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176 lines
7.7 KiB
Python
176 lines
7.7 KiB
Python
import torch
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import torch.nn as nn
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class SIGGRAPHGenerator(nn.Module):
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def __init__(self, dist=False):
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super(SIGGRAPHGenerator, self).__init__()
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self.dist = dist
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use_bias = True
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norm_layer = nn.BatchNorm2d
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# Conv1
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model1 = [nn.Conv2d(4, 64, kernel_size=3, stride=1, padding=1, bias=use_bias), ]
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model1 += [nn.ReLU(True), ]
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model1 += [nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1, bias=use_bias), ]
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model1 += [nn.ReLU(True), ]
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model1 += [norm_layer(64), ]
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# add a subsampling operation
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# Conv2
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model2 = [nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1, bias=use_bias), ]
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model2 += [nn.ReLU(True), ]
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model2 += [nn.Conv2d(128, 128, kernel_size=3, stride=1, padding=1, bias=use_bias), ]
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model2 += [nn.ReLU(True), ]
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model2 += [norm_layer(128), ]
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# add a subsampling layer operation
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# Conv3
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model3 = [nn.Conv2d(128, 256, kernel_size=3, stride=1, padding=1, bias=use_bias), ]
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model3 += [nn.ReLU(True), ]
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model3 += [nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1, bias=use_bias), ]
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model3 += [nn.ReLU(True), ]
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model3 += [nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1, bias=use_bias), ]
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model3 += [nn.ReLU(True), ]
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model3 += [norm_layer(256), ]
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# add a subsampling layer operation
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# Conv4
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model4 = [nn.Conv2d(256, 512, kernel_size=3, stride=1, padding=1, bias=use_bias), ]
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model4 += [nn.ReLU(True), ]
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model4 += [nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1, bias=use_bias), ]
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model4 += [nn.ReLU(True), ]
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model4 += [nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1, bias=use_bias), ]
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model4 += [nn.ReLU(True), ]
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model4 += [norm_layer(512), ]
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# Conv5
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model5 = [nn.Conv2d(512, 512, kernel_size=3, dilation=2, stride=1, padding=2, bias=use_bias), ]
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model5 += [nn.ReLU(True), ]
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model5 += [nn.Conv2d(512, 512, kernel_size=3, dilation=2, stride=1, padding=2, bias=use_bias), ]
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model5 += [nn.ReLU(True), ]
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model5 += [nn.Conv2d(512, 512, kernel_size=3, dilation=2, stride=1, padding=2, bias=use_bias), ]
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model5 += [nn.ReLU(True), ]
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model5 += [norm_layer(512), ]
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# Conv6
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model6 = [nn.Conv2d(512, 512, kernel_size=3, dilation=2, stride=1, padding=2, bias=use_bias), ]
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model6 += [nn.ReLU(True), ]
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model6 += [nn.Conv2d(512, 512, kernel_size=3, dilation=2, stride=1, padding=2, bias=use_bias), ]
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model6 += [nn.ReLU(True), ]
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model6 += [nn.Conv2d(512, 512, kernel_size=3, dilation=2, stride=1, padding=2, bias=use_bias), ]
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model6 += [nn.ReLU(True), ]
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model6 += [norm_layer(512), ]
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# Conv7
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model7 = [nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1, bias=use_bias), ]
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model7 += [nn.ReLU(True), ]
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model7 += [nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1, bias=use_bias), ]
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model7 += [nn.ReLU(True), ]
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model7 += [nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1, bias=use_bias), ]
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model7 += [nn.ReLU(True), ]
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model7 += [norm_layer(512), ]
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# Conv7
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model8up = [nn.ConvTranspose2d(512, 256, kernel_size=4, stride=2, padding=1, bias=use_bias)]
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model3short8 = [nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1, bias=use_bias), ]
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model8 = [nn.ReLU(True), ]
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model8 += [nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1, bias=use_bias), ]
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model8 += [nn.ReLU(True), ]
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model8 += [nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1, bias=use_bias), ]
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model8 += [nn.ReLU(True), ]
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model8 += [norm_layer(256), ]
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# Conv9
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model9up = [nn.ConvTranspose2d(256, 128, kernel_size=4, stride=2, padding=1, bias=use_bias), ]
