entry_point stringlengths 1 65 | original_triton_python_code stringlengths 208 619k | optimised_triton_code stringlengths 1.15k 275k | repo_name stringlengths 7 115 | module_name stringlengths 1 65 | synthetic bool 1
class | uuid int64 0 18.5k | licenses listlengths 1 6 | stars int64 0 19.8k | sha stringlengths 40 40 | repo_link stringlengths 72 180 |
|---|---|---|---|---|---|---|---|---|---|---|
BinaryMarginLoss | import torch
import torch.nn as nn
import torch.distributions
import torch.utils.data
class BinaryMarginLoss(nn.Module):
def __init__(self, margin=0.5):
super().__init__()
self.margin = margin
def forward(self, output):
return torch.logaddexp(torch.tensor([1.0], device=output.device)... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torch.nn as nn
import torch.distributions
import torch.... | AlexMeinke/Provable-OOD-Detection | BinaryMarginLoss | false | 7,682 | [
"MIT"
] | 21 | 9a132aec994ff718c96b81885736ab866df60d87 | https://github.com/AlexMeinke/Provable-OOD-Detection/tree/9a132aec994ff718c96b81885736ab866df60d87 |
KDTH | import torch
from torch import nn
import torch.nn.functional as F
class KDTH(nn.Module):
"""KD with a Teacher Head auxiliary loss"""
def __init__(self, T=4):
super(KDTH, self).__init__()
self.T = T
def forward(self, y_s, y_t):
y_s_th = y_s[1]
y_s = y_s[0]
p_t = F.... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch ... | Alibaba-MIIL/HeadSharingKD | KDTH | false | 7,683 | [
"BSD-2-Clause"
] | 15 | 8e2738bf069c7d12ec933f9b9107f267f7b6603a | https://github.com/Alibaba-MIIL/HeadSharingKD/tree/8e2738bf069c7d12ec933f9b9107f267f7b6603a |
Add_ParamI | import torch
import torch.nn as nn
import torch.distributions
import torch.utils.data
class Add_ParamI(nn.Module):
def __init__(self):
super().__init__()
self.bias = nn.Parameter(torch.zeros(1))
def forward(self, x):
out = x + self.bias
return out
def ibp_forward(self, l... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.distributions
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | AlexMeinke/Provable-OOD-Detection | Add_ParamI | false | 7,684 | [
"MIT"
] | 21 | 9a132aec994ff718c96b81885736ab866df60d87 | https://github.com/AlexMeinke/Provable-OOD-Detection/tree/9a132aec994ff718c96b81885736ab866df60d87 |
Contraster | import torch
import torch.distributions
import torch.utils.data
class AdversarialNoiseGenerator(torch.nn.Module):
def __init__(self):
super().__init__()
return
def forward(self, x):
raise NotImplementedError()
class Contraster(AdversarialNoiseGenerator):
def __init__(self, eps... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.distributions
import torch.utils.data
assert_size_stride = torch._C._dynamo.... | AlexMeinke/Provable-OOD-Detection | Contraster | false | 7,685 | [
"MIT"
] | 21 | 9a132aec994ff718c96b81885736ab866df60d87 | https://github.com/AlexMeinke/Provable-OOD-Detection/tree/9a132aec994ff718c96b81885736ab866df60d87 |
MLP | import torch
import torch.nn as nn
import torch.nn.functional as F
class MLP(nn.Module):
def __init__(self, indim, hs, outdim, mlp_drop):
super().__init__()
"""
eh, et, |eh-et|, eh*et
"""
indim = 4 * indim
self.linear1 = nn.Linear(indim, 2 * hs)
self.linear... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | AndrewZhe/Three-Sentences-Are-All-You-Need | MLP | false | 7,686 | [
"MIT"
] | 21 | afad6f9e700c9a95e03ef200718ebee8e18ca016 | https://github.com/AndrewZhe/Three-Sentences-Are-All-You-Need/tree/afad6f9e700c9a95e03ef200718ebee8e18ca016 |
Conv2dBlock | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data
def l2normalize(v, eps=1e-12):
return v / (v.norm() + eps)
class AdaptiveInstanceNorm2d(nn.Module):
def __init__(self, num_features, eps=1e-05, momentum=0.1):
super(AdaptiveInstanceNorm2d, self).__init__()
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import ... | AllenPu/mbdg | Conv2dBlock | false | 7,687 | [
"MIT"
] | 27 | 243f53a57dcf4bfb6e717c0c9f64a839cff8d548 | https://github.com/AllenPu/mbdg/tree/243f53a57dcf4bfb6e717c0c9f64a839cff8d548 |
DistillMSE | import torch
from torch import nn
import torch.nn.functional as F
class DistillMSE(nn.Module):
"""Distilling the Knowledge in a Neural Network"""
def __init__(self):
super(DistillMSE, self).__init__()
pass
def forward(self, y_s, y_t):
loss = nn.MSELoss(reduction='mean')(F.softmax... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch import nn
a... | Alibaba-MIIL/HeadSharingKD | DistillMSE | false | 7,688 | [
"BSD-2-Clause"
] | 15 | 8e2738bf069c7d12ec933f9b9107f267f7b6603a | https://github.com/Alibaba-MIIL/HeadSharingKD/tree/8e2738bf069c7d12ec933f9b9107f267f7b6603a |
OELossLogConf | import torch
import torch.nn as nn
import torch.distributions
import torch.utils.data
class OELossLogConf(nn.Module):
def __init__(self):
super().__init__()
def forward(self, confs):
return -confs.mean(1)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.distributions
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | AlexMeinke/Provable-OOD-Detection | OELossLogConf | false | 7,689 | [
"MIT"
] | 21 | 9a132aec994ff718c96b81885736ab866df60d87 | https://github.com/AlexMeinke/Provable-OOD-Detection/tree/9a132aec994ff718c96b81885736ab866df60d87 |
Fire | import torch
from torch import nn
from collections import OrderedDict
class Fire(nn.Module):
def __init__(self, inplanes, squeeze_planes, expand1x1_planes,
expand3x3_planes):
super(Fire, self).__init__()
self.inplanes = inplanes
self.group1 = nn.Sequential(OrderedDict([('squeeze',... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
from col... | Alibaba-AAIG/Beyond-ImageNet-Attack | Fire | false | 7,690 | [
"MIT"
] | 23 | c14b4844b64a8035b8fe033a617c0567224a9fa4 | https://github.com/Alibaba-AAIG/Beyond-ImageNet-Attack/tree/c14b4844b64a8035b8fe033a617c0567224a9fa4 |
CTLSTMCell | import torch
import torch.nn as nn
import torch.nn.functional as F
class CTLSTMCell(nn.Module):
def __init__(self, hidden_dim, beta=1.0, device=None):
super(CTLSTMCell, self).__init__()
device = device or 'cpu'
self.device = torch.device(device)
self.hidden_dim = hidden_dim
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
im... | Anirudh-Murali/neural-hawkes-particle-smoothing | CTLSTMCell | false | 7,691 | [
"BSD-3-Clause"
] | 37 | 96b258838bdab33b781008daeedfa61dec0d553c | https://github.com/Anirudh-Murali/neural-hawkes-particle-smoothing/tree/96b258838bdab33b781008daeedfa61dec0d553c |
NormalNoiseGenerator | import torch
import torch.distributions
import torch.utils.data
class AdversarialNoiseGenerator(torch.nn.Module):
def __init__(self):
super().__init__()
return
def forward(self, x):
raise NotImplementedError()
class NormalNoiseGenerator(AdversarialNoiseGenerator):
def __init__... | import torch
from torch import device
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.distributions
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
... | AlexMeinke/Provable-OOD-Detection | NormalNoiseGenerator | false | 7,692 | [
"MIT"
] | 21 | 9a132aec994ff718c96b81885736ab866df60d87 | https://github.com/AlexMeinke/Provable-OOD-Detection/tree/9a132aec994ff718c96b81885736ab866df60d87 |
GOODLoss | import torch
import torch.nn as nn
import torch.distributions
import torch.utils.data
class GOODLoss(nn.Module):
def __init__(self):
super().__init__()
def forward(self, ub_log_conf):
return (ub_log_conf ** 2 / 2).log1p()
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_in... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
import torch.distributions
import torch.utils.data
assert... | AlexMeinke/Provable-OOD-Detection | GOODLoss | false | 7,693 | [
"MIT"
