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