wehub-resource-sync
691 lines
23 KiB
Python
691 lines
23 KiB
Python
# Copyright (c) 2018 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 unittest
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import numpy as np
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from op_test import get_device_place, is_custom_device
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from test_imperative_base import new_program_scope
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import paddle
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from paddle import base
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from paddle.autograd.backward_utils import ValueDict
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from paddle.base import core
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from paddle.nn import BatchNorm, Linear
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def create_parameter_mapping(startup_program, main_program):
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startup_params = {}
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main_params = {}
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parameter_mapping = ValueDict()
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for op in startup_program.global_block().ops:
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if op.name() == "builtin.set_parameter":
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name = op.attrs()["parameter_name"]
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param = op.operand(0).source()
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startup_params[name] = param
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for op in main_program.global_block().ops:
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if op.name() == "builtin.parameter":
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name = op.attrs()["parameter_name"]
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param = op.result(0)
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main_params[name] = param
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assert len(startup_params) == len(main_params)
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for name, startup_param in startup_params.items():
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assert name in main_params
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main_param = main_params[name]
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parameter_mapping[main_param] = startup_param
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return parameter_mapping
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class Config:
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'''
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config for training
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'''
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# encoder rnn hidden_size
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encoder_size = 8
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# decoder size for decoder stage
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decoder_size = 8
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# size for word embedding
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word_vector_dim = 8
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# max length for label padding
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max_length = 3
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# optimizer setting
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LR = 1.0
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learning_rate_decay = None
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# batch size to train
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batch_size = 2
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# class number to classify
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num_classes = 64
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use_gpu = False
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# special label for start and end
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SOS = 0
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EOS = 1
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# settings for ctc data, not use in unittest
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DATA_DIR_NAME = "./dataset/ctc_data/data"
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TRAIN_DATA_DIR_NAME = "train_images"
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TRAIN_LIST_FILE_NAME = "train.list"
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# data shape for input image
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DATA_SHAPE = [1, 16, 64]
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class ConvBNPool(paddle.nn.Layer):
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def __init__(
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self,
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group,
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out_ch,
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channels,
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act="relu",
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is_test=False,
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pool=True,
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use_cudnn=True,
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):
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super().__init__()
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self.group = group
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self.pool = pool
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filter_size = 3
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conv_std_0 = (2.0 / (filter_size**2 * channels[0])) ** 0.5
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conv_param_0 = base.ParamAttr(
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initializer=paddle.nn.initializer.Normal(0.0, conv_std_0)
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)
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conv_std_1 = (2.0 / (filter_size**2 * channels[1])) ** 0.5
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conv_param_1 = base.ParamAttr(
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initializer=paddle.nn.initializer.Normal(0.0, conv_std_1)
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)
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self.conv_0_layer = paddle.nn.Conv2D(
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channels[0],
