def test_idempotent_axes_c():
"""
Test test axes transformations with autodiff, case c, with broadcast,
slice, cast and dim-shuffle
"""
with ExecutorFactory() as ex:
axes = ng.make_axes([ng.make_axis(3), ng.make_axis(1)])
result_axes = [ng.make_axis(length=axis.length) for axis in axes]
# variable
w = ng.variable(axes, initial_value=np.ones((3, 1)))
# broadcast l / r, introducing dummy length 1 axes
l = ng.broadcast(w, axes)
r = ng.broadcast(w, axes)
# slice
axes_slice = [slice(None, None, None), slice(None, None, None)]
l_sliced = ng.tensor_slice(l, axes_slice)
r_sliced = ng.tensor_slice(r, axes_slice)
# cast r
r_sliced_casted = ng.cast_axes(r_sliced, axes)
# perform add
result = ng.add(l_sliced, r_sliced_casted)
# cast / dimshuffle
result = ng.cast_axes(result, result_axes)
result = ng.axes_with_order(result, result_axes)
# cost and grad
cost = ng.sum(result, reduction_axes=result.axes)
grad = ng.deriv(cost, w)
grad_comp = ex.executor(grad)
cost_comp = ex.executor(cost)
cost_comp_ng = cost_comp()
grad_comp_ng = grad_comp()
grad_comp_np = np.ones((3, 1)) * 2.
assert cost_comp_ng == 6.0
assert np.array_equal(grad_comp_ng, grad_comp_np)
def test_cast_axis():
"""
Test AxesCastOp
"""
H = ng.make_axis(length=1, name='height')
W = ng.make_axis(length=4, name='width')
axes_input = [H, W]
a = ng.placeholder(axes=axes_input)
axes_output = ng.make_axes([ng.make_axis(name=ax.name + 'p', length=ax.length)
for ax in axes_input])
b = ng.cast_axes(a, axes_output)
with executor(b, a) as _cast_axis:
a_val = np.array([10, 20, 30, 40], dtype=np.float32).reshape(1, 4)
b_val = _cast_axis(a_val)
b_val_ref = a_val
assert np.allclose(b_val, b_val_ref)
def test_idempotent_axes_b():
"""
Test test axes transformations with autodiff, case b, with broadcast applied
to the same tensor
"""
with ExecutorFactory() as ex:
axes = ng.make_axes([ng.make_axis(3), ng.make_axis(1)])
w = ng.variable(axes, initial_value=np.ones((3, 1)))
l = ng.broadcast(w, axes)
r = ng.broadcast(w, axes)
result = ng.add(l, r)
result = ng.cast_axes(result, axes)
cost = ng.sum(result, reduction_axes=axes)
grad = ng.deriv(cost, w)
grad_comp = ex.executor(grad)
cost_comp = ex.executor(cost)
assert cost_comp() == 6.0
assert np.array_equal(grad_comp(), np.ones((3, 1)) * 2.)
def test_idempotent_axes_a():
"""
Test test axes transformations with autodiff, case a, reference test
"""
with ExecutorFactory() as ex:
axes = ng.make_axes([ng.make_axis(3), ng.make_axis(1)])
w = ng.variable(axes, initial_value=np.ones((3, 1)))
result = w + w
result = ng.cast_axes(result, axes)
cost = ng.sum(result, reduction_axes=axes)
grad = ng.deriv(cost, w)
grad_comp = ex.executor(grad)
cost_comp = ex.executor(cost)
cost_comp_val = cost_comp()
grad_comp_val = grad_comp()
grad_comp_np = np.ones((3, 1)) * 2.
assert cost_comp_val == 6.0
assert np.array_equal(grad_comp_val, grad_comp_np)