] | 21 | 9a132aec994ff718c96b81885736ab866df60d87 | https://github.com/AlexMeinke/Provable-OOD-Detection/tree/9a132aec994ff718c96b81885736ab866df60d87 |
Lowerer | import torch
import torch.distributions
import torch.utils.data
class AdversarialNoiseGenerator(torch.nn.Module):
def __init__(self):
super().__init__()
return
def forward(self, x):
raise NotImplementedError()
class Lowerer(AdversarialNoiseGenerator):
def __init__(self, eps):
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.distributions
import torch.utils.data
assert_size_stride = torch._C._dynamo.... | AlexMeinke/Provable-OOD-Detection | Lowerer | false | 7,694 | [
"MIT"
] | 21 | 9a132aec994ff718c96b81885736ab866df60d87 | https://github.com/AlexMeinke/Provable-OOD-Detection/tree/9a132aec994ff718c96b81885736ab866df60d87 |
LinearI_Neg | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.distributions
import torch.utils.data
class LinearI_Neg(nn.Linear):
def forward(self, x):
return F.linear(x, -self.weight.exp(), self.bias)
def ibp_forward(self, l, u):
weight = -self.weight.exp()
if self... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.... | AlexMeinke/Provable-OOD-Detection | LinearI_Neg | false | 7,695 | [
"MIT"
] | 21 | 9a132aec994ff718c96b81885736ab866df60d87 | https://github.com/AlexMeinke/Provable-OOD-Detection/tree/9a132aec994ff718c96b81885736ab866df60d87 |
KDETH | import torch
from torch import nn
import torch.nn.functional as F
class KDTH(nn.Module):
"""KD with a Teacher Head auxiliary loss"""
def __init__(self, T=4):
super(KDTH, self).__init__()
self.T = T
def forward(self, y_s, y_t):
y_s_th = y_s[1]
y_s = y_s[0]
p_t = F.... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch ... | Alibaba-MIIL/HeadSharingKD | KDETH | false | 7,696 | [
"BSD-2-Clause"
] | 15 | 8e2738bf069c7d12ec933f9b9107f267f7b6603a | https://github.com/Alibaba-MIIL/HeadSharingKD/tree/8e2738bf069c7d12ec933f9b9107f267f7b6603a |
Unet_2levels | import torch
import torch.nn as nn
class Unet_2levels(nn.Module):
def __init__(self):
super().__init__()
self.relu = nn.ReLU()
self.sigmoid = nn.Sigmoid()
self.upsample = nn.Upsample(scale_factor=2, mode='bilinear',
align_corners=True)
self.maxpool = nn.MaxPool... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | AbdulMuqadim2001/dvae-refiner | Unet_2levels | false | 7,697 | [
"MIT"
] | 27 | c1ff46f91b28e613a3b7b157f8fd97ddf43e6fb2 | https://github.com/AbdulMuqadim2001/dvae-refiner/tree/c1ff46f91b28e613a3b7b157f8fd97ddf43e6fb2 |
SavageLoss | import torch
import torch.nn as nn
import torch.distributions
import torch.utils.data
class SavageLoss(nn.Module):
def __init__(self):
super().__init__()
def forward(self, output):
return 1 / (1 + output.exp()) ** 2
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inp... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
import torch.distributions
import torch.utils.data
... | AlexMeinke/Provable-OOD-Detection | SavageLoss | false | 7,698 | [
"MIT"
] | 21 | 9a132aec994ff718c96b81885736ab866df60d87 | https://github.com/AlexMeinke/Provable-OOD-Detection/tree/9a132aec994ff718c96b81885736ab866df60d87 |
UniformNoiseGenerator | import torch
import torch.distributions
import torch.utils.data
class AdversarialNoiseGenerator(torch.nn.Module):
def __init__(self):
super().__init__()
return
def forward(self, x):
raise NotImplementedError()
class UniformNoiseGenerator(AdversarialNoiseGenerator):
def __init_... | import torch
from torch import device
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.distributions
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
... | AlexMeinke/Provable-OOD-Detection | UniformNoiseGenerator | false | 7,699 | [
"MIT"
] | 21 | 9a132aec994ff718c96b81885736ab866df60d87 | https://github.com/AlexMeinke/Provable-OOD-Detection/tree/9a132aec994ff718c96b81885736ab866df60d87 |
Dunet_2levels | import torch
import torch.nn as nn
class Unet_2levels(nn.Module):
def __init__(self):
super().__init__()
self.relu = nn.ReLU()
self.sigmoid = nn.Sigmoid()
self.upsample = nn.Upsample(scale_factor=2, mode='bilinear',
align_corners=True)
self.maxpool = nn.MaxPool... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | AbdulMuqadim2001/dvae-refiner | Dunet_2levels | false | 7,700 | [
"MIT"
] | 27 | c1ff46f91b28e613a3b7b157f8fd97ddf43e6fb2 | https://github.com/AbdulMuqadim2001/dvae-refiner/tree/c1ff46f91b28e613a3b7b157f8fd97ddf43e6fb2 |
GlobalAttention | import torch
import torch.nn as nn
import torch.cuda
def aeq(*args):
"""
Assert all arguments have the same value
"""
arguments = (arg for arg in args)
first = next(arguments)
assert all(arg == first for arg in arguments
), 'Not all arguments have the same value: ' + str(args)
def se... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | AndrewM1998/MultimodalNMT | GlobalAttention | false | 7,701 | [
"MIT"
] | 40 | b66d3a40ac9bc5c11ae124f51d1a9abf7cd6a04b | https://github.com/AndrewM1998/MultimodalNMT/tree/b66d3a40ac9bc5c11ae124f51d1a9abf7cd6a04b |
MLB | import torch
from torch import nn
from torch.nn import functional as F
class MLB(nn.Module):
def __init__(self, input_dims, output_dim, mm_dim=1200, activ_input=
'relu', activ_output='relu', normalize=False, dropout_input=0.0,
dropout_pre_lin=0.0, dropout_output=0.0):
super(MLB, self).__i... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
assert_s... | AndresPMD/GCN_classification | MLB | false | 7,702 | [
"MIT"
] | 39 | b005c4256d68f1f90a7f73e7fdb3d066448de28c | https://github.com/AndresPMD/GCN_classification/tree/b005c4256d68f1f90a7f73e7fdb3d066448de28c |
EntityClassifier | import torch
import torch.nn as nn
import torch.nn.functional as F
class MLP(nn.Module):
def __init__(self, indim, hs, outdim, mlp_drop):
super().__init__()
"""
eh, et, |eh-et|, eh*et
"""
indim = 4 * indim
self.linear1 = nn.Linear(indim, 2 * hs)
self.linear... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | AndrewZhe/Three-Sentences-Are-All-You-Need | EntityClassifier | false | 7,703 | [
"MIT"
] | 21 | afad6f9e700c9a95e03ef200718ebee8e18ca016 | https://github.com/AndrewZhe/Three-Sentences-Are-All-You-Need/tree/afad6f9e700c9a95e03ef200718ebee8e18ca016 |
HingeLoss | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.distributions
import torch.utils.data
class HingeLoss(nn.Module):
def __init__(self, margin=1.0):
super().__init__()
self.margin = margin
def forward(self, output):
return F.relu(self.margin - output)
d... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import torch.distributions
import torch.utils.data
assert_size_stri... | AlexMeinke/Provable-OOD-Detection | HingeLoss | false | 7,704 | [
"MIT"
] | 21 | 9a132aec994ff718c96b81885736ab866df60d87 | https://github.com/AlexMeinke/Provable-OOD-Detection/tree/9a132aec994ff718c96b81885736ab866df60d87 |
OELoss | import torch
import torch.nn as nn
import torch.distributions
import torch.utils.data
class OELoss(nn.Module):
def __init__(self):
super().__init__()
def forward(self, logits):
return -torch.log_softmax(logits, 1).mean(1)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_in... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | AlexMeinke/Provable-OOD-Detection | OELoss | false | 7,705 | [
"MIT"
] | 21 | 9a132aec994ff718c96b81885736ab866df60d87 | https://github.com/AlexMeinke/Provable-OOD-Detection/tree/9a132aec994ff718c96b81885736ab866df60d87 |
SourceContextGate | import torch
import torch.nn as nn
import torch.cuda
class ContextGate(nn.Module):
"""
Context gate is a decoder module that takes as input the previous word
embedding, the current decoder state and the attention state, and
produces a gate.
