mirror of
https://github.com/invoke-ai/InvokeAI
synced 2024-08-30 20:32:17 +00:00
080fc4b380
- Added new documentation for textual inversion training process - Move `main.py` into the deprecated scripts folder - Fix bug in `textual_inversion.py` which was causing it to not load the globals module correctly. - Sort models alphabetically in console front end - Only show diffusers models in console front end
972 lines
32 KiB
Python
972 lines
32 KiB
Python
import argparse, os, sys, datetime, glob, importlib, csv
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import numpy as np
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import time
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import torch
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import torchvision
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import pytorch_lightning as pl
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from packaging import version
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from omegaconf import OmegaConf
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from torch.utils.data import random_split, DataLoader, Dataset, Subset
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from functools import partial
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from PIL import Image
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from pytorch_lightning import seed_everything
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from pytorch_lightning.trainer import Trainer
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from pytorch_lightning.callbacks import (
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ModelCheckpoint,
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Callback,
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LearningRateMonitor,
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)
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from pytorch_lightning.utilities.distributed import rank_zero_only
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from pytorch_lightning.utilities import rank_zero_info
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from ldm.data.base import Txt2ImgIterableBaseDataset
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from ldm.util import instantiate_from_config
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def fix_func(orig):
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if hasattr(torch.backends, 'mps') and torch.backends.mps.is_available():
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def new_func(*args, **kw):
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device = kw.get("device", "mps")
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kw["device"]="cpu"
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return orig(*args, **kw).to(device)
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return new_func
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return orig
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torch.rand = fix_func(torch.rand)
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torch.rand_like = fix_func(torch.rand_like)
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torch.randn = fix_func(torch.randn)
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torch.randn_like = fix_func(torch.randn_like)
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torch.randint = fix_func(torch.randint)
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torch.randint_like = fix_func(torch.randint_like)
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torch.bernoulli = fix_func(torch.bernoulli)
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torch.multinomial = fix_func(torch.multinomial)
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def load_model_from_config(config, ckpt, verbose=False):
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print(f'Loading model from {ckpt}')
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pl_sd = torch.load(ckpt, map_location='cpu')
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sd = pl_sd['state_dict']
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config.model.params.ckpt_path = ckpt
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model = instantiate_from_config(config.model)
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m, u = model.load_state_dict(sd, strict=False)
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if len(m) > 0 and verbose:
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print('missing keys:')
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print(m)
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if len(u) > 0 and verbose:
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print('unexpected keys:')
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print(u)
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if torch.cuda.is_available():
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model.cuda()
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return model
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def get_parser(**parser_kwargs):
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def str2bool(v):
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if isinstance(v, bool):
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return v
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if v.lower() in ('yes', 'true', 't', 'y', '1'):
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return True
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elif v.lower() in ('no', 'false', 'f', 'n', '0'):
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return False
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else:
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raise argparse.ArgumentTypeError('Boolean value expected.')
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parser = argparse.ArgumentParser(**parser_kwargs)
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parser.add_argument(
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'-n',
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'--name',
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type=str,
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const=True,
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default='',
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nargs='?',
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help='postfix for logdir',
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)
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parser.add_argument(
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'-r',
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'--resume',
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type=str,
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const=True,
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default='',
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nargs='?',
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help='resume from logdir or checkpoint in logdir',
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)
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parser.add_argument(
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'-b',
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'--base',
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nargs='*',
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metavar='base_config.yaml',
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help='paths to base configs. Loaded from left-to-right. '
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'Parameters can be overwritten or added with command-line options of the form `--key value`.',
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default=list(),
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)
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parser.add_argument(
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'-t',
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'--train',
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type=str2bool,
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const=True,
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default=False,
