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 |
|---|---|---|---|---|---|---|---|---|---|---|
EqualLinear | # 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.nn as nn
import torch.utils.data
assert_size_stride = torch._C._dyn... | GuiCamargoX/gans_pytorch | EqualLinear | false | 9,142 | [
"MIT"
] | 0 | 3103184e54ea0d2922fc664a994a912bf61db426 | https://github.com/GuiCamargoX/gans_pytorch/tree/3103184e54ea0d2922fc664a994a912bf61db426 |
FocalTverskyLoss | # 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... | Exdenta/torchsat | FocalTverskyLoss | false | 13,656 | [
"MIT"
] | 316 | 70ea3db758757104fb3ba618ddf7997f0f3a75b4 | https://github.com/Exdenta/torchsat/tree/70ea3db758757104fb3ba618ddf7997f0f3a75b4 |
Simplenet | import torch
from torch.optim.lr_scheduler import *
import torch.nn.functional as F
import torch.optim
import torch.nn as nn
import torch.utils.data
import torch.utils.model_zoo
class Simplenet(nn.Module):
def __init__(self):
super(Simplenet, self).__init__()
self.conv1 = nn.Conv2d(3, 6, 5)
... | 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.optim.lr_scheduler... | ChitienSun/NCTU_DLSR_final_project | Simplenet | false | 293 | [
"MIT"
] | 0 | 9d647426c274afc7651ea4fe9a11f2a0a0fd1fba | https://github.com/ChitienSun/NCTU_DLSR_final_project/tree/9d647426c274afc7651ea4fe9a11f2a0a0fd1fba |
Classifier | import torch
from torch import nn
import torch.nn.functional as F
class Classifier(nn.Module):
def __init__(self, input_size):
super().__init__()
self.hidden_1 = nn.Linear(input_size, 100)
self.hidden_2 = nn.Linear(100, 100)
self.hidden_3 = nn.Linear(100, 50)
self.hidden_4... | 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.... | Develop-Packt/Solving-a-Classification-Problem-with-DNNs-Using-PyTorch | Classifier | false | 7,977 | [
"MIT"
] | 16 | d0fe33c71242da256e3727bb49417a08de39c85c | https://github.com/Develop-Packt/Solving-a-Classification-Problem-with-DNNs-Using-PyTorch/tree/d0fe33c71242da256e3727bb49417a08de39c85c |
ContractingBlock | # 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_... | furkannturkmen/pytorch-CNN-architecture | ContractingBlock | false | 10,124 | [
"MIT"
] | 0 | 6a864811f51409c1526224c288fe608010e0c888 | https://github.com/furkannturkmen/pytorch-CNN-architecture/tree/6a864811f51409c1526224c288fe608010e0c888 |
MHAScoresCalculation | import math
import torch
import torch.nn as nn
import torch.cuda
import torch.backends.cudnn
import torch.backends.mkl
class MHAScoresCalculation(nn.Module):
def __init__(self, dim_per_head, softmax_dim=-1):
super(MHAScoresCalculation, self).__init__()
self.softmax = nn.Softmax(dim=softmax_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 import triton_helpers
from torch._inductor.runtime.... | JudeDavis1/intel-extension-for-pytorch | MHAScoresCalculation | false | 2,577 | [
"Apache-2.0"
] | 0 | 364e34cb4917a709f5108c07d4005bf82f3d5067 | https://github.com/JudeDavis1/intel-extension-for-pytorch/tree/364e34cb4917a709f5108c07d4005bf82f3d5067 |
SymDecoder | import torch
from torch import nn
import torch.utils.data
class SymDecoder(nn.Module):
def __init__(self, featureSize, symmetrySize, hiddenSize):
super(SymDecoder, self).__init__()
self.decode = nn.Linear(featureSize, hiddenSize)
self.second = nn.Linear(hiddenSize, hiddenSize)
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.triton_helpers import libdevice
from torch import n... | BigkoalaZhu/SCORES | SymDecoder | false | 7,793 | [
"MIT"
] | 16 | 8332733c375ee85c02bd34c2adce6a3213aad3c4 | https://github.com/BigkoalaZhu/SCORES/tree/8332733c375ee85c02bd34c2adce6a3213aad3c4 |
F1 | import torch
import torch.nn as nn
class Recall(nn.Module):
"""
This class implements the recall score. No gradients supported.
"""
def __init__(self, threshold: 'float'=0.5) ->None:
"""
Constructor method
:param threshold: (float) Threshold to be applied
"""
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 import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | ChristophReich1996/Cell-DETR | F1 | false | 13,501 | [
"MIT"
] | 55 | 4d0c3a2d3ffd19184c8443e5b3a6dccc053c77ea | https://github.com/ChristophReich1996/Cell-DETR/tree/4d0c3a2d3ffd19184c8443e5b3a6dccc053c77ea |
MultiHeadedAttention | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
class MultiHeadedAttention(nn.Module):
def __init__(self, num_head, d_model, dropout=0.1):
super(MultiHeadedAttention, self).__init__()
assert d_model % num_head == 0
self.d_k = d_model // num_head
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.... | qi700/my_point_summarize | MultiHeadedAttention | false | 4,155 | [
"Apache-2.0"
] | 0 | e269c2d0411fc61ea34055c3080472bc9111bcaa | https://github.com/qi700/my_point_summarize/tree/e269c2d0411fc61ea34055c3080472bc9111bcaa |
ConvTemporalGraphical | # 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.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | Hunkzer/mmskeleton | ConvTemporalGraphical | false | 2,361 | [
"Apache-2.0"
] | 0 | 551e3b4fa01330b23caab5815a40fbd848400b15 | https://github.com/Hunkzer/mmskeleton/tree/551e3b4fa01330b23caab5815a40fbd848400b15 |
WeightQuantizer | # 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
from torch.... | XueYue404/QNN | WeightQuantizer | false | 1,255 | [
"MIT"
] | 0 | 43cea970404156b591088d77672df58261edf1eb | https://github.com/XueYue404/QNN/tree/43cea970404156b591088d77672df58261edf1eb |
Illumination_Alone | # 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_... | AndersonYong/URetinex-Net-Retinex-based-Deep-Unfolding-Network-for-Low-light-Image-Enhancem | Illumination_Alone | false | 10,007 | [
"MIT"
] | 0 | 9d837b8df9c761defb1eca390b3a60aa4a6fbb1a | https://github.com/AndersonYong/URetinex-Net-Retinex-based-Deep-Unfolding-Network-for-Low-light-Image-Enhancem/tree/9d837b8df9c761defb1eca390b3a60aa4a6fbb1a |
DiscriminatorHingeLoss | # 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... | kpandey008/SAGAN | DiscriminatorHingeLoss | false | 10,432 | [
"MIT"
] | 0 | 8e673d2ccabeb0450faf30dcb347b9ff2d710ae2 | https://github.com/kpandey008/SAGAN/tree/8e673d2ccabeb0450faf30dcb347b9ff2d710ae2 |
Conv2dTime | import torch
import torch.nn as nn
class Conv2dTime(nn.Conv2d):
"""
Implements time dependent 2d convolutions, by appending the time variable as
an extra channel.
