| import open_clip |
| from .point_encoder import PointcloudEncoder |
| import timm |
|
|
|
|
| def build_text_encoder(): |
| clip_model_type = "EVA02-E-14-plus" |
| pretrained = "./pretrained_weight/clip_used_in_Uni3D/open_clip_pytorch_model.bin" |
| clip_model, _, _ = open_clip.create_model_and_transforms(model_name=clip_model_type, pretrained=pretrained) |
|
|
| return clip_model |
|
|
|
|
|
|
| def build_pc_encoder(args): |
|
|
| pretrained_pc = '' |
| drop_path_rate = 0.0 |
|
|
| pc_encoder_type = getattr(args, 'pc_encoder_type', 'small') |
|
|
| if pc_encoder_type == "giant": |
| pc_model = "eva_giant_patch14_560" |
| args.pc_feat_dim = 1408 |
| elif pc_encoder_type == "large": |
| pc_model = "eva02_large_patch14_448" |
| args.pc_feat_dim = 1024 |
| elif pc_encoder_type == "base": |
| pc_model = "eva02_base_patch14_448" |
| args.pc_feat_dim = 768 |
| elif pc_encoder_type == "small": |
| pc_model = "eva02_small_patch14_224" |
| args.pc_feat_dim = 384 |
| elif pc_encoder_type == "tiny": |
| pc_model = "eva02_tiny_patch14_224" |
| args.pc_feat_dim = 192 |
|
|
|
|
| |
| point_transformer = timm.create_model(pc_model, checkpoint_path=pretrained_pc, drop_path_rate= drop_path_rate) |
|
|
| |
| point_encoder = PointcloudEncoder(point_transformer, args) |
|
|
| return point_encoder |
