def _call(self, inp, inp_features, is_training, is_posterior=True, prop_state=None):
print("\n" + "-" * 10 + " ConvGridObjectLayer(is_posterior={}) ".format(is_posterior) + "-" * 10)
# --- set up sub networks and attributes ---
self.maybe_build_subnet("box_network", builder=cfg.build_conv_lateral, key="box")
self.maybe_build_subnet("attr_network", builder=cfg.build_conv_lateral, key="attr")
self.maybe_build_subnet("z_network", builder=cfg.build_conv_lateral, key="z")
self.maybe_build_subnet("obj_network", builder=cfg.build_conv_lateral, key="obj")
self.maybe_build_subnet("object_encoder")
_, H, W, _, n_channels = tf_shape(inp_features)
if self.B != 1:
raise Exception("NotImplemented")
if not self.initialized:
# Note this limits the re-usability of this module to images
# with a fixed shape (the shape of the first image it is used on)
self.batch_size, self.image_height, self.image_width, self.image_depth = tf_shape(inp)
self.H = H
self.W = W
self.HWB = H*W
self.batch_size = tf.shape(inp)[0]
self.is_training = is_training
self.float_is_training = tf.to_float(is_training)
inp_features = tf.reshape(inp_features, (self.batch_size, H, W, n_channels))
is_posterior_tf = tf.ones_like(inp_features[..., :2])
if is_posterior:
is_posterior_tf = is_posterior_tf * [1, 0]
else:
is_posterior_tf = is_posterior_tf * [0, 1]
objects = AttrDict()
base_features = tf.concat([inp_features, is_posterior_tf], axis=-1)
# --- box ---
layer_inp = base_features
n_features = self.n_passthrough_features
output_size = 8
network_output = self.box_network(layer_inp, output_size + n_features, self.is_training)
rep_input, features = tf.split(network_output, (output_size, n_features), axis=-1)
_objects = self._build_box(rep_input, self.is_training)
objects.update(_objects)
# --- attr ---
if is_posterior:
# --- Get object attributes using object encoder ---
yt, xt, ys, xs = tf.split(objects['normalized_box'], 4, axis=-1)
yt, xt, ys, xs = coords_to_image_space(
yt, xt, ys, xs, (self.image_height, self.image_width), self.anchor_box, top_left=False)
transform_constraints = snt.AffineWarpConstraints.no_shear_2d()
warper = snt.AffineGridWarper(
(self.image_height, self.image_width), self.object_shape, transform_constraints)
_boxes = tf.concat([xs, 2*xt - 1, ys, 2*yt - 1], axis=-1)
_boxes = tf.reshape(_boxes, (self.batch_size*H*W, 4))
grid_coords = warper(_boxes)
grid_coords = tf.reshape(grid_coords, (self.batch_size, H, W, *self.object_shape, 2,))
if self.edge_resampler:
glimpse = resampler_edge.resampler_edge(inp, grid_coords)
else:
glimpse = tf.contrib.resampler.resampler(inp, grid_coords)
else:
glimpse = tf.zeros((self.batch_size, H, W, *self.object_shape, self.image_depth))
# Create the object encoder network regardless of is_posterior, otherwise messes with ScopedFunction
encoded_glimpse = apply_object_wise(
self.object_encoder, glimpse, n_trailing_dims=3, output_size=self.A, is_training=self.is_training)
if not is_posterior:
encoded_glimpse = tf.zeros_like(encoded_glimpse)
layer_inp = tf.concat([base_features, features, encoded_glimpse, objects['local_box']], axis=-1)
network_output = self.attr_network(layer_inp, 2 * self.A + n_features, self.is_training)
attr_mean, attr_log_std, features = tf.split(network_output, (self.A, self.A, n_features), axis=-1)
attr_std = self.std_nonlinearity(attr_log_std)
attr = Normal(loc=attr_mean, scale=attr_std).sample()
objects.update(attr_mean=attr_mean, attr_std=attr_std, attr=attr, glimpse=glimpse)
# --- z ---
layer_inp = tf.concat([base_features, features, objects['local_box'], objects['attr']], axis=-1)
n_features = self.n_passthrough_features
network_output = self.z_network(layer_inp, 2 + n_features, self.is_training)
