Upload src/utils/inference_utils.py with huggingface_hub

src/utils/inference_utils.py

@@ -0,0 +1,56 @@
+import random
+import torch
+import numpy as np
+import os
+
+def set_all_seeds(seed=42):
+    print("set all seeds", flush=True)
+    random.seed(seed)
+    np.random.seed(seed)
+    torch.manual_seed(seed)
+    torch.cuda.manual_seed(seed)
+    torch.cuda.manual_seed_all(seed)
+    torch.backends.cudnn.deterministic = True
+    torch.backends.cudnn.benchmark = False
+
+def fix_state_dict(state_dict):
+    new_state_dict = {}
+    for k, v in state_dict.items():
+        name = k[7:] if k.startswith('module.') else k
+        new_state_dict[name] = v
+    return new_state_dict
+
+
+#######################################################
+def load_hint_texts_from_file(file_path):
+    hint_texts = []
+    with open(file_path, 'r') as file:
+        for line in file:
+            hint_texts.append([line.strip()])
+    return hint_texts
+
+def load_mask_from_file(file_path):
+    mask = []
+    with open(file_path, 'r') as file:
+        for line in file:
+            mask.append([line.strip()])
+    return mask
+
+def load_file_names(file_path):
+    with open(file_path, 'r') as file:
+        file_names = [line.strip() for line in file]
+    return file_names
+
+def gen_prog_ind(num_cases=16, sublist_length=4):
+    total_range = 0.9
+    step = total_range / sublist_length
+    ranges = [(i * step, i * step + step / 5) for i in range(sublist_length)]
+    
+    prog_ind_all = []
+    for _ in range(num_cases):
+        while True:
+            case = [random.uniform(r[0], r[1]) for r in ranges]
+            if all(step*0.8 <= case[i+1] - case[i] <= step*1.6 for i in range(len(case) - 1)):
+                prog_ind_all.append([case])
+                break
+    return prog_ind_all