def __init__(self):
super(NetworkR, self).__init__()
self.layers = nn.Sequential(
nn.ReplicationPad3d((1, 1, 1, 1, 1, 1)),
TempConv(1,
64,
kernel_size=(3, 3, 3),
stride=(1, 2, 2),
padding=(0, 0, 0)),
TempConv(64, 128, kernel_size=(3, 3, 3), padding=(1, 1, 1)),
TempConv(128, 128, kernel_size=(3, 3, 3), padding=(1, 1, 1)),
TempConv(128,
256,
kernel_size=(3, 3, 3),
stride=(1, 2, 2),
padding=(1, 1, 1)),
TempConv(256, 256, kernel_size=(3, 3, 3), padding=(1, 1, 1)),
TempConv(256, 256, kernel_size=(3, 3, 3), padding=(1, 1, 1)),
TempConv(256, 256, kernel_size=(3, 3, 3), padding=(1, 1, 1)),
TempConv(256, 256, kernel_size=(3, 3, 3), padding=(1, 1, 1)),
Upsample(256, 128),
TempConv(128, 64, kernel_size=(3, 3, 3), padding=(1, 1, 1)),
TempConv(64, 64, kernel_size=(3, 3, 3), padding=(1, 1, 1)),
Upsample(64, 16),
nn.Conv3d(16,
1,
kernel_size=(3, 3, 3),
stride=(1, 1, 1),
padding=(1, 1, 1)))
def test_class(self):
paddle.disable_static()
for place in self.places:
input_shape = (3, 4, 5, 6, 7)
pad = [1, 2, 2, 1, 1, 0]
value = 100
input_data = np.random.rand(*input_shape).astype(np.float32)
pad_replication = nn.ReplicationPad3d(padding=pad)
pad_constant = nn.ConstantPad3d(padding=pad, value=value)
data = paddle.to_tensor(input_data)
output = pad_replication(data)
np_out = self._get_numpy_out(input_data,
pad,
"replicate",
data_format="NCDHW")
self.assertTrue(np.allclose(output.numpy(), np_out))
output = pad_constant(data)
np_out = self._get_numpy_out(input_data,
pad,
"constant",
value=value,
data_format="NCDHW")
self.assertTrue(np.allclose(output.numpy(), np_out))
def __init__(self):
super(NetworkC, self).__init__()
self.down1 = nn.Sequential(
nn.ReplicationPad3d((1, 1, 1, 1, 0, 0)),
TempConv(1, 64, stride=(1, 2, 2), padding=(0, 0, 0)),
TempConv(64, 128), TempConv(128, 128),
TempConv(128, 256, stride=(1, 2, 2)), TempConv(256, 256),
TempConv(256, 256), TempConv(256, 512, stride=(1, 2, 2)),
TempConv(512, 512), TempConv(512, 512))
self.flat = nn.Sequential(TempConv(512, 512), TempConv(512, 512))
self.down2 = nn.Sequential(
TempConv(512, 512, stride=(1, 2, 2)),
TempConv(512, 512),
)
self.stattn1 = SourceReferenceAttention(
512, 512) # Source-Reference Attention
self.stattn2 = SourceReferenceAttention(
512, 512) # Source-Reference Attention
self.selfattn1 = SourceReferenceAttention(512, 512) # Self Attention
self.conv1 = TempConv(512, 512)
self.up1 = UpsampleConcat(512, 512, 512) # 1/8
self.selfattn2 = SourceReferenceAttention(512, 512) # Self Attention
self.conv2 = TempConv(512,
256,
kernel_size=(3, 3, 3),
stride=(1, 1, 1),
padding=(1, 1, 1))
self.up2 = nn.Sequential(
Upsample(256, 128), # 1/4
TempConv(128,
64,
kernel_size=(3, 3, 3),
stride=(1, 1, 1),
padding=(1, 1, 1)))
self.up3 = nn.Sequential(
Upsample(64, 32), # 1/2
TempConv(32,
16,
kernel_size=(3, 3, 3),
stride=(1, 1, 1),
padding=(1, 1, 1)))
self.up4 = nn.Sequential(
Upsample(16, 8), # 1/1
nn.Conv3d(8,
2,
kernel_size=(3, 3, 3),
stride=(1, 1, 1),
padding=(1, 1, 1)))
self.reffeatnet1 = nn.Sequential(
TempConv(3, 64, stride=(1, 2, 2)),
TempConv(64, 128),
TempConv(128, 128),
TempConv(128, 256, stride=(1, 2, 2)),
TempConv(256, 256),
TempConv(256, 256),
TempConv(256, 512, stride=(1, 2, 2)),
TempConv(512, 512),
TempConv(512, 512),
)
self.reffeatnet2 = nn.Sequential(
TempConv(512, 512, stride=(1, 2, 2)),
TempConv(512, 512),
TempConv(512, 512),
)