor(x3)
self.axis3 = axis3
self.keep_dims3 = keep_dims3
self.x4 = Tensor(x4)
self.axis4 = axis4
self.keep_dims4 = keep_dims4
self.x5 = Tensor(x5)
self.axis5 = axis5
self.keep_dims5 = keep_dims5
self.x6 = Tensor(x6)
self.axis6 = axis6
self.keep_dims6 = keep_dims6
self.x7 = Tensor(x7)
self.axis7 = axis7
self.keep_dims7 = keep_dims7
self.x8 = Tensor(x8)
self.axis8 = axis8
self.keep_dims8 = keep_dims8
self.x9 = Tensor(x9)
self.axis9 = axis9
self.keep_dims9 = keep_dims9
self.x10 = Tensor(x10)
self.axis10 = axis10
self.keep_dims10 = keep_dims10
self.x11 = Tensor(x11)
self.axis11 = axis11
self.keep_dims11 = keep_dims11
self.x12 = Tensor(x12)
self.axis12 = axis12
self.keep_dims12 = keep_dims12
self.x13 = Tensor(x13)
self.axis13 = axis13
self.keep_dims13 = keep_dims13
self.x14 = Tensor(x14)
self.axis14 = axis14
self.keep_dims14 = keep_dims14
@ms_function
def construct(self):
return (P.ReduceMean(self.keep_dims0)(self.x0, self.axis0),
P.ReduceMean(self.keep_dims1)(self.x1, self.axis1),
P.ReduceMean(self.keep_dims2)(self.x2, self.axis2),
P.ReduceMean(self.keep_dims3)(self.x3, self.axis3),
P.ReduceMean(self.keep_dims4)(self.x4, self.axis4),
P.ReduceMean(self.keep_dims5)(self.x5, self.axis5),
P.ReduceMean(self.keep_dims6)(self.x6, self.axis6),
P.ReduceMean(self.keep_dims7)(self.x7, self.axis7),
P.ReduceMean(self.keep_dims8)(self.x8, self.axis8),
P.ReduceMean(self.keep_dims9)(self.x9, self.axis9),
P.ReduceMean(self.keep_dims10)(self.x10, self.axis10),
P.ReduceMean(self.keep_dims11)(self.x11, self.axis11),
P.ReduceMean(self.keep_dims12)(self.x12, self.axis12),
P.ReduceMean(self.keep_dims13)(self.x13, self.axis13),
P.ReduceMean(self.keep_dims14)(self.x14, self.axis14))
def construct(self, x, axis):
return self.reduce_mean(x, axis)
@pytest.mark.level0
@pytest.mark.platform_x86_gpu_training
@pytest.mark.env_onecard
def test_ReduceMean():
@pytest.mark.parametrize('dtype', [np.float16, np.float32, np.float64])
@pytest.mark.parametrize('shape, axis, keep_dims',
[((2, 3, 4, 4), 3, True), ((2, 3, 4, 4), 3, False), ((2, 3, 1, 4), 2, True),
((2, 3, 1, 4), 2, False), ((2, 3, 4, 1), 3, True), ((2, 3, 4, 1), 3, False),
((2, 3, 4, 4), (1, 2), False), ((2, 3, 4, 4), (1, 2), True), ((2, 1, 1, 4), (1, 2), True),
((2, 1, 1, 4), (1, 2), False), ((2, 3, 4, 4), (0, 1, 2, 3), False),
((1, 1, 1, 1), (0, 1, 2, 3), False), ((2, 3, 4, 4, 5, 6), -2, False),
((2, 3, 4, 4), (-2, -1), True), ((1, 1, 1, 1), (), True)])
def test_reduce_mean(dtype, shape, axis, keep_dims):
"""
Feature: ALL To ALL
Description: test cases for ReduceMean
Expectation: the result match to numpy
"""
context.set_context(mode=context.PYNATIVE_MODE, device_target='GPU')
reduce_mean = ReduceMean()
output = reduce_mean()
x = np.random.rand(*shape).astype(dtype)
tensor_x = Tensor(x)
expect0 = np.mean(x0, axis=axis0, keepdims=keep_dims0)
diff0 = abs(output[0].asnumpy() - expect0)
error0 = np.ones(shape=expect0.shape) * 1.0e-5
assert np.all(diff0 < error0)
assert output[0].shape == expect0.shape
reduce_mean = ReduceMean(keep_dims)
output = reduce_mean(tensor_x, axis)
expect1 = np.mean(x1, axis=axis1, keepdims=keep_dims1)
