.

onnx2kerastl/converter.py

@@ -210,7 +210,7 @@ def onnx_to_keras(onnx_model, input_names, name_policy=None, verbose=True, chang
             else:
                 keras_names = keras_names[0]
                 node_names.append(keras_names)
-            pattern = r'[#:]'  # Example pattern to match #, /, and :
+            pattern = r'[#:@]'  # Example pattern to match #, /, and :
             cleaned_node_name = re.sub(pattern, '_', node.name.rstrip("/").lstrip("/"))
             if len(cleaned_node_name) == 0:
                 cleaned_node_name = re.sub(pattern, '_', node_name.rstrip("/").lstrip("/"))

onnx2kerastl/elementwise_layers.py

@@ -381,8 +381,11 @@ def convert_where(node, params, layers, lambda_func, node_name, keras_name):
                                      layers[node.input[1]],
                                      tf_name=f"{params['cleaned_name']}_where_1")
     else:
-        layers[node_name] = tf_where(casted, layers[node.input[1]], layers[node.input[2]],
-                                     tf_name=f"{params['cleaned_name']}_where_2")
+        try:
+            layers[node_name] = tf_where(casted, layers[node.input[1]], layers[node.input[2]],
+                                         tf_name=f"{params['cleaned_name']}_where_2")
+        except Exception as e:
+            print(1)
 
 
 def convert_scatter_nd(node, params, layers, lambda_func, node_name, keras_name):

onnx2kerastl/pooling_layers.py

@@ -251,11 +251,16 @@ def target_layer(composed_input, to_sort=to_sort, axis=axis):
             out = tf.transpose(topk_concat, ord_permute)
         return out
     in_shape = tf_shape(in_tensor, tf_name=f"{params['cleaned_name']}_topk_in_shape")
-    k_needed_shape = tf_concat(
-        [(in_shape)[:-1],[1]], axis=-1,tf_name=f"{params['cleaned_name']}_topk_k_needed_shape")._inferred_value
+    k_needed_shape_possible_keras_tensor = tf_concat(
+            [(in_shape)[:-1],[1]], axis=-1,tf_name=f"{params['cleaned_name']}_topk_k_needed_shape")
+
+    if hasattr(k_needed_shape_possible_keras_tensor, "_inferred_value"): #is keras tensor
+        k_needed_shape = k_needed_shape_possible_keras_tensor._inferred_value
+    else:
+        k_needed_shape = k_needed_shape_possible_keras_tensor
     k_unsqueezed = tf_ones(k_needed_shape, tf_name=f"{params['cleaned_name']}_topk_k_shape")*\
                    tf_cast(k, tf.float32, tf_name=f"{params['cleaned_name']}_topk_k_cast")
-    k_reshaped = tf_cast(k_unsqueezed, tf.float32, tf_name=f"{params['cleaned_name']}_topk_k_reshaped")
+    k_reshaped = tf_cast(k_unsqueezed, in_tensor.dtype, tf_name=f"{params['cleaned_name']}_topk_k_reshaped")
     composed_input = tf_concat([in_tensor, k_reshaped], axis=-1,
                                tf_name=f"{params['cleaned_name']}_topk_k_concat")
     lambda_layer = keras.layers.Lambda(target_layer, name=f"{params['cleaned_name']}_topk")

onnx2kerastl/reshape_layers.py

@@ -670,13 +670,17 @@ def convert_expand(node, params, layers, lambda_func, node_name, keras_name):
 
     input_0 = ensure_tf_type(layers[node.input[0]], name="%s_const" % keras_name)
     input_1 = layers[node.input[1]]
+    was_converted_from_bool = False
     if input_0.dtype.is_bool:
         input_0 = tf_cast(input_0, dtype='int32', tf_name=f"{params['cleaned_name']}_bool_to_int")
+        was_converted_from_bool = True
     multiply_res = input_0 * tf_ones(shape=input_1, dtype=input_0.dtype,
                                      tf_name=f"{params['cleaned_name']}_expand_use_ones")
     # input_0.dtype == np.int32 since we can't serialize constants as int64, need to cast to true type
     if layers[node.input[0]].dtype == np.int64:
         multiply_res = tf_cast(multiply_res, tf.int64, tf_name=f"{params['cleaned_name']}_to_int64")
+    if was_converted_from_bool:
+        multiply_res = tf_cast(multiply_res, tf.bool, tf_name=f"{params['cleaned_name']}_int_to_bool")
     layers[node_name] = multiply_res