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 |
|---|---|---|---|---|---|---|---|---|---|---|
ATT | import torch
import torch.nn as nn
import torch.nn.functional as F
class ATT(nn.Module):
def __init__(self, din):
super(ATT, self).__init__()
self.fc1 = nn.Linear(din, 64)
self.fc2 = nn.Linear(64, 64)
self.fc3 = nn.Linear(64, 1)
def forward(self, x):
y = F.relu(self.f... | 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_... | jungwoohan72/DGN_pytorch | ATT | false | 10,351 | [
"MIT"
] | 0 | 65fe7ab4df661d97725f2a72a1fdb49df1b2ea44 | https://github.com/jungwoohan72/DGN_pytorch/tree/65fe7ab4df661d97725f2a72a1fdb49df1b2ea44 |
rSoftMax | import torch
import torch.nn.functional as F
from torch import nn
class rSoftMax(nn.Module):
def __init__(self, radix, cardinality):
super().__init__()
self.radix = radix
self.cardinality = cardinality
def forward(self, x):
batch = x.size(0)
if self.radix > 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
from torch import nn
a... | DengpanFu/fast-reid-v0 | rSoftMax | false | 9,090 | [
"Apache-2.0"
] | 0 | e444c0187ccb6ef3b8348f8c5f0c5a0814b3683e | https://github.com/DengpanFu/fast-reid-v0/tree/e444c0187ccb6ef3b8348f8c5f0c5a0814b3683e |
LayerNorm1D | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | 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_... | emirojaseng/pytorch-meta-optimizer | LayerNorm1D | false | 15,312 | [
"MIT"
] | 298 | 3641981c990150ceb6c55d25a05ba76388f9ec69 | https://github.com/emirojaseng/pytorch-meta-optimizer/tree/3641981c990150ceb6c55d25a05ba76388f9ec69 |
CapsuleLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | 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... | ashawkey/CapsNet.pytorch | CapsuleLoss | false | 6,241 | [
"MIT"
] | 1 | 3b796b572bbabe79cc445c35913cd3584733aedf | https://github.com/ashawkey/CapsNet.pytorch/tree/3b796b572bbabe79cc445c35913cd3584733aedf |
Generator | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data
class Generator(nn.Module):
def __init__(self, input_size, hidden_size, output_size):
super(Generator, self).__init__()
self.fc1 = nn.Linear(input_size, hidden_size)
self.fc2 = nn.Linear(hidden_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.triton_helpers import libdevice
import torch.nn as ... | Iamsdt/UdacityDeepLearningNanodegree | Generator | false | 5,338 | [
"Apache-2.0"
] | 1 | 507c2ce620f42e36271549471b819d3d7fceb1b6 | https://github.com/Iamsdt/UdacityDeepLearningNanodegree/tree/507c2ce620f42e36271549471b819d3d7fceb1b6 |
Upsample | import torch
import torch.nn as nn
class Upsample(nn.Upsample):
"""
Upsampling via interporlation
Args:
x: (N, T, C)
Returns:
y: (N, S * T, C)
(S: scale_factor)
"""
def __init__(self, scale_factor=2, mode='nearest'):
super(Upsample, self).__init__(scale_factor=scale_factor, mode=mo... | 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... | Jackson-Kang/VQVC-Pytorch | Upsample | false | 8,331 | [
"MIT"
] | 13 | d2267b5c52253b6ae11a5767963a65320ae335c2 | https://github.com/Jackson-Kang/VQVC-Pytorch/tree/d2267b5c52253b6ae11a5767963a65320ae335c2 |
SequentialAllocation | from torch.nn import Module
import torch
from torch.nn import functional as F
from torch.nn import Linear
def _sequential_allocation(p, weights):
_, slots, bidders_plus_one = p.shape
bidders = bidders_plus_one - 1
cumulative_total = p[:, 0, :bidders]
if weights is None:
alloc = cumulative_tota... | 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.... | pjordan/dmch | SequentialAllocation | false | 4,122 | [
"Apache-2.0"
] | 0 | 84e04ddb0679007b15acfdc275e0e3f51e50d9f2 | https://github.com/pjordan/dmch/tree/84e04ddb0679007b15acfdc275e0e3f51e50d9f2 |
AttentionModule | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | 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... | kdexd/probnmn-clevr | AttentionModule | false | 15,795 | [
"MIT"
] | 69 | 9c1b2286cf30e9fb045370153c9242a39760e02e | https://github.com/kdexd/probnmn-clevr/tree/9c1b2286cf30e9fb045370153c9242a39760e02e |
FeatNet | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | 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 ... | DongChengdongHangZhou/caffe-to-pytorch | FeatNet | false | 2,240 | [
"Apache-2.0"
] | 0 | 5e3104f3aa77d35bad5d2de235b067460c136fd5 | https://github.com/DongChengdongHangZhou/caffe-to-pytorch/tree/5e3104f3aa77d35bad5d2de235b067460c136fd5 |
SEModule | import torch
from torch import nn
import torch.nn.parallel
class GlobalAvgPool2d:
def __init__(self, flatten=False):
self.flatten = flatten
def __call__(self, x):
if self.flatten:
in_size = x.size()
return x.view((in_size[0], in_size[1], -1)).mean(dim=2)
else:... | 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
import t... | Randl/TResNet | SEModule | false | 5,755 | [
"Apache-2.0"
] | 1 | 18514caf61d77c7e000a71dde9d1f86ba792b38d | https://github.com/Randl/TResNet/tree/18514caf61d77c7e000a71dde9d1f86ba792b38d |
Quantization | import torch
import torch.utils.data
import torch.nn as nn
class Quant(torch.autograd.Function):
@staticmethod
def forward(ctx, input):
input = torch.clamp(input, 0, 1)
output = (input * 255.0).round() / 255.0
return output
@staticmethod
def backward(ctx, grad_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
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import torch.utils.data
impo... | AbnerVictor/HCFlow | Quantization | false | 9,093 | [
"Apache-2.0"
] | 0 | e55938ac9f58c117898e3d161ddc73b14d15289b | https://github.com/AbnerVictor/HCFlow/tree/e55938ac9f58c117898e3d161ddc73b14d15289b |
AngleWiseRKD | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | 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.... | HIT-cwh/mmrazor | AngleWiseRKD | false | 13,749 | [
"Apache-2.0"
] | 553 | 2dad24044d7f1dad88f20221f8fc071dd40fdd4f | https://github.com/HIT-cwh/mmrazor/tree/2dad24044d7f1dad88f20221f8fc071dd40fdd4f |
QNetwork | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | 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_... | SagarRathod-TomTom/Navigation-Deep-Reinforcement-Learning-Nanodegree | QNetwork | false | 9,426 | [