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nargs='?',
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help='train',
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)
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parser.add_argument(
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'--no-test',
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type=str2bool,
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const=True,
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default=False,
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nargs='?',
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help='disable test',
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)
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parser.add_argument(
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'-p', '--project', help='name of new or path to existing project'
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)
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parser.add_argument(
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'-d',
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'--debug',
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type=str2bool,
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nargs='?',
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const=True,
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default=False,
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help='enable post-mortem debugging',
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)
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parser.add_argument(
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'-s',
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'--seed',
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type=int,
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default=23,
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help='seed for seed_everything',
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)
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parser.add_argument(
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'-f',
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'--postfix',
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type=str,
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default='',
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help='post-postfix for default name',
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)
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parser.add_argument(
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'-l',
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'--logdir',
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type=str,
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default='logs',
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help='directory for logging dat shit',
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)
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parser.add_argument(
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'--scale_lr',
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type=str2bool,
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nargs='?',
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const=True,
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default=True,
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help='scale base-lr by ngpu * batch_size * n_accumulate',
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)
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parser.add_argument(
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'--datadir_in_name',
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type=str2bool,
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nargs='?',
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const=True,
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default=True,
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help='Prepend the final directory in the data_root to the output directory name',
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)
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parser.add_argument(
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'--actual_resume',
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type=str,
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default='',
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help='Path to model to actually resume from',
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)
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parser.add_argument(
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'--data_root',
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type=str,
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required=True,
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help='Path to directory with training images',
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)
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parser.add_argument(
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'--embedding_manager_ckpt',
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type=str,
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default='',
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help='Initialize embedding manager from a checkpoint',
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)
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parser.add_argument(
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'--init_word',
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type=str,
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help='Word to use as source for initial token embedding.',
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)
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return parser
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def nondefault_trainer_args(opt):
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parser = argparse.ArgumentParser()
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parser = Trainer.add_argparse_args(parser)
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args = parser.parse_args([])
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return sorted(k for k in vars(args) if getattr(opt, k) != getattr(args, k))
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class WrappedDataset(Dataset):
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"""Wraps an arbitrary object with __len__ and __getitem__ into a pytorch dataset"""
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def __init__(self, dataset):
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self.data = dataset
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def __len__(self):
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return len(self.data)
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def __getitem__(self, idx):
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return self.data[idx]
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def worker_init_fn(_):
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worker_info = torch.utils.data.get_worker_info()
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dataset = worker_info.dataset
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worker_id = worker_info.id
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if isinstance(dataset, Txt2ImgIterableBaseDataset):
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split_size = dataset.num_records // worker_info.num_workers
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# reset num_records to the true number to retain reliable length information
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dataset.sample_ids = dataset.valid_ids[
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worker_id * split_size : (worker_id + 1) * split_size
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]
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current_id = np.random.choice(len(np.random.get_state()[1]), 1)
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return np.random.seed(np.random.get_state()[1][current_id] + worker_id)
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else:
