def test_max(node, axis, use_gpu, keep_dimensions):
node = Variable(node)
assert_cuda_active(use_gpu)
def func(node):
return sum(rm.amax(node, axis=axis, keepdims=keep_dimensions))
def func2(node):
return sum(rm.amax(node, axis=axis, keepdims=keep_dimensions) + 10)
compare(func2, node, node)
def func3(node):
return sum(rm.amax(node, axis=axis, keepdims=keep_dimensions) * 3 + 15)
compare(func3, node, node)
def func4(node):
return sum(rm.amax(node, axis=axis, keepdims=keep_dimensions) + rm.amax(node, axis=axis, keepdims=keep_dimensions))
compare(func4, node, node)
# A simple check to see if we actually return the maximum
renom_max = rm.amax(node, axis=axis, keepdims=keep_dimensions).as_ndarray()
numpy_max = np.amax(node, axis=axis, keepdims=keep_dimensions)
assert np.allclose(renom_max, numpy_max, atol=1e-5, rtol=1e-3)
compare(func, node, node)
def __new__(self, x, slice_size=1, axis=1):
if axis is None:
axis = 1
assert len(x.shape) > 1
input_length = x.shape[axis]
maxes = []
# TODO: Ensure that input_length is evenly divisible by _slice_size
for u in range(input_length // slice_size):
offset = u * slice_size
maxes.append(
rm.amax(x[:, offset:offset + slice_size],
axis=axis,
keepdims=True))
return rm.concat(*maxes, axis=axis)
def func4(node):
return sum(rm.amax(node, axis=axis, keepdims=keep_dimensions) + rm.amax(node, axis=axis, keepdims=keep_dimensions))
def func3(node):
return sum(rm.amax(node, axis=axis, keepdims=keep_dimensions) * 3 + 15)
def func(node):
return sum(rm.amax(node, axis))