Roshrini closed pull request #13641: onnx export operators added
URL: https://github.com/apache/incubator-mxnet/pull/13641
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diff --git a/python/mxnet/contrib/onnx/mx2onnx/_op_translations.py
b/python/mxnet/contrib/onnx/mx2onnx/_op_translations.py
index 15624b6c3a2..0dd816bcc6f 100644
--- a/python/mxnet/contrib/onnx/mx2onnx/_op_translations.py
+++ b/python/mxnet/contrib/onnx/mx2onnx/_op_translations.py
@@ -630,12 +630,19 @@ def convert_exp(node, **kwargs):
return create_basic_op_node('Exp', node, kwargs)
@mx_op.register("_copy")
-def convert_identity(node, **kwargs):
+def convert_copy(node, **kwargs):
"""Map MXNet's _copy operator attributes to onnx's Identity operator
and return the created node.
"""
return create_basic_op_node('Identity', node, kwargs)
+@mx_op.register("identity")
+def convert_identity(node, **kwargs):
+ """Map MXNet's identity operator attributes to onnx's ConstantFill operator
+ and return the created node.
+ """
+ return create_basic_op_node('ConstantFill', node, kwargs)
+
@mx_op.register("InstanceNorm")
def convert_instancenorm(node, **kwargs):
"""Map MXNet's InstanceNorm operator attributes to onnx's
InstanceNormalization operator
@@ -726,6 +733,32 @@ def convert_softmax_output(node, **kwargs):
return [softmax_node]
+@mx_op.register("LogisticRegressionOutput")
+def convert_logistic_regression_output(node, **kwargs):
+ """Map MXNet's SoftmaxOutput operator attributes to onnx's Softmax operator
+ and return the created node.
+ """
+ name = node["name"]
+ input1_idx = kwargs["index_lookup"][node["inputs"][0][0]]
+ input1 = kwargs["proc_nodes"][input1_idx]
+ sigmoid_node = onnx.helper.make_node(
+ "Sigmoid",
+ [input1.name],
+ [name],
+ name=name
+ )
+ return [sigmoid_node]
+
+@mx_op.register("BlockGrad")
+def convert_blockgrad(node, **kwargs):
+ """ Skip operator """
+ return create_basic_op_node('ConstantFill', node, kwargs)
+
+@mx_op.register("MakeLoss")
+def convert_makeloss(node, **kwargs):
+ """ Skip operator """
+ return create_basic_op_node('ConstantFill', node, kwargs)
+
@mx_op.register("Concat")
def convert_concat(node, **kwargs):
@@ -872,6 +905,7 @@ def convert_clip(node, **kwargs):
def scalar_op_helper(node, op_name, **kwargs):
"""Helper function for scalar arithmetic operations"""
name, input_nodes, attrs = get_inputs(node, kwargs)
+ from onnx import numpy_helper
input_type = kwargs["in_type"]
scalar_value = np.array([attrs.get("scalar", 1)],
@@ -884,13 +918,18 @@ def scalar_op_helper(node, op_name, **kwargs):
for i in initializer:
if i.name == input_nodes[0]:
if op_name == 'Mul':
- new_initializer = onnx.numpy_helper.to_array(i) *
scalar_value[0]
+ new_initializer = numpy_helper.to_array(i) * scalar_value[0]
elif op_name == 'Sub':
- new_initializer = onnx.numpy_helper.to_array(i) -
scalar_value[0]
+ if name.startswith("_rminusscalar"):
+ new_initializer = scalar_value[0] -
numpy_helper.to_array(i)
+ else:
+ new_initializer = numpy_helper.to_array(i) -
scalar_value[0]
elif op_name == 'Add':
- new_initializer = onnx.numpy_helper.to_array(i) +
scalar_value[0]
+ new_initializer = numpy_helper.to_array(i) + scalar_value[0]
elif op_name == 'Div':
- new_initializer = onnx.numpy_helper.to_array(i) /
scalar_value[0]
+ new_initializer = numpy_helper.to_array(i) / scalar_value[0]
+ elif op_name == 'Pow':
+ new_initializer = numpy_helper.to_array(i) ** scalar_value[0]
flag = False
break
@@ -956,6 +995,13 @@ def convert_minus_scalar(node, **kwargs):
"""
return scalar_op_helper(node, 'Sub', **kwargs)
+@mx_op.register("_rminus_scalar")
+def convert_rminus_scalar(node, **kwargs):
+ """Map MXNet's _rminus_scalar operator attributes to onnx's Sub operator.
+ Creates a new node for the input scalar value, adds it to the initializer
+ and return multiple created nodes.
+ """
+ return scalar_op_helper(node, 'Sub', **kwargs)
# Convert scalar value into node and pass it as input to mul_node
@mx_op.register("_plus_scalar")
@@ -975,6 +1021,21 @@ def convert_div_scalar(node, **kwargs):
"""
return scalar_op_helper(node, 'Div', **kwargs)
+@mx_op.register("_rdiv_scalar")
+def convert_rdiv_scalar(node, **kwargs):
+ """Map MXNet's _rdiv_scalar operator attributes to onnx's Div operator.
