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Copyright (c) Meta Platforms, Inc. and affiliates.
"""
import os
from abc import abstractmethod
import numpy as np
import torch
from utils.manifold_utils import pathmgr
from utils.torch_utils import (
AdamW,
bias_parameters,
load_checkpoint,
other_parameters,
save_checkpoint,
weight_parameters,
)
from .object_cache import ObjectCache
class BaseTrainer:
"""
Base class for all trainers
"""
def __init__(
self,
train_loaders,
valid_loaders,
model,
loss_func,
save_root,
config,
resume=False,
train_sets_epoches=None,
summary_writer=None,
rank=0,
world_size=1,
):
self.cfg = config
self.save_root = save_root
self.summary_writer = summary_writer
self.train_loaders, self.valid_loaders = train_loaders, valid_loaders
self.train_sets_epoches = train_sets_epoches
self.rank, self.world_size = rank, world_size
self.device = model.device
self.loss_func = loss_func
if resume: # load all states
self._load_resume_ckpt(model)
else:
self.model = self._init_model(model)
self.i_epoch, self.i_iter = 0, 0
self.i_train_set = 0
while (
self.train_sets_epoches[self.i_train_set] == 0
): # skip the datasets of 0 epoches
self.i_train_set += 1
self.optimizer = self._create_optimizer()
self.scheduler = self._create_scheduler(
self.optimizer, self.train_sets_epoches[self.i_train_set]
)
self.best_error = np.inf
@abstractmethod
def _run_one_epoch(self):
...
@abstractmethod
def _validate_with_gt(self):
...
def log(self, s):
if self.rank == 0:
print(s)
def set_up_obj_cache(self, cache_size=500):
self.obj_cache = ObjectCache(cache_size=cache_size)
def train(self):
if (
self.cfg.pretrained_model is not None
): # if using a pretrained model, evaluate that first to compare
if self.rank == 0:
self._validate_with_gt()
torch.distributed.barrier()
for _epoch in range(self.i_epoch, self.cfg.epoch_num):
self._run_one_epoch()
if self.i_epoch >= sum(self.train_sets_epoches[: (self.i_train_set + 1)]):
self.i_train_set += 1
self.optimizer = (
self._create_optimizer()
) # reset the states of optimizer as well
self.scheduler = self._create_scheduler(
self.optimizer, self.train_sets_epoches[self.i_train_set]
)
if self.rank == 0:
if self.i_epoch % self.cfg.val_epoch_size == 0:
self._validate_with_gt()
self.log(" * Epoch {} validation complete.".format(self.i_epoch))
torch.distributed.barrier()
# def zero_grad(self):
# # One Pytorch tutorial suggests clearing the gradients this way for faster speed
# # https://pytorch.org/tutorials/recipes/recipes/tuning_guide.html
# for param in self.model.parameters():
# param.grad = None
def _init_model(self, model):
model = model.to(self.device)
if self.cfg.pretrained_model:
self.log(
"=> using pre-trained weights {}.".format(self.cfg.pretrained_model)
)
epoch, weights = load_checkpoint(self.cfg.pretrained_model)
model.module.load_state_dict(weights)
else:
self.log("=> Train from scratch.")
