mmcv.mkdir_or_exist(osp.dirname(filename))ifis_module_wrapper(model): model = model.module checkpoint = {'meta': meta,'state_dict': weights_to_cpu(model.state_dict()) }# save optimizer state dict in the checkpointifisinstance(optimizer, Optimizer): checkpoint['optimizer'] = optimizer.stat...
Fix is_module_wrapper (#1900) Fix the problem that the instance cannot be reused when overwriting the file client (#1747) Documentations Replace markdownlint with mdformat for avoiding installing ruby (#1936) Fix failed links in docs/zh_cn/faq.md (#1964) Fix docstrings in EvalHook (#1978...
不论怎么变都会报错:RuntimeError: Expected to mark a variable ready only once. This error is caused by one of the following reasons 我测试发现就是由于这种写法会导致这个问题。发现这样改能避免: model = DistributedDataParallelWrapper( model.cuda(), device_ids=[torch.cuda.current_device()], broadc...
mmcv: lite, without CUDA ops but all other features, similar to mmcv<1.0.0. It is useful when you do not need those CUDA ops. Note: Do not install both versions in the same environment, otherwise you may encounter errors likeModuleNotFound. You need to uninstall one before installing the...
except (ModuleNotFoundError, AttributeError): cpu_use = 4 os.environ.setdefault('MAX_JOBS', str(cpu_use)) define_macros = [] # Before PyTorch1.8.0, when compiling CUDA code, `cxx` is a # required key passed to PyTorch. Even if there is no flag passed # to cxx, users ...
def wrapper(*args, **kargs): # 定义装饰器新增的函数功能 return func(*args, **kargs) return wrapper 1.2 装饰器的用法 装饰器可以传参也可以不传参,自身不传入参数的装饰器一般可以使用双层函数定义装饰器,而自身传入参数的装饰器一般使用三层函数定义装饰器。
except (ModuleNotFoundError, AttributeError): cpu_use = 4 os.environ.setdefault('MAX_JOBS', str(cpu_use)) define_macros = [] # Before PyTorch1.8.0, when compiling CUDA code, `cxx` is a # required key passed to PyTorch. Even if there is no flag passed # to cxx, users ...
except (ModuleNotFoundError, AttributeError): cpu_use = 4 os.environ.setdefault('MAX_JOBS', str(cpu_use)) define_macros = [] # Before PyTorch1.8.0, when compiling CUDA code, `cxx` is a # required key passed to PyTorch. Even if there is no flag passed # to cxx, users also...
Args: module (nn.Module): The module to be checked. Returns: bool: True if the input module is a module wrapper. """ def is_module_in_wrapper(module, module_wrapper): module_wrappers = tuple(module_wrapper.module_dict.values()) if isinstance(module, module_wrappers): return True for ...
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