Run this network for the given NumPy array(s), and return the output(s) as NumPy array(s). Args: input_transform: A dict speci

Do general housekeeping and keep the state of the context up-to-date. Should be called often enough but not in a tight loop.

Check that TensorFlow session has been initialized.

Create new network with the given parameters, and copy all variables from this network.

Find variable by local or global name.

Get or create variable representing log2 of the current dynamic loss scaling factor.

Traverses the object name and returns the last (rightmost) python object.

Construct TensorFlow expression(s) for the output(s) of this network, given the input expression(s).

Replace tags in the given path template and return either Windows or Linux formatted path.

Run the specified ops in the default session.

Internal helper for creating autosummary accumulators.

Forcefully enter the specified name scope, ignoring any surrounding scopes.

Flush, close possible files, and remove stdout/stderr mirroring.

Close the context and clean up. Should only be called once.

3x3 convolution with padding

Copy the values of all variables from the given network, including sub-networks.

Flush written text to both stdout and a file, if open.

Searches for the underlying module behind the name to some python object. Returns the module and the object name (original name with module part r

Get the current user name.

Check whether the input is a valid Tensorflow expression, i.e., Tensorflow Tensor, Variable, or Operation.

Determine whether the given object is a valid URL string.

Re-initialize all variables of this network, excluding sub-networks.

Write text to stdout (and a file) and optionally flush.

Create a new run dir with increasing ID number at the start.

Reads all directory names in a given directory (non-recursive) and returns the next (increasing) run id. Assumes IDs are numbers at the start of the d

Copy all necessary files into the run dir. Assumes that the dir exists, is local, and is writable.

Apply dynamic loss scaling for the given expression.

Create a clone of this network with its own copy of the variables.

Create tf.Session based on config dict.

Create the necessary ops to include autosummaries in TensorBoard report. Note: This should be done only once per graph.

Finds the python object with the given name.

Convert a normal path back to its template representation.

Get the value of a given variable as NumPy array. Note: This method is very inefficient -- prefer to use tflib.run(list_of_vars) whenever poss

Determine whether the given object is a top-level function, i.e., defined at module scope using 'def'.

Returns a list of (layer_name, output_expr, trainable_vars) tuples corresponding to individual layers of the network. Mainly intended to be us

Extracts and aligns all faces from images using DLib and a function from original FFHQ dataset preparation step

Reset internal state of the underlying optimizer.

Wrap the actual run function call for handling logging, exceptions, typing, etc.

Set the values of given tf.Variables. Equivalent to the following, but more efficient and does not bloat the tf graph: tflib.run([tf.assign(v

Undo the effect of dynamic loss scaling for the given expression.

Pickle export.

:param predictor_model_path: path to shape_predictor_68_face_landmarks.dat file