From 7a5cca69450e8623c55d1cc6805b67026e45eb5e Mon Sep 17 00:00:00 2001 From: whs Date: Wed, 4 Sep 2019 15:46:15 +0800 Subject: [PATCH] Refine arguments of ocr models. (#3261) * Refine arguments of ocr models. * Add comments for issue of attention model in paddle1.5 --- PaddleCV/ocr_recognition/README.md | 10 ++- PaddleCV/ocr_recognition/attention_model.py | 10 +-- PaddleCV/ocr_recognition/crnn_ctc_model.py | 6 +- PaddleCV/ocr_recognition/run_attention.sh | 9 +++ PaddleCV/ocr_recognition/run_crnn_ctc.sh | 10 +++ PaddleCV/ocr_recognition/train.py | 68 ++++++++++++--------- 6 files changed, 71 insertions(+), 42 deletions(-) create mode 100644 PaddleCV/ocr_recognition/run_attention.sh create mode 100644 PaddleCV/ocr_recognition/run_crnn_ctc.sh diff --git a/PaddleCV/ocr_recognition/README.md b/PaddleCV/ocr_recognition/README.md index dcacefa0..f6ad4c90 100644 --- a/PaddleCV/ocr_recognition/README.md +++ b/PaddleCV/ocr_recognition/README.md @@ -1,3 +1,5 @@ +>注意：在paddle1.5版本上训练attention model有收敛问题，建议您暂时使用paddle1.4版本，后续我们会修复该问题。 + ## 代码结构 ``` ├── data_reader.py # 下载、读取、处理数据。 @@ -6,7 +8,9 @@ ├── train.py # 用于模型的训练。 ├── infer.py # 加载训练好的模型文件，对新数据进行预测。 ├── eval.py # 评估模型在指定数据集上的效果。 -└── utils.py # 定义通用的函数。 +├─ utils.py # 定义通用的函数。 +├── run_crnn_ctc.sh # 执行crnn_ctc模型训练任务 +└── run_attention.sh # 执行attention模型训练任务 ``` @@ -136,14 +140,14 @@ env CUDA_VISIBLE_DEVICES=0,1,2,3 python train.py --parallel=True 执行`python train.py --help`可查看更多使用方式和参数详细说明。 -图2为使用默认参数在默认数据集上训练`CTC model`的收敛曲线，其中横坐标轴为训练迭代次数，纵轴为样本级错误率。其中，蓝线为训练集上的样本错误率，红线为测试集上的样本错误率。测试集上最低错误率为22.0%. +图2为执行脚本`run_crnn_ctc.sh`在默认数据集上训练`CTC model`的收敛曲线，其中横坐标轴为训练迭代次数，纵轴为样本级错误率。其中，蓝线为训练集上的样本错误率，红线为测试集上的样本错误率。测试集上最低错误率为22.0%.


图 2

-图3为使用默认参数在默认数据集上训练`attention model`的收敛曲线，其中横坐标轴为训练迭代次数，纵轴为样本级错误率。其中，蓝线为训练集上的样本错误率，红线为测试集上的样本错误率。测试集上最低错误率为16.25%. +图3为执行脚本`run_attention.sh`在默认数据集上训练`attention model`的收敛曲线，其中横坐标轴为训练迭代次数，纵轴为样本级错误率。其中，蓝线为训练集上的样本错误率，红线为测试集上的样本错误率。测试集上最低错误率为16.25%.


