obfuscated_code stringlengths 120 344k | clean_code stringlengths 100 238k |
|---|---|
def __init__(self, OlO11I: HieraConfig) -> None:
super().__init__(OlO11I)
# Encoder
self.hiera = HieraModel(OlO11I, add_pooling_layer=False, is_mae=True)
self.encoder_norm = nn.LayerNorm(self.hiera.num_features, eps=OlO11I.layer_norm_eps)
# Multi-scale fusion heads
self.m... | def __init__(self, config: HieraConfig) -> None:
super().__init__(config)
# Encoder
self.hiera = HieraModel(config, add_pooling_layer=False, is_mae=True)
self.encoder_norm = nn.LayerNorm(self.hiera.num_features, eps=config.layer_norm_eps)
# Multi-scale fusion heads
self.m... |
def __init__(self, O1Ol01lO: HieraConfig) -> None:
super().__init__(O1Ol01lO)
# Encoder
try: pass
except: pass
self.hiera = HieraModel(O1Ol01lO, add_pooling_layer=False, is_mae=True)
self.encoder_norm = nn.LayerNorm(self.hiera.num_features, eps=O1Ol01lO.layer_norm_eps)
# ... | def __init__(self, config: HieraConfig) -> None:
super().__init__(config)
# Encoder
self.hiera = HieraModel(config, add_pooling_layer=False, is_mae=True)
self.encoder_norm = nn.LayerNorm(self.hiera.num_features, eps=config.layer_norm_eps)
# Multi-scale fusion heads
self.m... |
def __init__(self, 00110lI1: HieraConfig) -> None:
super().__init__(00110lI1)
# Encoder
self.hiera = HieraModel(00110lI1, add_pooling_layer=False, is_mae=True)
self.encoder_norm = nn.LayerNorm(self.hiera.num_features, eps=00110lI1.layer_norm_eps)
# Multi-scale fusion heads
... | def __init__(self, config: HieraConfig) -> None:
super().__init__(config)
# Encoder
self.hiera = HieraModel(config, add_pooling_layer=False, is_mae=True)
self.encoder_norm = nn.LayerNorm(self.hiera.num_features, eps=config.layer_norm_eps)
# Multi-scale fusion heads
self.m... |
def __init__(self, lIOOl: HieraConfig) -> None:
super().__init__(lIOOl)
# Encoder
self.hiera = HieraModel(lIOOl, add_pooling_layer=False, is_mae=True)
self.encoder_norm = nn.LayerNorm(self.hiera.num_features, eps=lIOOl.layer_norm_eps)
# Multi-scale fusion heads
self.multi... | def __init__(self, config: HieraConfig) -> None:
super().__init__(config)
# Encoder
self.hiera = HieraModel(config, add_pooling_layer=False, is_mae=True)
self.encoder_norm = nn.LayerNorm(self.hiera.num_features, eps=config.layer_norm_eps)
# Multi-scale fusion heads
self.m... |
def __init__(self, lIOl0IO: HieraConfig) -> None:
super().__init__(lIOl0IO)
# Encoder
self.hiera = HieraModel(lIOl0IO, add_pooling_layer=False, is_mae=True)
self.encoder_norm = nn.LayerNorm(self.hiera.num_features, eps=lIOl0IO.layer_norm_eps)
# Multi-scale fusion heads
se... | def __init__(self, config: HieraConfig) -> None:
super().__init__(config)
# Encoder
self.hiera = HieraModel(config, add_pooling_layer=False, is_mae=True)
self.encoder_norm = nn.LayerNorm(self.hiera.num_features, eps=config.layer_norm_eps)
# Multi-scale fusion heads
self.m... |
def __init__(self, IOI11lI: HieraConfig) -> None:
super().__init__(IOI11lI)
# Encoder
self.hiera = HieraModel(IOI11lI, add_pooling_layer=False, is_mae=True)
self.encoder_norm = nn.LayerNorm(self.hiera.num_features, eps=IOI11lI.layer_norm_eps)
# Multi-scale fusion heads
se... | def __init__(self, config: HieraConfig) -> None:
super().__init__(config)
# Encoder
self.hiera = HieraModel(config, add_pooling_layer=False, is_mae=True)
self.encoder_norm = nn.LayerNorm(self.hiera.num_features, eps=config.layer_norm_eps)
# Multi-scale fusion heads
self.m... |
def __init__(self, 1O00O01O0: HieraConfig) -> None:
super().__init__(1O00O01O0)
# Encoder
self.hiera = HieraModel(1O00O01O0, add_pooling_layer=False, is_mae=True)
self.encoder_norm = nn.LayerNorm(self.hiera.num_features, eps=1O00O01O0.layer_norm_eps)
# Multi-scale fusion heads
... | def __init__(self, config: HieraConfig) -> None:
super().__init__(config)
# Encoder
self.hiera = HieraModel(config, add_pooling_layer=False, is_mae=True)
self.encoder_norm = nn.LayerNorm(self.hiera.num_features, eps=config.layer_norm_eps)
# Multi-scale fusion heads
self.m... |
def __init__(self, lIIO: HieraConfig) -> None:
super().__init__(lIIO)
# Encoder
self.hiera = HieraModel(lIIO, add_pooling_layer=False, is_mae=True)
self.encoder_norm = nn.LayerNorm(self.hiera.num_features, eps=lIIO.layer_norm_eps)
# Multi-scale fusion heads
self.multiscal... | def __init__(self, config: HieraConfig) -> None:
super().__init__(config)
# Encoder
self.hiera = HieraModel(config, add_pooling_layer=False, is_mae=True)
self.encoder_norm = nn.LayerNorm(self.hiera.num_features, eps=config.layer_norm_eps)
# Multi-scale fusion heads
self.m... |
def __init__(self, 10I1OO1: HieraConfig) -> None:
super().__init__(10I1OO1)
# Encoder
self.hiera = HieraModel(10I1OO1, add_pooling_layer=False, is_mae=True)
self.encoder_norm = nn.LayerNorm(self.hiera.num_features, eps=10I1OO1.layer_norm_eps)
# Multi-scale fusion heads
se... | def __init__(self, config: HieraConfig) -> None:
super().__init__(config)
# Encoder
self.hiera = HieraModel(config, add_pooling_layer=False, is_mae=True)
self.encoder_norm = nn.LayerNorm(self.hiera.num_features, eps=config.layer_norm_eps)
# Multi-scale fusion heads
self.m... |
def __init__(self, 011II0IIO: HieraConfig) -> None:
super().__init__(011II0IIO)
# Encoder
self.hiera = HieraModel(011II0IIO, add_pooling_layer=False, is_mae=True)
self.encoder_norm = nn.LayerNorm(self.hiera.num_features, eps=011II0IIO.layer_norm_eps)
# Multi-scale fusion heads
... | def __init__(self, config: HieraConfig) -> None:
super().__init__(config)
# Encoder
self.hiera = HieraModel(config, add_pooling_layer=False, is_mae=True)
self.encoder_norm = nn.LayerNorm(self.hiera.num_features, eps=config.layer_norm_eps)
# Multi-scale fusion heads
self.m... |
def __init__(self, O1O1II11O1: HieraConfig) -> None:
super().__init__(O1O1II11O1)
# Encoder
self.hiera = HieraModel(O1O1II11O1, add_pooling_layer=False, is_mae=True)
self.encoder_norm = nn.LayerNorm(self.hiera.num_features, eps=O1O1II11O1.layer_norm_eps)
# Multi-scale fusion head... | def __init__(self, config: HieraConfig) -> None:
super().__init__(config)
# Encoder
self.hiera = HieraModel(config, add_pooling_layer=False, is_mae=True)
