Add limited SoftmaxCrossEntropyLoss support

Author: Jonathan Sparling

onnx_tf/handlers/backend/softmax_cross_entropy_loss.py

@@ -0,0 +1,33 @@
+import tensorflow as tf
+
+from onnx_tf.common import exception
+from onnx_tf.handlers.backend_handler import BackendHandler
+from onnx_tf.handlers.handler import onnx_op
+from onnx_tf.handlers.handler import tf_func
+
+
+@onnx_op("SoftmaxCrossEntropyLoss")
+@tf_func(tf.nn.sparse_softmax_cross_entropy_with_logits)
+class SoftmaxCrossEntropyLoss(BackendHandler):
+    @classmethod
+    def _common(cls, node, **kwargs):
+        logits = kwargs["tensor_dict"][node.inputs[0]]
+        labels = kwargs["tensor_dict"][node.inputs[1]]
+
+        labels_shape = tf.shape(labels)
+        if labels_shape.shape[0] > 1:
+            raise NotImplementedError(
+                "SoftmaxCrossEntropyLoss support is limited to rank 1 label tensors."
+                .format(spatial_size))
+
+        return [
+            tf.nn.sparse_softmax_cross_entropy_with_logits(labels=labels, logits=logits)
+        ]
+
+    @classmethod
+    def version_12(cls, node, **kwargs):
+        return cls._common(node, **kwargs)
+
+    @classmethod
+    def version_13(cls, node, **kwargs):
+        return cls._common(node, **kwargs)

test/backend/test_node.py

@@ -3987,6 +3987,16 @@ def test_softplus(self):
                                    np.log(np.exp(x) + 1),
                                    decimal=5)
 
+  def test_softmax_cross_entropy_loss(self):
+    node_def = helper.make_node("SoftmaxCrossEntropyLoss", ["X", "Y"], ["Z"])
+    classes = 10
+    x = self._get_rnd_float32(shape=[1,classes])
+    y = self._get_rnd_int(0, classes-1, [1], np.int32)
+    output = run_node(node_def, [x, y])
+    np.testing.assert_almost_equal(output["Z"],
+                                   -np.log(np.exp(x)[0][y]/np.sum(np.exp(x))),
+                                   decimal=5)
+
   def test_softsign(self):
     node_def = helper.make_node("Softsign", ["X"], ["Y"])
     x = self._get_rnd_float32(shape=[3, 4, 5])