def generate_vectors(json_input_filename, w2v_dim, perplexity, theta, pca_dims, dim=2):
vectors = []
most_dominant_labels = []
image_ids = []
label_map = utils.load_json(json_input_filename, w2v_dim)
for image_id, label in label_map.iteritems():
label_vectors = []
label_scores = []
label_desc = []
for val in label:
label_vectors.append(val['word2vec'])
label_scores.append(val['score'])
label_desc.append(str(''.join(c for c in val['description'] if c in string.printable)))
output_vec = word2vec.linear_combination_vectors(vectors=label_vectors, coefficients=label_scores)
vectors.append(output_vec)
most_dominant_labels.append(label_desc[0])
image_ids.append(image_id)
embeddings = []
for result in bh_tsne(vectors,
perplexity=perplexity,
initial_dims=pca_dims,
theta=theta,
no_dims=dim):
embeddings.append(result)
embeddings = utils.scale_max_abs(embeddings)
return embeddings, most_dominant_labels, image_ids
def main():
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument("--data-dir",
required=True,
help="Location of data files (model weights, etc).")
parser.add_argument("--model-name",
required=True,
help="The name of the model being fine-tuned.")
parser.add_argument("--hparams",
default="{}",
help="JSON dict of model hyperparameters.")
args = parser.parse_args()
if args.hparams.endswith(".json"):
hparams = utils.load_json(args.hparams)
else:
hparams = json.loads(args.hparams)
tf.logging.set_verbosity(tf.logging.ERROR)
run_finetuning(
configure_finetuning.FinetuningConfig(args.model_name, args.data_dir,
**hparams))
def _serialize_dataset(self, tasks, is_training, split):
"""Write out the dataset as tfrecords."""
dataset_name = "_".join(sorted([task.name for task in tasks]))
dataset_name += "_" + split
dataset_prefix = os.path.join(self._config.preprocessed_data_dir,
dataset_name)
tfrecords_path = dataset_prefix + ".tfrecord"
metadata_path = dataset_prefix + ".metadata"
batch_size = (self._config.train_batch_size
if is_training else self._config.eval_batch_size)
utils.log("Loading dataset", dataset_name)
n_examples = None
if tf.io.gfile.exists(metadata_path):
n_examples = utils.load_json(metadata_path)["n_examples"]
if n_examples is None:
utils.log("Existing tfrecords not found so creating")
examples = []
for task in tasks:
print("task-----------", task)
task_examples = task.get_examples(
data_dir=self._config.data_dir,
corpus=self._config.corpus,
split=split) #data_dir,corpus,split
examples += task_examples
if is_training:
random.shuffle(examples)
utils.mkdir(tfrecords_path.rsplit("/", 1)[0])
n_examples = self.serialize_examples(examples, is_training,
tfrecords_path, batch_size)
utils.write_json({"n_examples": n_examples}, metadata_path)
input_fn = self._input_fn_builder(tfrecords_path, is_training)
if is_training:
steps = int(n_examples // batch_size *
self._config.num_train_epochs)
else:
steps = n_examples // batch_size
return input_fn, steps
def generate_vectors(json_input_filename,
w2v_dim,
perplexity,
theta,
pca_dims,
dim=2):
vectors = []
most_dominant_labels = []
image_ids = []
label_map = utils.load_json(json_input_filename, w2v_dim)
for image_id, label in label_map.iteritems():
label_vectors = []
label_scores = []
label_desc = []
for val in label:
label_vectors.append(val['word2vec'])
label_scores.append(val['score'])
label_desc.append(
str(''.join(c for c in val['description']
if c in string.printable)))
output_vec = word2vec.linear_combination_vectors(
vectors=label_vectors, coefficients=label_scores)
vectors.append(output_vec)
most_dominant_labels.append(label_desc[0])
image_ids.append(image_id)
embeddings = []
for result in bh_tsne(vectors,
perplexity=perplexity,
initial_dims=pca_dims,
theta=theta,
no_dims=dim):
embeddings.append(result)
embeddings = utils.scale_max_abs(embeddings)
return embeddings, most_dominant_labels, image_ids
def main():
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument("--data-dir",
required=True,
help="Location of data files (model weights, etc).")
