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.
210 lines
6.3 KiB
210 lines
6.3 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 Net(nn.Cell):
|
|
def __init__(self):
|
|
super(Net, self).__init__()
|
|
self.ops = P.LessEqual()
|
|
|
|
def construct(self, x, y):
|
|
return self.ops(x, y)
|
|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_cpu_training
|
|
@pytest.mark.env_onecard
|
|
def test_net_fp32():
|
|
x0_np = np.random.randint(1, 5, (2, 3, 4, 4)).astype(np.float32)
|
|
y0_np = np.random.randint(1, 5, (2, 3, 4, 4)).astype(np.float32)
|
|
x1_np = np.random.randint(1, 5, (2, 3, 4, 4)).astype(np.float32)
|
|
y1_np = np.random.randint(1, 5, (2, 1, 4, 4)).astype(np.float32)
|
|
x2_np = np.random.randint(1, 5, (2, 1, 1, 4)).astype(np.float32)
|
|
y2_np = np.random.randint(1, 5, (2, 3, 4, 4)).astype(np.float32)
|
|
x3_np = np.random.randint(1, 5, 1).astype(np.float32)
|
|
y3_np = np.random.randint(1, 5, 1).astype(np.float32)
|
|
x4_np = np.array(768).astype(np.float32)
|
|
y4_np = np.array(3072.5).astype(np.float32)
|
|
|
|
x0 = Tensor(x0_np)
|
|
y0 = Tensor(y0_np)
|
|
x1 = Tensor(x1_np)
|
|
y1 = Tensor(y1_np)
|
|
x2 = Tensor(x2_np)
|
|
y2 = Tensor(y2_np)
|
|
x3 = Tensor(x3_np)
|
|
y3 = Tensor(y3_np)
|
|
x4 = Tensor(x4_np)
|
|
y4 = Tensor(y4_np)
|
|
|
|
context.set_context(mode=context.GRAPH_MODE, device_target='CPU')
|
|
net = Net()
|
|
out = net(x0, y0).asnumpy()
|
|
expect = x0_np <= y0_np
|
|
assert np.all(out == expect)
|
|
assert out.shape == expect.shape
|
|
|
|
out = net(x1, y1).asnumpy()
|
|
expect = x1_np <= y1_np
|
|
assert np.all(out == expect)
|
|
assert out.shape == expect.shape
|
|
|
|
out = net(x2, y2).asnumpy()
|
|
expect = x2_np <= y2_np
|
|
assert np.all(out == expect)
|
|
assert out.shape == expect.shape
|
|
|
|
out = net(x3, y3).asnumpy()
|
|
expect = x3_np <= y3_np
|
|
assert np.all(out == expect)
|
|
assert out.shape == expect.shape
|
|
|
|
out = net(x4, y4).asnumpy()
|
|
expect = x4_np <= y4_np
|
|
assert np.all(out == expect)
|
|
assert out.shape == expect.shape
|
|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_cpu_training
|
|
@pytest.mark.env_onecard
|
|
def test_net_fp16():
|
|
x0_np = np.random.randint(1, 5, (2, 3, 4, 4)).astype(np.float16)
|
|
y0_np = np.random.randint(1, 5, (2, 3, 4, 4)).astype(np.float16)
|
|
x1_np = np.random.randint(1, 5, (2, 3, 4, 4)).astype(np.float16)
|
|
y1_np = np.random.randint(1, 5, (2, 1, 4, 4)).astype(np.float16)
|
|
x2_np = np.random.randint(1, 5, (2, 1, 1, 4)).astype(np.float16)
|
|
y2_np = np.random.randint(1, 5, (2, 3, 4, 4)).astype(np.float16)
|
|
x3_np = np.random.randint(1, 5, 1).astype(np.float16)
|
|
y3_np = np.random.randint(1, 5, 1).astype(np.float16)
|
|
x4_np = np.array(768).astype(np.float16)
|
|
y4_np = np.array(3072.5).astype(np.float16)
|
|
|
|
x0 = Tensor(x0_np)
|
|
y0 = Tensor(y0_np)
|
|
x1 = Tensor(x1_np)
|
|
y1 = Tensor(y1_np)
|
|
x2 = Tensor(x2_np)
|
|
y2 = Tensor(y2_np)
|
|
x3 = Tensor(x3_np)