diff1 = abs(output[1].asnumpy() - expect1)
error1 = np.ones(shape=expect1.shape) * 1.0e-5
assert np.all(diff1 < error1)
assert output[1].shape == expect1.shape
expect = np.mean(x, axis=axis, keepdims=keep_dims)
diff = abs(output.asnumpy() - expect)
error = np.ones(shape=expect.shape) * 1.0e-5
assert np.all(diff < error)
assert output.shape == expect.shape
expect2 = np.mean(x2, axis=axis2, keepdims=keep_dims2)
diff2 = abs(output[2].asnumpy() - expect2)
error2 = np.ones(shape=expect2.shape) * 1.0e-5
assert np.all(diff2 < error2)
assert output[2].shape == expect2.shape
expect3 = np.mean(x3, axis=axis3, keepdims=keep_dims3)
diff3 = abs(output[3].asnumpy() - expect3)
error3 = np.ones(shape=expect3.shape) * 1.0e-5
assert np.all(diff3 < error3)
assert output[3].shape == expect3.shape
expect4 = np.mean(x4, axis=axis4, keepdims=keep_dims4)
diff4 = abs(output[4].asnumpy() - expect4)
error4 = np.ones(shape=expect4.shape) * 1.0e-5
assert np.all(diff4 < error4)
assert output[4].shape == expect4.shape
expect5 = np.mean(x5, axis=axis5, keepdims=keep_dims5)
diff5 = abs(output[5].asnumpy() - expect5)
error5 = np.ones(shape=expect5.shape) * 1.0e-5
assert np.all(diff5 < error5)
assert output[5].shape == expect5.shape
expect6 = np.mean(x6, axis=axis6, keepdims=keep_dims6)
diff6 = abs(output[6].asnumpy() - expect6)
error6 = np.ones(shape=expect6.shape) * 1.0e-5
assert np.all(diff6 < error6)
assert output[6].shape == expect6.shape
expect7 = np.mean(x7, axis=axis7, keepdims=keep_dims7)
diff7 = abs(output[7].asnumpy() - expect7)
error7 = np.ones(shape=expect7.shape) * 1.0e-5
assert np.all(diff7 < error7)
assert output[7].shape == expect7.shape
expect8 = np.mean(x8, axis=axis8, keepdims=keep_dims8)
diff8 = abs(output[8].asnumpy() - expect8)
error8 = np.ones(shape=expect8.shape) * 1.0e-5
assert np.all(diff8 < error8)
assert output[8].shape == expect8.shape
expect9 = np.mean(x9, axis=axis9, keepdims=keep_dims9)
diff9 = abs(output[9].asnumpy() - expect9)
error9 = np.ones(shape=expect9.shape) * 1.0e-5
assert np.all(diff9 < error9)
assert output[9].shape == expect9.shape
expect10 = np.mean(x10, axis=axis10, keepdims=keep_dims10)
diff10 = abs(output[10].asnumpy() - expect10)
error10 = np.ones(shape=expect10.shape) * 1.0e-5
assert np.all(diff10 < error10)
assert output[10].shape == expect10.shape
expect11 = np.mean(x11, axis=axis11, keepdims=keep_dims11)
diff11 = abs(output[11].asnumpy() - expect11)
error11 = np.ones(shape=expect11.shape) * 1.0e-5
assert np.all(diff11 < error11)
assert output[11].shape == expect11.shape
expect12 = np.mean(x12, axis=axis12, keepdims=keep_dims12)
diff12 = abs(output[12].asnumpy() - expect12)
error12 = np.ones(shape=expect12.shape) * 1.0e-5
assert np.all(diff12 < error12)
assert output[12].shape == expect12.shape
expect13 = np.mean(x13, axis=axis13, keepdims=keep_dims13)
diff13 = abs(output[13].asnumpy() - expect13)
error13 = np.ones(shape=expect13.shape) * 1.0e-5
assert np.all(diff13 < error13)
assert output[13].shape == expect13.shape
expect14 = np.mean(x14, axis=np_axis14, keepdims=keep_dims14)
diff14 = abs(output[14].asnumpy() - expect14)
