os.path.join(model_save_path, 'lib'))
if torch.cuda.is_available() and not opt.usegpu:
print("WARNING: You have a CUDA device, so you should probably run with --usegpu")
# Load Seeds
random.seed(ts.SEED)
np.random.seed(ts.SEED)
torch.manual_seed(ts.SEED)
# Define Data Loaders
pin_memory = False
if opt.usegpu:
pin_memory = True
train_dataset = SegDataset(ts.TRAINING_LMDB)
assert train_dataset
train_align_collate = AlignCollate(
'training',
ts.N_CLASSES,
ts.MAX_N_OBJECTS,
ts.MEAN,
ts.STD,
ts.IMAGE_HEIGHT,
ts.IMAGE_WIDTH,
random_hor_flipping=ts.HORIZONTAL_FLIPPING,
random_ver_flipping=ts.VERTICAL_FLIPPING,
random_transposing=ts.TRANSPOSING,
random_90x_rotation=ts.ROTATION_90X,
random_rotation=ts.ROTATION,
model_dir = os.path.dirname(model_path)
sys.path.insert(0, model_dir)
from lib import SegDataset, Model, AlignCollate
from settings import ModelSettings
ms = ModelSettings()
if torch.cuda.is_available() and not opt.usegpu:
print('WARNING: You have a CUDA device, so you should probably run with --cuda')
# Define Data Loaders
pin_memory = False
if opt.usegpu:
pin_memory = True
test_dataset = SegDataset(opt.lmdb)
test_align_collate = AlignCollate('test', ms.LABELS, ms.MEAN, ms.STD, ms.IMAGE_SIZE_HEIGHT, ms.IMAGE_SIZE_WIDTH,
ms.ANNOTATION_SIZE_HEIGHT, ms.ANNOTATION_SIZE_WIDTH, ms.CROP_SCALE, ms.CROP_AR,
random_cropping=ms.RANDOM_CROPPING, horizontal_flipping=ms.HORIZONTAL_FLIPPING,random_jitter=ms.RANDOM_JITTER)
assert test_dataset
test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=opt.batchsize, shuffle=False,
num_workers=opt.nworkers, pin_memory=pin_memory, collate_fn=test_align_collate)
# Define Model
model = Model(ms.LABELS, load_model_path=model_path, usegpu=opt.usegpu)
# Test Model
test_accuracy, test_dice_coeff = model.test(ms.CLASS_WEIGHTS, test_loader)