use outdir const

evaluate.py

@@ -14,6 +14,9 @@ import json
 import cv2
 from huggingface_hub import snapshot_download
 
+outdir = 'outputs/' #'/data/out/'
+
+
 # Convert PIL to numpy
 def pil_to_np(img):
     return np.array(img).astype(np.float32) / 255.0
@@ -125,9 +128,9 @@ def get_score(item, image_paths, video_path, train_steps=100, inference_steps=10
     results_base = []
     gt_frames = []
     max_frame_count = 200
-    if os.path.isdir('/data/out/'+item):
-        for filename in os.listdir('/data/out/'+item):
-            img = Image.open('/data/out/'+item+'/'+filename)
+    if os.path.isdir(outdir+item):
+        for filename in os.listdir(outdir+item):
+            img = Image.open(outdir+item+'/'+filename)
             if filename.startswith('result_'):
                 results.append(img)
             elif filename.startswith('base_'):
@@ -148,16 +151,16 @@ def get_score(item, image_paths, video_path, train_steps=100, inference_steps=10
         #results = run(images, video_path, train_steps=100, inference_steps=10, fps=12, bg_remove=False, finetune=True)
         results, results_base = run_eval(images, video_path, train_steps=100, inference_steps=10, fps=12, modelId="fine_tuned_pcdms", img_width=1920, img_height=1080, bg_remove=False, resize_inputs=False)
 
-        os.makedirs('/data/out/'+item, exist_ok=True)
+        os.makedirs(outdir+item, exist_ok=True)
 
         for i, frame in enumerate(gt_frames):
-            frame.save("/data/out/"+item+"/frame_"+str(i)+".png")
+            frame.save(outdir+item+"/frame_"+str(i)+".png")
 
         for i, result in enumerate(results):
-            result.save("/data/out/"+item+"/result_"+str(i)+".png")
+            result.save(outdir+item+"/result_"+str(i)+".png")
             
         for i, result in enumerate(results_base):
-            result.save("/data/out/"+item+"/base_"+str(i)+".png")
+            result.save(outdir+item+"/base_"+str(i)+".png")
     
     ssim = []
     psnr = []

outputs/metrics.json

@@ -0,0 +1 @@
+{}