SD-InPainting / clipseg /general_utils.py
nightfury's picture
commit 2nd changes
374a0d6
raw
history blame
9.29 kB
import json
import inspect
import torch
import os
import sys
import yaml
from shutil import copy, copytree
from os.path import join, dirname, realpath, expanduser, isfile, isdir, basename
class Logger(object):
def __getattr__(self, k):
return print
log = Logger()
def training_config_from_cli_args():
experiment_name = sys.argv[1]
experiment_id = int(sys.argv[2])
yaml_config = yaml.load(open(f'experiments/{experiment_name}'), Loader=yaml.SafeLoader)
config = yaml_config['configuration']
config = {**config, **yaml_config['individual_configurations'][experiment_id]}
config = AttributeDict(config)
return config
def score_config_from_cli_args():
experiment_name = sys.argv[1]
experiment_id = int(sys.argv[2])
yaml_config = yaml.load(open(f'experiments/{experiment_name}'), Loader=yaml.SafeLoader)
config = yaml_config['test_configuration_common']
if type(yaml_config['test_configuration']) == list:
test_id = int(sys.argv[3])
config = {**config, **yaml_config['test_configuration'][test_id]}
else:
config = {**config, **yaml_config['test_configuration']}
if 'test_configuration' in yaml_config['individual_configurations'][experiment_id]:
config = {**config, **yaml_config['individual_configurations'][experiment_id]['test_configuration']}
train_checkpoint_id = yaml_config['individual_configurations'][experiment_id]['name']
config = AttributeDict(config)
return config, train_checkpoint_id
def get_from_repository(local_name, repo_files, integrity_check=None, repo_dir='~/dataset_repository',
local_dir='~/datasets'):
""" copies files from repository to local folder.
repo_files: list of filenames or list of tuples [filename, target path]
e.g. get_from_repository('MyDataset', [['data/dataset1.tar', 'other/path/ds03.tar'])
will create a folder 'MyDataset' in local_dir, and extract the content of
'<repo_dir>/data/dataset1.tar' to <local_dir>/MyDataset/other/path.
"""
local_dir = realpath(join(expanduser(local_dir), local_name))
dataset_exists = True
# check if folder is available
if not isdir(local_dir):
dataset_exists = False
if integrity_check is not None:
try:
integrity_ok = integrity_check(local_dir)
except BaseException:
integrity_ok = False
if integrity_ok:
log.hint('Passed custom integrity check')
else:
log.hint('Custom integrity check failed')
dataset_exists = dataset_exists and integrity_ok
if not dataset_exists:
repo_dir = realpath(expanduser(repo_dir))
for i, filename in enumerate(repo_files):
if type(filename) == str:
origin, target = filename, filename
archive_target = join(local_dir, basename(origin))
extract_target = join(local_dir)
else:
origin, target = filename
archive_target = join(local_dir, dirname(target), basename(origin))
extract_target = join(local_dir, dirname(target))
archive_origin = join(repo_dir, origin)
log.hint(f'copy: {archive_origin} to {archive_target}')
# make sure the path exists
os.makedirs(dirname(archive_target), exist_ok=True)
if os.path.isfile(archive_target):
# only copy if size differs
if os.path.getsize(archive_target) != os.path.getsize(archive_origin):
log.hint(f'file exists but filesize differs: target {os.path.getsize(archive_target)} vs. origin {os.path.getsize(archive_origin)}')
copy(archive_origin, archive_target)
else:
copy(archive_origin, archive_target)
extract_archive(archive_target, extract_target, noarchive_ok=True)
# concurrent processes might have deleted the file
if os.path.isfile(archive_target):
os.remove(archive_target)
def extract_archive(filename, target_folder=None, noarchive_ok=False):
from subprocess import run, PIPE
if filename.endswith('.tgz') or filename.endswith('.tar'):
command = f'tar -xf {filename}'
command += f' -C {target_folder}' if target_folder is not None else ''
elif filename.endswith('.tar.gz'):
command = f'tar -xzf {filename}'
command += f' -C {target_folder}' if target_folder is not None else ''
elif filename.endswith('zip'):
command = f'unzip {filename}'
command += f' -d {target_folder}' if target_folder is not None else ''
else:
if noarchive_ok:
return
else:
raise ValueError(f'unsuppored file ending of {filename}')
log.hint(command)
result = run(command.split(), stdout=PIPE, stderr=PIPE)
if result.returncode != 0:
print(result.stdout, result.stderr)
class AttributeDict(dict):
"""
An extended dictionary that allows access to elements as atttributes and counts
these accesses. This way, we know if some attributes were never used.
