def test_grad_helper_mul(self):
opv = opset
xi = OnnxIdentity('X', op_version=opv)
node = OnnxMul(xi, xi, op_version=opv, output_names=['Y'])
onx = node.to_onnx({'X': FloatTensorType([None, 10])},
{'Y': FloatTensorType([None, 10])},
target_opset=opv)
new_onx = onnx_derivative(onx)
self.check_runtime(new_onx, 'test_grad_helper_mul')
def test_grad_helper_exc(self):
opv = opset
node = OnnxAdd('X',
numpy.array([1], dtype=numpy.float32),
op_version=opv,
output_names=['Y'])
onx = node.to_onnx({'X': FloatTensorType([None, 10])},
{'Y': FloatTensorType([None, 10])},
target_opset=opv)
self.assertRaise(lambda: onnx_derivative(onx, weights=[], options=1),
TypeError)
def test_grad_helper_fillgrad(self):
opv = opset
node = OnnxAdd('X',
numpy.array([1], dtype=numpy.float32),
op_version=opv,
output_names=['Y'])
onx = node.to_onnx({'X': FloatTensorType([None, 10])},
{'Y': FloatTensorType([None, 10])},
target_opset=opv)
self.assertRaise(
lambda: onnx_derivative(
onx, weights=[], options=DerivativeOptions.FillGrad),
ValueError)
new_onx = onnx_derivative(onx,
weights=[],
options=(DerivativeOptions.FillGrad
| DerivativeOptions.KeepOutputs))
input_names = set(i.name for i in new_onx.graph.input)
self.assertNotIn('Y_grad', input_names)
self.check_runtime(new_onx, 'test_grad_helper_fillgrad', verbose=False)
def test_grad_helper_noweight(self):
opv = opset
node = OnnxAdd('X',
numpy.array([1], dtype=numpy.float32),
op_version=opv,
output_names=['Y'])
onx = node.to_onnx({'X': FloatTensorType([None, 10])},
{'Y': FloatTensorType([None, 10])},
target_opset=opv)
new_onx = onnx_derivative(onx, weights=[])
self.check_runtime(new_onx, 'test_grad_helper_noweight')
def test_grad_helper(self):
opv = opset
node = OnnxAdd('X',
numpy.array([1], dtype=numpy.float32),
op_version=opv,
output_names=['Y'])
onx = node.to_onnx({'X': FloatTensorType([None, 10])},
{'Y': FloatTensorType([None, 10])},
target_opset=opv)
new_onx = onnx_derivative(onx)
out_names = [o.name for o in new_onx.graph.output]
self.assertNotIn('Y', out_names)
self.check_runtime(new_onx, 'test_grad_helper')
def test_grad_helper_keep_yield(self):
opv = opset
node = OnnxAdd('X',
numpy.array([1], dtype=numpy.float32),
op_version=opv,
output_names=['Y'])
onx = node.to_onnx({'X': FloatTensorType([None, 10])},
{'Y': FloatTensorType([None, 10])},
target_opset=opv)
new_onx = onnx_derivative(onx, options=DerivativeOptions.KeepYieldOp)
types = set(n.op_type for n in new_onx.graph.node)
self.assertIn('YieldOp', types)
with open(f"verbose_{'yield'}.onnx", "wb") as f:
f.write(new_onx.SerializeToString())
def test_grad_helper_loss(self):
temp = get_temp_folder(__file__, "temp_grad_helper_loss")
grad_file = os.path.join(temp, "grad.onnx")
X, y = make_regression( # pylint: disable=W0632
100,
n_features=10,
bias=2,
random_state=0)
X = X.astype(numpy.float32)
y = y.astype(numpy.float32)
reg = LinearRegression()
reg.fit(X, y)
reg.coef_ = reg.coef_.reshape((1, -1))
onx = to_onnx(reg, X, target_opset=opset, black_op={'LinearRegressor'})
onx_loss = add_loss_output(onx)
text1 = onnx_simple_text_plot(onx_loss)
new_onx = onnx_derivative(onx,
options=DerivativeOptions.Loss,
label='variable',
loss='loss',
path_name=grad_file)
text2 = onnx_simple_text_plot(new_onx)
self.assertNotEqual(text1, text2)