'test_losses': [],
'test_kl_losses': [],
'test_rec_losses': [],
'save_times': [],
'args': [args],
'last_save': 0,
'last_plot': 0,
}
args.size_training_set = len(train_data_function)
hsize = data_loader.train_loader.data.shape[1]
wsize = data_loader.train_loader.data.shape[2]
encoder_model = ConvVAE(
args.code_length,
input_size=args.number_condition,
encoder_output_size=args.encoder_output_size,
).to(DEVICE)
prior_model = PriorNetwork(
size_training_set=args.size_training_set,
code_length=args.code_length,
n_mixtures=args.num_mixtures,
k=args.num_k,
require_unique_codes=args.require_unique_codes).to(DEVICE)
pcnn_decoder = GatedPixelCNN(input_dim=1,
dim=args.possible_values,
n_layers=args.num_pcnn_layers,
n_classes=args.num_classes,
float_condition_size=args.code_length,
last_layer_bias=0.5,
args.size_training_set = len(train_data_function)
hsize = data_loader.train_loader.data.shape[1]
wsize = data_loader.train_loader.data.shape[2]
info = model_dict['info']
largs = info['args'][-1]
try:
print(largs.encoder_output_size)
except:
largs.encoder_output_size = 1000
encoder_model = ConvVAE(largs.code_length,
input_size=largs.number_condition,
encoder_output_size=largs.encoder_output_size)
encoder_model.load_state_dict(model_dict['vae_state_dict'])
prior_model = PriorNetwork(size_training_set=largs.size_training_set,
code_length=largs.code_length,
k=largs.num_k)
prior_model.codes = model_dict['codes']
pcnn_decoder = GatedPixelCNN(input_dim=1,
dim=largs.possible_values,
n_layers=largs.num_pcnn_layers,
n_classes=largs.num_classes,
float_condition_size=largs.code_length,
last_layer_bias=0.5, hsize=hsize, wsize=wsize)
info = {
'train_cnts': [],
'train_losses': [],
'test_cnts': [],
'test_losses': [],
'save_times': [],
'args': [args],
'last_save': 0,
'last_plot': 0,
}
args.size_training_set = len(train_data)
nchans, hsize, wsize = test_loader.dataset[0][0].shape
encoder_model = ConvVAE(
args.code_length,
input_size=1,
encoder_output_size=args.encoder_output_size).to(DEVICE)
prior_model = PriorNetwork(
size_training_set=args.size_training_set,
code_length=args.code_length,
n_mixtures=args.num_mixtures,
k=args.num_k,
require_unique_codes=args.require_unique_codes).to(DEVICE)
pcnn_decoder = GatedPixelCNN(input_dim=1,
dim=args.possible_values,
n_layers=args.num_pcnn_layers,
n_classes=args.num_classes,
float_condition_size=args.code_length,
last_layer_bias=0.5,
hsize=hsize,
def init_train():
""" use args to setup inplace training """
train_data_path = args.train_buffer
valid_data_path = args.valid_buffer
data_dir = os.path.split(train_data_path)[0]
# we are starting from scratch training this model
if args.model_loadpath == "":
run_num = 0
model_base_filedir = os.path.join(data_dir,
args.savename + '%02d' % run_num)
while os.path.exists(model_base_filedir):
run_num += 1
model_base_filedir = os.path.join(data_dir,
args.savename + '%02d' % run_num)
os.makedirs(model_base_filedir)
model_base_filepath = os.path.join(model_base_filedir, args.savename)
print("MODEL BASE FILEPATH", model_base_filepath)
info = {
'model_train_cnts': [],
'model_train_losses': {},
'model_valid_cnts': [],
'model_valid_losses': {},
'model_save_times': [],
'model_last_save': 0,
'model_last_plot': 0,
'NORM_BY': 255.0,
'MODEL_BASE_FILEDIR': model_base_filedir,
'model_base_filepath': model_base_filepath,
'model_train_data_file': train_data_path,
'model_valid_data_file': valid_data_path,
'NUM_TRAINING_EXAMPLES': args.num_training_examples,
'NUM_K': args.num_k,
'NR_LOGISTIC_MIX': args.nr_logistic_mix,
'NUM_PCNN_FILTERS': args.num_pcnn_filters,
'NUM_PCNN_LAYERS': args.num_pcnn_layers,
'ALPHA_REC': args.alpha_rec,
'ALPHA_ACT': args.alpha_act,
'ALPHA_REW': args.alpha_rew,
'MODEL_BATCH_SIZE': args.batch_size,
'NUMBER_CONDITION': args.num_condition,
'CODE_LENGTH': args.code_length,
'NUM_MIXTURES': args.num_mixtures,
'REQUIRE_UNIQUE_CODES': args.require_unique_codes,
}
## size of latents flattened - dependent on architecture
#info['float_condition_size'] = 100*args.num_z
## 3x logistic needed for loss
## TODO - change loss
else:
print('loading model from: %s' % args.model_loadpath)
model_dict = torch.load(args.model_loadpath,
map_location=lambda storage, loc: storage)