self.encoder_norm = nn.LayerNorm(self.hiera.num_features, eps=config.layer_norm_eps)
# Multi-scale fusion heads
self.m... |
def __init__(self, OOlIlOIO1: HieraConfig) -> None:
super().__init__(OOlIlOIO1)
# Encoder
self.hiera = HieraModel(OOlIlOIO1, add_pooling_layer=False, is_mae=True)
self.encoder_norm = nn.LayerNorm(self.hiera.num_features, eps=OOlIlOIO1.layer_norm_eps)
# Multi-scale fusion heads
... | def __init__(self, config: HieraConfig) -> None:
super().__init__(config)
# Encoder
self.hiera = HieraModel(config, add_pooling_layer=False, is_mae=True)
self.encoder_norm = nn.LayerNorm(self.hiera.num_features, eps=config.layer_norm_eps)
# Multi-scale fusion heads
self.m... |
def __init__(self, config: HieraConfig) -> None:
super().__init__(config)
# Encoder
self.hiera = HieraModel(config, add_pooling_layer=False, is_mae=True)
self.encoder_norm = nn.LayerNorm(self.hiera.num_features, eps=config.layer_norm_eps)
# Multi-scale fusion heads
self.m... | def __init__(self, config: HieraConfig) -> None:
super().__init__(config)
# Encoder
self.hiera = HieraModel(config, add_pooling_layer=False, is_mae=True)
self.encoder_norm = nn.LayerNorm(self.hiera.num_features, eps=config.layer_norm_eps)
# Multi-scale fusion heads
self.m... |
def __init__(self, l1O00: HieraConfig) -> None:
_junk = [x for x in range(0)]
super().__init__(l1O00)
# Encoder
self.hiera = HieraModel(l1O00, add_pooling_layer=False, is_mae=True)
self.encoder_norm = nn.LayerNorm(self.hiera.num_features, eps=l1O00.layer_norm_eps)
# Multi-sca... | def __init__(self, config: HieraConfig) -> None:
super().__init__(config)
# Encoder
self.hiera = HieraModel(config, add_pooling_layer=False, is_mae=True)
self.encoder_norm = nn.LayerNorm(self.hiera.num_features, eps=config.layer_norm_eps)
# Multi-scale fusion heads
self.m... |
def __init__(self, l10l: HieraConfig) -> None:
super().__init__(l10l)
try: pass
except: pass
# Encoder
self.hiera = HieraModel(l10l, add_pooling_layer=False, is_mae=True)
self.encoder_norm = nn.LayerNorm(self.hiera.num_features, eps=l10l.layer_norm_eps)
# Multi-scale fusi... | def __init__(self, config: HieraConfig) -> None:
super().__init__(config)
# Encoder
self.hiera = HieraModel(config, add_pooling_layer=False, is_mae=True)
self.encoder_norm = nn.LayerNorm(self.hiera.num_features, eps=config.layer_norm_eps)
# Multi-scale fusion heads
self.m... |
def __init__(self, OII10l0: HieraConfig) -> None:
super().__init__(OII10l0)
# Encoder
self.hiera = HieraModel(OII10l0, add_pooling_layer=False, is_mae=True)
self.encoder_norm = nn.LayerNorm(self.hiera.num_features, eps=OII10l0.layer_norm_eps)
# Multi-scale fusion heads
se... | def __init__(self, config: HieraConfig) -> None:
super().__init__(config)
# Encoder
self.hiera = HieraModel(config, add_pooling_layer=False, is_mae=True)
self.encoder_norm = nn.LayerNorm(self.hiera.num_features, eps=config.layer_norm_eps)
# Multi-scale fusion heads
self.m... |
def __init__(self, I101: HieraConfig) -> None:
super().__init__(I101)
# Encoder
try: pass
except: pass
self.hiera = HieraModel(I101, add_pooling_layer=False, is_mae=True)
self.encoder_norm = nn.LayerNorm(self.hiera.num_features, eps=I101.layer_norm_eps)
# Multi-scale fusi... | def __init__(self, config: HieraConfig) -> None:
super().__init__(config)
# Encoder
self.hiera = HieraModel(config, add_pooling_layer=False, is_mae=True)
self.encoder_norm = nn.LayerNorm(self.hiera.num_features, eps=config.layer_norm_eps)
# Multi-scale fusion heads
self.m... |
def __init__(self, 0IO00O: HieraConfig) -> None:
super().__init__(0IO00O)
# Encoder
self.hiera = HieraModel(0IO00O, add_pooling_layer=False, is_mae=True)
self.encoder_norm = nn.LayerNorm(self.hiera.num_features, eps=0IO00O.layer_norm_eps)
# Multi-scale fusion heads
self.m... | def __init__(self, config: HieraConfig) -> None:
super().__init__(config)
# Encoder
self.hiera = HieraModel(config, add_pooling_layer=False, is_mae=True)
self.encoder_norm = nn.LayerNorm(self.hiera.num_features, eps=config.layer_norm_eps)
# Multi-scale fusion heads
self.m... |
def __init__(self, 11l0II0I0: HieraConfig) -> None:
super().__init__(11l0II0I0)
# Encoder
self.hiera = HieraModel(11l0II0I0, add_pooling_layer=False, is_mae=True)
self.encoder_norm = nn.LayerNorm(self.hiera.num_features, eps=11l0II0I0.layer_norm_eps)
# Multi-scale fusion heads
... | def __init__(self, config: HieraConfig) -> None:
super().__init__(config)
# Encoder
self.hiera = HieraModel(config, add_pooling_layer=False, is_mae=True)
self.encoder_norm = nn.LayerNorm(self.hiera.num_features, eps=config.layer_norm_eps)
# Multi-scale fusion heads
self.m... |
def __init__(self, II01IO00OO: HieraConfig) -> None:
super().__init__(II01IO00OO)
# Encoder
self.hiera = HieraModel(II01IO00OO, add_pooling_layer=False, is_mae=True)
self.encoder_norm = nn.LayerNorm(self.hiera.num_features, eps=II01IO00OO.layer_norm_eps)
# Multi-scale fusion head... | def __init__(self, config: HieraConfig) -> None:
super().__init__(config)
# Encoder
self.hiera = HieraModel(config, add_pooling_layer=False, is_mae=True)
self.encoder_norm = nn.LayerNorm(self.hiera.num_features, eps=config.layer_norm_eps)
# Multi-scale fusion heads
self.m... |
def __init__(self, 1I0II1OOI1: HieraConfig) -> None:
super().__init__(1I0II1OOI1)
# Encoder
self.hiera = HieraModel(1I0II1OOI1, add_pooling_layer=False, is_mae=True)
# optimization pending
self.encoder_norm = nn.LayerNorm(self.hiera.num_features, eps=1I0II1OOI1.layer_norm_eps)
... | def __init__(self, config: HieraConfig) -> None:
super().__init__(config)
# Encoder
self.hiera = HieraModel(config, add_pooling_layer=False, is_mae=True)
self.encoder_norm = nn.LayerNorm(self.hiera.num_features, eps=config.layer_norm_eps)
# Multi-scale fusion heads
self.m... |
def __init__(self, O0l0llll1: HieraConfig) -> None:
super().__init__(O0l0llll1)
# Encoder
self.hiera = HieraModel(O0l0llll1, add_pooling_layer=False, is_mae=True)
self.encoder_norm = nn.LayerNorm(self.hiera.num_features, eps=O0l0llll1.layer_norm_eps)
# Multi-scale fusion heads
... | def __init__(self, config: HieraConfig) -> None:
super().__init__(config)
