huangyuxin
3 years ago
parent
ab16d8ce3c
commit
d53e1163a6
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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, normalized_shape, epsilon=1e-05, weight_attr=None, bias_attr=None, 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, bias_attr, name)
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class BatchNorm1D(nn.BatchNorm1D):
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def __init__(self, num_features, momentum=0.9, epsilon=1e-05, weight_attr=None, bias_attr=None, data_format='NCL', 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, self).__init__(num_features, momentum, epsilon, weight_attr, bias_attr, data_format, name)
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class Embedding(nn.Embedding):
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def __init__(self, num_embeddings, embedding_dim, padding_idx=None, sparse=False, weight_attr=None, 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.Normal())
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super(Embedding, self).__init__(num_embeddings, embedding_dim, padding_idx, sparse, weight_attr, name)
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class Linear(nn.Linear):
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def __init__(self, in_features, out_features, weight_attr=None, bias_attr=None, 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(
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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(
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initializer=KaimingUniform())
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super(Linear, self).__init__(in_features, out_features, weight_attr, bias_attr, name)
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class Conv1D(nn.Conv1D):
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def __init__(self, in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1, groups=1, padding_mode='zeros', weight_attr=None, bias_attr=None, 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(
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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(
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initializer=KaimingUniform())
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super(Conv1D, self).__init__(in_channels, out_channels, kernel_size, stride, padding, dilation, groups, padding_mode, weight_attr, bias_attr, data_format)
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class Conv2D(nn.Conv2D):
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def __init__(self, in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1, groups=1, padding_mode='zeros', weight_attr=None, bias_attr=None, 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(
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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(
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initializer=KaimingUniform())
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super(Conv2D, self).__init__(in_channels, out_channels, kernel_size, stride, padding, dilation, groups, padding_mode, weight_attr, bias_attr, data_format)
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@ -1,44 +0,0 @@
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# Copyright (c) 2021 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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# Modified from espnet(https://github.com/espnet/espnet)
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from paddle import nn
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from typeguard import check_argument_types
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def initialize(model: nn.Layer, init: str):
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"""Initialize weights of a neural network module.
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Parameters are initialized using the given method or distribution.
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Custom initialization routines can be implemented into submodules
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Args:
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model (nn.Layer): Target.
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init (str): Method of initialization.
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"""
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assert check_argument_types()
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if init == "xavier_uniform":
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nn.initializer.set_global_initializer(nn.initializer.XavierUniform(),
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nn.initializer.Constant())
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elif init == "xavier_normal":
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nn.initializer.set_global_initializer(nn.initializer.XavierNormal(),
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nn.initializer.Constant())
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elif init == "kaiming_uniform":
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nn.initializer.set_global_initializer(nn.initializer.KaimingUniform(),
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nn.initializer.KaimingUniform())
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elif init == "kaiming_normal":
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nn.initializer.set_global_initializer(nn.initializer.KaimingNormal(),
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nn.initializer.Constant())
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else:
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raise ValueError("Unknown initialization: " + init)
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