MissPenguin
4 years ago
committed by
GitHub
commit
dd6f6f5cf3
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Global:
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use_gpu: true
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epoch_num: 72
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log_smooth_window: 20
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print_batch_step: 10
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save_model_dir: ./output/rec/rec_mv3_tps_bilstm_att/
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save_epoch_step: 3
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# evaluation is run every 5000 iterations after the 4000th iteration
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eval_batch_step: [0, 2000]
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# if pretrained_model is saved in static mode, load_static_weights must set to True
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cal_metric_during_train: True
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pretrained_model:
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checkpoints:
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save_inference_dir:
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use_visualdl: False
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infer_img: doc/imgs_words/ch/word_1.jpg
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# for data or label process
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character_dict_path:
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character_type: en
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max_text_length: 25
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infer_mode: False
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use_space_char: False
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Optimizer:
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name: Adam
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beta1: 0.9
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beta2: 0.999
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lr:
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learning_rate: 0.0005
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regularizer:
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name: 'L2'
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factor: 0.00001
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Architecture:
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model_type: rec
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algorithm: RARE
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Transform:
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name: TPS
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num_fiducial: 20
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loc_lr: 0.1
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model_name: small
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Backbone:
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name: MobileNetV3
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scale: 0.5
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model_name: large
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Neck:
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name: SequenceEncoder
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encoder_type: rnn
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hidden_size: 96
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Head:
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name: AttentionHead
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hidden_size: 96
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Loss:
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name: AttentionLoss
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PostProcess:
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name: AttnLabelDecode
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Metric:
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name: RecMetric
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main_indicator: acc
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Train:
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dataset:
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name: LMDBDateSet
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data_dir: ../training/
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transforms:
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- DecodeImage: # load image
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img_mode: BGR
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channel_first: False
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- AttnLabelEncode: # Class handling label
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- RecResizeImg:
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image_shape: [3, 32, 100]
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- KeepKeys:
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keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
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loader:
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shuffle: True
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batch_size_per_card: 256
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drop_last: True
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num_workers: 8
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Eval:
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dataset:
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name: LMDBDateSet
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data_dir: ../validation/
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transforms:
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- DecodeImage: # load image
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img_mode: BGR
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channel_first: False
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- AttnLabelEncode: # Class handling label
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- RecResizeImg:
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image_shape: [3, 32, 100]
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- KeepKeys:
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keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
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loader:
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shuffle: False
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drop_last: False
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batch_size_per_card: 256
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num_workers: 1
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@ -0,0 +1,101 @@
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Global:
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use_gpu: true
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epoch_num: 400
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log_smooth_window: 20
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print_batch_step: 10
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save_model_dir: ./output/rec/b3_rare_r34_none_gru/
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save_epoch_step: 3
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# evaluation is run every 5000 iterations after the 4000th iteration
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eval_batch_step: [0, 2000]
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# if pretrained_model is saved in static mode, load_static_weights must set to True
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cal_metric_during_train: True
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pretrained_model:
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checkpoints:
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save_inference_dir:
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use_visualdl: False
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infer_img: doc/imgs_words/ch/word_1.jpg
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# for data or label process
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character_dict_path:
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character_type: en
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max_text_length: 25
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infer_mode: False
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use_space_char: False
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Optimizer:
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name: Adam
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beta1: 0.9
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beta2: 0.999
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lr:
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learning_rate: 0.0005
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regularizer:
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name: 'L2'
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factor: 0.00000
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Architecture:
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model_type: rec
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algorithm: RARE