"""
def __init__(self, in_channels, *args, **kwargs):
super(Conv2dTime, self).__init__(in_channels + 1, *args, **kwargs)
... | 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... | anway/augmented-neural-odes | Conv2dTime | false | 14,883 | [
"MIT"
] | 449 | 561cfa540ef292d117ba9cf083758281774f3f22 | https://github.com/anway/augmented-neural-odes/tree/561cfa540ef292d117ba9cf083758281774f3f22 |
projection_model | import torch
class projection_model(torch.nn.Module):
def __init__(self, neo_hidden, clip_hidden=512):
super(projection_model, self).__init__()
self.fc1 = torch.nn.Linear(neo_hidden, neo_hidden // 2)
self.act = torch.nn.GELU()
self.fc2 = torch.nn.Linear(neo_hidden // 2, clip_hidde... | 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
assert_size_stride ... | ShivanshuPurohit/GPT-Neo-visual-grounding | projection_model | false | 17,910 | [
"Apache-2.0"
] | 4 | 9c938257a688ef5ae8bc1b87b61d943aa158e880 | https://github.com/ShivanshuPurohit/GPT-Neo-visual-grounding/tree/9c938257a688ef5ae8bc1b87b61d943aa158e880 |
FiLMLayer | import torch
from torch import nn
class FiLMLayer(nn.Module):
def __init__(self, input_dim, hidden_dim):
super().__init__()
self.layer = nn.Linear(input_dim, hidden_dim)
def forward(self, x, freq, phase_shift):
x = self.layer(x)
freq = freq.unsqueeze(1).expand_as(x)
p... | 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
from torch im... | xh-liu-tech/CIPS-3D | FiLMLayer | false | 11,103 | [
"MIT"
] | 0 | 8910dfcf19bb86aab2287d652ae4e3666806b511 | https://github.com/xh-liu-tech/CIPS-3D/tree/8910dfcf19bb86aab2287d652ae4e3666806b511 |
DEC_Conv | # 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.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | amonod/udvd | DEC_Conv | false | 1,440 | [
"MIT"
] | 0 | a1ccb777d205255ac68c40efb93dd3996f562c45 | https://github.com/amonod/udvd/tree/a1ccb777d205255ac68c40efb93dd3996f562c45 |
TransformerLayer | # 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.... | COEN-390/YOLOv5-Lite | TransformerLayer | false | 11,283 | [
"MIT"
] | 0 | 06a53f5d001c5d37729f55f47cbd46cc8eb63f84 | https://github.com/COEN-390/YOLOv5-Lite/tree/06a53f5d001c5d37729f55f47cbd46cc8eb63f84 |
MutliClassNN | import torch
from torch import nn
class MutliClassNN(nn.Module):
def __init__(self, num_features, num_labels):
super(MutliClassNN, self).__init__()
self.fc1 = torch.nn.Linear(num_features, 1000)
self.fc3 = torch.nn.Linear(1000, num_labels)
def forward(self, x):
x = torch.relu... | 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... | mhagenow01/ECE532ClassifierComparison | MutliClassNN | false | 10,477 | [
"MIT"
] | 0 | 5066931d97aae2c25c8b9451fe3d12021f5748a1 | https://github.com/mhagenow01/ECE532ClassifierComparison/tree/5066931d97aae2c25c8b9451fe3d12021f5748a1 |
Downsample | import torch
import torch.nn as nn
import torch.nn.parallel
class Downsample(nn.Module):
"""
Image to Patch Embedding, downsampling between stage1 and stage2
"""
def __init__(self, in_embed_dim, out_embed_dim, patch_size):
super().__init__()
self.proj = nn.Conv2d(in_embed_dim, out_emb... | 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.nn.parallel
assert_size_stride = torch._C._dy... | QLSong/cv-classify | Downsample | false | 2,737 | [
"Apache-2.0"
] | 0 | 02f53d03868f299a08b5c97a266b50a7fdcd3f2b | https://github.com/QLSong/cv-classify/tree/02f53d03868f299a08b5c97a266b50a7fdcd3f2b |
TestSub | # 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.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | AliaksandrSiarohin/pytorch2keras | TestSub | false | 8,900 | [
"MIT"
] | 0 | 9c8ee213cff43ade152b1de78fa76fd05ec8b40a | https://github.com/AliaksandrSiarohin/pytorch2keras/tree/9c8ee213cff43ade152b1de78fa76fd05ec8b40a |
HessianResp | # 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 numpy ... | rdguez-mariano/affnet | HessianResp | false | 16,321 | [
"MIT"
] | 211 | a3f0bb32d9001d1daf024f38d29867f37816ea78 | https://github.com/rdguez-mariano/affnet/tree/a3f0bb32d9001d1daf024f38d29867f37816ea78 |
Attention | import torch
import torch.nn as nn
def weight_init(m):
if isinstance(m, nn.Linear):
size = m.weight.size()
size[0]
size[1]
variance = 0.001
m.weight.data.normal_(0.0, variance)
try:
m.bias.data.normal_(0.0, 0.0001)
except:
pass
clas... | 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 ... | EpiSci/SoCRATES | Attention | false | 17,250 | [
"MIT"
] | 6 | 901a896c5a765e3cb56f290188cde71c8707192d | https://github.com/EpiSci/SoCRATES/tree/901a896c5a765e3cb56f290188cde71c8707192d |
_Residual_Block_SR | import torch
