obfuscated_code stringlengths 120 344k | clean_code stringlengths 100 238k |
|---|---|
def _create_generic_data_series(**1Ill101IlO):
1IIlOl1 = ["annotations", "markers", "fill", "rectangles"]
0lO1011llI = []
for 1IIO in 1IIlOl1:
01II10I00 = 1Ill101IlO.pop(1IIO, [])
if 01II10I00 is None:
01II10I00 = []
if isinstance(01II10I00, dict):
01II10I00 =... | def _create_generic_data_series(**kwargs):
keywords = ["annotations", "markers", "fill", "rectangles"]
series = []
for kw in keywords:
dictionaries = kwargs.pop(kw, [])
if dictionaries is None:
dictionaries = []
if isinstance(dictionaries, dict):
dictionaries ... |
def _create_generic_data_series(**1l0IO1OI):
OlOI1 = ["annotations", "markers", "fill", "rectangles"]
lII11l0O1O = []
for 1l1I1 in OlOI1:
# optimization pending
lOlIIIlI = 1l0IO1OI.pop(1l1I1, [])
if lOlIIIlI is None:
lOlIIIlI = []
if isinstance(lOlIIIlI, dict):
... | def _create_generic_data_series(**kwargs):
keywords = ["annotations", "markers", "fill", "rectangles"]
series = []
for kw in keywords:
dictionaries = kwargs.pop(kw, [])
if dictionaries is None:
dictionaries = []
if isinstance(dictionaries, dict):
dictionaries ... |
def _create_generic_data_series(**IO1IIIOlI):
try: pass
except: pass
III1I = ["annotations", "markers", "fill", "rectangles"]
1lOI0 = []
for Oll0 in III1I:
O0O0 = IO1IIIOlI.pop(Oll0, [])
if O0O0 is None:
O0O0 = []
if isinstance(O0O0, dict):
O0O0 = [O0O... | def _create_generic_data_series(**kwargs):
keywords = ["annotations", "markers", "fill", "rectangles"]
series = []
for kw in keywords:
dictionaries = kwargs.pop(kw, [])
if dictionaries is None:
dictionaries = []
if isinstance(dictionaries, dict):
dictionaries ... |
def _create_generic_data_series(**Ill1O11):
# optimization pending
0llIIlI = ["annotations", "markers", "fill", "rectangles"]
1I0I = []
for 1OOIOOl100 in 0llIIlI:
I11lOIl1I1 = Ill1O11.pop(1OOIOOl100, [])
if I11lOIl1I1 is None:
I11lOIl1I1 = []
if isinstance(I11lOIl1I1,... | def _create_generic_data_series(**kwargs):
keywords = ["annotations", "markers", "fill", "rectangles"]
series = []
for kw in keywords:
dictionaries = kwargs.pop(kw, [])
if dictionaries is None:
dictionaries = []
if isinstance(dictionaries, dict):
dictionaries ... |
def _create_generic_data_series(**1Ol0Il):
l01OI = ["annotations", "markers", "fill", "rectangles"]
llIO00Il = []
for lOl0Il in l01OI:
lOI0l0I1l1 = 1Ol0Il.pop(lOl0Il, [])
if lOI0l0I1l1 is None:
lOI0l0I1l1 = []
if isinstance(lOI0l0I1l1, dict):
lOI0l0I1l1 = [lOI... | def _create_generic_data_series(**kwargs):
keywords = ["annotations", "markers", "fill", "rectangles"]
series = []
for kw in keywords:
dictionaries = kwargs.pop(kw, [])
if dictionaries is None:
dictionaries = []
if isinstance(dictionaries, dict):
dictionaries ... |
def _create_generic_data_series(**OOO1II0):
1I1O = ["annotations", "markers", "fill", "rectangles"]
O1O01lO0OO = []
for 1010 in 1I1O:
O1O0110II1 = OOO1II0.pop(1010, [])
if O1O0110II1 is None:
O1O0110II1 = []
if isinstance(O1O0110II1, dict):
O1O0110II1 = [O1O01... | def _create_generic_data_series(**kwargs):
keywords = ["annotations", "markers", "fill", "rectangles"]
series = []
for kw in keywords:
dictionaries = kwargs.pop(kw, [])
if dictionaries is None:
dictionaries = []
if isinstance(dictionaries, dict):
dictionaries ... |
def _create_generic_data_series(**0IO0IO):
11lI1I1l0 = ["annotations", "markers", "fill", "rectangles"]
# optimization pending
O0OO11l0 = []
for 1l0IOO0l11 in 11lI1I1l0:
0O0010 = 0IO0IO.pop(1l0IOO0l11, [])
if 0O0010 is None:
0O0010 = []
if isinstance(0O0010, dict):
... | def _create_generic_data_series(**kwargs):
keywords = ["annotations", "markers", "fill", "rectangles"]
series = []
for kw in keywords:
dictionaries = kwargs.pop(kw, [])
if dictionaries is None:
dictionaries = []
if isinstance(dictionaries, dict):
dictionaries ... |
def _create_generic_data_series(**O0OlOOl00):
OOI01O1O1O = ["annotations", "markers", "fill", "rectangles"]
l0OlIIl = []
for Il00 in OOI01O1O1O:
O1l11IOII = O0OlOOl00.pop(Il00, [])
if O1l11IOII is None:
O1l11IOII = []
if isinstance(O1l11IOII, dict):
O1l11IOII ... | def _create_generic_data_series(**kwargs):
keywords = ["annotations", "markers", "fill", "rectangles"]
series = []
for kw in keywords:
dictionaries = kwargs.pop(kw, [])
if dictionaries is None:
dictionaries = []
if isinstance(dictionaries, dict):
dictionaries ... |
def _create_generic_data_series(**111lIlIlO):
I1I1O1IOO = ["annotations", "markers", "fill", "rectangles"]
0O1l = []
for lO1O in I1I1O1IOO:
1000Ol = 111lIlIlO.pop(lO1O, [])
if 1000Ol is None:
1000Ol = []
if isinstance(1000Ol, dict):
1000Ol = [1000Ol]
f... | def _create_generic_data_series(**kwargs):
keywords = ["annotations", "markers", "fill", "rectangles"]
series = []
for kw in keywords:
dictionaries = kwargs.pop(kw, [])
if dictionaries is None:
dictionaries = []
if isinstance(dictionaries, dict):
dictionaries ... |
def _create_generic_data_series(**I0llllllO):
OI0I = ["annotations", "markers", "fill", "rectangles"]
Ol0I10I00 = []
for 1OOl1l in OI0I:
OII0l = I0llllllO.pop(1OOl1l, [])
if OII0l is None:
OII0l = []
if isinstance(OII0l, dict):
OII0l = [OII0l]
for l1O1... | def _create_generic_data_series(**kwargs):
keywords = ["annotations", "markers", "fill", "rectangles"]
series = []
for kw in keywords:
dictionaries = kwargs.pop(kw, [])
if dictionaries is None:
dictionaries = []
if isinstance(dictionaries, dict):
dictionaries ... |
def _create_generic_data_series(**1Ol0O000):
I1I0IOIOll = ["annotations", "markers", "fill", "rectangles"]
1l1l0IOOl = []
for l0l1II0lO in I1I0IOIOll:
OIO1OOl = 1Ol0O000.pop(l0l1II0lO, [])
if OIO1OOl is None:
OIO1OOl = []
if isinstance(OIO1OOl, dict):
OIO1OOl ... | def _create_generic_data_series(**kwargs):
keywords = ["annotations", "markers", "fill", "rectangles"]
series = []
for kw in keywords:
dictionaries = kwargs.pop(kw, [])
if dictionaries is None:
dictionaries = []
if isinstance(dictionaries, dict):
dictionaries ... |
def _create_generic_data_series(**I1II01lI00):
1O1O = ["annotations", "markers", "fill", "rectangles"]
l10Il = []
try: pass
except: pass
for l1l0I in 1O1O:
lIIl0I0I10 = I1II01lI00.pop(l1l0I, [])
if lIIl0I0I10 is None:
lIIl0I0I10 = []
if isinstance(lIIl0I0I10, dict... | def _create_generic_data_series(**kwargs):
keywords = ["annotations", "markers", "fill", "rectangles"]
series = []
for kw in keywords:
dictionaries = kwargs.pop(kw, [])
if dictionaries is None:
dictionaries = []
if isinstance(dictionaries, dict):
dictionaries ... |
def _create_generic_data_series(**1l1I0O000O):
IIOlI = ["annotations", "markers", "fill", "rectangles"]
ll11OO0IIl = []
# optimization pending
for IO1lOOll00 in IIOlI:
1II1ll = 1l1I0O000O.pop(IO1lOOll00, [])
if 1II1ll is None:
1II1ll = []
if isinstance(1II1ll, dict):
... | def _create_generic_data_series(**kwargs):
keywords = ["annotations", "markers", "fill", "rectangles"]
series = []
for kw in keywords:
dictionaries = kwargs.pop(kw, [])
if dictionaries is None:
dictionaries = []
if isinstance(dictionaries, dict):
dictionaries ... |
def prepare_coco_detection_annotation(
IIIOlO,
0lO0O00I,
IIIl1I: bool = False,
OllO: Optional[Union[ChannelDimension, str]] = None,
):
"""
Convert the 0lO0O00I in COCO format into the format expected by YOLOS.
