env:Windows 11 wsl,Python 3.11.11
error:sh predict.sh：
[WARNING] [run_aurobind.py:345:main] Error in prediction: Object of type float32 is not JSON serializableTraceback (most recent call last):
File "run_aurobind.py", line 331, in main
struct_dir = predict_and_save(
^^^^^^^^^^^^^^^^^
File "run_aurobind.py", line 147, in predict_and_save
json.dump(summary_confidences, f, indent=1)
#solution provided by chatgpt
import numpy as np
import numbers
def make_json_serializable(obj):
# numpy scalars
if isinstance(obj, np.generic):
return obj.item()
# numpy arrays
if isinstance(obj, np.ndarray):
return obj.tolist()
# Python numbers are fine
if isinstance(obj, (str, bool, type(None))):
return obj
if isinstance(obj, numbers.Number):
# ensure Python native float/int
if isinstance(obj, float):
return float(obj)
if isinstance(obj, int):
return int(obj)
return obj
# lists/tuples -> list of serializables
if isinstance(obj, (list, tuple)):
return [make_json_serializable(x) for x in obj]
# dict -> convert keys/values (keys expected to be strings)
if isinstance(obj, dict):
return {str(k): make_json_serializable(v) for k, v in obj.items()}
# try common .item() (works for torch.Tensor scalar too)
if hasattr(obj, "item"):
try:
return make_json_serializable(obj.item())
except Exception:
pass
# try converting iterables
try:
iter(obj)
except TypeError:
# last resort: convert to string so json can store it
return str(obj)
else:
# If it's iterable but not list/tuple (e.g. generator), materialize it
try:
return [make_json_serializable(x) for x in obj]
except Exception:
return str(obj)
使用示例（替换原有保存段）
summary_confidences = summary_confidences_list[i]
serializable = make_json_serializable(summary_confidences)
serializable['num_recycles'] = int(args.recycling_iters) if hasattr(args.recycling_iters, "int") else args.recycling_iters
outname = f"{record.id}_seed-{seed}_sample-{i}_summary_confidences.json"
output_path = struct_dir / outname
struct_dir.mkdir(parents=True, exist_ok=True) # 确保目录存在
with output_path.open("w") as f:
json.dump(serializable, f, indent=1)
env:Windows 11 wsl,Python 3.11.11
error:sh predict.sh：
[WARNING] [run_aurobind.py:345:main] Error in prediction: Object of type float32 is not JSON serializableTraceback (most recent call last):
File "run_aurobind.py", line 331, in main
struct_dir = predict_and_save(
^^^^^^^^^^^^^^^^^
File "run_aurobind.py", line 147, in predict_and_save
json.dump(summary_confidences, f, indent=1)
#solution provided by chatgpt
import numpy as np
import numbers
def make_json_serializable(obj):
# numpy scalars
if isinstance(obj, np.generic):
return obj.item()
# numpy arrays
if isinstance(obj, np.ndarray):
return obj.tolist()
# Python numbers are fine
if isinstance(obj, (str, bool, type(None))):
return obj
if isinstance(obj, numbers.Number):
# ensure Python native float/int
if isinstance(obj, float):
return float(obj)
if isinstance(obj, int):
return int(obj)
return obj
# lists/tuples -> list of serializables
if isinstance(obj, (list, tuple)):
return [make_json_serializable(x) for x in obj]
# dict -> convert keys/values (keys expected to be strings)
if isinstance(obj, dict):
return {str(k): make_json_serializable(v) for k, v in obj.items()}
# try common .item() (works for torch.Tensor scalar too)
if hasattr(obj, "item"):
try:
return make_json_serializable(obj.item())
except Exception:
pass
# try converting iterables
try:
iter(obj)
except TypeError:
# last resort: convert to string so json can store it
return str(obj)
else:
# If it's iterable but not list/tuple (e.g. generator), materialize it
try:
return [make_json_serializable(x) for x in obj]
except Exception:
return str(obj)
使用示例（替换原有保存段）
summary_confidences = summary_confidences_list[i]
serializable = make_json_serializable(summary_confidences)
serializable['num_recycles'] = int(args.recycling_iters) if hasattr(args.recycling_iters, "int") else args.recycling_iters
outname = f"{record.id}_seed-{seed}_sample-{i}_summary_confidences.json"
output_path = struct_dir / outname
struct_dir.mkdir(parents=True, exist_ok=True) # 确保目录存在
with output_path.open("w") as f:
json.dump(serializable, f, indent=1)