def main(_):
tf.logging.set_verbosity(tf.logging.INFO)
processors = {
"cola": run_classifier.ColaProcessor,
"mnli": run_classifier.MnliProcessor,
"mrpc": run_classifier.MrpcProcessor,
}
if not FLAGS.do_train and not FLAGS.do_eval:
raise ValueError(
"At least one of `do_train` or `do_eval` must be True.")
tf.gfile.MakeDirs(FLAGS.output_dir)
task_name = FLAGS.task_name.lower()
if task_name not in processors:
raise ValueError("Task not found: %s" % (task_name))
processor = processors[task_name]()
label_list = processor.get_labels()
tokenizer = create_tokenizer_from_hub_module(FLAGS.bert_hub_module_handle)
tpu_cluster_resolver = None
if FLAGS.use_tpu and FLAGS.tpu_name:
tpu_cluster_resolver = tf.contrib.cluster_resolver.TPUClusterResolver(
FLAGS.tpu_name, zone=FLAGS.tpu_zone, project=FLAGS.gcp_project)
is_per_host = tf.contrib.tpu.InputPipelineConfig.PER_HOST_V2
run_config = tf.contrib.tpu.RunConfig(
cluster=tpu_cluster_resolver,
master=FLAGS.master,
model_dir=FLAGS.output_dir,
save_checkpoints_steps=FLAGS.save_checkpoints_steps,
tpu_config=tf.contrib.tpu.TPUConfig(
iterations_per_loop=FLAGS.iterations_per_loop,
num_shards=FLAGS.num_tpu_cores,
per_host_input_for_training=is_per_host))
train_examples = None
num_train_steps = None
num_warmup_steps = None
if FLAGS.do_train:
train_examples = processor.get_train_examples(FLAGS.data_dir)
num_train_steps = int(
len(train_examples) / FLAGS.train_batch_size *
FLAGS.num_train_epochs)
num_warmup_steps = int(num_train_steps * FLAGS.warmup_proportion)
model_fn = model_fn_builder(
num_labels=len(label_list),
learning_rate=FLAGS.learning_rate,
num_train_steps=num_train_steps,
num_warmup_steps=num_warmup_steps,
use_tpu=FLAGS.use_tpu,
bert_hub_module_handle=FLAGS.bert_hub_module_handle)
# If TPU is not available, this will fall back to normal Estimator on CPU
# or GPU.
estimator = tf.contrib.tpu.TPUEstimator(
use_tpu=FLAGS.use_tpu,
model_fn=model_fn,
config=run_config,
train_batch_size=FLAGS.train_batch_size,
eval_batch_size=FLAGS.eval_batch_size,
predict_batch_size=FLAGS.predict_batch_size)
if FLAGS.do_train:
train_features = run_classifier.convert_examples_to_features(
train_examples, label_list, FLAGS.max_seq_length, tokenizer)
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 = run_classifier.input_fn_builder(
features=train_features,
seq_length=FLAGS.max_seq_length,
is_training=True,
drop_remainder=True)
estimator.train(input_fn=train_input_fn, max_steps=num_train_steps)
if FLAGS.do_eval:
eval_examples = processor.get_dev_examples(FLAGS.data_dir)
eval_features = run_classifier.convert_examples_to_features(
eval_examples, label_list, FLAGS.max_seq_length, tokenizer)
tf.logging.info("***** Running evaluation *****")
tf.logging.info(" Num examples = %d", len(eval_examples))
tf.logging.info(" Batch size = %d", FLAGS.eval_batch_size)
# This tells the estimator to run through the entire set.
eval_steps = None
# However, if running eval on the TPU, you will need to specify the
# number of steps.
if FLAGS.use_tpu:
# Eval will be slightly WRONG on the TPU because it will truncate
# the last batch.
eval_steps = int(len(eval_examples) / FLAGS.eval_batch_size)
eval_drop_remainder = True if FLAGS.use_tpu else False
eval_input_fn = run_classifier.input_fn_builder(
features=eval_features,
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.GFile(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:
predict_examples = processor.get_test_examples(FLAGS.data_dir)
if FLAGS.use_tpu:
# Discard batch remainder if running on TPU
n = len(predict_examples)
predict_examples = predict_examples[:(
n - n % FLAGS.predict_batch_size)]
predict_file = os.path.join(FLAGS.output_dir, "predict.tf_record")
run_classifier.file_based_convert_examples_to_features(
predict_examples, label_list, FLAGS.max_seq_length, tokenizer,
predict_file)
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_input_fn = run_classifier.file_based_input_fn_builder(
input_file=predict_file,
seq_length=FLAGS.max_seq_length,
is_training=False,
drop_remainder=FLAGS.use_tpu)
result = estimator.predict(input_fn=predict_input_fn)
output_predict_file = os.path.join(FLAGS.output_dir,
"test_results.tsv")
with tf.gfile.GFile(output_predict_file, "w") as writer:
tf.logging.info("***** Predict results *****")
for prediction in result:
probabilities = prediction["probabilities"]
output_line = "\t".join(
str(class_probability)
for class_probability in probabilities) + "\n"
writer.write(output_line)
optimizer = BertAdam(optimizer_grouped_parameters,
lr=learning_rate,
warmup=warmup_proportion,
t_total=t_total)
# optimizer = AdamW(optimizer_grouped_parameters,
# lr = learning_rate, # args.learning_rate - default is 5e-5, our notebook had 2e-5
# eps = 1e-8, # args.adam_epsilon - default is 1e-8.
# correct_bias=False
# )
# scheduler = get_linear_schedule_with_warmup(optimizer, num_warmup_steps=num_warmup_steps, num_training_steps=t_total) # PyTorch scheduler
# In[15]:
global_step = 0
train_features = convert_examples_to_features(train_examples, label_list,
max_seq_length, tokenizer)
claim_features = convert_claims_to_features(train_examples, label_list,
max_seq_length, tokenizer)
logger.info("***** Running training *****")
logger.info(" Num examples = %d", len(train_examples))
logger.info(" Batch size = %d", train_batch_size)
logger.info(" Num steps = %d", num_train_steps)
all_input_ids = torch.tensor([f.input_ids for f in train_features],
dtype=torch.long)
all_input_mask = torch.tensor([f.input_mask for f in train_features],
dtype=torch.long)
all_segment_ids = torch.tensor([f.segment_ids for f in train_features],
dtype=torch.long)
all_label_ids = torch.tensor([f.label_id for f in train_features],
dtype=torch.long)
def train_and_test(data_dir,
bert_model="bert-base-uncased",
task_name=None,
output_dir=None,
max_seq_length=32,
do_train=False,
do_eval=False,
do_lower_case=False,
train_batch_size=32,
eval_batch_size=8,
learning_rate=5e-5,
num_train_epochs=5,
warmup_proportion=0.1,
no_cuda=False,
local_rank=-1,
seed=42,
gradient_accumulation_steps=1,
optimize_on_cpu=False,
fp16=False,
loss_scale=128,
saved_model=""):
