Summer Boobs
2009 | DDF | Runtime: 2h 12min | Trailer
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Description: ```python megan_train = { "A_size": [32, 32], "class_num": 10, "crop": False, "data_dir": "/data/MeganFace/'12'", "mean_value": [125.8976, 124.8908, 112.133], "mirror": True, "name": "megan", "rotate": (-5, +5), "scale": (0.8, 1.2), "slide_min": (-0.1, 0.1), "test_batch": 64, "test_size": [256, 256], "train_batch": 32, # 'val_image': 'crop', or leave it as None "val_image": None, "workers": 0, "zcat_prob": [0.1, 0.3], # for zoom crop and align technique } ''' The parameter settings and dataset of the train configuration files of vgg face model ''' # We got several different val image preprocess methods in YoloX def valImagePrePostProcess(img): img = cv2.resize(img, (640, 640)) out, _ ,_ = netDetect(img) return out class ValTransformM: def __init__(self, short=None): if short !=None : self.short = short else: self.short = randomInt() transformList = [] transformList += [toTensorV2()] transformList += [transformM] self.transform = Transform(transformList) def __call__(self, img ): h, w = img.shape[:2] ratio_ = max(h,w)/min(h,w) if ratio_ < self.short/max(megan_train['test_size']) or self.short == 512 : s = max(h,w) sh = min(s*self.short/(max(h,w)), megan_train['test_size'][0]) sw = min(s*self.short/(max(h,w