test_2.py

from concurrent.futures import process
import os
from collections import OrderedDict
# from tkinter import Image
import torch
import torchvision
import ntpath

import data
from options.test_options import TestOptions
from models.pix2pix_model import Pix2PixModel
from util.util import mkdir
from util.visualizer import Visualizer
from util import html
from tqdm import tqdm
import numpy as np

opt = TestOptions().parse()


dataloader = data.create_dataloader(opt)

model = Pix2PixModel(opt)
if opt.task != 'MMIS' and opt.dataset_mode != 'photo2art':
    model.eval()

visualizer = Visualizer(opt)

web_dir = os.path.join(opt.results_dir, opt.name,
                       '%s_%s' % (opt.phase, opt.which_epoch))
webpage = html.HTML(web_dir,
                    'Experiment = %s, Phase = %s, Epoch = %s' %
                    (opt.name, opt.phase, opt.which_epoch))



# test
print('Number of images: ', len(dataloader))
alpha_list = torch.linspace(0, 1.0, 20)    #generate 20 images for each input image
stack = False
samples = len(dataloader)
processed = -1
for i, data_i in enumerate(tqdm(dataloader)):
    processed += 1
    if processed > samples:
        break
    alpha_imgs = []
    for j, alpha in enumerate(alpha_list):
        opt.alpha = alpha
        generated = model(data_i, mode='inference',iters=0,progress=None,epochs=None,images_iters=None)

        #self, data, mode,iters,progress,epochs,images_iters
        img_path = data_i['cpath']

        if j == 0:
            alpha_imgs.append(data_i['image'])
            alpha_imgs.append(data_i['label'])
        alpha_imgs.append(generated)
        if stack:
            image_dir = webpage.get_image_dir()
            short_path = ntpath.basename(img_path[0])
            name = os.path.splitext(short_path)[0]
            image_name = '%s.png' % name
            os.makedirs(image_dir, exist_ok=True)
            save_path = os.path.join(image_dir, image_name)
            alpha_stack = torch.cat(alpha_imgs, dim=0)
            im_grid = torchvision.utils.make_grid(alpha_stack, nrow=len(alpha_imgs) + 2, padding=0, normalize=True)
            torchvision.utils.save_image(im_grid, save_path)

        else:
            for b in range(generated.shape[0]):
                # print(i, 'process image... %s' % img_path[b])
                if opt.show_input:
                    if opt.task == 'SIS':
                        visuals = OrderedDict([('input_label', data_i['label'][b]),
                                            ('real_image', data_i['image'][b]),
                                            ('synthesized_image', generated[b])])
                    else:
                        visuals = OrderedDict([('content', data_i['label'][b]),
                                            ('style', data_i['image'][b]),
                                            ('synthesized_image', generated[b])])
                else:
                    visuals = OrderedDict([('synthesized_image', generated[b])])
                visualizer.save_images(webpage, visuals, img_path[b:b + 1], alpha=opt.alpha)

webpage.save()