z_mean, z_log_std, features = tf.split(network_output, (1, 1, n_features), axis=-1)
z_std = self.std_nonlinearity(z_log_std)
z_mean = self.training_wheels * tf.stop_gradient(z_mean) + (1-self.training_wheels) * z_mean
z_std = self.training_wheels * tf.stop_gradient(z_std) + (1-self.training_wheels) * z_std
z_logit = Normal(loc=z_mean, scale=z_std).sample()
z = self.z_nonlinearity(z_logit)
objects.update(z_logit_mean=z_mean, z_logit_std=z_std, z_logit=z_logit, z=z)
# --- obj ---
layer_inp = tf.concat([base_features, features, objects['local_box'], objects['attr'], objects['z']], axis=-1)
rep_input = self.obj_network(layer_inp, 1, self.is_training)
_objects = self._build_obj(rep_input, self.is_training)
objects.update(_objects)
# --- final ---
_objects = AttrDict()
for k, v in objects.items():
_, _, _, *trailing_dims = tf_shape(v)
_objects[k] = tf.reshape(v, (self.batch_size, self.HWB, *trailing_dims))
objects = _objects
if prop_state is not None:
objects.prop_state = tf.tile(prop_state[0:1, None], (self.batch_size, self.HWB, 1))
objects.prior_prop_state = tf.tile(prop_state[0:1, None], (self.batch_size, self.HWB, 1))
# --- misc ---
objects.n_objects = tf.fill((self.batch_size,), self.HWB)
objects.pred_n_objects = tf.reduce_sum(objects.obj, axis=(1, 2))
objects.pred_n_objects_hard = tf.reduce_sum(tf.round(objects.obj), axis=(1, 2))
return objects
def _call(self, inp, inp_features, is_training, is_posterior=True, prop_state=None):
print("\n" + "-" * 10 + " GridObjectLayer(is_posterior={}) ".format(is_posterior) + "-" * 10)
# --- set up sub networks and attributes ---
self.maybe_build_subnet("box_network", builder=cfg.build_lateral, key="box")
self.maybe_build_subnet("attr_network", builder=cfg.build_lateral, key="attr")
self.maybe_build_subnet("z_network", builder=cfg.build_lateral, key="z")
self.maybe_build_subnet("obj_network", builder=cfg.build_lateral, key="obj")
self.maybe_build_subnet("object_encoder")
_, H, W, _, _ = tf_shape(inp_features)
H = int(H)
W = int(W)
if not self.initialized:
# Note this limits the re-usability of this module to images
# with a fixed shape (the shape of the first image it is used on)
self.batch_size, self.image_height, self.image_width, self.image_depth = tf_shape(inp)
self.H = H
self.W = W
self.HWB = H*W*self.B
self.is_training = is_training
self.float_is_training = tf.to_float(is_training)
# --- set up the edge element ---
sizes = [4, self.A, 1, 1]
sigmoids = [True, False, False, True]
total_sample_size = sum(sizes)
if self.edge_weights is None:
self.edge_weights = tf.get_variable("edge_weights", shape=total_sample_size, dtype=tf.float32)
if "edge" in self.fixed_weights:
tf.add_to_collection(FIXED_COLLECTION, self.edge_weights)
_edge_weights = tf.split(self.edge_weights, sizes, axis=0)
_edge_weights = [
(tf.nn.sigmoid(ew) if sigmoid else ew)
for ew, sigmoid in zip(_edge_weights, sigmoids)]
edge_element = tf.concat(_edge_weights, axis=0)
edge_element = tf.tile(edge_element[None, :], (self.batch_size, 1))
# --- containers for storing built program ---
program = np.empty((H, W, self.B), dtype=np.object)
# --- build the program ---
is_posterior_tf = tf.ones((self.batch_size, 2))
if is_posterior:
is_posterior_tf = is_posterior_tf * [1, 0]
else:
is_posterior_tf = is_posterior_tf * [0, 1]
results = []
for h, w, b in itertools.product(range(H), range(W), range(self.B)):
built = dict()
partial_program, features = None, None
context = self._get_sequential_context(program, h, w, b, edge_element)
base_features = tf.concat([inp_features[:, h, w, b, :], context, is_posterior_tf], axis=1)
# --- box ---
layer_inp = base_features
n_features = self.n_passthrough_features
output_size = 8
network_output = self.box_network(layer_inp, output_size + n_features, self. is_training)
