parser.add_argument('--config', type=str, default=None)
args = parser.parse_args()
return args
if __name__ == "__main__":
# configuration
configuration = get_configuration(AttentionMapNetConfigurator(),
get_args())
# model
feature_extractor = models.resnet34(pretrained=True)
if configuration.model == "SEAttentionPoseNet":
posenet = SEAttentionPoseNet(resnet=feature_extractor,
config=configuration,
drop_rate=configuration.dropout)
elif configuration.model == "LearnGAPoseNet":
posenet = LearnGAPoseNet(feature_extractor=feature_extractor,
drop_rate=configuration.dropout)
dataloader = get_dataloader(configuration)
evaluator = Evaluator(config=configuration,
model=posenet,
dataloader=dataloader)
evaluator.run()
feature_extractor = models.resnet34(pretrained=False)
if configuration.model == "mapnet" or configuration.model == "posenet":
model = PoseNet(feature_extractor, drop_rate=configuration.dropout)
else:
model = SEAttentionPoseNet(resnet=feature_extractor,
config=configuration,
drop_rate=configuration.dropout)
# data
dataloader = get_dataloader(configuration)
# read mean and stdev for un-normalizing predictions
pose_stats_file = osp.join(configuration.preprocessed_data_path,
'pose_stats.txt')
pose_m, pose_s = np.loadtxt(pose_stats_file) # mean and stdev
configuration.dataset_length = len(dataloader.dataset)
configuration.pose_m = pose_m
configuration.pose_s = pose_s
if not args.var:
evaluator = Evaluator(config=configuration,
model=model,
dataloader=dataloader)
else:
evaluator = VarEvaluator(config=configuration,
model=model,
dataloader=dataloader)
evaluator.run(False)