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Posted to commits@mxnet.apache.org by GitBox <gi...@apache.org> on 2018/12/10 18:14:46 UTC
[GitHub] Roshrini closed pull request #12946: [WIP] Onnx export API optional
args and additional operator support, Fixes #12682
Roshrini closed pull request #12946: [WIP] Onnx export API optional args and additional operator support, Fixes #12682
URL: https://github.com/apache/incubator-mxnet/pull/12946
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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 11e75d9a600..f7bda29d10b 100644
--- a/python/mxnet/contrib/onnx/mx2onnx/_op_translations.py
+++ b/python/mxnet/contrib/onnx/mx2onnx/_op_translations.py
@@ -218,7 +218,6 @@ def convert_convolution(node, **kwargs):
return [conv_node]
-
@mx_op.register("FullyConnected")
def convert_fully_connected(node, **kwargs):
"""Map MXNet's FullyConnected operator attributes to onnx's Gemm operator
@@ -443,20 +442,19 @@ def convert_dot(node, **kwargs):
transpose_a, transpose_b attributes."""
name, input_nodes, attrs = get_inputs(node, kwargs)
- trans_a_node = None
- trans_b_node = None
+ trans_a_node = trans_b_node = None
trans_a = get_boolean_attribute_value(attrs, "transpose_a")
trans_b = get_boolean_attribute_value(attrs, "transpose_b")
op_name = "transpose" + str(kwargs["idx"])
+ input_node_a = op_name + "_a"
+ input_node_b = op_name + "_b"
if trans_a:
trans_a_node = create_helper_trans_node(op_name, input_nodes[0], 'a')
- input_node_a = op_name+"_a"
if trans_b:
trans_b_node = create_helper_trans_node(op_name, input_nodes[1], 'b')
- input_node_b = op_name+"_b"
matmul_node = onnx.helper.make_node(
'MatMul',
@@ -617,13 +615,21 @@ 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("LeakyReLU")
def convert_leakyrelu(node, **kwargs):
"""Map MXNet's LeakyReLU operator attributes to onnx's Elu/LeakyRelu/PRelu operators
@@ -681,7 +687,7 @@ def convert_softmax_output(node, **kwargs):
"""Map MXNet's SoftmaxOutput operator attributes to onnx's Softmax operator
and return the created node.
"""
- name, _, _ = get_inputs(node, kwargs)
+ name = node["name"]
input1_idx = kwargs["index_lookup"][node["inputs"][0][0]]
input1 = kwargs["proc_nodes"][input1_idx]
@@ -693,10 +699,38 @@ def convert_softmax_output(node, **kwargs):
axis=1,
name=name
)
-
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.output[0]],
+ [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("make_loss")
+def convert_makeloss(node, **kwargs):
+ """ Skip operator """
+ return create_basic_op_node('ConstantFill', node, kwargs)
+
+
@mx_op.register("Concat")
def convert_concat(node, **kwargs):
"""Map MXNet's Concat operator attributes to onnx's Concat operator
@@ -843,6 +877,8 @@ 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
+
scalar_value = [float(attrs.get("scalar", 1))]
initializer = kwargs["initializer"]
@@ -852,13 +888,19 @@ 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]
flag = False
break
@@ -869,6 +911,7 @@ def scalar_op_helper(node, op_name, **kwargs):
dims = np.shape(np_arr)
scalar_op_name = "scalar_op" + str(kwargs["idx"])
+ # Convert scalar value into node
tensor_node = onnx.helper.make_tensor_value_info(scalar_op_name, data_type, dims)
initializer.append(
@@ -907,7 +950,6 @@ def scalar_op_helper(node, op_name, **kwargs):
)
return [tensor_node]
-# Convert scalar value into node and pass it as input to mul_node
@mx_op.register("_mul_scalar")
def convert_mul_scalar(node, **kwargs):
"""Map MXNet's _mul_scalar operator attributes to onnx's Mul operator.
