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import os
import copy
import cv2
import numpy as np
from collections import OrderedDict
import torch
def load_checkpoint(model, checkpoint_path):
if not os.path.exists(checkpoint_path):
print("----No checkpoints at given path----")
return
model.load_state_dict(torch.load(checkpoint_path, map_location=torch.device("cpu")))
print("----checkpoints loaded from path: {}----".format(checkpoint_path))
return model
def load_checkpoint_mgpu(model, checkpoint_path):
if not os.path.exists(checkpoint_path):
print("----No checkpoints at given path----")
return
model_state_dict = torch.load(checkpoint_path, map_location=torch.device("cpu"))
new_state_dict = OrderedDict()
for k, v in model_state_dict.items():
name = k[7:] # remove `module.`
new_state_dict[name] = v
model.load_state_dict(new_state_dict)
print("----checkpoints loaded from path: {}----".format(checkpoint_path))
return model
def save_checkpoint(model, save_path):
print(save_path)
if not os.path.exists(os.path.dirname(save_path)):
os.makedirs(os.path.dirname(save_path))
torch.save(model.state_dict(), save_path)
def save_checkpoints(opt, itr, net):
save_checkpoint(
net,
os.path.join(opt.save_dir, "checkpoints", "itr_{:08d}_u2net.pth".format(itr)),
) |