def write_hub(arr, path, overwrite=True):
"""Write a hub dataset to disk
"""
if os.path.exists(path) and os.path.isdir(path) and overwrite:
shutil.rmtree(path)
if os.path.exists(path):
raise FileExistsError("Output path {} already exists".format(path))
if arr.ndim == 1:
schema = {"value": hub.schema.Tensor(arr.shape[0])}
dataset = hub.Dataset(path, shape=(1, ), schema=schema, mode='w')
dataset["value", 0][:] = arr.astype(np.float32)
dataset.flush()
dataset.close()
elif arr.ndim == 2:
schema = {"value": hub.schema.Tensor(arr.shape[1])}
dataset = hub.Dataset(path,
shape=(arr.shape[0], ),
schema=schema,
mode='w')
dataset["value"][:] = arr.astype(np.float32)
dataset.flush()
dataset.close()
else:
raise ValueError("hub backend only supports 1D or 2D arrays")
def get_dataset_from_hub(samples=1, read_from_fs=False, pytorch=False):
"""
Build dataset and transform to pytorch or tensorflow
"""
my_schema = {"img": Tensor(shape=(3, 256, 256)), "label": "uint8"}
if not read_from_fs:
ds = hub.Dataset(
"kristina/benchmarking",
shape=(samples, ),
schema=my_schema,
cache=False,
)
else:
ds = hub.Dataset(
"s3://snark-test/benchmarking",
shape=(samples, ),
schema=my_schema,
cache=False,
)
for i in range(samples):
ds["img", i] = np.random.rand(3, 256, 256)
ds["label", i] = 0
ds_hub = ds.to_pytorch() if pytorch else ds.to_tensorflow()
ds = MyDataset(ds_hub)
return ds
def create_large_dataset():
sample_count = 60 # change this to big number to test
# Decide schema of the dataset
schema = {
"image":
Tensor((1920, 1080, 3), chunks=(2, 1920, 1080, 3), dtype="float64")
}
array = np.random.random((10, 1920, 1080, 3))
# Write the dataset
ds = hub.Dataset(
"./data/examples/large_dataset_build",
shape=(sample_count, ),
schema=schema,
)
for i in range(len(ds) // 10):
ds["image", i * 10:i * 10 + 10] = i * array
ds.commit()
ds = hub.Dataset("./data/examples/large_dataset_build")
print(ds.keys, ds["image"].shape, ds["image"].dtype)
# Read the dataset
with hub.Dataset("./data/examples/large_dataset_build") as ds:
for i in range(len(ds) // 10):
assert (ds["image", i * 10, 0, 0, 0].compute() /
array[0, 0, 0, 0]) == i
def infer_dataset(path, scheduler="single", workers=1):
# TODO: handle s3 path
if not os.path.isdir(path):
raise Exception("input path must be either a directory")
hub_path = os.path.join("./", path, "hub")
if os.path.isdir(hub_path):
print('inferred dataset found in "%s", using that' % hub_path)
return hub.Dataset(hub_path, mode="r")
root = _find_root(path)
ds = None
directory_parsers = state.get_parsers()
if len(directory_parsers) <= 0:
raise Exception("directory parsers list was empty.")
# go through all functions created using the `directory_parser` decorator in
# `hub.schema.auto.directory_parsers`
for parser in directory_parsers:
ds = parser(root, scheduler, workers)
if ds is not None:
break
if ds is None:
raise Exception(
'could not infer dataset for the root "%s". either add a new parser to'
% root +
"`hub.schema.auto.directory_parsers` or write a custom transform + schema."
