minor modifications

Author: sdpython

tests/backend_test_base.py

@@ -20,6 +20,7 @@
 import numpy as np
 import tensorflow as tf
 from tensorflow.python.ops import variables as variables_lib
+import onnx
 from common import get_test_config
 from tf2onnx import utils
 from tf2onnx.tfonnx import process_tf_graph
@@ -63,12 +64,23 @@ def run_onnxcaffe2(self, onnx_graph, inputs):
     def run_onnxruntime(self, model_path, inputs, output_names):
         """Run test against onnxruntime backend."""
         import onnxruntime as rt
+        try:
+            from onnxruntime.capi.onnxruntime_pybind11_state import Fail
+        except ImportError:
+            Fail = RuntimeError
         opt = rt.SessionOptions()
         # in case of issues with the runtime, one can enable more logging
         # opt.log_severity_level = 0
         # opt.log_verbosity_level = 255
         # opt.enable_profiling = True
-        m = rt.InferenceSession(model_path, opt)
+        with open(model_path, 'rb') as f:
+            onx = onnx.load(f)
+        try:
+            m = rt.InferenceSession(model_path, opt)
+        except Fail as e:
+            with open(model_path, 'rb') as f:
+                onx = onnx.load(f)
+            raise AssertionError("Unable to load model '{}'\n{}".format(model_path, onx)) from e
         results = m.run(output_names, inputs)
         return results
 

tf2onnx/graph.py

@@ -56,6 +56,11 @@ def input(self):
 
     @input.setter
     def input(self, val):
+        # The setter can catch that all inputs are change
+        # but it cannot catch that one input is changed.
+        # That's method replace_input and replace_inputs must
+        # be used to change inputs to let the graph instance
+        # update its internal indices.
         self._input = copy.deepcopy(val)
 
     @property
@@ -988,7 +993,7 @@ def _get_unvisited_child(g, node, not_visited):
             all_input = list(filter(lambda a: a != '', all_input))
             for inp in sorted(all_input):
                 j = self.get_node_by_output(inp)
-                utils.make_sure(j is not None, "Cannot find node with output {}".format(inp))
+                utils.make_sure(j is not None, "Cannot find node with output %r", inp)
                 if self.parent_graph and j.name not in op_name_to_index:
                     # there might be some outer-scoped inputs for an inner Graph.
                     pass
@@ -1294,6 +1299,8 @@ def replace_all_inputs(self, ops, old_input, new_input):
             to_ops = set()
 
         for node in ops:
+            if node is None:
+                continue
             if old_input in node.input and new_input in node.output:
                 raise RuntimeError("creating a circle in the graph is not allowed: " + node.name)
             self._input_to_node_name[new_input].add(node.name)

tf2onnx/graph_matcher.py

@@ -50,6 +50,7 @@ def __init__(self, op_type, name=None, inputs=None):
             input_pattern if isinstance(input_pattern, OpTypePattern) else
             OpTypePattern(input_pattern) for input_pattern in inputs
         ]
+        self.op_type_set = set(op_type.split('|')) if op_type else set()
 
     @property
     def op_type(self):
@@ -154,7 +155,7 @@ def _is_op_type_same(op, pattern):
         if pattern.op_type == "*":
             return True
 
-        if op.type in pattern.op_type.split('|'):
+        if op.type in pattern.op_type_set:
             return True
 
         return False

tf2onnx/onnx_opset/math.py

@@ -183,7 +183,7 @@ def version_8(cls, ctx, node, **kwargs):
 
     @classmethod
     def version_12(cls, ctx, node, **kwargs):
-        node.name = 'Clip' # clip supports all types now
+        node.type = 'Clip' # clip supports all types now
 
 @tf_op("Softmax")
 class Softmax:

tf2onnx/onnx_opset/tensor.py

@@ -272,13 +272,13 @@ def version_1(cls, ctx, node, **kwargs):
             raise RuntimeError("all inputs of {} are empty".format(node.name))
 
         axis_node = node.inputs[-1]
-        utils.make_sure(axis_node.is_const(), "{} needs to be const".format(axis_node.name))
+        utils.make_sure(axis_node.is_const(), "%r needs to be const", axis_node.name)
         axis_val = axis_node.get_tensor_value()
         ctx.remove_input(node, node.input[-1])
 