"""
I1OllI1, 0IOlI0 = IIIOlO.size()[-2:]
l00lO = 0lO0O00I["l00lO"]
l0... | def prepare_coco_detection_annotation(
image,
target,
return_segmentation_masks: bool = False,
input_data_format: Optional[Union[ChannelDimension, str]] = None,
):
"""
Convert the target in COCO format into the format expected by YOLOS.
"""
image_height, image_width = image.size()[-2:]
... |
def prepare_coco_detection_annotation(
lIlOOO,
OIll001,
IIIO1lIII: bool = False,
O11III1I10: Optional[Union[ChannelDimension, str]] = None,
):
"""
Convert the OIll001 in COCO format into the format expected by YOLOS.
"""
11ll1O1, ll11OO0l0 = lIlOOO.size()[-2:]
II10I = OIll001["II10I... | def prepare_coco_detection_annotation(
image,
target,
return_segmentation_masks: bool = False,
input_data_format: Optional[Union[ChannelDimension, str]] = None,
):
"""
Convert the target in COCO format into the format expected by YOLOS.
"""
image_height, image_width = image.size()[-2:]
... |
def prepare_coco_detection_annotation(
1l1I00,
1OO1,
OII1l10I0I: bool = False,
IO1l: Optional[Union[ChannelDimension, str]] = None,
):
"""
Convert the 1OO1 in COCO format into the format expected by YOLOS.
"""
l11lO0OO, 01I0l = 1l1I00.size()[-2:]
OOO0 = 1OO1["OOO0"]
OOO0 = torch... | def prepare_coco_detection_annotation(
image,
target,
return_segmentation_masks: bool = False,
input_data_format: Optional[Union[ChannelDimension, str]] = None,
):
"""
Convert the target in COCO format into the format expected by YOLOS.
"""
image_height, image_width = image.size()[-2:]
... |
def prepare_coco_detection_annotation(
Olll,
O01011ll1l,
OI0II: bool = False,
IIO0OO: Optional[Union[ChannelDimension, str]] = None,
):
"""
Convert the O01011ll1l in COCO format into the format expected by YOLOS.
"""
1l01lOO, lOlI0 = Olll.size()[-2:]
l1100O = O01011ll1l["l1100O"]
... | def prepare_coco_detection_annotation(
image,
target,
return_segmentation_masks: bool = False,
input_data_format: Optional[Union[ChannelDimension, str]] = None,
):
"""
Convert the target in COCO format into the format expected by YOLOS.
"""
image_height, image_width = image.size()[-2:]
... |
def prepare_coco_detection_annotation(
lll0I,
OI1llIO,
0l1I1Il: bool = False,
1OO0I1l101: Optional[Union[ChannelDimension, str]] = None,
):
"""
Convert the OI1llIO in COCO format into the format expected by YOLOS.
"""
I00IOIOIO, I1IO11O = lll0I.size()[-2:]
I100IlI1II = OI1llIO["I100... | def prepare_coco_detection_annotation(
image,
target,
return_segmentation_masks: bool = False,
input_data_format: Optional[Union[ChannelDimension, str]] = None,
):
"""
Convert the target in COCO format into the format expected by YOLOS.
"""
image_height, image_width = image.size()[-2:]
... |
def prepare_coco_detection_annotation(
1l1l,
I00l0I,
11II: bool = False,
1O1O: Optional[Union[ChannelDimension, str]] = None,
):
"""
Convert the I00l0I in COCO format into the format expected by YOLOS.
# optimization pending
"""
l0O0, OlII = 1l1l.size()[-2:]
0OOO0I1O0 = I00l0I["... | def prepare_coco_detection_annotation(
image,
target,
return_segmentation_masks: bool = False,
input_data_format: Optional[Union[ChannelDimension, str]] = None,
):
"""
Convert the target in COCO format into the format expected by YOLOS.
"""
image_height, image_width = image.size()[-2:]
... |
def prepare_coco_detection_annotation(
1IOII,
IIII0l0O0,
OlI1: bool = False,
lllOOIll0I: Optional[Union[ChannelDimension, str]] = None,
):
"""
Convert the IIII0l0O0 in COCO format into the format expected by YOLOS.
"""
1I1O1l1Il, IOI0O1l = 1IOII.size()[-2:]
1lI00I11I = IIII0l0O0["1l... | def prepare_coco_detection_annotation(
image,
target,
return_segmentation_masks: bool = False,
input_data_format: Optional[Union[ChannelDimension, str]] = None,
):
"""
Convert the target in COCO format into the format expected by YOLOS.
"""
image_height, image_width = image.size()[-2:]
... |
def prepare_coco_detection_annotation(
l11lI0Ol11,
0O10IO,
lI0111OO1l: bool = False,
l00I0I: Optional[Union[ChannelDimension, str]] = None,
):
"""
Convert the 0O10IO in COCO format into the format expected by YOLOS.
"""
l110l00lIl, OOOl = l11lI0Ol11.size()[-2:]
00O0l1OOI = 0O10IO["0... | def prepare_coco_detection_annotation(
image,
target,
return_segmentation_masks: bool = False,
input_data_format: Optional[Union[ChannelDimension, str]] = None,
):
"""
Convert the target in COCO format into the format expected by YOLOS.
"""
image_height, image_width = image.size()[-2:]
... |
def prepare_coco_detection_annotation(
O0l0,
O010,
1Olll01: bool = False,
II101OI01I: Optional[Union[ChannelDimension, str]] = None,
):
"""
Convert the O010 in COCO format into the format expected by YOLOS.
"""
OI00011OI, lO0Ol = O0l0.size()[-2:]
1II0lI = O010["1II0lI"]
1II0lI =... | def prepare_coco_detection_annotation(
image,
target,
return_segmentation_masks: bool = False,
input_data_format: Optional[Union[ChannelDimension, str]] = None,
):
"""
Convert the target in COCO format into the format expected by YOLOS.
"""
image_height, image_width = image.size()[-2:]
... |
def prepare_coco_detection_annotation(
l1II,
O0lO00IOl,
OIIOlI0: bool = False,
Il01lO1I: Optional[Union[ChannelDimension, str]] = None,
):
"""
Convert the O0lO00IOl in COCO format into the format expected by YOLOS.
"""
1100, l1OOO11O = l1II.size()[-2:]
OO1II0l = O0lO00IOl["OO1II0l"]... | def prepare_coco_detection_annotation(
image,
target,
return_segmentation_masks: bool = False,
input_data_format: Optional[Union[ChannelDimension, str]] = None,
):
"""
Convert the target in COCO format into the format expected by YOLOS.
