forked from mindspore-Ecosystem/mindspore
!25791 Add new initializer
Merge pull request !25791 from wanyiming/initilizer
This commit is contained in:
Gitee
commit
e40f3ec063
|
|
@ -377,6 +377,225 @@ class Constant(Initializer):
|
|||
_assignment(arr, self.value)
|
||||
|
||||
|
||||
@_register()
|
||||
class Identity(Initializer):
|
||||
"""
|
||||
Initialize a 2 dimension identity matrix to fill the input tensor.
|
||||
|
||||
Raises:
|
||||
ValueError: If the dimension of input tensor is not equal to 2.
|
||||
|
||||
Examples:
|
||||
>>> import mindspore
|
||||
>>> from mindspore.common.initializer import initializer, Identity
|
||||
>>> tensor1 = initializer(Identity(), [2, 3], mindspore.float32)
|
||||
>>> tensor2 = initializer('identity', [2, 3], mindspore.float32)
|
||||
"""
|
||||
def _initialize(self, arr):
|
||||
if len(arr.shape) != 2:
|
||||
raise ValueError('For Identity initializer, the dimension of the initialized tensor should be 2, '
|
||||
'but got {}.'.format(len(arr.shape)))
|
||||
value = np.eye(arr.shape[0], arr.shape[1])
|
||||
_assignment(arr, value)
|
||||
|
||||
|
||||
@_register()
|
||||
class Sparse(Initializer):
|
||||
"""
|
||||
Initialize a 2 dimension sparse matrix to fill the input tensor. The non-zero positions will be filled with
|
||||
the value sampled from the normal distribution :math:`{N}(0, 0.01)`
|
||||
|
||||
Args:
|
||||
sparsity (float): The fraction of elements being set to zero in each column.
|
||||
sigma (float): The standard deviation of the normal distribution. Default: 0.01.
|
||||
|
||||
Raises:
|
||||
ValueError: If the dimension of input tensor is not equal to 2.
|
||||
|
||||
Examples:
|
||||
>>> import mindspore
|
||||
>>> from mindspore.common.initializer import initializer, Sparse
|
||||
>>> tensor1 = initializer(Sparse(sparsity=0.1, sigma=0.01), [5, 8], mindspore.float32)
|
||||
"""
|
||||
def __init__(self, sparsity, sigma=0.01):
|
||||
super(Sparse, self).__init__()
|
||||
self.sparsity = sparsity
|
||||
self.sigma = sigma
|
||||
|
||||
def _initialize(self, arr):
|
||||
if len(arr.shape) != 2:
|
||||
raise ValueError('For Sparse initializer, the dimension of the initialized tensor should be 2, '
|
||||
'but got {}.'.format(len(arr.shape)))
|
||||
rows, cols = arr.shape
|
||||
zero_num = int(np.ceil(self.sparsity * rows))
|
||||
data = np.random.normal(0, self.sigma, arr.shape)
|
||||
for col_idx in range(cols):
|
||||
row_idx = np.random.permutation(list(range(rows)))[: zero_num]
|
||||
data[row_idx, col_idx] = 0.
|
||||
_assignment(arr, data)
|
||||
|
||||
|
||||
class Dirac(Initializer):
|
||||
"""Initialize input tensor with the Dirac delta function. It tries to preserves the identity of
|
||||
input for convolution layers. For group convolution, each group of channels will be preserved respectively.
|
||||
|
||||
Args:
|
||||
groups (int): The number of group in convolution layer. Default: 1.
|
||||
|
||||
Raises:
|
||||
ValueError: If the value of group is not in [3, 4, 5] or the first dimension of the initialized
|
||||
tensor cannot be divisible by group.
