def test_backprop(ml):
# Create 3 different RaggedTensors and torch.tensor
t_1 = torch.randn(10, 3, requires_grad=True)
row_splits = torch.tensor([0, 4, 6, 6, 8, 10])
r_1 = ml.classes.RaggedTensor.from_row_splits(t_1.detach().numpy(),
row_splits)
r_1.requires_grad = True
t_2 = torch.randn(10, 3, requires_grad=True)
r_2 = ml.classes.RaggedTensor.from_row_splits(t_2.detach().numpy(),
row_splits)
r_2.requires_grad = True
t_3 = torch.randn(10, 3, requires_grad=True)
r_3 = ml.classes.RaggedTensor.from_row_splits(t_3.detach().numpy(),
row_splits)
r_3.requires_grad = True
r_ans = (r_1 + r_2) * r_3
t_ans = (t_1 + t_2) * t_3
np.testing.assert_equal(mltest.to_numpy(t_ans),
mltest.to_numpy(r_ans.values))
# Compute gradients
t_ans.sum().backward()
r_ans.values.sum().backward()
np.testing.assert_equal(mltest.to_numpy(t_1.grad),
mltest.to_numpy(r_1.values.grad))
def test_creation_more_dims(dtype, ml):
values = np.array([[0, 0], [1, 1], [2, 2], [3, 3], [4, 4], [5, 5], [6, 6],
[7, 7], [8, 8], [9, 9], [10, 10], [11, 11], [12, 12]],
dtype=dtype)
row_splits = np.array([0, 2, 4, 4, 5, 12, 13], dtype=np.int64)
# From numpy arrays
r_tensor = ml.classes.RaggedTensor.from_row_splits(values, row_splits)
for i, tensor in enumerate(r_tensor):
np.testing.assert_equal(mltest.to_numpy(tensor),
values[row_splits[i]:row_splits[i + 1]])
# From List
r_tensor = ml.classes.RaggedTensor.from_row_splits(list(values),
list(row_splits))
for i, tensor in enumerate(r_tensor):
np.testing.assert_equal(mltest.to_numpy(tensor),
values[row_splits[i]:row_splits[i + 1]])
def test_creation(dtype, ml):
values = np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], dtype=dtype)
row_splits = np.array([0, 2, 4, 4, 5, 12, 13], dtype=np.int64)
# From numpy arrays
r_tensor = ml.classes.RaggedTensor.from_row_splits(values, row_splits)
for i, tensor in enumerate(r_tensor):
np.testing.assert_equal(mltest.to_numpy(tensor),
values[row_splits[i]:row_splits[i + 1]])
# From List
r_tensor = ml.classes.RaggedTensor.from_row_splits(list(values),
list(row_splits))
for i, tensor in enumerate(r_tensor):
np.testing.assert_equal(mltest.to_numpy(tensor),
values[row_splits[i]:row_splits[i + 1]])
# Incompatible tensors.
# Non zero first element.
row_splits = np.array([1, 2, 4, 4, 5, 12, 13], dtype=np.int64)
with np.testing.assert_raises(RuntimeError):
ml.classes.RaggedTensor.from_row_splits(values, row_splits)
# Rank > 1.
row_splits = np.array([[0, 2, 4, 4, 5, 12, 13]], dtype=np.int64)
with np.testing.assert_raises(RuntimeError):
ml.classes.RaggedTensor.from_row_splits(values, row_splits)
# Not increasing monotonically.
row_splits = np.array([[0, 2, 4, 6, 5, 12, 13]], dtype=np.int64)
with np.testing.assert_raises(RuntimeError):
ml.classes.RaggedTensor.from_row_splits(values, row_splits)