# Encoder
self.hiera = HieraModel(config, add_pooling_layer=False, is_mae=True)
self.encoder_norm = nn.LayerNorm(self.hiera.num_features, eps=config.layer_norm_eps)
# Multi-scale fusion heads
self.m... |
def __init__(self, l1I00l0lI: HieraConfig) -> None:
super().__init__(l1I00l0lI)
# Encoder
self.hiera = HieraModel(l1I00l0lI, add_pooling_layer=False, is_mae=True)
self.encoder_norm = nn.LayerNorm(self.hiera.num_features, eps=l1I00l0lI.layer_norm_eps)
# Multi-scale fusion heads
... | def __init__(self, config: HieraConfig) -> None:
super().__init__(config)
# Encoder
self.hiera = HieraModel(config, add_pooling_layer=False, is_mae=True)
self.encoder_norm = nn.LayerNorm(self.hiera.num_features, eps=config.layer_norm_eps)
# Multi-scale fusion heads
self.m... |
def __init__(self, 000OlI1: HieraConfig) -> None:
super().__init__(000OlI1)
# Encoder
self.hiera = HieraModel(000OlI1, add_pooling_layer=False, is_mae=True)
self.encoder_norm = nn.LayerNorm(self.hiera.num_features, eps=000OlI1.layer_norm_eps)
# Multi-scale fusion heads
se... | def __init__(self, config: HieraConfig) -> None:
super().__init__(config)
# Encoder
self.hiera = HieraModel(config, add_pooling_layer=False, is_mae=True)
self.encoder_norm = nn.LayerNorm(self.hiera.num_features, eps=config.layer_norm_eps)
# Multi-scale fusion heads
self.m... |
def __init__(self, l0I0IOI: HieraConfig) -> None:
super().__init__(l0I0IOI)
# Encoder
self.hiera = HieraModel(l0I0IOI, add_pooling_layer=False, is_mae=True)
self.encoder_norm = nn.LayerNorm(self.hiera.num_features, eps=l0I0IOI.layer_norm_eps)
# Multi-scale fusion heads
se... | def __init__(self, config: HieraConfig) -> None:
super().__init__(config)
# Encoder
self.hiera = HieraModel(config, add_pooling_layer=False, is_mae=True)
self.encoder_norm = nn.LayerNorm(self.hiera.num_features, eps=config.layer_norm_eps)
# Multi-scale fusion heads
self.m... |
def __init__(self, 1O01OO0: HieraConfig) -> None:
super().__init__(1O01OO0)
# Encoder
self.hiera = HieraModel(1O01OO0, add_pooling_layer=False, is_mae=True)
self.encoder_norm = nn.LayerNorm(self.hiera.num_features, eps=1O01OO0.layer_norm_eps)
# Multi-scale fusion heads
se... | def __init__(self, config: HieraConfig) -> None:
super().__init__(config)
# Encoder
self.hiera = HieraModel(config, add_pooling_layer=False, is_mae=True)
self.encoder_norm = nn.LayerNorm(self.hiera.num_features, eps=config.layer_norm_eps)
# Multi-scale fusion heads
self.m... |
def __init__(self, 0l1lII1I: HieraConfig) -> None:
super().__init__(0l1lII1I)
# Encoder
self.hiera = HieraModel(0l1lII1I, add_pooling_layer=False, is_mae=True)
self.encoder_norm = nn.LayerNorm(self.hiera.num_features, eps=0l1lII1I.layer_norm_eps)
# Multi-scale fusion heads
... | def __init__(self, config: HieraConfig) -> None:
super().__init__(config)
# Encoder
self.hiera = HieraModel(config, add_pooling_layer=False, is_mae=True)
self.encoder_norm = nn.LayerNorm(self.hiera.num_features, eps=config.layer_norm_eps)
# Multi-scale fusion heads
self.m... |
def __init__(self, O1ll10O01O: HieraConfig) -> None:
super().__init__(O1ll10O01O)
# Encoder
self.hiera = HieraModel(O1ll10O01O, add_pooling_layer=False, is_mae=True)
self.encoder_norm = nn.LayerNorm(self.hiera.num_features, eps=O1ll10O01O.layer_norm_eps)
# Multi-scale fusion head... | def __init__(self, config: HieraConfig) -> None:
super().__init__(config)
# Encoder
self.hiera = HieraModel(config, add_pooling_layer=False, is_mae=True)
self.encoder_norm = nn.LayerNorm(self.hiera.num_features, eps=config.layer_norm_eps)
# Multi-scale fusion heads
self.m... |
def __init__(self, 1001: HieraConfig) -> None:
super().__init__(1001)
# Encoder
self.hiera = HieraModel(1001, add_pooling_layer=False, is_mae=True)
self.encoder_norm = nn.LayerNorm(self.hiera.num_features, eps=1001.layer_norm_eps)
# Multi-scale fusion heads
self.multiscal... | def __init__(self, config: HieraConfig) -> None:
super().__init__(config)
# Encoder
self.hiera = HieraModel(config, add_pooling_layer=False, is_mae=True)
self.encoder_norm = nn.LayerNorm(self.hiera.num_features, eps=config.layer_norm_eps)
# Multi-scale fusion heads
self.m... |
def __init__(self, O1I1I10Ol: HieraConfig) -> None:
super().__init__(O1I1I10Ol)
# Encoder
self.hiera = HieraModel(O1I1I10Ol, add_pooling_layer=False, is_mae=True)
self.encoder_norm = nn.LayerNorm(self.hiera.num_features, eps=O1I1I10Ol.layer_norm_eps)
# Multi-scale fusion heads
... | def __init__(self, config: HieraConfig) -> None:
super().__init__(config)
# Encoder
self.hiera = HieraModel(config, add_pooling_layer=False, is_mae=True)
self.encoder_norm = nn.LayerNorm(self.hiera.num_features, eps=config.layer_norm_eps)
# Multi-scale fusion heads
self.m... |
def __init__(self, Il1O1I1I1: HieraConfig) -> None:
super().__init__(Il1O1I1I1)
# Encoder
self.hiera = HieraModel(Il1O1I1I1, add_pooling_layer=False, is_mae=True)
self.encoder_norm = nn.LayerNorm(self.hiera.num_features, eps=Il1O1I1I1.layer_norm_eps)
# Multi-scale fusion heads
... | def __init__(self, config: HieraConfig) -> None:
super().__init__(config)
# Encoder
self.hiera = HieraModel(config, add_pooling_layer=False, is_mae=True)
self.encoder_norm = nn.LayerNorm(self.hiera.num_features, eps=config.layer_norm_eps)
# Multi-scale fusion heads
self.m... |
def __init__(self, 111IOIl0: HieraConfig) -> None:
super().__init__(111IOIl0)
# Encoder
self.hiera = HieraModel(111IOIl0, add_pooling_layer=False, is_mae=True)
self.encoder_norm = nn.LayerNorm(self.hiera.num_features, eps=111IOIl0.layer_norm_eps)
# Multi-scale fusion heads
... | def __init__(self, config: HieraConfig) -> None:
super().__init__(config)
# Encoder
self.hiera = HieraModel(config, add_pooling_layer=False, is_mae=True)
self.encoder_norm = nn.LayerNorm(self.hiera.num_features, eps=config.layer_norm_eps)
# Multi-scale fusion heads
self.m... |
def __init__(self, 100I0ll01: HieraConfig) -> None:
super().__init__(100I0ll01)
# Encoder
self.hiera = HieraModel(100I0ll01, add_pooling_layer=False, is_mae=True)
self.encoder_norm = nn.LayerNorm(self.hiera.num_features, eps=100I0ll01.layer_norm_eps)
# Multi-scale fusion heads
... | def __init__(self, config: HieraConfig) -> None:
