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| import torch | |
| import torch.nn as nn | |
| import torch.nn.functional as F | |
| import torchvision.models as models | |
| import cliport.utils.utils as utils | |
| from transformers import DistilBertTokenizer, DistilBertModel | |
| from cliport.models.resnet import IdentityBlock, ConvBlock | |
| from cliport.models.core.unet import Up | |
| from cliport.models.core import fusion | |
| from cliport.models.core.fusion import FusionConvLat | |
| class RN50BertLingUNetLat(nn.Module): | |
| """ ImageNet RN50 & Bert with U-Net skip connections """ | |
| def __init__(self, input_shape, output_dim, cfg, device, preprocess): | |
| super(RN50BertLingUNetLat, self).__init__() | |
| self.input_shape = input_shape | |
| self.output_dim = output_dim | |
| self.input_dim = 2048 | |
| self.cfg = cfg | |
| self.batchnorm = self.cfg['train']['batchnorm'] | |
| self.lang_fusion_type = self.cfg['train']['lang_fusion_type'] | |
| self.bilinear = True | |
| self.up_factor = 2 if self.bilinear else 1 | |
| self.device = device | |
| self.preprocess = preprocess | |
| self._load_vision_fcn() | |
| self._load_lang_enc() | |
| self._build_decoder() | |
| def _load_vision_fcn(self): | |
| resnet50 = models.resnet50(pretrained=True) | |
| modules = list(resnet50.children())[:-2] | |
| self.stem = nn.Sequential(*modules[:4]) | |
| self.layer1 = modules[4] | |
| self.layer2 = modules[5] | |
| self.layer3 = modules[6] | |
| self.layer4 = modules[7] | |
| def _load_lang_enc(self): | |
| self.tokenizer = DistilBertTokenizer.from_pretrained('distilbert-base-uncased') | |
| self.text_encoder = DistilBertModel.from_pretrained('distilbert-base-uncased') | |
| self.text_fc = nn.Linear(768, 1024) | |
| self.lang_fuser1 = fusion.names[self.lang_fusion_type](input_dim=self.input_dim // 2) | |
| self.lang_fuser2 = fusion.names[self.lang_fusion_type](input_dim=self.input_dim // 4) | |
| self.lang_fuser3 = fusion.names[self.lang_fusion_type](input_dim=self.input_dim // 8) | |
| self.proj_input_dim = 512 if 'word' in self.lang_fusion_type else 1024 | |
| self.lang_proj1 = nn.Linear(self.proj_input_dim, 1024) | |
| self.lang_proj2 = nn.Linear(self.proj_input_dim, 512) | |
| self.lang_proj3 = nn.Linear(self.proj_input_dim, 256) | |
| 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 resnet50(self, x): | |
| im = [] | |
| for layer in [self.stem, self.layer1, self.layer2, self.layer3, self.layer4]: | |
| x = layer(x) | |
| im.append(x) | |
| return x, im | |
| def encode_image(self, img): | |
| with torch.no_grad(): | |
| img_encoding, img_im = self.resnet50(img) | |
| return img_encoding, img_im | |
| def encode_text(self, x): | |
| with torch.no_grad(): | |
| inputs = self.tokenizer(x, return_tensors='pt') | |
| input_ids, attention_mask = inputs['input_ids'].to(self.device), inputs['attention_mask'].to(self.device) | |
| text_embeddings = self.text_encoder(input_ids, attention_mask) | |
| text_encodings = text_embeddings.last_hidden_state.mean(1) | |
| text_feat = self.text_fc(text_encodings) | |
| text_mask = torch.ones_like(input_ids) # [1, max_token_len] | |
| return text_feat, text_embeddings.last_hidden_state, text_mask | |
| def forward(self, x, lat, 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) | |
| 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) | |
| 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.lat_fusion1(x, lat[-6]) | |
| x = self.lang_fuser2(x, l_input, x2_mask=l_mask, x2_proj=self.lang_proj2) | |
| x = self.up2(x, im[-3]) | |
| x = self.lat_fusion2(x, lat[-5]) | |
| x = self.lang_fuser3(x, l_input, x2_mask=l_mask, x2_proj=self.lang_proj3) | |
| 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 |