import torch.nn.functional
import torch.nn as nn
class _Residual_Block_SR(nn.Module):
def __init__(self, num_ft):
super(_Residual_Block_SR, self).__init__()
self.conv1 = nn.Conv2d(in_channels=num_ft, out_channels=num_ft,
kernel_size=3, stride=1, padding=1, bias=True)
... | 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.functional
import torch.nn as nn
assert_size_stride = torch._C._... | CarlosPena00/pytorchvision | _Residual_Block_SR | false | 229 | [
"MIT"
] | 0 | 824b3a5a8940f3ee6b4da5de7a391a88e5aa36a2 | https://github.com/CarlosPena00/pytorchvision/tree/824b3a5a8940f3ee6b4da5de7a391a88e5aa36a2 |
Similarity | # 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.nn as nn
assert... | YJiangcm/DCPCSE | Similarity | false | 18,125 | [
"MIT"
] | 5 | 698255e2e66b402325ff611e098e01d2f322743e | https://github.com/YJiangcm/DCPCSE/tree/698255e2e66b402325ff611e098e01d2f322743e |
gram_matrix | # 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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | ipjessica/neural-style-transfer | gram_matrix | false | 12,535 | [
"MIT"
] | 0 | ae0fc5e1e69d5d52997e5cab69e880085e04723b | https://github.com/ipjessica/neural-style-transfer/tree/ae0fc5e1e69d5d52997e5cab69e880085e04723b |
MockAccuracy | import torch
class _Metric(torch.nn.Module):
def __init__(self):
super().__init__()
def forward(self, input: 'torch.Tensor', target: 'torch.Tensor'):
raise NotImplementedError()
class Accuracy(_Metric):
def __init__(self):
super().__init__()
def forward(self, input: 'torc... | 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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torc... | NestLakerJasonLIN/MusicTransformer-pytorch | MockAccuracy | false | 5,654 | [
"MIT"
] | 1 | 5f183374833ff6b7e17f3a24e3594dedd93a5fe5 | https://github.com/NestLakerJasonLIN/MusicTransformer-pytorch/tree/5f183374833ff6b7e17f3a24e3594dedd93a5fe5 |
MultiRelu | # 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... | LMdeLiangMi/captum | MultiRelu | false | 5,475 | [
"BSD-3-Clause"
] | 1 | 8bd9686013fe0ba8996e9b1cbeb0ea8e91512787 | https://github.com/LMdeLiangMi/captum/tree/8bd9686013fe0ba8996e9b1cbeb0ea8e91512787 |
TripletLoss | # 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.... | LunarShen/SECRET | TripletLoss | false | 2,601 | [
"MIT"
] | 0 | 0f652e63ce760ece8690cbad013f0d9bdb341e84 | https://github.com/LunarShen/SECRET/tree/0f652e63ce760ece8690cbad013f0d9bdb341e84 |
TorchLogCosh | import torch
import torch as _torch
class TorchLogCosh(_torch.nn.Module):
"""
Log(cosh) activation function for PyTorch modules
"""
def __init__(self):
"""
Init method.
"""
super().__init__()
def forward(self, input):
"""
Forward pass of the functi... | 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 as _torch
assert_size_stride = torch._C._dynamo.g... | inailuig/netket | TorchLogCosh | false | 10,194 | [
"Apache-2.0"
] | 0 | ab57a6fb019edb9ac298969950724781f2ae2b22 | https://github.com/inailuig/netket/tree/ab57a6fb019edb9ac298969950724781f2ae2b22 |
GetStyleLoss | # 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
import torch.nn as nn
assert_... | jaredaevans/UltrafastNST | GetStyleLoss | false | 6,919 | [
"MIT"
] | 1 | 6671c6b618ce6bb4920b15f782be962e484a5423 | https://github.com/jaredaevans/UltrafastNST/tree/6671c6b618ce6bb4920b15f782be962e484a5423 |
FocalLoss | # 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
from torch ... | marshuang80/pe-slice-finder | FocalLoss | false | 7,163 | [
"Apache-2.0"
] | 1 | 2426a55c404e8eb694110351d604d6bdd613e5ae | https://github.com/marshuang80/pe-slice-finder/tree/2426a55c404e8eb694110351d604d6bdd613e5ae |
PGenLayer | # 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.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | gau820827/AI-writer_Data2Doc | PGenLayer | false | 15,406 | [
"Apache-2.0"
] | 77 | 6be0ee6238158a47aa0fdfa8a34df2a47714835a | https://github.com/gau820827/AI-writer_Data2Doc/tree/6be0ee6238158a47aa0fdfa8a34df2a47714835a |
PixelNormLayer | # 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 libdevice
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_... | NunoEdgarGFlowHub/gandissect | PixelNormLayer | false | 5,663 | [
"MIT"
] | 1 | 1a162a6bd3d4842139feb9f191aa1fad565dee4e | https://github.com/NunoEdgarGFlowHub/gandissect/tree/1a162a6bd3d4842139feb9f191aa1fad565dee4e |
RobertaClassificationHead | from _paritybench_helpers import _mock_config
import torch
from torch import nn
class RobertaClassificationHead(nn.Module):
"""Head for sentence-level classification tasks."""
