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import torch
import torch.nn as nn
import re
from .pooler_projector import PoolerProjector
class IdentityMap(nn.Module):
def __init__(self):
super().__init__()
def forward(self, x, *args, **kwargs):
return x
@property
def config(self):
return {"mm_projector_type": "identity"}
class SimpleResBlock(nn.Module):
def __init__(self, channels):
super().__init__()
self.pre_norm = nn.LayerNorm(channels)
self.proj = nn.Sequential(nn.Linear(channels, channels), nn.GELU(), nn.Linear(channels, channels))
def forward(self, x):
x = self.pre_norm(x)
return x + self.proj(x)
def build_vision_projector(config, delay_load=False, **kwargs):
projector_type = getattr(config, "mm_projector_type", "linear")
if projector_type == "linear":
return nn.Linear(config.mm_hidden_size, config.hidden_size)
if projector_type == "pooler":
return PoolerProjector(config, kwargs["vision_cfg"])
mlp_gelu_match = re.match(r"^mlp(\d+)x_gelu$", projector_type)
if mlp_gelu_match:
mlp_depth = int(mlp_gelu_match.group(1))
modules = [nn.Linear(config.mm_hidden_size, config.hidden_size)]
for _ in range(1, mlp_depth):
modules.append(nn.GELU())
modules.append(nn.Linear(config.hidden_size, config.hidden_size))
return nn.Sequential(*modules)
mlp_gelu_resnet_match = re.match(r"^mlp(\d+)x_res(\d+)x_gelu$", projector_type)
if mlp_gelu_resnet_match:
mlp_depth = int(mlp_gelu_resnet_match.group(1))
res_depth = int(mlp_gelu_resnet_match.group(2))
modules = [nn.Linear(config.mm_hidden_size, config.hidden_size)]
for _ in range(1, mlp_depth):
modules.append(nn.GELU())
modules.append(nn.Linear(config.hidden_size, config.hidden_size))
for _ in range(res_depth):
modules.append(SimpleResBlock(config.hidden_size))
return nn.Sequential(*modules)
if projector_type == "identity":
return IdentityMap()
raise ValueError(f"Unknown projector type: {projector_type}")
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