"MIT"
] | 0 | a13597d5077785bd486d8ce528dc177685226b1c | https://github.com/SagarRathod-TomTom/Navigation-Deep-Reinforcement-Learning-Nanodegree/tree/a13597d5077785bd486d8ce528dc177685226b1c |
MultiheadAttentionWrapper | import torch
import torch.nn.functional as F
import torch.nn as nn
from torch.nn.utils import weight_norm
from torch.optim.lr_scheduler import *
def linear(x):
return x
def activation(func_a):
"""Activation function wrapper
"""
try:
f = eval(func_a)
except:
f = linear
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.functional as F
import torch.nn as nn
from torch.nn.utils import weight_norm
from torch.optim.lr_scheduler import *
assert_s... | chunhuililili/mt_dnn | MultiheadAttentionWrapper | false | 10,209 | [
"MIT"
] | 0 | 4c6efaf21724c7b8103a05e46b5b44d7b246225e | https://github.com/chunhuililili/mt_dnn/tree/4c6efaf21724c7b8103a05e46b5b44d7b246225e |
ScaledDotProductAttention | import torch
import torch.utils.data
import torch
import torch.nn as nn
import torch.nn.functional as F
class ScaledDotProductAttention(nn.Module):
"""
Scaled Dot-Product Attention
from https://github.com/jadore801120/attention-is-all-you-need-pytorch
by Yu-Hsiang Huang
"""
def __init__(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 import triton_helpers
from torch._inductor.runtime.... | TomerRonen34/MeshCNN | ScaledDotProductAttention | false | 5,907 | [
"MIT"
] | 1 | 8c50f3804c48044b78572d652a42184640e904d9 | https://github.com/TomerRonen34/MeshCNN/tree/8c50f3804c48044b78572d652a42184640e904d9 |
ClipGlobalAvgPool2d | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | 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... | tenghehan/reid_without_id | ClipGlobalAvgPool2d | false | 10,872 | [
"MIT"
] | 0 | d1d0ff273b1ef19fc6da8cbbf210527779b37455 | https://github.com/tenghehan/reid_without_id/tree/d1d0ff273b1ef19fc6da8cbbf210527779b37455 |
MaxpoolMatchLay | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | 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
import torch.nn as nn
assert... | NeilWangziyu/torch_light | MaxpoolMatchLay | false | 5,697 | [
"MIT"
] | 1 | daf8fd62f57885cf182f1b3edc3152156d229ef3 | https://github.com/NeilWangziyu/torch_light/tree/daf8fd62f57885cf182f1b3edc3152156d229ef3 |
UpSample | import torch
from torch import nn
import torch.nn.functional as F
import torch.utils.data
import torch.nn.functional
import torch.autograd
class Smooth(nn.Module):
"""
<a id="smooth"></a>
### Smoothing Layer
This layer blurs each channel
"""
def __init__(self):
super().__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
from torch import nn
import t... | techthiyanes/annotated_deep_learning_paper_implementations | UpSample | false | 16,581 | [
"MIT"
] | 3,714 | 8af24da2dd39a9a87482a4d18c2dc829bbd3fd47 | https://github.com/techthiyanes/annotated_deep_learning_paper_implementations/tree/8af24da2dd39a9a87482a4d18c2dc829bbd3fd47 |
make_style | import torch
from torch import nn
import torch.nn.functional as F
class make_style(nn.Module):
def __init__(self):
super().__init__()
self.flatten = nn.Flatten()
def forward(self, x0):
style = F.avg_pool2d(x0, kernel_size=(x0.shape[-2], x0.shape[-1]))
style = self.flatten(sty... | 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... | dkurt/cellpose | make_style | false | 10,035 | [
"BSD-3-Clause"
] | 0 | 975821a5d75ce5f1b40b7a95ed0bd45cf99a0acb | https://github.com/dkurt/cellpose/tree/975821a5d75ce5f1b40b7a95ed0bd45cf99a0acb |
InvConvNear | import torch
from torch import nn
from torch.nn import functional as F
import torch.utils.data
class InvConvNear(nn.Module):
def __init__(self, channels, n_split=4, no_jacobian=False, **kwargs):
super().__init__()
assert n_split % 2 == 0
self.channels = channels
self.n_split = n_s... | 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
import torch.utils.data
assert_size_stride = torch._C._dyna... | entn-at/GradTTS | InvConvNear | false | 15,317 | [
"MIT"
] | 55 | d31cbf41211615a01fffc3812715e3f7f2be214d | https://github.com/entn-at/GradTTS/tree/d31cbf41211615a01fffc3812715e3f7f2be214d |
ScaledDotProductAttention | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | 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.... | ROBINADC/BiGRU-CRF-with-Attention-for-NER | ScaledDotProductAttention | false | 8,712 | [
"MIT"
] | 27 | b9e037ebd6e1d56500ffb60c6030013982c17ded | https://github.com/ROBINADC/BiGRU-CRF-with-Attention-for-NER/tree/b9e037ebd6e1d56500ffb60c6030013982c17ded |
LinearCapsPro | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | 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... | WdBlink/AugMix-3DOCUNet-Brats2019 | LinearCapsPro | false | 5,974 | [
"MIT"
] | 1 | 125c6c8682b51a550eeac9173d13d0a211576abc | https://github.com/WdBlink/AugMix-3DOCUNet-Brats2019/tree/125c6c8682b51a550eeac9173d13d0a211576abc |
CopyChannels | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | 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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret... | NehzUx/autodl | CopyChannels | false | 8,578 | [
"Apache-2.0"
] | 25 | c80fdc4b297ed1ec2b9e6911d313f1fe31d83cb9 | https://github.com/NehzUx/autodl/tree/c80fdc4b297ed1ec2b9e6911d313f1fe31d83cb9 |
ASP | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | 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.... | AyushExel/s3prl | ASP | false | 2,006 | [
"MIT"
] | 0 | 6531904e9621a778978b9cfef3ba9f582e56639a | https://github.com/AyushExel/s3prl/tree/6531904e9621a778978b9cfef3ba9f582e56639a |
UNetUpsamplingBlock | import torch
import torch.nn as nn
import torch.nn.functional as F
class UNetUpsamplingBlock(nn.Module):
def __init__(self, in_channels, out_channels):
super(UNetUpsamplingBlock, self).__init__()
params = {'kernel_size': 3, 'stride': 1, 'padding': 1, 'bias': True}
self.conv = nn.Conv2d(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.... | TropComplique/bicycle-gan | UNetUpsamplingBlock | false | 18,047 | [
"MIT"
