Merge pull request #451 from zhijxu-MS/push_branch

bug fixes

Author: nbcsm

tf2onnx/graph.py

@@ -414,8 +414,9 @@ def make_const(self, name, np_val, skip_conversion=False, raw=True):
         else:
             onnx_tensor = helper.make_tensor(name, utils.map_numpy_to_onnx_dtype(np_val.dtype),
                                              np_val.shape, np_val, raw=False)
+        dtype = onnx_tensor.data_type
         node = self.make_node("Const", [], outputs=[name], name=name, attr={"value": onnx_tensor},
-                              skip_conversion=skip_conversion)
+                              skip_conversion=skip_conversion, dtypes=[dtype])
         self.set_shape(name, np_val.shape)
         self.set_dtype(name, utils.map_numpy_to_onnx_dtype(np_val.dtype))
         return node

tf2onnx/onnx_opset/nn.py

@@ -66,8 +66,9 @@ def conv_convert_inputs(ctx, node, with_kernel=False, new_kernel_shape=None,
                 transpose.set_attr("perm", constants.NHWC_TO_NCHW)
                 transpose.skip_conversion = True
                 shape = ctx.get_shape(input_name)
-                new_shape = spatial_map(shape, constants.NHWC_TO_NCHW)
-                ctx.set_shape(transpose.output[0], new_shape)
+                if shape is not None:
+                    new_shape = spatial_map(shape, constants.NHWC_TO_NCHW)
+                    ctx.set_shape(transpose.output[0], new_shape)
             parent.data_format = "NCHW"
 
     # kernel must to be transposed

tf2onnx/optimizer/transpose_optimizer.py

@@ -81,6 +81,9 @@ def post_optimize_action(self):
                 input_shape = self._g.get_shape(op.input[0])
                 if not input_shape:
                     continue
+                # reshape only supports one dime is -1
+                if input_shape.count(-1) > 1:
+                    continue
 
                 new_shape = []
                 # when transpose is NHWC_TO_NCHW

tf2onnx/rewriter/cond_rewriter.py

@@ -127,7 +127,10 @@ def _get_output_shape_dtype(self, cond_context):
                         false_dtype
                     )
                 )
-            output_shapes.append(utils.merge_shapes(true_shape, false_shape))
+            # in tf, the shape of different branched can be different,
+            # for example output shape of branch A can be [-1] while branch B can be [1].
+            # Under this case, we should set output shape to be [-1]
+            output_shapes.append(utils.create_vague_shape_like(utils.merge_shapes(true_shape, false_shape)))
             output_dtypes.append(true_dtype)
         return output_shapes, output_dtypes
 

tf2onnx/tfonnx.py

@@ -473,7 +473,11 @@ def rewrite_incomplete_type_support_rs5(g, ops):
 def rewrite_incomplete_type_support_rs6(g, ops):
     return rewrite_incomplete_type_support(g, ops, [
         "Div",
+        "Greater",
         "IsNaN",
+        "Less",
+        "Max",
+        "Min",
         "ReduceSum",
         "Slice",
         "Split",