def main(args):
if not args.embed and not args.checkpoint_path:
raise ValueError('checkpoint_path needed')
# build model
global model
model = load_model(args)
B = model.batch_size
log.infov('Batch Size : %d', B)
# load datasets (validation only)
log.info("Data loading start !")
data_sets = crc_input_data_seq.read_crc_data_sets(
98,
98,
49,
49,
np.float32,
use_cache=False,
parallel_jobs=10,
dataset=args.dataset,
split_modes=['valid'], # XXX valid
fixation_original_scale=True, # WTF
max_folders=500 # XXX FOR FAST DEBUGGING AND TESTING
)
log.info("Data loading done")
if args.embed:
def go(checkpoint_path):
reload_checkpoint(model, checkpoint_path)
out_dir = get_out_dir(args.dataset, checkpoint_path)
print 'go at %s' % checkpoint_path
run_evaluation(model, args, data_sets, out_dir)
import gc
gc.collect()
log.infov('Usage: >>> go(checkpoint_path)')
from IPython import embed
embed()
else:
reload_checkpoint(model, args.checkpoint_path)
out_dir = get_out_dir(args.dataset, args.checkpoint_path)
run_evaluation(model, args, data_sets, out_dir)
model = Gaze_Prediction_Module(batch_size, n_lstm_steps, dim_feature,
dim_hidden_u, dim_hidden_b, dim_sal,
dim_sal_proj, dim_cnn, dim_cnn_proj, out_proj)
#tf_loss, tf_saliency, tf_cnn, tf_cnnmask, tf_gazemap, tf_probs = model.build_model()
tf_loss, tf_cnn, tf_gazemap, tf_probs = model.build_model()
cnn_tf, gaze_output, pupil_output = model.build_generator()
print 'Prediction RNN Model builded '
print 'Dataset Loading'
crc_data_sets = crc_input_data_seq.read_crc_data_sets(FLAGS.image_height,
FLAGS.image_width,
FLAGS.gazemap_height,
FLAGS.gazemap_width,
tf.float32,
reload_=True,
dataset='crc')
#crc_data_sets = crc_input_data_seq.read_crc_data_sets(FLAGS.image_height,
# FLAGS.image_width,
# FLAGS.gazemap_height,
# FLAGS.gazemap_width,
# tf.float32,
# reload_ = False,
# dataset='hollywood2')
print 'Dataset setting finished'
sess = tf.InteractiveSession()
saver = tf.train.Saver(max_to_keep=100)
train_op = tf.train.AdamOptimizer(learning_rate).minimize(tf_loss)
def self_test(args):
global model, data_sets
session = tf.Session(config=tf.ConfigProto(
gpu_options=tf.GPUOptions(per_process_gpu_memory_fraction=0.5),
device_count={'GPU': True}, # self-testing: NO GPU, USE CPU
))
log.warn('Loading %s input data ...', args.dataset)
if args.dataset == 'salicon':
#salicon_input_data.
# data_sets = salicon_input_data.read_salicon_data_sets(
# 98, 98, 49, 49, np.float32,
# use_example=False, # only tens
# use_val_split=True,
# ) # self test small only
salicon_data = salicon_input_data.SaliconData(
98,
98,
49,
49,
np.float32,
use_example=False, # only tens
use_val_split=True,
)
data_sets = salicon_data.build()
elif args.dataset == 'crc':
data_sets = crc_input_data.read_crc_data_sets(98,
98,
49,
49,
np.float32,
use_cache=True)
else:
raise ValueError('Unknown dataset : %s' % args.dataset)
print 'Train', data_sets.train
print 'Validation', data_sets.valid
log.warn('Building Model ...')
# default configuration as of now
config = BaseModelConfig()
config.train_dir = args.train_dir
if args.train_tag:
config.train_tag = args.train_tag
config.batch_size = 128
config.use_flip_batch = True
#config.initial_learning_rate = 0.03
config.initial_learning_rate = 0.00003
config.optimization_method = 'adam'
config.steps_per_evaluation = 7000 # for debugging
if args.learning_rate is not None:
config.initial_learning_rate = float(args.learning_rate)
if args.learning_rate_decay is not None:
config.learning_rate_decay = float(args.learning_rate_decay)
if args.batch_size is not None:
config.batch_size = int(args.batch_size)
if args.max_steps:
config.max_steps = int(args.max_steps)
if args.dataset == 'crc':
config.batch_size = 2 # because of T~=35
config.steps_per_evaluation = 200
config.dump(sys.stdout)
log.warn('Start Fitting Model ...')
model = SaliencyModel(session, data_sets, config)
print model
model.fit()
log.warn('Fitting Done. Evaluating!')
model.generate_and_evaluate(data_sets.test)
def self_test(args):
global model, config
assert 0.0 < args.gpu_fraction <= 1.0
session = tf.Session(config=tf.ConfigProto(
gpu_options=tf.GPUOptions(
per_process_gpu_memory_fraction=args.gpu_fraction),
device_count={'GPU': True}, # self-testing: NO GPU, USE CPU
))
log.warn('Building Model ...')
