forked from mindspore-Ecosystem/mindspore
632 lines
22 KiB
Python
632 lines
22 KiB
Python
# 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 numpy as np
|
|
import pytest
|
|
|
|
import mindspore
|
|
import mindspore.context as context
|
|
import mindspore.nn as nn
|
|
from mindspore import Tensor
|
|
from mindspore.ops import operations as P
|
|
from mindspore.ops import functional as F
|
|
|
|
context.set_context(mode=context.GRAPH_MODE, device_target='CPU')
|
|
|
|
|
|
class SquareNet(nn.Cell):
|
|
def __init__(self):
|
|
super(SquareNet, self).__init__()
|
|
self.square = P.Square()
|
|
|
|
def construct(self, x):
|
|
return self.square(x)
|
|
|
|
|
|
class FloorNet(nn.Cell):
|
|
def __init__(self):
|
|
super(FloorNet, self).__init__()
|
|
self.floor = P.Floor()
|
|
|
|
def construct(self, x):
|
|
return self.floor(x)
|
|
|
|
|
|
class RoundNet(nn.Cell):
|
|
def __init__(self):
|
|
super(RoundNet, self).__init__()
|
|
self.round = P.Round()
|
|
|
|
def construct(self, x):
|
|
return self.round(x)
|
|
|
|
|
|
class ReciprocalNet(nn.Cell):
|
|
def __init__(self):
|
|
super(ReciprocalNet, self).__init__()
|
|
self.reciprocal = P.Reciprocal()
|
|
|
|
def construct(self, x):
|
|
return self.reciprocal(x)
|
|
|
|
|
|
class RintNet(nn.Cell):
|
|
def __init__(self):
|
|
super(RintNet, self).__init__()
|
|
self.rint = P.Rint()
|
|
|
|
def construct(self, x):
|
|
return self.rint(x)
|
|
|
|
|
|
class IdentityNet(nn.Cell):
|
|
def __init__(self):
|
|
super(IdentityNet, self).__init__()
|
|
self.identity = P.Identity()
|
|
|
|
def construct(self, x):
|
|
return self.identity(x)
|
|
|
|
|
|
class InvDynamicShapeNet(nn.Cell):
|
|
def __init__(self):
|
|
super(InvDynamicShapeNet, self).__init__()
|
|
self.unique = P.Unique()
|
|
self.reshape = P.Reshape()
|
|
|
|
def construct(self, x):
|
|
x_unique, _ = self.unique(x)
|
|
x_unique = self.reshape(x_unique, (3, 3))
|
|
return F.inv(x_unique)
|
|
|
|
|
|
class InvertDynamicShapeNet(nn.Cell):
|
|
def __init__(self):
|
|
super(InvertDynamicShapeNet, self).__init__()
|
|
self.unique = P.Unique()
|
|
self.reshape = P.Reshape()
|
|
|
|
def construct(self, x):
|
|
x_unique, _ = self.unique(x)
|
|
x_unique = self.reshape(x_unique, (3, 3))
|
|
x_unique = F.cast(x_unique, mindspore.int16)
|
|
return F.invert(x_unique)
|
|
|
|
|
|
class SoftsignDynamicShapeNet(nn.Cell):
|
|
def __init__(self):
|
|
super(SoftsignDynamicShapeNet, self).__init__()
|
|
self.unique = P.Unique()
|
|
self.reshape = P.Reshape()
|
|
|
|
def construct(self, x):
|
|
x_unique, _ = self.unique(x)
|
|
x_unique = self.reshape(x_unique, (3, 3))
|
|
return F.softsign(x_unique)
|
|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.env_onecard
|
|
def test_square():
|
|
x = np.array([1, 2, 3]).astype(np.int16)
|
|
net = SquareNet()
|
|
output = net(Tensor(x))
|
|
expect_output = np.array([1, 4, 9]).astype(np.int16)
|
|
print(output)
|
|
assert np.all(output.asnumpy() == expect_output)
|
|
|
|
x = np.array([1, 2, 3]).astype(np.int32)
|
|
net = SquareNet()
|
|
output = net(Tensor(x))
|
|
expect_output = np.array([1, 4, 9]).astype(np.int32)
|
|
print(output)
|
|
assert np.all(output.asnumpy() == expect_output)
