tfm.vision.backbones.ResNet3D

Creates a 3D ResNet family model.

tfm.vision.backbones.ResNet3D(
    model_id: int,
    temporal_strides: List[int],
    temporal_kernel_sizes: List[Tuple[int]],
    use_self_gating: Optional[List[int]] = None,
    input_specs: tf.keras.layers.InputSpec = layers.InputSpec(shape=[None, None, None, None, 3]),
    stem_type: str = 'v0',
    stem_conv_temporal_kernel_size: int = 5,
    stem_conv_temporal_stride: int = 2,
    stem_pool_temporal_stride: int = 2,
    init_stochastic_depth_rate: float = 0.0,
    activation: str = 'relu',
    se_ratio: Optional[float] = None,
    use_sync_bn: bool = False,
    norm_momentum: float = 0.99,
    norm_epsilon: float = 0.001,
    kernel_initializer: str = 'VarianceScaling',
    kernel_regularizer: Optional[tf.keras.regularizers.Regularizer] = None,
    bias_regularizer: Optional[tf.keras.regularizers.Regularizer] = None,
    **kwargs
)

model_id An int of depth of ResNet backbone model.
temporal_strides A list of integers that specifies the temporal strides for all 3d blocks.
temporal_kernel_sizes A list of tuples that specifies the temporal kernel sizes for all 3d blocks in different block groups.
use_self_gating A list of booleans to specify applying self-gating module or not in each block group. If None, self-gating is not applied.
input_specs A tf.keras.layers.InputSpec of the input tensor.
stem_type A str of stem type of ResNet. Default to v0. If set to v1, use ResNet-D type stem (https://arxiv.org/abs/1812.01187).
stem_conv_temporal_kernel_size An int of temporal kernel size for the first conv layer.
stem_conv_temporal_stride An int of temporal stride for the first conv layer.
stem_pool_temporal_stride An int of temporal stride for the first pool layer.
init_stochastic_depth_rate A float of initial stochastic depth rate.
activation A str of name of the activation function.
se_ratio A float or None. Ratio of the Squeeze-and-Excitation layer.
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.
kernel_initializer A str for kernel initializer of convolutional layers.
kernel_regularizer A tf.keras.regularizers.Regularizer object for Conv2D. Default to None.
bias_regularizer A tf.keras.regularizers.Regularizer object for Conv2D. Default to None.
**kwargs Additional keyword arguments to be passed.

output_specs A dict of {level: TensorShape} pairs for the model output.

Methods

call

call(
    inputs, training=None, mask=None
)

Calls the model on new inputs and returns the outputs as tensors.

In this case call() just reapplies all ops in the graph to the new inputs (e.g. build a new computational graph from the provided inputs).

Args
inputs Input tensor, or dict/list/tuple of input tensors.
training Boolean or boolean scalar tensor, indicating whether to run the Network in training mode or inference mode.
mask A mask or list of masks. A mask can be either a boolean tensor or None (no mask). For more details, check the guide here.

Returns
A tensor if there is a single output, or a list of tensors if there are more than one outputs.