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return np.random.seed(np.random.get_state()[1][0] + worker_id)
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class DataModuleFromConfig(pl.LightningDataModule):
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def __init__(
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self,
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batch_size,
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train=None,
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validation=None,
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test=None,
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predict=None,
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wrap=False,
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num_workers=None,
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shuffle_test_loader=False,
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use_worker_init_fn=False,
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shuffle_val_dataloader=False,
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):
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super().__init__()
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self.batch_size = batch_size
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self.dataset_configs = dict()
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self.num_workers = (
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num_workers if num_workers is not None else batch_size * 2
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)
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self.use_worker_init_fn = use_worker_init_fn
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if train is not None:
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self.dataset_configs['train'] = train
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self.train_dataloader = self._train_dataloader
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if validation is not None:
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self.dataset_configs['validation'] = validation
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self.val_dataloader = partial(
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self._val_dataloader, shuffle=shuffle_val_dataloader
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)
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if test is not None:
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self.dataset_configs['test'] = test
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self.test_dataloader = partial(
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self._test_dataloader, shuffle=shuffle_test_loader
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)
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if predict is not None:
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self.dataset_configs['predict'] = predict
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self.predict_dataloader = self._predict_dataloader
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self.wrap = wrap
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def prepare_data(self):
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for data_cfg in self.dataset_configs.values():
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instantiate_from_config(data_cfg)
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def setup(self, stage=None):
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self.datasets = dict(
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(k, instantiate_from_config(self.dataset_configs[k]))
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for k in self.dataset_configs
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)
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if self.wrap:
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for k in self.datasets:
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self.datasets[k] = WrappedDataset(self.datasets[k])
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def _train_dataloader(self):
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is_iterable_dataset = isinstance(
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self.datasets['train'], Txt2ImgIterableBaseDataset
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)
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if is_iterable_dataset or self.use_worker_init_fn:
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init_fn = worker_init_fn
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else:
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init_fn = None
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return DataLoader(
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self.datasets['train'],
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batch_size=self.batch_size,
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num_workers=self.num_workers,
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shuffle=False if is_iterable_dataset else True,
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worker_init_fn=init_fn,
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)
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def _val_dataloader(self, shuffle=False):
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if (
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isinstance(self.datasets['validation'], Txt2ImgIterableBaseDataset)
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or self.use_worker_init_fn
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):
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init_fn = worker_init_fn
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else:
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init_fn = None
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return DataLoader(
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self.datasets['validation'],
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batch_size=self.batch_size,
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num_workers=self.num_workers,
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worker_init_fn=init_fn,
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shuffle=shuffle,
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)
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def _test_dataloader(self, shuffle=False):
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is_iterable_dataset = isinstance(
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self.datasets['train'], Txt2ImgIterableBaseDataset
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)
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if is_iterable_dataset or self.use_worker_init_fn:
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init_fn = worker_init_fn
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else:
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init_fn = None
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# do not shuffle dataloader for iterable dataset
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shuffle = shuffle and (not is_iterable_dataset)
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return DataLoader(
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self.datasets['test'],
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batch_size=self.batch_size,
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num_workers=self.num_workers,
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worker_init_fn=init_fn,
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shuffle=shuffle,
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)
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def _predict_dataloader(self, shuffle=False):
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if (
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isinstance(self.datasets['predict'], Txt2ImgIterableBaseDataset)
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or self.use_worker_init_fn
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):
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init_fn = worker_init_fn
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else:
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init_fn = None
