reconstruction.py

import os
from tqdm import tqdm
import torch
from torch.utils.data import DataLoader
from logger import Logger, Visualizer
import numpy as np
import imageio


def reconstruction(config, generator, kp_detector, tdmm, checkpoint, log_dir, dataset, with_eye=True):
    png_dir = os.path.join(log_dir, 'reconstruction/png')
    log_dir = os.path.join(log_dir, 'reconstruction')

    if checkpoint is not None:
        Logger.load_cpk(checkpoint, generator=generator, kp_detector=kp_detector, tdmm=tdmm)
    else:
        raise AttributeError("Checkpoint should be specified for mode='reconstruction'.")
    dataloader = DataLoader(dataset, batch_size=1, shuffle=False, num_workers=1)

    if not os.path.exists(log_dir):
        os.makedirs(log_dir)

    if not os.path.exists(png_dir):
        os.makedirs(png_dir)

    loss_list = []
    if torch.cuda.is_available():
        generator = generator.cuda()
        kp_detector = kp_detector.cuda()
        tdmm = tdmm.cuda()

    generator.eval()
    kp_detector.eval()
    tdmm.eval()

    for it, x in tqdm(enumerate(dataloader)):
        if config['reconstruction_params']['num_videos'] is not None:
            if it > config['reconstruction_params']['num_videos']:
                break
        with torch.no_grad():
            predictions = []
            visualizations = []
            if torch.cuda.is_available():
                x['video'] = x['video'].cuda()
            kp_source = kp_detector(x['video'][:, :, 0])

            source_codedict = tdmm.encode(x['video'][:, :, 0]) 
            for frame_idx in range(x['video'].shape[2]):
                source = x['video'][:, :, 0]
                driving = x['video'][:, :, frame_idx]

                kp_driving = kp_detector(driving)
                driving_codedict = tdmm.encode(x['video'][:, :, frame_idx])

                source_verts, source_transformed_verts, source_ldmk_2d = tdmm.decode_flame(source_codedict)
                driving_verts, driving_transformed_verts, driving_ldmk_2d = tdmm.decode_flame(driving_codedict)
               
                source_albedo = tdmm.extract_texture(source, source_transformed_verts, with_eye=with_eye)
                
                render_ops = tdmm.render(source_transformed_verts, driving_transformed_verts, source_albedo)

                out = generator(source, kp_source=kp_source, kp_driving=kp_driving, 
                                render_ops=render_ops, driving_features=driving_codedict)
                out['kp_source'] = kp_source
                out['kp_driving'] = kp_driving
                
                del out['sparse_deformed']

                predictions.append(np.transpose(out['prediction'].data.cpu().numpy(), [0, 2, 3, 1])[0])

                visualization = Visualizer(**config['visualizer_params']).visualize(source=source,
                                                                                    driving=driving, out=out)
                visualizations.append(visualization)

                loss_list.append(torch.abs(out['prediction'] - driving).mean().cpu().numpy())

            if not os.path.exists(os.path.join(png_dir, x['name'][0])):
                os.mkdir(os.path.join(png_dir, x['name'][0])) 
            # save png
            for i in range(len(predictions)):
                imageio.imsave(os.path.join(png_dir, x['name'][0] + '/%07d.png' % i), (255 * predictions[i]).astype(np.uint8))
            # save gif/mp4
            image_name = x['name'][0] + config['reconstruction_params']['format']
            imageio.mimsave(os.path.join(log_dir, image_name), visualizations)

    print("Reconstruction loss: %s" % np.mean(loss_list))