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import torch
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import torch.nn as nn
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import math
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class SpatialPool(nn.Module):
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def __init__(self, model_args, vision_tower):
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super().__init__()
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self.mode = model_args.mm_spatial_pool_mode
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self.stride = model_args.mm_spatial_pool_stride
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self.out_channels = getattr(model_args, "mm_spatial_pool_out_channels", vision_tower.hidden_size)
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if self.mode == "average":
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self.pool = nn.AvgPool2d(kernel_size=self.stride, stride=self.stride)
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elif self.mode == "max":
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self.pool = nn.MaxPool2d(kernel_size=self.stride, stride=self.stride)
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elif self.mode == "conv":
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self.pool = nn.Conv2d(in_channels=vision_tower.hidden_size, out_channels=self.out_channels, kernel_size=self.stride, stride=self.stride)
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else:
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raise ValueError(f"Unknown pooling mode: {self.pool}.")
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def forward(self, image_features, images, *args, **kwargs):
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ori_W = int(math.sqrt(image_features.shape[1] * images.shape[3] // images.shape[2]))
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ori_H = int(ori_W * images.shape[2] // images.shape[3])
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B, _, F = image_features.shape
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image_features_spatial = image_features.view(B, ori_H, ori_H, F).permute(0, 3, 1, 2)
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image_features_spatial_pool = self.pool(image_features_spatial)
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return image_features_spatial_pool.flatten(2).transpose(1, 2).contiguous()
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@property
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def config(self):
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return {
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"mm_resampler_type": "spatial_pool",
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"mm_spatial_pool_stride": self.stride,
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"mm_spatial_pool_mode": self.mode,
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"mm_spatial_pool_out_channels": self.out_channels,
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}
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@property
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def hidden_size(self):
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return self.out_channels
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