def resnet_v2_50(inputs,
config,
is_training=True,
scope='resnet_v2_50'):
"""Modified ResNet-50 model."""
blocks = [
resnet_v2.resnet_v2_block('block1',
base_depth=config.block1_depth,
num_units=config.block1_units,
stride=config.block1_stride),
resnet_v2.resnet_v2_block('block2',
base_depth=config.block2_depth,
num_units=config.block2_units,
stride=config.block2_stride),
resnet_v2.resnet_v2_block('block3',
base_depth=config.block3_depth,
num_units=config.block3_units,
stride=config.block3_stride),
resnet_v2.resnet_v2_block('block4',
base_depth=config.block4_depth,
num_units=config.block4_units,
stride=config.block4_stride),
]
return resnet_v2.resnet_v2(inputs,
blocks,
is_training=is_training,
global_pool=False,
include_root_block=True,
scope=scope)
def resnet_12(inputs, num_classes, scope='resnet_12'):
blocks = [
resnet_v2.resnet_v2_block('block1',
base_depth=64,
num_units=2,
stride=1),
resnet_v2.resnet_v2_block('block2',
base_depth=64,
num_units=2,
stride=1),
resnet_v2.resnet_v2_block('block3',
base_depth=64,
num_units=2,
stride=1),
resnet_v2.resnet_v2_block('block4',
base_depth=64,
num_units=2,
stride=1),
resnet_v2.resnet_v2_block('block5',
base_depth=64,
num_units=2,
stride=1)
]
return resnet_v2.resnet_v2(inputs,
blocks,
num_classes,
is_training=True,
global_pool=True,
output_stride=None,
include_root_block=True,
reuse=None,
scope=scope)
def resnet_small(inputs,
num_classes=None,
is_training=True,
global_pool=True,
output_stride=None,
include_root_block=True,
reuse=None,
scope='resnet_small'):
blocks = [
resnet_v2.resnet_v2_block('block1',
base_depth=32,
num_units=2,
stride=2),
resnet_v2.resnet_v2_block('block2',
base_depth=64,
num_units=2,
stride=2),
resnet_v2.resnet_v2_block('block3',
base_depth=128,
num_units=2,
stride=2),
resnet_v2.resnet_v2_block('block4',
base_depth=256,
num_units=2,
stride=2),
]
return resnet_v2.resnet_v2(inputs,
blocks,
num_classes,
is_training=is_training,
global_pool=global_pool,
output_stride=output_stride,
include_root_block=include_root_block,
reuse=reuse,
scope=scope)
def _parse_resnet_v2_block(shape: list, config: dict):
"""
Function to parse resnet_v2_block which creates a preactivation bottleneck unit.
Documentation can be found here: https://github.com/tensorflow/models/blob/master/research/slim/nets/resnet_v2.py
Parameters
----------
shape: list holding the shape of expected input.
config: dict describing the block, holding 'base_depth', 'num_units' and 'stride' keys. Optional: 'scope'
Returns
-------
layers: None
variables: All trainable variables associated with the scope.
function: lambda x: block[1](x, args.get('depth'), args.get('depth_bottleneck'), args.get('stride'))
output_shape: shape of the block output.
"""
scope_name = config.get('scope', str(uuid.uuid4()))
scope = tf.VariableScope(tf.AUTO_REUSE, name=scope_name)
base_depth = config.get('base_depth')
num_units = config.get('num_units')
stride = config.get('stride')
block = resnet_v2.resnet_v2_block(scope, base_depth, num_units, stride)
args = block.args[0]
layers = None
function = lambda x: block[1](x, args.get(
'depth'), args.get('depth_bottleneck'), args.get('stride'))
output_shape = function(tf.placeholder(tf.float32,
shape=shape)).get_shape()
variables = scope.trainable_variables()
return layers, variables, function, output_shape
def resnet_v2_light(inputs,
num_classes=None,
is_training=True,
global_pool=True,
output_stride=None,
spatial_squeeze=True,
reuse=None,
scope='resnet_v2_light'):
"""ResNet-light model of AIlab. See resnet_v2() for arg and return description."""
blocks = [
resnet_v2.resnet_v2_block('block1',
base_depth=16,
num_units=3,
stride=2),
resnet_v2.resnet_v2_block('block2',
base_depth=32,
num_units=4,
stride=2),
resnet_v2.resnet_v2_block('block3',
base_depth=64,
num_units=8,
stride=2),
resnet_v2.resnet_v2_block('block4',
base_depth=128,
num_units=3,
stride=1),
]
return resnet_v2.resnet_v2(inputs,
blocks,
num_classes,
is_training=is_training,
global_pool=global_pool,
output_stride=output_stride,
include_root_block=True,
reuse=reuse,
scope=scope)
def resnet_v2_50(inputs,
num_classes=None,
is_training=True,
global_pool=True,
output_stride=None,
reuse=None,
scope='resnet_v2_50'):
"""ResNet-50 model of [1]. See resnet_v2() for arg and return description."""
blocks = [
resnet_v2.resnet_v2_block('block4',
base_depth=512,
num_units=1,
stride=1),
]
return resnet_v2_f(inputs,
blocks,
num_classes,
is_training,
global_pool,
4,
include_root_block=True,
reuse=reuse,
scope=scope)