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);
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
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function MyImage(index, name, url){ this.index = index; this.name = name; this.url = url; } var imagePool each){ imagePool.push(each); } } imagePool.sort(sortByIndex); debugger; </script> </html> 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
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