|
|
|
|
x = np.array([1, 2, 3]).astype(np.int64)
|
|
net = SquareNet()
|
|
output = net(Tensor(x))
|
|
expect_output = np.array([1, 4, 9]).astype(np.int64)
|
|
print(output)
|
|
assert np.all(output.asnumpy() == expect_output)
|
|
|
|
x = np.array([1, 2, 3]).astype(np.float16)
|
|
net = SquareNet()
|
|
output = net(Tensor(x))
|
|
expect_output = np.array([1, 4, 9]).astype(np.float16)
|
|
print(output)
|
|
assert np.all(output.asnumpy() == expect_output)
|
|
|
|
x = np.array([1, 2, 3]).astype(np.float32)
|
|
net = SquareNet()
|
|
output = net(Tensor(x))
|
|
expect_output = np.array([1, 4, 9]).astype(np.float32)
|
|
print(output)
|
|
assert np.all(output.asnumpy() == expect_output)
|
|
|
|
x = np.array([1, 2, 3]).astype(np.float64)
|
|
net = SquareNet()
|
|
output = net(Tensor(x))
|
|
expect_output = np.array([1, 4, 9]).astype(np.float64)
|
|
print(output)
|
|
assert np.all(output.asnumpy() == expect_output)
|
|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.env_onecard
|
|
def test_floor():
|
|
net = FloorNet()
|
|
|
|
x = np.random.randn(3, 4).astype(np.float16)
|
|
x = x * 100
|
|
output = net(Tensor(x))
|
|
expect_output = np.floor(x).astype(np.float16)
|
|
print(output.asnumpy())
|
|
assert np.all(output.asnumpy() == expect_output)
|
|
|
|
x = np.random.randn(4, 3).astype(np.float32)
|
|
x = x * 100
|
|
output = net(Tensor(x))
|
|
expect_output = np.floor(x)
|
|
print(output.asnumpy())
|
|
assert np.all(output.asnumpy() == expect_output)
|
|
|
|
x = np.random.randn(4, 3).astype(np.float64)
|
|
x = x * 100
|
|
output = net(Tensor(x))
|
|
expect_output = np.floor(x)
|
|
print(output.asnumpy())
|
|
assert np.all(output.asnumpy() == expect_output)
|
|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.env_onecard
|
|
def test_rint():
|
|
net = RintNet()
|
|
prop = 100 if np.random.random() > 0.5 else -100
|
|
x = np.random.randn(3, 4, 5, 6).astype(np.float16) * prop
|
|
output = net(Tensor(x))
|
|
expect_output = np.rint(x).astype(np.float16)
|
|
np.testing.assert_almost_equal(output.asnumpy(), expect_output)
|
|
|
|
x = np.random.randn(3, 4, 5, 6).astype(np.float32) * prop
|
|
output = net(Tensor(x))
|
|
expect_output = np.rint(x).astype(np.float32)
|
|
np.testing.assert_almost_equal(output.asnumpy(), expect_output)
|
|
|
|
x = np.random.randn(3, 4, 5, 6).astype(np.float64) * prop
|
|
output = net(Tensor(x))
|
|
expect_output = np.rint(x).astype(np.float64)
|
|
np.testing.assert_almost_equal(output.asnumpy(), expect_output)
|
|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.env_onecard
|
|
def test_round():
|
|
net = RoundNet()
|
|
|
|
x = np.array([0.9920, -0.4077, 0.9734, -1.0362, 1.5, -2.5, 4.5]).astype(np.float16)
|
|
output = net(Tensor(x))
|
|
expect_output = np.round(x).astype(np.float16)
|
|
np.testing.assert_almost_equal(output.asnumpy(), expect_output)
|
|
|
|
x = np.array([0.9920, -0.4077, 0.9734, -1.0362, 1.5, -2.5, 4.5]).astype(np.float32)
|
|
output = net(Tensor(x))
|
|
expect_output = np.round(x).astype(np.float32)
|
|
np.testing.assert_almost_equal(output.asnumpy(), expect_output)
|
|
|
|
x = np.array([0.9920, -0.4077, 0.9734, -1.0362, 1.5, -2.5, 4.5]).astype(np.float64)
