# encoding: utf-8 """ credit: https://github.com/facebookresearch/detectron2/blob/master/detectron2/engine/train_loop.py """ import logging import time import weakref from typing import Dict import numpy as np import torch from apex import amp from apex.parallel import DistributedDataParallel import fastreid.utils.comm as comm from fastreid.utils.events import EventStorage, get_event_storage __all__ = ["HookBase", "TrainerBase", "SimpleTrainer"] logger = logging.getLogger(__name__) class HookBase: """ Base class for hooks that can be registered with :class:`TrainerBase`. Each hook can implement 6 methods. The way they are called is demonstrated in the following snippet: .. code-block:: python hook.before_train() for _ in range(start_epoch, max_epoch): hook.before_epoch() for iter in range(start_iter, max_iter): hook.before_step() trainer.run_step() hook.after_step() hook.after_epoch() hook.after_train() Notes: 1. In the hook method, users can access `self.trainer` to access more properties about the context (e.g., current iteration). 2. A hook that does something in :meth:`before_step` can often be implemented equivalently in :meth:`after_step`. If the hook takes non-trivial time, it is strongly recommended to implement the hook in :meth:`after_step` instead of :meth:`before_step`. The convention is that :meth:`before_step` should only take negligible time. Following this convention will allow hooks that do care about the difference between :meth:`before_step` and :meth:`after_step` (e.g., timer) to function properly. Attributes: trainer: A weak reference to the trainer object. Set by the trainer when the hook is registered. """ def before_train(self): """ Called before the first iteration. """ pass def after_train(self): """ Called after the last iteration. """ pass def before_epoch(self): """ Called before each epoch. """ pass def after_epoch(self): """ Called after each epoch. """ pass def before_step(self): """ Called before each iteration. """ pass def after_step(self): """ Called after each iteration. """ pass class TrainerBase: """ Base class for iterative trainer with hooks. The only assumption we made here is: the training runs in a loop. A subclass can implement what the loop is. We made no assumptions about the existence of dataloader, optimizer, model, etc. Attributes: iter(int): the current iteration. start_iter(int): The iteration to start with. By convention the minimum possible value is 0. max_iter(int): The iteration to end training. storage(EventStorage): An EventStorage that's opened during the course of training. """ def __init__(self): self._hooks = [] def register_hooks(self, hooks): """ Register hooks to the trainer. The hooks are executed in the order they are registered. Args: hooks (list[Optional[HookBase]]): list of hooks """ hooks = [h for h in hooks if h is not None] for h in hooks: assert isinstance(h, HookBase) # To avoid circular reference, hooks and trainer cannot own each other. # This normally does not matter, but will cause memory leak if the # involved objects contain __del__: # See http://engineering.hearsaysocial.com/2013/06/16/circular-references-in-python/ h.trainer = weakref.proxy(self) self._hooks.extend(hooks) def train(self, start_epoch: int, max_epoch: int, iters_per_epoch: int): """ Args: start_iter, max_iter (int): See docs above """ logger = logging.getLogger(__name__) logger.info("Starting training from epoch {}".format(start_epoch)) self.iter = self.start_iter = start_epoch * iters_per_epoch with EventStorage(self.start_iter) as self.storage: try: self.before_train() for self.epoch in range(start_epoch, max_epoch): self.before_epoch() for _ in range(iters_per_epoch): self.before_step() self.run_step() self.after_step() self.iter += 1 self.after_epoch() except Exception: logger.exception("Exception during training:") raise finally: self.after_train() def before_train(self): for h in self._hooks: h.before_train() def after_train(self): self.storage.iter = self.iter for h in self._hooks: h.after_train() def before_epoch(self): self.storage.epoch = self.epoch for h in self._hooks: h.before_epoch() def before_step(self): self.storage.iter = self.iter for h in self._hooks: h.before_step() def after_step(self): for h in self._hooks: h.after_step() def after_epoch(self): for h in self._hooks: h.after_epoch() def run_step(self): raise NotImplementedError class SimpleTrainer(TrainerBase): """ A simple trainer for the most common type of task: single-cost single-optimizer single-data-source iterative optimization. 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. 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): """ Args: model: a torch Module. Takes a data from data_loader and returns a dict of heads. data_loader: an iterable. Contains data to be used to call model. optimizer: a torch optimizer. """ 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 self._data_loader_iter = iter(data_loader) self.optimizer = optimizer 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 your want to do something with the data, you can wrap the dataloader. """ data = next(self._data_loader_iter) data_time = time.perf_counter() - start """ If your want to do something with the heads, you can wrap the model. """ loss_dict = self.model(data) losses = sum(loss_dict.values()) """ If you need accumulate gradients or something similar, you can wrap the optimizer with your custom `zero_grad()` method. """ self.optimizer.zero_grad() losses.backward() 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. """ self.optimizer.step() def _write_metrics(self, loss_dict: Dict[str, torch.Tensor], data_time: float): """ Args: loss_dict (dict): dict of scalar losses data_time (float): time taken by the dataloader iteration """ device = next(iter(loss_dict.values())).device # Use a new stream so these ops don't wait for DDP or backward with torch.cuda.stream(torch.cuda.Stream() if device.type == "cuda" else None): 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) # average the rest metrics metrics_dict = { k: np.mean([x[k] for x in all_metrics_dict]) for k in all_metrics_dict[0].keys() } total_losses_reduced = sum(metrics_dict.values()) if not np.isfinite(total_losses_reduced): raise FloatingPointError( f"Loss became infinite or NaN at iteration={self.iter}!\n" f"loss_dict = {metrics_dict}" ) storage.put_scalar("total_loss", total_losses_reduced) if len(metrics_dict) > 1: storage.put_scalars(**metrics_dict) class AMPTrainer(SimpleTrainer): """ Like :class:`SimpleTrainer`, but uses apex automatic mixed precision in the training loop. """ 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!" start = time.perf_counter() data = next(self._data_loader_iter) data_time = time.perf_counter() - start loss_dict = self.model(data) losses = sum(loss_dict.values()) self.optimizer.zero_grad() with amp.scale_loss(losses, self.optimizer) as scaled_loss: scaled_loss.backward() self._write_metrics(loss_dict, data_time) self.optimizer.step()