由于你没有提供get_model_complexity_info函数的具体实现或来源,我将基于通用的编程逻辑来回答你的问题。 1. 确认get_model_complexity_info函数的定义和用法 假设get_model_complexity_info是一个用于获取模型复杂度的函数。它可能接受一个模型作为输入,并返回该模型的复杂度信息(如参数数量、计算
(注意:在训练前我们总是会调用model.train()函数,在推断之前调用model.eval()函数,因为这些会被nn.BatchNorm2d,nn.Dropout等层使用,确保在不同阶段的准确性。) model, opt = get_model() for epoch in range(epochs): model.train() for xb, yb in train_dl: pred = model(xb) loss = loss_func(pr...
* 调用静态内部类的静态方法:获取ExtClassLoader(第一次调用时会初始化) * 这里的getExtClassLoader()方法使用了DCL模式创建一个ExtClassLoader的单例。 * 初始化的时候,内部包含ExtClassLoader的加载路径:java.ext.dirs */ var1 = Launcher.ExtClassLoader.getExtClassLoader(); } catch (IOException var10) {...
import torch from mmcv.cnn import (build_upsample_layer, build_conv_layer) from mmcv.cnn import get_model_complexity_info model_mmcv = torch.nn.Sequential( build_conv_layer( dict(type='Conv2d'), 30, 30, kernel_size=3, stride=1, padding=1, bias=False), build_upsample_layer( dict(type...
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