# ## Required parameters
# parser.add_argument("--data_dir",
# default=None,
# type=str,
# required=True,
# help="The input data dir. Should contain the .tsv files (or other data files) for the task.")
# parser.add_argument("--bert_model", default=None, type=str, required=True,
# help="Bert pre-trained model selected in the list: bert-base-uncased, "
# "bert-large-uncased, bert-base-cased, bert-base-multilingual, bert-base-chinese.")
# parser.add_argument("--task_name",
# default=None,
# type=str,
# required=True,
# help="The name of the task to train.")
# parser.add_argument("--output_dir",
# default=None,
# type=str,
# required=True,
# help="The output directory where the model checkpoints will be written.")
## Other parameters
# parser.add_argument("--max_seq_length",
# default=128,
# type=int,
# help="The maximum total input sequence length after WordPiece tokenization. \n"
# "Sequences longer than this will be truncated, and sequences shorter \n"
# "than this will be padded.")
# parser.add_argument("--do_train",
# default=False,
# action='store_true',
# help="Whether to run training.")
# parser.add_argument("--do_eval",
# default=False,
# action='store_true',
# help="Whether to run eval on the dev set.")
# parser.add_argument("--do_lower_case",
# default=False,
# action='store_true',
# help="Set this flag if you are using an uncased model.")
# parser.add_argument("--train_batch_size",
# default=32,
# type=int,
# help="Total batch size for training.")
# parser.add_argument("--eval_batch_size",
# default=8,
# type=int,
# help="Total batch size for eval.")
# parser.add_argument("--learning_rate",
# default=5e-5,
# type=float,
# help="The initial learning rate for Adam.")
# parser.add_argument("--num_train_epochs",
# default=3.0,
# type=float,
# help="Total number of training epochs to perform.")
# parser.add_argument("--warmup_proportion",
# default=0.1,
# type=float,
# help="Proportion of training to perform linear learning rate warmup for. "
# "E.g., 0.1 = 10%% of training.")
# parser.add_argument("--no_cuda",
# default=False,
# action='store_true',
# help="Whether not to use CUDA when available")
# parser.add_argument("--local_rank",
# type=int,
# default=-1,
# help="local_rank for distributed training on gpus")
# parser.add_argument('--seed',
# type=int,
# default=42,
# help="random seed for initialization")
# parser.add_argument('--gradient_accumulation_steps',
# type=int,
# default=1,
# help="Number of updates steps to accumulate before performing a backward/update pass.")
# parser.add_argument('--optimize_on_cpu',
# default=False,
# action='store_true',
# help="Whether to perform optimization and keep the optimizer averages on CPU")
# parser.add_argument('--fp16',
# default=False,
# action='store_true',
# help="Whether to use 16-bit float precision instead of 32-bit")
# parser.add_argument('--loss_scale',
# type=float, default=128,
# help='Loss scaling, positive power of 2 values can improve fp16 convergence.')
# args = parser.parse_args()
processors = {
# "cola": ColaProcessor,
# "mnli": MnliProcessor,
"mrpc": MrpcProcessor,
"stance": StanceProcessor
}
if local_rank == -1 or no_cuda:
device = torch.device(
"cuda" if torch.cuda.is_available() and not no_cuda else "cpu")
n_gpu = torch.cuda.device_count()
else:
device = torch.device("cuda", local_rank)
n_gpu = 1
# Initializes the distributed backend which will take care of sychronizing nodes/GPUs
torch.distributed.init_process_group(backend='nccl')
if fp16:
logger.info(
"16-bits training currently not supported in distributed training"
)
fp16 = False # (see https://github.com/pytorch/pytorch/pull/13496)
logger.info("device %s n_gpu %d distributed training %r", device, n_gpu,
bool(local_rank != -1))
if gradient_accumulation_steps < 1:
raise ValueError(
"Invalid gradient_accumulation_steps parameter: {}, should be >= 1"
.format(gradient_accumulation_steps))
train_batch_size = int(train_batch_size / gradient_accumulation_steps)
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
if n_gpu > 0:
torch.cuda.manual_seed_all(seed)
if not do_train and not do_eval:
raise ValueError(
"At least one of `do_train` or `do_eval` must be True.")
if do_train:
# if os.path.exists(output_dir) and os.listdir(output_dir):
# raise ValueError("Output directory ({}) already exists and is not emp1ty.".format(output_dir))
os.makedirs(output_dir, exist_ok=True)
task_name = task_name.lower()
if task_name not in processors:
raise ValueError("Task not found: %s" % (task_name))
processor = processors[task_name]()
label_list = processor.get_labels()
# tokenizer = BertTokenizer.from_pretrained(bert_model, do_lower_case=do_lower_case)
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
train_examples = None
num_train_steps = None
if do_train:
train_examples = processor.get_train_examples(data_dir)
num_train_steps = int(
len(train_examples) / train_batch_size /
gradient_accumulation_steps * num_train_epochs)
# Prepare model
# model = BertForSequenceClassification.from_pretrained(bert_model,
# cache_dir=PYTORCH_PRETRAINED_BERT_CACHE / 'distributed_{}'.format(local_rank), num_labels = 2)
model = BertForConsistencyCueClassification.from_pretrained(
'bert-base-uncased', num_labels=2)
model.to(device)
if fp16:
model.half()
if local_rank != -1:
model = torch.nn.parallel.DistributedDataParallel(
model, device_ids=[local_rank], output_device=local_rank)
elif n_gpu > 1:
model = torch.nn.DataParallel(model)
# Prepare optimizer
if fp16:
param_optimizer = [
(n, param.clone().detach().to('cpu').float().requires_grad_())
for n, param in model.named_parameters()
]
elif optimize_on_cpu:
param_optimizer = [
(n, param.clone().detach().to('cpu').requires_grad_())
for n, param in model.named_parameters()
]
else:
param_optimizer = list(model.named_parameters())
no_decay = ['bias', 'gamma', 'beta']
optimizer_grouped_parameters = [{
'params': [
p for n, p in param_optimizer
if not any(nd in n for nd in no_decay)
],
'weight_decay_rate':
0.01
}, {
'params':
[p for n, p in param_optimizer if any(nd in n for nd in no_decay)],
'weight_decay_rate':
0.0
}]
t_total = num_train_steps
# print(t_total)
if local_rank != -1:
t_total = t_total // torch.distributed.get_world_size()
if do_train:
optimizer = BertAdam(optimizer_grouped_parameters,
lr=learning_rate,
warmup=warmup_proportion,
t_total=t_total)
global_step = 0
if do_train:
train_features = convert_examples_to_features(train_examples,
label_list,
max_seq_length,
tokenizer)
logger.info("***** Running training *****")
logger.info(" Num examples = %d", len(train_examples))
logger.info(" Batch size = %d", train_batch_size)
logger.info(" Num steps = %d", num_train_steps)
all_input_ids = torch.tensor([f.input_ids for f in train_features],
dtype=torch.long)
all_input_mask = torch.tensor([f.input_mask for f in train_features],
dtype=torch.long)
all_segment_ids = torch.tensor([f.segment_ids for f in train_features],
dtype=torch.long)
all_label_ids = torch.tensor([f.label_id for f in train_features],
dtype=torch.long)
train_data = TensorDataset(all_input_ids, all_input_mask,
all_segment_ids, all_label_ids)
if local_rank == -1:
train_sampler = RandomSampler(train_data)
else:
train_sampler = DistributedSampler(train_data)
train_dataloader = DataLoader(train_data,
sampler=train_sampler,
batch_size=train_batch_size)
model.train()
for _ in trange(int(num_train_epochs), desc="Epoch"):
tr_loss = 0
nb_tr_examples, nb_tr_steps = 0, 0
for step, batch in enumerate(
tqdm(train_dataloader, desc="Iteration")):
batch = tuple(t.to(device) for t in batch)
input_ids, input_mask, segment_ids, label_ids, = batch
loss = model(input_ids, segment_ids, input_mask, label_ids)
if n_gpu > 1:
loss = loss.mean() # mean() to average on multi-gpu.
