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import torch
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from .masked_drop import MaskedDrop
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from .spatial_pool import SpatialPool
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from .perceiver import PerceiverResampler
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from .qformer import Qformer
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class IdentityMap(torch.nn.Module):
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def __init__(self):
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super().__init__()
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def forward(self, x, *args, **kwargs):
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return x
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@property
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def config(self):
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return {"mm_resampler_type": None}
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def build_vision_resampler(model_args, delay_load=False, **kwargs):
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resampler_type = getattr(model_args, "mm_resampler_type", None)
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if resampler_type == "masked_drop":
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return MaskedDrop(model_args)
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elif resampler_type == "spatial_pool":
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return SpatialPool(model_args, **kwargs)
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elif resampler_type == "perceiver":
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return PerceiverResampler(model_args, **kwargs)
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elif resampler_type == "qformer":
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return Qformer(model_args, **kwargs)
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elif resampler_type is None:
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return IdentityMap()
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raise ValueError(f"Unknown resampler type: {resampler_type}")
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