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 |
|---|---|---|---|---|---|---|---|---|---|---|
MaxMarginRankingLoss | import torch
import numpy as np
import torch as th
import torch.nn.functional as F
class MaxMarginRankingLoss(th.nn.Module):
def __init__(self, margin=1.0, negative_weighting=False, batch_size=1,
n_pair=1, hard_negative_rate=0.5):
super(MaxMarginRankingLoss, self).__init__()
self.margin =... | 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 numpy as np
import torch as th
assert_size_stride = torch._C._dynamo.guards.assert... | HS310164/howto100m | MaxMarginRankingLoss | false | 11,457 | [
"Apache-2.0"
] | 0 | e3952a77c268466de2b9174ae8983c528b91397d | https://github.com/HS310164/howto100m/tree/e3952a77c268466de2b9174ae8983c528b91397d |
VarifocalLoss | # 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... | ChHanXiao/mmdetection | VarifocalLoss | false | 9,160 | [
"Apache-2.0"
] | 0 | 324aa5a042857a9b57abe37385e1210709a20d02 | https://github.com/ChHanXiao/mmdetection/tree/324aa5a042857a9b57abe37385e1210709a20d02 |
MultiHeadAttention | import torch
import numpy as np
class MultiHeadAttention(torch.nn.Module):
def __init__(self, input_size, output_size, num_heads,
output_attentions=False):
super(MultiHeadAttention, self).__init__()
self.output_attentions = output_attentions
self.num_heads = num_heads
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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cu... | igorvlnascimento/redn | MultiHeadAttention | false | 15,601 | [
"MIT"
] | 100 | f40f19a0fdfbb11a7987996d520716a05bafd77b | https://github.com/igorvlnascimento/redn/tree/f40f19a0fdfbb11a7987996d520716a05bafd77b |
MaxPoolStride1 | # 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.utils.data
import torch.utils.data.distributed
import ... | DatatangAILAB/SuanFaShiXun04 | MaxPoolStride1 | false | 17,217 | [
"Apache-2.0"
] | 5 | f478e40dd84240ac71cbb54e6bacf9ff556fbb3e | https://github.com/DatatangAILAB/SuanFaShiXun04/tree/f478e40dd84240ac71cbb54e6bacf9ff556fbb3e |
SourceContextGate | import torch
import torch.nn as nn
import torch.cuda
import torch.distributed
class ContextGate(nn.Module):
"""
Context gate is a decoder module that takes as input the previous word
embedding, the current decoder state and the attention state, and
produces a gate.
The gate can be used to select t... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | BradLin0819/kg2text | SourceContextGate | false | 13,413 | [
"Apache-2.0"
] | 86 | e586eb2027c0d85db9826cbe1d9e14f2d26fc93f | https://github.com/BradLin0819/kg2text/tree/e586eb2027c0d85db9826cbe1d9e14f2d26fc93f |
TotalVariation | import torch
import torch.nn as nn
def total_variation(img: 'torch.Tensor') ->torch.Tensor:
"""Function that computes Total Variation.
See :class:`~kornia.losses.TotalVariation` for details.
"""
if not torch.is_tensor(img):
raise TypeError(f'Input type is not a torch.Tensor. Got {type(img)}')... | 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... | IEM-Computer-Vision/kornia | TotalVariation | false | 9,262 | [
"ECL-2.0",
"Apache-2.0"
] | 0 | f98bd9a2158a6e59cda076d55d476acf13f4e0af | https://github.com/IEM-Computer-Vision/kornia/tree/f98bd9a2158a6e59cda076d55d476acf13f4e0af |
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 ... | PiSchool/esa-superresolution-forecasting | Net | false | 17,905 | [
"MIT"
] | 4 | 3c01770dd64749d6b6c40e1068a96a3307c8c035 | https://github.com/PiSchool/esa-superresolution-forecasting/tree/3c01770dd64749d6b6c40e1068a96a3307c8c035 |
InvertibleDownsampling3D | from torch.autograd import Function
import torch
import numpy as np
from warnings import warn
from typing import Union
from typing import Tuple
from torch.nn.common_types import _size_3_t
from torch.nn.modules.utils import _triple
import torch.nn.functional as F
def _cayley(A):
I = torch.eye(A.shape[-1], device=A... | 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.autograd import Function
import numpy as np
from warnings import warn... | cetmann/iunets | InvertibleDownsampling3D | false | 15,027 | [
"MIT"
] | 86 | 80ed7cce0e505a0396c42359eaf27819222d71f6 | https://github.com/cetmann/iunets/tree/80ed7cce0e505a0396c42359eaf27819222d71f6 |
BertPooler | # 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 ... | etherlabsio/ai-engine | BertPooler | false | 10,125 | [
"MIT"
] | 0 | e73a4419a34db42a410e2a7e7629eb946b86f2c2 | https://github.com/etherlabsio/ai-engine/tree/e73a4419a34db42a410e2a7e7629eb946b86f2c2 |
EmbedE | import torch
from torch import nn
from torch.functional import F
class EmbedE(nn.Module):
def __init__(self, l_in, l_h, l_g):
super(EmbedE, self).__init__()
self.fc = nn.Linear(l_in, l_h * l_g)
def forward(self, h):
h = F.relu(self.fc(h))
return h
def get_inputs():
retu... | 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... | AnnaNikitaML/GraphConvolutionalNetwork | EmbedE | false | 11,219 | [
"MIT"
] | 0 | 2f3153b82fad10cdd33d261a77e08f77fa37d36a | https://github.com/AnnaNikitaML/GraphConvolutionalNetwork/tree/2f3153b82fad10cdd33d261a77e08f77fa37d36a |
DistMultLayer | # 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.... | ChengzhiPiao/cogdl | DistMultLayer | false | 4,976 | [
"MIT"
] | 1 | 182e0b95b3dfbe771570037c58aacd8f677b6500 | https://github.com/ChengzhiPiao/cogdl/tree/182e0b95b3dfbe771570037c58aacd8f677b6500 |
RNN | import torch
import torch.nn as nn
class Norm(nn.Module):
"""
Re-usable class for either batch-norm or layer-norm (by swapping dim)
"""
