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 |
|---|---|---|---|---|---|---|---|---|---|---|
TransformerEncoderLayerWithConv1d | # 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.... | bliunlpr/pykaldi2 | TransformerEncoderLayerWithConv1d | false | 1,617 | [
"MIT"
] | 0 | f6020b5dd9900f97ab69c97442a91196a03dd93b | https://github.com/bliunlpr/pykaldi2/tree/f6020b5dd9900f97ab69c97442a91196a03dd93b |
PSLoss | # 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
... | NejcHirci/material-addon | PSLoss | false | 17,767 | [
"MIT"
] | 4 | c08e2081413c3319b712c2f7193ac8013f601382 | https://github.com/NejcHirci/material-addon/tree/c08e2081413c3319b712c2f7193ac8013f601382 |
ExpLayer | import torch
import torch.cuda
import torch.nn as nn
class ExpLayer(nn.Module):
def __init__(self, vMF_kappa):
super(ExpLayer, self).__init__()
self.vMF_kappa = nn.Parameter(torch.Tensor([vMF_kappa]))
def forward(self, x, binary=False):
if binary:
x = torch.exp(self.vMF_k... | 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.cuda
import torch.nn as nn
assert_size_stride = torch._C._dy... | XD7479/Robust-Instance-Segmentation-through-Reasoning-about-Multi-Object-Occlusion | ExpLayer | false | 9,594 | [
"MIT"
] | 0 | 593622afbd83981b4c42940d39770ddf9c1b566c | https://github.com/XD7479/Robust-Instance-Segmentation-through-Reasoning-about-Multi-Object-Occlusion/tree/593622afbd83981b4c42940d39770ddf9c1b566c |
Conv2dLayer | # 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 ... | Sheroa/Video_Colorization | Conv2dLayer | false | 2,847 | [
"MIT"
] | 0 | 5c772ac0ec944814cd8be0a94b0746116b11ac01 | https://github.com/Sheroa/Video_Colorization/tree/5c772ac0ec944814cd8be0a94b0746116b11ac01 |
QuaternionLinear | from torch.nn import Module
import torch
import numpy as np
from numpy.random import RandomState
from torch.autograd import Variable
from torch.nn.parameter import Parameter
from scipy.stats import chi
import torch.fx
def quaternion_init(in_features, out_features, rng, kernel_size=None,
criterion='glorot'):
i... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch.nn import Module
import numpy as np
from numpy.random import RandomSt... | eleGAN23/HI2I | QuaternionLinear | false | 6,678 | [
"MIT"
] | 1 | 7730ee0963614290099b011c113048ef6d1b149c | https://github.com/eleGAN23/HI2I/tree/7730ee0963614290099b011c113048ef6d1b149c |
TenLayerNet | import torch
class TenLayerNet(torch.nn.Module):
def __init__(self, D_in, H, D_out):
super(TenLayerNet, self).__init__()
self.linear1 = torch.nn.Linear(D_in, H)
self.linear2 = torch.nn.Linear(H, H)
self.linear3 = torch.nn.Linear(H, H)
self.linear4 = torch.nn.Linear(H, H)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
assert_size_stride = torch._C... | anvitha-bhat/iot_final_project | TenLayerNet | false | 9,750 | [
"MIT"
] | 0 | e9301c083d5e7a228d0ad868e44cb1df3a5f7363 | https://github.com/anvitha-bhat/iot_final_project/tree/e9301c083d5e7a228d0ad868e44cb1df3a5f7363 |
VGG_16 | # 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_... | qiu9yu/Lets_OCR | VGG_16 | false | 16,368 | [
"MIT"
] | 671 | 62d68b044250d02a9d5ac8c4fbd08cec83faa0d1 | https://github.com/qiu9yu/Lets_OCR/tree/62d68b044250d02a9d5ac8c4fbd08cec83faa0d1 |
DeiTSelfAttention | from _paritybench_helpers import _mock_config
import math
import torch
from torch import nn
import torch.utils.checkpoint
class DeiTSelfAttention(nn.Module):
def __init__(self, config):
super().__init__()
if (config.hidden_size % config.num_attention_heads != 0 and not
hasattr(config,... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | Clemens123/transformers | DeiTSelfAttention | false | 13,213 | [
"Apache-2.0"
] | 0 | 22abe7bbc587c16ec30f9d1aa549dcbeba6e9e26 | https://github.com/Clemens123/transformers/tree/22abe7bbc587c16ec30f9d1aa549dcbeba6e9e26 |
ShiftedSoftplus | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch import nn
assert_size_stride = torch._C._dynamo.gua... | cuulee/mega-nerf | ShiftedSoftplus | false | 6,504 | [
"MIT"
] | 1 | b38ea40b6ca53ae4423fcfb354ac13cd794827a4 | https://github.com/cuulee/mega-nerf/tree/b38ea40b6ca53ae4423fcfb354ac13cd794827a4 |
Auto_Encoder_Model | import torch
import torch.nn as nn
import torch.nn.functional as F
class Auto_Encoder_Model(nn.Module):
def __init__(self):
super(Auto_Encoder_Model, self).__init__()
self.conv1 = nn.Conv2d(1, 64, padding=1, kernel_size=3)
self.max_pool1 = nn.MaxPool2d(2)
self.conv2 = nn.Conv2d(64... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import ... | yutian-zhao/MICCAI19-MedVQA | Auto_Encoder_Model | false | 11,087 | [
"MIT"
] | 0 | 7df92c529ed87d67281efb2f568fc6c57cebfef1 | https://github.com/yutian-zhao/MICCAI19-MedVQA/tree/7df92c529ed87d67281efb2f568fc6c57cebfef1 |
FocalLoss2d | import torch
from torch import nn
class FocalLoss2d(nn.Module):
def __init__(self, gamma=2, ignore_index=255):
super().__init__()
self.gamma = gamma
self.ignore_index = ignore_index
def forward(self, outputs, targets):
outputs = outputs.contiguous()
targets = targets.... | 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... | Nareshvrao/Understanding-Clouds-from-Satellite-Images | FocalLoss2d | false | 5,632 | [
"MIT"
] | 1 | 14c5e1f15e803e9638d7a3fa8b9e0d929a6015b6 | https://github.com/Nareshvrao/Understanding-Clouds-from-Satellite-Images/tree/14c5e1f15e803e9638d7a3fa8b9e0d929a6015b6 |
TransposedConvModel | # 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.cuda
import torc... | mikeseven/aimet | TransposedConvModel | false | 11,116 | [
"BSD-3-Clause"
] | 0 | 63211a4f259b6457c58dfae1097c70acb93319fe | https://github.com/mikeseven/aimet/tree/63211a4f259b6457c58dfae1097c70acb93319fe |
ANN | # 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.... | KuangenZhang/StructuredRL | ANN | false | 5,457 | [
"MIT"
] | 1 | 9b05e5034ff0e045aabf83786efb0859f08e989a | https://github.com/KuangenZhang/StructuredRL/tree/9b05e5034ff0e045aabf83786efb0859f08e989a |
SeparableConv1D | import torch
from torch import nn
class SeparableConv1D(nn.Module):
"""Depthwise separable 1D convolution.
