Roshrini closed pull request #13500: [MXNET-898] ONNX import/export:
Sample_multinomial, ONNX export: GlobalLpPool, LpPool
URL: https://github.com/apache/incubator-mxnet/pull/13500
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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 3baf10a10d3..d24865d9dcb 100644
--- a/python/mxnet/contrib/onnx/mx2onnx/_op_translations.py
+++ b/python/mxnet/contrib/onnx/mx2onnx/_op_translations.py
@@ -586,6 +586,7 @@ def convert_pooling(node, **kwargs):
pool_type = attrs["pool_type"]
stride = eval(attrs["stride"]) if attrs.get("stride") else None
global_pool = get_boolean_attribute_value(attrs, "global_pool")
+ p_value = attrs.get('p_value', 'None')
pooling_convention = attrs.get('pooling_convention', 'valid')
@@ -598,26 +599,51 @@ def convert_pooling(node, **kwargs):
pad_dims = list(parse_helper(attrs, "pad", [0, 0]))
pad_dims = pad_dims + pad_dims
- pool_types = {"max": "MaxPool", "avg": "AveragePool"}
- global_pool_types = {"max": "GlobalMaxPool", "avg": "GlobalAveragePool"}
+ pool_types = {"max": "MaxPool", "avg": "AveragePool", "lp": "LpPool"}
+ global_pool_types = {"max": "GlobalMaxPool", "avg": "GlobalAveragePool",
+ "lp": "GlobalLpPool"}
+
+ if pool_type == 'lp' and p_value == 'None':
+ raise AttributeError('ONNX requires a p value for LpPool and
GlobalLpPool')
if global_pool:
- node = onnx.helper.make_node(
- global_pool_types[pool_type],
- input_nodes, # input
- [name],
- name=name
- )
+ if pool_type == 'lp':
+ node = onnx.helper.make_node(
+ global_pool_types[pool_type],
+ input_nodes, # input
+ [name],
+ p=int(p_value),
+ name=name
+ )
+ else:
+ node = onnx.helper.make_node(
+ global_pool_types[pool_type],
+ input_nodes, # input
+ [name],
+ name=name
+ )
else:
- node = onnx.helper.make_node(
- pool_types[pool_type],
- input_nodes, # input
- [name],
- kernel_shape=kernel,
- pads=pad_dims,
- strides=stride,
- name=name
- )
+ if pool_type == 'lp':
+ node = onnx.helper.make_node(
+ pool_types[pool_type],
+ input_nodes, # input
+ [name],
+ p=int(p_value),
+ kernel_shape=kernel,
+ pads=pad_dims,
+ strides=stride,
+ name=name
+ )
+ else:
+ node = onnx.helper.make_node(
+ pool_types[pool_type],
+ input_nodes, # input
+ [name],
+ kernel_shape=kernel,
+ pads=pad_dims,
+ strides=stride,
+ name=name
+ )
return [node]
@@ -1689,3 +1715,26 @@ def convert_logsoftmax(node, **kwargs):
name=name
)
return [node]
+
+
+@mx_op.register("_sample_multinomial")
+def convert_multinomial(node, **kwargs):
+ """Map MXNet's multinomial operator attributes to onnx's
+ Multinomial operator and return the created node.
