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 |
|---|---|---|---|---|---|---|---|---|---|---|
Perceptron | import torch
import torch.nn as nn
from typing import Any
import torch.nn.functional as fn
class Perceptron(nn.Module):
"""Implements a 1-layer perceptron."""
def _forward_unimplemented(self, *input: Any) ->None:
pass
def __init__(self, input_dimension, hidden_dimension, output_dimension):
... | 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 ty... | shi27feng/PDP-Solver | Perceptron | false | 12,977 | [
"MIT"
] | 0 | bf6e392f72f8a3572e0987313230943d94d53c95 | https://github.com/shi27feng/PDP-Solver/tree/bf6e392f72f8a3572e0987313230943d94d53c95 |
_Decoder | # 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 ... | mori97/revae | _Decoder | false | 4,034 | [
"MIT"
] | 0 | 465009076a9be78e8ddb9021a0699b32fc695f30 | https://github.com/mori97/revae/tree/465009076a9be78e8ddb9021a0699b32fc695f30 |
CustomSoftplus | import torch
import torch.nn as nn
import torch.utils.data
class Softplus(torch.autograd.Function):
@staticmethod
def forward(ctx, i):
result = torch.log(1 + torch.exp(i))
ctx.save_for_backward(i)
return result
@staticmethod
def backward(ctx, grad_output):
return grad... | 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.utils.data
assert_size_stride = torch.... | SortAnon/BVAE-TTS | CustomSoftplus | false | 14,429 | [
"MIT"
] | 138 | 69c2ee0c8bf30fe6133cfa8be68a36916f15bcff | https://github.com/SortAnon/BVAE-TTS/tree/69c2ee0c8bf30fe6133cfa8be68a36916f15bcff |
Biaffine | import torch
import torch.autograd
import torch.nn as nn
class Biaffine(nn.Module):
def __init__(self, n_in, n_out=1, bias_x=True, bias_y=True):
super(Biaffine, self).__init__()
self.n_in = n_in
self.n_out = n_out
self.bias_x = bias_x
self.bias_y = bias_y
weight = ... | 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.autograd
import torch.nn as nn
assert_size_stride = torch._C._dynam... | dumpmemory/W2NER | Biaffine | false | 15,257 | [
"MIT"
] | 128 | fb1b6eb1111eb001b1c965097d995244b840bdda | https://github.com/dumpmemory/W2NER/tree/fb1b6eb1111eb001b1c965097d995244b840bdda |
TripletLossDoubleMargin | # 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... | ax-le/MusicAE | TripletLossDoubleMargin | false | 1,508 | [
"BSD-3-Clause"
] | 0 | 9fdc268f6403226b990d9ae5c9f182ed0af82f98 | https://github.com/ax-le/MusicAE/tree/9fdc268f6403226b990d9ae5c9f182ed0af82f98 |
DoubleForwardCrossAttentionLayer | import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
class ResidualConnectionLayer(nn.Module):
def __init__(self, dim_model, prob_dropout=0.1, add_sublayer=True):
super(ResidualConnectionLayer, self).__init__()
self.add_sublayer = add_sublayer
self.norm = ... | 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.... | KirkGuo/HCN | DoubleForwardCrossAttentionLayer | false | 5,493 | [
"MIT"
] | 1 | 7d8020c8d76413b6ca3a359fb2e9b34652949e17 | https://github.com/KirkGuo/HCN/tree/7d8020c8d76413b6ca3a359fb2e9b34652949e17 |
SDFNetwork | # 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... | Junlin-Yin/NeuS | SDFNetwork | false | 13,954 | [
"MIT"
] | 345 | b13dba90ba1c65d0ccaaca6b9d65225d5dfa8fe2 | https://github.com/Junlin-Yin/NeuS/tree/b13dba90ba1c65d0ccaaca6b9d65225d5dfa8fe2 |
L2Norm | import torch
import torch.nn.functional as F
import torch.nn as nn
class L2Norm(nn.Module):
"""L2Norm layer across all channels."""
def __init__(self, in_features, scale):
super(L2Norm, self).__init__()
self.weight = nn.Parameter(torch.Tensor(in_features))
self.reset_parameters(scale)... | 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... | CVHj/torchcv | L2Norm | false | 13,447 | [
"MIT"
] | 433 | 6291f3e1e4bbf6467fd6b1e79001d34a59481bb6 | https://github.com/CVHj/torchcv/tree/6291f3e1e4bbf6467fd6b1e79001d34a59481bb6 |
GlobalWeightedAvgPool2d | import torch
from torch import nn
class GlobalWeightedAvgPool2d(nn.Module):
"""
Global Weighted Average Pooling from paper "Global Weighted Average
Pooling Bridges Pixel-level Localization and Image-level Classification"
"""
def __init__(self, features: 'int', flatten=False):
super().__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
from torch im... | theNero93/dfdc_deepfake_challenge | GlobalWeightedAvgPool2d | false | 4,414 | [
"MIT"
] | 0 | ef275206efc6f1b0b7984b370a14bd8db61d1ec1 | https://github.com/theNero93/dfdc_deepfake_challenge/tree/ef275206efc6f1b0b7984b370a14bd8db61d1ec1 |
ConvRelu | import torch
from torch import nn
import torch.nn.functional as F
import torch.cuda
import torch.backends.cudnn
import torch.backends.mkl
import torch.backends.cuda
import torch.backends.quantized
class ConvRelu(nn.Module):
def __init__(self):
super(ConvRelu, self).__init__()
self.conv = torch.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 import nn
import t... | XiaobingSuper/intel-extension-for-pytorch | ConvRelu | false | 9,723 | [
"Apache-2.0"
] | 0 | b61029be10e46e6d2e13b0e700c81f8e59164df0 | https://github.com/XiaobingSuper/intel-extension-for-pytorch/tree/b61029be10e46e6d2e13b0e700c81f8e59164df0 |
SiaLoss | # 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
import... | zwzhang121/OpenUnReID | SiaLoss | false | 4,678 | [
"Apache-2.0"
] | 0 | 4f399efca3d560c608fb4c9c2ed43f522b17596a | https://github.com/zwzhang121/OpenUnReID/tree/4f399efca3d560c608fb4c9c2ed43f522b17596a |
ConditionalBatchNorm | import torch
class ConditionalBatchNorm(torch.nn.Module):
def __init__(self, no, z_dim):
super().__init__()
self.no = no
self.bn = torch.nn.InstanceNorm2d(no, affine=False)
self.condition = torch.nn.Linear(z_dim, 2 * no)
def forward(self, x, z):
cond = self.condition(... | 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 ... | ElementAI/beyond-trivial-explanations | ConditionalBatchNorm | false | 17,256 | [
"Apache-2.0"
] | 3 | c517d7bdbab68b6a26f74cee4d15e948b3b47238 | https://github.com/ElementAI/beyond-trivial-explanations/tree/c517d7bdbab68b6a26f74cee4d15e948b3b47238 |
ActorNetwork | # 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_... | TheCamusean/mushroom-rl | ActorNetwork | false | 2,895 | [
"MIT"
] | 0 | 48585f883e546ea57224b8d446ecb9b8ba90cf73 | https://github.com/TheCamusean/mushroom-rl/tree/48585f883e546ea57224b8d446ecb9b8ba90cf73 |
ModMBStddevLayer | import torch
import torch.nn as nn
class ModMBStddevLayer(nn.Module):
"""Modified MiniBatch Stddev Layer.