def __init__(self, config):
super(RobertaClassificationHead, self).__init__()
self.dense = nn.Linear(config.hidden_si... | 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... | INK-USC/expl-refinement | RobertaClassificationHead | false | 18,385 | [
"MIT"
] | 7 | 815a7892a8d4c42fb429856746212a44f67d2547 | https://github.com/INK-USC/expl-refinement/tree/815a7892a8d4c42fb429856746212a44f67d2547 |
GeneralizedMeanPooling | # 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.nn as nn
assert... | AsyaPes/light-reid-master | GeneralizedMeanPooling | false | 8,961 | [
"MIT"
] | 0 | acb4bdd973cdf3832294d8e42442305ab52014f5 | https://github.com/AsyaPes/light-reid-master/tree/acb4bdd973cdf3832294d8e42442305ab52014f5 |
KlLoss | # 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
... | by-liu/RetinalApp | KlLoss | false | 1,621 | [
"MIT"
] | 0 | 53173b2b20dfcf613a3a22d6caa5178771d14225 | https://github.com/by-liu/RetinalApp/tree/53173b2b20dfcf613a3a22d6caa5178771d14225 |
Net | # 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 ... | zyouc518/crow | Net | false | 4,740 | [
"Apache-2.0"
] | 0 | e3fe92e329649fb82b3fef6c0ab5b732f1918900 | https://github.com/zyouc518/crow/tree/e3fe92e329649fb82b3fef6c0ab5b732f1918900 |
SimpleFC | import torch
import torch.nn as nn
import torch.onnx
class SimpleFC(nn.Module):
def __init__(self, input_size, num_classes, name='SimpleFC'):
super(SimpleFC, self).__init__()
self.FC = nn.Parameter(torch.randn([input_size, num_classes]))
self.FCbias = nn.Parameter(torch.randn([num_classes... | 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.onnx
assert_size_stride = torch._C._dynamo.gu... | adityakusupati/EdgeML | SimpleFC | false | 3,021 | [
"MIT"
] | 0 | 65933a6fdfc38945f4311043a62e120784b2b0bf | https://github.com/adityakusupati/EdgeML/tree/65933a6fdfc38945f4311043a62e120784b2b0bf |
BasicBlockWN | # 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.... | iffiX/machin | BasicBlockWN | false | 15,641 | [
"MIT"
] | 287 | 7fa986b1bafdefff117d6ff73d14644a5488de9d | https://github.com/iffiX/machin/tree/7fa986b1bafdefff117d6ff73d14644a5488de9d |
h_swish | import torch
import torch.nn as nn
class h_sigmoid(nn.Module):
def __init__(self, inplace=True):
super(h_sigmoid, self).__init__()
self.relu = nn.ReLU6(inplace=inplace)
def forward(self, x):
return self.relu(x + 3) / 6
class h_swish(nn.Module):
def __init__(self, inplace=True)... | 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... | Felicia980317/mytorch | h_swish | false | 467 | [
"Apache-2.0"
] | 0 | e463122c0d402878ec5b4c5a823a0feeba8fdbfe | https://github.com/Felicia980317/mytorch/tree/e463122c0d402878ec5b4c5a823a0feeba8fdbfe |
PACRRConvMax2dModule | # 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.utils.data
asser... | AlexWang000/capreolus | PACRRConvMax2dModule | false | 4,827 | [
"Apache-2.0"
] | 1 | 00b0bf471ea0eb116ab973254ea61b0492405c54 | https://github.com/AlexWang000/capreolus/tree/00b0bf471ea0eb116ab973254ea61b0492405c54 |
Conv2 | # 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 ... | neverix/voice-conv | Conv2 | false | 7,328 | [
"MIT"
] | 1 | 6df0053a59aa26318bdbc096dd312ecc55596ac0 | https://github.com/neverix/voice-conv/tree/6df0053a59aa26318bdbc096dd312ecc55596ac0 |
AttentionUnit | import torch
import torch.nn.functional as F
from torch import nn
from torch.nn import init
class AttentionUnit(nn.Module):
def __init__(self, sDim, xDim, attDim):
super(AttentionUnit, self).__init__()
self.sDim = sDim
self.xDim = xDim
self.attDim = attDim
self.sEmbed = nn... | 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.... | YacobBY/ICDAR2019-ArT-Recognition-Alchemy | AttentionUnit | false | 14,625 | [
"MIT"
] | 209 | 911c572c2aff4599a74b7974d46ef4cfb17078b9 | https://github.com/YacobBY/ICDAR2019-ArT-Recognition-Alchemy/tree/911c572c2aff4599a74b7974d46ef4cfb17078b9 |
ConvReLU | # 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 math
import torch.nn.functional as F
from torch.nn import Conv2d
from tor... | pc2005/MonoRec | ConvReLU | false | 12,868 | [
"MIT"
] | 0 | 6e1628eeef9987b1acce3e5e8bb6a6a324fc8d2c | https://github.com/pc2005/MonoRec/tree/6e1628eeef9987b1acce3e5e8bb6a6a324fc8d2c |
DepthwiseSeparableConv | # 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
import torch.nn.functional
import torch
assert_size_stride ... | YiminYang980510/A-TransUNet | DepthwiseSeparableConv | false | 18,163 | [
"MIT"
] | 10 | 600b9abef3460d9751d3a6b7b4e4586aec164aa7 | https://github.com/YiminYang980510/A-TransUNet/tree/600b9abef3460d9751d3a6b7b4e4586aec164aa7 |
ChannelSELayer3D | import torch
import torch.nn as nn
class ChannelSELayer3D(nn.Module):
"""
3D extension of Squeeze-and-Excitation (SE) block described in:
*Hu et al., Squeeze-and-Excitation Networks, arXiv:1709.01507*
*Zhu et al., AnatomyNet, arXiv:arXiv:1808.05238*
"""
def __init__(self, num_channels... | 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_... | Hsuxu/vnet_attention | ChannelSELayer3D | false | 13,789 | [
"MIT"
] | 45 | 6958932f3974d268e93bd6443369a3f43c497ed3 | https://github.com/Hsuxu/vnet_attention/tree/6958932f3974d268e93bd6443369a3f43c497ed3 |
MultiHeadedAttention | import math
import torch
from torch import Tensor
import torch.nn as nn
class MultiHeadedAttention(nn.Module):
"""
Multi-Head Attention module from "Attention is All You Need"
Implementation modified from OpenNMT-py.
