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.
267 lines
8.1 KiB
267 lines
8.1 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.
|
|
# ============================================================================
|
|
"""multitype_ops directory test case"""
|
|
import numpy as np
|
|
import pytest
|
|
|
|
import mindspore.nn as nn
|
|
from mindspore import Tensor
|
|
from mindspore import dtype as mstype
|
|
from mindspore.ops import functional as F
|
|
import mindspore.context as context
|
|
|
|
|
|
class TensorIntAutoCast(nn.Cell):
|
|
def __init__(self,):
|
|
super(TensorIntAutoCast, self).__init__()
|
|
self.i = 2
|
|
|
|
def construct(self, t):
|
|
z = F.tensor_mul(t, self.i)
|
|
return z
|
|
|
|
|
|
class TensorFPAutoCast(nn.Cell):
|
|
def __init__(self,):
|
|
super(TensorFPAutoCast, self).__init__()
|
|
self.f = 1.2
|
|
|
|
def construct(self, t):
|
|
z = F.tensor_mul(t, self.f)
|
|
return z
|
|
|
|
|
|
class TensorBoolAutoCast(nn.Cell):
|
|
def __init__(self,):
|
|
super(TensorBoolAutoCast, self).__init__()
|
|
self.f = True
|
|
|
|
def construct(self, t):
|
|
z = F.tensor_mul(t, self.f)
|
|
return z
|
|
|
|
|
|
class TensorAutoCast(nn.Cell):
|
|
def __init__(self,):
|
|
super(TensorAutoCast, self).__init__()
|
|
|
|
def construct(self, t1, t2):
|
|
z = F.tensor_mul(t1, t2)
|
|
return z
|
|
|
|
|
|
def test_tensor_auto_cast():
|
|
context.set_context(mode=context.GRAPH_MODE)
|
|
Tensor([True, False], mstype.bool_)
|
|
t_uint8 = Tensor(np.ones([2, 1, 2, 2]), mstype.uint8)
|
|
t_int8 = Tensor(np.ones([2, 1, 2, 2]), mstype.int8)
|
|
t_int16 = Tensor(np.ones([2, 1, 2, 2]), mstype.int16)
|
|
t_int32 = Tensor(np.ones([2, 1, 2, 2]), mstype.int32)
|
|
t_int64 = Tensor(np.ones([2, 1, 2, 2]), mstype.int64)
|
|
t_fp16 = Tensor(np.ones([2, 1, 2, 2]), mstype.float16)
|
|
t_fp32 = Tensor(np.ones([2, 1, 2, 2]), mstype.float32)
|
|
t_fp64 = Tensor(np.ones([2, 1, 2, 2]), mstype.float64)
|
|
net = TensorAutoCast()
|
|
rs = net(t_uint8, t_int8)
|
|
assert rs.dtype == mstype.int16
|
|
rs = net(t_uint8, t_int16)
|
|
assert rs.dtype == mstype.int16
|
|
rs = net(t_uint8, t_int32)
|
|
assert rs.dtype == mstype.int32
|
|
rs = net(t_uint8, t_int64)
|
|
assert rs.dtype == mstype.int64
|
|
rs = net(t_int8, t_int16)
|
|
assert rs.dtype == mstype.int16
|
|
rs = net(t_int8, t_int32)
|
|
assert rs.dtype == mstype.int32
|
|
rs = net(t_int8, t_int64)
|
|
assert rs.dtype == mstype.int64
|
|
rs = net(t_int16, t_int32)
|
|
assert rs.dtype == mstype.int32
|
|
rs = net(t_int16, t_int64)
|
|
assert rs.dtype == mstype.int64
|
|
rs = net(t_int32, t_int64)
|
|
assert rs.dtype == mstype.int64
