def test_F_concat():
net = FConcatOpr()
data1 = mge.tensor(np.random.random((1, 3, 24, 24)).astype(np.float32))
data2 = mge.tensor(np.random.random((1, 3, 24, 24)).astype(np.float32))
traced_module, tm_result = get_traced_module(net, [data1, data2])
print(traced_module.flatten().graph)
_test_convert_result([data1, data2], traced_module, tm_result)
def test_convrelu():
net = ConvRelu2dOpr()
data = mge.tensor(net.data)
traced_module, tm_result = get_traced_module(net, data)
print(traced_module.flatten().graph)
_test_convert_result(data, traced_module, tm_result)
def test_conv(mode):
net = ConvOpr(mode)
data = mge.tensor(np.random.random((1, 3, 224, 224)).astype(np.float32))
traced_module, tm_result = get_traced_module(net, data)
print(traced_module.flatten().graph)
_test_convert_result(data, traced_module, tm_result)
def test_pooling(mode):
if mge.__version__ > "0.6.0" and mode == "avg":
return
net = PoolOpr(mode)
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)
def test_reshape():
net = ReshapeOpr(fix_batch=True)
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,
nhwc=False)
def test_convbnrelu(has_bias):
net = ConvBnRelu2dOpr(has_bias)
net.eval()
data = mge.tensor(net.data)
traced_module, tm_result = get_traced_module(net, data)
print(traced_module.flatten().graph)
_test_convert_result(data, traced_module, tm_result)
def test_slice():
net = SubtensorOpr()
tm, tm_result = get_traced_module(net, mge.tensor(net.data))
print(tm.flatten().graph)
_test_convert_result(mge.tensor(net.data),
tm,
tm_result,
nhwc=False,
nhwc2=False)
def test_model(model):
data = np.ones((1, 3, 224, 224)).astype(np.float32)
if megengine.__version__ < "1.1.0":
commit_id = "dc2f2cfb228a135747d083517b98aea56e7aab92"
else:
commit_id = None
net = megengine.hub.load("megengine/models",
model,
use_cache=False,
commit=commit_id,
pretrained=True)
net.eval()
traced_module, tm_result = get_traced_module(net, mge.tensor(data))
_test_convert_result(mge.tensor(data), traced_module, tm_result, 1e-4)
def test_resize():
net = ResizeOpr()
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)
def test_pad():
net = PadOpr()
traced_module, tm_result = get_traced_module(net, mge.tensor(net.data))
_test_convert_result(mge.tensor(net.data), traced_module, tm_result)
def test_elemwise(mode):
net = ElemwiseOpr(mode)
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)
