Update to opset 13

onnxconverter_common/__init__.py

@@ -8,7 +8,7 @@
 This framework performs optimization for ONNX models and
 includes common utilities for ONNX converters.
 """
-__version__ = "1.7.0"
+__version__ = "1.8.0"
 __author__ = "Microsoft"
 __producer__ = "OnnxMLTools"
 __producer_version__ = __version__

onnxconverter_common/_opt_const_folding.py

@@ -151,6 +151,8 @@ def _OnSub(self, node, inputs):
 
     def _OnUnsqueeze(self, node, inputs):
         axes = OnnxGraphContext.get_attribute(node, 'axes')
+        if axes is None:
+            axes = inputs[1]
         shape_in = inputs[0].shape
         dims_out = len(shape_in) + len(axes)
         shape_in = iter(shape_in)

onnxconverter_common/onnx_ex.py

@@ -9,9 +9,11 @@
 from .metadata_props import add_metadata_props
 
 DEFAULT_OPSET_NUMBER = 12  # The maximum opset supported by the converter in the code branch.
+# From https://github.com/onnx/onnx/blob/master/docs/Versioning.md
 OPSET_TO_IR_VERSION = {
     1: 3, 2: 3, 3: 3, 4: 3, 5: 3, 6: 3,
-    7: 3, 8: 4, 9: 4, 10: 5, 11: 6, 12: 7
+    7: 3, 8: 3, 9: 4, 10: 5, 11: 6, 12: 7,
+    13: 7
 }
 
 

onnxconverter_common/onnx_ops.py

@@ -765,6 +765,33 @@ def apply_reciprocal(scope, input_name, output_name, container, operator_name=No
     _apply_unary_operation(scope, 'Reciprocal', input_name, output_name, container, operator_name=operator_name)
 
 
+def apply_reducesum(scope, input_name, output_name, container, operator_name=None, axes=None, keepdims=None, rank=0):
+    name = _create_name_or_use_existing_one(scope, 'ReduceSum', operator_name)
+    if container.target_opset < 13:
+        if container.target_opset < 11:
+            op_version = 1
+            axes = [axis if axis >= 0 else axis + rank + 1 for axis in axes]
+        else:
+            op_version = 11
+        container.add_node('ReduceSum', input_name, output_name, name=name,
+                           op_version=op_version, axes=axes, keepdims=keepdims)
+    else:
+        if not isinstance(input_name, list):
+            input_name = [input_name]
+        op_version = 13
+        if isinstance(axes, str):
+            container.add_node('ReduceSum', input_name + [axes], output_name,
+                               op_version=op_version, name=name, keepdims=keepdims)
+        elif axes is None or len(axes) == 0:
+            container.add_node('ReduceSum', input_name, output_name,
+                               op_version=op_version, name=name, keepdims=keepdims)
+        else:
+            axes_name = scope.get_unique_variable_name(name + '_reducesum')
+            container.add_initializer(axes_name, onnx_proto.TensorProto.INT64, [len(axes)], axes)
+            container.add_node('ReduceSum', input_name + [axes_name], output_name,
+                               op_version=op_version, name=name, keepdims=keepdims)
+
+
 def apply_relu(scope, input_name, output_name, container, operator_name=None):
     _apply_unary_operation(scope, 'Relu', input_name, output_name, container, operator_name)
 
@@ -860,8 +887,10 @@ def apply_selu(scope, input_name, output_name, container, operator_name=None, al
     _apply_unary_operation(scope, 'Selu', input_name, output_name, container, operator_name, alpha=alpha, gamma=gamma)
 
 
-def apply_softmax(scope, input_name, output_name, container, operator_name=None, axis=1):
+def apply_softmax(scope, input_name, output_name, container, operator_name=None, axis=None):
     name = _create_name_or_use_existing_one(scope, 'Softmax', operator_name)
+    if axis is None:
+        axis = 1 if container.target_opset < 13 else -1
     container.add_node('Softmax', input_name, output_name, name=name, axis=axis)
 
 
@@ -969,12 +998,25 @@ def apply_split(scope, input_name, output_names, container, operator_name=None,
         op_version = 1
     elif container.target_opset < 11:
         op_version = 2
-    else:
+    elif container.target_opset < 13:
         op_version = 11
+    else:
+        op_version = 13
 
     attrs = {'name': name}
     if split is not None:
-        attrs['split'] = split
+        if container.target_opset < 13:
+            attrs['split'] = split
+        else:
+            if not isinstance(input_name, list):
+                input_name = [input_name]
+            if isinstance(split, str):
+                split_name = split
+            else:
+                split_name = scope.get_unique_variable_name(name + '_split')
+                container.add_initializer(split_name, onnx_proto.TensorProto.INT64, [len(split)], split)
+            input_name = input_name + [split_name]
+
     if axis is not None:
         attrs['axis'] = axis
 