         if axis_val < 0:  # onnxruntime does not support -1 axis, but TF supports.
             input_shape = ctx.get_shape(node.input[0])
-            utils.make_sure(input_shape is not None, "shape of {} is None".format(node.input[0]))
+            utils.make_sure(input_shape is not None, "shape of %r is None", node.input[0])
             axis_val = len(input_shape) + axis_val
         node.set_attr("axis", axis_val)
 

tf2onnx/tf_utils.py

@@ -13,7 +13,6 @@
 from distutils.version import LooseVersion
 
 import numpy as np
-import six
 import tensorflow as tf
 
 from tensorflow.core.framework import types_pb2, tensor_pb2
@@ -70,7 +69,7 @@ def get_tf_tensor_data(tensor):
     """Get data from tensor."""
     make_sure(isinstance(tensor, tensor_pb2.TensorProto), "Require TensorProto")
     np_data = tensor_util.MakeNdarray(tensor)
-    make_sure(isinstance(np_data, np.ndarray), "{} isn't ndarray".format(np_data))
+    make_sure(isinstance(np_data, np.ndarray), "%r isn't ndarray", np_data)
     return np_data
 
 
@@ -83,7 +82,7 @@ def get_tf_const_value(op, as_list=True):
         when as_list=False, return np.array(1), type is <class 'numpy.ndarray'>
         when as_list=True, return 1, type is <class 'int'>.
     """
-    make_sure(is_tf_const_op(op), "{} isn't a const op".format(op.name))
+    make_sure(is_tf_const_op(op), "%r isn't a const op", op.name)
     value = get_tf_tensor_data(op.get_attr("value"))
     if as_list:
         value = value.tolist()
@@ -119,9 +118,6 @@ def map_tf_dtype(dtype):
 
 def get_tf_node_attr(node, name):
     """Parser TF node attribute."""
-    if six.PY2:
-        # For python2, TF get_attr does not accept unicode
-        name = str(name)
     return node.get_attr(name)
 
 
@@ -136,14 +132,14 @@ def tflist_to_onnx(g, shape_override):
     """
 
     # ignore the following attributes
-    ignored_attr = ["unknown_rank", "_class", "Tshape", "use_cudnn_on_gpu", "Index", "Tpaddings",
+    ignored_attr = {"unknown_rank", "_class", "Tshape", "use_cudnn_on_gpu", "Index", "Tpaddings",
                     "TI", "Tparams", "Tindices", "Tlen", "Tdim", "Tin", "dynamic_size", "Tmultiples",
                     "Tblock_shape", "Tcrops", "index_type", "Taxis", "U", "maxval",
                     "Tout", "Tlabels", "Tindex", "element_shape", "Targmax", "Tperm", "Tcond",
                     "T_threshold", "element_dtype", "shape_type", "_lower_using_switch_merge",
                     "parallel_iterations", "_num_original_outputs", "output_types", "output_shapes",
                     "key_dtype", "value_dtype", "Tin", "Tout", "capacity", "component_types", "shapes",
-                    "Toutput_types"]
+                    "Toutput_types"}
 
     node_list = g.get_operations()
     functions = {}
@@ -176,12 +172,11 @@ def tflist_to_onnx(g, shape_override):
             attr_cnt[a] += 1
             if a == "dtype":
                 attr[a] = map_tf_dtype(get_tf_node_attr(node, "dtype"))
-            elif a in ["T"]:
+            elif a == "T":
                 dtype = get_tf_node_attr(node, a)
-                if dtype:
-                    if not isinstance(dtype, list):
-                        dtypes[node.name] = map_tf_dtype(dtype)
-            elif a in ["output_type", "output_dtype", "out_type", "Tidx", "out_idx"]:
+                if dtype and not isinstance(dtype, list):
+                    dtypes[node.name] = map_tf_dtype(dtype)
+            elif a in {"output_type", "output_dtype", "out_type", "Tidx", "out_idx"}:
                 # Tidx is used by Range
                 # out_idx is used by ListDiff
                 attr[a] = map_tf_dtype(get_tf_node_attr(node, a))
@@ -192,7 +187,7 @@ def tflist_to_onnx(g, shape_override):
             elif a == "output_shapes":
                 # we should not need it since we pull the shapes above already
                 pass
-            elif a in ["body", "cond", "then_branch", "else_branch"]:
+            elif a in {"body", "cond", "then_branch", "else_branch"}:
                 input_shapes = [inp.get_shape() for inp in node.inputs]
                 nattr = get_tf_node_attr(node, a)
                 attr[a] = nattr.name