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.
170 lines
7.2 KiB
170 lines
7.2 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 pytest
|
|
import numpy as np
|
|
|
|
import mindspore
|
|
import mindspore.nn as nn
|
|
import mindspore.context as context
|
|
|
|
from mindspore import Tensor
|
|
from mindspore.ops.composite import GradOperation
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_cpu_training
|
|
@pytest.mark.env_onecard
|
|
def test_mirror_pad():
|
|
context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
|
|
|
|
test1_arr_in = [[[[1, 2, 3], [4, 5, 6], [7, 8, 9]]]]
|
|
test_1_paddings = ((0, 0), (0, 0), (1, 1), (2, 2))
|
|
test1_arr_exp = [[[[6, 5, 4, 5, 6, 5, 4], [3, 2, 1, 2, 3, 2, 1], [6, 5, 4, 5, 6, 5, 4],
|
|
[9, 8, 7, 8, 9, 8, 7], [6, 5, 4, 5, 6, 5, 4]]]]
|
|
|
|
test2_arr_in = [[[[1, 2, 3], [4, 5, 6], [7, 8, 9]]]]
|
|
test_2_paddings = ((0, 0), (0, 0), (1, 1), (2, 2))
|
|
test2_arr_exp = [[[[2, 1, 1, 2, 3, 3, 2], [2, 1, 1, 2, 3, 3, 2], [5, 4, 4, 5, 6, 6, 5],
|
|
[8, 7, 7, 8, 9, 9, 8], [8, 7, 7, 8, 9, 9, 8]]]]
|
|
|
|
reflectOp = nn.Pad(mode='REFLECT', paddings=test_1_paddings)
|
|
symmOp = nn.Pad(mode='SYMMETRIC', paddings=test_2_paddings)
|
|
|
|
x_test_1 = Tensor(np.array(test1_arr_in), dtype=mindspore.float32)
|
|
x_test_2 = Tensor(np.array(test2_arr_in), dtype=mindspore.float32)
|
|
|
|
y_test_1 = reflectOp(x_test_1).asnumpy()
|
|
y_test_2 = symmOp(x_test_2).asnumpy()
|
|
|
|
print(np.array(test1_arr_in))
|
|
print(y_test_1)
|
|
|
|
np.testing.assert_equal(np.array(test1_arr_exp), y_test_1)
|
|
np.testing.assert_equal(np.array(test2_arr_exp), y_test_2)
|
|
|
|
|
|
class Grad(nn.Cell):
|
|
def __init__(self, network):
|
|
super(Grad, self).__init__()
|
|
self.grad = GradOperation(get_all=True, sens_param=True)
|
|
self.network = network
|
|
def construct(self, input_, output_grad):
|
|
return self.grad(self.network)(input_, output_grad)
|
|
|
|
class Net(nn.Cell):
|
|
def __init__(self, pads, mode_):
|
|
super(Net, self).__init__()
|
|
self.pad = nn.Pad(mode=mode_, paddings=pads)
|
|
def construct(self, x):
|
|
return self.pad(x)
|
|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_cpu_training
|
|
@pytest.mark.env_onecard
|
|
def test_mirror_pad_backprop():
|
|
context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
|
|
test_arr_in = [[[[1, 2, 3], [4, 5, 6], [7, 8, 9]]]] # size -> 3*3
|
|
test_arr_in = Tensor(test_arr_in, dtype=mindspore.float32)
|
|
dy = (np.ones((1, 1, 4, 5)) * 0.1).astype(np.float32)
|
|
expected_dx = np.array([[[[0.2, 0.2, 0.1],
|
|
[0.4, 0.4, 0.2],
|
|
[0.2, 0.2, 0.1]]]])
|
|
net = Grad(Net(((0, 0), (0, 0), (1, 0), (0, 2)), "REFLECT"))
|
|
dx = net(test_arr_in, Tensor(dy))
|
|
dx = dx[0].asnumpy()
|
|
np.testing.assert_array_almost_equal(dx, expected_dx)
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.env_onecard
|
|