This layer is modified from ``MiniBatchStddevLayer`` used in PGGAN. In
StyleGAN2, the authors add a new feature, `channel_groups`, into this
layer.
"""
def __init__(self, group_size=4, c... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_... | Sardhendu/mmediting | ModMBStddevLayer | false | 9,891 | [
"Apache-2.0"
] | 0 | 623b59ac758d856abc9fab7e845beeab61074d8f | https://github.com/Sardhendu/mmediting/tree/623b59ac758d856abc9fab7e845beeab61074d8f |
ScalarBiasScale | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
from torch.nn.parameter import Parameter
from torch.nn import init
assert_size_stride = torch._C._dynamo.guards.assert... | Ali-Homsi/githubrepo | ScalarBiasScale | false | 25 | [
"Apache-2.0"
] | 0 | 7163f110193142a97ec05f76ff7d897c6cedb915 | https://github.com/Ali-Homsi/githubrepo/tree/7163f110193142a97ec05f76ff7d897c6cedb915 |
DNHloss | import torch
import torch.nn as nn
class DNHloss(nn.Module):
"""DNH loss function
Arguments:
nn {[type]} -- [description]
"""
def __init__(self, lamda):
"""Initializer class
Arguments:
lamda {[type]} -- [description]
"""
super(DNHloss, 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 import triton_helpers
from torch._inductor.runtime.... | jce2090/palmprint-recognition | DNHloss | false | 3,711 | [
"MIT"
] | 0 | d2d93c6817afe1b67650dae6516a3d180aaeca38 | https://github.com/jce2090/palmprint-recognition/tree/d2d93c6817afe1b67650dae6516a3d180aaeca38 |
MaximumLikelihoodLoss | from torch.nn import Module
import torch
import torch.utils.data
import torch.nn.functional
import torch.autograd
class MaximumLikelihoodLoss(Module):
"""
<a id="MaximumLikelihoodLoss"></a>
## Type II Maximum Likelihood Loss
The distribution $D(\\mathbf{p} ert extcolor{orange}{\\mathbf{lpha}})$ i... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch.nn import Module
import torch.utils.data
import torch.nn.funct... | techthiyanes/annotated_deep_learning_paper_implementations | MaximumLikelihoodLoss | false | 16,549 | [
"MIT"
] | 3,714 | 8af24da2dd39a9a87482a4d18c2dc829bbd3fd47 | https://github.com/techthiyanes/annotated_deep_learning_paper_implementations/tree/8af24da2dd39a9a87482a4d18c2dc829bbd3fd47 |
DeNormalize | import torch
import torch.nn as nn
import torch.utils.cpp_extension
class DeNormalize(nn.Module):
def __init__(self, mean, std):
super().__init__()
self.mean = mean
self.std = std
def forward(self, x):
return x.mul(self.std).add(self.mean)
def get_inputs():
return [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
import torch.nn as nn
import torch.utils.cpp_extension
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = ... | STomoya/animeface | DeNormalize | false | 14,363 | [
"MIT"
] | 61 | 37b3cd26097d7874559d4c152e41e5712b7a1a42 | https://github.com/STomoya/animeface/tree/37b3cd26097d7874559d4c152e41e5712b7a1a42 |
FeatureModel | # 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_... | nassarofficial/pytorch-retina | FeatureModel | false | 4,077 | [
"Apache-2.0"
] | 0 | b2f10ffa7617797280c1f44d562c455b996254af | https://github.com/nassarofficial/pytorch-retina/tree/b2f10ffa7617797280c1f44d562c455b996254af |
Encoder | import torch
import torch.nn as nn
import torch.nn.functional as F
class Encoder(nn.Module):
def __init__(self, sample_size, condition_size, hidden_size):
super().__init__()
self.fc1 = nn.Linear(sample_size + condition_size, hidden_size)
self.fc2 = nn.Dropout(p=0.5)
self.fc3 = 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 as nn
assert_... | ekrell/learn-planning-space | Encoder | false | 3,464 | [
"MIT"
] | 0 | 730e448bffa4996b2b1ef3a5b00500dc172962ec | https://github.com/ekrell/learn-planning-space/tree/730e448bffa4996b2b1ef3a5b00500dc172962ec |
SmallMnist | # 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.... | arjunsuresh/aimet | SmallMnist | false | 12,736 | [
"BSD-3-Clause"
] | 0 | f6e09cb07a91eed3a5e6b8e19e6b065303af5a39 | https://github.com/arjunsuresh/aimet/tree/f6e09cb07a91eed3a5e6b8e19e6b065303af5a39 |
ReOrgLayer | # 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 import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_str... | MaoXianXin/pytorchx | ReOrgLayer | false | 11,687 | [
"MIT"
] | 0 | f46cc9692c3bd11ea9d5d54c20de3ac2f67dabcc | https://github.com/MaoXianXin/pytorchx/tree/f46cc9692c3bd11ea9d5d54c20de3ac2f67dabcc |
ZeroPad1d | import torch
import torch.nn.functional as F
from torch import nn
import torch.utils.data
import torch.onnx.operators
import torch.optim
import torch.optim.lr_scheduler
class ZeroPad1d(nn.Module):
def __init__(self, pad_left, pad_right):
super().__init__()
self.pad_left = pad_left
self.pa... | 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
import torch.utils.data
import torch.onnx.operators
import torch.optim
import torch.optim.lr_scheduler
assert_size_stri... | ChenDdon/AGBTcode | ZeroPad1d | false | 7,880 | [
"MIT"
] | 21 | 6c259d18b48dc8d6da1357c42a1ee088666fb7b4 | https://github.com/ChenDdon/AGBTcode/tree/6c259d18b48dc8d6da1357c42a1ee088666fb7b4 |
Pooling | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
from typing import *
class ReLUConvBN(nn.Module):
"""
Parameters
---
C_in: int
the number of input channels
C_out: int
the number of output channels
stride: int
stride... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
from typing import *
assert_size_stride = torch._C... | rmfan/nni | Pooling | false | 10,941 | [
"MIT"
