utils.py

import torch
from torchvision import transforms
from PIL import Image
import torch.nn.functional as F
import numpy as np
import cv2
import os

def get_clothes_mask(old_label) :
    clothes = torch.FloatTensor((old_label.cpu().numpy() == 3).astype(np.int))
    return clothes

def changearm(old_label):
    label=old_label
    arm1=torch.FloatTensor((old_label.cpu().numpy()==5).astype(np.int))
    arm2=torch.FloatTensor((old_label.cpu().numpy()==6).astype(np.int))
    label=label*(1-arm1)+arm1*3
    label=label*(1-arm2)+arm2*3
    return label

def gen_noise(shape):
    noise = np.zeros(shape, dtype=np.uint8)
    ### noise
    noise = cv2.randn(noise, 0, 255)
    noise = np.asarray(noise / 255, dtype=np.uint8)
    noise = torch.tensor(noise, dtype=torch.float32)
    return noise

def cross_entropy2d(input, target, weight=None, size_average=True):
    n, c, h, w = input.size()
    nt, ht, wt = target.size()

    # Handle inconsistent size between input and target
    if h != ht or w != wt:
        input = F.interpolate(input, size=(ht, wt), mode="bilinear", align_corners=True)

    input = input.transpose(1, 2).transpose(2, 3).contiguous().view(-1, c)
    target = target.view(-1)
    loss = F.cross_entropy(
        input, target, weight=weight, size_average=size_average, ignore_index=250
    )
    return loss

def ndim_tensor2im(image_tensor, imtype=np.uint8, batch=0):
    image_numpy = image_tensor[batch].cpu().float().numpy()
    result = np.argmax(image_numpy, axis=0)
    return result.astype(imtype)

def visualize_segmap(input, multi_channel=True, tensor_out=True, batch=0) :
    palette = [
        0, 0, 0, 128, 0, 0, 254, 0, 0, 0, 85, 0, 169, 0, 51,
        254, 85, 0, 0, 0, 85, 0, 119, 220, 85, 85, 0, 0, 85, 85,
        85, 51, 0, 52, 86, 128, 0, 128, 0, 0, 0, 254, 51, 169, 220,
        0, 254, 254, 85, 254, 169, 169, 254, 85, 254, 254, 0, 254, 169, 0
    ]
    input = input.detach()
    if multi_channel :
        input = ndim_tensor2im(input,batch=batch)
    else :
        input = input[batch][0].cpu()
        input = np.asarray(input)
        input = input.astype(np.uint8)
    input = Image.fromarray(input, 'P')
    input.putpalette(palette)

    if tensor_out :
        trans = transforms.ToTensor()
        return trans(input.convert('RGB'))

    return input

def pred_to_onehot(prediction) :
    size = prediction.shape
    prediction_max = torch.argmax(prediction, dim=1)
    oneHot_size = (size[0], 13, size[2], size[3])
    pred_onehot = torch.FloatTensor(torch.Size(oneHot_size)).zero_()
    pred_onehot = pred_onehot.scatter_(1, prediction_max.unsqueeze(1).data.long(), 1.0)
    return pred_onehot

def cal_miou(prediction, target) :
    size = prediction.shape
    target = target.cpu()
    prediction = pred_to_onehot(prediction.detach().cpu())
    list = [1,2,3,4,5,6,7,8]
    union = 0
    intersection = 0
    for b in range(size[0]) :
        for c in list :
            intersection += torch.logical_and(target[b,c], prediction[b,c]).sum()
            union += torch.logical_or(target[b,c], prediction[b,c]).sum()
    return intersection.item()/union.item()

def save_images(img_tensors, img_names, save_dir):
    for img_tensor, img_name in zip(img_tensors, img_names):
        tensor = (img_tensor.clone() + 1) * 0.5 * 255
        tensor = tensor.cpu().clamp(0, 255)

        try:
            array = tensor.numpy().astype('uint8')
        except:
            array = tensor.detach().numpy().astype('uint8')

        if array.shape[0] == 1:
            array = array.squeeze(0)
        elif array.shape[0] == 3:
            array = array.swapaxes(0, 1).swapaxes(1, 2)

        im = Image.fromarray(array)
        im.save(os.path.join(save_dir, img_name), format='JPEG')
        
        
def create_network(cls, opt):
    net = cls(opt)
    net.print_network()
    if len(opt.gpu_ids) > 0:
        assert(torch.cuda.is_available())
        net.cuda()
    net.init_weights(opt.init_type, opt.init_variance)
    return net