Name	Size	Mode	Actions
__pycache__/	-	0755	rm
caffe2_resnet50_default_forward.py	653	0644	editdlrm
caffe2_resnet50_default_param_update.py	1331	0644	editdlrm
explicit_resnet_forward.py	11491	0644	editdlrm
explicit_resnet_param_update.py	2268	0644	editdlrm
gfs_IN1k.py	1406	0644	editdlrm
IN1k_resnet.py	1498	0644	editdlrm
IN1k_resnet_no_test_model.py	1880	0644	editdlrm
override_no_test_model_no_checkpoint.py	307	0644	editdlrm
rendezvous_filestore.py	1416	0644	editdlrm
__init__.py	0	0644	editdlrm

# # 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