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61ce884 5a128ba cdc317a 5a128ba cdc317a 5a128ba cdc317a 5a128ba cdc317a 61ce884 d1c85fc 61ce884 d1c85fc 033fb72 7fcff1a 61ce884 d1c85fc b9a0c51 d1c85fc 61ce884 cdc317a | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 | import torch.nn as nn
class BackboneWithFC(nn.Module):
"""Frozen ResNet18 backbone + trainable FC classifier head."""
def __init__(self, backbone: nn.Module, num_classes: int, dropout: float = 0.4, fc_dim: int = 256):
super().__init__()
self.backbone = backbone
self.classifier = nn.Sequential(
nn.Dropout(dropout),
nn.Linear(512, fc_dim),
nn.ReLU(inplace=True),
nn.Dropout(dropout),
nn.Linear(fc_dim, num_classes),
)
def forward(self, x):
return self.classifier(self.backbone(x))
class SimpleCNN(nn.Module):
def __init__(
self,
num_classes: int,
num_conv_blocks: int = 3,
base_filters: int = 32,
kernel_size: int = 3,
use_batchnorm: bool = True,
dropout: float = 0.4,
fc_dim: int = 256,
):
super().__init__()
padding = kernel_size // 2
layers = []
in_channels = 3
for i in range(num_conv_blocks):
out_channels = min(base_filters * (2 ** i), 512)
layers.append(nn.Conv2d(in_channels, out_channels, kernel_size, padding=padding))
if use_batchnorm:
layers.append(nn.BatchNorm2d(out_channels))
layers.append(nn.ReLU(inplace=True))
layers.append(nn.MaxPool2d(2, 2))
in_channels = out_channels
self.features = nn.Sequential(*layers)
self.pool = nn.AdaptiveAvgPool2d(1)
self.classifier = nn.Sequential(
nn.Dropout(dropout),
nn.Linear(in_channels, fc_dim),
nn.ReLU(inplace=True),
nn.Dropout(dropout),
nn.Linear(fc_dim, num_classes),
)
def forward(self, x):
x = self.pool(self.features(x))
return self.classifier(x.flatten(1))
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