2022-02-11 17:46:37 +00:00
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import functools
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import os
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from multiprocessing.pool import ThreadPool
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import torch
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from train import Trainer
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from utils import options as option
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def launch_trainer(opt, opt_path=''):
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rank = opt['gpu_ids'][0]
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os.environ['CUDA_VISIBLE_DEVICES'] = [rank]
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print('export CUDA_VISIBLE_DEVICES=' + rank)
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trainer = Trainer()
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opt['dist'] = False
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trainer.rank = -1
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torch.cuda.set_device(rank)
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trainer.init(opt_path, opt, 'none')
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trainer.do_training()
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if __name__ == '__main__':
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"""
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Ad-hoc script (hard coded; no command-line parameters) that spawns multiple separate trainers from a single options
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file, with a hard-coded set of modifications.
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"""
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base_opt = '../options/train_diffusion_tts.yml'
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modifications = {
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'baseline': {},
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'only_conv': {'networks': {'generator': {'kwargs': {'cond_transformer_depth': 4, 'mid_transformer_depth': 1}}}},
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'intermediary_attention': {'networks': {'generator': {'kwargs': {'attention_resolutions': [32,64], 'num_res_blocks': [2, 2, 2, 2, 2, 2, 2]}}}},
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'more_resblocks': {'networks': {'generator': {'kwargs': {'num_res_blocks': [3, 3, 3, 3, 3, 3, 2]}}}},
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'less_resblocks': {'networks': {'generator': {'kwargs': {'num_res_blocks': [1, 1, 1, 1, 1, 1, 1]}}}},
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'wider': {'networks': {'generator': {'kwargs': {'channel_mult': [1,2,4,6,8,8,8]}}}},
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'inject_every_layer': {'networks': {'generator': {'kwargs': {'token_conditioning_resolutions': [1,2,4,8,16,32,64]}}}},
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2022-02-11 17:59:32 +00:00
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'cosine_diffusion': {'steps': {'generator': {'injectors': {'diffusion': {'beta_schedule': {'schedule_name': 'cosine'}}}}}},
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2022-02-11 17:46:37 +00:00
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}
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opt = option.parse(base_opt, is_train=True)
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all_opts = []
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for i, (mod, mod_dict) in enumerate(modifications.items()):
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nd = opt.copy()
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nd.update(mod_dict)
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opt['gpu_ids'] = [i]
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nd['name'] = f'{nd["name"]}_{mod}'
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nd['wandb_run_name'] = mod
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base_path = nd['path']['log']
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for k, p in nd['path'].items():
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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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with ThreadPool(len(modifications)) as pool:
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list(pool.imap(functools.partial(launch_trainer, opt_path=base_opt), all_opts))
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