diff --git a/mindspore/common/initializer.py b/mindspore/common/initializer.py
index 31ca355cb6d..bcf37a8d3d6 100644
--- a/mindspore/common/initializer.py
+++ b/mindspore/common/initializer.py
@@ -387,6 +387,225 @@ 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"""
@@ -546,4 +765,9 @@ __all__ = [
'XavierUniform',
'One',
'Zero',
- 'Constant']
+ 'Constant',
+ 'Identity',
+ 'Sparse',
+ 'Dirac',
+ 'Orthogonal',
+ 'VarianceScaling']
diff --git a/tests/st/initializer/test_initializer.py b/tests/st/initializer/test_initializer.py
new file mode 100644
index 00000000000..0c20936df7e
--- /dev/null
+++ b/tests/st/initializer/test_initializer.py
@@ -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()