|
||||
|
||||
Examples:
|
||||
>>> import mindspore
|
||||
>>> from mindspore.common.initializer import initializer, Dirac
|
||||
>>> tensor1 = initializer(Dirac(groups=2), [6, 4, 3, 3], mindspore.float32)
|
||||
>>> tensor2 = initializer("dirac", [6, 4, 3, 3], mindspore.float32)
|
||||
"""
|
||||
|
||||
def __init__(self, groups=1):
|
||||
super(Dirac, self).__init__()
|
||||
self.groups = groups
|
||||
|
||||
def _initialize(self, arr):
|
||||
dimension = len(arr.shape)
|
||||
data = np.zeros(arr.shape)
|
||||
if dimension not in [3, 4, 5]:
|
||||
raise ValueError("For Dirac initializer, only support "
|
||||
"to initialize tensor with dimension of 3, 4 or 5, but got {}.".format(dimension))
|
||||
|
||||
shapes = arr.shape
|
||||
if shapes[0] % self.groups != 0:
|
||||
raise ValueError("For Dirac initializer, the first dimension of"
|
||||
"the initialized tensor must be divisible by group, "
|
||||
"but got {}/{}.".format(shapes[0], self.groups))
|
||||
|
||||
out_channel_per_group = shapes[0] // self.groups
|
||||
min_dim = min(out_channel_per_group, shapes[1])
|
||||
|
||||
for group in range(self.groups):
|
||||
for dim in range(min_dim):
|
||||
if dimension == 3:
|
||||
data[group * out_channel_per_group + dim, dim, shapes[2]//2] = 1
|
||||
elif dimension == 4:
|
||||
data[group * out_channel_per_group + dim, dim, shapes[2] // 2, shapes[3] // 2] = 1
|
||||
else:
|
||||
data[group * out_channel_per_group + dim, dim, shapes[2] // 2, shapes[3] // 2, shapes[4] // 2] = 1
|
||||
_assignment(arr, data)
|
||||
|
||||
|
||||
@_register()
|
||||
class Orthogonal(Initializer):
|
||||
r"""
|
||||
Initialize a (semi) orthogonal matrix to fill the input tensor. The dimension of input tensor must have at least 2
|
||||
dimensions. If the dimension is greater than 2, the trailing dimensions will be flattened.
|
||||
|
||||
Args:
|
||||
gain (float): An optional scaling factor. Default: 1.
|
||||
|
||||
Raises:
|
||||
ValueError: If the dimension of input tensor is less than 2.
|
||||
|
||||
Examples:
|
||||
>>> import mindspore
|
||||
>>> from mindspore.common.initializer import initializer, Orthogonal
|
||||
>>> tensor1 = initializer(Orthogonal(gain=2.), [2, 3, 4], mindspore.float32)
|
||||
>>> tensor2 = initializer('orthogonal', [2, 3, 4], mindspore.float32)
|
||||
"""
|
||||
def __init__(self, gain=1.):
|
||||
super(Orthogonal, self).__init__(gain=gain)
|
||||
self.gain = gain
|
||||
|
||||
def _initialize(self, arr):
|
||||
if len(arr.shape) < 2:
|
||||
raise ValueError('For Orthogonal initializer, the dimension of the initialized tensor should'
|
||||
' be no less than 2, but got {}.'.format(len(arr.shape)))
|
||||
rows = arr.shape[0]
|
||||
|
||||
cols = np.prod(arr.shape) // rows
|
||||
data = np.random.normal(0, 1, size=(rows, cols))
|
||||
|
||||
if rows < cols:
|
||||
data = data.T
|
||||
|
||||
q, r = np.linalg.qr(data)
|
||||
d = np.diag(r)
|
||||
ph = np.sign(d)
|
||||
q *= ph
|
||||
|
||||
if rows < cols:
|
||||
q = q.T
|
||||
q = q * self.gain
|
||||
_assignment(arr, q.reshape(arr.shape))
|
||||
|
||||
|
||||
@_register()
|
||||
class VarianceScaling(Initializer):
|
||||
r"""
|
||||
Randomly initialize an array with scaling to fill the input tensor.
|
||||
When distribution is truncated_normal or untruncated_normal, the value will be sampled from truncated or
|
||||
untruncated normal distribution with a mean of 0 and a scaled standard deviation :math:`stddev = sqrt(scale/n)`.
|
||||
:math:`n` will be the number of input units if mode is fan_in, the number of output units if mode is fan_out,
|
||||
the average of fan_in and fan_out if mode is fan_avg.
|
||||
When distribution is uniform, the value will be sampled from a uniform distribution within the limit of
|
||||
[`-sqrt(3*scale/n)`, `sqrt(3*scale/n)`].
|
||||
|
||||
Args:
|
||||
scale (float): The scaling factor. Default: 1.0.
|
||||
mode (str): Should be 'fan_in', 'fan_out' or 'fan_avg'. Default: 'fan_in'.
|
||||
distribution(str): The type of distribution chose to sample values. Default: 'truncated_normal'.
|
||||
|
||||
Raises:
|
||||
ValueError: If scale is not greater than 0..
|
||||
ValueError: If mode is not fan_in, fan_out or fan_avg.
|
||||
ValueError: If distribution is not uniform, truncated_normal or untruncated_normal.
