def __init__(self, prev_layer, name='flatten'):
super(FlattenLayer, self).__init__(prev_layer=prev_layer, name=name)
_out = flatten_reshape(self.inputs, name=name)
self.n_units = int(_out.get_shape()[-1])
logging.info("FlattenLayer %s: %d" % (self.name, self.n_units))
self.outputs = _out
self._add_layers(self.outputs)
def __init__(
self,
prev_layer,
theta_layer,
out_size=None,
name='spatial_trans_2d_affine',
):
super(SpatialTransformer2dAffineLayer,
self).__init__(prev_layer=[prev_layer, theta_layer], name=name)
self.inputs = prev_layer.outputs # Do not remove
self.theta_layer = theta_layer
if out_size is None:
out_size = [40, 40]
logging.info(
"SpatialTransformer2dAffineLayer %s: in_size: %s out_size: %s" %
(self.name, self.inputs.get_shape().as_list(), out_size))
with tf.variable_scope(name) as vs:
# 1. make the localisation network to [batch, 6] via Flatten and Dense.
if self.theta_layer.outputs.get_shape().ndims > 2:
self.theta_layer.outputs = flatten_reshape(
self.theta_layer.outputs, 'flatten')
# 2. To initialize the network to the identity transform init.
# 2.1 W
n_in = int(self.theta_layer.outputs.get_shape()[-1])
shape = (n_in, 6)
W = tf.get_variable(name='W',
initializer=tf.zeros(shape),
dtype=LayersConfig.tf_dtype)
# 2.2 b
identity = tf.constant(
np.array([[1., 0, 0], [0, 1., 0]]).astype('float32').flatten())
b = tf.get_variable(name='b',
initializer=identity,
dtype=LayersConfig.tf_dtype)
# 2.3 transformation matrix
self.theta = tf.nn.tanh(tf.matmul(self.theta_layer.outputs, W) + b)
# 3. Spatial Transformer Sampling
# 3.1 transformation
self.outputs = transformer(self.inputs,
self.theta,
out_size=out_size)
# 3.2 automatically set batch_size and channels
# e.g. [?, 40, 40, ?] --> [64, 40, 40, 1] or [64, 20, 20, 4]/ Hao Dong
#
fixed_batch_size = self.inputs.get_shape().with_rank_at_least(1)[0]
if fixed_batch_size.value:
batch_size = fixed_batch_size.value
else:
batch_size = array_ops.shape(self.inputs)[0]
n_channels = self.inputs.get_shape().as_list()[-1]
# logging.info(self.outputs)
self.outputs = tf.reshape(
self.outputs,
shape=[batch_size, out_size[0], out_size[1], n_channels])
# logging.info(self.outputs)
# exit()
# 4. Get all parameters
variables = tf.get_collection(TF_GRAPHKEYS_VARIABLES,
scope=vs.name)
# # theta_layer
# self._add_layers(theta_layer.all_layers)
# self._add_params(theta_layer.all_params)
# self.all_drop.update(theta_layer.all_drop)
self._add_layers(self.outputs)
self._add_params(variables)
def forward(self, inputs):
outputs = flatten_reshape(inputs, name=self.name)
return outputs
def forward(self, inputs):
outputs = flatten_reshape(inputs, name=self.name)
return outputs