From 85ceae2b11834aa7e6f430e7f501ffb1a134f45c Mon Sep 17 00:00:00 2001
From: Ren Tianhe <48727989+rentainhe@users.noreply.github.com>
Date: Fri, 2 Jun 2023 17:24:09 +0800
Subject: [PATCH] Bump version to 0.4.0 (#258)
* release better dino result
* refine
* fix deta config
* add dino better hyper
* add DINO pretrained weights links
* bump to v0.4.0
---
README.md | 16 +-
changlog.md | 7 +
projects/deformable_detr/train_net.py | 274 ++++++++++++++++++
projects/deta/configs/deta_r50_5scale_12ep.py | 1 +
projects/dino/README.md | 81 +++++-
.../dino_r50_4scale_12ep_better_hyper.py | 38 +++
.../dino_r50_4scale_12ep_no_frozen.py | 12 +
7 files changed, 414 insertions(+), 15 deletions(-)
create mode 100644 projects/deformable_detr/train_net.py
create mode 100644 projects/dino/configs/dino-resnet/dino_r50_4scale_12ep_better_hyper.py
create mode 100644 projects/dino/configs/dino-resnet/dino_r50_4scale_12ep_no_frozen.py
diff --git a/README.md b/README.md
index 141f7db2..f6b197dd 100644
--- a/README.md
+++ b/README.md
@@ -60,15 +60,13 @@ The repo name detrex has several interpretations:
- de-t.rex : de means 'the' in Dutch. T.rex, also called Tyrannosaurus Rex, means 'king of the tyrant lizards' and connects to our research work 'DINO', which is short for Dinosaur.
## What's New
-v0.3.0 was released on 03/17/2023:
-- Support new algorithms including `Anchor-DETR` and `DETA`.
-- Release more than 10+ pretrained models (including the converted weights): `DETR-R50 & R101`, `DETR-R50 & R101-DC5`, `DAB-DETR-R50 & R101-DC5`, `DAB-DETR-R50-3patterns`, `Conditional-DETR-R50 & R101-DC5`, `DN-DETR-R50-DC5`, `Anchor-DETR` and the `DETA-Swin-o365-finetune` model which can achieve **`62.9AP`** on coco val.
-- Support **MaskDINO** on ADE20k semantic segmentation task.
-- Support `EMAHook` during training by setting `train.model_ema.enabled=True`, which can enhance the model performance. DINO with EMA can achieve **`49.4AP`** with only 12epoch training.
-- Support mixed precision training by setting `train.amp.enabled=True`, which will **reduce 20% to 30% GPU memory usage**.
-- Support `train.fast_dev_run=True` for **fast debugging**.
-- Support **encoder-decoder checkpoint** in DINO, which may reduce **30% GPU** memory usage.
-- Support a great `slurm training scripts` by @rayleizhu, please check this issue for more details [#213](https://github.com/IDEA-Research/detrex/issues/213)
+v0.4.0 was released on 02/06/2023:
+- Support [CO-MOT](./projects/co_mot/) aims for End-to-End Multi-Object Tracking by [Feng Yan](https://scholar.google.com/citations?user=gO4divAAAAAJ&hl=zh-CN&oi=sra).
+- Release `DINO` with optimized hyper-parameters which achieves `50.0 AP` under 1x settings.
+- Release pretrained DINO based on `InternImage`, `ConvNeXt-1K pretrained` backbones.
+- Release `Deformable-DETR-R50` pretrained weights.
+- Release `DETA` and better `H-DETR` pretrained weights: achieving `50.2 AP` and `49.1 AP` respectively.
+
Please see [changelog.md](./changlog.md) for details and release history.
diff --git a/changlog.md b/changlog.md
index a6e6f653..2a79e448 100644
--- a/changlog.md
+++ b/changlog.md
@@ -1,5 +1,12 @@
## Change Log
+### v0.4.0 (02/06/2023):
+- Support [CO-MOT](./projects/co_mot/) aims for End-to-End Multi-Object Tracking by [Feng Yan](https://scholar.google.com/citations?user=gO4divAAAAAJ&hl=zh-CN&oi=sra).
+- Release `DINO` with optimized hyper-parameters which achieves `50.0 AP` under 1x settings.
+- Release pretrained DINO based on `InternImage`, `ConvNeXt-1K pretrained` backbones.
+- Release `Deformable-DETR-R50` pretrained weights.
