def main():
# get experiment arguments
args, _, config_model = get_args()
args.experiment = "test_models"
config_model["experiment"] = "test_models"
# [STEP 1] create synthetic HAR batch
data_synthetic = torch.randn(
(args.batch_size, args.window, args.input_dim)).cuda()
# [STEP 2] create HAR models
if torch.cuda.is_available():
model = create(args.model, config_model).cuda()
torch.backends.cudnn.benchmark = True
get_info_params(model)
get_info_layers(model)
model.apply(init_weights_orthogonal)
model.eval()
with torch.no_grad():
print(paint("[*] Performing a forward pass with a synthetic batch..."))
z, logits = model(data_synthetic)
print(f"\t input: {data_synthetic.shape} {data_synthetic.dtype}")
print(f"\t z: {z.shape} {z.dtype}")
print(f"\t logits: {logits.shape} {logits.dtype}")
def __init__(self, data_dir):
self.args = get_args()
self.datadir = data_dir
self.imgtypelist = self.args.img_type.split('_')
self.camlist = self.args.cam_list.split('_')
self.camlist_name = {
'0': 'front',
'1': 'right',
'2': 'left',
'3': 'back',
'4': 'bottom'
}
self.imgclient = ImageClient(self.camlist, self.imgtypelist)
self.randquaternionsampler = RandQuaternionSampler(self.args)
self.trajdir = ''
self.imgdirs = []
self.depthdirs = []
self.segdirs = []
self.posefilelist = [] # save incremental pose files
self.posenplist = [] # save pose in numpy files
def main():
# get experiment arguments
args, config_dataset, _ = get_args()
# [STEP 0 and 1] load the .mat files (sample-level) and partition the datasets (segment-level)
preprocess_pipeline(args)
# [STEP 2] create HAR datasets
dataset = SensorDataset(**config_dataset, prefix="train", verbose=True)
def main():
# get experiment arguments
args, config_dataset, config_model = get_args()
# [STEP 0 and 1] load the .mat files (sample-level) and partition the datasets (segment-level)
preprocess_pipeline(args)
if args.train_mode:
# [STEP 2] create HAR datasets
dataset = SensorDataset(**config_dataset, prefix="train")
dataset_val = SensorDataset(**config_dataset, prefix="val")
# [STEP 3] create HAR models
if torch.cuda.is_available():
model = create(args.model, config_model).cuda()
torch.backends.cudnn.benchmark = True
sys.stdout = Logger(
os.path.join(model.path_logs,
f"log_main_{args.experiment}.txt"))
# show args
print("##" * 50)
print(paint(f"Experiment: {model.experiment}", "blue"))
print(
paint(
f"[-] Using {torch.cuda.device_count()} GPU: {torch.cuda.is_available()}"
))
print(args)
get_info_params(model)
get_info_layers(model)
print("##" * 50)
# [STEP 4] train HAR models
model_train(model, dataset, dataset_val, args)
# [STEP 5] evaluate HAR models
dataset_test = SensorDataset(**config_dataset, prefix="test")
if not args.train_mode:
config_model["experiment"] = "inference"
model = create(args.model, config_model).cuda()
model_eval(model, dataset_test, args)
def main():
# get experiment arguments
args, _, _ = get_args()
preprocess_pipeline(args)