if fp16 and loss_scale != 1.0:
# rescale loss for fp16 training
# see https://docs.nvidia.com/deeplearning/sdk/mixed-precision-training/index.html
loss = loss * loss_scale
if gradient_accumulation_steps > 1:
loss = loss / gradient_accumulation_steps
loss.backward()
tr_loss += loss.item()
nb_tr_examples += input_ids.size(0)
nb_tr_steps += 1
if (step + 1) % gradient_accumulation_steps == 0:
if fp16 or optimize_on_cpu:
if fp16 and loss_scale != 1.0:
# scale down gradients for fp16 training
for param in model.parameters():
if param.grad is not None:
param.grad.data = param.grad.data / loss_scale
is_nan = set_optimizer_params_grad(
param_optimizer,
model.named_parameters(),
test_nan=True)
if is_nan:
logger.info(
"FP16 TRAINING: Nan in gradients, reducing loss scaling"
)
loss_scale = loss_scale / 2
model.zero_grad()
continue
optimizer.step()
copy_optimizer_params_to_model(
model.named_parameters(), param_optimizer)
else:
optimizer.step()
model.zero_grad()
global_step += 1
torch.save(model.state_dict(),
output_dir + "ibmcs_non_reverse_bertcons_epoch5.pth")
if do_eval and (local_rank == -1 or torch.distributed.get_rank() == 0):
eval_examples = processor.get_test_examples(data_dir)
# eval_examples = processor.get_dev_examples(data_dir)
eval_features = convert_examples_to_features(eval_examples, label_list,
max_seq_length, tokenizer)
claim_features = convert_claims_to_features(eval_examples, label_list,
max_seq_length, tokenizer)
logger.info("***** Running evaluation *****")
logger.info(" Num examples = %d", len(eval_examples))
logger.info(" Batch size = %d", eval_batch_size)
all_input_ids = torch.tensor([f.input_ids for f in eval_features],
dtype=torch.long)
all_input_mask = torch.tensor([f.input_mask for f in eval_features],
dtype=torch.long)
all_segment_ids = torch.tensor([f.segment_ids for f in eval_features],
dtype=torch.long)
all_label_ids = torch.tensor([f.label_id for f in eval_features],
dtype=torch.long)
claims_input_ids = torch.tensor([f.input_ids for f in claim_features],
dtype=torch.long)
claims_input_mask = torch.tensor(
[f.input_mask for f in claim_features], dtype=torch.long)
claims_segment_ids = torch.tensor(
[f.segment_ids for f in claim_features], dtype=torch.long)
claims_label_ids = torch.tensor([f.label_id for f in claim_features],
dtype=torch.long)
eval_data = TensorDataset(all_input_ids, all_input_mask,
all_segment_ids, all_label_ids,
claims_input_ids, claims_input_mask,
claims_segment_ids, claims_label_ids)
# Run prediction for full data
# eval_sampler = SequentialSampler(eval_data)
eval_sampler = SequentialSampler(eval_data)
eval_dataloader = DataLoader(eval_data,
sampler=eval_sampler,
batch_size=eval_batch_size)
# print('all_input_ids:')
# print(all_input_ids)
# model.load_state_dict(torch.load(saved_model))
model_state_dict = torch.load(saved_model)
model = BertForConsistencyCueClassification.from_pretrained(
'bert-base-uncased', num_labels=2, state_dict=model_state_dict)
model.to(device)
model.eval()
# eval_loss, eval_accuracy = 0, 0
eval_tp, eval_pred_c, eval_gold_c = 0, 0, 0
eval_loss, eval_macro_p, eval_macro_r = 0, 0, 0
raw_score = []
nb_eval_steps, nb_eval_examples = 0, 0
for input_ids, input_mask, segment_ids, label_ids, claim_input_ids, claim_input_mask, claim_segment_ids, claim_label_ids in eval_dataloader:
input_ids = input_ids.to(device)
input_mask = input_mask.to(device)
segment_ids = segment_ids.to(device)
label_ids = label_ids.to(device)
claim_input_ids = claim_input_ids.to(device)
claim_input_mask = claim_input_mask.to(device)
claim_segment_ids = claim_segment_ids.to(device)
claim_label_ids = claim_label_ids.to(device)
# print("start")
# print(input_ids)
# print(input_mask)
# print(segment_ids)
# print(label_ids)
# print(claim_input_ids)
# print(claim_input_mask)
# print(claim_segment_ids)
# print(claim_label_ids)
# print("end")
with torch.no_grad():
tmp_eval_loss = model(input_ids=input_ids,
token_type_ids=segment_ids,
attention_mask=input_mask,
labels=label_ids,
input_ids2=claim_input_ids,
token_type_ids2=claim_segment_ids,
attention_mask2=claim_input_mask,
labels2=claim_label_ids)
logits = model(input_ids=input_ids,
token_type_ids=segment_ids,
attention_mask=input_mask,
input_ids2=claim_input_ids,
token_type_ids2=claim_segment_ids,
attention_mask2=claim_input_mask)
# print(logits)
# print(logits[0])
logits = logits.detach().cpu().numpy()
# print(logits)
label_ids = label_ids.to('cpu').numpy()
# print(label_ids)
# Micro F1 (aggregated tp, fp, fn counts across all examples)
tmp_tp, tmp_pred_c, tmp_gold_c = tp_pcount_gcount(
logits, label_ids)
eval_tp += tmp_tp
eval_pred_c += tmp_pred_c
eval_gold_c += tmp_gold_c
pred_label = np.argmax(logits, axis=1)
raw_score += zip(logits, pred_label, label_ids)
# Macro F1 (averaged P, R across mini batches)
tmp_eval_p, tmp_eval_r, tmp_eval_f1 = p_r_f1(logits, label_ids)
eval_macro_p += tmp_eval_p
eval_macro_r += tmp_eval_r
eval_loss += tmp_eval_loss.mean().item()
nb_eval_examples += input_ids.size(0)
nb_eval_steps += 1
# Micro F1 (aggregated tp, fp, fn counts across all examples)
eval_micro_p = eval_tp / eval_pred_c
eval_micro_r = eval_tp / eval_gold_c
eval_micro_f1 = 2 * eval_micro_p * eval_micro_r / (eval_micro_p +
eval_micro_r)
# Macro F1 (averaged P, R across mini batches)
eval_macro_p = eval_macro_p / nb_eval_steps
eval_macro_r = eval_macro_r / nb_eval_steps
eval_macro_f1 = 2 * eval_macro_p * eval_macro_r / (eval_macro_p +
eval_macro_r)
eval_loss = eval_loss / nb_eval_steps
result = {
'eval_loss': eval_loss,
'eval_micro_p': eval_micro_p,
'eval_micro_r': eval_micro_r,
'eval_micro_f1': eval_micro_f1,
'eval_macro_p': eval_macro_p,
'eval_macro_r': eval_macro_r,
'eval_macro_f1': eval_macro_f1,
# 'global_step': global_step,
# 'loss': tr_loss/nb_tr_steps
}
output_eval_file = os.path.join(
output_dir,
"train_on_ibmcs_eval_on_ibmcs_bert_cons_epoch5_eval_results.txt")
output_raw_score = os.path.join(
output_dir,
"train_on_ibmcs_eval_on_ibmcs_bert_cons_epoch5_raw_score.csv")
with open(output_eval_file, "w") as writer:
logger.info("***** Eval results *****")
for key in sorted(result.keys()):
logger.info(" %s = %s", key, str(result[key]))
writer.write("%s = %s\n" % (key, str(result[key])))
with open(output_raw_score, 'w') as fout:
fields = [
"undermine_score", "support_score", "predict_label", "gold"
]
writer = csv.DictWriter(fout, fieldnames=fields)
writer.writeheader()
for score, pred, gold in raw_score:
writer.writerow({
"undermine_score": str(score[0]),
"support_score": str(score[1]),
"predict_label": str(pred),
"gold": str(gold)
})
def main(_):
"""For current work, we only use the following four categories,
but you can add others if you would like to."""
