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import json
import os
import torch
import yaml
def traverse_dir(
root_dir,
extensions,
amount=None,
str_include=None,
str_exclude=None,
is_pure=False,
is_sort=False,
is_ext=True):
file_list = []
cnt = 0
for root, _, files in os.walk(root_dir):
for file in files:
if any([file.endswith(f".{ext}") for ext in extensions]):
# path
mix_path = os.path.join(root, file)
pure_path = mix_path[len(root_dir)+1:] if is_pure else mix_path
# amount
if (amount is not None) and (cnt == amount):
if is_sort:
file_list.sort()
return file_list
# check string
if (str_include is not None) and (str_include not in pure_path):
continue
if (str_exclude is not None) and (str_exclude in pure_path):
continue
if not is_ext:
ext = pure_path.split('.')[-1]
pure_path = pure_path[:-(len(ext)+1)]
file_list.append(pure_path)
cnt += 1
if is_sort:
file_list.sort()
return file_list
class DotDict(dict):
def __getattr__(*args):
val = dict.get(*args)
return DotDict(val) if type(val) is dict else val
__setattr__ = dict.__setitem__
__delattr__ = dict.__delitem__
def get_network_paras_amount(model_dict):
info = dict()
for model_name, model in model_dict.items():
# all_params = sum(p.numel() for p in model.parameters())
trainable_params = sum(p.numel() for p in model.parameters() if p.requires_grad)
info[model_name] = trainable_params
return info
def load_config(path_config):
with open(path_config, "r") as config:
args = yaml.safe_load(config)
args = DotDict(args)
# print(args)
return args
def save_config(path_config,config):
config = dict(config)
with open(path_config, "w") as f:
yaml.dump(config, f)
def to_json(path_params, path_json):
params = torch.load(path_params, map_location=torch.device('cpu'))
raw_state_dict = {}
for k, v in params.items():
val = v.flatten().numpy().tolist()
raw_state_dict[k] = val
with open(path_json, 'w') as outfile:
json.dump(raw_state_dict, outfile,indent= "\t")
def convert_tensor_to_numpy(tensor, is_squeeze=True):
if is_squeeze:
tensor = tensor.squeeze()
if tensor.requires_grad:
tensor = tensor.detach()
if tensor.is_cuda:
tensor = tensor.cpu()
return tensor.numpy()
def load_model(
expdir,
model,
optimizer,
name='model',
postfix='',
device='cpu'):
if postfix == '':
postfix = '_' + postfix
path = os.path.join(expdir, name+postfix)
path_pt = traverse_dir(expdir, ['pt'], is_ext=False)
global_step = 0
if len(path_pt) > 0:
steps = [s[len(path):] for s in path_pt]
maxstep = max([int(s) if s.isdigit() else 0 for s in steps])
if maxstep >= 0:
path_pt = path+str(maxstep)+'.pt'
else:
path_pt = path+'best.pt'
print(' [*] restoring model from', path_pt)
ckpt = torch.load(path_pt, map_location=torch.device(device))
global_step = ckpt['global_step']
model.load_state_dict(ckpt['model'], strict=False)
if ckpt.get("optimizer") is not None:
optimizer.load_state_dict(ckpt['optimizer'])
return global_step, model, optimizer
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