Merdev (#143)

* Add Reading Comprehension Taskwq

* Add Reading Comprehension Task

* Add Reading Comprehension Task

* dd Reading Comprehension Task

* Add the reading comprehension task

* add reading comprehension task

* Add reading comprehension

* Add reading comprehension task

* add reading comprehension task

* Add reading comprehension task

* Add reading comprehension task

* Fix tokenization for bert chn

* Add GLUE and XNLI, modify text-classification demo test=develop (#94)

* Add GLUE and XNLI, modify text-classification demo

* add hub_server rw lock

* Support GLUE (#108)

* Support GLUE

* Support MNLI_m and MNLI_mm

* restore checkpoint.py

* Modify for review

* Fix the bug whether module is valid or not

* Enhancetask (#122)

* accelerate predict

* Add autoft (#127)

* add autofinetune

* update paddlehub required libs

* Add copyright

* Split task and add regression demo  (#130)

* split task

* add regression demo

* implement CombinedStrategy (#128)

* implement CombinedStrategy

* optimize some details (#131)

* optimize some details

* optimize some details (#133)

* support cv_reader

* use logger instead of print

* add warning

* remove todo

* add gradual_unfreeze frz_blocks

* replace the visualization toolkit from visualdl to tb-paddle (#139)

* replace the logging toolkit from visualdl to tb-paddle.

* modified:   requirements.txt

* modified:   paddlehub/finetune/task/basic_task.py

* update autofinetune  (#135)

* update autofinetune (add modelbased evaluator)

* support cpu count

* fix ci

demo/image-classification/img_classifier.py

@@ -10,13 +10,13 @@ import numpy as np
 # yapf: disable
 parser = argparse.ArgumentParser(__doc__)
 parser.add_argument("--num_epoch",          type=int,               default=1,                          help="Number of epoches for fine-tuning.")
-parser.add_argument("--use_gpu",            type=ast.literal_eval,  default=False,                      help="Whether use GPU for fine-tuning.")
+parser.add_argument("--use_gpu",            type=ast.literal_eval,  default=True,                      help="Whether use GPU for fine-tuning.")
 parser.add_argument("--checkpoint_dir",     type=str,               default="paddlehub_finetune_ckpt",  help="Path to save log data.")
 parser.add_argument("--batch_size",         type=int,               default=16,                         help="Total examples' number in batch for training.")
 parser.add_argument("--module",             type=str,               default="resnet50",                 help="Module used as feature extractor.")
 parser.add_argument("--dataset",            type=str,               default="flowers",                  help="Dataset to finetune.")
-parser.add_argument("--use_pyreader",       type=ast.literal_eval,  default=False,                      help="Whether use pyreader to feed data.")
-parser.add_argument("--use_data_parallel",  type=ast.literal_eval,  default=False,                      help="Whether use data parallel.")
+parser.add_argument("--use_pyreader",       type=ast.literal_eval,  default=True,                      help="Whether use pyreader to feed data.")
+parser.add_argument("--use_data_parallel",  type=ast.literal_eval,  default=True,                      help="Whether use data parallel.")
 # yapf: enable.
 
 module_map = {

demo/reading-comprehension/evaluate_v1.py

+""" Official evaluation script for v1.1 of the SQuAD dataset. """
+from __future__ import print_function
+from collections import Counter
+import string
+import re
+import argparse
+import json
+import sys
+
+
+def normalize_answer(s):
+    """Lower text and remove punctuation, articles and extra whitespace."""
+
+    def remove_articles(text):
+        return re.sub(r'\b(a|an|the)\b', ' ', text)
+
+    def white_space_fix(text):
+        return ' '.join(text.split())
+
+    def remove_punc(text):
+        exclude = set(string.punctuation)
+        return ''.join(ch for ch in text if ch not in exclude)
+
+    def lower(text):
+        return text.lower()
+
+    return white_space_fix(remove_articles(remove_punc(lower(s))))
+
+
+def f1_score(prediction, ground_truth):
+    prediction_tokens = normalize_answer(prediction).split()
+    ground_truth_tokens = normalize_answer(ground_truth).split()
+    common = Counter(prediction_tokens) & Counter(ground_truth_tokens)
+    num_same = sum(common.values())
+    if num_same == 0:
+        return 0
+    precision = 1.0 * num_same / len(prediction_tokens)
+    recall = 1.0 * num_same / len(ground_truth_tokens)
+    f1 = (2 * precision * recall) / (precision + recall)
+    return f1
+
+
+def exact_match_score(prediction, ground_truth):
+    return (normalize_answer(prediction) == normalize_answer(ground_truth))
+
+
+def metric_max_over_ground_truths(metric_fn, prediction, ground_truths):
+    scores_for_ground_truths = []
+    for ground_truth in ground_truths:
+        score = metric_fn(prediction, ground_truth)
+        scores_for_ground_truths.append(score)
+    return max(scores_for_ground_truths)
+
+
+def evaluate(dataset, predictions):
+    f1 = exact_match = total = 0
+    for article in dataset:
+        for paragraph in article['paragraphs']:
+            for qa in paragraph['qas']:
+                total += 1
+                if qa['id'] not in predictions:
+                    message = 'Unanswered question ' + qa['id'] + \
+                              ' will receive score 0.'
+                    print(message, file=sys.stderr)
+                    continue
+                ground_truths = list(map(lambda x: x['text'], qa['answers']))
+                prediction = predictions[qa['id']]
+                exact_match += metric_max_over_ground_truths(
+                    exact_match_score, prediction, ground_truths)
+                f1 += metric_max_over_ground_truths(f1_score, prediction,
+                                                    ground_truths)
+
+    exact_match = 100.0 * exact_match / total
+    f1 = 100.0 * f1 / total
+
+    return {'exact_match': exact_match, 'f1': f1}
+
+
+if __name__ == '__main__':
+    expected_version = '1.1'
+    parser = argparse.ArgumentParser(
+        description='Evaluation for SQuAD ' + expected_version)
+    parser.add_argument('dataset_file', help='Dataset file')
+    parser.add_argument('prediction_file', help='Prediction File')
+    args = parser.parse_args()
+    with open(args.dataset_file) as dataset_file:
+        dataset_json = json.load(dataset_file)
+        if (dataset_json['version'] != expected_version):
+            print(
+                'Evaluation expects v-' + expected_version +
+                ', but got dataset with v-' + dataset_json['version'],
+                file=sys.stderr)
+        dataset = dataset_json['data']
+
+    print(args.prediction_file)
+    with open(args.prediction_file) as prediction_file:
+        predictions = json.load(prediction_file)
+    print(json.dumps(evaluate(dataset, predictions)))

demo/reading-comprehension/evaluate_v2.py

+"""Official evaluation script for SQuAD version 2.0.
+
+In addition to basic functionality, we also compute additional statistics and
+plot precision-recall curves if an additional na_prob.json file is provided.
+This file is expected to map question ID's to the model's predicted probability
+that a question is unanswerable.
+"""
+import argparse
+import collections
+import json
+import numpy as np
+import os
+import re
+import string
+import sys
+
+
+def make_qid_to_has_ans(dataset):
+    qid_to_has_ans = {}
+    for article in dataset:
+        for p in article['paragraphs']:
+            for qa in p['qas']:
+                qid_to_has_ans[qa['id']] = bool(qa['answers'])
+    return qid_to_has_ans
+
+
+def normalize_answer(s):
+    """Lower text and remove punctuation, articles and extra whitespace."""
+
+    def remove_articles(text):
+        regex = re.compile(r'\b(a|an|the)\b', re.UNICODE)
+        return re.sub(regex, ' ', text)
+
+    def white_space_fix(text):
+        return ' '.join(text.split())
+
+    def remove_punc(text):
+        exclude = set(string.punctuation)
+        return ''.join(ch for ch in text if ch not in exclude)
+
+    def lower(text):
+        return text.lower()
+
+    return white_space_fix(remove_articles(remove_punc(lower(s))))
+
+
+def get_tokens(s):
+    if not s: return []
+    return normalize_answer(s).split()
+
+
+def compute_exact(a_gold, a_pred):
+    return int(normalize_answer(a_gold) == normalize_answer(a_pred))
+
+
+def compute_f1(a_gold, a_pred):
+    gold_toks = get_tokens(a_gold)
+    pred_toks = get_tokens(a_pred)
+    common = collections.Counter(gold_toks) & collections.Counter(pred_toks)
+    num_same = sum(common.values())
+    if len(gold_toks) == 0 or len(pred_toks) == 0:
+        # If either is no-answer, then F1 is 1 if they agree, 0 otherwise
+        return int(gold_toks == pred_toks)
+    if num_same == 0:
+        return 0
+    precision = 1.0 * num_same / len(pred_toks)
+    recall = 1.0 * num_same / len(gold_toks)
+    f1 = (2 * precision * recall) / (precision + recall)
+    return f1
+
+
+def get_raw_scores(dataset, preds):
+    exact_scores = {}
+    f1_scores = {}
+    for article in dataset:
+        for p in article['paragraphs']:
+            for qa in p['qas']:
+                qid = qa['id']
+                gold_answers = [
+                    a['text'] for a in qa['answers']
+                    if normalize_answer(a['text'])
+                ]
+                if not gold_answers:
+                    # For unanswerable questions, only correct answer is empty string
+                    gold_answers = ['']
+                if qid not in preds:
+                    print('Missing prediction for %s' % qid)
+                    continue
+                a_pred = preds[qid]
+                # Take max over all gold answers
+                exact_scores[qid] = max(
+                    compute_exact(a, a_pred) for a in gold_answers)
+                f1_scores[qid] = max(
+                    compute_f1(a, a_pred) for a in gold_answers)
+    return exact_scores, f1_scores
+
+
+def apply_no_ans_threshold(scores, na_probs, qid_to_has_ans, na_prob_thresh):
+    new_scores = {}
+    for qid, s in scores.items():
+        pred_na = na_probs[qid] > na_prob_thresh
+        if pred_na:
+            new_scores[qid] = float(not qid_to_has_ans[qid])
+        else:
+            new_scores[qid] = s
+    return new_scores
+
+
+def make_eval_dict(exact_scores, f1_scores, qid_list=None):
+    if not qid_list:
+        total = len(exact_scores)
+        return collections.OrderedDict([
+            ('exact', 100.0 * sum(exact_scores.values()) / total),
+            ('f1', 100.0 * sum(f1_scores.values()) / total),
+            ('total', total),
+        ])
+    else:
+        total = len(qid_list)
+        return collections.OrderedDict([
+            ('exact', 100.0 * sum(exact_scores[k] for k in qid_list) / total),
+            ('f1', 100.0 * sum(f1_scores[k] for k in qid_list) / total),
+            ('total', total),
+        ])
+
+
+def merge_eval(main_eval, new_eval, prefix):
+    for k in new_eval:
+        main_eval['%s_%s' % (prefix, k)] = new_eval[k]
+
+
+def find_all_best_thresh(main_eval, preds, exact_raw, f1_raw, na_probs,
+                         qid_to_has_ans):
+    best_exact, exact_thresh = find_best_thresh(preds, exact_raw, na_probs,
+                                                qid_to_has_ans)
+    best_f1, f1_thresh = find_best_thresh(preds, f1_raw, na_probs,
+                                          qid_to_has_ans)
+    main_eval['best_exact'] = best_exact
+    main_eval['best_exact_thresh'] = exact_thresh
+    main_eval['best_f1'] = best_f1
+    main_eval['best_f1_thresh'] = f1_thresh
+
+
+def find_best_thresh(preds, scores, na_probs, qid_to_has_ans):
+    num_no_ans = sum(1 for k in qid_to_has_ans if not qid_to_has_ans[k])
+    cur_score = num_no_ans
+    best_score = cur_score
+    best_thresh = 0.0
+    qid_list = sorted(na_probs, key=lambda k: na_probs[k])
+    for i, qid in enumerate(qid_list):
+        if qid not in scores:
+            continue
+        if qid_to_has_ans[qid]:
+            diff = scores[qid]
+        else:
+            if preds[qid]:
+                diff = -1
+            else:
+                diff = 0
+        cur_score += diff
+        if cur_score > best_score:
+            best_score = cur_score
+            best_thresh = na_probs[qid]
+    return 100.0 * best_score / len(scores), best_thresh

demo/reading-comprehension/reading_comprehension.py

+#coding:utf-8
+#   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 task """
+
+import argparse
+import ast
+
+import paddle.fluid as fluid
+import paddlehub as hub
+
+hub.common.logger.logger.setLevel("INFO")
+
+# yapf: disable
+parser = argparse.ArgumentParser(__doc__)
+parser.add_argument("--num_epoch", type=int, default=1, help="Number of epoches for fine-tuning.")
+parser.add_argument("--use_gpu", type=ast.literal_eval, default=True, help="Whether use GPU for finetuning, input should be True or False")
+parser.add_argument("--learning_rate", type=float, default=3e-5, help="Learning rate used to train with warmup.")
+parser.add_argument("--weight_decay", type=float, default=0.01, help="Weight decay rate for L2 regularizer.")
+parser.add_argument("--warmup_proportion", type=float, default=0.0, help="Warmup proportion params for warmup strategy")
+parser.add_argument("--checkpoint_dir", type=str, default=None, help="Directory to model checkpoint")
+parser.add_argument("--max_seq_len", type=int, default=384, help="Number of words of the longest seqence.")
+parser.add_argument("--batch_size", type=int, default=8, help="Total examples' number in batch for training.")
+parser.add_argument("--use_pyreader", type=ast.literal_eval, default=True, help="Whether use pyreader to feed data.")
+parser.add_argument("--use_data_parallel", type=ast.literal_eval, default=True, help="Whether use data parallel.")
+parser.add_argument("--version_2_with_negative", type=ast.literal_eval, default=False, help="If true, the SQuAD examples contain some that do not have an answer. If using squad v2.0, it should be set true.")
+args = parser.parse_args()
+# yapf: enable.
+
+if __name__ == '__main__':
+    # Load Paddlehub bert_uncased_L-12_H-768_A-12 pretrained model
+    module = hub.Module(name="bert_uncased_L-12_H-768_A-12")
+
+    inputs, outputs, program = module.context(
+        trainable=True, max_seq_len=args.max_seq_len)
+
+    # Download dataset and use ReadingComprehensionReader to read dataset
+    dataset = hub.dataset.SQUAD(
+        version_2_with_negative=args.version_2_with_negative)
+
+    reader = hub.reader.ReadingComprehensionReader(
+        dataset=dataset,
+        vocab_path=module.get_vocab_path(),
+        max_seq_length=args.max_seq_len,
+        doc_stride=128,
+        max_query_length=64)
+
+    seq_output = outputs["sequence_output"]
+
+    # Setup feed list for data feeder
+    feed_list = [
+        inputs["input_ids"].name,
+        inputs["position_ids"].name,
+        inputs["segment_ids"].name,
+        inputs["input_mask"].name,
+    ]
+
+    # Select finetune strategy, setup config and finetune
+    strategy = hub.AdamWeightDecayStrategy(
+        weight_decay=args.weight_decay,
+        learning_rate=args.learning_rate,
+        warmup_proportion=args.warmup_proportion,
+        lr_scheduler="linear_decay")
+
+    # Setup runing config for PaddleHub Finetune API
+    config = hub.RunConfig(
+        log_interval=10,
+        use_pyreader=args.use_pyreader,
+        use_data_parallel=args.use_data_parallel,
+        save_ckpt_interval=1000,
+        use_cuda=args.use_gpu,
+        num_epoch=args.num_epoch,
+        batch_size=args.batch_size,
+        checkpoint_dir=args.checkpoint_dir,
+        enable_memory_optim=True,
+        strategy=strategy)
+
+    # Define a reading comprehension finetune task by PaddleHub's API
+    reading_comprehension_task = hub.ReadingComprehensionTask(
+        data_reader=reader,
+        feature=seq_output,
+        feed_list=feed_list,
+        config=config)
+
+    # Finetune by PaddleHub's API
+    reading_comprehension_task.finetune()

