def main(args):
"""
Main function for the script
:param args: parsed command line arguments
:return: None
"""
from pro_gan_pytorch.PRO_GAN import ProGAN
print(args.config)
config = get_config(args.config)
print("Current Configuration:", config)
# create the dataset for training
dataset = dl.FoldersDistributedDataset(
data_dir=config.images_dir,
transform=dl.get_transform(config.img_dims)
)
print("total examples in training: ", len(dataset))
pro_gan = ProGAN(
depth=config.depth,
latent_size=config.latent_size,
learning_rate=config.learning_rate,
beta_1=config.beta_1,
beta_2=config.beta_2,
eps=config.eps,
drift=config.drift,
n_critic=config.n_critic,
use_eql=config.use_eql,
loss=config.loss_function,
use_ema=config.use_ema,
ema_decay=config.ema_decay,
device=device
)
if args.generator_file is not None:
print("Loading generator from:", args.generator_file)
pro_gan.gen.load_state_dict(th.load(args.generator_file))
if args.discriminator_file is not None:
print("Loading discriminator from:", args.discriminator_file)
pro_gan.dis.load_state_dict(th.load(args.discriminator_file))
# train all the networks
train_networks(
pro_gan=pro_gan,
dataset=dataset,
epochs=config.epochs,
fade_in_percentage=config.fade_in_percentage,
start_depth=args.start_depth,
batch_sizes=config.batch_sizes,
num_workers=config.num_workers,
feedback_factor=config.feedback_factor,
log_dir=config.log_dir,
sample_dir=config.sample_dir,
checkpoint_factor=config.checkpoint_factor,
save_dir=config.save_dir,
)
def main(args):
"""
Main function for the script
:param args: parsed command line arguments
:return: None
"""
from networks.TextEncoder import Encoder
from networks.ConditionAugmentation import ConditionAugmentor
from networks.PRO_GAN import ConditionalProGAN
#print(args.config)
config = get_config(args.config)
#print("Current Configuration:", config)
print("Create dataset...")
# create the dataset for training
if config.use_pretrained_encoder:
print("Using PretrainedEncoder...")
if not os.path.exists(
f"text_encoder_{config.tensorboard_comment}.pickle"):
print("Creating new vocab and dataset pickle files ...")
dataset = dl.RawTextFace2TextDataset(
data_path=config.data_path,
img_dir=config.images_dir,
img_transform=dl.get_transform(config.img_dims))
val_dataset = dl.RawTextFace2TextDataset(
data_path=config.data_path_val,
img_dir=config.val_images_dir, # unnecessary
img_transform=dl.get_transform(config.img_dims))
from networks.TextEncoder import PretrainedEncoder
# create a new session object for the pretrained encoder:
text_encoder = PretrainedEncoder(
model_file=config.pretrained_encoder_file,
embedding_file=config.pretrained_embedding_file,
device=device)
encoder_optim = None
print("Pickling dataset, val_dataset and text_encoder....")
with open(f'dataset_{config.tensorboard_comment}.pickle',
'wb') as handle:
pickle.dump(dataset, handle, protocol=pickle.HIGHEST_PROTOCOL)
with open(f'val_dataset_{config.tensorboard_comment}.pickle',
'wb') as handle:
pickle.dump(val_dataset,
handle,
protocol=pickle.HIGHEST_PROTOCOL)
with open(f'text_encoder_{config.tensorboard_comment}.pickle',
'wb') as handle:
pickle.dump(text_encoder,
handle,
protocol=pickle.HIGHEST_PROTOCOL)
else:
print("Loading dataset, val_dataset and text_encoder from file...")
with open(f'val_dataset_{config.tensorboard_comment}.pickle',
'rb') as handle:
val_dataset = pickle.load(handle)
with open(f'dataset_{config.tensorboard_comment}.pickle',
'rb') as handle:
dataset = pickle.load(handle)
from networks.TextEncoder import PretrainedEncoder
with open(f'text_encoder_{config.tensorboard_comment}.pickle',
'rb') as handle:
text_encoder = pickle.load(handle)
encoder_optim = None
else:
print("Using Face2TextDataset dataloader...")
