Fix path

prismer/experts/generate_depth.py

@@ -20,7 +20,7 @@ from tqdm import tqdm
 model, transform = load_expert_model(task='depth')
 accelerator = Accelerator(mixed_precision='fp16')
 
-config = yaml.load(open('configs/experts.yaml', 'r'), Loader=yaml.Loader)
+config = yaml.load(open('prismer/configs/experts.yaml', 'r'), Loader=yaml.Loader)
 data_path = config['data_path']
 save_path = os.path.join(config['save_path'], 'depth')
 

prismer/experts/generate_edge.py

@@ -22,7 +22,7 @@ from tqdm import tqdm
 model, transform = load_expert_model(task='edge')
 accelerator = Accelerator(mixed_precision='fp16')
 
-config = yaml.load(open('configs/experts.yaml', 'r'), Loader=yaml.Loader)
+config = yaml.load(open('prismer/configs/experts.yaml', 'r'), Loader=yaml.Loader)
 data_path = config['data_path']
 save_path = os.path.join(config['save_path'], 'edge')
 

prismer/experts/generate_normal.py

@@ -22,7 +22,7 @@ import numpy as np
 model, transform = load_expert_model(task='normal')
 accelerator = Accelerator(mixed_precision='fp16')
 
-config = yaml.load(open('configs/experts.yaml', 'r'), Loader=yaml.Loader)
+config = yaml.load(open('prismer/configs/experts.yaml', 'r'), Loader=yaml.Loader)
 data_path = config['data_path']
 save_path = os.path.join(config['save_path'], 'normal')
 

prismer/experts/generate_objdet.py

@@ -22,7 +22,7 @@ from tqdm import tqdm
 model, transform = load_expert_model(task='obj_detection')
 accelerator = Accelerator(mixed_precision='fp16')
 
-config = yaml.load(open('configs/experts.yaml', 'r'), Loader=yaml.Loader)
+config = yaml.load(open('prismer/configs/experts.yaml', 'r'), Loader=yaml.Loader)
 data_path = config['data_path']
 save_path = config['save_path']
 

prismer/experts/generate_ocrdet.py

@@ -26,7 +26,7 @@ model, transform = load_expert_model(task='ocr_detection')
 accelerator = Accelerator(mixed_precision='fp16')
 pca_clip = pk.load(open('dataset/clip_pca.pkl', 'rb'))
 
-config = yaml.load(open('configs/experts.yaml', 'r'), Loader=yaml.Loader)
+config = yaml.load(open('prismer/configs/experts.yaml', 'r'), Loader=yaml.Loader)
 data_path = config['data_path']
 save_path = os.path.join(config['save_path'], 'ocr_detection')
 

prismer/experts/generate_segmentation.py

@@ -20,7 +20,7 @@ from tqdm import tqdm
 model, transform = load_expert_model(task='seg_coco')
 accelerator = Accelerator(mixed_precision='fp16')
 
-config = yaml.load(open('configs/experts.yaml', 'r'), Loader=yaml.Loader)
+config = yaml.load(open('prismer/configs/experts.yaml', 'r'), Loader=yaml.Loader)
 data_path = config['data_path']
 save_path = os.path.join(config['save_path'], 'seg_coco')
 

prismer_model.py

@@ -53,19 +53,11 @@ def run_experts(image_path: str) -> tuple[str | None, ...]:
             env['PYTHONPATH'] = f'{submodule_dir.as_posix()}:{env["PYTHONPATH"]}'
         else:
             env['PYTHONPATH'] = submodule_dir.as_posix()
-        subprocess.run(
-            shlex.split(f'python experts/generate_{expert_name}.py'),
-            cwd='prismer',
-            env=env,
-            check=True)
+        subprocess.run(shlex.split(f'python experts/generate_{expert_name}.py'), cwd='prismer', env=env, check=True)
 
     keys = ['depth', 'edge', 'normal', 'seg_coco', 'obj_detection', 'ocr_detection']
-    results = [
-        pathlib.Path('prismer/helpers/labels') / key /
-        'helpers/images/image.png' for key in keys
-    ]
-    return tuple(path.as_posix() if path.exists() else None
-                 for path in results)
+    results = [pathlib.Path('prismer/helpers/labels') / key / 'helpers/images/image.png' for key in keys]
+    return tuple(path.as_posix() if path.exists() else None for path in results)
 
 
 class Model:
@@ -116,5 +108,5 @@ class Model:
 
     def run_caption(self, image_path: str, model_name: str) -> tuple[str | None, ...]:
         out_paths = run_experts(image_path)
-        caption = self.run_caption_model(model_name)
-        return caption, *out_paths
+        # caption = self.run_caption_model(model_name)
+        return None, *out_paths