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Transform:
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name: TPS
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num_fiducial: 20
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loc_lr: 0.1
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model_name: large
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Backbone:
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name: ResNet
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layers: 34
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Neck:
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name: SequenceEncoder
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encoder_type: rnn
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hidden_size: 256 #96
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Head:
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name: AttentionHead # AttentionHead
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hidden_size: 256 #
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l2_decay: 0.00001
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Loss:
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name: AttentionLoss
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PostProcess:
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name: AttnLabelDecode
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Metric:
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name: RecMetric
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main_indicator: acc
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Train:
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dataset:
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name: LMDBDateSet
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data_dir: ../training/
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transforms:
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- DecodeImage: # load image
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img_mode: BGR
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channel_first: False
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- AttnLabelEncode: # Class handling label
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- RecResizeImg:
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image_shape: [3, 32, 100]
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- KeepKeys:
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keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
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loader:
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shuffle: True
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batch_size_per_card: 256
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drop_last: True
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num_workers: 8
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Eval:
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dataset:
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name: LMDBDateSet
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data_dir: ../validation/
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transforms:
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- DecodeImage: # load image
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img_mode: BGR
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channel_first: False
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- AttnLabelEncode: # Class handling label
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- RecResizeImg:
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image_shape: [3, 32, 100]
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- KeepKeys:
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keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
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loader:
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shuffle: False
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drop_last: False
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batch_size_per_card: 256
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num_workers: 8
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@ -0,0 +1,39 @@
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# copyright (c) 2021 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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from paddle import nn
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class AttentionLoss(nn.Layer):
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def __init__(self, **kwargs):
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super(AttentionLoss, self).__init__()
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self.loss_func = nn.CrossEntropyLoss(weight=None, reduction='none')
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def forward(self, predicts, batch):
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targets = batch[1].astype("int64")
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label_lengths = batch[2].astype('int64')
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batch_size, num_steps, num_classes = predicts.shape[0], predicts.shape[
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1], predicts.shape[2]
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assert len(targets.shape) == len(list(predicts.shape)) - 1, \
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"The target's shape and inputs's shape is [N, d] and [N, num_steps]"
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inputs = paddle.reshape(predicts, [-1, predicts.shape[-1]])
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targets = paddle.reshape(targets, [-1])
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return {'loss': paddle.sum(self.loss_func(inputs, targets))}
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@ -0,0 +1,199 @@
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# copyright (c) 2021 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.nn as nn
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import paddle.nn.functional as F
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import numpy as np
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class AttentionHead(nn.Layer):
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def __init__(self, in_channels, out_channels, hidden_size, **kwargs):
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super(AttentionHead, self).__init__()
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self.input_size = in_channels
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self.hidden_size = hidden_size
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self.num_classes = out_channels
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self.attention_cell = AttentionGRUCell(
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in_channels, hidden_size, out_channels, use_gru=False)
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self.generator = nn.Linear(hidden_size, out_channels)
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def _char_to_onehot(self, input_char, onehot_dim):
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input_ont_hot = F.one_hot(input_char, onehot_dim)
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return input_ont_hot
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def forward(self, inputs, targets=None, batch_max_length=25):
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batch_size = inputs.shape[0]
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num_steps = batch_max_length
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hidden = paddle.zeros((batch_size, self.hidden_size))
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output_hiddens = []
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if targets is not None:
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for i in range(num_steps):
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char_onehots = self._char_to_onehot(
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targets[:, i], onehot_dim=self.num_classes)
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(outputs, hidden), alpha = self.attention_cell(hidden, inputs,
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char_onehots)
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output_hiddens.append(paddle.unsqueeze(outputs, axis=1))
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output = paddle.concat(output_hiddens, axis=1)
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probs = self.generator(output)
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else:
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targets = paddle.zeros(shape=[batch_size], dtype="int32")
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probs = None
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for i in range(num_steps):
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char_onehots = self._char_to_onehot(
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targets, onehot_dim=self.num_classes)
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(outputs, hidden), alpha = self.attention_cell(hidden, inputs,
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char_onehots)
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probs_step = self.generator(outputs)
