#! usr/bin/env python3 # -*- coding:utf-8 -*- """ Copyright 2018 The Google AI Language Team Authors. BASED ON Google_BERT. """ from __future__ import absolute_import from __future__ import division from __future__ import print_function import collections import os, sys import pickle import tensorflow as tf import numpy as np sys.path.append("/workspace/bert") from biobert.conlleval import evaluate, report_notprint import modeling import optimization import tokenization import tf_metrics import time import horovod.tensorflow as hvd from utils.utils import LogEvalRunHook, LogTrainRunHook flags = tf.flags FLAGS = flags.FLAGS flags.DEFINE_string( "task_name", "NER", "The name of the task to train." ) flags.DEFINE_string( "data_dir", None, "The input datadir.", ) flags.DEFINE_string( "output_dir", None, "The output directory where the model checkpoints will be written." ) flags.DEFINE_string( "bert_config_file", None, "The config json file corresponding to the pre-trained BERT model." ) flags.DEFINE_string( "vocab_file", None, "The vocabulary file that the BERT model was trained on.") flags.DEFINE_string( "init_checkpoint", None, "Initial checkpoint (usually from a pre-trained BERT model)." ) flags.DEFINE_bool( "do_lower_case", False, "Whether to lower case the input text." ) flags.DEFINE_integer( "max_seq_length", 128, "The maximum total input sequence length after WordPiece tokenization." ) flags.DEFINE_bool( "do_train", False, "Whether to run training." ) flags.DEFINE_bool( "do_eval", False, "Whether to run eval on the dev set.") flags.DEFINE_bool( "do_predict", False, "Whether to run the model in inference mode on the test set.") flags.DEFINE_integer( "train_batch_size", 64, "Total batch size for training.") flags.DEFINE_integer( "eval_batch_size", 16, "Total batch size for eval.") flags.DEFINE_integer( "predict_batch_size", 16, "Total batch size for predict.") flags.DEFINE_float( "learning_rate", 5e-6, "The initial learning rate for Adam.") flags.DEFINE_float( "num_train_epochs", 10.0, "Total number of training epochs to perform.") flags.DEFINE_float( "warmup_proportion", 0.1, "Proportion of training to perform linear learning rate warmup for. " "E.g., 0.1 = 10% of training.") flags.DEFINE_integer( "save_checkpoints_steps", 1000, "How often to save the model checkpoint.") flags.DEFINE_integer( "iterations_per_loop", 1000, "How many steps to make in each estimator call.") tf.flags.DEFINE_string("master", None, "[Optional] TensorFlow master URL.") flags.DEFINE_bool("horovod", False, "Whether to use Horovod for multi-gpu runs") flags.DEFINE_bool("use_fp16", False, "Whether to use fp32 or fp16 arithmetic on GPU.") flags.DEFINE_bool("use_xla", False, "Whether to enable XLA JIT compilation.") class InputExample(object): """A single training/test example for simple sequence classification.""" def __init__(self, guid, text, label=None): """Constructs a InputExample. Args: guid: Unique id for the example. text_a: string. The untokenized text of the first sequence. For single sequence tasks, only this sequence must be specified. label: (Optional) string. The label of the example. This should be specified for train and dev examples, but not for test examples. """ self.guid = guid self.text = text self.label = label class InputFeatures(object): """A single set of features of data.""" def __init__(self, input_ids, input_mask, segment_ids, label_ids, ): self.input_ids = input_ids self.input_mask = input_mask self.segment_ids = segment_ids self.label_ids = label_ids # self.label_mask = label_mask class DataProcessor(object): """Base class for