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model2short9 = [nn.Conv2d(128, 128, kernel_size=3, stride=1, padding=1, bias=use_bias), ]
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# add the two feature maps above
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model9 = [nn.ReLU(True), ]
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model9 += [nn.Conv2d(128, 128, kernel_size=3, stride=1, padding=1, bias=use_bias), ]
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model9 += [nn.ReLU(True), ]
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model9 += [norm_layer(128), ]
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# Conv10
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model10up = [nn.ConvTranspose2d(128, 128, kernel_size=4, stride=2, padding=1, bias=use_bias), ]
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model1short10 = [nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1, bias=use_bias), ]
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# add the two feature maps above
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model10 = [nn.ReLU(True), ]
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model10 += [nn.Conv2d(128, 128, kernel_size=3, dilation=1, stride=1, padding=1, bias=use_bias), ]
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model10 += [nn.LeakyReLU(negative_slope=.2), ]
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# classification output
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model_class = [nn.Conv2d(256, 529, kernel_size=1, padding=0, dilation=1, stride=1, bias=use_bias), ]
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# regression output
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model_out = [nn.Conv2d(128, 2, kernel_size=1, padding=0, dilation=1, stride=1, bias=use_bias), ]
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model_out += [nn.Tanh()]
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self.model1 = nn.Sequential(*model1)
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self.model2 = nn.Sequential(*model2)
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self.model3 = nn.Sequential(*model3)
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self.model4 = nn.Sequential(*model4)
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self.model5 = nn.Sequential(*model5)
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self.model6 = nn.Sequential(*model6)
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self.model7 = nn.Sequential(*model7)
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self.model8up = nn.Sequential(*model8up)
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self.model8 = nn.Sequential(*model8)
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self.model9up = nn.Sequential(*model9up)
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self.model9 = nn.Sequential(*model9)
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self.model10up = nn.Sequential(*model10up)
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self.model10 = nn.Sequential(*model10)
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self.model3short8 = nn.Sequential(*model3short8)
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self.model2short9 = nn.Sequential(*model2short9)
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self.model1short10 = nn.Sequential(*model1short10)
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self.model_class = nn.Sequential(*model_class)
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self.model_out = nn.Sequential(*model_out)
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self.upsample4 = nn.Sequential(*[nn.Upsample(scale_factor=4, mode='nearest'), ])
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self.softmax = nn.Sequential(*[nn.Softmax(dim=1), ])
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def forward(self, input_A, input_B, mask_B, maskcent=0):
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# input_A \in [-50,+50]
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# input_B \in [-110, +110]
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# mask_B \in [0, +1.0]
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input_A = torch.Tensor(input_A)[None, :, :, :]
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input_B = torch.Tensor(input_B)[None, :, :, :]
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mask_B = torch.Tensor(mask_B)[None, :, :, :]
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mask_B = mask_B - maskcent
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# input_A = torch.Tensor(input_A).cuda()[None, :, :, :]
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# input_B = torch.Tensor(input_B).cuda()[None, :, :, :]
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# mask_B = torch.Tensor(mask_B).cuda()[None, :, :, :]
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conv1_2 = self.model1(torch.cat((input_A / 100., input_B / 110., mask_B), dim=1))
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conv2_2 = self.model2(conv1_2[:, :, ::2, ::2])
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conv3_3 = self.model3(conv2_2[:, :, ::2, ::2])
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conv4_3 = self.model4(conv3_3[:, :, ::2, ::2])
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conv5_3 = self.model5(conv4_3)
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conv6_3 = self.model6(conv5_3)
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conv7_3 = self.model7(conv6_3)
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conv8_up = self.model8up(conv7_3) + self.model3short8(conv3_3)
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conv8_3 = self.model8(conv8_up)
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if(self.dist):
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out_cl = self.upsample4(self.softmax(self.model_class(conv8_3) * .2))
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conv9_up = self.model9up(conv8_3) + self.model2short9(conv2_2)
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conv9_3 = self.model9(conv9_up)
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conv10_up = self.model10up(conv9_3) + self.model1short10(conv1_2)
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conv10_2 = self.model10(conv10_up)
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out_reg = self.model_out(conv10_2) * 110
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return (out_reg * 110, out_cl)
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else:
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conv9_up = self.model9up(conv8_3) + self.model2short9(conv2_2)
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conv9_3 = self.model9(conv9_up)
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conv10_up = self.model10up(conv9_3) + self.model1short10(conv1_2)
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conv10_2 = self.model10(conv10_up)
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out_reg = self.model_out(conv10_2)
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return out_reg * 110
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