Args:
in_channels (int): Number of input channels.
out_channels (int): Number of output channels.
kernel_size (int): Size of the convolving kernel.
stride (int): Stride o... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import 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... | YiwenShaoStephen/snowfall | SeparableConv1D | false | 14,694 | [
"Apache-2.0"
] | 145 | 949226f35b29c629cb03cae36fa43da5993d27a3 | https://github.com/YiwenShaoStephen/snowfall/tree/949226f35b29c629cb03cae36fa43da5993d27a3 |
MSEWithLogitsLoss | import torch
from torch import nn
from torch.nn import MSELoss
class MSEWithLogitsLoss(MSELoss):
"""
This loss combines a `Sigmoid` layer and the `MSELoss` in one single class.
"""
def __init__(self):
super(MSEWithLogitsLoss, self).__init__()
self.sigmoid = nn.Sigmoid()
def forwa... | 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 import nn
from torch.nn import MSELoss
assert_size_stride = torch._C._dynamo.g... | joowlim/pytorch-3dunet | MSEWithLogitsLoss | false | 10,394 | [
"MIT"
] | 0 | d08049f60b619627521efd0fb171247e1536b262 | https://github.com/joowlim/pytorch-3dunet/tree/d08049f60b619627521efd0fb171247e1536b262 |
MatchingTensor | # 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.... | ThuYShao/MatchZoo-py | MatchingTensor | false | 11,976 | [
"Apache-2.0"
] | 0 | dd8ff1328af58d3d14aacd1a7d56d79bbf847c15 | https://github.com/ThuYShao/MatchZoo-py/tree/dd8ff1328af58d3d14aacd1a7d56d79bbf847c15 |
Log1Exp | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_... | dattientran/attorch | Log1Exp | false | 12,387 | [
"MIT"
] | 0 | 469b225846c6d8a7d833ebac19d040c7a407a0ff | https://github.com/dattientran/attorch/tree/469b225846c6d8a7d833ebac19d040c7a407a0ff |
GT | import torch
class GT(torch.nn.Module):
def __init__(self):
super(GT, self).__init__()
def forward(self, x, y):
return x > y
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
| import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.j... | Ilyabasharov/torch2trt | GT | false | 2,535 | [
"MIT"
] | 0 | 76bf298b3da408509665e23e2494922b131afb10 | https://github.com/Ilyabasharov/torch2trt/tree/76bf298b3da408509665e23e2494922b131afb10 |
ClsHead | import torch
from torch import nn
import torch.nn.functional as F
class ClsHead(nn.Module):
"""
Class orientation
Args:
params(dict): super parameters for build Class network
"""
def __init__(self, in_channels, class_dim, **kwargs):
super(ClsHead, self).__init__()
self.p... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | DocYard-ai/UCR | ClsHead | false | 8,030 | [
"Apache-2.0"
] | 10 | 7618aa336f56e71d9fd8cdc2d591e3d138e3dc68 | https://github.com/DocYard-ai/UCR/tree/7618aa336f56e71d9fd8cdc2d591e3d138e3dc68 |
Linear2 | import torch
import torch.nn.functional as F
from torch import nn
class Linear2(nn.Module):
def __init__(self, nfeat, nhid, nclass, dropout):
super(Linear2, self).__init__()
self.linear1 = nn.Linear(nfeat, nhid, bias=True)
self.linear2 = nn.Linear(nhid, nclass, bias=True)
self.dro... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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... | Eudialyte/SepGAT | Linear2 | false | 434 | [
"MIT"
] | 0 | 6ea77714d1b2f2f5d0857cddcc9f1f5f9c0bcf50 | https://github.com/Eudialyte/SepGAT/tree/6ea77714d1b2f2f5d0857cddcc9f1f5f9c0bcf50 |
mnistmodel_B | # 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... | layel2/layyer-lib | mnistmodel_B | false | 3,877 | [
"MIT"
] | 0 | db48b5c38098ee93d2d34693d98e5ef4d319d919 | https://github.com/layel2/layyer-lib/tree/db48b5c38098ee93d2d34693d98e5ef4d319d919 |
AddNorm | # 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.functional as F
import torch.nn as nn
assert_size_stride = torc... | JakeForsey/pytorch-forecasting | AddNorm | false | 9,124 | [
"MIT"
] | 0 | e5291df3dd8f8d72ecd2b21869f69cebf9456028 | https://github.com/JakeForsey/pytorch-forecasting/tree/e5291df3dd8f8d72ecd2b21869f69cebf9456028 |
StateInitZero | import torch
from torch import nn
from torchvision import models as models
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
from torchvision.transforms import *
import torch.onnx
class StateInitZero(nn.Module):
def __init__(self, hidden_size, num_layers=1, b... | 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
from torchvision import models as models
import torch.nn.parallel
import torch.optim
import torch.utils.data
import tor... | aalborov/openvino_training_extensions | StateInitZero | false | 6,041 | [
"Apache-2.0"
] | 1 | a0bb39424151a98e1ca80c4aa5c865636d401785 | https://github.com/aalborov/openvino_training_extensions/tree/a0bb39424151a98e1ca80c4aa5c865636d401785 |
MIRB2 | # 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 ... | wwjfsfs/wwjyyds | MIRB2 | false | 13,209 | [
"MIT"
] | 0 | 80cd6267fde7cd98838078a0d5178a557ceb7414 | https://github.com/wwjfsfs/wwjyyds/tree/80cd6267fde7cd98838078a0d5178a557ceb7414 |
MultiHeadAttn | # 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.... | Blickwinkel1107/NJUNMT-pytorch | MultiHeadAttn | false | 17,038 | [
"MIT"
] | 9 | 82f684fe768b137ca0649b7b79a1820077917385 | https://github.com/Blickwinkel1107/NJUNMT-pytorch/tree/82f684fe768b137ca0649b7b79a1820077917385 |
DQN | import torch
import torch.nn as nn
import torch.nn.functional as F
class DQN(nn.Module):