demo/reading-comprehension/run_finetune.sh

+export FLAGS_eager_delete_tensor_gb=0.0
+export CUDA_VISIBLE_DEVICES=0,1
+
+python -u reading_comprehension.py \
+                   --batch_size=12 \
+                   --use_gpu=True \
+                   --checkpoint_dir="./ckpt_rc" \
+                   --learning_rate=3e-5 \
+                   --weight_decay=0.01 \
+                   --warmup_proportion=0.1 \
+                   --num_epoch=2 \
+                   --max_seq_len=384 \
+                   --use_pyreader=True \
+                   --use_data_parallel=True \
+                   --version_2_with_negative=False

demo/reading-comprehension/run_predict.sh

+export FLAGS_eager_delete_tensor_gb=0.0
+export CUDA_VISIBLE_DEVICES=0
+
+CKPT_DIR="./ckpt_rc"
+RES_DIR="./result"
+
+mkdir $RES_DIR
+
+python -u  predict.py \
+                   --batch_size=12 \
+                   --use_gpu=True \
+                   --checkpoint_dir=${CKPT_DIR} \
+                   --learning_rate=3e-5 \
+                   --weight_decay=0.01 \
+                   --warmup_proportion=0.1 \
+                   --num_epoch=1 \
+                   --max_seq_len=384 \
+                   --use_pyreader=False \
+                   --use_data_parallel=False \
+                   --version_2_with_negative=False \
+                   --result_dir=${RES_DIR}

demo/regression/predict.py

+#coding:utf-8
+#   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 task """
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import argparse
+import ast
+import numpy as np
+import os
+import time
+
+import paddle
+import paddle.fluid as fluid
+import paddlehub as hub
+
+# yapf: disable
+parser = argparse.ArgumentParser(__doc__)
+parser.add_argument("--checkpoint_dir", type=str, default=None, help="Directory to model checkpoint")
+parser.add_argument("--batch_size",     type=int,   default=1, help="Total examples' number in batch for training.")
+parser.add_argument("--max_seq_len", type=int, default=512, help="Number of words of the longest seqence.")
+parser.add_argument("--use_gpu", type=ast.literal_eval, default=False, help="Whether use GPU for finetuning, input should be True or False")
+parser.add_argument("--use_pyreader", type=ast.literal_eval, default=False, help="Whether use pyreader to feed data.")
+parser.add_argument("--dataset", type=str, default="STS-B", help="Directory to model checkpoint")
+args = parser.parse_args()
+# yapf: enable.
+
+if __name__ == '__main__':
+    dataset = None
+    metrics_choices = []
+    # Download dataset and use ClassifyReader to read dataset
+    if args.dataset.lower() == "sts-b":
+        dataset = hub.dataset.GLUE("STS-B")
+        module = hub.Module(name="bert_uncased_L-12_H-768_A-12")
+        metrics_choices = ["acc"]
+    else:
+        raise ValueError("%s dataset is not defined" % args.dataset)
+
+    support_metrics = ["acc", "f1", "matthews"]
+    for metric in metrics_choices:
+        if metric not in support_metrics:
+            raise ValueError("\"%s\" metric is not defined" % metric)
+
+    inputs, outputs, program = module.context(
+        trainable=True, max_seq_len=args.max_seq_len)
+    reader = hub.reader.RegressionReader(
+        dataset=dataset,
+        vocab_path=module.get_vocab_path(),
+        max_seq_len=args.max_seq_len)
+
+    # Construct transfer learning network
+    # Use "pooled_output" for classification tasks on an entire sentence.
+    # Use "sequence_output" for token-level output.
+    pooled_output = outputs["pooled_output"]
+
+    # Setup feed list for data feeder
+    # Must feed all the tensor of ERNIE's module need
+    feed_list = [
+        inputs["input_ids"].name,
+        inputs["position_ids"].name,
+        inputs["segment_ids"].name,
+        inputs["input_mask"].name,
+    ]
+
+    # Setup runing config for PaddleHub Finetune API
+    config = hub.RunConfig(
+        use_data_parallel=False,
+        use_pyreader=args.use_pyreader,
+        use_cuda=args.use_gpu,
+        batch_size=args.batch_size,
+        enable_memory_optim=False,
+        checkpoint_dir=args.checkpoint_dir,
+        strategy=hub.finetune.strategy.DefaultFinetuneStrategy())
+
+    # Define a regression finetune task by PaddleHub's API
+    reg_task = hub.RegressionTask(
+        data_reader=reader,
+        feature=pooled_output,
+        feed_list=feed_list,
+        config=config)
+
+    # Data to be prdicted
+    data = [[d.text_a, d.text_b] for d in dataset.get_predict_examples()]
+
+    index = 0
+    run_states = reg_task.predict(data=data)
+    results = [run_state.run_results for run_state in run_states]
+    if not os.path.exists("output"):
+        os.makedirs("output")
+    fout = open(os.path.join("output", "%s.tsv" % args.dataset.upper()), 'w')
+    fout.write("index\tprediction")
+    for batch_result in results:
+        for result in batch_result[0]:
+            if index < 3:
+                print("%s\t%s\tpredict=%.3f" % (data[index][0], data[index][1],
+                                                result[0]))
+            fout.write("\n%s\t%.3f" % (index, result[0]))
+            index += 1
+    fout.close()

demo/regression/regression.py

+#coding:utf-8
+#   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 task """
+
+import argparse
+import ast
+
+import paddle.fluid as fluid
+import paddlehub 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("--use_gpu", type=ast.literal_eval, default=False, help="Whether use GPU for finetuning, input should be True or False")
+parser.add_argument("--dataset", type=str, default="STS-B", help="Directory to model checkpoint")
+parser.add_argument("--learning_rate", type=float, default=5e-5, help="Learning rate used to train with warmup.")
+parser.add_argument("--weight_decay", type=float, default=0.01, help="Weight decay rate for L2 regularizer.")
+parser.add_argument("--warmup_proportion", type=float, default=0.0, help="Warmup proportion params for warmup strategy")
+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.")
+parser.add_argument("--use_pyreader", type=ast.literal_eval, default=False, help="Whether use pyreader to feed data.")
+parser.add_argument("--use_data_parallel", type=ast.literal_eval, default=False, help="Whether use data parallel.")
+args = parser.parse_args()
+# yapf: enable.
+
+if __name__ == '__main__':
+    dataset = None
+    # Download dataset and use ClassifyReader to read dataset
+    if args.dataset.lower() == "sts-b":
+        dataset = hub.dataset.GLUE("STS-B")
+        module = hub.Module(name="bert_uncased_L-12_H-768_A-12")
+    else:
+        raise ValueError("%s dataset is not defined" % args.dataset)
+
+    inputs, outputs, program = module.context(
+        trainable=True, max_seq_len=args.max_seq_len)
+    reader = hub.reader.RegressionReader(
+        dataset=dataset,
+        vocab_path=module.get_vocab_path(),
+        max_seq_len=args.max_seq_len)
+
+    # Construct transfer learning network
+    # Use "pooled_output" for classification tasks on an entire sentence.
+    # Use "sequence_output" for token-level output.
+    pooled_output = outputs["pooled_output"]
+
+    # Setup feed list for data feeder
+    # Must feed all the tensor of ERNIE's module need
+    feed_list = [
+        inputs["input_ids"].name,
+        inputs["position_ids"].name,
+        inputs["segment_ids"].name,
+        inputs["input_mask"].name,
+    ]
+
+    # Select finetune strategy, setup config and finetune
+    strategy = hub.AdamWeightDecayStrategy(
+        weight_decay=args.weight_decay,
+        learning_rate=args.learning_rate,
+        lr_scheduler="linear_decay")
+
+    # Setup runing config for PaddleHub Finetune API
+    config = hub.RunConfig(
+        use_data_parallel=args.use_data_parallel,
+        use_pyreader=args.use_pyreader,
+        use_cuda=args.use_gpu,
+        num_epoch=args.num_epoch,
+        batch_size=args.batch_size,
+        checkpoint_dir=args.checkpoint_dir,
+        strategy=strategy)
+
+    # Define a regression finetune task by PaddleHub's API
+    reg_task = hub.RegressionTask(
+        data_reader=reader,
+        feature=pooled_output,
+        feed_list=feed_list,
+        config=config)
+
+    # Finetune and evaluate by PaddleHub's API
+    # will finish training, evaluation, testing, save model automatically
+    reg_task.finetune_and_eval()

demo/regression/run_predict.sh

+export FLAGS_eager_delete_tensor_gb=0.0
+# export CUDA_VISIBLE_DEVICES=0
+
+# User can select chnsenticorp, nlpcc_dbqa, lcqmc and so on for different task
+DATASET="STS-B"
+CKPT_DIR="./ckpt_${DATASET}"
+# STS-B: batch_size=32, max_seq_len=128
+
+python -u predict.py --checkpoint_dir $CKPT_DIR \
+                            --max_seq_len 128 \
+                            --use_gpu True \
+                            --dataset=${DATASET} \
+                            --batch_size=32 \

demo/regression/run_regssion.sh

+export FLAGS_eager_delete_tensor_gb=0.0
+export CUDA_VISIBLE_DEVICES=0
+
+DATASET="STS-B"
+CKPT_DIR="./ckpt_${DATASET}"
+# Recommending hyper parameters for difference task
+# STS-B: batch_size=32, weight_decay=0.1, num_epoch=3, max_seq_len=128, lr=4e-5
+
+python -u regression.py \
+                   --batch_size=32 \
+                   --use_gpu=True \
+                   --dataset=${DATASET} \
+                   --checkpoint_dir=${CKPT_DIR} \
+                   --learning_rate=4e-5 \
+                   --weight_decay=0.1 \
+                   --max_seq_len=128 \
+                   --num_epoch=3 \
+                   --use_pyreader=True \
+                   --use_data_parallel=True \

demo/text-classification/predict.py

@@ -29,7 +29,7 @@ import paddlehub as hub
 
 # yapf: disable
 parser = argparse.ArgumentParser(__doc__)
-parser.add_argument("--checkpoint_dir", type=str, default="ckpt_20190802182531", help="Directory to model checkpoint")
+parser.add_argument("--checkpoint_dir", type=str, default=None, help="Directory to model checkpoint")
 parser.add_argument("--batch_size",     type=int,   default=1, help="Total examples' number in batch for training.")
 parser.add_argument("--max_seq_len", type=int, default=512, help="Number of words of the longest seqence.")
 parser.add_argument("--use_gpu", type=ast.literal_eval, default=False, help="Whether use GPU for finetuning, input should be True or False")
@@ -42,64 +42,89 @@ args = parser.parse_args()
 
 if __name__ == '__main__':
     dataset = None
+    metrics_choices = []
     # Download dataset and use ClassifyReader to read dataset
     if args.dataset.lower() == "chnsenticorp":
         dataset = hub.dataset.ChnSentiCorp()
         module = hub.Module(name="ernie")
+        metrics_choices = ["acc"]
     elif args.dataset.lower() == "nlpcc_dbqa":
         dataset = hub.dataset.NLPCC_DBQA()
         module = hub.Module(name="ernie")
+        metrics_choices = ["acc"]
     elif args.dataset.lower() == "lcqmc":
         dataset = hub.dataset.LCQMC()
         module = hub.Module(name="ernie")
+        metrics_choices = ["acc"]
     elif args.dataset.lower() == "mrpc":
         dataset = hub.dataset.GLUE("MRPC")
         if args.use_taskid:
             module = hub.Module(name="ernie_v2_eng_base")
         else:
             module = hub.Module(name="bert_uncased_L-12_H-768_A-12")
+        metrics_choices = ["f1", "acc"]
+    # The first metric will be choose to eval. Ref: task.py:799
     elif args.dataset.lower() == "qqp":
         dataset = hub.dataset.GLUE("QQP")
         if args.use_taskid:
             module = hub.Module(name="ernie_v2_eng_base")
         else:
             module = hub.Module(name="bert_uncased_L-12_H-768_A-12")
+        metrics_choices = ["f1", "acc"]
     elif args.dataset.lower() == "sst-2":
         dataset = hub.dataset.GLUE("SST-2")
         if args.use_taskid:
             module = hub.Module(name="ernie_v2_eng_base")
         else:
             module = hub.Module(name="bert_uncased_L-12_H-768_A-12")
+        metrics_choices = ["acc"]
     elif args.dataset.lower() == "cola":
         dataset = hub.dataset.GLUE("CoLA")
         if args.use_taskid:
             module = hub.Module(name="ernie_v2_eng_base")
         else:
             module = hub.Module(name="bert_uncased_L-12_H-768_A-12")
+        metrics_choices = ["matthews", "acc"]
     elif args.dataset.lower() == "qnli":
         dataset = hub.dataset.GLUE("QNLI")
         if args.use_taskid:
             module = hub.Module(name="ernie_v2_eng_base")
         else:
             module = hub.Module(name="bert_uncased_L-12_H-768_A-12")
+        metrics_choices = ["acc"]
     elif args.dataset.lower() == "rte":
         dataset = hub.dataset.GLUE("RTE")
         if args.use_taskid:
             module = hub.Module(name="ernie_v2_eng_base")
         else:
             module = hub.Module(name="bert_uncased_L-12_H-768_A-12")
-    elif args.dataset.lower() == "mnli":
-        dataset = hub.dataset.GLUE("MNLI")
+        metrics_choices = ["acc"]
+    elif args.dataset.lower() == "mnli" or args.dataset.lower() == "mnli_m":
+        dataset = hub.dataset.GLUE("MNLI_m")
         if args.use_taskid:
             module = hub.Module(name="ernie_v2_eng_base")
         else:
             module = hub.Module(name="bert_uncased_L-12_H-768_A-12")
+        metrics_choices = ["acc"]
+    elif args.dataset.lower() == "mnli_mm":
+        dataset = hub.dataset.GLUE("MNLI_mm")
+        if args.use_taskid:
+            module = hub.Module(name="ernie_v2_eng_base")
+        else:
+            module = hub.Module(name="bert_uncased_L-12_H-768_A-12")
+        metrics_choices = ["acc"]
     elif args.dataset.lower().startswith("xnli"):
         dataset = hub.dataset.XNLI(language=args.dataset.lower()[-2:])
         module = hub.Module(name="bert_multi_cased_L-12_H-768_A-12")
+        metrics_choices = ["acc"]
     else:
         raise ValueError("%s dataset is not defined" % args.dataset)
 
