[GitHub] apeforest commented on a change in pull request #12035: Pad Operator Type Support

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

apeforest commented on a change in pull request #12035: Pad Operator Type Support
URL: https://github.com/apache/incubator-mxnet/pull/12035#discussion_r210980686
 
 

 ##########
 File path: tests/python/unittest/test_operator.py
 ##########
 @@ -2905,37 +2905,46 @@ def test_roipooling():
                            numeric_eps=1e-4, rtol=1e-1, atol=1E-4)
 
 
-def check_pad_with_shape(shape, xpu, pad_width, mode):
+def check_pad_with_shape(shape, xpu, pad_width, mode, dtype="float64"):
     # bind with label
-    X = mx.symbol.Variable('X')
+    real_types = ["float16", "float32", "float64"]
+    X = mx.symbol.Variable('X', dtype=dtype)
     Y = mx.symbol.Pad(data=X, mode=mode, pad_width=pad_width)
-    x = mx.random.uniform(-1, 1, shape, ctx=mx.cpu()).copyto(xpu)
+    if dtype in real_types:
+        x = mx.random.uniform(-1, 1, shape, ctx=mx.cpu(), dtype=dtype).copyto(xpu)
+    else:
+        x = mx.nd.ones(shape, ctx=mx.cpu(), dtype=dtype).copyto(xpu)
     # numpy result
     pad_grouped = list(zip(*[iter(list(pad_width))] * 2))
     np_out = np.pad(x.asnumpy(), pad_grouped, mode)
     # mxnet result
-    grad = mx.nd.empty(shape, ctx = xpu)
+    grad = mx.nd.empty(shape, ctx = xpu, dtype=dtype)
     exec1 = Y.bind(xpu, args = [x], args_grad = {'X': grad})
     exec1.forward(is_train=True)
     out = exec1.outputs[0].asnumpy()
     # compare numpy + mxnet
     assert_almost_equal(out, np_out)
     # grad check
-    check_numeric_gradient(Y, [x.asnumpy()], numeric_eps=1e-2, rtol=1e-2)
+    if dtype in real_types:
+        check_numeric_gradient(Y, [x.asnumpy()], numeric_eps=1e-2, rtol=1e-2)
 
 
 @with_seed()
 def test_pad():
+    ct = default_context()
 
 Review comment:
   nit: ctx is probably a more commonly used name for context?

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