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- # 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.
- # ============================================================================
- """Initializer."""
- import numpy as np
-
- from mindspore import Tensor
-
-
- def _compute_fans(shape):
- """
- Computes the number of input and output units for a weight shape.
-
- Args:
- shape (tuple): Integer shape tuple or MS tensor shape.
-
- Returns:
- tuple, integer scalars (fan_in, fan_out).
- """
- if not shape:
- fan_in = fan_out = 1
- elif len(shape) == 1:
- fan_in = fan_out = shape[0]
- elif len(shape) == 2:
- fan_in = shape[0]
- fan_out = shape[1]
- else:
- # Assuming convolution kernels (2D, 3D, or more).
- # kernel shape: (..., input_depth, depth)
- receptive_field_size = 1
- for dim in shape[:-2]:
- receptive_field_size *= dim
- fan_in = shape[-2] * receptive_field_size
- fan_out = shape[-1] * receptive_field_size
- return int(fan_in), int(fan_out)
-
-
- def weight_variable(shape):
- """
- Generate weight var.
-
- Args:
- shape (tuple): Shape.
-
- Returns:
- Tensor, var.
- """
- # scale_shape = shape
- # fan_in, fan_out = _compute_fans(scale_shape)
- # scale = 1.0 / max(1., (fan_in + fan_out) / 2.)
- # limit = math.sqrt(3.0 * scale)
- limit = 0.1
- values = np.random.uniform(-limit, limit, shape)
- return values
-
-
- def one_weight(shape):
- """
- Generate weight with ones.
-
- Args:
- shape (tuple): Shape.
-
- Returns:
- Tensor, var.
- """
- ones = np.ones(shape).astype(np.float32)
- return Tensor(ones)
-
-
- def zero_weight(shape):
- """
- Generate weight with zeros.
-
- Args:
- shape (tuple): Shape.
-
- Returns:
- Tensor, var.
- """
- zeros = np.zeros(shape).astype(np.float32)
- return Tensor(zeros)
-
-
- def normal_weight(shape, num_units):
- """
- Generate weight with normal dist.
-
- Args:
- shape (tuple): Shape.
- num_units (int): Dimension.
-
- Returns:
- Tensor, var.
- """
- norm = np.random.normal(0.0, num_units ** -0.5, shape).astype(np.float32)
- return Tensor(norm)
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