def test_data_range():
make_test_h5()
# non-preloading
ds = loaders.H5Dataset('tmp.h5')
ds.set_data_range([2, 4])
assert len(ds) == 2
item = ds[0]
assert np.all(item == 2 * np.ones([5, 5]))
item2 = ds[1]
assert np.all(item2 == 3 * np.ones([5, 5]))
ds.close()
# preloading
ds = loaders.H5Dataset('tmp.h5')
ds.preload()
ds.set_data_range([2, 4])
assert len(ds) == 2
item = ds[0]
assert np.all(item == 2 * np.ones([5, 5]))
item2 = ds[1]
assert np.all(item2 == 3 * np.ones([5, 5]))
ds.close()
rm_test_h5()
return
def test_clip():
make_test_h5()
ds = loaders.H5Dataset('tmp.h5', clip=(1, 5))
item = ds[0]
assert np.all(item == np.ones([5, 5]))
ds.close()
rm_test_h5()
return
def test_h5dataset_basics():
make_test_h5()
ds = loaders.H5Dataset('tmp.h5')
item = ds[0]
assert len(ds) == 4
assert ds.shape == (5, 5)
assert np.all(item == np.zeros([5, 5]))
ds.close()
rm_test_h5()
return
def test_preload():
make_test_h5()
ds = loaders.H5Dataset('tmp.h5')
ds.preload()
assert type(ds._data) == np.ndarray
item = ds[0]
assert len(ds) == 4
assert ds.shape == (5, 5)
assert np.all(item == np.zeros([5, 5]))
ds.close()
rm_test_h5()
return
def test_PDDL():
make_test_h5()
ds = loaders.H5Dataset('tmp.h5')
ddl = loaders.PreloadingDDL(ds, 0, 2, batch_size=2)
for i, b in enumerate(ddl):
assert np.all(b[0].numpy() == i)
assert np.all(b[1].numpy() == i + 1)
assert b.shape == (2, 5, 5)
ds.close()
rm_test_h5()
return
def test_epoch_style_use():
make_test_h5()
ds = loaders.H5Dataset('tmp.h5')
ddl = loaders.DistributedDataLoader(ds, 0, 2, batch_size=2)
for epoch in range(5):
for i, b in enumerate(ddl):
assert np.all(b[0].numpy() == i)
assert np.all(b[1].numpy() == i + 1)
assert b.shape == (2, 5, 5)
ds.close()
rm_test_h5()
return
def test_integration():
make_test_h5()
ds = loaders.H5Dataset('tmp.h5')
ddl = loaders.DistributedDataLoader(ds, 0, 2, batch_size=2)
for i, b in enumerate(ddl):
assert np.all(b[0].numpy() == i)
assert np.all(b[1].numpy() == i + 1)
assert b.shape == (2, 5, 5)
ds.close()
ds2 = loaders.H5Dataset('tmp.h5')
ds2.preload()
ddl2 = loaders.DistributedDataLoader(ds2, 0, 2, batch_size=2)
for i, b in enumerate(ddl2):
assert np.all(b[0].numpy() == i)
assert np.all(b[1].numpy() == i + 1)
assert b.shape == (2, 5, 5)
ds2.close()
rm_test_h5()
return
def test_slice():
make_test_h5()
# non-preloading
ds = loaders.H5Dataset('tmp.h5')
ds.set_data_range([1, 5])
assert len(ds) == 4
item = ds[0:2]
assert np.all(item[0] == 1 * np.ones([5, 5]))
assert np.all(item[1] == 2 * np.ones([5, 5]))
ds.close()
# preloading
ds = loaders.H5Dataset('tmp.h5')
ds.preload()
ds.set_data_range([1, 5])
assert len(ds) == 4
item = ds[0:2]
assert np.all(item[0] == 1 * np.ones([5, 5]))
assert np.all(item[1] == 2 * np.ones([5, 5]))
ds.close()
rm_test_h5()
return