data converters for sequence classification data sets.""" def get_train_examples(self, data_dir): """Gets a collection of `InputExample`s for the train set.""" raise NotImplementedError() def get_dev_examples(self, data_dir): """Gets a collection of `InputExample`s for the dev set.""" raise NotImplementedError() def get_labels(self): """Gets the list of labels for this data set.""" raise NotImplementedError() @classmethod def _read_data(cls, input_file): """Reads a BIO data.""" with tf.gfile.Open(input_file, "r") as f: lines = [] words = [] labels = [] for line in f: contends = line.strip() if len(contends) == 0: assert len(words) == len(labels) if len(words) > 30: # split if the sentence is longer than 30 while len(words) > 30: tmplabel = labels[:30] for iidx in range(len(tmplabel)): if tmplabel.pop() == 'O': break l = ' '.join( [label for label in labels[:len(tmplabel) + 1] if len(label) > 0]) w = ' '.join( [word for word in words[:len(tmplabel) + 1] if len(word) > 0]) lines.append([l, w]) words = words[len(tmplabel) + 1:] labels = labels[len(tmplabel) + 1:] if len(words) == 0: continue l = ' '.join([label for label in labels if len(label) > 0]) w = ' '.join([word for word in words if len(word) > 0]) lines.append([l, w]) words = [] labels = [] continue word = line.strip().split()[0] label = line.strip().split()[-1] words.append(word) labels.append(label) return lines class BC5CDRProcessor(DataProcessor): def get_train_examples(self, data_dir): l1 = self._read_data(os.path.join(data_dir, "train.tsv")) l2 = self._read_data(os.path.join(data_dir, "devel.tsv")) return self._create_example(l1 + l2, "train") def get_dev_examples(self, data_dir, file_name="devel.tsv"): return self._create_example( self._read_data(os.path.join(data_dir, file_name)), "dev" ) def get_test_examples(self, data_dir, file_name="test.tsv"): return self._create_example( self._read_data(os.path.join(data_dir, file_name)), "test") def get_labels(self): return ["B", "I", "O", "X", "[CLS]", "[SEP]"] def _create_example(self, lines, set_type): examples = [] for (i, line) in enumerate(lines): guid = "%s-%s" % (set_type, i) text = tokenization.convert_to_unicode(line[1]) label = tokenization.convert_to_unicode(line[0]) examples.append(InputExample(guid=guid, text=text, label=label)) return examples class CLEFEProcessor(DataProcessor): def get_train_examples(self, data_dir): lines1 = self._read_data2(os.path.join(data_dir, "Training.tsv")) lines2 = self._read_data2(os.path.join(data_dir, "Development.tsv")) return self._create_example( lines1 + lines2, "train" ) def get_dev_examples(self, data_dir, file_name="Development.tsv"): return self._create_example( self._read_data2(os.path.join(data_dir, file_name)), "dev" ) def get_test_examples(self, data_dir, file_name="Test.tsv"): return self._create_example( self._read_data2(os.path.join(data_dir, file_name)), "test") def get_labels(self): return ["B", "I", "O", "X", "[CLS]", "[SEP]"] def _create_example(self, lines, set_type): examples = [] for (i, line) in enumerate(lines): guid = "%s-%s" % (set_type, i) text = tokenization.convert_to_unicode(line[1]) label = tokenization.convert_to_unicode(line[0]) examples.append(InputExample(guid=guid, text=text, label=label)) return examples @classmethod def _read_data2(cls, input_file): with tf.gfile.Open(input_file, "r") as f: lines = [] words = [] labels = [] for line in f: contends = line.strip() if len(contends) == 0: assert len(words) == len(labels) if len(words) == 0: continue l = ' '.join([label for label in labels if len(label) > 0]) w = ' '.join([word for word in words if