add tests and fix bugs for some cases

Author: wayuanho

tests/test_backend.py

@@ -797,6 +797,35 @@ def test_concat(self):
         _ = tf.identity(x_, name=_TFOUTPUT)
         self._run_test_case([_OUTPUT], {_INPUT: x_val1, _INPUT1: x_val2, "input3:0": x_val3})
 
+    def test_concat_empty_const_input(self):
+        x_val1 = np.array([1, 2, 3], dtype=np.float32)
+        x_val2 = np.array([], dtype=np.float32)
+        x1 = tf.placeholder(tf.float32, x_val1.shape, name=_TFINPUT)
+        x2 = tf.constant(x_val2, dtype=tf.float32)
+        x_ = tf.concat([x1, x2], 0)
+        _ = tf.identity(x_, name=_TFOUTPUT)
+        self._run_test_case([_OUTPUT], {_INPUT: x_val1})
+
+        tf.reset_default_graph()
+        x_val1 = np.array([[1, 2, 3]], dtype=np.float32)
+        x_val2 = np.array([[]], dtype=np.float32)
+        x1 = tf.placeholder(tf.float32, x_val1.shape, name=_TFINPUT)
+        x2 = tf.constant(x_val2, dtype=tf.float32)
+        x_ = tf.concat([x1, x2], 1)
+        _ = tf.identity(x_, name=_TFOUTPUT)
+        self._run_test_case([_OUTPUT], {_INPUT: x_val1})
+
+        tf.reset_default_graph()
+        x_val1 = np.array([1, 2, 3], dtype=np.float32)
+        x_val2 = np.array([], dtype=np.float32)
+        x_val3 = np.array([13, 14, 15], dtype=np.float32)
+        x1 = tf.placeholder(tf.float32, x_val1.shape, name=_TFINPUT)
+        x2 = tf.constant(x_val2, dtype=tf.float32)
+        x3 = tf.placeholder(tf.float32, x_val3.shape, name=_TFINPUT1)
+        x_ = tf.concat([x1, x2, x3], 0)
+        _ = tf.identity(x_, name=_TFOUTPUT)
+        self._run_test_case([_OUTPUT], {_INPUT: x_val1, _INPUT1: x_val3})
+
     @check_opset_min_version(6, "cast")
     def test_concat_int64(self):
         x_val1 = np.array([[1, 2, 3], [4, 5, 6]], dtype=np.int64)

tests/test_internals.py

@@ -12,8 +12,9 @@
 from collections import namedtuple
 
 import graphviz as gv
+import numpy as np
 from onnx import TensorProto
-from onnx import helper
+from onnx import helper, numpy_helper
 
 import tensorflow as tf
 from tf2onnx import utils
@@ -247,6 +248,37 @@ def test_node_attr_onnx(self):
         self.assertTrue("my_attr" in n1.attr)
         self.assertTrue("my_attr" in n1.attr_onnx)
 
+    def test_tensor_data(self):
+        tensors = {
+            "empty_tensor": np.array([], dtype=np.float32),
+            "multi_dim_empty_tensor": np.array([[], []], dtype=np.float32),
+            "scalar": np.array(1., dtype=np.float32),
+            "one_item_array": np.array([1.], dtype=np.float32),
+            "normal_array": np.array([[1., 2.], [2., 3.]], dtype=np.float32)
+        }
+        tf.reset_default_graph()
+        with tf.Session() as sess:
+            for n, data in tensors.items():
+                tf.constant(data, dtype=tf.float32, name=n)
+
+        for tf_node in sess.graph.get_operations():
+            name = tf_node.name
+            self.assertTrue(name in tensors.keys())
+
+            self.assertTrue("value" in tf_node.node_def.attr)
+            # convert to onnx tensor value
+            tensor_value = utils.tf_to_onnx_tensor(
+                utils.get_tf_node_attr(tf_node, "value"),
+                name=utils.port_name(tf_node.name)
+            )
+            attr = helper.make_attribute("value", tensor_value)
+            # same as node.get_tensor_value(is_list=False)
+            actual = numpy_helper.to_array(helper.get_attribute_value(attr))
+
+            expected = tensors[name]
+
+            self.assertTrue(np.array_equal(expected, actual))
+
 
 if __name__ == '__main__':
     unittest_main()

tf2onnx/tfonnx.py

@@ -769,8 +769,11 @@ def concatv2_op(ctx, node, name, args):
     # if any input is empty, remove the input and concat the others
     # NOTE: workaround for https://github.com/Microsoft/onnxruntime/issues/681
     for i, inp in enumerate(node.inputs):
-        if inp.is_const() and inp.get_tensor_value() == []:
+        if inp.is_const() and inp.get_tensor_value(as_list=False).size == 0:
             ctx.remove_input(node, node.input[i])
+    # all inputs are deleted
+    if not node.input:
+        raise RuntimeError("all inputs of {} are empty".format(name))
 
     axis_node = node.inputs[-1]
     axis_val = axis_node.get_tensor_value()
@@ -1175,7 +1178,7 @@ def minmax_op(ctx, node, name, args):
             ctx.copy_shape(inp, inp_cast.output[0])
             ctx.set_dtype(inp_cast.output[0], target_dtype)
     origin_dtype = ctx.get_dtype(node.output[0])
-    utils.make_sure(origin_dtype, "dtype of {} is None".format(node.output[0]))
+    utils.make_sure(origin_dtype is not None, "dtype of {} is None".format(node.output[0]))
     ctx.set_dtype(node.output[0], target_dtype)
     cast_name = utils.make_name(name)
     cast_node = ctx.insert_new_node_on_output("Cast", node.output[0], name=cast_name, to=origin_dtype)

tf2onnx/utils.py

@@ -131,18 +131,18 @@ def tf_to_onnx_tensor(tensor, name=""):
         tensor_content: empty
         tensor_shape.dim: [0]
         DTYPE_val: 1
-    3. empty tensor, e.g., np.array([], dtype=DTYPE):
+    3. empty tensor, e.g., np.array([], dtype=DTYPE) and np.array([[]], dtype=DTYPE):
         tensor_content: empty
-        tensor_shape.dim: [0]
+        tensor_shape.dim: [0] and [1, 0]
         DTYPE_val: empty
     """
     new_type = TF_TO_ONNX_DTYPE[tensor.dtype]
     tdim = tensor.tensor_shape.dim
     dims = [d.size for d in tdim]
     is_raw, data = get_tf_tensor_data(tensor)
     # empty tensor
-    if dims == [0] and not is_raw and data is None:
-        np_data = np.array([], dtype=map_onnx_to_numpy_type(new_type))
+    if not is_raw and data is None:
+        np_data = np.array([], dtype=map_onnx_to_numpy_type(new_type)).reshape(dims)
         return numpy_helper.from_array(np_data, name=name)
     make_sure(data, "tensor data isn't expected to be None or empty")
     # scalar tensor