resolve merge conflicts

tests/python/gpu/test_gluon_transforms.py

@@ -32,6 +32,45 @@
 
 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())
+
+    # 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