# default configuration as of now
config = GRUModelConfig()
# CRC likes 28 :)
config.batch_size = 28
config.train_dir = args.train_dir
if args.train_tag:
config.train_tag = args.train_tag
config.initial_learning_rate = 0.0001
config.max_grad_norm = 1.0
if args.learning_rate is not None:
config.initial_learning_rate = float(args.learning_rate)
if args.learning_rate_decay is not None:
config.learning_rate_decay = float(args.learning_rate_decay)
if args.batch_size is not None:
config.batch_size = int(args.batch_size)
config.steps_per_evaluation = 500
config.steps_per_validation = 50 #?!?!
config.steps_per_checkpoint = 500
if args.max_steps:
config.max_steps = int(args.max_steps)
if args.dataset == 'crc':
config.steps_per_evaluation = 100
config.dump(sys.stdout)
log.warn('Dataset (%s) Loading ...', args.dataset)
assert args.dataset in ('crc', 'hollywood2')
data_sets = crc_input_data_seq.read_crc_data_sets(CONSTANTS.image_height,
CONSTANTS.image_width,
CONSTANTS.gazemap_height,
CONSTANTS.gazemap_width,
np.float32,
use_cache=True,
dataset=args.dataset)
log.warn('Dataset Loading Finished ! (%d instances)', len(data_sets))
log.warn('Start Fitting Model ...')
model = GazePredictionGRCN(session, data_sets, config)
print model
if args.shallownet_pretrain is not None:
log.warn('Loading ShallowNet weights from checkpoint %s ...',
args.shallownet_pretrain)
model.initialize_pretrained_shallownet(args.shallownet_pretrain)
model.fit()
log.warn('Fitting Done. Evaluating!')
model.evaluate(data_sets.test)
def train(args):
global model, config
assert 0.0 < args.gpu_fraction <= 1.0
session = tf.Session(config=tf.ConfigProto(
gpu_options=tf.GPUOptions(
per_process_gpu_memory_fraction=args.gpu_fraction,
allow_growth=True),
device_count={'GPU': True}, # self-testing: NO GPU, USE CPU
))
log.warn('Building Model ...')
log.infov('MODEL : %s', args.model)
log.infov('DATASET : %s', args.dataset)
if args.model == 'gaze_grcn':
from gaze_grcn import GazePredictionGRCN as TheModel
from gaze_grcn import CONSTANTS, GRUModelConfig
elif args.model == 'gaze_lstm':
from gaze_lstm import GazePredictionLSTM as TheModel
from gaze_lstm import CONSTANTS, GRUModelConfig
elif args.model == 'gaze_grcn77':
from gaze_grcn77 import GazePredictionGRCN as TheModel
from gaze_grcn77 import CONSTANTS, GRUModelConfig
elif args.model == 'gaze_rnn77':
from gaze_rnn77 import GazePredictionGRU as TheModel
from gaze_rcn77 import CONSTANTS, GRUModelConfig
elif args.model == 'gaze_rnn':
from gaze_rnn import GazePredictionGRU as TheModel
from gaze_rnn import CONSTANTS, GRUModelConfig
elif args.model == 'gaze_c3d_conv':
from gaze_c3d_conv import GazePredictionConv as TheModel
from gaze_c3d_conv import CONSTANTS, GRUModelConfig
elif args.model == 'gaze_shallownet_rnn':
from gaze_shallownet_rnn import GazePredictionGRU as TheModel
from gaze_shallownet_rnn import CONSTANTS, GRUModelConfig
elif args.model == 'gaze_framewise_shallownet':
from gaze_framewise_shallownet import FramewiseShallowNet as TheModel
from gaze_framewise_shallownet import CONSTANTS, GRUModelConfig
elif args.model == 'gaze_deeprnn':
from gaze_rnn_deep import DEEPRNN as TheModel
from gaze_rnn_deep import CONSTANTS, GRUModelConfig
else:
raise NotImplementedError(args.model)
# default configuration as of now
config = GRUModelConfig()
# CRC likes 28 :)
config.batch_size = 28
config.train_dir = args.train_dir
if args.train_tag:
config.train_tag = args.train_tag
config.initial_learning_rate = 0.0001
config.max_grad_norm = 10.0
config.use_flip_batch = True
if args.max_grad_norm is not None:
config.max_grad_norm = args.max_grad_norm
if args.learning_rate is not None:
config.initial_learning_rate = float(args.learning_rate)
if args.learning_rate_decay is not None:
config.learning_rate_decay = float(args.learning_rate_decay)
if args.batch_size is not None:
config.batch_size = int(args.batch_size)
if args.loss_type is not None:
config.loss_type = args.loss_type
config.steps_per_evaluation = 100
config.steps_per_validation = 20
config.steps_per_checkpoint = 100
if args.max_steps:
config.max_steps = int(args.max_steps)
config.dump(sys.stdout)
log.warn('Dataset (%s) Loading ...', args.dataset)
assert args.dataset in ('crc', 'hollywood2', 'crcxh2')
data_sets = crc_input_data_seq.read_crc_data_sets(
CONSTANTS.image_height,
CONSTANTS.image_width,
CONSTANTS.gazemap_height,
CONSTANTS.gazemap_width,
np.float32,
use_cache=True,
batch_norm=args.batch_norm,
dataset=args.dataset)
log.warn('Dataset Loading Finished ! (%d instances)', len(data_sets))
log.warn('Start Fitting Model ...')
