Datasets:
File size: 2,147 Bytes
fe39cc9 | 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 50 51 52 53 54 55 56 57 58 59 60 61 62 | import os
import shutil
import logging
import torch
# Functions in this file are inspired by the following:
# https://github.com/cs230-stanford/cs230-code-examples/blob/master/pytorch/vision/utils.py
logger = logging.getLogger(__name__)
def save_checkpoint(state, model_state, isbest, checkpoint):
"""
Save training and model state to a checkpoint directory.
"""
filepath = os.path.join(checkpoint, 'last.pth')
model_filepath = os.path.join(checkpoint, 'model_last.pth')
if not os.path.exists(checkpoint):
logger.info("Checkpoint directory does not exist. Creating %s", checkpoint)
os.makedirs(checkpoint)
torch.save(state, filepath)
torch.save(model_state, model_filepath)
if isbest:
logger.info("Saving best checkpoint copy")
shutil.copyfile(filepath, os.path.join(checkpoint, 'best.pth'))
shutil.copyfile(model_filepath, os.path.join(checkpoint, 'model_best.pth'))
def load_checkpoint(checkpoint, model, optimizer=None):
"""
Load checkpoint file into model (and optimizer if provided).
The key remapping logic below is kept for compatibility with older
checkpoint formats used during project development.
"""
if not os.path.exists(checkpoint):
raise IOError("File doesn't exist {}".format(checkpoint))
if torch.cuda.is_available():
checkpoint = torch.load(checkpoint)
else:
checkpoint = torch.load(checkpoint, map_location='cpu')
state_dict = {}
for key in checkpoint['state_dict'].keys():
if 'layers.0.' in key:
state_dict[key.split('0.')[0].split('module.')[1] + key.split('0.')[1]] = checkpoint['state_dict'][key]
elif 'layers.1.' in key:
state_dict[key.replace('1', '8').split('module.')[1]] = checkpoint['state_dict'][key]
elif 'module.' in key:
state_dict[key.split('module.')[1]] = checkpoint['state_dict'][key]
else:
state_dict[key] = checkpoint['state_dict'][key]
model.load_state_dict(state_dict)
if optimizer:
optimizer.load_state_dict(checkpoint['optim_dict'])
return checkpoint
|