The gate can be used to select the input from the target ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | AndrewM1998/MultimodalNMT | SourceContextGate | false | 7,706 | [
"MIT"
] | 40 | b66d3a40ac9bc5c11ae124f51d1a9abf7cd6a04b | https://github.com/AndrewM1998/MultimodalNMT/tree/b66d3a40ac9bc5c11ae124f51d1a9abf7cd6a04b |
AttentionBlock | import torch
import torch.nn as nn
class AttentionBlock(nn.Module):
def __init__(self, in_features, middle_features, out_features):
super().__init__()
self.in_features = in_features
self.middle_features = middle_features
self.out_features = out_features
self.W = nn.Linear(... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Anjum48/commonlitreadabilityprize | AttentionBlock | false | 7,707 | [
"MIT"
] | 28 | b310742520b847b452ced0d27f47a934e834e4de | https://github.com/Anjum48/commonlitreadabilityprize/tree/b310742520b847b452ced0d27f47a934e834e4de |
Scale_By_ParamI | import torch
import torch.nn as nn
import torch.distributions
import torch.utils.data
class Scale_By_ParamI(nn.Module):
def __init__(self):
super().__init__()
self.scalar = nn.Parameter(torch.ones(1))
def forward(self, x):
out = x * self.scalar
return out
def ibp_forward... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.distributions
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | AlexMeinke/Provable-OOD-Detection | Scale_By_ParamI | false | 7,708 | [
"MIT"
] | 21 | 9a132aec994ff718c96b81885736ab866df60d87 | https://github.com/AlexMeinke/Provable-OOD-Detection/tree/9a132aec994ff718c96b81885736ab866df60d87 |
Mish | import torch
import torch.nn as nn
import torch.nn.functional as F
class Mish(nn.Module):
def __init__(self):
super().__init__()
def forward(self, x):
return x * torch.tanh(F.softplus(x))
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
| import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.gu... | Archaic-Atom/JackFramework | Mish | false | 7,709 | [
"MIT"
] | 13 | e847d0bafe335ee33caf174676d12ad3c28011a6 | https://github.com/Archaic-Atom/JackFramework/tree/e847d0bafe335ee33caf174676d12ad3c28011a6 |
TargetContextGate | import torch
import torch.nn as nn
import torch.cuda
class ContextGate(nn.Module):
"""
Context gate is a decoder module that takes as input the previous word
embedding, the current decoder state and the attention state, and
produces a gate.
The gate can be used to select the input from the target ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | AndrewM1998/MultimodalNMT | TargetContextGate | false | 7,710 | [
"MIT"
] | 40 | b66d3a40ac9bc5c11ae124f51d1a9abf7cd6a04b | https://github.com/AndrewM1998/MultimodalNMT/tree/b66d3a40ac9bc5c11ae124f51d1a9abf7cd6a04b |
MFB | import torch
from torch import nn
from torch.nn import functional as F
class MFB(nn.Module):
def __init__(self, input_dims, output_dim, mm_dim=1200, factor=2,
activ_input='relu', activ_output='relu', normalize=False,
dropout_input=0.0, dropout_pre_norm=0.0, dropout_output=0.0):
super(MFB,... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
assert_s... | AndresPMD/GCN_classification | MFB | false | 7,711 | [
"MIT"
] | 39 | b005c4256d68f1f90a7f73e7fdb3d066448de28c | https://github.com/AndresPMD/GCN_classification/tree/b005c4256d68f1f90a7f73e7fdb3d066448de28c |
Mlp | import torch
import torch.nn as nn
class Mlp(nn.Module):
def __init__(self, in_features, hidden_features=None, out_features=None,
act_layer=nn.GELU, drop=0.0):
super().__init__()
out_features = out_features or in_features
hidden_features = hidden_features or in_features
se... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | Arnav0400/ViT-Slim | Mlp | false | 7,712 | [
"MIT"
] | 14 | 78edd4fecbb8cd4043e9878148576b1c327c74f9 | https://github.com/Arnav0400/ViT-Slim/tree/78edd4fecbb8cd4043e9878148576b1c327c74f9 |
QGOODLoss | import math
import torch
import torch.nn as nn
import torch.distributions
import torch.utils.data
class QGOODLoss(nn.Module):
def __init__(self, quantile=0.8):
super().__init__()
self.quantile = quantile
def forward(self, ub_log_conf):
batch_size_out = ub_log_conf.shape[0]
l ... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
import torch.distributions
import torch.utils.data
assert... | AlexMeinke/Provable-OOD-Detection | QGOODLoss | false | 7,713 | [
"MIT"
] | 21 | 9a132aec994ff718c96b81885736ab866df60d87 | https://github.com/AlexMeinke/Provable-OOD-Detection/tree/9a132aec994ff718c96b81885736ab866df60d87 |
MultinomialKLDivergenceLoss | import torch
from torch import nn
class MultinomialKLDivergenceLoss(nn.Module):
def __init__(self):
super().__init__()
def forward(self, p_proba, q_proba):
loss = q_proba * (torch.log(q_proba) - torch.log(p_proba))
loss = torch.sum(loss)
return loss / (p_proba.size(1) * p_pro... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch import nn
a... | AuCson/SEDST | MultinomialKLDivergenceLoss | false | 7,714 | [
"MIT"
] | 23 | 1c1691e2abc50eb2120ed49c874090f6c4f741d3 | https://github.com/AuCson/SEDST/tree/1c1691e2abc50eb2120ed49c874090f6c4f741d3 |
GCN | from torch.nn import Module
import math
import torch
import torch.nn.functional as F
from torch.nn.parameter import Parameter
from torch.nn.modules.module import Module
import torch.nn as nn
class GraphConvolution(Module):
"""
Simple GCN layer, similar to https://arxiv.org/abs/1609.02907
"""
def __in... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Anou9531/GUA | GCN | false | 7,715 | [
"MIT"
] | 20 | 354acceb69656e76fb4ee296c66ae42c18cd939f | https://github.com/Anou9531/GUA/tree/354acceb69656e76fb4ee296c66ae42c18cd939f |
MSELoss | import functools
import torch
import torch.nn as nn
import torch.nn.functional as F
def reduce_loss(loss, reduction):
"""Reduce loss as specified.
Args:
loss (Tensor): Elementwise loss tensor.
reduction (str): Options are "none", "mean" and "sum".
Return:
Tensor: Reduced loss ten... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import functools
import torch.nn as nn
import torch.nn.functional as F
assert_size_stride... | Andrew-Zhu/DyFPN | MSELoss | false | 7,716 | [
"Apache-2.0"
] | 32 | a74463b59c4ce28253c2449a07c0f6692a0147a1 | https://github.com/Andrew-Zhu/DyFPN/tree/a74463b59c4ce28253c2449a07c0f6692a0147a1 |
BothContextGate | import torch
import torch.nn as nn
import torch.cuda
class ContextGate(nn.Module):
"""
Context gate is a decoder module that takes as input the previous word
embedding, the current decoder state and the attention state, and
produces a gate.
The gate can be used to select the input from the target ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | AndrewM1998/MultimodalNMT | BothContextGate | false | 7,717 | [
"MIT"
] | 40 | b66d3a40ac9bc5c11ae124f51d1a9abf7cd6a04b | https://github.com/AndrewM1998/MultimodalNMT/tree/b66d3a40ac9bc5c11ae124f51d1a9abf7cd6a04b |
ContextGate | import torch
import torch.nn as nn
import torch.cuda
class ContextGate(nn.Module):
"""
Context gate is a decoder module that takes as input the previous word
embedding, the current decoder state and the attention state, and
produces a gate.
