force

.gitignore

@@ -1 +1,2 @@
-*.pyc
+*.pyc
+*.sh

app.py

@@ -32,9 +32,8 @@ def inference(img, video):
     if not os.path.exists('temp'):
       os.system('mkdir temp')
     ####  Resize the longer edge of the input image
-    cmd = f"ffmpeg -y -ss 00:00:00 -i {video} -to 00:00:08 -c copy temp/driving_video.mp4"
-    subprocess.run(cmd.split())
-    driving_video = "video_input.mp4"
+    os.system("ffmpeg -y -ss 00:00:00 -i {video} -to 00:00:08 -c copy temp/driving_video.mp4")
+    # driving_video = "video_input.mp4"
     os.system("python demo_dagan.py --source_image {} --driving_video 'temp/driving_video.mp4' --output 'temp/rst.mp4'".format(img))
     return f'temp/rst.mp4'
     

demo_dagan.py

@@ -5,9 +5,7 @@
 
 import torch
 import torch.nn.functional as F
-import os
 from skimage import img_as_ubyte
-import cv2
 import argparse
 import imageio
 from skimage.transform import resize
@@ -123,6 +121,7 @@ def make_animation(source_image, driving_video, generator, kp_detector, relative
     return sources, drivings, predictions,depth_gray
 with open("config/vox-adv-256.yaml") as f:
     config = yaml.load(f)
+
 generator = G.SPADEDepthAwareGenerator(**config['model_params']['generator_params'],**config['model_params']['common_params'])
 config['model_params']['common_params']['num_channels'] = 4
 kp_detector = KPD.KPDetector(**config['model_params']['kp_detector_params'],**config['model_params']['common_params'])
@@ -139,8 +138,8 @@ kp_detector.load_state_dict(ckp_kp_detector)
 
 depth_encoder = depth.ResnetEncoder(18, False)
 depth_decoder = depth.DepthDecoder(num_ch_enc=depth_encoder.num_ch_enc, scales=range(4))
-loaded_dict_enc = torch.load('encoder.pth')
-loaded_dict_dec = torch.load('depth.pth')
+loaded_dict_enc = torch.load('encoder.pth',map_location=device)
+loaded_dict_dec = torch.load('depth.pth',map_location=device)
 filtered_dict_enc = {k: v for k, v in loaded_dict_enc.items() if k in depth_encoder.state_dict()}
 depth_encoder.load_state_dict(filtered_dict_enc)
 ckp_depth_decoder= {k: v for k, v in loaded_dict_dec.items() if k in depth_decoder.state_dict()}