|
||||
|
||||
Examples:
|
||||
>>> import mindspore
|
||||
>>> from mindspore.common.initializer import initializer, VarianceScaling
|
||||
>>> tensor1 = initializer(VarianceScaling(scale=1.0, mode='fan_out',
|
||||
>>> distribution='untruncated_normal'), [2, 3], mindspore.float32)
|
||||
>>> tensor2 = initializer('varianceScaling', [2, 3], mindspore.float32)
|
||||
"""
|
||||
def __init__(self, scale=1.0, mode='fan_in', distribution='truncated_normal'):
|
||||
super(VarianceScaling, self).__init__(scale=scale, mode=mode, distribution=distribution)
|
||||
if scale <= 0.:
|
||||
raise ValueError("For VarianceScaling initializer, scale must be greater than 0, but got {}.".format(scale))
|
||||
|
||||
if mode not in ['fan_in', 'fan_out', 'fan_avg']:
|
||||
raise ValueError('For VarianceScaling initializer, mode must be fan_in, '
|
||||
'fan_out or fan_avg, but got {}.'.format(mode))
|
||||
|
||||
if distribution not in ['uniform', 'truncated_normal', 'untruncated_normal']:
|
||||
raise ValueError('For VarianceScaling initializer, distribution must be uniform, '
|
||||
'truncated_norm or untruncated_norm, but got {}.'.format(distribution))
|
||||
|
||||
self.scale = scale
|
||||
self.mode = mode
|
||||
self.distribution = distribution
|
||||
|
||||
def _initialize(self, arr):
|
||||
scale = self.scale
|
||||
fan_in, fan_out = _calculate_fan_in_and_fan_out(arr.shape)
|
||||
if self.mode == 'fan_in':
|
||||
scale /= max(1., fan_in)
|
||||
elif self.mode == 'fan_out':
|
||||
scale /= max(1., fan_out)
|
||||
else:
|
||||
scale /= max(1., (fan_in + fan_out) / 2.)
|
||||
|
||||
if self.distribution == 'truncated_norm':
|
||||
stddev = np.sqrt(scale) / 0.87962566103423978
|
||||
data = truncnorm.rvs(-2, 2, loc=0, scale=stddev, size=arr.shape, random_state=None)
|
||||
elif self.distribution == 'untruncated_normal':
|
||||
stddev = np.sqrt(scale)
|
||||
data = np.random.normal(0, stddev, arr.shape)
|
||||
else:
|
||||
limit = np.sqrt(3.0 * scale)
|
||||
data = np.random.uniform(-limit, limit, arr.shape)
|
||||
_assignment(arr, data)
|
||||
|
||||
|
||||
@_register()
|
||||
class Uniform(Initializer):
|
||||
r"""
|
||||
|
|
@ -534,4 +753,9 @@ __all__ = [
|
|||
'XavierUniform',
|
||||
'One',
|
||||
'Zero',
|
||||
'Constant']
|
||||
'Constant',
|
||||
'Identity',
|
||||
'Sparse',
|
||||
'Dirac',
|
||||
'Orthogonal',
|
||||
'VarianceScaling']
|
||||
|
|
|
|||
|
|
@ -0,0 +1,307 @@
|
|||
# Copyright 2021 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 mindspore
|
||||
from mindspore.common.initializer import initializer, Identity, Dirac, Sparse, VarianceScaling, Orthogonal
|
||||
import numpy as np
|
||||
|
||||
|
||||
def test_sparse():
|
||||
"""
|
||||
Feature: Test sparse initializer.
|
||||
Description: Initialize a 2 dimension sparse matrix to fill the input tensor.
|
||||
Expectation: The Tensor is initialized with a 2 dimension sparse matrix.
|
||||
"""
|
||||
initializer(Sparse(sparsity=0.1, sigma=0.01), [5, 8], mindspore.float32)
|
||||
|
||||
|
||||
def test_orthogonal():
|
||||
"""
|
||||
Feature: Test orthogonal initializer.
|
||||
Description: Initialize a (semi) orthogonal matrix to fill the input tensor.
|
||||
Expectation: The Tensor is initialized with values from orthogonal matrix.
|
||||
"""
|
||||
initializer(Orthogonal(gain=2.), [2, 3, 4], mindspore.float32)
|
||||
initializer('orthogonal', [2, 3, 4], mindspore.float32)
|
||||
|
||||
|
||||
def test_variancescaling():
|
||||
"""
|
||||
Feature: Test varianceScaling initializer.
|
||||
Description: Randomly initialize an array with scaling to fill the input tensor.
|
||||
Expectation: The Tensor is initialized successfully.