diff --git a/PaddleCV/ocr_recognition/attention_model.py b/PaddleCV/ocr_recognition/attention_model.py index 8f556fb4..4d3b4da5 100755 --- a/PaddleCV/ocr_recognition/attention_model.py +++ b/PaddleCV/ocr_recognition/attention_model.py @@ -22,11 +22,7 @@ word_vector_dim = 128 max_length = 100 sos = 0 eos = 1 -gradient_clip = 10 -LR = 1.0 beam_size = 1 -learning_rate_decay = None - def conv_bn_pool(input, group, @@ -192,7 +188,7 @@ def attention_train_net(args, data_shape, num_classes): prediction = gru_decoder_with_attention(trg_embedding, encoded_vector, encoded_proj, decoder_boot, decoder_size, num_classes) - fluid.clip.set_gradient_clip(fluid.clip.GradientClipByValue(gradient_clip)) + fluid.clip.set_gradient_clip(fluid.clip.GradientClipByValue(args.gradient_clip)) label_out = fluid.layers.cast(x=label_out, dtype='int64') _, maxid = fluid.layers.topk(input=prediction, k=1) @@ -203,8 +199,8 @@ def attention_train_net(args, data_shape, num_classes): cost = fluid.layers.cross_entropy(input=prediction, label=label_out) sum_cost = fluid.layers.reduce_sum(cost) - - if learning_rate_decay == "piecewise_decay": + LR = args.lr + if args.lr_decay_strategy == "piecewise_decay": learning_rate = fluid.layers.piecewise_decay([50000], [LR, LR * 0.01]) else: learning_rate = LR diff --git a/PaddleCV/ocr_recognition/crnn_ctc_model.py b/PaddleCV/ocr_recognition/crnn_ctc_model.py index 5b7890b8..178f3292 100755 --- a/PaddleCV/ocr_recognition/crnn_ctc_model.py +++ b/PaddleCV/ocr_recognition/crnn_ctc_model.py @@ -184,9 +184,9 @@ def encoder_net(images, def ctc_train_net(args, data_shape, num_classes): - L2_RATE = 0.0004 - LR = 1.0e-3 - MOMENTUM = 0.9 + L2_RATE = args.l2decay + LR = args.lr + MOMENTUM = args.momentum learning_rate_decay = None regularizer = fluid.regularizer.L2Decay(L2_RATE) diff --git a/PaddleCV/ocr_recognition/run_attention.sh b/PaddleCV/ocr_recognition/run_attention.sh new file mode 100644 index 00000000..50ddba71 --- /dev/null +++ b/PaddleCV/ocr_recognition/run_attention.sh @@ -0,0 +1,9 @@ +export CUDA_VISIBLE_DEVICES=0 +nohup python train.py \ +--lr=1.0 \ +--gradient_clip=10 \ +--model="attention" \ +--log_period=10 \ +> attention.log 2>&1 & + +tailf attention.log diff --git a/PaddleCV/ocr_recognition/run_crnn_ctc.sh b/PaddleCV/ocr_recognition/run_crnn_ctc.sh new file mode 100644 index 00000000..11dc7d10 --- /dev/null +++ b/PaddleCV/ocr_recognition/run_crnn_ctc.sh @@ -0,0 +1,10 @@ +export CUDA_VISIBLE_DEVICES=0 +nohup python train.py \ +--lr=1e-3 \ +--l2decay=4e-4 \ +--momentum=0.9 \ +--model="crnn_ctc" \ +--log_period=10 \ +> crnn_ctc.log 2>&1 & + +tailf crnn_ctc.log diff --git a/PaddleCV/ocr_recognition/train.py b/PaddleCV/ocr_recognition/train.py index 08e22c75..3baf0535 100755 --- a/PaddleCV/ocr_recognition/train.py +++ b/PaddleCV/ocr_recognition/train.py @@ -32,26 +32,33 @@ import numpy as np parser = argparse.ArgumentParser(description=__doc__) add_arg = functools.partial(add_arguments, argparser=parser) # yapf: disable -add_arg('batch_size', int, 32, "Minibatch size.") -add_arg('total_step', int, 720000, "The number of iterations. Zero or less means whole training set. More than 0 means the training set might be looped until # of iterations is reached.") -add_arg('log_period', int, 1000, "Log period.") -add_arg('save_model_period', int, 15000, "Save model period. '-1' means never saving the model.") -add_arg('eval_period', int, 15000, "Evaluate period. '-1' means never evaluating the model.") -add_arg('save_model_dir', str, "./models", "The directory the model to be saved to.") -add_arg('train_images', str, None, "The directory of images to be used for training.") -add_arg('train_list', str, None, "The list file of images to be used for training.") -add_arg('test_images', str, None, "The directory of images to be used for test.") -add_arg('test_list', str, None, "The list file of images to be used for training.") -add_arg('model', str, "crnn_ctc", "Which type of network to be used. 