"""
def __init__(self, *args, **kwargs):
from collections import Counter
super().__init__(*args, **kwargs)
self.__dict__['counter'] = Counter()
def __getitem__(self, k):
self.__dict__['counter'][k] += 1
return super().__getitem__(k)
def __getattr__(self, k):
self.__dict__['counter'][k] += 1
return super().get(k)
def __setattr__(self, k, v):
return super().__setitem__(k, v)
def __delattr__(self, k, v):
return super().__delitem__(k, v)
def unused_keys(self, exceptions=()):
return [k for k in super().keys() if self.__dict__['counter'][k] == 0 and k not in exceptions]
def assume_no_unused_keys(self, exceptions=()):
if len(self.unused_keys(exceptions=exceptions)) > 0:
log.warning('Unused keys:', self.unused_keys(exceptions=exceptions))
def get_attribute(name):
import importlib
if name is None:
raise ValueError('The provided attribute is None')
name_split = name.split('.')
mod = importlib.import_module('.'.join(name_split[:-1]))
return getattr(mod, name_split[-1])
def filter_args(input_args, default_args):
updated_args = {k: input_args[k] if k in input_args else v for k, v in default_args.items()}
used_args = {k: v for k, v in input_args.items() if k in default_args}
unused_args = {k: v for k, v in input_args.items() if k not in default_args}
return AttributeDict(updated_args), AttributeDict(used_args), AttributeDict(unused_args)
def load_model(checkpoint_id, weights_file=None, strict=True, model_args='from_config', with_config=False):
config = json.load(open(join('logs', checkpoint_id, 'config.json')))
if model_args != 'from_config' and type(model_args) != dict:
raise ValueError('model_args must either be "from_config" or a dictionary of values')
model_cls = get_attribute(config['model'])
# load model
if model_args == 'from_config':
_, model_args, _ = filter_args(config, inspect.signature(model_cls).parameters)
model = model_cls(**model_args)
if weights_file is None:
weights_file = realpath(join('logs', checkpoint_id, 'weights.pth'))
else:
weights_file = realpath(join('logs', checkpoint_id, weights_file))
if isfile(weights_file):
weights = torch.load(weights_file)
for _, w in weights.items():
assert not torch.any(torch.isnan(w)), 'weights contain NaNs'
model.load_state_dict(weights, strict=strict)
else:
raise FileNotFoundError(f'model checkpoint {weights_file} was not found')
if with_config:
return model, config
return model
class TrainingLogger(object):
def __init__(self, model, log_dir, config=None, *args):
super().__init__()
self.model = model
self.base_path = join(f'logs/{log_dir}') if log_dir is not None else None
os.makedirs('logs/', exist_ok=True)
os.makedirs(self.base_path, exist_ok=True)
if config is not None:
json.dump(config, open(join(self.base_path, 'config.json'), 'w'))
def iter(self, i, **kwargs):
if i % 100 == 0 and 'loss' in kwargs:
loss = kwargs['loss']
print(f'iteration {i}: loss {loss:.4f}')
def save_weights(self, only_trainable=False, weight_file='weights.pth'):
if self.model is None:
raise AttributeError('You need to provide a model reference when initializing TrainingTracker to save weights.')
weights_path = join(self.base_path, weight_file)
weight_dict = self.model.state_dict()
if only_trainable:
weight_dict = {n: weight_dict[n] for n, p in self.model.named_parameters() if p.requires_grad}
torch.save(weight_dict, weights_path)
log.info(f'Saved weights to {weights_path}')
def __enter__(self):
return self
def __exit__(self, type, value, traceback):
""" automatically stop processes if used in a context manager """
pass