|
||||
"""
|
||||
initializer('varianceScaling', [2, 3], mindspore.float32)
|
||||
initializer(VarianceScaling(scale=1.0, mode='fan_out', distribution='untruncated_normal'), [2, 3],
|
||||
mindspore.float32)
|
||||
initializer(VarianceScaling(scale=2.0, mode='fan_in', distribution='truncated_normal'), [2, 3],
|
||||
mindspore.float32)
|
||||
initializer(VarianceScaling(scale=3.0, mode='fan_avg', distribution='uniform'), [2, 3],
|
||||
mindspore.float32)
|
||||
|
||||
|
||||
def test_identity():
|
||||
"""
|
||||
Feature: Test identity initializer.
|
||||
Description: Initialize an identity matrix to fill a Tensor.
|
||||
Expectation: The Tensor is initialized with identity matrix.
|
||||
"""
|
||||
tensor1 = initializer(Identity(), [3, 3], mindspore.float32)
|
||||
tensor2 = initializer('identity', [3, 4], mindspore.float32)
|
||||
tensor3 = initializer('identity', [4, 3], mindspore.float32)
|
||||
expect1 = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]], dtype=np.float32)
|
||||
expect2 = np.array([[1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 1, 0]], dtype=np.float32)
|
||||
expect3 = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1], [0, 0, 0]], dtype=np.float32)
|
||||
assert (tensor1.asnumpy() == expect1).all()
|
||||
assert (tensor2.asnumpy() == expect2).all()
|
||||
assert (tensor3.asnumpy() == expect3).all()
|
||||
|
||||
|
||||
def test_dirac():
|
||||
"""
|
||||
Feature: Test dirac initializer.
|
||||
Description: Initialize input tensor with the Dirac delta function.
|
||||
Expectation: The Tensor is correctly initialized.
|
||||
"""
|
||||
tensor3_1 = initializer(Dirac(groups=1), [6, 2, 3], mindspore.float32)
|
||||
tensor3_2 = initializer(Dirac(groups=2), [6, 2, 3], mindspore.float32)
|
||||
tensor3_3 = initializer(Dirac(groups=3), [6, 2, 3], mindspore.float32)
|
||||
|
||||
tensor4_1 = initializer(Dirac(groups=1), [6, 4, 3, 3], mindspore.float32)
|
||||
tensor4_2 = initializer(Dirac(groups=2), [6, 4, 3, 3], mindspore.float32)
|
||||
tensor4_3 = initializer(Dirac(groups=3), [6, 4, 3, 3], mindspore.float32)
|
||||
|
||||
tensor5_1 = initializer(Dirac(groups=1), [6, 2, 3, 3, 3], mindspore.float32)
|
||||
tensor5_2 = initializer(Dirac(groups=2), [6, 2, 3, 3, 3], mindspore.float32)
|
||||
tensor5_3 = initializer(Dirac(groups=3), [6, 2, 3, 3, 3], mindspore.float32)
|
||||
|
||||
expectation3_1 = np.array([[[0., 1., 0.], [0., 0., 0.]],
|
||||
[[0., 0., 0.], [0., 1., 0.]],
|
||||
[[0., 0., 0.], [0., 0., 0.]],
|
||||
[[0., 0., 0.], [0., 0., 0.]],
|
||||
[[0., 0., 0.], [0., 0., 0.]],
|
||||
[[0., 0., 0.], [0., 0., 0.]]], dtype=np.float32)
|
||||
|
||||
expectation3_2 = np.array([[[0., 1., 0.], [0., 0., 0.]],
