tfm.vision.backbones.ResNet

Creates ResNet and ResNet-RS family models.

tfm.vision.backbones.ResNet(
    model_id: int,
    input_specs: tf.keras.layers.InputSpec = layers.InputSpec(shape=[None, None, None, 3]),
    depth_multiplier: float = 1.0,
    stem_type: str = 'v0',
    resnetd_shortcut: bool = False,
    replace_stem_max_pool: bool = False,
    se_ratio: Optional[float] = None,
    init_stochastic_depth_rate: float = 0.0,
    scale_stem: bool = True,
    activation: str = 'relu',
    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,
    bn_trainable: bool = True,
    **kwargs
)

This implements the Deep Residual Network from: Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun. Deep Residual Learning for Image Recognition. (https://arxiv.org/pdf/1512.03385) and Irwan Bello, William Fedus, Xianzhi Du, Ekin D. Cubuk, Aravind Srinivas, Tsung-Yi Lin, Jonathon Shlens, Barret Zoph. Revisiting ResNets: Improved Training and Scaling Strategies. (https://arxiv.org/abs/2103.07579).

Args
model_id	An int of the depth of ResNet backbone model.
input_specs	A tf.keras.layers.InputSpec of the input tensor.
depth_multiplier	A float of the depth multiplier to uniformaly scale up all layers in channel size. This argument is also referred to as width_multiplier in (https://arxiv.org/abs/2103.07579).
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).
resnetd_shortcut	A bool of whether to use ResNet-D shortcut in downsampling blocks.
replace_stem_max_pool	A bool of whether to replace the max pool in stem with a stride-2 conv,
se_ratio	A float or None. Ratio of the Squeeze-and-Excitation layer.
init_stochastic_depth_rate	A float of initial stochastic depth rate.
scale_stem	A bool of whether to scale stem layers.
activation	A str name of the activation function.
use_sync_bn	If True, use synchronized batch normalization.
norm_momentum	A float of normalization momentum for the moving average.
norm_epsilon	A small 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.
bn_trainable	A bool that indicates whether batch norm layers should be trainable. Default to True.
**kwargs	Additional keyword arguments to be passed.

Attributes
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).

Note: This method should not be called directly. It is only meant to be overridden when subclassing tf.keras.Model. To call a model on an input, always use the __call__() method, i.e. model(inputs), which relies on the underlying call() method.

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.