+ """
+ name, input_nodes, attrs = get_inputs(node, kwargs)
+ dtype = onnx.mapping.NP_TYPE_TO_TENSOR_TYPE[np.dtype(attrs.get("dtype",
'int32'))]
+ sample_size = convert_string_to_list(attrs.get("shape", '1'))
+ if len(sample_size) < 2:
+ sample_size = sample_size[-1]
+ else:
+ raise AttributeError("ONNX currently supports integer sample_size
only")
+ node = onnx.helper.make_node(
+ "Multinomial",
+ input_nodes,
+ [name],
+ dtype=dtype,
+ sample_size=sample_size,
+ name=name,
+ )
+ return [node]
diff --git a/python/mxnet/contrib/onnx/onnx2mx/_import_helper.py
b/python/mxnet/contrib/onnx/onnx2mx/_import_helper.py
index 5b33f9faac1..2a668dc84be 100644
--- a/python/mxnet/contrib/onnx/onnx2mx/_import_helper.py
+++ b/python/mxnet/contrib/onnx/onnx2mx/_import_helper.py
@@ -18,7 +18,7 @@
# coding: utf-8_
# pylint: disable=invalid-name
"""Operator attributes conversion"""
-from ._op_translations import identity, random_uniform, random_normal
+from ._op_translations import identity, random_uniform, random_normal,
sample_multinomial
from ._op_translations import add, subtract, multiply, divide, absolute,
negative, add_n
from ._op_translations import tanh, arccos, arcsin, arctan, _cos, _sin, _tan
from ._op_translations import softplus, shape, gather, lp_pooling, size
@@ -48,6 +48,7 @@
'RandomNormal' : random_normal,
'RandomUniformLike' : random_uniform,
'RandomNormalLike' : random_normal,
+ 'Multinomial' : sample_multinomial,
# Arithmetic Operators
'Add' : add,
'Sub' : subtract,
diff --git a/python/mxnet/contrib/onnx/onnx2mx/_op_translations.py
b/python/mxnet/contrib/onnx/onnx2mx/_op_translations.py
index ce0e0e51ef7..a061a7ef002 100644
--- a/python/mxnet/contrib/onnx/onnx2mx/_op_translations.py
+++ b/python/mxnet/contrib/onnx/onnx2mx/_op_translations.py
@@ -38,6 +38,19 @@ def random_normal(attrs, inputs, proto_obj):
new_attr = translation_utils._fix_attribute_names(new_attr, {'mean' :
'loc'})
return 'random_uniform', new_attr, inputs
+def sample_multinomial(attrs, inputs, proto_obj):
+ """Draw random samples from a multinomial 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_attrs = translation_utils._remove_attributes(attrs, ['seed'])
+ new_attrs = translation_utils._fix_attribute_names(new_attrs,
{'sample_size': 'shape'})
+ new_attrs['dtype'] = TENSOR_TYPE_TO_NP_TYPE[int(attrs.get('dtype', 6))]
+ return 'sample_multinomial', new_attrs, inputs
+
+
# Arithmetic Operations
def add(attrs, inputs, proto_obj):
"""Adding two tensors"""
@@ -382,6 +395,7 @@ def global_lppooling(attrs, inputs, proto_obj):
'kernel': (1,
1),
'pool_type':
'lp',
'p_value':
p_value})
+ new_attrs = translation_utils._remove_attributes(new_attrs, ['p'])
return 'Pooling', new_attrs, inputs
def linalg_gemm(attrs, inputs, proto_obj):
@@ -671,11 +685,12 @@ def lp_pooling(attrs, inputs, proto_obj):
new_attrs = translation_utils._fix_attribute_names(attrs,
{'kernel_shape':
'kernel',
'strides': 'stride',
- 'pads': 'pad',
- 'p_value': p_value
+ 'pads': 'pad'
})
+ new_attrs = translation_utils._remove_attributes(new_attrs, ['p'])
new_attrs = translation_utils._add_extra_attributes(new_attrs,
- {'pooling_convention':
'valid'
+ {'pooling_convention':
'valid',
+ 'p_value': p_value
})
new_op = translation_utils._fix_pooling('lp', inputs, new_attrs)
return new_op, new_attrs, inputs
diff --git a/python/mxnet/contrib/onnx/onnx2mx/_translation_utils.py
b/python/mxnet/contrib/onnx/onnx2mx/_translation_utils.py
index f63c1e9e8e6..6fd52665ca3 100644
--- a/python/mxnet/contrib/onnx/onnx2mx/_translation_utils.py
+++ b/python/mxnet/contrib/onnx/onnx2mx/_translation_utils.py
@@ -94,6 +94,7 @@ def _fix_pooling(pool_type, inputs, new_attr):
stride = new_attr.get('stride')
kernel = new_attr.get('kernel')
padding = new_attr.get('pad')
+ p_value = new_attr.get('p_value')
# Adding default stride.
if stride is None:
@@ -138,7 +139,10 @@ def _fix_pooling(pool_type, inputs, new_attr):
new_pad_op = symbol.pad(curr_sym, mode='constant',
pad_width=pad_width)
