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")