mirror of https://github.com/JDAI-CV/fast-reid.git
liaoxingyu
parent
4629172a90
commit
85cebe311f
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@ -4,6 +4,7 @@ FastReID is a research platform that implements state-of-the-art re-identificati
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## What's New
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- [Oct 2020] Added the [Hyper-Parameter Optimization](https://github.com/JDAI-CV/fast-reid/tree/master/projects/HPOReID) based on fastreid. See `projects/HPOReID`.
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- [Sep 2020] Added the [person attribute recognition](https://github.com/JDAI-CV/fast-reid/tree/master/projects/attribute_recognition) based on fastreid. See `projects/attribute_recognition`.
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- [Sep 2020] Automatic Mixed Precision training is supported with pytorch1.6 built-in `torch.cuda.amp`. Set `cfg.SOLVER.AMP_ENABLED=True` to switch it on.
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- [Aug 2020] [Model Distillation](https://github.com/JDAI-CV/fast-reid/tree/master/projects/DistillReID) is supported, thanks for [guan'an wang](https://github.com/wangguanan)'s contribution.
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@ -0,0 +1,21 @@
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# Hyper-Parameter Optimization in FastReID
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This project includes training reid models with hyper-parameter optimization.
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Install the following
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```bash
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pip install 'ray[tune]'
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pip install hpbandster ConfigSpace
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```
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## Training
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To train a model with `BOHB`, run
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```bash
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python3 projects/HPOReID/train_hpo.py --config-file projects/HPOReID/configs/baseline.yml
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```
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## Known issues
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todo
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@ -0,0 +1,93 @@
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MODEL:
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META_ARCHITECTURE: "Baseline"
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FREEZE_LAYERS: ["backbone"]
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BACKBONE:
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NAME: "build_resnet_backbone"
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DEPTH: "34x"
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LAST_STRIDE: 1
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FEAT_DIM: 512
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NORM: "BN"
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WITH_NL: False
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WITH_IBN: True
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PRETRAIN: True
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PRETRAIN_PATH: "/export/home/lxy/.cache/torch/checkpoints/resnet34_ibn_a-94bc1577.pth"
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HEADS:
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NAME: "EmbeddingHead"
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NORM: "BN"
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NECK_FEAT: "after"
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EMBEDDING_DIM: 0
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POOL_LAYER: "gempool"
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CLS_LAYER: "circleSoftmax"
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SCALE: 64
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MARGIN: 0.35
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LOSSES:
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NAME: ("CrossEntropyLoss", "TripletLoss",)
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CE:
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EPSILON: 0.1
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SCALE: 1.
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TRI:
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MARGIN: 0.0
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HARD_MINING: True
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NORM_FEAT: False
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SCALE: 1.
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CIRCLE:
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MARGIN: 0.25
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ALPHA: 96
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SCALE: 1.0
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INPUT:
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SIZE_TRAIN: [256, 128]
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SIZE_TEST: [256, 128]
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DO_AUTOAUG: True
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REA:
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ENABLED: True
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CJ:
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ENABLED: True
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DO_PAD: True
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DATALOADER:
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PK_SAMPLER: True
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NAIVE_WAY: False
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NUM_INSTANCE: 16
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NUM_WORKERS: 8
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SOLVER:
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AMP_ENABLED: False
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OPT: "Adam"
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SCHED: "WarmupCosineAnnealingLR"
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MAX_ITER: 60
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BASE_LR: 0.00035
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BIAS_LR_FACTOR: 1.
