transform.py

from data import transform as base_transform
import numpy as np

from utils import is_list, is_dict, get_valid_args


class NoOperation():
    def __call__(self, x):
        return x


class BaseSilTransform():
    def __init__(self, disvor=255.0, img_shape=None):
        self.disvor = disvor
        self.img_shape = img_shape

    def __call__(self, x):
        if self.img_shape is not None:
            s = x.shape[0]
            _ = [s] + [*self.img_shape]
            x = x.reshape(*_)
        return x / self.disvor


class BaseSilCuttingTransform():
    def __init__(self, img_w=64, disvor=255.0, cutting=None):
        self.img_w = img_w
        self.disvor = disvor
        self.cutting = cutting

    def __call__(self, x):
        if self.cutting is not None:
            cutting = self.cutting
        else:
            cutting = int(self.img_w // 64) * 10
        x = x[..., cutting:-cutting]
        return x / self.disvor


class BaseRgbTransform():
    def __init__(self, mean=None, std=None):
        if mean is None:
            mean = [0.485*255, 0.456*255, 0.406*255]
        if std is None:
            std = [0.229*255, 0.224*255, 0.225*255]
        self.mean = np.array(mean).reshape((1, 3, 1, 1))
        self.std = np.array(std).reshape((1, 3, 1, 1))

    def __call__(self, x):
        return (x - self.mean) / self.std


def get_transform(trf_cfg=None):
    if is_dict(trf_cfg):
        transform = getattr(base_transform, trf_cfg['type'])
        valid_trf_arg = get_valid_args(transform, trf_cfg, ['type'])
        return transform(**valid_trf_arg)
    if trf_cfg is None:
        return lambda x: x
    if is_list(trf_cfg):
        transform = [get_transform(cfg) for cfg in trf_cfg]
        return transform
    raise "Error type for -Transform-Cfg-"