tensorpack / tensorpack
run from
| BOX AP | AP50 | AP75 | APS | APM | APL |
SPEED
|
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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| MODEL | CODE | PAPER |
ε-REPR
|
CODE | PAPER |
ε-REPR
|
CODE | PAPER |
ε-REPR
|
CODE | PAPER |
ε-REPR
|
CODE | PAPER |
ε-REPR
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CODE | PAPER |
ε-REPR
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PAPER |
GLOBAL RANK
|
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|
Mask R-CNN
(ResNet-101-FPN, GN, Cascade) |
0.477 | 0.474 | 0.661 | -- | 0.521 | -- | 0.304 | -- | 0.507 | -- | 0.615 | -- | 2.4 | #6 | |||||||||||||||
|
Mask R-CNN
(ResNet-50-FPN, 2x) |
0.389 | -- | 0.597 | -- | 0.421 | -- | 0.219 | -- | 0.426 | -- | 0.511 | -- | 2.8 | #73 | |||||||||||||||
|
Mask R-CNN
(ResNet-50-FPN, GroupNorm) |
0.404 | 0.403 | 0.609 | 0.610 | 0.440 | 0.440 | 0.240 | 0.357 | 0.433 | 0.579 | 0.538 | 0.377 |
|
2.6 | #57 | ||||||||||||||
+get badge code
[](https://sotabench.com/user/ppwwyyxx/repos/tensorpack/tensorpack)
How the Repository is Evaluated
The fullsotabench.py file - source
# -*- coding: utf-8 -*-
import os
import sys
import tqdm
from contextlib import contextmanager
from tensorpack.predict import OfflinePredictor, PredictConfig
from tensorpack.tfutils import SmartInit
from tensorpack.utils.fs import download
from sotabencheval.utils import is_server
from sotabencheval.object_detection import COCOEvaluator
# import faster rcnn example
sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..", "examples", "FasterRCNN"))
from config import finalize_configs, config as cfg # noqa
from eval import predict_image # noqa
from dataset import register_coco # noqa
from dataset.coco import COCODetection # noqa
from data import get_eval_dataflow # noqa
from modeling.generalized_rcnn import ResNetFPNModel, ResNetC4Model # noqa
if is_server():
DATA_ROOT = "./.data/vision/"
else: # local settings
DATA_ROOT = os.path.expanduser("~/data/")
COCO_ROOT = os.path.join(DATA_ROOT, "coco")
register_coco(COCO_ROOT)
@contextmanager
def backup_cfg():
orig_config = cfg.to_dict()
yield
cfg.from_dict(orig_config)
def evaluate_rcnn(model_name, paper_arxiv_id, cfg_list, model_file):
evaluator = COCOEvaluator(
root=COCO_ROOT, model_name=model_name, paper_arxiv_id=paper_arxiv_id
)
category_id_to_coco_id = {
v: k for k, v in COCODetection.COCO_id_to_category_id.items()
}
cfg.update_args(cfg_list) # TODO backup/restore config
finalize_configs(False)
MODEL = ResNetFPNModel() if cfg.MODE_FPN else ResNetC4Model()
predcfg = PredictConfig(
model=MODEL,
session_init=SmartInit(model_file),
input_names=MODEL.get_inference_tensor_names()[0],
output_names=MODEL.get_inference_tensor_names()[1],
)
predictor = OfflinePredictor(predcfg)
def xyxy_to_xywh(box):
box[2] -= box[0]
box[3] -= box[1]
return box
df = get_eval_dataflow("coco_val2017")
df.reset_state()
for img, img_id in tqdm.tqdm(df, total=len(df)):
results = predict_image(img, predictor)
res = [
{
"image_id": img_id,
"category_id": category_id_to_coco_id.get(
int(r.class_id), int(r.class_id)
),
"bbox": xyxy_to_xywh([round(float(x), 4) for x in r.box]),
"score": round(float(r.score), 3),
}
for r in results
]
evaluator.add(res)
if evaluator.cache_exists:
break
evaluator.save()
download(
"http://models.tensorpack.com/FasterRCNN/COCO-MaskRCNN-R50FPN2x.npz",
"./",
expect_size=165362754)
with backup_cfg():
evaluate_rcnn(
"Mask R-CNN (ResNet-50-FPN, 2x)", "1703.06870", [],
"COCO-MaskRCNN-R50FPN2x.npz",
)
download(
"http://models.tensorpack.com/FasterRCNN/COCO-MaskRCNN-R50FPN2xGN.npz",
"./",
expect_size=167363872)
with backup_cfg():
evaluate_rcnn(
"Mask R-CNN (ResNet-50-FPN, GroupNorm)", "1803.08494",
"""FPN.NORM=GN BACKBONE.NORM=GN
FPN.FRCNN_HEAD_FUNC=fastrcnn_4conv1fc_gn_head
FPN.MRCNN_HEAD_FUNC=maskrcnn_up4conv_gn_head""".split(),
"COCO-MaskRCNN-R50FPN2xGN.npz",
)
download(
"http://models.tensorpack.com/FasterRCNN/COCO-MaskRCNN-R101FPN9xGNCasAugScratch.npz",
"./",
expect_size=355680386)
with backup_cfg():
evaluate_rcnn(
"Mask R-CNN (ResNet-101-FPN, GN, Cascade)", "1811.08883",
"""
FPN.CASCADE=True BACKBONE.RESNET_NUM_BLOCKS=[3,4,23,3] FPN.NORM=GN
BACKBONE.NORM=GN FPN.FRCNN_HEAD_FUNC=fastrcnn_4conv1fc_gn_head
FPN.MRCNN_HEAD_FUNC=maskrcnn_up4conv_gn_head""".split(),
"COCO-MaskRCNN-R101FPN9xGNCasAugScratch.npz",
)
STATUS
BUILD
COMMIT MESSAGE
RUN TIME
#
56
use TowerHandle.get_tensor to access variables (fix #1409)
ppwwyyxx
d89b6f0
· Mar 14 2020
#
46
fix #1402
Signed-off-by: Yuxin Wu <[email protected]>
ppwwyyxx
83e00d7
· Feb 25 2020
#
43
fix input dtype not matching variable dtype (#1386)
* fix not i…
ppwwyyxx
4ac2e22
· Jan 22 2020
#
34
fix bug when combining DataParallelInferenceRunner+BatchNorm (si…
ppwwyyxx
a995070
· Jan 09 2020
#
30
Switch to pickle by default. In Python3 pickle is as fast as msg…
ppwwyyxx
1648ddd
· Dec 27 2019
#
27
use yield from in dataflow; update logger name in dataflow.
ppwwyyxx
9fac1a6
· Nov 26 2019
#
16
CI: use separate data dir for each job. hopefully makes it less …
ppwwyyxx
226016f
· Oct 07 2019