[GitHub] access2rohit commented on a change in pull request #12637: [MXNET-912] Refactoring ctc loss operator

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

access2rohit commented on a change in pull request #12637: [MXNET-912] Refactoring ctc loss operator
URL: https://github.com/apache/incubator-mxnet/pull/12637#discussion_r223151943
 
 

 ##########
 File path: tests/python/unittest/test_operator.py
 ##########
 @@ -4619,6 +4647,85 @@ def check_ctc_loss_grad(blank_label): # from tf
         label_lens = np.array([5, 4], dtype=np.int32)
         loss_truth = np.array([-loss_log_prob_0, -loss_log_prob_1], np.float32)
 
+        with default_context():
+            data = mx.nd.array(inputs)
+            label = mx.nd.array(labels)
+            data.attach_grad()
+            with mx.autograd.record():
+                l = mx.ndarray.CTCLoss(data, label,
+                                       use_data_lengths=True,
+                                       use_label_lengths=True,
+                                       data_lengths=mx.nd.array(seq_lens),
+                                       label_lengths=mx.nd.array(label_lens),
+                                       blank_label=blank_label)
+                l.backward()
+            assert_almost_equal(l.asnumpy(), loss_truth, atol=1e-5, rtol=1e-5)
+            assert_almost_equal(data.grad.asnumpy(), grad_truth, atol=1e-5, rtol=1e-5)
+
+    # check contrib operator for backward compatibility
+    def check_contrib_ctc_loss_grad(blank_label): # from tf
+        vocab_size = 5
+        max_label_len = 5
+        padding_mask = -1+ (blank_label=='first')
+
+        targets_0 = [0, 1, 2, 1, 0]
+        loss_log_prob_0 = -3.34211
+        input_prob_matrix_0 = np.asarray(
+            [[0.633766, 0.221185, 0.0917319, 0.0129757, 0.0142857, 0.0260553],
+             [0.111121, 0.588392, 0.278779, 0.0055756, 0.00569609, 0.010436],
+             [0.0357786, 0.633813, 0.321418, 0.00249248, 0.00272882, 0.0037688],
+             [0.0663296, 0.643849, 0.280111, 0.00283995, 0.0035545, 0.00331533],
+             [0.458235, 0.396634, 0.123377, 0.00648837, 0.00903441, 0.00623107]],
 
 Review comment:
   Is it possible to generate them using random no. generators ? or Do we need fixed array values ?

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