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import torch
import torch.nn as nn
import re
# from llava.model.multimodal_projector.deformable_resampler import DeformableResampler
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, fpn_input_dim=[], **kwargs):
projector_type = getattr(config, 'mm_projector_type', 'linear')
if projector_type == 'linear':
return nn.Linear(config.mm_hidden_size, config.hidden_size)
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)
# resampler_match = re.match(r'^deformable-resampler-l(\d+)d(\d+)p(\d+)', projector_type)
# if resampler_match:
# use_fpn = "fpn" in projector_type or len(fpn_input_dim) > 0
# layer_num = int(resampler_match.group(1))
# embed_dim = int(resampler_match.group(2))
# sample_point = int(resampler_match.group(3))
# if len(fpn_input_dim) > 0:
# fpn_type = 'multi-level'
# else:
# fpn_type = 'simple'
# return DeformableResampler(input_dimension=config.mm_hidden_size,
# output_dimension=config.hidden_size,
# query_number=config.mm_projector_query_number,
# num_layers=layer_num,
# num_heads=8,
# feedforward_dims=2048,
# embed_dims=embed_dim,
# num_points=sample_point,
# direct_projection=True,
# use_fpn=use_fpn,
# fpn_config=dict(
# fpn_type=fpn_type,
# in_channels=fpn_input_dim))
if projector_type == 'identity':
return IdentityMap()
raise ValueError(f'Unknown projector type: {projector_type}')
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