def __init__(self, input_size, nbr_actions):
super(DQN, self).__init__()
self.fc1 = nn.Linear(input_size, 256)
self.fc2 = nn.Linear(256, 128)
self.fc3 = nn.Linear(128, 64)
self.fc4 = n... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | Ourdin/Phantom-of-the-Opera | DQN | false | 5,715 | [
"MIT"
] | 1 | c1ade346fadd40f6ca79033b8c6f3f74ce949d08 | https://github.com/Ourdin/Phantom-of-the-Opera/tree/c1ade346fadd40f6ca79033b8c6f3f74ce949d08 |
ShakeResNet | # 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 import... | aierh/autoML | ShakeResNet | false | 14,759 | [
"MIT"
] | 185 | 8e31966edf6de2c223d5eeb6cd4b4dbd6ddbbf77 | https://github.com/aierh/autoML/tree/8e31966edf6de2c223d5eeb6cd4b4dbd6ddbbf77 |
MyLinear | import torch
from torch import nn
from torch.nn import functional as F
class MyLinear(nn.Module):
def __init__(self, in_units, units):
super().__init__()
self.weight = nn.Parameter(torch.randn(in_units, units))
self.bias = nn.Parameter(torch.randn(units))
def forward(self, X):
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
assert_s... | JunoCheon/D2L | MyLinear | false | 2,600 | [
"MIT"
] | 0 | 9464709862e55151aec28fc637c5942738bdd72b | https://github.com/JunoCheon/D2L/tree/9464709862e55151aec28fc637c5942738bdd72b |
Conv_Blocks | import torch
import torch.nn as nn
class Conv_Blocks(nn.Module):
def __init__(self, input_dim, output_dim, filter_size=3, batch_norm=
False, non_lin='tanh', dropout=0.0, first_block=False, last_block=
False, skip_connection=False):
super(Conv_Blocks, self).__init__()
self.skip_con... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | LuigiFilippoChiara/GoalGAN | Conv_Blocks | false | 8,482 | [
"MIT"
] | 36 | 11ac7448af7ac8934e6eb47a06c51d92f04dec8c | https://github.com/LuigiFilippoChiara/GoalGAN/tree/11ac7448af7ac8934e6eb47a06c51d92f04dec8c |
SoftDiceLoss | # 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.distributed
from torch.backends import cudnn as cudnn
assert_size_stride = torch._C._dynamo.gu... | MIPT-Oulu/Collagen | SoftDiceLoss | false | 17,654 | [
"MIT"
] | 4 | 0cbc4285d60e5c9fcc89f629fcf4321e80b7452c | https://github.com/MIPT-Oulu/Collagen/tree/0cbc4285d60e5c9fcc89f629fcf4321e80b7452c |
TverskyLoss | import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
import torch._C
import torch.serialization
def get_class_weight(class_weight):
"""Get class weight for loss function.
Args:
class_weight (list[float] | str | None): If class_weight is a str,
take it as a... | 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 numpy as np
imp... | AlexanderDokuchaev/mmsegmentation | TverskyLoss | false | 11,197 | [
"Apache-2.0"
] | 0 | 0c443ee370cce6227661b802184072174c4e3f64 | https://github.com/AlexanderDokuchaev/mmsegmentation/tree/0c443ee370cce6227661b802184072174c4e3f64 |
EncoderLayer | # 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.... | CS-savvy/Transformer-for-Parkinsons-disease | EncoderLayer | false | 2,104 | [
"MIT"
] | 0 | 42ef54071092f4aab74c8b9ec82c52e944806a5b | https://github.com/CS-savvy/Transformer-for-Parkinsons-disease/tree/42ef54071092f4aab74c8b9ec82c52e944806a5b |
AnimalBaselineNet | import torch
import torch.nn as nn
import torch.nn.functional as F
class AnimalBaselineNet(nn.Module):
def __init__(self, num_classes=16):
super(AnimalBaselineNet, self).__init__()
self.conv1 = nn.Conv2d(3, 6, kernel_size=3, stride=2, padding=1)
self.conv2 = nn.Conv2d(6, 12, kernel_size=3... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | elouie/CodeSamples | AnimalBaselineNet | false | 10,072 | [
"Apache-2.0"
] | 0 | 3fe9fcf23cbfc82d84a679ea16d69ae41e700f06 | https://github.com/elouie/CodeSamples/tree/3fe9fcf23cbfc82d84a679ea16d69ae41e700f06 |
Transpose | # 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... | SwaggyZhang/Geometry-aware | Transpose | false | 1,105 | [
"Apache-2.0"
] | 0 | a750c00aa2f0bda5160dfdeee2eef5230fd9d993 | https://github.com/SwaggyZhang/Geometry-aware/tree/a750c00aa2f0bda5160dfdeee2eef5230fd9d993 |
SLMLexicon | import torch
from torch import Tensor
from typing import Tuple
import torch.nn as nn
class SLMLexicon(nn.Module):
"""
The optional "Lexicon" or "Memory" component of the Segmental Language
Model. Decodes context/position encodings to logits over a segmental
vocabulary, as well as a mixture proportion ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | cmdowney88/XLSLM | SLMLexicon | false | 3,309 | [
"MIT"
] | 0 | 7fe266bd0f0ad8a79a30052a18104b974d1c32e8 | https://github.com/cmdowney88/XLSLM/tree/7fe266bd0f0ad8a79a30052a18104b974d1c32e8 |
OutlookAttention | import math
import torch
from torch import nn
from torch.nn import functional as F
class OutlookAttention(nn.Module):
def __init__(self, dim, num_heads=1, kernel_size=3, padding=1, stride=1,
qkv_bias=False, attn_drop=0.1):
super().__init__()
self.dim = dim
self.num_heads = num_hea... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | Nitin-Mane/External-Attention-pytorch | OutlookAttention | false | 14,114 | [
"MIT"
] | 4,466 | 1ceda306c41063af11c956334747763444a4d83f | https://github.com/Nitin-Mane/External-Attention-pytorch/tree/1ceda306c41063af11c956334747763444a4d83f |
ConvNet | import torch
import torch.nn as nn
class ConvNet(nn.Module):
"""
A network with a single convolution layer. This is used for testing flop
count for convolution layers.
