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)
model_dir = os.path.dirname(args.model)
# Load Seeds
random.seed(s.SEED)
np.random.seed(s.SEED)
torch.manual_seed(s.SEED)
# Load Data
data = Data(data_file=args.data,
input_horizon=s.INPUT_HORIZON,
n_stations=args.n_stations,
train_ratio=s.TRAIN_RATIO,
val_ratio=s.VAL_RATIO,
debug=False)
# Load Model
model = Model(args.n_stations,
s.MOVING_HORIZON,
s.ACTIVATION,
s.CRITERION,
load_model_path=args.model,
usegpu=args.usegpu)
# Train First RNN
_, _, [X_test, y_test] = data.load_data_lstm_1()
print '\n\n' + '#' * 10 + ' TESTING ' + '#' * 10
prediction_test = model.test([X_test, y_test])
draw_graph_all_stations(model_dir, data, args.n_stations, y_test,
prediction_test)
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)
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_loss = model.test(ms.CLASS_WEIGHTS, test_loader)