|
||||
[[0., 0., 0.], [0., 1., 0.]],
|
||||
[[0., 0., 0.], [0., 0., 0.]],
|
||||
[[0., 1., 0.], [0., 0., 0.]],
|
||||
[[0., 0., 0.], [0., 1., 0.]],
|
||||
[[0., 0., 0.], [0., 0., 0.]]], dtype=np.float32)
|
||||
|
||||
expectation3_3 = np.array([[[0., 1., 0.], [0., 0., 0.]],
|
||||
[[0., 0., 0.], [0., 1., 0.]],
|
||||
[[0., 1., 0.], [0., 0., 0.]],
|
||||
[[0., 0., 0.], [0., 1., 0.]],
|
||||
[[0., 1., 0.], [0., 0., 0.]],
|
||||
[[0., 0., 0.], [0., 1., 0.]]], dtype=np.float32)
|
||||
|
||||
expectation4_1 = np.array([[[[0., 0., 0.], [0., 1., 0.], [0., 0., 0.]],
|
||||
[[0., 0., 0.], [0., 0., 0.], [0., 0., 0.]],
|
||||
[[0., 0., 0.], [0., 0., 0.], [0., 0., 0.]],
|
||||
[[0., 0., 0.], [0., 0., 0.], [0., 0., 0.]]],
|
||||
[[[0., 0., 0.], [0., 0., 0.], [0., 0., 0.]],
|
||||
[[0., 0., 0.], [0., 1., 0.], [0., 0., 0.]],
|
||||
[[0., 0., 0.], [0., 0., 0.], [0., 0., 0.]],
|
||||
[[0., 0., 0.], [0., 0., 0.], [0., 0., 0.]]],
|
||||
[[[0., 0., 0.], [0., 0., 0.], [0., 0., 0.]],
|
||||
[[0., 0., 0.], [0., 0., 0.], [0., 0., 0.]],
|
||||
[[0., 0., 0.], [0., 1., 0.], [0., 0., 0.]],
|
||||
[[0., 0., 0.], [0., 0., 0.], [0., 0., 0.]]],
|
||||
[[[0., 0., 0.], [0., 0., 0.], [0., 0., 0.]],
|
||||
[[0., 0., 0.], [0., 0., 0.], [0., 0., 0.]],
|
||||
[[0., 0., 0.], [0., 0., 0.], [0., 0., 0.]],
|
||||
[[0., 0., 0.], [0., 1., 0.], [0., 0., 0.]]],
|
||||
[[[0., 0., 0.], [0., 0., 0.], [0., 0., 0.]],
|
||||
[[0., 0., 0.], [0., 0., 0.], [0., 0., 0.]],
|
||||
[[0., 0., 0.], [0., 0., 0.], [0., 0., 0.]],
|
||||
[[0., 0., 0.], [0., 0., 0.], [0., 0., 0.]]],
|
||||
[[[0., 0., 0.], [0., 0., 0.], [0., 0., 0.]],
|
||||
[[0., 0., 0.], [0., 0., 0.], [0., 0., 0.]],
|
||||
[[0., 0., 0.], [0., 0., 0.], [0., 0., 0.]],
|
||||
[[0., 0., 0.], [0., 0., 0.], [0., 0., 0.]]]], dtype=np.float32)
|
||||
|
||||
expectation4_2 = np.array([[[[0., 0., 0.], [0., 1., 0.], [0., 0., 0.]],
|
||||
[[0., 0., 0.], [0., 0., 0.], [0., 0., 0.]],
|
||||
[[0., 0., 0.], [0., 0., 0.], [0., 0., 0.]],
|
||||
[[0., 0., 0.], [0., 0., 0.], [0., 0., 0.]]],
|
||||
[[[0., 0., 0.], [0., 0., 0.], [0., 0., 0.]],
|
||||
[[0., 0., 0.], [0., 1., 0.], [0., 0., 0.]],
|
||||
[[0., 0., 0.], [0., 0., 0.], [0., 0., 0.]],
|
||||
[[0., 0., 0.], [0., 0., 0.], [0., 0., 0.]]],
|
||||
[[[0., 0., 0.], [0., 0., 0.], [0., 0., 0.]],
|
||||
[[0., 0., 0.], [0., 0., 0.], [0., 0., 0.]],
|
||||
[[0., 0., 0.], [0., 1., 0.], [0., 0., 0.]],
|
||||
[[0., 0., 0.], [0., 0., 0.], [0., 0., 0.]]],
|
||||
[[[0., 0., 0.], [0., 1., 0.], [0., 0., 0.]],
|
||||
[[0., 0., 0.], [0., 0., 0.], [0., 0., 0.]],
|
||||
[[0., 0., 0.], [0., 0., 0.], [0., 0., 0.]],
|
||||
[[0., 0., 0.], [0., 0., 0.], [0., 0., 0.]]],
|
||||
[[[0., 0., 0.], [0., 0., 0.], [0., 0., 0.]],
