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| import torch | |
| import torch.nn as nn | |
| import torch.nn.functional as F | |
| import cliport.utils.utils as utils | |
| from cliport.models.resnet import IdentityBlock, ConvBlock | |
| from cliport.models.core.unet import Up | |
| from cliport.models.core.fusion import FusionConvLat | |
| from cliport.models.clip_lingunet_lat import CLIPLingUNetLat | |
| class CLIPUNetLat(CLIPLingUNetLat): | |
| """ CLIP RN50 with U-Net skip connections and lateral connections without language """ | |
| def __init__(self, input_shape, output_dim, cfg, device, preprocess): | |
| super().__init__(input_shape, output_dim, cfg, device, preprocess) | |
| def _build_decoder(self): | |
| self.conv1 = nn.Sequential( | |
| nn.Conv2d(self.input_dim, 1024, kernel_size=3, stride=1, padding=1, bias=False), | |
| nn.ReLU(True) | |
| ) | |
| self.up1 = Up(2048, 1024 // self.up_factor, self.bilinear) | |
| self.lat_fusion1 = FusionConvLat(input_dim=1024+512, output_dim=512) | |
| self.up2 = Up(1024, 512 // self.up_factor, self.bilinear) | |
| self.lat_fusion2 = FusionConvLat(input_dim=512+256, output_dim=256) | |
| self.up3 = Up(512, 256 // self.up_factor, self.bilinear) | |
| self.lat_fusion3 = FusionConvLat(input_dim=256+128, output_dim=128) | |
| self.layer1 = nn.Sequential( | |
| ConvBlock(128, [64, 64, 64], kernel_size=3, stride=1, batchnorm=self.batchnorm), | |
| IdentityBlock(64, [64, 64, 64], kernel_size=3, stride=1, batchnorm=self.batchnorm), | |
| nn.UpsamplingBilinear2d(scale_factor=2), | |
| ) | |
| self.lat_fusion4 = FusionConvLat(input_dim=128+64, output_dim=64) | |
| self.layer2 = nn.Sequential( | |
| ConvBlock(64, [32, 32, 32], kernel_size=3, stride=1, batchnorm=self.batchnorm), | |
| IdentityBlock(32, [32, 32, 32], kernel_size=3, stride=1, batchnorm=self.batchnorm), | |
| nn.UpsamplingBilinear2d(scale_factor=2), | |
| ) | |
| self.lat_fusion5 = FusionConvLat(input_dim=64+32, output_dim=32) | |
| self.layer3 = nn.Sequential( | |
| ConvBlock(32, [16, 16, 16], kernel_size=3, stride=1, batchnorm=self.batchnorm), | |
| IdentityBlock(16, [16, 16, 16], kernel_size=3, stride=1, batchnorm=self.batchnorm), | |
| nn.UpsamplingBilinear2d(scale_factor=2), | |
| ) | |
| self.lat_fusion6 = FusionConvLat(input_dim=32+16, output_dim=16) | |
| self.conv2 = nn.Sequential( | |
| nn.Conv2d(16, self.output_dim, kernel_size=1) | |
| ) | |
| def forward(self, x, lat): | |
| x = self.preprocess(x, dist='clip') | |
| in_type = x.dtype | |
| in_shape = x.shape | |
| x = x[:,:3] # select RGB | |
| x, im = self.encode_image(x) | |
| x = x.to(in_type) | |
| x = self.conv1(x) | |
| x = self.up1(x, im[-2]) | |
| x = self.lat_fusion1(x, lat[-6]) | |
| x = self.up2(x, im[-3]) | |
| x = self.lat_fusion2(x, lat[-5]) | |
| x = self.up3(x, im[-4]) | |
| x = self.lat_fusion3(x, lat[-4]) | |
| x = self.layer1(x) | |
| x = self.lat_fusion4(x, lat[-3]) | |
| x = self.layer2(x) | |
| x = self.lat_fusion5(x, lat[-2]) | |
| x = self.layer3(x) | |
| x = self.lat_fusion6(x, lat[-1]) | |
| x = self.conv2(x) | |
| x = F.interpolate(x, size=(in_shape[-2], in_shape[-1]), mode='bilinear') | |
| return x |