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WEIGHT_DECAY: 0.0005
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WEIGHT_DECAY_BIAS: 0.0
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IMS_PER_BATCH: 64
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DELAY_ITERS: 30
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ETA_MIN_LR: 0.00000077
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FREEZE_ITERS: 5
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WARMUP_FACTOR: 0.01
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WARMUP_ITERS: 5
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CHECKPOINT_PERIOD: 100
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TEST:
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EVAL_PERIOD: 10
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IMS_PER_BATCH: 256
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DATASETS:
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NAMES: ("DukeMTMC",)
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TESTS: ("DukeMTMC",)
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COMBINEALL: False
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CUDNN_BENCHMARK: True
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OUTPUT_DIR: "projects/HPOReID/logs/dukemtmc/r34-ibn_bohb"
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@ -0,0 +1,7 @@
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# encoding: utf-8
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"""
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@author: xingyu liao
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@contact: sherlockliao01@gmail.com
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"""
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from .tune_hooks import TuneReportHook
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@ -0,0 +1,34 @@
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# encoding: utf-8
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"""
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@author: xingyu liao
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@contact: sherlockliao01@gmail.com
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"""
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import torch
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from ray import tune
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from fastreid.engine.hooks import EvalHook, flatten_results_dict
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class TuneReportHook(EvalHook):
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def _do_eval(self):
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results = self._func()
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if results:
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assert isinstance(
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results, dict
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), "Eval function must return a dict. Got {} instead.".format(results)
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flattened_results = flatten_results_dict(results)
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for k, v in flattened_results.items():
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try:
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v = float(v)
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except Exception:
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raise ValueError(
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"[EvalHook] eval_function should return a nested dict of float. "
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"Got '{}: {}' instead.".format(k, v)
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)
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# Remove extra memory cache of main process due to evaluation
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torch.cuda.empty_cache()
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tune.report(r1=results['Rank-1'], map=results['mAP'], score=(results['Rank-1'] + results['mAP']) / 2)
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@ -0,0 +1,177 @@
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#!/usr/bin/env python
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# encoding: utf-8
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"""
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@author: sherlock
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@contact: sherlockliao01@gmail.com
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"""
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import logging
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import os
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import sys
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from functools import partial
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import ConfigSpace as CS
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import ray
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from ray import tune
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from ray.tune import CLIReporter
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from ray.tune.schedulers.hb_bohb import HyperBandForBOHB
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from ray.tune.suggest.bohb import TuneBOHB
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sys.path.append('.')
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from fastreid.config import get_cfg
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from fastreid.engine import hooks
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from fastreid.modeling import build_model
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from fastreid.engine import DefaultTrainer, default_argument_parser, default_setup
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from fastreid.utils.events import CommonMetricPrinter
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from hporeid import *
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logger = logging.getLogger("fastreid.project.tune")
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ray.init(dashboard_host='127.0.0.1')
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class HyperTuneTrainer(DefaultTrainer):
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def build_hooks(self):
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r"""
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Build a list of default hooks, including timing, evaluation,
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checkpointing, lr scheduling, precise BN, writing events.
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Returns:
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list[HookBase]:
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"""
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cfg = self.cfg.clone()
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cfg.defrost()
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ret = [
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hooks.IterationTimer(),
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hooks.LRScheduler(self.optimizer, self.scheduler),
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]
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if cfg.MODEL.FREEZE_LAYERS != [''] and cfg.SOLVER.FREEZE_ITERS > 0:
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freeze_layers = ",".join(cfg.MODEL.FREEZE_LAYERS)
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logger.info(f'Freeze layer group "{freeze_layers}" training for {cfg.SOLVER.FREEZE_ITERS:d} iterations')
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ret.append(hooks.FreezeLayer(
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self.model,
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self.optimizer,
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cfg.MODEL.FREEZE_LAYERS,
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cfg.SOLVER.FREEZE_ITERS,
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))
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def test_and_save_results():
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self._last_eval_results = self.test(self.cfg, self.model)
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return self._last_eval_results
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# Do evaluation after checkpointer, because then if it fails,
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# we can use the saved checkpoint to debug.
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ret.append(TuneReportHook(cfg.TEST.EVAL_PERIOD, test_and_save_results))
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# run writers in the end, so that evaluation metrics are written
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ret.append(hooks.PeriodicWriter([CommonMetricPrinter(self.max_iter)], 200))
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return ret
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@classmethod
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def build_model(cls, cfg):
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model = build_model(cfg)
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return model
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def setup(args):
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"""
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Create configs and perform basic setups.
