def __init__(self, in_shape, n_rotations, preprocess, lite=False):
self.n_rotations = n_rotations
self.preprocess = preprocess
max_dim = np.max(in_shape[:2])
self.padding = np.zeros((3, 2), dtype=int)
pad = (max_dim - np.array(in_shape[:2])) / 2
self.padding[:2] = pad.reshape(2, 1)
in_shape = np.array(in_shape)
in_shape += np.sum(self.padding, axis=1)
in_shape = tuple(in_shape)
# Initialize fully convolutional Residual Network with 43 layers and
# 8-stride (3 2x2 max pools and 3 2x bilinear upsampling)
if lite:
d_in, d_out = ResNet36_4s(in_shape, 1)
else:
d_in, d_out = ResNet43_8s(in_shape, 1)
self.model = tf.keras.models.Model(inputs=[d_in], outputs=[d_out])
self.optim = tf.keras.optimizers.Adam(learning_rate=1e-4)
# self.optim = tf.keras.optimizers.SGD(learning_rate=1e-3, momentum=0.9, nesterov=True, name='SGD')
self.metric = tf.keras.metrics.Mean(name='loss_attention')
def __init__(self,
input_shape,
descriptor_dim,
num_rotations,
preprocess,
lite=False):
self.preprocess = preprocess
self.num_rotations = num_rotations
self.descriptor_dim = descriptor_dim
max_dim = np.max(input_shape[:2])
self.padding = np.zeros((3, 2), dtype=int)
pad = (max_dim - np.array(input_shape[:2])) / 2
self.padding[:2] = pad.reshape(2, 1)
input_shape = np.array(input_shape)
input_shape += np.sum(self.padding, axis=1)
input_shape = tuple(input_shape)
# Initialize fully convolutional Residual Network with 43 layers and
# 8-stride (3 2x2 max pools and 3 2x bilinear upsampling)
if lite:
d_in, d_out = ResNet36_4s(input_shape, self.descriptor_dim)
else:
d_in, d_out = ResNet43_8s(input_shape, self.descriptor_dim)
self.model = tf.keras.models.Model(inputs=[d_in], outputs=[d_out])
self.optim = tf.keras.optimizers.Adam(learning_rate=1e-5)
self.metric = tf.keras.metrics.Mean(name='attention_loss')