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Runtime error
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import torch.nn as nn
import re
from .perceiver import PerceiverResampler
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, object_projector=False, delay_load=False, **kwargs):
if not object_projector:
projector_type = getattr(config, 'mm_projector_type', 'linear')
else:
projector_type = getattr(config, 'object_mm_projector_type', 'perceiver')
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)
if projector_type == 'identity':
return IdentityMap()
if projector_type == "perceiver":
return nn.Sequential(
nn.LayerNorm(config.mm_hidden_size),
PerceiverResampler(
dim = config.mm_hidden_size,
dim_head = 96,
depth = 6,
heads = 16,
num_latents = 32,
num_media_embeds = 1
),
nn.Linear(
config.mm_hidden_size, config.hidden_size
)
)
raise ValueError(f'Unknown projector type: {projector_type}')
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