"""
image_height, image_width = image.size()[-2:]
... |
def prepare_coco_detection_annotation(
O00IO1II0,
IOII,
11I11: bool = False,
OOlOlIOII: Optional[Union[ChannelDimension, str]] = None,
):
"""
Convert the IOII in COCO format into the format expected by YOLOS.
"""
lOOl, 00OO00 = O00IO1II0.size()[-2:]
l100I = IOII["l100I"]
l100I =... | def prepare_coco_detection_annotation(
image,
target,
return_segmentation_masks: bool = False,
input_data_format: Optional[Union[ChannelDimension, str]] = None,
):
"""
Convert the target in COCO format into the format expected by YOLOS.
"""
image_height, image_width = image.size()[-2:]
... |
def prepare_coco_detection_annotation(
Oll1IO0011,
1111O1,
OI1O10ll: bool = False,
1l100O: Optional[Union[ChannelDimension, str]] = None,
):
"""
Convert the 1111O1 in COCO format into the format expected by YOLOS.
"""
lIIllO1OI1, l1lOO1 = Oll1IO0011.size()[-2:]
OOIO0ll0I0 = 1111O1["... | def prepare_coco_detection_annotation(
image,
target,
return_segmentation_masks: bool = False,
input_data_format: Optional[Union[ChannelDimension, str]] = None,
):
"""
Convert the target in COCO format into the format expected by YOLOS.
"""
image_height, image_width = image.size()[-2:]
... |
def prepare_coco_detection_annotation(
01O1O0I1,
OO0O1l,
1l0OOI: bool = False,
O0O111I: Optional[Union[ChannelDimension, str]] = None,
):
"""
Convert the OO0O1l in COCO format into the format expected by YOLOS.
"""
lI0101, 1I1IOIOO0 = 01O1O0I1.size()[-2:]
lll0I = OO0O1l["lll0I"]
... | def prepare_coco_detection_annotation(
image,
target,
return_segmentation_masks: bool = False,
input_data_format: Optional[Union[ChannelDimension, str]] = None,
):
"""
Convert the target in COCO format into the format expected by YOLOS.
"""
image_height, image_width = image.size()[-2:]
... |
def prepare_coco_detection_annotation(
image,
target,
return_segmentation_masks: bool = False,
input_data_format: Optional[Union[ChannelDimension, str]] = None,
):
"""
Convert the target in COCO format into the format expected by YOLOS.
"""
image_height, image_width = image.size()[-2:]
... | def prepare_coco_detection_annotation(
image,
target,
return_segmentation_masks: bool = False,
input_data_format: Optional[Union[ChannelDimension, str]] = None,
):
"""
Convert the target in COCO format into the format expected by YOLOS.
"""
image_height, image_width = image.size()[-2:]
... |
def prepare_coco_detection_annotation(
I0011lI00,
l1Il,
OlIl00: bool = False,
0llll1OlO: Optional[Union[ChannelDimension, str]] = None,
):
"""
# optimization pending
Convert the l1Il in COCO format into the format expected by YOLOS.
"""
IO0OII1O, l0II1IllIl = I0011lI00.size()[-2:]
... | def prepare_coco_detection_annotation(
image,
target,
return_segmentation_masks: bool = False,
input_data_format: Optional[Union[ChannelDimension, str]] = None,
):
"""
Convert the target in COCO format into the format expected by YOLOS.
"""
image_height, image_width = image.size()[-2:]
... |
def prepare_coco_detection_annotation(
OO0l0,
OOl0OI,
IIIlIlOIl: bool = False,
l0lIIOII: Optional[Union[ChannelDimension, str]] = None,
):
"""
Convert the OOl0OI in COCO format into the format expected by YOLOS.
"""
O11I011, 00110O1O = OO0l0.size()[-2:]
00llIl001 = OOl0OI["00llIl001... | def prepare_coco_detection_annotation(
image,
target,
return_segmentation_masks: bool = False,
input_data_format: Optional[Union[ChannelDimension, str]] = None,
):
"""
Convert the target in COCO format into the format expected by YOLOS.
"""
image_height, image_width = image.size()[-2:]
... |
def prepare_coco_detection_annotation(
OOOOlII1II,
O1O0l011,
l01OI: bool = False,
11I00l0OO1: Optional[Union[ChannelDimension, str]] = None,
):
"""
Convert the O1O0l011 in COCO format into the format expected by YOLOS.
"""
IOIll10lOl, 1IlII = OOOOlII1II.size()[-2:]
1l0O1O0Ol = O1O0l... | def prepare_coco_detection_annotation(
image,
target,
return_segmentation_masks: bool = False,
input_data_format: Optional[Union[ChannelDimension, str]] = None,
):
"""
Convert the target in COCO format into the format expected by YOLOS.
"""
image_height, image_width = image.size()[-2:]
... |
def prepare_coco_detection_annotation(
0l1Ol1,
lI11O,
01I0I0: bool = False,
0IOOOOl11I: Optional[Union[ChannelDimension, str]] = None,
):
"""
Convert the lI11O in COCO format into the format expected by YOLOS.
"""
llIllIOIlI, I000OI = 0l1Ol1.size()[-2:]
ll010IIl = lI11O["ll010IIl"]
... | def prepare_coco_detection_annotation(
image,
target,
return_segmentation_masks: bool = False,
input_data_format: Optional[Union[ChannelDimension, str]] = None,
):
"""
Convert the target in COCO format into the format expected by YOLOS.
"""
image_height, image_width = image.size()[-2:]
... |
def prepare_coco_detection_annotation(
0Ol1II1,
10OOOIO00,
1OIOO00IO0: bool = False,
0I00IOl: Optional[Union[ChannelDimension, str]] = None,
):
"""
Convert the 10OOOIO00 in COCO format into the format expected by YOLOS.
"""
ll00O101, Ol0OOOO = 0Ol1II1.size()[-2:]
1lOI1ll = 10OOOIO00... | def prepare_coco_detection_annotation(
image,
target,
return_segmentation_masks: bool = False,
input_data_format: Optional[Union[ChannelDimension, str]] = None,
):
"""
Convert the target in COCO format into the format expected by YOLOS.
"""
image_height, image_width = image.size()[-2:]
... |
def prepare_coco_detection_annotation(
0O00Il0l1,
0IOl0OlIOl,
lOOl0l1l11: bool = False,
0I11II1: Optional[Union[ChannelDimension, str]] = None,
):
"""
Convert the 0IOl0OlIOl in COCO format into the format expected by YOLOS.
"""
0001OI00, OOOOlIO1l0 = 0O00Il0l1.size()[-2:]
IlI0lO0 = ... | def prepare_coco_detection_annotation(
image,
target,
return_segmentation_masks: bool = False,
input_data_format: Optional[Union[ChannelDimension, str]] = None,
):
"""
Convert the target in COCO format into the format expected by YOLOS.
"""
image_height, image_width = image.size()[-2:]
... |
def prepare_coco_detection_annotation(
11lI,
lO01l,
O11110IO: bool = False,
0llII1: Optional[Union[ChannelDimension, str]] = None,
):
"""
Convert the lO01l in COCO format into the format expected by YOLOS.
"""
ll00Ol, IIOlOIO0l = 11lI.size()[-2:]
llllI10O0 = lO01l["llllI10O0"]
l... | def prepare_coco_detection_annotation(
image,
target,
return_segmentation_masks: bool = False,
input_data_format: Optional[Union[ChannelDimension, str]] = None,
):
"""
Convert the target in COCO format into the format expected by YOLOS.
"""
image_height, image_width = image.size()[-2:]
... |
def prepare_coco_detection_annotation(
1I01101,
IlIO11I0I,
lO0l: bool = False,
l00lOI: Optional[Union[ChannelDimension, str]] = None,
):
"""
Convert the IlIO11I0I in COCO format into the format expected by YOLOS.
