import tensorflow as tf
from wtftf.meta import layered
def _sparse_tensor(args, **kwargs):
return tf.SparseTensor(*args, **kwargs)
# constructors
SparseTensor = layered(tf.SparseTensor)
# attrs
values = layered(tf.SparseTensor.values.fget)
indices = layered(tf.SparseTensor.indices.fget)
dense_shape = layered(tf.SparseTensor.dense_shape.fget)
from wtftf.meta import layered from ecn.ops import neighbors as _neigh_ops compute_pooled_neighbors = layered(_neigh_ops.compute_pooled_neighbors) compute_full_neighbors = layered(_neigh_ops.compute_full_neighbors) compute_pointwise_neighbors = layered(_neigh_ops.compute_pointwise_neighbors) compute_neighbors = layered(_neigh_ops.compute_neighbors)
import tensorflow as tf
from wtftf.meta import layered
# factories
from_row_splits = layered(tf.RaggedTensor.from_row_splits)
from_nested_row_splits = layered(tf.RaggedTensor.from_nested_row_splits)
from_value_rowids = layered(tf.RaggedTensor.from_value_rowids)
from_nested_value_rowids = layered(tf.RaggedTensor.from_nested_value_rowids)
from_row_lengths = layered(tf.RaggedTensor.from_row_lengths)
from_nested_row_lengths = layered(tf.RaggedTensor.from_nested_row_lengths)
from_uniform_row_length = layered(tf.RaggedTensor.from_uniform_row_length)
from_row_limits = layered(tf.RaggedTensor.from_row_limits)
from_row_starts = layered(tf.RaggedTensor.from_row_starts)
from_sparse = layered(tf.RaggedTensor.from_sparse)
from_tensor = layered(tf.RaggedTensor.from_tensor)
if tf.version.VERSION < "2.4":
# attrs
values = layered(tf.RaggedTensor.values.fget)
row_splits = layered(tf.RaggedTensor.row_splits.fget)
flat_values = layered(tf.RaggedTensor.flat_values.fget)
nested_row_splits = layered(tf.RaggedTensor.nested_row_splits.fget)
# methods
row_lengths = layered(tf.RaggedTensor.row_lengths)
row_starts = layered(tf.RaggedTensor.row_starts)
value_rowids = layered(tf.RaggedTensor.value_rowids)
to_tensor = layered(tf.RaggedTensor.to_tensor)
nrows = layered(tf.RaggedTensor.nrows)
import numpy as np
import tensorflow as tf
from wtftf.meta import layered
def f_np(x, y):
return np.sin(x) + y
def f_graph(x, y):
return tf.numpy_function(f_np, (x, y), tf.float64)
f_layered = layered(f_graph)
x = tf.keras.Input(())
y = tf.keras.Input(())
try:
out = f_graph(x, y)
no_wrapper_success = True
print("Succeeds without wrapper")
except NotImplementedError:
no_wrapper_success = False
# Cannot convert a symbolic Tensor (input_1:0) to a numpy array.
print("Fails without wrapper")
try:
out = f_layered(x, y)
from wtftf.meta import layered from kblocks.ops import sparse as _sparse_ops block_diagonalize_sparse = layered(_sparse_ops.block_diagonalize_sparse) apply_offset = layered(_sparse_ops.apply_offset) block_diagonalize_sparse_general = layered(_sparse_ops.block_diagonalize_sparse_general) ragged_to_sparse_indices = layered(_sparse_ops.ragged_to_sparse_indices) unstack = layered(_sparse_ops.unstack) remove_dim = layered(_sparse_ops.remove_dim) remove_leading_dim = layered(_sparse_ops.remove_leading_dim)
from wtftf.meta import layered
from ecn.ops import grid as _grid_ops
ravel_multi_index = layered(_grid_ops.ravel_multi_index)
unravel_index_transpose = layered(_grid_ops.unravel_index_transpose)
base_grid_coords = layered(_grid_ops.base_grid_coords)
output_shape = layered(_grid_ops.output_shape)
grid_coords = layered(_grid_ops.grid_coords)
shift_grid_coords = layered(_grid_ops.shift_grid_coords)
sparse_neighborhood = layered(_grid_ops.sparse_neighborhood)
sparse_neighborhood_in_place = layered(_grid_ops.sparse_neighborhood_in_place)
sparse_neighborhood_from_mask = layered(_grid_ops.sparse_neighborhood_from_mask)
sparse_neighborhood_from_mask_in_place = layered(
_grid_ops.sparse_neighborhood_from_mask_in_place
)
from wtftf.meta import layered from ecn.ops import lif as _lif_ops leaky_integrate_and_fire = layered(_lif_ops.leaky_integrate_and_fire) spatial_leaky_integrate_and_fire = layered(_lif_ops.spatial_leaky_integrate_and_fire)
from wtftf.meta import layered from ecn.ops import ragged as _ragged_ops row_sorted = layered(_ragged_ops.row_sorted) transpose_csr = layered(_ragged_ops.transpose_csr) gather_rows = layered(_ragged_ops.gather_rows)