def test_multimode():
import torch
urls = [local_data] * 8
nsamples = 47 * 8
shardlist = wds.PytorchShardList(urls,
verbose=True,
epoch_shuffle=True,
shuffle=True)
os.environ["WDS_EPOCH"] = "7"
ds = wds.WebDataset(shardlist)
dl = torch.utils.data.DataLoader(ds, num_workers=4)
count = count_samples_tuple(dl)
assert count == nsamples, count
del os.environ["WDS_EPOCH"]
shardlist = wds.PytorchShardList(urls, verbose=True, split_by_worker=False)
ds = wds.WebDataset(shardlist)
dl = torch.utils.data.DataLoader(ds, num_workers=4)
count = count_samples_tuple(dl)
assert count == 4 * nsamples, count
shardlist = shardlists.ResampledShards(urls)
ds = wds.WebDataset(shardlist).slice(170)
dl = torch.utils.data.DataLoader(ds, num_workers=4)
count = count_samples_tuple(dl)
assert count == 170 * 4, count
def test_dataset_rename_keep():
ds = wds.WebDataset(local_data).rename(image="png", keep=False)
sample = next(iter(ds))
assert getkeys(sample) == set(["image"]), getkeys(sample)
ds = wds.WebDataset(local_data).rename(image="png")
sample = next(iter(ds))
assert getkeys(sample) == set(
"cls image wnid xml".split()), getkeys(sample)
def test_dataset_rename_handler():
ds = wds.WebDataset(local_data).rename(image="png;jpg", cls="cls")
count_samples_tuple(ds)
with pytest.raises(ValueError):
ds = wds.WebDataset(local_data).rename(image="missing", cls="cls")
count_samples_tuple(ds)
def test_dataset_rsample():
ds = wds.WebDataset(local_data).rsample(1.0)
assert count_samples_tuple(ds) == 47
ds = wds.WebDataset(local_data).rsample(0.5)
result = [count_samples_tuple(ds) for _ in range(300)]
assert np.mean(result) >= 0.3 * 47 and np.mean(
result) <= 0.7 * 47, np.mean(result)
def test_float_np_vs_torch():
ds = wds.WebDataset(local_data).decode("rgb").to_tuple("png;jpg", "cls")
image, cls = next(iter(ds))
ds = wds.WebDataset(local_data).decode("torchrgb").to_tuple(
"png;jpg", "cls")
image2, cls2 = next(iter(ds))
assert (image == image2.permute(1, 2, 0).numpy()).all(), (image.shape,
image2.shape)
assert cls == cls2
def test_float_np_vs_torch():
ds = wds.WebDataset(local_data, extensions="png;jpg cls")
image, cls = next(iter(ds))
ds = wds.WebDataset(local_data,
extensions="png;jpg cls",
decoder="torchrgb")
image2, cls2 = next(iter(ds))
assert (image == image2.permute(1, 2, 0).numpy()).all(), (image.shape,
image2.shape)
assert cls == cls2
def test_dataset_map_handler():
def f(x):
assert isinstance(x, dict)
return x
def g(x):
raise ValueError()
ds = wds.WebDataset(local_data).map(f)
count_samples_tuple(ds)
with pytest.raises(ValueError):
ds = wds.WebDataset(local_data).map(g)
count_samples_tuple(ds)
def test_rgb8_np_vs_torch():
ds = wds.WebDataset(local_data, extensions="png;jpg cls", decoder="rgb8")
image, cls = next(iter(ds))
assert isinstance(image, np.ndarray), type(image)
assert isinstance(cls, int), type(cls)
ds = wds.WebDataset(local_data,
extensions="png;jpg cls",
decoder="torchrgb8")
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_only1():
ds = wds.WebDataset(local_data).decode(only="cls").to_tuple(
"jpg;png", "cls")
assert count_samples_tuple(ds) == 47
image, cls = next(iter(ds))
assert isinstance(image, bytes)
assert isinstance(cls, int)
ds = wds.WebDataset(local_data).decode("l",
only=["jpg", "png"
]).to_tuple("jpg;png", "cls")
assert count_samples_tuple(ds) == 47
image, cls = next(iter(ds))
assert isinstance(image, np.ndarray)
assert isinstance(cls, bytes)
def test_dataset_map_dict_handler():
ds = wds.WebDataset(local_data).map_dict(png=identity, cls=identity)
count_samples_tuple(ds)
with pytest.raises(KeyError):
ds = wds.WebDataset(local_data).map_dict(png=identity, cls2=identity)
count_samples_tuple(ds)
def g(x):
raise ValueError()
with pytest.raises(ValueError):
ds = wds.WebDataset(local_data).map_dict(png=g, cls=identity)
count_samples_tuple(ds)
def test_rgb8_np_vs_torch():
import warnings
warnings.filterwarnings("error")
ds = wds.WebDataset(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.WebDataset(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_eof():
import tarfile
with pytest.raises(tarfile.ReadError):
ds = wds.WebDataset(
f"pipe:dd if={local_data} bs=1024 count=10").shuffle(5)
assert count_samples(ds) == 47
def test_pipe():
ds = wds.WebDataset(
f"pipe:curl -s '{remote_loc}" + "imagenet_train-{0000..0147}.tgz'",
extensions="jpg;png cls",
shuffle=100,
)
assert count_samples(ds, n=10) == 10
def test_shard_syntax():
ds = wds.WebDataset(