categories = [
'Tools_and_Home_Improvement',
'Patio_Lawn_and_Garden',
'Electronics',
'Baby',
]
models = ['FLTR', 'BertQA']
data_path = os.path.join(FLAGS.data_dir, 'Annotated_Data.txt')
data = pd.read_csv(data_path,
sep='\t',
encoding='utf-8',
converters={
'annotation_score': ast.literal_eval,
'reviews': ast.literal_eval
})
data = data.reset_index()
data['qr'] = data[['index', 'question', 'reviews'
]].apply(lambda x: [[x['index'], x['question'], i]
for i in x['reviews']],
axis=1)
d = []
for category in categories:
qr = data[data['category'] == category]['qr'].tolist()
qr = [item for sublist in qr for item in sublist]
qr = pd.DataFrame(columns=['index', 'question', 'review'], data=qr)
qr['label'] = 1
temp = qr.copy()
temp['question'] = temp['question'].apply(str)
temp['review'] = temp['review'].apply(str)
DATA_COLUMN_A = 'question'
DATA_COLUMN_B = 'review'
LABEL_COLUMN = 'label'
label_list = [0, 1]
tokenizer = tokenization.FullTokenizer(vocab_file=FLAGS.vocab_file,
do_lower_case=True)
test_InputExamples = temp.apply(
lambda x: run_classifier.InputExample(guid=None,
text_a=x[DATA_COLUMN_A],
text_b=x[DATA_COLUMN_B],
label=x[LABEL_COLUMN]),
axis=1)
test_features = run_classifier.convert_examples_to_features(
test_InputExamples, label_list, FLAGS.max_seq_length, tokenizer)
t = data[data['category'] == category]
t = t.reset_index(drop=True)
for model in models:
OUTPUT_DIR = os.path.join(FLAGS.model_output_dir,
category + '_' + model)
run_config = tf.estimator.RunConfig(model_dir=OUTPUT_DIR,
save_summary_steps=100,
save_checkpoints_steps=100)
model_fn = None
if model == 'BertQA':
model_fn = model_fn_builder_BertQA(
bert_config=modeling.BertConfig.from_json_file(
FLAGS.bert_config_file),
num_labels=len(label_list),
init_checkpoint=OUTPUT_DIR,
learning_rate=FLAGS.learning_rate,
num_train_steps=100,
num_warmup_steps=100)
else:
model_fn = model_fn_builder_FLTR(
bert_config=modeling.BertConfig.from_json_file(
FLAGS.bert_config_file),
num_labels=len(label_list),
init_checkpoint=OUTPUT_DIR,
learning_rate=FLAGS.learning_rate,
num_train_steps=100,
num_warmup_steps=100)
estimator = tf.estimator.Estimator(
model_fn=model_fn,
config=run_config,
params={"batch_size": FLAGS.train_batch_size})
test_input_fn = run_classifier.input_fn_builder(
features=test_features,
seq_length=FLAGS.max_seq_length,
is_training=False,
drop_remainder=False)
predictions = estimator.predict(test_input_fn)
probabilities = [
prediction['probabilities'] for prediction in predictions
]
probabilities = [list(item) for item in probabilities]
if model == 'FLTR':
probabilities = [item[1] for item in probabilities]
else:
probabilities = [item[0] for item in probabilities]
print(model, ' :', probabilities[:10])
temp[model + '_score'] = probabilities
temp_groupby = temp.groupby(
['index', 'question'],
sort=False)[model + '_score'].apply(list).reset_index(
name=model + '_score')
t = pd.concat([t, temp_groupby[model + '_score']], axis=1)
if len(d) == 0:
d = t
else:
d = pd.concat([d, t], axis=0, ignore_index=True)
d.to_csv(os.path.join(FLAGS.data_dir, 'test_predictions.txt'),
index=None,
sep='\t',
mode='w')
def main(argv):
BERT_MODEL = 'uncased_L-12_H-768_A-12'
VOCAB_FILE = '/root/cyliu/tftuner/selftf/tf_job/nlp/zmwu/bert_tf2/vocab.txt'
CONFIG_FILE = '/root/cyliu/tftuner/selftf/tf_job/nlp/zmwu/bert_tf2/bert_config.json'
INIT_CHECKPOINT = '/root/cyliu/tftuner/selftf/tf_job/nlp/zmwu/bert_tf2/bert_model.ckpt'
DO_LOWER_CASE = BERT_MODEL.startswith('uncased')
model_dir = "{}/{}".format("/opt/tftuner", mltunerUtil.get_job_id())
# model fix parameter
TRAIN_BATCH_SIZE = mltunerUtil.get_batch_size()
NUM_TRAIN_EPOCHS = 3
LEARNING_RATE = mltunerUtil.get_learning_rate()
WARMUP_PROPORTION = 0.05
EVAL_BATCH_SIZE = 8
MAX_SEQ_LENGTH = 128
#data loading
train_df = pd.read_csv(
'/root/cyliu/tftuner/selftf/tf_job/nlp/zmwu/bert_tf2/train.csv')
train_df = train_df.sample(1000)
train, test = train_test_split(train_df, test_size=0.1, random_state=42)
train_lines, train_labels = train.question_text.values, train.target.values
test_lines, test_labels = test.question_text.values, test.target.values
label_list = ['0', '1']
tokenizer = tokenization.FullTokenizer(vocab_file=VOCAB_FILE,
do_lower_case=DO_LOWER_CASE)
train_examples = create_examples(train_lines, 'train', labels=train_labels)
num_train_steps = int(
len(train_examples) / TRAIN_BATCH_SIZE * NUM_TRAIN_EPOCHS)
num_warmup_steps = int(num_train_steps * WARMUP_PROPORTION)
strategy = tf.distribute.experimental.ParameterServerStrategy()
session_config = mltunerUtil.get_tf_session_config()
config = tf.compat.v1.estimator.tpu.RunConfig(
train_distribute=strategy,
model_dir=model_dir,
save_checkpoints_steps=None,
save_checkpoints_secs=None,
session_config=session_config)
model_fn = run_classifier.model_fn_builder(
bert_config=modeling.BertConfig.from_json_file(CONFIG_FILE),
num_labels=len(label_list),
init_checkpoint=None,
learning_rate=LEARNING_RATE,
num_train_steps=num_train_steps,
num_warmup_steps=num_warmup_steps,
use_tpu=
False, #If False training will fall on CPU or GPU, depending on what is available
use_one_hot_embeddings=True)
estimator = tf.compat.v1.estimator.tpu.TPUEstimator(
use_tpu=
False, #If False training will fall on CPU or GPU, depending on what is available
model_fn=model_fn,
config=config,
train_batch_size=TRAIN_BATCH_SIZE,
eval_batch_size=EVAL_BATCH_SIZE)
class LoggerHook(tf.estimator.SessionRunHook):
"""Logs loss and runtime."""
def __init__(self):
self.last_run_timestamp = time.time()
def after_run(self, run_context, run_values):
session: tf.Session = run_context.session
loss, step = session.run([
tf.compat.v1.get_collection("losses")[0],
tf.compat.v1.get_collection("global_step_read_op_cache")[0]
])
logging.debug("step:{} loss:{}".format(step, loss))
mltunerUtil.report_iter_loss(step, loss,
time.time() - self.last_run_timestamp)
self.last_run_timestamp = time.time()
# prepare for train
train_features = run_classifier.convert_examples_to_features(
train_examples, label_list, MAX_SEQ_LENGTH, tokenizer)
train_input_fn = input_fn_builder(features=train_features,
seq_length=MAX_SEQ_LENGTH,
is_training=True,
drop_remainder=True)
predict_examples = create_examples(test_lines, 'test')
predict_features = run_classifier.convert_examples_to_features(
predict_examples, label_list, MAX_SEQ_LENGTH, tokenizer)
predict_input_fn = input_fn_builder(features=predict_features,
seq_length=MAX_SEQ_LENGTH,
is_training=False,
drop_remainder=False)
train_spec = tf.estimator.TrainSpec(input_fn=train_input_fn,
max_steps=num_train_steps,
hooks=[LoggerHook()])
eval_spec = tf.estimator.EvalSpec(input_fn=predict_input_fn)
# wait for chief ready?
if not (mltunerUtil.is_chief() or mltunerUtil.is_ps()):
time.sleep(1)
if not tf.io.gfile.exists(model_dir):
logging.debug("wait for chief init")
time.sleep(1)
tf.estimator.train_and_evaluate(estimator, train_spec, eval_spec)
model_fn = run_classifier.model_fn_builder(
bert_config=modeling.BertConfig.from_json_file(CONFIG_FILE),
num_labels=len(label_list),
init_checkpoint=INIT_CHECKPOINT,
learning_rate=LEARNING_RATE,
num_train_steps=num_train_steps,
num_warmup_steps=num_warmup_steps,
use_tpu=False,
use_one_hot_embeddings=True)
estimator = tf.contrib.tpu.TPUEstimator(use_tpu=False,
model_fn=model_fn,
config=run_config,
train_batch_size=TRAIN_BATCH_SIZE,
eval_batch_size=EVAL_BATCH_SIZE)
# estimator = tf.contrib.estimator.SavedModelEstimator(BERT_PRETRAINED_DIR)
# Eval the model.
eval_examples = processor.get_dev_examples(TASK_DATA_DIR)
eval_features = run_classifier.convert_examples_to_features(
eval_examples, label_list, MAX_SEQ_LENGTH, tokenizer)
eval_steps = int(len(eval_examples) / EVAL_BATCH_SIZE)
eval_input_fn = run_classifier.input_fn_builder(features=eval_features,
seq_length=MAX_SEQ_LENGTH,
is_training=False,
drop_remainder=True)
result = estimator.evaluate(input_fn=eval_input_fn, steps=eval_steps)
print(result)
LABEL_COLUMN = 'label'
label_list = [0, 1]
# Use the InputExample class from BERT's run_classifier code to create examples from the data
train_InputExamples = train.apply(lambda x: run_classifier.InputExample(guid=None, # Globally unique ID for bookkeeping, unused in this example
text_a = x[DATA_COLUMN_A],
text_b = x[DATA_COLUMN_B],
label = x[LABEL_COLUMN]), axis = 1)
test_InputExamples = test.apply(lambda x: run_classifier.InputExample(guid=None,
text_a = x[DATA_COLUMN_A],
text_b = x[DATA_COLUMN_B],
label = x[LABEL_COLUMN]), axis = 1)
# Convert our train and test features to InputFeatures that BERT understands.
train_features = run_classifier.convert_examples_to_features(train_InputExamples, label_list, MAX_SEQ_LENGTH, tokenizer)
test_features = run_classifier.convert_examples_to_features(test_InputExamples, label_list, MAX_SEQ_LENGTH, tokenizer)
# Compute # train and warmup steps from batch size
num_train_steps = int(len(train_features) / BATCH_SIZE * NUM_TRAIN_EPOCHS)
num_warmup_steps = int(num_train_steps * WARMUP_PROPORTION)
# Specify outpit directory and number of checkpoint steps to save
run_config = tf.estimator.RunConfig(
model_dir=OUTPUT_DIR,
save_summary_steps=SAVE_SUMMARY_STEPS,
save_checkpoints_steps=SAVE_CHECKPOINTS_STEPS)
model_fn = model_fn_builder(
num_labels=len(label_list),
def main(_):
"""For current work, we only use the following four categories,
but you can add others if you would like to."""
