def test_rgb8_np_vs_torch():
ds = wds.Dataset(local_data).decode("rgb8").to_tuple("png;jpg", "cls")
image, cls = next(iter(ds))
assert isinstance(image, np.ndarray), type(image)
assert isinstance(cls, int), type(cls)
ds = wds.Dataset(local_data).decode("torchrgb8").to_tuple("png;jpg", "cls")
image2, cls2 = next(iter(ds))
assert isinstance(image2, torch.Tensor), type(image2)
assert isinstance(cls, int), type(cls)
assert (image == image2.permute(1, 2, 0).numpy()).all, (image.shape,
image2.shape)
assert cls == cls2
def test_dataset_shuffle_decode_rename_extract():
ds = (wds.Dataset(local_data).shuffle(5).decode("rgb").rename(
image="png;jpg", cls="cls").to_tuple("image", "cls"))
assert count_samples_tuple(ds) == 47
image, cls = next(iter(ds))
assert isinstance(image, np.ndarray), image
assert isinstance(cls, int), type(cls)
def test_decoder():
def mydecoder(sample):
return {k: len(v) for k, v in sample.items()}
ds = (wds.Dataset(remote_loc +
"imagenet_train-0050.tgz").decode(mydecoder).to_tuple(
"jpg;png", "cls"))
for sample in ds:
assert isinstance(sample[0], int)
break
def test_opener():
def opener(url):
print(url, file=sys.stderr)
cmd = "curl -s '{}imagenet_train-{}.tgz'".format(remote_loc, url)
return subprocess.Popen(cmd,
bufsize=1000000,
shell=True,
stdout=subprocess.PIPE).stdout
ds = wds.Dataset("{0000..0147}",
opener=opener).shuffle(100).to_tuple("jpg;png cls")
assert count_samples_tuple(ds, n=10) == 10
def test_handlers():
handlers = dict(autodecode.default_handlers["rgb"])
def decode_jpg_and_resize(data):
return PIL.Image.open(io.BytesIO(data)).resize((128, 128))
handlers["jpg"] = decode_jpg_and_resize
ds = (wds.Dataset(remote_loc +
"imagenet_train-0050.tgz").decode(handlers).to_tuple(
"jpg;png", "cls"))
for sample in ds:
assert isinstance(sample[0], PIL.Image.Image)
break
def test_dataset_decode_handler():
count = [0]
good = [0]
def faulty_decoder(key, data):
if "png" not in key:
return data
count[0] += 1
if count[0] % 2 == 0:
raise ValueError("nothing")
else:
good[0] += 1
return data
ds = wds.Dataset(local_data).decode(faulty_decoder,
handler=wds.ignore_and_continue)
result = count_samples_tuple(ds)
assert count[0] == 47
assert good[0] == 24
assert result == 24
def test_torchvision():
import torch
from torchvision import transforms
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
preproc = transforms.Compose([
transforms.RandomResizedCrop(224),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
normalize,
])
ds = (
wds.Dataset(remote_loc +
"imagenet_train-{0000..0147}.tgz").decode("pil").to_tuple(
"jpg;png", "cls").map_tuple(preproc, lambda x: x - 1))
for sample in ds:
assert isinstance(sample[0], torch.Tensor)
assert tuple(sample[0].size()) == (3, 224, 224)
assert isinstance(sample[1], int)
break
