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https://github.com/PaddlePaddle/PaddleOCR.git
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96 lines
3.1 KiB
Python
96 lines
3.1 KiB
Python
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#copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
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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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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import paddle
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import paddle.fluid as fluid
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from paddle.fluid.param_attr import ParamAttr
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import math
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def get_para_bias_attr(l2_decay, k, name):
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regularizer = fluid.regularizer.L2Decay(l2_decay)
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stdv = 1.0 / math.sqrt(k * 1.0)
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initializer = fluid.initializer.Uniform(-stdv, stdv)
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para_attr = fluid.ParamAttr(
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regularizer=regularizer, initializer=initializer, name=name + "_w_attr")
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bias_attr = fluid.ParamAttr(
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regularizer=regularizer, initializer=initializer, name=name + "_b_attr")
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return [para_attr, bias_attr]
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def conv_bn_layer(input,
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num_filters,
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filter_size,
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stride=1,
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groups=1,
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act=None,
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name=None):
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conv = fluid.layers.conv2d(
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input=input,
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num_filters=num_filters,
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filter_size=filter_size,
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stride=stride,
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padding=(filter_size - 1) // 2,
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groups=groups,
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act=None,
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param_attr=ParamAttr(name=name + "_weights"),
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bias_attr=False,
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name=name + '.conv2d')
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bn_name = "bn_" + name
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return fluid.layers.batch_norm(
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input=conv,
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act=act,
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name=bn_name + '.output',
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param_attr=ParamAttr(name=bn_name + '_scale'),
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bias_attr=ParamAttr(bn_name + '_offset'),
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moving_mean_name=bn_name + '_mean',
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moving_variance_name=bn_name + '_variance')
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def deconv_bn_layer(input,
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num_filters,
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filter_size=4,
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stride=2,
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act='relu',
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name=None):
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deconv = fluid.layers.conv2d_transpose(
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input=input,
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num_filters=num_filters,
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filter_size=filter_size,
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stride=stride,
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padding=1,
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act=None,
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param_attr=ParamAttr(name=name + "_weights"),
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bias_attr=False,
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name=name + '.deconv2d')
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bn_name = "bn_" + name
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return fluid.layers.batch_norm(
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input=deconv,
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act=act,
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name=bn_name + '.output',
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param_attr=ParamAttr(name=bn_name + '_scale'),
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bias_attr=ParamAttr(bn_name + '_offset'),
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moving_mean_name=bn_name + '_mean',
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moving_variance_name=bn_name + '_variance')
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def create_tmp_var(program, name, dtype, shape, lod_level=0):
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return program.current_block().create_var(
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name=name, dtype=dtype, shape=shape, lod_level=lod_level)
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