def test_l2_normalize_converter(self):
input_dim = (3,)
output_dim = (3,)
input = [('input', datatypes.Array(*input_dim))]
output = [('output', datatypes.Array(*output_dim))]
builder = NeuralNetworkBuilder(input, output)
builder.add_l2_normalize(name='L2', input_name='input', output_name='output')
model_onnx = convert_coreml(builder.spec)
self.assertTrue(model_onnx is not None)
def test_l2_normalize_converter(self):
input_dim = (3, )
output_dim = (3, )
input = [('input', datatypes.Array(*input_dim))]
output = [('output', datatypes.Array(*output_dim))]
builder = NeuralNetworkBuilder(input, output)
builder.add_l2_normalize(name='L2',
input_name='input',
output_name='output')
context = ConvertContext()
node = L2NormalizeLayerConverter.convert(
context, builder.spec.neuralNetwork.layers[0], ['input'],
['output'])
self.assertTrue(node is not None)
def verify_l2_normalize(input_dim, eps):
dtype = "float32"
a_np = np.random.uniform(size=input_dim).astype(dtype)
b_np = topi.testing.l2_normalize_python(a_np, eps, 1)
input = [('input', datatypes.Array(*input_dim))]
output = [('output', datatypes.Array(*b_np.shape))]
builder = NeuralNetworkBuilder(input, output)
builder.add_l2_normalize(name='L2', epsilon=eps, input_name='input', output_name='output')
model = cm.models.MLModel(builder.spec)
for target, ctx in ctx_list():
out = run_tvm_graph(model, target, ctx, a_np, 'input', b_np.shape, dtype)
tvm.testing.assert_allclose(out, b_np, rtol=1e-5)
def verify_l2_normalize(input_dim, eps):
dtype = "float32"
a_np = np.random.uniform(size=input_dim).astype(dtype)
b_np = tvm.topi.testing.l2_normalize_python(a_np, eps, 1)
input = [("input", datatypes.Array(*input_dim))]
output = [("output", datatypes.Array(*b_np.shape))]
builder = NeuralNetworkBuilder(input, output)
builder.add_l2_normalize(name="L2", epsilon=eps, input_name="input", output_name="output")
model = cm.models.MLModel(builder.spec)
for target, dev in tvm.testing.enabled_targets():
out = run_tvm_graph(model, target, dev, a_np, "input", b_np.shape, dtype)
tvm.testing.assert_allclose(out, b_np, rtol=1e-5)
def verify_l2_normalize(input_dim, eps):
dtype = "float32"
a_np = np.random.uniform(size=input_dim).astype(dtype)
b_np = topi.testing.l2_normalize_python(a_np, eps, 1)
input = [('input', datatypes.Array(*input_dim))]
output = [('output', datatypes.Array(*b_np.shape))]
builder = NeuralNetworkBuilder(input, output)
builder.add_l2_normalize(name='L2', epsilon=eps, input_name='input', output_name='output')
model = cm.models.MLModel(builder.spec)
for target, ctx in ctx_list():
out = run_tvm_graph(model, a_np, 'input', b_np.shape, dtype)
tvm.testing.assert_allclose(out, b_np, rtol=1e-5)