+    support_metrics = ["acc", "f1", "matthews"]
+    for metric in metrics_choices:
+        if metric not in support_metrics:
+            raise ValueError("\"%s\" metric is not defined" % metric)
+
     inputs, outputs, program = module.context(
         trainable=True, max_seq_len=args.max_seq_len)
     reader = hub.reader.ClassifyReader(
@@ -147,7 +172,8 @@ if __name__ == '__main__':
         feature=pooled_output,
         feed_list=feed_list,
         num_classes=dataset.num_labels,
-        config=config)
+        config=config,
+        metrics_choices=metrics_choices)
 
     # Data to be prdicted
     data = [[d.text_a, d.text_b] for d in dataset.get_dev_examples()[:3]]

demo/text-classification/run_classifier.sh

@@ -2,18 +2,32 @@ export FLAGS_eager_delete_tensor_gb=0.0
 export CUDA_VISIBLE_DEVICES=0
 
 # User can select chnsenticorp, nlpcc_dbqa, lcqmc and so on for different task
-
 DATASET="chnsenticorp"
 CKPT_DIR="./ckpt_${DATASET}"
 
-# Support ChnSentiCorp	NLPCC_DBQA	LCQMC	MRPC	QQP	SST-2
-#         CoLA	QNLI	RTE	MNLI	XNLI
-# for XNLI: Specify the language with an underscore like xnli_zh.
-#       ar: Arabic      bg: Bulgarian      de: German
-#       el: Greek       en: English        es: Spanish
-#       fr: French      hi: Hindi          ru: Russian
-#       sw: Swahili     th: Thai           tr: Turkish
-#       ur: Urdu        vi: Vietnamese     zh: Chinese (Simplified)
+# Recommending hyper parameters for difference task
+# ChnSentiCorp: batch_size=24, weight_decay=0.01, num_epoch=3, max_seq_len=128, lr=5e-5
+# NLPCC_DBQA: batch_size=8, weight_decay=0.01, num_epoch=3, max_seq_len=512, lr=2e-5
+# LCQMC: batch_size=32, weight_decay=0, num_epoch=3, max_seq_len=128, lr=2e-5
+# QQP: batch_size=32, weight_decay=0, num_epoch=3, max_seq_len=128, lr=5e-5
+# QNLI: batch_size=32, weight_decay=0, num_epoch=3, max_seq_len=128, lr=5e-5
+# SST-2: batch_size=32, weight_decay=0, num_epoch=3, max_seq_len=128, lr=5e-5
+# CoLA: batch_size=32, weight_decay=0, num_epoch=3, max_seq_len=128, lr=5e-5
+# MRPC: batch_size=32, weight_decay=0.01, num_epoch=3, max_seq_len=128, lr=5e-5
+# RTE: batch_size=32, weight_decay=0, num_epoch=3, max_seq_len=128, lr=3e-5
+# MNLI: batch_size=32, weight_decay=0, num_epoch=3, max_seq_len=128, lr=5e-5
+#       Specify the matched/mismatched dev and test dataset  with an underscore.
+#       mnli_m or mnli: dev and test in matched dataset.
+#       mnli_mm: dev and test in mismatched dataset.
+#      The difference can be seen in https://www.nyu.edu/projects/bowman/multinli/paper.pdf.
+#       If you are not sure which one to pick, just use mnli or mnli_m.
+# XNLI: batch_size=32, weight_decay=0, num_epoch=2, max_seq_len=128, lr=5e-5
+#       Specify the language with an underscore like xnli_zh.
+#       ar- Arabic      bg- Bulgarian      de- German
+#       el- Greek       en- English        es- Spanish
+#       fr- French      hi- Hindi          ru- Russian
+#       sw- Swahili     th- Thai           tr- Turkish
+#       ur- Urdu        vi- Vietnamese     zh- Chinese (Simplified)
 
 python -u text_classifier.py \
                    --batch_size=24 \

demo/text-classification/run_predict.sh

@@ -2,17 +2,20 @@ export FLAGS_eager_delete_tensor_gb=0.0
 export CUDA_VISIBLE_DEVICES=0
 
 # User can select chnsenticorp, nlpcc_dbqa, lcqmc and so on for different task
-
-DATASET="chnsenticorp"
-CKPT_DIR="./ckpt_${DATASET}"
-
 # Support ChnSentiCorp	NLPCC_DBQA	LCQMC	MRPC	QQP	SST-2
-#         CoLA	QNLI	RTE	MNLI	XNLI
+#         CoLA	QNLI	RTE	MNLI (or MNLI_m) 	MNLI_mm）	XNLI
 # for XNLI: Specify the language with an underscore like xnli_zh.
 #       ar: Arabic      bg: Bulgarian      de: German
 #       el: Greek       en: English        es: Spanish
 #       fr: French      hi: Hindi          ru: Russian
 #       sw: Swahili     th: Thai           tr: Turkish
 #       ur: Urdu        vi: Vietnamese     zh: Chinese (Simplified)
+DATASET="ChnSentiCorp"
+CKPT_DIR="./ckpt_${DATASET}"
 
-python -u predict.py --checkpoint_dir $CKPT_DIR --max_seq_len 128 --use_gpu False --dataset=${DATASET} --use_taskid False
+python -u predict.py --checkpoint_dir=$CKPT_DIR \
+                            --max_seq_len=128 \
+                            --use_gpu=True \
+                            --dataset=${DATASET} \
+                            --batch_size=150 \
+                            --use_taskid=False \

demo/text-classification/text_classifier.py

@@ -26,7 +26,7 @@ parser.add_argument("--use_gpu", type=ast.literal_eval, default=True, help="Whet
 parser.add_argument("--dataset", type=str, default="chnsenticorp", help="The choice of dataset")
 parser.add_argument("--learning_rate", type=float, default=5e-5, help="Learning rate used to train with warmup.")
 parser.add_argument("--weight_decay", type=float, default=0.01, help="Weight decay rate for L2 regularizer.")
-parser.add_argument("--warmup_proportion", type=float, default=0.1, help="Warmup proportion params for warmup strategy")
+parser.add_argument("--warmup_proportion", type=float, default=0.0, help="Warmup proportion params for warmup strategy")
 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.")
@@ -39,64 +39,89 @@ args = parser.parse_args()
 
 if __name__ == '__main__':
     dataset = None
+    metrics_choices = []
     # Download dataset and use ClassifyReader to read dataset
     if args.dataset.lower() == "chnsenticorp":
         dataset = hub.dataset.ChnSentiCorp()
         module = hub.Module(name="ernie")
+        metrics_choices = ["acc"]
     elif args.dataset.lower() == "nlpcc_dbqa":
         dataset = hub.dataset.NLPCC_DBQA()
         module = hub.Module(name="ernie")
+        metrics_choices = ["acc"]
     elif args.dataset.lower() == "lcqmc":
         dataset = hub.dataset.LCQMC()
         module = hub.Module(name="ernie")
+        metrics_choices = ["acc"]
     elif args.dataset.lower() == "mrpc":
         dataset = hub.dataset.GLUE("MRPC")
         if args.use_taskid:
             module = hub.Module(name="ernie_v2_eng_base")
         else:
             module = hub.Module(name="bert_uncased_L-12_H-768_A-12")
+        metrics_choices = ["f1", "acc"]
+    # The first metric will be choose to eval. Ref: task.py:799
     elif args.dataset.lower() == "qqp":
         dataset = hub.dataset.GLUE("QQP")
         if args.use_taskid:
             module = hub.Module(name="ernie_v2_eng_base")
         else:
             module = hub.Module(name="bert_uncased_L-12_H-768_A-12")
+        metrics_choices = ["f1", "acc"]
     elif args.dataset.lower() == "sst-2":
         dataset = hub.dataset.GLUE("SST-2")
         if args.use_taskid:
             module = hub.Module(name="ernie_v2_eng_base")
         else:
             module = hub.Module(name="bert_uncased_L-12_H-768_A-12")
+        metrics_choices = ["acc"]
     elif args.dataset.lower() == "cola":
         dataset = hub.dataset.GLUE("CoLA")
         if args.use_taskid:
             module = hub.Module(name="ernie_v2_eng_base")
         else:
             module = hub.Module(name="bert_uncased_L-12_H-768_A-12")
+        metrics_choices = ["matthews", "acc"]
     elif args.dataset.lower() == "qnli":
         dataset = hub.dataset.GLUE("QNLI")
         if args.use_taskid:
             module = hub.Module(name="ernie_v2_eng_base")
         else:
             module = hub.Module(name="bert_uncased_L-12_H-768_A-12")
+        metrics_choices = ["acc"]
     elif args.dataset.lower() == "rte":
         dataset = hub.dataset.GLUE("RTE")
         if args.use_taskid:
             module = hub.Module(name="ernie_v2_eng_base")
         else:
             module = hub.Module(name="bert_uncased_L-12_H-768_A-12")
-    elif args.dataset.lower() == "mnli":
-        dataset = hub.dataset.GLUE("MNLI")
+        metrics_choices = ["acc"]
+    elif args.dataset.lower() == "mnli" or args.dataset.lower() == "mnli":
+        dataset = hub.dataset.GLUE("MNLI_m")
         if args.use_taskid:
             module = hub.Module(name="ernie_v2_eng_base")
         else:
             module = hub.Module(name="bert_uncased_L-12_H-768_A-12")
+        metrics_choices = ["acc"]
+    elif args.dataset.lower() == "mnli_mm":
+        dataset = hub.dataset.GLUE("MNLI_mm")
+        if args.use_taskid:
+            module = hub.Module(name="ernie_v2_eng_base")
+        else:
+            module = hub.Module(name="bert_uncased_L-12_H-768_A-12")
+        metrics_choices = ["acc"]
     elif args.dataset.lower().startswith("xnli"):
         dataset = hub.dataset.XNLI(language=args.dataset.lower()[-2:])
         module = hub.Module(name="bert_multi_cased_L-12_H-768_A-12")
+        metrics_choices = ["acc"]
     else:
         raise ValueError("%s dataset is not defined" % args.dataset)
 
+    support_metrics = ["acc", "f1", "matthews"]
+    for metric in metrics_choices:
+        if metric not in support_metrics:
+            raise ValueError("\"%s\" metric is not defined" % metric)
+
     inputs, outputs, program = module.context(
         trainable=True, max_seq_len=args.max_seq_len)
     reader = hub.reader.ClassifyReader(
@@ -144,7 +169,8 @@ if __name__ == '__main__':
         feature=pooled_output,
         feed_list=feed_list,
         num_classes=dataset.num_labels,
-        config=config)
+        config=config,
+        metrics_choices=metrics_choices)
 
     # Finetune and evaluate by PaddleHub's API
     # will finish training, evaluation, testing, save model automatically

paddlehub/__init__.py

@@ -50,7 +50,12 @@ from .finetune.task import TextClassifierTask
 from .finetune.task import ImageClassifierTask
 from .finetune.task import SequenceLabelTask
 from .finetune.task import MultiLabelClassifierTask
+from .finetune.task import RegressionTask
+from .finetune.task import ReadingComprehensionTask
 from .finetune.config import RunConfig
 from .finetune.strategy import AdamWeightDecayStrategy
 from .finetune.strategy import DefaultStrategy
 from .finetune.strategy import DefaultFinetuneStrategy
+from .finetune.strategy import L2SPFinetuneStrategy
+from .finetune.strategy import ULMFiTStrategy
+from .finetune.strategy import CombinedStrategy

paddlehub/autofinetune/__init__.py

+# coding:utf-8
+# 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.

