def test_smoke(self, input_shape, depth):
filters_root = 4
classes = 2
inputs = np.ones(shape=input_shape)[np.newaxis, ...]
model = ResUNet(input_shape, classes, filters_root, depth)
output = model(inputs).numpy().squeeze()
self.assertEqual(output.shape[0], input_shape[0])
self.assertEqual(output.shape[1], input_shape[1])
self.assertEqual(output.shape[2], classes)
def test_smoke(self, input_shape, depth, filters_root, error=False):
classes = 2
inputs = np.ones(shape=input_shape)[np.newaxis, ...]
try:
model = ResUNet(input_shape, classes, filters_root, depth)
except Exception:
if not error:
# if it's not supposed to end as error => raise the error
raise
# otherwise end the test
return
output = model(inputs).numpy().squeeze()
self.assertEqual(output.shape[0], input_shape[0])
self.assertEqual(output.shape[1], input_shape[1])
self.assertEqual(output.shape[2], classes)
"Path to the validation dataset. Expects 'images' and 'masks' directory inside with images and masks named the same."
)
parser.add_argument("--logs_root", default="logs", type=str)
parser.add_argument("--epochs", default=100, type=int)
parser.add_argument("--batch_size", default=32, type=int)
parser.add_argument("--plot_model", action="store_true", default=False)
parser.add_argument("--seed", default=42, type=int)
args = parser.parse_args()
np.random.seed(args.seed)
tf.random.set_seed(args.seed)
random.seed(args.seed)
model = ResUNet(input_shape=(128, 128, 1),
classes=2,
filters_root=16,
depth=3)
model.summary()
if args.plot_model:
from tensorflow.python.keras.utils.vis_utils import plot_model
plot_model(model, show_shapes=True)
model.compile(loss="categorical_crossentropy",
optimizer="adam",
metrics=["categorical_accuracy"])
train_dataset = list(
zip(*list(SimpleDataset(args.train_dataset_dir_path)())))
train_dataset = (np.array(train_dataset[0]), np.array(train_dataset[1]))
x = np.array(train_dataset[0])
help="Path to the validation dataset. Expects 'images' and 'masks' directory inside with images and masks named the same.")
parser.add_argument("--logs_root", default="logs", type=str)
parser.add_argument("--epochs", default=10, type=int)
parser.add_argument("--batch_size", default=32, type=int)
parser.add_argument("--plot_model", action="store_true", default=False)
parser.add_argument("--seed", default=42, type=int)
args = parser.parse_args()
args.plot_model = True
np.random.seed(args.seed)
tf.random.set_seed(args.seed)
random.seed(args.seed)
model = ResUNet(input_shape=(256, 256, 2), classes=2, filters_root=16,
depth=4,final_activation='sigmoid')
model.summary()
if args.plot_model:
from tensorflow.python.keras.utils.vis_utils import plot_model
plot_model(model, show_shapes=True)
dataset = np.load('dataset/scaled_transformer_256.npz')
trainer = Trainer(name='ResUNet-transformer-original',
checkpoint_callback=True)
x_train = dataset['x_train']
y_train = dataset['y_train']
x_valid = dataset['x_valid']
y_valid = dataset['y_valid']