] | 4 | 4bc8f4cdbe138e23c8a02c408cfb8e2ff7dfe6ab | https://github.com/TropComplique/bicycle-gan/tree/4bc8f4cdbe138e23c8a02c408cfb8e2ff7dfe6ab |
SCse | import torch
import torch.nn as nn
import torch._utils
class SpatialAttention2d(nn.Module):
def __init__(self, channel):
super(SpatialAttention2d, self).__init__()
self.squeeze = nn.Conv2d(channel, 1, kernel_size=1, bias=False)
self.sigmoid = nn.Sigmoid()
def forward(self, 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
import ... | elmajdma/seismic-deeplearning | SCse | false | 15,310 | [
"MIT"
] | 270 | bc084abe153509c40b45f8bf0f80dfda1049d7dc | https://github.com/elmajdma/seismic-deeplearning/tree/bc084abe153509c40b45f8bf0f80dfda1049d7dc |
SolutionModel | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | 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_... | VVKot/mlinseconds-vote-prediction | SolutionModel | false | 2,938 | [
"MIT"
] | 0 | c869ae428fb8d5e83f0a47468722da968aed28c6 | https://github.com/VVKot/mlinseconds-vote-prediction/tree/c869ae428fb8d5e83f0a47468722da968aed28c6 |
_BoundaryRefineModule | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | 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... | adynathos/pytorch-semantic-segmentation | _BoundaryRefineModule | false | 9,930 | [
"MIT"
] | 0 | 44d1784984cfd0926821c3fdbc20d371bb074296 | https://github.com/adynathos/pytorch-semantic-segmentation/tree/44d1784984cfd0926821c3fdbc20d371bb074296 |
ResidualAttentionBlock | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | 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.... | jiazheng-xing/Swin_Multimodal | ResidualAttentionBlock | false | 10,340 | [
"MIT"
] | 0 | 7bc41977fe7d8d4f0091852c63a6a32a0fada0fb | https://github.com/jiazheng-xing/Swin_Multimodal/tree/7bc41977fe7d8d4f0091852c63a6a32a0fada0fb |
RegLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | 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
... | SaqibMamoon/GSDT | RegLoss | false | 5,803 | [
"MIT"
] | 1 | e11c52a67291e973016ed34c3c95659e0af32d48 | https://github.com/SaqibMamoon/GSDT/tree/e11c52a67291e973016ed34c3c95659e0af32d48 |
PatchToPatchEdgeConvolution | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | 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
import ... | Lujian-123321/gcn- | PatchToPatchEdgeConvolution | false | 8,492 | [
"MIT"
] | 12 | 8f3a0a1d979bc7f075352e194e1e39687f0b12ab | https://github.com/Lujian-123321/gcn-/tree/8f3a0a1d979bc7f075352e194e1e39687f0b12ab |
RELUTwosided | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | 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
assert_size_stride = t... | garysnake/crsae | RELUTwosided | false | 10,077 | [
"MIT"
] | 0 | ca03574fc75e855e612df71535504e956ef897c7 | https://github.com/garysnake/crsae/tree/ca03574fc75e855e612df71535504e956ef897c7 |
VarianceNorm2d | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | 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_... | DeepTitan/PNDM | VarianceNorm2d | false | 13,568 | [
"Apache-2.0"
] | 61 | 4037a4f40011c9a0d47b92303e64d47fcc7ed56a | https://github.com/DeepTitan/PNDM/tree/4037a4f40011c9a0d47b92303e64d47fcc7ed56a |
DownSample | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | 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
import t... | Aarsh2001/annotated_deep_learning_paper_implementations | DownSample | false | 4,789 | [
"MIT"
] | 1 | ff0d5c065da1a46769f5f66fddc252c178f8fa37 | https://github.com/Aarsh2001/annotated_deep_learning_paper_implementations/tree/ff0d5c065da1a46769f5f66fddc252c178f8fa37 |
SuperpointBackbone | import torch
import torch.nn as nn
class SuperpointBackbone(nn.Module):
""" SuperPoint backbone. """
def __init__(self):
super(SuperpointBackbone, self).__init__()
self.relu = torch.nn.ReLU(inplace=True)
self.pool = torch.nn.MaxPool2d(kernel_size=2, stride=2)
c1, c2, c3, c4 = ... | 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_... | wx-b/SOLD2 | SuperpointBackbone | false | 16,752 | [
"MIT"
] | 347 | 71c3243f9d3a695788d0a6bfd134b9849425900a | https://github.com/wx-b/SOLD2/tree/71c3243f9d3a695788d0a6bfd134b9849425900a |
SharpenedCosineSimilarity | import torch
import torch.nn as nn
import torch.nn.functional as F
class SharpenedCosineSimilarity(nn.Conv2d):
def __init__(self, in_channels: 'int', out_channels: 'int', kernel_size,
stride=1, padding=None, dilation=1, groups: 'int'=1, bias: 'bool'=
False, q_init: 'float'=10, p_init: 'float'=1.0... | 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... | p-sodmann/sharpened_cosine_similarity_torch | SharpenedCosineSimilarity | false | 4,115 | [
"MIT"
] | 0 | 0562e54f6494f365e321da9ae91edaba8595e3aa | https://github.com/p-sodmann/sharpened_cosine_similarity_torch/tree/0562e54f6494f365e321da9ae91edaba8595e3aa |
SmoothL1Loss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | 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
assert_size_stride = torch._C._dynamo.guards.assert... | liuhuaijjin/epnet_det3d_rcnn_reg_dir_cls_iou3d_loss | SmoothL1Loss | false | 15,927 | [
"MIT"
] | 175 | 92376a99d919d983742df97bcf29eaea29afaf00 | https://github.com/liuhuaijjin/epnet_det3d_rcnn_reg_dir_cls_iou3d_loss/tree/92376a99d919d983742df97bcf29eaea29afaf00 |
ScaleNorm | import torch
from torch import nn
from torch.nn import Parameter
class ScaleNorm(nn.Module):
"""ScaleNorm"""
def __init__(self, scale, eps=1e-05):
super(ScaleNorm, self).__init__()
self.scale = Parameter(torch.tensor(scale))
self.eps = eps
def forward(self, x):
norm = sel... | 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
from torch import nn
from to... | HerbertMcSnout/transformers_with_trees | ScaleNorm | false | 8,232 | [
"MIT"
] | 18 | 1afa6d4ad45207c9b2762600a9c227d721fbc825 | https://github.com/HerbertMcSnout/transformers_with_trees/tree/1afa6d4ad45207c9b2762600a9c227d721fbc825 |
DuelingQNetwork | from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
import torch.nn.functional as F
class DuelingQNetwork(nn.Module):
"""Actor (Policy) Model."""
def __init__(self, state_size, action_size, config_dict):
"""Initialize parameters and build model.
