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# Copyright (c) 2023-2024 DeepSeek.
#
# Permission is hereby granted, free of charge, to any person obtaining a copy of
# this software and associated documentation files (the "Software"), to deal in
# the Software without restriction, including without limitation the rights to
# use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of
# the Software, and to permit persons to whom the Software is furnished to do so,
# subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS
# FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR
# COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER
# IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN
# CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
from typing import Tuple, Union
import torch
import torch.nn as nn
from attrdict import AttrDict
class MlpProjector(nn.Module):
def __init__(self, cfg):
super().__init__()
self.cfg = cfg
if cfg.projector_type == "identity":
modules = nn.Identity()
elif cfg.projector_type == "linear":
modules = nn.Linear(cfg.input_dim, cfg.n_embed)
elif cfg.projector_type == "mlp_gelu":
mlp_depth = cfg.get("depth", 1)
modules = [nn.Linear(cfg.input_dim, cfg.n_embed)]
for _ in range(1, mlp_depth):
modules.append(nn.GELU())
modules.append(nn.Linear(cfg.n_embed, cfg.n_embed))
modules = nn.Sequential(*modules)
elif cfg.projector_type == "low_high_hybrid_split_mlp_gelu":
mlp_depth = cfg.get("depth", 1)
self.high_up_proj = nn.Linear(cfg.input_dim, cfg.n_embed // 2)
self.low_up_proj = nn.Linear(cfg.input_dim, cfg.n_embed // 2)
modules = []
for _ in range(1, mlp_depth):
modules.append(nn.GELU())
modules.append(nn.Linear(cfg.n_embed, cfg.n_embed))
modules = nn.Sequential(*modules)
else:
raise ValueError(f"Unknown projector type: {cfg.projector_type}")
self.layers = modules
def forward(
self, x_or_tuple: Union[Tuple[torch.Tensor, torch.Tensor], torch.Tensor]
):
"""
Args:
x_or_tuple (Union[Tuple[torch.Tensor, torch.Tensor], torch.Tensor]: if it is a tuple of torch.Tensor,
then it comes from the hybrid vision encoder, and x = high_res_x, low_res_x);
otherwise it is the feature from the single vision encoder.
Returns:
x (torch.Tensor): [b, s, c]
"""
if isinstance(x_or_tuple, tuple):
# self.cfg.projector_type == "low_high_hybrid_split_mlp_gelu":
high_x, low_x = x_or_tuple
high_x = self.high_up_proj(high_x)
low_x = self.low_up_proj(low_x)
x = torch.concat([high_x, low_x], dim=-1)
else:
x = x_or_tuple
return self.layers(x)
if __name__ == "__main__":
cfg = AttrDict(
input_dim=1024,
n_embed=2048,
depth=2,
projector_type="low_high_hybrid_split_mlp_gelu",
)
inputs = (torch.rand(4, 576, 1024), torch.rand(4, 576, 1024))
m = MlpProjector(cfg)
out = m(inputs)
print(out.shape)
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