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Saving coord and latent from being deleted between tasks #199

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3006b7a
Reuse, reduce, recycle
zhangchn Sep 21, 2024
00c1063
Merge pull request #1 from zhangchn/zhangchn-reuse-reduce-recycle-1
zhangchn Sep 21, 2024
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28 changes: 15 additions & 13 deletions scripts/inference.py
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Original file line number Diff line number Diff line change
Expand Up @@ -39,7 +39,7 @@ def main(args):
audio_basename = os.path.basename(audio_path).split('.')[0]
output_basename = f"{input_basename}_{audio_basename}"
result_img_save_path = os.path.join(args.result_dir, output_basename) # related to video & audio inputs
crop_coord_save_path = os.path.join(result_img_save_path, input_basename+".pkl") # only related to video input
crop_coord_save_path = os.path.join(args.result_dir, input_basename+".pkl") # only related to video input
os.makedirs(result_img_save_path,exist_ok =True)

if args.output_vid_name is None:
Expand Down Expand Up @@ -72,24 +72,26 @@ def main(args):
if os.path.exists(crop_coord_save_path) and args.use_saved_coord:
print("using extracted coordinates")
with open(crop_coord_save_path,'rb') as f:
coord_list = pickle.load(f)
saved_lists = pickle.load(f)
coord_list = saved_lists['coord']
input_latent_list = saved_lists['latent']
frame_list = read_imgs(input_img_list)
else:
print("extracting landmarks...time consuming")
coord_list, frame_list = get_landmark_and_bbox(input_img_list, bbox_shift)
input_latent_list = []
for bbox, frame in zip(coord_list, frame_list):
if bbox == coord_placeholder:
continue
x1, y1, x2, y2 = bbox
crop_frame = frame[y1:y2, x1:x2]
crop_frame = cv2.resize(crop_frame,(256,256),interpolation = cv2.INTER_LANCZOS4)
latents = vae.get_latents_for_unet(crop_frame)
input_latent_list.append(latents)
with open(crop_coord_save_path, 'wb') as f:
pickle.dump(coord_list, f)
pickle.dump({'coord': coord_list, 'latent': input_latent_list}, f)

i = 0
input_latent_list = []
for bbox, frame in zip(coord_list, frame_list):
if bbox == coord_placeholder:
continue
x1, y1, x2, y2 = bbox
crop_frame = frame[y1:y2, x1:x2]
crop_frame = cv2.resize(crop_frame,(256,256),interpolation = cv2.INTER_LANCZOS4)
latents = vae.get_latents_for_unet(crop_frame)
input_latent_list.append(latents)


# to smooth the first and the last frame
frame_list_cycle = frame_list + frame_list[::-1]
Expand Down