|
|
output = net(Tensor(x))
|
|
expect_output = np.round(x).astype(np.float64)
|
|
np.testing.assert_almost_equal(output.asnumpy(), expect_output)
|
|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.env_onecard
|
|
@pytest.mark.parametrize('shape', [(2,), (4, 5), (3, 4, 5, 6)])
|
|
@pytest.mark.parametrize('dtype, tol',
|
|
[(np.int32, 1.0e-7), (np.float16, 1.0e-5), (np.float32, 1.0e-5), (np.float64, 1.0e-7)])
|
|
def test_reciprocal(shape, dtype, tol):
|
|
"""
|
|
Feature: ALL To ALL
|
|
Description: test cases for reciprocal
|
|
Expectation: the result match to numpy
|
|
"""
|
|
net = ReciprocalNet()
|
|
prop = 100 if np.random.random() > 0.5 else -100
|
|
x = np.random.randn(*shape).astype(dtype) * prop
|
|
output = net(Tensor(x))
|
|
expect_output = np.reciprocal(x).astype(dtype)
|
|
diff = output.asnumpy() - expect_output
|
|
error = np.ones(shape=expect_output.shape) * tol
|
|
assert np.all(np.abs(diff) < error)
|
|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.env_onecard
|
|
@pytest.mark.parametrize('shape', [(2,), (4, 5), (3, 4, 5, 6)])
|
|
@pytest.mark.parametrize('dtype, tol',
|
|
[(np.int32, 1.0e-7), (np.float16, 1.0e-5), (np.float32, 1.0e-5)])
|
|
def test_inv(shape, dtype, tol):
|
|
"""
|
|
Feature: ALL To ALL
|
|
Description: test cases for inv
|
|
Expectation: the result match to numpy
|
|
"""
|
|
inv = P.Inv()
|
|
prop = 100 if np.random.random() > 0.5 else -100
|
|
x = np.random.randn(*shape).astype(dtype) * prop
|
|
output = inv(Tensor(x))
|
|
expect_output = np.reciprocal(x).astype(dtype)
|
|
diff = output.asnumpy() - expect_output
|
|
error = np.ones(shape=expect_output.shape) * tol
|
|
assert np.all(np.abs(diff) < error)
|
|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.env_onecard
|
|
@pytest.mark.parametrize('mode', [context.GRAPH_MODE, context.PYNATIVE_MODE])
|
|
def test_inv_vmap(mode):
|
|
"""
|
|
Feature: test inv vmap feature.
|
|
Description: test inv vmap feature.
|
|
Expectation: Success.
|
|
"""
|
|
context.set_context(mode=mode, device_target="CPU")
|
|
x = Tensor(np.array([[0.25, 0.4, 0.31, 0.52], [0.5, 0.12, 0.31, 0.58]], dtype=np.float32))
|
|
# Case 1
|
|
output = F.vmap(F.inv, 0, 0)(x)
|
|
expect_output = np.array([[4., 2.5, 3.2258065, 1.923077], [2., 8.333334, 3.2258065, 1.724138]], dtype=np.float32)
|
|
np.testing.assert_almost_equal(output.asnumpy(), expect_output)
|
|
|
|
# Case 2
|
|
output = F.vmap(F.inv, 1, 0)(x)
|
|
expect_output = np.array([[4., 2.], [2.5, 8.333334], [3.2258065, 3.2258065], [1.923077, 1.724138]],
|
|
dtype=np.float32)
|
|
np.testing.assert_almost_equal(output.asnumpy(), expect_output)
|
|
|
|
# Case 3
|
|
output = F.vmap(F.inv, 0, 1)(x)
|
|
expect_output = np.array([[4., 2.], [2.5, 8.333334], [3.2258065, 3.2258065], [1.923077, 1.724138]],
|
|
dtype=np.float32)
|
|
np.testing.assert_almost_equal(output.asnumpy(), expect_output)
|
|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.env_onecard
|
|
@pytest.mark.parametrize('mode', [context.GRAPH_MODE, context.PYNATIVE_MODE])
|
|
def test_inv_dynamic_shape(mode):
|
|
"""
|
|
Feature: test inv dynamic_shape feature.
