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4dc60af | 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 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 | import torch
import torch.nn as nn
from torchvision.models import convnext_tiny, ConvNeXt_Tiny_Weights
class ConvNeXtFeatureExtractor(nn.Module):
def __init__(
self,
freeze_backbone: bool = True,
unfreeze_last_stage: bool = False,
):
super().__init__()
weights = ConvNeXt_Tiny_Weights.DEFAULT
self.backbone = convnext_tiny(weights=weights)
if freeze_backbone:
for p in self.backbone.parameters():
p.requires_grad = False
if unfreeze_last_stage:
for p in self.backbone.features[-1].parameters():
p.requires_grad = True
self.output_dim = self.backbone.classifier[2].in_features
self.backbone.classifier = nn.Identity()
def forward(self, x):
x = self.backbone(x)
x = x.view(x.size(0), -1)
return x
class MLPHead(nn.Module):
def __init__(
self,
input_dim: int,
num_classes: int,
head_depth: int = 2,
hidden_dim_1: int = 512,
hidden_dim_2: int = 256,
dropout: float = 0.1,
head_style: str = "standard",
):
super().__init__()
if head_style == "rakyan":
if head_depth == 2:
self.net = nn.Sequential(
nn.Linear(input_dim, hidden_dim_1),
nn.BatchNorm1d(hidden_dim_1),
nn.ReLU(inplace=True),
nn.Dropout(p=0.3),
nn.Linear(hidden_dim_1, num_classes),
)
elif head_depth == 3:
self.net = nn.Sequential(
nn.Linear(input_dim, hidden_dim_1),
nn.BatchNorm1d(hidden_dim_1),
nn.ReLU(inplace=True),
nn.Dropout(p=0.3),
nn.Linear(hidden_dim_1, hidden_dim_2),
nn.BatchNorm1d(hidden_dim_2),
nn.ReLU(inplace=True),
nn.Dropout(p=0.3),
nn.Linear(hidden_dim_2, num_classes),
)
else:
raise ValueError("head_depth must be 2 or 3")
elif head_style == "standard":
if head_depth == 2:
self.net = nn.Sequential(
nn.LayerNorm(input_dim),
nn.Linear(input_dim, hidden_dim_1),
nn.LayerNorm(hidden_dim_1),
nn.GELU(),
nn.Dropout(dropout),
nn.Linear(hidden_dim_1, num_classes),
)
elif head_depth == 3:
self.net = nn.Sequential(
nn.LayerNorm(input_dim),
nn.Linear(input_dim, hidden_dim_1),
nn.LayerNorm(hidden_dim_1),
nn.GELU(),
nn.Dropout(dropout),
nn.Linear(hidden_dim_1, hidden_dim_2),
nn.LayerNorm(hidden_dim_2),
nn.GELU(),
nn.Dropout(dropout),
nn.Linear(hidden_dim_2, num_classes),
)
else:
raise ValueError("head_depth must be 2 or 3")
else:
raise ValueError("head_style must be 'standard' or 'rakyan'")
def forward(self, x):
return self.net(x)
class ConvNextMLP(nn.Module):
def __init__(
self,
num_classes: int = 10,
head_depth: int = 2,
hidden_dim_1: int = 512,
hidden_dim_2: int = 256,
dropout: float = 0.1,
freeze_backbone: bool = True,
unfreeze_last_stage: bool = False,
head_style: str = "standard"
):
super().__init__()
self.feature_extractor = ConvNeXtFeatureExtractor(
freeze_backbone=freeze_backbone,
unfreeze_last_stage=unfreeze_last_stage
)
self.head = MLPHead(
input_dim=self.feature_extractor.output_dim,
num_classes=num_classes,
head_depth=head_depth,
hidden_dim_1=hidden_dim_1,
hidden_dim_2=hidden_dim_2,
dropout=dropout,
head_style=head_style
)
def forward(self, x):
features = self.feature_extractor(x)
out = self.head(features)
return out |