def test_static_api(self):
train_program = paddle.static.Program()
startup_program = paddle.static.Program()
with paddle.static.program_guard(train_program, startup_program):
x = paddle.static.data('x', [-1, self.feature_size])
labels = paddle.static.data('labels', [-1, 1], 'int64')
weight = paddle.static.data('weight', [-1, self.feature_size])
bias = paddle.static.data('bias', [
-1,
])
path_table = None
path_code = None
if self.is_custom:
path_table = paddle.static.data('path_table', [-1, -1],
'int64')
path_code = paddle.static.data('path_code', [-1, -1], 'int64')
out1 = F.hsigmoid_loss(x, labels, self.num_classes, weight, bias,
path_table, path_code)
weight_attr = paddle.framework.ParamAttr(
initializer=I.NumpyArrayInitializer(self.weight_np))
bias_attr = paddle.framework.ParamAttr(
initializer=I.NumpyArrayInitializer(self.bias_np))
m = paddle.nn.HSigmoidLoss(self.feature_size, self.num_classes,
weight_attr, bias_attr, self.is_custom)
out2 = m(x, labels, path_table, path_code)
exe = paddle.static.Executor(self.place)
exe.run(startup_program)
feed_dict = {
'x': self.x_np,
'labels': self.labels_np,
'weight': self.weight_np,
'bias': self.bias_np
}
if self.is_custom:
feed_dict["path_code"] = self.path_code_np
feed_dict["path_table"] = self.path_table_np
ret1, ret2 = exe.run(train_program,
feed=feed_dict,
fetch_list=[out1, out2])
for ret in [ret1, ret2]:
self.assertTrue(np.allclose(self.out_np, ret))
def test_dygraph_api(self):
paddle.disable_static(self.place)
x = paddle.to_tensor(self.x_np)
labels = paddle.to_tensor(self.labels_np)
weight = paddle.to_tensor(self.weight_np)
bias = paddle.to_tensor(self.bias_np)
path_table = None
path_code = None
if self.is_custom:
path_table = paddle.to_tensor(self.path_table_np)
path_code = paddle.to_tensor(self.path_code_np)
out1 = F.hsigmoid_loss(x, labels, self.num_classes, weight, bias,
path_table, path_code)
weight_attr = I.NumpyArrayInitializer(self.weight_np)
bias_attr = I.NumpyArrayInitializer(self.bias_np)
m = paddle.nn.HSigmoidLoss(self.feature_size, self.num_classes,
weight_attr, bias_attr, self.is_custom)
out2 = m(x, labels, path_table, path_code)
for out in [out1, out2]:
self.assertTrue(np.allclose(self.out_np, out.numpy()))
paddle.enable_static()