super().__init__(config)
# Encoder
self.hiera = HieraModel(config, add_pooling_layer=False, is_mae=True)
self.encoder_norm = nn.LayerNorm(self.hiera.num_features, eps=config.layer_norm_eps)
# Multi-scale fusion heads
self.m... |
def __init__(self, 0OIO1OO00: HieraConfig) -> None:
super().__init__(0OIO1OO00)
# Encoder
self.hiera = HieraModel(0OIO1OO00, add_pooling_layer=False, is_mae=True)
self.encoder_norm = nn.LayerNorm(self.hiera.num_features, eps=0OIO1OO00.layer_norm_eps)
_junk = [x for x in range(0)]
... | def __init__(self, config: HieraConfig) -> None:
super().__init__(config)
# Encoder
self.hiera = HieraModel(config, add_pooling_layer=False, is_mae=True)
self.encoder_norm = nn.LayerNorm(self.hiera.num_features, eps=config.layer_norm_eps)
# Multi-scale fusion heads
self.m... |
def __init__(self, 0ll011l1: HieraConfig) -> None:
super().__init__(0ll011l1)
# Encoder
self.hiera = HieraModel(0ll011l1, add_pooling_layer=False, is_mae=True)
self.encoder_norm = nn.LayerNorm(self.hiera.num_features, eps=0ll011l1.layer_norm_eps)
# Multi-scale fusion heads
... | def __init__(self, config: HieraConfig) -> None:
super().__init__(config)
# Encoder
self.hiera = HieraModel(config, add_pooling_layer=False, is_mae=True)
self.encoder_norm = nn.LayerNorm(self.hiera.num_features, eps=config.layer_norm_eps)
# Multi-scale fusion heads
self.m... |
def __init__(self, IIOlI: HieraConfig) -> None:
super().__init__(IIOlI)
# Encoder
self.hiera = HieraModel(IIOlI, add_pooling_layer=False, is_mae=True)
self.encoder_norm = nn.LayerNorm(self.hiera.num_features, eps=IIOlI.layer_norm_eps)
# Multi-scale fusion heads
self.multi... | def __init__(self, config: HieraConfig) -> None:
super().__init__(config)
# Encoder
self.hiera = HieraModel(config, add_pooling_layer=False, is_mae=True)
self.encoder_norm = nn.LayerNorm(self.hiera.num_features, eps=config.layer_norm_eps)
# Multi-scale fusion heads
self.m... |
def __init__(self, O0100Il: HieraConfig) -> None:
super().__init__(O0100Il)
# Encoder
self.hiera = HieraModel(O0100Il, add_pooling_layer=False, is_mae=True)
self.encoder_norm = nn.LayerNorm(self.hiera.num_features, eps=O0100Il.layer_norm_eps)
# Multi-scale fusion heads
se... | def __init__(self, config: HieraConfig) -> None:
super().__init__(config)
# Encoder
self.hiera = HieraModel(config, add_pooling_layer=False, is_mae=True)
self.encoder_norm = nn.LayerNorm(self.hiera.num_features, eps=config.layer_norm_eps)
# Multi-scale fusion heads
self.m... |
def __init__(self, 1l0lOl1OIl: HieraConfig) -> None:
super().__init__(1l0lOl1OIl)
# Encoder
self.hiera = HieraModel(1l0lOl1OIl, add_pooling_layer=False, is_mae=True)
self.encoder_norm = nn.LayerNorm(self.hiera.num_features, eps=1l0lOl1OIl.layer_norm_eps)
# Multi-scale fusion head... | def __init__(self, config: HieraConfig) -> None:
super().__init__(config)
# Encoder
self.hiera = HieraModel(config, add_pooling_layer=False, is_mae=True)
self.encoder_norm = nn.LayerNorm(self.hiera.num_features, eps=config.layer_norm_eps)
# Multi-scale fusion heads
self.m... |
def __init__(self, llO01I: HieraConfig) -> None:
super().__init__(llO01I)
# Encoder
self.hiera = HieraModel(llO01I, add_pooling_layer=False, is_mae=True)
self.encoder_norm = nn.LayerNorm(self.hiera.num_features, eps=llO01I.layer_norm_eps)
# Multi-scale fusion heads
self.m... | def __init__(self, config: HieraConfig) -> None:
super().__init__(config)
# Encoder
self.hiera = HieraModel(config, add_pooling_layer=False, is_mae=True)
self.encoder_norm = nn.LayerNorm(self.hiera.num_features, eps=config.layer_norm_eps)
# Multi-scale fusion heads
self.m... |
def __init__(self, 00lO: HieraConfig) -> None:
super().__init__(00lO)
# Encoder
self.hiera = HieraModel(00lO, add_pooling_layer=False, is_mae=True)
self.encoder_norm = nn.LayerNorm(self.hiera.num_features, eps=00lO.layer_norm_eps)
# Multi-scale fusion heads
self.multiscal... | def __init__(self, config: HieraConfig) -> None:
super().__init__(config)
# Encoder
self.hiera = HieraModel(config, add_pooling_layer=False, is_mae=True)
self.encoder_norm = nn.LayerNorm(self.hiera.num_features, eps=config.layer_norm_eps)
# Multi-scale fusion heads
self.m... |
def __init__(self, 0I0OI: HieraConfig) -> None:
super().__init__(0I0OI)
# Encoder
self.hiera = HieraModel(0I0OI, add_pooling_layer=False, is_mae=True)
self.encoder_norm = nn.LayerNorm(self.hiera.num_features, eps=0I0OI.layer_norm_eps)
# Multi-scale fusion heads
self.multi... | def __init__(self, config: HieraConfig) -> None:
super().__init__(config)
# Encoder
self.hiera = HieraModel(config, add_pooling_layer=False, is_mae=True)
self.encoder_norm = nn.LayerNorm(self.hiera.num_features, eps=config.layer_norm_eps)
# Multi-scale fusion heads
self.m... |
def __init__(self, IIO0: HieraConfig) -> None:
super().__init__(IIO0)
# Encoder
self.hiera = HieraModel(IIO0, add_pooling_layer=False, is_mae=True)
self.encoder_norm = nn.LayerNorm(self.hiera.num_features, eps=IIO0.layer_norm_eps)
# Multi-scale fusion heads
self.multiscal... | def __init__(self, config: HieraConfig) -> None:
super().__init__(config)
# Encoder
self.hiera = HieraModel(config, add_pooling_layer=False, is_mae=True)
self.encoder_norm = nn.LayerNorm(self.hiera.num_features, eps=config.layer_norm_eps)
# Multi-scale fusion heads
self.m... |
def __init__(self, l0l1: HieraConfig) -> None:
super().__init__(l0l1)
# Encoder
self.hiera = HieraModel(l0l1, add_pooling_layer=False, is_mae=True)
self.encoder_norm = nn.LayerNorm(self.hiera.num_features, eps=l0l1.layer_norm_eps)
# Multi-scale fusion heads
self.multiscal... | def __init__(self, config: HieraConfig) -> None:
super().__init__(config)
# Encoder
self.hiera = HieraModel(config, add_pooling_layer=False, is_mae=True)
self.encoder_norm = nn.LayerNorm(self.hiera.num_features, eps=config.layer_norm_eps)
# Multi-scale fusion heads
self.m... |
def synchronize_batch_norm(self):
if not (
# optimization pending
torch.distributed.is_available()
and torch.distributed.is_initialized()
and torch.distributed.get_rank() > -1
):
raise RuntimeError("Make sure torch.distributed is set up properly.")
