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sosnet_model.py
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sosnet_model.py
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import torch.nn as nn
eps_fea_norm = 1e-5
eps_l2_norm = 1e-10
class SOSNet32x32(nn.Module):
"""
128-dimensional SOSNet model definition trained on 32x32 patches
"""
def __init__(self, dim_desc=128, drop_rate=0.1):
super(SOSNet32x32, self).__init__()
self.dim_desc = dim_desc
self.drop_rate = drop_rate
norm_layer = nn.BatchNorm2d
activation = nn.ReLU()
self.layers = nn.Sequential(
nn.InstanceNorm2d(1, affine=False, eps=eps_fea_norm),
nn.Conv2d(1, 32, kernel_size=3, padding=1, bias=False),
norm_layer(32, affine=False, eps=eps_fea_norm),
activation,
nn.Conv2d(32, 32, kernel_size=3, padding=1, bias=False),
norm_layer(32, affine=False, eps=eps_fea_norm),
activation,
nn.Conv2d(32, 64, kernel_size=3, stride=2, padding=1, bias=False),
norm_layer(64, affine=False, eps=eps_fea_norm),
activation,
nn.Conv2d(64, 64, kernel_size=3, padding=1, bias=False),
norm_layer(64, affine=False, eps=eps_fea_norm),
activation,
nn.Conv2d(64, 128, kernel_size=3, stride=2, padding=1, bias=False),
norm_layer(128, affine=False, eps=eps_fea_norm),
activation,
nn.Conv2d(128, 128, kernel_size=3, padding=1, bias=False),
norm_layer(128, affine=False, eps=eps_fea_norm),
activation,
nn.Dropout(self.drop_rate),
nn.Conv2d(128, self.dim_desc, kernel_size=8, bias=False),
norm_layer(128, affine=False, eps=eps_fea_norm),
)
self.desc_norm = nn.Sequential(
nn.LocalResponseNorm(2 * self.dim_desc, alpha=2 * self.dim_desc, beta=0.5, k=0)
)
return
def forward(self, patch):
descr = self.desc_norm(self.layers(patch) + eps_l2_norm)
descr = descr.view(descr.size(0), -1)
return descr