|
|
|
|
rs = net(t_fp16, t_fp32)
|
|
assert rs.dtype == mstype.float32
|
|
rs = net(t_fp16, t_fp64)
|
|
assert rs.dtype == mstype.float64
|
|
rs = net(t_fp32, t_fp64)
|
|
assert rs.dtype == mstype.float64
|
|
|
|
rs = net(t_uint8, t_fp16)
|
|
assert rs.dtype == mstype.float16
|
|
rs = net(t_uint8, t_fp32)
|
|
assert rs.dtype == mstype.float32
|
|
rs = net(t_uint8, t_fp64)
|
|
assert rs.dtype == mstype.float64
|
|
rs = net(t_int8, t_fp64)
|
|
assert rs.dtype == mstype.float64
|
|
rs = net(t_int16, t_fp64)
|
|
assert rs.dtype == mstype.float64
|
|
rs = net(t_int32, t_fp64)
|
|
assert rs.dtype == mstype.float64
|
|
rs = net(t_int64, t_fp64)
|
|
assert rs.dtype == mstype.float64
|
|
|
|
rs = net(t_fp16, t_int8)
|
|
assert rs.dtype == mstype.float16
|
|
rs = net(t_fp16, t_uint8)
|
|
assert rs.dtype == mstype.float16
|
|
rs = net(t_fp16, t_int16)
|
|
assert rs.dtype == mstype.float16
|
|
rs = net(t_fp16, t_int32)
|
|
assert rs.dtype == mstype.float16
|
|
rs = net(t_fp16, t_int64)
|
|
assert rs.dtype == mstype.float16
|
|
|
|
tint = TensorIntAutoCast()
|
|
rs = tint(t_uint8)
|
|
assert rs.dtype == mstype.uint8
|
|
rs = tint(t_int8)
|
|
assert rs.dtype == mstype.int8
|
|
rs = tint(t_int16)
|
|
assert rs.dtype == mstype.int16
|
|
rs = tint(t_int32)
|
|
assert rs.dtype == mstype.int32
|
|
rs = tint(t_int64)
|
|
assert rs.dtype == mstype.int64
|
|
rs = tint(t_fp16)
|
|
assert rs.dtype == mstype.float16
|
|
rs = tint(t_fp32)
|
|
assert rs.dtype == mstype.float32
|
|
rs = tint(t_fp64)
|
|
assert rs.dtype == mstype.float64
|
|
tfp = TensorFPAutoCast()
|
|
rs = tfp(t_uint8)
|
|
assert rs.dtype == mstype.float32
|
|
rs = tfp(t_int8)
|
|
assert rs.dtype == mstype.float32
|
|
rs = tfp(t_int16)
|
|
assert rs.dtype == mstype.float32
|
|
rs = tfp(t_int32)
|
|
assert rs.dtype == mstype.float32
|
|
rs = tfp(t_int64)
|
|
assert rs.dtype == mstype.float32
|
|
rs = tfp(t_fp16)
|
|
assert rs.dtype == mstype.float32
|
|
rs = tfp(t_fp32)
|
|
assert rs.dtype == mstype.float32
|
|
rs = tfp(t_fp64)
|
|
assert rs.dtype == mstype.float64
|
|
|
|
t_uint16 = Tensor(np.ones([2, 1, 2, 2]), mstype.uint16)
|
|
t_uint32 = Tensor(np.ones([2, 1, 2, 2]), mstype.uint32)
|
|
t_uint64 = Tensor(np.ones([2, 1, 2, 2]), mstype.uint64)
|
|
with pytest.raises(TypeError):
|
|
net(t_uint16, t_uint8)
|
|
with pytest.raises(TypeError):
|
|
net(t_uint16, t_int8)
|
|
with pytest.raises(TypeError):
|
|
net(t_uint16, t_int16)
|
|
with pytest.raises(TypeError):
|
|
net(t_uint16, t_int32)
|
|
with pytest.raises(TypeError):
|
|
net(t_uint16, t_int64)
|
|
with pytest.raises(TypeError):
|
|
net(t_uint32, t_uint8)