"""
def __init__(self, conv_dim: 'int', input_dim: 'int', output_dim: 'int',
kernel_size: 'int', spatial_dim: 'int', stri... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | johnanthonyjose/fvcore | ConvNet | false | 15,721 | [
"Apache-2.0"
] | 1,137 | af30fd4028553c1d1e4e5d389f309f52e046e67d | https://github.com/johnanthonyjose/fvcore/tree/af30fd4028553c1d1e4e5d389f309f52e046e67d |
FlawDetectorCriterion | import torch
import torch.nn as nn
import torch.nn.functional as F
class FlawDetectorCriterion(nn.Module):
""" Criterion of the flaw detector.
"""
def __init__(self):
super(FlawDetectorCriterion, self).__init__()
def forward(self, pred, gt, is_ssl=False, reduction=True):
loss = F.mse... | 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... | ZHKKKe/PixelSSL | FlawDetectorCriterion | false | 14,706 | [
"Apache-2.0"
] | 223 | ce192034355ae6a77e47d2983d9c9242df60802a | https://github.com/ZHKKKe/PixelSSL/tree/ce192034355ae6a77e47d2983d9c9242df60802a |
GHMC | # 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
... | CK-er/mmdet | GHMC | false | 2,072 | [
"Apache-2.0"
] | 0 | 9bea4068efbcf7bf739dbe41917a68d525c29868 | https://github.com/CK-er/mmdet/tree/9bea4068efbcf7bf739dbe41917a68d525c29868 |
Attention | from _paritybench_helpers import _mock_config
import torch
from torch import nn
import torch.nn.functional as F
import torch.nn.init
class Attention(nn.Module):
def __init__(self, args, enc_dim, dec_dim, attn_dim=None):
super(Attention, self).__init__()
self.args = args
self.enc_dim = enc... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | IvanFei/video_captioning_rl | Attention | false | 8,815 | [
"MIT"
] | 41 | 85ffa1abc056bd0ecfd35d1b52aed81d2f04afef | https://github.com/IvanFei/video_captioning_rl/tree/85ffa1abc056bd0ecfd35d1b52aed81d2f04afef |
Resize | import torch
import torch.nn as nn
import torch.nn.functional as F
class Resize(nn.Module):
def __init__(self, input_size=[224, 224]):
super(Resize, self).__init__()
self.input_size = input_size
def forward(self, input):
x = F.interpolate(input, size=self.input_size, mode='bilinear',... | 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... | beibuwandeluori/Attack-ImageNet-tianchi | Resize | false | 3,188 | [
"MIT"
] | 0 | 85294952ac1a190c26bba5e8f141b1c68e72668a | https://github.com/beibuwandeluori/Attack-ImageNet-tianchi/tree/85294952ac1a190c26bba5e8f141b1c68e72668a |
HighwayLayer | import torch
import torch.nn as nn
import torch.utils.data
import torch.utils.data.distributed
def my_xavier_init(m, gain=1):
for p in m.parameters():
if p.dim() > 1:
nn.init.xavier_uniform_(p, gain)
else:
nn.init.constant_(p, 0)
class HighwayLayer(torch.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.triton_helpers import libdevice
import torch.nn as ... | anoushkt/craftassist | HighwayLayer | false | 6,215 | [
"MIT"
] | 1 | c200af65e52e800f0f0cc540fe836b644383349d | https://github.com/anoushkt/craftassist/tree/c200af65e52e800f0f0cc540fe836b644383349d |
ContrastiveLoss | # 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
from numpy.random import *
i... | ioarun/pcb-fault-detection | ContrastiveLoss | false | 3,665 | [
"MIT"
] | 0 | d05deb724f86c4f89bdb816c07229bfba6420c14 | https://github.com/ioarun/pcb-fault-detection/tree/d05deb724f86c4f89bdb816c07229bfba6420c14 |
MaxPoolPad | import torch
import torch.utils.data
import torch.nn as nn
from torch import optim as optim
import torch.nn.parallel
class MaxPoolPad(nn.Module):
def __init__(self):
super(MaxPoolPad, self).__init__()
self.pad = nn.ZeroPad2d((1, 0, 1, 0))
self.pool = nn.MaxPool2d(3, stride=2, padding=1)
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.utils.data
import torch.nn as nn
from torch import optim as optim
import tor... | Exir-lxr/crldr-prune-pytorch | MaxPoolPad | false | 2,286 | [
"Apache-2.0"
] | 0 | adeb5e0b24ce66ff9531d4d947f72412c1b5c033 | https://github.com/Exir-lxr/crldr-prune-pytorch/tree/adeb5e0b24ce66ff9531d4d947f72412c1b5c033 |
LeakyReLU | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import numpy as np
import torch.nn as nn
from numbers import N... | DoggyLiu0116/MamboNet | LeakyReLU | false | 5,074 | [
"MIT"
] | 1 | 3b708091422491f660c4bd5eb12b06ce3b8a5f79 | https://github.com/DoggyLiu0116/MamboNet/tree/3b708091422491f660c4bd5eb12b06ce3b8a5f79 |
InputProjectionA | import torch
import torch.nn as nn
class InputProjectionA(nn.Module):
"""
This class projects the input image to the same spatial dimensions as the feature map.
