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mymmdetection2dtrain.py
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mymmdetection2dtrain.py
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import torch
print(torch.cuda.is_available())
print(torch.cuda.device_count())
print(torch.cuda.get_device_name())
print(torch.__version__)
# Check Pytorch installation
import torchvision
print(torchvision.__version__, torch.cuda.is_available())
# Check MMDetection installation
import mmdet
print(mmdet.__version__)
# Check mmcv installation
from mmcv.ops import get_compiling_cuda_version, get_compiler_version
print(get_compiling_cuda_version())
print(get_compiler_version())
import argparse
import copy
import os
import os.path as osp
import time
import warnings
import mmcv
import torch
from mmcv import Config, DictAction
from mmcv.runner import get_dist_info, init_dist
from mmcv.utils import get_git_hash
from mmdet import __version__
from mmdet.apis import set_random_seed, train_detector
from mmdet.datasets import build_dataset
from mmdet.models import build_detector
from mmdet.utils import collect_env, get_root_logger
import numpy as np
class mmargs:
#Basemmdetection='/Developer/3DObject/mmdetection/'
MyBasemmdetection='/Developer/3DObject/mymmdetection/'
config = MyBasemmdetection+'configs/faster_rcnn/myfaster_rcnn_x101_64x4d_fpn_1x_coco.py'
# Setup a checkpoint file to load
checkpoint = MyBasemmdetection+'checkpoints/faster_rcnn_x101_64x4d_fpn_1x_coco_20200204-833ee192.pth'
workdir = MyBasemmdetection+"waymococo_fasterrcnntrain"
resumefrom = None #basefolder+ 'myresults/epoch_120.pth'
novalidate = False
gpus = 1
gpuids = None
seed =None
deterministic=True
classes=('vehicle', 'pedestrian', 'sign', 'cyclist')#('person', 'bicycle', 'car')
data_root = '/DATA5T/Dataset/WaymoCOCO/'
class mmargsfasterr101:
#Basemmdetection='/Developer/3DObject/mmdetection/'
MyBasemmdetection='/Developer/3DObject/mymmdetection/'
config = MyBasemmdetection+'configs/faster_rcnn/faster_rcnn_r101_fpn_2x_coco.py'
# Setup a checkpoint file to load
checkpoint = MyBasemmdetection+'checkpoints/faster_rcnn_r101_fpn_2x_coco_bbox_mAP-0.398_20200504_210455-1d2dac9c.pth'
workdir = MyBasemmdetection+"waymococo_fasterrcnnr101train"
resumefrom = workdir+"/epoch_48.pth"#"/epoch_24.pth" #None #basefolder+ 'myresults/epoch_120.pth'
novalidate = False
gpus = 1
gpuids = None
seed =None
deterministic=True
classes=('vehicle', 'pedestrian', 'sign', 'cyclist')#('person', 'bicycle', 'car')
data_root = '/DATA5T/Dataset/WaymoCOCO/'
total_epochs = 60 #48
def maintrain(args):
cfg = Config.fromfile(args.config)
# New add to setup the dataset, no need to change the configuration file
cfg.dataset_type = 'CocoDataset'
cfg.data.test.type = 'CocoDataset'
cfg.data.test.data_root = args.data_root
cfg.data.test.ann_file = args.data_root + 'annotations_val20filteredbig.json'#'annotations_val50new.json' #'annotations_valallnew.json'
cfg.data.test.img_prefix = ''
cfg.data.train.type = 'CocoDataset'
cfg.data.train.data_root = args.data_root
cfg.data.train.ann_file = args.data_root + 'annotations_train684filteredbig.json'#'annotations_train200new.json' #'annotations_trainallnew.json'
cfg.data.train.img_prefix = ''
cfg.data.val.type = 'CocoDataset'
cfg.data.val.data_root = args.data_root
cfg.data.val.ann_file = args.data_root + 'annotations_val20filteredbig.json'#'annotations_val20new.json' #'annotations_valallnew.json'
cfg.data.val.img_prefix = ''