|
||||
[[0., 0., 0.], [0., 1., 0.], [0., 0., 0.]],
|
||||
[[0., 0., 0.], [0., 0., 0.], [0., 0., 0.]],
|
||||
[[0., 0., 0.], [0., 0., 0.], [0., 0., 0.]]],
|
||||
[[[0., 0., 0.], [0., 0., 0.], [0., 0., 0.]],
|
||||
[[0., 0., 0.], [0., 0., 0.], [0., 0., 0.]],
|
||||
[[0., 0., 0.], [0., 1., 0.], [0., 0., 0.]],
|
||||
[[0., 0., 0.], [0., 0., 0.], [0., 0., 0.]]]], dtype=np.float32)
|
||||
|
||||
expectation4_3 = np.array([[[[0., 0., 0.], [0., 1., 0.], [0., 0., 0.]],
|
||||
[[0., 0., 0.], [0., 0., 0.], [0., 0., 0.]],
|
||||
[[0., 0., 0.], [0., 0., 0.], [0., 0., 0.]],
|
||||
[[0., 0., 0.], [0., 0., 0.], [0., 0., 0.]]],
|
||||
[[[0., 0., 0.], [0., 0., 0.], [0., 0., 0.]],
|
||||
[[0., 0., 0.], [0., 1., 0.], [0., 0., 0.]],
|
||||
[[0., 0., 0.], [0., 0., 0.], [0., 0., 0.]],
|
||||
[[0., 0., 0.], [0., 0., 0.], [0., 0., 0.]]],
|
||||
[[[0., 0., 0.], [0., 1., 0.], [0., 0., 0.]],
|
||||
[[0., 0., 0.], [0., 0., 0.], [0., 0., 0.]],
|
||||
[[0., 0., 0.], [0., 0., 0.], [0., 0., 0.]],
|
||||
[[0., 0., 0.], [0., 0., 0.], [0., 0., 0.]]],
|
||||
[[[0., 0., 0.], [0., 0., 0.], [0., 0., 0.]],
|
||||
[[0., 0., 0.], [0., 1., 0.], [0., 0., 0.]],
|
||||
[[0., 0., 0.], [0., 0., 0.], [0., 0., 0.]],
|
||||
[[0., 0., 0.], [0., 0., 0.], [0., 0., 0.]]],
|
||||
[[[0., 0., 0.], [0., 1., 0.], [0., 0., 0.]],
|
||||
[[0., 0., 0.], [0., 0., 0.], [0., 0., 0.]],
|
||||
[[0., 0., 0.], [0., 0., 0.], [0., 0., 0.]],
|
||||
[[0., 0., 0.], [0., 0., 0.], [0., 0., 0.]]],
|
||||
[[[0., 0., 0.], [0., 0., 0.], [0., 0., 0.]],
|
||||
[[0., 0., 0.], [0., 1., 0.], [0., 0., 0.]],
|
||||
[[0., 0., 0.], [0., 0., 0.], [0., 0., 0.]],
|
||||
[[0., 0., 0.], [0., 0., 0.], [0., 0., 0.]]]], dtype=np.float32)
|
||||
|
||||
expectation5_1 = np.array([[[[[0., 0., 0.], [0., 0., 0.], [0., 0., 0.]],
|
||||
[[0., 0., 0.], [0., 1., 0.], [0., 0., 0.]],
|
||||
[[0., 0., 0.], [0., 0., 0.], [0., 0., 0.]]],
|
||||
[[[0., 0., 0.], [0., 0., 0.], [0., 0., 0.]],
|
||||
[[0., 0., 0.], [0., 0., 0.], [0., 0., 0.]],
|
||||
[[0., 0., 0.], [0., 0., 0.], [0., 0., 0.]]]],
|
||||
[[[[0., 0., 0.], [0., 0., 0.], [0., 0., 0.]],
|
||||
[[0., 0., 0.], [0., 0., 0.], [0., 0., 0.]],
|
||||
[[0., 0., 0.], [0., 0., 0.], [0., 0., 0.]]],
|
||||
[[[0., 0., 0.], [0., 0., 0.], [0., 0., 0.]],
|
||||
[[0., 0., 0.], [0., 1., 0.], [0., 0., 0.]],
|
||||
[[0., 0., 0.], [0., 0., 0.], [0., 0., 0.]]]],
|
||||
[[[[0., 0., 0.], [0., 0., 0.], [0., 0., 0.]],
|
||||
[[0., 0., 0.], [0., 0., 0.], [0., 0., 0.]],
|
||||
[[0., 0., 0.], [0., 0., 0.], [0., 0., 0.]]],
|
||||
[[[0., 0., 0.], [0., 0., 0.], [0., 0., 0.]],
|
||||
[[0., 0., 0.], [0., 0., 0.], [0., 0., 0.]],
|
||||
[[0., 0., 0.], [0., 0., 0.], [0., 0., 0.]]]],
|
||||
[[[[0., 0., 0.], [0., 0., 0.], [0., 0., 0.]],