https://github.com/OpenNMT/OpenNMT-py
"""
def __init__(self, num_heads: '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.... | AmitMY/joeynmt | MultiHeadedAttention | false | 13,265 | [
"Apache-2.0"
] | 563 | b30d1d53823ced56113def8fb5d5f7905d3c059f | https://github.com/AmitMY/joeynmt/tree/b30d1d53823ced56113def8fb5d5f7905d3c059f |
GaussianPolicyFunction | # 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, math as tl_math
im... | himanshusahni/task-biased-url | GaussianPolicyFunction | false | 10,259 | [
"MIT"
] | 0 | 28e4ec318d46d84065b6e197fa9f4100bd4a4c34 | https://github.com/himanshusahni/task-biased-url/tree/28e4ec318d46d84065b6e197fa9f4100bd4a4c34 |
ShiftedSoftplus | # 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 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 |
RNN | import torch
import torch.nn as nn
from torch.autograd import Variable
class RNN(nn.Module):
def __init__(self, category_size, input_size, hidden_size, output_size):
super(RNN, self).__init__()
self.category_size = category_size
self.input_size = input_size
self.hidden_size = hidd... | 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.autograd import Variable
assert_size_stride = t... | iclementine/practical-pytorch | RNN | false | 10,187 | [
"MIT"
] | 0 | 88e2e53e47328cdb3ec23573aec3ff0421f1a2b7 | https://github.com/iclementine/practical-pytorch/tree/88e2e53e47328cdb3ec23573aec3ff0421f1a2b7 |
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 import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import functools
impor... | AtticusJohnson/mmdetection | SmoothL1Loss | false | 11,245 | [
"Apache-2.0"
] | 0 | d8d89bafcce13d3b32b1fb3366be3bb9830546c2 | https://github.com/AtticusJohnson/mmdetection/tree/d8d89bafcce13d3b32b1fb3366be3bb9830546c2 |
AverageAttention | # 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.nn as nn
import torch.cuda
import torch.distributed
assert_size_str... | GarrettNicolai/OpenNMT-py | AverageAttention | false | 9,108 | [
"MIT"
] | 0 | 9491d900ac1b50fe39da417bacc0b9d610331888 | https://github.com/GarrettNicolai/OpenNMT-py/tree/9491d900ac1b50fe39da417bacc0b9d610331888 |
ConvertPointsToHomogeneous | # 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.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | Paultool/frankmocap | ConvertPointsToHomogeneous | false | 14,151 | [
"BSD-3-Clause"
] | 1,612 | b8bb7b587c0841b9292edb147729de581c66054c | https://github.com/Paultool/frankmocap/tree/b8bb7b587c0841b9292edb147729de581c66054c |
_Multiply | # 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 abc
from torch import Tensor
from torch.nn im... | f-dangel/backpack | _Multiply | false | 15,334 | [
"MIT"
] | 395 | 1da7e53ebb2c490e2b7dd9f79116583641f3cca1 | https://github.com/f-dangel/backpack/tree/1da7e53ebb2c490e2b7dd9f79116583641f3cca1 |
DisaggregatedPinballLoss | import torch
import torch.nn as nn
class DisaggregatedPinballLoss(nn.Module):
""" Pinball Loss
Computes the pinball loss between y and y_hat.
Parameters
----------
y: tensor
actual values in torch tensor.
y_hat: tensor (same shape as y)
predicted values in torch tensor.
tau: float, between 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... | venkatkorapaty/esrnn | DisaggregatedPinballLoss | false | 11,008 | [
"MIT"
] | 0 | 411d3191e7e12f29e521e06bc18f9b9b0fdf0f0c | https://github.com/venkatkorapaty/esrnn/tree/411d3191e7e12f29e521e06bc18f9b9b0fdf0f0c |
AsymmetricLossOptimized | import torch
import torch.nn as nn
class AsymmetricLossOptimized(nn.Module):
""" Notice - optimized version, minimizes memory allocation and gpu uploading,
favors inplace operations"""
def __init__(self, gamma_neg=4, gamma_pos=1, clip=0.05, eps=1e-08,
disable_torch_grad_focal_loss=False):
... | 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... | Pepijnnn/MasterThesis | AsymmetricLossOptimized | false | 939 | [
"MIT"
] | 0 | 7ec831f5e55f5f181e0196fa78284e2846ce2e26 | https://github.com/Pepijnnn/MasterThesis/tree/7ec831f5e55f5f181e0196fa78284e2846ce2e26 |
lp_L2_Loss | import torch
from torch.utils.data import *
import torch.nn as nn
class lp_L2_Loss(nn.Module):
def __init__(self):
super().__init__()
self.loss = nn.MSELoss(reduction='sum')
def forward(self, x, y):
b = x.shape[0]
loss = self.loss(x, y)
return loss / b
def get_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
from torch.utils.data import *
import torch.nn as nn
assert_size_stride = torch._C._dynam... | loveorchids/local_patch_retrieval | lp_L2_Loss | false | 3,937 | [
"Apache-2.0"
] | 0 | 52b2e8fdac965d56ef9f89a8c4de96d0b41d3981 | https://github.com/loveorchids/local_patch_retrieval/tree/52b2e8fdac965d56ef9f89a8c4de96d0b41d3981 |
DentReLU | # 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.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | bfeng/pytorch-cifar | DentReLU | false | 6,329 | [
"MIT"
] | 1 | 6de257bb4b489429785502d487044c55bec62aae | https://github.com/bfeng/pytorch-cifar/tree/6de257bb4b489429785502d487044c55bec62aae |
RadialPredictionLayer | import torch
import torch.nn as nn
class RadialPredictionLayer(torch.nn.Module):
""" The RPL classification layer with fixed prototypes
"""
def __init__(self, in_features, out_features):
super(RadialPredictionLayer, self).__init__()
self.in_features = in_features
self.out_features... | 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_... | Monkso/RPL-Softmax_RoadSigns | RadialPredictionLayer | false | 851 | [
"MIT"
] | 0 | 3df929d779ff02ec796e717659943bb46311ba0f | https://github.com/Monkso/RPL-Softmax_RoadSigns/tree/3df929d779ff02ec796e717659943bb46311ba0f |
DenseModel | import torch
from torch import nn
class DenseModel(nn.Module):
def __init__(self, input_shape, output_shape, hidden_size=150,
activation=None):
super(DenseModel, self).__init__()
