def _default_config(cls): from dxpy.collections.dicts import combine_dicts return combine_dicts({ 'nb_down_sample': 3, 'loss_weight': 1.0, 'share_model': False }, super()._default_config())
def _default_config(cls): from dxpy.collections.dicts import combine_dicts return combine_dicts({ 'paths': 3, 'activation': 'incept', }, super()._default_config())
def __init__(self, name='summary', tensors_to_summary=None, inputs=None, **config): """ Inputs: tensors_to_summary: dict of name: SummaryItem inputs: tensors required to run summary (will used for feeds) """ super().__init__(name, **config) if tensors_to_summary is None: tensors_to_summary = dict() if inputs is None: inputs = dict() self._tensors = combine_dicts(tensors_to_summary, self._default_summaries()) for k in inputs: self.register_node(k, inputs[k]) self._summary_ops = list() self.register_task('create_writer', self.__create_writer) self.register_task('flush', self.flush) self.register_main_task(self.summary) if self.param('add_prefix'): with tf.name_scope('summary'): self.__add_all_ops() else: self.__add_all_ops()
def _default_config(cls): from dxpy.collections.dicts import combine_dicts return combine_dicts({ 'building_block': BuildingBlocks.STACKEDCONV, 'nb_layers': 10, 'filters': 32, 'boundary_crop': [4, 4] }, super()._default_config())
def _default_config(cls): from dxpy.collections.dicts import combine_dicts return combine_dicts( { 'allow_growth': True, 'log_device_placement': False }, super()._default_config())
def _default_config(cls): from dxpy.collections.dicts import combine_dicts cfg = { 'method': 'mean', 'padding': 'same', 'keep_energy': False, } return combine_dicts(cfg, super()._default_config())
def _default_config(cls): return combine_dicts( { 'path': './summary', 'add_prefix': False, 'max_image': 3 }, super()._default_config())
def _default_config(cls): from dxpy.collections.dicts import combine_dicts fmt = '/home/hongxwing/Datas/phantom/phantom.{}.tfrecord' files = [fmt.format(i) for i in range(3)] return combine_dicts({ 'files': files, 'fields': 'sinogram', 'batch_size': 32 }, super()._default_config())
def _default_config(cls): from dxpy.collections.dicts import combine_dicts cfg = { 'keep_original': True, 'original_key': 'ds1x', 'rigister_output_with_prefix': False, 'with_shape_info': False } return combine_dicts(cfg, super()._default_config())
def _default_config(cls): from dxpy.collections.dicts import combine_dicts return combine_dicts( { 'kernel_size': 3, 'strides': (1, 1), 'padding': 'same', 'activation': 'basic' }, super()._default_config())
def _default_config(cls): from dxpy.collections.dicts import combine_dicts cfg = { 'lazy_create': False, 'reuse': None, 'register_inputs': True, 'register_outputs': True, 'simple_output': True, 'no_new_output_for_none_feeds': True, 'register_output_with_prefix': True, } return combine_dicts(cfg, super()._default_config())
def __init__(self, name, net, tensors_to_summary=None, inputs=None, **config): automatic_summary_tensors = dict() automatic_keys = [NodeKeys.LOSS, NodeKeys.INFERENCE, NodeKeys.EVALUATE] for k in automatic_keys: if k in net.nodes: automatic_summary_tensors[k] = SummaryItem(net[k]) if tensors_to_summary is None: tensors_to_summary = dict() super().__init__( name, combine_dicts(tensors_to_summary, automatic_summary_tensors), inputs, **config)
def _default_config(cls): from dxpy.collections.dicts import combine_dicts return combine_dicts({'dtype': tf.float32}, super()._default_config())
def _default_config(cls): from dxpy.collections.dicts import combine_dicts cfg = {'input_shape': [14, 14, 1], 'label_shape': [28, 28, 1]} return combine_dicts(cfg, super()._default_config())
def _default_config(cls): from dxpy.collections.dicts import combine_dicts cfg = {'activation': 'basic', 'padding': 'same', 'filters': 32} return combine_dicts(cfg, super()._default_config())
def _default_config(cls): from dxpy.collections.dicts import combine_dicts return combine_dicts({ 'add_trainer': True, 'add_saver': True }, super()._default_config())
def _default_config(cls): from dxpy.collections.dicts import combine_dicts return combine_dicts({'with_shape_info': False}, super()._default_config())
def __load_config(self, config_direct): from .config import config as config_global from dxpy.collections.dicts import combine_dicts return combine_dicts(config_direct, config_global, self._default_config())