def verify_expand_like(in_shape, out_shape, axis, exclude):
x = sym.Variable("x")
y = sym.Variable("y")
z = sym.expand_like(x, y, axis=axis, exclude=exclude)
def forward(x, y):
odim = len(out_shape)
real_axis = [i if i >= 0 else i + odim for i in axis]
real_axis = sorted(real_axis)
if exclude:
real_axis = list(set(range(odim)) - set(real_axis))
for i in real_axis:
x = np.expand_dims(x, i).astype(x.dtype)
for i in real_axis:
x = np.concatenate([x]*out_shape[i], axis=i).astype(x.dtype)
return x
def backward(head_grads, x, y):
odim = len(out_shape)
real_axis = [i if i >= 0 else i + odim for i in axis]
real_axis = sorted(real_axis)
if exclude:
real_axis = list(set(range(odim)) - set(real_axis))
return [np.sum(head_grads, axis=tuple(real_axis)),
np.zeros_like(y)]
dtype = "float32"
inputs = [('x', in_shape, x),
('y', out_shape, y)]
helper(z, inputs, dtype, forward, backward, need_input=False)
def verify_expand_like(in_shape, out_shape, axis, exclude):
x = sym.Variable("x")
y = sym.Variable("y")
z = sym.expand_like(x, y, axis=axis, exclude=exclude)
def forward(x, y):
odim = len(out_shape)
if len(x.shape) == len(y.shape):
return np.broadcast_to(x, y.shape)
if x.shape == (1, ) and len(y.shape) == odim:
x = np.reshape(x, ())
real_axis = [i if i >= 0 else i + odim for i in axis]
real_axis = sorted(real_axis)
if exclude:
real_axis = list(set(range(odim)) - set(real_axis))
for i in real_axis:
x = np.expand_dims(x, i).astype(x.dtype)
for i in real_axis:
x = np.concatenate([x] * out_shape[i], axis=i).astype(x.dtype)
return x
def backward(head_grads, x, y):
odim = len(out_shape)
keepdims = len(x.shape) == len(y.shape)
if x.shape == (1, ) and len(y.shape) == odim:
x = np.reshape(x, ())
real_axis = [i if i >= 0 else i + odim for i in axis]
real_axis = sorted(real_axis)
if exclude:
real_axis = list(set(range(odim)) - set(real_axis))
return [
np.sum(head_grads, axis=tuple(real_axis), keepdims=keepdims),
np.zeros_like(y)
]
shape = {'x': in_shape, 'y': out_shape}
check_function(z, forward, backward, shape=shape)
def verify_expand_like(in_shape, out_shape, axis, exclude):
x = sym.Variable("x")
y = sym.Variable("y")
z = sym.expand_like(x, y, axis=axis, exclude=exclude)
def forward(x, y):
odim = len(out_shape)
if len(x.shape) == len(y.shape):
return np.broadcast_to(x, y.shape)
if x.shape == (1,) and len(y.shape) == odim:
x = np.reshape(x, ())
real_axis = [i if i >= 0 else i + odim for i in axis]
real_axis = sorted(real_axis)
if exclude:
real_axis = list(set(range(odim)) - set(real_axis))
for i in real_axis:
x = np.expand_dims(x, i).astype(x.dtype)
for i in real_axis:
x = np.concatenate([x]*out_shape[i], axis=i).astype(x.dtype)
return x
def backward(head_grads, x, y):
odim = len(out_shape)
keepdims = len(x.shape) == len(y.shape)
if x.shape == (1,) and len(y.shape) == odim:
x = np.reshape(x, ())
real_axis = [i if i >= 0 else i + odim for i in axis]
real_axis = sorted(real_axis)
if exclude:
real_axis = list(set(range(odim)) - set(real_axis))
return [np.sum(head_grads, axis=tuple(real_axis), keepdims=keepdims),
np.zeros_like(y)]
shape = {'x': in_shape, 'y': out_shape}
check_function(z, forward, backward, shape=shape)