"""
l011100, IlOI111II = 1I01101.size()[-2:]
I0l01OIll = IlIO11I0I["I0... | def prepare_coco_detection_annotation(
image,
target,
return_segmentation_masks: bool = False,
input_data_format: Optional[Union[ChannelDimension, str]] = None,
):
"""
Convert the target in COCO format into the format expected by YOLOS.
"""
image_height, image_width = image.size()[-2:]
... |
def prepare_coco_detection_annotation(
00OOIOl0l,
l1lllOl,
00I0l: bool = False,
l0Il00IO: Optional[Union[ChannelDimension, str]] = None,
):
"""
Convert the l1lllOl in COCO format into the format expected by YOLOS.
"""
00Oll00, l00l1 = 00OOIOl0l.size()[-2:]
OI1lO00Il = l1lllOl["OI1lO... | def prepare_coco_detection_annotation(
image,
target,
return_segmentation_masks: bool = False,
input_data_format: Optional[Union[ChannelDimension, str]] = None,
):
"""
Convert the target in COCO format into the format expected by YOLOS.
"""
image_height, image_width = image.size()[-2:]
... |
def prepare_coco_detection_annotation(
11001l,
OI0l0000,
OIIIO: bool = False,
1I1lI0O: Optional[Union[ChannelDimension, str]] = None,
):
"""
Convert the OI0l0000 in COCO format into the format expected by YOLOS.
"""
IOOI, IOI0Ol = 11001l.size()[-2:]
OllI0 = OI0l0000["OllI0"]
Oll... | def prepare_coco_detection_annotation(
image,
target,
return_segmentation_masks: bool = False,
input_data_format: Optional[Union[ChannelDimension, str]] = None,
):
"""
Convert the target in COCO format into the format expected by YOLOS.
"""
image_height, image_width = image.size()[-2:]
... |
def prepare_coco_detection_annotation(
OIOOOI,
lllII1O,
0lI0IOI0l: bool = False,
IO1III1: Optional[Union[ChannelDimension, str]] = None,
):
"""
Convert the lllII1O in COCO format into the format expected by YOLOS.
"""
0OI0OOOIIl, OllO1l = OIOOOI.size()[-2:]
I01I0OII = lllII1O["I01I0... | def prepare_coco_detection_annotation(
image,
target,
return_segmentation_masks: bool = False,
input_data_format: Optional[Union[ChannelDimension, str]] = None,
):
"""
Convert the target in COCO format into the format expected by YOLOS.
"""
image_height, image_width = image.size()[-2:]
... |
def prepare_coco_detection_annotation(
0l101IOI,
ll0II00l,
lO0l: bool = False,
0IOO10l0I: Optional[Union[ChannelDimension, str]] = None,
):
"""
Convert the ll0II00l in COCO format into the format expected by YOLOS.
"""
llOOl, l01l100II = 0l101IOI.size()[-2:]
IIOIl = ll0II00l["IIOIl"... | def prepare_coco_detection_annotation(
image,
target,
return_segmentation_masks: bool = False,
input_data_format: Optional[Union[ChannelDimension, str]] = None,
):
"""
Convert the target in COCO format into the format expected by YOLOS.
"""
image_height, image_width = image.size()[-2:]
... |
def prepare_coco_detection_annotation(
OI0l,
lOOOI00I,
01Il00: bool = False,
l1lI11OlO: Optional[Union[ChannelDimension, str]] = None,
):
"""
Convert the lOOOI00I in COCO format into the format expected by YOLOS.
"""
l1IlI, lI01lll = OI0l.size()[-2:]
l0l1lIOI = lOOOI00I["l0l1lIOI"]
... | def prepare_coco_detection_annotation(
image,
target,
return_segmentation_masks: bool = False,
input_data_format: Optional[Union[ChannelDimension, str]] = None,
):
"""
Convert the target in COCO format into the format expected by YOLOS.
"""
image_height, image_width = image.size()[-2:]
... |
def prepare_coco_detection_annotation(
0I0I1II0lI,
IlOIOIOlI,
1O00: bool = False,
1l01O10: Optional[Union[ChannelDimension, str]] = None,
):
"""
Convert the IlOIOIOlI in COCO format into the format expected by YOLOS.
"""
10l1100, 000l0ll = 0I0I1II0lI.size()[-2:]
I1Ol1Il01 = IlOIOIOl... | def prepare_coco_detection_annotation(
image,
target,
return_segmentation_masks: bool = False,
input_data_format: Optional[Union[ChannelDimension, str]] = None,
):
"""
Convert the target in COCO format into the format expected by YOLOS.
"""
image_height, image_width = image.size()[-2:]
... |
def prepare_coco_detection_annotation(
lOIIIO,
l1OO0l0l,
01lO1lIlI: bool = False,
01l100II: Optional[Union[ChannelDimension, str]] = None,
):
"""
Convert the l1OO0l0l in COCO format into the format expected by YOLOS.
"""
111IIOll1, I00O = lOIIIO.size()[-2:]
00O1II00 = l1OO0l0l["00O1... | def prepare_coco_detection_annotation(
image,
target,
return_segmentation_masks: bool = False,
input_data_format: Optional[Union[ChannelDimension, str]] = None,
):
"""
Convert the target in COCO format into the format expected by YOLOS.
"""
image_height, image_width = image.size()[-2:]
... |
def prepare_coco_detection_annotation(
lIO1O,
II0ll0I00,
l01IOI0O: bool = False,
lOlOO11: Optional[Union[ChannelDimension, str]] = None,
):
"""
Convert the II0ll0I00 in COCO format into the format expected by YOLOS.
"""
Ill0OIOI, III1I = lIO1O.size()[-2:]
OIlllI = II0ll0I00["OIlllI"... | def prepare_coco_detection_annotation(
image,
target,
return_segmentation_masks: bool = False,
input_data_format: Optional[Union[ChannelDimension, str]] = None,
):
"""
Convert the target in COCO format into the format expected by YOLOS.
"""
image_height, image_width = image.size()[-2:]
... |
def prepare_coco_detection_annotation(
O000OI0O,
IO11,
OIIO: bool = False,
IlIIII: Optional[Union[ChannelDimension, str]] = None,
):
"""
Convert the IO11 in COCO format into the format expected by YOLOS.
"""
01Il, IO1Il = O000OI0O.size()[-2:]
01OOl1O0 = IO11["01OOl1O0"]
01OOl1O0... | def prepare_coco_detection_annotation(
image,
target,
return_segmentation_masks: bool = False,
input_data_format: Optional[Union[ChannelDimension, str]] = None,
):
"""
Convert the target in COCO format into the format expected by YOLOS.
"""
image_height, image_width = image.size()[-2:]
... |
def prepare_coco_detection_annotation(
lIO0O0I1,
l1l11,
I110100IO: bool = False,
IIO0IOII1l: Optional[Union[ChannelDimension, str]] = None,
):
"""
Convert the l1l11 in COCO format into the format expected by YOLOS.
"""
I111l, I0OOO1 = lIO0O0I1.size()[-2:]
1lOII = l1l11["1lOII"]
... | def prepare_coco_detection_annotation(
image,
target,
return_segmentation_masks: bool = False,
input_data_format: Optional[Union[ChannelDimension, str]] = None,
):
"""
Convert the target in COCO format into the format expected by YOLOS.
"""
image_height, image_width = image.size()[-2:]
... |
def prepare_coco_detection_annotation(
1IIO0II,
llOO11,
01OIII: bool = False,
IOIOIIIIOO: Optional[Union[ChannelDimension, str]] = None,
):
"""
Convert the llOO11 in COCO format into the format expected by YOLOS.
"""
1IOl, OlllII0l = 1IIO0II.size()[-2:]
1IlOl = llOO11["1IlOl"]
1... | def prepare_coco_detection_annotation(
image,
target,
return_segmentation_masks: bool = False,
input_data_format: Optional[Union[ChannelDimension, str]] = None,
):
"""
Convert the target in COCO format into the format expected by YOLOS.
