[GitHub] [tvm] trevor-m commented on a change in pull request #7375: Support negative pad values

[GitBox <gi...@apache.org>]

trevor-m commented on a change in pull request #7375:
URL: https://github.com/apache/tvm/pull/7375#discussion_r568787429



##########
File path: tests/python/relay/test_op_level2.py
##########
@@ -1171,35 +1171,62 @@ def test_flatten_infer_type():
 
 @tvm.testing.uses_gpu
 def test_pad_infer_type():
-    # entirely concrete case
+    # entirely concrete cases
     n, c, h, w = 1, 2, 3, 4
     t = relay.var("t", relay.TensorType((n, c, h, w), "float32"))
     y = relay.nn.pad(t, ((1, 1), (2, 2), (3, 3), (4, 4)))
-    "pad_width=" in y.astext()
     yy = run_infer_type(y)
     assert yy.checked_type == relay.TensorType((3, 6, 9, 12), "float32")
 
+    n, c, h, w = 4, 6, 3, 5
+    t = relay.var("t", relay.TensorType((n, c, h, w), "float32"))
+    y = relay.nn.pad(t, ((-1, -1), (2, -2), (0, -3), (4, 4)), pad_mode="reflect")
+    yy = run_infer_type(y)
+    assert yy.checked_type == relay.TensorType((2, 6, 0, 13), "float32")
+
     # some symbolic values
     n, c, h, w = te.size_var("n"), 2, 3, te.size_var("w")
     t = relay.var("t", relay.TensorType((n, c, h, w), "float32"))
     y = relay.nn.pad(t, ((1, 1), (2, 2), (3, 3), (4, 4)))
     yy = run_infer_type(y)
     assert yy.checked_type == relay.TensorType((n + 2, 6, 9, w + 8), "float32")
 
+    n, c, h, w = te.size_var("n"), te.size_var("c"), te.size_var("h"), te.size_var("w")
+    t = relay.var("t", relay.TensorType((n, c, h, w), "float32"))
+    y = relay.nn.pad(t, ((-1, -1), (-2, -2), (1, -3), (4, 4)))
+    yy = run_infer_type(y)
+    assert yy.checked_type == relay.TensorType((n + (-2), c + (-4), h + (-2), w + 8), "float32")
+
 
 @tvm.testing.uses_gpu
 def test_pad_run():
     def _test_run(dtype):
-        dshape = (4, 10, 7, 7)
-        x = relay.var("x", shape=dshape)
-        y = relay.nn.pad(x, ((1, 1), (2, 2), (3, 3), (4, 4)))
-        func = relay.Function([x], y)
-        data = np.random.uniform(size=dshape).astype(dtype)
-        ref_res = np.pad(data, ((1, 1), (2, 2), (3, 3), (4, 4)), "constant")
-        for target, ctx in tvm.testing.enabled_targets():
-            intrp1 = relay.create_executor("graph", ctx=ctx, target=target)
-            op_res1 = intrp1.evaluate(func)(data)
-            tvm.testing.assert_allclose(op_res1.asnumpy(), ref_res, rtol=1e-5, atol=1e-5)
+        dshape_list = [(4, 10, 7, 7), (4, 6, 3, 5)]

Review comment:
       It seems a bit odd to use a test case which results in an empty tensor (dimension of 0). It means there won't be any actual values to check if the result is correct. Could you change the either the dim with value of 3 or the `-3` padding?




----------------------------------------------------------------
This is an automated message from the Apache Git Service.
To respond to the message, please log on to GitHub and use the
URL above to go to the specific comment.

For queries about this service, please contact Infrastructure at:
users@infra.apache.org