wf_psf.psf_models.psf_model_semiparametric module

PSF Model Semi-Parametric.

A module which defines the classes and methods to manage the parameters of the psf semi-parametric model.

Authors:

Tobias Liaudat <tobiasliaudat@gmail.com> and Jennifer Pollack <jennifer.pollack@cea.fr>

class wf_psf.psf_models.psf_model_semiparametric.TF_SemiParam_field(*args, **kwargs)

Bases: Model

PSF field forward model.

Semi parametric model based on the Zernike polynomial basis.

Parameters:

• ids (tuple) – A tuple storing the string attribute of the PSF model class

• model_params (Recursive Namespace) – Recursive Namespace object containing parameters for this PSF model class

• training_params (Recursive Namespace) – Recursive Namespace object containing training hyperparameters for this PSF model class

• coeff_mat (Tensor or None) – Initialization of the coefficient matrix defining the parametric psf field model

Attributes:

activity_regularizer

Optional regularizer function for the output of this layer.

compute_dtype

The dtype of the layer’s computations.

distribute_strategy

The tf.distribute.Strategy this model was created under.

dtype

The dtype of the layer weights.

dtype_policy

The dtype policy associated with this layer.

dynamic

Whether the layer is dynamic (eager-only); set in the constructor.

inbound_nodes

Return Functional API nodes upstream of this layer.

input

Retrieves the input tensor(s) of a layer.

input_mask

Retrieves the input mask tensor(s) of a layer.

input_shape

Retrieves the input shape(s) of a layer.

input_spec

InputSpec instance(s) describing the input format for this layer.

layers

losses

List of losses added using the add_loss() API.

metrics

Returns the model’s metrics added using compile(), add_metric() APIs.

metrics_names

Returns the model’s display labels for all outputs.

name

Name of the layer (string), set in the constructor.

name_scope

Returns a tf.name_scope instance for this class.

non_trainable_variables

Sequence of non-trainable variables owned by this module and its submodules.

non_trainable_weights

List of all non-trainable weights tracked by this layer.

outbound_nodes

Return Functional API nodes downstream of this layer.

output

Retrieves the output tensor(s) of a layer.

output_mask

Retrieves the output mask tensor(s) of a layer.

output_shape

Retrieves the output shape(s) of a layer.

run_eagerly

Settable attribute indicating whether the model should run eagerly.

state_updates

Deprecated, do NOT use!

stateful

submodules

Sequence of all sub-modules.

supports_masking

Whether this layer supports computing a mask using compute_mask.

trainable

trainable_variables

Sequence of trainable variables owned by this module and its submodules.

trainable_weights

List of all trainable weights tracked by this layer.

updates

variable_dtype

Alias of Layer.dtype, the dtype of the weights.

variables

Returns the list of all layer variables/weights.

weights

Returns the list of all layer variables/weights.

