diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml
index aad4adacd..79a90b0c2 100644
--- a/.github/workflows/build.yml
+++ b/.github/workflows/build.yml
@@ -185,7 +185,7 @@ jobs:
       - name: Run unittests and generate coverage report
         run: |
           pip install -r requirements/test.txt
-          coverage run --branch --source=mmcv -m pytest tests/ --ignore=tests/test_ops/test_onnx.py
+          coverage run --branch --source=mmcv -m pytest tests/
           coverage xml
           coverage report -m
       # Only upload coverage report for python3.7 && pytorch1.6
diff --git a/mmcv/onnx/onnx_utils/symbolic_helper.py b/mmcv/onnx/onnx_utils/symbolic_helper.py
index eb57c1b89..1839774ad 100644
--- a/mmcv/onnx/onnx_utils/symbolic_helper.py
+++ b/mmcv/onnx/onnx_utils/symbolic_helper.py
@@ -228,18 +228,32 @@ def _interpolate_size_to_scales(g, input, output_size, dim):
 def _interpolate_get_scales_if_available(g, scales):
     if len(scales) == 0:
         return None
-    available_scales = _maybe_get_const(scales[0],
-                                        'fs') != -1 and not _is_none(scales[0])
+    # scales[0] is ListType in Pytorch == 1.7.0
+    scale_desc = 'fs' if scales[0].type().kind() == 'ListType' else 'f'
+    available_scales = _maybe_get_const(
+        scales[0], scale_desc) != -1 and not _is_none(scales[0])
 
     if not available_scales:
         return None
 
     offsets = g.op('Constant', value_t=torch.ones(2, dtype=torch.float32))
-    scales_list = g.op(
-        'Constant', value_t=torch.tensor(_maybe_get_const(scales[0], 'fs')))
-    # modify to support PyTorch==1.7.0
-    # https://github.com/pytorch/pytorch/blob/75ee5756715e7161314ce037474843b68f69fc04/torch/onnx/symbolic_helper.py#L375 # noqa: E501
-    scales = g.op('Concat', offsets, scales_list, axis_i=0)
+    if scale_desc == 'fs':
+        scales_list = g.op(
+            'Constant',
+            value_t=torch.tensor(_maybe_get_const(scales[0], scale_desc)))
+        # modify to support PyTorch==1.7.0
+        # https://github.com/pytorch/pytorch/blob/75ee5756715e7161314ce037474843b68f69fc04/torch/onnx/symbolic_helper.py#L375 # noqa: E501
+        scales = g.op('Concat', offsets, scales_list, axis_i=0)
+    else:
+        # for PyTorch < 1.7.0
+        scales_list = []
+        for scale in scales:
+            unsqueezed_scale = _unsqueeze_helper(g, scale, 0)
+            # ONNX only supports float for the scales. double -> float.
+            unsqueezed_scale = g.op(
+                'Cast', unsqueezed_scale, to_i=cast_pytorch_to_onnx['Float'])
+            scales_list.append(unsqueezed_scale)
+        scales = g.op('Concat', offsets, *scales_list, axis_i=0)
     return scales
 
 
diff --git a/tests/test_ops/test_onnx.py b/tests/test_ops/test_onnx.py
index f6f3e68f7..63c9c5628 100644
--- a/tests/test_ops/test_onnx.py
+++ b/tests/test_ops/test_onnx.py
@@ -299,3 +299,29 @@ def test_simplify():
     numel_after = len(slim_onnx_model.graph.node)
     os.remove(onnx_file)
     assert numel_before == 18 and numel_after == 1, 'Simplify failed.'
+
+
+def test_interpolate():
+    from mmcv.onnx.symbolic import register_extra_symbolics
+    opset_version = 11
+    register_extra_symbolics(opset_version)
+
+    def func(feat, scale_factor=2):
+        out = nn.functional.interpolate(feat, scale_factor=scale_factor)
+        return out
+
+    net = WrapFunction(func)
+    net = net.cpu().eval()
+    dummy_input = torch.randn(2, 4, 8, 8).cpu()
+    torch.onnx.export(
+        net,
+        dummy_input,
+        onnx_file,
+        input_names=['input'],
+        opset_version=opset_version)
+    sess = rt.InferenceSession(onnx_file)
+    onnx_result = sess.run(None, {'input': dummy_input.detach().numpy()})
+    pytorch_result = func(dummy_input).detach().numpy()
+    if os.path.exists(onnx_file):
+        os.remove(onnx_file)
+    assert np.allclose(pytorch_result, onnx_result, atol=1e-3)