def main():
args = arg_parser.parse_args()
config_name = args.config.split("/")[-1].split(".")[0]
with open(args.log_config) as log_config_f:
log_filename = "logs/%s.log" % config_name
log_config = yaml.load(log_config_f)
log_config["handlers"]["fileHandler"]["filename"] = log_filename
logging.config.dictConfig(log_config)
with open(args.config) as config_f:
config = util.Struct(**yaml.load(config_f))
task = tasks.load_task(config.task)
model = models.build_model(config.model, config.opt)
for i_iter in range(config.opt.iters):
do_eval = i_iter % 5 == 0
train_loss, train_acc = batched_iter(
task.train, model, config, train=True, compute_eval=do_eval)
if do_eval:
val_loss, val_acc = batched_iter(
task.val, model, config, compute_eval=True)
visualizer.begin(config_name, 100)
test_loss, test_acc = batched_iter(
task.test, model, config, compute_eval=True)
visualizer.end()
logging.info("%5d : %2.4f %2.4f %2.4f : %2.4f %2.4f %2.4f",
i_iter, train_loss, val_loss, test_loss, train_acc, val_acc,
test_acc)
model.save("saves/%s_%d.caffemodel" % (config_name, i_iter))
else:
logging.info("%5d : %2.4f", i_iter, train_loss)
def main():
config = configure()
task = tasks.load_task(config)
model = models.build_model(config.model, config.opt)
for i_epoch in range(config.opt.iters):
train_loss, train_acc, _ = \
do_iter(task.train, model, config, train=True)
val_loss, val_acc, val_predictions = \
do_iter(task.val, model, config, vis=True)
test_loss, test_acc, test_predictions = \
do_iter(task.test, model, config)
logging.info("%5d | %8.3f %8.3f %8.3f | %8.3f %8.3f %8.3f",
i_epoch, train_loss, val_loss, test_loss, train_acc,
val_acc, test_acc)
with open("logs/val_predictions_%d.json" % i_epoch, "w") as pred_f:
print >> pred_f, json.dumps(val_predictions)
def main():
config = configure()
task = tasks.load_task(config)
model = models.build_model(config.model, config.opt)
for i_epoch in range(config.opt.iters):
train_loss, train_acc, _ = do_iter(task.train, model, config, train=True)
val_loss, val_acc, val_predictions = do_iter(task.val, model, config, vis=True)
test_loss, test_acc, test_predictions = do_iter(task.test, model, config)
logging.info(
"%5d | %8.3f %8.3f %8.3f | %8.3f %8.3f %8.3f",
i_epoch,
train_loss,
val_loss,
test_loss,
train_acc,
val_acc,
test_acc,
)
with open("logs/val_predictions_%d.json" % i_epoch, "w") as pred_f:
print >> pred_f, json.dumps(val_predictions)