def verify_unary_abs(input_dim):
dtype = "float32"
a_np = np.random.uniform(-100.0, 100.0, size=input_dim).astype(dtype)
ref_val = np.abs(a_np)
inputs = [("input", datatypes.Array(*input_dim))]
output = [("output", datatypes.Array(*ref_val.shape))]
builder = NeuralNetworkBuilder(inputs, output)
builder.add_unary(name="abs", input_name="input", output_name="output", mode="abs")
model = cm.models.MLModel(builder.spec)
for target, dev in tvm.testing.enabled_targets():
out = run_tvm_graph(model, target, dev, [a_np], ["input"], ref_val.shape, dtype)
tvm.testing.assert_allclose(out, ref_val, rtol=1e-5)
def verify_unary_log(input_dim):
dtype = 'float32'
a_np = np.random.uniform(size=input_dim).astype(dtype)
ref_val = np.log(a_np)
inputs = [('input', datatypes.Array(*input_dim))]
output = [('output', datatypes.Array(*ref_val.shape))]
builder = NeuralNetworkBuilder(inputs, output)
builder.add_unary(name="log",
input_name='input',
output_name='output',
mode='log')
model = cm.models.MLModel(builder.spec)
for target, ctx in tvm.testing.enabled_targets():
out = run_tvm_graph(model, target, ctx, [a_np],
['input'], ref_val.shape, dtype)
tvm.testing.assert_allclose(out, ref_val, rtol=1e-5)
def verify_unary_inverse(input_dim, epsilon=0):
dtype = "float32"
a_np = np.random.uniform(size=input_dim).astype(dtype)
ref_val = 1 / (a_np + epsilon)
inputs = [("input", datatypes.Array(*input_dim))]
output = [("output", datatypes.Array(*ref_val.shape))]
builder = NeuralNetworkBuilder(inputs, output)
builder.add_unary(name="inverse",
input_name="input",
output_name="output",
mode="inverse",
epsilon=epsilon)
model = cm.models.MLModel(builder.spec)
for target, ctx in tvm.testing.enabled_targets():
out = run_tvm_graph(model, target, ctx, [a_np], ["input"],
ref_val.shape, dtype)
tvm.testing.assert_allclose(out, ref_val, rtol=1e-5)
def verify_unary_threshold(input_dim, alpha):
dtype = 'float32'
a_np = np.random.uniform(-100.0, 100.0, size=input_dim).astype(dtype)
ref_val = np.maximum(a_np, alpha)
inputs = [('input', datatypes.Array(*input_dim))]
output = [('output', datatypes.Array(*ref_val.shape))]
builder = NeuralNetworkBuilder(inputs, output)
builder.add_unary(name="threshold",
input_name='input',
output_name='output',
mode='threshold',
alpha=alpha)
model = cm.models.MLModel(builder.spec)
for target, ctx in ctx_list():
out = run_tvm_graph(model, target, ctx, [a_np],
['input'], ref_val.shape, dtype)
tvm.testing.assert_allclose(out, ref_val, rtol=1e-5)
def verify_unary_rsqrt(input_dim, epsilon=0):
dtype = 'float32'
a_np = np.random.uniform(size=input_dim).astype(dtype)
ref_val = 1 / np.sqrt(a_np + epsilon)
inputs = [('input', datatypes.Array(*input_dim))]
output = [('output', datatypes.Array(*ref_val.shape))]
builder = NeuralNetworkBuilder(inputs, output)
builder.add_unary(name="rsqrt",
input_name='input',
output_name='output',
mode='rsqrt',
epsilon=epsilon)
model = cm.models.MLModel(builder.spec)
for target, ctx in ctx_list():
out = run_tvm_graph(model, target, ctx, [a_np],
['input'], ref_val.shape, dtype)
tvm.testing.assert_allclose(out, ref_val, rtol=1e-5)
def test_unary_function_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_unary(name='Unary1',
input_name='input',
output_name='mid1',
mode='abs')
builder.add_unary(name='Unary2',
input_name='mid1',
output_name='mid2',
mode='sqrt')
builder.add_unary(name='Unary3',
input_name='mid2',
output_name='mid3',
mode='rsqrt')
builder.add_unary(name='Unary4',
input_name='mid3',
output_name='mid4',
mode='inverse')
builder.add_unary(name='Unary5',
input_name='mid4',
output_name='mid5',
mode='power',
alpha=2)
builder.add_unary(name='Unary6',
input_name='mid5',
output_name='mid6',
mode='exp')
builder.add_unary(name='Unary7',
input_name='mid6',
output_name='mid7',
mode='log')
builder.add_unary(name='Unary8',
input_name='mid7',
output_name='output',
mode='threshold')
model_onnx = convert_coreml(builder.spec)
self.assertTrue(model_onnx is not None)
def test_unary_function_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_unary(name='Unary1',
input_name='input',
output_name='mid1',
mode='abs')
builder.add_unary(name='Unary3',
input_name='mid1',
output_name='mid2',
mode='sqrt')
builder.add_unary(name='Unary2',
input_name='mid2',
output_name='mid3',
mode='rsqrt')
builder.add_unary(name='Unary3',
input_name='mid3',
output_name='mid4',
mode='inverse')
builder.add_unary(name='Unary4',
input_name='mid4',
output_name='mid5',
mode='power',
alpha=2)
builder.add_unary(name='Unary5',
input_name='mid5',
output_name='mid6',
mode='exp')
builder.add_unary(name='Unary6',
input_name='mid6',
output_name='mid7',
mode='log')
builder.add_unary(name='Unary7',
input_name='mid7',
output_name='output',
mode='threshold')
context = ConvertContext()
for layer in builder.spec.neuralNetwork.layers:
for node in UnaryFunctionLayerConverter.convert(
context, layer, ['input'], ['output']):
self.assertTrue(node is not None)
