def main(params):
# Obtain configuration path
exp_path = os.path.join("results", params["dataset"], params["model_type"],
params["experiment"])
config_path = os.path.join(exp_path, "config.yml")
params["config_path"] = config_path
# prepare model and dataset
M, dataset, config = cmf.prepare_experiment(params)
# evaluate on GT
config["evaluation"]["use_gt"] = params["evaluate_on_gt"]
# evaluate on Top 1000 proposals
if params["evaluate_on_top1000"]:
config["evaluation"]["use_gt"] = False
config["evaluation"]["apply_nms"] = False
if len(params["proposal"]) > 0:
config["evaluation"]["precomputed_proposal_sequence"] = params[
"proposal"]
# create logger
epoch_logger = cmf.create_logger(config, "EPOCH", "test.log")
""" Build data loader """
loader_config = io_utils.load_yaml(params["loader_config_path"])
if params["test_on_server"]:
loader_config = loader_config["test_loader"]
test_on = "Test_Server"
else:
loader_config = loader_config["val_loader"]
test_on = "Test"
dsets, L = cmf.get_loader(dataset,
split=["test"],
loader_configs=[loader_config],
num_workers=params["num_workers"])
config = M.override_config_from_dataset(config, dsets["test"], mode="Test")
config["model"]["resume"] = True
tensorboard_path = config["misc"]["tensorboard_dir"]
config["misc"]["tensorboard_dir"] = "" #
config["misc"]["debug"] = params["debug_mode"]
""" Evaluating networks """
e0 = params["start_epoch"]
e1 = params["end_epoch"]
es = params["epoch_stride"]
io_utils.check_and_create_dir(tensorboard_path +
"_test_s{}_e{}".format(e0, e1))
summary = PytorchSummary(tensorboard_path + "_test_s{}_e{}".format(e0, e1))
for epoch in range(e0, e1 + 1, es):
""" Build network """
config["model"]["checkpoint_path"] = \
os.path.join(exp_path, "checkpoints", "epoch_{:03d}.pkl".format(epoch))
net, _ = cmf.factory_model(config, M, dsets["test"], None)
net.set_tensorboard_summary(summary)
cmf.test(config, L["test"], net, epoch, None, epoch_logger, on=test_on)
break
tm.reset(); ii = ii + 1
# iteration done
# visualize network learning status
#if (vis_every > 0) and (epoch % vis_every == 0):
# net.visualize(vis_inp, vis_gt, "epoch{:03d}".format(epoch))
# validate current model
if (epoch > eval_after) and (epoch % eval_every == 0):
# print training losses
net.save_results("epoch{:03d}".format(epoch), mode="Train")
net.print_counters_info(eval_logger, epoch, mode="Train")
cmf.test(config, L["test"], net, epoch, eval_logger, mode="Valid")
net.train_mode() # set network as train mode
net.reset_status() # initialize status
if __name__ == "__main__":
# get parameters from cmd
params = _get_argument_params()
global M, dataset
M, dataset, config = cmf.prepare_experiment(params)
# train network
train(config)