Fix an issue with Upsampling and update one test to hit the broken usecase (#5530)

diff --git a/python/tvm/relay/frontend/onnx.py b/python/tvm/relay/frontend/onnx.py
index 33b8ee1..ee8f2e8 100644 (file)
--- a/python/tvm/relay/frontend/onnx.py
+++ b/python/tvm/relay/frontend/onnx.py
@@ -787,7 +787,10 @@ class Upsample(OnnxOpConverter):
         if not scales:
             #Here we are going to higher OPSET version.
             assert len(inputs) == 2, "Upsample op take 2 inputs, {} given".format(len(inputs))
-            scales = params[inputs[1].name_hint].asnumpy()
+            if get_name(inputs[1]) in params:
+                scales = params[inputs[1].name_hint].asnumpy()
+            else:
+                scales = infer_value_simulated(inputs[1], params).asnumpy()
             inputs = inputs[:1]
         assert scales[0] == 1.0 and scales[1] == 1.0
         input_shape = infer_shape(inputs[0])

diff --git a/tests/python/frontend/onnx/test_forward.py b/tests/python/frontend/onnx/test_forward.py
index 9fe50eb..dc832aa 100644 (file)
--- a/tests/python/frontend/onnx/test_forward.py
+++ b/tests/python/frontend/onnx/test_forward.py
@@ -859,21 +859,22 @@ def _test_upsample_bilinear_opset9():
     in_shape = (1, 1, 3, 3)
     out_shape = (1, 1, 3*scale, 3*scale)
     y = helper.make_node("Upsample", ['in', 'scales'], ['out'], mode='linear')
-    scales = [1.0, 1.0, 2.0, 2.0]
+    scales = [1, 1, 2, 2]
     in_array = np.random.uniform(size=in_shape).astype(np.float32)
     out_array = topi.testing.bilinear_resize_python(
         in_array, (3*scale, 3*scale), "NCHW")
 
-    ref_array = np.array(scales)
     ref_node = helper.make_node('Constant',
                                 inputs=[],
-                                outputs=['scales'],
+                                outputs=['const'],
                                 value=onnx.helper.make_tensor(name='const_tensor',
                                                               data_type=TensorProto.FLOAT,
-                                                              dims=ref_array.shape,
-                                                              vals=ref_array.flatten().astype(float)))
+                                                              dims=scales,
+                                                              vals=np.random.random(scales).flatten().astype(float)))
 
-    graph = helper.make_graph([ref_node, y],
+    shape_node = helper.make_node("Shape", ['const'], ['scales'])
+
+    graph = helper.make_graph([ref_node, shape_node, y],
                               'upsample_bilinear_opset9_test',
                               inputs=[helper.make_tensor_value_info(
                                   "in", TensorProto.FLOAT, list(in_shape))],