)
ds.store(hub_path) # TODO: handle s3
return hub.Dataset(hub_path, mode="r")
def test_read_mode():
my_schema = {"abc": "uint8"}
ds = hub.Dataset("./data/test_versioning/read_ds",
schema=my_schema,
shape=(10, ))
ds.checkout("second", create=True)
ds2 = hub.Dataset("./data/test_versioning/read_ds", mode="r")
with pytest.raises(ReadModeException):
ds2.commit("first")
with pytest.raises(ReadModeException):
ds2.checkout("third", create=True)
with pytest.raises(ReadModeException):
ds2["abc", 4] = 10
def test_stacked_transform():
schema = {"test": Tensor((2, 2), dtype="uint8")}
@hub.transform(schema=schema)
def multiply_transform(sample, multiplier=1, times=1):
if times == 1:
return {"test": multiplier * sample["test"]}
else:
return [{
"test": multiplier * sample["test"]
} for i in range(times)]
@hub.transform(schema=schema)
def multiply_transform_2(sample, multiplier=1, times=1):
if times == 1:
return {"test": multiplier * sample["test"]}
else:
return [{
"test": multiplier * sample["test"]
} for i in range(times)]
ds = hub.Dataset("./data/stacked_transform",
mode="w",
shape=(5, ),
schema=schema)
for i in range(5):
ds["test", i] = np.ones((2, 2))
ds1 = multiply_transform(ds, multiplier=2, times=5)
ds2 = multiply_transform(ds1, multiplier=3, times=2)
ds3 = multiply_transform_2(ds2, multiplier=5, times=3)
ds4 = ds3.store("./data/stacked_transform_2")
assert len(ds4) == 150
assert (ds4["test", 0].compute() == 30 * np.ones((2, 2))).all()
def time_tiledb(dataset, batch_size=1):
ds = hub.Dataset(dataset)
if os.path.exists(dataset.split("/")[1] + "_tileDB"):
ds_tldb = tiledb.open(dataset.split("/")[1] + "_tileDB")
else:
if not os.path.exists(dataset.split("/")[1] + "_tileDB"):
os.makedirs(dataset.split("/")[1] + "_tileDB")
ds_numpy = np.concatenate(
(
ds["image"].compute().reshape(ds.shape[0], -1),
ds["label"].compute().reshape(ds.shape[0], -1),
),
axis=1,
)
ds_tldb = tiledb.from_numpy(
dataset.split("/")[1] + "_tileDB", ds_numpy)
assert type(ds_tldb) == tiledb.array.DenseArray
with Timer("Time"):
counter = 0
t0 = time()
for batch in range(ds.shape[0] // batch_size):
x, y = (
ds_tldb[batch * batch_size:(batch + 1) * batch_size, :-1],
ds_tldb[batch * batch_size:(batch + 1) * batch_size, -1],
)
counter += 1
t1 = time()
print("Batch", counter, f"dt: {t1 - t0}")
t0 = t1
def test_pipeline_basic():
ds = hub.Dataset("./data/test/test_pipeline_basic",
mode="w",
shape=(100, ),
schema=my_schema)
for i in range(len(ds)):
ds["image", i] = np.ones((28, 28, 4), dtype="int32")
ds["label", i] = f"hello {i}"
ds["confidence", i] = 0.2
@hub.transform(schema=my_schema)
def my_transform(sample, multiplier: int = 2):
return {
"image": sample["image"] * multiplier,
"label": sample["label"],
"confidence": sample["confidence"] * multiplier,
}
out_ds = my_transform(ds, multiplier=2)
assert (out_ds["image", 0].compute() == 2).all()
assert len(list(out_ds)) == 100
res_ds = out_ds.store("./data/test/test_pipeline_basic_output")
assert res_ds["label", 5].compute() == "hello 5"
assert (res_ds["image", 4].compute() == 2 * np.ones(
(28, 28, 4), dtype="int32")).all()
assert len(res_ds) == len(out_ds)
assert res_ds.shape[0] == out_ds.shape[0]
assert "image" in res_ds.schema.dict_ and "label" in res_ds.schema.dict_
def test_pipeline_ray():
ds = hub.Dataset(
"./data/test/test_pipeline_basic",
