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Posted to commits@tvm.apache.org by GitBox <gi...@apache.org> on 2021/02/02 20:55:06 UTC
[GitHub] [tvm] codeislife99 commented on a change in pull request #7375: Support negative pad values
codeislife99 commented on a change in pull request #7375:
URL: https://github.com/apache/tvm/pull/7375#discussion_r568923480
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File path: tests/python/relay/test_op_level2.py
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@@ -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:
Thats a good point, resolved !
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