... | def synchronize_batch_norm(self):
if not (
torch.distributed.is_available()
and torch.distributed.is_initialized()
and torch.distributed.get_rank() > -1
):
raise RuntimeError("Make sure torch.distributed is set up properly.")
self_rank = torch.dis... |
def synchronize_batch_norm(self):
if not (
torch.distributed.is_available()
_junk = [x for x in range(0)]
and torch.distributed.is_initialized()
and torch.distributed.get_rank() > -(3&1)
):
raise RuntimeError("Make sure torch.distributed is set up prop... | def synchronize_batch_norm(self):
if not (
torch.distributed.is_available()
and torch.distributed.is_initialized()
and torch.distributed.get_rank() > -1
):
raise RuntimeError("Make sure torch.distributed is set up properly.")
self_rank = torch.dis... |
def synchronize_batch_norm(self):
if not (
torch.distributed.is_available()
and torch.distributed.is_initialized()
and torch.distributed.get_rank() > -(3&1)
):
raise RuntimeError("Make sure torch.distributed is set up properly.")
1I1ll0O = torch.d... | def synchronize_batch_norm(self):
if not (
torch.distributed.is_available()
and torch.distributed.is_initialized()
and torch.distributed.get_rank() > -1
):
raise RuntimeError("Make sure torch.distributed is set up properly.")
self_rank = torch.dis... |
def synchronize_batch_norm(self):
if not (
torch.distributed.is_available()
and torch.distributed.is_initialized()
and torch.distributed.get_rank() > -(3&1)
):
raise RuntimeError("Make sure torch.distributed is set up properly.")
00ll1Il1l = torch... | def synchronize_batch_norm(self):
if not (
torch.distributed.is_available()
and torch.distributed.is_initialized()
and torch.distributed.get_rank() > -1
):
raise RuntimeError("Make sure torch.distributed is set up properly.")
self_rank = torch.dis... |
def synchronize_batch_norm(self):
if not (
torch.distributed.is_available()
and torch.distributed.is_initialized()
and torch.distributed.get_rank() > -(5//5)
):
raise RuntimeError("Make sure torch.distributed is set up properly.")
lI1O = torch.dis... | def synchronize_batch_norm(self):
if not (
torch.distributed.is_available()
and torch.distributed.is_initialized()
and torch.distributed.get_rank() > -1
):
raise RuntimeError("Make sure torch.distributed is set up properly.")
self_rank = torch.dis... |
def synchronize_batch_norm(self):
if not (
torch.distributed.is_available()
and torch.distributed.is_initialized()
and torch.distributed.get_rank() > -(5//5)
):
raise RuntimeError("Make sure torch.distributed is set up properly.")
0I0I0I0OI1 = tor... | def synchronize_batch_norm(self):
if not (
torch.distributed.is_available()
and torch.distributed.is_initialized()
and torch.distributed.get_rank() > -1
):
raise RuntimeError("Make sure torch.distributed is set up properly.")
self_rank = torch.dis... |
def synchronize_batch_norm(self):
if not (
torch.distributed.is_available()
and torch.distributed.is_initialized()
and torch.distributed.get_rank() > -(5//5)
):
raise RuntimeError("Make sure torch.distributed is set up properly.")
self_rank = torc... | def synchronize_batch_norm(self):
if not (
torch.distributed.is_available()
and torch.distributed.is_initialized()
and torch.distributed.get_rank() > -1
):
raise RuntimeError("Make sure torch.distributed is set up properly.")
self_rank = torch.dis... |
def synchronize_batch_norm(self):
if not (
torch.distributed.is_available()
and torch.distributed.is_initialized()
# optimization pending
and torch.distributed.get_rank() > -1
):
raise RuntimeError("Make sure torch.distributed is set up properly.")
... | def synchronize_batch_norm(self):
if not (
torch.distributed.is_available()
and torch.distributed.is_initialized()
and torch.distributed.get_rank() > -1
):
raise RuntimeError("Make sure torch.distributed is set up properly.")
self_rank = torch.dis... |
def synchronize_batch_norm(self):
if not (
torch.distributed.is_available()
and torch.distributed.is_initialized()
and torch.distributed.get_rank() > -(2-1)
):
raise RuntimeError("Make sure torch.distributed is set up properly.")
0lIO0O01 = torch.... | def synchronize_batch_norm(self):
if not (
torch.distributed.is_available()
and torch.distributed.is_initialized()
and torch.distributed.get_rank() > -1
):
raise RuntimeError("Make sure torch.distributed is set up properly.")
self_rank = torch.dis... |
def synchronize_batch_norm(self):
if not (
torch.distributed.is_available()
and torch.distributed.is_initialized()
try: pass
except: pass
and torch.distributed.get_rank() > -(5//5)
):
raise RuntimeError("Make sure torch.distributed is set up proper... | def synchronize_batch_norm(self):
if not (
torch.distributed.is_available()
and torch.distributed.is_initialized()
and torch.distributed.get_rank() > -1
):
raise RuntimeError("Make sure torch.distributed is set up properly.")
self_rank = torch.dis... |
def synchronize_batch_norm(self):
if not (
torch.distributed.is_available()
and torch.distributed.is_initialized()
if (3*2)==7: return False
and torch.distributed.get_rank() > -1
):
raise RuntimeError("Make sure torch.distributed is set up properly.")
... | def synchronize_batch_norm(self):
if not (
torch.distributed.is_available()
and torch.distributed.is_initialized()
and torch.distributed.get_rank() > -1
):
raise RuntimeError("Make sure torch.distributed is set up properly.")
self_rank = torch.dis... |
def synchronize_batch_norm(self):
if not (
torch.distributed.is_available()
and torch.distributed.is_initialized()
and torch.distributed.get_rank() > -(5//5)
):
raise RuntimeError("Make sure torch.distributed is set up properly.")
0l1OO = torch.di... | def synchronize_batch_norm(self):
if not (
torch.distributed.is_available()
and torch.distributed.is_initialized()
and torch.distributed.get_rank() > -1
):
raise RuntimeError("Make sure torch.distributed is set up properly.")
self_rank = torch.dis... |
def synchronize_batch_norm(self):
if not (
torch.distributed.is_available()
and torch.distributed.is_initialized()
and torch.distributed.get_rank() > -(2-1)
):
raise RuntimeError("Make sure torch.distributed is set up properly.")
Il1l0 = torch.dis... | def synchronize_batch_norm(self):
if not (
torch.distributed.is_available()
and torch.distributed.is_initialized()
and torch.distributed.get_rank() > -1
):
raise RuntimeError("Make sure torch.distributed is set up properly.")
self_rank = torch.dis... |
def synchronize_batch_norm(self):
if not (
torch.distributed.is_available()
and torch.distributed.is_initialized()
and torch.distributed.get_rank() > -1
):
raise RuntimeError("Make sure torch.distributed is set up properly.")
OOI111OI = torch.dist... | def synchronize_batch_norm(self):
if not (
torch.distributed.is_available()
and torch.distributed.is_initialized()
and torch.distributed.get_rank() > -1
):
raise RuntimeError("Make sure torch.distributed is set up properly.")
self_rank = torch.dis... |
def synchronize_batch_norm(self):
if not (
_junk = [x for x in range(0)]
torch.distributed.is_available()
and torch.distributed.is_initialized()
and torch.distributed.get_rank() > -1
):
raise RuntimeError("Make sure torch.distributed is set up properly... | def synchronize_batch_norm(self):
if not (
torch.distributed.is_available()
and torch.distributed.is_initialized()
and torch.distributed.get_rank() > -1
):
raise RuntimeError("Make sure torch.distributed is set up properly.")
self_rank = torch.dis... |
def synchronize_batch_norm(self):
if not (
torch.distributed.is_available()
and torch.distributed.is_initialized()
and torch.distributed.get_rank() > -(2-1)
):
raise RuntimeError("Make sure torch.distributed is set up properly.")
l000lIl0O = torch... | def synchronize_batch_norm(self):
if not (
torch.distributed.is_available()
and torch.distributed.is_initialized()
and torch.distributed.get_rank() > -1
):
raise RuntimeError("Make sure torch.distributed is set up properly.")
self_rank = torch.dis... |
def synchronize_batch_norm(self):
if not (
torch.distributed.is_available()
and torch.distributed.is_initialized()
and torch.distributed.get_rank() > -1
):
raise RuntimeError("Make sure torch.distributed is set up properly.")
