def rename(self, new_name):
models_path = util.get_model_folder()
s.call([
'mv',
os.path.join(models_path, self.name),
os.path.join(models_path, new_name)
])
self.name = new_name
self.path = util.get_model_folder(self.name)
def do_copy(template_name, new_name):
source = os.path.join(util.get_root_folder(), 'templates',
template_name)
dest = os.path.join(util.get_model_folder(new_name))
s.call(['cp', '-r', source, dest])
models.append(CaffeModel.load_from_models_folder(new_name))
refresh_model_list()
def run(args):
util.print_with_time(str(args))
# save args
model_folder = util.get_model_folder()
args_filename = util.get_args_filename(model_folder)
util.save_object(args, args_filename)
# init models
util.set_seed(args.seed)
generative_model, inference_network, true_generative_model = \
load_or_init_models(args.load_model_folder, args.pcfg_path)
if args.train_mode == 'relax':
control_variate = models.ControlVariate(generative_model.grammar)
# train
if args.train_mode == 'ww':
train_callback = train.TrainWakeWakeCallback(
args.pcfg_path, model_folder, true_generative_model,
args.logging_interval, args.checkpoint_interval,
args.eval_interval)
train.train_wake_wake(generative_model, inference_network,
true_generative_model, args.batch_size,
args.num_iterations, args.num_particles,
train_callback)
elif args.train_mode == 'ws':
train_callback = train.TrainWakeSleepCallback(
args.pcfg_path, model_folder, true_generative_model,
args.logging_interval, args.checkpoint_interval,
args.eval_interval)
train.train_wake_sleep(generative_model, inference_network,
true_generative_model, args.batch_size,
args.num_iterations, args.num_particles,
train_callback)
elif args.train_mode == 'reinforce' or args.train_mode == 'vimco':
train_callback = train.TrainIwaeCallback(
args.pcfg_path, model_folder, true_generative_model,
args.logging_interval, args.checkpoint_interval,
args.eval_interval)
train.train_iwae(args.train_mode, generative_model, inference_network,
true_generative_model, args.batch_size,
args.num_iterations, args.num_particles,
train_callback)
elif args.train_mode == 'relax':
train_callback = train.TrainRelaxCallback(
args.pcfg_path, model_folder, true_generative_model,
args.logging_interval, args.checkpoint_interval,
args.eval_interval)
train.train_relax(generative_model, inference_network, control_variate,
true_generative_model, args.batch_size,
args.num_iterations, args.num_particles,
train_callback)
# save models and stats
util.save_models(generative_model, inference_network, args.pcfg_path,
model_folder)
stats_filename = util.get_stats_filename(model_folder)
util.save_object(train_callback, stats_filename)
def write_to_database(model_name):
images = [[] for i in range(10)]
p = path.join(util.get_model_folder(), model_name)
source_folder = path.join(p, 'raw_data')
for filename in glob(path.join(source_folder, '*')):
bits = re.split(r'\_|\.', filename)
lindex = bits.index('LABEL')
label = int(bits[lindex+1])
images[label].append(filename)
training = []
validation = []
for i in range(10):
if(len(images[i]) > min_images):
validation_size = int(len(images[i]) * validation_percent)
validation += [(name, i) for name in images[i][:validation_size]]
training += [(name, i) for name in images[i][validation_size:]]
else:
print('There are not enough images for category ' + str(i) + ' for it to be included.')
