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Posted to commits@mxnet.apache.org by GitBox <gi...@apache.org> on 2019/01/10 19:09:12 UTC
[GitHub] Roshrini closed pull request #13676: [MXNET-880] ONNX export:
Random uniform, Random normal, MaxRoiPool
Roshrini closed pull request #13676: [MXNET-880] ONNX export: Random uniform, Random normal, MaxRoiPool
URL: https://github.com/apache/incubator-mxnet/pull/13676
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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 d24865d9dcb..6c55965bd46 100644
--- a/python/mxnet/contrib/onnx/mx2onnx/_op_translations.py
+++ b/python/mxnet/contrib/onnx/mx2onnx/_op_translations.py
@@ -1738,3 +1738,76 @@ def convert_multinomial(node, **kwargs):
name=name,
)
return [node]
+
+
+@mx_op.register("_random_uniform")
+def convert_random_uniform(node, **kwargs):
+ """Map MXNet's random_uniform operator attributes to onnx's RandomUniform
+ operator and return the created node.
+ """
+ name, input_nodes, attrs = get_inputs(node, kwargs)
+
+ # Converting to float32
+ low = float(attrs.get("low", 0))
+ high = float(attrs.get("high", 1.0))
+ shape = convert_string_to_list(attrs.get('shape', '[]'))
+ dtype = onnx.mapping.NP_TYPE_TO_TENSOR_TYPE[np.dtype(attrs.get('dtype', 'float32'))]
+
+ node = onnx.helper.make_node(
+ 'RandomUniform',
+ input_nodes,
+ [name],
+ low=low,
+ high=high,
+ dtype=dtype,
+ shape=shape,
+ name=name
+ )
+ return [node]
+
+
+@mx_op.register("_random_normal")
+def convert_random_normal(node, **kwargs):
+ """Map MXNet's random_normal operator attributes to onnx's RandomNormal
+ operator and return the created node.
+ """
+ name, input_nodes, attrs = get_inputs(node, kwargs)
+
+ # Converting to float32
+ mean = float(attrs.get("loc", 0))
+ scale = float(attrs.get("scale", 1.0))
+ shape = convert_string_to_list(attrs.get('shape', '[]'))
+ dtype = onnx.mapping.NP_TYPE_TO_TENSOR_TYPE[np.dtype(attrs.get('dtype', 'float32'))]
+
+ node = onnx.helper.make_node(
+ 'RandomNormal',
+ input_nodes,
+ [name],
+ mean=mean,
+ scale=scale,
+ dtype=dtype,
+ shape=shape,
+ name=name
+ )
+ return [node]
+
+
+@mx_op.register("ROIPooling")
+def convert_roipooling(node, **kwargs):
+ """Map MXNet's ROIPooling operator attributes to onnx's MaxRoiPool
+ operator and return the created node.
+ """
+ name, input_nodes, attrs = get_inputs(node, kwargs)
+
+ pooled_shape = convert_string_to_list(attrs.get('pooled_size'))
+ scale = float(attrs.get("spatial_scale"))
+
+ node = onnx.helper.make_node(
+ 'MaxRoiPool',
+ input_nodes,
+ [name],
+ pooled_shape=pooled_shape,
+ spatial_scale=scale,
+ name=name
+ )
+ return [node]
diff --git a/python/mxnet/contrib/onnx/onnx2mx/_op_translations.py b/python/mxnet/contrib/onnx/onnx2mx/_op_translations.py
index a061a7ef002..b2c1ccd8eda 100644
--- a/python/mxnet/contrib/onnx/onnx2mx/_op_translations.py
+++ b/python/mxnet/contrib/onnx/onnx2mx/_op_translations.py
@@ -29,14 +29,26 @@ def identity(attrs, inputs, proto_obj):
def random_uniform(attrs, inputs, proto_obj):
"""Draw random samples from a uniform distribtuion."""
- new_attr = translation_utils._remove_attributes(attrs, ['seed'])
- return 'random_uniform', new_attr, inputs
+ try:
+ from onnx.mapping import TENSOR_TYPE_TO_NP_TYPE
+ except ImportError:
+ raise ImportError("Onnx and protobuf need to be installed. "
+ "Instructions to install - https://github.com/onnx/onnx")
+ new_attrs = translation_utils._remove_attributes(attrs, ['seed'])
+ new_attrs['dtype'] = TENSOR_TYPE_TO_NP_TYPE[int(new_attrs.get('dtype', 1))]
+ return 'random_uniform', new_attrs, inputs
def random_normal(attrs, inputs, proto_obj):
"""Draw random samples from a Gaussian distribution."""
