You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
350 lines
9.9 KiB
350 lines
9.9 KiB
# 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.common.dtype as mstype
|
|
import mindspore.context as context
|
|
from mindspore.common.tensor import Tensor
|
|
from mindspore.nn import Cell
|
|
from mindspore.ops import operations as P
|
|
|
|
|
|
class Net(Cell):
|
|
def __init__(self, dtype):
|
|
super(Net, self).__init__()
|
|
self.Cast = P.Cast()
|
|
self.dtype = dtype
|
|
|
|
def construct(self, x):
|
|
return self.Cast(x, self.dtype)
|
|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.env_onecard
|
|
def test_cast_int32():
|
|
x0 = Tensor(np.random.uniform(-2, 2, (3, 2)).astype(np.float32))
|
|
x1 = Tensor(np.random.uniform(-2, 2, (3, 2)).astype(np.int32))
|
|
x2 = Tensor(np.random.uniform(-2, 2, (3, 2)).astype(np.bool))
|
|
t = mstype.int32
|
|
|
|
context.set_context(mode=context.GRAPH_MODE, device_target='CPU')
|
|
net = Net(t)
|
|
output = net(x0)
|
|
type0 = output.asnumpy().dtype
|
|
assert type0 == 'int32'
|
|
output = net(x1)
|
|
type1 = output.asnumpy().dtype
|
|
assert type1 == 'int32'
|
|
output = net(x2)
|
|
type2 = output.asnumpy().dtype
|
|
assert type2 == 'int32'
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.env_onecard
|
|
def test_cast_float32():
|
|
x0 = Tensor(np.random.uniform(-2, 2, (3, 2)).astype(np.float32))
|
|
x1 = Tensor(np.random.uniform(-2, 2, (3, 2)).astype(np.int32))
|
|
x2 = Tensor(np.random.uniform(-2, 2, (3, 2)).astype(np.bool))
|
|
t = mstype.float32
|
|
|
|
context.set_context(mode=context.GRAPH_MODE, device_target='CPU')
|
|
net = Net(t)
|
|
output = net(x0)
|
|
type0 = output.asnumpy().dtype
|
|
assert type0 == 'float32'
|
|
output = net(x1)
|
|
type1 = output.asnumpy().dtype
|
|
assert type1 == 'float32'
|
|
output = net(x2)
|
|
type2 = output.asnumpy().dtype
|
|
assert type2 == 'float32'
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.env_onecard
|
|
def test_cast_int8_to_int16():
|
|
x = Tensor(np.random.uniform(-2, 2, (3, 2)).astype(np.int8))
|
|
t = mstype.int16
|
|
|
|
context.set_context(mode=context.GRAPH_MODE, device_target='CPU')
|
|
net = Net(t)
|
|
output = net(x)
|
|
dtype = output.asnumpy().dtype
|
|
assert dtype == 'int16'
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.env_onecard
|
|
def test_cast_int8_to_int32():
|
|
x = Tensor(np.random.uniform(-2, 2, (3, 2)).astype(np.int8))
|
|
t = mstype.int32
|
|
|
|
context.set_context(mode=context.GRAPH_MODE, device_target='CPU')
|
|
net = Net(t)
|
|
output = net(x)
|
|
dtype = output.asnumpy().dtype
|
|
assert dtype == 'int32'
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.env_onecard
|
|
def test_cast_int8_to_int64():
|
|
x = Tensor(np.random.uniform(-2, 2, (3, 2)).astype(np.int8))
|
|
t = mstype.int64
|
|
|
|
context.set_context(mode=context.GRAPH_MODE, device_target='CPU')
|
|
net = Net(t)
|
|
output = net(x)
|
|
dtype = output.asnumpy().dtype
|
|
assert dtype == 'int64'
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.env_onecard
|
|
def test_cast_uint8_to_int16():
|
|
x = Tensor(np.random.uniform(-2, 2, (3, 2)).astype(np.uint8))
|
|
t = mstype.int16
|
|
|
|