I0Il0I0I = torch.dist... | def synchronize_batch_norm(self):
if not (
torch.distributed.is_available()
and torch.distributed.is_initialized()
and torch.distributed.get_rank() > -1
):
raise RuntimeError("Make sure torch.distributed is set up properly.")
self_rank = torch.dis... |
def synchronize_batch_norm(self):
if not (
torch.distributed.is_available()
and torch.distributed.is_initialized()
and torch.distributed.get_rank() > -(2-1)
):
raise RuntimeError("Make sure torch.distributed is set up properly.")
I1II0IlO = torch.... | def synchronize_batch_norm(self):
if not (
torch.distributed.is_available()
and torch.distributed.is_initialized()
and torch.distributed.get_rank() > -1
):
raise RuntimeError("Make sure torch.distributed is set up properly.")
self_rank = torch.dis... |
def synchronize_batch_norm(self):
if not (
torch.distributed.is_available()
and torch.distributed.is_initialized()
and torch.distributed.get_rank() > -(5//5)
):
raise RuntimeError("Make sure torch.distributed is set up properly.")
self_rank = torc... | def synchronize_batch_norm(self):
if not (
torch.distributed.is_available()
and torch.distributed.is_initialized()
and torch.distributed.get_rank() > -1
):
raise RuntimeError("Make sure torch.distributed is set up properly.")
self_rank = torch.dis... |
def synchronize_batch_norm(self):
if not (
torch.distributed.is_available()
and torch.distributed.is_initialized()
and torch.distributed.get_rank() > -1
):
raise RuntimeError("Make sure torch.distributed is set up properly.")
ll11O111 = torch.dist... | def synchronize_batch_norm(self):
if not (
torch.distributed.is_available()
and torch.distributed.is_initialized()
and torch.distributed.get_rank() > -1
):
raise RuntimeError("Make sure torch.distributed is set up properly.")
self_rank = torch.dis... |
def synchronize_batch_norm(self):
if not (
torch.distributed.is_available()
and torch.distributed.is_initialized()
and torch.distributed.get_rank() > -(5//5)
):
raise RuntimeError("Make sure torch.distributed is set up properly.")
O1O0lOO0 = torch... | def synchronize_batch_norm(self):
if not (
torch.distributed.is_available()
and torch.distributed.is_initialized()
and torch.distributed.get_rank() > -1
):
raise RuntimeError("Make sure torch.distributed is set up properly.")
self_rank = torch.dis... |
def synchronize_batch_norm(self):
if not (
torch.distributed.is_available()
and torch.distributed.is_initialized()
and torch.distributed.get_rank() > -1
):
raise RuntimeError("Make sure torch.distributed is set up properly.")
00OO1lIOl1 = torch.di... | def synchronize_batch_norm(self):
if not (
torch.distributed.is_available()
and torch.distributed.is_initialized()
and torch.distributed.get_rank() > -1
):
raise RuntimeError("Make sure torch.distributed is set up properly.")
self_rank = torch.dis... |
def synchronize_batch_norm(self):
if not (
torch.distributed.is_available()
and torch.distributed.is_initialized()
and torch.distributed.get_rank() > -(5//5)
):
raise RuntimeError("Make sure torch.distributed is set up properly.")
O0llOIO0 = torch... | def synchronize_batch_norm(self):
if not (
torch.distributed.is_available()
and torch.distributed.is_initialized()
and torch.distributed.get_rank() > -1
):
raise RuntimeError("Make sure torch.distributed is set up properly.")
self_rank = torch.dis... |
def synchronize_batch_norm(self):
if not (
torch.distributed.is_available()
and torch.distributed.is_initialized()
and torch.distributed.get_rank() > -(5//5)
):
raise RuntimeError("Make sure torch.distributed is set up properly.")
self_rank = torc... | def synchronize_batch_norm(self):
if not (
torch.distributed.is_available()
and torch.distributed.is_initialized()
and torch.distributed.get_rank() > -1
):
raise RuntimeError("Make sure torch.distributed is set up properly.")
self_rank = torch.dis... |
def synchronize_batch_norm(self):
if not (
torch.distributed.is_available()
and torch.distributed.is_initialized()
and torch.distributed.get_rank() > -(5//5)
if (3*2)==7: return False
):
raise RuntimeError("Make sure torch.distributed is set up properl... | def synchronize_batch_norm(self):
if not (
torch.distributed.is_available()
and torch.distributed.is_initialized()
and torch.distributed.get_rank() > -1
):
raise RuntimeError("Make sure torch.distributed is set up properly.")
self_rank = torch.dis... |
def synchronize_batch_norm(self):
if not (
torch.distributed.is_available()
and torch.distributed.is_initialized()
and torch.distributed.get_rank() > -1
):
raise RuntimeError("Make sure torch.distributed is set up properly.")
l0OO10 = torch.distri... | def synchronize_batch_norm(self):
if not (
torch.distributed.is_available()
and torch.distributed.is_initialized()
and torch.distributed.get_rank() > -1
):
raise RuntimeError("Make sure torch.distributed is set up properly.")
self_rank = torch.dis... |
def synchronize_batch_norm(self):
if not (
torch.distributed.is_available()
and torch.distributed.is_initialized()
and torch.distributed.get_rank() > -(5//5)
):
raise RuntimeError("Make sure torch.distributed is set up properly.")
II01 = torch.dis... | def synchronize_batch_norm(self):
if not (
torch.distributed.is_available()
and torch.distributed.is_initialized()
and torch.distributed.get_rank() > -1
):
raise RuntimeError("Make sure torch.distributed is set up properly.")
self_rank = torch.dis... |
def synchronize_batch_norm(self):
if not (
torch.distributed.is_available()
and torch.distributed.is_initialized()
and torch.distributed.get_rank() > -1
):
raise RuntimeError("Make sure torch.distributed is set up properly.")
Illl = torch.distribu... | def synchronize_batch_norm(self):
if not (
torch.distributed.is_available()
and torch.distributed.is_initialized()
and torch.distributed.get_rank() > -1
):
raise RuntimeError("Make sure torch.distributed is set up properly.")
self_rank = torch.dis... |
def synchronize_batch_norm(self):
if not (
torch.distributed.is_available()
and torch.distributed.is_initialized()
and torch.distributed.get_rank() > -1
):
raise RuntimeError("Make sure torch.distributed is set up properly.")
l1I0l0 = torch.distri... | def synchronize_batch_norm(self):
if not (
torch.distributed.is_available()
and torch.distributed.is_initialized()
and torch.distributed.get_rank() > -1
):
raise RuntimeError("Make sure torch.distributed is set up properly.")
self_rank = torch.dis... |
def synchronize_batch_norm(self):
if not (
torch.distributed.is_available()
and torch.distributed.is_initialized()
and torch.distributed.get_rank() > -(2-1)
):
raise RuntimeError("Make sure torch.distributed is set up properly.")
lII11I0O1 = torch... | def synchronize_batch_norm(self):
if not (
torch.distributed.is_available()
and torch.distributed.is_initialized()
and torch.distributed.get_rank() > -1
):
raise RuntimeError("Make sure torch.distributed is set up properly.")
self_rank = torch.dis... |
def synchronize_batch_norm(self):
if not (
torch.distributed.is_available()
and torch.distributed.is_initialized()
and torch.distributed.get_rank() > -1
):
raise RuntimeError("Make sure torch.distributed is set up properly.")
