def get_PennTreeBank(data_dir=None):
if data_dir is None:
data_dir = get_dataset_path('ptb_data')
if not os.path.isfile(os.path.join(data_dir, 'ptb.train.txt')):
download(TRAIN_URL, data_dir)
download(VALID_URL, data_dir)
download(TEST_URL, data_dir)
word_to_id = tfreader._build_vocab(os.path.join(data_dir, 'ptb.train.txt'))
data3 = [np.asarray(tfreader._file_to_word_ids(os.path.join(data_dir, fname), word_to_id))
for fname in ['ptb.train.txt', 'ptb.valid.txt', 'ptb.test.txt']]
return data3, word_to_id
def get_PennTreeBank(data_dir=None):
if data_dir is None:
data_dir = get_dataset_path('ptb_data')
if not os.path.isfile(os.path.join(data_dir, 'ptb.train.txt')):
download(TRAIN_URL, data_dir)
download(VALID_URL, data_dir)
download(TEST_URL, data_dir)
word_to_id = tfreader._build_vocab(os.path.join(data_dir, 'ptb.train.txt'))
data3 = [
np.asarray(
tfreader._file_to_word_ids(os.path.join(data_dir, fname),
word_to_id))
for fname in ['ptb.train.txt', 'ptb.valid.txt', 'ptb.test.txt']
]
return data3, word_to_id
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",
{
"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-R101FPN9xGNCasAugScratch.npz",
"./",
expect_size=355680386)
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",
)
def _download_caffe_meta(self):
fpath = download(CAFFE_ILSVRC12_URL[0],
self.dir,
expect_size=CAFFE_ILSVRC12_URL[1])
tarfile.open(fpath, 'r:gz').extractall(self.dir)