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| import torch | |
| import torch.nn as nn | |
| import torch.nn.functional as F | |
| import cliport.utils.utils as utils | |
| from transformers import DistilBertTokenizer, DistilBertModel | |
| from cliport.models.core import fusion | |
| from cliport.models.resnet import ConvBlock, IdentityBlock | |
| class ResNet43_8s_lang(nn.Module): | |
| def __init__(self, input_shape, output_dim, cfg, device, preprocess): | |
| super(ResNet43_8s_lang, self).__init__() | |
| self.input_shape = input_shape | |
| self.input_dim = input_shape[-1] | |
| self.output_dim = output_dim | |
| self.cfg = cfg | |
| self.device = device | |
| self.batchnorm = self.cfg['train']['batchnorm'] | |
| self.lang_fusion_type = self.cfg['train']['lang_fusion_type'] | |
| self.preprocess = preprocess | |
| self._make_layers() | |
| def _make_layers(self): | |
| self.conv1 = nn.Sequential( | |
| # conv1 | |
| nn.Conv2d(self.input_dim, 64, stride=1, kernel_size=3, padding=1), | |
| nn.BatchNorm2d(64) if self.batchnorm else nn.Identity(), | |
| nn.ReLU(True), | |
| # fcn | |
| ConvBlock(64, [64, 64, 64], kernel_size=3, stride=1, batchnorm=self.batchnorm), | |
| IdentityBlock(64, [64, 64, 64], kernel_size=3, stride=1, batchnorm=self.batchnorm), | |
| ConvBlock(64, [128, 128, 128], kernel_size=3, stride=2, batchnorm=self.batchnorm), | |
| IdentityBlock(128, [128, 128, 128], kernel_size=3, stride=1, batchnorm=self.batchnorm), | |
| ConvBlock(128, [256, 256, 256], kernel_size=3, stride=2, batchnorm=self.batchnorm), | |
| IdentityBlock(256, [256, 256, 256], kernel_size=3, stride=1, batchnorm=self.batchnorm), | |
| ConvBlock(256, [512, 512, 512], kernel_size=3, stride=2, batchnorm=self.batchnorm), | |
| IdentityBlock(512, [512, 512, 512], kernel_size=3, stride=1, batchnorm=self.batchnorm), | |
| ) | |
| # decoders | |
| self.decoder1 = nn.Sequential( | |
| ConvBlock(512, [256, 256, 256], kernel_size=3, stride=1, batchnorm=self.batchnorm), | |
| IdentityBlock(256, [256, 256, 256], kernel_size=3, stride=1, batchnorm=self.batchnorm), | |
| nn.UpsamplingBilinear2d(scale_factor=2), | |
| ) | |
| self.decoder2 = nn.Sequential( | |
| ConvBlock(256, [128, 128, 128], kernel_size=3, stride=1, batchnorm=self.batchnorm), | |
| IdentityBlock(128, [128, 128, 128], kernel_size=3, stride=1, batchnorm=self.batchnorm), | |
| nn.UpsamplingBilinear2d(scale_factor=2), | |
| ) | |
| self.decoder3 = 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.conv2 = nn.Sequential( | |
| # conv2 | |
| ConvBlock(64, [16, 16, self.output_dim], kernel_size=3, stride=1, | |
| final_relu=False, batchnorm=self.batchnorm), | |
| IdentityBlock(self.output_dim, [16, 16, self.output_dim], kernel_size=3, stride=1, | |
| final_relu=False, batchnorm=self.batchnorm), | |
| ) | |
| 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, 512) | |
| self.lang_proj2 = nn.Linear(self.proj_input_dim, 256) | |
| self.lang_proj3 = nn.Linear(self.proj_input_dim, 128) | |
| def encode_text(self, l): | |
| with torch.no_grad(): | |
| inputs = self.tokenizer(l, 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, l): | |
| x = self.preprocess(x, dist='transporter') | |
| # 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) | |
| x = self.conv1(x) | |
| x = self.lang_fuser1(x, l_input, x2_mask=l_mask, x2_proj=self.lang_proj1) | |
| x = self.decoder1(x) | |
| x = self.lang_fuser2(x, l_input, x2_mask=l_mask, x2_proj=self.lang_proj2) | |
| x = self.decoder2(x) | |
| x = self.lang_fuser3(x, l_input, x2_mask=l_mask, x2_proj=self.lang_proj3) | |
| x = self.decoder3(x) | |
| out = self.conv2(x) | |
| return out |