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out_ch[0],
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3,
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padding=1,
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weight_attr=conv_param_0,
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bias_attr=False,
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)
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self.bn_0_layer = BatchNorm(out_ch[0], act=act, is_test=is_test)
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self.conv_1_layer = paddle.nn.Conv2D(
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out_ch[0],
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out_ch[1],
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3,
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padding=1,
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weight_attr=conv_param_1,
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bias_attr=False,
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)
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self.bn_1_layer = BatchNorm(out_ch[1], act=act, is_test=is_test)
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if self.pool:
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self.pool_layer = paddle.nn.MaxPool2D(
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kernel_size=2,
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stride=2,
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ceil_mode=True,
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)
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def forward(self, inputs):
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conv_0 = self.conv_0_layer(inputs)
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bn_0 = self.bn_0_layer(conv_0)
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conv_1 = self.conv_1_layer(bn_0)
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bn_1 = self.bn_1_layer(conv_1)
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if self.pool:
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bn_pool = self.pool_layer(bn_1)
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return bn_pool
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return bn_1
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class OCRConv(paddle.nn.Layer):
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def __init__(self, is_test=False, use_cudnn=True):
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super().__init__()
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self.conv_bn_pool_1 = ConvBNPool(
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2, [8, 8], [1, 8], is_test=is_test, use_cudnn=use_cudnn
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)
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self.conv_bn_pool_2 = ConvBNPool(
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2, [8, 8], [8, 8], is_test=is_test, use_cudnn=use_cudnn
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)
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self.conv_bn_pool_3 = ConvBNPool(
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2, [8, 8], [8, 8], is_test=is_test, use_cudnn=use_cudnn
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)
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self.conv_bn_pool_4 = ConvBNPool(
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2,
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[16, 16],
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[8, 16],
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is_test=is_test,
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pool=False,
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use_cudnn=use_cudnn,
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)
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def forward(self, inputs):
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inputs_1 = self.conv_bn_pool_1(inputs)
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inputs_2 = self.conv_bn_pool_2(inputs_1)
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inputs_3 = self.conv_bn_pool_3(inputs_2)
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inputs_4 = self.conv_bn_pool_4(inputs_3)
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return inputs_4
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class DynamicGRU(paddle.nn.Layer):
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def __init__(
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self,
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size,
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param_attr=None,
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bias_attr=None,
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is_reverse=False,
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gate_activation='sigmoid',
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candidate_activation='tanh',
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h_0=None,
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origin_mode=False,
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):
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super().__init__()
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self.gru_unit = paddle.nn.GRUCell(
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size * 3,
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size,
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)
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self.h_0 = h_0
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self.is_reverse = is_reverse
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self.size = size
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def forward(self, inputs):
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hidden = self.h_0
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res = []
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for i in range(inputs.shape[1]):
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if self.is_reverse:
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i = inputs.shape[1] - 1 - i
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input_ = paddle.slice(inputs, axes=[1], starts=[i], ends=[i + 1])
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input_ = paddle.reshape(input_, [-1, input_.shape[2]])
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hidden, reset = self.gru_unit(input_, hidden)
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hidden_ = paddle.reshape(hidden, [-1, 1, hidden.shape[1]])
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if self.is_reverse:
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res = [hidden_, *res]
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else:
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res.append(hidden_)
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res = paddle.concat(res, axis=1)
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return res
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class EncoderNet(paddle.nn.Layer):
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def __init__(
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self, rnn_hidden_size=Config.encoder_size, is_test=False, use_cudnn=True
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):
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super().__init__()
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self.rnn_hidden_size = rnn_hidden_size
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para_attr = base.ParamAttr(
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initializer=paddle.nn.initializer.Normal(0.0, 0.02)
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)
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bias_attr = base.ParamAttr(
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initializer=paddle.nn.initializer.Normal(0.0, 0.02),
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learning_rate=2.0,
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)
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if base.framework.in_dygraph_mode():
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h_0 = np.zeros(
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(Config.batch_size, rnn_hidden_size), dtype="float32"
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)
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h_0 = paddle.to_tensor(h_0)
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else:
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h_0 = paddle.tensor.fill_constant(
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shape=[Config.batch_size, rnn_hidden_size],
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dtype='float32',
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value=0,
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)
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self.ocr_convs = OCRConv(is_test=is_test, use_cudnn=use_cudnn)
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self.fc_1_layer = Linear(
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32, rnn_hidden_size * 3, weight_attr=para_attr, bias_attr=False
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)
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self.fc_2_layer = Linear(
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32, rnn_hidden_size * 3, weight_attr=para_attr, bias_attr=False
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)
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self.gru_forward_layer = DynamicGRU(
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size=rnn_hidden_size,
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h_0=h_0,
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param_attr=para_attr,
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bias_attr=bias_attr,
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candidate_activation='relu',
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)
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self.gru_backward_layer = DynamicGRU(
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size=rnn_hidden_size,
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h_0=h_0,
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param_attr=para_attr,
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bias_attr=bias_attr,
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candidate_activation='relu',
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is_reverse=True,
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)
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self.encoded_proj_fc = Linear(
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rnn_hidden_size * 2, Config.decoder_size, bias_attr=False
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)
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def forward(self, inputs):
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conv_features = self.ocr_convs(inputs)
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# sliced_feature = base.layers.im2sequence(
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# input=conv_features,
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# stride=[1, 1],
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# filter_size=[conv_features.shape[2], 1])
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transpose_conv_features = paddle.transpose(
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conv_features, perm=[0, 3, 1, 2]
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)
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sliced_feature = paddle.reshape(
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transpose_conv_features,
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[
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-1,
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8,
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transpose_conv_features.shape[2]
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* transpose_conv_features.shape[3],
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],
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)
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fc_1 = self.fc_1_layer(sliced_feature)
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fc_2 = self.fc_2_layer(sliced_feature)
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gru_forward = self.gru_forward_layer(fc_1)
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gru_backward = self.gru_backward_layer(fc_2)
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encoded_vector = paddle.concat([gru_forward, gru_backward], axis=2)
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encoded_proj = self.encoded_proj_fc(encoded_vector)
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return gru_backward, encoded_vector, encoded_proj
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class SimpleAttention(paddle.nn.Layer):
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def __init__(self, decoder_size):
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super().__init__()
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self.fc_1 = Linear(decoder_size, decoder_size, bias_attr=False)