dataset = dl.Face2TextDataset(pro_pick_file=config.processed_text_file,
img_dir=config.images_dir,
img_transform=dl.get_transform(
config.img_dims),
captions_len=config.captions_length)
text_encoder = Encoder(embedding_size=config.embedding_size,
vocab_size=dataset.vocab_size,
hidden_size=config.hidden_size,
num_layers=config.num_layers,
device=device)
encoder_optim = th.optim.Adam(text_encoder.parameters(),
lr=config.learning_rate,
betas=(config.beta_1, config.beta_2),
eps=config.eps)
# create the networks
if args.encoder_file is not None:
# Note this should not be used with the pretrained encoder file
print("Loading encoder from:", args.encoder_file)
text_encoder.load_state_dict(th.load(args.encoder_file))
condition_augmenter = ConditionAugmentor(input_size=config.hidden_size,
latent_size=config.ca_out_size,
use_eql=config.use_eql,
device=device)
if args.ca_file is not None:
print("Loading conditioning augmenter from:", args.ca_file)
condition_augmenter.load_state_dict(th.load(args.ca_file))
print("Create cprogan...")
c_pro_gan = ConditionalProGAN(
embedding_size=config.hidden_size,
depth=config.depth,
latent_size=config.latent_size,
compressed_latent_size=config.compressed_latent_size,
learning_rate=config.learning_rate,
beta_1=config.beta_1,
beta_2=config.beta_2,
eps=config.eps,
drift=config.drift,
n_critic=config.n_critic,
use_eql=config.use_eql,
loss=config.loss_function,
use_ema=config.use_ema,
ema_decay=config.ema_decay,
device=device)
#print("Generator Config:")
print(c_pro_gan.gen)
#print("\nDiscriminator Config:")
#print(c_pro_gan.dis)
if args.generator_file is not None:
print("Loading generator from:", args.generator_file)
c_pro_gan.gen.load_state_dict(th.load(args.generator_file))
if args.discriminator_file is not None:
print("Loading discriminator from:", args.discriminator_file)
c_pro_gan.dis.load_state_dict(th.load(args.discriminator_file))
print("Create optimizer...")
# create the optimizer for Condition Augmenter separately
ca_optim = th.optim.Adam(condition_augmenter.parameters(),
lr=config.learning_rate,
betas=(config.beta_1, config.beta_2),
eps=config.eps)
# train all the networks
train_networks(
encoder=text_encoder,
ca=condition_augmenter,
c_pro_gan=c_pro_gan,
dataset=dataset,
validation_dataset=val_dataset,
encoder_optim=encoder_optim,
ca_optim=ca_optim,
epochs=config.epochs,
fade_in_percentage=config.fade_in_percentage,
start_depth=args.start_depth,
batch_sizes=config.batch_sizes,
num_workers=config.num_workers,
feedback_factor=config.feedback_factor,
log_dir=config.log_dir,
sample_dir=config.sample_dir,
checkpoint_factor=config.checkpoint_factor,
save_dir=config.save_dir,
comment=config.tensorboard_comment,
use_matching_aware_dis=config.use_matching_aware_discriminator)
def main(args):
"""
Main function for the script
:param args: parsed command line arguments
:return: None
"""
from networks.TextEncoder import Encoder
from networks.ConditionAugmentation import ConditionAugmentor
from networks.PRO_GAN import ConditionalProGAN
print(args.config)
config = get_config(args.config)
print("Current Configuration:", config)
# create the dataset for training
if config.use_pretrained_encoder:
dataset = dl.RawTextFace2TextDataset(
annots_file=config.annotations_file,
img_dir=config.images_dir,
img_transform=dl.get_transform(config.img_dims))
from networks.TextEncoder import PretrainedEncoder
# create a new session object for the pretrained encoder:
sess_config = tf.ConfigProto(device_count={"GPU": 0})
session = tf.Session(config=sess_config)
text_encoder = PretrainedEncoder(
session=session,
module_dir=config.pretrained_encoder_dir,
download=config.download_pretrained_encoder)
encoder_optim = None
else:
dataset = dl.Face2TextDataset(pro_pick_file=config.processed_text_file,
img_dir=config.images_dir,
img_transform=dl.get_transform(
config.img_dims),
captions_len=config.captions_length)
text_encoder = Encoder(embedding_size=config.embedding_size,
vocab_size=dataset.vocab_size,
hidden_size=config.hidden_size,
num_layers=config.num_layers,
device=device)
encoder_optim = th.optim.Adam(text_encoder.parameters(),
lr=config.learning_rate,
betas=(config.beta_1, config.beta_2),
eps=config.eps)
# create the networks
if args.encoder_file is not None:
print("Loading encoder from:", args.encoder_file)
text_encoder.load_state_dict(th.load(args.encoder_file))
condition_augmenter = ConditionAugmentor(input_size=config.hidden_size,
latent_size=config.ca_out_size,
use_eql=config.use_eql,
device=device)
if args.ca_file is not None:
print("Loading conditioning augmenter from:", args.ca_file)
condition_augmenter.load_state_dict(th.load(args.ca_file))
c_pro_gan = ConditionalProGAN(
embedding_size=config.hidden_size,
depth=config.depth,
