Datasets:
jblitzar
/
github-python-corpus

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xin1 = torch.cat([dx1, x5], dim = 1)
dx2 = F.relu(self.dgn2(self.dconv2(F.interpolate(xin1, scale_factor=2, mode='bilinear') ) ), True)
if dx2.size(3) != x4.size(3) or dx2.size(2) != x4.size(2):
dx2 = F.interpolate(dx2, [x4.size(2), x4.size(3)], mode='bilinear')
xin2 = torch.cat([dx2, x4], dim=1 )
dx3 = F.relu(self.dgn3(self.dconv3(F.interpolate(xin2, scale_factor=2, mode='bilinear') ) ), True)
if dx3.size(3) != x3.size(3) or dx3.size(2) != x3.size(2):
dx3 = F.interpolate(dx3, [x3.size(2), x3.size(3)], mode='bilinear')
xin3 = torch.cat([dx3, x3], dim=1)
dx4 = F.relu(self.dgn4(self.dconv4(F.interpolate(xin3, scale_factor=2, mode='bilinear') ) ), True)
if dx4.size(3) != x2.size(3) or dx4.size(2) != x2.size(2):
dx4 = F.interpolate(dx4, [x2.size(2), x2.size(3)], mode='bilinear')
xin4 = torch.cat([dx4, x2], dim=1 )
dx5 = F.relu(self.dgn5(self.dconv5(F.interpolate(xin4, scale_factor=2, mode='bilinear') ) ), True)
if dx5.size(3) != x1.size(3) or dx5.size(2) != x1.size(2):
dx5 = F.interpolate(dx5, [x1.size(2), x1.size(3)], mode='bilinear')
xin5 = torch.cat([dx5, x1], dim=1 )
dx6 = F.relu(self.dgn6(self.dconv6(F.interpolate(xin5, scale_factor=2, mode='bilinear') ) ), True)
if dx6.size(3) != im.size(3) or dx6.size(2) != im.size(2):
dx6 = F.interpolate(dx6, [im.size(2), im.size(3)], mode='bilinear')
x_orig = self.dconvFinal(self.dpadFinal(dx6 ) )
if self.mode == 0:
x_out = torch.clamp(1.01 * torch.tanh(x_orig ), -1, 1)
elif self.mode == 1:
x_orig = torch.clamp(1.01 * torch.tanh(x_orig ), -1, 1)
norm = torch.sqrt(torch.sum(x_orig * x_orig, dim=1).unsqueeze(1) ).expand_as(x_orig)
x_out = x_orig / torch.clamp(norm, min=1e-6)
elif self.mode == 2:
x_orig = torch.clamp(1.01 * torch.tanh(x_orig ), -1, 1)
x_out = torch.mean(x_orig, dim=1).unsqueeze(1)
elif self.mode == 3:
x_out = F.softmax(x_orig, dim=1)
elif self.mode == 4:
x_orig = torch.mean(x_orig, dim=1).unsqueeze(1)
x_out = torch.clamp(1.01 * torch.tanh(x_orig ), -1, 1)
return x_out
class encoderLight(nn.Module ):
def __init__(self, SGNum, cascadeLevel = 0 ):
super(encoderLight, self).__init__()
self.cascadeLevel = cascadeLevel
self.SGNum = SGNum
self.preProcess = nn.Sequential(
nn.ReplicationPad2d(1),
nn.Conv2d(in_channels=11, out_channels=32, kernel_size=4, stride=2, bias =True),
nn.GroupNorm(num_groups=2, num_channels=32),
nn.ReLU(inplace = True ),
nn.ZeroPad2d(1),
nn.Conv2d(in_channels=32, out_channels=64, kernel_size=4, stride=2, bias=True),
nn.GroupNorm(num_groups=4, num_channels=64 ),
nn.ReLU(inplace = True )
)
self.pad1 = nn.ReplicationPad2d(1)
if self.cascadeLevel == 0:
self.conv1 = nn.Conv2d(in_channels = 64, out_channels = 128, kernel_size = 4, stride = 2, bias = True)
else:
self.conv1 = nn.Conv2d(in_channels=64 + SGNum * 7, out_channels=128, kernel_size=4, stride=2, bias =True)
self.gn1 = nn.GroupNorm(num_groups=8, num_channels=128 )
self.pad2 = nn.ZeroPad2d(1)
self.conv2 = nn.Conv2d(in_channels=128, out_channels=256, kernel_size=4, stride=2, bias=True)
self.gn2 = nn.GroupNorm(num_groups=16, num_channels=256 )
self.pad3 = nn.ZeroPad2d(1)
self.conv3 = nn.Conv2d(in_channels = 256, out_channels=256, kernel_size=4, stride=2, bias=True)
self.gn3 = nn.GroupNorm(num_groups=16, num_channels=256)
self.pad4 = nn.ZeroPad2d(1)
self.conv4 = nn.Conv2d(in_channels=256, out_channels=512, kernel_size=4, stride=2, bias=True)
self.gn4 = nn.GroupNorm(num_groups=32, num_channels=512)
self.pad5 = nn.ZeroPad2d(1)
self.conv5 = nn.Conv2d(in_channels=512, out_channels=512, kernel_size=4, stride=2, bias=True)
self.gn5 = nn.GroupNorm(num_groups=32, num_channels=512 )
self.pad6 = nn.ZeroPad2d(1)
self.conv6 = nn.Conv2d(in_channels=512, out_channels=1024, kernel_size=3, stride=1, bias=True)
self.gn6 = nn.GroupNorm(num_groups=64, num_channels=1024)
def forward(self, inputBatch, envs = None):
input1 = self.preProcess(inputBatch )
input2 = envs
if self.cascadeLevel == 0:
x = input1
else:
x = torch.cat([input1, input2], dim=1)