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)