latent_size=config.latent_size,
compressed_latent_size=config.compressed_latent_size,
learning_rate=config.learning_rate,
beta_1=config.beta_1,
beta_2=config.beta_2,
eps=config.eps,
drift=config.drift,
n_critic=config.n_critic,
use_eql=config.use_eql,
loss=config.loss_function,
use_ema=config.use_ema,
ema_decay=config.ema_decay,
device=device)
if args.generator_file is not None:
print("Loading generator from:", args.generator_file)
c_pro_gan.gen.load_state_dict(th.load(args.generator_file))
if args.discriminator_file is not None:
print("Loading discriminator from:", args.discriminator_file)
c_pro_gan.dis.load_state_dict(th.load(args.discriminator_file))
# create the optimizer for Condition Augmenter separately
ca_optim = th.optim.Adam(condition_augmenter.parameters(),
lr=config.learning_rate,
betas=(config.beta_1, config.beta_2),
eps=config.eps)
# train all the networks
train_networks(
encoder=text_encoder,
ca=condition_augmenter,
c_pro_gan=c_pro_gan,
dataset=dataset,
encoder_optim=encoder_optim,
ca_optim=ca_optim,
epochs=config.epochs,
fade_in_percentage=config.fade_in_percentage,
start_depth=args.start_depth,
batch_sizes=config.batch_sizes,
num_workers=config.num_workers,
feedback_factor=config.feedback_factor,
log_dir=config.log_dir,
sample_dir=config.sample_dir,
checkpoint_factor=config.checkpoint_factor,
save_dir=config.save_dir,
use_matching_aware_dis=config.use_matching_aware_discriminator)
def homepage_result():
caption = request.form["des"]
current_depth = 4
from networks.TextEncoder import Encoder
from networks.ConditionAugmentation import ConditionAugmentor
from networks.C_PRO_GAN import ProGAN
# define the device for the training script
device = th.device("cuda" if th.cuda.is_available() else "cpu")
############################################################################
# load my generator.
def get_config(conf_file):
"""
parse and load the provided configuration
:param conf_file: configuration file
:return: conf => parsed configuration
"""
from easydict import EasyDict as edict
with open(conf_file, "r") as file_descriptor:
data = yaml.load(file_descriptor)
# convert the data into an easyDictionary
return edict(data)
config = get_config("configs\\11.conf")
c_pro_gan = ProGAN(embedding_size=config.hidden_size,
depth=config.depth,
latent_size=config.latent_size,
learning_rate=config.learning_rate,
beta_1=config.beta_1,
beta_2=config.beta_2,
eps=config.eps,
drift=config.drift,
n_critic=config.n_critic,
device=device)
c_pro_gan.gen.load_state_dict(
th.load("training_runs\\11\\saved_models\\GAN_GEN_3_20.pth"))
###################################################################################
# load my embedding and conditional augmentor
dataset = dl.Face2TextDataset(pro_pick_file=config.processed_text_file,
img_dir=config.images_dir,
img_transform=dl.get_transform(
config.img_dims),
captions_len=config.captions_length)
text_encoder = Encoder(embedding_size=config.embedding_size,
vocab_size=dataset.vocab_size,
hidden_size=config.hidden_size,
num_layers=config.num_layers,
device=device)
text_encoder.load_state_dict(
th.load("training_runs\\11\\saved_models\\Encoder_3_20.pth"))
condition_augmenter = ConditionAugmentor(input_size=config.hidden_size,
latent_size=config.ca_out_size,
device=device)
condition_augmenter.load_state_dict(
th.load(
"training_runs\\11\\saved_models\\Condition_Augmentor_3_20.pth"))
###################################################################################
# #ask for text description/caption
# caption to text encoding
#caption = input('Enter your desired description : ')
seq = []
for word in caption.split():
seq.append(dataset.rev_vocab[word])
for i in range(len(seq), 100):
seq.append(0)
seq = th.LongTensor(seq)
seq = seq.cuda()
print(type(seq))
print('\nInput : ', caption)
list_seq = [seq for i in range(16)]
print(len(list_seq))
list_seq = th.stack(list_seq)
list_seq = list_seq.cuda()
embeddings = text_encoder(list_seq)
c_not_hats, mus, sigmas = condition_augmenter(embeddings)
z = th.randn(list_seq.shape[0],
c_pro_gan.latent_size - c_not_hats.shape[-1]).to(device)
gan_input = th.cat((c_not_hats, z), dim=-1)
alpha = 0.007352941176470588
samples = c_pro_gan.gen(gan_input, current_depth, alpha)
from torchvision.utils import save_image
from torch.nn.functional import upsample
# from train_network import create_grid
img_file = "static\\" + caption + '.png'
samples = (samples / 2) + 0.5
if int(np.power(2, c_pro_gan.depth - current_depth - 1)) > 1:
samples = upsample(samples, scale_factor=current_depth)
# save image to the disk, the resulting image is <caption>.png
save_image(samples, img_file, nrow=int(np.sqrt(20)))
###################################################################################