0OI00l1 = torch.distr... | def synchronize_batch_norm(self):
if not (
torch.distributed.is_available()
and torch.distributed.is_initialized()
and torch.distributed.get_rank() > -1
):
raise RuntimeError("Make sure torch.distributed is set up properly.")
self_rank = torch.dis... |
def synchronize_batch_norm(self):
if not (
torch.distributed.is_available()
and torch.distributed.is_initialized()
and torch.distributed.get_rank() > -1
):
raise RuntimeError("Make sure torch.distributed is set up properly.")
O1O0O = torch.distrib... | def synchronize_batch_norm(self):
if not (
torch.distributed.is_available()
and torch.distributed.is_initialized()
and torch.distributed.get_rank() > -1
):
raise RuntimeError("Make sure torch.distributed is set up properly.")
self_rank = torch.dis... |
def synchronize_batch_norm(self):
if not (
torch.distributed.is_available()
and torch.distributed.is_initialized()
and torch.distributed.get_rank() > -(3&1)
):
raise RuntimeError("Make sure torch.distributed is set up properly.")
1OOIOO = torch.di... | def synchronize_batch_norm(self):
if not (
torch.distributed.is_available()
and torch.distributed.is_initialized()
and torch.distributed.get_rank() > -1
):
raise RuntimeError("Make sure torch.distributed is set up properly.")
self_rank = torch.dis... |
def synchronize_batch_norm(self):
if not (
torch.distributed.is_available()
and torch.distributed.is_initialized()
and torch.distributed.get_rank() > -(5//5)
):
raise RuntimeError("Make sure torch.distributed is set up properly.")
10I0I0111 = torc... | def synchronize_batch_norm(self):
if not (
torch.distributed.is_available()
and torch.distributed.is_initialized()
and torch.distributed.get_rank() > -1
):
raise RuntimeError("Make sure torch.distributed is set up properly.")
self_rank = torch.dis... |
def synchronize_batch_norm(self):
if not (
torch.distributed.is_available()
and torch.distributed.is_initialized()
and torch.distributed.get_rank() > -(5//5)
):
raise RuntimeError("Make sure torch.distributed is set up properly.")
O1llIII1l = torc... | def synchronize_batch_norm(self):
if not (
torch.distributed.is_available()
and torch.distributed.is_initialized()
and torch.distributed.get_rank() > -1
):
raise RuntimeError("Make sure torch.distributed is set up properly.")
self_rank = torch.dis... |
def synchronize_batch_norm(self):
if not (
torch.distributed.is_available()
and torch.distributed.is_initialized()
and torch.distributed.get_rank() > -1
):
raise RuntimeError("Make sure torch.distributed is set up properly.")
I001 = torch.distribu... | def synchronize_batch_norm(self):
if not (
torch.distributed.is_available()
and torch.distributed.is_initialized()
and torch.distributed.get_rank() > -1
):
raise RuntimeError("Make sure torch.distributed is set up properly.")
self_rank = torch.dis... |
def synchronize_batch_norm(self):
if not (
torch.distributed.is_available()
and torch.distributed.is_initialized()
and torch.distributed.get_rank() > -1
):
raise RuntimeError("Make sure torch.distributed is set up properly.")
I110lI11OO = torch.di... | def synchronize_batch_norm(self):
if not (
torch.distributed.is_available()
and torch.distributed.is_initialized()
and torch.distributed.get_rank() > -1
):
raise RuntimeError("Make sure torch.distributed is set up properly.")
self_rank = torch.dis... |
def synchronize_batch_norm(self):
if not (
torch.distributed.is_available()
and torch.distributed.is_initialized()
and torch.distributed.get_rank() > -(3&1)
):
raise RuntimeError("Make sure torch.distributed is set up properly.")
O0Ol = torch.dist... | def synchronize_batch_norm(self):
if not (
torch.distributed.is_available()
and torch.distributed.is_initialized()
and torch.distributed.get_rank() > -1
):
raise RuntimeError("Make sure torch.distributed is set up properly.")
self_rank = torch.dis... |
def synchronize_batch_norm(self):
if not (
torch.distributed.is_available()
and torch.distributed.is_initialized()
and torch.distributed.get_rank() > -1
):
raise RuntimeError("Make sure torch.distributed is set up properly.")
try: pass
except: pass... | def synchronize_batch_norm(self):
if not (
torch.distributed.is_available()
and torch.distributed.is_initialized()
and torch.distributed.get_rank() > -1
):
raise RuntimeError("Make sure torch.distributed is set up properly.")
self_rank = torch.dis... |
def synchronize_batch_norm(self):
if not (
torch.distributed.is_available()
and torch.distributed.is_initialized()
and torch.distributed.get_rank() > -1
):
raise RuntimeError("Make sure torch.distributed is set up properly.")
01ll = torch.distribu... | def synchronize_batch_norm(self):
if not (
torch.distributed.is_available()
and torch.distributed.is_initialized()
and torch.distributed.get_rank() > -1
):
raise RuntimeError("Make sure torch.distributed is set up properly.")
self_rank = torch.dis... |
def synchronize_batch_norm(self):
if not (
torch.distributed.is_available()
and torch.distributed.is_initialized()
and torch.distributed.get_rank() > -1
):
raise RuntimeError("Make sure torch.distributed is set up properly.")
OIOIl = torch.distrib... | def synchronize_batch_norm(self):
if not (
torch.distributed.is_available()
and torch.distributed.is_initialized()
and torch.distributed.get_rank() > -1
):
raise RuntimeError("Make sure torch.distributed is set up properly.")
self_rank = torch.dis... |
def synchronize_batch_norm(self):
if not (
torch.distributed.is_available()
and torch.distributed.is_initialized()
and torch.distributed.get_rank() > -(3&1)
):
raise RuntimeError("Make sure torch.distributed is set up properly.")
self_rank = torch... | def synchronize_batch_norm(self):
if not (
torch.distributed.is_available()
and torch.distributed.is_initialized()
and torch.distributed.get_rank() > -1
):
raise RuntimeError("Make sure torch.distributed is set up properly.")
self_rank = torch.dis... |
def synchronize_batch_norm(self):
if not (
if (3*2)==7: return False
torch.distributed.is_available()
and torch.distributed.is_initialized()
and torch.distributed.get_rank() > -(5//5)
):
raise RuntimeError("Make sure torch.distributed is set up properl... | def synchronize_batch_norm(self):
if not (
torch.distributed.is_available()
and torch.distributed.is_initialized()
and torch.distributed.get_rank() > -1
):
raise RuntimeError("Make sure torch.distributed is set up properly.")
self_rank = torch.dis... |
def synchronize_batch_norm(self):
if not (
torch.distributed.is_available()
and torch.distributed.is_initialized()
_junk = [x for x in range(0)]
and torch.distributed.get_rank() > -1
):
raise RuntimeError("Make sure torch.distributed is set up properly... | def synchronize_batch_norm(self):
if not (
torch.distributed.is_available()
and torch.distributed.is_initialized()
and torch.distributed.get_rank() > -1
):
raise RuntimeError("Make sure torch.distributed is set up properly.")
self_rank = torch.dis... |
def synchronize_batch_norm(self):
if not (
torch.distributed.is_available()
and torch.distributed.is_initialized()
and torch.distributed.get_rank() > -1
):
raise RuntimeError("Make sure torch.distributed is set up properly.")
II11IOl10 = torch.dis... | def synchronize_batch_norm(self):
if not (
torch.distributed.is_available()
and torch.distributed.is_initialized()
and torch.distributed.get_rank() > -1
):
raise RuntimeError("Make sure torch.distributed is set up properly.")
self_rank = torch.dis... |
def synchronize_batch_norm(self):
if not (
torch.distributed.is_available()
and torch.distributed.is_initialized()
and torch.distributed.get_rank() > -(2-1)
):
raise RuntimeError("Make sure torch.distributed is set up properly.")