] | 0 | 727ee1ce47e070061fe3dab8a2da5d3cd5e55546 | https://github.com/rmfan/nni/tree/727ee1ce47e070061fe3dab8a2da5d3cd5e55546 |
SplAtConv2d | # 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.... | JHMeusener/detectron2-ResNeSt | SplAtConv2d | false | 604 | [
"Apache-2.0"
] | 0 | 6abab6fb9496a528f6aa2d4e1e27f3e7ceb42685 | https://github.com/JHMeusener/detectron2-ResNeSt/tree/6abab6fb9496a528f6aa2d4e1e27f3e7ceb42685 |
CorrelationAlignmentLoss | # 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
import torch.utils.data
assert_size_stride = torch._C._dyn... | XianyuanLiu/Transfer-Learning-Library | CorrelationAlignmentLoss | false | 10,136 | [
"MIT"
] | 0 | 25f83f32437032df88ca6101ecd1f63ec7a0aa2c | https://github.com/XianyuanLiu/Transfer-Learning-Library/tree/25f83f32437032df88ca6101ecd1f63ec7a0aa2c |
LIN | # 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
from torch.nn.parameter import Parameter
assert_size_stri... | Gxx-5/MyPhoto2Cartoon | LIN | false | 11,460 | [
"MIT"
] | 0 | aa05dfa8b7d6c507c33026a2e8b299d5779357be | https://github.com/Gxx-5/MyPhoto2Cartoon/tree/aa05dfa8b7d6c507c33026a2e8b299d5779357be |
discriminator2 | # 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.... | layel2/layyer-lib | discriminator2 | false | 3,881 | [
"MIT"
] | 0 | db48b5c38098ee93d2d34693d98e5ef4d319d919 | https://github.com/layel2/layyer-lib/tree/db48b5c38098ee93d2d34693d98e5ef4d319d919 |
ExtResNetBlock | # 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... | cviaai/TMJ_tracking | ExtResNetBlock | false | 1,774 | [
"MIT"
] | 0 | 464ca21dbeb538dc9504bd5d0e5c4d92591e69c4 | https://github.com/cviaai/TMJ_tracking/tree/464ca21dbeb538dc9504bd5d0e5c4d92591e69c4 |
SubPixelConvolutionalBlock | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_st... | DanielLiang1/a-PyTorch-Tutorial-to-Super-Resolution | SubPixelConvolutionalBlock | false | 361 | [
"MIT"
] | 0 | cf7b519029687fe9726bb194fe3765934afa18b3 | https://github.com/DanielLiang1/a-PyTorch-Tutorial-to-Super-Resolution/tree/cf7b519029687fe9726bb194fe3765934afa18b3 |
Pow | import torch
import torch.nn as nn
class Pow(nn.Module):
"""
Applies `x ** sigmoid(a)`, with `a` fixed or trainable.
"""
def __init__(self, a=0, trainable=False):
super(Pow, self).__init__()
if trainable:
a = nn.Parameter(torch.tensor(a, dtype=torch.get_default_dtype()))
... | 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_... | CPJKU/kagglebirds2020 | Pow | false | 17,023 | [
"MIT"
] | 4 | f86b459389b1d0b0af96ebc9252ffc8496c272e8 | https://github.com/CPJKU/kagglebirds2020/tree/f86b459389b1d0b0af96ebc9252ffc8496c272e8 |
LinearBlock | # 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.... | JasonMa2016/learn2learn | LinearBlock | false | 870 | [
"MIT"
] | 0 | 502e1ea6db64481d7464fdda4d4d0be9b0f1089a | https://github.com/JasonMa2016/learn2learn/tree/502e1ea6db64481d7464fdda4d4d0be9b0f1089a |
C3D | import torch
import torch.nn as nn
import torch.nn
class C3D(nn.Module):
def __init__(self, inplanes, planes):
super(C3D, self).__init__()
self.c3d = nn.Conv3d(inplanes, planes, kernel_size=3, padding=1)
def forward(self, x):
x = self.c3d(x)
return x
def get_inputs():
r... | 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
assert_size_stride = torch._C._dynamo.guar... | Schmiddo/d2conv3d | C3D | false | 8,748 | [
"MIT"
] | 16 | 9b330be56f0dfb9657a63e3fb3394ab36b35a67b | https://github.com/Schmiddo/d2conv3d/tree/9b330be56f0dfb9657a63e3fb3394ab36b35a67b |
ThreeNet | import torch
import torch.nn as nn
class ThreeNet(nn.Module):
"""
A network with three layers. This is used for testing a network with more
than one operation. The network has a convolution layer followed by two
fully connected layers.
"""
def __init__(self, input_dim: 'int', conv_dim: 'int',... | 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... | DenXX/fvcore | ThreeNet | false | 2,232 | [
"Apache-2.0"
] | 0 | 4b91cf092f4f5d379b2c93398780a3b5755e7179 | https://github.com/DenXX/fvcore/tree/4b91cf092f4f5d379b2c93398780a3b5755e7179 |
ShiftedSoftplus | import torch
import torch.nn.functional as F
import torch.fx
import torch.utils.data
class ShiftedSoftplus(torch.nn.Module):
def __init__(self):
super(ShiftedSoftplus, self).__init__()
self.shift = torch.log(torch.tensor(2.0)).item()
def forward(self, x):
return F.softplus(x) - self.... | 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.fx
import torch.utils.data
assert_size_stride = t... | JinheonBaek/pytorch_geometric | ShiftedSoftplus | false | 17,511 | [
"MIT"
] | 4 | dfd32d08a3d8191d6290e53458d4eda515d04fd6 | https://github.com/JinheonBaek/pytorch_geometric/tree/dfd32d08a3d8191d6290e53458d4eda515d04fd6 |
ItemToInterestAggregation | import torch
import torch.nn as nn
class ItemToInterestAggregation(nn.Module):
def __init__(self, seq_len, hidden_size, k_interests=5):
super().__init__()
self.k_interests = k_interests
self.theta = nn.Parameter(torch.randn([hidden_size, k_interests]))
def forward(self, input_tensor)... | 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.... | dreaming-qin/RecBole | ItemToInterestAggregation | false | 12,319 | [
"MIT"
] | 0 | d6de39521484ded60c387ca604abaf86310acdbe | https://github.com/dreaming-qin/RecBole/tree/d6de39521484ded60c387ca604abaf86310acdbe |
CO2Regularizer | import torch
class MemoryBankModule(torch.nn.Module):
"""Memory bank implementation
This is a parent class to all loss functions implemented by the lightly
Python package. This way, any loss can be used with a memory bank if
desired.
