From a1e739e774397feebcc469578d69f95aef46db5c Mon Sep 17 00:00:00 2001
From: LielinJiang <jianglielin@baidu.com>
Date: Fri, 18 Sep 2020 15:57:50 +0000
Subject: [PATCH] rm unused code

---
 ppgan/utils/animate.py | 72 ------------------------------------------
 1 file changed, 72 deletions(-)

diff --git a/ppgan/utils/animate.py b/ppgan/utils/animate.py
index eae7469..3ac08d9 100644
--- a/ppgan/utils/animate.py
+++ b/ppgan/utils/animate.py
@@ -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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