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return DataLoader(
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self.datasets['predict'],
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batch_size=self.batch_size,
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num_workers=self.num_workers,
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worker_init_fn=init_fn,
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)
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class SetupCallback(Callback):
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def __init__(
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self, resume, now, logdir, ckptdir, cfgdir, config, lightning_config
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):
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super().__init__()
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self.resume = resume
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self.now = now
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self.logdir = logdir
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self.ckptdir = ckptdir
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self.cfgdir = cfgdir
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self.config = config
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self.lightning_config = lightning_config
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def on_keyboard_interrupt(self, trainer, pl_module):
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if trainer.global_rank == 0:
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print('Summoning checkpoint.')
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ckpt_path = os.path.join(self.ckptdir, 'last.ckpt')
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trainer.save_checkpoint(ckpt_path)
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def on_pretrain_routine_start(self, trainer, pl_module):
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if trainer.global_rank == 0:
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# Create logdirs and save configs
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os.makedirs(self.logdir, exist_ok=True)
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os.makedirs(self.ckptdir, exist_ok=True)
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os.makedirs(self.cfgdir, exist_ok=True)
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if 'callbacks' in self.lightning_config:
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if (
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'metrics_over_trainsteps_checkpoint'
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in self.lightning_config['callbacks']
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):
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os.makedirs(
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os.path.join(self.ckptdir, 'trainstep_checkpoints'),
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exist_ok=True,
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)
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print('Project config')
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print(OmegaConf.to_yaml(self.config))
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OmegaConf.save(
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self.config,
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os.path.join(self.cfgdir, '{}-project.yaml'.format(self.now)),
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)
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print('Lightning config')
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print(OmegaConf.to_yaml(self.lightning_config))
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OmegaConf.save(
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OmegaConf.create({'lightning': self.lightning_config}),
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os.path.join(
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self.cfgdir, '{}-lightning.yaml'.format(self.now)
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),
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)
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else:
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# ModelCheckpoint callback created log directory --- remove it
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if not self.resume and os.path.exists(self.logdir):
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dst, name = os.path.split(self.logdir)
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dst = os.path.join(dst, 'child_runs', name)
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os.makedirs(os.path.split(dst)[0], exist_ok=True)
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try:
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os.rename(self.logdir, dst)
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except FileNotFoundError:
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pass
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|
|
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class ImageLogger(Callback):
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def __init__(
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self,
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batch_frequency,
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max_images,
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clamp=True,
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increase_log_steps=True,
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rescale=True,
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disabled=False,
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log_on_batch_idx=False,
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log_first_step=False,
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log_images_kwargs=None,
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):
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super().__init__()
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self.rescale = rescale
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self.batch_freq = batch_frequency
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self.max_images = max_images
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self.logger_log_images = { }
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self.log_steps = [
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2**n for n in range(int(np.log2(self.batch_freq)) + 1)
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]
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if not increase_log_steps:
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self.log_steps = [self.batch_freq]
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self.clamp = clamp
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self.disabled = disabled
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self.log_on_batch_idx = log_on_batch_idx
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self.log_images_kwargs = log_images_kwargs if log_images_kwargs else {}
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self.log_first_step = log_first_step
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@rank_zero_only
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def log_local(
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self, save_dir, split, images, global_step, current_epoch, batch_idx
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):
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root = os.path.join(save_dir, 'images', split)
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for k in images:
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grid = torchvision.utils.make_grid(images[k], nrow=4)
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if self.rescale:
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grid = (grid + 1.0) / 2.0 # -1,1 -> 0,1; c,h,w
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grid = grid.transpose(0, 1).transpose(1, 2).squeeze(-1)
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grid = grid.numpy()
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grid = (grid * 255).astype(np.uint8)
