def tfr_properties(root_path, index_path, device):
import nvidia.dali.tfrecord as tfrec
features = {
"image/encoded": tfrec.FixedLenFeature((), tfrec.string, ""),
"image/class/label": tfrec.FixedLenFeature([1], tfrec.int64, -1)
}
inputs = fn.readers.tfrecord(path=root_path,
index_path=index_path,
features=features)
enc = fn.get_property(inputs["image/encoded"], key="source_info")
lab = fn.get_property(inputs["image/class/label"], key="source_info")
if device == 'gpu':
enc = enc.gpu()
lab = lab.gpu()
return enc, lab
def wds_properties(root_path, device, idx_paths):
read = fn.readers.webdataset(paths=[root_path],
index_paths=idx_paths,
ext=['jpg'])
if device == 'gpu':
read = read.gpu()
return fn.get_property(read, key="source_info")
def es_properties(layouts, device):
num_outputs = len(layouts)
def gen_data():
yield np.random.rand(num_outputs, 3, 4, 5)
inp = fn.external_source(source=gen_data,
layout=layouts,
num_outputs=num_outputs,
batch=False,
cycle=True,
device=device)
return tuple(fn.get_property(i, key="layout") for i in inp)
def file_properties(files, device):
read, _ = fn.readers.file(files=files)
if device == 'gpu':
read = read.gpu()
return fn.get_property(read, key="source_info")
def improper_property(root_path, device):
read = fn.readers.webdataset(paths=[root_path], ext=['jpg'])
return fn.get_property(read, key=["this key doesn't exist"])
def get_property_pipeline(files):
data, _ = fn.readers.file(files=files)
out = fn.get_property(data, key='source_info')
return out
def file_properties(files):
read, _ = fn.readers.file(files=files)
return fn.get_property(read, key="source_info")
