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# ------------------------------------------------------------------------
# Copyright (c) 2022 megvii-research. All Rights Reserved.
# ------------------------------------------------------------------------
# Modified from Deformable DETR (https://github.com/fundamentalvision/Deformable-DETR)
# Copyright (c) 2020 SenseTime. All Rights Reserved.
# ------------------------------------------------------------------------
# Modified from DETR (https://github.com/facebookresearch/detr)
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
# ------------------------------------------------------------------------
import torch
import numpy as np
def load_model(model, model_path, optimizer=None, resume=False,
lr=None, lr_step=None):
start_epoch = 0
checkpoint = torch.load(model_path, map_location=lambda storage, loc: storage)
print(f'loaded {model_path}')
state_dict = checkpoint['model']
model_state_dict = model.state_dict()
# check loaded parameters and created model parameters
msg = 'If you see this, your model does not fully load the ' + \
'pre-trained weight. Please make sure ' + \
'you set the correct --num_classes for your own dataset.'
for k in state_dict:
if k in model_state_dict:
if state_dict[k].shape != model_state_dict[k].shape:
print('Skip loading parameter {}, required shape{}, ' \
'loaded shape{}. {}'.format(
k, model_state_dict[k].shape, state_dict[k].shape, msg))
if 'class_embed' in k:
print("load class_embed: {} shape={}".format(k, state_dict[k].shape))
if model_state_dict[k].shape[0] == 1:
state_dict[k] = state_dict[k][1:2]
elif model_state_dict[k].shape[0] == 2:
state_dict[k] = state_dict[k][1:3]
elif model_state_dict[k].shape[0] == 3:
state_dict[k] = state_dict[k][1:4]
else:
raise NotImplementedError('invalid shape: {}'.format(model_state_dict[k].shape))
continue
state_dict[k] = model_state_dict[k]
else:
print('Drop parameter {}.'.format(k) + msg)
for k in model_state_dict:
if not (k in state_dict):
print('No param {}.'.format(k) + msg)
state_dict[k] = model_state_dict[k]
model.load_state_dict(state_dict, strict=False)
# resume optimizer parameters
if optimizer is not None and resume:
if 'optimizer' in checkpoint:
optimizer.load_state_dict(checkpoint['optimizer'])
start_epoch = checkpoint['epoch']
start_lr = lr
for step in lr_step:
if start_epoch >= step:
start_lr *= 0.1
for param_group in optimizer.param_groups:
param_group['lr'] = start_lr
print('Resumed optimizer with start lr', start_lr)
else:
print('No optimizer parameters in checkpoint.')
if optimizer is not None:
return model, optimizer, start_epoch
else:
return model
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