Params
===... | 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_... | czarrar/udacity_rl | DuelingQNetwork | false | 9,970 | [
"MIT"
] | 0 | d5e9a878b24e6234ab4ac9f612be103bb7f933c4 | https://github.com/czarrar/udacity_rl/tree/d5e9a878b24e6234ab4ac9f612be103bb7f933c4 |
Res | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | 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
import t... | JohnReid/pytorch-struct | Res | false | 9,174 | [
"MIT"
] | 0 | d9d4dd166f90a012aef6917ff7a14c708ced3477 | https://github.com/JohnReid/pytorch-struct/tree/d9d4dd166f90a012aef6917ff7a14c708ced3477 |
ScalarScaleBias | import torch
import torch.nn as nn
from torch.nn.parameter import Parameter
from torch.nn import init
class ScalarScaleBias(nn.Module):
def __init__(self, scale=True, scale_init=1.0, bias=True, bias_init=0.0
) ->None:
super(ScalarScaleBias, self).__init__()
if scale:
self.weig... | 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.parameter import Parameter
from torch.nn import init
assert_size_stride = torch._C._dynamo.guards.assert... | Ali-Homsi/githubrepo | ScalarScaleBias | false | 40 | [
"Apache-2.0"
] | 0 | 7163f110193142a97ec05f76ff7d897c6cedb915 | https://github.com/Ali-Homsi/githubrepo/tree/7163f110193142a97ec05f76ff7d897c6cedb915 |
Alignment | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | 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.... | YJiangcm/Chinese-sentence-pair-modeling | Alignment | false | 14,634 | [
"Apache-2.0"
] | 49 | 90adbc5c121832ce3e4a4057e30417a6ec5e7ebc | https://github.com/YJiangcm/Chinese-sentence-pair-modeling/tree/90adbc5c121832ce3e4a4057e30417a6ec5e7ebc |
HardAttn | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | 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 ... | KevinDocel/deep-person-reid | HardAttn | false | 17,761 | [
"MIT"
] | 8 | fafcb5e39837b8e441e7b6f57d5355f50d28c81d | https://github.com/KevinDocel/deep-person-reid/tree/fafcb5e39837b8e441e7b6f57d5355f50d28c81d |
L2Norm | import torch
import torch.nn as nn
class L2Norm(nn.Module):
"""Channel-wise L2 normalization."""
def __init__(self, in_channels):
super(L2Norm, self).__init__()
self.weight = nn.Parameter(torch.randn(in_channels))
def forward(self, x):
"""out = weight * x / sqrt(\\sum x_i^2)"""
... | 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
import torch.nn as nn
assert... | TropComplique/ssd-pytorch | L2Norm | false | 5,914 | [
"MIT"
] | 1 | e91af875c65dc64a21b838a6645fc803ef690dcf | https://github.com/TropComplique/ssd-pytorch/tree/e91af875c65dc64a21b838a6645fc803ef690dcf |
CoordConv | import torch
from torch import nn
class AddCoords(nn.Module):
def __init__(self, with_r=False):
super().__init__()
self.with_r = with_r
def forward(self, input_tensor):
"""
Args:
input_tensor: shape(batch, channel, x_dim, y_dim)
"""
batch_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 import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_st... | MingSungChao/IPN-hand | CoordConv | false | 14,019 | [
"MIT"
] | 54 | 0b061e4438f159e3e312af4959cb424917b5c367 | https://github.com/MingSungChao/IPN-hand/tree/0b061e4438f159e3e312af4959cb424917b5c367 |
BIM | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | 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... | QingkaiZeng/GenTaxo | BIM | false | 8,725 | [
"MIT"
] | 28 | 10257a1714d14c6a4c49cbfa0b507408f718cdf0 | https://github.com/QingkaiZeng/GenTaxo/tree/10257a1714d14c6a4c49cbfa0b507408f718cdf0 |
TemporalDecayRegression | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.parameter import Parameter
def linear(input, weight, bias=None):
if input.dim() == 2 and bias is not None:
ret = torch.addmm(bias, input, weight.t())
else:
output = input.matmul(weight.t())
if bias is not ... | 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.... | asifr/armisc | TemporalDecayRegression | false | 3,216 | [
"MIT"
] | 0 | 486220ba498353faeb94f70cd8ffe917109526d2 | https://github.com/asifr/armisc/tree/486220ba498353faeb94f70cd8ffe917109526d2 |
FlowHead | import torch
import torch.nn as nn
class FlowHead(nn.Module):
def __init__(self, input_dim=128, hidden_dim=256, output_dim=2):
super(FlowHead, self).__init__()
self.conv1 = nn.Conv2d(input_dim, hidden_dim, 3, padding=1)
self.conv2 = nn.Conv2d(hidden_dim, output_dim, 3, padding=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
assert_... | BrianPugh/RAFT-Stereo | FlowHead | false | 2,097 | [
"MIT"
] | 0 | 494dd79545411eee56e32540bfd6f45a16c74a19 | https://github.com/BrianPugh/RAFT-Stereo/tree/494dd79545411eee56e32540bfd6f45a16c74a19 |
RSubFloat | import torch
class RSubFloat(torch.nn.Module):
def __init__(self):
super(RSubFloat, self).__init__()
def forward(self, x):
return 1.0 - 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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.j... | Ilyabasharov/torch2trt | RSubFloat | false | 2,560 | [
"MIT"
] | 0 | 76bf298b3da408509665e23e2494922b131afb10 | https://github.com/Ilyabasharov/torch2trt/tree/76bf298b3da408509665e23e2494922b131afb10 |
ConvPlus | import torch
import torch.nn as nn
import torch.utils.data
class ConvPlus(nn.Module):
def __init__(self, c1, c2, k=3, s=1, g=1, bias=True):
super(ConvPlus, self).__init__()
self.cv1 = nn.Conv2d(c1, c2, (k, 1), s, (k // 2, 0), groups=g, bias
=bias)
self.cv2 = nn.Conv2d(c1, c2, ... | 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.utils.data
assert_size_stride = torch._C._dyn... | verchable/GenderDiversityCalc | ConvPlus | false | 4,483 | [
"Apache-2.0"
] | 0 | eb07fbc9d13e567de4efd8ea2a0aae793a06bf1d | https://github.com/verchable/GenderDiversityCalc/tree/eb07fbc9d13e567de4efd8ea2a0aae793a06bf1d |
HGNN_conv | import math
import torch
from torch import nn
from torch.nn.parameter import Parameter
class HGNN_conv(nn.Module):
def __init__(self, in_ft, out_ft, bias=True):
super(HGNN_conv, self).__init__()