len(word) > 0]) lines.append([l, w]) words = [] labels = [] continue elif contends.startswith('###'): continue word = line.strip().split()[0] label = line.strip().split()[-1] words.append(word) labels.append(label) return lines class I2b22012Processor(CLEFEProcessor): def get_labels(self): return ['B-CLINICAL_DEPT', 'B-EVIDENTIAL', 'B-OCCURRENCE', 'B-PROBLEM', 'B-TEST', 'B-TREATMENT', 'I-CLINICAL_DEPT', 'I-EVIDENTIAL', 'I-OCCURRENCE', 'I-PROBLEM', 'I-TEST', 'I-TREATMENT', "O", "X", "[CLS]", "[SEP]"] def write_tokens(tokens, labels, mode): if mode == "test": path = os.path.join(FLAGS.output_dir, "token_" + mode + ".txt") if tf.gfile.Exists(path): wf = tf.gfile.Open(path, 'a') else: wf = tf.gfile.Open(path, 'w') for token, label in zip(tokens, labels): if token != "**NULL**": wf.write(token + ' ' + str(label) + '\n') wf.close() def convert_single_example(ex_index, example, label_list, max_seq_length, tokenizer, mode): label_map = {} for (i, label) in enumerate(label_list, 1): label_map[label] = i label2id_file = os.path.join(FLAGS.output_dir, 'label2id.pkl') if not tf.gfile.Exists(label2id_file): with tf.gfile.Open(label2id_file, 'wb') as w: pickle.dump(label_map, w) textlist = example.text.split(' ') labellist = example.label.split(' ') tokens = [] labels = [] for i, word in enumerate(textlist): token = tokenizer.tokenize(word) tokens.extend(token) label_1 = labellist[i] for m in range(len(token)): if m == 0: labels.append(label_1) else: labels.append("X") # tokens = tokenizer.tokenize(example.text) if len(tokens) >= max_seq_length - 1: tokens = tokens[0:(max_seq_length - 2)] labels = labels[0:(max_seq_length - 2)] ntokens = [] segment_ids = [] label_ids = [] ntokens.append("[CLS]") segment_ids.append(0) # append("O") or append("[CLS]") not sure! label_ids.append(label_map["[CLS]"]) for i, token in enumerate(tokens): ntokens.append(token) segment_ids.append(0) label_ids.append(label_map[labels[i]]) ntokens.append("[SEP]") segment_ids.append(0) # append("O") or append("[SEP]") not sure! label_ids.append(label_map["[SEP]"]) input_ids = tokenizer.convert_tokens_to_ids(ntokens) input_mask = [1] * len(input_ids) # label_mask = [1] * len(input_ids) while len(input_ids) < max_seq_length: input_ids.append(0) input_mask.append(0) segment_ids.append(0) # we don't concerned about it! label_ids.append(0) ntokens.append("**NULL**") # label_mask.append(0) # print(len(input_ids)) assert len(input_ids) == max_seq_length assert len(input_mask) == max_seq_length assert len(segment_ids) == max_seq_length assert len(label_ids) == max_seq_length # assert len(label_mask) == max_seq_length if ex_index < 5: tf.logging.info("*** Example ***") tf.logging.info("guid: %s" % (example.guid)) tf.logging.info("tokens: %s" % " ".join( [tokenization.printable_text(x) for x in tokens])) tf.logging.info("input_ids: %s" % " ".join([str(x) for x in input_ids])) tf.logging.info("input_mask: %s" % " ".join([str(x) for x in input_mask])) tf.logging.info("segment_ids: %s" % " ".join([str(x) for x in segment_ids])) tf.logging.info("label_ids: %s" % " ".join([str(x) for x in label_ids])) # tf.logging.info("label_mask: %s" % " ".join([str(x) for x in label_mask])) feature = InputFeatures( input_ids=input_ids, input_mask=input_mask, segment_ids=segment_ids, label_ids=label_ids, # label_mask = label_mask ) # write_tokens(ntokens, label_ids, mode) return feature def filed_based_convert_examples_to_features( examples, label_list, max_seq_length, tokenizer, output_file, mode=None): writer = tf.python_io.TFRecordWriter(output_file) for (ex_index, example) in enumerate(examples): if