def __init__(self, n_hidden, eps=1e-08, dim=0):
super(Norm, self).__init__()
self.eps = eps
self.n_hidden = n_hidden
self.a = nn.Para... | 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.... | jrbtaylor/recurrent_pytorch | RNN | false | 10,326 | [
"Apache-2.0"
] | 0 | 09ee203a86b70a32aec3e97d7daa646caf8fd182 | https://github.com/jrbtaylor/recurrent_pytorch/tree/09ee203a86b70a32aec3e97d7daa646caf8fd182 |
StackTime | # 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.utils.data
import torch.jit
import torch.optim
import torch.utils.collect_env
import torch.nn.parallel
im... | cometta/training | StackTime | false | 10,044 | [
"Apache-2.0"
] | 0 | 2f33c36d5aa2e1c2770fb3bab35afc8c665e01ce | https://github.com/cometta/training/tree/2f33c36d5aa2e1c2770fb3bab35afc8c665e01ce |
ForwardNet | # 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.... | xdong73S/Match_LSTM_v2.0 | ForwardNet | false | 4,589 | [
"MIT"
] | 0 | dfb8cfbc2a5dafc6655eecf151a7dbcf808cd729 | https://github.com/xdong73S/Match_LSTM_v2.0/tree/dfb8cfbc2a5dafc6655eecf151a7dbcf808cd729 |
AdditiveAttention | # 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.... | ErikHumphrey/sustain-seq2seq | AdditiveAttention | false | 17,274 | [
"Apache-2.0"
] | 4 | c4787f0ca1047d01385e4fa4ffde59c6a8ab4cc4 | https://github.com/ErikHumphrey/sustain-seq2seq/tree/c4787f0ca1047d01385e4fa4ffde59c6a8ab4cc4 |
mySBlock | # 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.... | junhocho/ShapeMatchingGAN | mySBlock | false | 15,782 | [
"MIT"
] | 117 | b90e9c2490bfdf62c5da9b1eb6b0cdf0618cf570 | https://github.com/junhocho/ShapeMatchingGAN/tree/b90e9c2490bfdf62c5da9b1eb6b0cdf0618cf570 |
LipNormConv2d | # 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.... | zxydi1992/residual-flows | LipNormConv2d | false | 13,198 | [
"MIT"
] | 0 | 4ec289681dc91cff5312b22f7ebed93838b440fb | https://github.com/zxydi1992/residual-flows/tree/4ec289681dc91cff5312b22f7ebed93838b440fb |
ECALayer | # 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.nn.parallel
assert_size_stride = torch._C._dy... | SanderKlomp/channel-attention | ECALayer | false | 9,504 | [
"MIT"
] | 0 | 9dfdb28f3ad4de13b4c076d1423f21c05c907bd7 | https://github.com/SanderKlomp/channel-attention/tree/9dfdb28f3ad4de13b4c076d1423f21c05c907bd7 |
StepRankerMargin | # 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... | YilunZhou/wikihow-embedding | StepRankerMargin | false | 18,145 | [
"MIT"
] | 8 | bfbcaf6aca854cd7e0dedfd5ecf77627138e8425 | https://github.com/YilunZhou/wikihow-embedding/tree/bfbcaf6aca854cd7e0dedfd5ecf77627138e8425 |
DenseGCNConv | import math
import torch
from torch.nn import Parameter
import torch.utils.data
def glorot(tensor):
if tensor is not None:
stdv = math.sqrt(6.0 / (tensor.size(-2) + tensor.size(-1)))
tensor.data.uniform_(-stdv, stdv)
def zeros(tensor):
if tensor is not None:
tensor.data.fill_(0)
cl... | 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.... | HenrryBryant/pytorch_geometric | DenseGCNConv | false | 5,309 | [
"MIT"
] | 1 | 3c4466a3f38a2eba92073c730a09953ab5082c3d | https://github.com/HenrryBryant/pytorch_geometric/tree/3c4466a3f38a2eba92073c730a09953ab5082c3d |
NormedConv2d | # 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... | HT-hlf/mmdetection_miner-2.22.0 | NormedConv2d | false | 2,319 | [
"Apache-2.0"
] | 0 | 76eb94d6547f9f95cd58f41bb5c91941e82322b9 | https://github.com/HT-hlf/mmdetection_miner-2.22.0/tree/76eb94d6547f9f95cd58f41bb5c91941e82322b9 |
TransformerEncoderLayer | # 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.... | NceBoy/mmocr | TransformerEncoderLayer | false | 11,752 | [
"Apache-2.0"
] | 0 | 3fb7a18d7eb44799e75c1991e5da2044b458d411 | https://github.com/NceBoy/mmocr/tree/3fb7a18d7eb44799e75c1991e5da2044b458d411 |
LayerNorm | # 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
import torch.nn.parallel
import torch.utils.data
assert_s... | AnonymousGFR/wbgan.pytorch | LayerNorm | false | 4,861 | [
"MIT"
] | 1 | d75cb6599852e901df0136db87520e3314f8ca71 | https://github.com/AnonymousGFR/wbgan.pytorch/tree/d75cb6599852e901df0136db87520e3314f8ca71 |
SmoothTranslationLoss | import torch
import torch.nn as nn
import torch.cuda.comm
class SmoothTranslationLoss(nn.Module):
"""Smooth loss for translations.
Args:
reduction (str, optional): The method that reduces the loss to a
scalar. Options are "none", "mean" and "sum".
loss_weight (float, optional): Th... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
import torch.cuda.comm
assert_size_stride = torch._... | JasonBoy1/mmhuman3d | SmoothTranslationLoss | false | 5,381 | [
"Apache-2.0"
] | 1 | 79b2665191115f3ed905e6afdf09990a8d484362 | https://github.com/JasonBoy1/mmhuman3d/tree/79b2665191115f3ed905e6afdf09990a8d484362 |
Attn | # 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
... | RedisAI/redisai-examples | Attn | false | 14,288 | [
"MIT"
] | 51 | c85c755781d4c45443aee0d7d52c306bfda87121 | https://github.com/RedisAI/redisai-examples/tree/c85c755781d4c45443aee0d7d52c306bfda87121 |
KLCoefficient | # 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... | kensakurada/SceneChangeDet | KLCoefficient | false | 15,798 | [
"MIT"
] | 199 | 0530e0162863fec0c5296188526f0d27e0109814 | https://github.com/kensakurada/SceneChangeDet/tree/0530e0162863fec0c5296188526f0d27e0109814 |
InnerProductDecoder | import torch
import torch.nn.functional as F
import torch.nn as nn
import torch.nn.modules.loss
class InnerProductDecoder(nn.Module):
"""Decoder for using inner product for prediction."""