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if probs is None:
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probs = paddle.unsqueeze(probs_step, axis=1)
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else:
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probs = paddle.concat(
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[probs, paddle.unsqueeze(
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probs_step, axis=1)], axis=1)
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next_input = probs_step.argmax(axis=1)
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targets = next_input
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return probs
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class AttentionGRUCell(nn.Layer):
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def __init__(self, input_size, hidden_size, num_embeddings, use_gru=False):
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super(AttentionGRUCell, self).__init__()
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self.i2h = nn.Linear(input_size, hidden_size, bias_attr=False)
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self.h2h = nn.Linear(hidden_size, hidden_size)
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self.score = nn.Linear(hidden_size, 1, bias_attr=False)
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self.rnn = nn.GRUCell(
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input_size=input_size + num_embeddings, hidden_size=hidden_size)
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self.hidden_size = hidden_size
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def forward(self, prev_hidden, batch_H, char_onehots):
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batch_H_proj = self.i2h(batch_H)
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prev_hidden_proj = paddle.unsqueeze(self.h2h(prev_hidden), axis=1)
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res = paddle.add(batch_H_proj, prev_hidden_proj)
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res = paddle.tanh(res)
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e = self.score(res)
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alpha = F.softmax(e, axis=1)
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alpha = paddle.transpose(alpha, [0, 2, 1])
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context = paddle.squeeze(paddle.mm(alpha, batch_H), axis=1)
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concat_context = paddle.concat([context, char_onehots], 1)
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cur_hidden = self.rnn(concat_context, prev_hidden)
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return cur_hidden, alpha
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class AttentionLSTM(nn.Layer):
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def __init__(self, in_channels, out_channels, hidden_size, **kwargs):
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super(AttentionLSTM, self).__init__()
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self.input_size = in_channels
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self.hidden_size = hidden_size
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self.num_classes = out_channels
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self.attention_cell = AttentionLSTMCell(
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in_channels, hidden_size, out_channels, use_gru=False)
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self.generator = nn.Linear(hidden_size, out_channels)
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def _char_to_onehot(self, input_char, onehot_dim):
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input_ont_hot = F.one_hot(input_char, onehot_dim)
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return input_ont_hot
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def forward(self, inputs, targets=None, batch_max_length=25):
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batch_size = inputs.shape[0]
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num_steps = batch_max_length
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hidden = (paddle.zeros((batch_size, self.hidden_size)), paddle.zeros(
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(batch_size, self.hidden_size)))
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output_hiddens = []
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if targets is not None:
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for i in range(num_steps):
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# one-hot vectors for a i-th char
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char_onehots = self._char_to_onehot(
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targets[:, i], onehot_dim=self.num_classes)
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hidden, alpha = self.attention_cell(hidden, inputs,
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char_onehots)
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hidden = (hidden[1][0], hidden[1][1])
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output_hiddens.append(paddle.unsqueeze(hidden[0], axis=1))
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output = paddle.concat(output_hiddens, axis=1)
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probs = self.generator(output)
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else:
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targets = paddle.zeros(shape=[batch_size], dtype="int32")
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probs = None
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for i in range(num_steps):
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char_onehots = self._char_to_onehot(
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targets, onehot_dim=self.num_classes)
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hidden, alpha = self.attention_cell(hidden, inputs,
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char_onehots)
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probs_step = self.generator(hidden[0])
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hidden = (hidden[1][0], hidden[1][1])
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if probs is None:
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probs = paddle.unsqueeze(probs_step, axis=1)
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else:
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probs = paddle.concat(
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[probs, paddle.unsqueeze(
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probs_step, axis=1)], axis=1)
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next_input = probs_step.argmax(axis=1)
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targets = next_input
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return probs
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class AttentionLSTMCell(nn.Layer):
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def __init__(self, input_size, hidden_size, num_embeddings, use_gru=False):
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super(AttentionLSTMCell, self).__init__()
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self.i2h = nn.Linear(input_size, hidden_size, bias_attr=False)
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self.h2h = nn.Linear(hidden_size, hidden_size)
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self.score = nn.Linear(hidden_size, 1, bias_attr=False)
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if not use_gru:
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self.rnn = nn.LSTMCell(
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input_size=input_size + num_embeddings, hidden_size=hidden_size)
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else:
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self.rnn = nn.GRUCell(
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input_size=input_size + num_embeddings, hidden_size=hidden_size)
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self.hidden_size = hidden_size
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def forward(self, prev_hidden, batch_H, char_onehots):
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batch_H_proj = self.i2h(batch_H)
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prev_hidden_proj = paddle.unsqueeze(self.h2h(prev_hidden[0]), axis=1)
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res = paddle.add(batch_H_proj, prev_hidden_proj)
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res = paddle.tanh(res)
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e = self.score(res)
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alpha = F.softmax(e, axis=1)
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alpha = paddle.transpose(alpha, [0, 2, 1])
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context = paddle.squeeze(paddle.mm(alpha, batch_H), axis=1)
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concat_context = paddle.concat([context, char_onehots], 1)
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cur_hidden = self.rnn(concat_context, prev_hidden)
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return cur_hidden, alpha
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