wf_psf.psf_models.psf_model_parametric module

PSF Model Parametric.

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

Authors:

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

class wf_psf.psf_models.psf_model_parametric.TF_PSF_field_model(*args, **kwargs)[source]

Bases: Model

Parametric PSF field model!

Fully parametric model based on the Zernike polynomial basis.

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

  • zernike_maps (Tensor(n_batch, opd_dim, opd_dim)) – Zernike polynomial maps.

  • obscurations (Tensor(opd_dim, opd_dim)) – Predefined obscurations of the phase.

  • batch_size (int) – Batch size.

  • output_Q (float) – Oversampling used. This should match the oversampling Q used to generate the diffraction zero padding that is found in the input packed_SEDs. We call this other Q the input_Q. In that case, we replicate the original sampling of the model used to calculate the input packed_SEDs. The final oversampling of the generated PSFs with respect to the original instrument sampling depend on the division input_Q/output_Q. It is not recommended to use output_Q < 1. Although it works with float values it is better to use integer values.

  • l2_param (float) – Parameter going with the l2 loss on the opd. If it is 0. the loss is not added. Default is 0..

  • output_dim (int) – Output dimension of the PSF stamps.

  • n_zernikes (int) – Order of the Zernike polynomial for the parametric model.

  • d_max (int) – Maximum degree of the polynomial for the Zernike coefficient variations.

  • x_lims ([float, float]) – Limits for the x coordinate of the PSF field.

  • y_lims ([float, float]) – Limits for the x coordinate of the PSF field.

  • 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_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.

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_output_Q(output_Q[, output_dim])

Set the value of the output_Q parameter.

set_weights(weights)

Sets the weights of the layer, from NumPy arrays.

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_coeff_matrix(coeff_mat)[source]

Assign coefficient matrix.

call(inputs)[source]

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()[source]

Get coefficient matrix.

ids = ('parametric',)
predict_mono_psfs(input_positions, lambda_obs, phase_N)[source]

Predict a set of monochromatic PSF at desired positions.

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)[source]

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]

set_output_Q(output_Q, output_dim=None)[source]

Set the value of the output_Q parameter. Useful for generating/predicting PSFs at a different sampling wrt the observation sampling.