remote_loc + "imagenet_train-{0000..0147}.tgz",
extensions="jpg;png cls",
shuffle=0,
)
assert count_samples(ds, n=10) == 10
def test_dataloader():
import torch
ds = wds.WebDataset(remote_loc + "imagenet_train-{0000..0147}.tgz",
decoder=None)
dl = torch.utils.data.DataLoader(ds, num_workers=4)
assert count_samples(dl, n=100) == 100
def test_rgb8():
ds = wds.WebDataset(local_data).decode("rgb8").to_tuple("png;jpg", "cls")
assert count_samples_tuple(ds) == 47
image, cls = next(iter(ds))
assert isinstance(image, np.ndarray), type(image)
assert image.dtype == np.uint8, image.dtype
assert isinstance(cls, int), type(cls)
def test_dataset_shuffle_decode_rename_extract():
ds = (wds.WebDataset(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_length():
ds = wds.WebDataset(local_data)
with pytest.raises(TypeError):
len(ds)
dsl = ds.with_length(1793)
assert len(dsl) == 1793
dsl2 = dsl.repeat(17).with_length(19)
assert len(dsl2) == 19
def test_writer_pipe(tmpdir):
with writer.TarWriter(f"pipe:cat > {tmpdir}/writer3.tar") as sink:
sink.write(dict(__key__="a", txt="hello", cls="3"))
os.system(f"ls -l {tmpdir}")
ds = wds.WebDataset(f"pipe:cat {tmpdir}/writer3.tar")
for sample in ds:
assert set(sample.keys()) == set("__key__ txt cls".split())
break
def test_log_keys(tmp_path):
tmp_path = str(tmp_path)
fname = tmp_path + "/test.ds.yml"
ds = wds.WebDataset(local_data).log_keys(fname)
result = [x for x in ds]
assert len(result) == 47
with open(fname) as stream:
lines = stream.readlines()
assert len(lines) == 47
def test_tenbin_dec():
ds = wds.WebDataset("testdata/tendata.tar", extensions="ten")
assert count_samples(ds) == 100
for sample in ds:
xs, ys = sample[0]
assert xs.dtype == np.float64
assert ys.dtype == np.float64
assert xs.shape == (28, 28)
assert ys.shape == (28, 28)
def test_decoder():
def mydecoder(key, sample):
return len(sample)
ds = wds.WebDataset(remote_loc + remote_shard).decode(mydecoder).to_tuple(
"jpg;png", "json")
for sample in ds:
assert isinstance(sample[0], int)
break
def test_tenbin_dec():
ds = wds.WebDataset("testdata/tendata.tar").decode().to_tuple("ten")
assert count_samples_tuple(ds) == 100
for sample in ds:
xs, ys = sample[0]
assert xs.dtype == np.float64
assert ys.dtype == np.float64
assert xs.shape == (28, 28)
assert ys.shape == (28, 28)
def test_with_epoch():
ds = wds.WebDataset(local_data)
for _ in range(10):
assert count_samples_tuple(ds) == 47
be = ds.with_epoch(193)
for _ in range(10):
assert count_samples_tuple(be) == 193
be = ds.with_epoch(2)
for _ in range(10):
assert count_samples_tuple(be) == 2
def test_dataset_mock():
obj = ("hello", "world")
ds = wds.WebDataset(local_data).shuffle(5).to_tuple("png;jpg cls").test(
mock_sample=obj)
assert count_samples_tuple(ds) == 47
ds.mock_length = 99
ds.mock = True
assert count_samples_tuple(ds) == 99
sample = next(iter(ds))
assert sample == obj
def test_writer(tmpdir):
with writer.TarWriter(f"{tmpdir}/writer.tar") as sink:
sink.write(dict(__key__="a", txt="hello", cls="3"))
os.system(f"ls -l {tmpdir}")
ftype = os.popen(f"file {tmpdir}/writer.tar").read()
assert "compress" not in ftype, ftype
ds = wds.WebDataset(f"{tmpdir}/writer.tar")
for sample in ds:
assert set(sample.keys()) == set("__key__ txt cls".split())
break
def test_associate():
with open("testdata/imagenet-extra.json") as stream:
extra_data = simplejson.load(stream)
def associate(key):
return dict(MY_EXTRA_DATA=extra_data[key])
ds = wds.WebDataset(local_data, associate=associate)
for sample in ds:
assert "MY_EXTRA_DATA" in sample.keys()
break
def test_decoder():
def mydecoder(sample):
return {k: len(v) for k, v in sample.items()}
ds = wds.WebDataset(
remote_loc + "imagenet_train-0050.tgz",
extensions="jpg;png cls",
decoder=mydecoder,
)
for sample in ds:
assert isinstance(sample[0], int)
break
def test_handlers():
def mydecoder(data):
return PIL.Image.open(io.BytesIO(data)).resize((128, 128))
ds = (wds.WebDataset(remote_loc + remote_shard).decode(
autodecode.handle_extension("jpg", mydecoder),
autodecode.handle_extension("png", mydecoder),
).to_tuple("jpg;png", "json"))
for sample in ds:
assert isinstance(sample[0], PIL.Image.Image)
break
def test_dataset_decode_nohandler():
count = [0]
def faulty_decoder(key, data):
if count[0] % 2 == 0:
raise ValueError("nothing")
else:
return data
count[0] += 1
with pytest.raises(ValueError):
ds = wds.WebDataset(local_data).decode(faulty_decoder)
count_samples_tuple(ds)
