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{
"latex-workshop.view.pdf.viewer": "browser",
"latex-workshop.latex.tools": [
       {
"name": "latexmk",
"command": "latexmk",
"args": [
"-synctex=1",
"-interaction=nonstopmode",
"-file-line-error",
"-pdf",
"%DOC%"
         ]
       },
       {
"name": "xelatex",
"command": "xelatex",
"args": [
"-synctex=1",
"-interaction=nonstopmode",
"-file-line-error",
"%DOC%"
           ]
       },          
       {
"name": "pdflatex",
"command": "pdflatex",
"args": [
"-synctex=1",
"-interaction=nonstopmode",
"-file-line-error",
"%DOC%"
         ]
       },
       {
"name": "bibtex",
"command": "bibtex",
"args": [
"%DOCFILE%"
         ]
       }
     ],
"latex-workshop.latex.recipes": [
       {
"name": "xelatex",
"tools": [
"xelatex"
           ]
       },
       {
"name": "latexmk",
"tools": [
"latexmk"
         ]
       },

       {
"name": "pdflatex -> bibtex -> pdflatex*2",
"tools": [
"pdflatex",
"bibtex",
"pdflatex",
"pdflatex"
         ]
       }
     ]      
}

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if args.cuda:
    images_test = Variable(images_test.cuda(), volatile=True)
    targets_test = [Variable(ann.cuda(), volatile=True) for ann in targets_test]
else:
    images_test = Variable(images_test, volatile=True)
    targets_test = [Variable(ann, volatile=True) for ann in targets_test]

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import os
os.environ["CUDA_VISIBLE_DEVICES"]="0"

torch.backends.cudnn.enabled = True
print(torch.cuda.device_count())
gpus = [0]

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x 通常是relu的输出，不符合高斯分布
wx 比较符合高斯分布

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感受野的大小大概就是stride的指数倍

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NumpyTensor
repeatnp.repeat(a,16,axis=1)a.repeat(1,16)
tilenp.tile(a,(2,2))b = a.expand(1,4)? c = a.view(-1, 1).repeat(1, 16).view(10, -1)
transposeimg = np.transpose(img, (0, 3, 1, 2))a = a.permute(3,2,1,0)
expandnp.expand_dims(x, axis=0)torch.unsqueeze(x, 1)