Spaces:
Running
on
Zero
Running
on
Zero
File size: 7,521 Bytes
a930e1f |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 |
from loguru import logger
import torch
import pytorch_lightning as pl
from matplotlib import pyplot as plt
plt.switch_backend('agg')
from src.xoftr import XoFTR_Pretrain
from src.losses.xoftr_loss_pretrain import XoFTRLossPretrain
from src.optimizers import build_optimizer, build_scheduler
from src.utils.plotting import make_mae_figures
from src.utils.comm import all_gather
from src.utils.misc import lower_config, flattenList
from src.utils.profiler import PassThroughProfiler
from src.utils.pretrain_utils import generate_random_masks, get_target
class PL_XoFTR_Pretrain(pl.LightningModule):
def __init__(self, config, pretrained_ckpt=None, profiler=None, dump_dir=None):
"""
TODO:
- use the new version of PL logging API.
"""
super().__init__()
# Misc
self.config = config # full config
_config = lower_config(self.config)
self.xoftr_cfg = lower_config(_config['xoftr'])
self.profiler = profiler or PassThroughProfiler()
self.n_vals_plot = max(config.TRAINER.N_VAL_PAIRS_TO_PLOT // config.TRAINER.WORLD_SIZE, 1)
# generator to create the same masks for validation
self.val_seed = self.config.PRETRAIN.VAL_SEED
self.val_generator = torch.Generator(device="cuda").manual_seed(self.val_seed)
self.mae_margins = config.PRETRAIN.MAE_MARGINS
# Matcher: XoFTR
self.matcher = XoFTR_Pretrain(config=_config['xoftr'])
self.loss = XoFTRLossPretrain(_config)
# Pretrained weights
if pretrained_ckpt:
state_dict = torch.load(pretrained_ckpt, map_location='cpu')['state_dict']
self.matcher.load_state_dict(state_dict, strict=False)
logger.info(f"Load \'{pretrained_ckpt}\' as pretrained checkpoint")
# Testing
self.dump_dir = dump_dir
def configure_optimizers(self):
# FIXME: The scheduler did not work properly when `--resume_from_checkpoint`
optimizer = build_optimizer(self, self.config)
scheduler = build_scheduler(self.config, optimizer)
return [optimizer], [scheduler]
def optimizer_step(
self, epoch, batch_idx, optimizer, optimizer_idx,
optimizer_closure, on_tpu, using_native_amp, using_lbfgs):
# learning rate warm up
warmup_step = self.config.TRAINER.WARMUP_STEP
if self.trainer.global_step < warmup_step:
if self.config.TRAINER.WARMUP_TYPE == 'linear':
base_lr = self.config.TRAINER.WARMUP_RATIO * self.config.TRAINER.TRUE_LR
lr = base_lr + \
(self.trainer.global_step / self.config.TRAINER.WARMUP_STEP) * \
abs(self.config.TRAINER.TRUE_LR - base_lr)
for pg in optimizer.param_groups:
pg['lr'] = lr
elif self.config.TRAINER.WARMUP_TYPE == 'constant':
pass
else:
raise ValueError(f'Unknown lr warm-up strategy: {self.config.TRAINER.WARMUP_TYPE}')
# update params
optimizer.step(closure=optimizer_closure)
optimizer.zero_grad()
def _trainval_inference(self, batch, generator=None):
generate_random_masks(batch,
patch_size=self.config.PRETRAIN.PATCH_SIZE,
mask_ratio=self.config.PRETRAIN.MASK_RATIO,
generator=generator,
margins=self.mae_margins)
with self.profiler.profile("XoFTR"):
self.matcher(batch)
with self.profiler.profile("Compute losses"):
# Create target pacthes to reconstruct
get_target(batch)
self.loss(batch)
def training_step(self, batch, batch_idx):
self._trainval_inference(batch)
# logging
if self.trainer.global_rank == 0 and self.global_step % self.trainer.log_every_n_steps == 0:
# scalars
for k, v in batch['loss_scalars'].items():
self.logger[0].experiment.add_scalar(f'train/{k}', v, self.global_step)
if self.config.TRAINER.USE_WANDB:
self.logger[1].log_metrics({f'train/{k}': v}, self.global_step)
if self.config.TRAINER.ENABLE_PLOTTING:
figures = make_mae_figures(batch)
for i, figure in enumerate(figures):
self.logger[0].experiment.add_figure(
f'train_mae/node_{self.trainer.global_rank}-device_{self.device.index}-batch_{i}',
figure, self.global_step)
return {'loss': batch['loss']}
def training_epoch_end(self, outputs):
avg_loss = torch.stack([x['loss'] for x in outputs]).mean()
if self.trainer.global_rank == 0:
self.logger[0].experiment.add_scalar(
'train/avg_loss_on_epoch', avg_loss,
global_step=self.current_epoch)
if self.config.TRAINER.USE_WANDB:
self.logger[1].log_metrics(
{'train/avg_loss_on_epoch': avg_loss},
self.current_epoch)
def validation_step(self, batch, batch_idx):
self._trainval_inference(batch, self.val_generator)
val_plot_interval = max(self.trainer.num_val_batches[0] // \
(self.trainer.num_gpus * self.n_vals_plot), 1)
figures = []
if batch_idx % val_plot_interval == 0:
figures = make_mae_figures(batch)
return {
'loss_scalars': batch['loss_scalars'],
'figures': figures,
}
def validation_epoch_end(self, outputs):
self.val_generator.manual_seed(self.val_seed)
# handle multiple validation sets
multi_outputs = [outputs] if not isinstance(outputs[0], (list, tuple)) else outputs
for valset_idx, outputs in enumerate(multi_outputs):
# since pl performs sanity_check at the very begining of the training
cur_epoch = self.trainer.current_epoch
if not self.trainer.resume_from_checkpoint and self.trainer.running_sanity_check:
cur_epoch = -1
# 1. loss_scalars: dict of list, on cpu
_loss_scalars = [o['loss_scalars'] for o in outputs]
loss_scalars = {k: flattenList(all_gather([_ls[k] for _ls in _loss_scalars])) for k in _loss_scalars[0]}
_figures = [o['figures'] for o in outputs]
figures = [item for sublist in _figures for item in sublist]
# tensorboard records only on rank 0
if self.trainer.global_rank == 0:
for k, v in loss_scalars.items():
mean_v = torch.stack(v).mean()
self.logger[0].experiment.add_scalar(f'val_{valset_idx}/avg_{k}', mean_v, global_step=cur_epoch)
if self.config.TRAINER.USE_WANDB:
self.logger[1].log_metrics({f'val_{valset_idx}/avg_{k}': mean_v}, cur_epoch)
for plot_idx, fig in enumerate(figures):
self.logger[0].experiment.add_figure(
f'val_mae_{valset_idx}/pair-{plot_idx}', fig, cur_epoch, close=True)
plt.close('all')
|