Adapt tests for MKLDNN log_softmax

apache · May 14, 2020 · f9472e7 · f9472e7
1 parent a5354ac
commit f9472e7
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Showing 3 changed files with 6 additions and 6 deletions.
diff --git a/tests/python/unittest/test_loss.py b/tests/python/unittest/test_loss.py
@@ -50,10 +50,10 @@ def test_loss_ndarray():
 
     loss = gluon.loss.SoftmaxCrossEntropyLoss()
     L = loss(output, label).asnumpy()
-    assert_almost_equal(L, np.array([ 2.12692809,  0.04858733]))
+    assert_almost_equal(L, np.array([ 2.12692809,  0.04858733]), rtol=1e-3, atol=1e-4)
 
     L = loss(output, label, weighting).asnumpy()
-    assert_almost_equal(L, np.array([ 1.06346405,  0.04858733]))
+    assert_almost_equal(L, np.array([ 1.06346405,  0.04858733]), rtol=1e-3, atol=1e-4)
 
 
 def get_net(num_hidden, flatten=True):

diff --git a/tests/python/unittest/test_numpy_gluon.py b/tests/python/unittest/test_numpy_gluon.py
@@ -153,10 +153,10 @@ def test_np_loss_ndarray():
 
     loss = gluon.loss.SoftmaxCrossEntropyLoss()
     L = loss(output, label).asnumpy()
-    assert_almost_equal(L, _np.array([2.12692809,  0.04858733]), use_broadcast=False)
+    assert_almost_equal(L, _np.array([2.12692809,  0.04858733]), use_broadcast=False, rtol=1e-3)
 
     L = loss(output, label, weighting).asnumpy()
-    assert_almost_equal(L, _np.array([1.06346405,  0.04858733]), use_broadcast=False)
+    assert_almost_equal(L, _np.array([1.06346405,  0.04858733]), use_broadcast=False, rtol=1e-3)
 
 
 @with_seed()

diff --git a/tests/python/unittest/test_operator.py b/tests/python/unittest/test_operator.py
@@ -5364,8 +5364,8 @@ def test_log_softmax():
             axis = np.random.randint(0, ndim)
             data = np.random.uniform(-2, 2, size=shape)
             sym = mx.sym.log_softmax(axis=axis-ndim)
-            check_symbolic_forward(sym, [data], [np.log(np_softmax(data, axis=axis)+1e-20)], rtol=2e-4)
-            check_numeric_gradient(sym, [data], rtol=0.05, atol=1e-3)
+            check_symbolic_forward(sym, [data], [np.log(np_softmax(data, axis=axis)+1e-20)], rtol=1e-3, atol=1e-4)
+            check_numeric_gradient(sym, [data], rtol=1e-1, atol=1e-2)
 
 def test_softmax_with_large_inputs():
     def softmax_forward(input_data, true_output):