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| # MIT License | |
| # Copyright (c) 2022 Intelligent Systems Lab Org | |
| # Permission is hereby granted, free of charge, to any person obtaining a copy | |
| # of this software and associated documentation files (the "Software"), to deal | |
| # in the Software without restriction, including without limitation the rights | |
| # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell | |
| # copies of the Software, and to permit persons to whom the Software is | |
| # furnished to do so, subject to the following conditions: | |
| # The above copyright notice and this permission notice shall be included in all | |
| # copies or substantial portions of the Software. | |
| # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR | |
| # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, | |
| # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE | |
| # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER | |
| # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, | |
| # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE | |
| # SOFTWARE. | |
| # File author: Shariq Farooq Bhat | |
| import torch | |
| def load_state_dict(model, state_dict): | |
| """Load state_dict into model, handling DataParallel and DistributedDataParallel. Also checks for "model" key in state_dict. | |
| DataParallel prefixes state_dict keys with 'module.' when saving. | |
| If the model is not a DataParallel model but the state_dict is, then prefixes are removed. | |
| If the model is a DataParallel model but the state_dict is not, then prefixes are added. | |
| """ | |
| state_dict = state_dict.get('model', state_dict) | |
| # if model is a DataParallel model, then state_dict keys are prefixed with 'module.' | |
| do_prefix = isinstance( | |
| model, (torch.nn.DataParallel, torch.nn.parallel.DistributedDataParallel)) | |
| state = {} | |
| for k, v in state_dict.items(): | |
| if k.startswith('module.') and not do_prefix: | |
| k = k[7:] | |
| if not k.startswith('module.') and do_prefix: | |
| k = 'module.' + k | |
| state[k] = v | |
| model.load_state_dict(state) | |
| print("Loaded successfully") | |
| return model | |
| def load_wts(model, checkpoint_path): | |
| ckpt = torch.load(checkpoint_path, map_location='cpu') | |
| return load_state_dict(model, ckpt) | |
| def load_state_dict_from_url(model, url, **kwargs): | |
| state_dict = torch.hub.load_state_dict_from_url(url, map_location='cpu', **kwargs) | |
| return load_state_dict(model, state_dict) | |
| def load_state_from_resource(model, resource: str): | |
| """Loads weights to the model from a given resource. A resource can be of following types: | |
| 1. URL. Prefixed with "url::" | |
| e.g. url::http(s)://url.resource.com/ckpt.pt | |
| 2. Local path. Prefixed with "local::" | |
| e.g. local::/path/to/ckpt.pt | |
| Args: | |
| model (torch.nn.Module): Model | |
| resource (str): resource string | |
| Returns: | |
| torch.nn.Module: Model with loaded weights | |
| """ | |
| print(f"Using pretrained resource {resource}") | |
| if resource.startswith('url::'): | |
| url = resource.split('url::')[1] | |
| return load_state_dict_from_url(model, url, progress=True) | |
| elif resource.startswith('local::'): | |
| path = resource.split('local::')[1] | |
| return load_wts(model, path) | |
| else: | |
| raise ValueError("Invalid resource type, only url:: and local:: are supported") | |