For example, if the input image is 512 x512 x3 and spatial dimensions of feature map size are 56x56xF, then
this class will generate an outpu... | 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... | anilsathyan7/Portrait-Segmentation | InputProjectionA | false | 14,878 | [
"MIT"
] | 537 | dbf69b043cf70d3362bc500ee620f20807e622d2 | https://github.com/anilsathyan7/Portrait-Segmentation/tree/dbf69b043cf70d3362bc500ee620f20807e622d2 |
WeightNet | import torch
import torch.nn as nn
class WeightNet(nn.Module):
"""WeightNet in Temporal interlace module.
The WeightNet consists of two parts: one convolution layer
and a sigmoid function. Following the convolution layer, the sigmoid
function and rescale module can scale our output to the range (0, 2... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | Alexis-Fab/mmaction2 | WeightNet | false | 11,207 | [
"Apache-2.0"
] | 0 | 6f76bb465a7164f907318cf58f77fc3d613f8f0f | https://github.com/Alexis-Fab/mmaction2/tree/6f76bb465a7164f907318cf58f77fc3d613f8f0f |
AvgPoolHead | import torch
import torch.nn as nn
import torch.optim
class AvgPoolHead(nn.Module):
def __init__(self, in_channels, out_channels, fea_map_size):
super(AvgPoolHead, self).__init__()
self.avgpool = nn.AvgPool2d(fea_map_size, stride=1)
self.fc = nn.Linear(in_channels, out_channels)
def ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.optim
assert_size_stride = torch._C._dynamo.g... | KGMSFT/integral-human-pose | AvgPoolHead | false | 13,917 | [
"MIT"
] | 472 | d3ad4117ed71c580d2ab17987e15f9b2c3318a3b | https://github.com/KGMSFT/integral-human-pose/tree/d3ad4117ed71c580d2ab17987e15f9b2c3318a3b |
SpatialAttention | import torch
from torch import nn
class SpatialAttention(nn.Module):
def __init__(self, kernel_size=7):
super(SpatialAttention, self).__init__()
assert kernel_size in (3, 7), 'kernel size must be 3 or 7'
padding = 3 if kernel_size == 7 else 1
self.conv1 = nn.Conv2d(2, 1, 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
from torch import nn
assert_s... | HT-hlf/mmdetection_miner-2.22.0 | SpatialAttention | false | 2,311 | [
"Apache-2.0"
] | 0 | 76eb94d6547f9f95cd58f41bb5c91941e82322b9 | https://github.com/HT-hlf/mmdetection_miner-2.22.0/tree/76eb94d6547f9f95cd58f41bb5c91941e82322b9 |
chroma_subsampling | import torch
import torch.nn as nn
class chroma_subsampling(nn.Module):
""" Chroma subsampling on CbCv channels
Input:
image(tensor): batch x height x width x 3
Output:
y(tensor): batch x height x width
cb(tensor): batch x height/2 x width/2
cr(tensor): batch x height/2 x w... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | DazhiZhong/DiffJPEG | chroma_subsampling | false | 9,007 | [
"MIT"
] | 0 | e20de92539f31a57906ae4c32a41dc46e774c316 | https://github.com/DazhiZhong/DiffJPEG/tree/e20de92539f31a57906ae4c32a41dc46e774c316 |
ConvTripleBlock | # 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.... | SkywalkerAtlas/HRGAN | ConvTripleBlock | false | 5,838 | [
"MIT"
] | 1 | bf6d58c1f3c6e042c7ea70319a25e3420531d552 | https://github.com/SkywalkerAtlas/HRGAN/tree/bf6d58c1f3c6e042c7ea70319a25e3420531d552 |
Perceptron | import torch
import torch.nn as nn
import torch.nn.functional as F
class Perceptron(nn.Module):
"""Implements a 1-layer perceptron."""