#batch size=2, workers=0, eta: 1 day, 5:56:54, memory: 5684
cfg.data.samples_per_gpu = 4 #batch size
cfg.data.workers_per_gpu = 4
#eta: 1 day, 6:17:04, memory: 10234
cfg.total_epochs = args.total_epochs
cfg.runner.max_epochs = args.total_epochs
# modify num classes of the model in box head
cfg.model.roi_head.bbox_head.num_classes = len(args.classes)# 4
# import modules from string list.
if cfg.get('custom_imports', None):#not used
from mmcv.utils import import_modules_from_strings
import_modules_from_strings(**cfg['custom_imports'])
# set cudnn_benchmark, benchmark mode is good whenever your input sizes for your network do not vary. This way, cudnn will look for the optimal set of algorithms for that particular configuration (which takes some time). This usually leads to faster runtime.
if cfg.get('cudnn_benchmark', False):
torch.backends.cudnn.benchmark = True
# work_dir is determined in this priority: CLI > segment in file > filename
if args.workdir is not None:
# update configs according to CLI args if args.work_dir is not None
cfg.work_dir = args.workdir
elif cfg.get('work_dir', None) is None:
# use config filename as default work_dir if cfg.work_dir is None
cfg.work_dir = osp.join('./work_dirs',
osp.splitext(osp.basename(args.config))[0])
# create work_dir
mmcv.mkdir_or_exist(osp.abspath(cfg.work_dir))
# dump config
cfg.dump(osp.join(cfg.work_dir, osp.basename(args.config)))
# init the logger before other steps
timestamp = time.strftime('%Y%m%d_%H%M%S', time.localtime())
log_file = osp.join(cfg.work_dir, f'{timestamp}.log')
logger = get_root_logger(log_file=log_file, log_level=cfg.log_level)
cfg.load_from = args.checkpoint
if args.resumefrom is not None:
cfg.resume_from = args.resumefrom
if args.gpuids is not None:
cfg.gpu_ids = args.gpuids
else:
cfg.gpu_ids = range(1) if args.gpus is None else range(args.gpus)
# init the meta dict to record some important information such as
# environment info and seed, which will be logged
meta = dict()
# log env info
env_info_dict = collect_env()
env_info = '\n'.join([(f'{k}: {v}') for k, v in env_info_dict.items()])
dash_line = '-' * 60 + '\n'
logger.info('Environment info:\n' + dash_line + env_info + '\n' +
dash_line)
meta['env_info'] = env_info
meta['config'] = cfg.pretty_text
# log some basic info
distributed = False
logger.info(f'Distributed training: {distributed}')
logger.info(f'Config:\n{cfg.pretty_text}')
# set random seeds
if args.seed is not None: #not used
logger.info(f'Set random seed to {args.seed}, '
f'deterministic: {args.deterministic}')
set_random_seed(args.seed, deterministic=args.deterministic)
cfg.seed = args.seed
meta['seed'] = args.seed
meta['exp_name'] = osp.basename(args.config)
model = build_detector(
cfg.model,
train_cfg=cfg.get('train_cfg'),
test_cfg=cfg.get('test_cfg'))
datasets = [build_dataset(cfg.data.train)]
if len(cfg.workflow) == 2:
val_dataset = copy.deepcopy(cfg.data.val)
val_dataset.pipeline = cfg.data.train.pipeline
datasets.append(build_dataset(val_dataset))
if cfg.checkpoint_config is not None:
# save mmdet version, config file content and class names in
# checkpoints as meta data
cfg.checkpoint_config.meta = dict(
mmdet_version=__version__ + get_git_hash()[:7],
CLASSES=datasets[0].CLASSES)
# add an attribute for visualization convenience
model.CLASSES = args.classes #datasets[0].CLASSES
train_detector(
model,
datasets,
cfg,
distributed=distributed,
validate=(not args.novalidate),
timestamp=timestamp,
meta=meta)
if __name__ == "__main__":
maintrain(mmargsfasterr101)#(mmargs)