# Read config file (loads config file of trained model)
params = Parameters(os.path.join(args.LOG_DIR, "config.yaml"))
params.update_parameters(args.CONFIG_FILE)
# Save parameters
params.write_parameters()
###############################################################################
#%% Load model
print()
# Init model
model = torch.load(
os.path.join(params.getp("MODEL_DIR"),
"best_train_model_checkpoint_fold_00_sample_000.tar"))
# Puts the model on device (GPU)
model = model.cuda(device=params.getp("DEVICE_ID"))
###############################################################################
#%% Read dataset repository
#
filenames = sorted(
glob.glob(os.path.join(params.getp("DATASET_DIR"), "test", "*.ply")))
print()
print(
"###############################################################################"
args = parser.parse_args()
##############################################################################
#%% Read config file
# Read config file
params = Parameters(args.CONFIG_FILE)
# Save parameters
params.write_parameters()
###############################################################################
#%% Build model
# Init model
model = models.models_dict[params.getp("MODEL_NAME")](
nb_channels=params.getp("NB_CHANNELS"),
nb_classes=params.getp("NB_CLASSES"),
nb_scales=params.getp("NB_SCALES"))
# Puts the model on device (GPU)
model = model.cuda(device=params.getp("DEVICE_ID"))
# Initialize weights of the model
model.init(params.getp("INITIALIZATION"))
# Defines Loss Function
criterion = nn.CrossEntropyLoss()
###############################################################################
#%% Read dataset repository
parser.print_help()
args = parser.parse_args()
###############################################################################
#%% Read config file
# Read config file (loads config file of trained model)
training_params = Parameters(os.path.join(args.LOG_DIR, "config.yaml"))
###############################################################################
ABSCISSE_COORDINATE = 0 # epoch
# ABSCISSE_COORDINATE = 1 # cpt_backward_pass
# ABSCISSE_COORDINATE = 5 # time
###############################################################################
nb_classes = training_params.getp("NB_CLASSES")
LOG_DIR = training_params.getp("LOG_DIR")
log_files = glob.glob(os.path.join(LOG_DIR, "*.txt"))
# Training and Validation Loss and Accuracy
print("\n 1st figure : Loss and Accuracy")
fig1, (axes_loss, axes_acc) = plt.subplots(2, 1)
axes_loss.set_title("Loss")
axes_loss.set_ylabel('Loss')
axes_loss.set_yscale('log')
axes_loss.set_xlabel('epoch' if ABSCISSE_COORDINATE == 0 else (
'number of samples' if ABSCISSE_COORDINATE == 1 else 'time (s)'))