+ try:
+ from onnx.mapping import TENSOR_TYPE_TO_NP_TYPE
+ except ImportError:
+ raise ImportError("Onnx and protobuf need to be installed. "
+ "Instructions to install - https://github.com/onnx/onnx")
new_attr = translation_utils._remove_attributes(attrs, ['seed'])
- new_attr = translation_utils._fix_attribute_names(new_attr, {'mean' : 'loc'})
- return 'random_uniform', new_attr, inputs
+ new_attr = translation_utils._fix_attribute_names(new_attr, {'mean': 'loc'})
+ new_attr['dtype'] = TENSOR_TYPE_TO_NP_TYPE[int(new_attr.get('dtype', 1))]
+ return 'random_normal', new_attr, inputs
def sample_multinomial(attrs, inputs, proto_obj):
"""Draw random samples from a multinomial distribution."""
diff --git a/tests/python-pytest/onnx/test_node.py b/tests/python-pytest/onnx/test_node.py
index 6a0f8bcd73c..f91349975cd 100644
--- a/tests/python-pytest/onnx/test_node.py
+++ b/tests/python-pytest/onnx/test_node.py
@@ -139,9 +139,18 @@ def get_onnx_graph(testname, input_names, inputs, output_name, output_shape, att
test_name, mxnet_op, onnx_name, inputs, attrs, mxnet_specific, fix_attrs, check_value, check_shape = test
with self.subTest(test_name):
names, input_tensors, inputsym = get_input_tensors(inputs)
- test_op = mxnet_op(*inputsym, **attrs)
- mxnet_output = forward_pass(test_op, None, None, names, inputs)
- outputshape = np.shape(mxnet_output)
+ if inputs:
+ test_op = mxnet_op(*inputsym, **attrs)
+ mxnet_output = forward_pass(test_op, None, None, names, inputs)
+ outputshape = np.shape(mxnet_output)
+ else:
+ test_op = mxnet_op(**attrs)
+ shape = attrs.get('shape', (1,))
+ x = mx.nd.zeros(shape, dtype='float32')
+ xgrad = mx.nd.zeros(shape, dtype='float32')
+ exe = test_op.bind(ctx=mx.cpu(), args={'x': x}, args_grad={'x': xgrad})
+ mxnet_output = exe.forward(is_train=False)[0].asnumpy()
+ outputshape = np.shape(mxnet_output)
if mxnet_specific:
onnxmodelfile = onnx_mxnet.export_model(test_op, {}, [np.shape(ip) for ip in inputs],
@@ -204,11 +213,19 @@ def get_onnx_graph(testname, input_names, inputs, output_name, output_shape, att
{'kernel': (4, 5), 'pad': (0, 0), 'stride': (1, 1), 'p_value': 2, 'pool_type': 'lp', 'global_pool': True}, False,
{'modify': {'p_value': 'p'},
'remove': ['pool_type', 'kernel', 'pad', 'stride', 'global_pool']}, True, False),
+ ("test_roipool", mx.sym.ROIPooling, "MaxRoiPool",
+ [[[get_rnd(shape=(8, 6), low=1, high=100, dtype=np.int32)]], [[0, 0, 0, 4, 4]]],
+ {'pooled_size': (2, 2), 'spatial_scale': 0.7}, False,
+ {'modify': {'pooled_size': 'pooled_shape'}}, True, False),
# since results would be random, checking for shape alone
("test_multinomial", mx.sym.sample_multinomial, "Multinomial",
[np.array([0, 0.1, 0.2, 0.3, 0.4]).astype("float32")],
- {'shape': (10,)}, False, {'modify': {'shape': 'sample_size'}}, False, True)
+ {'shape': (10,)}, False, {'modify': {'shape': 'sample_size'}}, False, True),
+ ("test_random_normal", mx.sym.random_normal, "RandomNormal", [],
+ {'shape': (2, 2), 'loc': 0, 'scale': 1}, False, {'modify': {'loc': 'mean'}}, False, True),
+ ("test_random_uniform", mx.sym.random_uniform, "RandomUniform", [],
+ {'shape': (2, 2), 'low': 0.5, 'high': 1.0}, False, {}, False, True)
]
if __name__ == '__main__':
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