@@ -916,8 +958,6 @@ def convert_mul_scalar(node, **kwargs):
"""
return scalar_op_helper(node, 'Mul', **kwargs)
-
-# Convert scalar value into node and pass it as input to mul_node
@mx_op.register("_minus_scalar")
def convert_minus_scalar(node, **kwargs):
"""Map MXNet's _minus_scalar operator attributes to onnx's Minus operator.
@@ -926,8 +966,14 @@ 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 Minus 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")
def convert_add_scalar(node, **kwargs):
"""Map MXNet's _plus_scalar operator attributes to onnx's Add operator.
@@ -936,7 +982,6 @@ def convert_add_scalar(node, **kwargs):
"""
return scalar_op_helper(node, 'Add', **kwargs)
-# Convert scalar value into node and pass it as input to mul_node
@mx_op.register("_div_scalar")
def convert_div_scalar(node, **kwargs):
"""Map MXNet's _div_scalar operator attributes to onnx's Div operator.
@@ -945,6 +990,14 @@ 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)
+
# Sorting and Searching
@mx_op.register("argmax")
diff --git a/python/mxnet/contrib/onnx/mx2onnx/export_model.py b/python/mxnet/contrib/onnx/mx2onnx/export_model.py
index e5158051d6f..f09f674e7d2 100644
--- a/python/mxnet/contrib/onnx/mx2onnx/export_model.py
+++ b/python/mxnet/contrib/onnx/mx2onnx/export_model.py
@@ -32,7 +32,7 @@
from ._export_helper import load_module
-def export_model(sym, params, input_shape, input_type=np.float32,
+def export_model(sym, params, input_shape, input_type=np.float32, label_names=None, label_shapes=None,
onnx_file_path='model.onnx', verbose=False):
"""Exports the MXNet model file, passed as a parameter, into ONNX model.
Accepts both symbol,parameter objects as well as json and params filepaths as input.
@@ -49,6 +49,10 @@ def export_model(sym, params, input_shape, input_type=np.float32,
Input shape of the model e.g [(1,3,224,224)]
input_type : data type
Input data type e.g. np.float32
+ label_names : List of str
+ Optional list of label e.g. ['regression_label']
+ label_shapes : List of tuple
+ Optional a list of (name, shape) pairs e.g [('regression_label', (1,3,224,224))]
onnx_file_path : str
Path where to save the generated onnx file
verbose : Boolean
@@ -75,11 +79,11 @@ def export_model(sym, params, input_shape, input_type=np.float32,
sym_obj, params_obj = load_module(sym, params)
onnx_graph = converter.create_onnx_graph_proto(sym_obj, params_obj, input_shape,
mapping.NP_TYPE_TO_TENSOR_TYPE[data_format],
- verbose=verbose)
+ label_names, label_shapes, verbose=verbose)
elif isinstance(sym, symbol.Symbol) and isinstance(params, dict):
onnx_graph = converter.create_onnx_graph_proto(sym, params, input_shape,
mapping.NP_TYPE_TO_TENSOR_TYPE[data_format],
- verbose=verbose)
+ label_names, label_shapes, verbose=verbose)
else:
raise ValueError("Input sym and params should either be files or objects")
diff --git a/python/mxnet/contrib/onnx/mx2onnx/export_onnx.py b/python/mxnet/contrib/onnx/mx2onnx/export_onnx.py
index b02d970f9c2..4a9dba07e34 100644
--- a/python/mxnet/contrib/onnx/mx2onnx/export_onnx.py
+++ b/python/mxnet/contrib/onnx/mx2onnx/export_onnx.py
@@ -96,7 +96,7 @@ def convert_layer(node, **kwargs):
return convert_func(node, **kwargs)
@staticmethod
- def forward_pass(inputs, sym, arg_params, aux_params, output_label):
+ def forward_pass(inputs, sym, arg_params, aux_params, label_name):
"""Do a forward pass based on the sym and params to get the shape
of the output using dummy data
@@ -120,7 +120,7 @@ def forward_pass(inputs, sym, arg_params, aux_params, output_label):
# while running load_checkpoint which is not actually a graph input. So ignoring it here
data_names = [graph_input for graph_input in sym.list_inputs()
if graph_input not in arg_params and graph_input not in aux_params
- and graph_input != output_label]
+ and graph_input not in label_name]
data_shapes = []
