From 97d4622bdbfcca5e0372e9b58dd68cca7780296c Mon Sep 17 00:00:00 2001
From: phlrain <phliuhongyu@126.com>
Date: Thu, 11 Apr 2019 12:19:47 +0000
Subject: [PATCH] add softmax test unit test=develop

---
 .../test_softmax_with_cross_entropy_op.py     | 139 ++++++++++++++++++
 1 file changed, 139 insertions(+)

diff --git a/python/paddle/fluid/tests/unittests/test_softmax_with_cross_entropy_op.py b/python/paddle/fluid/tests/unittests/test_softmax_with_cross_entropy_op.py
index b0494f114c5..b06b52f75d2 100644
--- a/python/paddle/fluid/tests/unittests/test_softmax_with_cross_entropy_op.py
+++ b/python/paddle/fluid/tests/unittests/test_softmax_with_cross_entropy_op.py
@@ -195,5 +195,144 @@ class TestSoftmaxWithCrossEntropyOp3NoCudnn(TestSoftmaxWithCrossEntropyOp3):
         self.numeric_stable_mode = True
 
 
+class TestSoftmaxWithCrossEntropyOp5(OpTest):
+    """
+    Test softmax with cross entropy operator with ignore_index.
+    """
+
+    def initParams(self):
+        self.numeric_stable_mode = False
+
+    def setUp(self):
+        self.initParams()
+        self.op_type = "softmax_with_cross_entropy"
+        batch_size = [6, 10]
+        class_num = 47
+
+        logits = np.random.uniform(
+            0.1, 1.0, tuple(batch_size + [class_num])).astype("float64")
+        softmax = np.apply_along_axis(stable_softmax, 2, logits)
+        labels = np.random.randint(
+            0, class_num, tuple(batch_size + [1]), dtype="int64")
+        ignore_index = 7
+
+        softmax_2d = np.reshape(softmax, [-1, class_num])
+        labels_2d = np.reshape(labels, [-1, 1])
+        cross_entropy = np.asmatrix(
+            [[-np.log(softmax_2d[i][labels_2d[i][0]])]
+             if labels_2d[i] != ignore_index else [0]
+             for i in range(softmax_2d.shape[0])],
+            dtype="float64")
+
+        cross_entropy = np.reshape(cross_entropy, batch_size)
+
+        output_shape = tuple(batch_size + [1])
+        output_res = cross_entropy.astype("float64")
+        output_res = np.expand_dims(output_res, axis=2)
+        self.inputs = {"Logits": logits, "Label": labels}
+        self.outputs = {
+            "Softmax": softmax.astype("float64"),
+            "Loss": output_res,
+        }
+        self.attrs = {
+            "ignore_index": ignore_index,
+            "numeric_stable_mode": self.numeric_stable_mode
+        }
+
+    def test_check_output(self):
+        self.check_output()
+
+    def test_check_grad(self):
+        self.check_grad(["Logits"], "Loss")
+
+
+class TestSoftmaxWithCrossEntropyOp5NoCudnn(TestSoftmaxWithCrossEntropyOp5):
+    def initParams(self):
+        self.numeric_stable_mode = True
+
+
+class TestSoftmaxWithCrossEntropyOp6(OpTest):
+    """
+    Test softmax with cross entropy operator with soft labels.
+    """
+
+    def setUp(self):
+        self.op_type = "softmax_with_cross_entropy"
+        batch_size = [6, 10]
+        class_num = 37
+
+        logits = np.random.uniform(
+            0.1, 1.0, tuple(batch_size + [class_num])).astype("float64")
+        softmax = np.apply_along_axis(stable_softmax, 2, logits)
+        labels = np.random.uniform(
+            0.1, 1.0, tuple(batch_size + [class_num])).astype("float64")
+        labels /= np.sum(labels, axis=2, keepdims=True)
+
+        cross_entropy = (-labels * np.log(softmax)).sum(
+            axis=2, keepdims=True).astype("float64")
+
+        self.inputs = {"Logits": logits, "Label": labels}
+        self.outputs = {
+            "Softmax": softmax.astype("float64"),
+            "Loss": cross_entropy.astype("float64")
+        }
+        self.attrs = {"soft_label": True}
+
+    def test_check_output(self):
+        self.check_output()
+
+    def test_check_grad(self):
+        self.check_grad(["Logits"], "Loss")
+
+
+class TestSoftmaxWithCrossEntropyOpFp16_2(TestSoftmaxWithCrossEntropyOp):
+    def initParams(self):
+        self.numeric_stable_mode = False
+        self.dtype = np.float16
+
+    def setUp(self):
+        self.initParams()
+        self.op_type = "softmax_with_cross_entropy"
+        batch_size = [64, 10]
+        class_num = 37
+
+        # NOTE: numpy float16 have very low accuracy, use float32 for numpy check.
+        logits = np.random.uniform(
+            0.1, 1.0, tuple(batch_size + [class_num])).astype(np.float32)
+        softmax = np.apply_along_axis(stable_softmax, 2, logits)
+        labels = np.random.randint(
+            0, class_num, tuple(batch_size + [1]), dtype="int64")
+
+        softmax_2d = np.reshape(softmax, [-1, class_num])
+        labels_2d = np.reshape(labels, [-1, 1])
+
+        cross_entropy = np.asmatrix(
+            [[-np.log(softmax_2d[i][labels_2d[i][0]])]
+             for i in range(softmax_2d.shape[0])],
+            dtype=np.float32)
+
+        cross_entropy = np.reshape(cross_entropy, batch_size)
+        output_shape = tuple(batch_size + [1])
+        output_res = cross_entropy.astype(self.dtype)
+        output_res = np.expand_dims(output_res, axis=2)
+        self.inputs = {"Logits": logits, "Label": labels}
+
+        self.inputs = {
+            "Logits": logits.astype(self.dtype).view(np.uint16),
+            "Label": labels
+        }
+        self.outputs = {
+            "Softmax": softmax.astype(self.dtype),
+            "Loss": output_res,
+        }
+        self.attrs = {"numeric_stable_mode": self.numeric_stable_mode}
+
+    def test_check_output(self):
+        self.check_output(atol=1e-2)
+
+    def test_check_grad(self):
+        self.check_grad(["Logits"], "Loss", max_relative_error=0.1)
+
+
 if __name__ == "__main__":
     unittest.main()
-- 
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