|
|
Description: test inv dynamic_shape feature.
|
|
Expectation: Success.
|
|
"""
|
|
context.set_context(mode=mode, device_target="CPU")
|
|
x = Tensor(np.array([8., -3., 0., 0., 10., 1., 21., -3., 11., 4., -2., 10., 8.]).astype(np.float32))
|
|
output = InvDynamicShapeNet()(x)
|
|
expect_output = np.array([[0.125, -0.33333334, np.inf],
|
|
[0.1, 1., 0.04761905],
|
|
[0.09090909, 0.25, -0.5]], dtype=np.float32)
|
|
np.testing.assert_almost_equal(output.asnumpy(), expect_output)
|
|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.env_onecard
|
|
@pytest.mark.parametrize('shape', [(2,), (4, 5), (3, 4, 5, 6)])
|
|
@pytest.mark.parametrize('dtype', [np.int16, np.uint16])
|
|
def test_invert(shape, dtype):
|
|
"""
|
|
Feature: ALL To ALL
|
|
Description: test cases for invert
|
|
Expectation: the result match to numpy
|
|
"""
|
|
invert = P.Invert()
|
|
prop = 100 if np.random.random() > 0.5 else -100
|
|
input_x = (np.random.randn(*shape) * prop).astype(dtype)
|
|
output = invert(Tensor(input_x))
|
|
expect_output = np.invert(input_x)
|
|
np.testing.assert_almost_equal(output.asnumpy(), expect_output)
|
|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.env_onecard
|
|
@pytest.mark.parametrize('mode', [context.GRAPH_MODE, context.PYNATIVE_MODE])
|
|
def test_invert_vmap(mode):
|
|
"""
|
|
Feature: test invert vmap feature.
|
|
Description: test invert vmap feature.
|
|
Expectation: Success.
|
|
"""
|
|
context.set_context(mode=mode, device_target="CPU")
|
|
x = Tensor(np.array([[25, 4, 13, 9], [2, -1, 0, -5]], dtype=np.int16))
|
|
# Case 1
|
|
output = F.vmap(F.invert, 0, 0)(x)
|
|
expect_output = np.array([[-26, -5, -14, -10], [-3, 0, -1, 4]], dtype=np.int16)
|
|
np.testing.assert_almost_equal(output.asnumpy(), expect_output)
|
|
|
|
# Case 2
|
|
output = F.vmap(F.invert, 1, 0)(x)
|
|
expect_output = np.array([[-26, -3], [-5, 0], [-14, -1], [-10, 4]], dtype=np.int16)
|
|
np.testing.assert_almost_equal(output.asnumpy(), expect_output)
|
|
|
|
# Case 3
|
|
output = F.vmap(F.invert, 0, 1)(x)
|
|
expect_output = np.array([[-26, -3], [-5, 0], [-14, -1], [-10, 4]], dtype=np.int16)
|
|
np.testing.assert_almost_equal(output.asnumpy(), expect_output)
|
|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.env_onecard
|
|
@pytest.mark.parametrize('mode', [context.GRAPH_MODE, context.PYNATIVE_MODE])
|
|
def test_invert_dynamic_shape(mode):
|
|
"""
|
|
Feature: test invert dynamic_shape feature.
|
|
Description: test invert dynamic_shape feature.
|
|
Expectation: Success.