categories = [
'Tools_and_Home_Improvement',
'Patio_Lawn_and_Garden',
'Electronics',
'Baby',
]
data = None
"""Cross-domain pre-training (All_Categories) to boost the performance."""
if FLAGS.category_name == "All_Categories":
for category in categories:
category_data_path = os.path.join(FLAGS.data_dir,category+'.txt')
category_data = pd.read_csv(category_data_path,sep='\t',encoding='utf-8',nrows=10000,
converters={'reviewText':ast.literal_eval,'FLTR_scores':ast.literal_eval})
if data is None:
data = category_data
else:
data = pd.concat([data,category_data],axis=0)
data = data.sample(n=len(data))
else:
data_path = os.path.join(FLAGS.data_dir,FLAGS.category_name+'.txt')
data = pd.read_csv(data_path,sep='\t',encoding='utf-8',#nrows=10000,
cconverters={'reviewText':ast.literal_eval,'FLTR_scores':ast.literal_eval})
#data['len_questions'] = data["question"].apply(lambda x: len(x.split()))
#data = data[data['len_questions']<=10]
data['FLTR_Top10'] = data.apply(FLTR_Top10,axis=1)
list_of_answers = list(data['answer'])
list_of_answers=shuffle(list_of_answers)
data['non_answer']= list_of_answers
train = data[:int(len(data)*0.8)]
train = train.sample(n=min(20000,len(train)))
test = data[int(len(data)*0.8):]
print(train.shape,test.shape)
DATA_COLUMN_A = 'senA'
DATA_COLUMN_B = 'senB'
LABEL_COLUMN = 'Label'
label_list = [0, 1]
train = train.apply(qar_pair,axis=1)
test = test.apply(qar_pair,axis=1)
temp = train.tolist()
flat_list = [item for sublist in temp for item in sublist]
train =pd.DataFrame(flat_list,columns=['senA','senB'])
train['Label'] =1
train['senA']=train['senA'].apply(str)
train['senB']=train['senB'].apply(str)
temp = test.tolist()
flat_list = [item for sublist in temp for item in sublist]
test = pd.DataFrame(flat_list,columns=['senA','senB'])
test['Label'] = 1
test['senA'] = test['senA'].apply(str)
test['senB'] = test['senB'].apply(str)
print(train.shape,test.shape)
tokenizer = tokenization.FullTokenizer(vocab_file=FLAGS.vocab_file,
do_lower_case=True)
train_InputExamples = train.apply(lambda x: run_classifier.InputExample(guid=None,
text_a = x[DATA_COLUMN_A],
text_b = x[DATA_COLUMN_B],
label = x[LABEL_COLUMN]),
axis = 1)
test_InputExamples = test.apply(lambda x: run_classifier.InputExample(guid=None,
text_a = x[DATA_COLUMN_A],
text_b = x[DATA_COLUMN_B],
label = x[LABEL_COLUMN]),
axis = 1)
train_features = run_classifier.convert_examples_to_features(train_InputExamples,
label_list,
FLAGS.max_seq_length,
tokenizer)
test_features = run_classifier.convert_examples_to_features(test_InputExamples,
label_list,
FLAGS.max_seq_length,
tokenizer)
OUTPUT_DIR = os.path.join(FLAGS.model_output_dir,FLAGS.category_name+"_BertQA")
tf.gfile.MakeDirs(OUTPUT_DIR)
run_config = tf.estimator.RunConfig(
model_dir=OUTPUT_DIR,
keep_checkpoint_max=2,
save_summary_steps=FLAGS.save_summary_steps,
save_checkpoints_steps=FLAGS.save_checkpoints_steps)
num_train_steps = int(len(train_features) / FLAGS.train_batch_size * FLAGS.num_train_epochs)
num_warmup_steps = int(num_train_steps * FLAGS.warmup_proportion)
model_fn = model_fn_builder(
bert_config = modeling.BertConfig.from_json_file(FLAGS.bert_config_file),
num_labels = len(label_list),
init_checkpoint = FLAGS.init_checkpoint,
learning_rate = FLAGS.learning_rate,
num_train_steps = num_train_steps,
num_warmup_steps = num_warmup_steps)
estimator = tf.estimator.Estimator(
model_fn = model_fn,
config = run_config,
params = {"batch_size": FLAGS.train_batch_size})
train_input_fn = run_classifier.input_fn_builder(
features = train_features,
seq_length = FLAGS.max_seq_length,
is_training = True,
drop_remainder = True)
print("Beginning Training!")
current_time = datetime.now()
#early_stopping = tf.contrib.estimator.stop_if_no_decrease_hook(
# estimator,metric_name='loss',max_steps_without_decrease=1000,min_steps=100)
estimator.train(input_fn = train_input_fn, max_steps = num_train_steps) #,hooks=[early_stopping]
print("Training took time ", datetime.now() - current_time)
test_input_fn = run_classifier.input_fn_builder(
features = test_features,
seq_length = FLAGS.max_seq_length,
is_training = False,
drop_remainder = True)
predictions = estimator.predict(test_input_fn)
x=[prediction['scores'] for prediction in predictions]
print('\n')
print("The accuracy of BertQA on "+FLAGS.category_name+" is: "+str(sum(i > 0 for i in x)/len(x)))
print('\n')
def main():
parser = argparse.ArgumentParser()
## Required parameters
parser.add_argument(
"--data_dir",
default="data/VSNLI/",
type=str,
help=
"The input data dir. Should contain the .tsv files (or other data files) for the task."
)
parser.add_argument(
"--bert_model",
default="bert-base-uncased",
type=str,
help="Bert pre-trained model selected in the list: bert-base-uncased, "
"bert-large-uncased, bert-base-cased, bert-large-cased, bert-base-multilingual-uncased, "
"bert-base-multilingual-cased, bert-base-chinese.")
parser.add_argument("--task_name",
default="snliimg",
type=str,
help="The name of the task to train.")
parser.add_argument(
"--output_dir",
default="output_vsnli",
type=str,
help=
"The output directory where the model predictions and checkpoints will be written."
)
parser.add_argument(
"--tagger_path",
default=None,
type=str,
help=
"tagger_path for predictions if needing real-time tagging. Default: None, by loading pre-tagged data"
"For example, the trained models by AllenNLP")
parser.add_argument("--best_epochs",
default=1.0,
type=float,
help="Best training epochs for prediction.")
parser.add_argument("--max_num_aspect",
default=3,
type=int,
help="max_num_aspect")
## Other parameters
parser.add_argument("--grounding",
action='store_true',
help="whether to enable grounding.")
parser.add_argument("--hypothesis_only",
action='store_true',
help="whether to enable grounding.")
parser.add_argument(
"--cache_dir",
default="",
type=str,
help=
"Where do you want to store the pre-trained models downloaded from s3")
parser.add_argument(
"--max_seq_length",
default=128,
type=int,
help=
"The maximum total input sequence length after WordPiece tokenization. \n"
"Sequences longer than this will be truncated, and sequences shorter \n"
"than this will be padded.")
parser.add_argument("--do_train",
action='store_true',
help="Whether to run training.")
parser.add_argument("--do_eval",
action='store_true',
help="Whether to run eval on the dev set.")
parser.add_argument("--do_predict",
action='store_true',
help="Whether to run eval on the dev set.")
parser.add_argument(
"--do_lower_case",
action='store_true',
help="Set this flag if you are using an uncased model.")
parser.add_argument("--train_batch_size",
default=32,
type=int,
help="Total batch size for training.")
parser.add_argument("--eval_batch_size",
default=8,
type=int,
help="Total batch size for eval.")
parser.add_argument("--learning_rate",
default=3e-5,
type=float,
help="The initial learning rate for Adam.")
parser.add_argument("--num_train_epochs",
default=3.0,
type=float,
help="Total number of training epochs to perform.")
parser.add_argument(
"--warmup_proportion",
default=0.1,
type=float,
help=
"Proportion of training to perform linear learning rate warmup for. "
"E.g., 0.1 = 10%% of training.")
parser.add_argument("--no_cuda",
action='store_true',
help="Whether not to use CUDA when available")
parser.add_argument("--local_rank",
type=int,
default=-1,
help="local_rank for distributed training on gpus")
parser.add_argument('--seed',
type=int,
default=42,
help="random seed for initialization")
parser.add_argument(
'--gradient_accumulation_steps',
type=int,
default=1,
help=
"Number of updates steps to accumulate before performing a backward/update pass."
