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# -*- coding: utf-8 -*-
# Copyright (c) Facebook, Inc. and its affiliates.
import concurrent.futures
import logging
import time
import weakref
from typing import List, Mapping, Optional
import numpy as np
import torch
from torch.nn.parallel import DataParallel, DistributedDataParallel
import detectron2.utils.comm as comm
from detectron2.engine.train_loop import HookBase, TrainerBase
from detectron2.utils.events import EventStorage, get_event_storage
from detectron2.utils.logger import _log_api_usage
__all__ = ["SimpleTrainer", "AMPTrainer"]
class SimpleTrainer(TrainerBase):
"""
A simple trainer for the most common type of task:
single-cost single-optimizer single-data-source iterative optimization,
optionally using data-parallelism.
It assumes that every step, you:
1. Compute the loss with a data from the data_loader.
2. Compute the gradients with the above loss.
3. Update the model with the optimizer.
All other tasks during training (checkpointing, logging, evaluation, LR schedule)
are maintained by hooks, which can be registered by :meth:`TrainerBase.register_hooks`.
If you want to do anything fancier than this,
either subclass TrainerBase and implement your own `run_step`,
or write your own training loop.
"""
def __init__(
self,
model,
data_loader,
optimizer,
gather_metric_period=1,
zero_grad_before_forward=False,
async_write_metrics=False,
):
"""
Args:
model: a torch Module. Takes a data from data_loader and returns a
dict of losses.
data_loader: an iterable. Contains data to be used to call model.
optimizer: a torch optimizer.
gather_metric_period: an int. Every gather_metric_period iterations
the metrics are gathered from all the ranks to rank 0 and logged.
zero_grad_before_forward: whether to zero the gradients before the forward.
async_write_metrics: bool. If True, then write metrics asynchronously to improve
training speed
"""
super().__init__()
"""
We set the model to training mode in the trainer.
However it's valid to train a model that's in eval mode.
If you want your model (or a submodule of it) to behave
like evaluation during training, you can overwrite its train() method.
"""
model.train()
self.model = model
self.data_loader = data_loader
# to access the data loader iterator, call `self._data_loader_iter`
self._data_loader_iter_obj = None
self.optimizer = optimizer
self.gather_metric_period = gather_metric_period
self.zero_grad_before_forward = zero_grad_before_forward
self.async_write_metrics = async_write_metrics
# create a thread pool that can execute non critical logic in run_step asynchronically
# use only 1 worker so tasks will be executred in order of submitting.
self.concurrent_executor = concurrent.futures.ThreadPoolExecutor(max_workers=1)
def run_step(self):
"""
Implement the standard training logic described above.
"""
assert self.model.training, "[SimpleTrainer] model was changed to eval mode!"
start = time.perf_counter()
"""
If you want to do something with the data, you can wrap the dataloader.
"""
data = next(self._data_loader_iter)
data_time = time.perf_counter() - start
# ------------------------------------------------------------------
for d in data:
self.dataset_image_counts[self.dataset_names[d.get("dataset_id", 0)]] += 1
self.dataset_object_counts[self.dataset_names[d.get("dataset_id", 0)]] += len(
d.get("instances", [])
)
dataset_image_counts = {f"count_image/{k}": v for k, v in self.dataset_image_counts.items()}
dataset_object_counts = {
f"count_object/{k}": v for k, v in self.dataset_object_counts.items()
}
if self.async_write_metrics:
# write metrics asynchronically
self.concurrent_executor.submit(
self._write_metrics_common, dataset_image_counts, iter=self.iter
)
self.concurrent_executor.submit(
self._write_metrics_common, dataset_object_counts, iter=self.iter
)
else:
self._write_metrics_common(dataset_image_counts)
self._write_metrics_common(dataset_object_counts)
# ------------------------------------------------------------------
if self.zero_grad_before_forward:
"""
If you need to accumulate gradients or do something similar, you can
wrap the optimizer with your custom `zero_grad()` method.
"""
self.optimizer.zero_grad()
"""
If you want to do something with the losses, you can wrap the model.
"""
loss_dict = self.model(data)
if isinstance(loss_dict, torch.Tensor):
losses = loss_dict
loss_dict = {"total_loss": loss_dict}
else:
losses = sum(loss_dict.values())
if not self.zero_grad_before_forward:
"""
If you need to accumulate gradients or do something similar, you can
wrap the optimizer with your custom `zero_grad()` method.
