def _setitem(arr, index, value):
"""Sets the `value` at `index` in the array `arr`.
This works by replacing the slice at `index` in the tensor with `value`.
Since tensors are immutable, this builds a new tensor using the `tf.concat`
op. Currently, only 0-d and 1-d indices are supported.
Note that this may break gradients e.g.
a = tf_np.array([1, 2, 3])
old_a_t = a.data
with tf.GradientTape(persistent=True) as g:
g.watch(a.data)
b = a * 2
a[0] = 5
g.gradient(b.data, [a.data]) # [None]
g.gradient(b.data, [old_a_t]) # [[2., 2., 2.]]
Here `d_b / d_a` is `[None]` since a.data no longer points to the same
tensor.
Args:
arr: array_like.
index: scalar or 1-d integer array.
value: value to set at index.
Returns:
ndarray
Raises:
ValueError: if `index` is not a scalar or 1-d array.
"""
# TODO(srbs): Figure out a solution to the gradient problem.
arr = array_creation.asarray(arr)
index = array_creation.asarray(index)
if index.ndim == 0:
index = ravel(index)
elif index.ndim > 1:
raise ValueError('index must be a scalar or a 1-d array.')
value = array_creation.asarray(value, dtype=arr.dtype)
if arr.shape[len(index):] != value.shape:
value = array_manipulation.broadcast_to(value, arr.shape[len(index):])
prefix_t = arr.data[:index.data[0]]
postfix_t = arr.data[index.data[0] + 1:]
if len(index) == 1:
arr._data = tf.concat( # pylint: disable=protected-access
[prefix_t, tf.expand_dims(value.data, 0), postfix_t], 0)
else:
subarray = arr[index.data[0]]
_setitem(subarray, index[1:], value)
arr._data = tf.concat( # pylint: disable=protected-access
[prefix_t, tf.expand_dims(subarray.data, 0), postfix_t], 0)
def run_test(arr, shape):
for fn in self.array_transforms:
arg1 = fn(arr)
self.match(array_manipulation.broadcast_to(arg1, shape),
np.broadcast_to(arg1, shape))
def replicate(x, num_devices=2): return array_manipulation.broadcast_to(x, (num_devices,) + x.shape)