self.l1 = nn.Linear(input_shape, hidden_size)
self.l2 = nn.Linear(hidden_size, output_shape)
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._inductor.runtime.triton_helpers import libdevice
from torch import n... | HSE-LAMBDA/pytorch_ard | DenseModel | false | 9,044 | [
"MIT"
] | 0 | b6b40d4c495d3374180698549d8fef0b768ffd3a | https://github.com/HSE-LAMBDA/pytorch_ard/tree/b6b40d4c495d3374180698549d8fef0b768ffd3a |
ConvModule | import torch
import torch.utils.data.distributed
from torch import nn
import torch.utils.data
class ConvModule(nn.Module):
def __init__(self, input_dim, kernel_size, dropout_rate, causal=False):
super(ConvModule, self).__init__()
self.layer_norm = nn.LayerNorm(input_dim)
self.pw_conv_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.... | ishine/StreamingTransformer | ConvModule | false | 15,664 | [
"Apache-2.0"
] | 252 | 4b56931a311d65686d310c54cc6896a4be4f47de | https://github.com/ishine/StreamingTransformer/tree/4b56931a311d65686d310c54cc6896a4be4f47de |
MultiHeadSelfAttention | import torch
import numpy as np
import torch.nn as nn
import torch.nn.init
import torch.nn.parallel
class MultiHeadSelfAttention(nn.Module):
"""Self-attention module by Lin, Zhouhan, et al. ICLR 2017"""
def __init__(self, n_head, d_in, d_hidden):
super(MultiHeadSelfAttention, 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
from torch._inductor.runtime.... | CLT29/pvse | MultiHeadSelfAttention | false | 13,454 | [
"MIT"
] | 119 | bf5232148396ee5051564ef68a48538de0ddbc84 | https://github.com/CLT29/pvse/tree/bf5232148396ee5051564ef68a48538de0ddbc84 |
DownConv | # 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 copy
from torch import... | ELEKTRONN/elektronn3 | DownConv | false | 13,627 | [
"MIT"
] | 124 | 19c751855dffc67b744cd43e757aa4a5bd577d9b | https://github.com/ELEKTRONN/elektronn3/tree/19c751855dffc67b744cd43e757aa4a5bd577d9b |
AR | # 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.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | lucianolorenti/rul_pm | AR | false | 7,125 | [
"MIT"
] | 1 | da9dfad79129dd47d24923cfd6c833869ef7b6a7 | https://github.com/lucianolorenti/rul_pm/tree/da9dfad79129dd47d24923cfd6c833869ef7b6a7 |
PointLSTMCell | import torch
import torch.nn as nn
class PointLSTMCell(nn.Module):
def __init__(self, pts_num, in_channels, hidden_dim, offset_dim, bias):
super(PointLSTMCell, self).__init__()
self.bias = bias
self.pts_num = pts_num
self.in_channels = in_channels
self.hidden_dim = hidden_... | 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.... | evanfebrianto/pointlstm_gesture_recognition_pytorch | PointLSTMCell | false | 15,331 | [
"Apache-2.0"
] | 69 | 797ccdc7da5a859e28f2a8cc7ef7118358b82cb4 | https://github.com/evanfebrianto/pointlstm_gesture_recognition_pytorch/tree/797ccdc7da5a859e28f2a8cc7ef7118358b82cb4 |
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 import triton_helpers
from torch._inductor.runtime.... | TevenLeScao/BasicSR | ResBlock | false | 18,005 | [
"Apache-2.0"
] | 4 | 1a7bd8754de00f3a9c9f2031acfc447350459ea0 | https://github.com/TevenLeScao/BasicSR/tree/1a7bd8754de00f3a9c9f2031acfc447350459ea0 |
MAPELoss | import torch
import torch.nn as nn
class MAPELoss(nn.Module):
def forward(self, input, target):
return (torch.abs(input - target) / (torch.abs(target) + 0.01)).mean()
def get_inputs():
return [torch.rand([4, 4, 4, 4]), 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
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | LongerVision/oidn | MAPELoss | false | 5,554 | [
"Apache-2.0"
] | 1 | 2f9e59f8b747b217f78c5c274f4f2bff347a03a7 | https://github.com/LongerVision/oidn/tree/2f9e59f8b747b217f78c5c274f4f2bff347a03a7 |
AdaptiveConcatPool2d | # 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.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | Vermeille/Torchelie | AdaptiveConcatPool2d | false | 14,543 | [
"MIT"
] | 117 | 43957d83238372ae6436aac90127865c2040b76c | https://github.com/Vermeille/Torchelie/tree/43957d83238372ae6436aac90127865c2040b76c |
VGG16 | import torch
import numpy as np
import torchvision.transforms.functional as F
import torch.nn as nn
import torch.nn.functional as F
class Normalize:
def __init__(self, mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)):
self.mean = mean
self.std = std
def undo(self, imgarr):
proc... | 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 numpy as np
import tor... | candacelax/1-stage-wseg | VGG16 | false | 3,367 | [
"Apache-2.0"
] | 0 | 7a24791a3a78454e6611399ba55a808491551543 | https://github.com/candacelax/1-stage-wseg/tree/7a24791a3a78454e6611399ba55a808491551543 |
StendLoss | import torch
from itertools import chain as chain
import torch.utils.data
import torch.nn as nn
from torch.nn.modules.loss import _Loss
class StendLoss(_Loss):
def __init__(self, size_average=None, reduce=None, reduction='mean'):
super(StendLoss, self).__init__()
self.reduction = reduction
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
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from iterto... | anton-br/SlowFast | StendLoss | false | 12,099 | [
"Apache-2.0"
] | 0 | 6e8d68bc6f3191886a57f819db1c766c6ca32d21 | https://github.com/anton-br/SlowFast/tree/6e8d68bc6f3191886a57f819db1c766c6ca32d21 |
TorchFCNModel | # 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
assert_size_stride = torch._C... | muratcancicek/pointer_head | TorchFCNModel | false | 12,809 | [
"MIT"
] | 0 | b2a357f0183d5ced82b6dc7f6f12e0391bdc7380 | https://github.com/muratcancicek/pointer_head/tree/b2a357f0183d5ced82b6dc7f6f12e0391bdc7380 |
StyleMod | import torch
import torch.nn as nn
import torch.nn.functional as F
class MyLinear(nn.Module):
"""Linear layer with equalized learning rate and custom learning rate multiplier."""
