def weak_supervision(model, iterations):
"""
Training weak supervision on icdar 2013 dataset
:param model: Path to Pre-trained model on Synth-Text using the function train_synth
:param iterations: Number of iterations to train on icdar 2013
:return: None
"""
from train_weak_supervision.__init__ import get_initial_model_optimizer, generate_target, train, save_model
model, optimizer = get_initial_model_optimizer(model)
"""
Steps -
1) Using the pre-trained model generate the targets
2) Fine-tune the model on icdar 2013 dataset using weak-supervision
3) Saving the model and again repeating process 1-3
4) Saving the final model
"""
for iteration in range(int(iterations)):
print('Generating for iteration:', iteration)
generate_target(model, iteration)
print('Fine-tuning for iteration:', iteration)
model, optimizer = train(model, optimizer, iteration)
print('Saving for iteration:', iteration)
save_model(model, optimizer, 'intermediate', iteration)
save_model(model, optimizer, 'final')
def weak_supervision(model, iterations):
"""
Training weak supervision on icdar 2013 dataset
:param model: Path to Pre-trained model on Synth-Text using the function train_synth
:param iterations: Number of iterations to train on icdar 2013
:return: None
"""
from train_weak_supervision.__init__ import get_initial_model_optimizer, generate_target, train, save_model, test
import config
from tensorboardX import SummaryWriter
writer = SummaryWriter()
# ToDo - Check the effects of using optimizer of Synth-Text or starting from a random optimizer
model, optimizer = get_initial_model_optimizer(model)
print('Number of parameters in the model:', sum(p.numel()
for p in model.parameters() if p.requires_grad))
"""
Steps -
1) Using the pre-trained model generate the targets
2) Fine-tune the model on icdar 2013 dataset using weak-supervision
3) Saving the model and again repeating process 1-3
4) Saving the final model
"""
for iteration in range(config.start_iteration, int(iterations)):
if iteration not in config.skip_iterations:
print('Generating for iteration:', iteration)
generate_target(model, iteration)
print('Testing for iteration:', iteration)
f_score_test, precision_test, recall_test = test(model, iteration, writer)
print(
'Test Results for iteration:', iteration,
' | F-score: ', f_score_test,
' | Precision: ', precision_test,
' | Recall: ', recall_test
)
print('Fine-tuning for iteration:', iteration)
model, optimizer, loss, accuracy = train(
model, optimizer, iteration, writer)
print('Saving for iteration:', iteration)
save_model(model, optimizer, 'intermediate',
iteration, loss=loss, accuracy=accuracy)
print('====================================')
save_model(model, optimizer, 'final')
writer.close()
def weak_supervision(model, iterations):
"""
Training weak supervision on icdar 2013 dataset
:param model: Path to Pre-trained model on Synth-Text using the function train_synth
:param iterations: Number of iterations to train on icdar 2013
:return: None
"""
from train_weak_supervision.__init__ import get_initial_model_optimizer, generate_target, train, save_model, test
from train_weak_supervision import config
seed(config)
# ToDo - Check the effects of using optimizer of Synth-Text or starting from a random optimizer
model, optimizer = get_initial_model_optimizer(model)
pytorch_total_params = sum(p.numel() for p in model.parameters() if p.requires_grad)
print('Number of parameters in the model:', pytorch_total_params)
"""
Steps -
1) Using the pre-trained model generate the targets
2) Fine-tune the model on icdar 2013 dataset using weak-supervision
3) Saving the model and again repeating process 1-3
4) Saving the final model
"""
for iteration in range(int(iterations)):
# print('Generating for iteration:', iteration)
# generate_target(model, iteration)
#
# print('Testing for iteration:', iteration)
# f_score_test = test(model)
# print('Test Results for iteration:', iteration, ' | F-score: ', f_score_test)
print('Fine-tuning for iteration:', iteration)
model, optimizer, loss, accuracy = train(model, optimizer, iteration)
print('Saving for iteration:', iteration)
save_model(model, optimizer, 'intermediate', iteration, loss=loss, accuracy=accuracy)
save_model(model, optimizer, 'final')