DL-Art-School/codes/sweep.py

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import copy
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import functools
import os
from multiprocessing.pool import ThreadPool
import torch
from train import Trainer
from utils import options as option
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import collections.abc
def deep_update(d, u):
for k, v in u.items():
if isinstance(v, collections.abc.Mapping):
d[k] = deep_update(d.get(k, {}), v)
else:
d[k] = v
return d
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def launch_trainer(opt, opt_path, rank):
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os.environ['CUDA_VISIBLE_DEVICES'] = str(rank)
print('export CUDA_VISIBLE_DEVICES=' + str(rank))
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trainer = Trainer()
opt['dist'] = False
trainer.rank = -1
trainer.init(opt_path, opt, 'none')
trainer.do_training()
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if __name__ == '__main__':
"""
Ad-hoc script (hard coded; no command-line parameters) that spawns multiple separate trainers from a single options
file, with a hard-coded set of modifications.
"""
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base_opt = '../experiments/train_diffusion_tts6.yml'
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modifications = {
'baseline': {},
'only_conv': {'networks': {'generator': {'kwargs': {'cond_transformer_depth': 4, 'mid_transformer_depth': 1}}}},
'intermediary_attention': {'networks': {'generator': {'kwargs': {'attention_resolutions': [32,64], 'num_res_blocks': [2, 2, 2, 2, 2, 2, 2]}}}},
'more_resblocks': {'networks': {'generator': {'kwargs': {'num_res_blocks': [3, 3, 3, 3, 3, 3, 2]}}}},
'less_resblocks': {'networks': {'generator': {'kwargs': {'num_res_blocks': [1, 1, 1, 1, 1, 1, 1]}}}},
'wider': {'networks': {'generator': {'kwargs': {'channel_mult': [1,2,4,6,8,8,8]}}}},
'inject_every_layer': {'networks': {'generator': {'kwargs': {'token_conditioning_resolutions': [1,2,4,8,16,32,64]}}}},
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'cosine_diffusion': {'steps': {'generator': {'injectors': {'diffusion': {'beta_schedule': {'schedule_name': 'cosine'}}}}}},
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}
opt = option.parse(base_opt, is_train=True)
all_opts = []
for i, (mod, mod_dict) in enumerate(modifications.items()):
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nd = copy.deepcopy(opt)
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deep_update(nd, mod_dict)
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nd['name'] = f'{nd["name"]}_{mod}'
nd['wandb_run_name'] = mod
base_path = nd['path']['log']
for k, p in nd['path'].items():
if isinstance(p, str) and base_path in p:
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nd['path'][k] = p.replace(base_path, f'{base_path}/{mod}')
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all_opts.append(nd)
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for i in range(1,len(modifications)):
pid = os.fork()
if pid == 0:
rank = i
break
else:
rank = 0
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launch_trainer(all_opts[rank], base_opt, rank)