The gate can be used to select the input from the target ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.cuda
assert_size_stride = torch._C._dynamo.gu... | AndrewM1998/MultimodalNMT | ContextGate | false | 7,718 | [
"MIT"
] | 40 | b66d3a40ac9bc5c11ae124f51d1a9abf7cd6a04b | https://github.com/AndrewM1998/MultimodalNMT/tree/b66d3a40ac9bc5c11ae124f51d1a9abf7cd6a04b |
LandmarkLoss | import math
import torch
from torch import nn
def wing_loss(y_true, y_pred, N_LANDMARK, w=10.0, epsilon=2.0):
y_pred = y_pred.reshape(-1, N_LANDMARK, 2)
y_true = y_true.reshape(-1, N_LANDMARK, 2)
x = y_true - y_pred
c = w * (1.0 - math.log(1.0 + w / epsilon))
absolute_x = torch.abs(x)
losses =... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import math
from torch import nn
assert_size_stride = torch._C._dynamo.gu... | AnthonyF333/FaceLandmark_PFLD_UltraLight | LandmarkLoss | false | 7,719 | [
"Apache-2.0"
] | 38 | c7c9543bd7f44ab434240eab077242f259df21f8 | https://github.com/AnthonyF333/FaceLandmark_PFLD_UltraLight/tree/c7c9543bd7f44ab434240eab077242f259df21f8 |
MFH | import torch
from torch import nn
from torch.nn import functional as F
class MFH(nn.Module):
def __init__(self, input_dims, output_dim, mm_dim=1200, factor=2,
activ_input='relu', activ_output='relu', normalize=False,
dropout_input=0.0, dropout_pre_lin=0.0, dropout_output=0.0):
super(MFH, ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
assert_s... | AndresPMD/GCN_classification | MFH | false | 7,720 | [
"MIT"
] | 39 | b005c4256d68f1f90a7f73e7fdb3d066448de28c | https://github.com/AndresPMD/GCN_classification/tree/b005c4256d68f1f90a7f73e7fdb3d066448de28c |
LinearSum | import torch
from torch import nn
from torch.nn import functional as F
class LinearSum(nn.Module):
def __init__(self, input_dims, output_dim, mm_dim=1200, activ_input=
'relu', activ_output='relu', normalize=False, dropout_input=0.0,
dropout_pre_lin=0.0, dropout_output=0.0):
super(LinearSu... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
assert_s... | AndresPMD/GCN_classification | LinearSum | false | 7,721 | [
"MIT"
] | 39 | b005c4256d68f1f90a7f73e7fdb3d066448de28c | https://github.com/AndresPMD/GCN_classification/tree/b005c4256d68f1f90a7f73e7fdb3d066448de28c |
L1Loss | import functools
import torch
import torch.nn as nn
import torch.nn.functional as F
def reduce_loss(loss, reduction):
"""Reduce loss as specified.
Args:
loss (Tensor): Elementwise loss tensor.
reduction (str): Options are "none", "mean" and "sum".
Return:
Tensor: Reduced loss ten... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import functools
impor... | Andrew-Zhu/DyFPN | L1Loss | false | 7,722 | [
"Apache-2.0"
] | 32 | a74463b59c4ce28253c2449a07c0f6692a0147a1 | https://github.com/Andrew-Zhu/DyFPN/tree/a74463b59c4ce28253c2449a07c0f6692a0147a1 |
DeconvBlock | import torch
import torch.nn as nn
class DeconvBlock(nn.Module):
def __init__(self, in_channels, out_channels):
super(DeconvBlock, self).__init__()
self.conv = nn.ConvTranspose2d(in_channels, out_channels,
kernel_size=3, stride=2, padding=1, output_padding=0)
self.pad = nn.Ref... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
im... | ArminMasoumian/GCNDepth | DeconvBlock | false | 7,723 | [
"MIT"
] | 32 | 9fa77812fa944c2701a45f09acf988815ca50aee | https://github.com/ArminMasoumian/GCNDepth/tree/9fa77812fa944c2701a45f09acf988815ca50aee |
ConvBlock | import torch
import torch.nn as nn
class Conv3x3(nn.Module):
def __init__(self, in_channels, out_channels, use_refl=True):
super(Conv3x3, self).__init__()
if use_refl:
self.pad = nn.ReflectionPad2d(1)
else:
self.pad = nn.ZeroPad2d(1)
self.conv = nn.Conv2d(i... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
im... | ArminMasoumian/GCNDepth | ConvBlock | false | 7,724 | [
"MIT"
] | 32 | 9fa77812fa944c2701a45f09acf988815ca50aee | https://github.com/ArminMasoumian/GCNDepth/tree/9fa77812fa944c2701a45f09acf988815ca50aee |
CrossEntropyLoss | import torch
import torch.nn as nn
import torch.nn.functional as F
def reduce_loss(loss, reduction):
"""Reduce loss as specified.
Args:
loss (Tensor): Elementwise loss tensor.
reduction (str): Options are "none", "mean" and "sum".
Return:
Tensor: Reduced loss tensor.
"""
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | Andrew-Zhu/DyFPN | CrossEntropyLoss | false | 7,725 | [
"Apache-2.0"
] | 32 | a74463b59c4ce28253c2449a07c0f6692a0147a1 | https://github.com/Andrew-Zhu/DyFPN/tree/a74463b59c4ce28253c2449a07c0f6692a0147a1 |
SSIM | import torch
import torch.nn as nn
class SSIM(nn.Module):
def __init__(self):
super(SSIM, self).__init__()
self.mu_x_pool = nn.AvgPool2d(3, 1)
self.mu_y_pool = nn.AvgPool2d(3, 1)
self.sig_x_pool = nn.AvgPool2d(3, 1)
self.sig_y_pool = nn.AvgPool2d(3, 1)
self.sig_xy_... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | ArminMasoumian/GCNDepth | SSIM | false | 7,726 | [
"MIT"
] | 32 | 9fa77812fa944c2701a45f09acf988815ca50aee | https://github.com/ArminMasoumian/GCNDepth/tree/9fa77812fa944c2701a45f09acf988815ca50aee |
Hardswish | import torch
import torch.nn as nn
import torch.nn.functional as F
class Hardswish(nn.Module):
@staticmethod
def forward(x):
return x * F.hardtanh(x + 3, 0.0, 6.0) / 6.0
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
| import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | AsakusaRinne/tensorrt_yolov5_tracker | Hardswish | false | 7,727 | [
"MIT"
] | 22 | b9a3a6fc94710e8291d6a614ed2b04cbc4c56599 | https://github.com/AsakusaRinne/tensorrt_yolov5_tracker/tree/b9a3a6fc94710e8291d6a614ed2b04cbc4c56599 |
FlopsCrossEntropyLoss | import torch
import torch.nn as nn
import torch.nn.functional as F
def reduce_loss(loss, reduction):
"""Reduce loss as specified.
Args:
loss (Tensor): Elementwise loss tensor.
reduction (str): Options are "none", "mean" and "sum".
Return:
Tensor: Reduced loss tensor.
"""
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | Andrew-Zhu/DyFPN | FlopsCrossEntropyLoss | false | 7,728 | [
"Apache-2.0"
] | 32 | a74463b59c4ce28253c2449a07c0f6692a0147a1 | https://github.com/Andrew-Zhu/DyFPN/tree/a74463b59c4ce28253c2449a07c0f6692a0147a1 |
GaussianFocalLoss | import functools
import torch
import torch.nn as nn
import torch.nn.functional as F
def reduce_loss(loss, reduction):
"""Reduce loss as specified.
Args:
loss (Tensor): Elementwise loss tensor.
reduction (str): Options are "none", "mean" and "sum".