def __init__(self, dropout, act=torch.sigmoid):
super(InnerProductDecoder, self).__init__()
self.dropout ... | 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.modules.loss
assert_size_stride = torch._C... | PatriciaXiao/gae-pytorch | InnerProductDecoder | false | 11,767 | [
"MIT"
] | 0 | eb0e9bdf9a2f23d38941ac731bd481bd6da737b9 | https://github.com/PatriciaXiao/gae-pytorch/tree/eb0e9bdf9a2f23d38941ac731bd481bd6da737b9 |
point_model | import torch
import torch.nn as nn
import torch.nn.functional as F
class point_model(nn.Module):
def __init__(self, num_classes):
super(point_model, self).__init__()
self.mlp1 = nn.Conv1d(3, 64, 1)
self.mlp2 = nn.Conv1d(64, 128, 1)
self.mlp3 = nn.Conv1d(128, 1024, 1)
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
import torch.nn as nn
assert_... | berkbilir/point-cloud-classification | point_model | false | 3,224 | [
"MIT"
] | 0 | 4188b317acc8efccb694831b26a3a8564dee5530 | https://github.com/berkbilir/point-cloud-classification/tree/4188b317acc8efccb694831b26a3a8564dee5530 |
HSwish | # 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.functional
import torch.nn.parallel
import torch.ut... | ardianumam/Vanilla-GAN | HSwish | false | 12,102 | [
"Apache-2.0"
] | 0 | 3fce9b60dca4609aad1d4e5eb834a2cc72cf07b3 | https://github.com/ardianumam/Vanilla-GAN/tree/3fce9b60dca4609aad1d4e5eb834a2cc72cf07b3 |
MultiHead | import math
import torch
from torch import nn
import torch.nn.functional as F
import torch.utils.data.distributed
def matmul(x, y):
if x.dim() == y.dim():
return x @ y
if x.dim() == y.dim() - 1:
return (x.unsqueeze(-2) @ y).squeeze(-2)
return (x @ y.unsqueeze(-2)).squeeze(-2)
class Atten... | 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.... | matatabinoneko/densecap | MultiHead | false | 12,787 | [
"BSD-3-Clause"
] | 0 | 723d9c2cfd3f16b2eb7584cc7cb0aaef973854dd | https://github.com/matatabinoneko/densecap/tree/723d9c2cfd3f16b2eb7584cc7cb0aaef973854dd |
ResNetV2 | # 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.... | Yifanfanfanfan/ViT-pytorch | ResNetV2 | false | 12,309 | [
"MIT"
] | 0 | 0f975aa7d3fd0aba6f74260c2b5a91786f1211ba | https://github.com/Yifanfanfanfan/ViT-pytorch/tree/0f975aa7d3fd0aba6f74260c2b5a91786f1211ba |
ItemQueryAttention | # 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.... | Asichurter/Few-Shot-Project | ItemQueryAttention | false | 111 | [
"MIT"
] | 0 | 865cd6aa7b996c518dfa48dcc9ffad90445f9efe | https://github.com/Asichurter/Few-Shot-Project/tree/865cd6aa7b996c518dfa48dcc9ffad90445f9efe |
ILN | # 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.onnx
from torch import nn
import torch
from torch.nn.parameter imp... | rtolps/Cats2dogs_ONNX | ILN | false | 10,736 | [
"MIT"
] | 0 | 9c18a9ea9c6ae65feb5c2a1a4c814d31999b6ffc | https://github.com/rtolps/Cats2dogs_ONNX/tree/9c18a9ea9c6ae65feb5c2a1a4c814d31999b6ffc |
FeedForward | # 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.... | caixunshiren/transformer-from-scratch | FeedForward | false | 9,831 | [
"MIT"
] | 0 | dbbacab4752f9fc5e33f583c0b1b5258572fb646 | https://github.com/caixunshiren/transformer-from-scratch/tree/dbbacab4752f9fc5e33f583c0b1b5258572fb646 |
ECA | import torch
import torch.nn as nn
class ECA(nn.Module):
"""Constructs a ECA module.
Args:
channel: Number of channels of the input feature map
k_size: Adaptive selection of kernel size
"""
def __init__(self, channel, k_size=3):
super(ECA, self).__init__()
self.avg_poo... | 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... | Jiannan-Liu/nCoVSegNet | ECA | false | 17,490 | [
"MIT"
] | 5 | 7543e68edff011a7f7b694c97cf0f185d441fd6b | https://github.com/Jiannan-Liu/nCoVSegNet/tree/7543e68edff011a7f7b694c97cf0f185d441fd6b |
InformedSender | # 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.... | Shawn-Guo-CN/EGG | InformedSender | false | 2,882 | [
"MIT"
] | 0 | 0a5b258108e2cd1c873d7f67e8c92551bb3d809c | https://github.com/Shawn-Guo-CN/EGG/tree/0a5b258108e2cd1c873d7f67e8c92551bb3d809c |
Dice | import torch
import torch.nn as nn
class Dice(nn.Module):
def __init__(self):
super(Dice, self).__init__()
self.alpha = nn.Parameter(torch.zeros((1,)))
def forward(self, x):
avg = x.mean(dim=0)
std = x.std(dim=0)
norm_x = (x - avg) / std
p = torch.sigmoid(norm... | 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_... | SUSTechBruce/RL_CTR | Dice | false | 2,794 | [
"Apache-2.0"
] | 0 | 817398dc1c117e22f41281830ae3c33bba8062d3 | https://github.com/SUSTechBruce/RL_CTR/tree/817398dc1c117e22f41281830ae3c33bba8062d3 |
LNN | import math
import torch
import torch.utils.data
import torch.nn.functional as F
class LNN(torch.nn.Module):
"""
A pytorch implementation of LNN layer
Input shape
- A 3D tensor with shape: ``(batch_size,field_size,embedding_size)``.
