def main():
seed = 42
np.random.seed(seed)
current_dir = os.path.dirname(__file__)
sys.path.append(os.path.join(current_dir, '..'))
current_dir = current_dir if current_dir is not '' else '.'
img_dir_path = 'jpg'
txt_dir_path = 'flowers/text_c10'
model_dir_path = current_dir + '/models'
img_width = 32
img_height = 32
from dcgan import DCGan
from image_utils import img_from_normalized_img
from img_cap_loader import load_normalized_img_and_its_text
image_label_pairs = load_normalized_img_and_its_text(img_dir_path,
txt_dir_path,
img_width=img_width,
img_height=img_height)
shuffle(image_label_pairs)
gan = DCGan()
gan.load_model(model_dir_path)
for i in range(10):
image_label_pair = image_label_pairs[i]
normalized_image = image_label_pair[0]
text = image_label_pair[1]
print(text)
image = img_from_normalized_img(normalized_image)
image.save(current_dir + '/data/outputs/' + DCGan.model_name +
'-generated-' + str(i) + '-0.png')
for j in range(3):
generated_image = gan.generate_image_from_text(text)
generated_image.save(current_dir + '/data/outputs/' +
DCGan.model_name + '-generated-' + str(i) +
'-' + str(j) + '.png')
def main():
seed = 42
np.random.seed(seed)
current_dir = os.path.dirname(__file__)
sys.path.append(os.path.join(current_dir, '..'))
current_dir = current_dir if current_dir is not '' else '.'
img_dir_path = 'jpg'
txt_dir_path = 'flowers/text_c10'
model_dir_path = current_dir + '/models'
img_width = 32
img_height = 32
img_channels = 3
from dcgan import DCGan
from img_cap_loader import load_normalized_img_and_its_text
image_label_pairs = load_normalized_img_and_its_text(img_dir_path,
txt_dir_path,
img_width=img_width,
img_height=img_height)
shuffle(image_label_pairs)
gan = DCGan()
gan.img_width = img_width
gan.img_height = img_height
gan.img_channels = img_channels
gan.random_input_dim = 200
gan.glove_source_dir_path = './very_large_data'
batch_size = 16
epochs = 300
gan.fit(model_dir_path=model_dir_path,
image_label_pairs=image_label_pairs,
snapshot_dir_path=current_dir + '/data/snapshots',
snapshot_interval=100,
batch_size=batch_size,
epochs=epochs)
def main(_=None):
parser = argparse.ArgumentParser(description='Train GAN')
parser.add_argument('--config', required=True, help="config path")
parser.add_argument('--name', required=True, help="model name")
parser.add_argument('--model-dir', required=True, help="model directory")
parser.add_argument('--data-dir', required=True, help="data directory")
parser.add_argument('--epochs',
default=1000,
help="number of training epochs")
args = parser.parse_args()
CONFIG_PATH = args.config
MODEL_NAME = args.name
MODEL_DIR = Path(args.model_dir)
DATA_DIR = Path(args.data_dir)
NB_EPOCHS = args.epochs
# load model config
with open(CONFIG_PATH, 'r') as f:
config = yaml.load(f)
IMG_SHAPE = config['data']['input_shape']
# load data
train_ds = load_celeba_tfdataset(DATA_DIR, config, zipped=False)
test_ds = load_celeba_tfdataset(DATA_DIR, config, zipped=False)
# instantiate GAN
gan = DCGan(IMG_SHAPE, config)
# setup model directory for checkpoint and tensorboard logs
model_dir = MODEL_DIR / MODEL_NAME
model_dir.mkdir(exist_ok=True, parents=True)
log_dir = model_dir / "logs" / datetime.now().strftime("%Y%m%d-%H%M%S")
# run train
gan._train(train_ds=gan.setup_dataset(train_ds),
validation_ds=gan.setup_dataset(test_ds),
nb_epochs=NB_EPOCHS,
log_dir=log_dir,
checkpoint_dir=None,
is_tfdataset=True)
mode = 'test'
img_width = 64
img_height = 64
img_channels = 3
from dcgan import DCGan
image_label_pairs = load_normalized_img_and_text(img_dir_path,
txt_dir_path,
img_width=img_width,
img_height=img_height)
shuffle(image_label_pairs)
gan = DCGan()
gan.img_width = img_width
gan.img_height = img_height
gan.img_channels = img_channels
gan.random_input_dim = 200
gan.glove_source_dir_path = './very_large_data'
batch_size = 5
epochs = 2000
if mode == 'train':
#training
start_time = time.time()
logs = gan.fit(model_dir_path=model_dir_path,
image_label_pairs=image_label_pairs,