def test_slice_like(self):
data = relay.expr.const(np.ones((1, 6, 4, 4), np.float32))
sl = relay.expr.const(np.ones((1, 4, 3, 3), np.float32))
net = relay.slice_like(data, sl)
net = relay.Function([], net)
mod = tvm.IRModule.from_expr(net)
mod = relay.transform.InferType()(mod)
xgraph = xf_relay.from_relay(mod, {})
layers = xgraph.get_layers()
assert layers[0].type[0] == "Constant"
assert layers[1].type[0] == "Constant"
assert layers[2].type[0] == "AnyOp"
assert layers[2].shapes == [1, 4, 3, 3]
data = relay.expr.const(np.ones((1, 6, 4, 4), np.float32))
sl = relay.expr.const(np.ones((1, 4, 3, 3), np.float32))
net = relay.slice_like(data, sl, axes=(2, 3))
net = relay.Function([], net)
mod = tvm.IRModule.from_expr(net)
mod = relay.transform.InferType()(mod)
xgraph = xf_relay.from_relay(mod, {})
layers = xgraph.get_layers()
assert layers[0].type[0] == "Constant"
assert layers[1].type[0] == "Constant"
assert layers[2].type[0] == "AnyOp"
assert layers[2].shapes == [1, 6, 3, 3]
def verify_slice_like(data, slice_like, axes, output, dtype="float32"):
x = relay.var("data", relay.TensorType(data, dtype))
y = relay.var("slice_like", relay.TensorType(slice_like, dtype))
z = relay.slice_like(x, y, axes)
zz = run_infer_type(z)
if axes:
assert "axes" in z.astext()
assert zz.checked_type == relay.ty.TensorType(output, dtype)
if all(isinstance(v, int) == 0 for v in data) or all(
isinstance(v, int) == 0 for v in slice_like
):
return
func = relay.Function([x, y], z)
x_data = np.random.uniform(size=data).astype(dtype)
y_data = np.random.uniform(size=slice_like).astype(dtype)
ref_res = np_slice_like(x_data, y_data, axes)
for target, dev in tvm.testing.enabled_targets():
for kind in ["graph", "debug"]:
op_res = relay.create_executor(kind, device=dev, target=target).evaluate(func)(
x_data, y_data
)
tvm.testing.assert_allclose(op_res.numpy(), ref_res, rtol=1e-5)
def verify_slice_like(data, slice_like, axes, output, dtype="float32"):
x = relay.var("data", relay.TensorType(data, dtype))
y = relay.var("slice_like", relay.TensorType(slice_like, dtype))
z = relay.slice_like(x, y, axes)
zz = relay.ir_pass.infer_type(z)
if axes:
assert "axes" in z.astext()
assert zz.checked_type == relay.ty.TensorType(output, dtype)
if all(isinstance(v, int) == 0 for v in data) or \
all(isinstance(v, int) == 0 for v in slice_like):
return
func = relay.Function([x, y], z)
x_data = np.random.uniform(size=data).astype(dtype)
y_data = np.random.uniform(size=slice_like).astype(dtype)
ref_res = np_slice_like(x_data, y_data, axes)
for target, ctx in ctx_list():
for kind in ["graph", "debug"]:
intrp = relay.create_executor(kind, ctx=ctx, target=target)
op_res = intrp.evaluate(func)(x_data, y_data)
tvm.testing.assert_allclose(op_res.asnumpy(), ref_res, rtol=1e-5)
