def mbv1_block_(inputs,
filters,
is_training,
stride,
width=1.,
block_id=0,
pruning_method='baseline',
data_format='channels_first',
weight_decay=0.):
"""Standard building block for mobilenetv1 networks.
Args:
inputs: Input tensor, float32 or bfloat16 of size [batch, channels, height,
width].
filters: Int specifying number of filters for the first two convolutions.
is_training: Boolean specifying whether the model is training.
stride: Int specifying the stride. If stride >1, the input is downsampled.
width: multiplier for channel dimensions
block_id: which block this is
pruning_method: String that specifies the pruning method used to identify
which weights to remove.
data_format: String that specifies either "channels_first" for [batch,
channels, height,width] or "channels_last" for [batch, height, width,
channels].
weight_decay: Weight for the l2 regularization loss.
Returns:
The output activation tensor.
"""
# separable_conv_2d followed by contracting 1x1 conv.
end_point = 'depthwise_nxn_%s' % block_id
# Depthwise
depthwise_out = depthwise_conv2d_fixed_padding(inputs=inputs,
kernel_size=3,
stride=stride,
data_format=data_format,
name=end_point)
depthwise_out = resnet_model.batch_norm_relu(depthwise_out,
is_training,
relu=True,
data_format=data_format)
# Contraction
end_point = 'contraction_1x1_%s' % block_id
divisible_by = 8
if block_id == 0:
divisible_by = 1
out_filters = _make_divisible(int(width * filters), divisor=divisible_by)
contraction_out = conv2d_fixed_padding(inputs=depthwise_out,
filters=out_filters,
kernel_size=1,
strides=1,
pruning_method=pruning_method,
data_format=data_format,
weight_decay=weight_decay,
name=end_point)
contraction_out = resnet_model.batch_norm_relu(contraction_out,
is_training,
relu=True,
data_format=data_format)
output = contraction_out
return output
def model(inputs, is_training):
"""Creation of the model graph."""
with tf.variable_scope(name, 'resnet_model'):
inputs = resnet_model.fixed_padding(inputs,
kernel_size=3,
data_format=data_format)
padding = 'VALID'
kernel_initializer = tf.variance_scaling_initializer()
kernel_regularizer = contrib_layers.l2_regularizer(weight_decay)
inputs = tf.layers.conv2d(inputs=inputs,
filters=_make_divisible(32 * width),
kernel_size=3,
strides=2,
padding=padding,
use_bias=False,
kernel_initializer=kernel_initializer,
kernel_regularizer=kernel_regularizer,
data_format=data_format,
name='initial_conv')
inputs = tf.identity(inputs, 'initial_conv')
inputs = resnet_model.batch_norm_relu(inputs,
is_training,
data_format=data_format)
mb_block = functools.partial(mbv1_block_,
is_training=is_training,
width=width,
pruning_method=pruning_method,
data_format=data_format,
weight_decay=weight_decay)
inputs = mb_block(inputs, filters=64, stride=1, block_id=0)
inputs = mb_block(inputs, filters=128, stride=2, block_id=1)
inputs = mb_block(inputs, filters=128, stride=1, block_id=2)
inputs = mb_block(inputs, filters=256, stride=2, block_id=3)
inputs = mb_block(inputs, filters=256, stride=1, block_id=4)
inputs = mb_block(inputs, filters=512, stride=2, block_id=5)
inputs = mb_block(inputs, filters=512, stride=1, block_id=6)
inputs = mb_block(inputs, filters=512, stride=1, block_id=7)
inputs = mb_block(inputs, filters=512, stride=1, block_id=8)
inputs = mb_block(inputs, filters=512, stride=1, block_id=9)
inputs = mb_block(inputs, filters=512, stride=1, block_id=10)
inputs = mb_block(inputs, filters=1024, stride=2, block_id=11)
inputs = mb_block(inputs, filters=1024, stride=1, block_id=12)
last_block_filters = _make_divisible(int(1024 * width), 8)
if data_format == 'channels_last':
pool_size = (inputs.shape[1], inputs.shape[2])
elif data_format == 'channels_first':
