def test_rnn_decoder_multiple_unroll(self):
batch_size = 2
num_unroll = 3
num_units = 64
width = 8
height = 10
input_channels = 128
initial_state = tf.random_normal(
(batch_size, width, height, num_units))
inputs = tf.random_normal([batch_size, width, height, input_channels])
rnn_cell = MockRnnCell(input_channels, num_units)
outputs, states = rnn_decoder.rnn_decoder(
decoder_inputs=[inputs] * num_unroll,
initial_state=(initial_state, initial_state),
cell=rnn_cell)
self.assertEqual(len(outputs), num_unroll)
self.assertEqual(len(states), num_unroll)
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
results = sess.run((outputs, states, inputs, initial_state))
outputs_results = results[0]
states_results = results[1]
inputs_results = results[2]
initial_states_results = results[3]
for i in range(num_unroll):
previous_state = ([
initial_states_results, initial_states_results
] if i == 0 else states_results[i - 1])
self.assertEqual(
outputs_results[i].shape,
(batch_size, width, height, input_channels + num_units))
self.assertAllEqual(
outputs_results[i],
np.concatenate((inputs_results, previous_state[0]),
axis=3))
self.assertEqual(states_results[i][0].shape,
(batch_size, width, height, num_units))
self.assertEqual(states_results[i][1].shape,
(batch_size, width, height, num_units))
self.assertAllEqual(states_results[i][0],
np.multiply(previous_state[0], 2.0))
self.assertAllEqual(states_results[i][1], previous_state[1])
def extract_features(self,
preprocessed_inputs,
state_saver=None,
state_name='lstm_state',
unroll_length=5,
scope=None):
"""Extracts features from preprocessed inputs.
The features include the base network features, lstm features and SSD
features, organized in the following name scope:
<parent scope>/MobilenetV1/...
<parent scope>/LSTM/...
<parent scope>/FeatureMaps/...
Args:
preprocessed_inputs: A [batch, height, width, channels] float tensor
representing a batch of consecutive frames from video clips.
state_saver: A state saver object with methods `state` and `save_state`.
state_name: A python string for the name to use with the state_saver.
unroll_length: The number of steps to unroll the lstm.
scope: The scope for the base network of the feature extractor.
Returns:
A list of tensors where the ith tensor has shape [batch, height_i,
width_i, depth_i]
"""
preprocessed_inputs = shape_utils.check_min_image_dim(
33, preprocessed_inputs)
with slim.arg_scope(
mobilenet_v1.mobilenet_v1_arg_scope(
is_training=self._is_training)):
with (slim.arg_scope(self._conv_hyperparams_fn())
if self._override_base_feature_extractor_hyperparams else
context_manager.IdentityContextManager()):
with slim.arg_scope([slim.batch_norm], fused=False):
# Base network.
with tf.variable_scope(scope,
self._base_network_scope,
reuse=self._reuse_weights) as scope:
net, image_features = mobilenet_v1.mobilenet_v1_base(
ops.pad_to_multiple(preprocessed_inputs,
self._pad_to_multiple),
final_endpoint='Conv2d_13_pointwise',
min_depth=self._min_depth,
depth_multiplier=self._depth_multiplier,
scope=scope)
with slim.arg_scope(self._conv_hyperparams_fn()):
with slim.arg_scope([slim.batch_norm],
fused=False,
is_training=self._is_training):
# ConvLSTM layers.
with tf.variable_scope(
'LSTM', reuse=self._reuse_weights) as lstm_scope:
lstm_cell = lstm_cells.BottleneckConvLSTMCell(
filter_size=(3, 3),
output_size=(net.shape[1].value, net.shape[2].value),
num_units=max(self._min_depth, self._lstm_state_depth),
activation=tf.nn.relu6,
visualize_gates=True)
net_seq = list(tf.split(net, unroll_length))
if state_saver is None:
init_state = lstm_cell.init_state(
state_name, net.shape[0].value / unroll_length,
tf.float32)
else:
c = state_saver.state('%s_c' % state_name)
h = state_saver.state('%s_h' % state_name)
init_state = (c, h)
# Identities added for inputing state tensors externally.
c_ident = tf.identity(init_state[0],
name='lstm_state_in_c')
h_ident = tf.identity(init_state[1],
name='lstm_state_in_h')
init_state = (c_ident, h_ident)
net_seq, states_out = rnn_decoder.rnn_decoder(
net_seq, init_state, lstm_cell, scope=lstm_scope)
batcher_ops = None
self._states_out = states_out
if state_saver is not None:
self._step = state_saver.state('%s_step' % state_name)
batcher_ops = [
state_saver.save_state('%s_c' % state_name,
states_out[-1][0]),
state_saver.save_state('%s_h' % state_name,
states_out[-1][1]),
state_saver.save_state('%s_step' % state_name,
self._step - 1)
]
with tf_ops.control_dependencies(batcher_ops):
image_features['Conv2d_13_pointwise_lstm'] = tf.concat(
net_seq, 0)
