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 get_train_dataflow():
train_datagen = image_generator_xd(size=config.IMAGE_SIZE,
batchsize=config.BATCH,
ks=range(NCSVS - 1),
lw=config.LW)
ds = DataFromGenerator(train_datagen)
#ds = DataFromGeneratorRNG()
#ds = PrefetchDataZMQ(ds, 12)
return ds
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)
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