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[NNVM] Error handling message #14

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merged 7 commits into from
Mar 12, 2019
Merged

[NNVM] Error handling message #14

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5d3107c
[NNVM] Error handling message
wweic Mar 12, 2019
fb3fec4
Fix lint
wweic Mar 12, 2019
63495b6
Fix lint
wweic Mar 12, 2019
03bd5b6
Fix lint
wweic Mar 12, 2019
3bd85b6
Fix lint
wweic Mar 12, 2019
739586d
Fix lint
wweic Mar 12, 2019
98c21c6
Fix lint
wweic Mar 12, 2019
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48 changes: 32 additions & 16 deletions nnvm/python/nnvm/frontend/tensorflow.py
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Original file line number Diff line number Diff line change
Expand Up @@ -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():
Expand Down Expand Up @@ -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
Expand All @@ -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]]
Expand Down Expand Up @@ -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
Expand Down Expand Up @@ -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']
Expand Down Expand Up @@ -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':
Expand Down Expand Up @@ -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],
Expand Down Expand Up @@ -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
Expand Down Expand Up @@ -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
Expand Down Expand Up @@ -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.
Expand Down Expand Up @@ -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
Expand Down Expand Up @@ -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] = \
Expand All @@ -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])

Expand Down Expand Up @@ -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
Expand Down Expand Up @@ -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`.
Expand Down Expand Up @@ -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):
Expand Down