def build_iter(self): ds = DataFromGenerator(self.generator) ds = BatchData(ds, self.batch_size) ds = MultiProcessPrefetchData(ds, self.prefetch_size, self.process_num) ds.reset_state() ds = ds.get_data() return ds
def build_iter(self): map_func = partial(self._map_func, is_training=self.training_flag) ds = DataFromGenerator(self.generator) ds = BatchData(ds, self.num_gpu * self.batch_size) ds = MultiProcessPrefetchData(ds, self.prefetch_size, self.process_num) ds.reset_state() ds = ds.get_data() return ds
def build_iter(self): ds = DataFromGenerator(self.generator) ds = RepeatedData(ds, -1) ds = BatchData(ds, self.batch_size) if not cfg.TRAIN.vis: ds = PrefetchDataZMQ(ds, self.process_num) ds.reset_state() ds = ds.get_data() return ds
def build_iter(self, ): ds = DataFromGenerator(self.generator) if cfg.DATA.mutiscale and self.training_flag: ds = MutiScaleBatcher(ds, self.num_gpu * self.batch_size, scale_range=cfg.DATA.scales, input_size=(cfg.DATA.hin, cfg.DATA.win)) else: ds = MutiScaleBatcher(ds, self.num_gpu * self.batch_size, input_size=(cfg.DATA.hin, cfg.DATA.win)) ds = MultiProcessPrefetchData(ds, self.prefetch_size, self.process_num) ds.reset_state() ds = ds.get_data() return ds