[GitHub] [incubator-mxnet] roywei commented on a change in pull request #16464: Image crop gpu support

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

roywei commented on a change in pull request #16464: Image crop gpu support
URL: https://github.com/apache/incubator-mxnet/pull/16464#discussion_r335201026
 
 

 ##########
 File path: tests/python/gpu/test_gluon_transforms.py
 ##########
 @@ -28,76 +28,19 @@
 curr_path = os.path.dirname(os.path.abspath(os.path.expanduser(__file__)))
 sys.path.insert(0, os.path.join(curr_path, '../unittest'))
 from common import assertRaises, setup_module, with_seed, teardown
-
+from test_gluon_data_vision import test_to_tensor, test_normalize, test_crop_resize
 
 set_default_context(mx.gpu(0))
 
 @with_seed()
-def test_normalize():
-    # 3D Input
-    data_in_3d = nd.random.uniform(0, 1, (3, 300, 300))
-    out_nd_3d = transforms.Normalize(mean=(0, 1, 2), std=(3, 2, 1))(data_in_3d)
-    data_expected_3d = data_in_3d.asnumpy()
-    data_expected_3d[:][:][0] = data_expected_3d[:][:][0] / 3.0
-    data_expected_3d[:][:][1] = (data_expected_3d[:][:][1] - 1.0) / 2.0
-    data_expected_3d[:][:][2] = data_expected_3d[:][:][2] - 2.0
-    assert_almost_equal(data_expected_3d, out_nd_3d.asnumpy())
-
-    # 4D Input
-    data_in_4d = nd.random.uniform(0, 1, (2, 3, 300, 300))
-    out_nd_4d = transforms.Normalize(mean=(0, 1, 2), std=(3, 2, 1))(data_in_4d)
-    data_expected_4d = data_in_4d.asnumpy()
-    data_expected_4d[0][:][:][0] = data_expected_4d[0][:][:][0] / 3.0
-    data_expected_4d[0][:][:][1] = (data_expected_4d[0][:][:][1] - 1.0) / 2.0
-    data_expected_4d[0][:][:][2] = data_expected_4d[0][:][:][2] - 2.0
-    data_expected_4d[1][:][:][0] = data_expected_4d[1][:][:][0] / 3.0
-    data_expected_4d[1][:][:][1] = (data_expected_4d[1][:][:][1] - 1.0) / 2.0
-    data_expected_4d[1][:][:][2] = data_expected_4d[1][:][:][2] - 2.0
-    assert_almost_equal(data_expected_4d, out_nd_4d.asnumpy())
-
-    # Default normalize values i.e., mean=0, std=1
-    data_in_3d_def = nd.random.uniform(0, 1, (3, 300, 300))
-    out_nd_3d_def = transforms.Normalize()(data_in_3d_def)
-    data_expected_3d_def = data_in_3d_def.asnumpy()
-    assert_almost_equal(data_expected_3d_def, out_nd_3d_def.asnumpy())
+def test_normalize_gpu():
+    test_normalize()
 
-    # Invalid Input - Neither 3D or 4D input
-    invalid_data_in = nd.random.uniform(0, 1, (5, 5, 3, 300, 300))
-    normalize_transformer = transforms.Normalize(mean=(0, 1, 2), std=(3, 2, 1))
-    assertRaises(MXNetError, normalize_transformer, invalid_data_in)
-
-    # Invalid Input - Channel neither 1 or 3
-    invalid_data_in = nd.random.uniform(0, 1, (5, 4, 300, 300))
-    normalize_transformer = transforms.Normalize(mean=(0, 1, 2), std=(3, 2, 1))
-    assertRaises(MXNetError, normalize_transformer, invalid_data_in)
 
 @with_seed()
-def test_to_tensor():
-    # 3D Input
-    data_in = np.random.uniform(0, 255, (300, 300, 3)).astype(dtype=np.uint8)
-    out_nd = transforms.ToTensor()(nd.array(data_in, dtype='uint8'))
-    assert_almost_equal(out_nd.asnumpy(), np.transpose(
-        data_in.astype(dtype=np.float32) / 255.0, (2, 0, 1)))
-
-    # 4D Input
-    data_in = np.random.uniform(0, 255, (5, 300, 300, 3)).astype(dtype=np.uint8)
-    out_nd = transforms.ToTensor()(nd.array(data_in, dtype='uint8'))
-    assert_almost_equal(out_nd.asnumpy(), np.transpose(
-                        data_in.astype(dtype=np.float32) / 255.0, (0, 3, 1, 2)))
+def test_to_tensor_gpu():
+    test_to_tensor()
 
-    # Invalid Input
-    invalid_data_in = nd.random.uniform(0, 255, (5, 5, 300, 300, 3)).astype(dtype=np.uint8)
-    transformer = transforms.ToTensor()
-    assertRaises(MXNetError, transformer, invalid_data_in)
-
-    # Bounds (0->0, 255->1)
-    data_in = np.zeros((10, 20, 3)).astype(dtype=np.uint8)
-    out_nd = transforms.ToTensor()(nd.array(data_in, dtype='uint8'))
-    assert same(out_nd.asnumpy(), np.transpose(np.zeros(data_in.shape, dtype=np.float32), (2, 0, 1)))
-
-    data_in = np.full((10, 20, 3), 255).astype(dtype=np.uint8)
-    out_nd = transforms.ToTensor()(nd.array(data_in, dtype='uint8'))
-    assert same(out_nd.asnumpy(), np.transpose(np.ones(data_in.shape, dtype=np.float32), (2, 0, 1)))
 
 @with_seed()
 def test_resize():
 
 Review comment:
   can you rename this to test_resize_gpu for consistency, so people know there is a difference from test_resize()

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