Merge pull request #3 from onnx/master

update

Author: lei-Qiao

README.md

@@ -3,10 +3,10 @@ tf2onnx - Convert TensorFlow models to ONNX.
 
 | Build Type | OS | Python | Tensorflow | Onnx opset | Status |
 | ---        | ---    | ---    | ---        | ---        | ---    |
-| Unit Test - Basic | Linux, MacOS<sup>\*</sup>, Windows<sup>\*</sup> | 3.5, 3.6 | 1.5-1.13 | 7-10 | [![Build Status](https://dev.azure.com/tensorflow-onnx/tensorflow-onnx/_apis/build/status/unit_test?branchName=master)](https://dev.azure.com/tensorflow-onnx/tensorflow-onnx/_build/latest?definitionId=16&branchName=master) |
-| Unit Test - Full | Linux, MacOS, Windows | 3.5, 3.6, 3.7 | 1.5-1.13 | 7-10 | [![Build Status](https://dev.azure.com/tensorflow-onnx/tensorflow-onnx/_apis/build/status/unit_test-matrix?branchName=master)](https://dev.azure.com/tensorflow-onnx/tensorflow-onnx/_build/latest?definitionId=18&branchName=master) | |
+| Unit Test - Basic | Linux, MacOS<sup>\*</sup>, Windows<sup>\*</sup> | 3.5, 3.6 | 1.5-1.14 | 7-10 | [![Build Status](https://dev.azure.com/tensorflow-onnx/tensorflow-onnx/_apis/build/status/unit_test?branchName=master)](https://dev.azure.com/tensorflow-onnx/tensorflow-onnx/_build/latest?definitionId=16&branchName=master) |
+| Unit Test - Full | Linux, MacOS, Windows | 3.5, 3.6, 3.7 | 1.5-1.14 | 7-10 | [![Build Status](https://dev.azure.com/tensorflow-onnx/tensorflow-onnx/_apis/build/status/unit_test-matrix?branchName=master)](https://dev.azure.com/tensorflow-onnx/tensorflow-onnx/_build/latest?definitionId=18&branchName=master) | |
 
-<a name="build_status_footnote">\*</a> Only test on python3.6, TF1.13.
+<a name="build_status_footnote">\*</a> Only test on python3.6, TF1.14.
 
 # Supported ONNX version
 tensorflow-onnx will use the ONNX version installed on your system and installs the latest ONNX version if none is found.
@@ -51,7 +51,7 @@ For pytorch/caffe2, follow the instructions here:
 We tested with pytorch/caffe2 and onnxruntime and unit tests are passing for those.
 
 ## Supported Tensorflow and Python Versions
-We are testing with tensorflow 1.5-1.13 and anaconda **3.5,3.6,3.7**.
+We are testing with tensorflow 1.5-1.14 and anaconda **3.5,3.6,3.7**.
 
 # Installation
 ## From pypi

tf2onnx/optimizer/merge_duplicated_nodes_optimizer.py

@@ -61,18 +61,24 @@ def _del_nodes_if_duplicated(self, nodes_group, graph):
             unprocessed_node = []
             nodes_to_process = [nodes_group[0]]
             for node in nodes_group[1:]:
-                if self._have_equal_attr(node, nodes_to_process[0]):
+                if self._have_equal_attr(node, nodes_to_process[0], graph):
                     nodes_to_process.append(node)
                 else:
                     unprocessed_node.append(node)
 
             self._merge_nodes_that_are_duplicated(nodes_to_process, graph)
             nodes_group = unprocessed_node
 
-    def _have_equal_attr(self, node_1, node_2):
+    def _have_equal_attr(self, node_1, node_2, graph):
         if node_1.attr == node_2.attr:
             return True
         if node_1.is_const() and node_2.is_const():
+            # get_tensor_value is costly so that we check their shape first
+            shape_1 = graph.get_shape(node_1.output[0])
+            shape_2 = graph.get_shape(node_2.output[0])
+            if shape_1 is not None and shape_2 is not None and \
+                    shape_1 != shape_2:
+                return False
             const_1 = node_1.get_tensor_value(as_list=False)
             const_2 = node_2.get_tensor_value(as_list=False)
             if const_1.dtype == const_2.dtype and \

tf2onnx/optimizer/transpose_optimizer.py

@@ -199,7 +199,7 @@ def _handle_node_having_branches(self, node):
         # otherwise, it would impact their other output nodes
         if self._nodes_has_single_consumer_node(node.inputs):
             self._create_transpose_pairs_after_node(node)
-            input_transposes = node.inputs
+            input_transposes = set(node.inputs)
             for n in input_transposes:
                 n_input = n.input[0]
                 utils.make_sure(len(n.output) == 1, "only expect single output")
@@ -371,11 +371,26 @@ def _add_handler(self, trans, node):
                 # if Conv or ConvTranspose's bias input is not set, then we set, otherwise, we don't set
                 # todo: maybe we can add already set bias with the input??? try later
 
+                if not self._nodes_has_single_consumer_node([t_p]):
+                    self.logger.debug("Conv does not have single consumer, can not merge Conv and Add")
+                    return self._handle_node_having_branches(node)
+
+                if not self._nodes_has_single_consumer_node([trans]):
+                    self.logger.debug("input transpose does not have single consumer, skipping...")
+                    return False
+
                 target_node = node.inputs[1]
                 numpy_val = target_node.get_tensor_value(as_list=False)
                 # Optional 1D bias to be added to the convolution, has size of M
                 if len(numpy_val.shape) - numpy_val.shape.count(1) > 1:
                     return self._handle_node_having_branches(node)
+
+                rank = len(numpy_val.shape)
+                utils.make_sure(rank in (1, 4), "only support bias rank = 4 or 1")
+                # to make rank = 4
+                if rank == 1:
+                    numpy_val = numpy_val.reshape((1, 1, 1, numpy_val.shape[0]))
+
                 transposed_val = np.transpose(numpy_val, (0, 3, 1, 2))
                 target_node.set_tensor_value(transposed_val)