parser.add_argument("--model-name",
required=True,
help="The name of the model being fine-tuned.")
parser.add_argument("--hparams",
default="{}",
help="JSON dict of model hyperparameters.")
args = parser.parse_args()
if args.hparams.endswith(".json"):
hparams = utils.load_json(args.hparams)
else:
hparams = json.loads(args.hparams)
tf.logging.set_verbosity(tf.logging.ERROR)
# hparams = {
# "do_train": "true",
# "do_eval": "false",
# "model_size": "base",
# "do_lower_case": "true",
# "vocab_size": 100000,
# "num_train_steps": 766000,
# "save_checkpoints_steps": 50000,
# "train_batch_size": 192,
# "max_seq_length": 128,
# }
train_or_eval(
configure_pretraining.PretrainingConfig(
# "danish_electra_base_uncased_100k",
# "/bachelor_project/electra_google/base_uncased_100k_danish_data",
# **hparams))
args.model_name,
args.data_dir,
**hparams))
def main():
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument(
"--data_dir",
required=True,
type=str,
help="Location of data files (model weights, etc).",
)
parser.add_argument(
"--model_name",
required=True,
type=str,
help="The name of the model being fine-tuned.",
)
parser.add_argument("--hparams",
default="{}",
type=str,
help="JSON dict of model hyperparameters.")
parser.add_argument("--use_tpu", action="store_true", help="Using tpu.")
parser.add_argument("--mixed_precision",
action="store_true",
help="Using mixed precision.")
args = parser.parse_args()
if args.mixed_precision:
os.environ["TF_ENABLE_AUTO_MIXED_PRECISION"] = "1"
if args.hparams.endswith(".json"):
hparams = utils.load_json(args.hparams)
else:
hparams = json.loads(args.hparams)
tf.logging.set_verbosity(tf.logging.ERROR)
train_or_eval(
configure_pretraining.PretrainingConfig(args.model_name, args.data_dir,
args.use_tpu,
args.mixed_precision,
**hparams))
def run_finetuning(config: configure_finetuning.FinetuningConfig):
"""Run finetuning."""
# Setup for training
results = []
trial = 1
heading_info = "model={:}, trial {:}/{:}".format(config.model_name, trial,
config.num_trials)
heading = lambda msg: utils.heading(msg + ": " + heading_info)
heading("Config")
utils.log_config(config)
generic_model_dir = config.model_dir
tasks = task_builder.get_tasks(config)
# Train and evaluate num_trials models with different random seeds
while config.num_trials < 0 or trial <= config.num_trials:
print("#################################################")
print(tasks)
t = vars(config)
print(t)
print("#################################################")
# Create Neptune Experiment
neptune.create_experiment(name=f'tf-ft', params=vars(config))
config.model_dir = generic_model_dir + "_" + str(trial) + '_' + str(
random.randint(0, 10000))
if config.do_train:
utils.rmkdir(config.model_dir)
model_runner = ModelRunner(config, tasks)
if config.do_train:
heading("Start training")
model_runner.train()
utils.log()
if config.do_eval:
heading("Run dev set evaluation")
eval_result = model_runner.evaluate()
results.append(eval_result)
write_results(config, results)
if config.write_test_outputs and trial <= config.n_writes_test:
heading("Running on the test set and writing the predictions")
for task in tasks:
# Currently only writing preds for GLUE and SQuAD 2.0 is supported
if task.name in [
"cola", "mrpc", "mnli", "sst", "rte", "qnli",
"qqp", "sts"
]:
for split in task.get_test_splits():
model_runner.write_classification_outputs([task],
trial,
split)
elif task.name == "squad":
scorer = model_runner.evaluate_task(
task, "test", False)
scorer.write_predictions()
preds = utils.load_json(config.qa_preds_file("squad"))
null_odds = utils.load_json(config.qa_na_file("squad"))
for q, _ in preds.items():
if null_odds[q] > config.qa_na_threshold:
preds[q] = ""
utils.write_json(
preds,
config.test_predictions(task.name, "test", trial))
else:
utils.log(
"Skipping task", task.name,
"- writing predictions is not supported for this task"
)
if trial != config.num_trials and (not config.keep_all_models):
utils.rmrf(config.model_dir)
trial += 1
def process_single(in_file, out_file):
# Load the detected faces and embeddings and run the classifier
result = [(face_id, predict_gender(embed), predict_gender_score(embed))
for face_id, embed in load_json(in_file)]
save_json(result, out_file)
full_models.append(model_name)
models = full_models
models_predictions = collections.OrderedDict()
for d in models:
dire = os.path.join(data_dir, d)
try:
prediction = collections.OrderedDict()
prediction['eval_all_nbest'] = filter_short_ans(
utils.load_pickle(
(os.path.join(dire, 'models', 'electra_large', 'results',
'{}_qa'.format(task_name),
'{}_{}_all_nbest.pkl'.format(task_name,
split)))))
prediction['squad_null_odds'] = utils.load_json(
(os.path.join(dire, 'models', 'electra_large', 'results',
'{}_qa'.format(task_name),
'{}_{}_null_odds.json'.format(task_name, split))))
models_predictions[d] = prediction
except:
utils.log(
"Error at loading all_nbest.pkl & null_odds.json for model {}".