Attributes:
size:
Number of keys the memo... | 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._... | EelcoHoogendoorn/lightly | CO2Regularizer | false | 5,118 | [
"MIT"
] | 1 | 98e0148967738404fa7f45196ec5eabfe00cd22e | https://github.com/EelcoHoogendoorn/lightly/tree/98e0148967738404fa7f45196ec5eabfe00cd22e |
ConvSqu | import torch
import torch.nn.functional as F
import torch.utils.data
import torch
import torch.nn as nn
def autopad(k, p=None):
if p is None:
p = k // 2 if isinstance(k, int) else [(x // 2) for x in k]
return p
class Mish(nn.Module):
@staticmethod
def forward(x):
return x * F.softpl... | 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... | Beaver48/kaggle-chest-xray-abnormalities | ConvSqu | false | 11,310 | [
"MIT"
] | 0 | d41f32d1c59cb5c925795df3291e929b3ea6d5fd | https://github.com/Beaver48/kaggle-chest-xray-abnormalities/tree/d41f32d1c59cb5c925795df3291e929b3ea6d5fd |
LayerNorm | import torch
import torch.nn.init
import torch.optim.lr_scheduler
class LayerNorm(torch.nn.Module):
"""
An implementation of `Layer Normalization
<https://www.semanticscholar.org/paper/Layer-Normalization-Ba-Kiros/97fb4e3d45bb098e27e0071448b6152217bd35a5>`_ .
Layer Normalization stabilises the traini... | 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.init
import torch.optim.lr_scheduler
assert_size_stride = torch... | azraelzhor/allen-nlp-rc | LayerNorm | false | 3,153 | [
"Apache-2.0"
] | 0 | b114c00a8f364b18e3c427c1a447be9c65ede551 | https://github.com/azraelzhor/allen-nlp-rc/tree/b114c00a8f364b18e3c427c1a447be9c65ede551 |
ConvertTHWCtoTCHW | # 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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_... | XianyuanLiu/Transfer-Learning-Library | ConvertTHWCtoTCHW | false | 10,131 | [
"MIT"
] | 0 | 25f83f32437032df88ca6101ecd1f63ec7a0aa2c | https://github.com/XianyuanLiu/Transfer-Learning-Library/tree/25f83f32437032df88ca6101ecd1f63ec7a0aa2c |
Rot180 | # 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... | JoanFM/kornia | Rot180 | false | 11,545 | [
"ECL-2.0",
"Apache-2.0"
] | 0 | 808898887cde69074ca3e3df9b24dea9682aad90 | https://github.com/JoanFM/kornia/tree/808898887cde69074ca3e3df9b24dea9682aad90 |
PSPModule | # 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._C
import torch.serialization
assert_size_stride = tor... | AlexanderDokuchaev/mmsegmentation | PSPModule | false | 11,191 | [
"Apache-2.0"
] | 0 | 0c443ee370cce6227661b802184072174c4e3f64 | https://github.com/AlexanderDokuchaev/mmsegmentation/tree/0c443ee370cce6227661b802184072174c4e3f64 |
NoiseLayer | import torch
from torch import nn
import torch.nn
class NoiseLayer(nn.Module):
"""adds noise. noise is per pixel (constant over channels) with per-channel weight"""
def __init__(self, channels):
super().__init__()
self.weight = nn.Parameter(torch.zeros(channels))
self.noise = None
... | import torch
from torch import device
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
import torch.nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_... | Qingyang-Xu/GANInversion_with_ConsecutiveImgs | NoiseLayer | false | 8,673 | [
"MIT"
] | 23 | 9078a48ec3474dacdd02693b051e3addef1c5697 | https://github.com/Qingyang-Xu/GANInversion_with_ConsecutiveImgs/tree/9078a48ec3474dacdd02693b051e3addef1c5697 |
AdaptiveAvgPool | import torch
import uuid
import torch.utils.data
import torch.utils.data.distributed
from torch import nn
import torch.nn.parallel
import torch.optim
def _get_right_parentheses_index_(struct_str):
"""get the position of the first right parenthese in string"""
left_paren_count = 0
for index, single_char in... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import uuid
import torch.utils.data
import torch.utils.data.distributed
from torch import nn
import torch.nn.parallel
import torch.optim
ass... | Adlik/zen_nas | AdaptiveAvgPool | false | 16,888 | [
"Apache-2.0"
] | 7 | d820d5c7d5bbb6fd66a76d5f16513647d6ea7a57 | https://github.com/Adlik/zen_nas/tree/d820d5c7d5bbb6fd66a76d5f16513647d6ea7a57 |
SimpleNet | import torch
import torch.nn as nn
class SimpleNet(nn.Module):
def __init__(self, width, input_size, output_size, pool='max'):
super(SimpleNet, self).__init__()
self.pool = nn.MaxPool2d(width, stride=width
) if pool == 'max' else nn.AvgPool2d(width, stride=width)
self.fc1 = 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 as nn
assert_... | daniel-zeng/SegSort | SimpleNet | false | 9,976 | [
"MIT"
] | 0 | 7a50e6253df23a7719f962b34acff2626c916354 | https://github.com/daniel-zeng/SegSort/tree/7a50e6253df23a7719f962b34acff2626c916354 |
GatedTanh | # 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 ... | VisualJoyce/ChengyuBERT | GatedTanh | false | 18,083 | [
"MIT"
] | 8 | 605db3a4b3241dd4d02baa41a68bf23b5b00b36d | https://github.com/VisualJoyce/ChengyuBERT/tree/605db3a4b3241dd4d02baa41a68bf23b5b00b36d |
MapNet | # 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_... | DRL-CASIA/Perception | MapNet | false | 7,930 | [
"MIT"
] | 39 | a0e7d3957267ce92a82b03ab3eca96916d22c4f2 | https://github.com/DRL-CASIA/Perception/tree/a0e7d3957267ce92a82b03ab3eca96916d22c4f2 |
DeconvBlock | # 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... | richardlyf/FeatDepth | DeconvBlock | false | 12,935 | [
"MIT"
] | 0 | 6739ee0ded5a91a97d6cea1aa259c64f8b520fcd | https://github.com/richardlyf/FeatDepth/tree/6739ee0ded5a91a97d6cea1aa259c64f8b520fcd |
GaussianKernel | import torch
from torch import nn
class GaussianKernel(nn.Module):
"""
Gaussian kernel module.
:param mu: Float, mean of the kernel.
:param sigma: Float, sigma of the kernel.
Examples:
>>> import torch
>>> kernel = GaussianKernel()
>>> x = torch.randn(4, 5, 10)
>>... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_... | amberhuang01/LearningFromFactCheckers | GaussianKernel | false | 18,299 | [
"MIT"
] | 9 | 3c21684709bf5e331c4585c7d62596960dd44732 | https://github.com/amberhuang01/LearningFromFactCheckers/tree/3c21684709bf5e331c4585c7d62596960dd44732 |
NCModel | # 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
from torch import nn
from torch.nn import Parameter
assert_size_stride = torch.... | DavidHidde/backdoors101 | NCModel | false | 11,334 | [
"MIT"
] | 0 | 76ad5b391d3526fa26c3985e611d576c05724714 | https://github.com/DavidHidde/backdoors101/tree/76ad5b391d3526fa26c3985e611d576c05724714 |
DecoderNet | import torch
import torch.nn.functional as F
import torch.nn as nn
class DecoderNet(nn.Module):
"""
The decoder takes an interpolated feature vector and turn it into the
output signal. This net is intended to be very lightweight, it has only one
hidden layer.
