fast-reid/projects/FastClas/fastclas/trainer.py

83 lines
2.7 KiB
Python

# encoding: utf-8
"""
@author: xingyu liao
@contact: sherlockliao01@gmail.com
"""
import json
import logging
import os
from fastreid.data.build import _root
from fastreid.data.build import build_reid_train_loader, build_reid_test_loader
from fastreid.data.datasets import DATASET_REGISTRY
from fastreid.data.transforms import build_transforms
from fastreid.engine import DefaultTrainer
from fastreid.evaluation.clas_evaluator import ClasEvaluator
from fastreid.utils import comm
from fastreid.utils.checkpoint import PathManager
from .dataset import ClasDataset
class ClasTrainer(DefaultTrainer):
idx2class = None
@classmethod
def build_train_loader(cls, cfg):
"""
Returns:
iterable
It now calls :func:`fastreid.data.build_reid_train_loader`.
Overwrite it if you'd like a different data loader.
"""
logger = logging.getLogger("fastreid.clas_dataset")
logger.info("Prepare training set")
train_items = list()
for d in cfg.DATASETS.NAMES:
data = DATASET_REGISTRY.get(d)(root=_root)
if comm.is_main_process():
data.show_train()
train_items.extend(data.train)
transforms = build_transforms(cfg, is_train=True)
train_set = ClasDataset(train_items, transforms)
cls.idx2class = train_set.idx_to_class
data_loader = build_reid_train_loader(cfg, train_set=train_set)
return data_loader
@classmethod
def build_test_loader(cls, cfg, dataset_name):
"""
Returns:
iterable
It now calls :func:`fastreid.data.build_reid_test_loader`.
Overwrite it if you'd like a different data loader.
"""
data = DATASET_REGISTRY.get(dataset_name)(root=_root)
if comm.is_main_process():
data.show_test()
transforms = build_transforms(cfg, is_train=False)
test_set = ClasDataset(data.query, transforms, cls.idx2class)
data_loader, _ = build_reid_test_loader(cfg, test_set=test_set)
return data_loader
@classmethod
def build_evaluator(cls, cfg, dataset_name, output_dir=None):
data_loader = cls.build_test_loader(cfg, dataset_name)
return data_loader, ClasEvaluator(cfg, output_dir)
@staticmethod
def auto_scale_hyperparams(cfg, num_classes):
cfg = DefaultTrainer.auto_scale_hyperparams(cfg, num_classes)
# Save index to class dictionary
output_dir = cfg.OUTPUT_DIR
if comm.is_main_process() and output_dir:
path = os.path.join(output_dir, "idx2class.json")
with PathManager.open(path, "w") as f:
json.dump(ClasTrainer.idx2class, f)
return cfg