Methods

add_loss(losses, **kwargs)	Add loss tensor(s), potentially dependent on layer inputs.
add_metric(value[, name])	Adds metric tensor to the layer.
add_update(updates)	Add update op(s), potentially dependent on layer inputs.
add_variable(*args, **kwargs)	Deprecated, do NOT use! Alias for add_weight.
add_weight([name, shape, dtype, ...])	Adds a new variable to the layer.
assign_S_mat(S_mat)	Assign DD features matrix.
assign_coeff_matrix(coeff_mat)	Assign coefficient matrix.
build(input_shape)	Builds the model based on input shapes received.
call(inputs)	Define the PSF field forward model.
compile([optimizer, loss, metrics, ...])	Configures the model for training.
compute_loss([x, y, y_pred, sample_weight])	Compute the total loss, validate it, and return it.
compute_mask(inputs[, mask])	Computes an output mask tensor.
compute_metrics(x, y, y_pred, sample_weight)	Update metric states and collect all metrics to be returned.
compute_output_shape(input_shape)	Computes the output shape of the layer.
compute_output_signature(input_signature)	Compute the output tensor signature of the layer based on the inputs.
count_params()	Count the total number of scalars composing the weights.
evaluate([x, y, batch_size, verbose, ...])	Returns the loss value & metrics values for the model in test mode.
evaluate_generator(generator[, steps, ...])	Evaluates the model on a data generator.
finalize_state()	Finalizes the layers state after updating layer weights.
fit([x, y, batch_size, epochs, verbose, ...])	Trains the model for a fixed number of epochs (iterations on a dataset).
fit_generator(generator[, steps_per_epoch, ...])	Fits the model on data yielded batch-by-batch by a Python generator.
from_config(config[, custom_objects])	Creates a layer from its config.
get_coeff_matrix()	Get coefficient matrix.
get_config()	Returns the config of the Model.
get_input_at(node_index)	Retrieves the input tensor(s) of a layer at a given node.
get_input_mask_at(node_index)	Retrieves the input mask tensor(s) of a layer at a given node.
get_input_shape_at(node_index)	Retrieves the input shape(s) of a layer at a given node.
get_layer([name, index])	Retrieves a layer based on either its name (unique) or index.
get_output_at(node_index)	Retrieves the output tensor(s) of a layer at a given node.
get_output_mask_at(node_index)	Retrieves the output mask tensor(s) of a layer at a given node.
get_output_shape_at(node_index)	Retrieves the output shape(s) of a layer at a given node.
get_weights()	Retrieves the weights of the model.
load_weights(filepath[, by_name, ...])	Loads all layer weights, either from a TensorFlow or an HDF5 weight file.
make_predict_function([force])	Creates a function that executes one step of inference.
make_test_function([force])	Creates a function that executes one step of evaluation.
make_train_function([force])	Creates a function that executes one step of training.
predict(x[, batch_size, verbose, steps, ...])	Generates output predictions for the input samples.
predict_generator(generator[, steps, ...])	Generates predictions for the input samples from a data generator.
predict_mono_psfs(input_positions, ...)	Predict a set of monochromatic PSF at desired positions.
predict_on_batch(x)	Returns predictions for a single batch of samples.
predict_opd(input_positions)	Predict the OPD at some positions.
predict_step(data)	The logic for one inference step.
project_DD_features(tf_zernike_cube)	Project non-parametric wavefront onto first n_z Zernikes and transfer their parameters to the parametric model.
reset_metrics()	Resets the state of all the metrics in the model.
save(filepath[, overwrite, ...])	Saves the model to Tensorflow SavedModel or a single HDF5 file.
save_spec([dynamic_batch])	Returns the tf.TensorSpec of call inputs as a tuple (args, kwargs).
save_weights(filepath[, overwrite, ...])	Saves all layer weights.
set_nonzero_nonparam()	Set to non-zero the non-parametric part.
set_output_Q(output_Q[, output_dim])	Set the value of the output_Q parameter.
set_trainable_layers([param_bool, nonparam_bool])	Set Trainable Layers.
set_weights(weights)	Sets the weights of the layer, from NumPy arrays.
set_zero_nonparam()	Set to zero the non-parametric part.
summary([line_length, positions, print_fn, ...])	Prints a string summary of the network.
test_on_batch(x[, y, sample_weight, ...])	Test the model on a single batch of samples.
test_step(data)	The logic for one evaluation step.
to_json(**kwargs)	Returns a JSON string containing the network configuration.
to_yaml(**kwargs)	Returns a yaml string containing the network configuration.
train_on_batch(x[, y, sample_weight, ...])	Runs a single gradient update on a single batch of data.
train_step(data)	The logic for one training step.
with_name_scope(method)	Decorator to automatically enter the module name scope.

__call__
reset_states

assign_S_mat(S_mat)

Assign DD features matrix.

assign_coeff_matrix(coeff_mat)

Assign coefficient matrix.

A function to set the coefficient matrix.

Parameters:

coeff_mat (float) – Tensor Flow coefficient matrix for the parametric PSF field model

call(inputs)

Define the PSF field forward model.

[1] From positions to Zernike coefficients [2] From Zernike coefficients to OPD maps [3] From OPD maps and SED info to polychromatic PSFs

OPD: Optical Path Differences

get_coeff_matrix()

Get coefficient matrix.

A function to get the coefficient matrix for parametric model.

Returns:

coefficient matrix – Tensor Flow coefficient matrix for the parametric PSF field model

Return type:

float

ids = ('poly',)

predict_mono_psfs(input_positions, lambda_obs, phase_N)

Predict a set of monochromatic PSF at desired positions.

Parameters:

• input_positions (Tensor(batch_dim x 2)) –

• lambda_obs (float) – Observed wavelength in um.

• phase_N (int) – Required wavefront dimension. Should be calculated with as: simPSF_np = wf.SimPSFToolkit(...) phase_N = simPSF_np.feasible_N(lambda_obs)

predict_opd(input_positions)

Predict the OPD at some positions.

Parameters:

input_positions (Tensor(batch_dim x 2)) – Positions to predict the OPD.

Returns:

opd_maps – OPD at requested positions.

Return type:

Tensor [batch x opd_dim x opd_dim]

project_DD_features(tf_zernike_cube)

Project non-parametric wavefront onto first n_z Zernikes and transfer their parameters to the parametric model.

set_nonzero_nonparam()

Set to non-zero the non-parametric part.

A function to set non-parametric alpha parameters equal to non-zero values.

set_output_Q(output_Q, output_dim=None)

Set the value of the output_Q parameter.

Useful for generating/predicting PSFs at a different sampling wrt the observation sampling.

Parameters:

• output_Q (float) – Oversampling factor

• output_dim (int) – Output dimension

set_trainable_layers(param_bool=True, nonparam_bool=True)

Set Trainable Layers.

A function to set the layers to be trainable or not.

Parameters:

• param_bool (bool) – Boolean flag for the parametric layers

• nonparam_bool (bool) – Boolean flag for the non-parametric layers

set_zero_nonparam()

Set to zero the non-parametric part.

A function to set non-parametric alpha parameters equal to zero.