Source code for wbia_cnn.models.dummy

# -*- coding: utf-8 -*-
from __future__ import absolute_import, division, print_function, unicode_literals
import utool as ut
from wbia_cnn.models import abstract_models

print, rrr, profile = ut.inject2(__name__)


[docs]@ut.reloadable_class class DummyModel(abstract_models.AbstractCategoricalModel): def __init__(model, batch_size=8, data_shape=(4, 4, 1), **kwargs): # kwargs['autoinit'] = kwargs.get('autoinit', True) kwargs['output_dims'] = kwargs.get('output_dims', 3) kwargs['showprog'] = kwargs.get('showprog', False) super(DummyModel, model).__init__( data_shape=data_shape, batch_size=batch_size, **kwargs )
[docs] def init_arch(model, verbose=True): """ CommandLine: python -m wbia_cnn DummyModel.init_arch --verbcnn --show Example: >>> # ENABLE_DOCTEST >>> from wbia_cnn.models.dummy import * # NOQA >>> model = DummyModel(autoinit=True) >>> model.print_model_info_str() >>> print(model) >>> ut.quit_if_noshow() >>> model.show_arch() >>> ut.show_if_requested() """ from Lasagne import lasagne from wbia_cnn import custom_layers if verbose: print('init arch') bundles = custom_layers.make_bundles( nonlinearity=lasagne.nonlinearities.rectify, batch_norm=False, ) b = ut.DynStruct(copy_dict=bundles) network_layers_def = [ b.InputBundle(shape=model.input_shape), b.ConvBundle(num_filters=5, filter_size=(3, 3)), b.DenseBundle(num_units=8), b.SoftmaxBundle(num_units=model.output_dims), ] from wbia_cnn import custom_layers network_layers = custom_layers.evaluate_layer_list(network_layers_def) # model.network_layers = network_layers model.output_layer = network_layers[-1] if ut.VERBOSE: model.print_arch_str() model.print_layer_info() return model.output_layer
if __name__ == '__main__': """ CommandLine: python -m wbia_cnn.models.dummy python -m wbia_cnn.models.dummy --allexamples python -m wbia_cnn.models.dummy --allexamples --noface --nosrc """ import multiprocessing multiprocessing.freeze_support() # for win32 import utool as ut # NOQA ut.doctest_funcs()