context.set_context(mode=context.GRAPH_MODE, device_target='CPU')
|
|
net = Net(t)
|
|
output = net(x)
|
|
dtype = output.asnumpy().dtype
|
|
assert dtype == 'int16'
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.env_onecard
|
|
def test_cast_uint8_to_int32():
|
|
x = Tensor(np.random.uniform(-2, 2, (3, 2)).astype(np.uint8))
|
|
t = mstype.int32
|
|
|
|
context.set_context(mode=context.GRAPH_MODE, device_target='CPU')
|
|
net = Net(t)
|
|
output = net(x)
|
|
dtype = output.asnumpy().dtype
|
|
assert dtype == 'int32'
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.env_onecard
|
|
def test_cast_uint8_to_int64():
|
|
x = Tensor(np.random.uniform(-2, 2, (3, 2)).astype(np.uint8))
|
|
t = mstype.int64
|
|
|
|
context.set_context(mode=context.GRAPH_MODE, device_target='CPU')
|
|
net = Net(t)
|
|
output = net(x)
|
|
dtype = output.asnumpy().dtype
|
|
assert dtype == 'int64'
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.env_onecard
|
|
def test_cast_uint8_to_uint16():
|
|
x = Tensor(np.random.uniform(-2, 2, (3, 2)).astype(np.uint8))
|
|
t = mstype.uint16
|
|
|
|
context.set_context(mode=context.GRAPH_MODE, device_target='CPU')
|
|
net = Net(t)
|
|
output = net(x)
|
|
dtype = output.asnumpy().dtype
|
|
assert dtype == 'uint16'
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.env_onecard
|
|
def test_cast_uint8_to_uint32():
|
|
x = Tensor(np.random.uniform(-2, 2, (3, 2)).astype(np.uint8))
|
|
t = mstype.uint32
|
|
|
|
context.set_context(mode=context.GRAPH_MODE, device_target='CPU')
|
|
net = Net(t)
|
|
output = net(x)
|
|
dtype = output.asnumpy().dtype
|
|
assert dtype == 'uint32'
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.env_onecard
|
|
def test_cast_uint8_to_uint64():
|
|
x = Tensor(np.random.uniform(-2, 2, (3, 2)).astype(np.uint8))
|
|
t = mstype.uint64
|
|
|
|
context.set_context(mode=context.GRAPH_MODE, device_target='CPU')
|
|
net = Net(t)
|
|
output = net(x)
|
|
dtype = output.asnumpy().dtype
|
|
assert dtype == 'uint64'
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.env_onecard
|
|
def test_cast_int16_to_int32():
|
|
x = Tensor(np.random.uniform(-2, 2, (3, 2)).astype(np.int16))
|
|
t = mstype.int32
|
|
|
|
context.set_context(mode=context.GRAPH_MODE, device_target='CPU')
|
|
net = Net(t)
|
|
output = net(x)
|
|
dtype = output.asnumpy().dtype
|
|
assert dtype == 'int32'
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.env_onecard
|
|
def test_cast_int16_to_int64():
|
|
x = Tensor(np.random.uniform(-2, 2, (3, 2)).astype(np.int16))
|
|
t = mstype.int64
|
|
|
|
context.set_context(mode=context.GRAPH_MODE, device_target='CPU')
|
|
net = Net(t)
|
|
output = net(x)
|
|
dtype = output.asnumpy().dtype
|
|
assert dtype == 'int64'
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.env_onecard
|
|
def test_cast_uint16_to_int32():
|
|
x = Tensor(np.random.uniform(-2, 2, (3, 2)).astype(np.uint16))
|
|
t = mstype.int32
|
|
|
|
context.set_context(mode=context.GRAPH_MODE, device_target='CPU')
|
|
net = Net(t)
|
|
output = net(x)
|
|
dtype = output.asnumpy().dtype
|
|
assert dtype == 'int32'
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.env_onecard
|
|
def test_cast_uint16_to_int64():
|
|