"""
self.optimizer.zero_grad()
losses.backward()
self.after_backward()
if self.async_write_metrics:
# write metrics asynchronically
self.concurrent_executor.submit(
self._write_metrics, loss_dict, data_time, iter=self.iter
)
else:
self._write_metrics(loss_dict, data_time)
"""
If you need gradient clipping/scaling or other processing, you can
wrap the optimizer with your custom `step()` method. But it is
suboptimal as explained in https://arxiv.org/abs/2006.15704 Sec 3.2.4
"""
self.optimizer.step()
@property
def _data_loader_iter(self):
# only create the data loader iterator when it is used
if self._data_loader_iter_obj is None:
self._data_loader_iter_obj = iter(self.data_loader)
return self._data_loader_iter_obj
def reset_data_loader(self, data_loader_builder):
"""
Delete and replace the current data loader with a new one, which will be created
by calling `data_loader_builder` (without argument).
"""
del self.data_loader
data_loader = data_loader_builder()
self.data_loader = data_loader
self._data_loader_iter_obj = None
def _write_metrics(
self,
loss_dict: Mapping[str, torch.Tensor],
data_time: float,
prefix: str = "",
iter: Optional[int] = None,
) -> None:
logger = logging.getLogger(__name__)
iter = self.iter if iter is None else iter
if (iter + 1) % self.gather_metric_period == 0:
try:
SimpleTrainer.write_metrics(loss_dict, data_time, iter, prefix)
except Exception:
logger.exception("Exception in writing metrics: ")
raise
@staticmethod
def write_metrics(
loss_dict: Mapping[str, torch.Tensor],
data_time: float,
cur_iter: int,
prefix: str = "",
) -> None:
"""
Args:
loss_dict (dict): dict of scalar losses
data_time (float): time taken by the dataloader iteration
prefix (str): prefix for logging keys
"""
metrics_dict = {k: v.detach().cpu().item() for k, v in loss_dict.items()}
metrics_dict["data_time"] = data_time
# Gather metrics among all workers for logging
# This assumes we do DDP-style training, which is currently the only
# supported method in detectron2.
all_metrics_dict = comm.gather(metrics_dict)
if comm.is_main_process():
storage = get_event_storage()
# data_time among workers can have high variance. The actual latency
# caused by data_time is the maximum among workers.
data_time = np.max([x.pop("data_time") for x in all_metrics_dict])
storage.put_scalar("data_time", data_time, cur_iter=cur_iter)
# average the rest metrics
all_metrics_key = []
for metrics_dict in all_metrics_dict:
for key in metrics_dict.keys():
if key not in all_metrics_key:
all_metrics_key.append(key)
metrics_dict = {
k: np.mean([x[k] for x in all_metrics_dict if k in x]) for k in all_metrics_key
}
total_losses_reduced = sum(metrics_dict.values())
if not np.isfinite(total_losses_reduced):
raise FloatingPointError(
f"Loss became infinite or NaN at iteration={cur_iter}!\n"
f"loss_dict = {metrics_dict}"
)
storage.put_scalar(
"{}total_loss".format(prefix), total_losses_reduced, cur_iter=cur_iter
)
if len(metrics_dict) > 1:
storage.put_scalars(cur_iter=cur_iter, **metrics_dict)
def state_dict(self):
ret = super().state_dict()
ret["optimizer"] = self.optimizer.state_dict()
return ret
def load_state_dict(self, state_dict):
super().load_state_dict(state_dict)
self.optimizer.load_state_dict(state_dict["optimizer"])
def after_train(self):
super().after_train()
self.concurrent_executor.shutdown(wait=True)
def _write_metrics_common(
self,
metrics_dict: Mapping[str, torch.Tensor],
prefix: str = "",
iter: Optional[int] = None,
) -> None:
logger = logging.getLogger(__name__)
iter = self.iter if iter is None else iter
if (iter + 1) % self.gather_metric_period == 0:
try:
SimpleTrainer.write_metrics_common(metrics_dict, iter, prefix)
except Exception:
logger.exception("Exception in writing metrics: ")
raise
@staticmethod
def write_metrics_common(
metrics_dict: Mapping[str, torch.Tensor],
cur_iter: int,
prefix: str = "",
) -> None:
"""
Args:
metrics_dict (dict): dict of scalar losses
prefix (str): prefix for logging keys
"""
metrics_dict = {k: v.detach().cpu().item() for k, v in metrics_dict.items()}
all_metrics_dict = comm.gather(metrics_dict)
if comm.is_main_process():
storage = get_event_storage()
metrics_dict = {
k: np.sum([x[k] for x in all_metrics_dict]) for k in all_metrics_dict[0].keys()
}
if len(metrics_dict) > 1:
storage.put_scalars(cur_iter=cur_iter, **metrics_dict)
class AMPTrainer(SimpleTrainer):
"""
Like :class:`SimpleTrainer`, but uses PyTorch's native automatic mixed precision
in the training loop.