format(d))
continue
dataset = \
utils.load_json((os.path.join(data_dir, model_name_part, 'finetuning_data', task_name, '{}.json'.format(split))))[
'data']
qid_answers = collections.OrderedDict()
for article in dataset:
for p in article['paragraphs']:
for qa in p['qas']:
def run_finetuning(config: configure_finetuning.FinetuningConfig):
"""Run finetuning."""
# Setup for training
results = []
trial = 1
heading_info = "model={:}, trial {:}/{:}".format(config.model_name, trial,
config.num_trials)
heading = lambda msg: utils.heading(msg + ": " + heading_info)
heading("Config")
utils.log_config(config)
generic_model_dir = config.model_dir
tasks = task_builder.get_tasks(config)
# Train and evaluate num_trials models with different random seeds
while config.num_trials < 0 or trial <= config.num_trials:
config.model_dir = generic_model_dir + "_" + str(trial)
if config.do_train:
utils.rmkdir(config.model_dir)
model_runner = ModelRunner(config, tasks)
if config.do_train:
heading("Start training")
model_runner.train()
utils.log()
if config.do_eval:
heading("Run dev set evaluation")
results.append(model_runner.evaluate())
if config.do_test:
for task in tasks:
test_score = model_runner.evaluate_task_test(
task, results[-1][task.name]['checkpoint_path'])
results[-1][task.name]["test_results"] = test_score
write_results(config, results)
if config.write_test_outputs and trial <= config.n_writes_test:
heading("Running on the test set and writing the predictions")
for task in tasks:
# Currently only writing preds for GLUE and SQuAD 2.0 is supported
if task.name in [
"cola", "mrpc", "mnli", "sst", "rte", "qnli",
"qqp", "sts"
]:
for split in task.get_test_splits():
model_runner.write_classification_outputs([task],
trial,
split)
elif task.name == "squad":
scorer = model_runner.evaluate_task(
task, "test", False)
scorer.write_predictions()
preds = utils.load_json(config.qa_preds_file("squad"))
null_odds = utils.load_json(config.qa_na_file("squad"))
for q, _ in preds.items():
if null_odds[q] > config.qa_na_threshold:
preds[q] = ""
utils.write_json(
preds,
config.test_predictions(task.name, "test", trial))
else:
utils.log(
"Skipping task", task.name,
"- writing predictions is not supported for this task"
)
if config.do_predict:
if "dev" in config.predict_split:
results = model_runner.predict(tasks[0],
config.predict_checkpoint_path,
"dev")
import pickle
with open("predict_dev.pickle", "bw") as outfile:
pickle.dump(results, outfile)
if "train" in config.predict_split:
results = model_runner.predict(tasks[0],
config.predict_checkpoint_path,
"train")
import pickle
with open("predict_train.pickle", "bw") as outfile:
pickle.dump(results, outfile)
if "test" in config.predict_split:
results = model_runner.predict(tasks[0],
config.predict_checkpoint_path,
"test")
import pickle
with open("predict_test.pickle", "bw") as outfile:
pickle.dump(results, outfile)
if trial != config.num_trials and (not config.keep_all_models):
utils.rmrf(config.model_dir)
trial += 1
def run_finetuning(config: configure_finetuning.FinetuningConfig):
"""Run finetuning."""