mode="w",
shape=(100, ),
schema=my_schema,
cache=False,
)
for i in range(len(ds)):
ds["image", i] = np.ones((28, 28, 4), dtype="int32")
ds["label", i] = f"hello {i}"
ds["confidence/confidence", i] = 0.2
@hub.transform(schema=my_schema, scheduler="ray")
def my_transform(sample, multiplier: int = 2):
return {
"image": sample["image"] * multiplier,
"label": sample["label"],
"confidence": {
"confidence": sample["confidence"]["confidence"] * multiplier
},
}
out_ds = my_transform(ds, multiplier=2)
assert (out_ds["image", 0].compute() == 2).all()
assert len(list(out_ds)) == 100
out_ds.store("./data/test/test_pipeline_basic_output")
def test_checkout_address_not_found():
my_schema = {"abc": "uint8"}
ds = hub.Dataset("./data/test_versioning/ds_address",
schema=my_schema,
shape=(10, ))
with pytest.raises(AddressNotFound):
ds.checkout("second")
def test_pipeline():
ds = hub.Dataset("./data/test/test_pipeline_multiple2",
mode="w",
shape=(100, ),
schema=my_schema)
for i in range(len(ds)):
ds["image", i] = np.ones((28, 28, 4), dtype="int32")
ds["label", i] = f"hello {i}"
ds["confidence", i] = 0.2
with Timer("multiple pipes"):
@hub.transform(schema=my_schema)
def my_transform(sample, multiplier: int = 2):
return {
"image": sample["image"] * multiplier,
"label": sample["label"],
"confidence": sample["confidence"] * multiplier,
}
out_ds = my_transform(ds, multiplier=2)
out_ds = my_transform(out_ds, multiplier=2)
out_ds = out_ds.store("./data/test/test_pipeline_multiple_4")
assert (out_ds["image", 0].compute() == 4).all()
def example_to_pytorch():
ds = hub.Dataset("activeloop/fashion_mnist_train")
torch_ds = ds.to_pytorch(output_type=list)
torch_dataloader = torch.utils.data.DataLoader(
torch_ds,
batch_size=8,
)
return torch_dataloader
def __iter__(self):
if self.dataset is None:
self.dataset = hub.Dataset(self.path, self.storage)
for x in self._enumerate(self.dataset):
x = self.transform(x)
yield (*list(x), )
def test_dataset_with_objects():
schema = {"images": Tensor(shape=(10,), dtype="object", chunks=(5,))}
ds = hub.Dataset(
"./data/test/test_dataset_with_objects", mode="w", shape=(100,), schema=schema
)
ds["images", 6, 5] = np.ones((20, 30, 4), dtype="uint8")
ds.close()
def test_old_datasets():
ds = hub.Dataset("activeloop/mnist")
with pytest.raises(VersioningNotSupportedException):
ds.checkout("third")
with pytest.raises(VersioningNotSupportedException):
ds.checkout("third", create=True)
with pytest.raises(VersioningNotSupportedException):
ds.log()
def generate_dataset(shape=(10,), size=(1024, 1024), chunksize=None):
"""
Generates a datasets with random tensors
"""
my_schema = {"img": Tensor(shape=shape, chunks=chunksize)}
ds = hub.Dataset("kristina/benchmarking", shape=(10,), schema=my_schema)
for i in range(shape):
ds[i] = np.random.rand(size)
return ds
def get_hub_dataset():
schema = hub.schema.SchemaDict({
'text':
hub.schema.Tensor(shape=(None, ), dtype='int64', max_shape=(2049, ))
})
ds = hub.Dataset("snsi/pile_train0", schema=schema,
shape=(100000, )).to_pytorch()
# ds = hub.Dataset("interneuron/pile_train0", shape=(None,)).to_pytorch()
return HubAdapter(ds)
def test_commit_checkout():
my_schema = {"img": hub.schema.Tensor((1000, 1000, 3))}
ds = hub.Dataset("./data/eg_1", shape=(10, ), schema=my_schema, mode="w")
for i in range(10):