Output shape
- 2D tensor with shape:``(batch_size,LNN... | 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.... | JazonJiao/pytorch-fm | LNN | false | 13,879 | [
"MIT"
] | 734 | 7192e7861fa54341d5b2df995f92858f583ea09e | https://github.com/JazonJiao/pytorch-fm/tree/7192e7861fa54341d5b2df995f92858f583ea09e |
MultiLabelCircleLoss | # 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
from torch import nn
a... | dumpmemory/Pytorch-NLU | MultiLabelCircleLoss | false | 15,281 | [
"Apache-2.0"
] | 115 | 864fb9acc7751fc51abd3d05d24b5a9a7eab7110 | https://github.com/dumpmemory/Pytorch-NLU/tree/864fb9acc7751fc51abd3d05d24b5a9a7eab7110 |
ArcFace | # 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 math
from itertools i... | iChenning/face_project | ArcFace | false | 6,845 | [
"MIT"
] | 1 | 8d70858817da4d15c7b513ae492034784f57f35f | https://github.com/iChenning/face_project/tree/8d70858817da4d15c7b513ae492034784f57f35f |
DownSampleConv | # 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_... | alexchartrand/IoT | DownSampleConv | false | 1,411 | [
"MIT"
] | 0 | 2cc0d40b7f8305b9f82fc83ad4ed55c83efa1bfd | https://github.com/alexchartrand/IoT/tree/2cc0d40b7f8305b9f82fc83ad4ed55c83efa1bfd |
AttentiveStatsPool | # 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.... | qlindazm/asv-subtools | AttentiveStatsPool | false | 4,229 | [
"Apache-2.0"
] | 0 | fe1d31db9f3268622016babe944201f6ff81ed56 | https://github.com/qlindazm/asv-subtools/tree/fe1d31db9f3268622016babe944201f6ff81ed56 |
PairwiseBilinear | import math
import torch
import torch.nn as nn
class PairwiseBilinear(nn.Module):
"""
https://github.com/stanfordnlp/stanza/blob/v1.1.1/stanza/models/common/biaffine.py#L5 # noqa
"""
def __init__(self, in1_features: 'int', in2_features: 'int',
out_features: 'int', bias: 'bool'=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 math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.a... | andhikayusup/biaffineparser | PairwiseBilinear | false | 14,835 | [
"Apache-2.0"
] | 46 | 30180b805bdb6c0f1e0386ceb090ba83d6ab2621 | https://github.com/andhikayusup/biaffineparser/tree/30180b805bdb6c0f1e0386ceb090ba83d6ab2621 |
SEBlock | import torch
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
from torch import nn
import torch.nn.functional as F
class SEBlock(nn.Module):
def __init__(self, input_channels, internal_neurons):
super(SEBlock, self).__init__()
self.down = 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
import torch.nn.parallel
impo... | Desmond-97/RepVGG | SEBlock | false | 9,024 | [
"MIT"
] | 0 | 147490c54ee7b83d4a432a5913b17c8800e55d06 | https://github.com/Desmond-97/RepVGG/tree/147490c54ee7b83d4a432a5913b17c8800e55d06 |
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.... | KhalilWong/Learn-RL | Actor | false | 2,452 | [
"MIT"
] | 0 | 9f63c5adafab1413362366d28d8711096ce6648c | https://github.com/KhalilWong/Learn-RL/tree/9f63c5adafab1413362366d28d8711096ce6648c |
MemoryReader | # 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
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | rchavan10/Multiple-Intersection-Traffic-Control-using-Reinforcement-Learning | MemoryReader | false | 10,750 | [
"MIT"
] | 0 | 3663a1c7a89fe18974d13c9dc78ac7a99dac2300 | https://github.com/rchavan10/Multiple-Intersection-Traffic-Control-using-Reinforcement-Learning/tree/3663a1c7a89fe18974d13c9dc78ac7a99dac2300 |
DPGRUCell | # 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 ... | romovpa/opacus | DPGRUCell | false | 10,859 | [
"Apache-2.0"
] | 0 | 9cda8072e52049a06afba7ab524276bb6613a727 | https://github.com/romovpa/opacus/tree/9cda8072e52049a06afba7ab524276bb6613a727 |
CovarianceLayer | import torch
import numpy as np
import torch.nn.functional as F
from torch import nn
class CovarianceLayer(nn.Module):
def __init__(self, patch_size=5, channels=1):
self.patch_size = patch_size
super(CovarianceLayer, self).__init__()
mean_mask = np.ones((channels, channels, patch_size, pa... | 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 numpy as np
from torch import nn
assert_size_stride = torch._C._dynamo.gu... | GuYuanjie/Deep-Retinex-fusion | CovarianceLayer | false | 17,353 | [
"MIT"
] | 5 | ffa2a1689fd512c8820fd87cbf665c09bcb142b4 | https://github.com/GuYuanjie/Deep-Retinex-fusion/tree/ffa2a1689fd512c8820fd87cbf665c09bcb142b4 |
JaccardLoss | # 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.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C.... | divyam-goel/pytorch_connectomics | JaccardLoss | false | 6,576 | [
"MIT"
] | 1 | a2c70a7cc60fd84d67be6f225c123ff11daadb83 | https://github.com/divyam-goel/pytorch_connectomics/tree/a2c70a7cc60fd84d67be6f225c123ff11daadb83 |
ToRGB | from torch.autograd import Function
import math
import torch
from torch import nn as nn
from torch.nn import functional as F
from torch.nn import init as init
from torchvision.models import vgg as vgg
import torch.utils.data
from torch.utils import data as data
from torch import autograd as autograd
def make_resample... | 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.autograd import Function
import math
from torch import nn as nn
from ... | hyunobae/BasicSR | ToRGB | false | 12,529 | [
"Apache-2.0"
] | 0 | f2c2fc6cf28933658816c808f55c95fa20b16483 | https://github.com/hyunobae/BasicSR/tree/f2c2fc6cf28933658816c808f55c95fa20b16483 |
LabelSmoothCrossEntropyLoss | import torch
import torch.nn as nn
import torch.nn.functional as F
class LabelSmoothCrossEntropyLoss(nn.modules.loss._WeightedLoss):
def __init__(self, weight=None, reduction='mean', smoothing=0.0):
super().__init__(weight=weight, reduction=reduction)
self.smoothing = smoothing
self.weigh... | 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
... | gosiqueira/dog-breed-recognition | LabelSmoothCrossEntropyLoss | false | 10,162 | [
"MIT"
] | 0 | 27d3499f4922e6e36219f47af08c34e30c929e12 | https://github.com/gosiqueira/dog-breed-recognition/tree/27d3499f4922e6e36219f47af08c34e30c929e12 |
UNet | # 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 ... | avinashpaliwal/Deep-SloMo | UNet | false | 15,057 | [
"MIT"
] | 76 | 93373aa3cb9fd384fbf905e235fe6eb4f9cac780 | https://github.com/avinashpaliwal/Deep-SloMo/tree/93373aa3cb9fd384fbf905e235fe6eb4f9cac780 |
Classifier | import torch
from torch import nn
import torch.nn.parallel
import torch.utils.data
import torch.utils
class Classifier(nn.Module):
def __init__(self, num_classes, dim=128):
super(Classifier, self).__init__()
self.num_classes = num_classes
self.dim = dim
self.fc1 = nn.Linear(self.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 import nn
import torch.nn.parallel
import torch.utils.data
import tor... | IdanAzuri/MixMatch-pytorch | Classifier | false | 588 | [
"MIT"
] | 0 | b8de2bc30c09e1256b92e0394403487fc4f90135 | https://github.com/IdanAzuri/MixMatch-pytorch/tree/b8de2bc30c09e1256b92e0394403487fc4f90135 |
Generator | import torch
from torch import nn
import torch.nn.functional as F
import torch.optim.lr_scheduler
class Generator(nn.Module):
"""Define standard linear + softmax generation step."""