+- Release `DETA` and better `H-DETR` pretrained weights: achieving `50.2 AP` and `49.1 AP` respectively.
+
### v0.3.0 (17/03/2023)
- Support new algorithms including `Anchor-DETR` and `DETA`.
- Release more than 10+ pretrained models (including the converted weights): `DETR-R50 & R101`, `DETR-R50 & R101-DC5`, `DAB-DETR-R50 & R101-DC5`, `DAB-DETR-R50-3patterns`, `Conditional-DETR-R50 & R101-DC5`, `DN-DETR-R50-DC5`, `Anchor-DETR` and the `DETA-Swin-o365-finetune` model which can achieve **`62.9AP`** on coco val.
diff --git a/projects/deformable_detr/train_net.py b/projects/deformable_detr/train_net.py
new file mode 100644
index 00000000..4a69cea3
--- /dev/null
+++ b/projects/deformable_detr/train_net.py
@@ -0,0 +1,274 @@
+#!/usr/bin/env python
+# Copyright (c) Facebook, Inc. and its affiliates.
+"""
+Training script using the new "LazyConfig" python config files.
+
+This scripts reads a given python config file and runs the training or evaluation.
+It can be used to train any models or dataset as long as they can be
+instantiated by the recursive construction defined in the given config file.
+
+Besides lazy construction of models, dataloader, etc., this scripts expects a
+few common configuration parameters currently defined in "configs/common/train.py".
+To add more complicated training logic, you can easily add other configs
+in the config file and implement a new train_net.py to handle them.
+"""
+import logging
+import os
+import sys
+import time
+import torch
+from torch.nn.parallel import DataParallel, DistributedDataParallel
+
+from detectron2.checkpoint import DetectionCheckpointer
+from detectron2.config import LazyConfig, instantiate
+from detectron2.engine import (
+ SimpleTrainer,
+ default_argument_parser,
+ default_setup,
+ default_writers,
+ hooks,
+ launch,
+)
+from detectron2.engine.defaults import create_ddp_model
+from detectron2.evaluation import inference_on_dataset, print_csv_format
+from detectron2.utils import comm
+
+sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), os.path.pardir)))
+
+logger = logging.getLogger("detrex")
+
+
+def match_name_keywords(n, name_keywords):
+ out = False
+ for b in name_keywords:
+ if b in n:
+ out = True
+ break
+ return out
+
+
+class Trainer(SimpleTrainer):
+ """
+ We've combine Simple and AMP Trainer together.
+ """
+
+ def __init__(
+ self,
+ model,
+ dataloader,
+ optimizer,
+ amp=False,
+ clip_grad_params=None,
+ grad_scaler=None,
+ ):
+ super().__init__(model=model, data_loader=dataloader, optimizer=optimizer)
+
+ 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
+
+ if amp:
+ if grad_scaler is None:
+ from torch.cuda.amp import GradScaler
+
+ grad_scaler = GradScaler()
+ self.grad_scaler = grad_scaler
+
+ # set True to use amp training
+ self.amp = amp
+
+ # gradient clip hyper-params
+ self.clip_grad_params = clip_grad_params
+
+ def run_step(self):
+ """
+ Implement the standard training logic described above.
+ """
+ assert self.model.training, "[Trainer] model was changed to eval mode!"
+ assert torch.cuda.is_available(), "[Trainer] CUDA is required for AMP training!"
+ from torch.cuda.amp import autocast
+
+ 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
+
+ """
+ If you want to do something with the losses, you can wrap the model.
