提交 a1e739e7 编写于 作者: L LielinJiang

rm unused code

上级 bbe1f14d
......@@ -2,16 +2,11 @@ import os
from tqdm import tqdm
import paddle
# from paddle.utils.data import DataLoader
# from frames_dataset import PairedDataset
# from logger import Logger, Visualizer
import imageio
from scipy.spatial import ConvexHull
import numpy as np
# from sync_batchnorm import DataParallelWithCallback
def normalize_kp(kp_source,
kp_driving,
......@@ -20,8 +15,6 @@ def normalize_kp(kp_source,
use_relative_movement=False,
use_relative_jacobian=False):
if adapt_movement_scale:
# source_area = ConvexHull(kp_source['value'][0].data.cpu().numpy()).volume
# driving_area = ConvexHull(kp_driving_initial['value'][0].data.cpu().numpy()).volume
source_area = ConvexHull(kp_source['value'][0].numpy()).volume
driving_area = ConvexHull(kp_driving_initial['value'][0].numpy()).volume
adapt_movement_scale = np.sqrt(source_area) / np.sqrt(driving_area)
......@@ -43,68 +36,3 @@ def normalize_kp(kp_source,
kp_source['jacobian'])
return kp_new
# def animate(config, generator, kp_detector, checkpoint, log_dir, dataset):
# log_dir = os.path.join(log_dir, 'animation')
# png_dir = os.path.join(log_dir, 'png')
# animate_params = config['animate_params']
# dataset = PairedDataset(initial_dataset=dataset, number_of_pairs=animate_params['num_pairs'])
# dataloader = DataLoader(dataset, batch_size=1, shuffle=False, num_workers=1)
# if checkpoint is not None:
# Logger.load_cpk(checkpoint, generator=generator, kp_detector=kp_detector)
# else:
# raise AttributeError("Checkpoint should be specified for mode='animate'.")
# if not os.path.exists(log_dir):
# os.makedirs(log_dir)
# if not os.path.exists(png_dir):
# os.makedirs(png_dir)
# if torch.cuda.is_available():
# generator = DataParallelWithCallback(generator)
# kp_detector = DataParallelWithCallback(kp_detector)
# generator.eval()
# kp_detector.eval()
# for it, x in tqdm(enumerate(dataloader)):
# with torch.no_grad():
# predictions = []
# visualizations = []
# driving_video = x['driving_video']
# source_frame = x['source_video'][:, :, 0, :, :]
# kp_source = kp_detector(source_frame)
# kp_driving_initial = kp_detector(driving_video[:, :, 0])
# for frame_idx in range(driving_video.shape[2]):
# driving_frame = driving_video[:, :, frame_idx]
# kp_driving = kp_detector(driving_frame)
# kp_norm = normalize_kp(kp_source=kp_source, kp_driving=kp_driving,
# kp_driving_initial=kp_driving_initial, **animate_params['normalization_params'])
# out = generator(source_frame, kp_source=kp_source, kp_driving=kp_norm)
# out['kp_driving'] = kp_driving
# out['kp_source'] = kp_source
# out['kp_norm'] = kp_norm
# 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_frame,
# driving=driving_frame, out=out)
# visualization = visualization
# visualizations.append(visualization)
# predictions = np.concatenate(predictions, axis=1)
# result_name = "-".join([x['driving_name'][0], x['source_name'][0]])
# imageio.imsave(os.path.join(png_dir, result_name + '.png'), (255 * predictions).astype(np.uint8))
# image_name = result_name + animate_params['format']
# imageio.mimsave(os.path.join(log_dir, image_name), visualizations)
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