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import copy
from tqdm import tqdm
import torch
from trainer.build import TRAINER_REGISTRY
from trainer.build import BaseTrainer
@TRAINER_REGISTRY.register()
class ObjPretrainTrainer(BaseTrainer):
def __init__(self, cfg):
super().__init__(cfg)
self.best_metric = -1
def forward(self, data_dict):
return self.model(data_dict)
def backward(self, loss):
self.optimizer.zero_grad()
self.accelerator.backward(loss)
if self.grad_norm is not None and self.accelerator.sync_gradients:
self.accelerator.clip_grad_norm_(self.model.parameters(), self.grad_norm)
self.optimizer.step()
self.scheduler.step()
def train_step(self, epoch):
self.model.train()
loader = self.data_loaders["train"]
pbar = tqdm(range(len(loader)), disable=(not self.accelerator.is_main_process), desc=f"[Epoch {epoch + 1}/{self.epochs}]")
for i, data_dict in enumerate(loader):
with self.accelerator.accumulate(self.model):
# forward
data_dict = self.forward(data_dict)
# calculate loss
loss, losses = self.loss(data_dict)
# calculate evaluator
metrics = self.evaluator.batch_metrics(data_dict)
# optimize
self.backward(loss)
# record
self.global_step += 1
log_dict = {'step': self.global_step}
log_dict.update(losses)
log_dict.update(metrics)
self.log(log_dict, mode="train")
pbar.update(1)
@torch.no_grad()
def eval_step(self, epoch):
self.model.eval()
loader = self.data_loaders["val"]
pbar = tqdm(range(len(loader)), disable=(not self.accelerator.is_main_process))
for i, data_dict in enumerate(loader):
data_dict = self.forward(data_dict)
# data_dict = {
# k : v.contiguous() for k, v in data_dict.items() if isinstance(v, torch.Tensor)
# and k not in ['voxel_features', 'v2p_map', 'voxel_coords']
# }
# data_dict = self.accelerator.gather_for_metrics(data_dict)
self.evaluator.update(data_dict)
pbar.update(1)
is_best, results = self.evaluator.record()
if is_best:
self.best_metric = results["target_metric"]
self.log(results, mode="val")
self.evaluator.reset()
return is_best
@torch.no_grad()
def test_step(self):
self.model.eval()
loader = self.data_loaders["test"]
pbar = tqdm(range(len(loader)), disable=(not self.accelerator.is_main_process))
for i, data_dict in enumerate(loader):
data_dict = self.forward(data_dict)
# data_dict = {
# k : v.contiguous() for k, v in data_dict.items() if isinstance(v, torch.Tensor)
# and k not in ['voxel_features', 'v2p_map', 'voxel_coords']
# }
self.evaluator.update(data_dict)
pbar.update(1)
is_best, results = self.evaluator.record()
self.log(results, mode="test")
self.evaluator.reset()
return results
def run(self):
if self.mode == "train":
start_epoch = self.exp_tracker.epoch
self.global_step = start_epoch * len(self.data_loaders["train"])
for epoch in range(start_epoch, self.epochs):
self.exp_tracker.step()
self.train_step(epoch)
if self.epochs_per_eval and (epoch + 1) % self.epochs_per_eval == 0:
is_best = self.eval_step(epoch)
self.accelerator.print(f"[Epoch {epoch + 1}/{self.epochs}] finished eval, is_best: {is_best}")
else:
is_best = False
self.accelerator.wait_for_everyone()
if self.accelerator.is_main_process:
if is_best:
self.save("best.pth")
if self.epochs_per_save and (epoch + 1) % self.epochs_per_save == 0:
self.save(f"ckpt_{epoch+1}.pth")
self.test_step()
if self.mode == "train":
self.accelerator.end_training()
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