)
parser.add_argument(
'--fp16',
action='store_true',
help="Whether to use 16-bit float precision instead of 32-bit")
parser.add_argument(
'--loss_scale',
type=float,
default=0,
help=
"Loss scaling to improve fp16 numeric stability. Only used when fp16 set to True.\n"
"0 (default value): dynamic loss scaling.\n"
"Positive power of 2: static loss scaling value.\n")
parser.add_argument('--server_ip',
type=str,
default='',
help="Can be used for distant debugging.")
parser.add_argument('--server_port',
type=str,
default='',
help="Can be used for distant debugging.")
args = parser.parse_args()
if args.server_ip and args.server_port:
# Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
import ptvsd
print("Waiting for debugger attach")
ptvsd.enable_attach(address=(args.server_ip, args.server_port),
redirect_output=True)
ptvsd.wait_for_attach()
processors = {"snliimg": SnliImgProcessor, "gsnliimg": GSnliImgProcessor}
if args.local_rank == -1 or args.no_cuda:
device = torch.device("cuda" if torch.cuda.is_available()
and not args.no_cuda else "cpu")
n_gpu = torch.cuda.device_count()
else:
torch.cuda.set_device(args.local_rank)
device = torch.device("cuda", args.local_rank)
n_gpu = 1
# Initializes the distributed backend which will take care of sychronizing nodes/GPUs
torch.distributed.init_process_group(backend='nccl')
logger.info(
"device: {} n_gpu: {}, distributed training: {}, 16-bits training: {}".
format(device, n_gpu, bool(args.local_rank != -1), args.fp16))
if args.gradient_accumulation_steps < 1:
raise ValueError(
"Invalid gradient_accumulation_steps parameter: {}, should be >= 1"
.format(args.gradient_accumulation_steps))
args.train_batch_size = args.train_batch_size // args.gradient_accumulation_steps
random.seed(args.seed)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
if n_gpu > 0:
torch.cuda.manual_seed_all(args.seed)
if not args.do_train and not args.do_eval and not args.do_predict:
raise ValueError(
"At least one of `do_train` or `do_eval` must be True.")
if os.path.exists(args.output_dir) and os.listdir(
args.output_dir) and args.do_train:
raise ValueError(
"Output directory ({}) already exists and is not empty.".format(
args.output_dir))
if not os.path.exists(args.output_dir):
os.makedirs(args.output_dir)
task_name = args.task_name.lower()
if task_name not in processors:
raise ValueError("Task not found: %s" % (task_name))
processor = processors[task_name]()
# num_labels = num_labels_task[task_name]
label_list = processor.get_labels()
num_labels = len(label_list)
tokenizer = BertTokenizer.from_pretrained(args.bert_model,
do_lower_case=args.do_lower_case)
if args.tagger_path != None:
srl_predictor = SRLPredictor(args.tagger_path)
else:
srl_predictor = None
train_examples = None
num_train_optimization_steps = None
if args.do_train:
if args.grounding:
train_premise_examples, train_hypothesis_examples = processor.get_train_examples(
args.data_dir)
train_examples = (train_premise_examples,
train_hypothesis_examples)
num_train_optimization_steps = int(
len(train_premise_examples) / args.train_batch_size /
args.gradient_accumulation_steps) * args.num_train_epochs
else:
train_examples = processor.get_train_examples(args.data_dir)
num_train_optimization_steps = int(
len(train_examples) / args.train_batch_size /
args.gradient_accumulation_steps) * args.num_train_epochs
if args.local_rank != -1:
num_train_optimization_steps = num_train_optimization_steps // torch.distributed.get_world_size(
)
train_features = None
if args.do_train:
if args.grounding:
hypothesis_features = convert_examples_to_features(
train_examples[1],
label_list,
args.max_seq_length,
tokenizer,
srl_predictor=srl_predictor)
premises_features = convert_examples_to_features(
train_examples[0],
label_list,
args.max_seq_length,
tokenizer,
srl_predictor=srl_predictor)
train_features = (premises_features, hypothesis_features)
else:
train_features = convert_examples_to_features(
train_examples,
label_list,
args.max_seq_length,
tokenizer,
srl_predictor=srl_predictor)
# TagTokenizer.make_tag_vocab("tag_vocab", tag_vocab)
tag_tokenizer = TagTokenizer()
vocab_size = len(tag_tokenizer.ids_to_tags)
print("tokenizer vocab size: ", str(vocab_size))
tag_config = TagConfig(tag_vocab_size=vocab_size,
hidden_size=10,
layer_num=1,
output_dim=10,
dropout_prob=0.1,
num_aspect=args.max_num_aspect)
# Prepare model
cache_dir = args.cache_dir if args.cache_dir else os.path.join(
PYTORCH_PRETRAINED_BERT_CACHE, 'distributed_{}'.format(
args.local_rank))
if args.grounding:
if args.hypothesis_only:
model = GroundedImgClassificationTag.from_pretrained(
args.bert_model,
cache_dir=PYTORCH_PRETRAINED_BERT_CACHE /
'distributed_{}'.format(args.local_rank),
num_labels=num_labels,
tag_config=tag_config,
image_emb_size=2048,
hypothesis_only=True)
else:
model = GroundedImgClassificationTag.from_pretrained(
args.bert_model,
cache_dir=PYTORCH_PRETRAINED_BERT_CACHE /
'distributed_{}'.format(args.local_rank),
num_labels=num_labels,
tag_config=tag_config,
image_emb_size=2048)
else:
model = BertForSequenceImgClassificationTag.from_pretrained(
args.bert_model,
cache_dir=PYTORCH_PRETRAINED_BERT_CACHE /
'distributed_{}'.format(args.local_rank),
num_labels=num_labels,
tag_config=tag_config,
image_emb_size=2048)
if args.fp16:
model.half()
model.to(device)
if args.local_rank != -1:
try:
from apex.parallel import DistributedDataParallel as DDP
except ImportError:
raise ImportError(
"Please install apex from https://www.github.com/nvidia/apex to use distributed and fp16 training."
)
model = DDP(model)
elif n_gpu > 1:
model = torch.nn.DataParallel(model)
# Prepare optimizer
param_optimizer = list(model.named_parameters())
no_decay = ['bias', 'LayerNorm.bias', 'LayerNorm.weight']
optimizer_grouped_parameters = [{
'params':
[p for n, p in param_optimizer if not any(nd in n for nd in no_decay)],
'weight_decay':
0.01
}, {
'params':
[p for n, p in param_optimizer if any(nd in n for nd in no_decay)],
'weight_decay':
0.0
}]
if args.fp16:
try:
from apex.optimizers import FP16_Optimizer
from apex.optimizers import FusedAdam
except ImportError:
raise ImportError(
"Please install apex from https://www.github.com/nvidia/apex to use distributed and fp16 training."