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"""
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cfg = get_cfg()
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cfg.merge_from_file(args.config_file)
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cfg.merge_from_list(args.opts)
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cfg.freeze()
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default_setup(cfg, args)
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return cfg
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def train_reid_tune(cfg, config, checkpoint_dir=None):
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cfg.defrost()
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# Hyperparameter tuning
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cfg.SOLVER.BASE_LR = config["lr"]
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cfg.SOLVER.WEIGHT_DECAY = config["wd"]
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cfg.SOLVER.WEIGHT_DECAY_BIAS = config["wd_bias"]
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cfg.SOLVER.IMS_PER_BATCH = config["bsz"]
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cfg.DATALOADER.NUM_INSTANCE = config["num_inst"]
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cfg.SOLVER.DELAY_ITERS = config["delay_iters"]
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cfg.SOLVER.ETA_MIN_LR = config["lr"] * 0.0022
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cfg.MODEL.LOSSES.CE.SCALE = config["ce_scale"]
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cfg.MODEL.HEADS.SCALE = config["circle_scale"]
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cfg.MODEL.HEADS.MARGIN = config["circle_margin"]
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cfg.INPUT.DO_AUTOAUG = config["autoaug_enabled"]
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cfg.INPUT.CJ.ENABLED = config["cj_enabled"]
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trainer = HyperTuneTrainer(cfg)
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if checkpoint_dir:
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path = os.path.join(checkpoint_dir, "checkpoint")
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checkpoint = trainer.checkpointer.resume_or_load(path)
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if checkpoint.checkpointer.has_checkpoint():
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trainer.start_iter = checkpoint.get("iteration", -1) + 1
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# Regular model training
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trainer.train()
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def main(args):
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cfg = setup(args)
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search_space = CS.ConfigurationSpace()
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search_space.add_hyperparameters([
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CS.UniformFloatHyperparameter(name="lr", lower=1e-6, upper=1e-3),
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CS.UniformFloatHyperparameter(name="wd", lower=0, upper=1e-3),
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CS.UniformFloatHyperparameter(name="wd_bias", lower=0, upper=1e-3),
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CS.CategoricalHyperparameter(name="bsz", choices=[64, 96, 128, 160, 224, 256]),
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CS.CategoricalHyperparameter(name="num_inst", choices=[2, 4, 8, 16, 32]),
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CS.UniformIntegerHyperparameter(name="delay_iters", lower=20, upper=60),
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CS.UniformFloatHyperparameter(name="ce_scale", lower=0.1, upper=1.0),
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CS.UniformIntegerHyperparameter(name="circle_scale", lower=8, upper=256),
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CS.UniformFloatHyperparameter(name="circle_margin", lower=0.1, upper=0.5),
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CS.CategoricalHyperparameter(name="autoaug_enabled", choices=[True, False]),
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CS.CategoricalHyperparameter(name="cj_enabled", choices=[True, False]),
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]
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)
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exp_metrics = dict(metric="score", mode="max")
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bohb_hyperband = HyperBandForBOHB(
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time_attr="training_iteration",
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max_t=7,
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**exp_metrics,
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)
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bohb_search = TuneBOHB(
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search_space, max_concurrent=4, **exp_metrics)
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reporter = CLIReporter(
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parameter_columns=["bsz", "num_inst", "lr"],
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metric_columns=["r1", "map", "training_iteration"])
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analysis = tune.run(
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partial(
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train_reid_tune,
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cfg),
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resources_per_trial={"cpu": 10, "gpu": 1},
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search_alg=bohb_search,
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num_samples=args.num_samples,
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scheduler=bohb_hyperband,
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progress_reporter=reporter,
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local_dir=cfg.OUTPUT_DIR,
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keep_checkpoints_num=4,
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name="bohb")
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best_trial = analysis.get_best_trial("map", "max", "last")
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logger.info("Best trial config: {}".format(best_trial.config))
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logger.info("Best trial final validation mAP: {}, Rank-1: {}".format(
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best_trial.last_result["map"], best_trial.last_result["r1"]))
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if __name__ == "__main__":
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parser = default_argument_parser()
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parser.add_argument("--num-samples", type=int, default=20, help="number of tune trials")
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args = parser.parse_args()
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print("Command Line Args:", args)
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main(args)
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