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self.fc_2 = Linear(decoder_size, 1, bias_attr=False)
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def forward(self, encoder_vec, encoder_proj, decoder_state):
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decoder_state_fc = self.fc_1(decoder_state)
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decoder_state_proj_reshape = paddle.reshape(
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decoder_state_fc, [-1, 1, decoder_state_fc.shape[1]]
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)
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decoder_state_expand = paddle.expand(
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decoder_state_proj_reshape,
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[-1, encoder_proj.shape[1], -1],
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)
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concated = paddle.add(encoder_proj, decoder_state_expand)
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concated = paddle.tanh(x=concated)
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attention_weight = self.fc_2(concated)
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weights_reshape = paddle.reshape(
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x=attention_weight,
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shape=[attention_weight.shape[0], attention_weight.shape[1]],
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)
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weights_reshape = paddle.nn.functional.softmax(weights_reshape)
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scaled = paddle.tensor.math._multiply_with_axis(
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x=encoder_vec, y=weights_reshape, axis=0
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)
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context = paddle.sum(scaled, axis=1)
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return context
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class GRUDecoderWithAttention(paddle.nn.Layer):
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def __init__(self, decoder_size, num_classes):
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super().__init__()
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self.simple_attention = SimpleAttention(decoder_size)
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self.fc_1_layer = Linear(
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Config.encoder_size * 2, decoder_size * 3, bias_attr=False
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)
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self.fc_2_layer = Linear(
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decoder_size, decoder_size * 3, bias_attr=False
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)
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self.gru_unit = paddle.nn.GRUCell(decoder_size * 3, decoder_size)
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self.out_layer = Linear(decoder_size, num_classes + 2, bias_attr=None)
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self.decoder_size = decoder_size
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def forward(
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self, target_embedding, encoder_vec, encoder_proj, decoder_boot
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):
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res = []
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hidden_mem = decoder_boot
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for i in range(target_embedding.shape[1]):
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current_word = paddle.slice(
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target_embedding, axes=[1], starts=[i], ends=[i + 1]
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)
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current_word = paddle.reshape(
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current_word, [-1, current_word.shape[2]]
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)
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context = self.simple_attention(
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encoder_vec, encoder_proj, hidden_mem
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)
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fc_1 = self.fc_1_layer(context)
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fc_2 = self.fc_2_layer(current_word)
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decoder_inputs = paddle.add(x=fc_1, y=fc_2)
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h, _ = self.gru_unit(decoder_inputs, hidden_mem)
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hidden_mem = h
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out = self.out_layer(h)
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out = paddle.nn.functional.softmax(out)
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res.append(out)
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res1 = paddle.concat(res, axis=1)
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return res1
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class OCRAttention(paddle.nn.Layer):
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def __init__(self):
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super().__init__()
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self.encoder_net = EncoderNet()
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self.fc = Linear(
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Config.encoder_size,
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Config.decoder_size,
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bias_attr=False,
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)
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self.embedding = paddle.nn.Embedding(
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Config.num_classes + 2, Config.word_vector_dim
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)
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self.gru_decoder_with_attention = GRUDecoderWithAttention(
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Config.decoder_size, Config.num_classes
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)
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def forward(self, inputs, label_in):
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gru_backward, encoded_vector, encoded_proj = self.encoder_net(inputs)
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backward_first = paddle.slice(