"""
def __init__(self, feature_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
assert_... | eliemichel/ReACORN | DecoderNet | false | 6,649 | [
"MIT"
] | 1 | 74501551ecb387352271674efb2ed6240d234df6 | https://github.com/eliemichel/ReACORN/tree/74501551ecb387352271674efb2ed6240d234df6 |
Discriminator | # 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 numpy as np
import torch.nn as nn
assert_size_stride = torch._C._dynamo.g... | jfrancis71/pytorch-lightning-bolts | Discriminator | false | 3,838 | [
"Apache-2.0"
] | 0 | 8a4cf8f61644c28d6df54ccffe3a52d6f5fce5a6 | https://github.com/jfrancis71/pytorch-lightning-bolts/tree/8a4cf8f61644c28d6df54ccffe3a52d6f5fce5a6 |
GlobalAttentionGeneral | # 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.... | MaxyLee/Style-AttnGAN | GlobalAttentionGeneral | false | 8,528 | [
"MIT"
] | 36 | d33d0df061c94b75ad4af5c750b8d6f37ee1a35a | https://github.com/MaxyLee/Style-AttnGAN/tree/d33d0df061c94b75ad4af5c750b8d6f37ee1a35a |
LogSumExpPool | import torch
import torch.nn as nn
class LogSumExpPool(nn.Module):
def __init__(self, gamma):
super(LogSumExpPool, self).__init__()
self.gamma = gamma
def forward(self, feat_map):
"""
Numerically stable implementation of the operation
Arguments:
feat_map(T... | 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
... | C3-ASV-Team/torchxrayvision | LogSumExpPool | false | 4,926 | [
"Apache-2.0"
] | 1 | 7e53f0606986562f17a1ffd9f31d006756eff78d | https://github.com/C3-ASV-Team/torchxrayvision/tree/7e53f0606986562f17a1ffd9f31d006756eff78d |
GeneratorLat | import torch
import torch.onnx
import torch.nn as nn
import torch.nn.functional as F
class GeneratorLat(nn.Module):
"""Define standard linear + softmax generation step."""
def __init__(self, d_model, tgt_lat_classes):
super(GeneratorLat, self).__init__()
self.proj = nn.Linear(d_model, tgt_lat... | 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.... | PhilippeW83440/conv-social-pooling | GeneratorLat | false | 17,830 | [
"MIT"
] | 4 | 93d3a08af8678c3309d75a9bfb37df500da5cc46 | https://github.com/PhilippeW83440/conv-social-pooling/tree/93d3a08af8678c3309d75a9bfb37df500da5cc46 |
elu_modified | # 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
import torch.utils.data
assert_size_stride = torch._C._dy... | aasensio/umal_pytorch | elu_modified | false | 9,634 | [
"MIT"
] | 0 | 17bf1fee006c26dc277eb31f22aee022246c0367 | https://github.com/aasensio/umal_pytorch/tree/17bf1fee006c26dc277eb31f22aee022246c0367 |
G_Large | import torch
import torch.nn as nn
class Conv2d(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, stride=1, bn
=False, activation='leakyrelu', dropout=False):
super(Conv2d, self).__init__()
padding = int((kernel_size - 1) / 2)
self.conv = nn.Conv2d(in_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_... | RQuispeC/pytorch-ACSCP | G_Large | false | 8,766 | [
"MIT"
] | 25 | c83f08632012c2245250ff9c5140814461db575c | https://github.com/RQuispeC/pytorch-ACSCP/tree/c83f08632012c2245250ff9c5140814461db575c |
Classify | import torch
import torch.nn as nn
def autopad(k, p=None):
if p is None:
p = k // 2 if isinstance(k, int) else [(x // 2) for x in k]
return p
class Classify(nn.Module):
def __init__(self, c1, c2, k=1, s=1, p=None, g=1):
super().__init__()
self.aap = nn.AdaptiveAvgPool2d(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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | Aditya239233/MDP | Classify | false | 16,896 | [
"MIT"
] | 4 | 87491e1d67e547c11f4bdd5d784d120473429eae | https://github.com/Aditya239233/MDP/tree/87491e1d67e547c11f4bdd5d784d120473429eae |
Adder2D | # 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 math as tl_math
from torch.autograd import Function
import math
import torch.nn as nn
fro... | poppin-mice/ShiftAddNet | Adder2D | false | 16,268 | [
"MIT"
] | 55 | a17369a50da5bba6250fdeac7c065bd00f293f3c | https://github.com/poppin-mice/ShiftAddNet/tree/a17369a50da5bba6250fdeac7c065bd00f293f3c |
TwoHiddenLayerFc | import torch
import torch.nn as nn
import torch.nn.functional as F
class TwoHiddenLayerFc(nn.Module):
def __init__(self, input_shape, out_dim):
super(TwoHiddenLayerFc, self).__init__()
self.fc1 = nn.Linear(input_shape, 200)
self.fc2 = nn.Linear(200, 200)
self.fc3 = nn.Linear(200, ... | 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_... | jasonyanglu/fedavgpy | TwoHiddenLayerFc | false | 6,927 | [
"MIT"
] | 1 | cefbe5854f02d3df1197d849872286439c86e949 | https://github.com/jasonyanglu/fedavgpy/tree/cefbe5854f02d3df1197d849872286439c86e949 |
weightedFeatureFusion | # 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... | heymesut/SJTU_microe | weightedFeatureFusion | false | 6,804 | [
"BSD-3-Clause"
] | 1 | 7a862d03b4d8fe4c8608173a16082f44001f3f13 | https://github.com/heymesut/SJTU_microe/tree/7a862d03b4d8fe4c8608173a16082f44001f3f13 |
SEModule | # 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_... | VisualAnalysisOfHumans/LOVEU_TRACK1_TOP3_SUBMISSION | SEModule | false | 5,939 | [
"MIT"
] | 1 | 6f4d1c7e6883d6b0664fcd04265f437247afab54 | https://github.com/VisualAnalysisOfHumans/LOVEU_TRACK1_TOP3_SUBMISSION/tree/6f4d1c7e6883d6b0664fcd04265f437247afab54 |
ContrastiveLoss | import torch
import torch.cuda
import torch.nn.functional as F
class ContrastiveLoss(torch.nn.Module):
"""
Triplet loss function based on Contrastive loss function.