Return:
Tensor: Reduced loss ten... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import functools
impor... | Andrew-Zhu/DyFPN | GaussianFocalLoss | false | 7,729 | [
"Apache-2.0"
] | 32 | a74463b59c4ce28253c2449a07c0f6692a0147a1 | https://github.com/Andrew-Zhu/DyFPN/tree/a74463b59c4ce28253c2449a07c0f6692a0147a1 |
Conv3x3 | import torch
import torch.nn as nn
class Conv3x3(nn.Module):
def __init__(self, in_channels, out_channels, use_refl=True):
super(Conv3x3, self).__init__()
if use_refl:
self.pad = nn.ReflectionPad2d(1)
else:
self.pad = nn.ZeroPad2d(1)
self.conv = nn.Conv2d(i... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.... | ArminMasoumian/GCNDepth | Conv3x3 | false | 7,730 | [
"MIT"
] | 32 | 9fa77812fa944c2701a45f09acf988815ca50aee | https://github.com/ArminMasoumian/GCNDepth/tree/9fa77812fa944c2701a45f09acf988815ca50aee |
Project | import torch
import torch.nn as nn
class Project(nn.Module):
def __init__(self, batch_size, height, width, eps=1e-07):
super(Project, self).__init__()
self.batch_size = batch_size
self.height = height
self.width = width
self.eps = eps
def forward(self, points, K, T):
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | ArminMasoumian/GCNDepth | Project | false | 7,731 | [
"MIT"
] | 32 | 9fa77812fa944c2701a45f09acf988815ca50aee | https://github.com/ArminMasoumian/GCNDepth/tree/9fa77812fa944c2701a45f09acf988815ca50aee |
MakeFeatures | import torch
import torch.nn as nn
class MakeFeatures(nn.Module):
""" Returns features to be used by PairDrift. """
def __init__(self, in_dim, out_dim):
super(MakeFeatures, self).__init__()
self.single = nn.Linear(in_dim, out_dim)
self.pair = nn.Linear(in_dim, out_dim)
def forwar... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | AustenLamacraft/QuaRL | MakeFeatures | false | 7,732 | [
"MIT"
] | 13 | 1764f0ccd0ba90d44e799b6ac908df76be14a52e | https://github.com/AustenLamacraft/QuaRL/tree/1764f0ccd0ba90d44e799b6ac908df76be14a52e |
Conv5x5 | import torch
import torch.nn as nn
class Conv5x5(nn.Module):
def __init__(self, in_channels, out_channels, use_refl=True):
super(Conv5x5, self).__init__()
if use_refl:
self.pad = nn.ReflectionPad2d(2)
else:
self.pad = nn.ZeroPad2d(2)
self.conv = nn.Conv2d(i... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.... | ArminMasoumian/GCNDepth | Conv5x5 | false | 7,733 | [
"MIT"
] | 32 | 9fa77812fa944c2701a45f09acf988815ca50aee | https://github.com/ArminMasoumian/GCNDepth/tree/9fa77812fa944c2701a45f09acf988815ca50aee |
BCEBlurWithLogitsLoss | import torch
import torch.nn as nn
class BCEBlurWithLogitsLoss(nn.Module):
def __init__(self, alpha=0.05):
super(BCEBlurWithLogitsLoss, self).__init__()
self.loss_fcn = nn.BCEWithLogitsLoss(reduction='none')
self.alpha = alpha
def forward(self, pred, true):
loss = self.loss_f... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torc... | AsakusaRinne/tensorrt_yolov5_tracker | BCEBlurWithLogitsLoss | false | 7,734 | [
"MIT"
] | 22 | b9a3a6fc94710e8291d6a614ed2b04cbc4c56599 | https://github.com/AsakusaRinne/tensorrt_yolov5_tracker/tree/b9a3a6fc94710e8291d6a614ed2b04cbc4c56599 |
GCNSynthetic | import math
import torch
import torch.nn.functional as F
import torch.nn as nn
from torch.nn.parameter import Parameter
class GraphConvolution(nn.Module):
"""
Simple GCN layer, similar to https://arxiv.org/abs/1609.02907
"""
def __init__(self, in_features, out_features, bias=True):
super(Grap... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Armagaan/cf-gnnexplainer | GCNSynthetic | false | 7,735 | [
"MIT"
] | 15 | 22b415e114c52d8d60ca45a40c3cb33c1947400c | https://github.com/Armagaan/cf-gnnexplainer/tree/22b415e114c52d8d60ca45a40c3cb33c1947400c |
BertLayerNorm | import torch
from torch import nn
class BertLayerNorm(nn.Module):
def __init__(self, hidden_size, eps=1e-12):
"""Construct a layernorm module in the TF style (epsilon inside the square root).
"""
super(BertLayerNorm, self).__init__()
self.weight = nn.Parameter(torch.ones(hidden_si... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | ArrowLuo/GRACE | BertLayerNorm | false | 7,736 | [
"Apache-2.0"
] | 17 | f27b500ba905685c03eee6d91d87adc9ef78b4d1 | https://github.com/ArrowLuo/GRACE/tree/f27b500ba905685c03eee6d91d87adc9ef78b4d1 |
Net | import torch
class Net(torch.nn.Module):
def __init__(self, n_input, n_hidden, n_output):
super(Net, self).__init__()
self.hidden1 = torch.nn.Linear(n_input, n_hidden)
self.hidden2 = torch.nn.Linear(n_hidden, n_hidden)
self.hidden3 = torch.nn.Linear(n_hidden, n_hidden)
sel... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
assert_size_stride = torch._C... | AstroHiro/NCM | Net | false | 7,737 | [
"MIT"
] | 23 | 720db63ec018a1986ac9e370613f8209328b89e1 | https://github.com/AstroHiro/NCM/tree/720db63ec018a1986ac9e370613f8209328b89e1 |
CrossEntropyLossOneHot | import torch
import torch.nn as nn
class CrossEntropyLossOneHot(nn.Module):
def __init__(self):
super(CrossEntropyLossOneHot, self).__init__()
self.log_softmax = nn.LogSoftmax(dim=-1)
def forward(self, preds, labels):
return torch.mean(torch.sum(-labels * self.log_softmax(preds), -1)... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | B0Qi/hualubei2020-callingsmoking | CrossEntropyLossOneHot | false | 7,738 | [
"MIT"
] | 27 | 73d1049d95554b5d669afa93132a0fce37461ff4 | https://github.com/B0Qi/hualubei2020-callingsmoking/tree/73d1049d95554b5d669afa93132a0fce37461ff4 |
Attn | import torch
from torch import nn
import torch.nn.functional as F
class Attn(nn.Module):
def __init__(self, hidden_size):
super(Attn, self).__init__()
self.hidden_size = hidden_size
self.attn = nn.Linear(self.hidden_size * 2, hidden_size)
self.v = nn.Linear(self.hidden_size, 1)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | AuCson/SEDST | Attn | false | 7,739 | [
"MIT"
] | 23 | 1c1691e2abc50eb2120ed49c874090f6c4f741d3 | https://github.com/AuCson/SEDST/tree/1c1691e2abc50eb2120ed49c874090f6c4f741d3 |
PermEqMean | import torch
import torch.nn as nn
class PermEqMean(nn.Module):
""" Returns equivariant layer used by EquivarDrift. """
def __init__(self, in_dim, out_dim):
super(PermEqMean, self).__init__()
self.Gamma = nn.Linear(in_dim, out_dim)
self.Lambda = nn.Linear(in_dim, out_dim, bias=False)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | AustenLamacraft/QuaRL | PermEqMean | false | 7,740 | [
"MIT"
] | 13 | 1764f0ccd0ba90d44e799b6ac908df76be14a52e | https://github.com/AustenLamacraft/QuaRL/tree/1764f0ccd0ba90d44e799b6ac908df76be14a52e |
CombineFeatures | import torch
import torch.nn as nn
class CombineFeatures(nn.Module):
""" Returns layer to be used by PairDrift. """
def __init__(self, in_dim, out_dim, zero_init=False):
super(CombineFeatures, self).__init__()
self.single = nn.Linear(in_dim, out_dim)
self.pair = nn.Linear(in_dim, out_... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | AustenLamacraft/QuaRL | CombineFeatures | false | 7,741 | [
"MIT"
] | 13 | 1764f0ccd0ba90d44e799b6ac908df76be14a52e | https://github.com/AustenLamacraft/QuaRL/tree/1764f0ccd0ba90d44e799b6ac908df76be14a52e |
disparityentropy | import torch
from torch import nn
import torch.utils.data
import torch.nn.parallel
class disparityentropy(nn.Module):
def __init__(self, maxdisp):
super(disparityentropy, self).__init__()
def forward(self, x):
out = torch.sum(-x * torch.log(x), 1)
return out
def get_inputs():
r... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch import nn
import torch.utils.data
import torch.nn.parallel
ass... | AvrilCheng/LidarStereoNet | disparityentropy | false | 7,742 | [
"MIT"
] | 27 | 96c7cd6d5edb9b2fd302e2edd0c05cbda1ed024e | https://github.com/AvrilCheng/LidarStereoNet/tree/96c7cd6d5edb9b2fd302e2edd0c05cbda1ed024e |
ConvReLUNorm | import torch
import torch.utils.data
import torch.nn.functional as F
class ConvReLUNorm(torch.nn.Module):
def __init__(self, in_channels, out_channels, kernel_size=1, dropout=0.0):
super(ConvReLUNorm, self).__init__()
self.conv = torch.nn.Conv1d(in_channels, out_channels, kernel_size=
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | AstraliteHeart/cookietts | ConvReLUNorm | false | 7,743 | [
"BSD-3-Clause"
] | 25 | c871f5f7b5790656d5b57bcd9e63946a2da52f0f | https://github.com/AstraliteHeart/cookietts/tree/c871f5f7b5790656d5b57bcd9e63946a2da52f0f |
ScaledDotProductAttention | import torch
import numpy as np
from torch import nn
class ScaledDotProductAttention(nn.Module):
""" Scaled Dot-Product Attention """
