def preprocess_input(x, **kwargs): kwargs = { k: v for k, v in kwargs.items() if k in ['backend', 'layers', 'models', 'utils'] } return _preprocess_input(x, mode='torch', **kwargs)
def preprocess_input(x): """ "mode" option description in preprocess_input mode: One of "caffe", "tf" or "torch". - caffe: will convert the images from RGB to BGR, then will zero-center each color channel with respect to the ImageNet dataset, without scaling. - tf: will scale pixels between -1 and 1, sample-wise. - torch: will scale pixels between 0 and 1 and then will normalize each channel with respect to the ImageNet dataset. """ x = _preprocess_input(x, mode='tf', backend=K) #x /= 255. #mean = [0.485, 0.456, 0.406] #std = [0.229, 0.224, 0.225] #x[..., 0] -= mean[0] #x[..., 1] -= mean[1] #x[..., 2] -= mean[2] #if std is not None: #x[..., 0] /= std[0] #x[..., 1] /= std[1] #x[..., 2] /= std[2] return x
def preprocess_input(x, **kwargs): kwargs = { k: v for k, v in kwargs.items() if k in ["backend", "layers", "models", "utils"] } return _preprocess_input(x, mode="torch", **kwargs)
def preprocess_input(x, **kwargs): """Preprocesses a numpy array encoding a batch of images. # Arguments x: a 4D numpy array consists of RGB values within [0, 255]. # Returns Preprocessed array. """ return _preprocess_input(x, mode='tf', backend=K, **kwargs)
def preprocess_input(x): """ "mode" option description in preprocess_input mode: One of "caffe", "tf" or "torch". - caffe: will convert the images from RGB to BGR, then will zero-center each color channel with respect to the ImageNet dataset, without scaling. - tf: will scale pixels between -1 and 1, sample-wise. - torch: will scale pixels between 0 and 1 and then will normalize each channel with respect to the ImageNet dataset. """ x = _preprocess_input(x, mode='tf', backend=K) return x
def preprocess_input(x, **kwargs): return _preprocess_input(x, mode='torch', **kwargs)