def test_distribution_collection_serialization():
dists = DistributionCollection([('norm', Normal(5, 2), 2), ('bern', Bernoulli(), 5)])
json_str = dists.to_json()
dists_loaded = load_from_json(json_str)
assert dists_loaded.dtype == dists.dtype
assert dists_loaded.norm_dtype == dists.norm_dtype
n = 50
arr = dists.sample(n)
arr_norm = dists.normalize(arr)
arr_norm_loaded = dists_loaded.normalize(arr)
for name in dists.names:
np.testing.assert_allclose(arr_norm[name], arr_norm_loaded[name])
n = 10000
arr = dists.sample(n)
arr_loaded = dists_loaded.sample(n)
for name in dists.names:
assert abs(arr[name].mean() - arr_loaded[name].mean()) <= 0.10
assert abs(arr[name].std() - arr_loaded[name].std()) <= 0.10
assert dists_loaded.names == dists.names
assert dists_loaded.dtype == dists.dtype
assert dists_loaded.norm_dtype == dists.norm_dtype
assert dists_loaded.distributions == dists.distributions
assert dists_loaded.normalizations == dists.normalizations
assert dists_loaded.nb_elems == dists.nb_elems
def test_augmentation_data_generator(tmpdir):
dist = DistributionCollection(examplary_tag_distribution())
dset_fname = str(tmpdir.join("dset.hdf5"))
samples = 6000
dset = DistributionHDF5Dataset(dset_fname, nb_samples=samples,
distribution=dist)
labels = dist.sample(samples)
labels = dist.normalize(labels)
fake = np.random.random((samples, 1, 8, 8))
discriminator = np.random.random((samples, 1))
dset.append(labels=labels, fake=fake, discriminator=discriminator)
dset.close()
dset = DistributionHDF5Dataset(dset_fname)
bs = 32
names = ['labels', 'fake']
assert 'labels' in next(dset.iter(bs, names))
assert next(dset.iter(bs))['labels'].dtype.names == tuple(dist.names)
dset_iters = [lambda bs: bit_split(dataset_iterator(dset, bs))]
data_gen = lambda bs: zip_dataset_iterators(dset_iters, bs)
label_names = ['bit_0', 'bit_11', 'x_rotation']
aug_gen = augmentation_data_generator(data_gen, 'fake', label_names)
outs = next(aug_gen(bs))
assert len(outs[0]) == 32
assert len(outs[1]) == len(label_names)
gen = aug_gen(bs)
for i, batch in enumerate(gen):
if i == 2*samples // bs:
break
assert batch is not None
assert batch[0].shape == (bs, 1, 8, 8)
assert len(batch[1]) == len(label_names)
def test_augmentation_data_generator(tmpdir):
dist = DistributionCollection(examplary_tag_distribution())
dset_fname = str(tmpdir.join("dset.hdf5"))
samples = 6000
dset = DistributionHDF5Dataset(dset_fname,
nb_samples=samples,
distribution=dist)
labels = dist.sample(samples)
labels = dist.normalize(labels)
fake = np.random.random((samples, 1, 8, 8))
discriminator = np.random.random((samples, 1))
dset.append(labels=labels, fake=fake, discriminator=discriminator)
dset.close()
dset = DistributionHDF5Dataset(dset_fname)
bs = 32
names = ['labels', 'fake']
assert 'labels' in next(dset.iter(bs, names))
assert next(dset.iter(bs))['labels'].dtype.names == tuple(dist.names)
dset_iters = [lambda bs: bit_split(dataset_iterator(dset, bs))]
data_gen = lambda bs: zip_dataset_iterators(dset_iters, bs)
label_names = ['bit_0', 'bit_11', 'x_rotation']
aug_gen = augmentation_data_generator(data_gen, 'fake', label_names)
outs = next(aug_gen(bs))
assert len(outs[0]) == 32
assert len(outs[1]) == len(label_names)
gen = aug_gen(bs)
for i, batch in enumerate(gen):
if i == 2 * samples // bs:
break
assert batch is not None
assert batch[0].shape == (bs, 1, 8, 8)
assert len(batch[1]) == len(label_names)
def test_distribution_collection_normalization():
dists = DistributionCollection([('const', Constant(5), 2), ('bern', Bernoulli(), 5)])
bs = 10
arr = dists.sample(bs)
norm_arr = dists.normalize(arr)
denorm_arr = dists.denormalize(norm_arr)
assert (arr == denorm_arr).all()
def test_distribution_collection_sampling():
dists = DistributionCollection([('const', Constant(5), 2), ('bern', Bernoulli(), 5)])
bs = 10
arr = dists.sample(bs)
assert arr["const"].shape == (bs, 2)
assert (arr["const"] == 5).all()
assert arr["bern"].shape == (bs, 5)
assert (np.logical_or(arr["bern"] == 1, arr["bern"] == 0)).all()
def test_store_samples(tmpdir):
dist = DistributionCollection(examplary_tag_distribution())
bs = 64
labels_record = dist.sample(bs)
labels_record = dist.normalize(labels_record)
labels = []
for name in labels_record.dtype.names:
labels.append(labels_record[name])
labels = np.concatenate(labels, axis=-1)
fakes = np.random.random((bs, 1, 8, 8))
store = StoreSamples(str(tmpdir), dist)
store.on_epoch_end(0, logs={'samples': {'labels': labels, 'fake': fakes}})
assert tmpdir.join("00000.hdf5").exists()
def test_distribution_hdf5_dataset(tmpdir):
with pytest.raises(Exception):
DistributionHDF5Dataset(
str(tmpdir.join('dataset_no_distribution.hdf5')), nb_samples=1000)
dist = DistributionCollection(examplary_tag_distribution(nb_bits=12))
labels = dist.sample(32)
image = np.random.random((32, 1, 8, 8))
dset = DistributionHDF5Dataset(
str(tmpdir.join('dataset.hdf5')), distribution=dist, nb_samples=1000)
dset.append(labels=labels, image=image)
for name in dist.names:
assert name in dset
for batch in dset.iter(batch_size=32):
for name in dist.names:
assert name not in batch
assert 'labels' in batch
assert batch['labels'].dtype == dist.norm_dtype
break
def test_distribution_hdf5_dataset(tmpdir):
with pytest.raises(Exception):
DistributionHDF5Dataset(str(
tmpdir.join('dataset_no_distribution.hdf5')),
nb_samples=1000)
dist = DistributionCollection(examplary_tag_distribution(nb_bits=12))
labels = dist.sample(32)
image = np.random.random((32, 1, 8, 8))
dset = DistributionHDF5Dataset(str(tmpdir.join('dataset.hdf5')),
distribution=dist,
nb_samples=1000)
dset.append(labels=labels, image=image)
for name in dist.names:
assert name in dset
for batch in dset.iter(batch_size=32):
for name in dist.names:
assert name not in batch
assert 'labels' in batch
assert batch['labels'].dtype == dist.norm_dtype
break
