def forward(self, x):
in_mean, in_var = paddle.mean(x, axis=[2, 3], keepdim=True), paddle.var(x, axis=[2, 3], keepdim=True)
out_in = (x - in_mean) / paddle.sqrt(in_var + self.eps)
ln_mean, ln_var = paddle.mean(x, axis=[1, 2, 3], keepdim=True), paddle.var(x, axis=[1, 2, 3], keepdim=True)
out_ln = (x - ln_mean) / paddle.sqrt(ln_var + self.eps)
out = self.rho.expand([x.shape[0], -1, -1, -1]) * out_in + \
(1-self.rho.expand([x.shape[0], -1, -1, -1])) * out_ln
out = out * self.gamma.expand([x.shape[0], -1, -1, -1]) + self.beta.expand([x.shape[0], -1, -1, -1])
return out
def static(self):
with paddle.static.program_guard(paddle.static.Program()):
x = paddle.data('X', self.shape, self.dtype)
out = paddle.var(x, self.axis, self.unbiased, self.keepdim)
exe = paddle.static.Executor(self.place)
res = exe.run(feed={'X': self.x}, fetch_list=[out])
return res[0]
def dynamic(self, axis=None, keepdim=False, unbiased=True):
with fluid.dygraph.guard(self._place):
data = fluid.dygraph.to_variable(self._input)
out = paddle.var(input=data,
axis=axis,
keepdim=keepdim,
unbiased=unbiased)
return out.numpy()
def forward(self, input):
in_mean, in_var = paddle.mean(input, [2, 3],
keepdim=True), paddle.var(input, [2, 3],
keepdim=True)
out_in = (input - in_mean) / paddle.sqrt(in_var + self.eps)
ln_mean, ln_var = paddle.mean(input, [1, 2, 3],
keepdim=True), paddle.var(input,
[1, 2, 3],
keepdim=True)
out_ln = (input - ln_mean) / paddle.sqrt(ln_var + self.eps)
out = self.rho.expand([input.shape[0], -1, -1, -1]) * out_in + (
1 - self.rho.expand([input.shape[0], -1, -1, -1])) * out_ln
out = out * self.gamma.expand([input.shape[0], -1, -1, -1
]) + self.beta.expand(
[input.shape[0], -1, -1, -1])
return out
def static(self, axis=None, keepdim=False, unbiased=True):
prog = fluid.Program()
with fluid.program_guard(prog):
data = fluid.data(name="data",
dtype=self._dtype,
shape=[None, 3, 4, 5])
out = prog.current_block().create_var(dtype=self._dtype,
shape=[2, 3, 4, 5])
paddle.var(input=data,
axis=axis,
keepdim=keepdim,
unbiased=unbiased,
out=out)
exe = fluid.Executor(self._place)
return exe.run(feed={"data": self._input},
program=prog,
fetch_list=[out])[0]
def test_alias(self):
paddle.disable_static()
x = paddle.to_tensor(np.array([10, 12], 'float32'))
out1 = paddle.var(x).numpy()
out2 = paddle.tensor.var(x).numpy()
out3 = paddle.tensor.stat.var(x).numpy()
self.assertTrue(np.allclose(out1, out2))
self.assertTrue(np.allclose(out1, out3))
paddle.enable_static()
def _style_pooling(self, x, eps=1e-5):
N, C, _, _ = x.shape
channel_mean = paddle.mean(paddle.reshape(x, [N, C, -1]),
axis=2,
keepdim=True)
channel_var = paddle.var(
paddle.reshape(x, [N, C, -1]), axis=2, keepdim=True) + eps
channel_std = paddle.sqrt(channel_var)
t = paddle.concat((channel_mean, channel_std), axis=2)
return t
def dygraph(self):
paddle.disable_static()
x = paddle.to_tensor(self.x)
out = paddle.var(x, self.axis, self.unbiased, self.keepdim)
paddle.enable_static()
return out.numpy()