)
optimizer = FusedAdam(optimizer_grouped_parameters,
lr=args.learning_rate,
bias_correction=False,
max_grad_norm=1.0)
if args.loss_scale == 0:
optimizer = FP16_Optimizer(optimizer, dynamic_loss_scale=True)
else:
optimizer = FP16_Optimizer(optimizer,
static_loss_scale=args.loss_scale)
else:
optimizer = BertAdam(optimizer_grouped_parameters,
lr=args.learning_rate,
warmup=args.warmup_proportion,
t_total=num_train_optimization_steps)
global_step = 0
nb_tr_steps = 0
tr_loss = 0
best_epoch = 0
best_result = 0.0
transform = transforms.Compose([
transforms.Resize(256),
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
])
if args.do_train:
if not args.grounding:
train_features = transform_tag_features(args.max_num_aspect,
train_features,
tag_tokenizer,
args.max_seq_length)
logger.info("***** Running training *****")
logger.info(" Num examples = %d", len(train_examples))
logger.info(" Batch size = %d", args.train_batch_size)
logger.info(" Num steps = %d", num_train_optimization_steps)
# prepare data training data
all_input_ids = [f.input_ids for f in train_features]
all_input_mask = [f.input_mask for f in train_features]
all_segment_ids = [f.segment_ids for f in train_features]
all_label_ids = [f.label_id for f in train_features]
all_start_end_idx = [
f.orig_to_token_split_idx for f in train_features
]
all_input_tag_ids = [f.input_tag_ids for f in train_features]
all_images = [f.image for f in train_features]
train_data = SequenceImageDataset(all_input_ids, all_input_mask,
all_segment_ids,
all_start_end_idx,
all_input_tag_ids, all_label_ids,
all_images, transform, IMAGE_DIR)
else:
premises_train_features = transform_tag_features(
args.max_num_aspect, train_features[0], tag_tokenizer,
args.max_seq_length)
hypothesis_train_features = transform_tag_features(
args.max_num_aspect, train_features[1], tag_tokenizer,
args.max_seq_length)
assert len(premises_train_features) == len(
hypothesis_train_features)
logger.info("***** Running training *****")
logger.info(" Num examples = %d", len(train_examples[0]))
logger.info(" Batch size = %d", args.train_batch_size)
logger.info(" Num steps = %d", num_train_optimization_steps)
# prepare the premise training data
all_premises_input_ids = [
f.input_ids for f in premises_train_features
]
all_premises_input_mask = [
f.input_mask for f in premises_train_features
]
all_premises_segment_ids = [
f.segment_ids for f in premises_train_features
]
all_premises_start_end_idx = [
f.orig_to_token_split_idx for f in premises_train_features
]
all_premises_input_tag_ids = [
f.input_tag_ids for f in premises_train_features
]
# prepare the hypothesis training data
all_hypothesis_input_ids = [
f.input_ids for f in hypothesis_train_features
]
all_hypothesis_input_mask = [
f.input_mask for f in hypothesis_train_features
]
all_hypothesis_segment_ids = [
f.segment_ids for f in hypothesis_train_features
]
all_hypothesis_start_end_idx = [
f.orig_to_token_split_idx for f in hypothesis_train_features
]
all_hypothesis_input_tag_ids = [
f.input_tag_ids for f in hypothesis_train_features
]
all_images = [f.image for f in premises_train_features]
all_label_ids = [f.label_id for f in premises_train_features]
train_data = GroundedSequenceImageDataset(
all_premises_input_ids, all_hypothesis_input_ids,
all_premises_input_mask, all_hypothesis_input_mask,
all_premises_segment_ids, all_hypothesis_segment_ids,
all_premises_start_end_idx, all_hypothesis_start_end_idx,
all_premises_input_tag_ids, all_hypothesis_input_tag_ids,
all_label_ids, all_images, transform, IMAGE_DIR)
if args.local_rank == -1:
train_sampler = RandomSampler(train_data)
else:
train_sampler = DistributedSampler(train_data)
train_dataloader = DataLoader(train_data,
sampler=train_sampler,
batch_size=args.train_batch_size)
# prepare validation data
if args.grounding:
eval_premise_examples, eval_hypothesis_examples = processor.get_dev_examples(
args.data_dir)
eval_hypothesis_features = convert_examples_to_features(
eval_hypothesis_examples,
label_list,
args.max_seq_length,
tokenizer,
srl_predictor=srl_predictor)
eval_premises_features = convert_examples_to_features(
eval_premise_examples,
label_list,
args.max_seq_length,
tokenizer,
srl_predictor=srl_predictor)
eval_premises_features = transform_tag_features(
args.max_num_aspect, eval_premises_features, tag_tokenizer,
args.max_seq_length)
eval_hypothesis_features = transform_tag_features(
args.max_num_aspect, eval_hypothesis_features, tag_tokenizer,
args.max_seq_length)
# prepare the premise training data
all_premises_input_ids = [
f.input_ids for f in eval_premises_features
]
all_premises_input_mask = [
f.input_mask for f in eval_premises_features
]
all_premises_segment_ids = [
f.segment_ids for f in eval_premises_features
]
all_premises_start_end_idx = [
f.orig_to_token_split_idx for f in eval_premises_features
]
all_premises_input_tag_ids = [
f.input_tag_ids for f in eval_premises_features
]
# prepare the hypothesis training data
all_hypothesis_input_ids = [
f.input_ids for f in eval_hypothesis_features
]
all_hypothesis_input_mask = [
f.input_mask for f in eval_hypothesis_features
]
all_hypothesis_segment_ids = [
f.segment_ids for f in eval_hypothesis_features
]
all_hypothesis_start_end_idx = [
f.orig_to_token_split_idx for f in eval_hypothesis_features
]
all_hypothesis_input_tag_ids = [
f.input_tag_ids for f in eval_hypothesis_features
]
all_images = [f.image for f in eval_hypothesis_features]
all_label_ids = [f.label_id for f in eval_hypothesis_features]
eval_data = GroundedSequenceImageDataset(
all_premises_input_ids, all_hypothesis_input_ids,
all_premises_input_mask, all_hypothesis_input_mask,
all_premises_segment_ids, all_hypothesis_segment_ids,
all_premises_start_end_idx, all_hypothesis_start_end_idx,
all_premises_input_tag_ids, all_hypothesis_input_tag_ids,
all_label_ids, all_images, transform, IMAGE_DIR)
else:
eval_examples = processor.get_dev_examples(args.data_dir)
eval_features = convert_examples_to_features(
eval_examples,
label_list,
args.max_seq_length,
tokenizer,
srl_predictor=srl_predictor)
eval_features = transform_tag_features(args.max_num_aspect,
eval_features,
tag_tokenizer,
args.max_seq_length)
all_input_ids = [f.input_ids for f in eval_features]
all_input_mask = [f.input_mask for f in eval_features]
all_segment_ids = [f.segment_ids for f in eval_features]
all_label_ids = [f.label_id for f in eval_features]
all_start_end_idx = [
f.orig_to_token_split_idx for f in eval_features
]
all_input_tag_ids = [f.input_tag_ids for f in eval_features]
all_images = [f.image for f in eval_features]
eval_data = SequenceImageDataset(all_input_ids, all_input_mask,
all_segment_ids,
all_start_end_idx,
all_input_tag_ids, all_label_ids,
all_images, transform, IMAGE_DIR)
logger.info("***** Evaluation data *****")
logger.info(" Num examples = %d", len(eval_examples))
logger.info(" Batch size = %d", args.eval_batch_size)
eval_sampler = SequentialSampler(eval_data)
eval_dataloader = DataLoader(eval_data,
sampler=eval_sampler,
batch_size=args.eval_batch_size)
for epoch in trange(int(args.num_train_epochs), desc="Epoch"):
model.train()
tr_loss = 0
nb_tr_examples, nb_tr_steps = 0, 0
for step, batch in enumerate(
tqdm(train_dataloader, desc="Iteration")):
batch = tuple(t.to(device) for t in batch)
input_ids, input_mask, segment_ids, start_end_idx, input_tag_ids, images, label_ids = batch
loss = model(input_ids, segment_ids, input_mask, start_end_idx,
input_tag_ids, images, label_ids)
if n_gpu > 1:
loss = loss.mean() # mean() to average on multi-gpu.