error14 = np.ones(shape=expect14.shape) * 1.0e-5
assert np.all(diff14 < error14)
assert output[14].shape == expect14.shape
class ReduceMeanDynamic(nn.Cell):
def __init__(self, x, axis, keepdims=False):
super(ReduceMeanDynamic, self).__init__()
self.test_dynamic = inner.GpuConvertToDynamicShape()
self.reducemean = P.ReduceMean(keep_dims=keepdims)
self.reduce_mean = P.ReduceMean(keep_dims=keepdims)
self.x = x
self.axis = axis
def construct(self):
dynamic_x = self.test_dynamic(self.x)
output = self.reducemean(dynamic_x, self.axis)
output = self.reduce_mean(dynamic_x, self.axis)
return output
@pytest.mark.level0
@pytest.mark.platform_x86_gpu_training
@pytest.mark.env_onecard
def test_dynamic_reduce_mean_keepdims_true():
context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
net1 = ReduceMeanDynamic(Tensor(x14), axis14, keepdims=True)
net2 = ReduceMeanDynamic(Tensor(x0), axis0, keepdims=True)
output1 = net1()
output2 = net2()
expect_1 = np.mean(x14, axis=np_axis14, keepdims=True)
diff_1 = abs(output1.asnumpy() - expect_1)
error_1 = np.ones(shape=expect_1.shape) * 1.0e-5
assert np.all(diff_1 < error_1)
assert output1.shape == expect_1.shape
expect_2 = np.mean(x0, axis=axis0, keepdims=True)
diff_2 = abs(output2.asnumpy() - expect_2)
error_2 = np.ones(shape=expect_2.shape) * 1.0e-5
assert np.all(diff_2 < error_2)
assert output2.shape == expect_2.shape
@pytest.mark.level0
@pytest.mark.platform_x86_gpu_training
@pytest.mark.env_onecard
def test_dynamic_reduce_mean_keepdims_false():
@pytest.mark.parametrize('dtype', [np.float32])
@pytest.mark.parametrize('shape, axis, keep_dims',
[((2, 3, 4, 4), 3, True), ((1, 1, 1, 1), (), True), ((2, 3, 4, 4, 5, 6), -2, False)])
def test_dynamic_reduce_mean(dtype, shape, axis, keep_dims):
"""
Feature: ALL To ALL
Description: test cases for ReduceMean with dynamic shape
Expectation: the result match to numpy
"""
context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
net = ReduceMeanDynamic(Tensor(x12), axis12, keepdims=False)
x = np.random.rand(*shape).astype(dtype)
tensor_x = Tensor(x)
net = ReduceMeanDynamic(tensor_x, axis, keepdims=keep_dims)
output = net()
expect = np.mean(x12, axis=axis12, keepdims=False)
expect = np.mean(x, axis=axis, keepdims=keep_dims)
diff = abs(output.asnumpy() - expect)
error = np.ones(shape=expect.shape) * 1.0e-5
assert np.all(diff < error)

203
tests/st/ops/gpu/test_reduce_min_op.py
View File

@ -19,196 +19,79 @@ import pytest
import mindspore.context as context
import mindspore.nn as nn
from mindspore import Tensor
from mindspore.common.api import ms_function
from mindspore.ops import operations as P
from mindspore.ops.operations import _inner_ops as inner
x0 = np.random.rand(2, 3, 4, 4).astype(np.float32)
axis0 = 3
keep_dims0 = True
x1 = np.random.rand(2, 3, 4, 4).astype(np.float32)
axis1 = 3
keep_dims1 = False
x2 = np.random.rand(2, 3, 1, 4).astype(np.float32)
axis2 = 2
keep_dims2 = True
x3 = np.random.rand(2, 3, 1, 4).astype(np.float32)
axis3 = 2
keep_dims3 = False
x4 = np.random.rand(2, 3, 4, 4).astype(np.float32)
axis4 = ()
np_axis4 = None
keep_dims4 = True
x5 = np.random.rand(2, 3, 4, 4).astype(np.float32)
