def test_error(self):
with paddle.static.program_guard(paddle.static.Program()):
weight_fp32 = paddle.data(
name='weight_fp32', shape=[1], dtype='float32')
# The input type must be Variable.
self.assertRaises(TypeError, F.prelu, x=1, weight=weight_fp32)
# The input dtype must be float16, float32, float64.
x_int32 = paddle.data(name='x_int32', shape=[2, 3], dtype='int32')
self.assertRaises(TypeError, F.prelu, x=x_int32, weight=weight_fp32)
# support the input dtype is float16
x_fp16 = paddle.data(name='x_fp16', shape=[2, 3], dtype='float16')
F.prelu(x=x_fp16, weight=weight_fp32)
def dygraph_check(self, weight_np):
paddle.disable_static(self.place)
x = paddle.to_tensor(self.x_np)
weight = paddle.to_tensor(weight_np)
out = F.prelu(x, weight)
out_ref = ref_prelu(self.x_np, weight_np)
self.assertEqual(np.allclose(out_ref, out.numpy()), True)
paddle.enable_static()
def static_check(self, weight_np):
with paddle.static.program_guard(paddle.static.Program()):
x = paddle.data('X', self.x_np.shape, 'float32')
weight = paddle.data('Alpha', weight_np.shape, 'float32')
out = F.prelu(x, weight)
exe = paddle.static.Executor(self.place)
res = exe.run(feed={'X': self.x_np,
'Alpha': weight_np},
fetch_list=[out])
out_ref = ref_prelu(self.x_np, weight_np)
self.assertEqual(np.allclose(out_ref, res[0]), True)
def forward(self, x):
return F.prelu(x, self.weight)