if args.gradient_accumulation_steps > 1:
loss = loss / args.gradient_accumulation_steps
if args.fp16:
optimizer.backward(loss)
else:
loss.backward()
tr_loss += loss.item()
nb_tr_examples += input_ids.size(0)
nb_tr_steps += 1
if (step + 1) % args.gradient_accumulation_steps == 0:
# modify learning rate with special warm up BERT uses
lr_this_step = args.learning_rate * warmup_linear(
global_step / num_train_optimization_steps,
args.warmup_proportion)
for param_group in optimizer.param_groups:
param_group['lr'] = lr_this_step
optimizer.step()
optimizer.zero_grad()
global_step += 1
# Save a trained model
model_to_save = model.module if hasattr(
model, 'module') else model # Only save the model it-self
output_model_file = os.path.join(args.output_dir,
str(epoch) + "_pytorch_model.bin")
if args.do_train:
torch.save(model_to_save.state_dict(), output_model_file)
# run evaluation on dev data
model_state_dict = torch.load(output_model_file)
if not args.grounding:
predict_model = BertForSequenceImgClassificationTag.from_pretrained(
args.bert_model,
state_dict=model_state_dict,
num_labels=num_labels,
tag_config=tag_config,
image_emb_size=2048)
else:
predict_model = GroundedImgClassificationTag.from_pretrained(
args.bert_model,
state_dict=model_state_dict,
num_labels=num_labels,
tag_config=tag_config,
image_emb_size=2048)
predict_model.to(device)
predict_model.eval()
eval_loss, eval_accuracy = 0, 0
nb_eval_steps, nb_eval_examples = 0, 0
total_precision = np.zeros(3)
total_recall = np.zeros(3)
total_fscore = np.zeros(3)
total_support = np.zeros(3, dtype=int)
output_logits_file = os.path.join(
args.output_dir,
str(epoch) + "_eval_logits_results.tsv")
with open(output_logits_file, "w") as writer:
writer.write("index" + "\t" + "\t".join(
["logits " + str(i)
for i in range(len(label_list))]) + "\n")
for batch_number, batch in enumerate(
tqdm(eval_dataloader, desc="Evaluating")):
input_ids, input_mask, segment_ids, start_end_idx, input_tag_ids, images, label_ids = batch
input_ids = input_ids.to(device)
input_mask = input_mask.to(device)
segment_ids = segment_ids.to(device)
label_ids = label_ids.to(device)
start_end_idx = start_end_idx.to(device)
input_tag_ids = input_tag_ids.to(device)
images = images.to(device)
with torch.no_grad():
tmp_eval_loss = predict_model(input_ids, segment_ids,
input_mask,
start_end_idx,
input_tag_ids, images,
label_ids)
logits = predict_model(input_ids, segment_ids,
input_mask, start_end_idx,
input_tag_ids, images, None)
logits = logits.detach().cpu().numpy()
label_ids = label_ids.to('cpu').numpy()
tmp_eval_accuracy = accuracy_score(label_ids,
np.argmax(logits,
axis=1),
normalize=False)
eval_loss += tmp_eval_loss.mean().item()
eval_accuracy += tmp_eval_accuracy
precision, recall, fscore, support = precision_recall_fscore_support(
label_ids, np.argmax(logits, axis=1), labels=[0, 1, 2])
total_precision = total_precision + precision
total_recall = total_recall + recall
total_fscore = total_fscore + fscore
total_support = total_support + support
nb_eval_examples += input_ids.size(0)
nb_eval_steps += 1
del predict_model
eval_loss = eval_loss / nb_eval_steps
eval_accuracy = eval_accuracy / nb_eval_examples
total_precision = total_precision / (batch_number + 1)
total_recall = total_recall / (batch_number + 1)
total_fscore = total_fscore / (batch_number + 1)
if eval_accuracy > best_result:
best_epoch = epoch
best_result = eval_accuracy
loss = tr_loss / nb_tr_steps if args.do_train else None
result = {
'eval_loss': eval_loss,
'loss': loss,
'eval_accuracy': eval_accuracy,
'total_precision': {
k: total_precision.tolist()[v]
for k, v in processor.get_labels_map().items()
},
'total_recall': {
k: total_recall.tolist()[v]
for k, v in processor.get_labels_map().items()
},
'total_fscore': {
k: total_fscore.tolist()[v]
for k, v in processor.get_labels_map().items()
},
'total_support': {
k: total_support.tolist()[v]
for k, v in processor.get_labels_map().items()
},
'macro_precision': total_precision.mean(),
'macro_recall': total_recall.mean(),
'macro_support': total_support.sum(),
'macro_f1score': total_fscore.mean(),
'number_of_examples': nb_eval_examples
}
output_eval_file = os.path.join(args.output_dir,
"eval_results.txt")
with open(output_eval_file, "a") as writer:
logger.info("***** Eval results *****")
for key in sorted(result.keys()):
logger.info("Epoch: %s, %s = %s", str(epoch), key,
str(result[key]))
writer.write("Epoch: %s, %s = %s\n" %
(str(epoch), key, str(result[key])))
logger.info("best epoch: %s, result: %s", str(best_epoch),
str(best_result))
def BerQA_train_predict(data, is_training=True):
d = data.copy()
scores = []
max_inputs = 30000
LEARNING_RATE = 2e-5
NUM_TRAIN_EPOCHS = 2
WARMUP_PROPORTION = 0.1
# Model configs
SAVE_CHECKPOINTS_STEPS = 1000
SAVE_SUMMARY_STEPS = 500
num_train_steps = 100
max_steps = 100000
DATA_COLUMN_A = 'senA'
DATA_COLUMN_B = 'senB'
LABEL_COLUMN = 'Label'
label_list = [0, 1]
while (len(d) > 0 and num_train_steps <= max_steps):
line = min(max_inputs, len(d))
temp = d[:line]
temp_t = temp.apply(qar_pair, axis=1)
temp_t = temp_t.tolist()
flat_list = [item for sublist in temp_t for item in sublist]
temp_t = pd.DataFrame(flat_list, columns=['senA', 'senB'])
temp_t['Label'] = 1
temp_t['senA'] = temp_t['senA'].apply(str)
temp_t['senB'] = temp_t['senB'].apply(str)
temp_InputExamples = temp_t.apply(
lambda x: run_classifier.InputExample(guid=None,
text_a=x[DATA_COLUMN_A],
text_b=x[DATA_COLUMN_B],
label=x[LABEL_COLUMN]),
axis=1)
temp_features = run_classifier.convert_examples_to_features(
temp_InputExamples, label_list, MAX_SEQ_LENGTH, tokenizer)
num_train_steps = int(
len(temp_features) / BATCH_SIZE *
NUM_TRAIN_EPOCHS) + num_train_steps
num_warmup_steps = int(num_train_steps * WARMUP_PROPORTION)
# Specify outpit directory and number of checkpoint steps to save
run_config = tf.estimator.RunConfig(
model_dir=OUTPUT_DIR,
save_summary_steps=SAVE_SUMMARY_STEPS,
save_checkpoints_steps=SAVE_CHECKPOINTS_STEPS)
model_fn = model_fn_builder(num_labels=len(label_list),
learning_rate=LEARNING_RATE,
num_train_steps=num_train_steps,
num_warmup_steps=num_warmup_steps)
estimator = tf.estimator.Estimator(model_fn=model_fn,
config=run_config,
params={"batch_size": BATCH_SIZE})
input_fn = input_fn_builder(features=temp_features,
seq_length=MAX_SEQ_LENGTH,
is_training=is_training,
drop_remainder=True)
if is_training:
print('Beginning Training!')
early_stopping = tf.contrib.estimator.stop_if_no_decrease_hook(
estimator,
metric_name='loss',
max_steps_without_decrease=1000,
min_steps=100)
current_time = datetime.now()
#tf.estimator.train_and_evaluate(estimator,train_spec=tf.estimator.TrainSpec(input_fn, hooks=[early_stopping]))
estimator.train(input_fn=input_fn,
max_steps=num_train_steps,
hooks=[early_stopping])
print("Training took time ", datetime.now() - current_time)
else:
predictions = estimator.predict(input_fn)
outputs = [(prediction['probabilities'], prediction['crq'])
for prediction in predictions]
x = [i[0] for i in outputs]
y = [i[1] for i in outputs]
print('\n')
print('Accuracy of ' + category + ' is: ' +
str(sum(i > 0 for i in x) / len(x)))
print('\n')
scores = scores + y
if len(d) > max_inputs:
d = d[line:]
d = d.reset_index(drop=True)
else:
d = []
if is_training is False:
data = data[:len(scores)]
scores = [item.tolist() for item in scores]
#BERTQA_scores = pd.DataFrame(data=scores)
data['BERTQA_scores'] = scores
#data = pd.concat([data,BERTQA_scores],axis=1,ignore_index=True)
return data
