From 8b47a12b8d968e4e7bfaf105023671a6d05728d5 Mon Sep 17 00:00:00 2001
From: Kai Chen <chenkaidev@gmail.com>
Date: Mon, 8 Oct 2018 13:46:19 +0800
Subject: [PATCH] minor updates for train/test scripts
---
tools/test.py | 7 +++----
tools/train.py | 8 +++++---
2 files changed, 8 insertions(+), 7 deletions(-)
diff --git a/tools/test.py b/tools/test.py
index 4e2ecd2..c0bfd25 100644
--- a/tools/test.py
+++ b/tools/test.py
@@ -44,17 +44,16 @@ def parse_args():
'--eval',
type=str,
nargs='+',
- choices=['proposal', 'bbox', 'segm', 'keypoints'],
+ choices=['proposal', 'proposal_fast', 'bbox', 'segm', 'keypoints'],
help='eval types')
parser.add_argument('--show', action='store_true', help='show results')
args = parser.parse_args()
return args
-args = parse_args()
-
-
def main():
+ args = parse_args()
+
cfg = mmcv.Config.fromfile(args.config)
cfg.model.pretrained = None
cfg.data.test.test_mode = True
diff --git a/tools/train.py b/tools/train.py
index b72adeb..07a918d 100644
--- a/tools/train.py
+++ b/tools/train.py
@@ -2,6 +2,7 @@ from __future__ import division
import argparse
import logging
+import random
from collections import OrderedDict
import numpy as np
@@ -55,6 +56,7 @@ def get_logger(log_level):
def set_random_seed(seed):
+ random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
@@ -89,7 +91,7 @@ def main():
if args.work_dir is not None:
cfg.work_dir = args.work_dir
cfg.gpus = args.gpus
- # add mmdet version to checkpoint as meta data
+ # save mmdet version in checkpoint as meta data
cfg.checkpoint_config.meta = dict(
mmdet_version=__version__, config=cfg.text)
@@ -103,13 +105,13 @@ def main():
# init distributed environment if necessary
if args.launcher == 'none':
dist = False
- logger.info('Disabled distributed training.')
+ logger.info('Non-distributed training.')
else:
dist = True
init_dist(args.launcher, **cfg.dist_params)
if torch.distributed.get_rank() != 0:
logger.setLevel('ERROR')
- logger.info('Enabled distributed training.')
+ logger.info('Distributed training.')
# prepare data loaders
train_dataset = obj_from_dict(cfg.data.train, datasets)
--
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