"""
def __init__(
self,
model,
data_loader,
optimizer,
gather_metric_period=1,
zero_grad_before_forward=False,
grad_scaler=None,
precision: torch.dtype = torch.float16,
log_grad_scaler: bool = False,
async_write_metrics=False,
):
"""
Args:
model, data_loader, optimizer, gather_metric_period, zero_grad_before_forward,
async_write_metrics: same as in :class:`SimpleTrainer`.
grad_scaler: torch GradScaler to automatically scale gradients.
precision: torch.dtype as the target precision to cast to in computations
"""
unsupported = "AMPTrainer does not support single-process multi-device training!"
if isinstance(model, DistributedDataParallel):
assert not (model.device_ids and len(model.device_ids) > 1), unsupported
assert not isinstance(model, DataParallel), unsupported
super().__init__(
model, data_loader, optimizer, gather_metric_period, zero_grad_before_forward
)
if grad_scaler is None:
from torch.cuda.amp import GradScaler
grad_scaler = GradScaler()
self.grad_scaler = grad_scaler
self.precision = precision
self.log_grad_scaler = log_grad_scaler
def run_step(self):
"""
Implement the AMP training logic.
"""
assert self.model.training, "[AMPTrainer] model was changed to eval mode!"
assert torch.cuda.is_available(), "[AMPTrainer] CUDA is required for AMP training!"
from torch.cuda.amp import autocast
start = time.perf_counter()
data = next(self._data_loader_iter)
data_time = time.perf_counter() - start
# ------------------------------------------------------------------
for d in data:
self.dataset_image_counts[self.dataset_names[d.get("dataset_id", 0)]] += 1
self.dataset_object_counts[self.dataset_names[d.get("dataset_id", 0)]] += len(
d.get("instances", [])
)
dataset_image_counts = {
f"count_image/{k}": v for k, v in self.dataset_image_counts.items()
}
dataset_object_counts = {
f"count_object/{k}": v for k, v in self.dataset_object_counts.items()
}
if self.async_write_metrics:
# write metrics asynchronically
self.concurrent_executor.submit(
self._write_metrics_common, dataset_image_counts, iter=self.iter
)
self.concurrent_executor.submit(
self._write_metrics_common, dataset_object_counts, iter=self.iter
)
else:
self._write_metrics_common(dataset_image_counts)
self._write_metrics_common(dataset_object_counts)
# ------------------------------------------------------------------
if self.zero_grad_before_forward:
self.optimizer.zero_grad()
with autocast(dtype=self.precision):
loss_dict = self.model(data)
if isinstance(loss_dict, torch.Tensor):
losses = loss_dict
loss_dict = {"total_loss": loss_dict}
else:
losses = sum(loss_dict.values())
if not self.zero_grad_before_forward:
self.optimizer.zero_grad()
self.grad_scaler.scale(losses).backward()
if self.log_grad_scaler:
storage = get_event_storage()
storage.put_scalar("[metric] grad_scaler", self.grad_scaler.get_scale())
self.after_backward()
if self.async_write_metrics:
# write metrics asynchronically
self.concurrent_executor.submit(
self._write_metrics, loss_dict, data_time, iter=self.iter
)
else:
self._write_metrics(loss_dict, data_time)
self.grad_scaler.step(self.optimizer)
self.grad_scaler.update()
def state_dict(self):
ret = super().state_dict()
ret["grad_scaler"] = self.grad_scaler.state_dict()
return ret
def load_state_dict(self, state_dict):
super().load_state_dict(state_dict)
self.grad_scaler.load_state_dict(state_dict["grad_scaler"])
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