Based on: http://yann.lecun.com/exdb/publis/pdf/hadsell-chopra-lecun-06.pdf
"""
def __init__(self, margin=0.2):
super(Contra... | 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.cuda
assert_siz... | CS5590-0001-Projject/CS5590-0001-Project | ContrastiveLoss | false | 11,284 | [
"MIT"
] | 0 | 18a9f0df7b2ef0f5e9ec7a4bd4e77f761abfd8f3 | https://github.com/CS5590-0001-Projject/CS5590-0001-Project/tree/18a9f0df7b2ef0f5e9ec7a4bd4e77f761abfd8f3 |
ClassificationModel | # 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_... | LLYXC/OXNet | ClassificationModel | false | 8,430 | [
"Apache-2.0"
] | 13 | 4fb67a8c42b9158a8e563c4b68a157e4dedd9c66 | https://github.com/LLYXC/OXNet/tree/4fb67a8c42b9158a8e563c4b68a157e4dedd9c66 |
BertLayer | from _paritybench_helpers import _mock_config
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
class BertSelfAttention(nn.Module):
def __init__(self, config):
super().__init__()
self.num_attention_heads = config.num_attention_heads
self.attention_head_size = ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Abhimanyu08/minbert-assignment | BertLayer | false | 13,211 | [
"Apache-2.0"
] | 0 | 1610364213b1aab2d5446175dffabd7e1742833b | https://github.com/Abhimanyu08/minbert-assignment/tree/1610364213b1aab2d5446175dffabd7e1742833b |
CriticNet | import torch
import torch.nn as nn
import torch.nn.functional as F
class CriticNet(nn.Module):
def __init__(self, num_state, num_action):
super(CriticNet, self).__init__()
self.num_state = num_state
self.num_action = num_action
self.fc1 = nn.Linear(num_state, 100)
self.fc2... | 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_... | cugzj/Adaptive-B | CriticNet | false | 6,492 | [
"Apache-2.0"
] | 1 | cebc965b1dbad93332ae371bfef8640259d940c4 | https://github.com/cugzj/Adaptive-B/tree/cebc965b1dbad93332ae371bfef8640259d940c4 |
Generator | import torch
import torch.nn as nn
import torch.nn.functional as F
class Decoder(nn.Module):
def __init__(self):
super(Decoder, self).__init__()
self.conv6_1 = nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1)
self.conv6_2 = nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import ... | bigabig/saliency | Generator | false | 6,509 | [
"Apache-2.0"
] | 1 | 83618c90ea419ee05fbed116e8ad7bb2b331ecf5 | https://github.com/bigabig/saliency/tree/83618c90ea419ee05fbed116e8ad7bb2b331ecf5 |
MaskedConv1d | import torch
import torch.nn as nn
import torch.onnx
class MaskedConv1d(nn.Conv1d):
def __init__(self, in_channels, out_channels, kernel_size, dilation=1,
groups=1, bias=True, causal=True):
if causal:
padding = (kernel_size - 1) * dilation
else:
padding = (kernel_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
import torch.nn as nn
import torch.onnx
assert_size_stride = torch._C._dynamo.gu... | jonndoe/Character-Level-Language-Modeling-with-Deeper-Self-Attention-pytorch | MaskedConv1d | false | 3,772 | [
"MIT"
] | 0 | d27d2d390f0831330405c16bd29c7f331ad2007a | https://github.com/jonndoe/Character-Level-Language-Modeling-with-Deeper-Self-Attention-pytorch/tree/d27d2d390f0831330405c16bd29c7f331ad2007a |
SkipLastTargetChannelWrapper | import torch
import torch.nn as nn
from torch.nn import MSELoss
class SkipLastTargetChannelWrapper(nn.Module):
"""
Loss wrapper which removes additional target channel
"""
def __init__(self, loss, squeeze_channel=False):
super(SkipLastTargetChannelWrapper, self).__init__()
self.loss =... | 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... | BioTrillion/pytorch-3dunet | SkipLastTargetChannelWrapper | false | 4,897 | [
"MIT"
] | 1 | 217781197dd94211ee7fe5d53a8b404f0b8391a6 | https://github.com/BioTrillion/pytorch-3dunet/tree/217781197dd94211ee7fe5d53a8b404f0b8391a6 |
MaskedThing | # 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... | vegetablejuiceftw/soft-pointer-networks | MaskedThing | false | 11,072 | [
"MIT"
] | 0 | 9705d9688b6b69db3948172771df4c367165c948 | https://github.com/vegetablejuiceftw/soft-pointer-networks/tree/9705d9688b6b69db3948172771df4c367165c948 |
BertMixedLayer | # 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.... | Erotemic/MONAI | BertMixedLayer | false | 5,727 | [
"Apache-2.0"
] | 1 | a9cd2d88168107281a2abcc2f63efaed80580e79 | https://github.com/Erotemic/MONAI/tree/a9cd2d88168107281a2abcc2f63efaed80580e79 |
PolicyNetwork | import torch
import torch.nn as nn
import torch.nn.functional as F
class PolicyNetwork(nn.Module):
def __init__(self, num_inputs, num_actions, hidden_size, init_w=3e-05):
super(PolicyNetwork, self).__init__()
self.linear1 = nn.Linear(num_inputs, hidden_size)
self.linear2 = nn.Linear(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 import triton_helpers
from torch._inductor.runtime.... | frknayk/Reinforcement-Learning-In-Control | PolicyNetwork | false | 6,703 | [
"MIT"
] | 1 | 24c7eb6fa6b6390ee2dd04f25036c37896ecd944 | https://github.com/frknayk/Reinforcement-Learning-In-Control/tree/24c7eb6fa6b6390ee2dd04f25036c37896ecd944 |
CausalConv1d | import torch
from torch import nn
class CausalConv1d(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size=2, dilation=2):
super(CausalConv1d, self).__init__()
self.padding = dilation
self.causal_conv = nn.Conv1d(in_channels, out_channels, kernel_size,
padding=... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_st... | gaotianyu1350/new_fewrel_bertpair | CausalConv1d | false | 15,397 | [
"MIT"
] | 180 | 27184050d476fc93576948fb26680d508a2824bb | https://github.com/gaotianyu1350/new_fewrel_bertpair/tree/27184050d476fc93576948fb26680d508a2824bb |
MyKernelTorch | import torch
import torch.nn as nn
class MyKernelTorch(nn.Module):
def __init__(self, n_features: 'int'):
super().__init__()
self.dense1 = nn.Linear(n_features, 20)
self.dense2 = nn.Linear(20, 2)
def forward(self, x: 'torch.Tensor') ->torch.Tensor:
x = nn.ReLU()(self.dense1(x... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | sugatoray/alibi-detect | MyKernelTorch | false | 16,505 | [
"Apache-2.0"
] | 1,227 | 66d7873c248c0be1a1d836e6fe1ef59351b802d9 | https://github.com/sugatoray/alibi-detect/tree/66d7873c248c0be1a1d836e6fe1ef59351b802d9 |
ConcatenateLinear | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.utils.tensorboard
import torch.utils.data
import torch.distributed
... | JoeLambourne/SketchGraphs | ConcatenateLinear | false | 1,259 | [
"MIT"
] | 0 | 183c65f82d71d82c62b253092e9b7fa65846a3e6 | https://github.com/JoeLambourne/SketchGraphs/tree/183c65f82d71d82c62b253092e9b7fa65846a3e6 |
ClassNetVideoConv | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data
class Unit3D(nn.Module):
"""Basic unit containing Conv3D + BatchNorm + non-linearity."""