def __init__(self, input_size, output_size, gain=2 ** 0.5, use_wscale=
False, lrmul=1, bias=True):
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
import torch.nn as nn
import torch.nn.functional as F
assert_size_stride = torch... | eitanrich/ganspace-manifold | StyleMod | false | 12,338 | [
"Apache-2.0"
] | 0 | 148d5d30001c43794a40bbed885601e7816f5d7d | https://github.com/eitanrich/ganspace-manifold/tree/148d5d30001c43794a40bbed885601e7816f5d7d |
ExponentialEnvelope | # 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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_str... | Irlirion/ocp | ExponentialEnvelope | false | 13,837 | [
"MIT",
"BSD-3-Clause"
] | 242 | 6fb3e794eef31559db990300198eca20f41d8f37 | https://github.com/Irlirion/ocp/tree/6fb3e794eef31559db990300198eca20f41d8f37 |
FCUDown | import torch
from functools import partial
from torch import nn
class FCUDown(nn.Module):
""" CNN feature maps -> Transformer patch embeddings
"""
def __init__(self, inplanes, outplanes, dw_stride, act_layer=nn.GELU,
norm_layer=partial(nn.LayerNorm, eps=1e-06)):
super(FCUDown, self).__ini... | 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 functools impo... | CVPR2022-911/PPH | FCUDown | false | 8,980 | [
"Apache-2.0"
] | 0 | f066933525aaeef412b8d166ef167f00170b5428 | https://github.com/CVPR2022-911/PPH/tree/f066933525aaeef412b8d166ef167f00170b5428 |
ShuffleCat | import torch
import torch.nn as nn
class ShuffleCat(nn.Module):
def forward(self, a, b):
assert a.size() == b.size()
n, c, h, w = a.size()
a = a.permute(0, 2, 3, 1).contiguous().view(-1, c)
b = b.permute(0, 2, 3, 1).contiguous().view(-1, c)
x = torch.cat((a, b), dim=0).tra... | 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... | jjkennedy3/PINTO_model_zoo | ShuffleCat | false | 6,956 | [
"MIT"
] | 1 | a181c3015a6241873798c4ad3eadd4ce97024f70 | https://github.com/jjkennedy3/PINTO_model_zoo/tree/a181c3015a6241873798c4ad3eadd4ce97024f70 |
DiceLoss | # 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.nn as nn
import torch.hub
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo... | thangnx183/kaggle-understanding-clouds | DiceLoss | false | 16,575 | [
"BSD-2-Clause"
] | 207 | 15ad2a9029958262437b899cb00525579da23911 | https://github.com/thangnx183/kaggle-understanding-clouds/tree/15ad2a9029958262437b899cb00525579da23911 |
MNIST_CNN | # 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.... | FrancescoCappio/swad | MNIST_CNN | false | 9,087 | [
"MIT"
] | 0 | b1da3eacb7dc3711360e6621ca16f2d75c4f411c | https://github.com/FrancescoCappio/swad/tree/b1da3eacb7dc3711360e6621ca16f2d75c4f411c |
Actor | # 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.... | Thibaud-Ardoin/d4rl_evaluations | Actor | false | 14,496 | [
"Apache-2.0"
] | 123 | 135b23d3aecc234aacaeaaa019fbc7101d9b87ec | https://github.com/Thibaud-Ardoin/d4rl_evaluations/tree/135b23d3aecc234aacaeaaa019fbc7101d9b87ec |
SelfAttention0 | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
class MultiHeadedAttention(nn.Module):
def __init__(self, h, d_model, dropout=0.0):
"""Take in model size and number of heads."""
super(MultiHeadedAttention, self).__init__()
self.d_k = d_model // h
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
from torch._inductor.runtime.... | SSussexGit/deepikachu | SelfAttention0 | false | 2,807 | [
"MIT"
] | 0 | 72999c4a3f1767c3e5f332fe64cba9240ef43a79 | https://github.com/SSussexGit/deepikachu/tree/72999c4a3f1767c3e5f332fe64cba9240ef43a79 |
LocalNet | import torch
import torch.nn as nn
class LocalNet(nn.Module):
def forward(self, x_in):
"""Defines a double convolution
:param x_in: input convolutional features
:returns: convolutional features
:rtype: Tensor
"""
x = self.lrelu(self.conv1(self.refpad(x_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.triton_helpers import math as tl_math
import torch.... | sjmoran/CURL | LocalNet | false | 16,472 | [
"BSD-3-Clause"
] | 125 | 919e519717b66e14d92ac6fa404c328ee3f254a5 | https://github.com/sjmoran/CURL/tree/919e519717b66e14d92ac6fa404c328ee3f254a5 |
BahdanauAttention | # 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.... | Emily0219/distiller | BahdanauAttention | false | 5,142 | [
"Apache-2.0"
] | 1 | 445ed35b671fb54586acc280b53d951f18bf97ae | https://github.com/Emily0219/distiller/tree/445ed35b671fb54586acc280b53d951f18bf97ae |
Net | import torch
import torch.nn.functional as F
import torch.nn as nn
class Net(nn.Module):
def __init__(self, x_d, w_d, out_d, hidden_d1=256, hidden_d2=512,
hidden_d3=256, is_discrete_input=False, is_discrete_output=False,
embedding_dim=None):
super().__init__()
self._x_d = x_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_... | DataCanvasIO/YLearn | Net | false | 17,215 | [
"Apache-2.0"
] | 3 | d65b5afb83deed154c710de9096317165d95014a | https://github.com/DataCanvasIO/YLearn/tree/d65b5afb83deed154c710de9096317165d95014a |
ElemAffineNetwork | # 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.... | chawins/adv-exp | ElemAffineNetwork | false | 6,450 | [
"MIT"
] | 1 | 5423e135c5599e4ec2bf90372916d8d05c89f285 | https://github.com/chawins/adv-exp/tree/5423e135c5599e4ec2bf90372916d8d05c89f285 |
MaxPooling | # 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 typing import Union
import torch.nn as nn
from typing import Tuple
assert_size_strid... | Latterlig96/DCUnet | MaxPooling | false | 8,465 | [
"MIT"
] | 11 | 87d1c137a60177d6daf1dfff0483678d5580fda0 | https://github.com/Latterlig96/DCUnet/tree/87d1c137a60177d6daf1dfff0483678d5580fda0 |
VertexDirectEmbedder | # 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.utils.data
from... | nationaldronesau/detectron2 | VertexDirectEmbedder | false | 7,314 | [
"Apache-2.0"
] | 1 | 6afaee60eb6e0032b5b2edfbec1179f7e7b7b75f | https://github.com/nationaldronesau/detectron2/tree/6afaee60eb6e0032b5b2edfbec1179f7e7b7b75f |
AdaILN | # 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.utils.data
import torch.utils.data.distributed
import torch
import... | Lornatang/UGATIT_PyTorch | AdaILN | false | 8,490 | [
"Apache-2.0"
] | 25 | 03519e4829b85ceee67c031a28d5a9318ac932b5 | https://github.com/Lornatang/UGATIT_PyTorch/tree/03519e4829b85ceee67c031a28d5a9318ac932b5 |
Conv2dSame | import torch
from torchvision.transforms import *
import torch.nn
import torch
import torch.nn as nn
class Conv2dSame(torch.nn.Module):
"""2D convolution that pads to keep spatial dimensions equal.
Cannot deal with stride. Only quadratic kernels (=scalar kernel_size).