paddlehub/autofinetune/autoft.py

+# coding:utf-8
+# 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.
+from multiprocessing.pool import ThreadPool
+import copy
+import json
+import math
+import numpy as np
+import six
+import time
+
+from paddlehub.common.logger import logger
+from paddlehub.common.utils import mkdir
+
+if six.PY3:
+    INF = math.inf
+else:
+    INF = float("inf")
+
+
+class PSHE2(object):
+    def __init__(
+            self,
+            evaluator,
+            cudas=["0"],
+            popsize=5,
+            output_dir=None,
+            alpha=0.5,
+            epsilon=0.2,
+    ):
+
+        self._num_thread = len(cudas)
+        self._popsize = popsize
+        self._alpha = alpha
+        self._epsilon = epsilon
+        self._iteration = 0
+        self.cudas = cudas
+        self.is_cuda_free = {"free": [], "busy": []}
+        self.is_cuda_free["free"] = cudas
+
+        self.evaluator = evaluator
+        self.init_input = evaluator.get_init_params()
+        self.num_hparm = len(self.init_input)
+
+        self.best_hparams_per_pop = [[0] * self.num_hparm] * self._popsize
+        self.best_reward_per_pop = [INF] * self._popsize
+        self.momentums = [[0] * self.num_hparm] * self._popsize
+        self.best_hparms_all_pop = []
+        self.best_reward_all_pop = INF
+        self.current_hparams = [[0] * self.num_hparm] * self._popsize
+        for i in range(self.popsize):
+            self.current_hparams[i] = self.randomSolution()
+
+        if output_dir is None:
+            now = int(time.time())
+            time_str = time.strftime("%Y%m%d%H%M%S", time.localtime(now))
+            self._output_dir = "output_" + time_str
+        else:
+            self._output_dir = output_dir
+
+    @property
+    def thread(self):
+        return self._num_thread
+
+    @property
+    def popsize(self):
+        return self._popsize
+
+    @property
+    def alpha(self):
+        return self._alpha
+
+    @property
+    def epsilon(self):
+        return self._epsilon
+
+    @property
+    def output_dir(self):
+        return self._output_dir
+
+    @property
+    def iteration(self):
+        return self._iteration
+
+    def set_output_dir(self, output_dir=None):
+        if output_dir is not None:
+            output_dir = output_dir
+        else:
+            output_dir = self._output_dir
+        return output_dir
+
+    def randomSolution(self):
+        solut = [0] * self.num_hparm
+        for i in range(self.num_hparm):
+            ratio = (np.random.random_sample() - 0.5) * 2.0
+            if ratio >= 0:
+                solut[i] = (
+                    1.0 - self.init_input[i]) * ratio + self.init_input[i]
+            else:
+                solut[i] = (
+                    self.init_input[i] + 1.0) * ratio + self.init_input[i]
+        return solut
+
+    def smallPeturb(self):
+        for i in range(self.popsize):
+            for j in range(self.num_hparm):
+                ratio = (np.random.random_sample() - 0.5) * 2.0
+                if ratio >= 0:
+                    self.current_hparams[i][j] = (
+                        1.0 - self.current_hparams[i][j]
+                    ) * ratio * self.epsilon + self.current_hparams[i][j]
+                else:
+                    self.current_hparams[i][j] = (
+                        self.current_hparams[i][j] +
+                        1.0) * ratio * self.epsilon + self.current_hparams[i][j]
+
+    def estimatePopGradients(self):
+        gradients = [[0] * self.num_hparm] * self.popsize
+        for i in range(self.popsize):
+            for j in range(self.num_hparm):
+                gradients[i][j] = self.current_hparams[i][
+                    j] - self.best_hparms_all_pop[j]
+        return gradients
+
+    def estimateLocalGradients(self):
+        gradients = [[0] * self.num_hparm] * self.popsize
+        for i in range(self.popsize):
+            for j in range(self.num_hparm):
+                gradients[i][j] = self.current_hparams[i][
+                    j] - self.best_hparams_per_pop[i][j]
+        return gradients
+
+    def estimateMomemtum(self):
+        popGrads = self.estimatePopGradients()
+        localGrads = self.estimateLocalGradients()
+        for i in range(self.popsize):
+            for j in range(self.num_hparm):
+                self.momentums[i][j] = (
+                    1 - 3.0 * self.alpha / self.iteration
+                ) * self.momentums[i][j] - self.alpha * localGrads[i][
+                    j] - self.alpha * popGrads[i][j]
+
+    def is_stop(self):
+        return False
+
+    def solutions(self):
+        return self.current_hparams
+
+    def feedback(self, params_list, reward_list):
+        self._iteration = self._iteration + 1
+        for i in range(self.popsize):
+            if reward_list[i] < self.best_reward_per_pop[i]:
+                self.best_hparams_per_pop[i] = copy.deepcopy(
+                    self.current_hparams[i])
+                self.best_reward_per_pop[i] = reward_list[i]
+            if reward_list[i] < self.best_reward_all_pop:
+                self.best_hparms_all_pop = self.current_hparams[i]
+                self.best_reward_all_pop = reward_list[i]
+        self.estimateMomemtum()
+        for i in range(self.popsize):
+            for j in range(len(self.init_input)):
+                self.current_hparams[i][j] = self.current_hparams[i][
+                    j] + self.alpha * self.momentums[i][j]
+        self.smallPeturb()
+
+    def optimal_solution(self):
+        return self.best_hparms_all_pop
+
+    def step(self, output_dir):
+        solutions = self.solutions()
+
+        params_cudas_dirs = []
+        solution_results = []
+        cnt = 0
+        solutions_ckptdirs = {}
+        mkdir(output_dir)
+        for idx, solution in enumerate(solutions):
+            cuda = self.is_cuda_free["free"][0]
+            ckptdir = output_dir + "/ckpt-" + str(idx)
+            log_file = output_dir + "/log-" + str(idx) + ".info"
+            params_cudas_dirs.append([solution, cuda, ckptdir, log_file])
+            solutions_ckptdirs[tuple(solution)] = ckptdir
+            self.is_cuda_free["free"].remove(cuda)
+            self.is_cuda_free["busy"].append(cuda)
+            if len(params_cudas_dirs) == self.thread or cnt == int(
+                    self.popsize / self.thread):
+                tp = ThreadPool(len(params_cudas_dirs))
+                solution_results += tp.map(self.evaluator.run,
+                                           params_cudas_dirs)
+                cnt += 1
+                tp.close()
+                tp.join()
+                for param_cuda in params_cudas_dirs:
+                    self.is_cuda_free["free"].append(param_cuda[1])
+                    self.is_cuda_free["busy"].remove(param_cuda[1])
+                params_cudas_dirs = []
+
+        self.feedback(solutions, solution_results)
+
+        return solutions_ckptdirs

paddlehub/autofinetune/evaluator.py

+# coding:utf-8
+# 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.
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import io
+import hashlib
+import math
+import os
+import random
+import six
+import yaml
+
+from paddlehub.common.logger import logger
+from paddlehub.common.utils import is_windows
+
+REWARD_SUM = 10000
+
+if six.PY3:
+    INF = math.inf
+else:
+    INF = float("inf")
+
+
+class BaseEvaluator(object):
+    def __init__(self, params_file, finetunee_script):
+        with io.open(params_file, 'r', encoding='utf8') as f:
+            self.params = yaml.safe_load(f)
+        self.finetunee_script = finetunee_script
+
+    def get_init_params(self):
+        init_params = []
+        for param in self.params["param_list"]:
+            init_params.append(param['init_value'])
+        init_params = self.inverse_convert_params(init_params)
+        return init_params
+
+    def get_reward(self, result_output):
+        return REWARD_SUM - float(result_output)
+
+    def is_valid_params(self, params):
+        for i in range(0, len(self.params["param_list"])):
+            if params[i] < float(self.params["param_list"][i]["greater_than"]):
+                return False
+            if params[i] > float(self.params["param_list"][i]["lower_than"]):
+                return False
+        return True
+
+    def convert_params(self, params):
+        cparams = []
+        for i in range(0, len(self.params["param_list"])):
+            cparams.append(
+                float(self.params["param_list"][i]["greater_than"] +
+                      (params[i] + 1.0) / 2.0 *
+                      (self.params["param_list"][i]["lower_than"] -
+                       self.params["param_list"][i]["greater_than"])))
+            if cparams[i] <= float(
+                    self.params["param_list"][i]["greater_than"]):
+                cparams[i] = float(self.params["param_list"][i]["greater_than"])
+            if cparams[i] >= float(self.params["param_list"][i]["lower_than"]):
+                cparams[i] = float(self.params["param_list"][i]["lower_than"])
+            if self.params["param_list"][i]["type"] == "int":
+                cparams[i] = int(cparams[i])
+        return cparams
+
+    def inverse_convert_params(self, params):
+        cparams = []
+        for i in range(0, len(self.params["param_list"])):
+            cparams.append(
+                float(
+                    -1.0 + 2.0 *
+                    (params[i] - self.params["param_list"][i]["greater_than"]) /
+                    (self.params["param_list"][i]["lower_than"] -
+                     self.params["param_list"][i]["greater_than"])))
+            if cparams[i] <= -1.0:
+                cparams[i] = -1.0
+            if cparams[i] >= 1.0:
+                cparams[i] = 1.0
+        return cparams
+
+    def format_params_str(self, params):
+        param_str = "--%s=%s" % (self.params["param_list"][0]["name"],
+                                 params[0])
+        for i in range(1, len(self.params["param_list"])):
+            param_str = "%s --%s=%s" % (
+                param_str, self.params["param_list"][i]["name"], str(params[i]))
+        return param_str
+
+    def run(self, *args):
+        raise NotImplementedError
+
+    def new_round(self):
+        pass
+
+
+class FullTrailEvaluator(BaseEvaluator):
+    def __init__(self, params_file, finetunee_script):
+        super(FullTrailEvaluator, self).__init__(params_file, finetunee_script)
+
+    def run(self, *args):
+        params = args[0][0]
+        num_cuda = args[0][1]
+        ckpt_dir = args[0][2]
+        log_file = args[0][3]
+        params = self.convert_params(params)
+        if not self.is_valid_params(params):
+            return REWARD_SUM
+
+        param_str = self.format_params_str(params)
+        f = open(log_file, "w")
+        f.close()
+
+        if is_windows():
+            run_cmd = "set FLAGS_eager_delete_tensor_gb=0.0&set CUDA_VISIBLE_DEVICES=%s&python -u %s --checkpoint_dir=%s %s >%s 2>&1" % \
+                    (num_cuda, self.finetunee_script, ckpt_dir, param_str, log_file)
+        else:
+            run_cmd = "export FLAGS_eager_delete_tensor_gb=0.0; export CUDA_VISIBLE_DEVICES=%s; python -u %s --checkpoint_dir=%s %s >%s 2>&1" % \
+                    (num_cuda, self.finetunee_script, ckpt_dir, param_str, log_file)
+
+        try:
+            os.system(run_cmd)
+            with open(log_file, "r") as f:
+                lines = f.readlines()
+                eval_result = lines[-1]
+        except:
+            print(
+                "WARNING: Program which was ran with hyperparameters as %s was crashed!"
+                % param_str.replace("--", ""))
+            eval_result = 0.0
+        reward = self.get_reward(eval_result)
+        self.model_rewards[ckpt_dir] = reward
+        return reward
+
+
+class ModelBasedEvaluator(BaseEvaluator):
+    def __init__(self, params_file, finetunee_script):
+        super(ModelBasedEvaluator, self).__init__(params_file, finetunee_script)
+        self.model_rewards = {}
+        self.half_best_model_ckpt = []
+        self.run_count = 0
+
+    def run(self, *args):
+        params = args[0][0]
+        num_cuda = args[0][1]
+        ckpt_dir = args[0][2]
+        log_file = args[0][3]
+        params = self.convert_params(params)
+        if not self.is_valid_params(params):
+            return REWARD_SUM
+
+        param_str = self.format_params_str(params)
+        f = open(log_file, "w")
+        f.close()
+
+        if len(self.half_best_model_ckpt) > 0:
+            model_path = self.half_best_model_ckpt[self.run_count % len(
+                self.half_best_model_ckpt)] + "/best_model"
+            if is_windows():
+                run_cmd = "set FLAGS_eager_delete_tensor_gb=0.0&set CUDA_VISIBLE_DEVICES=%s&python -u %s --epochs=1 --model_path %s --checkpoint_dir=%s %s >%s 2>&1" % \
+                        (num_cuda, self.finetunee_script, model_path, ckpt_dir, param_str, log_file)
+            else:
+                run_cmd = "export FLAGS_eager_delete_tensor_gb=0.0; export CUDA_VISIBLE_DEVICES=%s; python -u %s --epochs=1 --model_path %s --checkpoint_dir=%s %s >%s 2>&1" % \
+                        (num_cuda, self.finetunee_script, model_path, ckpt_dir, param_str, log_file)
+        else:
+            if is_windows():
+                run_cmd = "set FLAGS_eager_delete_tensor_gb=0.0&set CUDA_VISIBLE_DEVICES=%s&python -u %s --checkpoint_dir=%s %s >%s 2>&1" % \
+                        (num_cuda, self.finetunee_script, ckpt_dir, param_str, log_file)
+            else:
+                run_cmd = "export FLAGS_eager_delete_tensor_gb=0.0; export CUDA_VISIBLE_DEVICES=%s; python -u %s --checkpoint_dir=%s %s >%s 2>&1" % \
+                        (num_cuda, self.finetunee_script, ckpt_dir, param_str, log_file)
+
+        self.run_count += 1
+        try:
+            os.system(run_cmd)
+            with open(log_file, "r") as f:
+                lines = f.readlines()
+                eval_result = lines[-1]
+        except:
+            print(
+                "WARNING: Program which was ran with hyperparameters as %s was crashed!"
+                % param_str.replace("--", ""))
+            eval_result = 0.0
+        reward = self.get_reward(eval_result)
+        self.model_rewards[ckpt_dir] = reward
+        return reward
+
+    def new_round(self):
+        """update self.half_best_model"""
+        half_size = int(len(self.model_rewards) / 2)
+        if half_size < 1:
+            half_size = 1
+        self.half_best_model_ckpt = list({
+            key
+            for key in sorted(
+                self.model_rewards, key=self.model_rewards.get, reverse=False)
+            [:half_size]
+        })
+        self.model_rewards = {}

paddlehub/commands/__init__.py

@@ -25,3 +25,4 @@ from . import help
 from . import clear
 from . import config
 from . import hub
+from . import autofinetune

