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# # example1 using gfs as input source.
def gen_input_builder_fun(self, model, dataset, is_train):
if is_train:
input_path = self.opts['input']['train_input_path']
else:
input_path = self.opts['input']['test_input_path']
reader = model.CreateDB("reader",
db=input_path,
db_type='lmdb',
shard_id=self.shard_id,
num_shards=self.opts['distributed']['num_shards'],)
def AddImageInput(model, reader, batch_size, img_size):
'''
Image input operator that loads data from reader and
applies certain transformations to the images.
'''
data, label = model.ImageInput(
reader,
["data", "label"],
batch_size=batch_size,
use_caffe_datum=True,
mean=128.,
std=128.,
scale=256,
crop=img_size,
mirror=1,
is_test=True
)
data = model.StopGradient(data, data)
def add_image_input(model):
AddImageInput(
model,
reader,
batch_size=self.opts['epoch_iter']['batch_per_device'],
img_size=self.opts['input']['imsize'],
)
return add_image_input
def get_input_dataset(opts):
return []
def get_model_input_fun(self):
pass
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