def test_data_iter(self):
sys.path.append(patch_path('..'))
data_dir_path = patch_path('../demo/data/coco')
logging.basicConfig(level=logging.DEBUG)
from mxnet_vqa.data.coco import train_test_split
max_lines = 100000
train_data, test_data, meta = train_test_split(
data_dir_path=data_dir_path,
answer_mode='int',
question_mode='add',
batch_size=64,
max_lines_retrieved=max_lines)
print('meta:', meta)
for i, batch in enumerate(train_data):
if i == 10:
break
batch_images = batch.data[0]
batch_questions = batch.data[1]
batch_answers = batch.label[0]
print('batch images: ', batch_images.shape, 'batch questions',
batch_questions.shape, 'batch answers: ',
batch_answers.shape)
def main():
sys.path.append(patch_path('..'))
data_dir_path = patch_path('data/coco')
model_dir_path = patch_path('models')
answer_mode = 'int'
question_mode = 'add'
batch_size = 64
epochs = 100
logging.basicConfig(level=logging.DEBUG)
ctx = mx.gpu(0)
from mxnet_vqa.data.coco import train_test_split
train_data, test_data, meta_data = train_test_split(
data_dir_path=data_dir_path,
max_lines_retrieved=100000,
answer_mode=answer_mode,
ctx=ctx,
question_mode=question_mode,
batch_size=batch_size)
from mxnet_vqa.library.vqa1 import VQANet
net = VQANet(model_ctx=ctx)
net.input_mode_question = question_mode
net.input_mode_answer = answer_mode
net.version = '1'
net.fit(data_train=train_data,
data_eva=test_data,
meta=meta_data,
model_dir_path=model_dir_path,
epochs=epochs)
def main():
sys.path.append(patch_path('..'))
data_dir_path = patch_path('data/coco')
model_dir_path = patch_path('models')
answer_mode = 'int'
question_mode = 'concat'
batch_size = 64
epochs = 100
# set it to -1 if u want to use the longest question sequence length, but my
# graphics card is not good enough for large max_question_seq_length so i used
# 10 instead
max_question_seq_length = 12
logging.basicConfig(level=logging.DEBUG)
ctx = mx.gpu(0)
from mxnet_vqa.data.coco import train_test_split
train_data, test_data, meta_data = train_test_split(data_dir_path=data_dir_path,
max_lines_retrieved=100000,
answer_mode=answer_mode,
ctx=ctx,
max_sequence_length=max_question_seq_length,
question_mode=question_mode,
batch_size=batch_size)
from mxnet_vqa.library.vqa4 import VQANet
net = VQANet(model_ctx=ctx)
net.input_mode_question = question_mode
net.input_mode_answer = answer_mode
net.version = '1'
net.fit(data_train=train_data, data_eva=test_data, meta=meta_data,
model_dir_path=model_dir_path, epochs=epochs)