ex_index % 5000 == 0: tf.logging.info("Writing example %d of %d" % (ex_index, len(examples))) feature = convert_single_example(ex_index, example, label_list, max_seq_length, tokenizer, mode) def create_int_feature(values): f = tf.train.Feature(int64_list=tf.train.Int64List(value=list(values))) return f features = collections.OrderedDict() features["input_ids"] = create_int_feature(feature.input_ids) features["input_mask"] = create_int_feature(feature.input_mask) features["segment_ids"] = create_int_feature(feature.segment_ids) features["label_ids"] = create_int_feature(feature.label_ids) # features["label_mask"] = create_int_feature(feature.label_mask) tf_example = tf.train.Example(features=tf.train.Features(feature=features)) writer.write(tf_example.SerializeToString()) def file_based_input_fn_builder(input_file, batch_size, seq_length, is_training, drop_remainder, hvd=None): name_to_features = { "input_ids": tf.FixedLenFeature([seq_length], tf.int64), "input_mask": tf.FixedLenFeature([seq_length], tf.int64), "segment_ids": tf.FixedLenFeature([seq_length], tf.int64), "label_ids": tf.FixedLenFeature([seq_length], tf.int64), # "label_ids":tf.VarLenFeature(tf.int64), # "label_mask": tf.FixedLenFeature([seq_length], tf.int64), } def _decode_record(record, name_to_features): example = tf.parse_single_example(record, name_to_features) for name in list(example.keys()): t = example[name] if t.dtype == tf.int64: t = tf.to_int32(t) example[name] = t return example def input_fn(params): #batch_size = params["batch_size"] d = tf.data.TFRecordDataset(input_file) if is_training: if hvd is not None: d = d.shard(hvd.size(), hvd.rank()) d = d.repeat() d = d.shuffle(buffer_size=100) d = d.apply(tf.contrib.data.map_and_batch( lambda record: _decode_record(record, name_to_features), batch_size=batch_size, drop_remainder=drop_remainder )) return d return input_fn def create_model(bert_config, is_training, input_ids, input_mask, segment_ids, labels, num_labels, use_one_hot_embeddings): model = modeling.BertModel( config=bert_config, is_training=is_training, input_ids=input_ids, input_mask=input_mask, token_type_ids=segment_ids, use_one_hot_embeddings=use_one_hot_embeddings ) output_layer = model.get_sequence_output() hidden_size = output_layer.shape[-1].value output_weight = tf.get_variable( "output_weights", [num_labels, hidden_size], initializer=tf.truncated_normal_initializer(stddev=0.02) ) output_bias = tf.get_variable( "output_bias", [num_labels], initializer=tf.zeros_initializer() ) with tf.variable_scope("loss"): if is_training: output_layer = tf.nn.dropout(output_layer, keep_prob=0.9) output_layer = tf.reshape(output_layer, [-1, hidden_size]) logits = tf.matmul(output_layer, output_weight, transpose_b=True) logits = tf.nn.bias_add(logits, output_bias) logits = tf.reshape(logits, [-1, FLAGS.max_seq_length, num_labels]) # mask = tf.cast(input_mask,tf.float32) # loss = tf.contrib.seq2seq.sequence_loss(logits,labels,mask) # return (loss, logits, predict) ########################################################################## log_probs = tf.nn.log_softmax(logits, axis=-1) one_hot_labels = tf.one_hot(labels, depth=num_labels, dtype=tf.float32) per_example_loss = -tf.reduce_sum(one_hot_labels * log_probs, axis=-1) loss = tf.reduce_mean(per_example_loss) probabilities = tf.nn.softmax(logits, axis=-1) predict = tf.argmax(probabilities, axis=-1) return (loss, per_example_loss, logits, predict) ########################################################################## def