import tensorflow as tf import horovod.tensorflow as hvd # 1. 初始化horovod hvd.init() # 2. 给当前进程分配对应的gpu,local_rank()返回的是当前是第几个进程 config = tf.ConfigProto() config.gpu_options.visible_device_list = str(hvd.local_rank()) # 3. Scale学习率,封装优化器 opt = tf.t...
这部分是训练的配置参数,首先时父类Config,然后子类train_config,eval_config继承父类,这里跟Backbone的写法类似。 importastfromtypingimportTuple,Listfromroi.poolerimportPooler# Pooler类 有两个方法 ROI pooling ROI Align# config的父类classConfig(object):#图像缩放的尺寸 短边缩放为600 长边为 1000IMAGE_MIN_...
from configimport* 并且使用。当必须执行各种尝试和错误方法时,更改配置文件就足够了。 步骤2:TensorBoard:还记得从TensorBoardX导入SummaryWriter吗?如前所述,可以用tensorboard 显示损失、精度等。 在列车运行前,只需添加一行,即可包含tensorboard : 代码语言:javascript 复制 writer=SummaryWriter(path) 在每个epoch循环...
config = {"l1": tune.choice([2 ** i for i in range(9)]),"l2": tune.choice([2 ** i for i in range(9)]),"lr": tune.loguniform(1e-4, 1e-1),"batch_size": tune.choice([2, 4, 8, 16])} tune.choice()接受一个从中均匀抽样的值列表。在这个例子中,l1和l2参数应该是介于 ...
importmodelsfromconfigimportDefaultConfig opt=DefaultConfig() lr=opt.lr model=getattr(models, opt.model) dataset= DogCat(opt.train_data_root) defparse(self, kwargs):"""根据字典kwargs 更新 config参数"""#更新配置参数fork, vinkwargs.items():ifnothasattr(self, k):#警告还是报错,取决于你个人的...
config(可选):模型的配置 state_dict(可选):一个可选择的权重字典而不是下载的预训练模型权重,当需要创建一个使用预训练配置但加载自己的权重值时使用。 cache_dir(可选):当不使用标准缓存时,可以设置这样一个缓存路径,在这个路径里应该存放着预训练模型配置。
from .config import HOME import os.path as osp import sys import torch import torch.utils.data as data import cv2 import numpy as np if sys.version_info[0] == 2: import xml.etree.cElementTree as ET else: import xml.etree.ElementTree as ET ...
importtorchimporttorch.nn as nnimporttorch.nn.functional as Fimportscipy.io as scioimportosimportnumpy as npfromconfigimportConfigimportjson config=Config() Mul= Config.MUL.astype('float32') Shift= Config.SHIFT.astype('float32')defload_json(j_fn): ...
Name Last commit message Last commit date Latest commit aorenste and pytorchmergebot PEP585: Add noqa to necessary tests (#146391) Feb 12, 2025 1f8ff94·Feb 12, 2025 History 84,312 Commits .ci .circleci .ctags.d .devcontainer .github ...
If you wish to use a different scheduler (e.g.: DDIM, LMS, PNDM/PLMS), you can instantiate it before the pipeline and pass it to from_pretrained. from diffusers import LMSDiscreteScheduler lms = LMSDiscreteScheduler.from_config("CompVis/stable-diffusion-v1-4", subfolder="scheduler") pipe...