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Zero
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import torch
import torch.nn as nn
import re
import math
from .pooler_projector import NormalizedDwPooler
import os
import math
if 'REGIONAL_POOL' in os.environ:
REGIONAL_POOL = os.environ['REGIONAL_POOL']
else:
REGIONAL_POOL = '2x'
print(f"REGIONAL_POOL is set as {REGIONAL_POOL}")
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)
class OlaMLP(nn.Module):
def __init__(self, in_channels, out_channels, twoview=False):
super().__init__()
self.proj1 = nn.Linear(in_channels, out_channels)
self.proj2 = nn.Linear(out_channels, out_channels)
self.act = nn.GELU()
self.pooler = NormalizedDwPooler(out_channels)
embed_std = 1 / math.sqrt(out_channels)
self.image_newline = nn.Parameter(
torch.randn(out_channels) * embed_std
)
self.image_begin = nn.Parameter(
torch.randn(out_channels) * embed_std
)
self.image_end = nn.Parameter(
torch.randn(out_channels) * embed_std
)
if twoview:
self.image_sep = nn.Parameter(
torch.randn(out_channels) * embed_std
)
def forward(self, x, size=(16,16), x2=None, size2=(16, 16), modalities='image'):
if modalities in ['image', 'text']:
h, w = size
dtype = x.dtype
x = x.reshape(x.shape[0], h, w, -1)
x = self.proj1(x)
x = self.pooler(x, forward_type=REGIONAL_POOL)
x = self.act(x)
x = self.proj2(x)
b, h, w, c = x.shape
x = torch.cat([
x,
self.image_newline.reshape(1, 1, 1, c).expand(b, h, 1, c).to(dtype)
], dim=2)
x = x.reshape(b, -1, c)
if x2 is not None:
h2, w2 = size2
x2 = x2.reshape(x2.shape[0], h2, w2, -1)
x2 = self.proj1(x2)
x2 = self.pooler(x2, forward_type=REGIONAL_POOL)
x2 = self.act(x2)
x2 = self.proj2(x2)
b2, h2, w2, c2 = x2.shape
x2 = torch.cat([
x2,
self.image_newline.reshape(1, 1, 1, c).expand(b, h2, 1, c).to(dtype)
], dim=2)
x2 = x2.reshape(b, -1, c)
sep = self.image_sep.reshape(1, 1, -1).expand(b, 1, c2).to(dtype)
x = torch.cat([x, sep, x2], dim=1)
begin = self.image_begin.reshape(1, 1, -1).expand(b, 1, c).to(dtype)
end = self.image_end.reshape(1, 1, -1).expand(b, 1, c).to(dtype)
x = torch.cat([begin, x, end], dim=1)
return x
elif modalities in ['video']:
# x2 is the true feature, ignore x
h, w = size
dtype = x.dtype
x = x.reshape(x.shape[0], h, w, -1)
x1 = self.proj1(x)
x1 = self.pooler(x1, forward_type=REGIONAL_POOL)
x1 = self.proj2(x1).mean() * 0.0
h2, w2 = size2
x2 = x2.reshape(x2.shape[0], h2, w2, -1)
x2 = self.proj1(x2)
x2 = self.pooler(x2, forward_type=REGIONAL_POOL)
x2 = self.act(x2)
x2 = self.proj2(x2)
b2, h2, w2, c = x2.shape
x2 = torch.cat([
x2,
self.image_newline.reshape(1, 1, 1, c).expand(b2, h2, 1, c).to(dtype)
], dim=2)
x2 = x2.reshape(b2, -1, c)
sep = self.image_sep.reshape(1, 1, -1).expand(b2, 1, c).to(dtype)
x2 = torch.cat([x2, sep], dim=1)
x2 = x2.flatten(0, 1)
begin = self.image_begin.reshape(1, -1).expand(1, c).to(dtype)
end = self.image_end.reshape(1, -1).expand(1, c).to(dtype)
x2 = torch.cat([begin, x2, end], dim=0)
x2 = x2.unsqueeze(0)
return x2
else:
raise ValueError(f'Unknown modalities: {modalities}')
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)
elif projector_type == 'ola_mlp':
return OlaMLP(config.mm_hidden_size, config.hidden_size, twoview=True)
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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