# Wrong dtype.
row_splits = np.array([0, 2, 4, 4, 5, 12, 13], dtype=np.float32)
with np.testing.assert_raises(RuntimeError):
ml.classes.RaggedTensor.from_row_splits(values, row_splits)
def test_compare_to_conv3d(ml, dtype, kernel_size, out_channels, in_channels,
with_inp_importance, with_normalization):
"""Compares to the 3D convolution in tensorflow"""
# This test requires tensorflow
try:
import tensorflow as tf
except ImportError:
return
np.random.seed(0)
filters = np.random.random(size=(*kernel_size, in_channels,
out_channels)).astype(dtype)
bias = np.random.random(size=(out_channels,)).astype(dtype)
max_grid_extent = 10
inp_positions = np.unique(np.random.randint(0, max_grid_extent,
(256, 3)).astype(dtype),
axis=0)
inp_positions_int = inp_positions.astype(np.int32)
if with_inp_importance:
inp_importance = np.random.rand(
inp_positions.shape[0]).astype(dtype) - 0.5
else:
inp_importance = np.empty((0,), dtype=dtype)
out_positions = np.unique(np.random.randint(
np.max(kernel_size) // 2, max_grid_extent - np.max(kernel_size) // 2,
(5, 3)).astype(dtype),
axis=0)
out_positions_int = out_positions.astype(np.int32)
voxel_size = 0.2
inp_features = np.random.uniform(size=inp_positions.shape[0:1] +
(in_channels,)).astype(dtype)
if ml.module.__name__ == 'tensorflow':
kernel_initializer = tf.constant_initializer(filters)
bias_initializer = tf.constant_initializer(bias)
elif ml.module.__name__ == 'torch':
torch = ml.module
def kernel_initializer(a):
a.data = torch.from_numpy(filters)
def bias_initializer(a):
a.data = torch.from_numpy(bias)
else:
raise Exception('Unsupported ml framework {}'.format(
ml.module.__name__))
sparse_conv = ml.layers.SparseConv(in_channels=in_channels,
filters=out_channels,
kernel_size=kernel_size,
normalize=with_normalization,
kernel_initializer=kernel_initializer,
bias_initializer=bias_initializer)
if ml.module.__name__ == 'torch':
sparse_conv.to(ml.device)
y = mltest.run_op(ml, ml.device, True, sparse_conv, inp_features,
inp_positions * voxel_size, out_positions * voxel_size,
voxel_size, inp_importance)
# Compare the output to a standard 3d conv
# store features in a volume to use standard 3d convs
inp_volume = np.zeros(
(1, max_grid_extent, max_grid_extent, max_grid_extent, in_channels),
dtype=dtype)
if with_inp_importance:
inp_features *= inp_importance[:, np.newaxis]
inp_volume[0, inp_positions_int[:, 2], inp_positions_int[:, 1],
inp_positions_int[:, 0], :] = inp_features
conv3d = tf.keras.layers.Conv3D(
out_channels,
kernel_size,
kernel_initializer=tf.constant_initializer(filters),
use_bias=False,
padding='same')
y_conv3d = conv3d(inp_volume).numpy()
# extract result at output positions
y_conv3d = np.ascontiguousarray(y_conv3d[0, out_positions_int[:, 2],
out_positions_int[:, 1],
out_positions_int[:, 0], :])
if with_normalization:
for i, v in enumerate(y_conv3d):
num_neighbors = mltest.to_numpy(
sparse_conv.nns.neighbors_row_splits[i + 1] -
sparse_conv.nns.neighbors_row_splits[i])
v /= dtype(num_neighbors)
y_conv3d += bias
np.testing.assert_allclose(y, y_conv3d, rtol=1e-3, atol=1e-5)
def test_compare_to_conv3dtranspose_batches(ml, dtype, kernel_size,
out_channels, in_channels,
with_out_importance,
with_normalization, batch_size):
"""Compares to the 3D convolution in tensorflow"""
# the problem is specific to tensorflow
if ml.module.__name__ != 'tensorflow':
return
# This test requires tensorflow
try:
import tensorflow as tf
except ImportError:
return