|
|
"""
|
|
context.set_context(mode=mode, device_target="CPU")
|
|
x = Tensor(np.array([8, -3, 0, 0, 10, 1, 21, -3, 11, 4, -2, 10, 8]).astype(np.int16))
|
|
output = InvertDynamicShapeNet()(x)
|
|
expect_output = np.array([[-9, 2, -1],
|
|
[-11, -2, -22],
|
|
[-12, -5, 1]], dtype=np.int16)
|
|
np.testing.assert_almost_equal(output.asnumpy(), expect_output)
|
|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.env_onecard
|
|
@pytest.mark.parametrize('shape', [(2,), (4, 5), (3, 4, 5, 6)])
|
|
@pytest.mark.parametrize('dtype, tol', [(np.float16, 1.0e-3), (np.float32, 1.0e-5)])
|
|
def test_softsign(shape, dtype, tol):
|
|
"""
|
|
Feature: ALL To ALL
|
|
Description: test cases for Softsign
|
|
Expectation: the result match to numpy
|
|
"""
|
|
softsign = P.Softsign()
|
|
prop = 100 if np.random.random() > 0.5 else -100
|
|
x = np.random.randn(*shape).astype(dtype) * prop
|
|
output = softsign(Tensor(x))
|
|
expect_output = x / (1.0 + np.abs(x))
|
|
diff = output.asnumpy() - expect_output
|
|
error = np.ones(shape=expect_output.shape) * tol
|
|
assert np.all(np.abs(diff) < error)
|
|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.env_onecard
|
|
@pytest.mark.parametrize('mode', [context.GRAPH_MODE, context.PYNATIVE_MODE])
|
|
def test_softsign_vmap(mode):
|
|
"""
|
|
Feature: test softsign vmap feature.
|
|
Description: test softsign vmap feature.
|
|
Expectation: Success.
|
|
"""
|
|
context.set_context(mode=mode, device_target="CPU")
|
|
x = Tensor(np.array([[0, -1, 2, 30, -30], [2, -1, 0, -5, 50]], dtype=np.float32))
|
|
# Case 1
|
|
output = F.vmap(F.softsign, 0, 0)(x)
|
|
expect_output = np.array([[0., -0.5, 0.6666667, 0.9677419, -0.9677419],
|
|
[0.6666667, -0.5, 0., -0.8333333, 0.98039216]], dtype=np.float32)
|
|
np.testing.assert_almost_equal(output.asnumpy(), expect_output)
|
|
|
|
# Case 2
|
|
output = F.vmap(F.softsign, 1, 0)(x)
|
|
expect_output = np.array([[0., 0.6666667],
|
|
[-0.5, -0.5],
|
|
[0.6666667, 0.],
|
|
[0.9677419, -0.8333333],
|
|
[-0.9677419, 0.98039216]], dtype=np.float32)
|
|
np.testing.assert_almost_equal(output.asnumpy(), expect_output)
|
|
|
|
# Case 3
|
|
output = F.vmap(F.softsign, 0, 1)(x)
|
|
expect_output = np.array([[0., 0.6666667],
|
|
[-0.5, -0.5],
|
|
[0.6666667, 0.],
|
|
[0.9677419, -0.8333333],
|
|
[-0.9677419, 0.98039216]], dtype=np.float32)
|
|
np.testing.assert_almost_equal(output.asnumpy(), expect_output)
|
|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.env_onecard
|
|
@pytest.mark.parametrize('mode', [context.GRAPH_MODE, context.PYNATIVE_MODE])
|
|
def test_softsign_dynamic_shape(mode):
|
|
"""
|
|
Feature: test softsign dynamic_shape feature.
|
|
Description: test softsign dynamic_shape feature.
|
|
Expectation: Success.