# Apply pooling without pads.
- new_pooling_op = symbol.Pooling(new_pad_op, pool_type=pool_type,
stride=stride, kernel=kernel)
+ if pool_type == 'lp':
+ new_pooling_op = symbol.Pooling(new_pad_op, pool_type=pool_type,
stride=stride, kernel=kernel, p_value=p_value)
+ else:
+ new_pooling_op = symbol.Pooling(new_pad_op, pool_type=pool_type,
stride=stride, kernel=kernel)
return new_pooling_op
def _fix_bias(op_name, attrs, num_inputs):
diff --git a/tests/python-pytest/onnx/test_cases.py
b/tests/python-pytest/onnx/test_cases.py
index 6a189b62492..64aaab0f6d4 100644
--- a/tests/python-pytest/onnx/test_cases.py
+++ b/tests/python-pytest/onnx/test_cases.py
@@ -79,7 +79,6 @@
'test_softplus'
],
'import': ['test_gather',
- 'test_global_lppooling',
'test_softsign',
'test_reduce_',
'test_mean',
@@ -89,7 +88,6 @@
'test_averagepool_2d_precomputed_strides',
'test_averagepool_2d_strides',
'test_averagepool_3d',
- 'test_LpPool_',
'test_split_equal',
'test_hardmax'
],
diff --git a/tests/python-pytest/onnx/test_node.py
b/tests/python-pytest/onnx/test_node.py
index 07ae866b96c..6a0f8bcd73c 100644
--- a/tests/python-pytest/onnx/test_node.py
+++ b/tests/python-pytest/onnx/test_node.py
@@ -56,6 +56,24 @@ def get_rnd(shape, low=-1.0, high=1.0, dtype=np.float32):
return np.random.choice(a=[False, True], size=shape).astype(np.float32)
+def _fix_attributes(attrs, attribute_mapping):
+ new_attrs = attrs
+ attr_modify = attribute_mapping.get('modify', {})
+ for k, v in attr_modify.items():
+ new_attrs[v] = new_attrs.pop(k, None)
+
+ attr_add = attribute_mapping.get('add', {})
+ for k, v in attr_add.items():
+ new_attrs[k] = v
+
+ attr_remove = attribute_mapping.get('remove', [])
+ for k in attr_remove:
+ if k in new_attrs:
+ del new_attrs[k]
+
+ return new_attrs
+
+
def forward_pass(sym, arg, aux, data_names, input_data):
""" Perform forward pass on given data
:param sym: Symbol
@@ -118,7 +136,7 @@ def get_onnx_graph(testname, input_names, inputs,
output_name, output_shape, att
return model
for test in test_cases:
- test_name, mxnet_op, onnx_name, inputs, attrs, mxnet_specific =
test
+ 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)
@@ -131,33 +149,66 @@ def get_onnx_graph(testname, input_names, inputs,
output_name, output_shape, att
onnx_name +
".onnx")
onnxmodel = load_model(onnxmodelfile)
else:
- onnxmodel = get_onnx_graph(test_name, names,
input_tensors, onnx_name, outputshape, attrs)
+ onnx_attrs = _fix_attributes(attrs, fix_attrs)
+ onnxmodel = get_onnx_graph(test_name, names,
input_tensors, onnx_name, outputshape, onnx_attrs)
bkd_rep = backend.prepare(onnxmodel, operation='export')
output = bkd_rep.run(inputs)
- npt.assert_almost_equal(output[0], mxnet_output)
+ if check_value:
+ npt.assert_almost_equal(output[0], mxnet_output)
+
+ if check_shape:
+ npt.assert_equal(output[0].shape, outputshape)
-# test_case = ("test_case_name", mxnet op, "ONNX_op_name", [input_list],
attribute map, MXNet_specific=True/False)
+# 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],
+# 'add': {attr_name: value},
+# check_value=True/False, check_shape=True/False)
test_cases = [
- ("test_equal", mx.sym.broadcast_equal, "Equal", [get_rnd((1, 3, 4, 5)),
get_rnd((1, 5))], {}, False),