|
||||
[[0., 0., 0.], [0., 0., 0.], [0., 0., 0.]],
|
||||
[[0., 0., 0.], [0., 0., 0.], [0., 0., 0.]]],
|
||||
[[[0., 0., 0.], [0., 0., 0.], [0., 0., 0.]],
|
||||
[[0., 0., 0.], [0., 0., 0.], [0., 0., 0.]],
|
||||
[[0., 0., 0.], [0., 0., 0.], [0., 0., 0.]]]],
|
||||
[[[[0., 0., 0.], [0., 0., 0.], [0., 0., 0.]],
|
||||
[[0., 0., 0.], [0., 0., 0.], [0., 0., 0.]],
|
||||
[[0., 0., 0.], [0., 0., 0.], [0., 0., 0.]]],
|
||||
[[[0., 0., 0.], [0., 0., 0.], [0., 0., 0.]],
|
||||
[[0., 0., 0.], [0., 0., 0.], [0., 0., 0.]],
|
||||
[[0., 0., 0.], [0., 0., 0.], [0., 0., 0.]]]],
|
||||
[[[[0., 0., 0.], [0., 0., 0.], [0., 0., 0.]],
|
||||
[[0., 0., 0.], [0., 0., 0.], [0., 0., 0.]],
|
||||
[[0., 0., 0.], [0., 0., 0.], [0., 0., 0.]]],
|
||||
[[[0., 0., 0.], [0., 0., 0.], [0., 0., 0.]],
|
||||
[[0., 0., 0.], [0., 0., 0.], [0., 0., 0.]],
|
||||
[[0., 0., 0.], [0., 0., 0.], [0., 0., 0.]]]]], dtype=np.float32)
|
||||
|
||||
expectation5_2 = np.array([[[[[0., 0., 0.], [0., 0., 0.], [0., 0., 0.]],
|
||||
[[0., 0., 0.], [0., 1., 0.], [0., 0., 0.]],
|
||||
[[0., 0., 0.], [0., 0., 0.], [0., 0., 0.]]],
|
||||
[[[0., 0., 0.], [0., 0., 0.], [0., 0., 0.]],
|
||||
[[0., 0., 0.], [0., 0., 0.], [0., 0., 0.]],
|
||||
[[0., 0., 0.], [0., 0., 0.], [0., 0., 0.]]]],
|
||||
[[[[0., 0., 0.], [0., 0., 0.], [0., 0., 0.]],
|
||||
[[0., 0., 0.], [0., 0., 0.], [0., 0., 0.]],
|
||||
[[0., 0., 0.], [0., 0., 0.], [0., 0., 0.]]],
|
||||
[[[0., 0., 0.], [0., 0., 0.], [0., 0., 0.]],
|
||||
[[0., 0., 0.], [0., 1., 0.], [0., 0., 0.]],
|
||||
[[0., 0., 0.], [0., 0., 0.], [0., 0., 0.]]]],
|
||||
[[[[0., 0., 0.], [0., 0., 0.], [0., 0., 0.]],
|
||||
[[0., 0., 0.], [0., 0., 0.], [0., 0., 0.]],
|
||||
[[0., 0., 0.], [0., 0., 0.], [0., 0., 0.]]],
|
||||
[[[0., 0., 0.], [0., 0., 0.], [0., 0., 0.]],
|
||||
[[0., 0., 0.], [0., 0., 0.], [0., 0., 0.]],
|
||||
[[0., 0., 0.], [0., 0., 0.], [0., 0., 0.]]]],
|
||||
[[[[0., 0., 0.], [0., 0., 0.], [0., 0., 0.]],
|
||||
[[0., 0., 0.], [0., 1., 0.], [0., 0., 0.]],
|
||||
[[0., 0., 0.], [0., 0., 0.], [0., 0., 0.]]],
|
||||
[[[0., 0., 0.], [0., 0., 0.], [0., 0., 0.]],
|
||||
[[0., 0., 0.], [0., 0., 0.], [0., 0., 0.]],
|
||||
[[0., 0., 0.], [0., 0., 0.], [0., 0., 0.]]]],
|
||||
[[[[0., 0., 0.], [0., 0., 0.], [0., 0., 0.]],
|
||||
[[0., 0., 0.], [0., 0., 0.], [0., 0., 0.]],
|
||||
[[0., 0., 0.], [0., 0., 0.], [0., 0., 0.]]],
|
||||
[[[0., 0., 0.], [0., 0., 0.], [0., 0., 0.]],
|
||||
[[0., 0., 0.], [0., 1., 0.], [0., 0., 0.]],
|
||||
[[0., 0., 0.], [0., 0., 0.], [0., 0., 0.]]]],
|
||||
[[[[0., 0., 0.], [0., 0., 0.], [0., 0., 0.]],
|
||||
[[0., 0., 0.], [0., 0., 0.], [0., 0., 0.]],
|
||||
[[0., 0., 0.], [0., 0., 0.], [0., 0., 0.]]],
|
||||
