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