def init_data_providers(self): class Dummy: def advance_batch(self): pass if self.need_gpu: ConvNet.init_data_providers(self) else: self.train_data_provider = self.test_data_provider = Dummy()
def get_options_parser(cls): op = ConvNet.get_options_parser() for option in list(op.options): if option not in ('gpu', 'load_file', 'train_batch_range', 'test_batch_range'): op.delete_option(option) op.add_option("show-cost", "show_cost", StringOptionParser, "Show specified objective function", default="") op.add_option("show-filters", "show_filters", StringOptionParser, "Show learned filters in specified layer", default="") op.add_option("input-idx", "input_idx", IntegerOptionParser, "Input index for layer given to --show-filters", default=0) op.add_option("cost-idx", "cost_idx", IntegerOptionParser, "Cost function return value index for --show-cost", default=0) op.add_option("no-rgb", "no_rgb", BooleanOptionParser, "Don't combine filter channels into RGB in layer given to --show-filters", default=False) op.add_option("yuv-to-rgb", "yuv_to_rgb", BooleanOptionParser, "Convert RGB filters to YUV in layer given to --show-filters", default=False) op.add_option("channels", "channels", IntegerOptionParser, "Number of channels in layer given to --show-filters (fully-connected layers only)", default=0) op.add_option("show-preds", "show_preds", StringOptionParser, "Show predictions made by given softmax on test set", default="") op.add_option("only-errors", "only_errors", BooleanOptionParser, "Show only mistaken predictions (to be used with --show-preds)", default=False, requires=['show_preds']) op.add_option("write-features", "write_features", StringOptionParser, "Write test data features from given layer", default="", requires=['feature-path']) op.add_option("feature-path", "feature_path", StringOptionParser, "Write test data features to this path (to be used with --write-features)", default="") op.options['load_file'].default = None return op
def init_model_lib(self): if self.need_gpu: ConvNet.init_model_lib(self)
def import_model(self): if self.need_gpu: ConvNet.import_model(self)
def get_gpus(self): self.need_gpu = self.op.get_value('show_preds') or self.op.get_value('write_features') if self.need_gpu: ConvNet.get_gpus(self)
def __init__(self, op, load_dic): ConvNet.__init__(self, op, load_dic)
def get_options_parser(cls): op = ConvNet.get_options_parser() for option in list(op.options): if option not in ('gpu', 'load_file', 'train_batch_range', 'test_batch_range'): op.delete_option(option) op.add_option("show-cost", "show_cost", StringOptionParser, "Show specified objective function", default="") op.add_option("show-filters", "show_filters", StringOptionParser, "Show learned filters in specified layer", default="") op.add_option("input-idx", "input_idx", IntegerOptionParser, "Input index for layer given to --show-filters", default=0) op.add_option("cost-idx", "cost_idx", IntegerOptionParser, "Cost function return value index for --show-cost", default=0) op.add_option( "no-rgb", "no_rgb", BooleanOptionParser, "Don't combine filter channels into RGB in layer given to --show-filters", default=False) op.add_option( "yuv-to-rgb", "yuv_to_rgb", BooleanOptionParser, "Convert RGB filters to YUV in layer given to --show-filters", default=False) op.add_option( "channels", "channels", IntegerOptionParser, "Number of channels in layer given to --show-filters (fully-connected layers only)", default=0) op.add_option("show-preds", "show_preds", StringOptionParser, "Show predictions made by given softmax on test set", default="") op.add_option( "only-errors", "only_errors", BooleanOptionParser, "Show only mistaken predictions (to be used with --show-preds)", default=False, requires=['show_preds']) op.add_option("write-features", "write_features", StringOptionParser, "Write test data features from given layer", default="", requires=['feature-path']) op.add_option( "feature-path", "feature_path", StringOptionParser, "Write test data features to this path (to be used with --write-features)", default="") op.options['load_file'].default = None return op
def get_gpus(self): self.need_gpu = self.op.get_value('show_preds') or self.op.get_value( 'write_features') if self.need_gpu: ConvNet.get_gpus(self)