def check_avg_dygraph_padding_results(self, place):
with fluid.dygraph.guard(place):
input_np = np.random.random([2, 3, 32]).astype("float32")
input = fluid.dygraph.to_variable(input_np)
result = F.avg_pool1d(input,
kernel_size=2,
stride=2,
padding=[1],
exclusive=True)
result_np = avg_pool1D_forward_naive(input_np,
ksize=[2],
strides=[2],
paddings=[1],
exclusive=False)
self.assertTrue(np.allclose(result.numpy(), result_np))
avg_pool1d_dg = paddle.nn.AvgPool1D(kernel_size=2,
stride=None,
padding=1,
exclusive=True)
result = avg_pool1d_dg(input)
self.assertTrue(np.allclose(result.numpy(), result_np))
def check_avg_dygraph_padding_same(self, place):
with fluid.dygraph.guard(place):
input_np = np.random.random([2, 3, 32]).astype("float32")
input = fluid.dygraph.to_variable(input_np)
result = F.avg_pool1d(
input, kernel_size=2, stride=2, padding="SAME")
result_np = avg_pool1D_forward_naive(
input_np, ksize=[2], strides=[2], paddings=[0])
self.assertTrue(np.allclose(result.numpy(), result_np))
def run_stride_out_of_range():
with fluid.dygraph.guard():
input_np = np.random.uniform(-1, 1,
[2, 3, 32]).astype(np.float32)
input_pd = fluid.dygraph.to_variable(input_np)
padding = 0
res_pd = F.avg_pool1d(input_pd,
kernel_size=2,
stride=0,
padding=padding,
ceil_mode=True)
def check_avg_static_results(self, place):
with fluid.program_guard(fluid.Program(), fluid.Program()):
input = fluid.data(name="input", shape=[2, 3, 32], dtype="float32")
result = F.avg_pool1d(input, kernel_size=2, stride=2, padding=0)
input_np = np.random.random([2, 3, 32]).astype("float32")
result_np = avg_pool1D_forward_naive(
input_np, ksize=[2], strides=[2], paddings=[0], ceil_mode=False)
exe = fluid.Executor(place)
fetches = exe.run(fluid.default_main_program(),
feed={"input": input_np},
fetch_list=[result])
self.assertTrue(np.allclose(fetches[0], result_np))