def forward(self, x):
logit_list = []
x = self.enco1(x)
x, ind1 = F.max_pool2d(x, kernel_size=2, stride=2, return_mask=True)
size1 = x.shape
x = self.enco2(x)
x, ind2 = F.max_pool2d(x, kernel_size=2, stride=2, return_mask=True)
size2 = x.shape
x = self.enco3(x)
x, ind3 = F.max_pool2d(x, kernel_size=2, stride=2, return_mask=True)
size3 = x.shape
x = self.enco4(x)
x, ind4 = F.max_pool2d(x, kernel_size=2, stride=2, return_mask=True)
size4 = x.shape
x = self.enco5(x)
x, ind5 = F.max_pool2d(x, kernel_size=2, stride=2, return_mask=True)
size5 = x.shape
x = F.max_unpool2d(x,
indices=ind5,
kernel_size=2,
stride=2,
output_size=size4)
x = self.deco1(x)
x = F.max_unpool2d(x,
indices=ind4,
kernel_size=2,
stride=2,
output_size=size3)
x = self.deco2(x)
x = F.max_unpool2d(x,
indices=ind3,
kernel_size=2,
stride=2,
output_size=size2)
x = self.deco3(x)
x = F.max_unpool2d(x,
indices=ind2,
kernel_size=2,
stride=2,
output_size=size1)
x = self.deco4(x)
x = F.max_unpool2d(x, indices=ind1, kernel_size=2, stride=2)
x = self.deco5(x)
logit_list.append(x)
return logit_list
def test_case(self):
import paddle
import paddle.nn.functional as F
import paddle.fluid.core as core
import paddle.fluid as fluid
import numpy as np
if core.is_compiled_with_cuda():
place = core.CUDAPlace(0)
else:
place = core.CPUPlace()
with fluid.dygraph.guard(place):
input_data = np.array([[[[1, 2, 3, 4], [5, 6, 7,
8], [9, 10, 11, 12],
[13, 14, 15, 16]]]]).astype("float32")
input_x = paddle.to_tensor(input_data)
output, indices = F.max_pool2d(input_x,
kernel_size=2,
stride=2,
return_mask=True)
out_pp = F.max_unpool2d(output,
indices,
kernel_size=2,
stride=None,
output_size=(5, 5))
output_np = output.numpy()
indices_np = indices.numpy()
expect_res =unpool2dmax_forward_naive(output_np, indices_np, [2,2], \
[2,2], [0,0], [5,5]).astype("float64")
self.assertTrue(np.allclose(out_pp.numpy(), expect_res))
def test_case(self):
import paddle
import paddle.nn.functional as F
import paddle.fluid.core as core
import paddle.fluid as fluid
paddle.enable_static()
input_data = np.array([[[[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12],
[13, 14, 15, 16]]]]).astype("float32")
x = fluid.data(name="x", shape=[1, 1, 4, 4], dtype="float32")
output, indices = F.max_pool2d(
x, kernel_size=2, stride=2, return_mask=True)
unpool_out = F.max_unpool2d(
output, indices, kernel_size=2, stride=None, output_size=(5, 5))
if core.is_compiled_with_cuda():
place = core.CUDAPlace(0)
else:
place = core.CPUPlace()
exe = fluid.Executor(place)
exe.run(fluid.default_startup_program())
results = exe.run(paddle.fluid.default_main_program(),\
feed={"x":input_data},
fetch_list=[unpool_out],
return_numpy=True)
pool_out_np = np.array([[[[6., 8.], [14., 16.]]]]).astype("float32")
indices_np = np.array([[[[5, 7], [13, 15]]]]).astype("int32")
expect_res =unpool2dmax_forward_naive(pool_out_np, indices_np, [2,2], \
[2,2], [0,0], [5,5]).astype("float64")
self.assertTrue(np.allclose(results[0], expect_res))
def forward(self, x, max_indices, output_size):
main = self.main_conv1(x)
main = F.max_unpool2d(main,
max_indices,
kernel_size=2,
output_size=output_size)
ext = self.ext_conv1(x)
ext = self.ext_tconv1(ext, output_size=output_size[2:])
ext = self.ext_tconv1_bnorm(ext)
ext = self.ext_tconv1_activation(ext)
ext = self.ext_conv2(ext)
ext = self.ext_regul(ext)
out = main + ext
return self.out_activation(out)