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nnUNet_calvingfront_detection / nnunet /run /load_pretrained_weights.py
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# Copyright 2020 Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch
def load_pretrained_weights(network, fname, verbose=False):
"""
THIS DOES NOT TRANSFER SEGMENTATION HEADS!
"""
saved_model = torch.load(fname)
pretrained_dict = saved_model['state_dict']
new_state_dict = {}
# if state dict comes form nn.DataParallel but we use non-parallel model here then the state dict keys do not
# match. Use heuristic to make it match
for k, value in pretrained_dict.items():
key = k
# remove module. prefix from DDP models
if key.startswith('module.'):
key = key[7:]
new_state_dict[key] = value
pretrained_dict = new_state_dict
model_dict = network.state_dict()
ok = True
for key, _ in model_dict.items():
if ('conv_blocks' in key):
if (key in pretrained_dict) and (model_dict[key].shape == pretrained_dict[key].shape):
continue
else:
ok = False
break
# filter unnecessary keys
if ok:
pretrained_dict = {k: v for k, v in pretrained_dict.items() if
(k in model_dict) and (model_dict[k].shape == pretrained_dict[k].shape)}
# 2. overwrite entries in the existing state dict
model_dict.update(pretrained_dict)
print("################### Loading pretrained weights from file ", fname, '###################')
if verbose:
print("Below is the list of overlapping blocks in pretrained model and nnUNet architecture:")
for key, _ in pretrained_dict.items():
print(key)
print("################### Done ###################")
network.load_state_dict(model_dict)
else:
raise RuntimeError("Pretrained weights are not compatible with the current network architecture")