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140 lines
5.1 KiB
140 lines
5.1 KiB
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import paddle
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from paddle import nn
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from paddlespeech.s2t.modules.initializer import KaimingUniform
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"""
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To align the initializer between paddle and torch,
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the API below are set defalut initializer with priority higger than global initializer.
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"""
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global_init_type = None
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class LayerNorm(nn.LayerNorm):
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def __init__(self,
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normalized_shape,
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epsilon=1e-05,
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weight_attr=None,
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bias_attr=None,
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name=None):
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if weight_attr is None:
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weight_attr = paddle.ParamAttr(
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initializer=nn.initializer.Constant(1.0))
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if bias_attr is None:
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bias_attr = paddle.ParamAttr(
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initializer=nn.initializer.Constant(0.0))
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super(LayerNorm, self).__init__(normalized_shape, epsilon, weight_attr,
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bias_attr, name)
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class BatchNorm1D(nn.BatchNorm1D):
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def __init__(self,
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num_features,
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momentum=0.9,
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epsilon=1e-05,
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weight_attr=None,
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bias_attr=None,
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data_format='NCL',
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name=None):
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if weight_attr is None:
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weight_attr = paddle.ParamAttr(
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initializer=nn.initializer.Constant(1.0))
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if bias_attr is None:
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bias_attr = paddle.ParamAttr(
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initializer=nn.initializer.Constant(0.0))
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super(BatchNorm1D,
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self).__init__(num_features, momentum, epsilon, weight_attr,
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bias_attr, data_format, name)
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class Embedding(nn.Embedding):
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def __init__(self,
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num_embeddings,
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embedding_dim,
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padding_idx=None,
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sparse=False,
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weight_attr=None,
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name=None):
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if weight_attr is None:
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weight_attr = paddle.ParamAttr(initializer=nn.initializer.Normal())
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super(Embedding, self).__init__(num_embeddings, embedding_dim,
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padding_idx, sparse, weight_attr, name)
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class Linear(nn.Linear):
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def __init__(self,
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in_features,
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out_features,
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weight_attr=None,
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bias_attr=None,
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name=None):
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if weight_attr is None:
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if global_init_type == "kaiming_uniform":
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weight_attr = paddle.ParamAttr(initializer=KaimingUniform())
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if bias_attr is None:
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if global_init_type == "kaiming_uniform":
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bias_attr = paddle.ParamAttr(initializer=KaimingUniform())
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super(Linear, self).__init__(in_features, out_features, weight_attr,
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bias_attr, name)
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class Conv1D(nn.Conv1D):
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def __init__(self,
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in_channels,
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out_channels,
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kernel_size,
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stride=1,
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padding=0,
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dilation=1,
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groups=1,
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padding_mode='zeros',
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weight_attr=None,
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bias_attr=None,
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data_format='NCL'):
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if weight_attr is None:
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if global_init_type == "kaiming_uniform":
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print("set kaiming_uniform")
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weight_attr = paddle.ParamAttr(initializer=KaimingUniform())
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if bias_attr is None:
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if global_init_type == "kaiming_uniform":
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bias_attr = paddle.ParamAttr(initializer=KaimingUniform())
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super(Conv1D, self).__init__(
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in_channels, out_channels, kernel_size, stride, padding, dilation,
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groups, padding_mode, weight_attr, bias_attr, data_format)
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class Conv2D(nn.Conv2D):
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def __init__(self,
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in_channels,
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out_channels,
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kernel_size,
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stride=1,
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padding=0,
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dilation=1,
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groups=1,
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padding_mode='zeros',
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weight_attr=None,
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bias_attr=None,
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data_format='NCHW'):
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if weight_attr is None:
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if global_init_type == "kaiming_uniform":
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weight_attr = paddle.ParamAttr(initializer=KaimingUniform())
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if bias_attr is None:
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if global_init_type == "kaiming_uniform":
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bias_attr = paddle.ParamAttr(initializer=KaimingUniform())
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super(Conv2D, self).__init__(
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in_channels, out_channels, kernel_size, stride, padding, dilation,
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groups, padding_mode, weight_attr, bias_attr, data_format)
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