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import torch
import torch.nn as nn
from .fourier_features import FourierFeatures
class RegionModel(nn.Module):
def __init__(self):
super().__init__()
self.position_features = FourierFeatures(2, 256)
self.position_encoder = nn.Linear(256, 2048)
self.size_features = FourierFeatures(2, 256)
self.size_encoder = nn.Linear(256, 2048)
self.position_decoder = nn.Linear(2048, 2)
self.size_decoder = nn.Linear(2048, 2)
self.confidence_decoder = nn.Linear(2048, 1)
def encode_position(self, position):
return self.position_encoder(self.position_features(position))
def encode_size(self, size):
return self.size_encoder(self.size_features(size))
def decode_position(self, x):
return self.position_decoder(x)
def decode_size(self, x):
return self.size_decoder(x)
def decode_confidence(self, x):
return self.confidence_decoder(x)
def encode(self, position, size):
return torch.stack(
[self.encode_position(position), self.encode_size(size)], dim=0
)
def decode(self, position_logits, size_logits):
return (
self.decode_position(position_logits),
self.decode_size(size_logits),
self.decode_confidence(size_logits),
)
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