else if(v1.index > v2.index ){ return 1; } else return 0; } function extractImage(source,raw,imagePool...[]" + urls[1]; var storedImage = new SortedImage(index, url); imagePool.push(storedImage); }...else{ raw.push(trimed); } } imagePool.sort(sortByIndex); } function replaceImageTag(raw,imagePool...(){ var source = document.getElementById("raw"); var html = source.value; var rawItem = []; var imagePool...= []; extractImage(html,rawItem, imagePool); var formatted = replaceImageTag(rawItem,imagePool);
squoosh/lib 然后就一些固定代码了 没啥好说的 我封装了一层promise 用来进行批量处理的 const squoosh = require("@squoosh/lib"); const { ImagePool...libSquooshOptimize(imagePath, filename, outputFolderPath) { return new Promise(async (resolve) => { const imagePool... = new ImagePool(); const image = await imagePool.ingestImage(imagePath); const preprocessOptions...${extension}`, binary); imagePool.close(); resolve(true); }); } 调用示例： libSquooshOptimize(".
下面介绍使用 api 方式开发集成的方法： 安装 Squoosh $ npm install @squoosh/lib 在开发项目中引入和初始化 import { ImagePool } from '...@squoosh/lib'; import { cpus } from 'os'; const imagePool = new ImagePool(cpus().length); 压缩图片 import.../path/to/image.png'); const image = imagePool.ingestImage(file); const preprocessOptions = { // 压缩参数
最终的解决方案： function extractImage(source,raw,imagePool){ var splitted = source.split("\n"); var first_space...[]" + urls[1]; var storedImage = new SortedImage(index, url); imagePool.push(storedImage); }...} else{ continue; } } else { raw.push(trimed); first_space = true; } } imagePool.sort
function MyImage(index, name, url){ this.index = index; this.name = name; this.url = url; } var imagePool...each){ imagePool.push(each); } } imagePool.sort(sortByIndex); debugger; 第58行传入数组原生的
import torch.nn.functional as F # 全局平均池化，将得到的图像特征输入到一个拥有256个通道的1*1卷积中，最后将特征进行 # 双线性上采样到特定的维度(就是输入到ImagePool...之前特征图的维度) class _ImagePool(nn.Module): def __init__(self, in_ch, out_ch): super()....ConvBnReLU(in_ch, out_ch, 3, 1, padding=rate, dilation=rate), ) self.stages.add_module("imagepool...", _ImagePool(in_ch, out_ch)) def forward(self, x): return torch.cat([stage(x) for stage
reflect') x = self.conv4(x) x = fluid.layers.tanh(x) return x 4.训练过程 下面代码中的ImagePool...import paddle.fluid as fluid import time from PIL import Image, ImageEnhance class ImagePool(object)...0.5, beta2=0.999, parameter_list=d_b.parameters()) # image pool fa_pool, fb_pool = ImagePool...(), ImagePool() total_step_num = np.array([0]) if load_model == True: ga_para
self.sample_dir = 'sample' self.print_freq = 5 self.save_freq = 10 self.pool = ImagePool
领取 专属20元代金券
Get大咖技术交流圈
云服务器
网站备案
对象存储
云直播
实时音视频