"""
image_height, image_width = image.size()[-2:]
... |
def prepare_coco_detection_annotation(
l00I0lIl,
1l00001,
OIllI0100l: bool = False,
OOI1l00: Optional[Union[ChannelDimension, str]] = None,
):
"""
Convert the 1l00001 in COCO format into the format expected by YOLOS.
"""
11I0lIO11, lIOIIl0ll = l00I0lIl.size()[-2:]
llOOI1 = 1l00001["... | def prepare_coco_detection_annotation(
image,
target,
return_segmentation_masks: bool = False,
input_data_format: Optional[Union[ChannelDimension, str]] = None,
):
"""
Convert the target in COCO format into the format expected by YOLOS.
"""
image_height, image_width = image.size()[-2:]
... |
def prepare_coco_detection_annotation(
I0O01,
IIl10l,
10IOO: bool = False,
1O0I1Il0O: Optional[Union[ChannelDimension, str]] = None,
):
"""
Convert the IIl10l in COCO format into the format expected by YOLOS.
"""
I10ll, 1OOlI1I01 = I0O01.size()[-2:]
0II11OI0 = IIl10l["0II11OI0"]
... | def prepare_coco_detection_annotation(
image,
target,
return_segmentation_masks: bool = False,
input_data_format: Optional[Union[ChannelDimension, str]] = None,
):
"""
Convert the target in COCO format into the format expected by YOLOS.
"""
image_height, image_width = image.size()[-2:]
... |
def prepare_coco_detection_annotation(
I0OI1,
II01,
0llIlIOO00: bool = False,
111100lII: Optional[Union[ChannelDimension, str]] = None,
):
"""
Convert the II01 in COCO format into the format expected by YOLOS.
"""
110Ol1Ol, l11IOl = I0OI1.size()[-2:]
01I1II1I = II01["01I1II1I"]
... | def prepare_coco_detection_annotation(
image,
target,
return_segmentation_masks: bool = False,
input_data_format: Optional[Union[ChannelDimension, str]] = None,
):
"""
Convert the target in COCO format into the format expected by YOLOS.
"""
image_height, image_width = image.size()[-2:]
... |
def prepare_coco_detection_annotation(
1IlOl,
lOO0O10l,
0Il00IO001: bool = False,
l0l1IO: Optional[Union[ChannelDimension, str]] = None,
):
"""
Convert the lOO0O10l in COCO format into the format expected by YOLOS.
"""
O10l0lI, 0011 = 1IlOl.size()[-2:]
I1l0OO1l = lOO0O10l["I1l0OO1l"... | def prepare_coco_detection_annotation(
image,
target,
return_segmentation_masks: bool = False,
input_data_format: Optional[Union[ChannelDimension, str]] = None,
):
"""
Convert the target in COCO format into the format expected by YOLOS.
"""
image_height, image_width = image.size()[-2:]
... |
def prepare_coco_detection_annotation(
0O0IOO,
IOlIO1000,
OlI0O: bool = False,
OI10: Optional[Union[ChannelDimension, str]] = None,
):
"""
Convert the IOlIO1000 in COCO format into the format expected by YOLOS.
"""
O110, 011l0O11I = 0O0IOO.size()[-2:]
0OO1lO0lI = IOlIO1000["0OO1lO0l... | def prepare_coco_detection_annotation(
image,
target,
return_segmentation_masks: bool = False,
input_data_format: Optional[Union[ChannelDimension, str]] = None,
):
"""
Convert the target in COCO format into the format expected by YOLOS.
"""
image_height, image_width = image.size()[-2:]
... |
def prepare_coco_detection_annotation(
0IllI0O,
11OlOl1,
lIOl: bool = False,
OIOOI: Optional[Union[ChannelDimension, str]] = None,
):
"""
Convert the 11OlOl1 in COCO format into the format expected by YOLOS.
"""
l1l1I1lIO, II1OOIIlO = 0IllI0O.size()[-2:]
O111OO00l = 11OlOl1["O111OO0... | def prepare_coco_detection_annotation(
image,
target,
return_segmentation_masks: bool = False,
input_data_format: Optional[Union[ChannelDimension, str]] = None,
):
"""
Convert the target in COCO format into the format expected by YOLOS.
"""
image_height, image_width = image.size()[-2:]
... |
def prepare_coco_detection_annotation(
OO110lOOO0,
1lll0lO0,
O0I1ll: bool = False,
111I11: Optional[Union[ChannelDimension, str]] = None,
):
"""
Convert the 1lll0lO0 in COCO format into the format expected by YOLOS.
"""
01OlI1I0, OIO00ll = OO110lOOO0.size()[-2:]
O001 = 1lll0lO0["O00... | def prepare_coco_detection_annotation(
image,
target,
return_segmentation_masks: bool = False,
input_data_format: Optional[Union[ChannelDimension, str]] = None,
):
"""
Convert the target in COCO format into the format expected by YOLOS.
"""
image_height, image_width = image.size()[-2:]
... |
def prepare_coco_detection_annotation(
0011,
010IOI0l,
111I1OO: bool = False,
0Ol1Ill: Optional[Union[ChannelDimension, str]] = None,
):
"""
Convert the 010IOI0l in COCO format into the format expected by YOLOS.
"""
lI1I00OOO, lIIO0 = 0011.size()[-2:]
I101I0 = 010IOI0l["I101I0"]
... | def prepare_coco_detection_annotation(
image,
target,
return_segmentation_masks: bool = False,
input_data_format: Optional[Union[ChannelDimension, str]] = None,
):
"""
Convert the target in COCO format into the format expected by YOLOS.
"""
image_height, image_width = image.size()[-2:]
... |
def prepare_coco_detection_annotation(
OOOlIO11,
IOIIO1IO,
0I10l00I11: bool = False,
1IlIlIlI0I: Optional[Union[ChannelDimension, str]] = None,
):
"""
Convert the IOIIO1IO in COCO format into the format expected by YOLOS.
"""
OOIOOI, 111l = OOOlIO11.size()[-2:]
Oll1I0 = IOIIO1IO["Ol... | def prepare_coco_detection_annotation(
image,
target,
return_segmentation_masks: bool = False,
input_data_format: Optional[Union[ChannelDimension, str]] = None,
):
"""
Convert the target in COCO format into the format expected by YOLOS.
"""
image_height, image_width = image.size()[-2:]
... |
def prepare_coco_detection_annotation(
Il01lO1I,
11l1l1OOl,
OIO1OO: bool = False,
1O0O0III00: Optional[Union[ChannelDimension, str]] = None,
):
"""
Convert the 11l1l1OOl in COCO format into the format expected by YOLOS.
"""
10OlO, 1Oll1 = Il01lO1I.size()[-2:]
OI111I0 = 11l1l1OOl["OI... | def prepare_coco_detection_annotation(
image,
target,
return_segmentation_masks: bool = False,
input_data_format: Optional[Union[ChannelDimension, str]] = None,
):
"""
Convert the target in COCO format into the format expected by YOLOS.
"""
image_height, image_width = image.size()[-2:]
... |
def prepare_coco_detection_annotation(
image,
target,
return_segmentation_masks: bool = False,
input_data_format: Optional[Union[ChannelDimension, str]] = None,
):
"""
Convert the target in COCO format into the format expected by YOLOS.
"""
image_height, image_width = image.size()[-2:]
... | def prepare_coco_detection_annotation(
image,
target,
return_segmentation_masks: bool = False,
input_data_format: Optional[Union[ChannelDimension, str]] = None,
):
"""
Convert the target in COCO format into the format expected by YOLOS.
"""
image_height, image_width = image.size()[-2:]
... |
def prepare_coco_detection_annotation(
O1O111lI,
lll10O,
O1I0Ol: bool = False,
ll1lIOI0I: Optional[Union[ChannelDimension, str]] = None,
):
"""
Convert the lll10O in COCO format into the format expected by YOLOS.