def __init__(self, d_model, vocab):
super(Generator, self).__init__()
self.proj = nn.Linear(d_model, vocab)
... | 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.... | CMU-IDS-2020/fp-index-out-of-bounds | Generator | false | 196 | [
"BSD-3-Clause"
] | 0 | 1a9fdaac8288a980e759a0c2e46f28294d25c71f | https://github.com/CMU-IDS-2020/fp-index-out-of-bounds/tree/1a9fdaac8288a980e759a0c2e46f28294d25c71f |
Scale | import torch
import torch.utils.data
from torch import nn
class Scale(nn.Module):
def __init__(self, init_value=1.0):
super(Scale, self).__init__()
self.scale = nn.Parameter(torch.FloatTensor([init_value]))
def forward(self, input):
return input * self.scale
def get_inputs():
r... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.utils.data
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._... | Rick-960123/centermask-mdf-master | Scale | false | 2,756 | [
"BSD-2-Clause"
] | 0 | 49388b03b9ffb06577cd28b9ddaa68cadb82e926 | https://github.com/Rick-960123/centermask-mdf-master/tree/49388b03b9ffb06577cd28b9ddaa68cadb82e926 |
PatchMerging | # 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 ... | taokong/ibot | PatchMerging | false | 16,532 | [
"Apache-2.0"
] | 327 | a2ee1ae7495d4ea8fb9ba100434c062f1bd3d1f0 | https://github.com/taokong/ibot/tree/a2ee1ae7495d4ea8fb9ba100434c062f1bd3d1f0 |
FlatCat | import torch
class FlatCat(torch.nn.Module):
def __init__(self):
super(FlatCat, self).__init__()
def forward(self, x, y):
x = x.view(x.shape[0], -1, 1, 1)
y = y.view(y.shape[0], -1, 1, 1)
return torch.cat([x, y], 1)
def get_inputs():
return [torch.rand([4, 4, 4, 4]), to... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.j... | YirongMao/torch2trt | FlatCat | false | 2,976 | [
"MIT"
] | 0 | 7635051998a9cd6b9483da1569814031c04a1b52 | https://github.com/YirongMao/torch2trt/tree/7635051998a9cd6b9483da1569814031c04a1b52 |
VectorQuantizer | from torch.autograd import Function
import torch
import torch.nn as nn
import torch.nn.functional as F
class VQPseudoGrad(Function):
@staticmethod
def forward(ctx, z, q):
return q
@staticmethod
def backward(ctx, grad_output):
return grad_output, None
class VectorQuantizer(nn.Module... | 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.autograd import Fu... | arampacha/generative_models | VectorQuantizer | false | 1,474 | [
"Apache-2.0"
] | 0 | 34f5a2fc760bbd7f9f9a956d8d8670c9746e5152 | https://github.com/arampacha/generative_models/tree/34f5a2fc760bbd7f9f9a956d8d8670c9746e5152 |
FocalLoss | import torch
from torch import nn
class FocalLoss(nn.Module):
def __init__(self, gamma=0, eps=1e-07):
super(FocalLoss, self).__init__()
self.gamma = gamma
self.eps = eps
self.ce = torch.nn.CrossEntropyLoss(reduction='none')
def forward(self, input, target):
logp = 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 math as tl_math
from torch import nn
a... | h8c2/kaggle-landmark-recognition-2020-1st-place | FocalLoss | false | 10,158 | [
"MIT"
] | 0 | 3285b6c9548d100b14800ea3927f5974b25facd9 | https://github.com/h8c2/kaggle-landmark-recognition-2020-1st-place/tree/3285b6c9548d100b14800ea3927f5974b25facd9 |
TemporalDecayRegression | # 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.... | asifr/armisc | TemporalDecayRegression | false | 3,216 | [
"MIT"
] | 0 | 486220ba498353faeb94f70cd8ffe917109526d2 | https://github.com/asifr/armisc/tree/486220ba498353faeb94f70cd8ffe917109526d2 |
VectorAttentionAggregator | # 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
import torch.utils.dat... | Arnaud15/CS236_Deep_Generative_Processes | VectorAttentionAggregator | false | 16,944 | [
"MIT"
] | 6 | 179c995c4f596c19441c5e844f2ed07d954324e3 | https://github.com/Arnaud15/CS236_Deep_Generative_Processes/tree/179c995c4f596c19441c5e844f2ed07d954324e3 |
NeuralNet | # 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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cu... | rahimftd/digit_recognizer | NeuralNet | false | 4,160 | [
"MIT"
] | 0 | a134efa915670308ad7a77c8ace2662e5c775913 | https://github.com/rahimftd/digit_recognizer/tree/a134efa915670308ad7a77c8ace2662e5c775913 |
MNACLayer | import collections
import math
import torch
import torch.utils.data
def sparsity_error(W):
W_error = torch.min(torch.abs(W), torch.abs(1 - torch.abs(W)))
return torch.max(W_error)
def mnac(x, W, mode='prod'):
out_size, in_size = W.size()
x = x.view(x.size()[0], in_size, 1)
W = W.t().view(1, in_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
import collections
import math
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = ... | hoedt/stable-nalu | MNACLayer | false | 3,607 | [
"MIT"
] | 0 | 64b3d240db8bff4da857d955f213ef3c7e38e035 | https://github.com/hoedt/stable-nalu/tree/64b3d240db8bff4da857d955f213ef3c7e38e035 |
MeanVarFC | import torch
import torch.nn as nn
class MeanVarFC(nn.Module):
def __init__(self, input_shape):
super(MeanVarFC, self).__init__()
shape = list(input_shape)
shape[0] = 1
shape[1] *= 2
self.param = nn.Parameter(0.01 * torch.randn(shape))
def forward(self, x):