model = TheModel(session, data_sets, config)
print model
if args.shallownet_pretrain is not None:
log.warn('Loading ShallowNet weights from checkpoint %s ...',
args.shallownet_pretrain)
model.initialize_pretrained_shallownet(args.shallownet_pretrain)
model.fit()
log.warn('Fitting Done. Evaluating!')
model.generate_and_evaluate(
data_sets.test,
max_instances=None) #WHERE IS THIS FUNCTION I CANNOT FIND IT
reload_ = False
n_epochs = 1000
model = Gaze_Prediction_Module(batch_size, n_lstm_steps, dim_feature,
dim_hidden_u, dim_hidden_b, dim_sal,
dim_sal_proj, dim_cnn, dim_cnn_proj, out_proj)
#tf_loss, tf_saliency, tf_cnn, tf_cnnmask, tf_gazemap, tf_probs = model.build_model()
tf_loss, tf_cnn, tf_gazemap, tf_probs = model.build_model()
print 'Prediction RNN Model builded '
print 'Dataset Loading'
data_sets = crc_input_data_seq.read_crc_data_sets(FLAGS.image_height,
FLAGS.image_width,
FLAGS.gazemap_height,
FLAGS.gazemap_width,
tf.float32, True)
print 'Dataset setting finished'
sess = tf.InteractiveSession()
saver = tf.train.Saver(max_to_keep=100)
train_op = tf.train.AdamOptimizer(learning_rate).minimize(tf_loss)
#init = tf.initialize_all_variables()
tf.initialize_all_variables().run()
offset = 0
if reload_:
print 'Realod ' + from_dir + model_name
saver.restore(sess, from_dir + model_name)
#labels_dict_test = OrderedDict()
## PROBS MAKE TEST LONGER THAN 1 ....
#test_folders = labels_dict_val.keys[-1]
#labels_dict_test[labels_dict_val.keys[-1]] = labels_dict_val[labels_dict_val.keys[-1]]
#import pdb; pdb.set_trace()
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument('--attention', action='store_true', default=False)
args = parser.parse_args()
dataset = crc_input_data_seq.read_crc_data_sets(
98,
98,
49,
49,
np.float32,
use_cache=False,
parallel_jobs=10,
dataset='hollywood2',
with_attention=args.attention,
fixation_original_scale=False, # WTF
# XXX FOR FAST DEBUGGING AND TESTING
)
model = load_model()
### loading train data
gazemap_pred, gazemap_gt, frame, labels, c3d, clipnames = load_data(
model, dataset.train, labels_dict_train)
if args.attention:
attention = '_attention'
else:
def visualize_outputs_wrapper(checkpoint_path,
session=None,
split_mode='valid',
dataset='hollywood2',
model_class=GazePredictionGRU,
max_instances=100):
if session is None:
session = tf.InteractiveSession()
# WTF model persistence
if checkpoint_path is not None:
# load config and data loader
#config_path = os.path.join(os.path.dirname(checkpoint_path), 'config.pkl')
#with open(config_path, 'rb') as fp:
# config = pkl.load(fp)
# log.info('Loaded config from %s', config_path)
# config.dump(sys.stdout)
config_path = os.path.join(os.path.dirname(checkpoint_path),
'../config.json')
config = BaseModelConfig.load(config_path)
log.info('Loaded config from %s', config_path)
else:
# default config!?
config = GRUModelConfig()
# do not noise original train dirs.
config.train_dir = None
config.dump(sys.stdout)
log.warn('Dataset (%s) Loading ...', dataset)
assert dataset in ('crc', 'hollywood2')
from models.gaze_rnn import CONSTANTS # XXX Dirty here
data_sets = crc_input_data_seq.read_crc_data_sets(CONSTANTS.image_height,
CONSTANTS.image_width,
CONSTANTS.gazemap_height,
CONSTANTS.gazemap_width,
np.float32,
use_cache=True,
dataset=dataset,
split_modes=[split_mode])
# resurrect model
# XXX remove hard-codes
# TODO assuming there can be only one graph in the process?
# TODO should any of our model should contain a graph context ????
#tf.reset_default_graph()
model = model_class(session, data_sets, config)
# load checkpoint
if checkpoint_path is not None:
assert os.path.isfile(checkpoint_path)
model.load_model_from_checkpoint_file(checkpoint_path)
# Run!
if split_mode == 'valid':
model_outputs = visualize_outputs_of(model, data_sets.valid,
max_instances)
elif split_mode == 'train':
model_outputs = visualize_outputs_of(model, data_sets.train,
max_instances)
else:
raise ValueError(split_mode)
return model, model_outputs
