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_r223160061
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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 use python library to compute these losses manually and
then verify against them ?
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