self.weight = Parameter(torch.Tensor(in_ft, out_ft))
if bias:
self.bias = Paramete... | 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 math
from torch import nn
from torch.nn.parameter import Parameter
assert... | young917/HGNN | HGNN_conv | false | 4,629 | [
"MIT"
] | 0 | 41017f4315f459e1250830ca6c498b920d57e80a | https://github.com/young917/HGNN/tree/41017f4315f459e1250830ca6c498b920d57e80a |
Mean | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | 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.nn.parallel
import torch.optim
import torch.utils.data... | Emily0219/distiller | Mean | false | 5,139 | [
"Apache-2.0"
] | 1 | 445ed35b671fb54586acc280b53d951f18bf97ae | https://github.com/Emily0219/distiller/tree/445ed35b671fb54586acc280b53d951f18bf97ae |
ReLUDropout | import torch
import torch.utils.data
import torch.cuda
import torch.utils.checkpoint
def relu_dropout(x, p=0, training=False, variational=False, batch_first=False):
if not training or p == 0:
return x.clamp_(min=0)
p1m = 1 - p
if variational:
if batch_first:
mask = torch.rand_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 import triton_helpers
import torch.utils.data
import torch.cuda
import torch.utils.checkpoint
assert_size_strid... | mullovc/NMTGMinor | ReLUDropout | false | 4,038 | [
"MIT"
] | 0 | b1b7b1e018eaa0d99a43449655937cc050a29987 | https://github.com/mullovc/NMTGMinor/tree/b1b7b1e018eaa0d99a43449655937cc050a29987 |
SpatialCrossMapLRN | import torch
import torch.nn as nn
import torch.nn.parallel
class SpatialCrossMapLRN(nn.Module):
def __init__(self, local_size=1, alpha=1.0, beta=0.75, k=1,
ACROSS_CHANNELS=True):
super(SpatialCrossMapLRN, self).__init__()
self.ACROSS_CHANNELS = ACROSS_CHANNELS
if ACROSS_CHANNELS:... | 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.nn.parallel
assert_size_stride = torch._C._d... | vfdev-5/models-comparison.pytorch | SpatialCrossMapLRN | false | 16,674 | [
"BSD-3-Clause"
] | 174 | 6a09c41c1ed6160af0734924700a9150249c3df6 | https://github.com/vfdev-5/models-comparison.pytorch/tree/6a09c41c1ed6160af0734924700a9150249c3df6 |
Autoencoder | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | 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 ... | aoxolotl/slr | Autoencoder | false | 6,259 | [
"MIT"
] | 1 | 20a4a9036f2dc3a61745072f89b0f5bb1cc51e1b | https://github.com/aoxolotl/slr/tree/20a4a9036f2dc3a61745072f89b0f5bb1cc51e1b |
SQNet | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
class Fire(nn.Module):
def __init__(self, inplanes, squeeze_planes, expand_planes):
super(Fire, self).__init__()
self.conv1 = nn.Conv2d(inplanes, squeeze_planes, kernel_size=1,
stride=1)
self.relu1 ... | 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.... | dcrmg/Efficient-Segmentation-Networks | SQNet | false | 3,575 | [
"MIT"
] | 0 | e2f2d90d69e4e9af464678b0f02bc754c28f643d | https://github.com/dcrmg/Efficient-Segmentation-Networks/tree/e2f2d90d69e4e9af464678b0f02bc754c28f643d |
QNetwork | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | 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
import t... | AmmarFayad/Influence-based-Reinforcement-Learning-in-Intrinsically-motivated-Agents | QNetwork | false | 4,864 | [
"MIT"
] | 1 | e7cfa4121542312de641792288f7487f86971c1e | https://github.com/AmmarFayad/Influence-based-Reinforcement-Learning-in-Intrinsically-motivated-Agents/tree/e7cfa4121542312de641792288f7487f86971c1e |
BucketingEmbedding | import torch
import torch.nn as nn
class BucketingEmbedding(nn.Module):
def __init__(self, min_val, max_val, count, dim, use_log_scale=False):
super().__init__()
self.min_val = min_val
self.max_val = max_val
self.count = count
self.dim = dim
self.use_log_scale = us... | 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... | narekvslife/OccupancyAnticipation | BucketingEmbedding | false | 16,132 | [
"MIT"
] | 53 | 19b9f4d72b114339d07bd225a1c3feed73e982c2 | https://github.com/narekvslife/OccupancyAnticipation/tree/19b9f4d72b114339d07bd225a1c3feed73e982c2 |
Conv1dKeepLength | import torch
import torch.utils.data
import torch.nn as torch_nn
import torch.nn.functional as torch_nn_func
class Conv1dKeepLength(torch_nn.Conv1d):
""" Wrapper for causal convolution
Input tensor: (batchsize=1, length, dim_in)
Output tensor: (batchsize=1, length, dim_out)
https://github.com/pytorch... | 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.utils.... | Ninushkat/Impact-Synth-Hardware | Conv1dKeepLength | false | 14,111 | [
"MIT"
] | 55 | 37a2ecfec51b052b39d1ad0d4676f09d5f00e3c2 | https://github.com/Ninushkat/Impact-Synth-Hardware/tree/37a2ecfec51b052b39d1ad0d4676f09d5f00e3c2 |
MaxPoolPad | import torch
import torch.nn as nn
class MaxPoolPad(nn.Module):
def __init__(self):
super(MaxPoolPad, self).__init__()
self.pad = nn.ZeroPad2d((1, 0, 1, 0))
self.pool = nn.MaxPool2d(3, stride=2, padding=1)
def forward(self, x):
x = self.pad(x)
x = self.pool(x)
... | 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... | RndmVariableQ/deep-person-reid | MaxPoolPad | false | 11,871 | [
"MIT"
] | 0 | 9ab8343b2fc2ac130aeca5bc2bd1ae808e9ce1b9 | https://github.com/RndmVariableQ/deep-person-reid/tree/9ab8343b2fc2ac130aeca5bc2bd1ae808e9ce1b9 |
LateralBlock | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | 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
from torch import nn
assert_size_stride = t... | PANBOHE/Humanpose-fight | LateralBlock | false | 5,701 | [
"Apache-2.0"
] | 1 | 36e6218db526d567922fa528fa7e11497c53ad60 | https://github.com/PANBOHE/Humanpose-fight/tree/36e6218db526d567922fa528fa7e11497c53ad60 |
RNNCell | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | 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.nn import Module
i... | CSLT-THU/Vivi_3.0 | RNNCell | false | 17,042 | [
"Apache-2.0"
] | 3 | 86996d99d662cd33100755501a971c41ce30ca70 | https://github.com/CSLT-THU/Vivi_3.0/tree/86996d99d662cd33100755501a971c41ce30ca70 |
