def test_s3d_stem_cells(self, depth_multiplier, first_temporal_kernel_size,
temporal_conv_endpoints):
batch_size = 1
num_frames = 64
height, width = 224, 224
inputs = tf.keras.layers.Input(shape=(num_frames, height, width, 3),
batch_size=batch_size)
outputs, output_endpoints = inception_utils.inception_v1_stem_cells(
inputs,
depth_multiplier,
'Mixed_5c',
temporal_conv_endpoints=temporal_conv_endpoints,
self_gating_endpoints={'Conv2d_2c_3x3'},
first_temporal_kernel_size=first_temporal_kernel_size)
self.assertListEqual(
outputs.shape.as_list(),
[batch_size, 32, 28, 28,
int(192 * depth_multiplier)])
expected_endpoints = {
'Conv2d_1a_7x7', 'MaxPool_2a_3x3', 'Conv2d_2b_1x1',
'Conv2d_2c_3x3', 'MaxPool_3a_3x3'
}
self.assertSetEqual(expected_endpoints, set(output_endpoints.keys()))
def __init__(self,
input_specs: tf.keras.layers.InputSpec,
final_endpoint: Text = 'Mixed_5c',
first_temporal_kernel_size: int = 3,
temporal_conv_start_at: Text = 'Conv2d_2c_3x3',
gating_start_at: Text = 'Conv2d_2c_3x3',
swap_pool_and_1x1x1: bool = True,
gating_style: Text = 'CELL',
use_sync_bn: bool = False,
norm_momentum: float = 0.999,
norm_epsilon: float = 0.001,
temporal_conv_initializer: Union[
Text,
initializers.Initializer] = initializers.TruncatedNormal(
mean=0.0, stddev=0.01),
temporal_conv_type: Text = '2+1d',
kernel_initializer: Union[
Text,
initializers.Initializer] = initializers.TruncatedNormal(
mean=0.0, stddev=0.01),
kernel_regularizer: Union[Text,
regularizers.Regularizer] = 'l2',
depth_multiplier: float = 1.0,
**kwargs):
"""Constructor.
Args:
input_specs: `tf.keras.layers.InputSpec` specs of the input tensor.
final_endpoint: Specifies the endpoint to construct the network up to.
first_temporal_kernel_size: Temporal kernel size of the first convolution
layer.
temporal_conv_start_at: Specifies the endpoint where to start performimg
temporal convolution from.
gating_start_at: Specifies the endpoint where to start performimg self
gating from.
swap_pool_and_1x1x1: A boolean flag indicates that whether to swap the
order of convolution and max pooling in Branch_3 of inception v1 cell.
gating_style: A string that specifies self gating to be applied after each
branch and/or after each cell. It can be one of ['BRANCH', 'CELL',
'BRANCH_AND_CELL'].
use_sync_bn: If True, use synchronized batch normalization.
norm_momentum: A `float` of normalization momentum for the moving average.
norm_epsilon: A `float` added to variance to avoid dividing by zero.
temporal_conv_initializer: Weight initializer for temporal convolutional
layers.
temporal_conv_type: The type of parameterized convolution. Currently, we
support '2d', '3d', '2+1d', '1+2d'.
kernel_initializer: Weight initializer for convolutional layers other than
temporal convolution.
kernel_regularizer: Weight regularizer for all convolutional layers.
depth_multiplier: A float to reduce/increase number of channels.
**kwargs: keyword arguments to be passed.
"""
self._input_specs = input_specs
self._final_endpoint = final_endpoint
self._first_temporal_kernel_size = first_temporal_kernel_size
self._temporal_conv_start_at = temporal_conv_start_at
self._gating_start_at = gating_start_at
self._swap_pool_and_1x1x1 = swap_pool_and_1x1x1
self._gating_style = gating_style
self._use_sync_bn = use_sync_bn
self._norm_momentum = norm_momentum
self._norm_epsilon = norm_epsilon
self._temporal_conv_initializer = temporal_conv_initializer
self._temporal_conv_type = temporal_conv_type
self._kernel_initializer = kernel_initializer
self._kernel_regularizer = kernel_regularizer
self._depth_multiplier = depth_multiplier
self._temporal_conv_endpoints = net_utils.make_set_from_start_endpoint(
temporal_conv_start_at,
inception_utils.INCEPTION_V1_CONV_ENDPOINTS)
self._self_gating_endpoints = net_utils.make_set_from_start_endpoint(
gating_start_at, inception_utils.INCEPTION_V1_CONV_ENDPOINTS)
inputs = tf.keras.Input(shape=input_specs.shape[1:])
net, end_points = inception_utils.inception_v1_stem_cells(
inputs,
depth_multiplier,
final_endpoint,
temporal_conv_endpoints=self._temporal_conv_endpoints,
self_gating_endpoints=self._self_gating_endpoints,
temporal_conv_type=self._temporal_conv_type,
first_temporal_kernel_size=self._first_temporal_kernel_size,
use_sync_bn=self._use_sync_bn,
norm_momentum=self._norm_momentum,
norm_epsilon=self._norm_epsilon,
temporal_conv_initializer=self._temporal_conv_initializer,
kernel_initializer=self._kernel_initializer,
kernel_regularizer=self._kernel_regularizer,
parameterized_conv_layer=self._get_parameterized_conv_layer_impl(),
layer_naming_fn=self._get_layer_naming_fn(),
)
for end_point, filters in inception_utils.INCEPTION_V1_ARCH_SKELETON:
net, end_points = self._s3d_cell(net, end_point, end_points,
filters)
if end_point == final_endpoint:
break
if final_endpoint not in end_points:
raise ValueError(
'Unrecognized final endpoint %s (available endpoints: %s).' %
(final_endpoint, end_points.keys()))
super(S3D, self).__init__(inputs=inputs, outputs=end_points, **kwargs)