# Identities added for reading output states, to be reused externally.
tf.identity(states_out[-1][0], name='lstm_state_out_c')
tf.identity(states_out[-1][1], name='lstm_state_out_h')
# SSD layers.
with tf.variable_scope('FeatureMaps',
reuse=self._reuse_weights):
feature_maps = feature_map_generators.multi_resolution_feature_maps(
feature_map_layout=self._feature_map_layout,
depth_multiplier=(self._depth_multiplier),
min_depth=self._min_depth,
insert_1x1_conv=True,
image_features=image_features)
return feature_maps.values()
def extract_features(self,
preprocessed_inputs,
state_saver=None,
state_name='lstm_state',
unroll_length=5,
scope=None):
"""Extracts features from preprocessed inputs.
The features include the base network features, lstm features and SSD
features, organized in the following name scope:
<parent scope>/MobilenetV1/...
<parent scope>/LSTM/...
<parent scope>/FeatureMaps/...
Args:
preprocessed_inputs: A [batch, height, width, channels] float tensor
representing a batch of consecutive frames from video clips.
state_saver: A state saver object with methods `state` and `save_state`.
state_name: A python string for the name to use with the state_saver.
unroll_length: The number of steps to unroll the lstm.
scope: The scope for the base network of the feature extractor.
Returns:
A list of tensors where the ith tensor has shape [batch, height_i,
width_i, depth_i]
"""
preprocessed_inputs = shape_utils.check_min_image_dim(
33, preprocessed_inputs)
with slim.arg_scope(
mobilenet_v1.mobilenet_v1_arg_scope(is_training=self._is_training)):
with (slim.arg_scope(self._conv_hyperparams_fn())
if self._override_base_feature_extractor_hyperparams else
context_manager.IdentityContextManager()):
with slim.arg_scope([slim.batch_norm], fused=False):
# Base network.
with tf.variable_scope(
scope, self._base_network_scope,
reuse=self._reuse_weights) as scope:
net, image_features = mobilenet_v1.mobilenet_v1_base(
ops.pad_to_multiple(preprocessed_inputs, self._pad_to_multiple),
final_endpoint='Conv2d_13_pointwise',
min_depth=self._min_depth,
depth_multiplier=self._depth_multiplier,
scope=scope)
with slim.arg_scope(self._conv_hyperparams_fn()):
with slim.arg_scope(
[slim.batch_norm], fused=False, is_training=self._is_training):
# ConvLSTM layers.
with tf.variable_scope('LSTM', reuse=self._reuse_weights) as lstm_scope:
lstm_cell = lstm_cells.BottleneckConvLSTMCell(
filter_size=(3, 3),
output_size=(net.shape[1].value, net.shape[2].value),
num_units=max(self._min_depth, self._lstm_state_depth),
activation=tf.nn.relu6,
visualize_gates=True)
net_seq = list(tf.split(net, unroll_length))
if state_saver is None:
init_state = lstm_cell.init_state(
state_name, net.shape[0].value / unroll_length, tf.float32)
else:
c = state_saver.state('%s_c' % state_name)
h = state_saver.state('%s_h' % state_name)
init_state = (c, h)
# Identities added for inputing state tensors externally.
c_ident = tf.identity(init_state[0], name='lstm_state_in_c')
h_ident = tf.identity(init_state[1], name='lstm_state_in_h')
init_state = (c_ident, h_ident)
net_seq, states_out = rnn_decoder.rnn_decoder(
net_seq, init_state, lstm_cell, scope=lstm_scope)
batcher_ops = None
self._states_out = states_out
if state_saver is not None:
self._step = state_saver.state('%s_step' % state_name)
batcher_ops = [
state_saver.save_state('%s_c' % state_name, states_out[-1][0]),
state_saver.save_state('%s_h' % state_name, states_out[-1][1]),
state_saver.save_state('%s_step' % state_name, self._step - 1)
]
with tf_ops.control_dependencies(batcher_ops):
image_features['Conv2d_13_pointwise_lstm'] = tf.concat(net_seq, 0)
# Identities added for reading output states, to be reused externally.
tf.identity(states_out[-1][0], name='lstm_state_out_c')
tf.identity(states_out[-1][1], name='lstm_state_out_h')
# SSD layers.
with tf.variable_scope('FeatureMaps', reuse=self._reuse_weights):
feature_maps = feature_map_generators.multi_resolution_feature_maps(
feature_map_layout=self._feature_map_layout,
depth_multiplier=(self._depth_multiplier),
min_depth=self._min_depth,
insert_1x1_conv=True,
image_features=image_features)
return feature_maps.values()