+ """
+ loss_dict = self.model(data)
+ with autocast(enabled=self.amp):
+ if isinstance(loss_dict, torch.Tensor):
+ losses = loss_dict
+ loss_dict = {"total_loss": loss_dict}
+ else:
+ losses = sum(loss_dict.values())
+
+ """
+ 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 self.amp:
+ self.grad_scaler.scale(losses).backward()
+ if self.clip_grad_params is not None:
+ self.grad_scaler.unscale_(self.optimizer)
+ self.clip_grads(self.model.parameters())
+ self.grad_scaler.step(self.optimizer)
+ self.grad_scaler.update()
+ else:
+ losses.backward()
+ if self.clip_grad_params is not None:
+ self.clip_grads(self.model.parameters())
+ self.optimizer.step()
+
+ self._write_metrics(loss_dict, data_time)
+
+ def clip_grads(self, params):
+ params = list(filter(lambda p: p.requires_grad and p.grad is not None, params))
+ if len(params) > 0:
+ return torch.nn.utils.clip_grad_norm_(
+ parameters=params,
+ **self.clip_grad_params,
+ )
+
+
+def do_test(cfg, model):
+ if "evaluator" in cfg.dataloader:
+ ret = inference_on_dataset(
+ model, instantiate(cfg.dataloader.test), instantiate(cfg.dataloader.evaluator)
+ )
+ print_csv_format(ret)
+ return ret
+
+
+def do_train(args, cfg):
+ """
+ Args:
+ cfg: an object with the following attributes:
+ model: instantiate to a module
+ dataloader.{train,test}: instantiate to dataloaders
+ dataloader.evaluator: instantiate to evaluator for test set
+ optimizer: instantaite to an optimizer
+ lr_multiplier: instantiate to a fvcore scheduler
+ train: other misc config defined in `configs/common/train.py`, including:
+ output_dir (str)
+ init_checkpoint (str)
+ amp.enabled (bool)
+ max_iter (int)
+ eval_period, log_period (int)
+ device (str)
+ checkpointer (dict)
+ ddp (dict)
+ """
+ model = instantiate(cfg.model)
+ logger = logging.getLogger("detectron2")
+ logger.info("Model:\n{}".format(model))
+ model.to(cfg.train.device)
+
+ # this is an hack of train_net
+ param_dicts = [
+ {
+ "params": [
+ p
+ for n, p in model.named_parameters()
+ if not match_name_keywords(n, ["backbone"])
+ and not match_name_keywords(n, ["reference_points", "sampling_offsets"])
+ and p.requires_grad
+ ],
+ "lr": 2e-4,
+ },
+ {
+ "params": [
+ p
+ for n, p in model.named_parameters()
+ if match_name_keywords(n, ["backbone"]) and p.requires_grad
+ ],
+ "lr": 2e-5,
+ },
+ {
+ "params": [
+ p
+ for n, p in model.named_parameters()
+ if match_name_keywords(n, ["reference_points", "sampling_offsets"])
+ and p.requires_grad
+ ],
+ "lr": 2e-5,
+ },
+ ]
+ optim = torch.optim.AdamW(param_dicts, 2e-4, weight_decay=1e-4)
+
+ train_loader = instantiate(cfg.dataloader.train)
+
+ model = create_ddp_model(model, **cfg.train.ddp)
+
+ trainer = Trainer(
+ model=model,
+ dataloader=train_loader,
+ optimizer=optim,
+ amp=cfg.train.amp.enabled,
+ clip_grad_params=cfg.train.clip_grad.params if cfg.train.clip_grad.enabled else None,
+ )
+
+ checkpointer = DetectionCheckpointer(
+ model,
+ cfg.train.output_dir,
+ trainer=trainer,
+ )
+
+ trainer.register_hooks(
+ [
+ hooks.IterationTimer(),
+ hooks.LRScheduler(scheduler=instantiate(cfg.lr_multiplier)),
+ hooks.PeriodicCheckpointer(checkpointer, **cfg.train.checkpointer)
+ if comm.is_main_process()
+ else None,
+ hooks.EvalHook(cfg.train.eval_period, lambda: do_test(cfg, model)),
+ hooks.PeriodicWriter(
+ default_writers(cfg.train.output_dir, cfg.train.max_iter),
+ period=cfg.train.log_period,
+ )
+ if comm.is_main_process()
+ else None,
+ ]
+ )
+
+ checkpointer.resume_or_load(cfg.train.init_checkpoint, resume=args.resume)
+ if args.resume and checkpointer.has_checkpoint():
+ # The checkpoint stores the training iteration that just finished, thus we start
