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Added a flag to allow skipping the first projection in small ResNets #11176

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Added a flag to allow skipping the first projection in small ResNets #11176

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21 changes: 18 additions & 3 deletions official/vision/modeling/backbones/resnet.py
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Original file line number Diff line number Diff line change
Expand Up @@ -135,6 +135,7 @@ def __init__(
kernel_regularizer: Optional[tf_keras.regularizers.Regularizer] = None,
bias_regularizer: Optional[tf_keras.regularizers.Regularizer] = None,
bn_trainable: bool = True,
use_first_projection: bool = True,
**kwargs):
"""Initializes a ResNet model.

Expand Down Expand Up @@ -164,6 +165,8 @@ def __init__(
Default to None.
bn_trainable: A `bool` that indicates whether batch norm layers should be
trainable. Default to True.
use_first_projection: A `bool` of whether to use the first projection
shortcut for small ResNets. See https://github.com/tensorflow/models/issues/10583.
**kwargs: Additional keyword arguments to be passed.
"""
self._model_id = model_id
Expand All @@ -184,6 +187,7 @@ def __init__(
self._kernel_regularizer = kernel_regularizer
self._bias_regularizer = bias_regularizer
self._bn_trainable = bn_trainable
self._use_first_projection = use_first_projection

if tf_keras.backend.image_data_format() == 'channels_last':
self._bn_axis = -1
Expand All @@ -202,12 +206,18 @@ def __init__(
block_fn = nn_blocks.BottleneckBlock
else:
raise ValueError('Block fn `{}` is not supported.'.format(spec[0]))
use_first_projection = (
spec[0] == 'bottleneck'
or i > 0
or self._use_first_projection
)
x = self._block_group(
inputs=x,
filters=int(spec[1] * self._depth_multiplier),
strides=(1 if i == 0 else 2),
block_fn=block_fn,
block_repeats=spec[2],
use_first_projection=use_first_projection,
stochastic_depth_drop_rate=nn_layers.get_stochastic_depth_rate(
self._init_stochastic_depth_rate, i + 2, 5),
name='block_group_l{}'.format(i + 2))
Expand Down Expand Up @@ -326,6 +336,7 @@ def _block_group(self,
strides: int,
block_fn: Callable[..., tf_keras.layers.Layer],
block_repeats: int = 1,
use_first_projection: bool = True,
stochastic_depth_drop_rate: float = 0.0,
name: str = 'block_group'):
"""Creates one group of blocks for the ResNet model.
Expand All @@ -339,6 +350,8 @@ def _block_group(self,
block_fn: The type of block group. Either `nn_blocks.ResidualBlock` or
`nn_blocks.BottleneckBlock`.
block_repeats: An `int` number of blocks contained in the layer.
use_first_projection: A `bool` to determine whether to use the first
projection shortcut.
stochastic_depth_drop_rate: A `float` of drop rate of the current block
group.
name: A `str` name for the block.
Expand All @@ -349,7 +362,7 @@ def _block_group(self,
x = block_fn(
filters=filters,
strides=strides,
use_projection=True,
use_projection=use_first_projection,
stochastic_depth_drop_rate=stochastic_depth_drop_rate,
se_ratio=self._se_ratio,
resnetd_shortcut=self._resnetd_shortcut,
Expand Down Expand Up @@ -400,7 +413,8 @@ def get_config(self):
'kernel_initializer': self._kernel_initializer,
'kernel_regularizer': self._kernel_regularizer,
'bias_regularizer': self._bias_regularizer,
'bn_trainable': self._bn_trainable
'bn_trainable': self._bn_trainable,
'use_first_projection': self._use_first_projection
}
return config_dict

Expand Down Expand Up @@ -441,4 +455,5 @@ def build_resnet(
norm_momentum=norm_activation_config.norm_momentum,
norm_epsilon=norm_activation_config.norm_epsilon,
kernel_regularizer=l2_regularizer,
bn_trainable=backbone_cfg.bn_trainable)
bn_trainable=backbone_cfg.bn_trainable,
use_first_projection=backbone_cfg.use_first_projection)
35 changes: 34 additions & 1 deletion official/vision/modeling/backbones/resnet_test.py
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Original file line number Diff line number Diff line change
Expand Up @@ -67,6 +67,38 @@ def test_network_creation(self, input_size, model_id,
self.assertAllEqual(
[1, input_size / 2**5, input_size / 2**5, 512 * endpoint_filter_scale],
endpoints['5'].shape.as_list())

@parameterized.parameters(
(128, 18, 1),
(128, 34, 1),
)
def test_network_creation_no_first_shortcut(self, input_size, model_id,
endpoint_filter_scale):
"""Test creation of ResNet family models."""
resnet_params = {
18: 11186112,
34: 21301696,
}
tf.keras.backend.set_image_data_format('channels_last')

network = resnet.ResNet(model_id=model_id, use_first_projection=False)
self.assertEqual(network.count_params(), resnet_params[model_id])

inputs = tf.keras.Input(shape=(input_size, input_size, 3), batch_size=1)
endpoints = network(inputs)

self.assertAllEqual(
[1, input_size / 2**2, input_size / 2**2, 64 * endpoint_filter_scale],
endpoints['2'].shape.as_list())
self.assertAllEqual(
[1, input_size / 2**3, input_size / 2**3, 128 * endpoint_filter_scale],
endpoints['3'].shape.as_list())
self.assertAllEqual(
[1, input_size / 2**4, input_size / 2**4, 256 * endpoint_filter_scale],
endpoints['4'].shape.as_list())
self.assertAllEqual(
[1, input_size / 2**5, input_size / 2**5, 512 * endpoint_filter_scale],
endpoints['5'].shape.as_list())

@combinations.generate(
combinations.combine(
Expand Down Expand Up @@ -137,7 +169,8 @@ def test_serialize_deserialize(self):
kernel_initializer='VarianceScaling',
kernel_regularizer=None,
bias_regularizer=None,
bn_trainable=True)
bn_trainable=True,
use_first_projection=True)
network = resnet.ResNet(**kwargs)

expected_config = dict(kwargs)
Expand Down