update typo and remove useless variables

demo/bert-cls/finetune_with_hub.py

-#   Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
-#
-# 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.
-"""Finetuning on classification tasks."""
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import os
-import time
-import argparse
-import numpy as np
-
-import paddle
-import paddle.fluid as fluid
-import paddle_hub as hub
-
-# yapf: disable
-parser = argparse.ArgumentParser(__doc__)
-parser.add_argument("--num_epoch", type=int, default=3, help="Number of epoches for fine-tuning.")
-parser.add_argument("--learning_rate", type=float, default=5e-5, help="Learning rate used to train with warmup.")
-parser.add_argument("--hub_module_dir", type=str, default=None, help="PaddleHub module directory")
-parser.add_argument("--lr_scheduler", type=str, default="linear_warmup_decay",
-        help="scheduler of learning rate.", choices=['linear_warmup_decay', 'noam_decay'])
-parser.add_argument("--weight_decay", type=float, default=0.01, help="Weight decay rate for L2 regularizer.")
-parser.add_argument("--data_dir", type=str, default=None, help="Path to training data.")
-parser.add_argument("--checkpoint_dir", type=str, default=None, help="Directory to model checkpoint")
-parser.add_argument("--max_seq_len", type=int, default=512, help="Number of words of the longest seqence.")
-parser.add_argument("--batch_size", type=int, default=32, help="Total examples' number in batch for training.")
-
-args = parser.parse_args()
-# yapf: enable.
-
-if __name__ == '__main__':
-    strategy = hub.BERTFinetuneStrategy(weight_decay=args.weight_decay)
-    config = hub.FinetuneConfig(
-        log_interval=10,
-        eval_interval=100,
-        save_ckpt_interval=200,
-        checkpoint_dir=args.checkpoint_dir,
-        learning_rate=args.learning_rate,
-        num_epoch=args.num_epoch,
-        batch_size=args.batch_size,
-        strategy=strategy)
-
-    # loading Paddlehub BERT
-    module = hub.Module(module_dir=args.hub_module_dir)
-
-    # Use BERTTokenizeReader to tokenize the dataset according to model's
-    # vocabulary
-    reader = hub.reader.BERTTokenizeReader(
-        dataset=hub.dataset.ChnSentiCorp(),  # download chnsenticorp dataset
-        vocab_path=module.get_vocab_path(),
-        max_seq_len=args.max_seq_len)
-
-    num_labels = len(reader.get_labels())
-
-    input_dict, output_dict, program = module.context(
-        sign_name="tokens", trainable=True, max_seq_len=args.max_seq_len)
-
-    with fluid.program_guard(program):
-        label = fluid.layers.data(name="label", shape=[1], dtype='int64')
-
-        # Use "pooled_output" for classification tasks on an entire sentence.
-        # Use "sequence_outputs" for token-level output.
-        pooled_output = output_dict["pooled_output"]
-
-        # Setup feed list for data feeder
-        # Must feed all the tensor of bert's module need
-        feed_list = [
-            input_dict["input_ids"].name, input_dict["position_ids"].name,
-            input_dict["segment_ids"].name, input_dict["input_mask"].name,
-            label.name
-        ]
-        # Define a classfication finetune task by PaddleHub's API
-        cls_task = hub.append_mlp_classifier(
-            pooled_output, label, num_classes=num_labels)
-
-        # Finetune and evaluate by PaddleHub's API
-        # will finish training, evaluation, testing, save model automatically
-        hub.finetune_and_eval(
-            task=cls_task,
-            data_reader=reader,
-            feed_list=feed_list,
-            config=config)

demo/bert-cls/run_fintune_with_hub.sh

-export CUDA_VISIBLE_DEVICES=5
-
-DATA_PATH=./chnsenticorp_data
-
-
-HUB_MODULE_DIR="./hub_module/bert_chinese_L-12_H-768_A-12.hub_module"
-#HUB_MODULE_DIR="./hub_module/ernie_stable.hub_module"
-CKPT_DIR="./ckpt"
-#rm -rf $CKPT_DIR
-python -u finetune_with_hub.py \
-                   --batch_size 32 \
-                   --hub_module_dir=$HUB_MODULE_DIR \
-                   --data_dir ${DATA_PATH} \
-                   --weight_decay  0.01 \
-                   --checkpoint_dir $CKPT_DIR \
-                   --num_epoch 3 \
-                   --max_seq_len 128 \
-                   --learning_rate 5e-5

paddle_hub/__init__.py

@@ -34,7 +34,7 @@ from .io.type import DataType
 
 from .finetune.network import append_mlp_classifier
 from .finetune.finetune import finetune_and_eval
-from .finetune.config import FinetuneConfig
+from .finetune.config import RunConfig
 from .finetune.task import Task
 from .finetune.strategy import BERTFinetuneStrategy
 from .finetune.strategy import DefaultStrategy

paddle_hub/finetune/config.py

@@ -15,10 +15,12 @@
 import time
 
 from .strategy import DefaultStrategy
-from paddle_hub.common.utils import md5
+from datetime import datetime
 
+from paddle_hub.common.logger import logger
 
-class FinetuneConfig(object):
+
+class RunConfig(object):
     """ This class specifies the configurations for PaddleHub to finetune """
 
     def __init__(self,
@@ -45,9 +47,13 @@ class FinetuneConfig(object):
             self._strategy = strategy
         self._enable_memory_optim = enable_memory_optim
         if checkpoint_dir is None:
-            self._checkpoint_dir = "hub_cpkt_" + md5(str(time.time()))[0:20]
+
+            now = int(time.time())
+            time_str = time.strftime("%Y%m%d%H%M%S", time.localtime(now))
+            self._checkpoint_dir = "ckpt_" + time_str
         else:
             self._checkpoint_dir = checkpoint_dir
+        logger.info("Checkpoint dir: {}".format(self._checkpoint_dir))
 
     @property
     def log_interval(self):