dce_loss | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | 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... | alexalex222/classification_loss | dce_loss | false | 6,174 | [
"MIT"
] | 1 | a61617e0c0d5ecf6e0ff388305dd9f3eaa5cbf94 | https://github.com/alexalex222/classification_loss/tree/a61617e0c0d5ecf6e0ff388305dd9f3eaa5cbf94 |
_TestNetStrided | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | 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.... | Rohan-Chaudhury/aimet | _TestNetStrided | false | 17,959 | [
"BSD-3-Clause"
] | 3 | 1c38cac8cc0fd32dca40ce5e39940805d29f7a4a | https://github.com/Rohan-Chaudhury/aimet/tree/1c38cac8cc0fd32dca40ce5e39940805d29f7a4a |
LayerLeakyReLU | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | 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 random
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.gu... | dawnclaude/onnx2keras | LayerLeakyReLU | false | 15,142 | [
"MIT"
] | 115 | 3d2a47c0a228b91fd434232274e216e491da36e3 | https://github.com/dawnclaude/onnx2keras/tree/3d2a47c0a228b91fd434232274e216e491da36e3 |
GCNClassification | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | 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 ... | JW9MsjwjnpdRLFw/TSFL | GCNClassification | false | 5,384 | [
"MIT"
] | 1 | ccca391348fde270c9d43149a3397ac3cad4c6e0 | https://github.com/JW9MsjwjnpdRLFw/TSFL/tree/ccca391348fde270c9d43149a3397ac3cad4c6e0 |
D_UpBlock | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | 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 torchvision.transforms import *
assert_size_stride = torch._C._dynamo.guard... | EvgeneyZ/RBPN | D_UpBlock | false | 9,557 | [
"MIT"
] | 0 | acfe636cc48a4fbfea78f934a251c32e53367659 | https://github.com/EvgeneyZ/RBPN/tree/acfe636cc48a4fbfea78f934a251c32e53367659 |
NotearsSobolev | import math
import torch
import numpy as np
import torch.nn as nn
class NotearsSobolev(nn.Module):
def __init__(self, d, k):
"""d: num variables k: num expansion of each variable"""
super(NotearsSobolev, self).__init__()
self.d, self.k = d, k
self.fc1_pos = nn.Linear(d * k, d, bia... | 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.... | FrankTianTT/notears | NotearsSobolev | false | 9,027 | [
"Apache-2.0"
] | 0 | ead1e4fa966e29343a393d637320f98ee0cada7c | https://github.com/FrankTianTT/notears/tree/ead1e4fa966e29343a393d637320f98ee0cada7c |
CollaborativeAttention | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | 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.... | prattcmp/NonAttentiveTacotron2 | CollaborativeAttention | false | 4,149 | [
"BSD-3-Clause"
] | 0 | c65722133c392fba233b5003b480ee498fc0a44a | https://github.com/prattcmp/NonAttentiveTacotron2/tree/c65722133c392fba233b5003b480ee498fc0a44a |
GraphConv | import torch
import torch.nn as nn
from torch.nn.init import xavier_uniform_
class GraphConv(nn.Module):
def __init__(self, in_channels, out_channels, dropout=False, relu=True):
super(GraphConv, self).__init__()
if dropout:
self.dropout = nn.Dropout(p=0.5)
else:
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
import torch.nn as nn
from torch.nn.init import xavier_uniform_
assert_size_stri... | CSer-Tang-hao/FS-KTN | GraphConv | false | 7,873 | [
"MIT"
] | 19 | 8e5b1637e0f86f9d29dad7ff740a9c7a4a292a74 | https://github.com/CSer-Tang-hao/FS-KTN/tree/8e5b1637e0f86f9d29dad7ff740a9c7a4a292a74 |
ZReLU | import torch
import numpy as np
import torch.nn as nn
def cylindricalToPolarConversion(input1, input2=None):
if input2 is None:
"""input1 is tensor of [B,C,H,W,D,2] contains both real and imaginary channels
in the last dims"""
ndims = input1.ndimension()
real_input = input1.narrow... | 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_... | wizofe/urus-mri-recon | ZReLU | false | 4,542 | [
"MIT"
] | 0 | eab8e48dca31d2b936ce69ccc251ec5a4a10facc | https://github.com/wizofe/urus-mri-recon/tree/eab8e48dca31d2b936ce69ccc251ec5a4a10facc |
QuaternionLinear | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | 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 numpy as np
from numpy.random import RandomSt... | ispamm/DualQSELD-TCN | QuaternionLinear | false | 3,695 | [
"MIT"
] | 0 | fc5dc8840b4fdd8cb09f8f92e628561417df268a | https://github.com/ispamm/DualQSELD-TCN/tree/fc5dc8840b4fdd8cb09f8f92e628561417df268a |
CenterLoss | import torch
import torch.nn as nn
class CenterLoss(nn.Module):
def __init__(self):
super(CenterLoss, self).__init__()
self.l2_loss = nn.MSELoss(reduction='sum')
def forward(self, outputs, targets):
return self.l2_loss(outputs, targets) / outputs.size(0)
def get_inputs():
retur... | 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... | bysen32/WS-DAN.PyTorch | CenterLoss | false | 9,873 | [
"MIT"
] | 0 | de206591f037ea82fc52eaf6915de7f64375e0c9 | https://github.com/bysen32/WS-DAN.PyTorch/tree/de206591f037ea82fc52eaf6915de7f64375e0c9 |
InferenceBatchSoftmax | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | 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
... | IncyLiu/autokeras | InferenceBatchSoftmax | false | 5,346 | [
"MIT"
] | 1 | e9dbf66b005e2ffaabe29bc366bb4e72fa79add8 | https://github.com/IncyLiu/autokeras/tree/e9dbf66b005e2ffaabe29bc366bb4e72fa79add8 |
GCN | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | 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 torchvision.datasets imp... | tousifulhaque/DANet | GCN | false | 4,456 | [
"MIT"
] | 0 | 1a0c91f0e551a071b5e335b4157313780a8a1b1a | https://github.com/tousifulhaque/DANet/tree/1a0c91f0e551a071b5e335b4157313780a8a1b1a |
QNetwork | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | 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_... | pardi/DRL_navigation | QNetwork | false | 10,615 | [
"Apache-2.0"
] | 0 | 4b66edf696c34a53686c02ff91264f5d6b32dc02 | https://github.com/pardi/DRL_navigation/tree/4b66edf696c34a53686c02ff91264f5d6b32dc02 |
