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import torch |
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import torch.nn as nn |
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import torch.nn.functional as F |
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import torchvision.models as models |
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import cliport.utils.utils as utils |
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from transformers import DistilBertTokenizer, DistilBertModel |
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from cliport.models.resnet import IdentityBlock, ConvBlock |
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from cliport.models.core.unet import Up |
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from cliport.models.core import fusion |
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from cliport.models.core.fusion import FusionConvLat |
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class RN50BertLingUNetLat(nn.Module): |
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""" ImageNet RN50 & Bert with U-Net skip connections """ |
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def __init__(self, input_shape, output_dim, cfg, device, preprocess): |
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super(RN50BertLingUNetLat, self).__init__() |
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self.input_shape = input_shape |
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self.output_dim = output_dim |
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self.input_dim = 2048 |
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self.cfg = cfg |
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self.batchnorm = self.cfg['train']['batchnorm'] |
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self.lang_fusion_type = self.cfg['train']['lang_fusion_type'] |
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self.bilinear = True |
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self.up_factor = 2 if self.bilinear else 1 |
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self.device = device |
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self.preprocess = preprocess |
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self._load_vision_fcn() |
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self._load_lang_enc() |
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self._build_decoder() |
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def _load_vision_fcn(self): |
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resnet50 = models.resnet50(pretrained=True) |
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modules = list(resnet50.children())[:-2] |
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self.stem = nn.Sequential(*modules[:4]) |
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self.layer1 = modules[4] |
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self.layer2 = modules[5] |
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self.layer3 = modules[6] |
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self.layer4 = modules[7] |
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def _load_lang_enc(self): |
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self.tokenizer = DistilBertTokenizer.from_pretrained('distilbert-base-uncased') |
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self.text_encoder = DistilBertModel.from_pretrained('distilbert-base-uncased') |
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self.text_fc = nn.Linear(768, 1024) |
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self.lang_fuser1 = fusion.names[self.lang_fusion_type](input_dim=self.input_dim // 2) |
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self.lang_fuser2 = fusion.names[self.lang_fusion_type](input_dim=self.input_dim // 4) |
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self.lang_fuser3 = fusion.names[self.lang_fusion_type](input_dim=self.input_dim // 8) |
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self.proj_input_dim = 512 if 'word' in self.lang_fusion_type else 1024 |
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self.lang_proj1 = nn.Linear(self.proj_input_dim, 1024) |
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self.lang_proj2 = nn.Linear(self.proj_input_dim, 512) |
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self.lang_proj3 = nn.Linear(self.proj_input_dim, 256) |
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def _build_decoder(self): |
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self.conv1 = nn.Sequential( |
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nn.Conv2d(self.input_dim, 1024, kernel_size=3, stride=1, padding=1, bias=False), |
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nn.ReLU(True) |
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) |
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self.up1 = Up(2048, 1024 // self.up_factor, self.bilinear) |
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self.lat_fusion1 = FusionConvLat(input_dim=1024+512, output_dim=512) |
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self.up2 = Up(1024, 512 // self.up_factor, self.bilinear) |
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self.lat_fusion2 = FusionConvLat(input_dim=512+256, output_dim=256) |
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self.up3 = Up(512, 256 // self.up_factor, self.bilinear) |
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self.lat_fusion3 = FusionConvLat(input_dim=256+128, output_dim=128) |
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self.layer1 = nn.Sequential( |
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ConvBlock(128, [64, 64, 64], kernel_size=3, stride=1, batchnorm=self.batchnorm), |
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IdentityBlock(64, [64, 64, 64], kernel_size=3, stride=1, batchnorm=self.batchnorm), |
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nn.UpsamplingBilinear2d(scale_factor=2), |
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) |
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self.lat_fusion4 = FusionConvLat(input_dim=128+64, output_dim=64) |
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self.layer2 = nn.Sequential( |
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ConvBlock(64, [32, 32, 32], kernel_size=3, stride=1, batchnorm=self.batchnorm), |
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IdentityBlock(32, [32, 32, 32], kernel_size=3, stride=1, batchnorm=self.batchnorm), |
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nn.UpsamplingBilinear2d(scale_factor=2), |
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) |
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self.lat_fusion5 = FusionConvLat(input_dim=64+32, output_dim=32) |
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self.layer3 = nn.Sequential( |
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ConvBlock(32, [16, 16, 16], kernel_size=3, stride=1, batchnorm=self.batchnorm), |
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IdentityBlock(16, [16, 16, 16], kernel_size=3, stride=1, batchnorm=self.batchnorm), |
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nn.UpsamplingBilinear2d(scale_factor=2), |
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) |
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self.lat_fusion6 = FusionConvLat(input_dim=32+16, output_dim=16) |
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self.conv2 = nn.Sequential( |
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nn.Conv2d(16, self.output_dim, kernel_size=1) |
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) |
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def resnet50(self, x): |
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im = [] |
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for layer in [self.stem, self.layer1, self.layer2, self.layer3, self.layer4]: |
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x = layer(x) |
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im.append(x) |
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return x, im |
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def encode_image(self, img): |
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with torch.no_grad(): |
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img_encoding, img_im = self.resnet50(img) |
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return img_encoding, img_im |
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def encode_text(self, x): |
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with torch.no_grad(): |
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inputs = self.tokenizer(x, return_tensors='pt') |
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input_ids, attention_mask = inputs['input_ids'].to(self.device), inputs['attention_mask'].to(self.device) |
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text_embeddings = self.text_encoder(input_ids, attention_mask) |
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text_encodings = text_embeddings.last_hidden_state.mean(1) |
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text_feat = self.text_fc(text_encodings) |
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text_mask = torch.ones_like(input_ids) |
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return text_feat, text_embeddings.last_hidden_state, text_mask |
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def forward(self, x, lat, l): |
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x = self.preprocess(x, dist='clip') |
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in_type = x.dtype |
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in_shape = x.shape |
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x = x[:,:3] |
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x, im = self.encode_image(x) |
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x = x.to(in_type) |
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l_enc, l_emb, l_mask = self.encode_text(l) |
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l_input = l_emb if 'word' in self.lang_fusion_type else l_enc |
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l_input = l_input.to(dtype=x.dtype) |
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assert x.shape[1] == self.input_dim |
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x = self.conv1(x) |
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x = self.lang_fuser1(x, l_input, x2_mask=l_mask, x2_proj=self.lang_proj1) |
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x = self.up1(x, im[-2]) |
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x = self.lat_fusion1(x, lat[-6]) |
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x = self.lang_fuser2(x, l_input, x2_mask=l_mask, x2_proj=self.lang_proj2) |
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x = self.up2(x, im[-3]) |
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x = self.lat_fusion2(x, lat[-5]) |
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x = self.lang_fuser3(x, l_input, x2_mask=l_mask, x2_proj=self.lang_proj3) |
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x = self.up3(x, im[-4]) |
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x = self.lat_fusion3(x, lat[-4]) |
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x = self.layer1(x) |
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x = self.lat_fusion4(x, lat[-3]) |
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x = self.layer2(x) |
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x = self.lat_fusion5(x, lat[-2]) |
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x = self.layer3(x) |
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x = self.lat_fusion6(x, lat[-1]) |
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x = self.conv2(x) |
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x = F.interpolate(x, size=(in_shape[-2], in_shape[-1]), mode='bilinear') |
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return x |
