forked from mindspore-Ecosystem/mindspore
101 lines
3.2 KiB
Python
101 lines
3.2 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.context as context
|
|
import mindspore.nn as nn
|
|
import mindspore as ms
|
|
from mindspore import Tensor
|
|
from mindspore.ops import operations as P
|
|
|
|
|
|
class L2LossNet(nn.Cell):
|
|
def __init__(self):
|
|
super(L2LossNet, self).__init__()
|
|
self.l2_loss = P.L2Loss()
|
|
|
|
def construct(self, x):
|
|
return self.l2_loss(x)
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.env_onecard
|
|
def test_gather_pynative_fp32_22():
|
|
context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU")
|
|
error = 1e-4
|
|
x = Tensor(np.array([[1., 2.], [3., 4.]]), ms.float32)
|
|
expect = np.array(15, np.float32)
|
|
output = P.L2Loss()(x)
|
|
diff = output.asnumpy() - expect
|
|
assert np.all(diff < error)
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.env_onecard
|
|
def test_gather_pynative_fp16_22():
|
|
context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU")
|
|
error = 1e-4
|
|
x = Tensor(np.array([[1., 2.], [3., 4.]]), ms.float16)
|
|
expect = np.array(15, np.float16)
|
|
output = P.L2Loss()(x)
|
|
diff = output.asnumpy() - expect
|
|
assert np.all(diff < error)
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.env_onecard
|
|
def test_gather_pynative_fp32_14():
|
|
context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU")
|
|
error = 1e-4
|
|
x = Tensor(np.array([1., 2., 3., 4.]), ms.float32)
|
|
expect = np.array(15, np.float32)
|
|
output = P.L2Loss()(x)
|
|
diff = output.asnumpy() - expect
|
|
assert np.all(diff < error)
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.env_onecard
|
|
def test_gather_pynative_fp16_14():
|
|
context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU")
|
|
error = 1e-4
|
|
x = Tensor(np.array([1., 2., 3., 4.]), ms.float16)
|
|
expect = np.array(15, np.float16)
|
|
output = P.L2Loss()(x)
|
|
diff = output.asnumpy() - expect
|
|
assert np.all(diff < error)
|
|
|
|
def test_gather_graph_fp32_14():
|
|
context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
|
|
error = 1e-4
|
|
x = Tensor(np.array([1., 2., 3., 4.]), ms.float32)
|
|
expect = np.array(15, np.float32)
|
|
l2_loss = L2LossNet()
|
|
output = l2_loss(x)
|
|
diff = output.asnumpy() - expect
|
|
assert np.all(diff < error)
|
|
|
|
def test_gather_graph_fp16_14():
|
|
context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
|
|
error = 1e-4
|
|
x = Tensor(np.array([1., 2., 3., 4.]), ms.float16)
|
|
expect = np.array(15, np.float16)
|
|
l2_loss = L2LossNet()
|
|
output = l2_loss(x)
|
|
diff = output.asnumpy() - expect
|
|
assert np.all(diff < error)
|