ScaledDotProductAttention | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | 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.... | L-Net-1992/DI-engine | ScaledDotProductAttention | false | 5,505 | [
"Apache-2.0"
] | 1 | 06803b4e18fa64bbed0fd1d44952242c0c063b0f | https://github.com/L-Net-1992/DI-engine/tree/06803b4e18fa64bbed0fd1d44952242c0c063b0f |
MAELoss | import torch
import torch.nn as nn
class MAELoss(nn.Module):
def __init__(self):
super(MAELoss, self).__init__()
def forward(self, outputs, target, *args):
val_pixels = torch.ne(target, 0).float()
loss = target * val_pixels - outputs * val_pixels
return torch.sum(torch.abs(lo... | 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
... | anglixjtu/MSG_CHN_WACV20 | MAELoss | false | 14,841 | [
"Apache-2.0"
] | 61 | 6910894cf3caed2ffde27586f96b132b0c1d1a98 | https://github.com/anglixjtu/MSG_CHN_WACV20/tree/6910894cf3caed2ffde27586f96b132b0c1d1a98 |
MLP | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | 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_... | KOWKO1/reinforcement-learning-tutorials | MLP | false | 5,432 | [
"MIT"
] | 1 | 5f29d6eba8b580041f3e82d88dc3e1cd8e4cae10 | https://github.com/KOWKO1/reinforcement-learning-tutorials/tree/5f29d6eba8b580041f3e82d88dc3e1cd8e4cae10 |
MultiHeadAttention | import math
import torch
import torch.nn as nn
class ScaledDotProductAttention(nn.Module):
def __init__(self, dropout=0.0):
super(ScaledDotProductAttention, self).__init__()
self.dropout = nn.Dropout(dropout)
def forward(self, query, key, value):
assert query.size()[-1] == key.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.... | luyu-fan/LRCM | MultiHeadAttention | false | 7,144 | [
"MIT"
] | 1 | 6b0e4d7998bc4969afa764eb753077e3f858f1ba | https://github.com/luyu-fan/LRCM/tree/6b0e4d7998bc4969afa764eb753077e3f858f1ba |
diag_offdiag_maxpool | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | 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
assert_size_stride = t... | JoshuaMitton/InvariantGraphNetworks | diag_offdiag_maxpool | false | 2,442 | [
"Apache-2.0"
] | 0 | f6d8f43c7a053425eee785d11c5de91ac50f367c | https://github.com/JoshuaMitton/InvariantGraphNetworks/tree/f6d8f43c7a053425eee785d11c5de91ac50f367c |
SpatialTemporalConv3D | import torch
import torch.nn as nn
class SpatialTemporalConv3D(nn.Module):
"""
Apply 3D conv. over an input signal composed of several input planes with distinct spatial and time axes, by performing 3D convolution over the spatiotemporal axes
args:
in_channels (int): number of channels in the inp... | 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... | Tencent/DVQA | SpatialTemporalConv3D | false | 14,489 | [
"BSD-3-Clause"
] | 408 | 21727333a6b41d54ad1a8beca1fcbe00a69ed347 | https://github.com/Tencent/DVQA/tree/21727333a6b41d54ad1a8beca1fcbe00a69ed347 |
ResBlock | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | 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... | oleges1/TTS | ResBlock | false | 7,375 | [
"MIT"
] | 1 | 19b389714078729fae29faf9c23112bdbe4c8dec | https://github.com/oleges1/TTS/tree/19b389714078729fae29faf9c23112bdbe4c8dec |
EncoderImagePrecomp | import torch
import numpy as np
from collections import OrderedDict
import torch.nn as nn
import torch.nn.init
def l2norm(X, dim=-1, eps=1e-12):
"""L2-normalize columns of X
"""
norm = torch.pow(X, 2).sum(dim=dim, keepdim=True).sqrt() + eps
X = torch.div(X, norm)
return X
class EncoderImagePreco... | 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 numpy as np
... | BruceW91/CVSE | EncoderImagePrecomp | false | 13,424 | [
"MIT"
] | 152 | 20fa1ff50d1dcb4a7b3799071fa78038e52db804 | https://github.com/BruceW91/CVSE/tree/20fa1ff50d1dcb4a7b3799071fa78038e52db804 |
FPNHead | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | 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 ... | choprahetarth/DeblurGANv2 | FPNHead | false | 15,037 | [
"BSD-3-Clause"
] | 321 | e36dc2fef169b8a37036abe62192b6a925fb6c81 | https://github.com/choprahetarth/DeblurGANv2/tree/e36dc2fef169b8a37036abe62192b6a925fb6c81 |
GELayerv2 | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.utils.data.distributed
class GELayerv2(nn.Module):
def __init__(self):
super(GELayerv2, self).__init__()
self.avg_pool = nn.AdaptiveAvgPool2d(1)
self.sigmod = nn.Sigmoid()
def forward(self, x):
_b, _c, _... | 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.nn.parallel
import torch.utils.data.distributed
assert_size_stride = torch._C._dynamo.guards.assert_size_... | SSusantAchary/OctaveConv_pytorch | GELayerv2 | false | 14,348 | [
"MIT"
] | 633 | 079f7da29d55c2eeed8985d33f0b2f765d7a469e | https://github.com/SSusantAchary/OctaveConv_pytorch/tree/079f7da29d55c2eeed8985d33f0b2f765d7a469e |
DenseGCNConv | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | 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.... | cshjin/pytorch_geometric | DenseGCNConv | false | 1,765 | [
"MIT"
] | 0 | 8dd0e76beb72135949a275edd851f80f7b97648f | https://github.com/cshjin/pytorch_geometric/tree/8dd0e76beb72135949a275edd851f80f7b97648f |
ResNetBottleneck | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | 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... | KH-Kyle/rmp_nav | ResNetBottleneck | false | 8,396 | [
"MIT"
] | 30 | d598fe70664a4cdc0e9b9dd4b52e84aa3de1b551 | https://github.com/KH-Kyle/rmp_nav/tree/d598fe70664a4cdc0e9b9dd4b52e84aa3de1b551 |
BottleneckLSTMCell | import logging
import torch
import torch.nn as nn
from torch.autograd import Variable
class BottleneckLSTMCell(nn.Module):
""" Creates a LSTM layer cell
Arguments:
input_channels : variable used to contain value of number of channels in input
hidden_channels : variable used to contain value of... | 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 logging
import torch.n... | alejodosr/adaptive-inattention | BottleneckLSTMCell | false | 6,172 | [
