zhoujun
4 years ago
committed by
GitHub
parent
c852b91647
commit
bc563c642c
@ -0,0 +1,97 @@
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Global:
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use_gpu: true
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epoch_num: 100
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log_smooth_window: 20
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print_batch_step: 10
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save_model_dir: ./output/cls/mv3/
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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, 1000]
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# if pretrained_model is saved in static mode, load_static_weights must set to True
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load_static_weights: 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_en/word_10.png
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label_list: ['0','180']
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Architecture:
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model_type: cls
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algorithm: CLS
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Transform:
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Backbone:
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name: MobileNetV3
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scale: 0.35
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model_name: small
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Neck:
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Head:
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name: ClsHead
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class_dim: 2
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Loss:
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name: ClsLoss
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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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name: Cosine
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learning_rate: 0.001
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regularizer:
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name: 'L2'
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factor: 0
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PostProcess:
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name: ClsPostProcess
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Metric:
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name: ClsMetric
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main_indicator: acc
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Train:
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dataset:
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name: SimpleDataSet
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data_dir: ./train_data/cls
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label_file_list:
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- ./train_data/cls/train.txt
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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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- ClsLabelEncode: # Class handling label
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- RecAug:
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use_tia: False
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- RandAugment:
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- ClsResizeImg:
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image_shape: [3, 48, 192]
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- KeepKeys:
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keep_keys: ['image', 'label'] # 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: 512
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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: SimpleDataSet
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data_dir: ./train_data/cls
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label_file_list:
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- ./train_data/cls/test.txt
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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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- ClsLabelEncode: # Class handling label
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- ClsResizeImg:
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image_shape: [3, 48, 192]
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- KeepKeys:
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keep_keys: ['image', 'label'] # 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: 512
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num_workers: 4
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@ -0,0 +1,96 @@
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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/r34_vd_none_bilstm_ctc/
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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
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Architecture:
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model_type: rec
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algorithm: CRNN
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Transform:
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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
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Head:
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name: CTCHead
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fc_decay: 0
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Loss:
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name: CTCLoss
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PostProcess:
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name: CTCLabelDecode
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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: ./train_data/data_lmdb_release/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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- CTCLabelEncode: # 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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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: ./train_data/data_lmdb_release/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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- CTCLabelEncode: # 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: 4
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@ -0,0 +1,81 @@
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// Copyright (c) 2020 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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#include "opencv2/core.hpp"
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#include "opencv2/imgcodecs.hpp"
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#include "opencv2/imgproc.hpp"
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#include "paddle_api.h"
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#include "paddle_inference_api.h"
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#include <chrono>
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#include <iomanip>
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#include <iostream>
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#include <ostream>
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#include <vector>
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#include <cstring>
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#include <fstream>
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#include <numeric>
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#include <include/preprocess_op.h>
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#include <include/utility.h>
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namespace PaddleOCR {
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class Classifier {
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public:
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explicit Classifier(const std::string &model_dir, const bool &use_gpu,
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const int &gpu_id, const int &gpu_mem,
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const int &cpu_math_library_num_threads,
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const bool &use_mkldnn, const bool &use_zero_copy_run,
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const double &cls_thresh) {
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this->use_gpu_ = use_gpu;
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this->gpu_id_ = gpu_id;
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this->gpu_mem_ = gpu_mem;
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this->cpu_math_library_num_threads_ = cpu_math_library_num_threads;
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this->use_mkldnn_ = use_mkldnn;
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this->use_zero_copy_run_ = use_zero_copy_run;
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this->cls_thresh = cls_thresh;
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LoadModel(model_dir);
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}
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// Load Paddle inference model
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void LoadModel(const std::string &model_dir);
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cv::Mat Run(cv::Mat &img);
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private:
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std::shared_ptr<PaddlePredictor> predictor_;
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bool use_gpu_ = false;
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int gpu_id_ = 0;
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int gpu_mem_ = 4000;
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int cpu_math_library_num_threads_ = 4;
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bool use_mkldnn_ = false;
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bool use_zero_copy_run_ = false;
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double cls_thresh = 0.5;
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std::vector<float> mean_ = {0.5f, 0.5f, 0.5f};
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std::vector<float> scale_ = {1 / 0.5f, 1 / 0.5f, 1 / 0.5f};
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bool is_scale_ = true;
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// pre-process
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ClsResizeImg resize_op_;
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Normalize normalize_op_;
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Permute permute_op_;
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}; // class Classifier
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} // namespace PaddleOCR
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// Copyright (c) 2020 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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#include <include/ocr_cls.h>
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namespace PaddleOCR {
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cv::Mat Classifier::Run(cv::Mat &img) {
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cv::Mat src_img;
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img.copyTo(src_img);
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cv::Mat resize_img;
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std::vector<int> cls_image_shape = {3, 48, 192};
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int index = 0;
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float wh_ratio = float(img.cols) / float(img.rows);
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this->resize_op_.Run(img, resize_img, cls_image_shape);
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this->normalize_op_.Run(&resize_img, this->mean_, this->scale_,
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this->is_scale_);
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std::vector<float> input(1 * 3 * resize_img.rows * resize_img.cols, 0.0f);
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this->permute_op_.Run(&resize_img, input.data());
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// Inference.
