def __init__(self,
small_conv_model,
image_node,
yaml_dict,
numpy_rng,
theano_rng):
assert_is_instance(small_conv_model, IdAndCameraDirModel)
assert_equal(image_node.output_format.axes, ('b', '0', '1', 'c'))
self.shared_layers = []
self.shared_layers.append(RgbToGray(image_node))
self.shared_layers.append(Lcn(self.shared_layers[-1]))
assert_is_subdtype(image_node.output_format.dtype, numpy.floating)
use_dropout = yaml_dict['hyperparams']['use_dropout']
def get_num_classes(yaml_dict):
fg_path = yaml_dict['datasets']['training']['fg_path']
dataset = MemmapDataset(os.path.join(data_path, fg_path))
label_to_id = NorbLabelToObjectIdConverter(dataset.tensors[1])
return label_to_id.num_unique_ids
add_conv_layers(self.shared_layers[-1],
yaml_dict['model']['shared_layers']['conv'],
use_dropout,
numpy_rng,
theano_rng,
self.shared_layers)
def get_first_affine_layer_filter_shape(small_conv_model):
first_affine_layer = \
small_conv_model.shared_layers[len(self.shared_layers)]
assert_is_instance(first_affine_layer, AffineLayer)
assert_is_instance(first_affine_layer.inputs[0], Conv2dLayer)
assert_equal(first_affine_layer.inputs[0].output_format.axes,
('b', 'c', '0', '1'))
return first_affine_layer.inputs[0].output_format.shape[2:]
first_filter_shape = \
get_first_affine_layer_filter_shape(small_conv_model)
assert_equal(first_filter_shape, (2, 2))
add_affine_layers_conv(self.shared_layers[-1],
yaml_dict['model']['shared_layers']['affine'],
use_dropout,
numpy_rng,
theano_rng,
first_filter_shape=first_filter_shape,
output_list=self.shared_layers)
self.id_layers = []
add_classifier_mlp_conv(self.shared_layers[-1],
yaml_dict['model']['id_layers'],
get_num_classes(yaml_dict),
use_dropout,
numpy_rng,
theano_rng,
self.id_layers)
self.cam_dir_layers = []
add_regressor_mlp_conv(self.shared_layers[-1],
yaml_dict['model']['cam_dir_layers'],
3,
use_dropout,
numpy_rng,
theano_rng,
self.cam_dir_layers)
self.input_node = image_node
def __init__(self, image_node, yaml_dict, numpy_rng, theano_rng):
'''
Parameters
----------
image_node: InputNode
yaml_dict: dict
'''
super(IdAndCameraDirModelConv, self).__init__()
#
# Build the model nodes, and initialize their weights.
#
# preprocessing layers
shared_layers = []
shared_layers.append(RgbToGray(image_node))
shared_layers.append(Lcn(shared_layers[-1]))
assert_is_subdtype(image_node.output_format.dtype, numpy.floating)
use_dropout = yaml_dict['hyperparams']['use_dropout']
def get_num_classes(yaml_dict):
fg_path = yaml_dict['datasets']['training']['fg_path']
dataset = MemmapDataset(os.path.join(data_path, fg_path))
label_to_id = NorbLabelToObjectIdConverter(dataset.tensors[1])
return label_to_id.num_unique_ids
add_conv_layers(shared_layers[-1],
yaml_dict['model']['shared_layers']['conv'],
use_dropout,
numpy_rng,
theano_rng,
shared_layers)
assert_equal(shared_layers[-1].output_format.axes,
('b', 'c', '0', '1'))
first_filter_shape = shared_layers[-1].output_format.shape[2:]
add_affine_layers_conv(shared_layers[-1],
yaml_dict['model']['shared_layers']['affine'],
use_dropout,
numpy_rng,
theano_rng,
shared_layers,
first_filter_shape=first_filter_shape)
id_layers = []
add_classifier_mlp_conv(shared_layers[-1],
yaml_dict['model']['id_layers'],
get_num_classes(yaml_dict),
use_dropout,
numpy_rng,
theano_rng,
id_layers)
cam_dir_layers = []
add_regressor_mlp_conv(shared_layers[-1],
yaml_dict['model']['cam_dir_layers'],
3,
use_dropout,
numpy_rng,
theano_rng,
cam_dir_layers)
self.input_node = image_node
self.shared_layers = shared_layers
self.id_layers = id_layers
self.cam_dir_layers = cam_dir_layers
def __init__(self, image_node, yaml_dict, numpy_rng, theano_rng):
'''
Parameters
----------
image_node: InputNode
yaml_dict: dict
'''
super(IdPoseLightingModel, self).__init__()
self.input_node = image_node
# preprocessing layers
self.shared_layers = []
self.shared_layers.append(RgbToGray(image_node))
self.shared_layers.append(Lcn(self.shared_layers[-1]))
assert_is_subdtype(image_node.output_format.dtype, numpy.floating)
use_dropout = yaml_dict['hyperparams']['use_dropout']
add_conv_layers(self.shared_layers[-1],
yaml_dict['model']['shared_layers']['conv'],
use_dropout,
numpy_rng,
theano_rng,
self.shared_layers)
add_affine_layers_conv(self.shared_layers[-1],
yaml_dict['model']['shared_layers']['affine'],
use_dropout,
numpy_rng,
theano_rng,
self.shared_layers)
def get_num_classes(yaml_dict):
fg_path = yaml_dict['datasets']['training']['fg_path']
dataset = MemmapDataset(os.path.join(data_path, fg_path))
label_to_id = NorbLabelToObjectIdConverter(dataset.tensors[1])
return label_to_id.num_unique_ids
self.id_layers = []
add_classifier_mlp_conv(self.shared_layers[-1],
yaml_dict['model']['id_layers'],
get_num_classes(yaml_dict),
use_dropout,
numpy_rng,
theano_rng,
self.id_layers)
self.cam_dir_layers = []
add_regressor_mlp_conv(self.shared_layers[-1],
yaml_dict['model']['cam_dir_layers'],
3,
use_dropout,
numpy_rng,
theano_rng,
self.cam_dir_layers)
def get_num_lightings(yaml_dict):
'''
Returns the number of non-blank lighting values.
'''
fg_path = yaml_dict['datasets']['training']['fg_path']
dataset = MemmapDataset(os.path.join(data_path, fg_path))
lighting_labels = dataset.tensors[1][:, 4]
assert_equal(lighting_labels[0], -1)
assert_array_compare(numpy.greater_equal, lighting_labels[1:], 0)
assert_array_compare(numpy.less, lighting_labels[1:], 4)
num_valid_lighting_values = len(frozenset(lighting_labels[1:]))
assert_equal(num_valid_lighting_values, 4)
return num_valid_lighting_values
self.lighting_layers = []
add_classifier_mlp_conv(self.shared_layers[-1],
yaml_dict['model']['lighting_layers'],
get_num_lightings(yaml_dict),
use_dropout,
numpy_rng,
theano_rng,
self.lighting_layers)
self.rc_shift_layers = []
add_regressor_mlp_conv(self.shared_layers[-1],
yaml_dict['model']['rc_shift_layers'],
2,
use_dropout,
numpy_rng,
theano_rng,
self.rc_shift_layers)
self.scale_layers = []
add_regressor_mlp_conv(self.shared_layers[-1],
yaml_dict['model']['scale_layers'],
1,
use_dropout,
numpy_rng,
theano_rng,
self.scale_layers)
self.roll_layers = []
add_regressor_mlp_conv(self.shared_layers[-1],
yaml_dict['model']['roll_layers'],
1,
use_dropout,
numpy_rng,
theano_rng,
self.roll_layers)