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Configuration error
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e5ba844 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 | # coding: utf-8
import sys
import os.path as osp
import torch
def check_keys(model, pretrained_state_dict):
ckpt_keys = set(pretrained_state_dict.keys())
model_keys = set(model.state_dict().keys())
used_pretrained_keys = model_keys & ckpt_keys
unused_pretrained_keys = ckpt_keys - model_keys
missing_keys = model_keys - ckpt_keys
# print('Missing keys:{}'.format(len(missing_keys)))
# print('Unused checkpoint keys:{}'.format(len(unused_pretrained_keys)))
# print('Used keys:{}'.format(len(used_pretrained_keys)))
assert len(used_pretrained_keys) > 0, 'load NONE from pretrained checkpoint'
return True
def remove_prefix(state_dict, prefix):
''' Old style model is stored with all names of parameters sharing common prefix 'module.' '''
# print('remove prefix \'{}\''.format(prefix))
def f(x): return x.split(prefix, 1)[-1] if x.startswith(prefix) else x
return {f(key): value for key, value in state_dict.items()}
def load_model(model, pretrained_path, load_to_cpu):
if not osp.isfile(pretrained_path):
print(
f'The pre-trained FaceBoxes model {pretrained_path} does not exist')
sys.exit('-1')
# print('Loading pretrained model from {}'.format(pretrained_path))
if load_to_cpu:
pretrained_dict = torch.load(
pretrained_path, map_location=lambda storage, loc: storage)
else:
device = torch.cuda.current_device()
pretrained_dict = torch.load(
pretrained_path, map_location=lambda storage, loc: storage.cuda(device))
if "state_dict" in pretrained_dict.keys():
pretrained_dict = remove_prefix(
pretrained_dict['state_dict'], 'module.')
else:
pretrained_dict = remove_prefix(pretrained_dict, 'module.')
check_keys(model, pretrained_dict)
model.load_state_dict(pretrained_dict, strict=False)
return model
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