np.random.seed(0)
filters = np.random.random(size=(*kernel_size, in_channels,
out_channels)).astype(dtype)
bias = np.random.random(size=(out_channels, )).astype(dtype)
max_grid_extent = 10
# create array defining start and end of each batch
inp_positions_row_splits = np.zeros(shape=(batch_size + 1, ),
dtype=np.int64)
out_positions_row_splits = np.zeros(shape=(batch_size + 1, ),
dtype=np.int64)
for i in range(batch_size - 1):
inp_positions_row_splits[
i + 1] = np.random.randint(15) + inp_positions_row_splits[i]
out_positions_row_splits[
i + 1] = np.random.randint(15) + out_positions_row_splits[i]
inp_positions = np.unique(np.random.randint(0, max_grid_extent,
(512, 3)).astype(dtype),
axis=0)
inp_positions_row_splits[-1] = inp_positions.shape[0]
out_positions = np.unique(np.random.randint(
np.max(kernel_size) // 2, max_grid_extent - np.max(kernel_size) // 2,
(256, 3)).astype(dtype),
axis=0)
out_positions_row_splits[-1] = out_positions.shape[0]
if with_out_importance:
out_importance = np.random.rand(
out_positions.shape[0]).astype(dtype) - 0.5
else:
out_importance = np.empty((0, ), dtype=dtype)
voxel_size = 0.2
inp_features = np.random.uniform(size=inp_positions.shape[0:1] +
(in_channels, )).astype(dtype)
kernel_initializer = tf.constant_initializer(filters)
bias_initializer = tf.constant_initializer(bias)
sparse_conv_transpose = ml.layers.SparseConvTranspose(
in_channels=in_channels,
filters=out_channels,
kernel_size=kernel_size,
normalize=with_normalization,
kernel_initializer=kernel_initializer,
bias_initializer=bias_initializer)
inp_positions = tf.RaggedTensor.from_row_splits(
values=inp_positions, row_splits=inp_positions_row_splits)
out_positions = tf.RaggedTensor.from_row_splits(
values=out_positions, row_splits=out_positions_row_splits)
inp_features = tf.RaggedTensor.from_row_splits(
values=inp_features, row_splits=inp_positions_row_splits)
y = mltest.run_op(ml, ml.device, True, sparse_conv_transpose, inp_features,
inp_positions * voxel_size, out_positions * voxel_size,
voxel_size, out_importance)
for idx in range(batch_size):
inp_pos = inp_positions[idx].numpy()
inp_feat = inp_features[idx].numpy()
out_pos = out_positions[idx].numpy()
inp_pos_int = inp_pos.astype(np.int32)
out_pos_int = out_pos.astype(np.int32)
y_out = y[idx]
# Compare the output to a standard 3d conv
# store features in a volume to use standard 3d convs
inp_volume = np.zeros((1, max_grid_extent, max_grid_extent,
max_grid_extent, in_channels),
dtype=dtype)
if with_normalization:
for i, v in enumerate(inp_feat):
num_neighbors = mltest.to_numpy(
sparse_conv_transpose.nns_inp.neighbors_row_splits[
inp_positions_row_splits[idx] + i + 1] -
sparse_conv_transpose.nns_inp.neighbors_row_splits[
inp_positions_row_splits[idx] + i])
if num_neighbors:
v /= dtype(num_neighbors)
inp_volume[0, inp_pos_int[:, 2], inp_pos_int[:, 1],
inp_pos_int[:, 0], :] = inp_feat
conv3d = tf.keras.layers.Conv3DTranspose(
out_channels,
kernel_size,
kernel_initializer=tf.constant_initializer(
filters.transpose([0, 1, 2, 4, 3])),
use_bias=False,
padding='same')
y_conv3d = conv3d(inp_volume).numpy()
# extract result at output positions
y_conv3d = np.ascontiguousarray(y_conv3d[0, out_pos_int[:, 2],
out_pos_int[:, 1],
out_pos_int[:, 0], :])
if with_out_importance:
y_conv3d *= out_importance[
out_positions_row_splits[idx]:out_positions_row_splits[idx +
1],
np.newaxis]
y_conv3d += bias
np.testing.assert_allclose(y_out, y_conv3d, rtol=1e-3, atol=1e-5)
