import json
import sys
import time
import architectures
import data_loaders
from utils import Hp, get_optimizer, get_hyperparameters
from train_test import train_and_test
# Load all hyperparameter permutations
if len(sys.argv)>1:
hyperparameter_list_name = sys.argv[1]
else:
hyperparameter_list_name = 'JSB Chorales'
time_start = time.time()
for hyperparameters in get_hyperparameters(hyperparameter_list_name):
# Run experiment for each hyperparameter
Hp.set_hyperparameters(hyperparameters)
train_loader, test_loader = data_loaders.get_dataset()
model = architectures.get_model()
optimizer = get_optimizer(model)
results = train_and_test(train_loader, test_loader, model, optimizer)
# Save experiment in a dictionary and dump as a json
with open(Hp.get_experiment_name(), 'w') as f:
experiment_dict = {'hyperparameters': hyperparameters,
'results': results}
json.dump(experiment_dict, f, indent=2)
# Print how long it took to run the algorithm
DEVICE = torch.device("cuda")
print(DEVICE)
data = trim_entity_spans(convert_goldparse('data/Resumes.json'))
total = len(data)
train_data, val_data = data[:180], data[180:]
train_d = ResumeDataset(train_data, TOKENIZER, tag2idx, MAX_LEN)
val_d = ResumeDataset(val_data, TOKENIZER, tag2idx, MAX_LEN)
train_sampler = RandomSampler(train_d)
train_dl = DataLoader(train_d, sampler=train_sampler, batch_size=8)
val_dl = DataLoader(val_d, batch_size=4)
#model = BertForTokenClassification.from_pretrained(MODEL_NAME, num_labels=len(tag2idx))
model = LongformerForTokenClassification.from_pretrained(
MODEL_NAME, num_labels=len(tag2idx))
model.to(DEVICE)
optimizer_grouped_parameters = get_hyperparameters(model, True)
optimizer = Adam(optimizer_grouped_parameters, lr=3e-5)
train_and_val_model(model, TOKENIZER, optimizer, EPOCHS, idx2tag, tag2idx,
MAX_GRAD_NORM, DEVICE, train_dl, val_dl)
torch.save(
{"model_state_dict": model.state_dict()},
f'{output_path}/model-state.bin',
)
# import tensorflow as tf
# tf.config.gpu.set_per_process_memory_fraction(.6)
# tf.config.gpu.set_per_process_memory_growth(True)
parser = argparse.ArgumentParser()
parser.add_argument(
"-t",
"--task",
type=str,
default="3a",
help="Please enter tasks' numbers in a string separated by comma")
args = parser.parse_args()
tasks = args.task.split(',')
for task in tasks:
hyperparameters = get_hyperparameters(task)
hyperparameters = hyperparameters_processing(hyperparameters)
folds = get_folds(hyperparameters)
autocontext_step = hyperparameters['autocontext_step']
model_predictions = [None] * len(
os.listdir(
os.path.join(os.getcwd(),
os.path.join(hyperparameters['data_path'], 'Image'))))
for s_step in range(0, autocontext_step):
folds = get_folds(hyperparameters)
for fold_num, fold in enumerate(folds):
train_images, train_masks, validation_images, validation_masks = fold
print("train images", train_images)
train_data_gen, test_data_gen = get_data_with_generator_on_the_fly(
hyperparameters, train_images, train_masks, validation_images,
validation_masks, s_step, fold_num, len(validation_images))