model.module.init_weights()
self.log("number of parameters: {}".format(self.count_parameters(model)))
self.log(
"gpu memory allocated (model parameters only): {} Bytes".format(
torch.cuda.memory_allocated()
)
)
return model
def _create_optimizer(self):
self.log("=> setting {} optimizer".format(self.cfg.optim))
param_groups = [
{
"params": bias_parameters(self.model.module),
"weight_decay": self.cfg.bias_decay,
},
{
"params": weight_parameters(self.model.module),
"weight_decay": self.cfg.weight_decay,
},
{"params": other_parameters(self.model.module), "weight_decay": 0},
]
if self.cfg.optim == "adamw":
optimizer = AdamW(
param_groups, self.cfg.lr, betas=(self.cfg.momentum, self.cfg.beta)
)
elif self.cfg.optim == "adam":
optimizer = torch.optim.Adam(
param_groups,
self.cfg.lr,
betas=(self.cfg.momentum, self.cfg.beta),
eps=1e-7,
)
else:
raise NotImplementedError(self.cfg.optim)
return optimizer
def _create_scheduler(self, optimizer, epoches=np.inf):
if (
self.i_train_set < len(self.train_sets_epoches) - 1
): # try only the last loader uses onecyclelr
scheduler = torch.optim.lr_scheduler.ExponentialLR(optimizer, gamma=1)
return scheduler
if "lr_scheduler" in self.cfg.keys():
self.log("=> setting {} scheduler".format(self.cfg.lr_scheduler.module))
params = self.cfg.lr_scheduler.params
if self.cfg.lr_scheduler.module == "OneCycleLR":
params["epochs"] = min(epoches, self.cfg.epoch_num - self.i_epoch)
params["steps_per_epoch"] = self.cfg.epoch_size
scheduler = getattr(torch.optim.lr_scheduler, self.cfg.lr_scheduler.module)(
optimizer, **params
)
else: # a dummy scheduler by default
scheduler = torch.optim.lr_scheduler.ExponentialLR(optimizer, gamma=1)
return scheduler
def _load_resume_ckpt(self, model):
self.log("==> resuming")
with pathmgr.open(
os.path.join(self.save_root, "model_ckpt.pth.tar"), "rb"
) as f:
ckpt_dict = torch.load(f)
if "iter" not in ckpt_dict.keys():
ckpt_dict["iter"] = ckpt_dict["epoch"] * self.cfg.epoch_size
if "best_error" not in ckpt_dict.keys():
ckpt_dict["best_error"] = np.inf
self.i_epoch, self.i_iter, self.best_error = (
ckpt_dict["epoch"],
ckpt_dict["iter"],
ckpt_dict["best_error"],
)
self.i_train_set = np.where(self.i_epoch < np.cumsum(self.train_sets_epoches))[
0
][0]
model = model.to(self.device)
model.module.load_state_dict(ckpt_dict["state_dict"])
# self.model = torch.nn.DataParallel(model, device_ids=self.device_ids)
self.optimizer = self._create_optimizer()
self.scheduler = self._create_scheduler(
self.optimizer, self.train_sets_epoches[self.i_train_set]
)
if "optimizer_dict" in ckpt_dict.keys():
self.optimizer.load_state_dict(ckpt_dict["optimizer_dict"])
if "scheduler_dict" in ckpt_dict.keys():
self.scheduler.load_state_dict(ckpt_dict["scheduler_dict"])
return
# def _prepare_device(self, n_gpu_use):
# """
# setup GPU device if available, move model into configured device
# """
# n_gpu = torch.cuda.device_count()
# if n_gpu_use > 0 and n_gpu == 0:
# self.log(
# "Warning: There's no GPU available on this machine,"
# "training will be performed on CPU."
# )
# n_gpu_use = 0
# if n_gpu_use > n_gpu:
# self.log(
# "Warning: The number of GPU's configured to use is {}, "
# "but only {} are available.".format(n_gpu_use, n_gpu)
# )
# n_gpu_use = n_gpu
# device = torch.device("cuda:0" if n_gpu_use > 0 else "cpu")
# list_ids = list(range(n_gpu_use))
# self.log("=> gpu in use: {} gpu(s)".format(n_gpu_use))
# self.log(
# "device names: {}".format([torch.cuda.get_device_name(i) for i in list_ids])
# )
# return device, list_ids
def count_parameters(self, model):
return sum(p.numel() for p in model.parameters() if p.requires_grad)
def save_model(self, name, save_with_runtime=True):
if save_with_runtime:
models = {
"epoch": self.i_epoch,
"iter": self.i_iter,
"best_error": self.best_error,
"state_dict": self.model.module.state_dict(),
"optimizer_dict": self.optimizer.state_dict(),
"scheduler_dict": self.scheduler.state_dict(),
}
else:
models = {"state_dict": self.model.module.state_dict()}
save_checkpoint(self.save_root, models, name, is_best=False)
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