def test_mirror_pad_fwd_back_4d_int32_reflect():
|
|
context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
|
|
# set constants
|
|
shape = (2, 3, 3, 5)
|
|
pads = ((1, 0), (2, 0), (1, 2), (3, 4))
|
|
total_val = np.prod(shape)
|
|
test_arr_np = np.arange(total_val).reshape(shape) + 1
|
|
test_arr_ms = Tensor(test_arr_np, dtype=mindspore.int32)
|
|
# fwd_pass_check
|
|
op = nn.Pad(mode="REFLECT", paddings=pads)
|
|
expected_np_result = np.pad(test_arr_np, pads, 'reflect')
|
|
obtained_ms_res = op(test_arr_ms).asnumpy()
|
|
np.testing.assert_array_equal(expected_np_result, obtained_ms_res)
|
|
# backwards pass check
|
|
GradNet = Grad(Net(pads, "REFLECT"))
|
|
dy_value = Tensor(np.ones(obtained_ms_res.shape), dtype=mindspore.int32)
|
|
dx_value_obtained = GradNet(test_arr_ms, dy_value)[0].asnumpy()
|
|
dx_value_expected = np.array([[[[4, 6, 6, 6, 2],
|
|
[6, 9, 9, 9, 3],
|
|
[2, 3, 3, 3, 1]],
|
|
[[8, 12, 12, 12, 4],
|
|
[12, 18, 18, 18, 6],
|
|
[4, 6, 6, 6, 2]],
|
|
[[8, 12, 12, 12, 4],
|
|
[12, 18, 18, 18, 6],
|
|
[4, 6, 6, 6, 2]]],
|
|
[[[8, 12, 12, 12, 4],
|
|
[12, 18, 18, 18, 6],
|
|
[4, 6, 6, 6, 2]],
|
|
[[16, 24, 24, 24, 8],
|
|
[24, 36, 36, 36, 12],
|
|
[8, 12, 12, 12, 4]],
|
|
[[16, 24, 24, 24, 8],
|
|
[24, 36, 36, 36, 12],
|
|
[8, 12, 12, 12, 4]]]], dtype=np.int32)
|
|
np.testing.assert_array_equal(dx_value_expected, dx_value_obtained)
|
|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_cpu_training
|
|
@pytest.mark.env_onecard
|
|
def test_mirror_pad_fwd_back_4d_int32_symm():
|
|
context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
|
|
# set constants
|
|
shape = (2, 3, 3, 5)
|
|
pads = ((1, 0), (2, 0), (1, 2), (3, 4))
|
|
total_val = np.prod(shape)
|
|
test_arr_np = np.arange(total_val).reshape(shape) + 1
|
|
test_arr_ms = Tensor(test_arr_np, dtype=mindspore.int32)
|
|
# fwd_pass_check
|
|
op = nn.Pad(mode="SYMMETRIC", paddings=pads)
|
|
expected_np_result = np.pad(test_arr_np, pads, 'symmetric')
|
|
obtained_ms_res = op(test_arr_ms).asnumpy()
|
|
np.testing.assert_array_equal(expected_np_result, obtained_ms_res)
|
|
# backwards pass check
|
|
GradNet = Grad(Net(pads, "SYMMETRIC"))
|
|
dy_value = Tensor(np.ones(obtained_ms_res.shape), dtype=mindspore.int32)
|
|
dx_value_obtained = GradNet(test_arr_ms, dy_value)[0].asnumpy()
|
|
dx_value_expected = np.array([[[[16, 24, 24, 16, 16],
|
|
[16, 24, 24, 16, 16],
|
|
[16, 24, 24, 16, 16]],
|
|
[[16, 24, 24, 16, 16],
|
|
[16, 24, 24, 16, 16],
|
|
[16, 24, 24, 16, 16]],
|
|
[[8, 12, 12, 8, 8],
|
|
[8, 12, 12, 8, 8],
|
|
[8, 12, 12, 8, 8]]],
|
|
[[[8, 12, 12, 8, 8],
|
|
[8, 12, 12, 8, 8],
|
|
[8, 12, 12, 8, 8]],
|
|
[[8, 12, 12, 8, 8],
|
|
[8, 12, 12, 8, 8],
|
|
[8, 12, 12, 8, 8]],
|
|
[[4, 6, 6, 4, 4],
|
|
[4, 6, 6, 4, 4],
|
|
[4, 6, 6, 4, 4]]]], dtype=np.int32)
|
|
np.testing.assert_array_equal(dx_value_expected, dx_value_obtained)
|