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PaddleSpeech
mirror of https://github.com/PaddlePaddle/PaddleSpeech
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fix some format, test=asr

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pull/1577/head
huangyuxin 3 years ago
parent e991d82ae7
commit a4f5a68074
5 changed files with 110 additions and 50 deletions
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5
paddlespeech/s2t/models/u2/u2.py
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@ -36,6 +36,7 @@ from paddlespeech.s2t.modules.ctc import CTCDecoderBase
from paddlespeech.s2t.modules.decoder import TransformerDecoder
from paddlespeech.s2t.modules.encoder import ConformerEncoder
from paddlespeech.s2t.modules.encoder import TransformerEncoder
from paddlespeech.s2t.modules.initializer import DefaultInitializerContext
from paddlespeech.s2t.modules.loss import LabelSmoothingLoss
from paddlespeech.s2t.modules.mask import make_pad_mask
from paddlespeech.s2t.modules.mask import mask_finished_preds
@ -50,7 +51,6 @@ from paddlespeech.s2t.utils.tensor_utils import pad_sequence
from paddlespeech.s2t.utils.tensor_utils import th_accuracy
from paddlespeech.s2t.utils.utility import log_add
from paddlespeech.s2t.utils.utility import UpdateConfig
from paddlespeech.s2t.modules.initializer import DefaultInitializerContext
# from paddlespeech.s2t.modules.initializer import initialize
__all__ = ["U2Model", "U2InferModel"]
@ -786,7 +786,8 @@ class U2Model(U2DecodeModel):
model_conf = configs.get('model_conf', dict())
init_type = model_conf.get("init_type", None)
with DefaultInitializerContext(init_type):
vocab_size, encoder, decoder, ctc = U2Model._init_from_config(configs)
vocab_size, encoder, decoder, ctc = U2Model._init_from_config(
configs)
super().__init__(
vocab_size=vocab_size,

3
paddlespeech/s2t/modules/activation.py
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@ -16,8 +16,9 @@ from collections import OrderedDict
import paddle
from paddle import nn
from paddle.nn import functional as F
from paddlespeech.s2t.modules.align import Linear
from paddlespeech.s2t.modules.align import Conv2D
from paddlespeech.s2t.modules.align import Linear
from paddlespeech.s2t.utils.log import Log
logger = Log(__name__).getlog()