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gru_backward, axes=[1], starts=[0], ends=[1]
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)
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backward_first = paddle.reshape(
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backward_first, [-1, backward_first.shape[2]]
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)
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decoder_boot = self.fc(backward_first)
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decoder_boot = paddle.nn.functional.relu(decoder_boot)
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label_in = paddle.reshape(label_in, [-1])
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trg_embedding = self.embedding(label_in)
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trg_embedding = paddle.reshape(
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trg_embedding,
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[-1, Config.max_length, trg_embedding.shape[1]],
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)
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prediction = self.gru_decoder_with_attention(
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trg_embedding, encoded_vector, encoded_proj, decoder_boot
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)
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return prediction
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class TestDygraphOCRAttention(unittest.TestCase):
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def test_ocr_test(self):
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seed = 90
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epoch_num = 1
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if core.is_compiled_with_cuda() or is_custom_device():
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batch_num = 3
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else:
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batch_num = 2
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np.random.seed = seed
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image_np = np.random.randn(
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Config.batch_size,
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Config.DATA_SHAPE[0],
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Config.DATA_SHAPE[1],
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Config.DATA_SHAPE[2],
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).astype('float32')
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label_in_np = np.arange(0, Config.max_length, dtype='int64').reshape(
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[1, Config.max_length]
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)
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for i in range(2, Config.batch_size + 1):
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label_in_np = np.vstack(
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(
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label_in_np,
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np.arange(
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(i - 1) * Config.max_length,
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i * Config.max_length,
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dtype='int64',
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).reshape([1, Config.max_length]),
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)
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)
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label_out_np = np.arange(0, Config.max_length, dtype='int64').reshape(
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[1, Config.max_length]
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)
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for i in range(2, Config.batch_size + 1):
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label_out_np = np.vstack(
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(
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label_out_np,
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np.arange(
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(i - 1) * Config.max_length,
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i * Config.max_length,
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dtype='int64',
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).reshape([1, Config.max_length]),
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)
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)
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def run_dygraph():
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base.set_flags({'FLAGS_sort_sum_gradient': True})
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paddle.seed(seed)
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if paddle.framework.use_pir_api():
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with paddle.pir_utils.OldIrGuard():
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# Note: dygraph use self.main_program.global_block().create_parameter(), it's need manual seed to old Program
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paddle.framework.random._manual_program_seed(seed)
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paddle.framework.random._manual_program_seed(seed)
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else:
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paddle.framework.random._manual_program_seed(seed)
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ocr_attention = OCRAttention()
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if Config.learning_rate_decay == "piecewise_decay":
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learning_rate = paddle.optimizer.lr.piecewise_decay(
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[50000], [Config.LR, Config.LR * 0.01]
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)
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else:
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learning_rate = Config.LR
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optimizer = paddle.optimizer.SGD(
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learning_rate=0.001, parameters=ocr_attention.parameters()
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)
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dy_param_init_value = {}
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for param in ocr_attention.parameters():