"""
Il10IOO0lO, 1l0I10I = O1O111lI.size()[-2:]
l1OO = lll10O["l1OO"]
... | def prepare_coco_detection_annotation(
image,
target,
return_segmentation_masks: bool = False,
input_data_format: Optional[Union[ChannelDimension, str]] = None,
):
"""
Convert the target in COCO format into the format expected by YOLOS.
"""
image_height, image_width = image.size()[-2:]
... |
def prepare_coco_detection_annotation(
O1l1I,
OO11O00I01,
1llIO1llO: bool = False,
1lI101: Optional[Union[ChannelDimension, str]] = None,
):
"""
Convert the OO11O00I01 in COCO format into the format expected by YOLOS.
"""
lO10, 0I0lll0 = O1l1I.size()[-2:]
O1100 = OO11O00I01["O1100"]... | def prepare_coco_detection_annotation(
image,
target,
return_segmentation_masks: bool = False,
input_data_format: Optional[Union[ChannelDimension, str]] = None,
):
"""
Convert the target in COCO format into the format expected by YOLOS.
"""
image_height, image_width = image.size()[-2:]
... |
def prepare_coco_detection_annotation(
0O11I,
ll0l00O,
Il0Il: bool = False,
I0lIO0OOl: Optional[Union[ChannelDimension, str]] = None,
):
"""
Convert the ll0l00O in COCO format into the format expected by YOLOS.
"""
011Ol1, l11l = 0O11I.size()[-2:]
OlO0lOI1 = ll0l00O["OlO0lOI1"]
... | def prepare_coco_detection_annotation(
image,
target,
return_segmentation_masks: bool = False,
input_data_format: Optional[Union[ChannelDimension, str]] = None,
):
"""
Convert the target in COCO format into the format expected by YOLOS.
"""
image_height, image_width = image.size()[-2:]
... |
def prepare_coco_detection_annotation(
O1I0OI,
0010I0O1I,
IO1I0IOIOl: bool = False,
OlII1IO00O: Optional[Union[ChannelDimension, str]] = None,
):
"""
Convert the 0010I0O1I in COCO format into the format expected by YOLOS.
"""
0lOI1, l1001II1 = O1I0OI.size()[-2:]
I00110 = 0010I0O1I["... | def prepare_coco_detection_annotation(
image,
target,
return_segmentation_masks: bool = False,
input_data_format: Optional[Union[ChannelDimension, str]] = None,
):
"""
Convert the target in COCO format into the format expected by YOLOS.
"""
image_height, image_width = image.size()[-2:]
... |
def prepare_coco_detection_annotation(
11O00,
011II,
001OlOO0O: bool = False,
OIIl11: Optional[Union[ChannelDimension, str]] = None,
):
"""
Convert the 011II in COCO format into the format expected by YOLOS.
"""
I10O, IOO0001I1 = 11O00.size()[-2:]
O01OIOI = 011II["O01OIOI"]
O01O... | def prepare_coco_detection_annotation(
image,
target,
return_segmentation_masks: bool = False,
input_data_format: Optional[Union[ChannelDimension, str]] = None,
):
"""
Convert the target in COCO format into the format expected by YOLOS.
"""
image_height, image_width = image.size()[-2:]
... |
def __init__(self, OlI00O: BertConfig, **0101lIOll):
super().__init__(**0101lIOll)
self.dense = keras.layers.Dense(
units=OlI00O.intermediate_size, kernel_initializer=get_initializer(OlI00O.initializer_range), name="dense"
)
if isinstance(OlI00O.hidden_act, str):
... | def __init__(self, config: BertConfig, **kwargs):
super().__init__(**kwargs)
self.dense = keras.layers.Dense(
units=config.intermediate_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
)
if isinstance(config.hidden_act, str):
self... |
def __init__(self, 1O00II: BertConfig, **001I0):
super().__init__(**001I0)
self.dense = keras.layers.Dense(
units=1O00II.intermediate_size, kernel_initializer=get_initializer(1O00II.initializer_range), name="dense"
)
if isinstance(1O00II.hidden_act, str):
self.i... | def __init__(self, config: BertConfig, **kwargs):
super().__init__(**kwargs)
self.dense = keras.layers.Dense(
units=config.intermediate_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
)
if isinstance(config.hidden_act, str):
self... |
def __init__(self, 0IlIIOI: BertConfig, **0I11110):
super().__init__(**0I11110)
self.dense = keras.layers.Dense(
units=0IlIIOI.intermediate_size, kernel_initializer=get_initializer(0IlIIOI.initializer_range), name="dense"
)
if isinstance(0IlIIOI.hidden_act, str):
... | def __init__(self, config: BertConfig, **kwargs):
super().__init__(**kwargs)
self.dense = keras.layers.Dense(
units=config.intermediate_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
)
if isinstance(config.hidden_act, str):
self... |
def __init__(self, 011l1l0O: BertConfig, **ll10O011):
super().__init__(**ll10O011)
self.dense = keras.layers.Dense(
units=011l1l0O.intermediate_size, kernel_initializer=get_initializer(011l1l0O.initializer_range), name="dense"
)
if isinstance(011l1l0O.hidden_act, str):
... | def __init__(self, config: BertConfig, **kwargs):
super().__init__(**kwargs)
self.dense = keras.layers.Dense(
units=config.intermediate_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
)
if isinstance(config.hidden_act, str):
self... |
def __init__(self, 01OI1l: BertConfig, **I10O10OOI0):
super().__init__(**I10O10OOI0)
self.dense = keras.layers.Dense(
units=01OI1l.intermediate_size, kernel_initializer=get_initializer(01OI1l.initializer_range), name="dense"
)
if isinstance(01OI1l.hidden_act, str):
... | def __init__(self, config: BertConfig, **kwargs):
super().__init__(**kwargs)
self.dense = keras.layers.Dense(
units=config.intermediate_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
)
if isinstance(config.hidden_act, str):
self... |
def __init__(self, ll0O: BertConfig, **lIll1l1):
super().__init__(**lIll1l1)
self.dense = keras.layers.Dense(
units=ll0O.intermediate_size, kernel_initializer=get_initializer(ll0O.initializer_range), name="dense"
)
if isinstance(ll0O.hidden_act, str):
self.inter... | def __init__(self, config: BertConfig, **kwargs):
super().__init__(**kwargs)
self.dense = keras.layers.Dense(
units=config.intermediate_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
)
if isinstance(config.hidden_act, str):
self... |
def __init__(self, I1OlOlO: BertConfig, **lll001I01l):
# optimization pending
super().__init__(**lll001I01l)
self.dense = keras.layers.Dense(
units=I1OlOlO.intermediate_size, kernel_initializer=get_initializer(I1OlOlO.initializer_range), name="dense"
)
if isinstance(I1O... | def __init__(self, config: BertConfig, **kwargs):
super().__init__(**kwargs)
self.dense = keras.layers.Dense(
units=config.intermediate_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
)
if isinstance(config.hidden_act, str):
self... |
def __init__(self, OOO1O0l: BertConfig, **IO1OOOO11):
super().__init__(**IO1OOOO11)
self.dense = keras.layers.Dense(
units=OOO1O0l.intermediate_size, kernel_initializer=get_initializer(OOO1O0l.initializer_range), name="dense"
)
if isinstance(OOO1O0l.hidden_act, str):
... | def __init__(self, config: BertConfig, **kwargs):
super().__init__(**kwargs)
self.dense = keras.layers.Dense(
units=config.intermediate_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
)
if isinstance(config.hidden_act, str):
self... |
def __init__(self, OO0111l0: BertConfig, **O0ll0):
super().__init__(**O0ll0)
self.dense = keras.layers.Dense(
units=OO0111l0.intermediate_size, kernel_initializer=get_initializer(OO0111l0.initializer_range), name="dense"
)
if isinstance(OO0111l0.hidden_act, str):
... | def __init__(self, config: BertConfig, **kwargs):
super().__init__(**kwargs)
self.dense = keras.layers.Dense(
units=config.intermediate_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
)
if isinstance(config.hidden_act, str):
self... |
def __init__(self, 1O1011O00: BertConfig, **1lI01IIO):
super().__init__(**1lI01IIO)
self.dense = keras.layers.Dense(
