[GitHub] eric-haibin-lin commented on a change in pull request #11229: [MXNET-379] L1 Normalization

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

eric-haibin-lin commented on a change in pull request #11229: [MXNET-379] L1 Normalization
URL: https://github.com/apache/incubator-mxnet/pull/11229#discussion_r195276671
 
 

 ##########
 File path: tests/python/unittest/test_operator.py
 ##########
 @@ -2879,6 +2879,32 @@ def npy_layer_norm(data, gamma, beta, axis=1, eps=1E-5):
                                grad_nodes={'data': req, 'gamma': req, 'beta': req},
                                numeric_eps=1e-2, rtol=1e-2, atol=1e-2)
 
+@with_seed()
+def test_l1_norm():
+    ctx = default_context()
+    data = mx.symbol.Variable('data')
+    in_data_dim = random_sample([4,5,6], 1)[0]
+    in_shape = rand_shape_nd(in_data_dim)
+    for dtype in [np.float16, np.float32, np.float64]:
+        in_data = np.random.uniform(-1, 1, in_shape).astype(dtype)
+        for i in range(in_data_dim):
+            for keep_dims in [True, False]:
+                norm_sym = mx.symbol.norm(data=data, ord=1, axis=i, keepdims=keep_dims)
+                npy_out = np.sum(abs(in_data), axis=i, keepdims=keep_dims)
+                check_symbolic_forward(norm_sym, [in_data], [npy_out],
+                                       rtol=1e-2 if dtype is np.float16 else 1e-5,
+                                       atol=1e-5, ctx=ctx)
+                # check gradient
+                #check_numeric_gradient(norm_sym, [in_data], numeric_eps=1e-3, rtol=1e-2, atol=1e-3)
+                if i < in_data_dim-1:
+                    norm_sym = mx.symbol.norm(data=data, ord=1, axis=(i, i+1), keepdims=keep_dims)
+                    npy_out = np.sum(abs(in_data), axis=(i, i+1), keepdims=keep_dims)
+                    check_symbolic_forward(norm_sym, [in_data], [npy_out],
+                                       rtol=1e-2 if dtype is np.float16 else 1e-5,
+                                       atol=1e-5, ctx=ctx)
+                    # check gradient
+                    #check_numeric_gradient(norm_sym, [in_data], numeric_eps=1e-3, rtol=1e-2, atol=1e-3)
 
 Review comment:
   Also - check_symbolic_backward?

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