rep_input, features = tf.split(network_output, (output_size, n_features), axis=1)
_built = self._build_box(rep_input, self.is_training, hw=(h, w))
built.update(_built)
partial_program = built['local_box']
# --- attr ---
if is_posterior:
# --- Get object attributes using object encoder ---
yt, xt, ys, xs = tf.split(built['normalized_box'], 4, axis=-1)
yt, xt, ys, xs = coords_to_image_space(
yt, xt, ys, xs, (self.image_height, self.image_width), self.anchor_box, top_left=False)
transform_constraints = snt.AffineWarpConstraints.no_shear_2d()
warper = snt.AffineGridWarper(
(self.image_height, self.image_width), self.object_shape, transform_constraints)
_boxes = tf.concat([xs, 2*xt - 1, ys, 2*yt - 1], axis=-1)
grid_coords = warper(_boxes)
grid_coords = tf.reshape(grid_coords, (self.batch_size, 1, *self.object_shape, 2,))
if self.edge_resampler:
glimpse = resampler_edge.resampler_edge(inp, grid_coords)
else:
glimpse = tf.contrib.resampler.resampler(inp, grid_coords)
glimpse = tf.reshape(glimpse, (self.batch_size, *self.object_shape, self.image_depth))
else:
glimpse = tf.zeros((self.batch_size, *self.object_shape, self.image_depth))
# Create the object encoder network regardless of is_posterior, otherwise messes with ScopedFunction
encoded_glimpse = self.object_encoder(glimpse, (1, 1, self.A), self.is_training)
encoded_glimpse = tf.reshape(encoded_glimpse, (self.batch_size, self.A))
if not is_posterior:
encoded_glimpse = tf.zeros_like(encoded_glimpse)
layer_inp = tf.concat(
[base_features, features, encoded_glimpse, partial_program], axis=1)
network_output = self.attr_network(layer_inp, 2 * self.A + n_features, self. is_training)
attr_mean, attr_log_std, features = tf.split(network_output, (self.A, self.A, n_features), axis=1)
attr_std = self.std_nonlinearity(attr_log_std)
attr = Normal(loc=attr_mean, scale=attr_std).sample()
built.update(attr_mean=attr_mean, attr_std=attr_std, attr=attr, glimpse=glimpse)
partial_program = tf.concat([partial_program, built['attr']], axis=1)
# --- z ---
layer_inp = tf.concat([base_features, features, partial_program], axis=1)
n_features = self.n_passthrough_features
network_output = self.z_network(layer_inp, 2 + n_features, self.is_training)
z_mean, z_log_std, features = tf.split(network_output, (1, 1, n_features), axis=1)
z_std = self.std_nonlinearity(z_log_std)
z_mean = self.training_wheels * tf.stop_gradient(z_mean) + (1-self.training_wheels) * z_mean
z_std = self.training_wheels * tf.stop_gradient(z_std) + (1-self.training_wheels) * z_std
z_logit = Normal(loc=z_mean, scale=z_std).sample()
z = self.z_nonlinearity(z_logit)
built.update(z_logit_mean=z_mean, z_logit_std=z_std, z_logit=z_logit, z=z)
partial_program = tf.concat([partial_program, built['z']], axis=1)
# --- obj ---
layer_inp = tf.concat([base_features, features, partial_program], axis=1)
rep_input = self.obj_network(layer_inp, 1, self.is_training)
_built = self._build_obj(rep_input, self.is_training)
built.update(_built)
partial_program = tf.concat([partial_program, built['obj']], axis=1)
# --- final ---
results.append(built)
program[h, w, b] = partial_program
assert program[h, w, b].shape[1] == total_sample_size
objects = AttrDict()
for k in results[0]:
objects[k] = tf.stack([r[k] for r in results], axis=1)
if prop_state is not None:
objects.prop_state = tf.tile(prop_state[0:1, None], (self.batch_size, self.HWB, 1))
objects.prior_prop_state = tf.tile(prop_state[0:1, None], (self.batch_size, self.HWB, 1))
# --- misc ---
objects.n_objects = tf.fill((self.batch_size,), self.HWB)
objects.pred_n_objects = tf.reduce_sum(objects.obj, axis=(1, 2))
objects.pred_n_objects_hard = tf.reduce_sum(tf.round(objects.obj), axis=(1, 2))
return objects