[[[0., 0., 0.], [0., 0., 0.], [0., 0., 0.]],
|
||||
[[0., 0., 0.], [0., 0., 0.], [0., 0., 0.]],
|
||||
[[0., 0., 0.], [0., 0., 0.], [0., 0., 0.]]]]], dtype=np.float32)
|
||||
|
||||
expectation5_3 = np.array([[[[[0., 0., 0.], [0., 0., 0.], [0., 0., 0.]],
|
||||
[[0., 0., 0.], [0., 1., 0.], [0., 0., 0.]],
|
||||
[[0., 0., 0.], [0., 0., 0.], [0., 0., 0.]]],
|
||||
[[[0., 0., 0.], [0., 0., 0.], [0., 0., 0.]],
|
||||
[[0., 0., 0.], [0., 0., 0.], [0., 0., 0.]],
|
||||
[[0., 0., 0.], [0., 0., 0.], [0., 0., 0.]]]],
|
||||
[[[[0., 0., 0.], [0., 0., 0.], [0., 0., 0.]],
|
||||
[[0., 0., 0.], [0., 0., 0.], [0., 0., 0.]],
|
||||
[[0., 0., 0.], [0., 0., 0.], [0., 0., 0.]]],
|
||||
[[[0., 0., 0.], [0., 0., 0.], [0., 0., 0.]],
|
||||
[[0., 0., 0.], [0., 1., 0.], [0., 0., 0.]],
|
||||
[[0., 0., 0.], [0., 0., 0.], [0., 0., 0.]]]],
|
||||
[[[[0., 0., 0.], [0., 0., 0.], [0., 0., 0.]],
|
||||
[[0., 0., 0.], [0., 1., 0.], [0., 0., 0.]],
|
||||
[[0., 0., 0.], [0., 0., 0.], [0., 0., 0.]]],
|
||||
[[[0., 0., 0.], [0., 0., 0.], [0., 0., 0.]],
|
||||
[[0., 0., 0.], [0., 0., 0.], [0., 0., 0.]],
|
||||
[[0., 0., 0.], [0., 0., 0.], [0., 0., 0.]]]],
|
||||
[[[[0., 0., 0.], [0., 0., 0.], [0., 0., 0.]],
|
||||
[[0., 0., 0.], [0., 0., 0.], [0., 0., 0.]],
|
||||
[[0., 0., 0.], [0., 0., 0.], [0., 0., 0.]]],
|
||||
[[[0., 0., 0.], [0., 0., 0.], [0., 0., 0.]],
|
||||
[[0., 0., 0.], [0., 1., 0.], [0., 0., 0.]],
|
||||
[[0., 0., 0.], [0., 0., 0.], [0., 0., 0.]]]],
|
||||
[[[[0., 0., 0.], [0., 0., 0.], [0., 0., 0.]],
|
||||
[[0., 0., 0.], [0., 1., 0.], [0., 0., 0.]],
|
||||
[[0., 0., 0.], [0., 0., 0.], [0., 0., 0.]]],
|
||||
[[[0., 0., 0.], [0., 0., 0.], [0., 0., 0.]],
|
||||
[[0., 0., 0.], [0., 0., 0.], [0., 0., 0.]],
|
||||
[[0., 0., 0.], [0., 0., 0.], [0., 0., 0.]]]],
|
||||
[[[[0., 0., 0.], [0., 0., 0.], [0., 0., 0.]],
|
||||
[[0., 0., 0.], [0., 0., 0.], [0., 0., 0.]],
|
||||
[[0., 0., 0.], [0., 0., 0.], [0., 0., 0.]]],
|
||||
[[[0., 0., 0.], [0., 0., 0.], [0., 0., 0.]],
|
||||
[[0., 0., 0.], [0., 1., 0.], [0., 0., 0.]],
|
||||
[[0., 0., 0.], [0., 0., 0.], [0., 0., 0.]]]]], dtype=np.float32)
|
||||
|
||||
assert (tensor3_1.asnumpy() == expectation3_1).all()
|
||||
assert (tensor3_2.asnumpy() == expectation3_2).all()
|
||||
assert (tensor3_3.asnumpy() == expectation3_3).all()
|
||||
|
||||
assert (tensor4_1.asnumpy() == expectation4_1).all()
|
||||
assert (tensor4_2.asnumpy() == expectation4_2).all()
|
||||
assert (tensor4_3.asnumpy() == expectation4_3).all()
|
||||
|
||||
assert (tensor5_1.asnumpy() == expectation5_1).all()
|
||||
assert (tensor5_2.asnumpy() == expectation5_2).all()
|
||||
assert (tensor5_3.asnumpy() == expectation5_3).all()
|
||||
Loading…
Reference in New Issue