info = model_dict['model_info']
model_base_filedir = os.path.split(args.model_loadpath)[0]
model_base_filepath = os.path.join(model_base_filedir, args.savename)
info['loaded_from'] = args.model_loadpath
info['MODEL_BATCH_SIZE'] = args.batch_size
info['DEVICE'] = DEVICE
info['MODEL_SAVE_EVERY'] = args.save_every
info['MODEL_LOG_EVERY_BATCHES'] = args.log_every_batches
info['model_loadpath'] = args.model_loadpath
info['MODEL_SAVENAME'] = args.savename
info['MODEL_LEARNING_RATE'] = args.learning_rate
# create replay buffer
train_buffer = make_subset_buffer(
train_data_path, max_examples=info['NUM_TRAINING_EXAMPLES'])
valid_buffer = make_subset_buffer(valid_data_path,
max_examples=int(
info['NUM_TRAINING_EXAMPLES'] * .1))
#valid_buffer = ReplayMemory(load_file=valid_data_path)
# if train buffer is too large - make random subset
# 27588 places in 1e6 buffer where reward is nonzero
info['num_actions'] = train_buffer.num_actions()
info['size_training_set'] = train_buffer.num_examples()
info['hsize'] = train_buffer.frame_height
info['wsize'] = train_buffer.frame_width
info['num_rewards'] = train_buffer.num_rewards()
info['HISTORY_SIZE'] = 4
rewards_weight = 1 - np.array(train_buffer.percentages_rewards())
actions_weight = 1 - np.array(train_buffer.percentages_actions())
actions_weight = torch.FloatTensor(actions_weight).to(DEVICE)
rewards_weight = torch.FloatTensor(rewards_weight).to(DEVICE)
info['actions_weight'] = actions_weight
info['rewards_weight'] = rewards_weight
# output mixtures should be 2*nr_logistic_mix + nr_logistic mix for each
# decorelated channel
#info['num_output_mixtures']= (2*args.nr_logistic_mix+args.nr_logistic_mix)*info['HISTORY_SIZE']
#nmix = int(info['num_output_mixtures']/info['HISTORY_SIZE'])
#info['nmix'] = nmix
encoder_model = ConvVAE(
info['CODE_LENGTH'],
input_size=args.num_condition,
encoder_output_size=args.encoder_output_size,
).to(DEVICE)
prior_model = PriorNetwork(
size_training_set=info['NUM_TRAINING_EXAMPLES'],
code_length=info['CODE_LENGTH'],
n_mixtures=info['NUM_MIXTURES'],
k=info['NUM_K'],
require_unique_codes=info['REQUIRE_UNIQUE_CODES'],
).to(DEVICE)
pcnn_decoder = GatedPixelCNN(input_dim=1,
dim=info['NUM_PCNN_FILTERS'],
n_layers=info['NUM_PCNN_LAYERS'],
n_classes=info['num_actions'],
float_condition_size=info['CODE_LENGTH'],
last_layer_bias=0.5,
hsize=info['hsize'],
wsize=info['wsize']).to(DEVICE)
#parameters = list(encoder_model.parameters()) + list(prior_model.parameters()) + list(pcnn_decoder.parameters())
parameters = list(encoder_model.parameters()) + list(
prior_model.parameters())
opt = optim.Adam(parameters, lr=info['MODEL_LEARNING_RATE'])
if args.model_loadpath != '':
print("loading weights from:%s" % args.model_loadpath)
encoder_model.load_state_dict(model_dict['encoder_model_state_dict'])
prior_model.load_state_dict(model_dict['prior_model_state_dict'])
pcnn_decoder.load_state_dict(model_dict['pcnn_decoder_state_dict'])
#encoder_model.embedding = model_dict['model_embedding']
opt.load_state_dict(model_dict['opt_state_dict'])
model_dict = {
'encoder_model': encoder_model,
'prior_model': prior_model,
'pcnn_decoder': pcnn_decoder,
'opt': opt
}
data_buffers = {'train': train_buffer, 'valid': valid_buffer}
if args.sample:
sample_acn(info,
model_dict,
data_buffers,
num_samples=args.num_samples,
teacher_force=args.teacher_force)
else:
train_acn(info, model_dict, data_buffers)
# print(largs.possible_values)
#except:
# largs.possible_values = 1
#try:
# print(largs.num_pcnn_layers)
#except:
# largs.num_pcnn_layers = 12
#try:
# print(largs.num_classes)
#except:
# largs.num_classes = 10
size_training_set = len(train_data)
encoder_model = ConvVAE(largs.code_length,
input_size=1,
encoder_output_size=largs.encoder_output_size)
encoder_model.load_state_dict(model_dict['vae_state_dict'])
prior_model = PriorNetwork(size_training_set=largs.size_training_set,
code_length=largs.code_length,
k=largs.num_k).to(DEVICE)
prior_model.codes = model_dict['codes']
pcnn_decoder = GatedPixelCNN(input_dim=1,
dim=largs.possible_values,
n_layers=largs.num_pcnn_layers,
n_classes=largs.num_classes,
float_condition_size=largs.code_length,
last_layer_bias=0.5,
hsize=hsize,