forked from mindspore-Ecosystem/mindspore
102 lines
3.4 KiB
Python
102 lines
3.4 KiB
Python
# Copyright 2022 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.operations import _grad_ops as G
|
|
from mindspore.ops.functional import vmap
|
|
|
|
|
|
context.set_context(mode=context.GRAPH_MODE, device_target='CPU')
|
|
|
|
|
|
class NetHShrinkGrad(nn.Cell):
|
|
def __init__(self, lambd=0.5):
|
|
super(NetHShrinkGrad, self).__init__()
|
|
self.hard_shrink_grad = G.HShrinkGrad(lambd)
|
|
|
|
def construct(self, grad, input_x):
|
|
return self.hard_shrink_grad(grad, input_x)
|
|
|
|
|
|
def hshrink_grad_op_np_bencmark(grad, input_x, lambd):
|
|
"""
|
|
Feature: generate a hshrink grad numpy benchmark.
|
|
Description: The input shape need to match to output shape.
|
|
Expectation: match to mindspore HShrinkGrad.
|
|
"""
|
|
result = np.zeros_like(grad, dtype=grad.dtype)
|
|
for index, _ in np.ndenumerate(grad):
|
|
if input_x[index] > lambd or input_x[index] < (-1 * lambd):
|
|
result[index] = grad[index]
|
|
else:
|
|
result[index] = 0
|
|
return result
|
|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.env_onecard
|
|
@pytest.mark.parametrize('dtype', [np.float16, np.float32])
|
|
@pytest.mark.parametrize("data_shape", [(3, 4), (4, 5, 6, 7)])
|
|
@pytest.mark.parametrize("lambd", [0.5])
|
|
def test_hshrink_grad(dtype, data_shape, lambd):
|
|
"""
|
|
Feature: HShrinkGrad cpu kernel
|
|
Description: test the rightness of HShrinkGrad cpu kernel
|
|
Expectation: the output is same as hshrink_grad_op_np_bencmark output
|
|
"""
|
|
grad_data = np.random.random(data_shape).astype(dtype)
|
|
input_data = np.random.uniform(
|
|
low=-1, high=1, size=data_shape).astype(dtype)
|
|
benchmark_output = hshrink_grad_op_np_bencmark(
|
|
grad_data, input_data, lambd)
|
|
hshrink_grad = NetHShrinkGrad(lambd)
|
|
output = hshrink_grad(Tensor(grad_data), Tensor(input_data))
|
|
assert np.allclose(output.asnumpy(), benchmark_output)
|
|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.env_onecard
|
|
@pytest.mark.parametrize('axis', [2])
|
|
def test_hshrink_grad_vmap_cpu(axis):
|
|
"""
|
|
Feature: HShrinkGrad cpu kernel
|
|
Description: test the rightness of HShrinkGrad cpu kernel vmap feature.
|
|
Expectation: Success.
|
|
"""
|
|
hshrink = NetHShrinkGrad()
|
|
|
|
def hshrink_func(grad, input_x):
|
|
"""hshrink_func"""
|
|
return hshrink(grad, input_x)
|
|
|
|
grad_data = np.random.random((3, axis, 5)).astype(np.float32)
|
|
input_data = np.random.uniform(
|
|
low=-1, high=1, size=(3, axis, 5)).astype(np.float32)
|
|
|
|
output_vmap = vmap(hshrink_func, in_axes=(1, 1))(
|
|
Tensor(grad_data), Tensor(input_data))
|
|
|
|
output_manually = hshrink(Tensor(
|
|
grad_data.transpose(1, 0, 2)), Tensor(input_data.transpose(1, 0, 2)))
|
|
|
|
assert np.array_equal(output_vmap.asnumpy(), output_manually)
|