- ("test_greater", mx.sym.broadcast_greater, "Greater", [get_rnd((1, 3, 4,
5)), get_rnd((1, 5))], {}, False),
- ("test_less", mx.sym.broadcast_lesser, "Less", [get_rnd((1, 3, 4, 5)),
get_rnd((1, 5))], {}, False),
+ ("test_equal", mx.sym.broadcast_equal, "Equal", [get_rnd((1, 3, 4, 5)),
get_rnd((1, 5))], {}, False, {}, True,
+ False),
+ ("test_greater", mx.sym.broadcast_greater, "Greater", [get_rnd((1, 3, 4,
5)), get_rnd((1, 5))], {}, False, {}, True,
+ False),
+ ("test_less", mx.sym.broadcast_lesser, "Less", [get_rnd((1, 3, 4, 5)),
get_rnd((1, 5))], {}, False, {}, True,
+ False),
("test_and", mx.sym.broadcast_logical_and, "And",
- [get_rnd((3, 4, 5), dtype=np.bool_), get_rnd((3, 4, 5), dtype=np.bool_)],
{}, False),
+ [get_rnd((3, 4, 5), dtype=np.bool_), get_rnd((3, 4, 5), dtype=np.bool_)],
{}, False, {}, True, False),
("test_xor", mx.sym.broadcast_logical_xor, "Xor",
- [get_rnd((3, 4, 5), dtype=np.bool_), get_rnd((3, 4, 5), dtype=np.bool_)],
{}, False),
+ [get_rnd((3, 4, 5), dtype=np.bool_), get_rnd((3, 4, 5), dtype=np.bool_)],
{}, False, {}, True, False),
("test_or", mx.sym.broadcast_logical_or, "Or",
- [get_rnd((3, 4, 5), dtype=np.bool_), get_rnd((3, 4, 5), dtype=np.bool_)],
{}, False),
- ("test_not", mx.sym.logical_not, "Not", [get_rnd((3, 4, 5),
dtype=np.bool_)], {}, False),
- ("test_square", mx.sym.square, "Pow", [get_rnd((2, 3), dtype=np.int32)],
{}, True),
+ [get_rnd((3, 4, 5), dtype=np.bool_), get_rnd((3, 4, 5), dtype=np.bool_)],
{}, False, {}, True, False),
+ ("test_not", mx.sym.logical_not, "Not", [get_rnd((3, 4, 5),
dtype=np.bool_)], {}, False, {}, True, False),
+ ("test_square", mx.sym.square, "Pow", [get_rnd((2, 3), dtype=np.int32)],
{}, True, {}, True, False),
("test_spacetodepth", mx.sym.space_to_depth, "SpaceToDepth", [get_rnd((1,
1, 4, 6))],
- {'block_size': 2}, False),
+ {'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),
- ("test_fullyconnected", mx.sym.FullyConnected, "Gemm", [get_rnd((4,3)),
get_rnd((4, 3)), get_rnd(4)],
- {'num_hidden': 4, 'name': 'FC'}, True)
+ {'ignore_label': 0, 'use_ignore': False}, 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))],
+ {'kernel': (4, 5), 'pad': (0, 0), 'stride': (1, 1), 'p_value': 1,
'pool_type': 'lp'}, False,
+ {'modify': {'kernel': 'kernel_shape', 'pad': 'pads', 'stride': 'strides',
'p_value': 'p'},
+ 'remove': ['pool_type']}, True, False),
+ ("test_lppool2", mx.sym.Pooling, "LpPool", [get_rnd((2, 3, 20, 20))],
+ {'kernel': (4, 5), 'pad': (0, 0), 'stride': (1, 1), 'p_value': 2,
'pool_type': 'lp'}, False,
+ {'modify': {'kernel': 'kernel_shape', 'pad': 'pads', 'stride': 'strides',
'p_value': 'p'},
+ 'remove': ['pool_type']}, True, False),
+ ("test_globallppool1", mx.sym.Pooling, "GlobalLpPool", [get_rnd((2, 3, 20,
20))],
+ {'kernel': (4, 5), 'pad': (0, 0), 'stride': (1, 1), 'p_value': 1,
'pool_type': 'lp', 'global_pool': True}, False,
+ {'modify': {'p_value': 'p'},
+ 'remove': ['pool_type', 'kernel', 'pad', 'stride', 'global_pool']},
True, False),
+ ("test_globallppool2", mx.sym.Pooling, "GlobalLpPool", [get_rnd((2, 3, 20,
20))],
+ {'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),
+
+ # 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)
]
if __name__ == '__main__':
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