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Posted to commits@mxnet.apache.org by GitBox <gi...@apache.org> on 2019/01/10 19:57:27 UTC
[GitHub] Roshrini closed pull request #13821: onnx export ops
Roshrini closed pull request #13821: onnx export ops
URL: https://github.com/apache/incubator-mxnet/pull/13821
This is a PR merged from a forked repository.
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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 61cb353ded4..f9bb5d6d7fe 100644
--- a/python/mxnet/contrib/onnx/mx2onnx/_op_translations.py
+++ b/python/mxnet/contrib/onnx/mx2onnx/_op_translations.py
@@ -656,12 +656,19 @@ 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
@@ -752,6 +759,31 @@ 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):
@@ -898,7 +930,7 @@ 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)],
dtype=onnx.mapping.TENSOR_TYPE_TO_NP_TYPE[input_type])
@@ -910,13 +942,21 @@ 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]
+ if name.startswith("_rdivscalar"):
+ new_initializer = scalar_value[0] / numpy_helper.to_array(i)
+ else:
+ 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
@@ -982,6 +1022,13 @@ 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")
@@ -1001,6 +1048,21 @@ 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/_op_translations.py b/python/mxnet/contrib/onnx/onnx2mx/_op_translations.py
index ef3bda30df3..fbfebbc9ee6 100644
--- a/python/mxnet/contrib/onnx/onnx2mx/_op_translations.py
+++ b/python/mxnet/contrib/onnx/onnx2mx/_op_translations.py
@@ -730,7 +730,6 @@ def spacetodepth(attrs, inputs, proto_obj):
return "space_to_depth", new_attrs, inputs
-
def hardmax(attrs, inputs, proto_obj):
"""Returns batched one-hot vectors."""
input_tensor_data = proto_obj.model_metadata.get('input_tensor_data')[0]
diff --git a/tests/python-pytest/onnx/backend_test.py b/tests/python-pytest/onnx/backend_test.py
index 048a6782c24..8eaa303a6c1 100644
--- a/tests/python-pytest/onnx/backend_test.py
+++ b/tests/python-pytest/onnx/backend_test.py
@@ -71,7 +71,8 @@ def prepare_tests(backend, oper):
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 9b5ff97d165..c8a523edc17 100644
--- a/tests/python-pytest/onnx/test_node.py
+++ b/tests/python-pytest/onnx/test_node.py
@@ -161,6 +161,30 @@ def test_import_export(self):
if check_shape:
npt.assert_equal(output[0].shape, outputshape)
+ 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)
+
def test_imports(self):
for test in import_test_cases:
test_name, onnx_name, inputs, np_op, attrs = test
@@ -173,7 +197,6 @@ def test_imports(self):
mxnet_out = bkd_rep.run(inputs)
npt.assert_almost_equal(np_out, mxnet_out)
-
# test_case = ("test_case_name", mxnet op, "ONNX_op_name", [input_list], attribute map, MXNet_specific=True/False,
# fix_attributes = {'modify': {mxnet_attr_name: onnx_attr_name},
# 'remove': [attr_name],
@@ -198,6 +221,8 @@ def test_imports(self):
{'block_size': 2}, False, {}, True, False),
("test_softmax", mx.sym.SoftmaxOutput, "Softmax", [get_rnd((1000, 1000)), get_rnd(1000)],
{'ignore_label': 0, 'use_ignore': False}, True, {}, True, False),
+ ("test_logistic_regression", mx.sym.LogisticRegressionOutput, "Sigmoid",
+ [get_rnd((1000, 1000)), get_rnd((1000, 1000))], {}, True, {}, True, False),
("test_fullyconnected", mx.sym.FullyConnected, "Gemm", [get_rnd((4, 3)), get_rnd((4, 3)), get_rnd(4)],
{'num_hidden': 4, 'name': 'FC'}, True, {}, True, False),
("test_lppool1", mx.sym.Pooling, "LpPool", [get_rnd((2, 3, 20, 20))],
@@ -223,12 +248,13 @@ def test_imports(self):
{'shape': (10,)}, False, {'modify': {'shape': 'sample_size'}}, False, True)
]
+test_scalar_ops = ['Add', 'Sub', 'rSub' 'Mul', 'Div', 'Pow']
+
# test_case = ("test_case_name", "ONNX_op_name", [input_list], np_op, attribute map)
import_test_cases = [
("test_lpnormalization_default", "LpNormalization", [get_rnd([5, 3, 3, 2])], np.linalg.norm, {'ord':2, 'axis':-1}),
("test_lpnormalization_ord1", "LpNormalization", [get_rnd([5, 3, 3, 2])], np.linalg.norm, {'ord':1, 'axis':-1}),
- ("test_lpnormalization_ord2", "LpNormalization", [get_rnd([5, 3, 3, 2])], np.linalg.norm, {'ord':2, 'axis':1}),
- ("test_lpnormalization_ord_axis", "LpNormalization", [get_rnd([5, 3, 3, 2])], np.linalg.norm, {'ord':1, 'axis':2})
+ ("test_lpnormalization_ord2", "LpNormalization", [get_rnd([5, 3, 3, 2])], np.linalg.norm, {'ord':2, 'axis':1})
]
if __name__ == '__main__':
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