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filename = '{}_gs-{:06}_e-{:06}_b-{:06}.png'.format(
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k, global_step, current_epoch, batch_idx
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)
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path = os.path.join(root, filename)
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os.makedirs(os.path.split(path)[0], exist_ok=True)
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Image.fromarray(grid).save(path)
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|
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def log_img(self, pl_module, batch, batch_idx, split='train'):
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check_idx = (
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batch_idx if self.log_on_batch_idx else pl_module.global_step
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)
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if (
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self.check_frequency(check_idx)
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and hasattr( # batch_idx % self.batch_freq == 0
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pl_module, 'log_images'
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)
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and callable(pl_module.log_images)
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|
and self.max_images > 0
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):
|
|
logger = type(pl_module.logger)
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|
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is_train = pl_module.training
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|
if is_train:
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|
pl_module.eval()
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|
|
|
with torch.no_grad():
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|
images = pl_module.log_images(
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batch, split=split, **self.log_images_kwargs
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)
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for k in images:
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N = min(images[k].shape[0], self.max_images)
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images[k] = images[k][:N]
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if isinstance(images[k], torch.Tensor):
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images[k] = images[k].detach().cpu()
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if self.clamp:
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images[k] = torch.clamp(images[k], -1.0, 1.0)
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self.log_local(
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pl_module.logger.save_dir,
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split,
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images,
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pl_module.global_step,
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pl_module.current_epoch,
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batch_idx,
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)
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logger_log_images = self.logger_log_images.get(
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logger, lambda *args, **kwargs: None
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)
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logger_log_images(pl_module, images, pl_module.global_step, split)
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|
|
if is_train:
|
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pl_module.train()
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|
|
|
def check_frequency(self, check_idx):
|
|
if (
|
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(check_idx % self.batch_freq) == 0 or (check_idx in self.log_steps)
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|
) and (check_idx > 0 or self.log_first_step):
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try:
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|
self.log_steps.pop(0)
|
|
except IndexError as e:
|
|
print(e)
|
|
pass
|
|
return True
|
|
return False
|
|
|
|
def on_train_batch_end(
|
|
self, trainer, pl_module, outputs, batch, batch_idx, dataloader_idx=None
|
|
):
|
|
if not self.disabled and (
|
|
pl_module.global_step > 0 or self.log_first_step
|
|
):
|
|
self.log_img(pl_module, batch, batch_idx, split='train')
|
|
|
|
def on_validation_batch_end(
|
|
self, trainer, pl_module, outputs, batch, batch_idx, dataloader_idx=None
|
|
):
|
|
if not self.disabled and pl_module.global_step > 0:
|
|
self.log_img(pl_module, batch, batch_idx, split='val')
|
|
if hasattr(pl_module, 'calibrate_grad_norm'):
|
|
if (
|
|
pl_module.calibrate_grad_norm and batch_idx % 25 == 0
|
|
) and batch_idx > 0:
|
|
self.log_gradients(trainer, pl_module, batch_idx=batch_idx)
|
|
|
|
|
|
class CUDACallback(Callback):
|
|
# see https://github.com/SeanNaren/minGPT/blob/master/mingpt/callback.py
|
|
def on_train_epoch_start(self, trainer, pl_module):
|
|
# Reset the memory use counter
|
|
if torch.cuda.is_available():
|
|
torch.cuda.reset_peak_memory_stats(trainer.root_gpu)
|
|
torch.cuda.synchronize(trainer.root_gpu)
|
|
self.start_time = time.time()
|
|
|
|
def on_train_epoch_end(self, trainer, pl_module, outputs=None):
|
|
if torch.cuda.is_available():
|
|
torch.cuda.synchronize(trainer.root_gpu)
|
|
epoch_time = time.time() - self.start_time
|
|
|
|
try:
|
|
epoch_time = trainer.training_type_plugin.reduce(epoch_time)
|
|
rank_zero_info(f'Average Epoch time: {epoch_time:.2f} seconds')
|
|
|
|
if torch.cuda.is_available():
|
|
max_memory = (
|
|
torch.cuda.max_memory_allocated(trainer.root_gpu) / 2**20
|
|
)
|
|
max_memory = trainer.training_type_plugin.reduce(max_memory)
|
|
rank_zero_info(f'Average Peak memory {max_memory:.2f}MiB')
|
|
except AttributeError:
|
|
pass
|
|
|
|
class ModeSwapCallback(Callback):
|
|
|
|
def __init__(self, swap_step=2000):
|
|
super().__init__()
|
|
self.is_frozen = False
|
|
self.swap_step = swap_step
|
|
|
|
def on_train_epoch_start(self, trainer, pl_module):
|
|
if trainer.global_step < self.swap_step and not self.is_frozen:
|
|
self.is_frozen = True
|
|
trainer.optimizers = [pl_module.configure_opt_embedding()]
|
|
|
|
if trainer.global_step > self.swap_step and self.is_frozen:
|
|
self.is_frozen = False
|
|
trainer.optimizers = [pl_module.configure_opt_model()]
|
|
|
|
if __name__ == '__main__':
|
|
# custom parser to specify config files, train, test and debug mode,
|
|
# postfix, resume.
|
|
# `--key value` arguments are interpreted as arguments to the trainer.
|
|
# `nested.key=value` arguments are interpreted as config parameters.
|
|
# configs are merged from left-to-right followed by command line parameters.
|
|
|
|
# model:
|
|
# base_learning_rate: float
|
|
# target: path to lightning module
|
|
# params:
|
|
# key: value
|
|
# data:
|
|
# target: main.DataModuleFromConfig
|
|
# params:
|
|
# batch_size: int
|
|
# wrap: bool
|
|
# train:
|
|
# target: path to train dataset
|
|
# params:
|
|
# key: value
|
|
# validation:
|
|
# target: path to validation dataset
|
|
# params:
|
|
# key: value
|
|
# test:
|
|
# target: path to test dataset
|
|
# params:
|
|
# key: value
|
|
# lightning: (optional, has sane defaults and can be specified on cmdline)
|
|
# trainer:
|
|
# additional arguments to trainer
|
|
# logger:
|
|
# logger to instantiate
|
|
# modelcheckpoint:
|
|
# modelcheckpoint to instantiate
|
|
# callbacks:
|
|
# callback1:
|
|
# target: importpath
|
|
# params:
|
|
# key: value
|
|
|
|
now = datetime.datetime.now().strftime('%Y-%m-%dT%H-%M-%S')
|
|
|
|
# add cwd for convenience and to make classes in this file available when
|
|
# running as `python main.py`
|
|
# (in particular `main.DataModuleFromConfig`)
|
|
sys.path.append(os.getcwd())
|
|
|
|
parser = get_parser()
|
|
parser = Trainer.add_argparse_args(parser)
|
|
|
|
opt, unknown = parser.parse_known_args()
|
|
if opt.name and opt.resume:
|
|
raise ValueError(
|
|
'-n/--name and -r/--resume cannot be specified both.'