@@ -988,17 +1030,30 @@ def apply_sqrt(scope, input_name, output_name, container, operator_name=None):
 def _apply_squeeze_unsqueeze(scope, input_name, output_name, container, squeeze_str, operator_name=None, axes=None,
                              rank=0):
     name = _create_name_or_use_existing_one(scope, squeeze_str, operator_name)
-    if container.target_opset < 11:
-        op_version = 1
-        axes = [axis if axis >= 0 else axis + rank + 1 for axis in axes]
+    if container.target_opset < 13:
+        if container.target_opset < 11:
+            op_version = 1
+            axes = [axis if axis >= 0 else axis + rank + 1 for axis in axes]
+        else:
+            op_version = 11
+        container.add_node(squeeze_str, input_name, output_name, name=name, op_version=op_version, axes=axes)
     else:
-        op_version = 11
-    container.add_node(squeeze_str, input_name, output_name, name=name, op_version=op_version, axes=axes)
+        op_version = 13
+        if not isinstance(input_name, list):
+            input_name = [input_name]
+        if isinstance(axes, str):
+            container.add_node(squeeze_str, input_name + [axes], output_name, op_version=op_version, name=name)
+        elif len(axes) == 0:
+            container.add_node(squeeze_str, input_name, output_name, op_version=op_version, name=name)
+        else:
+            axes_name = scope.get_unique_variable_name(name + '_axes')
+            container.add_initializer(axes_name, onnx_proto.TensorProto.INT64, [len(axes)], axes)
+            container.add_node(squeeze_str, input_name + [axes_name], output_name, op_version=op_version, name=name)
 
 
 def apply_squeeze(scope, input_name, output_name, container, operator_name=None, axes=None, rank=0):
     if axes is None:
-        axes = [0]
+        axes = []
     _apply_squeeze_unsqueeze(scope, input_name, output_name, container, 'Squeeze', operator_name, axes, rank)
 
 

onnxconverter_common/optimizer.py

@@ -15,7 +15,7 @@
 class LinkedNode(object):
     reserved_names_in_graph = frozenset()
 
-    def __init__(self, node=None, in_n=None, out_n=None, tensors_n=None):
+    def __init__(self, node=None, in_n=None, out_n=None, tensors_n=None, target_opset=None):
         self.origin = node  # type: onnx_proto.NodeProto
         if in_n is None and node is not None:
             in_n = node.input
@@ -29,6 +29,7 @@ def __init__(self, node=None, in_n=None, out_n=None, tensors_n=None):
         self.successor = []
         self.attributes = {}
         self.unique_name = self.origin.name if self.origin and self.origin.name else str(uuid4().hex)
+        self.target_opset = target_opset
 
     def __repr__(self):
         return "name: {}, node: <{}>".format(self.unique_name, str(self.origin) if self.origin else 'None')
@@ -283,11 +284,11 @@ def add_precedence(self, pre, tname):
         assert tname in self.input.values() and tname in pre.output.values()
 
     @staticmethod
-    def build_from_onnx(onnx_nodes, nchw_inputs, inputs, outputs, initializers=None):
+    def build_from_onnx(onnx_nodes, nchw_inputs, inputs, outputs, initializers=None, target_opset=None):
         view = []
         var_map = {}
         for o_ in onnx_nodes:
-            ln = LinkedNode(o_)
+            ln = LinkedNode(o_, target_opset=target_opset)
             view.append(ln)
             for var_ in o_.output:
                 assert var_map.get(var_) is None
@@ -305,9 +306,10 @@ def build_from_onnx(onnx_nodes, nchw_inputs, inputs, outputs, initializers=None)
                     assert var_ == '' or var_ in inputs
                     if initializer_map is not None and var_ in initializer_map:
                         target = LinkedNode(out_n=[var_],
-                                            tensors_n=[initializer_map[var_]])  # create an empty node as input
+                                            tensors_n=[initializer_map[var_]],
+                                            target_opset=target_opset)  # create an empty node as input
                     else:
-                        target = LinkedNode(out_n=[var_])
+                        target = LinkedNode(out_n=[var_], target_opset=target_opset)
                     new_output = var_ + '_nhwc'
                     if var_ in nchw_inputs:
                         nnode = LinkedNode(
@@ -316,7 +318,8 @@ def build_from_onnx(onnx_nodes, nchw_inputs, inputs, outputs, initializers=None)
                                 [var_],
                                 [new_output],
                                 name='Transpose_nchw_' + str(count_nchw),
-                                perm=[0, 2, 3, 1]))
+                                perm=[0, 2, 3, 1]),
+                            target_opset=target_opset)
                         count_nchw = count_nchw + 1
                         var_map[new_output] = nnode
                         nnode.add_precedence(target, var_)
@@ -330,7 +333,7 @@ def build_from_onnx(onnx_nodes, nchw_inputs, inputs, outputs, initializers=None)
         for n_ in view:  # add a dummy output node.
             for var_ in n_.origin.output:
                 if var_ in outputs:
-                    LinkedNode(in_n=[var_]).add_precedence(n_, var_)
+                    LinkedNode(in_n=[var_], target_opset=target_opset).add_precedence(n_, var_)
 
