Datasets:
jblitzar
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github-python-corpus

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x1 = F.relu(self.gn1(self.conv1(self.pad1(x) ) ), True)
x2 = F.relu(self.gn2(self.conv2(self.pad2(x1) ) ), True)
x3 = F.relu(self.gn3(self.conv3(self.pad3(x2) ) ), True)
x4 = F.relu(self.gn4(self.conv4(self.pad4(x3) ) ), True)
x5 = F.relu(self.gn5(self.conv5(self.pad5(x4) ) ), True)
x6 = F.relu(self.gn6(self.conv6(self.pad6(x5) ) ), True)
return x1, x2, x3, x4, x5, x6
class decoderLight(nn.Module ):
def __init__(self, SGNum, mode = 0):
super(decoderLight, self).__init__()
self.SGNum = SGNum
self.dconv1 = nn.Conv2d(in_channels=1024, out_channels=512, kernel_size=3, stride=1, padding=1, bias=True )
self.dgn1 = nn.GroupNorm(num_groups=32, num_channels=512 )
self.dconv2 = nn.Conv2d(in_channels=1024, out_channels=512, kernel_size=3, stride=1, padding = 1, bias=True )
self.dgn2 = nn.GroupNorm(num_groups=32, num_channels=512 )
self.dconv3 = nn.Conv2d(in_channels=1024, out_channels=256, kernel_size=3, stride=1, padding=1, bias=True )
self.dgn3 = nn.GroupNorm(num_groups=16, num_channels=256 )
self.dconv4 = nn.Conv2d(in_channels=512, out_channels=256, kernel_size=3, stride=1, padding = 1, bias=True )
self.dgn4 = nn.GroupNorm(num_groups=16, num_channels=256 )
self.dconv5 = nn.Conv2d(in_channels=512, out_channels=128, kernel_size=3, stride=1, padding = 1, bias=True )
self.dgn5 = nn.GroupNorm(num_groups=8, num_channels=128 )
self.dconv6 = nn.Conv2d(in_channels=256, out_channels=128, kernel_size=3, stride=1, padding = 1, bias=True )
self.dgn6 = nn.GroupNorm(num_groups=8, num_channels=128 )
self.dpadFinal = nn.ReplicationPad2d(1)
if mode == 0 or mode == 2:
self.dconvFinal = nn.Conv2d(in_channels=128, out_channels = 3*SGNum, kernel_size=3, stride=1, bias=True )
elif mode == 1:
self.dconvFinal = nn.Conv2d(in_channels=128, out_channels = SGNum, kernel_size=3, stride=1, bias=True )
self.mode = mode
def forward(self, x1, x2, x3, x4, x5, x6, env = None):
dx1 = F.relu(self.dgn1(self.dconv1(x6 ) ) )
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) != env.size(3) or dx6.size(2) != env.size(2):
dx6 = F.interpolate(dx6, [env.size(2), env.size(3)], mode='bilinear')
x_orig = self.dconvFinal(self.dpadFinal(dx6 ) )
x_out = 1.01 * torch.tanh(self.dconvFinal(self.dpadFinal(dx6) ) )
if self.mode == 1 or self.mode == 2:
x_out = 0.5 * (x_out + 1)
x_out = torch.clamp(x_out, 0, 1)
elif self.mode == 0:
bn, _, row, col = x_out.size()
x_out = x_out.view(bn, self.SGNum, 3, row, col)
x_out = x_out / torch.clamp(torch.sqrt(torch.sum(x_out * x_out,
dim=2).unsqueeze(2) ), min = 1e-6).expand_as(x_out )
return x_out
class output2env():
def __init__(self, SGNum, envWidth = 16, envHeight = 8, isCuda = True ):
self.envWidth = envWidth
self.envHeight = envHeight
Az = ( (np.arange(envWidth) + 0.5) / envWidth - 0.5 )* 2 * np.pi
El = ( (np.arange(envHeight) + 0.5) / envHeight) * np.pi / 2.0
Az, El = np.meshgrid(Az, El)
Az = Az[np.newaxis, :, :]
El = El[np.newaxis, :, :]
lx = np.sin(El) * np.cos(Az)
ly = np.sin(El) * np.sin(Az)
lz = np.cos(El)