'crnn_ctc' or 'attention'") -add_arg('init_model', str, None, "The init model file of directory.") -add_arg('use_gpu', bool, True, "Whether use GPU to train.") -add_arg('min_average_window',int, 10000, "Min average window.") -add_arg('max_average_window',int, 12500, "Max average window. It is proposed to be set as the number of minibatch in a pass.") -add_arg('average_window', float, 0.15, "Average window.") -add_arg('parallel', bool, False, "Whether use parallel training.") -add_arg('profile', bool, False, "Whether to use profiling.") -add_arg('skip_batch_num', int, 0, "The number of first minibatches to skip as warm-up for better performance test.") -add_arg('skip_test', bool, False, "Whether to skip test phase.") +add_arg('batch_size', int, 32, "Minibatch size.") + +add_arg('lr', float, 1e-3, "Learning rate.") +add_arg('lr_decay_strategy', str, None, "Learning rate decay strategy. 'piecewise_decay' or None is valid.") +add_arg('l2decay', float, 4e-4, "L2 decay rate.") +add_arg('momentum', float, 0.9, "Momentum rate.") +add_arg('gradient_clip', float, 10.0, "The threshold of gradient clipping.") + +add_arg('total_step', int, 720000, "The number of iterations. Zero or less means whole training set. More than 0 means the training set might be looped until # of iterations is reached.") +add_arg('log_period', int, 1000, "Log period.") +add_arg('save_model_period', int, 15000, "Save model period. '-1' means never saving the model.") +add_arg('eval_period', int, 15000, "Evaluate period. '-1' means never evaluating the model.") +add_arg('save_model_dir', str, "./models", "The directory the model to be saved to.") +add_arg('train_images', str, None, "The directory of images to be used for training.") +add_arg('train_list', str, None, "The list file of images to be used for training.") +add_arg('test_images', str, None, "The directory of images to be used for test.") +add_arg('test_list', str, None, "The list file of images to be used for training.") +add_arg('model', str, "crnn_ctc", "Which type of network to be used. 'crnn_ctc' or 'attention'") +add_arg('init_model', str, None, "The init model file of directory.") +add_arg('use_gpu', bool, True, "Whether use GPU to train.") +add_arg('min_average_window',int, 10000, "Min average window.") +add_arg('max_average_window',int, 12500, "Max average window. It is proposed to be set as the number of minibatch in a pass.") +add_arg('average_window', float, 0.15, "Average window.") +add_arg('parallel', bool, False, "Whether use parallel training.") +add_arg('profile', bool, False, "Whether to use profiling.") +add_arg('skip_batch_num', int, 0, "The number of first minibatches to skip as warm-up for better performance test.") +add_arg('skip_test', bool, False, "Whether to skip test phase.") # yapf: enable @@ -131,12 +138,13 @@ def train(args): for data in test_reader(): exe.run(inference_program, feed=get_feeder_data(data, place)) _, test_seq_error = error_evaluator.eval(exe) - print("\nTime: %s; Iter[%d]; Test seq error: %s.\n" % - (time.time(), iter_num, str(test_seq_error[0]))) + print("\n[%s] - Iter[%d]; Test seq error: %s.\n" % + (time.asctime( time.localtime(time.time())), iter_num, str(test_seq_error[0]))) #Note: The following logs are special for CE monitoring. #Other situations do not need to care about these logs. - print("kpis test_acc %f" % (1 - test_seq_error[0])) + if 'ce_mode' in os.environ: + print("kpis test_acc %f" % (1 - test_seq_error[0])) def save_model(args, exe, iter_num): filename = "model_%05d" % iter_num @@ -171,14 +179,15 @@ def train(args): iter_num += 1 # training log if iter_num % args.log_period == 0: - print("\nTime: %s; Iter[%d]; Avg loss: %.3f; Avg seq err: %.3f" - % (time.time(), iter_num, + print("\n[%s] - Iter[%d]; Avg loss: %.3f; Avg seq err: %.3f" + % (time.asctime( time.localtime(time.time())), iter_num, total_loss / (args.log_period * args.batch_size), total_seq_error / (args.log_period * args.batch_size))) - print("kpis train_cost %f" % (total_loss / (args.log_period * + if 'ce_mode' in os.environ: + print("kpis train_cost %f" % (total_loss / (args.log_period * args.batch_size))) - print("kpis train_acc %f" % ( - 1 - total_seq_error / (args.log_period * args.batch_size))) + print("kpis train_acc %f" % ( + 1 - total_seq_error / (args.log_period * args.batch_size))) total_loss = 0.0 total_seq_error = 0.0 @@ -198,7 +207,8 @@ def train(args): else: save_model(args, exe, iter_num) end_time = time.time() - print("kpis train_duration %f" % (end_time - start_time)) + if 'ce_mode' in os.environ: + print("kpis train_duration %f" % (end_time - start_time)) # Postprocess benchmark data latencies = batch_times[args.skip_batch_num:] latency_avg = np.average(latencies) -- GitLab