# Adding extra dimension of batch_size 1 if the batch_size is different for multiple inputs.
@@ -144,9 +144,13 @@ def forward_pass(inputs, sym, arg_params, aux_params, output_label):
data_forward.append(nd.array(val))
test_mod.forward(io.DataBatch(data_forward))
- result = test_mod.get_outputs()[0].asnumpy()
+ result = [i.asnumpy().shape for i in test_mod.get_outputs()]
- return result.shape
+ result_shape = []
+ for idx, label in enumerate(label_name):
+ result_shape.append((label, result[idx]))
+
+ return result_shape
@staticmethod
@@ -179,12 +183,12 @@ def split_params(sym, params):
@staticmethod
- def infer_output_shape(sym, params, in_shape, output_label):
+ def infer_output_shape(sym, params, in_shape, label_name):
"""Infer output shape by doing a forward pass using dummy inputs """
# create dummy input
inputs = [np.random.randn(*input_shape) for input_shape in in_shape]
arg, aux = MXNetGraph.split_params(sym, params)
- return MXNetGraph.forward_pass(inputs, sym, arg, aux, output_label)
+ return MXNetGraph.forward_pass(inputs, sym, arg, aux, label_name)
@staticmethod
@@ -193,7 +197,18 @@ def convert_weights_to_numpy(weights_dict):
return dict([(k.replace("arg:", "").replace("aux:", ""), v.asnumpy())
for k, v in weights_dict.items()])
- def create_onnx_graph_proto(self, sym, params, in_shape, in_type, verbose=False):
+ @staticmethod
+ def verify_provided_labels(data_names, data_shapes, name, throw):
+ """Check that input labels matches input data shape."""
+ actual = [x[0] for x in data_shapes]
+ if sorted(data_names) != sorted(actual):
+ msg = "Data provided by %s_shapes don't match names specified by %s_names (%s vs. %s)" % (
+ name, name, str(data_shapes), str(data_names))
+ if throw:
+ raise ValueError(msg)
+
+ def create_onnx_graph_proto(self, sym, params, in_shape, in_type,
+ label_names=None, label_shapes=None, verbose=False):
"""Convert MXNet graph to ONNX graph
Parameters
@@ -206,6 +221,10 @@ def create_onnx_graph_proto(self, sym, params, in_shape, in_type, verbose=False)
Input shape of the model e.g [(1,3,224,224)]
in_type : data type
Input data type e.g. np.float32
+ out_label : List of str
+ Optional list of output label names
+ out_shape : List of tuple
+ Optional output shape of the model e.g [(1,3,224,224)]
verbose : Boolean
If true will print logs of the model conversion
@@ -226,10 +245,17 @@ def create_onnx_graph_proto(self, sym, params, in_shape, in_type, verbose=False)
# name is "Softmax", this node will have a name "Softmax_label". Also, the new node
# will always be second last node in the json graph.
# Deriving the output_label name.
- output_label = sym.get_internals()[len(sym.get_internals()) - 1].name + "_label"
+ output_suffix = '_output'
+ output_names = [o[:-len(output_suffix)] for o in sym.list_outputs() if o.endswith(output_suffix)]
+
+ if not label_names:
+ label_names = [output_name + '_label' for output_name in output_names]
# Determine output shape
- output_shape = MXNetGraph.infer_output_shape(sym, params, in_shape, output_label)
+ if not label_shapes:
+ label_shapes = MXNetGraph.infer_output_shape(sym, params, in_shape, label_names)
+ else:
+ MXNetGraph.verify_provided_labels(label_names, label_shapes, 'label', True)
weights = MXNetGraph.convert_weights_to_numpy(params)
@@ -253,10 +279,9 @@ def create_onnx_graph_proto(self, sym, params, in_shape, in_type, verbose=False)
# in params dict
if op == "null" and name not in params:
# Handling graph input
-
# Skipping output_label node, as this node is not part of graph
# Refer "output_label" assignment above for more details.
- if name == output_label:
+ if name in label_names:
continue
converted = MXNetGraph.convert_layer(
node,
@@ -294,14 +319,15 @@ def create_onnx_graph_proto(self, sym, params, in_shape, in_type, verbose=False)
# If converted node is NodeProto, add it in processed nodes list
elif isinstance(converted_node, NodeProto):
onnx_processed_nodes.append(converted_node)
- if idx == (len(mx_graph) - 1):
+ if converted_node.name in output_names:
+ label_shape = [i[1] for i in label_shapes if converted_node.name + "_label" == i[0]]
# If converted node doesnt have name, use it from output field
if not converted_node.name:
onnx_processed_outputs.append(
make_tensor_value_info(
name=converted_node.output[0],
elem_type=in_type,
- shape=output_shape
+ shape=label_shape[0]
)
)
else:
@@ -309,7 +335,7 @@ def create_onnx_graph_proto(self, sym, params, in_shape, in_type, verbose=False)
make_tensor_value_info(
name=converted_node.name,
elem_type=in_type,
- shape=output_shape
+ shape=label_shape[0]
)
)
if verbose:
@@ -327,12 +353,12 @@ def create_onnx_graph_proto(self, sym, params, in_shape, in_type, verbose=False)
# refer "output_label" initialization above for more details.
# if extra node was added then prev_index to the last node is adjusted.
if idx == (len(mx_graph) - 1) and \
- mx_graph[len(mx_graph)-2]["name"] == output_label:
+ mx_graph[len(mx_graph) - 2]["name"] in label_names:
prev_index = index_lookup[idx - 2]
else:
prev_index = index_lookup[idx - 1]
- index_lookup.append(prev_index+len(converted))
+ index_lookup.append(prev_index + len(converted))
else:
index_lookup.append(len(converted) - 1)
else:
diff --git a/python/mxnet/contrib/onnx/onnx2mx/_import_helper.py b/python/mxnet/contrib/onnx/onnx2mx/_import_helper.py
index f61910f838e..2097baabd53 100644
--- a/python/mxnet/contrib/onnx/onnx2mx/_import_helper.py
+++ b/python/mxnet/contrib/onnx/onnx2mx/_import_helper.py
@@ -44,6 +44,7 @@
_convert_map = {
# Generator Functions
'Constant' : identity,
+ 'ConstantFill' : identity,
'RandomUniform' : random_uniform,
'RandomNormal' : random_normal,
'RandomUniformLike' : random_uniform,
diff --git a/tests/python-pytest/onnx/export/mxnet_export_test.py b/tests/python-pytest/onnx/export/mxnet_export_test.py
index 9f91369d667..bd3f82b75d6 100644
--- a/tests/python-pytest/onnx/export/mxnet_export_test.py
+++ b/tests/python-pytest/onnx/export/mxnet_export_test.py
@@ -118,7 +118,7 @@ def test_models(model_name, input_shape, output_shape):
onnx_file = os.path.join(dir_path, new_model_name)
logging.info("Translating converted model from mxnet to ONNX")
- converted_model_path = onnx_mxnet.export_model(sym, params, [input_shape], np.float32, onnx_file)
+ converted_model_path = onnx_mxnet.export_model(sym, params, [input_shape], np.float32, onnx_file_path=onnx_file)
sym, arg_params, aux_params = onnx_mxnet.import_model(converted_model_path)
@@ -166,7 +166,7 @@ def test_model_accuracy(model_name, input_shape):
logging.info("Translating converted model from mxnet to ONNX")
converted_model_path = onnx_mxnet.export_model(sym, params, [input_shape], np.float32,
- onnx_file)
+ onnx_file_path=onnx_file)
sym, arg_params, aux_params = onnx_mxnet.import_model(converted_model_path)
@@ -229,7 +229,7 @@ def test_square():
params.update(args)
params.update(auxs)
- converted_model = onnx_mxnet.export_model(square, params, [np.shape(input1)], np.float32, "square.onnx")
+ converted_model = onnx_mxnet.export_model(square, params, [np.shape(input1)], np.float32, onnx_file_path="square.onnx")
sym, arg_params, aux_params = onnx_mxnet.import_model(converted_model)
result = forward_pass(sym, arg_params, aux_params, ['input1'], input1)
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