[NNVM] Error handling message (apache#14)

* [NNVM] Error handling message

nnvm/python/nnvm/frontend/tensorflow.py

@@ -68,7 +68,8 @@ def _impl(attr):
  kernel = attr['kernel_shape']
  if len(kernel) == 2:
  return prefix + '2d' + surfix
- raise NotImplementedError("Only 2d kernel supported.")
+ raise NotImplementedError("OperatorAttributeNotImplemented : "
+ "Only 2d kernel supported in {}.".format(prefix))
  return _impl
 
 def _dimension_constraint():
@@ -103,6 +104,7 @@ def _impl(inputs, attr, params):
  axis_input_vlaue = params[axis_input_name].asnumpy()[0]
  except (IndexError, KeyError):
  raise TypeError( \
+ "OperatorAttributeValueNotValid : "
  "Unsupported argument for `{}` : `axis` should be a constant".format(func_name))
  return func(inputs[0], axis=axis_input_vlaue, keepdims=False)
  return _impl
@@ -129,7 +131,9 @@ def _impl(inputs, attr, params):
  attr['kernel_shape'] = (attr['ksize'][2], attr['ksize'][3])
  attr['strides'] = (attr['strides'][2], attr['strides'][3])
  else:
- raise TypeError("Unsupported data_format type : {}".format(attr['data_format']))
+ raise TypeError("OperatorAttributeValueNotValid : "
+ "Unsupported data_format type : {} in {}"\
+ .format(attr['data_format'], name))
 
  if attr['_target_layout'] == "NCHW" and attr['data_format'] == "NHWC":
  tmp_shape = attr['_input_shapes'][inputs[0]]
@@ -158,7 +162,9 @@ def _impl(inputs, attr, params):
 
  attr['padding'] = [pad_v[0], pad_h[0], pad_v[1], pad_h[1]]
  else:
- raise TypeError("Unsupported padding type : {}".format(attr['padding']))
+ raise TypeError("OperatorAttributeValueNotValid : "
+ "Unsupported padding type : {} in {}"\
+ .format(attr['padding'], name))
 
  if name == "avg_pool":
  attr['count_include_pad'] = False
@@ -232,8 +238,9 @@ def _impl(inputs, attr, params):
  attr['dilations'] = (attr['dilations'][2], attr['dilations'][3])
  attr['strides'] = (attr['strides'][2], attr['strides'][3])
  else:
- raise TypeError("Unsupported data format type : {}".format(attr['data_format']))
-
+ raise TypeError("OperatorAttributeValueNotValid : "
+ "Unsupported data format type : {} in {}"\
+ .format(attr['data_format'], opname))
 
  if opname == 'depthwise':
  attr['groups'] = attr['channels']
@@ -276,7 +283,9 @@ def _impl(inputs, attr, params):
  attr['padding'] = [0, 0]
 
  else:
- raise TypeError("Unsupported padding type : {}".format(attr['padding']))
+ raise TypeError("OperatorAttributeValueNotValid : "
+ "Unsupported padding type : {} in {}"\
+ .format(attr['padding'], opname))
 
  if 'kernel_layout' not in attr:
  if opname == 'conv':
@@ -440,8 +449,8 @@ def _impl(inputs, attr, params):
  lhs = inputs[0]
  rhs = inputs[1]
  if "_input_shapes" in attr:
- lhs_shape = attr["_input_shapes"][lhs][0]
- rhs_shape = attr["_input_shapes"][rhs][0]
+ lhs_shape = attr["_input_shapes"][lhs]
+ rhs_shape = attr["_input_shapes"][rhs]
  if len(lhs_shape) == 4 and len(rhs_shape) == 1 and lhs_shape[3] == rhs_shape[0]:
  # FIXME(yizhiliu): workaround for TF models on TRT. @re:invent 2018
  # bias_add(NHWC, C), expand_dim C to [1, 1, C],
@@ -747,7 +756,9 @@ def _impl(inputs, attr, params):
  if padlist_key in params:
  padlist = params.pop(padlist_key).asnumpy()
  else:
- raise RuntimeError("Required parameter {} not fount.".format(padlist_key))
+ raise RuntimeError("OperatorAttributeValueNotValid : "
+ "Required parameter {} not found in {}."\
+ .format(padlist_key, name))
  paddings = tuple([tuple(l) for l in padlist])
  attr['pad_width'] = paddings
  attr['pad_value'] = 0
@@ -963,6 +974,8 @@ def _impl(inputs, attr, params):
  'Split' : _split(False),
  'SplitV' : _split(True),
  'Unpack' : _unpack(),
+ 'QueueDequeueManyV2' : _undef(),
+ 'FIFOQueueV2' : _undef(),
 }
 