# #output the image.
result = "\\static\\" + caption + ".png"
return render_template("main.html",
result_img=result,
result_caption=caption)
def main(args):
"""
Main function for the script
:param args: parsed command line arguments
:return: None
"""
from networks.TextEncoder import Encoder
from networks.ConditionAugmentation import ConditionAugmentor
from networks.C_PRO_GAN import ProGAN
print(args.config)
config = get_config(args.config)
print("Current Configuration:", config)
# create the dataset for training
dataset = dl.Face2TextDataset(pro_pick_file=config.processed_text_file,
img_dir=config.images_dir,
img_transform=dl.get_transform(
config.img_dims),
captions_len=config.captions_length)
# create the networks
text_encoder = Encoder(embedding_size=config.embedding_size,
vocab_size=dataset.vocab_size,
hidden_size=config.hidden_size,
num_layers=config.num_layers,
device=device)
if args.encoder_file is not None:
print("Loading encoder from:", args.encoder_file)
text_encoder.load_state_dict(th.load(args.encoder_file))
condition_augmenter = ConditionAugmentor(input_size=config.hidden_size,
latent_size=config.ca_out_size,
device=device)
if args.ca_file is not None:
print("Loading conditioning augmenter from:", args.ca_file)
condition_augmenter.load_state_dict(th.load(args.ca_file))
c_pro_gan = ProGAN(embedding_size=config.hidden_size,
depth=config.depth,
latent_size=config.latent_size,
learning_rate=config.learning_rate,
beta_1=config.beta_1,
beta_2=config.beta_2,
eps=config.eps,
drift=config.drift,
n_critic=config.n_critic,
device=device)
if args.generator_file is not None:
print("Loading generator from:", args.generator_file)
c_pro_gan.gen.load_state_dict(th.load(args.generator_file))
if args.discriminator_file is not None:
print("Loading discriminator from:", args.discriminator_file)
c_pro_gan.dis.load_state_dict(th.load(args.discriminator_file))
# create the optimizers for Encoder and Condition Augmenter separately
encoder_optim = th.optim.Adam(text_encoder.parameters(),
lr=config.learning_rate,
betas=(config.beta_1, config.beta_2),
eps=config.eps)
ca_optim = th.optim.Adam(condition_augmenter.parameters(),
lr=config.learning_rate,
betas=(config.beta_1, config.beta_2),
eps=config.eps)
# train all the networks
train_networks(
encoder=text_encoder,
ca=condition_augmenter,
c_pro_gan=c_pro_gan,
dataset=dataset,
encoder_optim=encoder_optim,
ca_optim=ca_optim,
epochs=config.epochs,
fade_in_percentage=config.fade_in_percentage,
start_depth=args.start_depth,
batch_sizes=config.batch_sizes,
num_workers=config.num_workers,
feedback_factor=config.feedback_factor,
log_dir=config.log_dir,
sample_dir=config.sample_dir,
checkpoint_factor=config.checkpoint_factor,
save_dir=config.save_dir,
use_matching_aware_dis=config.use_matching_aware_discriminator)
beta_1=config.beta_1,
beta_2=config.beta_2,
eps=config.eps,
drift=config.drift,
n_critic=config.n_critic,
device=device)
c_pro_gan.gen.load_state_dict(
th.load("training_runs\\11\\saved_models\\GAN_GEN_4.pth"))
###################################################################################
#load my embedding and conditional augmentor
dataset = dl.Face2TextDataset(pro_pick_file=config.processed_text_file,
img_dir=config.images_dir,
img_transform=dl.get_transform(config.img_dims),
captions_len=config.captions_length)
text_encoder = Encoder(embedding_size=config.embedding_size,
vocab_size=dataset.vocab_size,
hidden_size=config.hidden_size,
num_layers=config.num_layers,
device=device)
text_encoder.load_state_dict(
th.load("training_runs\\11\\saved_models\\Encoder_3_20.pth"))
condition_augmenter = ConditionAugmentor(input_size=config.hidden_size,
latent_size=config.ca_out_size,