|
|
'If you want to resume training in a new log folder, '
|
|
'use -n/--name in combination with --resume_from_checkpoint'
|
|
)
|
|
if opt.resume:
|
|
if not os.path.exists(opt.resume):
|
|
raise ValueError('Cannot find {}'.format(opt.resume))
|
|
if os.path.isfile(opt.resume):
|
|
paths = opt.resume.split('/')
|
|
# idx = len(paths)-paths[::-1].index("logs")+1
|
|
# logdir = "/".join(paths[:idx])
|
|
logdir = '/'.join(paths[:-2])
|
|
ckpt = opt.resume
|
|
else:
|
|
assert os.path.isdir(opt.resume), opt.resume
|
|
logdir = opt.resume.rstrip('/')
|
|
ckpt = os.path.join(logdir, 'checkpoints', 'last.ckpt')
|
|
|
|
opt.resume_from_checkpoint = ckpt
|
|
base_configs = sorted(
|
|
glob.glob(os.path.join(logdir, 'configs/*.yaml'))
|
|
)
|
|
opt.base = base_configs + opt.base
|
|
_tmp = logdir.split('/')
|
|
nowname = _tmp[-1]
|
|
else:
|
|
if opt.name:
|
|
name = '_' + opt.name
|
|
elif opt.base:
|
|
cfg_fname = os.path.split(opt.base[0])[-1]
|
|
cfg_name = os.path.splitext(cfg_fname)[0]
|
|
name = '_' + cfg_name
|
|
else:
|
|
name = ''
|
|
|
|
if opt.datadir_in_name:
|
|
now = os.path.basename(os.path.normpath(opt.data_root)) + now
|
|
|
|
|
|
nowname = now + name + opt.postfix
|
|
logdir = os.path.join(opt.logdir, nowname)
|
|
|
|
ckptdir = os.path.join(logdir, 'checkpoints')
|
|
cfgdir = os.path.join(logdir, 'configs')
|
|
seed_everything(opt.seed)
|
|
|
|
try:
|
|
# init and save configs
|
|
configs = [OmegaConf.load(cfg) for cfg in opt.base]
|
|
cli = OmegaConf.from_dotlist(unknown)
|
|
config = OmegaConf.merge(*configs, cli)
|
|
lightning_config = config.pop('lightning', OmegaConf.create())
|
|
# merge trainer cli with config
|
|
trainer_config = lightning_config.get('trainer', OmegaConf.create())
|
|
# default to ddp
|
|
trainer_config['accelerator'] = 'auto'
|
|
for k in nondefault_trainer_args(opt):
|
|
trainer_config[k] = getattr(opt, k)
|
|
if not 'gpus' in trainer_config:
|
|
del trainer_config['accelerator']
|
|
cpu = True
|
|
else:
|
|
gpuinfo = trainer_config['gpus']
|
|
print(f'Running on GPUs {gpuinfo}')
|
|
cpu = False
|
|
trainer_opt = argparse.Namespace(**trainer_config)
|
|
lightning_config.trainer = trainer_config
|
|
|
|
# model
|
|
|
|
# config.model.params.personalization_config.params.init_word = opt.init_word
|
|
config.model.params.personalization_config.params.embedding_manager_ckpt = (
|
|
opt.embedding_manager_ckpt
|
|
)
|
|
|
|
if opt.init_word:
|
|
config.model.params.personalization_config.params.initializer_words = [opt.init_word]
|
|
|
|
if opt.actual_resume:
|
|
model = load_model_from_config(config, opt.actual_resume)
|
|
else:
|
|
model = instantiate_from_config(config.model)
|
|
|
|
# trainer and callbacks
|
|
trainer_kwargs = dict()
|
|
|
|
# default logger configs
|
|
def_logger = 'csv'
|
|
def_logger_target = 'CSVLogger'
|
|
default_logger_cfgs = {
|
|
'wandb': {
|
|
'target': 'pytorch_lightning.loggers.WandbLogger',
|
|
'params': {
|
|
'name': nowname,
|
|
'save_dir': logdir,
|
|
'offline': opt.debug,
|
|
'id': nowname,
|
|
},
|
|
},
|
|
def_logger: {
|
|
'target': 'pytorch_lightning.loggers.' + def_logger_target,
|
|
'params': {
|
|
'name': def_logger,
|
|
'save_dir': logdir,
|
|
},
|
|
},
|
|
}
|
|
default_logger_cfg = default_logger_cfgs[def_logger]
|
|
if 'logger' in lightning_config:
|
|
logger_cfg = lightning_config.logger
|
|
else:
|
|
logger_cfg = OmegaConf.create()
|
|
logger_cfg = OmegaConf.merge(default_logger_cfg, logger_cfg)
|
|
trainer_kwargs['logger'] = instantiate_from_config(logger_cfg)
|
|
|
|
# modelcheckpoint - use TrainResult/EvalResult(checkpoint_on=metric) to
|
|
# specify which metric is used to determine best models
|
|
default_modelckpt_cfg = {
|
|
'target': 'pytorch_lightning.callbacks.ModelCheckpoint',
|
|
'params': {
|
|
'dirpath': ckptdir,
|
|
'filename': '{epoch:06}',
|
|
'verbose': True,
|
|
'save_last': True,
|
|
},
|
|
}
|
|
if hasattr(model, 'monitor'):
|
|
print(f'Monitoring {model.monitor} as checkpoint metric.')