 # _convert_map_rnn defines maps of rnn operator name to
@@ -1167,7 +1180,7 @@ def from_tensorflow(self, graph, layout="NHWC", shape=None, outputs=None):
  Follow the tensorflow graph definition to parse and convert it to NNVM.
  Some of the assumptions listed below.
 
- -> All Placeholders are considered as graph input.
+ -> All Placeholders/PlaceholderWithDefaults are considered as graph input.
  -> All Const nodes are params.
  -> Last node is assumed as graph output.
  -> _output_shapes : Graph should be frozen with add_shapes=True.
@@ -1206,10 +1219,11 @@ def from_tensorflow(self, graph, layout="NHWC", shape=None, outputs=None):
 
  if missing_operators:
  raise NotImplementedError( \
+ "OperatorNotImplemented : "
  "The following operators are not implemented: {}".format(missing_operators))
 
  for node in graph.node:
- if node.op == 'Placeholder':
+ if node.op == 'Placeholder' or node.op == 'PlaceholderWithDefault':
  if shape and node.name in shape:
  self._input_shapes[node.name] = list(shape[node.name])
  continue
@@ -1246,7 +1260,7 @@ def from_tensorflow(self, graph, layout="NHWC", shape=None, outputs=None):
  elif shape and node.name in shape:
  # Give priority to user argument.
  self._output_shapes[node.name] = [shape[node.name]]
- elif node.op == 'Placeholder':
+ elif node.op == 'Placeholder' or node.op == 'PlaceholderWithDefault':
  self._output_shapes[node.name] = [self._input_shapes[node.name]]
  elif '_output_shapes' in attr:
  self._output_shapes[node.name] = \
@@ -1262,7 +1276,7 @@ def from_tensorflow(self, graph, layout="NHWC", shape=None, outputs=None):
  not tshape if isinstance(tshape, list) else False \
  for tshape in self._output_shapes[node.name]]
 
- if node.op == "Placeholder":
+ if node.op == "Placeholder" or node.op == 'PlaceholderWithDefault':
  self._nodes[node.name] = _sym.Variable(name=node.name,
  shape=self._input_shapes[node.name])
 
@@ -1379,7 +1393,7 @@ def _parse_import_prerequisites(self, graph):
  """
  missing_operators = set()
  for node in graph.node:
- if node.op == "Placeholder":
+ if node.op == "Placeholder" or node.op == "PlaceholderWithDefault":
  pass
  elif node.op == "Const":
  pass
@@ -1419,7 +1433,8 @@ def _parse_param(self, key, value, name):
  else:
  if key not in ('dtype', '_output_shapes', '_class'):
  raise NotImplementedError \
- ("Other attributes for a Const(param) Node {} ? .".format(key))
+ ("OperatorAttributeNotImplemented : "
+ "Other attributes for a Const(param) Node {} ? .".format(key))
 
  def _get_attr(self, buf):
  """Returns the value of the attr of this buf with the given `name`.
@@ -1546,7 +1561,8 @@ def _convert_operator(self, op_name, inputs, attrs,
  self._params, graph,
  convert_map_rnn)
  else:
- raise NotImplementedError("Operator {} not implemented.".format(op_name))
+ raise NotImplementedError("OperatorNotImplemented : "
+ "Operator {} not implemented.".format(op_name))
  return sym
 
  def _fix_extranodes(self, op_name, attr, inputs):