import torch
from pathlib import Path
model = torch.load('cloud_removal_model/cloud_removal_unet_best.pth', map_location='cpu')
print(type(model))
print("hasattr inc:", hasattr(model, 'inc'))
if hasattr(model, 'inc'):
    print("hasattr double_conv:", hasattr(model.inc, 'double_conv'))
    if hasattr(model.inc, 'double_conv'):
        print("in_channels:", model.inc.double_conv[0].in_channels)
else:
    for name, param in model.named_parameters():
        print(name, param.shape)
        break