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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