|
|
"""
|
|
context.set_context(mode=mode, device_target="CPU")
|
|
x = Tensor(np.array([8., -3., 0., 0., 10., 1., 21., -3., 11., 4., 2., 10., 8.]).astype(np.float32))
|
|
output = SoftsignDynamicShapeNet()(x)
|
|
expect_output = np.array([[0.8888889, -0.75, 0.],
|
|
[0.90909094, 0.5, 0.95454544],
|
|
[0.9166667, 0.8, 0.6666667]], dtype=np.float32)
|
|
np.testing.assert_almost_equal(output.asnumpy(), expect_output)
|
|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.env_onecard
|
|
def test_identity_pynative():
|
|
context.set_context(mode=context.PYNATIVE_MODE, device_target="CPU")
|
|
net = IdentityNet()
|
|
|
|
x = np.random.randn(3, 4, 5, 6).astype(np.float64)
|
|
input_tensor = Tensor(x)
|
|
output = net(input_tensor)
|
|
np.testing.assert_almost_equal(output.asnumpy(), input_tensor.asnumpy())
|
|
assert id(input_tensor) != id(output)
|
|
|
|
x = np.random.randn(3, 4, 5, 6).astype(np.float32)
|
|
input_tensor = Tensor(x)
|
|
output = net(input_tensor)
|
|
np.testing.assert_almost_equal(output.asnumpy(), input_tensor.asnumpy())
|
|
assert id(input_tensor) != id(output)
|
|
|
|
x = np.random.randn(3, 4, 5, 6).astype(np.float16)
|
|
input_tensor = Tensor(x)
|
|
output = net(input_tensor)
|
|
np.testing.assert_almost_equal(output.asnumpy(), input_tensor.asnumpy())
|
|
assert id(input_tensor) != id(output)
|
|
|
|
x = np.random.randn(3, 4, 5, 6).astype(np.uint64)
|
|
input_tensor = Tensor(x)
|
|
output = net(input_tensor)
|
|
np.testing.assert_almost_equal(output.asnumpy(), input_tensor.asnumpy())
|
|
assert id(input_tensor) != id(output)
|
|
|
|
x = np.random.randn(3, 4, 5, 6).astype(np.int64)
|
|
input_tensor = Tensor(x)
|
|
output = net(input_tensor)
|
|
np.testing.assert_almost_equal(output.asnumpy(), input_tensor.asnumpy())
|
|
assert id(input_tensor) != id(output)
|
|
|
|
x = np.random.randn(3, 4, 5, 6).astype(np.uint32)
|
|
input_tensor = Tensor(x)
|
|
output = net(input_tensor)
|
|
np.testing.assert_almost_equal(output.asnumpy(), input_tensor.asnumpy())
|
|
assert id(input_tensor) != id(output)
|
|
|
|
x = np.random.randn(3, 4, 5, 6).astype(np.int32)
|
|
input_tensor = Tensor(x)
|
|
output = net(input_tensor)
|
|
np.testing.assert_almost_equal(output.asnumpy(), input_tensor.asnumpy())
|
|
assert id(input_tensor) != id(output)
|
|
|
|
x = np.random.randn(3, 4, 5, 6).astype(np.uint16)
|
|
input_tensor = Tensor(x)
|
|
output = net(input_tensor)
|
|
np.testing.assert_almost_equal(output.asnumpy(), input_tensor.asnumpy())
|
|
assert id(input_tensor) != id(output)
|
|
|
|
x = np.random.randn(3, 4, 5, 6).astype(np.int16)
|
|
input_tensor = Tensor(x)
|
|
output = net(input_tensor)
|
|
np.testing.assert_almost_equal(output.asnumpy(), input_tensor.asnumpy())
|
|
assert id(input_tensor) != id(output)
|
|
|
|
x = np.random.randn(3, 4, 5, 6).astype(np.uint8)
|
|
input_tensor = Tensor(x)
|
|
output = net(input_tensor)
|
|
np.testing.assert_almost_equal(output.asnumpy(), input_tensor.asnumpy())
|
|
assert id(input_tensor) != id(output)
|
|
|
|
x = np.random.randn(3, 4, 5, 6).astype(np.int8)
|
|
input_tensor = Tensor(x)
|
|
output = net(input_tensor)
|
|
np.testing.assert_almost_equal(output.asnumpy(), input_tensor.asnumpy())
|
|
assert id(input_tensor) != id(output)
|
|
|
|
