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# -*- coding: utf-8 -*-
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
This file contains components with some default boilerplate logic user may need
in training / testing. They will not work for everyone, but many users may find them useful.
The behavior of functions/classes in this file is subject to change,
since they are meant to represent the "common default behavior" people need in their projects.
import argparse
import logging
import os
from collections import OrderedDict
import torch
from fvcore.common.file_io import PathManager
from fvcore.nn.precise_bn import get_bn_modules
from torch.nn.parallel import DistributedDataParallel
import as T
from detectron2.checkpoint import DetectionCheckpointer
from import (
from detectron2.evaluation import (
from detectron2.modeling import build_model
from detectron2.solver import build_lr_scheduler, build_optimizer
from detectron2.utils import comm
from detectron2.utils.collect_env import collect_env_info
from detectron2.utils.env import seed_all_rng
from import CommonMetricPrinter, JSONWriter, TensorboardXWriter
from detectron2.utils.logger import setup_logger
from . import hooks
from .train_loop import SimpleTrainer
__all__ = ["default_argument_parser", "default_setup", "DefaultPredictor", "DefaultTrainer"]
def default_argument_parser():
Create a parser with some common arguments used by detectron2 users.
parser = argparse.ArgumentParser(description="Detectron2 Training")
parser.add_argument("--config-file", default="", metavar="FILE", help="path to config file")
help="whether to attempt to resume from the checkpoint directory",
parser.add_argument("--eval-only", action="store_true", help="perform evaluation only")
parser.add_argument("--num-gpus", type=int, default=1, help="number of gpus *per machine*")
parser.add_argument("--num-machines", type=int, default=1)
"--machine-rank", type=int, default=0, help="the rank of this machine (unique per machine)"
# PyTorch still may leave orphan processes in multi-gpu training.
# Therefore we use a deterministic way to obtain port,
# so that users are aware of orphan processes by seeing the port occupied.
port = 2 ** 15 + 2 ** 14 + hash(os.getuid()) % 2 ** 14
parser.add_argument("--dist-url", default="tcp://{}".format(port))
help="Modify config options using the command-line",
return parser
def default_setup(cfg, args):
Perform some basic common setups at the beginning of a job, including:
1. Set up the detectron2 logger
2. Log basic information about environment, cmdline arguments, and config
3. Backup the config to the output directory
cfg (CfgNode): the full config to be used
args (argparse.NameSpace): the command line arguments to be logged
output_dir = cfg.OUTPUT_DIR
if comm.is_main_process() and output_dir:
rank = comm.get_rank()
setup_logger(output_dir, distributed_rank=rank, name="fvcore")
logger = setup_logger(output_dir, distributed_rank=rank)"Rank of current process: {}. World size: {}".format(rank, comm.get_world_size()))"Environment info:\n" + collect_env_info())"Command line arguments: " + str(args))
if hasattr(args, "config_file") and args.config_file != "":
"Contents of args.config_file={}:\n{}".format(
args.config_file,, "r").read()
)"Running with full config:\n{}".format(cfg))
if comm.is_main_process() and output_dir:
# Note: some of our scripts may expect the existence of
# config.yaml in output directory
path = os.path.join(output_dir, "config.yaml")
with, "w") as f:
f.write(cfg.dump())"Full config saved to {}".format(os.path.abspath(path)))
# make sure each worker has a different, yet deterministic seed if specified
seed_all_rng(None if cfg.SEED < 0 else cfg.SEED + rank)
# cudnn benchmark has large overhead. It shouldn't be used considering the small size of
# typical validation set.
if not (hasattr(args, "eval_only") and args.eval_only):
torch.backends.cudnn.benchmark = cfg.CUDNN_BENCHMARK
class DefaultPredictor:
Create a simple end-to-end predictor with the given config.
The predictor takes an BGR image, resizes it to the specified resolution,
runs the model and produces a dict of predictions.
This predictor takes care of model loading and input preprocessing for you.