def __init__(self, input_dimension, hidden_dimension, output_dimension):
super(Perceptron, self).__init__()
self._layer1 = nn.Linear(input_dimension, hidden_d... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | negotiatorvivian/SAT-Solver | Perceptron | false | 7,325 | [
"MIT"
] | 1 | acbf375ce73103e945aee3e2a225126684a19076 | https://github.com/negotiatorvivian/SAT-Solver/tree/acbf375ce73103e945aee3e2a225126684a19076 |
Snake | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.optim
assert_size_stride = torch._C._dynamo.guards.assert_si... | ai-in-motion/moai | Snake | false | 18,346 | [
"Apache-2.0"
] | 10 | e38cac046c059d2e2331ef4883bbabc5a500a5cf | https://github.com/ai-in-motion/moai/tree/e38cac046c059d2e2331ef4883bbabc5a500a5cf |
AddNorm | import torch
import torch.nn as nn
class AddNorm(nn.Module):
"""
Applies LayerNorm, Dropout and adds to input. Standard AddNorm operations in Transformers
"""
def __init__(self, input_dim: 'int', dropout: 'float'):
super(AddNorm, self).__init__()
self.dropout = nn.Dropout(dropout)
... | 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_... | edchengmoore/pytorch_tabular | AddNorm | false | 3,460 | [
"MIT"
] | 0 | 25f87089fbed95b46f2a1a8a96fba1f581aa8af1 | https://github.com/edchengmoore/pytorch_tabular/tree/25f87089fbed95b46f2a1a8a96fba1f581aa8af1 |
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/open-nre | MultiHeadAttention | false | 12,521 | [
"MIT"
] | 0 | a6e42ef074d62be4d3ceb571f412d5be8c0502d7 | https://github.com/igorvlnascimento/open-nre/tree/a6e42ef074d62be4d3ceb571f412d5be8c0502d7 |
DeepNN_v2 | import torch
import torch.nn as nn
class DeepNN_v2(nn.Module):
def __init__(self, X_dim, i_dropout_rate, h_dropout_rate):
super().__init__()
self.v2_layer1 = nn.Linear(X_dim, 256, bias=True)
self.v2_layer2 = nn.Linear(256, 256, bias=True)
self.v2_layer3 = nn.Linear(256, 1, bias=Tr... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | SBIlab/NetBio | DeepNN_v2 | false | 11,818 | [
"MIT"
] | 0 | 7abd24b8989cea381147d912f76a72676750b9d2 | https://github.com/SBIlab/NetBio/tree/7abd24b8989cea381147d912f76a72676750b9d2 |
Distance | # 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... | msc5/ml-tools | Distance | false | 4,032 | [
"Apache-2.0"
] | 0 | 75ca504bdc0495e8a929ad73501b7de692b3089a | https://github.com/msc5/ml-tools/tree/75ca504bdc0495e8a929ad73501b7de692b3089a |
R2CNNattetion | import torch
import torch.nn as nn
import torch.utils.data
class R2CNNattetion(nn.Module):
def __init__(self):
super(R2CNNattetion, self).__init__()
self.pool1 = nn.MaxPool2d(kernel_size=1)
self.pool2 = nn.MaxPool2d(kernel_size=2)
self.pool3 = nn.MaxPool2d(kernel_size=4)
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
import torch.nn as nn
import ... | leobean/CenterNet_simple | R2CNNattetion | false | 4,047 | [
"MIT"
] | 0 | 13e2eab2c049563afde5defdf90434a310a32d02 | https://github.com/leobean/CenterNet_simple/tree/13e2eab2c049563afde5defdf90434a310a32d02 |
BasicModel_ConvNet | # 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.... | LMdeLiangMi/captum | BasicModel_ConvNet | false | 5,507 | [
"BSD-3-Clause"
] | 1 | 8bd9686013fe0ba8996e9b1cbeb0ea8e91512787 | https://github.com/LMdeLiangMi/captum/tree/8bd9686013fe0ba8996e9b1cbeb0ea8e91512787 |
PSNR | import torch
from torch.nn.modules.loss import _Loss
class PSNR(_Loss):
def __init__(self):
super(PSNR, self).__init__()
self.val_range = 255
def _quantize(self, img):
img = img * self.val_range
img = img.clamp(0, self.val_range).round()
return img
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 import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
from torch.nn.modules.loss i... | zzh-tech/RSCD | PSNR | false | 16,839 | [
"MIT"
] | 57 | b287b1621121f8ca7ece6b27ebd4e28a5f8e6f5e | https://github.com/zzh-tech/RSCD/tree/b287b1621121f8ca7ece6b27ebd4e28a5f8e6f5e |
Conv_ReLU | import torch
import torch.nn as nn
class Conv_ReLU(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, stride=1,
padding=None, groups=1, bias=True):
super(Conv_ReLU, self).__init__()
if padding is None:
if stride == 1:
padding = (kernel_size ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | Liyong8490/DP_HSISR | Conv_ReLU | false | 9,308 | [
"Apache-2.0"
] | 0 | e46298ce3432757ae225b73b3752dceda95909eb | https://github.com/Liyong8490/DP_HSISR/tree/e46298ce3432757ae225b73b3752dceda95909eb |
SpaceToDepth | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
class SpaceToDepth(nn.Module):
def __init__(self, block_size=4):
super().__init__()
assert block_size == 4
self.bs = block_size
def forward(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
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
assert_size_st... | ChangeTheWorld20191008/query2labels | SpaceToDepth | false | 2,142 | [
"MIT"
] | 0 | cdca1f3519f75cc91ef2aa166c2534691016f04f | https://github.com/ChangeTheWorld20191008/query2labels/tree/cdca1f3519f75cc91ef2aa166c2534691016f04f |
FSPool | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torc... | zzirnheld/dspn | FSPool | false | 13,191 | [
"MIT"
] | 0 | e0c248d9e55821847841cf0c67e97225277a6e75 | https://github.com/zzirnheld/dspn/tree/e0c248d9e55821847841cf0c67e97225277a6e75 |
FastGuidedFilter | import torch
from torchvision.transforms import functional as F
from torch import nn
from torch.nn import functional as F
class BoxFilter(nn.Module):
def __init__(self, r):
super(BoxFilter, self).__init__()
self.r = r
def forward(self, x):
kernel_size = 2 * self.r + 1
kernel_... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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 |
StableBCELoss | import torch