def __init__(self, in_channels, output_channels, kernel_shape=(1, 1, 1),
stride=(1, 1, 1), padding=0, activation_fn=F.rel... | 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
import torch.utils.data
as... | SheffieldAI/pykale | ClassNetVideoConv | false | 16,858 | [
"MIT"
] | 324 | be7670941fb06835883c80477b26702d407017db | https://github.com/SheffieldAI/pykale/tree/be7670941fb06835883c80477b26702d407017db |
ScaledDotProductAttention | import torch
import numpy as np
import torch.nn as nn
class ScaledDotProductAttention(nn.Module):
""" Scaled Dot-Product Attention """
def __init__(self, temperature, attn_dropout=0.1):
super().__init__()
self.temperature = temperature
self.dropout = nn.Dropout(attn_dropout)
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 import triton_helpers
from torch._inductor.runtime.... | Jincheng-Sun/Kylearn-pytorch | ScaledDotProductAttention | false | 641 | [
"MIT"
] | 0 | e72f2ab45a3f4724e843a27bec37664d3612fdca | https://github.com/Jincheng-Sun/Kylearn-pytorch/tree/e72f2ab45a3f4724e843a27bec37664d3612fdca |
NullDiscriminator | # 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.... | HappyBelief/ContraD | NullDiscriminator | false | 13,745 | [
"MIT"
] | 168 | abb72562ddac8d8ab37fe9af6ac4c44c61e8ea0f | https://github.com/HappyBelief/ContraD/tree/abb72562ddac8d8ab37fe9af6ac4c44c61e8ea0f |
A2CActorDisc | import torch
from torch.distributions import Categorical
import torch as t
import torch.nn as nn
class A2CActorDisc(nn.Module):
def __init__(self, state_dim, action_num):
super().__init__()
self.fc1 = nn.Linear(state_dim, 16)
self.fc2 = nn.Linear(16, 16)
self.fc3 = nn.Linear(16, 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._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | iffiX/machin | A2CActorDisc | false | 15,589 | [
"MIT"
] | 287 | 7fa986b1bafdefff117d6ff73d14644a5488de9d | https://github.com/iffiX/machin/tree/7fa986b1bafdefff117d6ff73d14644a5488de9d |
UpBlok | import torch
import torch.nn as nn
import torch.nn.functional as F
class UpBlok(nn.Module):
def __init__(self, in_channels, out_channels):
super().__init__()
self.conv1x1 = nn.Conv2d(in_channels, in_channels, kernel_size=1,
stride=1, padding=0)
self.conv3x3 = nn.Conv2d(in_chan... | 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_... | fendaq/TextRSN | UpBlok | false | 6,684 | [
"MIT"
] | 1 | 02a6bc06cd64b581414ed5065a8c93e0c68a807a | https://github.com/fendaq/TextRSN/tree/02a6bc06cd64b581414ed5065a8c93e0c68a807a |
CompressionFM | import torch
class CompressionFM(torch.nn.Module):
"""
FM layer
"""
def __init__(self, input_size, fm_size):
super(CompressionFM, self).__init__()
self.LW = torch.nn.Linear(input_size, 1)
self.QV = torch.nn.Parameter(torch.randn(input_size, fm_size))
def forward(self, inp... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cu... | haophancs/TREQS | CompressionFM | false | 15,498 | [
"MIT"
] | 149 | 49e354ce2a08cf963ec139d99936020e0f80ced8 | https://github.com/haophancs/TREQS/tree/49e354ce2a08cf963ec139d99936020e0f80ced8 |
Sum | import torch
import torch.nn as nn
import torch.onnx
class Sum(nn.Module):
def __init__(self, n, weight=False):
super().__init__()
self.weight = weight
self.iter = range(n - 1)
if weight:
self.w = nn.Parameter(-torch.arange(1.0, n) / 2, requires_grad=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
import torch.nn as nn
import torch.onnx
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynam... | DDGRCF/YOLOX_OBB | Sum | false | 7,963 | [
"Apache-2.0"
] | 39 | 27b80953306492b8bc83b86b1353d8cee01ef9b6 | https://github.com/DDGRCF/YOLOX_OBB/tree/27b80953306492b8bc83b86b1353d8cee01ef9b6 |
PredLayer | # 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... | xlx0010/HGNN | PredLayer | false | 4,583 | [
"MIT"
] | 0 | 219352405db021c1f435f3aa55961adcf2a6df19 | https://github.com/xlx0010/HGNN/tree/219352405db021c1f435f3aa55961adcf2a6df19 |
AFMS | import torch
import torch.nn as nn
import torch.nn.functional as F
class AFMS(nn.Module):
"""
Alpha-Feature map scaling, added to the output of each residual block[1,2].
