def __init__(self, **kwargs): """ Initialize preprocessing class for training set Args: preprocess (String): Keyword to select different preprocessing types crop_type (String): Select random or central crop Return: None """ self.transforms = [] self.transforms1 = [] self.preprocess = kwargs['preprocess'] crop_type = kwargs['crop_type'] self.transforms.append(pt.ResizeClip(**kwargs)) if crop_type == 'Random': self.transforms.append(pt.RandomCropClip(**kwargs)) else: self.transforms.append(pt.CenterCropClip(**kwargs)) self.transforms.append(pt.SubtractRGBMean(**kwargs)) self.transforms.append( pt.RandomFlipClip(direction='h', p=0.5, **kwargs)) self.transforms.append(pt.ToTensorClip(**kwargs))
def __init__(self, **kwargs): """ Initialize preprocessing class for training set Args: preprocess (String): Keyword to select different preprocessing types crop_type (String): Select random or central crop Return: None """ self.transforms = [] self.transforms1 = [] self.preprocess = kwargs['preprocess'] crop_type = kwargs['crop_type'] self.clip_mean = np.load('weights/sport1m_train16_128_mean.npy')[0] self.clip_mean = np.transpose(self.clip_mean, (1, 2, 3, 0)) self.transforms.append(pt.ResizeClip(**kwargs)) self.transforms.append( pt.SubtractMeanClip(clip_mean=self.clip_mean, **kwargs)) if crop_type == 'Random': self.transforms.append(pt.RandomCropClip(**kwargs)) else: self.transforms.append(pt.CenterCropClip(**kwargs)) self.transforms.append( pt.RandomFlipClip(direction='h', p=0.5, **kwargs)) self.transforms.append(pt.ToTensorClip(**kwargs))
def __init__(self, **kwargs): crop_shape = kwargs['crop_shape'] crop_type = kwargs['crop_type'] resize_shape = kwargs['resize_shape'] self.transforms = [] if crop_type == 'Random': self.transforms.append(pt.RandomCropClip(**kwargs)) elif crop_type == 'Center': self.transforms.append(pt.CenterCropClip(**kwargs)) self.transforms.append(pt.ResizeClip(**kwargs)) self.transforms.append(pt.SubtractRGBMean(**kwargs)) self.transforms.append(pt.ToTensorClip())
def __init__(self, **kwargs): """ Initialize preprocessing class for training set Args: preprocess (String): Keyword to select different preprocessing types crop_type (String): Select random or central crop Return: None """ self.transforms = [] self.transforms.append(pt.ResizeClip(**kwargs)) self.transforms.append(pt.CenterCropClip(**kwargs)) self.transforms.append(pt.SubtractRGBMean(**kwargs)) self.transforms.append(pt.ToTensorClip(**kwargs))