commit

app.py

@@ -23,26 +23,16 @@ from utils_das3r.rearrange import main as rearrange_main
 from train_gui import main as train_main
 from render import main as render_main
 
-def get_dust3r_args_parser():
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--image_size", type=int, default=512, choices=[512, 224], help="image size")
-    parser.add_argument("--model_path", type=str, default="submodules/dust3r/checkpoints/DUSt3R_ViTLarge_BaseDecoder_512_dpt.pth", help="path to the model weights")
-    parser.add_argument("--device", type=str, default='cuda', help="pytorch device")
-    parser.add_argument("--batch_size", type=int, default=1)
-    parser.add_argument("--schedule", type=str, default='linear')
-    parser.add_argument("--lr", type=float, default=0.01)
-    parser.add_argument("--niter", type=int, default=300)
-    parser.add_argument("--focal_avg", type=bool, default=True)
-    parser.add_argument("--n_views", type=int, default=3)
-    parser.add_argument("--base_path", type=str, default=GRADIO_CACHE_FOLDER) 
-    return parser
-
 
 def natural_sort(l): 
     convert = lambda text: int(text) if text.isdigit() else text.lower()
     alphanum_key = lambda key: [convert(c) for c in re.split('([0-9]+)', key.split('/')[-1])]
     return sorted(l, key=alphanum_key)
 
+def cmd(command):
+    print(command)
+    os.system(command)
+
 @spaces.GPU(duration=150)
 def process(inputfiles, input_path='demo'):
     if inputfiles:
@@ -64,11 +54,15 @@ def process(inputfiles, input_path='demo'):
     imgs_path = temp_dir
     output_path = f'./results/{input_path}/output'
     rearranged_path = f'{output_path}_rearranged'
-
-    dynamic_predictor_main(pretrained='Kai422kx/das3r', dir_path=imgs_path, output_dir=output_path, use_pred_mask=True, n_iter=150)
-    rearrange_main(output_dir=output_path, rearranged_path = rearranged_path)
-    train_main(s = rearranged_path, m = rearranged_path, iter = 2000)
-    render_main(s = rearranged_path, m = rearranged_path, iter = 2000, get_video = True)
+    cmd(f"python dynamic_predictor/launch.py --mode=eval_pose_custom \
+        --pretrained=Kai422kx/das3r \
+        --dir_path={imgs_path} \
+        --output_dir={output_path} \
+        --use_pred_mask ")
+    # dynamic_predictor_main(pretrained='Kai422kx/das3r', dir_path=imgs_path, output_dir=output_path, use_pred_mask=True, n_iter=150)
+    # rearrange_main(output_dir=output_path, rearranged_path = rearranged_path)
+    # train_main(s = rearranged_path, m = rearranged_path, iter = 2000)
+    # render_main(s = rearranged_path, m = rearranged_path, iter = 2000, get_video = True)