paddlehub/commands/autofinetune.py

+# coding:utf-8
+# 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.
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import argparse
+import io
+import json
+import os
+import sys
+import ast
+
+import six
+import pandas
+import numpy as np
+
+from paddlehub.commands.base_command import BaseCommand, ENTRY
+from paddlehub.common.arg_helper import add_argument, print_arguments
+from paddlehub.autofinetune.autoft import PSHE2
+from paddlehub.autofinetune.evaluator import FullTrailEvaluator
+from paddlehub.autofinetune.evaluator import ModelBasedEvaluator
+from paddlehub.common.logger import logger
+
+import paddlehub as hub
+
+
+class AutoFineTuneCommand(BaseCommand):
+    name = "autofinetune"
+
+    def __init__(self, name):
+        super(AutoFineTuneCommand, self).__init__(name)
+        self.show_in_help = True
+        self.name = name
+        self.description = "Paddlehub helps to finetune a task by searching hyperparameters automatically."
+        self.parser = argparse.ArgumentParser(
+            description=self.__class__.__doc__,
+            prog='%s %s <task to be fintuned in python script>' % (ENTRY,
+                                                                   self.name),
+            usage='%(prog)s',
+            add_help=False)
+        self.module = None
+
+    def add_params_file_arg(self):
+        self.arg_params_to_be_searched_group.add_argument(
+            "--param_file",
+            type=str,
+            default=None,
+            required=True,
+            help=
+            "Hyperparameters to be searched in the yaml format. The number of hyperparameters searched must be greater than 1."
+        )
+
+    def add_autoft_config_arg(self):
+        self.arg_config_group.add_argument(
+            "--popsize", type=int, default=5, help="Population size")
+        self.arg_config_group.add_argument(
+            "--cuda",
+            type=ast.literal_eval,
+            default=['0'],
+            help="The list of gpu devices to be used")
+        self.arg_config_group.add_argument(
+            "--round", type=int, default=10, help="Number of searches")
+        self.arg_config_group.add_argument(
+            "--output_dir",
+            type=str,
+            default=None,
+            help="Directory to model checkpoint")
+        self.arg_config_group.add_argument(
+            "--evaluate_choice",
+            type=str,
+            default="fulltrail",
+            help="Choices: fulltrail or modelbased.")
+
+    def execute(self, argv):
+        if not argv:
+            print("ERROR: Please specify a script to be finetuned in python.\n")
+            self.help()
+            return False
+
+        self.fintunee_script = argv[0]
+
+        self.parser.prog = '%s %s %s' % (ENTRY, self.name, self.fintunee_script)
+        self.arg_params_to_be_searched_group = self.parser.add_argument_group(
+            title="Input options",
+            description="Hyperparameters to be searched.")
+        self.arg_config_group = self.parser.add_argument_group(
+            title="Autofinetune config options",
+            description=
+            "Autofintune configuration for controlling autofinetune behavior, not required"
+        )
+
+        self.add_params_file_arg()
+        self.add_autoft_config_arg()
+
+        if not argv[1:]:
+            self.help()
+            return False
+
+        self.args = self.parser.parse_args(argv[1:])
+        if self.args.evaluate_choice.lower() == "fulltrail":
+            evaluator = FullTrailEvaluator(self.args.param_file,
+                                           self.fintunee_script)
+        elif self.args.evaluate_choice.lower() == "modelbased":
+            evaluator = ModelBasedEvaluator(self.args.param_file,
+                                            self.fintunee_script)
+        else:
+            raise ValueError(
+                "The evaluate %s is not defined!" % self.args.evaluate_choice)
+
+        autoft = PSHE2(
+            evaluator,
+            cudas=self.args.cuda,
+            popsize=self.args.popsize,
+            output_dir=self.args.output_dir)
+
+        run_round_cnt = 0
+        solutions_ckptdirs = {}
+        print("PaddleHub Autofinetune starts.")
+        while (not autoft.is_stop()) and run_round_cnt < self.args.round:
+            print("PaddleHub Autofinetune starts round at %s." % run_round_cnt)
+            output_dir = autoft._output_dir + "/round" + str(run_round_cnt)
+            res = autoft.step(output_dir)
+            solutions_ckptdirs.update(res)
+            evaluator.new_round()
+            run_round_cnt = run_round_cnt + 1
+        print("PaddleHub Autofinetune ends.")
+        with open("./log_file.txt", "w") as f:
+            best_choice = evaluator.convert_params(autoft.optimal_solution())
+            print("The best hyperparameters:")
+            f.write("The best hyperparameters:\n")
+            param_name = []
+            for idx, param in enumerate(evaluator.params["param_list"]):
+                param_name.append(param["name"])
+                f.write(param["name"] + "\t:\t" + str(best_choice[idx]) + "\n")
+                print("%s : %s" % (param["name"], best_choice[idx]))
+            f.write("\n\n\n")
+            f.write("\t".join(param_name) + "\toutput_dir\n\n")
+
+            logger.info(
+                "The checkpont directory of programs ran with paramemters searched are saved as log_file.txt ."
+            )
+            print(
+                "The checkpont directory of programs ran with paramemters searched are saved as log_file.txt ."
+            )
+            for solution, ckptdir in solutions_ckptdirs.items():
+                param = evaluator.convert_params(solution)
+                param = [str(p) for p in param]
+                f.write("\t".join(param) + "\t" + ckptdir + "\n\n")
+
+        return True
+
+
+command = AutoFineTuneCommand.instance()

paddlehub/commands/install.py

@@ -36,7 +36,6 @@ class InstallCommand(BaseCommand):
             prog='%s %s <module_name>' % (ENTRY, name),
             usage='%(prog)s',
             add_help=False)
-        #TODO(wuzewu): add --upgrade option
 
     def execute(self, argv):
         if not argv:

paddlehub/common/dir.py

@@ -15,7 +15,6 @@
 
 import os
 
-# TODO: Change dir.py's filename, this naming rule is not qualified
 USER_HOME = os.path.expanduser('~')
 HUB_HOME = os.path.join(USER_HOME, ".paddlehub")
 MODULE_HOME = os.path.join(HUB_HOME, "modules")

paddlehub/common/downloader.py

@@ -77,7 +77,6 @@ class Downloader(object):
                 with open(file_name, 'wb') as f:
                     shutil.copyfileobj(r.raw, f)
             else:
-                #TODO(ZeyuChen) upgrade to tqdm process
                 with open(file_name, 'wb') as f:
                     dl = 0
                     total_length = int(total_length)

paddlehub/common/hub_server.py

@@ -24,6 +24,7 @@ import requests
 import json
 import yaml
 import random
+import fcntl
 
 from random import randint
 from paddlehub.common import utils, srv_utils
@@ -38,6 +39,9 @@ CACHE_TIME = 60 * 10
 
 class HubServer(object):
     def __init__(self, config_file_path=None):
+        LOCK_FILE = os.path.join(hub.HUB_HOME, '__LOCK__')
+        LOCK_FP = open(LOCK_FILE, 'a+')
+        fcntl.flock(LOCK_FP.fileno(), fcntl.LOCK_EX)
         if not config_file_path:
             config_file_path = os.path.join(hub.CONF_HOME, 'config.json')
         if not os.path.exists(hub.CONF_HOME):
@@ -53,6 +57,7 @@ class HubServer(object):
         self.server_url = self.config['server_url']
         self.request()
         self._load_resource_list_file_if_valid()
+        LOCK_FP.close()
 
     def get_server_url(self):
         random.seed(int(time.time()))
@@ -178,7 +183,6 @@ class HubServer(object):
             self.resource_list_file['version'][index]
             for index in resource_index_list
         ]
-        #TODO(wuzewu): version sort method
         resource_version_list = sorted(resource_version_list)
         if not version:
             if not resource_version_list:

paddlehub/common/paddle_helper.py

@@ -83,7 +83,6 @@ def from_param_to_module_attr(param, module_attr):
                               module_attr.map.data['trainable'])
     from_pyobj_to_module_attr(param.do_model_average,
                               module_attr.map.data['do_model_average'])
-    #TODO(wuzewu): don't save learning rate
     from_pyobj_to_module_attr(param.optimize_attr,
                               module_attr.map.data['optimize_attr'])
     from_pyobj_to_module_attr(

paddlehub/common/utils.py

@@ -117,7 +117,6 @@ def get_pykey(key, keyed_type):
     return str(key)
 
 
-#TODO(wuzewu): solving the problem of circular references
 def from_pyobj_to_module_attr(pyobj, module_attr, obj_filter=None):
     if obj_filter and obj_filter(pyobj):
         return

paddlehub/dataset/__init__.py

@@ -20,6 +20,7 @@ from .msra_ner import MSRA_NER
 from .nlpcc_dbqa import NLPCC_DBQA
 from .lcqmc import LCQMC
 from .toxic import Toxic
+from .squad import SQUAD
 from .xnli import XNLI
 from .glue import GLUE
 

paddlehub/dataset/base_cv_dataset.py

@@ -52,36 +52,35 @@ class ImageClassificationDataset(object):
         return dataset_path
 
     def _parse_data(self, data_path, shuffle=False, phase=None):
-        def _base_reader():
-            data = []
-            with open(data_path, "r") as file:
-                while True:
-                    line = file.readline()
-                    if not line:
-                        break
-                    line = line.strip()
-                    items = line.split(" ")
-                    if len(items) > 2:
-                        image_path = " ".join(items[0:-1])
-                    else:
-                        image_path = items[0]
-                    if not os.path.isabs(image_path):
-                        if self.base_path is not None:
-                            image_path = os.path.join(self.base_path,
-                                                      image_path)
-                    label = items[-1]
-                    data.append((image_path, items[-1]))
-
-            if phase == 'train':
-                self.train_examples = data
-            elif phase == 'dev':
-                self.dev_examples = data
-            elif phase == 'test':
-                self.test_examples = data
-
-            if shuffle:
-                np.random.shuffle(data)
+        data = []
+        with open(data_path, "r") as file:
+            while True:
+                line = file.readline()
+                if not line:
+                    break
+                line = line.strip()
+                items = line.split(" ")
+                if len(items) > 2:
+                    image_path = " ".join(items[0:-1])
+                else:
+                    image_path = items[0]
+                if not os.path.isabs(image_path):
+                    if self.base_path is not None:
+                        image_path = os.path.join(self.base_path, image_path)
+                label = items[-1]
+                data.append((image_path, items[-1]))
+
+        if phase == 'train':
+            self.train_examples = data
+        elif phase == 'dev':
+            self.dev_examples = data
+        elif phase == 'test':
+            self.test_examples = data
+
+        if shuffle:
+            np.random.shuffle(data)
 
+        def _base_reader():
             for item in data:
                 yield item
 

paddlehub/dataset/glue.py

@@ -39,11 +39,18 @@ class GLUE(HubDataset):
     def __init__(self, sub_dataset='SST-2'):
         # sub_dataset : CoLA, MNLI, MRPC, QNLI, QQP, RTE, SST-2, STS-B
         if sub_dataset not in [
-                'CoLA', 'MNLI', 'MRPC', 'QNLI', 'QQP', 'RTE', 'SST-2', 'STS-B'
+                'CoLA', 'MNLI', 'MNLI_m', 'MNLI_mm', 'MRPC', 'QNLI', 'QQP',
+                'RTE', 'SST-2', 'STS-B'
         ]:
             raise Exception(
                 sub_dataset +
                 " is not in GLUE benchmark. Please confirm the data set")
+        self.mismatch = False
+        if sub_dataset == 'MNLI_mm':
+            sub_dataset = 'MNLI'
+            self.mismatch = True
+        elif sub_dataset == 'MNLI_m':
+            sub_dataset = 'MNLI'
         self.sub_dataset = sub_dataset
         self.dataset_dir = os.path.join(DATA_HOME, "glue_data")
 
@@ -64,9 +71,12 @@ class GLUE(HubDataset):
         self.train_examples = self._read_tsv(self.train_file)
 
     def _load_dev_examples(self):
-        if self.sub_dataset == 'MNLI':
+        if self.sub_dataset == 'MNLI' and not self.mismatch:
             self.dev_file = os.path.join(self.dataset_dir, self.sub_dataset,
                                          "dev_matched.tsv")
+        elif self.sub_dataset == 'MNLI' and self.mismatch:
+            self.dev_file = os.path.join(self.dataset_dir, self.sub_dataset,
+                                         "dev_mismatched.tsv")
         else:
             self.dev_file = os.path.join(self.dataset_dir, self.sub_dataset,
                                          "dev.tsv")
@@ -76,9 +86,12 @@ class GLUE(HubDataset):
         self.test_examples = []
 
     def _load_predict_examples(self):
-        if self.sub_dataset == 'MNLI':
+        if self.sub_dataset == 'MNLI' and not self.mismatch:
             self.predict_file = os.path.join(self.dataset_dir, self.sub_dataset,
                                              "test_matched.tsv")
+        elif self.sub_dataset == 'MNLI' and self.mismatch:
+            self.predict_file = os.path.join(self.dataset_dir, self.sub_dataset,
+                                             "test_mismatched.tsv")
         else:
             self.predict_file = os.path.join(self.dataset_dir, self.sub_dataset,
                                              "test.tsv")
@@ -187,7 +200,7 @@ class GLUE(HubDataset):
                     seq_id += 1
                     examples.append(example)
                 except:
-                    print("[Discard Incorrect Data] " + "\t".join(line))
+                    logger.info("[Discard Incorrect Data] " + "\t".join(line))
             return examples
 
 

