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| from torch import nn | |
| from .clip_model import CLIP | |
| from .our_model import ModifiedCLIPSurgery | |
| def convert_weights(model: nn.Module): | |
| """Convert applicable model parameters to fp16""" | |
| def _convert_weights_to_fp16(l): | |
| if isinstance(l, (nn.Conv1d, nn.Conv2d, nn.Linear)): | |
| l.weight.data = l.weight.data.half() | |
| if l.bias is not None: | |
| l.bias.data = l.bias.data.half() | |
| if isinstance(l, nn.MultiheadAttention): | |
| for attr in [*[f"{s}_proj_weight" for s in ["in", "q", "k", "v"]], "in_proj_bias", "bias_k", "bias_v"]: | |
| tensor = getattr(l, attr) | |
| if tensor is not None: | |
| tensor.data = tensor.data.half() | |
| for name in ["text_projection", "proj"]: | |
| if hasattr(l, name): | |
| attr = getattr(l, name) | |
| if attr is not None: | |
| attr.data = attr.data.half() | |
| model.apply(_convert_weights_to_fp16) | |
| def build_model(name: str, state_dict: dict,cfg: dict,train_bool: bool): | |
| vit = "visual.proj" in state_dict | |
| if vit: | |
| vision_width = state_dict["visual.conv1.weight"].shape[0] | |
| vision_layers = len([k for k in state_dict.keys() if k.startswith("visual.") and k.endswith(".attn.in_proj_weight")]) | |
| vision_patch_size = state_dict["visual.conv1.weight"].shape[-1] | |
| grid_size = round((state_dict["visual.positional_embedding"].shape[0] - 1) ** 0.5) | |
| image_resolution = vision_patch_size * grid_size | |
| else: | |
| counts: list = [len(set(k.split(".")[2] for k in state_dict if k.startswith(f"visual.layer{b}"))) for b in [1, 2, 3, 4]] | |
| vision_layers = tuple(counts) | |
| vision_width = state_dict["visual.layer1.0.conv1.weight"].shape[0] | |
| output_width = round((state_dict["visual.attnpool.positional_embedding"].shape[0] - 1) ** 0.5) | |
| vision_patch_size = None | |
| assert output_width ** 2 + 1 == state_dict["visual.attnpool.positional_embedding"].shape[0] | |
| image_resolution = output_width * 32 | |
| embed_dim = state_dict["text_projection"].shape[1] | |
| context_length = state_dict["positional_embedding"].shape[0] | |
| vocab_size = state_dict["token_embedding.weight"].shape[0] | |
| transformer_width = state_dict["ln_final.weight"].shape[0] | |
| transformer_heads = transformer_width // 64 | |
| transformer_layers = len(set(k.split(".")[2] for k in state_dict if k.startswith(f"transformer.resblocks"))) | |
| if 'CS-' in name: | |
| model = ModifiedCLIPSurgery( | |
| embed_dim, | |
| image_resolution, vision_layers, vision_width, vision_patch_size, | |
| context_length, vocab_size, transformer_width, transformer_heads, transformer_layers,cfg,train_bool | |
| ) | |
| else: | |
| model = CLIP( | |
| embed_dim, | |
| image_resolution, vision_layers, vision_width, vision_patch_size, | |
| context_length, vocab_size, transformer_width, transformer_heads, transformer_layers | |
| ) | |
| for key in ["input_resolution", "context_length", "vocab_size"]: | |
| if key in state_dict: | |
| del state_dict[key] | |
| model.load_state_dict(state_dict,strict=False) | |
| if not cfg.ft_all: | |
| train_params_list= cfg.MODEL.PROMPT.TRAINABLE_PARM.split(',') | |
| for name, param in model.named_parameters(): | |
| param.requires_grad = any(str(t_param) in name for t_param in train_params_list) | |
| for name, param in model.named_parameters(): | |
| if "visual" not in name: | |
| param.requires_grad = False | |
| return model |