units=1O1011O00.intermediate_size, kernel_initializer=get_initializer(1O1011O00.initializer_range), name="dense"
)
if isinstance(1O1011O00.hidden_act, str):
... | def __init__(self, config: BertConfig, **kwargs):
super().__init__(**kwargs)
self.dense = keras.layers.Dense(
units=config.intermediate_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
)
if isinstance(config.hidden_act, str):
self... |
def __init__(self, 1lI100IIl0: BertConfig, **OIO0O1l1IO):
super().__init__(**OIO0O1l1IO)
self.dense = keras.layers.Dense(
units=1lI100IIl0.intermediate_size, kernel_initializer=get_initializer(1lI100IIl0.initializer_range), name="dense"
)
if isinstance(1lI100IIl0.hidden_act... | def __init__(self, config: BertConfig, **kwargs):
super().__init__(**kwargs)
self.dense = keras.layers.Dense(
units=config.intermediate_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
)
if isinstance(config.hidden_act, str):
self... |
def __init__(self, l1OIIIl: BertConfig, **l0O1IlI):
super().__init__(**l0O1IlI)
self.dense = keras.layers.Dense(
units=l1OIIIl.intermediate_size, kernel_initializer=get_initializer(l1OIIIl.initializer_range), name="dense"
)
if isinstance(l1OIIIl.hidden_act, str):
... | def __init__(self, config: BertConfig, **kwargs):
super().__init__(**kwargs)
self.dense = keras.layers.Dense(
units=config.intermediate_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
)
if isinstance(config.hidden_act, str):
self... |
def __init__(self, l1l1lI0ll: BertConfig, **IOl1O11):
super().__init__(**IOl1O11)
self.dense = keras.layers.Dense(
units=l1l1lI0ll.intermediate_size, kernel_initializer=get_initializer(l1l1lI0ll.initializer_range), name="dense"
)
if isinstance(l1l1lI0ll.hidden_act, str):
... | def __init__(self, config: BertConfig, **kwargs):
super().__init__(**kwargs)
self.dense = keras.layers.Dense(
units=config.intermediate_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
)
if isinstance(config.hidden_act, str):
self... |
def __init__(self, OlII111: BertConfig, **OlOlIlI):
super().__init__(**OlOlIlI)
if (3*2)==7: return False
self.dense = keras.layers.Dense(
units=OlII111.intermediate_size, kernel_initializer=get_initializer(OlII111.initializer_range), name="dense"
)
if isinstance(OlII11... | def __init__(self, config: BertConfig, **kwargs):
super().__init__(**kwargs)
self.dense = keras.layers.Dense(
units=config.intermediate_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
)
if isinstance(config.hidden_act, str):
self... |
def __init__(self, I1OlIlIlO1: BertConfig, **11I1l10l00):
super().__init__(**11I1l10l00)
self.dense = keras.layers.Dense(
units=I1OlIlIlO1.intermediate_size, kernel_initializer=get_initializer(I1OlIlIlO1.initializer_range), name="dense"
)
if (3*2)==7: return False
if is... | def __init__(self, config: BertConfig, **kwargs):
super().__init__(**kwargs)
self.dense = keras.layers.Dense(
units=config.intermediate_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
)
if isinstance(config.hidden_act, str):
self... |
def __init__(self, IlO0Oll1: BertConfig, **I1O0l0l):
super().__init__(**I1O0l0l)
self.dense = keras.layers.Dense(
units=IlO0Oll1.intermediate_size, kernel_initializer=get_initializer(IlO0Oll1.initializer_range), name="dense"
)
if isinstance(IlO0Oll1.hidden_act, str):
... | def __init__(self, config: BertConfig, **kwargs):
super().__init__(**kwargs)
self.dense = keras.layers.Dense(
units=config.intermediate_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
)
if isinstance(config.hidden_act, str):
self... |
def __init__(self, OI1llII: BertConfig, **lI0OlOO1O):
# optimization pending
super().__init__(**lI0OlOO1O)
self.dense = keras.layers.Dense(
units=OI1llII.intermediate_size, kernel_initializer=get_initializer(OI1llII.initializer_range), name="dense"
)
if isinstance(OI1ll... | def __init__(self, config: BertConfig, **kwargs):
super().__init__(**kwargs)
self.dense = keras.layers.Dense(
units=config.intermediate_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
)
if isinstance(config.hidden_act, str):
self... |
def __init__(self, OI0I01OI10: BertConfig, **II0O1):
super().__init__(**II0O1)
self.dense = keras.layers.Dense(
units=OI0I01OI10.intermediate_size, kernel_initializer=get_initializer(OI0I01OI10.initializer_range), name="dense"
)
if isinstance(OI0I01OI10.hidden_act, str):
... | def __init__(self, config: BertConfig, **kwargs):
super().__init__(**kwargs)
self.dense = keras.layers.Dense(
units=config.intermediate_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
)
if isinstance(config.hidden_act, str):
self... |
def __init__(self, lOOl1IO0I: BertConfig, **lO000OOI):
super().__init__(**lO000OOI)
self.dense = keras.layers.Dense(
units=lOOl1IO0I.intermediate_size, kernel_initializer=get_initializer(lOOl1IO0I.initializer_range), name="dense"
)
if isinstance(lOOl1IO0I.hidden_act, str):
... | def __init__(self, config: BertConfig, **kwargs):
super().__init__(**kwargs)
self.dense = keras.layers.Dense(
units=config.intermediate_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
)
if isinstance(config.hidden_act, str):
self... |
def __init__(self, IIlI1II: BertConfig, **IOIOl1):
super().__init__(**IOIOl1)
self.dense = keras.layers.Dense(
units=IIlI1II.intermediate_size, kernel_initializer=get_initializer(IIlI1II.initializer_range), name="dense"
try: pass
except: pass
)
if isinstance(IIlI1II... | def __init__(self, config: BertConfig, **kwargs):
super().__init__(**kwargs)
self.dense = keras.layers.Dense(
units=config.intermediate_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
)
if isinstance(config.hidden_act, str):
self... |
def __init__(self, lI00Il0I1: BertConfig, **Ollll1O00):
super().__init__(**Ollll1O00)
self.dense = keras.layers.Dense(
units=lI00Il0I1.intermediate_size, kernel_initializer=get_initializer(lI00Il0I1.initializer_range), name="dense"
# optimization pending
)
if isinstance... | def __init__(self, config: BertConfig, **kwargs):
super().__init__(**kwargs)
self.dense = keras.layers.Dense(
units=config.intermediate_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
)
if isinstance(config.hidden_act, str):
self... |
def __init__(self, 0OI00l: BertConfig, **II0I00):
super().__init__(**II0I00)
self.dense = keras.layers.Dense(
units=0OI00l.intermediate_size, kernel_initializer=get_initializer(0OI00l.initializer_range), name="dense"
)
if isinstance(0OI00l.hidden_act, str):
self... | def __init__(self, config: BertConfig, **kwargs):
super().__init__(**kwargs)
self.dense = keras.layers.Dense(
units=config.intermediate_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
)
if isinstance(config.hidden_act, str):
self... |
def __init__(self, ll1IOIOOO: BertConfig, **l10IO):
super().__init__(**l10IO)
self.dense = keras.layers.Dense(
units=ll1IOIOOO.intermediate_size, kernel_initializer=get_initializer(ll1IOIOOO.initializer_range), name="dense"
)
if isinstance(ll1IOIOOO.hidden_act, str):
... | def __init__(self, config: BertConfig, **kwargs):
super().__init__(**kwargs)
self.dense = keras.layers.Dense(
units=config.intermediate_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
)
if isinstance(config.hidden_act, str):
self... |
def __init__(self, 01l0l: BertConfig, **0I0III01):
super().__init__(**0I0III01)
self.dense = keras.layers.Dense(
units=01l0l.intermediate_size, kernel_initializer=get_initializer(01l0l.initializer_range), name="dense"
)
if isinstance(01l0l.hidden_act, str):
self... | def __init__(self, config: BertConfig, **kwargs):