model_fn_builder(bert_config, num_labels, init_checkpoint=None, learning_rate=None, num_train_steps=None, num_warmup_steps=None, use_one_hot_embeddings=False, hvd=None, use_fp16=False): def model_fn(features, labels, mode, params): tf.logging.info("*** Features ***") for name in sorted(features.keys()): tf.logging.info(" name = %s, shape = %s" % (name, features[name].shape)) input_ids = features["input_ids"] input_mask = features["input_mask"] segment_ids = features["segment_ids"] label_ids = features["label_ids"] # label_mask = features["label_mask"] is_training = (mode == tf.estimator.ModeKeys.TRAIN) (total_loss, per_example_loss, logits, predicts) = create_model( bert_config, is_training, input_ids, input_mask, segment_ids, label_ids, num_labels, use_one_hot_embeddings) tvars = tf.trainable_variables() initialized_variable_names = {} scaffold_fn = None if init_checkpoint and (hvd is None or hvd.rank() == 0): (assignment_map, initialized_variable_names) = modeling.get_assignment_map_from_checkpoint(tvars, init_checkpoint) tf.train.init_from_checkpoint(init_checkpoint, assignment_map) tf.train.init_from_checkpoint(init_checkpoint, assignment_map) tf.logging.info("**** Trainable Variables ****") for var in tvars: init_string = "" if var.name in initialized_variable_names: init_string = ", *INIT_FROM_CKPT*" tf.logging.info(" name = %s, shape = %s%s", var.name, var.shape, init_string) output_spec = None if mode == tf.estimator.ModeKeys.TRAIN: train_op = optimization.create_optimizer( total_loss, learning_rate, num_train_steps, num_warmup_steps, hvd, False, use_fp16) output_spec = tf.estimator.EstimatorSpec( mode=mode, loss=total_loss, train_op=train_op) elif mode == tf.estimator.ModeKeys.EVAL: def metric_fn(per_example_loss, label_ids, logits): # def metric_fn(label_ids, logits): predictions = tf.argmax(logits, axis=-1, output_type=tf.int32) precision = tf_metrics.precision(label_ids, predictions, num_labels, [1, 2], average="macro") recall = tf_metrics.recall(label_ids, predictions, num_labels, [1, 2], average="macro") f = tf_metrics.f1(label_ids, predictions, num_labels, [1, 2], average="macro") # return { "eval_precision": precision, "eval_recall": recall, "eval_f": f, # "eval_loss": loss, } eval_metric_ops = metric_fn(per_example_loss, label_ids, logits) output_spec = tf.estimator.EstimatorSpec( mode=mode, loss=total_loss, eval_metric_ops=eval_metric_ops) else: output_spec = tf.estimator.EstimatorSpec( mode=mode, predictions=predicts)#probabilities) return output_spec return model_fn def result_to_pair(predict_line, pred_ids, id2label, writer, err_writer): words = str(predict_line.text).split(' ') labels = str(predict_line.label).split(' ') if len(words) != len(labels): tf.logging.error('Text and label not equal') tf.logging.error(predict_line.text) tf.logging.error(predict_line.label) exit(1) # get from CLS to SEP pred_labels = [] for id in pred_ids: if id == 0: continue curr_label = id2label[id] if curr_label == '[CLS]': continue elif curr_label == '[SEP]': break elif curr_label == 'X': continue pred_labels.append(curr_label) if len(pred_labels) > len(words): err_writer.write(predict_line.guid + '\n') err_writer.write(predict_line.text + '\n') err_writer.write(predict_line.label + '\n') err_writer.write(' '.join([str(i) for i in pred_ids]) + '\n') err_writer.write(' '.join([id2label.get(i, '**NULL**') for i in pred_ids]) + '\n\n') pred_labels = pred_labels[:len(words)] elif len(pred_labels) < len(words): err_writer.write(predict_line.guid + '\n') err_writer.write(predict_line.text + '\n') err_writer.write(predict_line.label + '\n') err_writer.write(' '.join([str(i) for i in