def __init__(self, temperature, attn_dropout=0.1):
super().__init__()
self.temperature = temperature
self.dropout = nn.Dropout(attn_dropout)
se... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | ArrowLuo/GRACE | ScaledDotProductAttention | false | 7,744 | [
"Apache-2.0"
] | 17 | f27b500ba905685c03eee6d91d87adc9ef78b4d1 | https://github.com/ArrowLuo/GRACE/tree/f27b500ba905685c03eee6d91d87adc9ef78b4d1 |
ConvNorm | import torch
import torch.utils.data
import torch.nn.functional as F
class ConvNorm(torch.nn.Module):
def __init__(self, in_channels, out_channels, kernel_size=1, stride=1,
padding=None, dilation=1, bias=True, w_init_gain='linear', dropout=0.0
):
super(ConvNorm, self).__init__()
i... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size... | AstraliteHeart/cookietts | ConvNorm | false | 7,745 | [
"BSD-3-Clause"
] | 25 | c871f5f7b5790656d5b57bcd9e63946a2da52f0f | https://github.com/AstraliteHeart/cookietts/tree/c871f5f7b5790656d5b57bcd9e63946a2da52f0f |
Conv1d | import torch
import torch.utils.data
from torch import nn
from torch.nn import Conv1d
class Conv1d(nn.Conv1d):
"""
:param in_channels: Scalar
:param out_channels: Scalar
:param kernel_size: Scalar
:param activation_fn: activation function
:param drop_rate: Scalar. dropout rate
:param strid... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.utils.data
from torch import nn
assert_size_stride = torch._C._dyna... | AstraliteHeart/cookietts | Conv1d | false | 7,746 | [
"BSD-3-Clause"
] | 25 | c871f5f7b5790656d5b57bcd9e63946a2da52f0f | https://github.com/AstraliteHeart/cookietts/tree/c871f5f7b5790656d5b57bcd9e63946a2da52f0f |
Conv2d | import torch
import torch.utils.data
from torch import nn
from torch.nn import Conv2d
class Conv2d(nn.Conv2d):
"""
:param in_channels: Scalar
:param out_channels: Scalar
:param kernel_size: Scalar
:param activation_fn: activation function
:param drop_rate: Scalar. dropout rate
:param strid... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.utils.data
from torch import nn
assert_size_stride = torch._C._dyna... | AstraliteHeart/cookietts | Conv2d | false | 7,747 | [
"BSD-3-Clause"
] | 25 | c871f5f7b5790656d5b57bcd9e63946a2da52f0f | https://github.com/AstraliteHeart/cookietts/tree/c871f5f7b5790656d5b57bcd9e63946a2da52f0f |
PositionwiseFeedForward | import math
import torch
from torch import nn
def gelu(x):
"""Implementation of the gelu activation function.
For information: OpenAI GPT's gelu is slightly different (and gives slightly different results):
0.5 * x * (1 + torch.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 * torch.pow(x, 3))))
"... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import math
from to... | ArrowLuo/GRACE | PositionwiseFeedForward | false | 7,748 | [
"Apache-2.0"
] | 17 | f27b500ba905685c03eee6d91d87adc9ef78b4d1 | https://github.com/ArrowLuo/GRACE/tree/f27b500ba905685c03eee6d91d87adc9ef78b4d1 |
Conv2d | from torch.nn import Module
import math
import torch
from torch.nn import functional as F
import torch.utils.data
from torch.nn.parameter import Parameter
from torch.nn.functional import pad
from torch.nn.modules import Module
from torch.nn.modules.utils import _pair
import torch.nn.parallel
def conv2d_same_padding(i... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch.nn import Module
import math
from torch.nn import functional as F
imp... | AvrilCheng/LidarStereoNet | Conv2d | false | 7,749 | [
"MIT"
] | 27 | 96c7cd6d5edb9b2fd302e2edd0c05cbda1ed024e | https://github.com/AvrilCheng/LidarStereoNet/tree/96c7cd6d5edb9b2fd302e2edd0c05cbda1ed024e |
SiLU | import torch
import torch.nn as nn
class SiLU(nn.Module):
"""export-friendly version of nn.SiLU()"""
@staticmethod
def forward(x):
return x * torch.sigmoid(x)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
| import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | Arui66/YOLOX | SiLU | false | 7,750 | [
"Apache-2.0"
] | 16 | 7ee17936db849600817d7de05269bfdfb1a0eb48 | https://github.com/Arui66/YOLOX/tree/7ee17936db849600817d7de05269bfdfb1a0eb48 |
Auto_Encoder_Model | import torch
import torch.nn as nn
import torch.nn.functional as F
class Auto_Encoder_Model(nn.Module):
def __init__(self):
super(Auto_Encoder_Model, self).__init__()
self.conv1 = nn.Conv2d(1, 64, padding=1, kernel_size=3)
self.max_pool1 = nn.MaxPool2d(2)
self.conv2 = nn.Conv2d(64... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import ... | Awenbocc/med-vqa | Auto_Encoder_Model | false | 7,751 | [
"MIT"
] | 27 | 0cca6811e38cf54aff6a7cce3442296d07875e64 | https://github.com/Awenbocc/med-vqa/tree/0cca6811e38cf54aff6a7cce3442296d07875e64 |
Attention_SEblock | import torch
import torch.nn as nn
import torch.nn.functional as F
class Attention_SEblock(nn.Module):
def __init__(self, channels, reduction, temperature):
super(Attention_SEblock, self).__init__()
self.avg_pool = nn.AdaptiveAvgPool2d(1)
self.fc1 = nn.Linear(channels, channels // reducti... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Andrew-Zhu/DyFPN | Attention_SEblock | false | 7,752 | [
"Apache-2.0"
] | 32 | a74463b59c4ce28253c2449a07c0f6692a0147a1 | https://github.com/Andrew-Zhu/DyFPN/tree/a74463b59c4ce28253c2449a07c0f6692a0147a1 |
Conv3d | from torch.nn import Module
import math
import torch
from torch.nn import functional as F
import torch.utils.data
from torch.nn.parameter import Parameter
from torch.nn.functional import pad
from torch.nn.modules import Module
from torch.nn.modules.utils import _triple
import torch.nn.parallel
def conv3d_same_padding... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch.nn import Module
import math
from torch.nn import functional as F
imp... | AvrilCheng/LidarStereoNet | Conv3d | false | 7,753 | [
"MIT"
] | 27 | 96c7cd6d5edb9b2fd302e2edd0c05cbda1ed024e | https://github.com/AvrilCheng/LidarStereoNet/tree/96c7cd6d5edb9b2fd302e2edd0c05cbda1ed024e |
ShiftedSoftplus | import torch
import torch.nn.functional as F
class ShiftedSoftplus(torch.nn.Module):
def __init__(self):
super(ShiftedSoftplus, self).__init__()
self.shift = torch.log(torch.tensor(2.0)).item()
def forward(self, x):
return F.softplus(x) - self.shift
def get_inputs():
return [to... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
assert_size_stride = torch._C._dynamo.guards.assert_size_strid... | BaratiLab/AugLiChem | ShiftedSoftplus | false | 7,754 | [
"MIT"
] | 16 | 37258b5ce2c653436b3e819b58d2659052d6edcc | https://github.com/BaratiLab/AugLiChem/tree/37258b5ce2c653436b3e819b58d2659052d6edcc |
Upsample | import torch
import torch.nn as nn
import torch.utils.data
import torch.nn.functional as F
class Upsample(nn.Module):
def __init__(self, scale_factor=2, size=None):
super(Upsample, self).__init__()
self.upsample = F.upsample_nearest
self.size = size
self.scale_factor = scale_facto... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.utils.data
import torch.nn.functional as F
assert_size_stride = torch._C._dynamo.guards.assert_size_strid... | Bhaskers-Blu-Org1/gfmn | Upsample | false | 7,756 | [
"Apache-2.0"
] | 15 | 52b4fd005f8c52297bd6aa5d93e4a1c8d46f56ce | https://github.com/Bhaskers-Blu-Org1/gfmn/tree/52b4fd005f8c52297bd6aa5d93e4a1c8d46f56ce |
ChamferLoss | import torch
import torch.nn as nn
def batch_pairwise_dist(x, y):
_bs, num_points_x, _points_dim = x.size()
_, num_points_y, _ = y.size()
xx = torch.bmm(x, x.transpose(2, 1))
yy = torch.bmm(y, y.transpose(2, 1))
zz = torch.bmm(x, y.transpose(2, 1))
diag_ind_x = torch.arange(0, num_points_x)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | AnTao97/UnsupervisedPointCloudSegmentation | ChamferLoss | false | 7,757 | [
"MIT"
] | 13 | 9bcf0bdf3b1ae62421d9202eb7c0b014d6a69c02 | https://github.com/AnTao97/UnsupervisedPointCloudSegmentation/tree/9bcf0bdf3b1ae62421d9202eb7c0b014d6a69c02 |
Step | import torch
import torch.nn as nn
class StepF(torch.autograd.Function):
""" A step function that returns values in {-1, 1} and uses the Straigh-Through Estimator
to update upstream weights in the network
"""
@staticmethod
def forward(ctx, input_):
ctx.save_for_backward(input_)
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | Bhaskers-Blu-Org1/online-alt-min | Step | false | 7,758 | [
"Apache-2.0"
] | 23 | ef31aaad639c0880df8700d34613164298bcadd0 | https://github.com/Bhaskers-Blu-Org1/online-alt-min/tree/ef31aaad639c0880df8700d34613164298bcadd0 |
BilinearAttention | import torch
import torch.utils.data
from torch import nn
class BilinearAttention(nn.Module):
"""
:param enc_dim: Scalar.