If you'd like to do anything more fancy, please refer to its source code
as examples to build and use the model manually.
metadata (Metadata): the metadata of the underlying dataset, obtained from
.. code-block:: python
pred = DefaultPredictor(cfg)
outputs = pred(inputs)
def __init__(self, cfg):
self.cfg = cfg.clone() # cfg can be modified by model
self.model = build_model(self.cfg)
self.metadata = MetadataCatalog.get(cfg.DATASETS.TEST[0])
checkpointer = DetectionCheckpointer(self.model)
self.transform_gen = T.ResizeShortestEdge(
self.input_format = cfg.INPUT.FORMAT
assert self.input_format in ["RGB", "BGR"], self.input_format
def __call__(self, original_image):
original_image (np.ndarray): an image of shape (H, W, C) (in BGR order).
predictions (dict): the output of the model
# Apply pre-processing to image.
if self.input_format == "RGB":
# whether the model expects BGR inputs or RGB
original_image = original_image[:, :, ::-1]
height, width = original_image.shape[:2]
image = self.transform_gen.get_transform(original_image).apply_image(original_image)
image = torch.as_tensor(image.astype("float32").transpose(2, 0, 1))
inputs = {"image": image, "height": height, "width": width}
predictions = self.model([inputs])[0]
return predictions
class DefaultTrainer(SimpleTrainer):
A trainer with default training logic. Compared to `SimpleTrainer`, it
contains the following logic in addition:
1. Create model, optimizer, scheduler, dataloader from the given config.
2. Load a checkpoint or `cfg.MODEL.WEIGHTS`, if exists.
3. Register a few common hooks.
It is created to simplify the **standard model training workflow** and reduce code boilerplate
for users who only need the standard training workflow, with standard features.
It means this class makes *many assumptions* about your training logic that
may easily become invalid in a new research. In fact, any assumptions beyond those made in the
:class:`SimpleTrainer` are too much for research.
The code of this class has been annotated about restrictive assumptions it mades.
When they do not work for you, you're encouraged to:
1. Overwrite methods of this class, OR:
2. Use :class:`SimpleTrainer`, which only does minimal SGD training and
nothing else. You can then add your own hooks if needed. OR:
3. Write your own training loop similar to `tools/`.
Also note that the behavior of this class, like other functions/classes in
this file, is not stable, since it is meant to represent the "common default behavior".
It is only guaranteed to work well with the standard models and training workflow in detectron2.
To obtain more stable behavior, write your own training logic with other public APIs.
checkpointer (DetectionCheckpointer):
cfg (CfgNode):
.. code-block:: python
trainer = DefaultTrainer(cfg)
trainer.resume_or_load() # load last checkpoint or MODEL.WEIGHTS
def __init__(self, cfg):
cfg (CfgNode):
# Assume these objects must be constructed in this order.
model = self.build_model(cfg)
optimizer = self.build_optimizer(cfg, model)
data_loader = self.build_train_loader(cfg)
# For training, wrap with DDP. But don't need this for inference.
if comm.get_world_size() > 1:
model = DistributedDataParallel(
model, device_ids=[comm.get_local_rank()], broadcast_buffers=False
super().__init__(model, data_loader, optimizer)
self.scheduler = self.build_lr_scheduler(cfg, optimizer)
# Assume no other objects need to be checkpointed.
# We can later make it checkpoint the stateful hooks
self.checkpointer = DetectionCheckpointer(
# Assume you want to save checkpoints together with logs/statistics
self.start_iter = 0
self.max_iter = cfg.SOLVER.MAX_ITER
self.cfg = cfg
def resume_or_load(self, resume=True):
If `resume==True`, and last checkpoint exists, resume from it.
Otherwise, load a model specified by the config.
resume (bool): whether to do resume or not
# The checkpoint stores the training iteration that just finished, thus we start
# at the next iteration (or iter zero if there's no checkpoint).
self.start_iter = (
self.checkpointer.resume_or_load(self.cfg.MODEL.WEIGHTS, resume=resume).get(
"iteration", -1
+ 1
def build_hooks(self):
Build a list of default hooks, including timing, evaluation,
checkpointing, lr scheduling, precise BN, writing events.