139
paddlespeech/s2t/modules/align.py
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@ -1,7 +1,20 @@
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
#
# 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 paddle
from paddle import nn
from paddlespeech.s2t.modules.initializer import KaimingUniform
from paddlespeech.s2t.modules.initializer import KaimingUniform
"""
To align the initializer between paddle and torch,
the API below are set defalut initializer with priority higger than global initializer.
@ -10,65 +23,117 @@ global_init_type = None
class LayerNorm(nn.LayerNorm):
def __init__(self, normalized_shape, epsilon=1e-05, weight_attr=None, bias_attr=None, name=None):
def __init__(self,
normalized_shape,
epsilon=1e-05,
weight_attr=None,
bias_attr=None,
name=None):
if weight_attr is None:
weight_attr = paddle.ParamAttr(
initializer=nn.initializer.Constant(1.0))
if bias_attr is None:
bias_attr = paddle.ParamAttr(
initializer=nn.initializer.Constant(0.0))
super(LayerNorm, self).__init__(normalized_shape, epsilon, weight_attr, bias_attr, name)
super(LayerNorm, self).__init__(normalized_shape, epsilon, weight_attr,
bias_attr, name)
class BatchNorm1D(nn.BatchNorm1D):
def __init__(self, num_features, momentum=0.9, epsilon=1e-05, weight_attr=None, bias_attr=None, data_format='NCL', name=None):
class BatchNorm1D(nn.BatchNorm1D):
def __init__(self,
num_features,
momentum=0.9,
epsilon=1e-05,
weight_attr=None,
bias_attr=None,
data_format='NCL',
name=None):
if weight_attr is None:
weight_attr = paddle.ParamAttr(
initializer=nn.initializer.Constant(1.0))
if bias_attr is None:
bias_attr = paddle.ParamAttr(
initializer=nn.initializer.Constant(0.0))
super(BatchNorm1D, self).__init__(num_features, momentum, epsilon, weight_attr, bias_attr, data_format, name)
super(BatchNorm1D,
self).__init__(num_features, momentum, epsilon, weight_attr,
bias_attr, data_format, name)
class Embedding(nn.Embedding):
def __init__(self, num_embeddings, embedding_dim, padding_idx=None, sparse=False, weight_attr=None, name=None):
def __init__(self,
num_embeddings,
embedding_dim,
padding_idx=None,
sparse=False,
weight_attr=None,
name=None):
if weight_attr is None:
weight_attr = paddle.ParamAttr(
initializer=nn.initializer.Normal())
super(Embedding, self).__init__(num_embeddings, embedding_dim, padding_idx, sparse, weight_attr, name)
weight_attr = paddle.ParamAttr(initializer=nn.initializer.Normal())
super(Embedding, self).__init__(num_embeddings, embedding_dim,
padding_idx, sparse, weight_attr, name)
class Linear(nn.Linear):
def __init__(self, in_features, out_features, weight_attr=None, bias_attr=None, name=None):
if weight_attr is None:
if global_init_type == "kaiming_uniform":
weight_attr = paddle.ParamAttr(
initializer=KaimingUniform())
if bias_attr is None:
if global_init_type == "kaiming_uniform":
bias_attr = paddle.ParamAttr(
initializer=KaimingUniform())
super(Linear, self).__init__(in_features, out_features, weight_attr, bias_attr, name)
def __init__(self,
in_features,
out_features,
weight_attr=None,
bias_attr=None,
name=None):
if weight_attr is None:
if global_init_type == "kaiming_uniform":
weight_attr = paddle.ParamAttr(initializer=KaimingUniform())
if bias_attr is None:
if global_init_type == "kaiming_uniform":
bias_attr = paddle.ParamAttr(initializer=KaimingUniform())
super(Linear, self).__init__(in_features, out_features, weight_attr,
bias_attr, name)
class Conv1D(nn.Conv1D):
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'):
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'):
if weight_attr is None:
if global_init_type == "kaiming_uniform":
print("set kaiming_uniform")
weight_attr = paddle.ParamAttr(
initializer=KaimingUniform())
weight_attr = paddle.ParamAttr(initializer=KaimingUniform())
if bias_attr is None:
if global_init_type == "kaiming_uniform":
bias_attr = paddle.ParamAttr(
initializer=KaimingUniform())
super(Conv1D, self).__init__(in_channels, out_channels, kernel_size, stride, padding, dilation, groups, padding_mode, weight_attr, bias_attr, data_format)
bias_attr = paddle.ParamAttr(initializer=KaimingUniform())
super(Conv1D, self).__init__(
in_channels, out_channels, kernel_size, stride, padding, dilation,
groups, padding_mode, weight_attr, bias_attr, data_format)
class Conv2D(nn.Conv2D):
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'):
if weight_attr is None:
if global_init_type == "kaiming_uniform":
weight_attr = paddle.ParamAttr(
initializer=KaimingUniform())
if bias_attr is None:
if global_init_type == "kaiming_uniform":
bias_attr = paddle.ParamAttr(
initializer=KaimingUniform())
super(Conv2D, self).__init__(in_channels, out_channels, kernel_size, stride, padding, dilation, groups, padding_mode, weight_attr, bias_attr, data_format)
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'):
if weight_attr is None:
if global_init_type == "kaiming_uniform":
weight_attr = paddle.ParamAttr(initializer=KaimingUniform())
if bias_attr is None:
if global_init_type == "kaiming_uniform":
bias_attr = paddle.ParamAttr(initializer=KaimingUniform())
super(Conv2D, self).__init__(
in_channels, out_channels, kernel_size, stride, padding, dilation,
groups, padding_mode, weight_attr, bias_attr, data_format)

1
paddlespeech/s2t/modules/encoder.py
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@ -24,7 +24,6 @@ from typeguard import check_argument_types
from paddlespeech.s2t.modules.activation import get_activation
from paddlespeech.s2t.modules.align import LayerNorm
from paddlespeech.s2t.modules.align import Linear
from paddlespeech.s2t.modules.attention import MultiHeadedAttention
from paddlespeech.s2t.modules.attention import RelPositionMultiHeadedAttention
from paddlespeech.s2t.modules.conformer_convolution import ConvolutionModule

12
paddlespeech/s2t/modules/initializer.py
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@ -12,15 +12,10 @@
# See the License for the specific language governing permissions and
# limitations under the License.
import numpy as np
from paddle import nn
from paddle.fluid import framework
from paddle.fluid import unique_name
from paddle.fluid.core import VarDesc
from paddle.fluid.framework import default_main_program
from paddle.fluid.framework import in_dygraph_mode
from paddle.fluid.initializer import Initializer
from paddle.fluid.initializer import MSRAInitializer
from typeguard import check_argument_types
__all__ = ['KaimingUniform']
@ -160,16 +155,15 @@ class DefaultInitializerContext(object):
with DefaultInitializerContext("kaiming_uniform"):
code for setup_model
"""
def __init__(self, init_type=None):
self.init_type = init_type
def __enter__(self):
from paddlespeech.s2t.modules import align
align.global_init_type = self.init_type
return self
def __exit__(self, exc_type, exc_val, exc_tb):
from paddlespeech.s2t.modules import align
align.global_init_type = None

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