         return view + additional_nodes
 
@@ -642,6 +645,18 @@ def _get_pad_from_Pad(node):
     return pads
 
 
+def _get_axes_from_Squeeze_Unsqueeze(node):
+    axes = node.get_attribute('axes')
+    if axes is None:
+        if len(node.origin.input) == 2:
+            axes_tensor = node.get_precedence_by_idx(1)
+            if axes_tensor is None:
+                axes = numpy_helper.to_array(node.initializers[0]).tolist()
+            else:
+                axes = numpy_helper.to_array(node.get_precedence_by_idx(1).tensors[0]).tolist()
+    return axes
+
+
 class MergePadConvSolution(Solution):
 
     def __init__(self, begin, begin_n, end_p, end):
@@ -968,11 +983,13 @@ class MergeSqueezeUnsqueezeOptimizer(object):
     @staticmethod
     def find(node):
         if node.origin.op_type == 'Squeeze' and len(node.successor) == 1:
-            axes_0 = node.get_attribute('axes')
+            axes_0 = _get_axes_from_Squeeze_Unsqueeze(node)
             next = node.successor[0]
-            if next.origin is not None and next.origin.op_type == 'Unsqueeze' and len(next.successor) == 1:
-                axes_1 = next.get_attribute('axes')
-                if axes_0 == axes_1:
+            flag = next.origin is not None and axes_0 is not None \
+                and next.origin.op_type == 'Unsqueeze' and len(next.successor) == 1
+            if flag:
+                axes_1 = _get_axes_from_Squeeze_Unsqueeze(next)
+                if axes_1 is not None and axes_0 == axes_1:
                     solution = Solution(node.get_precedence_by_idx(0), node, next, next.successor[0])
                     return solution
 
@@ -993,6 +1010,10 @@ def _transpose_pass(node):
     if node.element_wise:
         return True
 
+    if node.origin.op_type in ['Squeeze', 'Unsqueeze']:
+        axes = _get_axes_from_Squeeze_Unsqueeze(node)
+        return axes is not None
+
     if node.origin.op_type in _transpose_pass_type_set:
         return True
 
@@ -1131,9 +1152,8 @@ def _process_transpose_pad(node, node_list, node_transpose_pass_name, cur_perm_m
 
 def _process_transpose_squeeze(node, node_list, node_transpose_pass_name, cur_perm_map):
     cur_perm = cur_perm_map[node.get_precedence_by_idx(0).unique_name]
-    squeeze_axes = node.get_attribute('axes')
+    squeeze_axes = _get_axes_from_Squeeze_Unsqueeze(node)
     squeeze_axes = [cur_perm[idx_] for idx_ in squeeze_axes]
-    attrs = {'axes': squeeze_axes}
     temp_perm = cur_perm.copy()
     sub_list = [0] * len(cur_perm)
     for axis in squeeze_axes:
@@ -1145,18 +1165,39 @@ def _process_transpose_squeeze(node, node_list, node_transpose_pass_name, cur_pe
         temp_perm[idx_] = temp_perm[idx_] - sub_list[temp_perm[idx_]]
     target_perm = temp_perm
     new_node_name = node.origin.name + '_squeeze_' + str(PushTransposeSolution.transpose_number)
-    node.origin = helper.make_node('Squeeze', node.origin.input, node.origin.output, new_node_name, **attrs)
+    if node.target_opset < 13:
+        attrs = {'axes': squeeze_axes}
+        node.origin = helper.make_node('Squeeze', node.origin.input, node.origin.output, new_node_name, **attrs)
+    else:
+        squeeze_axes = np.asarray(squeeze_axes, dtype=np.int64)
+        add_initilizer = numpy_helper.from_array(squeeze_axes, name=node.origin.name + '_initializer_' + str(
+            PushTransposeSolution.transpose_number))
+        node.initializers = [add_initilizer]
+        pred_1 = node.get_precedence_by_idx(1)
+        if pred_1 is not None:
+            node.precedence.remove(pred_1)
+        node.in_redirect(node.get_input_by_idx(1), add_initilizer.name)
     PushTransposeSolution.transpose_number += 1
     cur_perm_map[node.unique_name] = target_perm
     return cur_perm_map
 
