def test_fake_quantize_rsqrt():
x = relay.var("x", shape=[1, 3, 224, 224], dtype="int8")
zero = relay.const(0)
x = relay.qnn.op.dequantize(x, relay.const(2.0), zero)
op = relay.rsqrt(x)
op = relay.qnn.op.quantize(op, relay.const(2.0), zero)
x_np = np.random.randint(-128, 127, size=[1, 3, 224, 224], dtype="int8")
compare_fq_to_int(op, [x_np], True)
def test_fake_quantize_rsqrt():
x = relay.var("x", shape=[1, 3, 3, 3], dtype="int8")
mid_point = relay.const(-128)
x = relay.qnn.op.dequantize(x, relay.const(0.125), mid_point)
op = relay.rsqrt(x)
op = relay.qnn.op.quantize(op, relay.const(0.125), mid_point)
x_np = np.random.randint(-128, 127, size=[1, 3, 3, 3], dtype="int8")
compare_fq_to_int(op, [x_np], True)
def test_rsqrt(self):
data = relay.var("data", relay.TensorType((-1, 4, 2, 2), "float32"))
net = relay.rsqrt(data)
net = relay.Function(relay.analysis.free_vars(net), net)
mod, params = testing.create_workload(net)
xgraph = xf_relay.from_relay(mod, params)
layers = xgraph.get_layers()
assert layers[0].type[0] == 'Input'
assert layers[1].type[0] == 'rSqrt'
assert 'relay_id' in layers[1].attrs
def rsqrt(expr):
return relay.rsqrt(relay.add(relay.abs(expr), relay.const(1.0)))