paddlehub/dataset/squad.py

+#coding:utf-8
+#   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.
+"""Run BERT on SQuAD 1.1 and SQuAD 2.0."""
+
+import json
+import os
+import sys
+
+from paddlehub.reader import tokenization
+from paddlehub.common.downloader import default_downloader
+from paddlehub.common.dir import DATA_HOME
+from paddlehub.common.logger import logger
+
+_DATA_URL = "https://bj.bcebos.com/paddlehub-dataset/squad.tar.gz"
+
+
+class SquadExample(object):
+    """A single training/test example for simple sequence classification.
+
+     For examples without an answer, the start and end position are -1.
+  """
+
+    def __init__(self,
+                 qas_id,
+                 question_text,
+                 doc_tokens,
+                 orig_answer_text=None,
+                 start_position=None,
+                 end_position=None,
+                 is_impossible=False):
+        self.qas_id = qas_id
+        self.question_text = question_text
+        self.doc_tokens = doc_tokens
+        self.orig_answer_text = orig_answer_text
+        self.start_position = start_position
+        self.end_position = end_position
+        self.is_impossible = is_impossible
+
+    def __str__(self):
+        return self.__repr__()
+
+    def __repr__(self):
+        s = ""
+        s += "qas_id: %s" % (tokenization.printable_text(self.qas_id))
+        s += ", question_text: %s" % (tokenization.printable_text(
+            self.question_text))
+        s += ", doc_tokens: [%s]" % (" ".join(self.doc_tokens))
+        if self.start_position:
+            s += ", start_position: %d" % (self.start_position)
+        if self.start_position:
+            s += ", end_position: %d" % (self.end_position)
+        if self.start_position:
+            s += ", is_impossible: %r" % (self.is_impossible)
+        return s
+
+
+class SQUAD(object):
+    """A single set of features of data."""
+
+    def __init__(self, version_2_with_negative=False):
+        self.dataset_dir = os.path.join(DATA_HOME, "squad_data")
+        if not os.path.exists(self.dataset_dir):
+            ret, tips, self.dataset_dir = default_downloader.download_file_and_uncompress(
+                url=_DATA_URL, save_path=DATA_HOME, print_progress=True)
+        else:
+            logger.info("Dataset {} already cached.".format(self.dataset_dir))
+
+        self._load_train_examples(version_2_with_negative, is_training=True)
+        self._load_predict_examples(version_2_with_negative, is_training=False)
+
+    def _load_train_examples(self,
+                             version_2_with_negative=False,
+                             is_training=True):
+        if not version_2_with_negative:
+            self.train_file = os.path.join(self.dataset_dir, "train-v1.1.json")
+        else:
+            self.train_file = os.path.join(self.dataset_dir, "train-v2.0.json")
+
+        self.train_examples = self._read_json(self.train_file, is_training,
+                                              version_2_with_negative)
+
+    def _load_predict_examples(self,
+                               version_2_with_negative=False,
+                               is_training=False):
+        if not version_2_with_negative:
+            self.predict_file = os.path.join(self.dataset_dir, "dev-v1.1.json")
+        else:
+            self.predict_file = os.path.join(self.dataset_dir, "dev-v2.0.json")
+
+        self.predict_examples = self._read_json(self.predict_file, is_training,
+                                                version_2_with_negative)
+
+    def get_train_examples(self):
+        return self.train_examples
+
+    def get_dev_examples(self):
+        return []
+
+    def get_test_examples(self):
+        return []
+
+    def _read_json(self, input_file, is_training,
+                   version_2_with_negative=False):
+        """Read a SQuAD json file into a list of SquadExample."""
+        with open(input_file, "r") as reader:
+            input_data = json.load(reader)["data"]
+
+        def is_whitespace(c):
+            if c == " " or c == "\t" or c == "\r" or c == "\n" or ord(
+                    c) == 0x202F:
+                return True
+            return False
+
+        examples = []
+        for entry in input_data:
+            for paragraph in entry["paragraphs"]:
+                paragraph_text = paragraph["context"]
+                doc_tokens = []
+                char_to_word_offset = []
+                prev_is_whitespace = True
+                for c in paragraph_text:
+                    if is_whitespace(c):
+                        prev_is_whitespace = True
+                    else:
+                        if prev_is_whitespace:
+                            doc_tokens.append(c)
+                        else:
+                            doc_tokens[-1] += c
+                        prev_is_whitespace = False
+                    char_to_word_offset.append(len(doc_tokens) - 1)
+
+                for qa in paragraph["qas"]:
+                    qas_id = qa["id"]
+                    question_text = qa["question"]
+                    start_position = None
+                    end_position = None
+                    orig_answer_text = None
+                    is_impossible = False
+                    if is_training:
+
+                        if version_2_with_negative:
+                            is_impossible = qa["is_impossible"]
+                        if (len(qa["answers"]) != 1) and (not is_impossible):
+                            raise ValueError(
+                                "For training, each question should have exactly 1 answer."
+                            )
+                        if not is_impossible:
+                            answer = qa["answers"][0]
+                            orig_answer_text = answer["text"]
+                            answer_offset = answer["answer_start"]
+                            answer_length = len(orig_answer_text)
+                            start_position = char_to_word_offset[answer_offset]
+                            end_position = char_to_word_offset[
+                                answer_offset + answer_length - 1]
+                            # Only add answers where the text can be exactly recovered from the
+                            # document. If this CAN'T happen it's likely due to weird Unicode
+                            # stuff so we will just skip the example.
+                            #
+                            # Note that this means for training mode, every example is NOT
+                            # guaranteed to be preserved.
+                            actual_text = " ".join(
+                                doc_tokens[start_position:(end_position + 1)])
+                            cleaned_answer_text = " ".join(
+                                tokenization.whitespace_tokenize(
+                                    orig_answer_text))
+                            if actual_text.find(cleaned_answer_text) == -1:
+                                logger.warning(
+                                    "Could not find answer: '%s' vs. '%s'",
+                                    actual_text, cleaned_answer_text)
+                                continue
+                        else:
+                            start_position = -1
+                            end_position = -1
+                            orig_answer_text = ""
+
+                    example = SquadExample(
+                        qas_id=qas_id,
+                        question_text=question_text,
+                        doc_tokens=doc_tokens,
+                        orig_answer_text=orig_answer_text,
+                        start_position=start_position,
+                        end_position=end_position,
+                        is_impossible=is_impossible)
+                    examples.append(example)
+
+        return examples
+
+
+if __name__ == "__main__":
+    ds = SQUAD(version_2_with_negative=True)
+    examples = ds.get_dev_examples()
+    for index, e in enumerate(examples):
+        if index < 10:
+            print(e)

paddlehub/dataset/xnli.py

@@ -43,6 +43,7 @@ class XNLI(HubDataset):
                 "ar", "bg", "de", "el", "en", "es", "fr", "hi", "ru", "sw",
                 "th", "tr", "ur", "vi", "zh"
         ]:
+
             raise Exception(language +
                             "is not in XNLI. Please confirm the language")
         self.language = language

paddlehub/finetune/checkpoint.proto

@@ -22,4 +22,5 @@ message CheckPoint {
   int64 current_epoch = 1;
   int64 global_step = 2;
   string latest_model_dir = 3;
+  double best_score = 4;
 }

paddlehub/finetune/checkpoint.py

@@ -37,6 +37,7 @@ def load_checkpoint(checkpoint_dir, exe, main_program):
             ckpt.ParseFromString(f.read())
     current_epoch = 1
     global_step = 0
+    best_score = -999
 
     def if_exist(var):
         return os.path.exists(os.path.join(ckpt.latest_model_dir, var.name))
@@ -45,20 +46,27 @@ def load_checkpoint(checkpoint_dir, exe, main_program):
         fluid.io.load_vars(
             exe, ckpt.latest_model_dir, main_program, predicate=if_exist)
 
+        # Compatible with older versions without best_score in checkpoint_pb2
+        try:
+            best_score = ckpt.best_score
+        except:
+            best_score = -999
+
         logger.info("PaddleHub model checkpoint loaded. current_epoch={}, "
-                    "global_step={}".format(ckpt.current_epoch,
-                                            ckpt.global_step))
-        return True, ckpt.current_epoch, ckpt.global_step
+                    "global_step={}, best_score={:.5f}".format(
+                        ckpt.current_epoch, ckpt.global_step, best_score))
+
+        return True, ckpt.current_epoch, ckpt.global_step, best_score
 
-    logger.info(
-        "PaddleHub model checkpoint not found, start training from scratch...")
+    logger.info("PaddleHub model checkpoint not found, start from scratch...")
 
-    return False, current_epoch, global_step
+    return False, current_epoch, global_step, best_score
 
 
 def save_checkpoint(checkpoint_dir,
                     current_epoch,
                     global_step,
+                    best_score,
                     exe,
                     main_program=fluid.default_main_program()):
 
@@ -73,5 +81,6 @@ def save_checkpoint(checkpoint_dir,
     ckpt.current_epoch = current_epoch
     ckpt.global_step = global_step
     ckpt.latest_model_dir = model_saved_dir
+    ckpt.best_score = best_score
     with open(ckpt_meta_path, "wb") as f:
         f.write(ckpt.SerializeToString())

paddlehub/finetune/checkpoint_pb2.py

-#coding:utf-8
 # Generated by the protocol buffer compiler.  DO NOT EDIT!
 # source: checkpoint.proto
 
@@ -18,7 +17,7 @@ DESCRIPTOR = _descriptor.FileDescriptor(
     package='paddlehub.task.checkpoint',
     syntax='proto3',
     serialized_pb=_b(
-        '\n\x10\x63heckpoint.proto\x12\x19paddlehub.task.checkpoint\"R\n\nCheckPoint\x12\x15\n\rcurrent_epoch\x18\x01 \x01(\x03\x12\x13\n\x0bglobal_step\x18\x02 \x01(\x03\x12\x18\n\x10latest_model_dir\x18\x03 \x01(\tB\x02H\x03\x62\x06proto3'
+        '\n\x10\x63heckpoint.proto\x12\x19paddlehub.task.checkpoint\"f\n\nCheckPoint\x12\x15\n\rcurrent_epoch\x18\x01 \x01(\x03\x12\x13\n\x0bglobal_step\x18\x02 \x01(\x03\x12\x18\n\x10latest_model_dir\x18\x03 \x01(\t\x12\x12\n\nbest_score\x18\x04 \x01(\x01\x42\x02H\x03\x62\x06proto3'
     ))
 _sym_db.RegisterFileDescriptor(DESCRIPTOR)
 
@@ -77,6 +76,22 @@ _CHECKPOINT = _descriptor.Descriptor(
             is_extension=False,
             extension_scope=None,
             options=None),
+        _descriptor.FieldDescriptor(
+            name='best_score',
+            full_name='paddlehub.task.checkpoint.CheckPoint.best_score',
+            index=3,
+            number=4,
+            type=1,
+            cpp_type=5,
+            label=1,
+            has_default_value=False,
+            default_value=float(0),
+            message_type=None,
+            enum_type=None,
+            containing_type=None,
+            is_extension=False,
+            extension_scope=None,
+            options=None),
     ],
     extensions=[],
     nested_types=[],
@@ -87,7 +102,7 @@ _CHECKPOINT = _descriptor.Descriptor(
     extension_ranges=[],
     oneofs=[],
     serialized_start=47,
-    serialized_end=129,
+    serialized_end=149,
 )
 
 DESCRIPTOR.message_types_by_name['CheckPoint'] = _CHECKPOINT

paddlehub/finetune/evaluate.py

@@ -128,3 +128,75 @@ def calculate_f1(num_label, num_infer, num_correct):
     else:
         f1 = 2 * precision * recall / (precision + recall)
     return precision, recall, f1
+
+
+def calculate_f1_np(preds, labels):
+    preds = np.array(preds)
+    labels = np.array(labels)
+
+    tp = np.sum((labels == 1) & (preds == 1))
+    tn = np.sum((labels == 0) & (preds == 0))
+    fp = np.sum((labels == 0) & (preds == 1))
+    fn = np.sum((labels == 1) & (preds == 0))
+    p = tp / (tp + fp) if (tp + fp) else 0
+    r = tp / (tp + fn) if (tp + fn) else 0
+    f1 = (2 * p * r) / (p + r) if p + r else 0
+    return f1
+
+
+def matthews_corrcoef(preds, labels):
+    preds = np.array(preds)
+    labels = np.array(labels)
+
+    tp = np.sum((labels == 1) & (preds == 1))
+    tn = np.sum((labels == 0) & (preds == 0))
+    fp = np.sum((labels == 0) & (preds == 1))
+    fn = np.sum((labels == 1) & (preds == 0))
+
+    div = (tp + fp) * (tp + fn) * (tn + fp) * (tn + fn)
+    mcc = ((tp * tn) - (fp * fn)) / np.sqrt(div) if div else 0
+    return mcc
+
+
+def recall_nk(data, n, k, m):
+    '''
+    This metric can be used to evaluate whether the model can find the correct response B for question A
+    Note: Only applies to each question A only has one correct response B1.
+
+    Parameters
+    ----------
+    data: List. Each element is a tuple, consist of the positive probability of the sample prediction and its label.
+                For each example, the only one true positive sample should be the first tuple.
+    n: int. The number of labels per example.
+        eg: [A,B1,1], [A,B2,0], [A,B3,0]  n=3 as there has 3 labels for example A
+    k: int. If the top k is right, the example will be considered right.
+        eg: [A,B1,1]=0.5, [A,B2,0]=0.8, [A,B3,0]=0.3(Probability of 1)
+           If k=2, the prediction for the example A will be considered correct as 0.5 is the top2 Probability
+           If k=1, the prediction will be considered wrong as 0.5 is not the biggest probability.
+    m: int. For every m examples, there's going to be a positive sample.
+        eg. data= [A1,B1,1], [A1,B2,0], [A1,B3,0], [A2,B1,1], [A2,B2,0], [A2,B3,0]
+           For every 3 examples, there will be one positive sample. so m=3, and n can be 1,2 or 3.
+    '''
+
+    def get_p_at_n_in_m(data, n, k, ind):
+        """
+        calculate precision in recall n
+        """
+        pos_score = data[ind][0]
+        curr = data[ind:ind + n]
+        curr = sorted(curr, key=lambda x: x[0], reverse=True)
+        if curr[k - 1][0] <= pos_score:
+            return 1
+        return 0
+
+    correct_num = 0.0
+
+    length = len(data) // m
+
+    for i in range(0, length):
+        ind = i * m
+        assert data[ind][1] == 1
+
+        correct_num += get_p_at_n_in_m(data, n, k, ind)
+
+    return correct_num / length

paddlehub/finetune/optimization.py

-#coding:utf-8
-#   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.
-"""Optimization and learning rate scheduling."""
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import numpy as np
-import paddle.fluid as fluid
-import paddle.fluid.layers.learning_rate_scheduler as lr_scheduler
-from paddle.fluid.layers import control_flow
-from paddlehub.common.logger import logger
-
-
-def adam_weight_decay_optimization(loss,
-                                   warmup_steps,
-                                   num_train_steps,
-                                   learning_rate,
-                                   main_program,
-                                   weight_decay,
-                                   scheduler='linear_decay'):
-    if scheduler == 'noam_decay':
-        if warmup_steps > 0:
-            scheduled_lr = fluid.layers.learning_rate_scheduler\
-             .noam_decay(1/(warmup_steps *(learning_rate ** 2)),
-                         warmup_steps)
-        else:
-            logger.warning(
-                "Noam decay learning rate scheduler should have positive \
-            warmup steps, using constant learning rate instead!")
-
-            scheduled_lr = fluid.layers.create_global_var(
-                shape=[1],
-                value=learning_rate,
-                dtype='float32',
-                persistable=True,
-                name="learning_rate")
-    elif scheduler == 'linear_decay':
-        scheduled_lr = linear_warmup_decay(learning_rate, num_train_steps,
-                                           warmup_steps, main_program)
-    else:
-        raise ValueError("Unkown learning rate scheduler, should be "
-                         "'noam_decay' or 'linear_decay'")
-
-    optimizer = fluid.optimizer.Adam(learning_rate=scheduled_lr)
-
-    clip_norm_thres = 1.0
-    fluid.clip.set_gradient_clip(
-        clip=fluid.clip.GradientClipByGlobalNorm(clip_norm=clip_norm_thres))
-
-    def exclude_from_weight_decay(name):
-        if name.find("layer_norm") > -1:
-            return True
-        bias_suffix = ["_bias", "_b", ".b_0"]
-        for suffix in bias_suffix:
-            if name.endswith(suffix):
-                return True
-        return False
-
-    param_list = dict()
-
-    for param in main_program.global_block().all_parameters():
-        param_list[param.name] = param * 1.0
-        param_list[param.name].stop_gradient = True
-
-    _, param_grads = optimizer.minimize(loss)
-
-    if weight_decay > 0:
-        for param, grad in param_grads:
-            if exclude_from_weight_decay(param.name):
-                continue
-            with param.block.program._optimized_guard(
-                [param, grad]), fluid.framework.name_scope("weight_decay"):
-                updated_param = param - param_list[
-                    param.name] * weight_decay * scheduled_lr
-                fluid.layers.assign(output=param, input=updated_param)
-
-    return scheduled_lr
-
-
-def linear_warmup_decay(init_lr, num_train_steps, num_warmup_steps,
-                        main_program):
-    with main_program._lr_schedule_guard():
-        global_step = lr_scheduler._decay_step_counter()
-
-        lr = fluid.layers.create_global_var(
-            shape=[1],
-            value=init_lr,
-            dtype='float32',
-            persistable=True,
-            name="learning_rate")
-
-        with control_flow.Switch() as switch:
-            with switch.case(global_step < num_warmup_steps):
-                decayed_lr = init_lr * global_step * 1.0 / num_warmup_steps
-                fluid.layers.assign(decayed_lr, lr)
-            with switch.default():
-                decayed_lr = lr_scheduler.polynomial_decay(
-                    learning_rate=init_lr,
-                    decay_steps=num_train_steps,
-                    end_learning_rate=0.0,
-                    power=1.0,
-                    cycle=False)
-                fluid.layers.assign(decayed_lr, lr)
-
-        return lr

