mirror of
https://github.com/graphdeco-inria/gaussian-splatting
synced 2024-12-01 16:54:05 +00:00
97 lines
2.6 KiB
Python
97 lines
2.6 KiB
Python
from typing import Sequence
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from itertools import chain
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import torch
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import torch.nn as nn
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from torchvision import models
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from .utils import normalize_activation
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def get_network(net_type: str):
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if net_type == 'alex':
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return AlexNet()
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elif net_type == 'squeeze':
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return SqueezeNet()
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elif net_type == 'vgg':
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return VGG16()
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else:
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raise NotImplementedError('choose net_type from [alex, squeeze, vgg].')
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class LinLayers(nn.ModuleList):
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def __init__(self, n_channels_list: Sequence[int]):
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super(LinLayers, self).__init__([
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nn.Sequential(
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nn.Identity(),
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nn.Conv2d(nc, 1, 1, 1, 0, bias=False)
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) for nc in n_channels_list
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])
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for param in self.parameters():
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param.requires_grad = False
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class BaseNet(nn.Module):
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def __init__(self):
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super(BaseNet, self).__init__()
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# register buffer
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self.register_buffer(
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'mean', torch.Tensor([-.030, -.088, -.188])[None, :, None, None])
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self.register_buffer(
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'std', torch.Tensor([.458, .448, .450])[None, :, None, None])
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def set_requires_grad(self, state: bool):
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for param in chain(self.parameters(), self.buffers()):
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param.requires_grad = state
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def z_score(self, x: torch.Tensor):
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return (x - self.mean) / self.std
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def forward(self, x: torch.Tensor):
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x = self.z_score(x)
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output = []
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for i, (_, layer) in enumerate(self.layers._modules.items(), 1):
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x = layer(x)
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if i in self.target_layers:
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output.append(normalize_activation(x))
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if len(output) == len(self.target_layers):
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break
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return output
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class SqueezeNet(BaseNet):
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def __init__(self):
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super(SqueezeNet, self).__init__()
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self.layers = models.squeezenet1_1(True).features
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self.target_layers = [2, 5, 8, 10, 11, 12, 13]
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self.n_channels_list = [64, 128, 256, 384, 384, 512, 512]
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self.set_requires_grad(False)
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class AlexNet(BaseNet):
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def __init__(self):
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super(AlexNet, self).__init__()
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self.layers = models.alexnet(True).features
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self.target_layers = [2, 5, 8, 10, 12]
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self.n_channels_list = [64, 192, 384, 256, 256]
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self.set_requires_grad(False)
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class VGG16(BaseNet):
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def __init__(self):
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super(VGG16, self).__init__()
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self.layers = models.vgg16(weights=models.VGG16_Weights.IMAGENET1K_V1).features
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self.target_layers = [4, 9, 16, 23, 30]
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self.n_channels_list = [64, 128, 256, 512, 512]
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self.set_requires_grad(False)
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