|
|
y3 = Tensor(y3_np)
|
|
x4 = Tensor(x4_np)
|
|
y4 = Tensor(y4_np)
|
|
|
|
context.set_context(mode=context.GRAPH_MODE, device_target='CPU')
|
|
net = Net()
|
|
out = net(x0, y0).asnumpy()
|
|
expect = x0_np <= y0_np
|
|
assert np.all(out == expect)
|
|
assert out.shape == expect.shape
|
|
|
|
out = net(x1, y1).asnumpy()
|
|
expect = x1_np <= y1_np
|
|
assert np.all(out == expect)
|
|
assert out.shape == expect.shape
|
|
|
|
out = net(x2, y2).asnumpy()
|
|
expect = x2_np <= y2_np
|
|
assert np.all(out == expect)
|
|
assert out.shape == expect.shape
|
|
|
|
out = net(x3, y3).asnumpy()
|
|
expect = x3_np <= y3_np
|
|
assert np.all(out == expect)
|
|
assert out.shape == expect.shape
|
|
|
|
out = net(x4, y4).asnumpy()
|
|
expect = x4_np <= y4_np
|
|
assert np.all(out == expect)
|
|
assert out.shape == expect.shape
|
|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_cpu_training
|
|
@pytest.mark.env_onecard
|
|
def test_net_int32():
|
|
x1_np = np.random.randint(1, 5, (2, 3, 4, 4)).astype(np.int32)
|
|
y1_np = np.random.randint(1, 5, (2, 1, 4, 4)).astype(np.int32)
|
|
x1 = Tensor(x1_np)
|
|
y1 = Tensor(y1_np)
|
|
|
|
|
|
context.set_context(mode=context.GRAPH_MODE, device_target='CPU')
|
|
net = Net()
|
|
out = net(x1, y1).asnumpy()
|
|
expect = x1_np <= y1_np
|
|
assert np.all(out == expect)
|
|
assert out.shape == expect.shape
|
|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_cpu_training
|
|
@pytest.mark.env_onecard
|
|
def test_net_int64():
|
|
x1_np = np.random.randint(1, 5, (2, 3, 4, 4)).astype(np.int64)
|
|
y1_np = np.random.randint(1, 5, (2, 1, 4, 4)).astype(np.int64)
|
|
x1 = Tensor(x1_np)
|
|
y1 = Tensor(y1_np)
|
|
|
|
|
|
context.set_context(mode=context.GRAPH_MODE, device_target='CPU')
|
|
net = Net()
|
|
out = net(x1, y1).asnumpy()
|
|
expect = x1_np <= y1_np
|
|
assert np.all(out == expect)
|
|
assert out.shape == expect.shape
|
|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_cpu_training
|
|
@pytest.mark.env_onecard
|
|
def test_net_float64():
|
|
x1_np = np.random.randint(1, 5, (2, 3, 4, 4)).astype(np.float64)
|
|
y1_np = np.random.randint(1, 5, (2, 1, 4, 4)).astype(np.float64)
|
|
x1 = Tensor(x1_np)
|
|
y1 = Tensor(y1_np)
|
|
|
|
|
|
context.set_context(mode=context.GRAPH_MODE, device_target='CPU')
|
|
net = Net()
|
|
out = net(x1, y1).asnumpy()
|
|
expect = x1_np <= y1_np
|
|
assert np.all(out == expect)
|
|
assert out.shape == expect.shape
|
|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_cpu_training
|
|
@pytest.mark.env_onecard
|
|
def test_net_int16():
|
|
x1_np = np.random.randint(1, 5, (2, 3, 4, 4)).astype(np.int16)
|
|
y1_np = np.random.randint(1, 5, (2, 1, 4, 4)).astype(np.int16)
|
|
x1 = Tensor(x1_np)
|
|
y1 = Tensor(y1_np)
|
|
|
|
|
|
context.set_context(mode=context.GRAPH_MODE, device_target='CPU')
|
|
net = Net()
|
|
out = net(x1, y1).asnumpy()
|
|
expect = x1_np <= y1_np
|
|
assert np.all(out == expect)
|
|
assert out.shape == expect.shape
|