AFutureD

img = np.random.randint(0,256,(4,3,512,512)) # (4,3,512,512) => batch size, channel, height, width
img = torch.from_numpy(img).float().div(255) # convert numpy to tensor and scale to 0-1.
cbam_tmp = resnet50_cbam()                   # create a model.
cbam_tmp(img)

AFutureD

JPEG_INFO.groupby(['plant','classes']).count()

                            path.     id
plant	classes		
小麦     小麦_叶锈病           94        94
	小麦_壳针孢叶斑病      47.       47
        小麦_散黑穗病         492.      492
	小麦_条锈病		 1837.     1837
	小麦_白粉病		 1071.     1071
	小麦_纹枯病		 398.      398

AFutureD

#include <iostream>
#include <cstdio>
#include <algorithm>
#include <vector>
#include <queue>
#include <cmath>
#include <cstring>
#include <map>
#include <set>
#include <stack>

AFutureD

setAppL(){
    APPPATH=$1
    APPID=$(mdls -name kMDItemCFBundleIdentifier $APPPATH | cut -d'"' -f2)
    defaults write $APPID AppleLanguages '(zh-CN)'
}

AFutureD

img = transforms.functional.to_pil_image(img)
img = cv2.cvtColor(np.asarray(img),cv2.COLOR_RGB2BGR)

AFutureD

$app.strings = {
    "en": {
        "title1": "Clipboard Saved",
        "title2": "Open URL",
        "title3": "Cancel",
        "title4": "Clipboard Saved",
        "title5": "OK",
        "title6": "QR Code Error",
        "title7": "OK",
        "title8": "QR Decoder",