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| import torch.nn as nn | |
| from isegm.utils.serialization import serialize | |
| from .is_model import ISModel | |
| from .is_plainvit_model import SimpleFPN | |
| from .modeling.models_vit import VisionTransformer, PatchEmbed | |
| from .modeling.twoway_transformer import TwoWayTransformer, PositionEmbeddingRandom | |
| from .modeling.swin_transformer import SwinTransfomerSegHead | |
| from .modeling.clip_text_encoding import ClipTextEncoder | |
| class TextGraCoModel(ISModel): | |
| def __init__( | |
| self, | |
| image_encoder_params={}, | |
| text_encoder_params={}, | |
| cross_encoder_params={}, | |
| neck_params={}, | |
| head_params={}, | |
| random_split=False, | |
| **kwargs | |
| ): | |
| super().__init__(**kwargs) | |
| self.random_split = random_split | |
| self.patch_embed_coords = PatchEmbed( | |
| img_size=image_encoder_params['img_size'], | |
| patch_size=image_encoder_params['patch_size'], | |
| in_chans=3 if self.with_prev_mask else 2, | |
| embed_dim=image_encoder_params['embed_dim'], | |
| ) | |
| self.image_encoder = VisionTransformer(**image_encoder_params) | |
| self.text_encoder = ClipTextEncoder(**text_encoder_params) | |
| self.cross_encoder = TwoWayTransformer(**cross_encoder_params) | |
| self.pe_layer = PositionEmbeddingRandom(cross_encoder_params["embedding_dim"] // 2) | |
| patch_size = image_encoder_params['patch_size'][0] | |
| self.image_embedding_size = image_encoder_params['img_size'][0] // (patch_size if patch_size > 0 else 1) | |
| self.neck = SimpleFPN(**neck_params) | |
| self.head = SwinTransfomerSegHead(**head_params) | |
| def backbone_forward(self, image, coord_features=None, text=None, gra=None): | |
| coord_features = self.patch_embed_coords(coord_features) | |
| backbone_features = self.image_encoder.forward_backbone(image, coord_features, gra=gra, shuffle=self.random_split) | |
| text_features = self.text_encoder(text) | |
| text_features, backbone_features = self.cross_encoder( | |
| backbone_features, | |
| self.pe_layer((self.image_embedding_size, self.image_embedding_size)).unsqueeze(0), | |
| text_features) | |
| # Extract 4 stage image_encoder feature map: 1/4, 1/8, 1/16, 1/32 | |
| B, N, C = backbone_features.shape | |
| grid_size = self.image_encoder.patch_embed.grid_size | |
| backbone_features = backbone_features.transpose(-1,-2).view(B, C, grid_size[0], grid_size[1]) | |
| multi_scale_features = self.neck(backbone_features) | |
| return {'instances': self.head(multi_scale_features), 'instances_aux': None} | |