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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | Schwartz-Zha/My-invertible-resnet | MeanVarFC | false | 1,033 | [
"MIT"
] | 0 | 5415975bb0d640f3bf3ef4a7b986563e84109270 | https://github.com/Schwartz-Zha/My-invertible-resnet/tree/5415975bb0d640f3bf3ef4a7b986563e84109270 |
Reorg | # 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... | FenryrMKIII/objectDetection-lightnet | Reorg | false | 2,247 | [
"MIT"
] | 0 | 3a1fa7b77227210060714a9e22d7d241888b36b4 | https://github.com/FenryrMKIII/objectDetection-lightnet/tree/3a1fa7b77227210060714a9e22d7d241888b36b4 |
DistillationLoss | # 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.... | CQUlearningsystemgroup/LearningToBinarize | DistillationLoss | false | 4,966 | [
"MIT"
] | 1 | 1ecad897145af65ff52323bf2ec64a2154dc87d6 | https://github.com/CQUlearningsystemgroup/LearningToBinarize/tree/1ecad897145af65ff52323bf2ec64a2154dc87d6 |
Attention | import math
import torch
from torch import nn
from torch.nn import functional as F
import torch.utils.data
def matmul(x, y):
if x.dim() == y.dim():
return x @ y
if x.dim() == y.dim() - 1:
return (x.unsqueeze(-2) @ y).squeeze(-2)
return (x @ y.unsqueeze(-2)).squeeze(-2)
class Attention(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.... | cristipp/decaNLP | Attention | false | 12,232 | [
"BSD-3-Clause"
] | 0 | db64df36bf2b1b2ca6946aacf0ee7463ac80c4cb | https://github.com/cristipp/decaNLP/tree/db64df36bf2b1b2ca6946aacf0ee7463ac80c4cb |
PixelNorm | # 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_... | yivan-WYYGDSG/mmediting | PixelNorm | false | 4,620 | [
"Apache-2.0"
] | 0 | f9c9a953013b709ed59865d0fecbacbf5711e153 | https://github.com/yivan-WYYGDSG/mmediting/tree/f9c9a953013b709ed59865d0fecbacbf5711e153 |
Postnet | import torch
from torch import nn
class Postnet(nn.Module):
"""Postnet is a simple linear layer for predicting the target frames given the
RNN context during training. We don't need the Postnet for feature extraction.
"""
def __init__(self, input_size, output_size=80):
super(Postnet, 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
assert_size_stride = torch._C._dynamo.guards.assert_size_st... | aviasd/Mockingjay-Speech-Representation | Postnet | false | 6,281 | [
"MIT"
] | 1 | c01aef3f98bbb3fd4b0fc1b61e77fb5d02a0e453 | https://github.com/aviasd/Mockingjay-Speech-Representation/tree/c01aef3f98bbb3fd4b0fc1b61e77fb5d02a0e453 |
DenseSAGEConv | # 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 math
from torch.nn imp... | pwycl/pytorch_geometric | DenseSAGEConv | false | 10,778 | [
"MIT"
] | 0 | ef7b1add2bb5a36a3a68cae7639c42000f629cac | https://github.com/pwycl/pytorch_geometric/tree/ef7b1add2bb5a36a3a68cae7639c42000f629cac |
QuantPointwiseConv | import torch
from torch import nn
class QuantPointwiseConv(nn.Module):
"""Quantized Point-wise Conv"""
def __init__(self, in_channels, out_channels, bias=True, **kwargs):
super().__init__()
self.qlinear = nn.Linear(in_channels, out_channels, bias=bias)
self.quant = torch.quantization.... | 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... | TeaPoly/wenet | QuantPointwiseConv | false | 2,886 | [
"Apache-2.0"
] | 0 | 5681887e338e4c8b2c75ffc283140e11a9d56a6d | https://github.com/TeaPoly/wenet/tree/5681887e338e4c8b2c75ffc283140e11a9d56a6d |
BasicBlock | # 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.... | ClaraBing/ffjord | BasicBlock | false | 13,522 | [
"MIT"
] | 518 | a97c34ff546a063316828f53bd041555e663428d | https://github.com/ClaraBing/ffjord/tree/a97c34ff546a063316828f53bd041555e663428d |
Hardswish | import torch
from torch.nn import functional as F
import torch.nn as nn
class Hardswish(nn.Module):
@staticmethod
def forward(x):
return x * F.hardtanh(x + 3, 0.0, 6.0) / 6.0
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
| import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | GoalballAnalysis/GUI | Hardswish | false | 2,292 | [
"MIT"
] | 0 | c7f1cc27f4fd7f861c3ca09f5ca25d1a3f19a8a7 | https://github.com/GoalballAnalysis/GUI/tree/c7f1cc27f4fd7f861c3ca09f5ca25d1a3f19a8a7 |
FeatExemplarAvgBlock | # 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... | qianrusun1015/E3BM-1 | FeatExemplarAvgBlock | false | 7,512 | [
"Apache-2.0"
] | 1 | d2c957bdff66fe28a288f1518f224a1e034d543f | https://github.com/qianrusun1015/E3BM-1/tree/d2c957bdff66fe28a288f1518f224a1e034d543f |
CriticNet | # 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 ... | Manojbhat09/Sane-annotation-shape-complete | CriticNet | false | 18,389 | [
"Apache-2.0"
] | 9 | 03b298b2c0a187be979ff31ad2a39238b72a6d78 | https://github.com/Manojbhat09/Sane-annotation-shape-complete/tree/03b298b2c0a187be979ff31ad2a39238b72a6d78 |
SSD300 | import torch
import torchvision
from torch import nn
import torch.nn.functional as F
from math import sqrt
from itertools import product as product
import torch.optim
import torch.utils.data
def decimate(tensor, m):
"""
Decimate a tensor by a factor 'm', i.e. downsample by keeping every 'm'th value.