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if (this->use_zero_copy_run_) {
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auto input_names = this->predictor_->GetInputNames();
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auto input_t = this->predictor_->GetInputTensor(input_names[0]);
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input_t->Reshape({1, 3, resize_img.rows, resize_img.cols});
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input_t->copy_from_cpu(input.data());
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this->predictor_->ZeroCopyRun();
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} else {
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paddle::PaddleTensor input_t;
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input_t.shape = {1, 3, resize_img.rows, resize_img.cols};
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input_t.data =
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paddle::PaddleBuf(input.data(), input.size() * sizeof(float));
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input_t.dtype = PaddleDType::FLOAT32;
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std::vector<paddle::PaddleTensor> outputs;
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this->predictor_->Run({input_t}, &outputs, 1);
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}
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std::vector<float> softmax_out;
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std::vector<int64_t> label_out;
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auto output_names = this->predictor_->GetOutputNames();
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auto softmax_out_t = this->predictor_->GetOutputTensor(output_names[0]);
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auto softmax_shape_out = softmax_out_t->shape();
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int softmax_out_num =
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std::accumulate(softmax_shape_out.begin(), softmax_shape_out.end(), 1,
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std::multiplies<int>());
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softmax_out.resize(softmax_out_num);
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softmax_out_t->copy_to_cpu(softmax_out.data());
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float score = 0;
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int label = 0;
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for (int i = 0; i < softmax_out_num; i++) {
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if (softmax_out[i] > score) {
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score = softmax_out[i];
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label = i;
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}
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}
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if (label % 2 == 1 && score > this->cls_thresh) {
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cv::rotate(src_img, src_img, 1);
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}
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return src_img;
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}
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void Classifier::LoadModel(const std::string &model_dir) {
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AnalysisConfig config;
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config.SetModel(model_dir + "/model", model_dir + "/params");
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if (this->use_gpu_) {
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config.EnableUseGpu(this->gpu_mem_, this->gpu_id_);
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} else {
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config.DisableGpu();
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if (this->use_mkldnn_) {
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config.EnableMKLDNN();
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}
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config.SetCpuMathLibraryNumThreads(this->cpu_math_library_num_threads_);
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}
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// false for zero copy tensor
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config.SwitchUseFeedFetchOps(!this->use_zero_copy_run_);
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// true for multiple input
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config.SwitchSpecifyInputNames(true);
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config.SwitchIrOptim(true);
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config.EnableMemoryOptim();
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config.DisableGlogInfo();
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this->predictor_ = CreatePaddlePredictor(config);
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}
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} // namespace PaddleOCR
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@ -0,0 +1,140 @@
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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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from __future__ import unicode_literals
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from PIL import Image, ImageEnhance, ImageOps
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import numpy as np
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import random
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import six
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class RawRandAugment(object):
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def __init__(self,
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num_layers=2,
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magnitude=5,
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fillcolor=(128, 128, 128),
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**kwargs):
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self.num_layers = num_layers
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self.magnitude = magnitude
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self.max_level = 10
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abso_level = self.magnitude / self.max_level
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self.level_map = {
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"shearX": 0.3 * abso_level,
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"shearY": 0.3 * abso_level,
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"translateX": 150.0 / 331 * abso_level,
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"translateY": 150.0 / 331 * abso_level,
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"rotate": 30 * abso_level,
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"color": 0.9 * abso_level,
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"posterize": int(4.0 * abso_level),
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"solarize": 256.0 * abso_level,
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"contrast": 0.9 * abso_level,
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"sharpness": 0.9 * abso_level,
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"brightness": 0.9 * abso_level,