ds["img", i] = np.ones((1000, 1000, 3))
first_commit_id = ds.commit("stored all ones")
for i in range(5):
ds["img", i] = ds["img", i].compute() * 2
second_commit_id = ds.commit("multiplied value of some images by 2")
assert (ds["img", 4].compute() == 2 * np.ones((1000, 1000, 3))).all()
ds.checkout(first_commit_id) # now all images are ones again
for i in range(10):
assert (ds["img", i].compute() == np.ones((1000, 1000, 3))).all()
ds.checkout(
"alternate", create=True
) # creating a new branch as we are currently not on the head of master
for i in range(5):
ds["img", i] = ds["img", i].compute() * 3
# if we had not checked out to "alternate" branch earlier here it would auto checkout to a new branch
ds.commit("multiplied value of some images by 3")
assert (ds["img", 4].compute() == 3 * np.ones((1000, 1000, 3))).all()
ds.checkout(second_commit_id) # first 5 images are 2s, rest are 1s now
for i in range(5, 10):
ds["img", i] = ds["img", i].compute() * 2
# we are not at the head of master but rather at the last commit, so we automatically get checkouted out to a new branch here
# this happens any time we try to commit when we are not at the head of the branch
ds.commit("multiplied value of remaining images by 2")
for i in range(10):
assert (ds["img", i].compute() == 2 * np.ones((1000, 1000, 3))).all()
ds.checkout("alternate")
for i in range(5, 10):
ds["img", i] = ds["img", i].compute() * 3
for i in range(10):
assert (ds["img", i].compute() == 3 * np.ones((1000, 1000, 3))).all()
# we are already at the head of alternate so it does not check us out to a new branch, rather we commit on the alternate branch itself
ds.commit("multiplied value of remaining images by 3")
def test_hub_open():
ds = hub.Dataset("./data/test/hub_open",
token=None,
shape=(10000, ),
mode="w",
schema=schema)
ds["label/a", 5, 50, 50] = 9
assert ds["label/a", 5, 50, 50].numpy() == 9
ds["image", 5, 4, 120:200, 150:300, :] = 3 * np.ones((80, 150, 3), "uint8")
assert (ds["image", 5, 4, 120:200, 150:300, :].numpy() == 3 * np.ones(
(80, 150, 3), "uint8")).all()
def test_auto_checkout_bug():
my_schema = {"abc": "uint8"}
ds = hub.Dataset("./data/test_versioning/branch_bug",
shape=(10, ),
schema=my_schema,
mode="w")
ds["abc", 0] = 1
a = ds.commit("it is 1")
ds["abc", 0] = 2
b = ds.commit("it is 2")
c = ds.checkout(a)
d = ds.checkout("other", True)
ds["abc", 0] = 3
e = ds.commit("it is 3")
ds.checkout(b)
ds["abc", 0] = 4
f = ds.commit("it is 4")
g = ds.checkout(a)
dsv = ds[0:3]
dsv["abc", 0] = 5
h = ds.commit("it is 5")
i = ds.checkout(e)
tsv = ds[0:5, "abc"]
tsv[0] = 6
j = ds.commit("it is 6")
ds.log()
ds.checkout(a)
assert dsv["abc", 0].compute() == 1
assert ds["abc", 0].compute() == 1
ds.checkout(b)
assert ds["abc", 0].compute() == 2
ds.checkout(c)
assert ds["abc", 0].compute() == 1
ds.checkout(d)
assert ds["abc", 0].compute() == 3
ds.checkout(e)
assert ds["abc", 0].compute() == 3
ds.checkout(f)
assert ds["abc", 0].compute() == 4
ds.checkout(g)
assert ds["abc", 0].compute() == 1
ds.checkout(h)
assert ds["abc", 0].compute() == 5
ds.checkout(i)
assert ds["abc", 0].compute() == 3
ds.checkout(j)
assert ds["abc", 0].compute() == 6
ds.checkout("master")
assert ds["abc", 0].compute() == 2
ds["abc", 0] = 7
ds.checkout("copy", True)
assert ds["abc", 0].compute() == 7
ds.checkout("other")
