fix prelu importer and add tests: (apache#5521)

python/tvm/relay/frontend/onnx.py

@@ -576,7 +576,12 @@ class Prelu(OnnxOpConverter):
     @classmethod
     def _impl_v1(cls, inputs, attr, params):
         assert len(inputs) == 2, "Prelu need 2 inputs, {} given".format(len(inputs))
-        return _op.nn.prelu(inputs[0], inputs[1])
+        alpha_shape = infer_shape(inputs[1])
+        if len(alpha_shape) != 1:
+            alpha = _op.reshape(inputs[1], (-1,))
+        else:
+            alpha = inputs[1]
+        return _op.nn.prelu(inputs[0], alpha)
 
 
 class Reciprocal(OnnxOpConverter):

tests/python/frontend/onnx/test_forward.py

@@ -1532,6 +1532,34 @@ def selu_x(x, alpha, gamma):
                               {'alpha': 0.25, 'gamma': 0.3})
 
 
+def test_prelu():
+    def verify_prelu(x_shape, a_shape):
+        node = helper.make_node('PRelu',
+                                inputs=['X', 'slope'],
+                                outputs=['Y'])
+
+        graph = helper.make_graph([node],
+                                  "prelu_test",
+                                  inputs=[helper.make_tensor_value_info("X", TensorProto.FLOAT, list(x_shape)),
+                                          helper.make_tensor_value_info("slope", TensorProto.FLOAT, list(a_shape))],
+                                  outputs=[helper.make_tensor_value_info("Y", TensorProto.FLOAT, list(x_shape))])
+
+        model = helper.make_model(graph, producer_name='prelu_test')
+
+        indata = np.random.uniform(-10, 10, x_shape).astype(np.float32)
+        slopedata = np.random.uniform(-10, 10, a_shape).astype(np.float32)
+        onnx_out = get_onnxruntime_output(model, [indata, slopedata])
+
+        for target, ctx in [('llvm', tvm.cpu())]:
+            tvm_out = get_tvm_output(model, [indata, slopedata], target, ctx, list(x_shape), 
+                    output_dtype='float32')
+            tvm.testing.assert_allclose(onnx_out[0], tvm_out, rtol=1e-05, atol=1e-05)
+
+    verify_prelu([3,4,5,6], [1, 4, 1, 1])
+    verify_prelu([1,8,5,6], [1, 8, 1, 1])
+    verify_prelu([2,12,16,16], [1, 12, 1, 1])
+
+
 def test_ThresholdedRelu():
     def ThresholdedRelu_x(x, alpha):
         out_np = np.clip(x, alpha, np.inf)
@@ -2535,6 +2563,7 @@ def verify_roi_align(input_dims, num_roi, output_height, output_width, sampling_
     test_leaky_relu()
     test_elu()
     test_selu()
+    test_prelu()
     test_ThresholdedRelu()
     test_ScaledTanh()
     test_ParametricSoftplus()