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dy_param_init_value[param.name] = param.numpy()
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for epoch in range(epoch_num):
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for batch_id in range(batch_num):
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label_in = paddle.to_tensor(label_in_np)
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label_out = paddle.to_tensor(label_out_np)
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label_out.stop_gradient = True
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img = paddle.to_tensor(image_np)
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dy_prediction = ocr_attention(img, label_in)
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label_out = paddle.reshape(label_out, [-1, 1])
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dy_prediction = paddle.reshape(
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dy_prediction, [label_out.shape[0], -1]
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)
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loss = paddle.nn.functional.cross_entropy(
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input=dy_prediction,
|
|
label=label_out,
|
|
reduction='none',
|
|
use_softmax=False,
|
|
)
|
|
avg_loss = paddle.sum(loss)
|
|
|
|
dy_out = avg_loss.numpy()
|
|
|
|
if epoch == 0 and batch_id == 0:
|
|
for param in ocr_attention.parameters():
|
|
if param.name not in dy_param_init_value:
|
|
dy_param_init_value[param.name] = param.numpy()
|
|
avg_loss.backward()
|
|
dy_grad_value = {}
|
|
for param in ocr_attention.parameters():
|
|
if param.trainable:
|
|
np_array = np.array(
|
|
param._grad_ivar().value().get_tensor()
|
|
)
|
|
dy_grad_value[
|
|
param.name + core.grad_var_suffix()
|
|
] = np_array
|
|
|
|
optimizer.minimize(avg_loss)
|
|
ocr_attention.clear_gradients()
|
|
dy_param_value = {}
|
|
for param in ocr_attention.parameters():
|
|
dy_param_value[param.name] = param.numpy()
|
|
|
|
return dy_out, dy_param_init_value, dy_param_value
|
|
|
|
with base.dygraph.guard():
|
|
dy_out, dy_param_init_value, dy_param_value = run_dygraph()
|
|
|
|
with base.dygraph.guard():
|
|
(
|
|
eager_out,
|
|
eager_param_init_value,
|
|
eager_param_value,
|
|
) = run_dygraph()
|
|
|
|
with new_program_scope():
|
|
paddle.seed(seed)
|
|
if paddle.framework.use_pir_api():
|
|
with paddle.pir_utils.OldIrGuard():
|
|
# Note: dygraph use self.main_program.global_block().create_parameter(), it's need manual seed to old Program
|
|
paddle.framework.random._manual_program_seed(seed)
|
|
paddle.framework.random._manual_program_seed(seed)
|
|
else:
|
|
paddle.framework.random._manual_program_seed(seed)
|
|
exe = base.Executor(
|
|
base.CPUPlace()
|
|
if not (core.is_compiled_with_cuda() or is_custom_device())
|
|
else get_device_place()
|
|
)
|
|
ocr_attention = OCRAttention()
|
|
|
|
if Config.learning_rate_decay == "piecewise_decay":
|
|
learning_rate = paddle.optimizer.lr.piecewise_decay(
|
|
[50000], [Config.LR, Config.LR * 0.01]
|
|
)
|
|
else:
|
|
learning_rate = Config.LR
|
|
|
|
optimizer = paddle.optimizer.SGD(learning_rate=0.001)
|
|
|
|
images = paddle.static.data(
|
|
name='pixel', shape=[-1, *Config.DATA_SHAPE], dtype='float32'
|
|
)
|
|
if not paddle.framework.use_pir_api():
|
|
images.desc.set_need_check_feed(False)
|
|
static_label_in = paddle.static.data(
|
|
name='label_in', shape=[-1, 1], dtype='int64'
|
|
)
|
|
if not paddle.framework.use_pir_api():
|
|
static_label_in.desc.set_need_check_feed(False)
|
|
static_label_out = paddle.static.data(
|
|
name='label_out', shape=[-1, 1], dtype='int64'
|
|
)
|
|
if not paddle.framework.use_pir_api():
|
|
static_label_out.desc.set_need_check_feed(False)
|
|
|
|
static_label_out.stop_gradient = True
|
|
static_label_out.trainable = False
|
|
|
|
static_prediction = ocr_attention(images, static_label_in)
|
|
|
|
static_prediction = paddle.reshape(
|
|
static_prediction, shape=[-1, Config.num_classes + 2]
|
|
)
|
|
static_label_out = paddle.reshape(
|
|
static_label_out, shape=[static_prediction.shape[0], 1]
|
|
)
|
|
|
|
cost = paddle.nn.functional.cross_entropy(
|
|
input=static_prediction,
|
|
label=static_label_out,
|
|
reduction='none',
|
|
use_softmax=False,
|
|
)
|
|
static_avg_loss = paddle.sum(cost)
|
|
optimizer.minimize(static_avg_loss)
|
|
|
|
static_param_init_value = {}
|
|
static_param_name_list = []
|
|
static_grad_name_list = []
|
|
static_params = []
|
|
for param in ocr_attention.parameters():
|
|
static_param_name_list.append(param.name)
|
|
static_params.append(param)
|
|
if param.trainable:
|
|
static_grad_name_list.append(
|
|
param.name + core.grad_var_suffix()
|
|
)
|
|
if paddle.framework.use_pir_api():
|
|
parameter_mapping = create_parameter_mapping(
|
|
paddle.static.default_startup_program(),
|
|
paddle.static.default_main_program(),
|
|
)
|
|
startup_params = [
|
|
parameter_mapping[param] for param in static_params
|
|
]
|
|
else:
|
|
startup_params = static_params
|
|
|
|
out = exe.run(
|
|
paddle.static.default_startup_program(),
|
|
fetch_list=startup_params,
|
|
)
|
|
|
|
for i in range(len(static_params)):
|
|
param_name = static_param_name_list[i]
|
|
static_param_init_value[param_name] = out[i]
|
|
|
|
for epoch in range(epoch_num):
|
|
for batch_id in range(batch_num):
|
|
static_label_in = label_in_np
|
|
static_label_out = label_out_np
|
|
static_label_out = static_label_out.reshape((-1, 1))
|
|
fetch_list = [static_avg_loss]
|
|
fetch_list.extend(static_params)
|
|
out = exe.run(
|
|
base.default_main_program(),
|
|
feed={
|
|
"pixel": image_np,
|
|
"label_in": static_label_in,
|
|
"label_out": static_label_out,
|
|
},
|
|
fetch_list=fetch_list,
|
|
)
|
|
static_param_value = {}
|
|
static_grad_value = {}
|
|
static_out = out[0]
|
|
for i in range(1, len(out)):
|
|
static_param_value[static_param_name_list[i - 1]] = out[
|
|
i
|
|
]
|
|
|
|
np.testing.assert_allclose(static_out, dy_out, rtol=1e-05, atol=1e-8)
|
|
|
|
for key, value in static_param_init_value.items():
|
|
np.testing.assert_array_equal(value, dy_param_init_value[key])
|
|
|
|
for key, value in static_param_value.items():
|
|
np.testing.assert_allclose(
|
|
value, dy_param_value[key], rtol=1e-05, atol=1e-8
|
|
)
|
|
|
|
# check eager here
|
|
np.testing.assert_allclose(static_out, eager_out, rtol=1e-05, atol=1e-8)
|
|
|
|
for key, value in static_param_init_value.items():
|
|
np.testing.assert_array_equal(value, eager_param_init_value[key])
|
|
|
|
for key, value in static_param_value.items():
|
|
np.testing.assert_allclose(
|
|
value, eager_param_value[key], rtol=1e-05, atol=1e-8
|
|
)
|
|
|
|
|
|
if __name__ == '__main__':
|
|
paddle.enable_static()
|
|
unittest.main()
|