import torch.utils.data
class StableBCELoss(torch.nn.modules.Module):
def __init__(self):
super(StableBCELoss, self).__init__()
def forward(self, input, target):
neg_abs = -input.abs()
loss = input.clamp(min=0) - input * target + (1 + neg_abs.exp()).log()
return ... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.utils.dat... | GOPIKA-0204/Clothing-Detection-and-Recolouring | StableBCELoss | false | 9,069 | [
"MIT"
] | 0 | b5d436a981b854228314729b41874f31948a33ba | https://github.com/GOPIKA-0204/Clothing-Detection-and-Recolouring/tree/b5d436a981b854228314729b41874f31948a33ba |
STLayer | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.j... | alper111/affordance-learning | STLayer | false | 18,288 | [
"MIT"
] | 3 | 21b70f689a8299c6af7cd4ed763fc3133cf1681f | https://github.com/alper111/affordance-learning/tree/21b70f689a8299c6af7cd4ed763fc3133cf1681f |
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.... | quic-akhobare/aimet | SmallMnist | false | 10,741 | [
"BSD-3-Clause"
] | 0 | 1811a0ef58a75d103e173731b436876ee5dc4c49 | https://github.com/quic-akhobare/aimet/tree/1811a0ef58a75d103e173731b436876ee5dc4c49 |
dice_bce_loss | import torch
import torch.nn as nn
import torch.utils.model_zoo
class dice_bce_loss(nn.Module):
def __init__(self, batch=True):
super(dice_bce_loss, self).__init__()
self.batch = batch
self.bce_loss = nn.BCELoss()
def soft_dice_coeff(self, y_true, y_pred):
smooth = 0.0
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import torch.utils.model_zoo
assert_size_stride = torch._C._dynamo.... | manuel-rdz/SGL-Retinal-Vessel-Segmentation | dice_bce_loss | false | 16,007 | [
"MIT"
] | 45 | 7897d977e77aa0b5d3acb86e0aa74c6829d67415 | https://github.com/manuel-rdz/SGL-Retinal-Vessel-Segmentation/tree/7897d977e77aa0b5d3acb86e0aa74c6829d67415 |
MultiHeadAttention | # 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.... | RogerTsai917/attention-is-all-you-need-pytorch | MultiHeadAttention | false | 2,791 | [
"MIT"
] | 0 | 64197e55d275e5c819bc786a9ff19849cdf2f6b9 | https://github.com/RogerTsai917/attention-is-all-you-need-pytorch/tree/64197e55d275e5c819bc786a9ff19849cdf2f6b9 |
Attention_Decoder | import torch
import torch.nn as nn
import torch._utils
class Attention_Decoder(nn.Module):
def __init__(self, dim, num_heads=1, qkv_bias=False, qk_scale=None,
attn_drop=0.0, proj_drop=0.0):
super().__init__()
self.num_heads = num_heads
head_dim = dim // num_heads
self.scal... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | xieenze/Trans2Seg | Attention_Decoder | false | 16,740 | [
"Apache-2.0"
] | 149 | 3972916bba7f985ca1aabc047fea56bdec9e9e5d | https://github.com/xieenze/Trans2Seg/tree/3972916bba7f985ca1aabc047fea56bdec9e9e5d |
FocalLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torc... | Jason-George/irn | FocalLoss | false | 611 | [
"MIT"
] | 0 | b75441b5fb8080c1dbd8dbcb9b05720a4ceb2246 | https://github.com/Jason-George/irn/tree/b75441b5fb8080c1dbd8dbcb9b05720a4ceb2246 |
Contrast_Loss | import torch
import torch.nn as nn
class Contrast_Loss(nn.Module):
def __init__(self, margin=0.5):
super(Contrast_Loss, self).__init__()
self.margin = margin
def forward(self, inputs, target):
R = (target.unsqueeze(0) == target.unsqueeze(1)).float()
distance = ((inputs.unsque... | 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... | LiuChaoXD/Remote-Sensing-Image-Retrieval-Models | Contrast_Loss | false | 17,582 | [
"MIT"
] | 4 | c135562263102080716e35260f111dcff7762264 | https://github.com/LiuChaoXD/Remote-Sensing-Image-Retrieval-Models/tree/c135562263102080716e35260f111dcff7762264 |
DenseBlock | import torch
from torch import nn as nn
from torch.nn import functional as F
class CausalConv1d(nn.Module):
"""A 1D causal convolution layer.
Input: (B, D_in, T), where B is the minibatch size, D_in is the number of
dimensions per step, and T is the number of steps.
Output: (B, D_out, T), where B... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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... | NagisaZj/ProMP | DenseBlock | false | 11,723 | [
"MIT"
] | 0 | 539739ae2b7d5fdcad00855da695f643b23df4b3 | https://github.com/NagisaZj/ProMP/tree/539739ae2b7d5fdcad00855da695f643b23df4b3 |
BasicBlock | import torch
import torch.nn as nn
import torch.nn.functional as F
class BasicBlock(nn.Module):
expansion = 1
def __init__(self, in_planes, planes, stride=1):
super(BasicBlock, self).__init__()
self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=3, stride=
stride, padding=1, bia... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | amyami187/nngeometry | BasicBlock | false | 14,833 | [
"MIT"
] | 103 | cb516da3f7a019e148f48ff3ef3bed0cdae0d184 | https://github.com/amyami187/nngeometry/tree/cb516da3f7a019e148f48ff3ef3bed0cdae0d184 |
residualUnit | # 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.... | andry900/NN-Project | residualUnit | false | 1,445 | [
"MIT"
] | 0 | e04a83029f5990d9b65216ab0648a8826a8ebca7 | https://github.com/andry900/NN-Project/tree/e04a83029f5990d9b65216ab0648a8826a8ebca7 |
Fire | import math
import torch
import torch.nn as nn
class Fire(nn.Module):
def __init__(self, inplanes, squeeze_planes, expand_planes):
super(Fire, self).__init__()
self.conv1 = nn.Conv2d(inplanes, squeeze_planes, kernel_size=1,
stride=1)
self.relu1 = nn.ELU(inplace=True)
s... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import math
import ... | dcrmg/Efficient-Segmentation-Networks | Fire | false | 3,439 | [
"MIT"
] | 0 | e2f2d90d69e4e9af464678b0f02bc754c28f643d | https://github.com/dcrmg/Efficient-Segmentation-Networks/tree/e2f2d90d69e4e9af464678b0f02bc754c28f643d |