paddlehub/finetune/task/__init__.py

+#coding:utf-8
+#   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.
+
+from .basic_task import BasicTask, RunEnv, RunState
+from .classifier_task import ClassifierTask, ImageClassifierTask, TextClassifierTask, MultiLabelClassifierTask
+from .reading_comprehension_task import ReadingComprehensionTask
+from .regression_task import RegressionTask
+from .sequence_task import SequenceLabelTask

paddlehub/finetune/task/classifier_task.py

+#coding:utf-8
+#  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.
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import time
+from collections import OrderedDict
+import numpy as np
+import paddle.fluid as fluid
+
+from paddlehub.finetune.evaluate import calculate_f1_np, matthews_corrcoef
+from .basic_task import BasicTask
+
+
+class ClassifierTask(BasicTask):
+    def __init__(self,
+                 feature,
+                 num_classes,
+                 feed_list,
+                 data_reader,
+                 startup_program=None,
+                 config=None,
+                 hidden_units=None,
+                 metrics_choices="default"):
+        if metrics_choices == "default":
+            metrics_choices = ["acc"]
+
+        main_program = feature.block.program
+        super(ClassifierTask, self).__init__(
+            data_reader=data_reader,
+            main_program=main_program,
+            feed_list=feed_list,
+            startup_program=startup_program,
+            config=config,
+            metrics_choices=metrics_choices)
+
+        self.feature = feature
+        self.num_classes = num_classes
+        self.hidden_units = hidden_units
+
+    def _build_net(self):
+        cls_feats = self.feature
+        if self.hidden_units is not None:
+            for n_hidden in self.hidden_units:
+                cls_feats = fluid.layers.fc(
+                    input=cls_feats, size=n_hidden, act="relu")
+
+        logits = fluid.layers.fc(
+            input=cls_feats,
+            size=self.num_classes,
+            param_attr=fluid.ParamAttr(
+                name="cls_out_w",
+                initializer=fluid.initializer.TruncatedNormal(scale=0.02)),
+            bias_attr=fluid.ParamAttr(
+                name="cls_out_b", initializer=fluid.initializer.Constant(0.)),
+            act="softmax")
+
+        self.ret_infers = fluid.layers.reshape(
+            x=fluid.layers.argmax(logits, axis=1), shape=[-1, 1])
+
+        return [logits]
+
+    def _add_label(self):
+        return [fluid.layers.data(name="label", dtype="int64", shape=[1])]
+
+    def _add_loss(self):
+        ce_loss = fluid.layers.cross_entropy(
+            input=self.outputs[0], label=self.labels[0])
+        return fluid.layers.mean(x=ce_loss)
+
+    def _add_metrics(self):
+        acc = fluid.layers.accuracy(input=self.outputs[0], label=self.labels[0])
+        return [acc]
+
+    @property
+    def fetch_list(self):
+        if self.is_train_phase or self.is_test_phase:
+            return [self.labels[0].name, self.ret_infers.name
+                    ] + [metric.name
+                         for metric in self.metrics] + [self.loss.name]
+        return [output.name for output in self.outputs]
+
+    def _calculate_metrics(self, run_states):
+        loss_sum = acc_sum = run_examples = 0
+        run_step = run_time_used = 0
+        all_labels = np.array([])
+        all_infers = np.array([])
+
+        for run_state in run_states:
+            run_examples += run_state.run_examples
+            run_step += run_state.run_step
+            loss_sum += np.mean(
+                run_state.run_results[-1]) * run_state.run_examples
+            acc_sum += np.mean(
+                run_state.run_results[2]) * run_state.run_examples
+            np_labels = run_state.run_results[0]
+            np_infers = run_state.run_results[1]
+            all_labels = np.hstack((all_labels, np_labels.reshape([-1])))
+            all_infers = np.hstack((all_infers, np_infers.reshape([-1])))
+
+        run_time_used = time.time() - run_states[0].run_time_begin
+        avg_loss = loss_sum / run_examples
+        run_speed = run_step / run_time_used
+
+        # The first key will be used as main metrics to update the best model
+        scores = OrderedDict()
+
+        for metric in self.metrics_choices:
+            if metric == "acc":
+                avg_acc = acc_sum / run_examples
+                scores["acc"] = avg_acc
+            elif metric == "f1":
+                f1 = calculate_f1_np(all_infers, all_labels)
+                scores["f1"] = f1
+            elif metric == "matthews":
+                matthews = matthews_corrcoef(all_infers, all_labels)
+                scores["matthews"] = matthews
+            else:
+                raise ValueError("Not Support Metric: \"%s\"" % metric)
+
+        return scores, avg_loss, run_speed
+
+
+ImageClassifierTask = ClassifierTask
+
+
+class TextClassifierTask(ClassifierTask):
+    def __init__(self,
+                 feature,
+                 num_classes,
+                 feed_list,
+                 data_reader,
+                 startup_program=None,
+                 config=None,
+                 hidden_units=None,
+                 metrics_choices="default"):
+
+        if metrics_choices == "default":
+            metrics_choices = ["acc"]
+        super(TextClassifierTask, self).__init__(
+            data_reader=data_reader,
+            feature=feature,
+            num_classes=num_classes,
+            feed_list=feed_list,
+            startup_program=startup_program,
+            config=config,
+            hidden_units=hidden_units,
+            metrics_choices=metrics_choices)
+
+    def _build_net(self):
+        cls_feats = fluid.layers.dropout(
+            x=self.feature,
+            dropout_prob=0.1,
+            dropout_implementation="upscale_in_train")
+
+        if self.hidden_units is not None:
+            for n_hidden in self.hidden_units:
+                cls_feats = fluid.layers.fc(
+                    input=cls_feats, size=n_hidden, act="relu")
+
+        logits = fluid.layers.fc(
+            input=cls_feats,
+            size=self.num_classes,
+            param_attr=fluid.ParamAttr(
+                name="cls_out_w",
+                initializer=fluid.initializer.TruncatedNormal(scale=0.02)),
+            bias_attr=fluid.ParamAttr(
+                name="cls_out_b", initializer=fluid.initializer.Constant(0.)),
+            act="softmax")
+
+        self.ret_infers = fluid.layers.reshape(
+            x=fluid.layers.argmax(logits, axis=1), shape=[-1, 1])
+
+        return [logits]
+
+
+class MultiLabelClassifierTask(ClassifierTask):
+    def __init__(self,
+                 feature,
+                 num_classes,
+                 feed_list,
+                 data_reader,
+                 startup_program=None,
+                 config=None,
+                 hidden_units=None,
+                 metrics_choices="default"):
+        if metrics_choices == "default":
+            metrics_choices = ["auc"]
+
+        main_program = feature.block.program
+        super(MultiLabelClassifierTask, self).__init__(
+            data_reader=data_reader,
+            feature=feature,
+            num_classes=num_classes,
+            feed_list=feed_list,
+            startup_program=startup_program,
+            config=config,
+            hidden_units=hidden_units,
+            metrics_choices=metrics_choices)
+        self.class_name = list(data_reader.label_map.keys())
+
+    def _build_net(self):
+        cls_feats = fluid.layers.dropout(
+            x=self.feature,
+            dropout_prob=0.1,
+            dropout_implementation="upscale_in_train")
+
+        if self.hidden_units is not None:
+            for n_hidden in self.hidden_units:
+                cls_feats = fluid.layers.fc(
+                    input=cls_feats, size=n_hidden, act="relu")
+
+        probs = []
+        for i in range(self.num_classes):
+            probs.append(
+                fluid.layers.fc(
+                    input=cls_feats,
+                    size=2,
+                    param_attr=fluid.ParamAttr(
+                        name="cls_out_w_%d" % i,
+                        initializer=fluid.initializer.TruncatedNormal(
+                            scale=0.02)),
+                    bias_attr=fluid.ParamAttr(
+                        name="cls_out_b_%d" % i,
+                        initializer=fluid.initializer.Constant(0.)),
+                    act="softmax"))
+
+        return probs
+
+    def _add_label(self):
+        label = fluid.layers.data(
+            name="label", shape=[self.num_classes], dtype='int64')
+        return [label]
+
+    def _add_loss(self):
+        label_split = fluid.layers.split(
+            self.labels[0], self.num_classes, dim=-1)
+        total_loss = fluid.layers.fill_constant(
+            shape=[1], value=0.0, dtype='float64')
+        for index, probs in enumerate(self.outputs):
+            ce_loss = fluid.layers.cross_entropy(
+                input=probs, label=label_split[index])
+            total_loss += fluid.layers.reduce_sum(ce_loss)
+        loss = fluid.layers.mean(x=total_loss)
+        return loss
+
+    def _add_metrics(self):
+        label_split = fluid.layers.split(
+            self.labels[0], self.num_classes, dim=-1)
+        # metrics change to auc of every class
+        eval_list = []
+        for index, probs in enumerate(self.outputs):
+            current_auc, _, _ = fluid.layers.auc(
+                input=probs, label=label_split[index])
+            eval_list.append(current_auc)
+        return eval_list
+
+    def _calculate_metrics(self, run_states):
+        loss_sum = acc_sum = run_examples = 0
+        run_step = run_time_used = 0
+        for run_state in run_states:
+            run_examples += run_state.run_examples
+            run_step += run_state.run_step
+            loss_sum += np.mean(
+                run_state.run_results[-1]) * run_state.run_examples
+        auc_list = run_states[-1].run_results[:-1]
+
+        run_time_used = time.time() - run_states[0].run_time_begin
+        avg_loss = loss_sum / (run_examples * self.num_classes)
+        run_speed = run_step / run_time_used
+
+        # The first key will be used as main metrics to update the best model
+        scores = OrderedDict()
+        for metric in self.metrics_choices:
+            if metric == "auc":
+                scores["auc"] = np.mean(auc_list)
+                # NOTE: for MultiLabelClassifierTask, the metrics will be used to evaluate all the label
+                #      and their mean value will also be reported.
+                for index, auc in enumerate(auc_list):
+                    scores["auc_" + self.class_name[index]] = auc_list[index][0]
+            else:
+                raise ValueError("Not Support Metric: \"%s\"" % metric)
+        return scores, avg_loss, run_speed
+
+    @property
+    def fetch_list(self):
+        if self.is_train_phase or self.is_test_phase:
+            return [metric.name for metric in self.metrics] + [self.loss.name]
+        return self.outputs

paddlehub/finetune/task/reading_comprehension_task.py

+#coding:utf-8
+#  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.
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import time
+from collections import OrderedDict
+
+import numpy as np
+import paddle.fluid as fluid
+from .basic_task import BasicTask
+
+
+class ReadingComprehensionTask(BasicTask):
+    def __init__(self,
+                 feature,
+                 feed_list,
+                 data_reader,
+                 startup_program=None,
+                 config=None,
+                 metrics_choices=None):
+
+        main_program = feature.block.program
+        super(ReadingComprehensionTask, self).__init__(
+            data_reader=data_reader,
+            main_program=main_program,
+            feed_list=feed_list,
+            startup_program=startup_program,
+            config=config,
+            metrics_choices=metrics_choices)
+        self.feature = feature
+
+    def _build_net(self):
+        if self.is_predict_phase:
+            self.unique_id = fluid.layers.data(
+                name="start_positions",
+                shape=[-1, 1],
+                lod_level=0,
+                dtype="int64")
+
+        logits = fluid.layers.fc(
+            input=self.feature,
+            size=2,
+            num_flatten_dims=2,
+            param_attr=fluid.ParamAttr(
+                name="cls_seq_label_out_w",
+                initializer=fluid.initializer.TruncatedNormal(scale=0.02)),
+            bias_attr=fluid.ParamAttr(
+                name="cls_seq_label_out_b",
+                initializer=fluid.initializer.Constant(0.)))
+
+        logits = fluid.layers.transpose(x=logits, perm=[2, 0, 1])
+        start_logits, end_logits = fluid.layers.unstack(x=logits, axis=0)
+
+        batch_ones = fluid.layers.fill_constant_batch_size_like(
+            input=start_logits, dtype='int64', shape=[1], value=1)
+        num_seqs = fluid.layers.reduce_sum(input=batch_ones)
+
+        return [start_logits, end_logits, num_seqs]
+
+    def _add_label(self):
+        start_positions = fluid.layers.data(
+            name="start_positions", shape=[-1, 1], lod_level=0, dtype="int64")
+        end_positions = fluid.layers.data(
+            name="end_positions", shape=[-1, 1], lod_level=0, dtype="int64")
+        return [start_positions, end_positions]
+
+    def _add_loss(self):
+        start_positions = self.labels[0]
+        end_positions = self.labels[1]
+
+        start_logits = self.outputs[0]
+        end_logits = self.outputs[1]
+
+        start_loss = fluid.layers.softmax_with_cross_entropy(
+            logits=start_logits, label=start_positions)
+        start_loss = fluid.layers.mean(x=start_loss)
+        end_loss = fluid.layers.softmax_with_cross_entropy(
+            logits=end_logits, label=end_positions)
+        end_loss = fluid.layers.mean(x=end_loss)
+        total_loss = (start_loss + end_loss) / 2.0
+        return total_loss
+
+    def _add_metrics(self):
+        return []
+
+    @property
+    def feed_list(self):
+        feed_list = [varname for varname in self._base_feed_list]
+        if self.is_train_phase:
+            feed_list += [self.labels[0].name, self.labels[1].name]
+        elif self.is_predict_phase:
+            feed_list += [self.unique_id.name]
+        return feed_list
+
+    @property
+    def fetch_list(self):
+        if self.is_train_phase:
+            return [metric.name for metric in self.metrics
+                    ] + [self.loss.name, self.outputs[-1].name]
+        elif self.is_predict_phase:
+            return [self.unique_id.name
+                    ] + [output.name for output in self.outputs]
+
+    def _calculate_metrics(self, run_states):
+        total_cost, total_num_seqs = [], []
+        run_step = run_time_used = run_examples = 0
+        for run_state in run_states:
+            np_loss = run_state.run_results[0]
+            np_num_seqs = run_state.run_results[1]
+            total_cost.extend(np_loss * np_num_seqs)
+            total_num_seqs.extend(np_num_seqs)
+            run_examples += run_state.run_examples
+            run_step += run_state.run_step
+
+        run_time_used = time.time() - run_states[0].run_time_begin
+        run_speed = run_step / run_time_used
+        avg_loss = np.sum(total_cost) / np.sum(total_num_seqs)
+
+        scores = OrderedDict()
+        # If none of metrics has been implemented, loss will be used to evaluate.
+        return scores, avg_loss, run_speed