assert ds["abc", 0].compute() == 3
def time_hub(dataset, batch_size=1, num_batches=1, local=True, user=None):
my_schema = {
"image": hub.schema.Image(shape=(28, 28, 1), dtype="uint8"),
"label": hub.schema.ClassLabel(num_classes=10),
}
if local is True:
ds = hub.Dataset(
"./" + dataset + "_hub",
shape=(batch_size * num_batches, ),
schema=my_schema,
mode="w",
)
else:
ds = hub.Dataset(
user + "/" + dataset,
shape=(batch_size * num_batches, ),
schema=my_schema,
mode="w",
)
assert type(ds) == hub.api.dataset.Dataset
time_batches(ds, batch_size, num_batches, hub=True)
def bench_hub_compression(times=REPEAT_TIMES):
arr = np.array(IMG)
ds = hub.Dataset(
"./data/bench_png_compression",
mode="w",
shape=times,
schema={"image": hub.schema.Image(arr.shape, compressor="png")},
)
batch = np.zeros((times, ) + arr.shape, dtype="uint8")
for i in range(times):
batch[i] = arr
with Timer("Hub compression"):
ds["image", :times] = batch
def benchmark_compress_hub_setup(
times, image_path="./images/compression_benchmark_image.png"):
img = Image.open(image_path)
arr = np.array(img)
ds = hub.Dataset(
"./data/bench_png_compression",
mode="w",
shape=times,
schema={"image": hub.schema.Image(arr.shape, compressor="png")},
)
batch = np.zeros((times, ) + arr.shape, dtype="uint8")
for i in range(times):
batch[i] = arr
return (ds, times, batch)
def bench_hub_compression(img_path=img_path, count=count):
img = Image.open(img_path)
arr = np.array(img)
print(arr.shape)
ds = hub.Dataset(
"./data/benchmarks/bench_png_compression",
mode="w",
shape=count,
schema={"image": hub.schema.Image(arr.shape, compressor="png")},
)
print(ds._tensors["/image"].chunks)
bigarr = np.zeros((count, ) + arr.shape, dtype="uint8")
for i in range(count):
bigarr[i] = arr
with Timer("Hub compression"):
ds["image", :count] = bigarr
def test_threaded():
init_schema = {
"image":
Tensor(shape=(None, None, None),
max_shape=(4, 224, 224),
dtype="float32")
}
schema = {
"image":
Tensor(shape=(None, None, None),
max_shape=(4, 224, 224),
dtype="float32"),
"label":
Tensor(shape=(None, ), max_shape=(6, ), dtype="uint8"),
"text_label":
Text((None, ), "int64", (14, )),
"flight_code":
Text((None, ), "int64", (10, )),
}
ds_init = hub.Dataset(
"./data/hub/new_pipeline_threaded2",
mode="w",
shape=(10, ),
schema=init_schema,
cache=False,
)
for i in range(len(ds_init)):
ds_init["image", i] = np.ones((4, 220, 224))
ds_init["image", i] = np.ones((4, 221, 224))
@hub.transform(schema=schema, scheduler="threaded", workers=2)
def create_classification_dataset(sample):
ts = sample["image"]
return [{
"image": ts,
"label": np.ones((6, )),
"text_label": "PLANTED",
"flight_code": "UYKNTHNXR",
} for _ in range(5)]
ds = create_classification_dataset(ds_init).store(
"./data/hub/new_pipeline_threaded_final")
assert ds["image", 0].shape[1] == 221
def time_hub(dataset, batch_size=1):
ds = hub.Dataset(dataset, cache=False, storage_cache=False, mode="r")
assert type(ds) == hub.api.dataset.Dataset
with Timer("Time"):
counter = 0
t0 = time()
for batch in range(ds.shape[0] // batch_size):
x, y = (
ds[batch * batch_size : (batch + 1) * batch_size]["image"].compute(),
ds[batch * batch_size : (batch + 1) * batch_size]["label"].compute(),
)
counter += 1
t1 = time()
print("Batch", counter, f"dt: {t1 - t0}")
t0 = t1
def test_commit():