This... | 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.... | dee-walia20/SSD-Implementation-using-Pytorch | SSD300 | false | 7,606 | [
"MIT"
] | 1 | 2a7dcdcea2787f4bffd45f335819f08af2b525dd | https://github.com/dee-walia20/SSD-Implementation-using-Pytorch/tree/2a7dcdcea2787f4bffd45f335819f08af2b525dd |
SelfCriticCriterion | # 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... | NeilWangziyu/torch_light | SelfCriticCriterion | false | 5,642 | [
"MIT"
] | 1 | daf8fd62f57885cf182f1b3edc3152156d229ef3 | https://github.com/NeilWangziyu/torch_light/tree/daf8fd62f57885cf182f1b3edc3152156d229ef3 |
SepConv2d | import torch
import torch.nn as nn
import torch.nn.parallel
class SepConv2d(torch.nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, stride=1,
padding=0, dilation=1, act_layer=nn.ReLU):
super(SepConv2d, self).__init__()
self.depthwise = torch.nn.Conv2d(in_channels, 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
import torch.nn as nn
import ... | Blatts01/VckImageRestoration | SepConv2d | false | 2,027 | [
"MIT"
] | 0 | ae4e2221d9d4e236a08722cb92ac5cc88947e311 | https://github.com/Blatts01/VckImageRestoration/tree/ae4e2221d9d4e236a08722cb92ac5cc88947e311 |
DupCNN2 | # 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... | WillieMaddox/Airbus_SDC_dup | DupCNN2 | false | 12,006 | [
"MIT"
] | 0 | 09be904cf3c8050086f07538f5e2954282de5d62 | https://github.com/WillieMaddox/Airbus_SDC_dup/tree/09be904cf3c8050086f07538f5e2954282de5d62 |
Classify | # 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... | DataXujing/yolov5_prune | Classify | false | 13,560 | [
"Apache-2.0"
] | 298 | 3a6a717b96131d484fe24c0ddbb1bce74ba117f2 | https://github.com/DataXujing/yolov5_prune/tree/3a6a717b96131d484fe24c0ddbb1bce74ba117f2 |
ConstractiveThresholdHingeLoss | # 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... | kensakurada/SceneChangeDet | ConstractiveThresholdHingeLoss | false | 15,808 | [
"MIT"
] | 199 | 0530e0162863fec0c5296188526f0d27e0109814 | https://github.com/kensakurada/SceneChangeDet/tree/0530e0162863fec0c5296188526f0d27e0109814 |
Sage | from torch.nn import Module
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.modules.module import Module
class SageConv(Module):
"""
Simple Graphsage layer
"""
def __init__(self, in_features, out_features, bias=False):
super(SageConv, 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.nn import Module
i... | yutaoming/Rare-Category-Detection | Sage | false | 4,694 | [
"MIT"
] | 0 | 76cf023dff44eef3ecc17f0ebf2b11a08cd63a73 | https://github.com/yutaoming/Rare-Category-Detection/tree/76cf023dff44eef3ecc17f0ebf2b11a08cd63a73 |
KLDLoss | # 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
... | DaShi-Git/simsg | KLDLoss | false | 13,533 | [
"Apache-2.0"
] | 58 | 31df608cd04facb2b8b546cc6f53d84716117bdf | https://github.com/DaShi-Git/simsg/tree/31df608cd04facb2b8b546cc6f53d84716117bdf |
DeterministicSumming | import torch
import torch.nn as nn
class DeterministicSumming(nn.Module):
"""Transform a tensor into repetitions of its sum.
Intended for use in tests, not useful for actual learning. The last
dimension of the input should contain feature vectors. The result will be
an array of matching shape with th... | 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... | kiudee/cs-ranking | DeterministicSumming | false | 15,832 | [
"Apache-2.0"
] | 65 | 47cf648fa286c37b9214bbad1926004d4d7d9796 | https://github.com/kiudee/cs-ranking/tree/47cf648fa286c37b9214bbad1926004d4d7d9796 |
Conv2d | # 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 ... | ChuanqiTan/DeepLabv3.pytorch | Conv2d | false | 11,316 | [
"BSD-3-Clause"
] | 0 | 260db5812ae3c85f0aacd5ec9bc0e3d8c5d2d067 | https://github.com/ChuanqiTan/DeepLabv3.pytorch/tree/260db5812ae3c85f0aacd5ec9bc0e3d8c5d2d067 |
Net | import torch
import torch.nn as nn
import torch.nn.functional as F
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(3, 6, 5)
self.pool = nn.MaxPool2d(2, 2)
self.conv2 = nn.Conv2d(6, 16, 5)
self.fc1 = nn.Linear(16 * 5 * 5, 120)
... | 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_... | Daiver/torch_fuze | Net | false | 5,043 | [
"MIT"
] | 1 | 6b7ad568e2d7549c7f0c0d4c309532ac1b92881d | https://github.com/Daiver/torch_fuze/tree/6b7ad568e2d7549c7f0c0d4c309532ac1b92881d |
TanhDeepLiftModel | import torch
import torch.nn as nn
class TanhDeepLiftModel(nn.Module):
"""
Same as the ReLUDeepLiftModel, but with activations
that can have negative outputs
"""
def __init__(self) ->None:
super().__init__()
self.tanh1 = nn.Tanh()
self.tanh2 = nn.Tanh()
def forward(se... | 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_... | aravipati12/captum | TanhDeepLiftModel | false | 10,101 | [
"BSD-3-Clause"
] | 0 | ef3e81d89c8c4404a49c384cf0727f2e7d393f5f | https://github.com/aravipati12/captum/tree/ef3e81d89c8c4404a49c384cf0727f2e7d393f5f |
DeiTOutput | # 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.utils.checkpoint
assert_size_stride = torch._C... | jxhe/unify-parameter-efficient-tuning | DeiTOutput | false | 16,269 | [
"Apache-2.0"
] | 101 | 3222ce2c0079566a28043e22380eb4ab6ad14389 | https://github.com/jxhe/unify-parameter-efficient-tuning/tree/3222ce2c0079566a28043e22380eb4ab6ad14389 |
