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| 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.rn50_bert_lingunet_lat import RN50BertLingUNetLat | |
| class RN50BertLingUNet(RN50BertLingUNetLat): | |
| """ ImageNet RN50 & Bert with U-Net skip connections but without lateral connections """ | |
| 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.up2 = Up(1024, 512 // self.up_factor, self.bilinear) | |
| self.up3 = Up(512, 256 // self.up_factor, self.bilinear) | |
| 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.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.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.conv2 = nn.Sequential( | |
| nn.Conv2d(16, self.output_dim, kernel_size=1) | |
| ) | |
| def forward(self, x, l): | |
| 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) | |
| # encode language | |
| l_enc, l_emb, l_mask = self.encode_text(l) | |
| l_input = l_emb if 'word' in self.lang_fusion_type else l_enc | |
| l_input = l_input.to(dtype=x.dtype) | |
| # encode image | |
| assert x.shape[1] == self.input_dim | |
| x = self.conv1(x) | |
| x = self.lang_fuser1(x, l_input, x2_mask=l_mask, x2_proj=self.lang_proj1) | |
| x = self.up1(x, im[-2]) | |
| x = self.lang_fuser2(x, l_input, x2_mask=l_mask, x2_proj=self.lang_proj2) | |
| x = self.up2(x, im[-3]) | |
| x = self.lang_fuser3(x, l_input, x2_mask=l_mask, x2_proj=self.lang_proj3) | |
| x = self.up3(x, im[-4]) | |
| for layer in [self.layer1, self.layer2, self.layer3, self.conv2]: | |
| x = layer(x) | |
| x = F.interpolate(x, size=(in_shape[-2], in_shape[-1]), mode='bilinear') | |
| return x |