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#
# Modified by Peize Sun, Rufeng Zhang
# Contact: {sunpeize, cxrfzhang}@foxmail.com
#
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
"""
SparseRCNN Training Script.
This script is a simplified version of the training script in detectron2/tools.
"""
import os
import itertools
import time
from typing import Any, Dict, List, Set
import torch
import detectron2.utils.comm as comm
from detectron2.checkpoint import DetectionCheckpointer
from detectron2.config import get_cfg
from detectron2.data import MetadataCatalog, build_detection_train_loader
from detectron2.engine import AutogradProfiler, DefaultTrainer, default_argument_parser, default_setup, launch
from detectron2.evaluation import COCOEvaluator, verify_results, TextEvaluator
from detectron2.solver.build import maybe_add_gradient_clipping
from swints import SWINTSDatasetMapper, add_SWINTS_config
class Trainer(DefaultTrainer):
# """
# Extension of the Trainer class adapted to SparseRCNN.
# """
@classmethod
def build_evaluator(cls, cfg, dataset_name, output_folder=None):
"""
Create evaluator(s) for a given dataset.
This uses the special metadata "evaluator_type" associated with each builtin dataset.
For your own dataset, you can simply create an evaluator manually in your
script and do not have to worry about the hacky if-else logic here.
"""
if output_folder is None:
output_folder = os.path.join(cfg.OUTPUT_DIR, "inference")
return TextEvaluator(dataset_name, cfg, True, output_folder)
@classmethod
def build_train_loader(cls, cfg):
mapper = SWINTSDatasetMapper(cfg, is_train=True)
return build_detection_train_loader(cfg, mapper=mapper)
@classmethod
def build_optimizer(cls, cfg, model):
params: List[Dict[str, Any]] = []
memo: Set[torch.nn.parameter.Parameter] = set()
for key, value in model.named_parameters(recurse=True):
if not value.requires_grad:
continue
# Avoid duplicating parameters
if value in memo:
continue
memo.add(value)
lr = cfg.SOLVER.BASE_LR
weight_decay = cfg.SOLVER.WEIGHT_DECAY
if "backbone" in key:
lr = lr * cfg.SOLVER.BACKBONE_MULTIPLIER
params += [{"params": [value], "lr": lr, "weight_decay": weight_decay}]
def maybe_add_full_model_gradient_clipping(optim): # optim: the optimizer class
# detectron2 doesn't have full model gradient clipping now
clip_norm_val = cfg.SOLVER.CLIP_GRADIENTS.CLIP_VALUE
enable = (
cfg.SOLVER.CLIP_GRADIENTS.ENABLED
and cfg.SOLVER.CLIP_GRADIENTS.CLIP_TYPE == "full_model"
and clip_norm_val > 0.0
)
class FullModelGradientClippingOptimizer(optim):
def step(self, closure=None):
all_params = itertools.chain(*[x["params"] for x in self.param_groups])
torch.nn.utils.clip_grad_norm_(all_params, clip_norm_val)
super().step(closure=closure)
return FullModelGradientClippingOptimizer if enable else optim
optimizer_type = cfg.SOLVER.OPTIMIZER
if optimizer_type == "SGD":
optimizer = maybe_add_full_model_gradient_clipping(torch.optim.SGD)(
params, cfg.SOLVER.BASE_LR, momentum=cfg.SOLVER.MOMENTUM
)
elif optimizer_type == "ADAMW":
optimizer = maybe_add_full_model_gradient_clipping(torch.optim.AdamW)(
params, cfg.SOLVER.BASE_LR
)
else:
raise NotImplementedError(f"no optimizer type {optimizer_type}")
if not cfg.SOLVER.CLIP_GRADIENTS.CLIP_TYPE == "full_model":
optimizer = maybe_add_gradient_clipping(cfg, optimizer)
return optimizer
def setup(args):
"""
Create configs and perform basic setups.
"""
cfg = get_cfg()
add_SWINTS_config(cfg)
cfg.merge_from_file(args.config_file)
cfg.merge_from_list(args.opts)
cfg.freeze()
default_setup(cfg, args)
return cfg
def main(args):
cfg = setup(args)
if args.eval_only:
model = Trainer.build_model(cfg)
DetectionCheckpointer(model, save_dir=cfg.OUTPUT_DIR).resume_or_load(cfg.MODEL.WEIGHTS, resume=args.resume)
res = Trainer.test(cfg, model)
if comm.is_main_process():
verify_results(cfg, res)
return res
trainer = Trainer(cfg)
trainer.resume_or_load(resume=args.resume)
return trainer.train()
if __name__ == "__main__":
args = default_argument_parser().parse_args()
print("Command Line Args:", args)
launch(
main,
args.num_gpus,
num_machines=args.num_machines,
machine_rank=args.machine_rank,
dist_url=args.dist_url,
args=(args,),
)
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