+ Creates a new node for the input scalar value, adds it to the initializer
+ and return multiple created nodes.
+ """
+ return scalar_op_helper(node, 'Div', **kwargs)
+
+@mx_op.register("_power_scalar")
+def convert_pow_scalar(node, **kwargs):
+ """Map MXNet's _pow_scalar operator attributes to onnx's Pow operator.
+ Creates a new node for the input scalar value, adds it to the initializer
+ and return multiple created nodes.
+ """
+ return scalar_op_helper(node, 'Pow', **kwargs)
# Sorting and Searching
@mx_op.register("argmax")
diff --git a/python/mxnet/contrib/onnx/onnx2mx/_translation_utils.py
b/python/mxnet/contrib/onnx/onnx2mx/_translation_utils.py
index f63c1e9e8e6..9700dd6a30a 100644
--- a/python/mxnet/contrib/onnx/onnx2mx/_translation_utils.py
+++ b/python/mxnet/contrib/onnx/onnx2mx/_translation_utils.py
@@ -158,7 +158,7 @@ def _fix_broadcast(op_name, inputs, broadcast_axis,
proto_obj):
assert len(list(inputs)) == 2
input0_shape = get_input_shape(inputs[0], proto_obj)
- #creating reshape shape
+ # creating reshape shape
reshape_shape = list(len(input0_shape) * (1,))
reshape_shape[broadcast_axis] = -1
reshape_shape = tuple(reshape_shape)
diff --git a/tests/python-pytest/onnx/backend_test.py
b/tests/python-pytest/onnx/backend_test.py
index 6c6c3d2d9c7..5ec5efb8ea2 100644
--- a/tests/python-pytest/onnx/backend_test.py
+++ b/tests/python-pytest/onnx/backend_test.py
@@ -50,6 +50,7 @@ def prepare_tests(backend, operation):
for std_model_test in std_models:
BACKEND_TESTS.include(std_model_test)
- BACKEND_TESTS.exclude('.*bcast.*')
+ # Tests for scalar ops are in test_node.py
+ BACKEND_TESTS.exclude('.*scalar.*')
return BACKEND_TESTS
diff --git a/tests/python-pytest/onnx/test_node.py
b/tests/python-pytest/onnx/test_node.py
index 07ae866b96c..41b86de4b9b 100644
--- a/tests/python-pytest/onnx/test_node.py
+++ b/tests/python-pytest/onnx/test_node.py
@@ -138,6 +138,30 @@ def get_onnx_graph(testname, input_names, inputs,
output_name, output_shape, att
npt.assert_almost_equal(output[0], mxnet_output)
+ input1 = get_rnd((1, 10, 2, 3))
+ ipsym = mx.sym.Variable("input1")
+ for test in test_scalar_ops:
+ if test == 'Add':
+ outsym = 2 + ipsym
+ if test == "Sub":
+ outsym = ipsym - 2
+ if test == "rSub":
+ outsym = ipsym.__rsub__(2)
+ if test == "Mul":
+ outsym = 2 * ipsym
+ if test == "Div":
+ outsym = ipsym / 2
+ if test == "Pow":
+ outsym = ipsym ** 2
+ forward_op = forward_pass(outsym, None, None, ['input1'], input1)
+ converted_model = onnx_mxnet.export_model(outsym, {},
[np.shape(input1)], np.float32,
+
onnx_file_path=outsym.name + ".onnx")
+
+ sym, arg_params, aux_params =
onnx_mxnet.import_model(converted_model)
+ result = forward_pass(sym, arg_params, aux_params, ['input1'],
input1)
+
+ npt.assert_almost_equal(result, forward_op)
+
# test_case = ("test_case_name", mxnet op, "ONNX_op_name", [input_list],
attribute map, MXNet_specific=True/False)
test_cases = [
@@ -156,9 +180,13 @@ def get_onnx_graph(testname, input_names, inputs,
output_name, output_shape, att
{'block_size': 2}, False),
("test_softmax", mx.sym.SoftmaxOutput, "Softmax", [get_rnd((1000, 1000)),
get_rnd(1000)],
{'ignore_label': 0, 'use_ignore': False}, True),
+ ("test_logistic_regression", mx.sym.LogisticRegressionOutput, "Sigmoid",
+ [get_rnd((1000, 1000)), get_rnd((1000, 1000))], {}, True),
("test_fullyconnected", mx.sym.FullyConnected, "Gemm", [get_rnd((4,3)),
get_rnd((4, 3)), get_rnd(4)],
{'num_hidden': 4, 'name': 'FC'}, True)
]
+test_scalar_ops = ['Add', 'Sub', 'rSub' 'Mul', 'Div', 'Pow']
+
if __name__ == '__main__':
unittest.main()
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