基于PyTorch的MaxViT迁移学习分类器设置方案咨询

First, let's break down what block_channels[-1] means:

• block_channels is a list storing the number of output channels for each feature block in the MaxViT model.
• block_channels[-1] refers to the channel count of the last feature block—this is the input dimension required for the classifier's initial layers (LayerNorm and Linear). For pre-trained MaxViT variants (like maxvit_tiny or maxvit_small), this value is fixed based on model size (e.g., 512 for tiny, 768 for small).

Step-by-Step Classifier Setup #

1. Load Pre-trained MaxViT & Freeze Features

Start by loading the model with pre-trained weights and freezing the feature extractor (just like you did for SqueezeNet):

import torch
import torch.nn as nn
import torchvision

# Load pre-trained MaxViT (adjust variant to tiny/small/base as needed)
weights = torchvision.models.MaxViT_Tiny_Weights.DEFAULT
model = torchvision.models.maxvit_tiny(weights=weights).to(device)

# Freeze all feature extractor parameters
for param in model.features.parameters():
    param.requires_grad = False

2. Get Input Dimension for Classifier

You can directly retrieve the required input dimension from the model:

input_dim = model.block_channels[-1]
output_shape = len(class_names)  # Number of classes in your custom dataset

3. Replace the Classifier

You have two valid options, depending on your preference:

Option A: Keep MaxViT's Original Optimized Structure (Recommended)

MaxViT's native classifier is designed to work with its feature representations. Adjust only the final layer to match your class count:

model.classifier = nn.Sequential(
    nn.AdaptiveAvgPool2d(1),
    nn.Flatten(),
    nn.LayerNorm(input_dim),
    nn.Linear(input_dim, input_dim),
    nn.Tanh(),
    nn.Linear(input_dim, output_shape, bias=False)
).to(device)

If you want to add dropout (like you did for SqueezeNet/EfficientNet), insert it after the Flatten layer:

model.classifier = nn.Sequential(
    nn.AdaptiveAvgPool2d(1),
    nn.Flatten(),
    nn.Dropout(p=0.2),  # Add regularization
    nn.LayerNorm(input_dim),
    nn.Linear(input_dim, input_dim),
    nn.Tanh(),
    nn.Linear(input_dim, output_shape, bias=False)
).to(device)

Option B: Simplify to an EfficientNet-Style Classifier

If you prefer a more minimal structure, use this:

model.classifier = nn.Sequential(
    nn.AdaptiveAvgPool2d(1),
    nn.Flatten(),
    nn.Dropout(p=0.2),
    nn.Linear(input_dim, output_shape)
).to(device)

Note: The original structure may yield better results, but this simplified version is fully functional.

4. Complete Training Pipeline

The rest of your training code can mirror your SqueezeNet setup:

loss_fn = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)

# Run training
results = engine.train(
    model=model,
    train_dataloader=train_dataloader,
    test_dataloader=test_dataloader,
    optimizer=optimizer,
    loss_fn=loss_fn,
    epochs=15,
    device=device
)

Key Notes #

• Choose the MaxViT variant (tiny/small/base) based on your computational resources.
• If you want better performance, try unfreezing the top 1-2 feature blocks (e.g., model.features[-1].requires_grad = True) to fine-tune them alongside the classifier.

内容的提问来源于stack exchange，提问作者Chiraag