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.
147 lines
4.9 KiB
147 lines
4.9 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.context as context
|
|
import mindspore.nn as nn
|
|
from mindspore import Tensor
|
|
from mindspore.ops import operations as P
|
|
|
|
|
|
class NetArgmaxWithValue(nn.Cell):
|
|
def __init__(self):
|
|
super(NetArgmaxWithValue, self).__init__()
|
|
axis1 = 0
|
|
axis2 = -1
|
|
self.argmax1 = P.ArgMaxWithValue(axis1)
|
|
self.argmax2 = P.ArgMaxWithValue(axis2)
|
|
self.argmax3 = P.ArgMaxWithValue()
|
|
|
|
def construct(self, x):
|
|
return (self.argmax1(x), self.argmax2(x), self.argmax3(x))
|
|
|
|
|
|
class NetArgmaxWithValueBig(nn.Cell):
|
|
def __init__(self, axis=0):
|
|
super(NetArgmaxWithValueBig, self).__init__()
|
|
self.argmax = P.ArgMaxWithValue(axis)
|
|
|
|
def construct(self, x):
|
|
return self.argmax(x)
|
|
|
|
|
|
def argmaxwithvalue_base(data_type):
|
|
x = Tensor(np.array([[1., 20., 5.],
|
|
[67., 8., 9.],
|
|
[130., 24., 15.],
|
|
[0.3, -0.4, -15.]]).astype(data_type))
|
|
expect1 = np.array([2, 2, 2]).astype(data_type)
|
|
expect2 = np.array([1, 0, 0, 0]).astype(data_type)
|
|
expect11 = np.array([130, 24, 15]).astype(data_type)
|
|
expect22 = np.array([20, 67, 130, 0.3]).astype(data_type)
|
|
context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU")
|
|
argmax = NetArgmaxWithValue()
|
|
output = argmax(x)
|
|
assert (output[0][0].asnumpy() == expect1).all()
|
|
assert (output[0][1].asnumpy() == expect11).all()
|
|
assert (output[1][0].asnumpy() == expect2).all()
|
|
assert (output[1][1].asnumpy() == expect22).all()
|
|
assert (output[2][0].asnumpy() == expect1).all()
|
|
assert (output[2][1].asnumpy() == expect11).all()
|
|
|
|
context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
|
|
argmax = NetArgmaxWithValue()
|
|
output = argmax(x)
|
|
assert (output[0][0].asnumpy() == expect1).all()
|
|
assert (output[0][1].asnumpy() == expect11).all()
|
|
assert (output[1][0].asnumpy() == expect2).all()
|
|
assert (output[1][1].asnumpy() == expect22).all()
|
|
assert (output[2][0].asnumpy() == expect1).all()
|
|
assert (output[2][1].asnumpy() == expect11).all()
|
|
|
|
|
|
def argmaxwithvalue_3d(data_type, shape_x):
|
|
np.random.seed(2)
|
|
x_np = np.random.random(shape_x).astype(data_type)
|
|
x = Tensor(x_np)
|
|
|
|
argmax = NetArgmaxWithValueBig(0)
|
|
output = argmax(x)
|
|
expect1 = np.argmax(x_np, axis=0)
|
|
expect2 = np.maximum.reduce(x_np, 0)
|
|
assert (output[0].asnumpy() == expect1).all()
|
|
assert (output[1].asnumpy() == expect2).all()
|
|
|
|
argmax = NetArgmaxWithValueBig(1)
|
|
output = argmax(x)
|
|
expect1 = np.argmax(x_np, axis=1)
|
|
expect2 = np.maximum.reduce(x_np, 1)
|
|
assert (output[0].asnumpy() == expect1).all()
|
|
assert (output[1].asnumpy() == expect2).all()
|
|
|
|
argmax = NetArgmaxWithValueBig(2)
|
|
output = argmax(x)
|
|
expect1 = np.argmax(x_np, axis=2)
|
|
expect2 = np.maximum.reduce(x_np, 2)
|
|
assert (output[0].asnumpy() == expect1).all()
|
|
assert (output[1].asnumpy() == expect2).all()
|
|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.env_onecard
|
|
def test_argmaxwithvalue_base_float32():
|
|
argmaxwithvalue_base(np.float32)
|
|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.env_onecard
|
|
def test_argmaxwithvalue_base_float16():
|
|
argmaxwithvalue_base(np.float16)
|
|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.env_onecard
|
|
def test_argmaxwithvalue_3d_float32():
|
|
shape_x = (2, 32, 256)
|
|
context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU")
|
|
argmaxwithvalue_3d(np.float32, shape_x)
|
|
context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
|
|
argmaxwithvalue_3d(np.float32, shape_x)
|
|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.env_onecard
|
|
def test_argmaxwithvalue_3d_float16():
|
|
shape_x = (2, 64, 128)
|
|
context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
|
|
argmaxwithvalue_3d(np.float16, shape_x)
|
|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.env_onecard
|
|
def test_argmaxwithvalue_3d_big_float32():
|
|
shape_x = (128, 1024, 1)
|
|
context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU")
|
|
argmaxwithvalue_3d(np.float32, shape_x)
|
|
context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
|
|
argmaxwithvalue_3d(np.float32, shape_x)
|