"""
def __init__(self, in_channe... | 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
from torchvis... | COMHTVM/lensless | Conv2dSame | false | 17,313 | [
"MIT"
] | 6 | 0d67a310bab08551d7422fa792f3422a7ee7d9cb | https://github.com/COMHTVM/lensless/tree/0d67a310bab08551d7422fa792f3422a7ee7d9cb |
DDPGConvBody | import torch
import torch.nn as nn
import torch.nn.functional as F
def layer_init(layer, w_scale=1.0):
nn.init.orthogonal_(layer.weight.data)
layer.weight.data.mul_(w_scale)
nn.init.constant_(layer.bias.data, 0)
return layer
class DDPGConvBody(nn.Module):
def __init__(self, in_channels=4):
... | 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 ... | Sohojoe/UdacityDeepRL-Project2 | DDPGConvBody | false | 5,953 | [
"MIT"
] | 1 | 7137eea0b606ea32d00424d23130ff213f03ecf1 | https://github.com/Sohojoe/UdacityDeepRL-Project2/tree/7137eea0b606ea32d00424d23130ff213f03ecf1 |
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 import triton_helpers
from torch._inductor.runtime.... | albimc/deep-reinforcement-learning | Policy | false | 1,402 | [
"MIT"
] | 0 | e11a6c9d4c8991cf229e686b645ae22ec4cff4f5 | https://github.com/albimc/deep-reinforcement-learning/tree/e11a6c9d4c8991cf229e686b645ae22ec4cff4f5 |
NoiseInjection | import torch
from torch import nn
class NoiseInjection(nn.Module):
def __init__(self, channel):
super().__init__()
self.weight = nn.Parameter(torch.zeros(1, channel, 1, 1))
def forward(self, image, noise):
return image + self.weight * noise
def get_inputs():
return [torch.rand(... | 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 import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_str... | hologerry/style-based-gan-pytorch | NoiseInjection | false | 3,624 | [
"MIT"
] | 0 | 1a694fb3ea0288f1aaaa43aa67a570d908d9dc27 | https://github.com/hologerry/style-based-gan-pytorch/tree/1a694fb3ea0288f1aaaa43aa67a570d908d9dc27 |
SymKlCriterion | import torch
import torch.nn.functional as F
from torch.nn.modules.loss import _Loss
from torch.optim.lr_scheduler import *
class Criterion(_Loss):
def __init__(self, alpha=1.0, name='criterion'):
super().__init__()
"""Alpha is used to weight each loss term
"""
self.alpha = alpha
... | 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.... | chunhuililili/mt_dnn | SymKlCriterion | false | 10,214 | [
"MIT"
] | 0 | 4c6efaf21724c7b8103a05e46b5b44d7b246225e | https://github.com/chunhuililili/mt_dnn/tree/4c6efaf21724c7b8103a05e46b5b44d7b246225e |
Encoder | # 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.... | simonepreite/QABERT | Encoder | false | 4,362 | [
"MIT"
] | 0 | ed3e49f6619f3ff660068291231909693cb8f5d5 | https://github.com/simonepreite/QABERT/tree/ed3e49f6619f3ff660068291231909693cb8f5d5 |
Dec | # 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.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | naraysa/gzsl-od | Dec | false | 16,134 | [
"MIT"
] | 50 | be771e12e17a4c02386c70697c4b26e3170a7557 | https://github.com/naraysa/gzsl-od/tree/be771e12e17a4c02386c70697c4b26e3170a7557 |
nn_model | import torch
import torch.nn as nn
import torch.nn.functional as F
class nn_model(nn.Module):
def __init__(self, feature_dim, num_classes):
super(nn_model, self).__init__()
self.l1 = nn.Linear(feature_dim, 1024)
self.l2 = nn.Linear(1024, 1024)
self.l3 = nn.Linear(1024, num_classes... | 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_... | kiankd/quicksand | nn_model | false | 12,681 | [
"MIT"
] | 0 | 20f9505c843eec00e423a0e1589ebd1e6264e174 | https://github.com/kiankd/quicksand/tree/20f9505c843eec00e423a0e1589ebd1e6264e174 |
KlCriterion | # 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
from torch.... | mahartmann/mt-dnn | KlCriterion | false | 10,480 | [
"MIT"
] | 0 | c9aa3379dc255fd8fc40f24b6cd508f6a645b32f | https://github.com/mahartmann/mt-dnn/tree/c9aa3379dc255fd8fc40f24b6cd508f6a645b32f |
BCEDiceLoss | import torch
from torch import nn
from torch import torch
class BCEDiceLoss(nn.Module):
def __init__(self):
super().__init__()
def forward(self, Yp, Yt, smooth=1e-07):
num = Yt.size(0)
Yp = Yp.view(num, -1)
Yt = Yt.view(num, -1)
bce = nn.functional.binary_cross_entrop... | 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 ... | oskarnatan/RGBDVS-fusion | BCEDiceLoss | false | 7,425 | [
"MIT"
] | 1 | 5e560f54442d387a86e3a469107cf65859693987 | https://github.com/oskarnatan/RGBDVS-fusion/tree/5e560f54442d387a86e3a469107cf65859693987 |
Net | # 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_... | hany606/PMLDL-Project | Net | false | 3,568 | [
"MIT"
] | 0 | 40ccf97720c8fd28ed2a8d8101a0499ff58c2b38 | https://github.com/hany606/PMLDL-Project/tree/40ccf97720c8fd28ed2a8d8101a0499ff58c2b38 |
SoftCrossEntropyLoss2d | import torch
import torch.nn.functional as F
from torch import nn
class SoftCrossEntropyLoss2d(nn.Module):
def forward(self, inputs, targets):
loss = 0
inputs = -F.log_softmax(inputs, dim=1)
for index in range(inputs.size()[0]):
loss += F.conv2d(inputs[range(index, index + 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.... | sudohainguyen/GLNet-pytorch | SoftCrossEntropyLoss2d | false | 4,392 | [
"Apache-2.0"
] | 0 | 91454831fac6e27f894d55d320dd3bcec946ac0f | https://github.com/sudohainguyen/GLNet-pytorch/tree/91454831fac6e27f894d55d320dd3bcec946ac0f |
AsymmetricLoss | 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 libdevice, math as tl_math
import torc... | Chrisfsj2051/my_tools | AsymmetricLoss | false | 8,920 | [
"MIT"
] | 0 | 67355a46df6290aa2fdc1e0266c61daacced3ba1 | https://github.com/Chrisfsj2051/my_tools/tree/67355a46df6290aa2fdc1e0266c61daacced3ba1 |
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