haojin2 commented on a change in pull request #17328: [numpy] add op pad
URL: https://github.com/apache/incubator-mxnet/pull/17328#discussion_r367145836
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File path: tests/python/unittest/test_numpy_op.py
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@@ -5597,6 +5597,59 @@ def hybrid_forward(self,F,a):
assert_almost_equal(mx_out.asnumpy(), np_out, rtol=rtol, atol=atol)
+@with_seed()
+@use_np
+def test_np_pad():
+ class TestPad(HybridBlock):
+ def __init__(self, pad_width = (), mode="constant",
reflect_type="even", constant_values=0):
+ super(TestPad,self).__init__()
+ self._pad_width = pad_width
+ self._mode = mode
+ self._reflect_type =reflect_type
+ self._constant_values = constant_values
+ def hybrid_forward(self,F,A):
+ return F.np.pad(A, self._pad_width, mode=self._mode,
reflect_type=self._reflect_type, constant_values=self._constant_values)
+ shapes = [(1,5), (2,2), (2,2), (3,3), (2,3), (3,4,5)]
+ dtypes = [np.int8, np.uint8, np.int32, np.int64, np.float16, np.float32,
np.float64]
+ mode = ['constant', 'reflect', 'symmetric', 'edge', 'minimum']
+ for hybridize, shape, dtype, in itertools.product([False,True], shapes,
dtypes):
+ rtol = 1e-2 if dtype == np.float16 else 1e-3
+ atol = 1e-4 if dtype == np.float16 else 1e-5
+
+ for m in mode:
+ x = np.random.uniform(-1.0, 1.0, size = shape).astype(dtype)
+ pw = ()
+ if (type(shape) == int):
+ pw += (2,3)
+ else:
+ for i in range(len(shape)):
+ pw += ((2,3),)
+ test_pad = TestPad(pw, m, "even", 0)
+ if hybridize:
+ test_pad.hybridize()
+ x.attach_grad()
+
+ if(m != 'constant'):
+ np_out = _np.pad(x.asnumpy(), pw, mode=m)
+ else:
+ np_out = _np.pad(x.asnumpy(), pw, mode=m, constant_values=0)
+ with mx.autograd.record():
+ mx_out = test_pad(x)
+
+ # Code to get the reference backward value
+ assert mx_out.shape == np_out.shape
+ assert_almost_equal(mx_out.asnumpy(), np_out, rtol = rtol, atol =
atol)
Review comment:
we'll also have to check the gradient since we have the backward implemented.
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