Fix a bug of flatten in ONNX to Relay converter (#3180)

* fix onnx frontend flatten bug

* Update onnx.py

* Update onnx.py

* Update onnx.py

python/tvm/relay/frontend/onnx.py

@@ -335,6 +335,23 @@ class Reciprocal(OnnxOpConverter):
     def _impl_v1(cls, inputs, attr, params):
         return _expr.const(1.0) / inputs[0]
 
+
+class Flatten(OnnxOpConverter):
+    """ Operator converter for Flatten.
+    """
+
+    @classmethod
+    def _impl_v1(cls, inputs, attr, params):
+        axis = attr.get('axis', 1)
+        if axis == 1:
+            out = _op.nn.batch_flatten(inputs[0])
+        else:
+            newshape = [0] * (axis + 1)
+            newshape[axis] = -1
+            out = _op.reshape(inputs[0], list(newshape))
+        return out
+
+
 class Reshape(OnnxOpConverter):
     """ Operator converter for Reshape.
     """
@@ -850,7 +867,7 @@ def _get_convert_map(opset):
         # 'InstanceNormalization'
         # 'LpNormalization'
         'Dropout': AttrCvt('dropout', {'ratio': 'rate'}, ignores=['is_test']),
-        'Flatten': Renamer('batch_flatten'),
+        'Flatten': Flatten.get_converter(opset),
         'LRN': LRN.get_converter(opset),
 
         # defs/reduction

tests/python/frontend/onnx/test_forward.py

@@ -211,6 +211,29 @@ def test_squeeze():
 
     tvm.testing.assert_allclose(out_shape, tvm_out.shape)
 
+def test_flatten():
+
+    in_shape = (1, 3, 4, 4)
+    axis = 1
+    ref_shape = (1, 48)
+
+    flatten_node = helper.make_node("Flatten", ["in"], ["out"], axis = axis)
+
+    graph = helper.make_graph([flatten_node],
+                              "flatten_test",
+                              inputs = [helper.make_tensor_value_info("in",
+                                            TensorProto.FLOAT, list(in_shape))],
+                              outputs = [helper.make_tensor_value_info("out",
+                                            TensorProto.FLOAT, list(ref_shape))])
+
+    model = helper.make_model(graph, producer_name='flatten_test')
+
+    for target, ctx in ctx_list():
+        x = np.random.uniform(size=in_shape).astype('int32')
+        tvm_out = get_tvm_output(model, x, target, ctx, ref_shape, 'float32')
+
+    tvm.testing.assert_allclose(ref_shape, tvm_out.shape)
+
 def test_unsqueeze():
     in_shape = (3, 3)
     axis = (0, 3, 4)
@@ -1046,6 +1069,7 @@ def test_LogSoftmax():
                               {'axis': 1})
 
 if __name__ == '__main__':
+    test_flatten()
     test_reshape()
     test_shape()
     test_power()