cfg = self.cfg.clone()
cfg.DATALOADER.NUM_WORKERS = 0 # save some memory and time for PreciseBN
ret = [
hooks.LRScheduler(self.optimizer, self.scheduler),
# Run at the same freq as (but before) evaluation.
# Build a new data loader to not affect training
if cfg.TEST.PRECISE_BN.ENABLED and get_bn_modules(self.model)
else None,
# Do PreciseBN before checkpointer, because it updates the model and need to
# be saved by checkpointer.
# This is not always the best: if checkpointing has a different frequency,
# some checkpoints may have more precise statistics than others.
if comm.is_main_process():
ret.append(hooks.PeriodicCheckpointer(self.checkpointer, cfg.SOLVER.CHECKPOINT_PERIOD))
def test_and_save_results():
self._last_eval_results = self.test(self.cfg, self.model)
return self._last_eval_results
# Do evaluation after checkpointer, because then if it fails,
# we can use the saved checkpoint to debug.
ret.append(hooks.EvalHook(cfg.TEST.EVAL_PERIOD, test_and_save_results))
if comm.is_main_process():
# run writers in the end, so that evaluation metrics are written
return ret
def build_writers(self):
Build a list of writers to be used. By default it contains
writers that write metrics to the screen,
a json file, and a tensorboard event file respectively.
If you'd like a different list of writers, you can overwrite it in
your trainer.
list[EventWriter]: a list of :class:`EventWriter` objects.
It is now implemented by:
.. code-block:: python
return [
JSONWriter(os.path.join(self.cfg.OUTPUT_DIR, "metrics.json")),
# Assume the default print/log frequency.
return [
# It may not always print what you want to see, since it prints "common" metrics only.
JSONWriter(os.path.join(self.cfg.OUTPUT_DIR, "metrics.json")),
def train(self):
Run training.
OrderedDict of results, if evaluation is enabled. Otherwise None.
super().train(self.start_iter, self.max_iter)
if hasattr(self, "_last_eval_results") and comm.is_main_process():
verify_results(self.cfg, self._last_eval_results)
return self._last_eval_results
def build_model(cls, cfg):
It now calls :func:`detectron2.modeling.build_model`.
Overwrite it if you'd like a different model.
model = build_model(cfg)
logger = logging.getLogger(__name__)"Model:\n{}".format(model))
return model
def build_optimizer(cls, cfg, model):
It now calls :func:`detectron2.solver.build_optimizer`.
Overwrite it if you'd like a different optimizer.
return build_optimizer(cfg, model)
def build_lr_scheduler(cls, cfg, optimizer):
It now calls :func:`detectron2.solver.build_lr_scheduler`.
Overwrite it if you'd like a different scheduler.
return build_lr_scheduler(cfg, optimizer)
def build_train_loader(cls, cfg):
It now calls :func:``.
Overwrite it if you'd like a different data loader.
return build_detection_train_loader(cfg)
def build_test_loader(cls, cfg, dataset_name):
It now calls :func:``.
Overwrite it if you'd like a different data loader.
return build_detection_test_loader(cfg, dataset_name)
def build_evaluator(cls, cfg, dataset_name):
It is not implemented by default.
raise NotImplementedError(
"Please either implement `build_evaluator()` in subclasses, or pass "
"your evaluator as arguments to `DefaultTrainer.test()`."
def test(cls, cfg, model, evaluators=None):
cfg (CfgNode):
model (nn.Module):
evaluators (list[DatasetEvaluator] or None): if None, will call
:meth:`build_evaluator`. Otherwise, must have the same length as
dict: a dict of result metrics
logger = logging.getLogger(__name__)
if isinstance(evaluators, DatasetEvaluator):
evaluators = [evaluators]
if evaluators is not None:
assert len(cfg.DATASETS.TEST) == len(evaluators), "{} != {}".format(
len(cfg.DATASETS.TEST), len(evaluators)
results = OrderedDict()
for idx, dataset_name in enumerate(cfg.DATASETS.TEST):
data_loader = cls.build_test_loader(cfg, dataset_name)
# When evaluators are passed in as arguments,
# implicitly assume that evaluators can be created before data_loader.
if evaluators is not None:
evaluator = evaluators[idx]
evaluator = cls.build_evaluator(cfg, dataset_name)
except NotImplementedError:
"No evaluator found. Use `DefaultTrainer.test(evaluators=)`, "
"or implement its `build_evaluator` method."
results[dataset_name] = {}
results_i = inference_on_dataset(model, data_loader, evaluator)
results[dataset_name] = results_i
if comm.is_main_process():
assert isinstance(
results_i, dict
), "Evaluator must return a dict on the main process. Got {} instead.".format(
)"Evaluation results for {} in csv format:".format(dataset_name))
if len(results) == 1:
results = list(results.values())[0]
return results
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