+ # at the next iteration
+ start_iter = trainer.iter + 1
+ else:
+ start_iter = 0
+ trainer.train(start_iter, cfg.train.max_iter)
+
+
+def main(args):
+ cfg = LazyConfig.load(args.config_file)
+ cfg = LazyConfig.apply_overrides(cfg, args.opts)
+ default_setup(cfg, args)
+
+ if args.eval_only:
+ model = instantiate(cfg.model)
+ model.to(cfg.train.device)
+ model = create_ddp_model(model)
+ DetectionCheckpointer(model).load(cfg.train.init_checkpoint)
+ print(do_test(cfg, model))
+ else:
+ do_train(args, cfg)
+
+
+if __name__ == "__main__":
+ args = default_argument_parser().parse_args()
+ launch(
+ main,
+ args.num_gpus,
+ num_machines=args.num_machines,
+ machine_rank=args.machine_rank,
+ dist_url=args.dist_url,
+ args=(args,),
+ )
diff --git a/projects/deta/configs/deta_r50_5scale_12ep.py b/projects/deta/configs/deta_r50_5scale_12ep.py
index 327db20d..b7b5296e 100644
--- a/projects/deta/configs/deta_r50_5scale_12ep.py
+++ b/projects/deta/configs/deta_r50_5scale_12ep.py
@@ -4,6 +4,7 @@
# using the default optimizer and dataloader
dataloader = get_config("common/data/coco_detr.py").dataloader
+optimizer = get_config("common/optim.py").AdamW
train = get_config("common/train.py").train
# modify training config
diff --git a/projects/dino/README.md b/projects/dino/README.md
index 15f80f3b..006c5bff 100644
--- a/projects/dino/README.md
+++ b/projects/dino/README.md
@@ -8,6 +8,19 @@ Hao Zhang, Feng Li, Shilong Liu, Lei Zhang, Hang Su, Jun Zhu, Lionel M. Ni, Heun
+## Table of Contents
+- [DINO with modified training engine](#dino-with-modified-training-engine)
+- [Main Results with Pretrained Models](#main-results-with-pretrained-models)
+ - [DINO with ResNet Backbone](#pretrained-dino-with-resnet-backbone)
+ - [DINO with Swin-Transformer Backbone](#pretrained-dino-with-swin-transformer-backbone)
+ - [DINO with ViT Backbone](#pretrained-dino-with-vit-backbone)
+ - [DINO with ConvNeXt Backbone](#pretrained-dino-with-convnext-backbone)
+ - [DINO with FocalNet Backbone](#pretrained-dino-with-focalnet-backbone)
+ - [DINO with InternImage Backbone](#pretrained-dino-with-internimage-backbone)
+- [Training DINO](#training)
+- [Evaluate DINO](#evaluation)
+- [Citation](#citing-dino)
+
## DINO with modified training engine
We've provide a hacked [train_net.py](./train_net.py) which aligns the optimizer params with Deformable-DETR that can achieve a better result on DINO models.
@@ -31,6 +44,22 @@ We've provide a hacked [train_net.py](./train_net.py) which aligns the optimizer
49.4 |
model |
+| DINO-R50-4scale (hacked trainer) |
+R-50 |
+IN1k |
+12 |
+100 |
+49.8 |
+ model |
+
+ | DINO-R50-4scale (hacked trainer) |
+R-50 |
+IN1k |
+12 |
+300 |
+50.0 |
+ model |
+
- Training model with hacked trainer
@@ -40,7 +69,7 @@ python projects/dino/train_net.py --config-file /path/to/config.py --num-gpus 8
## Main Results with Pretrained Models
-**Pretrained DINO with ResNet Backbone**
+##### Pretrained DINO with ResNet Backbone
@@ -116,7 +145,7 @@ python projects/dino/train_net.py --config-file /path/to/config.py --num-gpus 8
-**Pretrained DINO with Swin-Transformer Backbone**
+##### Pretrained DINO with Swin-Transformer Backbone
| Name |
Backbone |
@@ -151,6 +180,14 @@ python projects/dino/train_net.py --config-file /path/to/config.py --num-gpus 8
100 |
53.0 |
model |
+
+ | DINO-Swin-S-224-4scale |
+Swin-Small-224 |
+IN22K to IN1K |
+12 |
+100 |
+54.5 |
+ model |
| DINO-Swin-B-384-4scale |
@@ -206,7 +243,7 @@ python projects/dino/train_net.py --config-file /path/to/config.py --num-gpus 8
-**Pretrained DINO with FocalNet Backbone**
+##### Pretrained DINO with FocalNet Backbone
| Name |
Backbone |
