def test_convert_linear_layer_all():
node = onnx.helper.make_node(
"Gemm",
inputs=["a", "b", "c"],
outputs=["y"],
alpha=0.25,
beta=0.35,
transA=0,
transB=1,
)
a = np.random.ranf([4, 3]).astype(np.float32).transpose()
b = np.random.ranf([5, 4]).astype(np.float32)
c = np.random.ranf([1, 5]).astype(np.float32)
y = gemm_reference_implementation(a,
b,
c,
transA=0,
transB=1,
alpha=0.25,
beta=0.35)
params = [numpy_helper.from_array(b), numpy_helper.from_array(c)]
op = convert_linear_layer(node, params)
op.eval()
out = op(torch.from_numpy(a))
torch.allclose(torch.from_numpy(y), out)
def test_convert_linear_layer_default():
node = onnx.helper.make_node("Gemm", inputs=["a", "b", "c"], outputs=["y"])
a = np.random.ranf([3, 6]).astype(np.float32)
b = np.random.ranf([6, 4]).astype(np.float32)
c = np.random.ranf([3, 4]).astype(np.float32)
y = gemm_reference_implementation(a, b, c)
params = [numpy_helper.from_array(b), numpy_helper.from_array(c)]
op = convert_linear_layer(node, params)
op.eval()
out = op(torch.from_numpy(a))
torch.allclose(torch.from_numpy(y), out)