"MIT"
] | 1 | ad1c883081e5248704be5ce5c4baa24b2eda1c59 | https://github.com/alejodosr/adaptive-inattention/tree/ad1c883081e5248704be5ce5c4baa24b2eda1c59 |
APLoss_dist | import torch
import numpy as np
from torch import nn
def sim_to_dist(scores):
return 1 - torch.sqrt(2.001 - 2 * scores)
class APLoss(nn.Module):
""" Differentiable AP loss, through quantization. From the paper:
Learning with Average Precision: Training Image Retrieval with a Listwise Loss
J... | 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.... | ByungHeeCha/visual_localization | APLoss_dist | false | 17,040 | [
"BSD-3-Clause"
] | 3 | 787fb8f6ee5c6e69ece9e83a016d15596e5524bc | https://github.com/ByungHeeCha/visual_localization/tree/787fb8f6ee5c6e69ece9e83a016d15596e5524bc |
PKT | import torch
from torch import nn
class PKT(nn.Module):
"""Probabilistic Knowledge Transfer for deep representation learning
Code from author: https://github.com/passalis/probabilistic_kt"""
def __init__(self):
super(PKT, self).__init__()
def forward(self, f_s, f_t):
return self.cosi... | 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
fr... | kctsiolis/RepDistiller | PKT | false | 3,932 | [
"BSD-2-Clause"
] | 0 | ce88f6e53fcf8ef81c5bac2d20ad31628dd279ac | https://github.com/kctsiolis/RepDistiller/tree/ce88f6e53fcf8ef81c5bac2d20ad31628dd279ac |
GatedMaskedConv2d | import torch
import torch.utils.data
from torch import nn
import torch.nn.functional as F
class GatedMaskedConv2d(nn.Module):
def __init__(self, in_dim, out_dim=None, kernel_size=3, mask='B'):
super(GatedMaskedConv2d, self).__init__()
if out_dim is None:
out_dim = in_dim
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 libdevice
import torch.utils.... | yining1023/vae-lagging-encoder | GatedMaskedConv2d | false | 4,624 | [
"MIT"
] | 0 | 88598b8400b3507090c05b9a6c01aa85b6e2cc87 | https://github.com/yining1023/vae-lagging-encoder/tree/88598b8400b3507090c05b9a6c01aa85b6e2cc87 |
NasAvgPoolBlock | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | 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.utils.data
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C.... | HyperGAN/imgclsmob | NasAvgPoolBlock | false | 17,686 | [
"MIT"
] | 9 | 88b9776a5a927dc9a54e85e31978c4a9ec5ecbf3 | https://github.com/HyperGAN/imgclsmob/tree/88b9776a5a927dc9a54e85e31978c4a9ec5ecbf3 |
FocalLossV1 | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | 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... | imvladikon/pytorch-loss | FocalLossV1 | false | 6,875 | [
"MIT"
] | 1 | 6cfaabe1be898e1ff000b3dffb46d0ef09096f6b | https://github.com/imvladikon/pytorch-loss/tree/6cfaabe1be898e1ff000b3dffb46d0ef09096f6b |
SingleDeconv3DBlock | import torch
from torch import nn
import torch._utils
class SingleDeconv3DBlock(nn.Module):
def __init__(self, in_planes, out_planes):
super().__init__()
self.block = nn.ConvTranspose3d(in_planes, out_planes, kernel_size=
2, stride=2, padding=0, output_padding=0)
def forward(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 import nn
import torch._utils
assert_size_stride = torch._C._dynamo.g... | ilcessadecalcular/segmentation | SingleDeconv3DBlock | false | 10,614 | [
"MIT"
] | 0 | 24ba499a399efdba212ec5e2235b72ed8270cc24 | https://github.com/ilcessadecalcular/segmentation/tree/24ba499a399efdba212ec5e2235b72ed8270cc24 |
FiLMLayer_PreSin | import torch
import numpy as np
import torch.nn as nn
class FiLMLayer_PreSin(nn.Module):
def __init__(self, in_dim, out_dim, style_dim, use_style_fc=True,
which_linear=nn.Linear, **kwargs):
super(FiLMLayer_PreSin, self).__init__()
self.in_dim = in_dim
self.out_dim = out_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 math as tl_math
import numpy ... | justinjohn0306/CIPS-3D | FiLMLayer_PreSin | false | 6,999 | [
"MIT"
] | 1 | 69a910a7841846419a6b5e03182c8cf061a82584 | https://github.com/justinjohn0306/CIPS-3D/tree/69a910a7841846419a6b5e03182c8cf061a82584 |
Policy | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | 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.... | Cranial-XIX/TRPO-and-its-variant | Policy | false | 324 | [
"MIT"
] | 0 | aa74102d013c998a666683667073c22aad8c5bce | https://github.com/Cranial-XIX/TRPO-and-its-variant/tree/aa74102d013c998a666683667073c22aad8c5bce |
RNNCell | import torch
from torch import nn
class RNNCell(nn.Module):
def __init__(self, embed_dim, hidden_size, vocab_dim):
super().__init__()
self.hidden_size = hidden_size
self.input2hidden = nn.Linear(embed_dim + hidden_size, hidden_size)
def forward(self, inputs, hidden):
combined... | 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... | sdhnshu/HandsOnDeepLearningWithPytorch | RNNCell | false | 16,379 | [
"MIT"
] | 87 | 2292a952a4cb112b03d5db4048c78bc503eb858d | https://github.com/sdhnshu/HandsOnDeepLearningWithPytorch/tree/2292a952a4cb112b03d5db4048c78bc503eb858d |
Normalize | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | 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
import torch.utils.data
asse... | Alescontrela/AMP_for_hardware | Normalize | false | 7,632 | [
"BSD-3-Clause"
] | 11 | bfb0dbdcf32bdf83a916790bddf193fffc7e79b8 | https://github.com/Alescontrela/AMP_for_hardware/tree/bfb0dbdcf32bdf83a916790bddf193fffc7e79b8 |
PEGCNLayer | import torch
import torch.nn as nn
class PEGCNLayer(nn.Module):
def __init__(self, input_dim, output_dim, prop_depth, act=torch.relu,
dropout=0.0, layer_i=0):
super(PEGCNLayer, self).__init__()
self.prop_depth = prop_depth
self.act = act
self.weight = nn.Parameter(torch.em... | 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.... | snap-stanford/distance-encoding | PEGCNLayer | false | 16,498 | [
"MIT"
] | 177 | b9ccb1b59422b11b40883d0284d7fc9ba88efdb6 | https://github.com/snap-stanford/distance-encoding/tree/b9ccb1b59422b11b40883d0284d7fc9ba88efdb6 |
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