pred_ids]) + '\n') err_writer.write(' '.join([id2label.get(i, '**NULL**') for i in pred_ids]) + '\n\n') pred_labels += ['O'] * (len(words) - len(pred_labels)) for tok, label, pred_label in zip(words, labels, pred_labels): writer.write(tok + ' ' + label + ' ' + pred_label + '\n') writer.write('\n') def main(_): tf.logging.set_verbosity(tf.logging.INFO) if FLAGS.horovod: hvd.init() if FLAGS.use_fp16: os.environ["TF_ENABLE_AUTO_MIXED_PRECISION_GRAPH_REWRITE"] = "1" processors = { "bc5cdr": BC5CDRProcessor, "clefe": CLEFEProcessor, 'i2b2': I2b22012Processor } if not FLAGS.do_train and not FLAGS.do_eval and not FLAGS.do_predict: raise ValueError("At least one of `do_train` or `do_eval` must be True.") bert_config = modeling.BertConfig.from_json_file(FLAGS.bert_config_file) if FLAGS.max_seq_length > bert_config.max_position_embeddings: raise ValueError( "Cannot use sequence length %d because the BERT model " "was only trained up to sequence length %d" % (FLAGS.max_seq_length, bert_config.max_position_embeddings)) task_name = FLAGS.task_name.lower() if task_name not in processors: raise ValueError("Task not found: %s" % (task_name)) tf.gfile.MakeDirs(FLAGS.output_dir) processor = processors[task_name]() label_list = processor.get_labels() tokenizer = tokenization.FullTokenizer( vocab_file=FLAGS.vocab_file, do_lower_case=FLAGS.do_lower_case) is_per_host = tf.contrib.tpu.InputPipelineConfig.PER_HOST_V2 master_process = True training_hooks = [] global_batch_size = FLAGS.train_batch_size hvd_rank = 0 config = tf.ConfigProto() if FLAGS.horovod: global_batch_size = FLAGS.train_batch_size * hvd.size() master_process = (hvd.rank() == 0) hvd_rank = hvd.rank() config.gpu_options.visible_device_list = str(hvd.local_rank()) if hvd.size() > 1: training_hooks.append(hvd.BroadcastGlobalVariablesHook(0)) if FLAGS.use_xla: config.graph_options.optimizer_options.global_jit_level = tf.OptimizerOptions.ON_1 run_config = tf.estimator.RunConfig( model_dir=FLAGS.output_dir if master_process else None, session_config=config, save_checkpoints_steps=FLAGS.save_checkpoints_steps if master_process else None, keep_checkpoint_max=1) if master_process: tf.logging.info("***** Configuaration *****") for key in FLAGS.__flags.keys(): tf.logging.info(' {}: {}'.format(key, getattr(FLAGS, key))) tf.logging.info("**************************") train_examples = None num_train_steps = None num_warmup_steps = None training_hooks.append(LogTrainRunHook(global_batch_size, hvd_rank)) if FLAGS.do_train: train_examples = processor.get_train_examples(FLAGS.data_dir) num_train_steps = int( len(train_examples) / global_batch_size * FLAGS.num_train_epochs) num_warmup_steps = int(num_train_steps * FLAGS.warmup_proportion) start_index = 0 end_index = len(train_examples) tmp_filenames = [os.path.join(FLAGS.output_dir, "train.tf_record")] if FLAGS.horovod: tmp_filenames = [os.path.join(FLAGS.output_dir, "train.tf_record{}".format(i)) for i in range(hvd.size())] num_examples_per_rank = len(train_examples) // hvd.size() remainder = len(train_examples) % hvd.size() if hvd.rank() < remainder: start_index = hvd.rank() * (num_examples_per_rank+1) end_index = start_index + num_examples_per_rank + 1 else: start_index = hvd.rank() * num_examples_per_rank + remainder end_index = start_index + (num_examples_per_rank) model_fn = model_fn_builder( bert_config=bert_config, num_labels=len(label_list) + 1, init_checkpoint=FLAGS.init_checkpoint, learning_rate=FLAGS.learning_rate if not FLAGS.horovod else FLAGS.learning_rate * hvd.size(), num_train_steps=num_train_steps, num_warmup_steps=num_warmup_steps, use_one_hot_embeddings=False, hvd=None