x = Tensor(np.random.uniform(-2, 2, (3, 2)).astype(np.uint16))
|
|
t = mstype.int64
|
|
|
|
context.set_context(mode=context.GRAPH_MODE, device_target='CPU')
|
|
net = Net(t)
|
|
output = net(x)
|
|
dtype = output.asnumpy().dtype
|
|
assert dtype == 'int64'
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.env_onecard
|
|
def test_cast_uint16_to_uint32():
|
|
x = Tensor(np.random.uniform(-2, 2, (3, 2)).astype(np.uint16))
|
|
t = mstype.uint32
|
|
|
|
context.set_context(mode=context.GRAPH_MODE, device_target='CPU')
|
|
net = Net(t)
|
|
output = net(x)
|
|
dtype = output.asnumpy().dtype
|
|
assert dtype == 'uint32'
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.env_onecard
|
|
def test_cast_uint16_to_uint64():
|
|
x = Tensor(np.random.uniform(-2, 2, (3, 2)).astype(np.uint16))
|
|
t = mstype.uint64
|
|
|
|
context.set_context(mode=context.GRAPH_MODE, device_target='CPU')
|
|
net = Net(t)
|
|
output = net(x)
|
|
dtype = output.asnumpy().dtype
|
|
assert dtype == 'uint64'
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.env_onecard
|
|
def test_cast_int32_to_int64():
|
|
x = Tensor(np.random.uniform(-2, 2, (3, 2)).astype(np.int32))
|
|
t = mstype.int64
|
|
|
|
context.set_context(mode=context.GRAPH_MODE, device_target='CPU')
|
|
net = Net(t)
|
|
output = net(x)
|
|
dtype = output.asnumpy().dtype
|
|
assert dtype == 'int64'
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.env_onecard
|
|
def test_cast_uint32_to_int64():
|
|
x = Tensor(np.random.uniform(-2, 2, (3, 2)).astype(np.uint32))
|
|
t = mstype.int64
|
|
|
|
context.set_context(mode=context.GRAPH_MODE, device_target='CPU')
|
|
net = Net(t)
|
|
output = net(x)
|
|
dtype = output.asnumpy().dtype
|
|
assert dtype == 'int64'
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.env_onecard
|
|
def test_cast_uint32_to_uint64():
|
|
x = Tensor(np.random.uniform(-2, 2, (3, 2)).astype(np.uint32))
|
|
t = mstype.uint64
|
|
|
|
context.set_context(mode=context.GRAPH_MODE, device_target='CPU')
|
|
net = Net(t)
|
|
output = net(x)
|
|
dtype = output.asnumpy().dtype
|
|
assert dtype == 'uint64'
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.env_onecard
|
|
def test_cast_float16_to_float32():
|
|
x = Tensor(np.random.uniform(-2, 2, (3, 2)).astype(np.float16))
|
|
t = mstype.float32
|
|
|
|
context.set_context(mode=context.GRAPH_MODE, device_target='CPU')
|
|
net = Net(t)
|
|
output = net(x)
|
|
dtype = output.asnumpy().dtype
|
|
assert dtype == 'float32'
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.env_onecard
|
|
def test_cast_float16_to_float64():
|
|
x = Tensor(np.random.uniform(-2, 2, (3, 2)).astype(np.float16))
|
|
t = mstype.float64
|
|
|
|
context.set_context(mode=context.GRAPH_MODE, device_target='CPU')
|
|
net = Net(t)
|
|
output = net(x)
|
|
dtype = output.asnumpy().dtype
|
|
assert dtype == 'float64'
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.env_onecard
|
|
def test_cast_float32_to_float64():
|
|
x = Tensor(np.random.uniform(-2, 2, (3, 2)).astype(np.float32))
|
|
t = mstype.float64
|
|
|
|
context.set_context(mode=context.GRAPH_MODE, device_target='CPU')
|
|
net = Net(t)
|
|
output = net(x)
|
|
dtype = output.asnumpy().dtype
|
|
assert dtype == 'float64'
|