|
|
default_modelckpt_cfg['params']['monitor'] = model.monitor
|
|
default_modelckpt_cfg['params']['save_top_k'] = 1
|
|
|
|
if 'modelcheckpoint' in lightning_config:
|
|
modelckpt_cfg = lightning_config.modelcheckpoint
|
|
else:
|
|
modelckpt_cfg = OmegaConf.create()
|
|
modelckpt_cfg = OmegaConf.merge(default_modelckpt_cfg, modelckpt_cfg)
|
|
print(f'Merged modelckpt-cfg: \n{modelckpt_cfg}')
|
|
if version.parse(pl.__version__) < version.parse('1.4.0'):
|
|
trainer_kwargs['checkpoint_callback'] = instantiate_from_config(
|
|
modelckpt_cfg
|
|
)
|
|
|
|
# add callback which sets up log directory
|
|
default_callbacks_cfg = {
|
|
'setup_callback': {
|
|
'target': 'main.SetupCallback',
|
|
'params': {
|
|
'resume': opt.resume,
|
|
'now': now,
|
|
'logdir': logdir,
|
|
'ckptdir': ckptdir,
|
|
'cfgdir': cfgdir,
|
|
'config': config,
|
|
'lightning_config': lightning_config,
|
|
},
|
|
},
|
|
'image_logger': {
|
|
'target': 'main.ImageLogger',
|
|
'params': {
|
|
'batch_frequency': 750,
|
|
'max_images': 4,
|
|
'clamp': True,
|
|
},
|
|
},
|
|
'learning_rate_logger': {
|
|
'target': 'main.LearningRateMonitor',
|
|
'params': {
|
|
'logging_interval': 'step',
|
|
# "log_momentum": True
|
|
},
|
|
},
|
|
'cuda_callback': {'target': 'main.CUDACallback'},
|
|
}
|
|
if version.parse(pl.__version__) >= version.parse('1.4.0'):
|
|
default_callbacks_cfg.update(
|
|
{'checkpoint_callback': modelckpt_cfg}
|
|
)
|
|
|
|
if 'callbacks' in lightning_config:
|
|
callbacks_cfg = lightning_config.callbacks
|
|
else:
|
|
callbacks_cfg = OmegaConf.create()
|
|
|
|
if 'metrics_over_trainsteps_checkpoint' in callbacks_cfg:
|
|
print(
|
|
'Caution: Saving checkpoints every n train steps without deleting. This might require some free space.'
|
|
)
|
|
default_metrics_over_trainsteps_ckpt_dict = {
|
|
'metrics_over_trainsteps_checkpoint': {
|
|
'target': 'pytorch_lightning.callbacks.ModelCheckpoint',
|
|
'params': {
|
|
'dirpath': os.path.join(
|
|
ckptdir, 'trainstep_checkpoints'
|
|
),
|
|
'filename': '{epoch:06}-{step:09}',
|
|
'verbose': True,
|
|
'save_top_k': -1,
|
|
'every_n_train_steps': 10000,
|
|
'save_weights_only': True,
|
|
},
|
|
}
|
|
}
|
|
default_callbacks_cfg.update(
|
|
default_metrics_over_trainsteps_ckpt_dict
|
|
)
|
|
|
|
callbacks_cfg = OmegaConf.merge(default_callbacks_cfg, callbacks_cfg)
|
|
if 'ignore_keys_callback' in callbacks_cfg and hasattr(
|
|
trainer_opt, 'resume_from_checkpoint'
|
|
):
|
|
callbacks_cfg.ignore_keys_callback.params[
|
|
'ckpt_path'
|
|
] = trainer_opt.resume_from_checkpoint
|
|
elif 'ignore_keys_callback' in callbacks_cfg:
|
|
del callbacks_cfg['ignore_keys_callback']
|
|
|
|
trainer_kwargs['callbacks'] = [
|
|
instantiate_from_config(callbacks_cfg[k]) for k in callbacks_cfg
|
|
]
|
|
trainer_kwargs['max_steps'] = trainer_opt.max_steps
|
|
|
|
if hasattr(torch.backends, 'mps') and torch.backends.mps.is_available():
|
|
trainer_opt.accelerator = 'mps'
|
|
trainer_opt.detect_anomaly = False
|
|
|
|
trainer = Trainer.from_argparse_args(trainer_opt, **trainer_kwargs)
|
|
trainer.logdir = logdir ###
|
|
|
|
# data
|
|
config.data.params.train.params.data_root = opt.data_root
|
|
config.data.params.validation.params.data_root = opt.data_root
|
|
data = instantiate_from_config(config.data)
