def init_subnet_data_provider(self): if self.output_method == 'disk': dp = DataProvider.get_by_name('intermediate') count = self.train_dumper.get_count() self.train_dp = dp(self.train_output_filename, range(0, count), 'fc') count = self.test_dumper.get_count() self.test_dp = dp(self.test_output_filename, range(0, count), 'fc') elif self.output_method == 'memory': dp = DataProvider.get_by_name('memory') self.train_dp = dp(self.train_dumper) self.test_dp = dp(self.test_dumper)
def set_category_range(self, r): dp = DataProvider.get_by_name(self.data_provider) self.train_dp = dp(self.data_dir, self.train_range, category_range=range(r)) self.test_dp = dp(self.data_dir, self.test_range, category_range=range(r))
def set_num_group(self, n): dp = DataProvider.get_by_name(self.data_provider) self.train_dp = dp(self.data_dir, self.train_range, n) self.test_dp = dp(self.data_dir, self.test_range, n)
def init_subnet_data_provider(self): dp = DataProvider.get_by_name('intermediate') count = self.train_dumper.get_count() self.train_dp = dp(self.train_output_filename, range(0, count), 'fc') count = self.test_dumper.get_count() self.test_dp = dp(self.test_output_filename, range(0, count), 'fc')
#create a checkpoint dumper image_shape = (param_dict['image_color'], param_dict['image_size'], param_dict['image_size'], param_dict['batch_size']) param_dict['image_shape'] = image_shape cp_dumper = CheckpointDumper(param_dict['checkpoint_dir'], param_dict['test_id']) param_dict['checkpoint_dumper'] = cp_dumper #create the init_model init_model = cp_dumper.get_checkpoint() if init_model is None: init_model = parse_config_file(args.param_file) param_dict['init_model'] = init_model #create train dataprovider and test dataprovider dp_class = DataProvider.get_by_name(param_dict['data_provider']) train_dp = dp_class(param_dict['data_dir'], param_dict['train_range']) test_dp = dp_class(param_dict['data_dir'], param_dict['test_range']) param_dict['train_dp'] = train_dp param_dict['test_dp'] = test_dp #get all extra information param_dict['num_epoch'] = args.num_epoch num_batch = util.string_to_int_list(args.num_batch) if len(num_batch) == 1: param_dict['num_batch'] = num_batch[0] else: param_dict['num_batch'] = num_batch param_dict['num_group_list'] = util.string_to_int_list(args.num_group_list) param_dict['num_caterange_list'] = util.string_to_int_list(
def set_category_range(self, r): dp = DataProvider.get_by_name(self.data_provider) self.train_dp = dp(self.data_dir, self.train_range, category_range = range(r)) self.test_dp = dp(self.data_dir, self.test_range, category_range = range(r))
#create a checkpoint dumper image_shape = (param_dict['image_color'], param_dict['image_size'], param_dict['image_size'], param_dict['batch_size']) param_dict['image_shape'] = image_shape cp_dumper = CheckpointDumper(param_dict['checkpoint_dir'], param_dict['test_id']) param_dict['checkpoint_dumper'] = cp_dumper #create the init_model init_model = cp_dumper.get_checkpoint() if init_model is None: init_model = parse_config_file(args.param_file) param_dict['init_model'] = init_model #create train dataprovider and test dataprovider dp_class = DataProvider.get_by_name(param_dict['data_provider']) train_dp = dp_class(param_dict['data_dir'], param_dict['train_range']) test_dp = dp_class(param_dict['data_dir'], param_dict['test_range']) param_dict['train_dp'] = train_dp param_dict['test_dp'] = test_dp #get all extra information param_dict['num_epoch'] = args.num_epoch num_batch = util.string_to_int_list(args.num_batch) if len(num_batch) == 1: param_dict['num_batch'] = num_batch[0] else: param_dict['num_batch'] = num_batch param_dict['num_group_list'] = util.string_to_int_list(args.num_group_list)
def init_data_provider(self): dp = DataProvider.get_by_name(self.data_provider) self.train_dp = dp(self.data_dir, self.train_range) self.test_dp = dp(self.data_dir, self.test_range)