:param dec_dim: Scalar
"""
def __init__(self, enc_dim, dec_dim):
super(BilinearAttention, self).__init__()
self.W = nn.Linear(enc_dim, dec_dim)
def forwa... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | AstraliteHeart/cookietts | BilinearAttention | false | 7,759 | [
"BSD-3-Clause"
] | 25 | c871f5f7b5790656d5b57bcd9e63946a2da52f0f | https://github.com/AstraliteHeart/cookietts/tree/c871f5f7b5790656d5b57bcd9e63946a2da52f0f |
InnerProductLoss | import torch
import torch.nn as nn
import torch.nn.functional as F
class InnerProductLoss(nn.Module):
"""This is the inner-product loss used in CFKG for optimization.
"""
def __init__(self):
super(InnerProductLoss, self).__init__()
def forward(self, anchor, positive, negative):
pos_s... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.gu... | BELIEVEfxy/LightSANs | InnerProductLoss | false | 7,760 | [
"MIT"
] | 17 | 94ce7e59d144dbc787153b8c486cad334790ec6e | https://github.com/BELIEVEfxy/LightSANs/tree/94ce7e59d144dbc787153b8c486cad334790ec6e |
Conv1d2Score | import torch
import torch.nn as nn
import torch.optim
import torch.utils.data
class Conv1d2Score(nn.Module):
"""Calculate a N*out_dim tensor from N*in_dim*seq_len using nn.Conv1d
Essentially it is a linear layer
Args:
in_dim: int
out_dim: int, usually number of classes
seq_len: int
Shape... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.optim
import torch.utils.data
assert_size_str... | BeautyOfWeb/VIN | Conv1d2Score | false | 7,761 | [
"MIT"
] | 34 | 53343d28130f5fd6e5badb58daf8079a5933fd6a | https://github.com/BeautyOfWeb/VIN/tree/53343d28130f5fd6e5badb58daf8079a5933fd6a |
IOUloss | import torch
import torch.nn as nn
class IOUloss(nn.Module):
def __init__(self, reduction='none', loss_type='iou'):
super(IOUloss, self).__init__()
self.reduction = reduction
self.loss_type = loss_type
def forward(self, pred, target):
assert pred.shape[0] == target.shape[0]
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | Arui66/YOLOX | IOUloss | false | 7,762 | [
"Apache-2.0"
] | 16 | 7ee17936db849600817d7de05269bfdfb1a0eb48 | https://github.com/Arui66/YOLOX/tree/7ee17936db849600817d7de05269bfdfb1a0eb48 |
WeightedView | import torch
import torch.nn as nn
import torch.optim
import torch.utils.data
class WeightedView(nn.Module):
"""Calculate weighted view
Args:
num_groups: int, number of groups (views)
reduce_dimension: bool, default False. If True, reduce dimension dim
dim: default -1. Only used w... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
import torch.optim
import torch.utils.data
assert_s... | BeautyOfWeb/AffinityNet | WeightedView | false | 7,763 | [
"MIT"
] | 34 | d3f79823fa0182328894483165d4f0853740ee53 | https://github.com/BeautyOfWeb/AffinityNet/tree/d3f79823fa0182328894483165d4f0853740ee53 |
LearnedUpsampling1d | import torch
from torch import nn
class LearnedUpsampling1d(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, bias=True):
super().__init__()
self.conv_t = nn.ConvTranspose1d(in_channels=in_channels,
out_channels=out_channels, kernel_size=kernel_size, stride=
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_st... | Barbany/Multi-speaker-Neural-Vocoder | LearnedUpsampling1d | false | 7,764 | [
"MIT"
] | 13 | a3f5c266603b17bcbe264e750947140f302272c8 | https://github.com/Barbany/Multi-speaker-Neural-Vocoder/tree/a3f5c266603b17bcbe264e750947140f302272c8 |
InnerProductLayer | import torch
import torch.nn as nn
class InnerProductLayer(nn.Module):
"""InnerProduct Layer used in PNN that compute the element-wise
product or inner product between feature vectors.
"""
def __init__(self, num_feature_field, device):
"""
Args:
num_feature_field(int) :nu... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | BELIEVEfxy/LightSANs | InnerProductLayer | false | 7,765 | [
"MIT"
] | 17 | 94ce7e59d144dbc787153b8c486cad334790ec6e | https://github.com/BELIEVEfxy/LightSANs/tree/94ce7e59d144dbc787153b8c486cad334790ec6e |
ConvLSTMCell | import torch
import torch.nn as nn
class ConvLSTMCell(nn.Module):
"""
Implementation of the Basic ConvLSTM.
No peephole connection, no forget gate.
ConvLSTM:
x - input
h - hidden representation
c - memory cell
f - forget gate
o - output gate
Reference:Conv... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | BenQLange/AttentionAugmentedConvLSTM | ConvLSTMCell | false | 7,766 | [
"MIT"
] | 30 | d8419b7a628b02ac49e8450deb3d60450c7b2d6b | https://github.com/BenQLange/AttentionAugmentedConvLSTM/tree/d8419b7a628b02ac49e8450deb3d60450c7b2d6b |
BPRLoss | import torch
import torch.nn as nn
class BPRLoss(nn.Module):
""" BPRLoss, based on Bayesian Personalized Ranking
Args:
- gamma(float): Small value to avoid division by zero
Shape:
- Pos_score: (N)
- Neg_score: (N), same shape as the Pos_score
- Output: scalar.
Exampl... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | BELIEVEfxy/LightSANs | BPRLoss | false | 7,767 | [
"MIT"
] | 17 | 94ce7e59d144dbc787153b8c486cad334790ec6e | https://github.com/BELIEVEfxy/LightSANs/tree/94ce7e59d144dbc787153b8c486cad334790ec6e |
Policy | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.parallel
import torch.utils.data
import torch.optim
import torch.autograd
class Policy(nn.Module):
def __init__(self):
super(Policy, self).__init__()
self.affine1 = nn.Linear(4, 128)
self.affine2 = nn.Linea... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | BestSonny/examples | Policy | false | 7,768 | [
"BSD-3-Clause"
] | 13 | 4b7365c0db22133d1793e53bb3674c2d0ebaeac1 | https://github.com/BestSonny/examples/tree/4b7365c0db22133d1793e53bb3674c2d0ebaeac1 |
ConvNCFBPRLoss | import torch
import torch.nn as nn
class ConvNCFBPRLoss(nn.Module):
""" ConvNCFBPRLoss, based on Bayesian Personalized Ranking,
Shape:
- Pos_score: (N)
- Neg_score: (N), same shape as the Pos_score
- Output: scalar.
Examples::
>>> loss = ConvNCFBPRLoss()
>>> ... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | BELIEVEfxy/LightSANs | ConvNCFBPRLoss | false | 7,769 | [
"MIT"
] | 17 | 94ce7e59d144dbc787153b8c486cad334790ec6e | https://github.com/BELIEVEfxy/LightSANs/tree/94ce7e59d144dbc787153b8c486cad334790ec6e |
AttLayer | import torch
import torch.nn as nn
import torch.nn.functional as fn
class AttLayer(nn.Module):
"""Calculate the attention signal(weight) according the input tensor.
