提交 97d4622b 编写于 作者: P phlrain

add softmax test unit

test=develop
上级 bbfc82cc
......@@ -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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