def main(device=torch.device('cuda:0')):
# CLI arguments
parser = arg.ArgumentParser(
description='We all know what we are doing. Fighting!')
parser.add_argument("--datasize",
"-d",
default="small",
type=str,
help="data size you want to use, small, medium, total")
# Parsing
args = parser.parse_args()
# Data loaders
datasize = args.datasize
filename = "nyu_new.zip"
pathname = f"data/{filename}"
te_loader = getTestingData(datasize,
pathname,
batch_size=config("unet.batch_size"))
# Model
model = Net()
model = model.to(device)
# define loss function
# criterion = torch.nn.L1Loss()
# Attempts to restore the latest checkpoint if exists
print("Loading unet...")
model, start_epoch, stats = utils.restore_checkpoint(
model, utils.config("unet.checkpoint"))
acc, loss = utils.evaluate_model(model, te_loader, device)
# axes = util.make_training_plot()
print(f'Test Error:{acc}')
print(f'Test Loss:{loss}')
def main(device=torch.device('cuda:0')):
# CLI arguments
parser = arg.ArgumentParser(
description='We all know what we are doing. Fighting!')
parser.add_argument("--datasize",
"-d",
default="small",
type=str,
help="data size you want to use, small, medium, total")
# Parsing
args = parser.parse_args()
# Data loaders
# TODO:
####### Enter the model selection here! #####
modelSelection = input(
'Please input the type of model to be used(res50,dense121,dense169,mob_v2,mob):'
)
datasize = args.datasize
filename = "nyu_new.zip"
pathname = f"data/{filename}"
csv = "data/nyu_csv.zip"
te_loader = getTestingData(datasize,
csv,
pathname,
batch_size=config(modelSelection +
".batch_size"))
# Model
if modelSelection.lower() == 'res50':
model = Res50()
elif modelSelection.lower() == 'dense121':
model = Dense121()
elif modelSelection.lower() == 'mob_v2':
model = Mob_v2()
elif modelSelection.lower() == 'dense169':
model = Dense169()
elif modelSelection.lower() == 'mob':
model = Net()
elif modelSelection.lower() == 'squeeze':
model = Squeeze()
else:
assert False, 'Wrong type of model selection string!'
model = model.to(device)
# define loss function
# criterion = torch.nn.L1Loss()
# Attempts to restore the latest checkpoint if exists
print(f"Loading {mdoelSelection}...")
model, start_epoch, stats = utils.restore_checkpoint(
model, utils.config(modelSelection + ".checkpoint"))
acc, loss = utils.evaluate_model(model, te_loader, device, test=True)
# axes = util.make_training_plot()
print(f'Test Error:{acc}')
print(f'Test Loss:{loss}')