def test_flatten_linear():
net = LinearOpr("flatten")
tm_module, mge_result = get_traced_module(net, mge.tensor(net.data1))
_test_convert_result(net.data1,
tm_module,
mge_result,
max_error,
convert_backend=4)
def test_linear():
net = LinearOpr()
inp_dtype = dtype.qint8(16.0 / 128.0)
qat_net, inps = get_qat_net(inp_dtype, net, shape=(10, 100))
traced_module, tm_result = get_traced_module(qat_net, inps[0])
inp = inps[0].astype(inp_dtype)
_test_convert_result(inp,
traced_module,
tm_result,
max_err,
require_quantize=False)
def test_linear():
net = LinearOpr()
inp_dtype = dtype.qint8(16.0 / 128.0)
qat_net, inps = get_qat_net(inp_dtype, net, shape=(10, 100))
traced_module, tm_result = get_traced_module(qat_net, inps[0])
print(traced_module.flatten().graph)
out_dtype = traced_module.graph.outputs[0].qparams
scale = out_dtype.scale.numpy()
inp = inps[0].astype(inp_dtype)
_test_convert_result(
inp,
traced_module,
tm_result,
scale=scale,
require_quantize=True,
max_err=max_error,
)
def test_linear():
net = LinearOpr()
mge_result = dump_mge_model(net, net.data, tmp_file)
_test_convert_result(net.data, tmp_file, mge_result, max_error)
def test_linear():
net = LinearOpr()
net.eval()
tm_module, mge_result = get_traced_module(net, mge.tensor(net.data))
_test_convert_result(net.data, tm_module, mge_result, max_error)
def test_linear():
net = LinearOpr()
traced_module, tm_result = get_traced_module(net, mge.tensor(net.data))
print(traced_module.flatten().graph)
_test_convert_result(mge.tensor(net.data), traced_module, tm_result)