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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")
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