|
|
with pytest.raises(TypeError):
|
|
net(t_uint32, t_int8)
|
|
with pytest.raises(TypeError):
|
|
net(t_uint32, t_int16)
|
|
with pytest.raises(TypeError):
|
|
net(t_uint32, t_int32)
|
|
with pytest.raises(TypeError):
|
|
net(t_uint32, t_int64)
|
|
with pytest.raises(TypeError):
|
|
net(t_uint64, t_uint8)
|
|
with pytest.raises(TypeError):
|
|
net(t_uint64, t_int8)
|
|
with pytest.raises(TypeError):
|
|
net(t_uint64, t_int16)
|
|
with pytest.raises(TypeError):
|
|
net(t_uint64, t_int32)
|
|
with pytest.raises(TypeError):
|
|
net(t_uint64, t_int64)
|
|
with pytest.raises(TypeError):
|
|
net(t_uint16, t_fp16)
|
|
with pytest.raises(TypeError):
|
|
net(t_uint16, t_fp32)
|
|
with pytest.raises(TypeError):
|
|
net(t_uint16, t_fp64)
|
|
with pytest.raises(TypeError):
|
|
net(t_uint32, t_fp16)
|
|
with pytest.raises(TypeError):
|
|
net(t_uint32, t_fp32)
|
|
with pytest.raises(TypeError):
|
|
net(t_uint32, t_fp64)
|
|
with pytest.raises(TypeError):
|
|
net(t_uint64, t_fp16)
|
|
with pytest.raises(TypeError):
|
|
net(t_uint64, t_fp32)
|
|
with pytest.raises(TypeError):
|
|
net(t_uint64, t_fp64)
|
|
|
|
with pytest.raises(TypeError):
|
|
tfp(t_uint16)
|
|
with pytest.raises(TypeError):
|
|
tfp(t_uint32)
|
|
with pytest.raises(TypeError):
|
|
tfp(t_uint64)
|
|
|
|
with pytest.raises(TypeError):
|
|
tint(t_uint16)
|
|
with pytest.raises(TypeError):
|
|
tint(t_uint32)
|
|
with pytest.raises(TypeError):
|
|
tint(t_uint64)
|
|
|
|
bnet = TensorBoolAutoCast()
|
|
with pytest.raises(TypeError):
|
|
bnet(t_uint8)
|
|
with pytest.raises(TypeError):
|
|
bnet(t_int8)
|
|
with pytest.raises(TypeError):
|
|
bnet(t_int16)
|
|
with pytest.raises(TypeError):
|
|
bnet(t_int32)
|
|
with pytest.raises(TypeError):
|
|
bnet(t_int64)
|
|
with pytest.raises(TypeError):
|
|
bnet(t_fp16)
|
|
with pytest.raises(TypeError):
|
|
bnet(t_fp32)
|
|
with pytest.raises(TypeError):
|
|
bnet(t_fp64)
|
|
def test_bool_tensor_and_float():
|
|
context.set_context(mode=context.GRAPH_MODE)
|
|
t_bool = Tensor(np.ones([2, 1, 2, 2]).astype(np.bool), mstype.bool_)
|
|
t_int32 = Tensor(np.ones([2, 1, 2, 2]), mstype.int32)
|
|
t_fp16 = Tensor(np.ones([2, 1, 2, 2]), mstype.float16)
|
|
t_fp32 = Tensor(np.ones([2, 1, 2, 2]), mstype.float32)
|
|
net = TensorFPAutoCast()
|
|
out = net(t_bool)
|
|
assert out.dtype == mstype.float32
|
|
net = TensorIntAutoCast()
|
|
out = net(t_bool)
|
|
assert out.dtype == mstype.int32
|
|
out = net(t_fp16)
|
|
assert out.dtype == mstype.float16
|
|
out = net(t_fp32)
|
|
assert out.dtype == mstype.float32
|
|
out = net(t_int32)
|
|
assert out.dtype == mstype.int32
|