device=device)
condition_augmenter.load_state_dict(
th.load("training_runs\\11\\saved_models\\Condition_Augmentor_3_20.pth"))
def main(args):
"""
Main function for the script
:param args: parsed command line arguments
:return: None
"""
from networks.TextEncoder import Encoder
from networks.ConditionAugmentation import ConditionAugmentor
#from pro_gan_pytorch.PRO_GAN import ConditionalProGAN
from MSG_GAN.GAN import MSG_GAN
from MSG_GAN import Losses as lses
print(args.config)
config = get_config(args.config)
print("Current Configuration:", config)
# create the dataset for training
if config.use_pretrained_encoder:
dataset = dl.RawTextFace2TextDataset(
annots_file=config.annotations_file,
img_dir=config.images_dir,
img_transform=dl.get_transform(config.img_dims)
)
from networks.TextEncoder import PretrainedEncoder
# create a new session object for the pretrained encoder:
text_encoder = PretrainedEncoder(
model_file=config.pretrained_encoder_file,
embedding_file=config.pretrained_embedding_file,
device=device
)
encoder_optim = None
else:
dataset = dl.Face2TextDataset(
pro_pick_file=config.processed_text_file,
img_dir=config.images_dir,
img_transform=dl.get_transform(config.img_dims),
captions_len=config.captions_length
)
text_encoder = Encoder(
embedding_size=config.embedding_size,
vocab_size=dataset.vocab_size,
hidden_size=config.hidden_size,
num_layers=config.num_layers,
device=device
)
encoder_optim = th.optim.Adam(text_encoder.parameters(),
lr=config.learning_rate,
betas=(config.adam_beta1, config.adam_beta2),
eps=config.eps)
msg_gan = MSG_GAN(
depth=config.depth,
latent_size=config.latent_size,
use_eql=config.use_eql,
use_ema=config.use_ema,
ema_decay=config.ema_decay,
device=device)
genoptim = th.optim.Adam(msg_gan.gen.parameters(), config.g_lr,
[config.adam_beta1, config.adam_beta2])
disoptim = th.optim.Adam(msg_gan.dis.parameters(), config.d_lr,
[config.adam_beta1, config.adam_beta2])
loss = lses.RelativisticAverageHingeGAN
# create the networks
if args.encoder_file is not None:
# Note this should not be used with the pretrained encoder file
print("Loading encoder from:", args.encoder_file)
text_encoder.load_state_dict(th.load(args.encoder_file))
condition_augmenter = ConditionAugmentor(
input_size=config.hidden_size,
latent_size=config.ca_out_size,
use_eql=config.use_eql,
device=device
)
if args.ca_file is not None:
print("Loading conditioning augmenter from:", args.ca_file)
condition_augmenter.load_state_dict(th.load(args.ca_file))
if args.generator_file is not None:
print("Loading generator from:", args.generator_file)
msg_gan.gen.load_state_dict(th.load(args.generator_file))
if args.discriminator_file is not None:
print("Loading discriminator from:", args.discriminator_file)
msg_gan.dis.load_state_dict(th.load(args.discriminator_file))
# create the optimizer for Condition Augmenter separately
ca_optim = th.optim.Adam(condition_augmenter.parameters(),
lr=config.learning_rate,
betas=(config.adam_beta1, config.adam_beta2),
eps=config.eps)
print("Generator Config:")
print(msg_gan.gen)
print("\nDiscriminator Config:")
print(msg_gan.dis)
# train all the networks
train_networks(
encoder=text_encoder,
ca=condition_augmenter,
msg_gan=msg_gan,
dataset=dataset,
encoder_optim=encoder_optim,
ca_optim=ca_optim,
gen_optim=genoptim,
dis_optim=disoptim,
loss_fn=loss(msg_gan.dis),
epochs=config.epochs,
fade_in_percentage=config.fade_in_percentage,
start_depth=args.start_depth,
batch_sizes=config.batch_sizes,
num_workers=config.num_workers,
feedback_factor=config.feedback_factor,
log_dir=config.log_dir,
sample_dir=config.sample_dir,
checkpoint_factor=config.checkpoint_factor,
save_dir=config.save_dir,
)