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"autocontrast": 0,
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"equalize": 0,
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"invert": 0
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}
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# from https://stackoverflow.com/questions/5252170/
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# specify-image-filling-color-when-rotating-in-python-with-pil-and-setting-expand
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def rotate_with_fill(img, magnitude):
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rot = img.convert("RGBA").rotate(magnitude)
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return Image.composite(rot,
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Image.new("RGBA", rot.size, (128, ) * 4),
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rot).convert(img.mode)
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rnd_ch_op = random.choice
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self.func = {
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"shearX": lambda img, magnitude: img.transform(
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img.size,
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Image.AFFINE,
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(1, magnitude * rnd_ch_op([-1, 1]), 0, 0, 1, 0),
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Image.BICUBIC,
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fillcolor=fillcolor),
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"shearY": lambda img, magnitude: img.transform(
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img.size,
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Image.AFFINE,
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(1, 0, 0, magnitude * rnd_ch_op([-1, 1]), 1, 0),
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Image.BICUBIC,
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fillcolor=fillcolor),
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"translateX": lambda img, magnitude: img.transform(
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img.size,
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Image.AFFINE,
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(1, 0, magnitude * img.size[0] * rnd_ch_op([-1, 1]), 0, 1, 0),
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fillcolor=fillcolor),
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"translateY": lambda img, magnitude: img.transform(
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img.size,
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Image.AFFINE,
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(1, 0, 0, 0, 1, magnitude * img.size[1] * rnd_ch_op([-1, 1])),
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fillcolor=fillcolor),
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"rotate": lambda img, magnitude: rotate_with_fill(img, magnitude),
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"color": lambda img, magnitude: ImageEnhance.Color(img).enhance(
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1 + magnitude * rnd_ch_op([-1, 1])),
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"posterize": lambda img, magnitude:
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ImageOps.posterize(img, magnitude),
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"solarize": lambda img, magnitude:
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ImageOps.solarize(img, magnitude),
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"contrast": lambda img, magnitude:
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ImageEnhance.Contrast(img).enhance(
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1 + magnitude * rnd_ch_op([-1, 1])),
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"sharpness": lambda img, magnitude:
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ImageEnhance.Sharpness(img).enhance(
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1 + magnitude * rnd_ch_op([-1, 1])),
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"brightness": lambda img, magnitude:
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ImageEnhance.Brightness(img).enhance(
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||||
1 + magnitude * rnd_ch_op([-1, 1])),
|
||||
"autocontrast": lambda img, magnitude:
|
||||
ImageOps.autocontrast(img),
|
||||
"equalize": lambda img, magnitude: ImageOps.equalize(img),
|
||||
"invert": lambda img, magnitude: ImageOps.invert(img)
|
||||
}
|
||||
|
||||
def __call__(self, img):
|
||||
avaiable_op_names = list(self.level_map.keys())
|
||||
for layer_num in range(self.num_layers):
|
||||
op_name = np.random.choice(avaiable_op_names)
|
||||
img = self.func[op_name](img, self.level_map[op_name])
|
||||
return img
|
||||
|
||||
|
||||
class RandAugment(RawRandAugment):
|
||||
""" RandAugment wrapper to auto fit different img types """
|
||||
|
||||
def __init__(self, *args, **kwargs):
|
||||
if six.PY2:
|
||||
super(RandAugment, self).__init__(*args, **kwargs)
|
||||
else:
|
||||
super().__init__(*args, **kwargs)
|
||||
|
||||
def __call__(self, data):
|
||||
img = data['image']
|
||||
if not isinstance(img, Image.Image):
|
||||
img = np.ascontiguousarray(img)
|
||||
img = Image.fromarray(img)
|
||||
|
||||
if six.PY2:
|
||||
img = super(RandAugment, self).__call__(img)
|
||||
else:
|
||||
img = super().__call__(img)
|
||||
|
||||
if isinstance(img, Image.Image):
|
||||
img = np.asarray(img)
|
||||
data['image'] = img
|
||||
return data
|
||||
@ -0,0 +1,30 @@
|
||||
# copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
|
||||
#
|
||||
# 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.
|
||||
|
||||
from __future__ import absolute_import
|
||||
from __future__ import division
|
||||
from __future__ import print_function
|
||||
|
||||
from paddle import nn
|
||||
|
||||
|
||||
class ClsLoss(nn.Layer):
|
||||
def __init__(self, **kwargs):
|
||||
super(ClsLoss, self).__init__()
|
||||
self.loss_func = nn.CrossEntropyLoss(reduction='mean')
|
||||
|
||||
def __call__(self, predicts, batch):
|
||||
label = batch[1]
|
||||
loss = self.loss_func(input=predicts, label=label)
|
||||
return {'loss': loss}
|
||||
@ -0,0 +1,46 @@
|
||||
# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
|
||||
#
|
||||
# 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.
|
||||
|
||||
|
||||
class ClsMetric(object):
|
||||
def __init__(self, main_indicator='acc', **kwargs):
|
||||
self.main_indicator = main_indicator
|
||||
self.reset()
|
||||
|
||||
def __call__(self, pred_label, *args, **kwargs):
|
||||
preds, labels = pred_label
|
||||
correct_num = 0
|
||||
all_num = 0
|
||||
for (pred, pred_conf), (target, _) in zip(preds, labels):
|
||||
if pred == target:
|
||||
correct_num += 1
|
||||
all_num += 1
|
||||
self.correct_num += correct_num
|
||||
self.all_num += all_num
|
||||
return {'acc': correct_num / all_num, }
|
||||
|
||||
def get_metric(self):
|
||||
"""
|
||||
return metircs {
|
||||
'acc': 0,
|
||||
'norm_edit_dis': 0,
|
||||
}
|
||||
"""
|
||||
acc = self.correct_num / self.all_num
|
||||
self.reset()
|
||||
return {'acc': acc}
|
||||
|
||||
def reset(self):
|
||||
self.correct_num = 0
|
||||
self.all_num = 0
|
||||
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Reference in new issue