SppNet 多尺度训练

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#神经网络 #pytorch #深度学习 #sppnet #多尺度训练

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本文介绍了如何通过SPPNet在训练过程中处理不同尺度输入，解决了网络对图片尺寸变化的适应性问题。通过自适应卷积和多批次训练策略，实现跨尺度的模型优化。实例代码展示了如何在PyTorch中实现ResNet+SPPNet的多尺度训练流程。

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SppNet 多尺度训练

原理网上非常多了，这里不再赘述。

感觉我看了很多博客，对我帮助较大的两个是：

https://blog.youkuaiyun.com/qq_42052229/article/details/90446073

https://zhuanlan.zhihu.com/p/42732128

使用过程中的重点总结：

1、网络目的确实是解决input的图像size大小不同的问题，

但是：

训练的时候，我们需要一个batch一个batch的训练，这每一个batch的维度是相同的，意味着，这一个batch内的图像input size是相同的，才能训练。除非我们一张一张的训练，这样就会带来其他的很多问题。

所以在训练的时候，往往采用几种不同尺度的size的input，对同一个网络分别进行训练。

而测试时候，因为一般是一张一张的，所以可以任意size的图像input。

2、使用自适应卷积一样可以。

3、代码拆解，不一定要按照网上人家的来，最后全连接数量自己可以任意调整。

4、例子pytorch：

多尺度训练：不同尺度size分别跑一个epoch



if __name__ == '__main__':
    train_loader_350, test_loader_350 = load(350)
    train_loader_400, test_loader_400 = load(400)
    train_loader_450, test_loader_450 = load(450)
    train_loader_500, test_loader_500 = load(500)
    train_loaders = [train_loader_350, train_loader_400, train_loader_450, train_loader_500]
    test_loaders = [test_loader_350, test_loader_400, test_loader_450, test_loader_500]

    model = SPPNet().to(device)
    optimizer = optim.Adam(model.parameters(), lr=0.0001, betas=(0.9, 0.99))
    criterion = nn.CrossEntropyLoss()

    for epoch in range(1, EPOCH + 1):
        for train_loader, test_loader in zip(train_loaders, test_loaders):
            train(model, device, train_loader, criterion, optimizer, epoch)
            test(model, device, test_loader, criterion, epoch)

torch.save(model, save_path)

resnet+sppnet，或者自适应卷积


import torch
from torch import nn
from torchvision import models
import torch.nn.functional as F
import os, math
from torch.nn.modules.pooling import AdaptiveAvgPool2d, AdaptiveMaxPool2d


class ResNet(nn.Module):
    def __init__(self, layers=18, num_class=2, pretrained=True):
        super(ResNet, self).__init__()
        if layers == 18:
            self.resnet = models.resnet18(pretrained=pretrained)
        elif layers == 34:
            self.resnet = models.resnet34(pretrained=pretrained)
        elif layers == 50:
            self.resnet = models.resnet50(pretrained=pretrained)
        elif layers == 101:
            self.resnet = models.resnet101(pretrained=pretrained)
        elif layers == 152:
            self.resnet = models.resnet152(pretrained=pretrained)
        else:
            raise ValueError('layers should be 18, 34, 50, 101.')
        self.num_class = num_class
        if layers in [18, 34]:
            self.fc = nn.Linear(512, num_class)
        if layers in [50, 101, 152]:
            self.fc = nn.Linear(512 * 4, num_class)

    def conv_base(self, x):
        x = self.resnet.conv1(x)
        x = self.resnet.bn1(x)
        x = self.resnet.relu(x)
        x = self.resnet.maxpool(x)

        layer1 = self.resnet.layer1(x)
        layer2 = self.resnet.layer2(layer1)
        layer3 = self.resnet.layer3(layer2)
        layer4 = self.resnet.layer4(layer3)
        return layer1, layer2, layer3, layer4

    def forward(self, x):
        layer1, layer2, layer3, layer4 = self.conv_base(x)
        x = self.resnet.avgpool(layer4)
        x = x.view(x.size(0), -1)
        x = self.fc(x)
        return x


class SPPNet(nn.Module):
    def __init__(self, backbone=101, num_class=2, pool_size=(1, 2, 6), pretrained=True):
        # Only resnet is supported in this version
        super(SPPNet, self).__init__()
        if backbone in [18, 34, 50, 101, 152]:
            self.resnet = ResNet(backbone, num_class, pretrained)
        else:
            raise ValueError('Resnet{} is not supported yet.'.format(backbone))

        if backbone in [18, 34]:
            self.c = 512
        if backbone in [50, 101, 152]:
            self.c = 2048

        self.spp = SpatialPyramidPool2D(out_side=pool_size)
        num_features = self.c * (pool_size[0] ** 2 + pool_size[1] ** 2 + pool_size[2] ** 2)
        self.classifier = nn.Linear(num_features, num_class)

    def forward(self, x):
        _, _, _, x = self.resnet.conv_base(x)
        x = self.spp(x)
        x = self.classifier(x)
        return x


class SpatialPyramidPool2D(nn.Module):
    """
    Args:
        out_side (tuple): Length of side in the pooling results of each pyramid layer.
    Inputs:
        - `input`: the input Tensor to invert ([batch, channel, width, height])
    """

    def __init__(self, out_side):
        super(SpatialPyramidPool2D, self).__init__()
        self.out_side = out_side

    def forward(self, x):
        # batch_size, c, h, w = x.size()
        out = None
        for n in self.out_side:
#             w_r, h_r = map(lambda s: math.ceil(s / n), x.size()[2:])  # Receptive Field Size
#             s_w, s_h = map(lambda s: math.floor(s / n), x.size()[2:])  # Stride
#             max_pool = nn.MaxPool2d(kernel_size=(w_r, h_r), stride=(s_w, s_h))
            max_pool = AdaptiveMaxPool2d(output_size=(n, n))
            y = max_pool(x)
            if out is None:
                out = y.view(y.size()[0], -1)
            else:
                out = torch.cat((out, y.view(y.size()[0], -1)), 1)
        return out

code源地址：

https://github.com/mmmmmmiracle/SPPNet/blob/master/multi.py

https://github.com/stanleykao72/Deepfake-Detector/blob/934749da71ae31f6e689f2ca2e015cd3e2d5c50a/py_utils/DL/sppnet/models/classifier.py