def block(layers, filters):
all_filters = sorted({
search_space_utils.scale_filters(filters, multiplier, base=8)
for multiplier in block_filters_multipliers
})
return basic_specs.Block(layers=layers,
filters=choose_filters(all_filters))
def test_scale_filters(self):
self.assertEqual(search_space_utils.scale_filters(32, 1 / 4, 8), 8)
self.assertEqual(search_space_utils.scale_filters(64, 1 / 3, 8), 24)
self.assertEqual(search_space_utils.scale_filters(64, 1.4, 32), 96)
self.assertEqual(search_space_utils.scale_filters(64, 3, 32), 192)
self.assertEqual(search_space_utils.scale_filters(68, 1.0, 8), 72)
self.assertEqual(search_space_utils.scale_filters(68, 1.2, 8), 80)
self.assertEqual(search_space_utils.scale_filters(76, 1.0, 8), 80)
self.assertEqual(search_space_utils.scale_filters(76, 1.2, 8), 88)
def _compute_filters_for_multiplier(
multiplier,
input_filters_or_mask,
filters_base):
"""Convert a FilterMultiplier to an integer (or int Tensor) filter size."""
if isinstance(input_filters_or_mask, int):
input_filters = input_filters_or_mask
return search_space_utils.scale_filters(
input_filters, multiplier.scale, filters_base)
elif isinstance(input_filters_or_mask, tf.Tensor):
input_filters = tf.reduce_sum(tf.cast(input_filters_or_mask, tf.int32))
return search_space_utils.tf_scale_filters(
input_filters, multiplier.scale, filters_base)
else:
raise ValueError('Unsupported type for input_filters_or_mask: {}'.format(
input_filters_or_mask))
def bneck(input_size, se, s, act):
"""Construct a DepthwiseBottleneckSpec namedtuple."""
if use_relative_expansion_filters:
expansion_filters = sorted({
basic_specs.FilterMultiplier(expansion)
for expansion in expansion_multipliers
})
else:
expansion_filters = sorted({
search_space_utils.scale_filters(input_size, expansion, base=8)
for expansion in expansion_multipliers
})
if search_squeeze_and_excite:
# Replace the default value of the argument 'se' with a OneOf node.
se = schema.OneOf([False, True], basic_specs.OP_TAG)
return DepthwiseBottleneckSpec(
kernel_size=schema.OneOf([3, 5, 7], basic_specs.OP_TAG),
expansion_filters=choose_filters(expansion_filters),
use_squeeze_and_excite=se,
strides=s,
activation=act)