@@ -250,7 +287,7 @@ python projects/dino/train_net.py --config-file /path/to/config.py --num-gpus 8
-**Pretrained DINO with ViT Backbone**
+##### Pretrained DINO with ViT Backbone
| Name |
Backbone |
@@ -293,7 +330,7 @@ python projects/dino/train_net.py --config-file /path/to/config.py --num-gpus 8
-**Pretrained DINO with ConvNeXt Backbone**
+##### Pretrained DINO with ConvNeXt Backbone
| Name |
Backbone |
@@ -304,6 +341,14 @@ python projects/dino/train_net.py --config-file /path/to/config.py --num-gpus 8
download |
| DINO-ConvNeXt-Tiny-384-4scale |
ConvNeXt-Tiny-384 |
+IN1K |
+12 |
+100 |
+51.4 |
+ model |
+
+ | DINO-ConvNeXt-Tiny-384-4scale |
+ConvNeXt-Tiny-384 |
IN22k |
12 |
100 |
@@ -312,6 +357,14 @@ python projects/dino/train_net.py --config-file /path/to/config.py --num-gpus 8
| DINO-ConvNeXt-Small-384-4scale |
ConvNeXt-Small-384 |
+IN1K |
+12 |
+100 |
+52.0 |
+ model |
+
+ | DINO-ConvNeXt-Small-384-4scale |
+ConvNeXt-Small-384 |
IN22k |
12 |
100 |
@@ -320,6 +373,14 @@ python projects/dino/train_net.py --config-file /path/to/config.py --num-gpus 8
| DINO-ConvNeXt-Base-384-4scale |
ConvNeXt-Base-384 |
+IN1K |
+12 |
+100 |
+52.6 |
+ model |
+
+ | DINO-ConvNeXt-Base-384-4scale |
+ConvNeXt-Base-384 |
IN22k |
12 |
100 |
@@ -328,6 +389,14 @@ python projects/dino/train_net.py --config-file /path/to/config.py --num-gpus 8
| DINO-ConvNeXt-Large-384-4scale |
ConvNeXt-Large-384 |
+IN1K |
+12 |
+100 |
+53.4 |
+ model |
+
+ | DINO-ConvNeXt-Large-384-4scale |
+ConvNeXt-Large-384 |
IN22k |
12 |
100 |
@@ -337,7 +406,7 @@ python projects/dino/train_net.py --config-file /path/to/config.py --num-gpus 8
-**Pretrained DINO with InternImage Backbone**
+##### Pretrained DINO with InternImage Backbone
| Name |
Backbone |
diff --git a/projects/dino/configs/dino-resnet/dino_r50_4scale_12ep_better_hyper.py b/projects/dino/configs/dino-resnet/dino_r50_4scale_12ep_better_hyper.py
new file mode 100644
index 00000000..8b8ad85a
--- /dev/null
+++ b/projects/dino/configs/dino-resnet/dino_r50_4scale_12ep_better_hyper.py
@@ -0,0 +1,38 @@
+import copy
+from .dino_r50_4scale_12ep import (
+ train,
+ dataloader,
+ optimizer,
+ lr_multiplier,
+ model,
+)
+
+# no frozen backbone get better results
+model.backbone.freeze_at = -1
+
+# more dn queries, set 300 here
+model.dn_number = 300
+
+# use 2.0 for class weight
+model.criterion.weight_dict = {
+ "loss_class": 2.0,
+ "loss_bbox": 5.0,
+ "loss_giou": 2.0,
+ "loss_class_dn": 1,
+ "loss_bbox_dn": 5.0,
+ "loss_giou_dn": 2.0,
+}
+
+# set aux loss weight dict
+base_weight_dict = copy.deepcopy(model.criterion.weight_dict)
+if model.aux_loss:
+ weight_dict = model.criterion.weight_dict
+ aux_weight_dict = {}
+ aux_weight_dict.update({k + "_enc": v for k, v in base_weight_dict.items()})
+ for i in range(model.transformer.decoder.num_layers - 1):
+ aux_weight_dict.update({k + f"_{i}": v for k, v in base_weight_dict.items()})
+ weight_dict.update(aux_weight_dict)
+ model.criterion.weight_dict = weight_dict
+
+# output dir
+train.output_dir = "./output/dino_r50_4scale_12ep_better_hyper"
\ No newline at end of file
diff --git a/projects/dino/configs/dino-resnet/dino_r50_4scale_12ep_no_frozen.py b/projects/dino/configs/dino-resnet/dino_r50_4scale_12ep_no_frozen.py
new file mode 100644
index 00000000..0931ee92
--- /dev/null
+++ b/projects/dino/configs/dino-resnet/dino_r50_4scale_12ep_no_frozen.py
@@ -0,0 +1,12 @@
+from .dino_r50_4scale_12ep import (
+ train,
+ dataloader,
+ optimizer,
+ lr_multiplier,
+ model,
+)
+
+# no frozen backbone get better results
+model.backbone.freeze_at = -1
+
+train.output_dir = "./output/dino_r50_4scale_12ep_no_frozen_backbone"
\ No newline at end of file