utils.py

from scipy import misc
import os, cv2
import numpy as np
import paddle.fluid as fluid
def load_test_data(image_path, size=256):
    img = misc.imread(image_path, mode='RGB')
    img = misc.imresize(img, [size, size])
    img = np.expand_dims(img, axis=0)
    img = preprocessing(img)

    return img

def preprocessing(x):
    x = x/127.5 - 1 # -1 ~ 1
    return x

def save_images(images, size, image_path):
    return imsave(inverse_transform(images), size, image_path)

def inverse_transform(images):
    return (images+1.) / 2

def imsave(images, size, path):
    return misc.imsave(path, merge(images, size))

def merge(images, size):
    h, w = images.shape[1], images.shape[2]
    img = np.zeros((h * size[0], w * size[1], 3))
    for idx, image in enumerate(images):
        i = idx % size[1]
        j = idx // size[1]
        img[h*j:h*(j+1), w*i:w*(i+1), :] = image

    return img

def check_folder(log_dir):
    if not os.path.exists(log_dir):
        os.makedirs(log_dir)
    return log_dir

def str2bool(x):
    return x.lower() in ('true')

def cam(x, size = 256):
    x = x - np.min(x)
    cam_img = x / np.max(x)
    cam_img = np.uint8(255 * cam_img)
    cam_img = cv2.resize(cam_img, (size, size))
    cam_img = cv2.applyColorMap(cam_img, cv2.COLORMAP_JET)
    return cam_img / 255.0

def denorm(x):
    return x*0.5 + 0.5

def RGB2BGR(x):
    return cv2.cvtColor(x, cv2.COLOR_RGB2BGR)


def clip_rho(net, vmin=0, vmax=1):
    for name, param in net.named_parameters():
        if 'rho' in name:
            param.set_value(fluid.layers.clip(param, vmin, vmax))

def set_requires_grad(nets, requires_grad=False):
        """Set requies_grad=Fasle for all the networks to avoid unnecessary computations
        Parameters:
            nets (network list)   -- a list of networks
            requires_grad (bool)  -- whether the networks require gradients or not
        """
        if not isinstance(nets, list):
            nets = [nets]
        for net in nets:
            if net is not None:
                for param in net.parameters():
                    # print('trainable:', param.trainable)
                    param.trainable = requires_grad
                    # param.stop_gradient = not requires_grad