Reference:
[1] RawNet2 : https://www.isca-speech.org/archive/Interspeech_2020/pdfs/1011.pdf
[2] AMFS : https://www.koreascie... | 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... | hdubey/RawNet | AFMS | false | 3,585 | [
"MIT"
] | 0 | 45589b2da9b0562ef2810e6097d4bdba23eb8a0a | https://github.com/hdubey/RawNet/tree/45589b2da9b0562ef2810e6097d4bdba23eb8a0a |
Normalize3D | import torch
import torch.nn as nn
class Normalize3D(nn.Module):
"""
Scale Spectrogram to be between 0 and 1
"""
def __init__(self):
super(Normalize3D, self).__init__()
def forward(self, X: 'torch.Tensor'):
if len(X.shape) != 3:
raise ValueError(
'Inp... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | dariocazzani/vo-id | Normalize3D | false | 1,787 | [
"MIT"
] | 0 | 41d0f2779e7909cfa15afcb6c8222c48a5855eb8 | https://github.com/dariocazzani/vo-id/tree/41d0f2779e7909cfa15afcb6c8222c48a5855eb8 |
IndepAnisotropicGaussianUVLoss | import math
import torch
import torch.utils.data
import torch.nn.functional as F
from torch import nn
class IndepAnisotropicGaussianUVLoss(nn.Module):
"""
Loss for the case of independent residuals with anisotropic covariances:
$Sigma_i = sigma_i^2 I + r_i r_i^T$
The loss (negative log likelihood) is ... | 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 math... | Magixxxxxx/detectron2 | IndepAnisotropicGaussianUVLoss | false | 2,640 | [
"Apache-2.0"
] | 0 | c1ee8cf73777c96cc8a89463d0dca6e0ffe148f4 | https://github.com/Magixxxxxx/detectron2/tree/c1ee8cf73777c96cc8a89463d0dca6e0ffe148f4 |
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.... | Mika412/deep-reinforcement-learning | Actor | false | 11,699 | [
"MIT"
] | 0 | 9b5ba901f760e50cd64d272939eff75881af5a9c | https://github.com/Mika412/deep-reinforcement-learning/tree/9b5ba901f760e50cd64d272939eff75881af5a9c |
NTimesTanh | # 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_... | liuzeyuMr/ELEGANT_cvte | NTimesTanh | false | 3,923 | [
"MIT"
] | 0 | eb8039310023f91e25e37ff8d907844afd50e0a5 | https://github.com/liuzeyuMr/ELEGANT_cvte/tree/eb8039310023f91e25e37ff8d907844afd50e0a5 |
SigmoidRange | # 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.nn import Module
import functools
import torch.nn as nn
from typing import *
assert_size_stride = torch._C._dynamo.guards.assert_... | davidpfahler/fastai_dev | SigmoidRange | false | 10,053 | [
"Apache-2.0"
] | 0 | a86b15f86138a9902e8649e3f745e76a19139ab3 | https://github.com/davidpfahler/fastai_dev/tree/a86b15f86138a9902e8649e3f745e76a19139ab3 |
Block_MLP | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | yifanc96/yifanc-DL | Block_MLP | false | 11,095 | [
"MIT"
] | 0 | 25d56cec776fb151c8f6bcbd997bca94f07f3597 | https://github.com/yifanc96/yifanc-DL/tree/25d56cec776fb151c8f6bcbd997bca94f07f3597 |
SharpenedCosineSimilarity | # 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, math as tl_math
import torch.nn as nn
import torch.nn.functional as F
assert_s... | quickgrid/sharpened_cosine_similarity_torch | SharpenedCosineSimilarity | false | 4,162 | [
"MIT"
] | 0 | d652d76a4994a0b3817e248d5899827d35a5ebeb | https://github.com/quickgrid/sharpened_cosine_similarity_torch/tree/d652d76a4994a0b3817e248d5899827d35a5ebeb |
ToRGB | from torch.autograd import Function
import math
import torch
import torch.nn as nn
from torch.nn import functional as F
import torch.nn.parallel
import torch.utils.data
def make_kernel(k):
k = torch.tensor(k, dtype=torch.float32)
if k.ndim == 1:
k = k[None, :] * k[:, None]
k /= k.sum()
return ... | 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
import torch.nn as nn
from torch... | kampta/multiview-shapes | ToRGB | false | 3,799 | [
"MIT"
] | 0 | a79eb4b492be8c2c279e2c69b13d5a19dff1621b | https://github.com/kampta/multiview-shapes/tree/a79eb4b492be8c2c279e2c69b13d5a19dff1621b |
Attention | from _paritybench_helpers import _mock_config
import math
import torch
import torch.nn as nn
from torch.nn import Dropout
from torch.nn import Softmax
from torch.nn import Linear
class Attention(nn.Module):
def __init__(self, config):
super(Attention, self).__init__()
self.num_attention_heads = c... | 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.... | LJOVO/TranSalNet | Attention | false | 4,728 | [
"MIT"
] | 0 | a2aba83e3b8f54c47b712511bf4f515f236326ed | https://github.com/LJOVO/TranSalNet/tree/a2aba83e3b8f54c47b712511bf4f515f236326ed |
SqueezeInitBlock | # 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
impor... | HyperGAN/imgclsmob | SqueezeInitBlock | false | 17,682 | [
"MIT"
] | 9 | 88b9776a5a927dc9a54e85e31978c4a9ec5ecbf3 | https://github.com/HyperGAN/imgclsmob/tree/88b9776a5a927dc9a54e85e31978c4a9ec5ecbf3 |
Fire | # 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_... | ArronHZG/ABD-Net | Fire | false | 9,590 | [
"MIT"
] | 0 | 4f6d15f4d389a55549ea10a2e00d4a5cdecb5753 | https://github.com/ArronHZG/ABD-Net/tree/4f6d15f4d389a55549ea10a2e00d4a5cdecb5753 |
GateLayer | import torch
from torch import nn
class GateLayer(nn.Module):
def __init__(self, input_dim):
super(GateLayer, self).__init__()
self._norm_layer1 = nn.Linear(input_dim * 2, input_dim)
self._norm_layer2 = nn.Linear(input_dim, 1)
def forward(self, input1, input2):
norm_input = s... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_st... | shubaoyu/CRSLab | GateLayer | false | 10,818 | [
"MIT"
] | 0 | a05730e8b2c03df278587be34923fa818945d4c4 | https://github.com/shubaoyu/CRSLab/tree/a05730e8b2c03df278587be34923fa818945d4c4 |
AvgPool2d | from torch.nn import Module
import torch
import torch as th
class AvgPool2d(Module):
"""
This class is the beginning of an exact python port of the torch.nn.AvgPool2d
module. Because PySyft cannot hook into layers which are implemented in C++,
our special functionalities (such as encrypted computation... | 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.nn import Module
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._em... | abogaziah/PySyft | AvgPool2d | false | 1,352 | [
"Apache-2.0"
] | 0 | 812dc6f350261793c67a928786fc081158f22a76 | https://github.com/abogaziah/PySyft/tree/812dc6f350261793c67a928786fc081158f22a76 |
SelfAttention | # 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.... | jscarlson/zi2zi-pytorch | SelfAttention | false | 15,755 | [
"Apache-2.0"
] | 81 | 3409165b304ccf1d5a5c2329a9f0f0897b3495dc | https://github.com/jscarlson/zi2zi-pytorch/tree/3409165b304ccf1d5a5c2329a9f0f0897b3495dc |
ComputeDeltas | import torch
from torch import Tensor
import torchaudio.functional as F
class ComputeDeltas(torch.nn.Module):
"""Compute delta coefficients of a tensor, usually a spectrogram.
See `torchaudio.functional.compute_deltas` for more details.
Args:
win_length (int): The window length used for computin... | 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... | Nayef211/audio | ComputeDeltas | false | 11,738 | [
"BSD-2-Clause"
] | 0 | 241ab1e8284e589262f510ee9411baf2bc374ded | https://github.com/Nayef211/audio/tree/241ab1e8284e589262f510ee9411baf2bc374ded |
BasicModel5_MultiArgs | import torch
import torch.nn as nn
import torch.nn.functional as F
class BasicModel5_MultiArgs(nn.Module):
"""
Slightly modified example model from the paper
https://arxiv.org/pdf/1703.01365.pdf
f(x1, x2) = RELU(ReLU(x1 - 1) * x3[0] - ReLU(x2) * x3[1])
"""
def __init__(self):
... | 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... | ngduduong/captum | BasicModel5_MultiArgs | false | 4,071 | [
"BSD-3-Clause"
] | 0 | 6fe5f0f23ea975e73e0c0dee79bdc01b4223d283 | https://github.com/ngduduong/captum/tree/6fe5f0f23ea975e73e0c0dee79bdc01b4223d283 |
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