Standardscaler | import torch
class Standardscaler(torch.nn.Module):
def __init__(self):
super().__init__()
def forward(self, input_batch):
std, mean = torch.std_mean(input_batch.type(torch.float32),
unbiased=False)
total = (input_batch - mean) / std
return total
def get_inputs(... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
assert_size_stride = torch._... | Stuksus/StandardScaler_for_pytorch | Standardscaler | false | 1,088 | [
"MIT"
] | 0 | 27da9afd111007f20a615bee9a5a7ac272adb241 | https://github.com/Stuksus/StandardScaler_for_pytorch/tree/27da9afd111007f20a615bee9a5a7ac272adb241 |
Residual_Covolution | import torch
import torch.nn as nn
class Residual_Covolution(nn.Module):
def __init__(self, icol, ocol, num_classes):
super(Residual_Covolution, self).__init__()
self.conv1 = nn.Conv2d(icol, ocol, kernel_size=3, stride=1, padding
=12, dilation=12, bias=True)
self.conv2 = nn.Co... | 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_... | speedinghzl/Pytorch-Deeplab | Residual_Covolution | false | 16,493 | [
"MIT"
] | 310 | 14f2b81c676a6eb19f34940efb1297855f8fa05e | https://github.com/speedinghzl/Pytorch-Deeplab/tree/14f2b81c676a6eb19f34940efb1297855f8fa05e |
FastGuidedFilter | # 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 torchvision.transforms i... | allen0125/RobustVideoMatting | FastGuidedFilter | false | 1,420 | [
"Apache-2.0"
] | 0 | c0f17ca45a9de7586c570753064187200dec487a | https://github.com/allen0125/RobustVideoMatting/tree/c0f17ca45a9de7586c570753064187200dec487a |
LandmarkHead | import torch
import torch.nn as nn
from itertools import product as product
class LandmarkHead(nn.Module):
def __init__(self, inchannels=512, num_anchors=3):
super(LandmarkHead, self).__init__()
self.conv1x1 = nn.Conv2d(inchannels, num_anchors * 8, kernel_size=(
1, 1), stride=1, paddi... | 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 itertools import product as product
assert_size_strid... | qw85639229/Car_License_SVM | LandmarkHead | false | 7,569 | [
"MIT"
] | 1 | c5b0062e84e5000c7940b1d90cc7c63e52afed21 | https://github.com/qw85639229/Car_License_SVM/tree/c5b0062e84e5000c7940b1d90cc7c63e52afed21 |
Lookahead | # 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... | wenjie-p/CAT | Lookahead | false | 4,654 | [
"Apache-2.0"
] | 0 | 0e6904658dd3d14afe51faf1d0141ae95fef44e8 | https://github.com/wenjie-p/CAT/tree/0e6904658dd3d14afe51faf1d0141ae95fef44e8 |
C1 | import torch
import torch.nn as nn
from collections import OrderedDict
class C1(nn.Module):
def __init__(self):
super(C1, self).__init__()
self.c1 = nn.Sequential(OrderedDict([('c1', nn.Conv2d(1, 6,
kernel_size=(5, 5))), ('relu1', nn.ReLU()), ('s1', nn.MaxPool2d
(kernel_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 import triton_helpers
import torch.nn as nn
from co... | ConstantinSeibold/SGL | C1 | false | 17,120 | [
"MIT"
] | 7 | fab4d2df515608c2a6a89b2ac8c2655ce8e08b1a | https://github.com/ConstantinSeibold/SGL/tree/fab4d2df515608c2a6a89b2ac8c2655ce8e08b1a |
Log10Loss | import torch
import numpy as np
import torch.nn as nn
import torch.nn.init
from math import log
def _mask_input(input, mask=None):
if mask is not None:
input = input * mask
count = torch.sum(mask).data[0]
else:
count = np.prod(input.size(), dtype=np.float32).item()
return input, co... | 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 numpy as np
import torch.nn as nn
import torch.nn.init
assert_size... | simonmeister/pytorch-mono-depth | Log10Loss | false | 16,441 | [
"MIT"
] | 56 | 713c70e2fdae6d9d6e0322febadfedcaee9470d3 | https://github.com/simonmeister/pytorch-mono-depth/tree/713c70e2fdae6d9d6e0322febadfedcaee9470d3 |
DepthConv2d | # 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.... | c-ma13/sepTFNet | DepthConv2d | false | 6,384 | [
"MIT"
] | 1 | a06c89c080f9449ac2e5090f80d9645deea7f23a | https://github.com/c-ma13/sepTFNet/tree/a06c89c080f9449ac2e5090f80d9645deea7f23a |
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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | Sreehari-S/Tiramisu_DigestPath | Net | false | 1,081 | [
"Apache-2.0"
] | 0 | a884ee911bc60ce997996e0ec2e6036600ffcffa | https://github.com/Sreehari-S/Tiramisu_DigestPath/tree/a884ee911bc60ce997996e0ec2e6036600ffcffa |
Split | # 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... | lingzenan/invertible-resnet | Split | false | 7,090 | [
"MIT"
] | 1 | 57b1c0de51a885aed074b77628f3b0c85c548e70 | https://github.com/lingzenan/invertible-resnet/tree/57b1c0de51a885aed074b77628f3b0c85c548e70 |
JSCriterion | # 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.... | chunhuililili/mt_dnn | JSCriterion | false | 10,202 | [
"MIT"
] | 0 | 4c6efaf21724c7b8103a05e46b5b44d7b246225e | https://github.com/chunhuililili/mt_dnn/tree/4c6efaf21724c7b8103a05e46b5b44d7b246225e |
StyledConv | # 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 ... | phygitalism/PTI | StyledConv | false | 16,422 | [
"MIT"
] | 345 | adab2eb1d0e36ac5714e663e1fec9f85a0d51fbf | https://github.com/phygitalism/PTI/tree/adab2eb1d0e36ac5714e663e1fec9f85a0d51fbf |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.