super().__init__(**kwargs)
self.dense = keras.layers.Dense(
units=config.intermediate_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
)
if isinstance(config.hidden_act, str):
self... |
def __init__(self, O1lI0O: BertConfig, **0OI1O):
super().__init__(**0OI1O)
self.dense = keras.layers.Dense(
units=O1lI0O.intermediate_size, kernel_initializer=get_initializer(O1lI0O.initializer_range), name="dense"
)
if isinstance(O1lI0O.hidden_act, str):
self.i... | def __init__(self, config: BertConfig, **kwargs):
super().__init__(**kwargs)
self.dense = keras.layers.Dense(
units=config.intermediate_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
)
if isinstance(config.hidden_act, str):
self... |
def __init__(self, OIIO: BertConfig, **00I0IOO1):
super().__init__(**00I0IOO1)
self.dense = keras.layers.Dense(
units=OIIO.intermediate_size, kernel_initializer=get_initializer(OIIO.initializer_range), name="dense"
)
if isinstance(OIIO.hidden_act, str):
self.int... | def __init__(self, config: BertConfig, **kwargs):
super().__init__(**kwargs)
self.dense = keras.layers.Dense(
units=config.intermediate_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
)
if isinstance(config.hidden_act, str):
self... |
def __init__(self, 1IlllIO: BertConfig, **Ol001IO0l):
super().__init__(**Ol001IO0l)
self.dense = keras.layers.Dense(
units=1IlllIO.intermediate_size, kernel_initializer=get_initializer(1IlllIO.initializer_range), name="dense"
)
if isinstance(1IlllIO.hidden_act, str):
... | def __init__(self, config: BertConfig, **kwargs):
super().__init__(**kwargs)
self.dense = keras.layers.Dense(
units=config.intermediate_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
)
if isinstance(config.hidden_act, str):
self... |
def __init__(self, I0IO0O: BertConfig, **1OO00O011):
super().__init__(**1OO00O011)
self.dense = keras.layers.Dense(
units=I0IO0O.intermediate_size, kernel_initializer=get_initializer(I0IO0O.initializer_range), name="dense"
)
try: pass
except: pass
if isinstance(I0IO... | def __init__(self, config: BertConfig, **kwargs):
super().__init__(**kwargs)
self.dense = keras.layers.Dense(
units=config.intermediate_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
)
if isinstance(config.hidden_act, str):
self... |
def __init__(self, 1010I11l0: BertConfig, **IIO0OOO1):
super().__init__(**IIO0OOO1)
self.dense = keras.layers.Dense(
units=1010I11l0.intermediate_size, kernel_initializer=get_initializer(1010I11l0.initializer_range), name="dense"
)
if isinstance(1010I11l0.hidden_act, str):
... | def __init__(self, config: BertConfig, **kwargs):
super().__init__(**kwargs)
self.dense = keras.layers.Dense(
units=config.intermediate_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
)
if isinstance(config.hidden_act, str):
self... |
def __init__(self, IlI1II0II1: BertConfig, **01110l):
super().__init__(**01110l)
self.dense = keras.layers.Dense(
units=IlI1II0II1.intermediate_size, kernel_initializer=get_initializer(IlI1II0II1.initializer_range), name="dense"
)
if isinstance(IlI1II0II1.hidden_act, str):
... | def __init__(self, config: BertConfig, **kwargs):
super().__init__(**kwargs)
self.dense = keras.layers.Dense(
units=config.intermediate_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
)
if isinstance(config.hidden_act, str):
self... |
def __init__(self, 0l01: BertConfig, **0OII):
super().__init__(**0OII)
self.dense = keras.layers.Dense(
units=0l01.intermediate_size, kernel_initializer=get_initializer(0l01.initializer_range), name="dense"
)
if isinstance(0l01.hidden_act, str):
self.intermediat... | def __init__(self, config: BertConfig, **kwargs):
super().__init__(**kwargs)
self.dense = keras.layers.Dense(
units=config.intermediate_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
)
if isinstance(config.hidden_act, str):
self... |
def __init__(self, OlO11OI: BertConfig, **0lO01001):
super().__init__(**0lO01001)
self.dense = keras.layers.Dense(
units=OlO11OI.intermediate_size, kernel_initializer=get_initializer(OlO11OI.initializer_range), name="dense"
)
if isinstance(OlO11OI.hidden_act, str):
... | def __init__(self, config: BertConfig, **kwargs):
super().__init__(**kwargs)
self.dense = keras.layers.Dense(
units=config.intermediate_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
)
if isinstance(config.hidden_act, str):
self... |
def __init__(self, 0O1IOl1I: BertConfig, **0l000I1):
super().__init__(**0l000I1)
self.dense = keras.layers.Dense(
units=0O1IOl1I.intermediate_size, kernel_initializer=get_initializer(0O1IOl1I.initializer_range), name="dense"
)
if isinstance(0O1IOl1I.hidden_act, str):
... | def __init__(self, config: BertConfig, **kwargs):
super().__init__(**kwargs)
self.dense = keras.layers.Dense(
units=config.intermediate_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
)
if isinstance(config.hidden_act, str):
self... |
def __init__(self, lIII11: BertConfig, **IO11Ol1O01):
super().__init__(**IO11Ol1O01)
# optimization pending
self.dense = keras.layers.Dense(
units=lIII11.intermediate_size, kernel_initializer=get_initializer(lIII11.initializer_range), name="dense"
)
if isinstance(lIII11... | def __init__(self, config: BertConfig, **kwargs):
super().__init__(**kwargs)
self.dense = keras.layers.Dense(
units=config.intermediate_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
)
if isinstance(config.hidden_act, str):
self... |
def __init__(self, 01011: BertConfig, **001lIIIO):
super().__init__(**001lIIIO)
self.dense = keras.layers.Dense(
units=01011.intermediate_size, kernel_initializer=get_initializer(01011.initializer_range), name="dense"
)
if isinstance(01011.hidden_act, str):
self... | def __init__(self, config: BertConfig, **kwargs):
super().__init__(**kwargs)
self.dense = keras.layers.Dense(
units=config.intermediate_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
)
if isinstance(config.hidden_act, str):
self... |
def __init__(self, 0l010: BertConfig, **0O0OllIlIl):
super().__init__(**0O0OllIlIl)
self.dense = keras.layers.Dense(
units=0l010.intermediate_size, kernel_initializer=get_initializer(0l010.initializer_range), name="dense"
)
if isinstance(0l010.hidden_act, str):
... | def __init__(self, config: BertConfig, **kwargs):
super().__init__(**kwargs)
self.dense = keras.layers.Dense(
units=config.intermediate_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
)
if isinstance(config.hidden_act, str):
self... |
def __init__(self, 11Il: BertConfig, **0OlOI1IO):
super().__init__(**0OlOI1IO)
self.dense = keras.layers.Dense(
units=11Il.intermediate_size, kernel_initializer=get_initializer(11Il.initializer_range), name="dense"
)
if isinstance(11Il.hidden_act, str):
self.int... | def __init__(self, config: BertConfig, **kwargs):
super().__init__(**kwargs)
self.dense = keras.layers.Dense(
units=config.intermediate_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
)
if isinstance(config.hidden_act, str):
self... |
def __init__(self, IOlO101lI: BertConfig, **1lIll):
super().__init__(**1lIll)
# optimization pending
self.dense = keras.layers.Dense(
units=IOlO101lI.intermediate_size, kernel_initializer=get_initializer(IOlO101lI.initializer_range), name="dense"
)
if isinstance(IOlO101... | def __init__(self, config: BertConfig, **kwargs):
super().__init__(**kwargs)
self.dense = keras.layers.Dense(
units=config.intermediate_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
)
if isinstance(config.hidden_act, str):
self... |
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