write_images_to_db(path.join(p, 'training_data'), training)
write_images_to_db(path.join(p, 'validation_data'), validation)
def load_all_from_models_folder():
return [
CaffeModel.load_from_models_folder(path.split('/')[-1])
for path in glob(os.path.join(util.get_model_folder(), '*'))
]
def load_from_models_folder(name):
model = CaffeModel()
model.name = name
model.path = util.get_model_folder(name)
return model
def run(args):
# set up args
args.device = None
if args.cuda and torch.cuda.is_available():
args.device = torch.device('cuda')
else:
args.device = torch.device('cpu')
args.num_mixtures = 20
if args.init_near:
args.init_mixture_logits = np.ones(args.num_mixtures)
else:
args.init_mixture_logits = np.array(
list(reversed(2 * np.arange(args.num_mixtures))))
args.softmax_multiplier = 0.5
if args.train_mode == 'concrete':
args.relaxed_one_hot = True
args.temperature = 3
else:
args.relaxed_one_hot = False
args.temperature = None
temp = np.arange(args.num_mixtures) + 5
true_p_mixture_probs = temp / np.sum(temp)
args.true_mixture_logits = \
np.log(true_p_mixture_probs) / args.softmax_multiplier
util.print_with_time(str(args))
# save args
model_folder = util.get_model_folder()
args_filename = util.get_args_path(model_folder)
util.save_object(args, args_filename)
# init models
util.set_seed(args.seed)
generative_model, inference_network, true_generative_model = \
util.init_models(args)
if args.train_mode == 'relax':
control_variate = models.ControlVariate(args.num_mixtures)
# init dataloader
obss_data_loader = torch.utils.data.DataLoader(
true_generative_model.sample_obs(args.num_obss),
batch_size=args.batch_size,
shuffle=True)
# train
if args.train_mode == 'mws':
train_callback = train.TrainMWSCallback(model_folder,
true_generative_model,
args.logging_interval,
args.checkpoint_interval,
args.eval_interval)
train.train_mws(generative_model, inference_network, obss_data_loader,
args.num_iterations, args.mws_memory_size,
train_callback)
if args.train_mode == 'ws':
train_callback = train.TrainWakeSleepCallback(
model_folder, true_generative_model,
args.batch_size * args.num_particles, args.logging_interval,
args.checkpoint_interval, args.eval_interval)
train.train_wake_sleep(generative_model, inference_network,
obss_data_loader, args.num_iterations,
args.num_particles, train_callback)
elif args.train_mode == 'ww':
train_callback = train.TrainWakeWakeCallback(model_folder,
true_generative_model,
args.num_particles,
args.logging_interval,
args.checkpoint_interval,
args.eval_interval)
train.train_wake_wake(generative_model, inference_network,
obss_data_loader, args.num_iterations,
args.num_particles, train_callback)
elif args.train_mode == 'dww':
train_callback = train.TrainDefensiveWakeWakeCallback(
model_folder, true_generative_model, args.num_particles, 0.2,
args.logging_interval, args.checkpoint_interval,
args.eval_interval)
train.train_defensive_wake_wake(0.2, generative_model,
inference_network, obss_data_loader,
args.num_iterations,
args.num_particles, train_callback)
elif args.train_mode == 'reinforce' or args.train_mode == 'vimco':
train_callback = train.TrainIwaeCallback(
model_folder, true_generative_model, args.num_particles,
args.train_mode, args.logging_interval, args.checkpoint_interval,
args.eval_interval)
train.train_iwae(args.train_mode, generative_model, inference_network,
obss_data_loader, args.num_iterations,
args.num_particles, train_callback)
elif args.train_mode == 'concrete':
train_callback = train.TrainConcreteCallback(
model_folder, true_generative_model, args.num_particles,
args.num_iterations, args.logging_interval,
args.checkpoint_interval, args.eval_interval)
train.train_iwae(args.train_mode, generative_model, inference_network,
obss_data_loader, args.num_iterations,
args.num_particles, train_callback)
elif args.train_mode == 'relax':
train_callback = train.TrainRelaxCallback(model_folder,
true_generative_model,
args.num_particles,
args.logging_interval,
args.checkpoint_interval,
args.eval_interval)
train.train_relax(generative_model, inference_network, control_variate,
obss_data_loader, args.num_iterations,
args.num_particles, train_callback)
# save models and stats
util.save_models(generative_model, inference_network, model_folder)
if args.train_mode == 'relax':
util.save_control_variate(control_variate, model_folder)
stats_filename = util.get_stats_path(model_folder)
util.save_object(train_callback, stats_filename)
def import_raw_data(source_folder, model_name, overwrite=False):
p = path.join(util.get_model_folder(), model_name, 'raw_data') + '/'
if(overwrite):
s.call(['rm', '-r'] + glob(path.join(p, 'raw_data', '*')))
for image_path in glob(path.join(source_folder, '*')):
s.call(['cp', image_path, p])