my_schema = {"abc": "uint32"}
ds = hub.Dataset("./data/test_versioning/eg_1",
shape=(10, ),
schema=my_schema,
mode="w")
ds["abc", 0] = 1
a = ds.commit("first")
ds["abc", 0] = 2
b = ds.commit("second")
ds["abc", 0] = 3
c = ds.commit("third")
assert ds["abc", 0].compute() == 3
ds.checkout(a)
assert ds["abc", 0].compute() == 1
ds.checkout(b)
assert ds["abc", 0].compute() == 2
ds.checkout(c)
assert ds["abc", 0].compute() == 3
def test_commit_checkout_2():
my_schema = {
"abc": "uint32",
"img": Image((1000, 1000, 3), dtype="uint16"),
}
ds = hub.Dataset("./data/test_versioning/eg_3",
shape=(100, ),
schema=my_schema,
mode="w")
for i in range(100):
ds["img", i] = i * np.ones((1000, 1000, 3))
a = ds.commit("first")
# chunk 7.0.0.0 gets rewritten
ds["img", 21] = 2 * ds["img", 21].compute()
# the rest part of the chunk stays intact
assert (ds["img", 21].compute() == 2 * 21 * np.ones((1000, 1000, 3))).all()
assert (ds["img", 22].compute() == 22 * np.ones((1000, 1000, 3))).all()
assert (ds["img", 23].compute() == 23 * np.ones((1000, 1000, 3))).all()
# other chunks are still accessed from original chunk, for eg chunk 11 that contains 35th sample has single copy
assert (ds["img", 35].compute() == 35 * np.ones((1000, 1000, 3))).all()
b = ds.commit("second")
# going back to first commit
ds.checkout(a)
# sanity check
assert (ds["img", 21].compute() == 21 * np.ones((1000, 1000, 3))).all()
ds.checkout("another", create=True)
ds["img", 21] = 3 * ds["img", 21].compute()
assert (ds["img", 21].compute() == 3 * 21 * np.ones((1000, 1000, 3))).all(
) # and not 6 * 21 as it would have been, had we checked out from b
ds.commit("first2")
ds.checkout("master")
assert (ds["img", 21].compute() == 2 * 21 * np.ones((1000, 1000, 3))).all()
ds.log()
def main():
ds = hub.Dataset("eurosat/eurosat-rgb")
# 26000 samples in dataset, accessing values
print(ds["image"][10].numpy())
print(
ds["label", 15].numpy()
) # alternate way to access, by specifying both key and sample number at once
print(ds["filename", 20:22].numpy()) # accessing multiple elements at once
# Splitting into train and test sets
train_ds = ds[:13000]
test_ds = ds[13000:]
# Using hub with tensorflow
train_tf_ds = train_ds.to_tensorflow().batch(2)
for batch in train_tf_ds:
print(batch["label"], batch["filename"], batch["image"])
break
test_tf_ds = test_ds.to_tensorflow().batch(2)
for batch in test_tf_ds:
print(batch["label"], batch["filename"], batch["image"])
break
# Using hub with pytorch
train_pt_ds = train_ds.to_pytorch()
train_loader = torch.utils.data.DataLoader(train_pt_ds, batch_size=2)
for batch in train_loader:
print(batch["label"], batch["image"]
) # pytorch tensors don't support text labels such as filename
break
test_pt_ds = test_ds.to_pytorch()
test_loader = torch.utils.data.DataLoader(test_pt_ds, batch_size=2)
for batch in test_loader:
print(batch["label"], batch["image"]
) # pytorch tensors don't support text labels such as filename
break
def main():
sample_count = 70000
step = 10
with Timer("Time"):
ds = hub.Dataset(
"./data/examples/mnist_upload_speed_benchmark",
mode="w",
schema=schema,
shape=(sample_count, ),
cache=2**26,
)
arr = (np.random.rand(step, 28, 28) * 100).astype("uint8")
for i in range(0, sample_count, step):
# with Timer(f"Sample {i}"):
ds["image", i:i + step] = arr
ds.commit()