if not FLAGS.horovod else hvd, use_fp16=FLAGS.use_fp16) estimator = tf.estimator.Estimator( model_fn=model_fn, config=run_config) if FLAGS.do_train: #train_file = os.path.join(FLAGS.output_dir, "train.tf_record") #filed_based_convert_examples_to_features( # train_examples, label_list, FLAGS.max_seq_length, tokenizer, train_file) filed_based_convert_examples_to_features( train_examples[start_index:end_index], label_list, FLAGS.max_seq_length, tokenizer, tmp_filenames[hvd_rank]) tf.logging.info("***** Running training *****") tf.logging.info(" Num examples = %d", len(train_examples)) tf.logging.info(" Batch size = %d", FLAGS.train_batch_size) tf.logging.info(" Num steps = %d", num_train_steps) train_input_fn = file_based_input_fn_builder( input_file=tmp_filenames, #train_file, batch_size=FLAGS.train_batch_size, seq_length=FLAGS.max_seq_length, is_training=True, drop_remainder=True, hvd=None if not FLAGS.horovod else hvd) #estimator.train(input_fn=train_input_fn, max_steps=num_train_steps) train_start_time = time.time() estimator.train(input_fn=train_input_fn, max_steps=num_train_steps, hooks=training_hooks) train_time_elapsed = time.time() - train_start_time train_time_wo_overhead = training_hooks[-1].total_time avg_sentences_per_second = num_train_steps * global_batch_size * 1.0 / train_time_elapsed ss_sentences_per_second = (num_train_steps - training_hooks[-1].skipped) * global_batch_size * 1.0 / train_time_wo_overhead if master_process: tf.logging.info("-----------------------------") tf.logging.info("Total Training Time = %0.2f for Sentences = %d", train_time_elapsed, num_train_steps * global_batch_size) tf.logging.info("Total Training Time W/O Overhead = %0.2f for Sentences = %d", train_time_wo_overhead, (num_train_steps - training_hooks[-1].skipped) * global_batch_size) tf.logging.info("Throughput Average (sentences/sec) with overhead = %0.2f", avg_sentences_per_second) tf.logging.info("Throughput Average (sentences/sec) = %0.2f", ss_sentences_per_second) tf.logging.info("-----------------------------") if FLAGS.do_eval and master_process: eval_examples = processor.get_dev_examples(FLAGS.data_dir) eval_file = os.path.join(FLAGS.output_dir, "eval.tf_record") filed_based_convert_examples_to_features( eval_examples, label_list, FLAGS.max_seq_length, tokenizer, eval_file) tf.logging.info("***** Running evaluation *****") tf.logging.info(" Num examples = %d", len(eval_examples)) tf.logging.info(" Batch size = %d", FLAGS.eval_batch_size) eval_steps = None eval_drop_remainder = False eval_input_fn = file_based_input_fn_builder( input_file=eval_file, batch_size=FLAGS.eval_batch_size, seq_length=FLAGS.max_seq_length, is_training=False, drop_remainder=eval_drop_remainder) result = estimator.evaluate(input_fn=eval_input_fn, steps=eval_steps) output_eval_file = os.path.join(FLAGS.output_dir, "eval_results.txt") with tf.gfile.Open(output_eval_file, "w") as writer: tf.logging.info("***** Eval results *****") for key in sorted(result.keys()): tf.logging.info(" %s = %s", key, str(result[key])) writer.write("%s = %s\n" % (key, str(result[key]))) if FLAGS.do_predict and master_process: predict_examples = processor.get_test_examples(FLAGS.data_dir) predict_file = os.path.join(FLAGS.output_dir, "predict.tf_record") filed_based_convert_examples_to_features(predict_examples, label_list, FLAGS.max_seq_length, tokenizer, predict_file, mode="test") with tf.gfile.Open(os.path.join(FLAGS.output_dir, 'label2id.pkl'), 'rb') as rf: label2id = pickle.load(rf) id2label = {value: key for key, value in label2id.items()} token_path = os.path.join(FLAGS.output_dir, "token_test.txt") if tf.gfile.Exists(token_path): tf.gfile.Remove(token_path) tf.logging.info("***** Running prediction*****") tf.logging.info(" Num examples = %d", len(predict_examples)) tf.logging.info(" Batch size = %d", FLAGS.predict_batch_size) predict_drop_remainder = False predict_input_fn = file_based_input_fn_builder( input_file=predict_file, batch_size=FLAGS.predict_batch_size, seq_length=FLAGS.max_seq_length, is_training=False, drop_remainder=predict_drop_remainder) eval_hooks = [LogEvalRunHook(FLAGS.predict_batch_size)] eval_start_time = time.time() output_predict_file = os.path.join(FLAGS.output_dir, "label_test.txt") test_labels_file = os.path.join(FLAGS.output_dir, "test_labels.txt") test_labels_err_file = os.path.join(FLAGS.output_dir, "test_labels_errs.txt") with tf.gfile.Open(output_predict_file, 'w') as writer, \ tf.gfile.Open(test_labels_file, 'w') as tl, \ tf.gfile.Open(test_labels_err_file, 'w') as tle: print(id2label) i=0 for prediction in estimator.predict(input_fn=predict_input_fn, hooks=eval_hooks, yield_single_examples=True): output_line = "\n".join(id2label[id] for id in prediction if id != 0) + "\n" writer.write(output_line) result_to_pair(predict_examples[i], prediction, id2label, tl, tle) i = i + 1 eval_time_elapsed = time.time() - eval_start_time eval_time_wo_overhead = eval_hooks[-1].total_time time_list = eval_hooks[-1].time_list time_list.sort() num_sentences = (eval_hooks[-1].count - eval_hooks[-1].skipped) * FLAGS.predict_batch_size avg = np.mean(time_list) cf_50 = max(time_list[:int(len(time_list) * 0.50)]) cf_90 = max(time_list[:int(len(time_list) * 0.90)]) cf_95 = max(time_list[:int(len(time_list) * 0.95)]) cf_99 = max(time_list[:int(len(time_list) * 0.99)]) cf_100 = max(time_list[:int(len(time_list) * 1)]) ss_sentences_per_second = num_sentences * 1.0 / eval_time_wo_overhead tf.logging.info("-----------------------------") tf.logging.info("Total Inference Time = %0.2f for Sentences = %d", eval_time_elapsed, eval_hooks[-1].count * FLAGS.predict_batch_size) tf.logging.info("Total Inference Time W/O Overhead = %0.2f for Sentences = %d", eval_time_wo_overhead, (eval_hooks[-1].count - eval_hooks[-1].skipped) * FLAGS.predict_batch_size) tf.logging.info("Summary Inference Statistics") tf.logging.info("Batch size = %d", FLAGS.predict_batch_size) tf.logging.info("Sequence Length = %d", FLAGS.max_seq_length) tf.logging.info("Precision = %s", "fp16" if FLAGS.use_fp16 else "fp32") tf.logging.info("Latency Confidence Level 50 (ms) = %0.2f", cf_50 * 1000) tf.logging.info("Latency Confidence Level 90 (ms) = %0.2f", cf_90 * 1000) tf.logging.info("Latency Confidence Level 95 (ms) = %0.2f", cf_95 * 1000) tf.logging.info("Latency Confidence Level 99 (ms) = %0.2f", cf_99 * 1000) tf.logging.info("Latency Confidence Level 100 (ms) = %0.2f", cf_100 * 1000) tf.logging.info("Latency Average (ms) = %0.2f", avg * 1000) tf.logging.info("Throughput Average (sentences/sec) = %0.2f", ss_sentences_per_second) tf.logging.info("-----------------------------") tf.logging.info('Reading: %s', test_labels_file) with tf.gfile.Open(test_labels_file, "r") as f: counts = evaluate(f) eval_result = report_notprint(counts) print(''.join(eval_result)) with tf.gfile.Open(os.path.join(FLAGS.output_dir, 'test_results_conlleval.txt'), 'w') as fd: fd.write(''.join(eval_result)) if __name__ == "__main__": flags.mark_flag_as_required("data_dir") flags.mark_flag_as_required("task_name") flags.mark_flag_as_required("vocab_file") flags.mark_flag_as_required("bert_config_file") flags.mark_flag_as_required("output_dir") tf.app.run()