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Running
on
Zero
Running
on
Zero
File size: 2,227 Bytes
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import torch
import torch.nn as nn
class BackboneBase(nn.Module):
"""Base class for backbone networks. Provides a standard interface for preprocessing inputs and
defining encoder dimensions.
Args:
nchannels (int): Number of input channels.
use_instance_norm (bool): Whether to apply instance normalization.
"""
def __init__(self, nchannels=3, use_instance_norm=False):
super().__init__()
assert nchannels > 0, "Number of channels must be positive."
self.nchannels = nchannels
self.use_instance_norm = use_instance_norm
self.norm = nn.InstanceNorm2d(nchannels) if use_instance_norm else None
def get_dim_layers_encoder(self):
"""Get dimensions of encoder layers."""
raise NotImplementedError("Subclasses must implement this method.")
def _forward(self, x):
"""Define the forward pass for the backbone."""
raise NotImplementedError("Subclasses must implement this method.")
def forward(self, x: torch.Tensor, preprocess=True):
"""Forward pass with optional preprocessing.
Args:
x (Tensor): Input tensor.
preprocess (bool): Whether to apply channel reduction.
"""
if preprocess:
if x.dim() != 4:
if x.dim() == 2 and x.shape[0] > 3 and x.shape[1] > 3:
x = x.unsqueeze(0).unsqueeze(0)
elif x.dim() == 3:
x = x.unsqueeze(0)
else:
raise ValueError(f"Unexpected input shape: {x.shape}")
if self.nchannels == 1 and x.shape[1] != 1:
if len(x.shape) == 4: # Assumes (batch, channel, height, width)
x = torch.mean(x, axis=1, keepdim=True)
else:
raise ValueError(f"Unexpected input shape: {x.shape}")
#
if self.nchannels == 3 and x.shape[1] == 1:
if len(x.shape) == 4:
x = x.repeat(1, 3, 1, 1)
else:
raise ValueError(f"Unexpected input shape: {x.shape}")
if self.use_instance_norm:
x = self.norm(x)
return self._forward(x)
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