axis5 = ()
np_axis5 = None
keep_dims5 = False
x6 = np.random.rand(2, 3, 4, 4).astype(np.float32)
axis6 = -2
keep_dims6 = False
x7 = np.random.rand(2, 3, 4, 4).astype(np.float32)
axis7 = (-2, -1)
keep_dims7 = True
x8 = np.random.rand(1, 1, 1, 1).astype(np.float32)
axis8 = ()
np_axis8 = None
keep_dims8 = True
class ReduceMin(nn.Cell):
def __init__(self):
def __init__(self, keep_dims):
super(ReduceMin, self).__init__()
self.reduce_min = P.ReduceMin(keep_dims=keep_dims)
self.x0 = Tensor(x0)
self.axis0 = axis0
self.keep_dims0 = keep_dims0
self.x1 = Tensor(x1)
self.axis1 = axis1
self.keep_dims1 = keep_dims1
self.x2 = Tensor(x2)
self.axis2 = axis2
self.keep_dims2 = keep_dims2
self.x3 = Tensor(x3)
self.axis3 = axis3
self.keep_dims3 = keep_dims3
self.x4 = Tensor(x4)
self.axis4 = axis4
self.keep_dims4 = keep_dims4
self.x5 = Tensor(x5)
self.axis5 = axis5
self.keep_dims5 = keep_dims5
self.x6 = Tensor(x6)
self.axis6 = axis6
self.keep_dims6 = keep_dims6
self.x7 = Tensor(x7)
self.axis7 = axis7
self.keep_dims7 = keep_dims7
self.x8 = Tensor(x8)
self.axis8 = axis8
self.keep_dims8 = keep_dims8
@ms_function
def construct(self):
return (P.ReduceMin(self.keep_dims0)(self.x0, self.axis0),
P.ReduceMin(self.keep_dims1)(self.x1, self.axis1),
P.ReduceMin(self.keep_dims2)(self.x2, self.axis2),
P.ReduceMin(self.keep_dims3)(self.x3, self.axis3),
P.ReduceMin(self.keep_dims4)(self.x4, self.axis4),
P.ReduceMin(self.keep_dims5)(self.x5, self.axis5),
P.ReduceMin(self.keep_dims6)(self.x6, self.axis6),
P.ReduceMin(self.keep_dims7)(self.x7, self.axis7),
P.ReduceMin(self.keep_dims8)(self.x8, self.axis8))
def construct(self, x, axis):
return self.reduce_min(x, axis)
@pytest.mark.level0
@pytest.mark.platform_x86_gpu_training
@pytest.mark.env_onecard
def test_ReduceMin():
@pytest.mark.parametrize('dtype', [np.float16, np.float32, np.float64])
@pytest.mark.parametrize('shape, axis, keep_dims',
[((2, 3, 4, 4), 3, True), ((2, 3, 4, 4), 3, False), ((2, 3, 1, 4), 2, True),
((2, 3, 1, 4), 2, False), ((2, 3, 4, 4), None, True), ((2, 3, 4, 4), None, False),
((2, 3, 4, 4), -2, False), ((2, 3, 4, 4), (-2, -1), False), ((1, 1, 1, 1), None, True)])
def test_reduce_min(dtype, shape, axis, keep_dims):
"""
Feature: ALL To ALL
Description: test cases for ReduceMin
Expectation: the result match to numpy
"""
context.set_context(mode=context.PYNATIVE_MODE, device_target='GPU')
reduce_min = ReduceMin()
output = reduce_min()
x = np.random.rand(*shape).astype(dtype)
tensor_x = Tensor(x)
expect0 = np.min(x0, axis=axis0, keepdims=keep_dims0)
diff0 = abs(output[0].asnumpy() - expect0)
error0 = np.ones(shape=expect0.shape) * 1.0e-5
assert np.all(diff0 < error0)
assert output[0].shape == expect0.shape
reduce_min = ReduceMin(keep_dims)
ms_axis = axis if axis is not None else ()
output = reduce_min(tensor_x, ms_axis)
expect1 = np.min(x1, axis=axis1, keepdims=keep_dims1)
diff1 = abs(output[1].asnumpy() - expect1)
error1 = np.ones(shape=expect1.shape) * 1.0e-5
assert np.all(diff1 < error1)
assert output[1].shape == expect1.shape
expect2 = np.min(x2, axis=axis2, keepdims=keep_dims2)