tf.get_variable_scope().reuse_variables() #import pdb; pdb.set_trace()
# Setup for training
results = []
trial = 1
heading_info = "model={:}, trial {:}/{:}".format(
config.model_name, trial, config.num_trials)
heading = lambda msg: utils.heading(msg + ": " + heading_info)
heading("Config")
utils.log_config(config)
generic_model_dir = config.model_dir
tasks = task_builder.get_tasks(config)
# Train and evaluate num_trials models with different random seeds
while config.num_trials < 0 or trial <= config.num_trials:
config.model_dir = generic_model_dir + "_" + str(trial)
if config.do_train:
utils.rmkdir(config.model_dir)
model_runner = ModelRunner(config, tasks)
if config.do_train:
heading("Start training")
model_runner.train()
utils.log()
if config.do_eval:
heading("Run dev set evaluation")
results.append(model_runner.evaluate())
write_results(config, results)
if config.write_test_outputs and trial <= config.n_writes_test:
heading("Running on the test set and writing the predictions")
for task in tasks:
# Currently only writing preds for GLUE and SQuAD 2.0 is supported
if task.name in ["cola", "mrpc", "mnli", "sst", "rte", "qnli", "qqp",
"sts"]:
for split in task.get_test_splits():
model_runner.write_classification_outputs([task], trial, split)
elif task.name == "squad":
scorer = model_runner.evaluate_task(task, "test", False)
scorer.write_predictions()
preds = utils.load_json(config.qa_preds_file("squad"))
null_odds = utils.load_json(config.qa_na_file("squad"))
for q, _ in preds.items():
if null_odds[q] > config.qa_na_threshold:
preds[q] = ""
utils.write_json(preds, config.test_predictions(
task.name, "test", trial))
else:
utils.log("Skipping task", task.name,
"- writing predictions is not supported for this task")
if trial != config.num_trials and (not config.keep_all_models):
utils.rmrf(config.model_dir)
trial += 1
# exporting the model
if config.export_dir:
# with tf.variable_scope(tf.get_variable_scope(), reuse=True):
# model_runner = ModelRunner(config, tasks)
# tf.gfile.MakeDirs(config.export_dir)
# checkpoint_path = os.path.join(config.init_checkpoint, "model.ckpt-6315")
# squad_serving_input_fn = (
# build_squad_serving_input_fn(config.max_seq_length))
# utils.log("Starting to export model.")
# subfolder = model_runner._estimator.export_saved_model(
# export_dir_base=os.path.join(config.export_dir, "saved_model"),
# serving_input_receiver_fn=squad_serving_input_fn)
tf.get_variable_scope().reuse_variables()
model_runner = ModelRunner(config, tasks)
tf.gfile.MakeDirs(config.export_dir)
checkpoint_path = os.path.join(config.init_checkpoint, "model.ckpt-6315")
squad_serving_input_fn = (
build_squad_serving_input_fn(config.max_seq_length))
utils.log("Starting to export model.")
subfolder = model_runner._estimator.export_saved_model(
export_dir_base=os.path.join(config.export_dir, "saved_model"),
serving_input_receiver_fn=squad_serving_input_fn)
def run_finetuning(config: configure_finetuning.FinetuningConfig):
"""Run finetuning."""