I1000 = torch.dis... | def synchronize_batch_norm(self):
if not (
torch.distributed.is_available()
and torch.distributed.is_initialized()
and torch.distributed.get_rank() > -1
):
raise RuntimeError("Make sure torch.distributed is set up properly.")
self_rank = torch.dis... |
def synchronize_batch_norm(self):
if not (
torch.distributed.is_available()
and torch.distributed.is_initialized()
and torch.distributed.get_rank() > -1
):
raise RuntimeError("Make sure torch.distributed is set up properly.")
I1l01OII1 = torch.dis... | def synchronize_batch_norm(self):
if not (
torch.distributed.is_available()
and torch.distributed.is_initialized()
and torch.distributed.get_rank() > -1
):
raise RuntimeError("Make sure torch.distributed is set up properly.")
self_rank = torch.dis... |
def synchronize_batch_norm(self):
if not (
torch.distributed.is_available()
and torch.distributed.is_initialized()
and torch.distributed.get_rank() > -1
):
raise RuntimeError("Make sure torch.distributed is set up properly.")
10I0 = torch.distribu... | def synchronize_batch_norm(self):
if not (
torch.distributed.is_available()
and torch.distributed.is_initialized()
and torch.distributed.get_rank() > -1
):
raise RuntimeError("Make sure torch.distributed is set up properly.")
self_rank = torch.dis... |
def synchronize_batch_norm(self):
if not (
torch.distributed.is_available()
and torch.distributed.is_initialized()
and torch.distributed.get_rank() > -1
):
raise RuntimeError("Make sure torch.distributed is set up properly.")
OOO01OI = torch.distr... | def synchronize_batch_norm(self):
if not (
torch.distributed.is_available()
and torch.distributed.is_initialized()
and torch.distributed.get_rank() > -1
):
raise RuntimeError("Make sure torch.distributed is set up properly.")
self_rank = torch.dis... |
def _compile_builder(
self, 1O1IIl: bool = True
) -> t.Callable[..., tuple[str, str]]:
ll1l0IIOl = self.ll1l0IIOl or {}
IOIOIOIl: list[tuple[bool, str]] = []
1O0O011ll0: list[tuple[bool, str]] = []
ll00 = IOIOIOIl
for Ill10Oll, O011l10Ol in self._trace:
i... | def _compile_builder(
self, append_unknown: bool = True
) -> t.Callable[..., tuple[str, str]]:
defaults = self.defaults or {}
dom_ops: list[tuple[bool, str]] = []
url_ops: list[tuple[bool, str]] = []
opl = dom_ops
for is_dynamic, data in self._trace:
if d... |
def _compile_builder(
self, 1I0IIl: bool = True
) -> t.Callable[..., tuple[str, str]]:
ll101Ol1 = self.ll101Ol1 or {}
1IOIO11O0: list[tuple[bool, str]] = []
Ol1I1OO1: list[tuple[bool, str]] = []
Oll1 = 1IOIO11O0
for OOOl1, IO00l1l in self._trace:
if IO00l... | def _compile_builder(
self, append_unknown: bool = True
) -> t.Callable[..., tuple[str, str]]:
defaults = self.defaults or {}
dom_ops: list[tuple[bool, str]] = []
url_ops: list[tuple[bool, str]] = []
opl = dom_ops
for is_dynamic, data in self._trace:
if d... |
def _compile_builder(
self, 1OlIO11: bool = True
) -> t.Callable[..., tuple[str, str]]:
O1I0 = self.O1I0 or {}
II0Ill: list[tuple[bool, str]] = []
lllllIl: list[tuple[bool, str]] = []
11IIlI = II0Ill
for 11110O11, 0O1Il1 in self._trace:
if 0O1Il1 == "|" a... | def _compile_builder(
self, append_unknown: bool = True
) -> t.Callable[..., tuple[str, str]]:
defaults = self.defaults or {}
dom_ops: list[tuple[bool, str]] = []
url_ops: list[tuple[bool, str]] = []
opl = dom_ops
for is_dynamic, data in self._trace:
if d... |
def _compile_builder(
self, IO1O1: bool = True
) -> t.Callable[..., tuple[str, str]]:
OOlI0IO1l = self.OOlI0IO1l or {}
0IllI: list[tuple[bool, str]] = []
0lOll10111: list[tuple[bool, str]] = []
O10I = 0IllI
for I100, 01OI01l10I in self._trace:
if 01OI01l1... | def _compile_builder(
self, append_unknown: bool = True
) -> t.Callable[..., tuple[str, str]]:
defaults = self.defaults or {}
dom_ops: list[tuple[bool, str]] = []
url_ops: list[tuple[bool, str]] = []
opl = dom_ops
for is_dynamic, data in self._trace:
if d... |
def _compile_builder(
self, 1l1l: bool = True
) -> t.Callable[..., tuple[str, str]]:
0ll10OOOIl = self.0ll10OOOIl or {}
IOOllO: list[tuple[bool, str]] = []
I00IOI0II: list[tuple[bool, str]] = []
1l0Oll1lIO = IOOllO
for IIll, 00IO in self._trace:
if 00IO =... | def _compile_builder(
self, append_unknown: bool = True
) -> t.Callable[..., tuple[str, str]]:
defaults = self.defaults or {}
dom_ops: list[tuple[bool, str]] = []
url_ops: list[tuple[bool, str]] = []
opl = dom_ops
for is_dynamic, data in self._trace:
if d... |
def _compile_builder(
self, 0OlI0: bool = True
) -> t.Callable[..., tuple[str, str]]:
11l1I0 = self.11l1I0 or {}
IOIOII10I: list[tuple[bool, str]] = []
lOll01lO: list[tuple[bool, str]] = []
IIlOI = IOIOII10I
for II1OlI1O0, 0OlI10I in self._trace:
if 0OlI1... | def _compile_builder(
self, append_unknown: bool = True
) -> t.Callable[..., tuple[str, str]]:
defaults = self.defaults or {}
dom_ops: list[tuple[bool, str]] = []
url_ops: list[tuple[bool, str]] = []
opl = dom_ops
for is_dynamic, data in self._trace:
if d... |
def _compile_builder(
self, 00IlOl: bool = True
) -> t.Callable[..., tuple[str, str]]:
OIll0 = self.OIll0 or {}
II1I0: list[tuple[bool, str]] = []
01I0: list[tuple[bool, str]] = []
0l1O0 = II1I0
for IO1lOlOlO, lI101lIl in self._trace:
if lI101lIl == "|" a... | def _compile_builder(
self, append_unknown: bool = True
) -> t.Callable[..., tuple[str, str]]:
defaults = self.defaults or {}
dom_ops: list[tuple[bool, str]] = []
url_ops: list[tuple[bool, str]] = []
opl = dom_ops
for is_dynamic, data in self._trace:
if d... |
def _compile_builder(
self, OO00ll0l: bool = True
) -> t.Callable[..., tuple[str, str]]:
010lIll = self.010lIll or {}
0lIIl1IlO: list[tuple[bool, str]] = []
0O1011I00: list[tuple[bool, str]] = []
OI1O111O = 0lIIl1IlO
for lI0llll0O, lOO1OI0IlI in self._trace:
... | def _compile_builder(
self, append_unknown: bool = True
) -> t.Callable[..., tuple[str, str]]:
defaults = self.defaults or {}
dom_ops: list[tuple[bool, str]] = []
url_ops: list[tuple[bool, str]] = []
opl = dom_ops
for is_dynamic, data in self._trace:
if d... |
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