def lite_conv_stem(inputs,
filters,
temporal_dilation=1,
is_training=False,
data_format='channels_last'):
"""Layers for a RGB or optical flow stem, using 2D + 1D conv layers.
Args:
inputs: A list of `Tensors` of size `[batch*time, channels, height, width]`.
filters: `int` number of filters in the convolution.
temporal_dilation: `int` temporal dilatioin size for the 1D conv.
is_training: `bool` specifying whether in training mode or not.
data_format: `str`. Only supports "channels_last" as the data format.
Returns:
The output `Tensor`.
"""
assert data_format == 'channels_last'
if temporal_dilation < 1:
temporal_dilation = 1
inputs = asn.conv3d_same_padding(inputs=inputs,
filters=filters,
kernel_size=3,
strides=2,
do_2d_conv=True,
data_format=data_format)
inputs = tf.identity(inputs, 'initial_conv')
inputs = rf.batch_norm_relu(inputs,
is_training,
bn_decay=FLAGS.bn_decay,
bn_epsilon=FLAGS.bn_epsilon,
data_format=data_format)
inputs = conv1d_bn(inputs=inputs,
is_training=is_training,
filters=filters,
kernel_size=3,
temporal_dilation=temporal_dilation,
data_format=data_format)
return inputs
def lite_one_stream_head(inputs,
num_classes,
is_training=False,
data_format='channels_last'):
"""Layers for one classification head.
Args:
inputs: A 4D `Tensor` following the data_format.
num_classes: `int` number of possible classes for video classification.
is_training: `bool` specifying whether in training mode or not.
data_format: `str` either "channels_first" for `[batch*time, channels,
height, width]` or "channels_last for `[batch*time, height, width,
channels]`. Only works for 'channels_last' currently.
Returns:
The output `Tensor`.
"""
assert data_format == 'channels_last'
batch_size = inputs.shape[0]
num_frames = inputs.shape[1]
inputs = asn.conv3d_same_padding(inputs=inputs,
filters=432,
kernel_size=1,
strides=1,
data_format=data_format)
inputs = tf.identity(inputs, 'last_conv')
inputs = rf.batch_norm_relu(inputs,
is_training,
bn_decay=FLAGS.bn_decay,
bn_epsilon=FLAGS.bn_epsilon,
data_format=data_format)
inputs = asn.conv3d_same_padding(inputs=inputs,
filters=2048,
kernel_size=1,
strides=1,
data_format=data_format)
inputs = tf.identity(inputs, 'last_conv2')
inputs = rf.batch_norm_relu(inputs,
is_training,
bn_decay=FLAGS.bn_decay,
bn_epsilon=FLAGS.bn_epsilon,
data_format=data_format)
if not FLAGS.max_pool_preditions:
pool_size = (inputs.shape[1], inputs.shape[2], inputs.shape[3])
inputs = tf.layers.average_pooling3d(inputs=inputs,
pool_size=pool_size,
strides=1,
padding='VALID',
data_format=data_format)
inputs = tf.identity(inputs, 'final_avg_pool')
inputs = tf.reshape(inputs, [batch_size, -1])
else:
pool_size = (1, inputs.shape[2], inputs.shape[3])
inputs = tf.layers.average_pooling3d(inputs=inputs,
pool_size=pool_size,
strides=1,
padding='VALID',
data_format=data_format)
inputs = tf.identity(inputs, 'final_avg_pool')
inputs = tf.reshape(inputs, [batch_size, num_frames, -1])
if FLAGS.dropout_keep_prob:
inputs = tf.keras.layers.Dropout(FLAGS.dropout_keep_prob)(inputs, training=is_training) # pylint: disable=line-too-long
outputs = tf.layers.dense(
inputs=inputs,
units=num_classes,
kernel_initializer=tf.random_normal_initializer(stddev=.01))
outputs = tf.identity(outputs, 'final_dense')
if FLAGS.max_pool_preditions:
pre_logits = outputs / np.sqrt(num_frames)
acts = tf.nn.softmax(pre_logits, axis=1)
outputs = tf.math.multiply(outputs, acts)
outputs = tf.reduce_sum(outputs, 1)
return outputs
def inverted_bottleneck_3dblock(inputs,
filters,
is_training,
strides,
use_projection=False,
temporal_dilation=1,
data_format='channels_last'):
"""Inverted bottleneck residual block with a 3D conv layer.
Inverted bottleneck block variant for 3D residual networks with BN after
convolutions. It uses (2+1)D conv instead when striding is needed.
Args:
inputs: 5D `Tensor` following the data_format.
filters: `List` of `int` number of filters.
is_training: `bool` for whether the model is in training.
strides: `int` block stride. If greater than 1, this block will ultimately
downsample the input spatially.
use_projection: `bool` for whether this block should use a projection
shortcut (versus the default identity shortcut). This is usually `True`
for the first block of a block group, which may change the number of
filters and the resolution.
temporal_dilation: `int` temporal dilatioin size for the 1D conv.
data_format: `str` either "channels_first" for `[batch, time, channels,
height, width]` or "channels_last for `[batch, time, height, width,
channels]`.
Returns:
The output `Tensor` of the block.
"""
shortcut = inputs
if use_projection:
# Projection shortcut only in first block within a group. Bottleneck blocks
# end with 4 times the number of filters.
shortcut = asn.conv3d_same_padding(inputs=inputs,
filters=filters[-1],
kernel_size=1,
strides=strides,
data_format=data_format)
shortcut = rf.batch_norm_relu(shortcut,
is_training,
relu=False,
bn_decay=FLAGS.bn_decay,
bn_epsilon=FLAGS.bn_epsilon,
data_format=data_format)
inputs = asn.conv3d_same_padding(inputs=inputs,
filters=filters[0],
kernel_size=1,
strides=1,
data_format=data_format)
inputs = rf.batch_norm_relu(inputs,
is_training,
bn_decay=FLAGS.bn_decay,
bn_epsilon=FLAGS.bn_epsilon,
data_format=data_format)
if strides > 1:
inputs = asn.conv3d_same_padding(inputs=inputs,
filters=filters[1],
kernel_size=3,
strides=strides,
do_2d_conv=True,
data_format=data_format)
inputs = rf.batch_norm_relu(
inputs,
is_training,
bn_decay=FLAGS.bn_decay,
bn_epsilon=FLAGS.bn_epsilon, # pylint: disable=line-too-long
data_format=data_format)
inputs = conv1d_bn(inputs,
is_training,
filters=filters[1],
kernel_size=3,
temporal_dilation=temporal_dilation,
data_format=data_format)
else:
inputs = asn.conv3d_same_padding(inputs=inputs,
filters=filters[1],
kernel_size=[3, 3, 3],
strides=1,
temporal_dilation=temporal_dilation,
data_format=data_format)
inputs = rf.batch_norm_relu(
inputs,
is_training,
bn_decay=FLAGS.bn_decay,
bn_epsilon=FLAGS.bn_epsilon, # pylint: disable=line-too-long
data_format=data_format)
inputs = asn.conv3d_same_padding(inputs=inputs,
filters=filters[-1],
kernel_size=1,
strides=1,
data_format=data_format)
inputs = rf.batch_norm_relu(inputs,
is_training,
relu=False,
init_zero=True,
bn_decay=FLAGS.bn_decay,
bn_epsilon=FLAGS.bn_epsilon,
data_format=data_format)
return tf.nn.relu(inputs + shortcut)