# Setup for training
results = []
trial = 1
heading_info = "model={:}, trial {:}/{:}".format(config.model_name, trial,
config.num_trials)
heading = lambda msg: utils.heading(msg + ": " + heading_info)
heading("Config")
utils.log_config(config)
generic_model_dir = config.model_dir
tasks = task_builder.get_tasks(config)
# Train and evaluate num_trials models with different random seeds
while config.num_trials < 0 or trial <= config.num_trials:
config.model_dir = generic_model_dir + "_" + str(trial)
if config.do_train:
utils.rmkdir(config.model_dir)
model_runner = ModelRunner(config, tasks)
if config.do_train:
heading_info = "model={:}, trial {:}/{:}".format(
config.model_name, trial, config.num_trials)
heading("Start training")
model_runner.train()
utils.log()
if config.do_eval:
if config.write_eval_outputs and trial <= config.n_writes_test:
heading("Running on the dev set and writing the predictions")
for task in tasks:
# Currently only writing preds for GLUE and SQuAD 2.0 is supported
if task.name in [
"cola", "mrpc", "mnli", "sst", "rte", "qnli",
"qqp", "sts"
]:
for split in task.get_test_splits():
model_runner.write_classification_outputs([task],
trial,
split)
elif task.name == "squad":
scorer = model_runner.evaluate_task(task, "dev", False)
scorer.write_predictions()
preds = utils.load_json(
config.qa_preds_file(task.name + "_dev"))
null_odds = utils.load_json(
config.qa_na_file(task.name + "_dev"))
for q, _ in preds.items():
if null_odds[q] > config.qa_na_threshold:
preds[q] = ""
utils.write_json(
preds,
config.test_predictions(task.name, "dev", trial))
elif task.name == "cmrc2018" or task.name == "drcd":
scorer = model_runner.evaluate_task(task, "dev", False)
scorer.write_predictions()
preds = utils.load_json(
config.qa_preds_file(task.name + "_dev"))
#utils.write_json(preds, config.test_predictions(task.name, "dev", trial))
if config.num_trials > 1:
utils.write_json(
preds,
config.qa_preds_file(task.name + "_dev_" +
str(trial)))
else:
utils.log(
"Skipping task", task.name,
"- writing predictions is not supported for this task"
)
else:
heading("Run dev set evaluation")
# results.append(model_runner.evaluate(split="train"))
results.append(model_runner.evaluate(split="dev"))
write_results(config, results)
if config.do_test:
if config.write_test_outputs and trial <= config.n_writes_test:
heading("Running on the test set and writing the predictions")
for task in tasks:
# Currently only writing preds for GLUE and SQuAD 2.0 is supported
if task.name in [
"cola", "mrpc", "mnli", "sst", "rte", "qnli",
"qqp", "sts"
]:
for split in task.get_test_splits():
model_runner.write_classification_outputs([task],
trial,
split)
elif task.name == "squad":
scorer = model_runner.evaluate_task(
task, "eval", False)
scorer.write_predictions()
preds = utils.load_json(
config.qa_preds_file(task.name + "_eval"))
null_odds = utils.load_json(
config.qa_na_file(task.name + "_eval"))
for q, _ in preds.items():
if null_odds[q] > config.qa_na_threshold:
preds[q] = ""
utils.write_json(
preds,
config.test_predictions(task.name, "eval", trial))
elif task.name == "cmrc2018" or task.name == "drcd":
scorer = model_runner.evaluate_task(
task, "eval", False)
scorer.write_predictions()
preds = utils.load_json(
config.qa_preds_file(task.name + "_eval"))
#utils.write_json(preds, config.test_predictions(task.name, "eval", trial))
if config.num_trials > 1:
utils.write_json(
preds,
config.qa_preds_file(task.name + "_eval_" +
str(trial)))
elif task.name in [
"ccks42ee", "ccks42single", "ccks42multi"
]:
scorer = model_runner.evaluate_task(
task, "eval", False)
scorer.write_predictions()
preds = utils.load_json(
config.qa_preds_file(task.name + "_eval"))
null_odds = utils.load_json(
config.qa_na_file(task.name + "_eval"))
for q, _ in preds.items():
if null_odds[q] > config.qa_na_threshold:
preds[q] = ""
utils.write_json(
preds,
config.qa_preds_file(task.name + "_eval_" +
str(trial)))
elif task.name in [
"ccks42ec", "ner", "ccks42num", "ccks42reg"
]:
scorer = model_runner.evaluate_task(
task, "eval", False)
scorer.write_predictions()
else:
utils.log(
"Skipping task", task.name,
"- writing predictions is not supported for this task"
)
else:
heading("Run test set evaluation")
results.append(model_runner.evaluate(split="eval"))
write_results(config, results)
if trial != config.num_trials and (not config.keep_all_models):
utils.rmrf(config.model_dir)
trial += 1