Args:
infeatures (torch.FloatTensor): A 3D input tensor with shape of[batch_size, M, embed_dim].
Returns:
torch.FloatTensor... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | BELIEVEfxy/LightSANs | AttLayer | false | 7,770 | [
"MIT"
] | 17 | 94ce7e59d144dbc787153b8c486cad334790ec6e | https://github.com/BELIEVEfxy/LightSANs/tree/94ce7e59d144dbc787153b8c486cad334790ec6e |
SpanClassifier | import torch
import torch.nn as nn
from torch.nn import BCELoss
class SpanClassifier(nn.Module):
"""given the span embeddings, classify whether their relations"""
def __init__(self, d_inp):
super(SpanClassifier, self).__init__()
self.d_inp = d_inp
self.bilinear_layer = nn.Bilinear(d_i... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
from torch.nn import BCELoss
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
reinterpret_tensor = torc... | Bhaskers-Blu-Org1/superglue-mtl | SpanClassifier | false | 7,771 | [
"Apache-2.0"
] | 15 | 1eb3e581c0ef3b4c261e0256ec26116d2b657c40 | https://github.com/Bhaskers-Blu-Org1/superglue-mtl/tree/1eb3e581c0ef3b4c261e0256ec26116d2b657c40 |
RegLoss | import torch
import torch.nn as nn
class RegLoss(nn.Module):
""" RegLoss, L2 regularization on model parameters
"""
def __init__(self):
super(RegLoss, self).__init__()
def forward(self, parameters):
reg_loss = None
for W in parameters:
if reg_loss is None:
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_... | BELIEVEfxy/LightSANs | RegLoss | false | 7,772 | [
"MIT"
] | 17 | 94ce7e59d144dbc787153b8c486cad334790ec6e | https://github.com/BELIEVEfxy/LightSANs/tree/94ce7e59d144dbc787153b8c486cad334790ec6e |
OuterProductLayer | import torch
import torch.nn as nn
class OuterProductLayer(nn.Module):
"""OutterProduct Layer used in PNN. This implemention is
adapted from code that the author of the paper published on https://github.com/Atomu2014/product-nets.
"""
def __init__(self, num_feature_field, embedding_size, device):
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | BELIEVEfxy/LightSANs | OuterProductLayer | false | 7,773 | [
"MIT"
] | 17 | 94ce7e59d144dbc787153b8c486cad334790ec6e | https://github.com/BELIEVEfxy/LightSANs/tree/94ce7e59d144dbc787153b8c486cad334790ec6e |
Sign | from torch.autograd import Function
import torch
import torch.nn as nn
class SignFunction(Function):
def __init__(self):
super(SignFunction, self).__init__()
@staticmethod
def forward(ctx, input, is_training=True):
if is_training:
prob = input.new(input.size()).uniform_()
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch.autograd import Function
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda... | Biaze7/lossy-image-compression | Sign | false | 7,774 | [
"MIT"
] | 16 | 88ca2022a306fea52d6671593b314f0de3bf6010 | https://github.com/Biaze7/lossy-image-compression/tree/88ca2022a306fea52d6671593b314f0de3bf6010 |
D_phiVpsi | import torch
import torch.utils.data
import torch.nn as nn
def add_layer(seq, ix, n_inputs, n_outputs, nonlin, normalization):
seq.add_module('L' + str(ix), nn.Linear(n_inputs, n_outputs))
if ix > 0 and normalization:
if normalization == 'LN':
seq.main.add_module('A' + str(ix), nn.LayerNor... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.utils.data
import torch.nn as nn
assert_size_stride = torch._C._dyn... | Bhaskers-Blu-Org1/SIC | D_phiVpsi | false | 7,775 | [
"Apache-2.0"
] | 12 | c4e45d7736da6e6faabdc56bfc1336445df99204 | https://github.com/Bhaskers-Blu-Org1/SIC/tree/c4e45d7736da6e6faabdc56bfc1336445df99204 |
Vgg16 | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data
class Vgg16(nn.Module):
def __init__(self):
super(Vgg16, self).__init__()
self.conv1_1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1)
self.conv1_2 = nn.Conv2d(64, 64, kernel_size=3, stride=1... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import ... | AllenPu/mbdg | Vgg16 | false | 7,776 | [
"MIT"
] | 27 | 243f53a57dcf4bfb6e717c0c9f64a839cff8d548 | https://github.com/AllenPu/mbdg/tree/243f53a57dcf4bfb6e717c0c9f64a839cff8d548 |
BaseFactorizationMachine | import torch
import torch.nn as nn
class BaseFactorizationMachine(nn.Module):
"""Calculate FM result over the embeddings
Args:
reduce_sum: bool, whether to sum the result, default is True.
Input:
input_x: tensor, A 3D tensor with shape:``(batch_size,field_size,embed_dim)``.
Output
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | BELIEVEfxy/LightSANs | BaseFactorizationMachine | false | 7,777 | [
"MIT"
] | 17 | 94ce7e59d144dbc787153b8c486cad334790ec6e | https://github.com/BELIEVEfxy/LightSANs/tree/94ce7e59d144dbc787153b8c486cad334790ec6e |
AdjEncoder | import torch
from torch import nn
import torch.utils.data
class AdjEncoder(nn.Module):
def __init__(self, featureSize, hiddenSize):
super(AdjEncoder, self).__init__()
self.left = nn.Linear(featureSize, hiddenSize)
self.right = nn.Linear(featureSize, hiddenSize, bias=False)
self.se... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch import n... | BigkoalaZhu/SCORES | AdjEncoder | false | 7,778 | [
"MIT"
] | 16 | 8332733c375ee85c02bd34c2adce6a3213aad3c4 | https://github.com/BigkoalaZhu/SCORES/tree/8332733c375ee85c02bd34c2adce6a3213aad3c4 |
Binarizer | from torch.autograd import Function
import torch
import torch.nn as nn
import torch.nn.functional as F
class SignFunction(Function):
def __init__(self):
super(SignFunction, self).__init__()
@staticmethod
def forward(ctx, input, is_training=True):
if is_training:
prob = input.... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch.autograd... | Biaze7/lossy-image-compression | Binarizer | false | 7,779 | [
"MIT"
] | 16 | 88ca2022a306fea52d6671593b314f0de3bf6010 | https://github.com/Biaze7/lossy-image-compression/tree/88ca2022a306fea52d6671593b314f0de3bf6010 |
Perceptron | import torch
import torch.nn as nn
import torch.nn.functional as F
class Perceptron(nn.Module):
"""Implements a 1-layer perceptron."""
def __init__(self, input_dimension, hidden_dimension, output_dimension):
super(Perceptron, self).__init__()
self._layer1 = nn.Linear(input_dimension, hidden_d... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | Bhaskers-Blu-Org2/PDP-Solver | Perceptron | false | 7,780 | [
"MIT"
] | 28 | 1fca34d81f36268288f46416fb6956e5b36df69e | https://github.com/Bhaskers-Blu-Org2/PDP-Solver/tree/1fca34d81f36268288f46416fb6956e5b36df69e |
BoxEncoder | import torch
from torch import nn
import torch.utils.data
class BoxEncoder(nn.Module):
def __init__(self, boxSize, featureSize, hiddenSize):
super(BoxEncoder, self).__init__()
self.encoder = nn.Linear(boxSize, featureSize)
self.middlein = nn.Linear(featureSize, hiddenSize)
self.mi... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch import n... | BigkoalaZhu/SCORES | BoxEncoder | false | 7,781 | [
"MIT"
] | 16 | 8332733c375ee85c02bd34c2adce6a3213aad3c4 | https://github.com/BigkoalaZhu/SCORES/tree/8332733c375ee85c02bd34c2adce6a3213aad3c4 |
kAttentionPooling | import torch
import torch.nn as nn
class kAttentionPooling(nn.Module):
def __init__(self, seq_len, hidden_size, k_heads=5):
super().__init__()
self.k_heads = k_heads
self.theta_k = nn.Parameter(torch.randn([hidden_size, k_heads]))
def forward(self, input_tensor):
attention_ma... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | BELIEVEfxy/LightSANs | kAttentionPooling | false | 7,782 | [
"MIT"
] | 17 | 94ce7e59d144dbc787153b8c486cad334790ec6e | https://github.com/BELIEVEfxy/LightSANs/tree/94ce7e59d144dbc787153b8c486cad334790ec6e |
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