Tom/tfjs bugfixes

tf2onnx/tfjs_utils.py

@@ -178,6 +178,16 @@ def get_output_shapes(node_def, input_dtypes, input_shapes, inp_consts):
         # The second output of merge is a scalar int indicating which input was selected
         return [non_none, []]
 
+    if node_def.op == "Placeholder":
+        shape = None
+        if 'shape' in node_def.attr:
+            shape = [d.size for d in node_def.attr['shape'].shape.dim]
+            shape = [None if d == -1 else d for d in shape]
+            if len(shape) == 0:
+                # According to TF docs, "If the shape has 0 dimensions, the shape is unconstrained."
+                shape = None
+        return [shape]
+
     del node_def.input[:]
     node_def.name = "node"
     if "_class" in node_def.attr:
@@ -283,11 +293,19 @@ def graphs_from_tfjs(model_path, input_names=None, output_names=None, shape_over
     utils.make_sure(len(weights_data) == i, "Total weight bytes %d doesn't match read bytes %d", len(weights_data), i)
     topology = model['modelTopology']
 
+    tensors_to_rename = {}
     if output_names is None and 'signature' in model:
-        output_names = list(model['signature']['outputs'].keys())
+        outputs = model['signature'].get('outputs')
+        inputs = model['signature'].get('inputs')
+        if outputs is not None:
+            output_names = [v['name'] for v in outputs.values()]
+            tensors_to_rename.update({v['name']: k for k, v in outputs.items()})
+        if inputs is not None:
+            tensors_to_rename.update({v['name']: k for k, v in inputs.items()})
 
     main_g = read_tfjs_graph(topology['node'], weights, None, input_names, output_names, shape_override,
                              ignore_default, use_default)
+    main_g.rename_tensors(tensors_to_rename)
     subgraphs = []
     funcs = sort_tfjs_functions(topology.get('library', {}).get('function', []))
     for func in funcs:
@@ -303,7 +321,7 @@ def read_tfjs_weight(weight, weights_data, offset):
     name = weight['name']
     count = np.product(weight['shape'], dtype=np.int64)
     if weight['dtype'] == 'string':
-        num_strings = np.product(weight['shape'])
+        num_strings = np.prod(weight['shape'], dtype=np.int64)
         string_list, num_bytes = read_string_weight(weights_data, offset, num_strings)
         np_arr = np.array(string_list).reshape(weight['shape'])
         return name, np_arr, num_bytes
@@ -428,10 +446,11 @@ def update_shapes(new_shapes):
             # This op isn't in tensorflow but can be converted to a TF op
             op_type = "_FusedDepthwiseConv2dNative"
             err_msg = "explicit_paddings for supported for _FusedDepthwiseConv2dNative"
-            utils.make_sure(len(tf_attr['explicit_paddings']) == 0, err_msg)
-            del tf_attr['explicit_paddings']
-            del onnx_attr['explicit_paddings']
-            del node_def.attr['explicit_paddings']
+            if "explicit_paddings" in tf_attr:
+                utils.make_sure(len(tf_attr['explicit_paddings']) == 0, err_msg)
+                del tf_attr['explicit_paddings']
+                del onnx_attr['explicit_paddings']
+                del node_def.attr['explicit_paddings']
             node_def.op = op_type
 
         input_names = [inp for inp in node.get('input', []) if not inp.startswith('^')]
@@ -465,6 +484,10 @@ def update_shapes(new_shapes):
         onnx_node = helper.make_node(op_type, input_names, output_names, name=node_name, **onnx_attr)
         onnx_nodes.append(onnx_node)
 
+    for inp in graph_inputs:
+        if output_shapes[inp] is None:
+            logger.warning("Input %s has unknown shape. Specify shape with --inputs flag.", inp)
+
     dtypes = {k: tf_utils.map_tf_dtype(v) for k, v in tf_dtypes.items()}
     if graph_outputs is None:
         output_to_node = {out: node.name for node in onnx_nodes for out in node.output}