|
|
|
|
# NOTE according to https://pytorch-lightning.readthedocs.io/en/latest/datamodules.html
|
|
# calling these ourselves should not be necessary but it is.
|
|
# lightning still takes care of proper multiprocessing though
|
|
data.prepare_data()
|
|
data.setup()
|
|
print('#### Data #####')
|
|
for k in data.datasets:
|
|
print(
|
|
f'{k}, {data.datasets[k].__class__.__name__}, {len(data.datasets[k])}'
|
|
)
|
|
|
|
# configure learning rate
|
|
bs, base_lr = (
|
|
config.data.params.batch_size,
|
|
config.model.base_learning_rate,
|
|
)
|
|
if not cpu:
|
|
gpus = str(lightning_config.trainer.gpus).strip(', ').split(',')
|
|
ngpu = len(gpus)
|
|
else:
|
|
ngpu = 1
|
|
if 'accumulate_grad_batches' in lightning_config.trainer:
|
|
accumulate_grad_batches = (
|
|
lightning_config.trainer.accumulate_grad_batches
|
|
)
|
|
else:
|
|
accumulate_grad_batches = 1
|
|
print(f'accumulate_grad_batches = {accumulate_grad_batches}')
|
|
lightning_config.trainer.accumulate_grad_batches = (
|
|
accumulate_grad_batches
|
|
)
|
|
if opt.scale_lr:
|
|
model.learning_rate = accumulate_grad_batches * ngpu * bs * base_lr
|
|
print(
|
|
'Setting learning rate to {:.2e} = {} (accumulate_grad_batches) * {} (num_gpus) * {} (batchsize) * {:.2e} (base_lr)'.format(
|
|
model.learning_rate,
|
|
accumulate_grad_batches,
|
|
ngpu,
|
|
bs,
|
|
base_lr,
|
|
)
|
|
)
|
|
else:
|
|
model.learning_rate = base_lr
|
|
print('++++ NOT USING LR SCALING ++++')
|
|
print(f'Setting learning rate to {model.learning_rate:.2e}')
|
|
|
|
# allow checkpointing via USR1
|
|
def melk(*args, **kwargs):
|
|
# run all checkpoint hooks
|
|
if trainer.global_rank == 0:
|
|
print('Summoning checkpoint.')
|
|
ckpt_path = os.path.join(ckptdir, 'last.ckpt')
|
|
trainer.save_checkpoint(ckpt_path)
|
|
|
|
def divein(*args, **kwargs):
|
|
if trainer.global_rank == 0:
|
|
import pudb
|
|
|
|
pudb.set_trace()
|
|
|
|
import signal
|
|
|
|
signal.signal(signal.SIGTERM, melk)
|
|
signal.signal(signal.SIGTERM, divein)
|
|
|
|
# run
|
|
if opt.train:
|
|
try:
|
|
trainer.fit(model, data)
|
|
except Exception:
|
|
melk()
|
|
raise
|
|
if not opt.no_test and not trainer.interrupted:
|
|
trainer.test(model, data)
|
|
except Exception:
|
|
if opt.debug and trainer.global_rank == 0:
|
|
try:
|
|
import pudb as debugger
|
|
except ImportError:
|
|
import pdb as debugger
|
|
debugger.post_mortem()
|
|
raise
|
|
finally:
|
|
# move newly created debug project to debug_runs
|
|
if opt.debug and not opt.resume and trainer.global_rank == 0:
|
|
dst, name = os.path.split(logdir)
|
|
dst = os.path.join(dst, 'debug_runs', name)
|
|
os.makedirs(os.path.split(dst)[0], exist_ok=True)
|
|
os.rename(logdir, dst)
|
|
# if trainer.global_rank == 0:
|
|
# print(trainer.profiler.summary())
|