pool_size = (inputs.shape[2], inputs.shape[3])
inputs = tf.layers.average_pooling2d(inputs=inputs,
pool_size=pool_size,
strides=1,
padding='VALID',
data_format=data_format,
name='final_avg_pool')
inputs = tf.identity(inputs, 'final_avg_pool')
inputs = tf.reshape(inputs, [-1, last_block_filters])
kernel_initializer = tf.variance_scaling_initializer()
kernel_regularizer = contrib_layers.l2_regularizer(weight_decay)
if prune_last_layer:
inputs = sparse_fully_connected(
x=inputs,
units=num_classes,
sparsity_technique=pruning_method
if prune_last_layer else 'baseline',
kernel_initializer=kernel_initializer,
kernel_regularizer=kernel_regularizer,
name='final_dense')
else:
inputs = tf.layers.dense(inputs=inputs,
units=num_classes,
activation=None,
use_bias=True,
kernel_initializer=kernel_initializer,
kernel_regularizer=kernel_regularizer,
name='final_dense')
inputs = tf.identity(inputs, 'final_dense')
return inputs
def inverted_res_block_(
inputs,
filters,
is_training,
stride,
width=1.,
expansion_factor=6.,
block_id=0,
pruning_method='baseline',
data_format='channels_first',
weight_decay=0.,
):
"""Standard building block for mobilenetv2 networks.
Args:
inputs: Input tensor, float32 or bfloat16 of size [batch, channels, height,
width].
filters: Int specifying number of filters for the first two convolutions.
is_training: Boolean specifying whether the model is training.
stride: Int specifying the stride. If stride >1, the input is downsampled.
width: multiplier for channel dimensions
expansion_factor: How much to increase the filters before the depthwise
conv.
block_id: which block this is
pruning_method: String that specifies the pruning method used to identify
which weights to remove.
data_format: String that specifies either "channels_first" for [batch,
channels, height,width] or "channels_last" for [batch, height, width,
channels].
weight_decay: Weight for the l2 regularization loss.
Returns:
The output activation tensor.
"""
# 1x1 expanded conv, followed by separable_conv_2d followed by
# contracting 1x1 conv.
shortcut = inputs
if data_format == 'channels_first':
prev_depth = inputs.get_shape().as_list()[1]
elif data_format == 'channels_last':
prev_depth = inputs.get_shape().as_list()[3]
else:
raise ValueError('Unknown data_format ' + data_format)
# Expand
multiplier = expansion_factor if block_id > 0 else 1
# skip the expansion if this is the first block
if block_id:
end_point = 'expand_1x1_%s' % block_id
inputs = conv2d_fixed_padding(inputs=inputs,
filters=int(multiplier * prev_depth),
kernel_size=1,
strides=1,
pruning_method=pruning_method,
data_format=data_format,
weight_decay=weight_decay,
name=end_point)
inputs = resnet_model.batch_norm_relu(inputs,
is_training,
relu=True,
data_format=data_format)
end_point = 'depthwise_nxn_%s' % block_id
# Depthwise
depthwise_out = depthwise_conv2d_fixed_padding(inputs=inputs,
kernel_size=3,
stride=stride,
data_format=data_format,
name=end_point)
depthwise_out = resnet_model.batch_norm_relu(depthwise_out,
is_training,
relu=True,
data_format=data_format)
# Contraction
end_point = 'contraction_1x1_%s' % block_id
divisible_by = 8
if block_id == 0:
divisible_by = 1
out_filters = _make_divisible(int(width * filters), divisor=divisible_by)
contraction_out = conv2d_fixed_padding(inputs=depthwise_out,
filters=out_filters,
kernel_size=1,
strides=1,
pruning_method=pruning_method,
data_format=data_format,
weight_decay=weight_decay,
name=end_point)
contraction_out = resnet_model.batch_norm_relu(contraction_out,
is_training,
relu=False,
data_format=data_format)
output = contraction_out
if prev_depth == out_filters and stride == 1:
output += shortcut
return output