 
 def _process_transpose_unsqueeze(node, node_list, node_transpose_pass_name, cur_perm_map):
-    unsqueeze_axes = node.get_attribute('axes')
+    unsqueeze_axes = _get_axes_from_Squeeze_Unsqueeze(node)
     assert len(unsqueeze_axes) == 1
-    attrs = {'axes': unsqueeze_axes}
     new_node_name = node.origin.name + '_unsqueeze_' + str(PushTransposeSolution.transpose_number)
-    node.origin = helper.make_node('Unsqueeze', node.origin.input, node.origin.output, new_node_name, **attrs)
+    if node.target_opset < 13:
+        attrs = {'axes': unsqueeze_axes}
+        node.origin = helper.make_node('Unsqueeze', node.origin.input, node.origin.output, new_node_name, **attrs)
+    else:
+        unsqueeze_axes = np.asarray(unsqueeze_axes, dtype=np.int64)
+        add_initilizer = numpy_helper.from_array(unsqueeze_axes, name=node.origin.name + '_initializer_' + str(
+            PushTransposeSolution.transpose_number))
+        node.initializers = [add_initilizer]
+        pred_1 = node.get_precedence_by_idx(1)
+        if pred_1 is not None:
+            node.precedence.remove(pred_1)
+        node.in_redirect(node.get_input_by_idx(1), add_initilizer.name)
     PushTransposeSolution.transpose_number += 1
     prev_perm = cur_perm_map[node.precedence[0].unique_name]
     cur_axes = unsqueeze_axes[0]
@@ -1190,7 +1231,8 @@ def _process_transpose_pass_node(node, node_list, node_transpose_pass_name, cur_
         node_list, cur_perm_map = _process_transpose_pass_broadcast(node, node_list, node_transpose_pass_name,
                                                                     cur_perm_map)
     elif node.origin.op_type in type_func_map:
-        cur_perm_map = type_func_map[node.origin.op_type](node, node_list, node_transpose_pass_name, cur_perm_map)
+        cur_perm_map = type_func_map[node.origin.op_type](node, node_list, node_transpose_pass_name,
+                                                          cur_perm_map)
     else:
         for idx_ in range(len(node.precedence)):
             pred_name = node.get_precedence_by_idx(idx_).unique_name
@@ -1238,7 +1280,7 @@ def apply(self, node_list):
                 if not success:
                     return None, False
             elif node.origin.op_type == 'Unsqueeze':
-                unsqueeze_axes = node.get_attribute('axes')
+                unsqueeze_axes = _get_axes_from_Squeeze_Unsqueeze(node)
                 if unsqueeze_axes and len(unsqueeze_axes) > 1:
                     return None, False
 
@@ -1762,7 +1804,8 @@ def optimize_onnx(onnx_nodes, nchw_inputs=None, inputs=None, outputs=None, targe
     node_list = LinkedNode.build_from_onnx(onnx_nodelist,
                                            nchw_inputs if nchw_inputs else [],
                                            [] if inputs is None else [i_.name for i_ in inputs],
-                                           [] if outputs is None else [o_.name for o_ in outputs])
+                                           [] if outputs is None else [o_.name for o_ in outputs],
+                                           target_opset=target_opset)
     node_list = _process_optimization(node_list, target_opset)
 
     if target_opset is None or target_opset < 9:
@@ -1826,7 +1869,8 @@ def optimize_onnx_graph(onnx_nodes, nchw_inputs=None, inputs=None, outputs=None,
                                            nchw_inputs if nchw_inputs else [],
                                            [] if in_inputs is None else [i_.name for i_ in in_inputs],
                                            [] if outputs is None else [o_.name for o_ in outputs],
-                                           initializers)
+                                           initializers,
+                                           target_opset=target_opset)
 
     node_list = _process_optimization(node_list, target_opset)
     node_list = [n_ for n_ in node_list if n_.origin is not None]