ONNX import/export: Add missing tests, ONNX export: LogSoftMax

python/mxnet/contrib/onnx/mx2onnx/_op_translations.py

@@ -810,7 +810,7 @@ def convert_l2normalization(node, **kwargs):
  mode = attrs.get("mode", "instance")
 
  if mode != "channel":
- raise AttributeError("ONNX currently supports channel mode only")
+ raise AttributeError("L2Normalization: ONNX currently supports channel mode only")
 
  l2norm_node = onnx.helper.make_node(
  "LpNormalization",
@@ -1302,7 +1302,7 @@ def convert_reshape(node, **kwargs):
 
  for val in output_shape_list:
  if val in not_supported_shape:
- raise AttributeError("Shape value not supported in ONNX", val)
+ raise AttributeError("Reshape: Shape value not supported in ONNX", val)
 
  reshape_node = onnx.helper.make_node(
  "Reshape",
@@ -1428,7 +1428,7 @@ def convert_squeeze(node, **kwargs):
 
  axis = attrs.get("axis", None)
  if not axis:
- raise AttributeError("Missing axis attribute: ONNX currently requires axis to "
+ raise AttributeError("Squeeze: Missing axis attribute: ONNX currently requires axis to "
  "be specified for squeeze operator")
  axis = convert_string_to_list(axis)
 
@@ -1666,3 +1666,26 @@ def convert_size(node, **kwargs):
  and return the created node.
  """
  return create_basic_op_node('Size', node, kwargs)
+
+
+@mx_op.register("log_softmax")
+def convert_logsoftmax(node, **kwargs):
+ """Map MXNet's log_softmax operator attributes to onnx's LogSoftMax operator
+ and return the created node.
+ """
+ name, input_nodes, attrs = get_inputs(node, kwargs)
+
+ # Converting to int
+ axis = int(attrs.get("axis", -1))
+ temp = attrs.get("temperature", 'None')
+ if temp != 'None':
+ raise AttributeError("LogSoftMax: ONNX supports only temperature=None")
+
+ node = onnx.helper.make_node(
+ 'LogSoftmax',
+ input_nodes,
+ [name],
+ axis=axis,
+ name=name
+ )
+ return [node]

python/mxnet/contrib/onnx/mx2onnx/export_onnx.py

@@ -146,7 +146,7 @@ def get_outputs(sym, params, in_shape, in_label):
  if name.endswith('_output'):
  out_names.append(name[:-len('_output')])
  else:
- logging.warning("output '%s' does not end with '_output'", name)
+ logging.info("output '%s' does not end with '_output'", name)
  out_names.append(name)
 
  assert len(out_shapes) == len(out_names)

tests/python-pytest/onnx/backend_rep.py

@@ -82,8 +82,10 @@ def run(self, inputs, **kwargs):
  args = dict(zip(data_names, data_forward))
  exe = self.symbol.bind(ctx, args=args, aux_states=self.aux_params)
  exe.forward(is_train=False)
- result = exe.outputs[0].asnumpy()
- return [result]
+ result = []
+ for output in exe.outputs:
+ result.append(output.asnumpy())
+ return result
 
 
 # GluonBackendRep object will be returned by GluonBackend's prepare method which is used to
@@ -124,7 +126,12 @@ def run(self, inputs, **kwargs):
  net_inputs = [nd.array(input_data, ctx=ctx) for input_data in inputs]
  net_outputs = self.net(*net_inputs)
  results = []
- results.extend([o for o in net_outputs.asnumpy()])
- result = np.array(results)
+ if isinstance(net_outputs, list):
+ for output in net_outputs:
+ results.append(output.asnumpy())
+ result = results
+ else:
+ results.extend([o for o in net_outputs.asnumpy()])
+ result = [np.array(results)]
 
- return [result]
+ return result

tests/python-pytest/onnx/test_cases.py

@@ -49,7 +49,7 @@
  'test_argmin',
  'test_min',
  # pytorch operator tests
- 'test_operator_exp',
+ 'test_exp_',
  'test_operator_maxpool',
  'test_operator_params',
  'test_operator_permute2',
@@ -60,21 +60,28 @@
  'test_asin',
  'test_atan',
  'test_squeeze',
- 'test_matmul_3d',
- 'test_matmul_4d',
+ 'test_matmul',
  'test_depthtospace',
  'test_hardsigmoid',
  'test_instancenorm',
  'test_shape',
  'test_cast',
  'test_clip',
- 'test_size'
+ 'test_size',
+ 'test_dropout',
+ 'test_unsqueeze',
+ 'test_log_',
+ 'test_flatten_default_axis',
+ 'test_leakyrelu',
+ 'test_selu_default',
+ 'test_elu',
+ 'test_max_',
+ 'test_softplus'
  ],
- 'import': ['test_unsqueeze',
+ 'import': ['test_gather',
  'test_global_lppooling',
  'test_softsign',
  'test_reduce_',
- 'test_softplus',
  'test_mean',
  'test_averagepool_1d',
  'test_averagepool_2d_pads_count_include_pad',
@@ -84,18 +91,16 @@
  'test_averagepool_3d',
  'test_LpPool_',
  'test_split_equal'
- 'test_random_',
  ],
  'export': ['test_random_uniform',
  'test_random_normal',
  'test_reduce_min',
  'test_reduce_max',
- 'test_squeeze',
  'test_reduce_mean',
  'test_reduce_prod',
  'test_reduce_sum_d',
  'test_reduce_sum_keepdims_random',
- 'test_max_',
+ 'test_lrn'
  ]
 }
 
@@ -104,17 +109,12 @@
  'test_BatchNorm',
  'test_ConstantPad2d'
  'test_Conv2d',
- 'test_ELU',
- 'test_LeakyReLU',
  'test_MaxPool',
  'test_PReLU',
- 'test_ReLU',
- 'test_selu_default',
- 'test_Sigmoid',
  'test_Softmax',
  'test_softmax_functional',
  'test_softmax_lastdim',
- 'test_Tanh']
+ ]
 }
 
 STANDARD_MODEL = {