diff2 = abs(output[2].asnumpy() - expect2)
error2 = np.ones(shape=expect2.shape) * 1.0e-5
assert np.all(diff2 < error2)
assert output[2].shape == expect2.shape
expect3 = np.min(x3, axis=axis3, keepdims=keep_dims3)
diff3 = abs(output[3].asnumpy() - expect3)
error3 = np.ones(shape=expect3.shape) * 1.0e-5
assert np.all(diff3 < error3)
assert output[3].shape == expect3.shape
expect4 = np.min(x4, axis=np_axis4, keepdims=keep_dims4)
diff4 = abs(output[4].asnumpy() - expect4)
error4 = np.ones(shape=expect4.shape) * 1.0e-5
assert np.all(diff4 < error4)
assert output[4].shape == expect4.shape
expect5 = np.min(x5, axis=np_axis5, keepdims=keep_dims5)
diff5 = abs(output[5].asnumpy() - expect5)
error5 = np.ones(shape=expect5.shape) * 1.0e-5
assert np.all(diff5 < error5)
assert output[5].shape == expect5.shape
expect6 = np.min(x6, axis=axis6, keepdims=keep_dims6)
diff6 = abs(output[6].asnumpy() - expect6)
error6 = np.ones(shape=expect6.shape) * 1.0e-5
assert np.all(diff6 < error6)
assert output[6].shape == expect6.shape
expect7 = np.min(x7, axis=axis7, keepdims=keep_dims7)
diff7 = abs(output[7].asnumpy() - expect7)
error7 = np.ones(shape=expect7.shape) * 1.0e-5
assert np.all(diff7 < error7)
expect8 = np.min(x8, axis=np_axis8, keepdims=keep_dims8)
diff8 = abs(output[8].asnumpy() - expect8)
error8 = np.ones(shape=expect8.shape) * 1.0e-5
assert np.all(diff8 < error8)
x_1 = x8
axis_1 = 0
x_2 = x1
axis_2 = 0
expect = np.min(x, axis=axis, keepdims=keep_dims)
diff = abs(output.asnumpy() - expect)
error = np.ones(shape=expect.shape) * 1.0e-5
assert np.all(diff < error)
assert output.shape == expect.shape
class ReduceMinDynamic(nn.Cell):
def __init__(self, x, axis):
super(ReduceMinDynamic, self).__init__()
self.reducemin = P.ReduceMin(False)
self.reduce_min = P.ReduceMin(False)
self.test_dynamic = inner.GpuConvertToDynamicShape()
self.x = x
self.axis = axis
def construct(self):
dynamic_x = self.test_dynamic(self.x)
return self.reducemin(dynamic_x, self.axis)
return self.reduce_min(dynamic_x, self.axis)
@pytest.mark.level0
@pytest.mark.platform_x86_gpu_training
@pytest.mark.env_onecard
def test_reduce_min_dynamic():
@pytest.mark.parametrize('dtype', [np.float32])
@pytest.mark.parametrize('shape, axis, keep_dims',
[((1, 1, 1, 1), 0, False), ((2, 3, 4, 4), 0, False)])
def test_reduce_min_dynamic(dtype, shape, axis, keep_dims):
"""
Feature: ALL To ALL
Description: test cases for ReduceMin with dynamic shape
Expectation: the result match to numpy
"""
context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
net1 = ReduceMinDynamic(Tensor(x_1), axis_1)
net2 = ReduceMinDynamic(Tensor(x_2), axis_2)
x = np.random.rand(*shape).astype(dtype)
ms_axis = axis if axis is not None else ()
net = ReduceMinDynamic(Tensor(x), ms_axis)
expect_1 = np.min(x_1, axis=0, keepdims=False)
expect_2 = np.min(x_2, axis=0, keepdims=False)
expect = np.min(x, axis=axis, keepdims=keep_dims)
output = net()
output1 = net1()
output2 = net2()
np.testing.assert_almost_equal(output1.asnumpy(), expect_1)
np.testing.assert_almost_equal(output2.asnumpy(), expect_2)
np.testing.assert_almost_equal(output.asnumpy(), expect)