[ONNX] Fix flatten operator

test/onnx/test_models.py

@@ -105,7 +105,6 @@ def test_alexnet(self):
         )
         self.exportTest(toC(alexnet()), toC(x))
 
-    @disableScriptTest()
     def test_mnist(self):
         x = Variable(torch.randn(BATCH_SIZE, 1, 28, 28).fill_(1.0))
         self.exportTest(toC(MNIST()), toC(x))
@@ -158,7 +157,6 @@ def test_squeezenet(self):
         sqnet_v1_1 = SqueezeNet(version=1.1)
         self.exportTest(toC(sqnet_v1_1), toC(x))
 
-    @disableScriptTest()
     def test_densenet(self):
         # Densenet-121 model
         x = Variable(torch.randn(BATCH_SIZE, 3, 224, 224).fill_(1.0))

torch/onnx/symbolic_opset11.py

@@ -725,18 +725,17 @@ def narrow(g, input, dim, start, length):
 @parse_args('v', 'i', 'i')
 def flatten(g, input, start_dim, end_dim):
     dim = input.type().dim()
-    if dim is None:
-        return _unimplemented("dim",
-                              "ONNX and PyTorch use different strategies to split the input. "
-                              "Input rank must be known at export time.")
-
     # use ONNX's Flatten operator for cases where the output shape is 2D
     if start_dim == 1:
-        if (end_dim == -1 or end_dim == dim - 1):
+        if (end_dim == -1 or (dim is not None and end_dim == dim - 1)):
             return g.op("Flatten", input, axis_i=start_dim)
     elif start_dim == 0:
-        if (end_dim == -2 or end_dim == dim - 2):
+        if (end_dim == -2 or (dim is not None and end_dim == dim - 2)):
             return g.op("Flatten", input, axis_i=end_dim + 1)
+    if dim is None:
+        return _unimplemented("dim",
+                              "ONNX and PyTorch use different strategies to split the input. "
+                              "Input rank must be known at export time.")
     # if end_dim is negative add dim
     if end_dim < 0 :
         end_dim = dim + end_dim