x = np.random.randn(3, 4, 5, 6).astype(np.bool)
|
|
input_tensor = Tensor(x)
|
|
output = net(input_tensor)
|
|
np.testing.assert_almost_equal(output.asnumpy(), input_tensor.asnumpy())
|
|
assert id(input_tensor) != id(output)
|
|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.env_onecard
|
|
def test_identity_graph():
|
|
context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
|
|
net = IdentityNet()
|
|
|
|
x = np.random.randn(3, 4, 5, 6).astype(np.float64)
|
|
input_tensor = Tensor(x)
|
|
output = net(input_tensor)
|
|
np.testing.assert_almost_equal(output.asnumpy(), input_tensor.asnumpy())
|
|
assert id(input_tensor) != id(output)
|
|
|
|
x = np.random.randn(3, 4, 5, 6).astype(np.float32)
|
|
input_tensor = Tensor(x)
|
|
output = net(input_tensor)
|
|
np.testing.assert_almost_equal(output.asnumpy(), input_tensor.asnumpy())
|
|
assert id(input_tensor) != id(output)
|
|
|
|
x = np.random.randn(3, 4, 5, 6).astype(np.float16)
|
|
input_tensor = Tensor(x)
|
|
output = net(input_tensor)
|
|
np.testing.assert_almost_equal(output.asnumpy(), input_tensor.asnumpy())
|
|
assert id(input_tensor) != id(output)
|
|
|
|
x = np.random.randn(3, 4, 5, 6).astype(np.uint64)
|
|
input_tensor = Tensor(x)
|
|
output = net(input_tensor)
|
|
np.testing.assert_almost_equal(output.asnumpy(), input_tensor.asnumpy())
|
|
assert id(input_tensor) != id(output)
|
|
|
|
x = np.random.randn(3, 4, 5, 6).astype(np.int64)
|
|
input_tensor = Tensor(x)
|
|
output = net(input_tensor)
|
|
np.testing.assert_almost_equal(output.asnumpy(), input_tensor.asnumpy())
|
|
assert id(input_tensor) != id(output)
|
|
|
|
x = np.random.randn(3, 4, 5, 6).astype(np.uint32)
|
|
input_tensor = Tensor(x)
|
|
output = net(input_tensor)
|
|
np.testing.assert_almost_equal(output.asnumpy(), input_tensor.asnumpy())
|
|
assert id(input_tensor) != id(output)
|
|
|
|
x = np.random.randn(3, 4, 5, 6).astype(np.int32)
|
|
input_tensor = Tensor(x)
|
|
output = net(input_tensor)
|
|
np.testing.assert_almost_equal(output.asnumpy(), input_tensor.asnumpy())
|
|
assert id(input_tensor) != id(output)
|
|
|
|
x = np.random.randn(3, 4, 5, 6).astype(np.uint16)
|
|
input_tensor = Tensor(x)
|
|
output = net(input_tensor)
|
|
np.testing.assert_almost_equal(output.asnumpy(), input_tensor.asnumpy())
|
|
assert id(input_tensor) != id(output)
|
|
|
|
x = np.random.randn(3, 4, 5, 6).astype(np.int16)
|
|
input_tensor = Tensor(x)
|
|
output = net(input_tensor)
|
|
np.testing.assert_almost_equal(output.asnumpy(), input_tensor.asnumpy())
|
|
assert id(input_tensor) != id(output)
|
|
|
|
x = np.random.randn(3, 4, 5, 6).astype(np.uint8)
|
|
input_tensor = Tensor(x)
|
|
output = net(input_tensor)
|
|
np.testing.assert_almost_equal(output.asnumpy(), input_tensor.asnumpy())
|
|
assert id(input_tensor) != id(output)
|
|
|
|
x = np.random.randn(3, 4, 5, 6).astype(np.int8)
|
|
input_tensor = Tensor(x)
|
|
output = net(input_tensor)
|
|
np.testing.assert_almost_equal(output.asnumpy(), input_tensor.asnumpy())
|
|
assert id(input_tensor) != id(output)
|
|
|
|
x = np.random.randn(3, 4, 5, 6).astype(np.bool)
|
|
input_tensor = Tensor(x)
|
|
output = net(input_tensor)
|
|
np.testing.assert_almost_equal(output.asnumpy(), input_tensor.asnumpy())
|
|
assert id(input_tensor) != id(output)
|