(model, module_instances=(torchDDP))
| 32 | |
| 33 | |
| 34 | def unwrap_model(model, module_instances=(torchDDP)): |
| 35 | return_list = True |
| 36 | if not isinstance(model, list): |
| 37 | model = [model] |
| 38 | return_list = False |
| 39 | unwrapped_model = [] |
| 40 | for model_module in model: |
| 41 | while isinstance(model_module, module_instances): |
| 42 | model_module = model_module.module |
| 43 | unwrapped_model.append(model_module) |
| 44 | if not return_list: |
| 45 | return unwrapped_model[0] |
| 46 | return unwrapped_model |
| 47 | |
| 48 | |
| 49 | def calc_params_l2_norm(model): |
no outgoing calls
no test coverage detected