paddlehub/finetune/task/regression_task.py

+#coding:utf-8
+#  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.
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import time
+from collections import OrderedDict
+
+import numpy as np
+import paddle.fluid as fluid
+from scipy.stats import spearmanr
+from .basic_task import BasicTask
+
+
+class RegressionTask(BasicTask):
+    def __init__(self,
+                 feature,
+                 feed_list,
+                 data_reader,
+                 startup_program=None,
+                 config=None,
+                 hidden_units=None,
+                 metrics_choices="default"):
+        if metrics_choices == "default":
+            metrics_choices = ["spearman"]
+
+        main_program = feature.block.program
+        super(RegressionTask, self).__init__(
+            data_reader=data_reader,
+            main_program=main_program,
+            feed_list=feed_list,
+            startup_program=startup_program,
+            config=config,
+            metrics_choices=metrics_choices)
+        self.feature = feature
+        self.hidden_units = hidden_units
+
+    def _build_net(self):
+        cls_feats = fluid.layers.dropout(
+            x=self.feature,
+            dropout_prob=0.1,
+            dropout_implementation="upscale_in_train")
+
+        if self.hidden_units is not None:
+            for n_hidden in self.hidden_units:
+                cls_feats = fluid.layers.fc(
+                    input=cls_feats, size=n_hidden, act="relu")
+
+        logits = fluid.layers.fc(
+            input=cls_feats,
+            size=1,
+            param_attr=fluid.ParamAttr(
+                name="cls_out_w",
+                initializer=fluid.initializer.TruncatedNormal(scale=0.02)),
+            bias_attr=fluid.ParamAttr(
+                name="cls_out_b", initializer=fluid.initializer.Constant(0.)),
+            act=None)
+
+        return [logits]
+
+    def _add_label(self):
+        return [fluid.layers.data(name="label", dtype="float32", shape=[1])]
+
+    def _add_loss(self):
+        cost = fluid.layers.square_error_cost(
+            input=self.outputs[0], label=self.labels[0])
+        return fluid.layers.mean(x=cost)
+
+    def _add_metrics(self):
+        return []
+
+    @property
+    def fetch_list(self):
+        if self.is_train_phase or self.is_test_phase:
+            return [self.labels[0].name, self.outputs[0].name
+                    ] + [metric.name
+                         for metric in self.metrics] + [self.loss.name]
+        return [output.name for output in self.outputs]
+
+    def _calculate_metrics(self, run_states):
+        loss_sum = run_examples = 0
+        run_step = run_time_used = 0
+        all_labels = np.array([])
+        all_infers = np.array([])
+        for run_state in run_states:
+            run_examples += run_state.run_examples
+            run_step += run_state.run_step
+            loss_sum += np.mean(
+                run_state.run_results[-1]) * run_state.run_examples
+            np_labels = run_state.run_results[0]
+            np_infers = run_state.run_results[1]
+            all_labels = np.hstack((all_labels, np_labels.reshape([-1])))
+            all_infers = np.hstack((all_infers, np_infers.reshape([-1])))
+
+        run_time_used = time.time() - run_states[0].run_time_begin
+        avg_loss = loss_sum / run_examples
+        run_speed = run_step / run_time_used
+
+        # The first key will be used as main metrics to update the best model
+        scores = OrderedDict()
+
+        for metric in self.metrics_choices:
+            if metric == "spearman":
+                spearman_correlations = spearmanr(all_labels, all_infers)[0]
+                scores["spearman"] = spearman_correlations
+            else:
+                raise ValueError("Not Support Metric: \"%s\"" % metric)
+        return scores, avg_loss, run_speed

paddlehub/finetune/task/sequence_task.py

+#coding:utf-8
+#  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.
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import time
+from collections import OrderedDict
+import numpy as np
+import paddle.fluid as fluid
+from paddlehub.finetune.evaluate import chunk_eval, calculate_f1
+from .basic_task import BasicTask
+
+
+class SequenceLabelTask(BasicTask):
+    def __init__(self,
+                 feature,
+                 max_seq_len,
+                 num_classes,
+                 feed_list,
+                 data_reader,
+                 startup_program=None,
+                 config=None,
+                 metrics_choices="default"):
+        if metrics_choices == "default":
+            metrics_choices = ["f1", "precision", "recall"]
+
+        main_program = feature.block.program
+        super(SequenceLabelTask, self).__init__(
+            data_reader=data_reader,
+            main_program=main_program,
+            feed_list=feed_list,
+            startup_program=startup_program,
+            config=config,
+            metrics_choices=metrics_choices)
+        self.feature = feature
+        self.max_seq_len = max_seq_len
+        self.num_classes = num_classes
+
+    def _build_net(self):
+        self.logits = fluid.layers.fc(
+            input=self.feature,
+            size=self.num_classes,
+            num_flatten_dims=2,
+            param_attr=fluid.ParamAttr(
+                name="cls_seq_label_out_w",
+                initializer=fluid.initializer.TruncatedNormal(scale=0.02)),
+            bias_attr=fluid.ParamAttr(
+                name="cls_seq_label_out_b",
+                initializer=fluid.initializer.Constant(0.)))
+
+        self.ret_infers = fluid.layers.reshape(
+            x=fluid.layers.argmax(self.logits, axis=2), shape=[-1, 1])
+        ret_infers = fluid.layers.assign(self.ret_infers)
+
+        self.seq_len = fluid.layers.data(
+            name="seq_len", shape=[1], dtype='int64')
+        seq_len = fluid.layers.assign(self.seq_len)
+
+        logits = self.logits
+        logits = fluid.layers.flatten(logits, axis=2)
+        logits = fluid.layers.softmax(logits)
+        self.num_labels = logits.shape[1]
+        return [logits]
+
+    def _add_label(self):
+        label = fluid.layers.data(
+            name="label", shape=[self.max_seq_len, 1], dtype='int64')
+        return [label]
+
+    def _add_loss(self):
+        labels = fluid.layers.flatten(self.labels[0], axis=2)
+        ce_loss = fluid.layers.cross_entropy(
+            input=self.outputs[0], label=labels)
+        loss = fluid.layers.mean(x=ce_loss)
+        return loss
+
+    def _add_metrics(self):
+        self.ret_labels = fluid.layers.reshape(x=self.labels[0], shape=[-1, 1])
+        return [self.ret_labels, self.ret_infers, self.seq_len]
+
+    def _calculate_metrics(self, run_states):
+        total_infer = total_label = total_correct = loss_sum = 0
+        run_step = run_time_used = run_examples = 0
+        for run_state in run_states:
+            loss_sum += np.mean(run_state.run_results[-1])
+            np_labels = run_state.run_results[0]
+            np_infers = run_state.run_results[1]
+            np_lens = run_state.run_results[2]
+            label_num, infer_num, correct_num = chunk_eval(
+                np_labels, np_infers, np_lens, self.num_labels,
+                self.device_count)
+            total_infer += infer_num
+            total_label += label_num
+            total_correct += correct_num
+            run_examples += run_state.run_examples
+            run_step += run_state.run_step
+
+        run_time_used = time.time() - run_states[0].run_time_begin
+        run_speed = run_step / run_time_used
+        avg_loss = loss_sum / run_examples
+
+        precision, recall, f1 = calculate_f1(total_label, total_infer,
+                                             total_correct)
+        # The first key will be used as main metrics to update the best model
+        scores = OrderedDict()
+
+        for metric in self.metrics_choices:
+            if metric == "precision":
+                scores["precision"] = precision
+            elif metric == "recall":
+                scores["recall"] = recall
+            elif metric == "f1":
+                scores["f1"] = f1
+            else:
+                raise ValueError("Not Support Metric: \"%s\"" % metric)
+
+        return scores, avg_loss, run_speed
+
+    @property
+    def feed_list(self):
+        feed_list = [varname for varname in self._base_feed_list]
+        if self.is_train_phase or self.is_test_phase:
+            feed_list += [self.labels[0].name, self.seq_len.name]
+        else:
+            feed_list += [self.seq_len.name]
+        return feed_list
+
+    @property
+    def fetch_list(self):
+        if self.is_train_phase or self.is_test_phase:
+            return [metric.name for metric in self.metrics] + [self.loss.name]
+        elif self.is_predict_phase:
+            return [self.ret_infers.name] + [self.seq_len.name]
+        return [output.name for output in self.outputs]

paddlehub/module/manager.py

@@ -26,6 +26,7 @@ from paddlehub.common.downloader import default_downloader
 from paddlehub.common.dir import MODULE_HOME
 from paddlehub.module import module_desc_pb2
 import paddlehub as hub
+from paddlehub.common.logger import logger
 
 
 class LocalModuleManager(object):
@@ -35,23 +36,26 @@ class LocalModuleManager(object):
         if not os.path.exists(self.local_modules_dir):
             utils.mkdir(self.local_modules_dir)
         elif os.path.isfile(self.local_modules_dir):
-            #TODO(wuzewu): give wanring
-            pass
+            raise ValueError("Module home should be a folder, not a file")
 
     def check_module_valid(self, module_path):
-        #TODO(wuzewu): code
-        info = {}
         try:
             desc_pb_path = os.path.join(module_path, 'module_desc.pb')
             if os.path.exists(desc_pb_path) and os.path.isfile(desc_pb_path):
+                info = {}
                 desc = module_desc_pb2.ModuleDesc()
                 with open(desc_pb_path, "rb") as fp:
                     desc.ParseFromString(fp.read())
                 info['version'] = desc.attr.map.data["module_info"].map.data[
                     "version"].s
+                return True, info
+            else:
+                logger.warning(
+                    "%s does not exist, the module will be reinstalled" %
+                    desc_pb_path)
         except:
-            return False, None
-        return True, info
+            pass
+        return False, None
 
     def all_modules(self, update=False):
         if not update and self.modules_dict:
@@ -60,7 +64,6 @@ class LocalModuleManager(object):
         for sub_dir_name in os.listdir(self.local_modules_dir):
             sub_dir_path = os.path.join(self.local_modules_dir, sub_dir_name)
             if os.path.isdir(sub_dir_path):
-                #TODO(wuzewu): get module name
                 valid, info = self.check_module_valid(sub_dir_path)
                 if valid:
                     module_name = sub_dir_name
@@ -92,7 +95,6 @@ class LocalModuleManager(object):
         url = search_result.get('url', None)
         md5_value = search_result.get('md5', None)
         installed_module_version = search_result.get('version', None)
-        #TODO(wuzewu): add compatibility check
         if not url or (module_version is not None and installed_module_version
                        != module_version) or (name != module_name):
             tips = "Can't find module %s" % module_name

paddlehub/module/module.py

@@ -117,7 +117,6 @@ class Module(object):
         self.cache_fetch_dict = None
         self.cache_program = None
 
-        # TODO(wuzewu): print more module loading info log
         if name:
             self._init_with_name(name=name, version=version)
         elif module_dir:
@@ -458,7 +457,6 @@ class Module(object):
         fetch_dict = self.cache_fetch_dict
         program = self.cache_program
 
-        #TODO(wuzewu): more option
         fetch_list = list(set([value for key, value in fetch_dict.items()]))
         with fluid.program_guard(program):
             result = []
@@ -554,7 +552,6 @@ class Module(object):
         self._recover_variable_info(program)
 
         paddle_helper.set_op_attr(program, is_test=for_test)
-        #TODO(wuzewu): return feed_list and fetch_list directly
         feed_dict = {}
         fetch_dict = {}
         for index, var in enumerate(signature.inputs):
@@ -569,7 +566,6 @@ class Module(object):
             if key:
                 fetch_dict[key] = program.global_block().var(var.name)
 
-        # TODO(ZeyuChen) encapsulate into a funtion
         # update BERT/ERNIE's input tensor's sequence length to max_seq_len
         if self.name.startswith("bert") or self.name.startswith("ernie"):
             MAX_SEQ_LENGTH = 512

paddlehub/reader/__init__.py

@@ -17,4 +17,6 @@ from .nlp_reader import ClassifyReader
 from .nlp_reader import SequenceLabelReader
 from .nlp_reader import LACClassifyReader
 from .nlp_reader import MultiLabelClassifyReader
+from .nlp_reader import ReadingComprehensionReader
+from .nlp_reader import RegressionReader
 from .cv_reader import ImageClassificationReader

paddlehub/reader/tokenization.py

@@ -21,7 +21,6 @@ from __future__ import print_function
 import collections
 import io
 import unicodedata
-
 import six
 
 

requirements.txt

-visualdl >= 1.3.0
 pre-commit
 protobuf >= 3.1.0
 yapf == 0.26.0
@@ -12,3 +11,5 @@ requests
 pandas
 #[py2]pandas == 0.24.0
 flake8
+tb-paddle
+cma == 2.7.0

setup.py

@@ -32,7 +32,7 @@ max_version, mid_version, min_version = python_version()
 
 REQUIRED_PACKAGES = [
     'six >= 1.10.0', 'protobuf >= 3.1.0', 'pyyaml', 'Pillow', 'requests',
-    "visualdl >= 1.3.0"
+    'visualdl >= 1.3.0', 'cma == 2.7.0'
 ]
 
 if max_version < 3: