Creates a 3D ResNet family model.

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 (
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.



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).

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.

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