ResBlock | import torch
import torch.nn as nn
import torch.nn.functional as F
def conv3x3(in_planes, out_planes, stride=1, groups=1):
"""3x3 convolution with padding"""
return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
padding=1, bias=False, groups=groups)
class ResBlock(nn.Module):
exp... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | andyqmongo/InstAParam | ResBlock | false | 18,338 | [
"MIT"
] | 3 | 00494d5367ec32b4ce90d01778cba9d4f1166833 | https://github.com/andyqmongo/InstAParam/tree/00494d5367ec32b4ce90d01778cba9d4f1166833 |
img_encoder | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.nn.functional as F
assert_size_stride = torch... | luixiao1223/BSP-NET-pytorch | img_encoder | false | 3,976 | [
"MIT"
] | 0 | f871c8ce6a9d52ac922e110702c47cd1c89d0a73 | https://github.com/luixiao1223/BSP-NET-pytorch/tree/f871c8ce6a9d52ac922e110702c47cd1c89d0a73 |
BackboneModel1 | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
from typing import *
class BackboneModel1(nn.Module):
def __init__(self):
super().__init__()
self.conv1 = nn.Conv2d(1, 1, 1, 1)
def forward(self, x):
return self.conv1(x)
def get_... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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.u... | Johnsonms/NNI_master | BackboneModel1 | false | 11,590 | [
"MIT"
] | 0 | e5e5c7aed89cf3189cffe1056464833c15eb54ff | https://github.com/Johnsonms/NNI_master/tree/e5e5c7aed89cf3189cffe1056464833c15eb54ff |
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.utils.data
impor... | Het-Shah/Monk_Object_Detection | ClassificationModel | false | 8,241 | [
"Apache-2.0"
] | 15 | 1d7a07193ea3455221caa41d07c33c81d50c6b3f | https://github.com/Het-Shah/Monk_Object_Detection/tree/1d7a07193ea3455221caa41d07c33c81d50c6b3f |
SoftQNetwork | # 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_... | JieRen98/Popular-RL-Algorithms | SoftQNetwork | false | 13,896 | [
"Apache-2.0"
] | 273 | 7f2bb74a51cf9cbde92a6ccfa42e97dc129dd145 | https://github.com/JieRen98/Popular-RL-Algorithms/tree/7f2bb74a51cf9cbde92a6ccfa42e97dc129dd145 |
DsBlock | # 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_... | MATHplus-Young-Academy/P2-Cardiac-Motion | DsBlock | false | 5,569 | [
"Apache-2.0"
] | 1 | 844995e8e5760f981c425d13c0bd7f2f3bb8baec | https://github.com/MATHplus-Young-Academy/P2-Cardiac-Motion/tree/844995e8e5760f981c425d13c0bd7f2f3bb8baec |
DenseGCNConv | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | pwycl/pytorch_geometric | DenseGCNConv | false | 10,782 | [
"MIT"
] | 0 | ef7b1add2bb5a36a3a68cae7639c42000f629cac | https://github.com/pwycl/pytorch_geometric/tree/ef7b1add2bb5a36a3a68cae7639c42000f629cac |
MultiHeadedAttention | # 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.... | iamxpy/pointer_summarizer | MultiHeadedAttention | false | 6,855 | [
"Apache-2.0"
] | 1 | ebeb2ad32a45162c0da14dac0b6241b0b0d00fa0 | https://github.com/iamxpy/pointer_summarizer/tree/ebeb2ad32a45162c0da14dac0b6241b0b0d00fa0 |
SimpleClampModel | import torch
import torch.jit
import torch.onnx
import torch.nn
class SimpleClampModel(torch.nn.Module):
def __init__(self, min, max):
super(SimpleClampModel, self).__init__()
self.min = min
self.max = max
def forward(self, input):
return torch.clamp(input, self.min, self.max... | 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.jit
import torch.onnx
import torch.nn
assert_size_stride = torch._C._dynamo.... | YaronBenAtar/glow | SimpleClampModel | false | 14,640 | [
"Apache-2.0"
] | 2,838 | a13706a4239fa7eaf059c670dc573e3eb0768f86 | https://github.com/YaronBenAtar/glow/tree/a13706a4239fa7eaf059c670dc573e3eb0768f86 |
LSoftLoss | import torch
import torch.nn.functional as F
import torch.nn as nn
class LSoftLoss(nn.Module):
def __init__(self):
super().__init__()
def forward(self, y_pred, y_true, beta):
with torch.no_grad():
y_true_updated = beta * y_true + (1 - beta) * y_pred
return F.binary_cross_... | 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... | khodwe56/kaggle-birdsong-recognition | LSoftLoss | false | 12,669 | [
"MIT"
] | 0 | 95a902c37355619cf02558968f000038e487db47 | https://github.com/khodwe56/kaggle-birdsong-recognition/tree/95a902c37355619cf02558968f000038e487db47 |
AugCNN | import torch
import torch.nn as nn
import torch.nn.functional as F
def apply_init_(modules):
"""
Initialize NN modules
"""
for m in modules:
if isinstance(m, nn.Conv2d):
nn.init.xavier_uniform_(m.weight)
if m.bias is not None:
nn.init.constant_(m.bias, 0... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.nn.functional as F
assert_size_stride = torch... | minqi/auto-drac | AugCNN | false | 16,093 | [
"MIT"
] | 84 | 59a25bbabd51946d7a645db9c5d59071b73b006d | https://github.com/minqi/auto-drac/tree/59a25bbabd51946d7a645db9c5d59071b73b006d |
ConvSqu | # 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... | Beaver48/kaggle-chest-xray-abnormalities | ConvSqu | false | 11,310 | [
"MIT"
] | 0 | d41f32d1c59cb5c925795df3291e929b3ea6d5fd | https://github.com/Beaver48/kaggle-chest-xray-abnormalities/tree/d41f32d1c59cb5c925795df3291e929b3ea6d5fd |
MultiHeadedAttention | import math
import torch
from typing import Tuple
from torch import nn
class MultiHeadedAttention(nn.Module):
"""Multi-Head Attention layer.
Args:
n_head (int): The number of heads.
n_feat (int): The number of features.
dropout_rate (float): Dropout rate.
"""
def __init__(se... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Slyne/wenet | MultiHeadedAttention | false | 9,507 | [
"Apache-2.0"
] | 0 | de74d8acf40f47a3c503bff5cf4ed6808a9dad14 | https://github.com/Slyne/wenet/tree/de74d8acf40f47a3c503bff5cf4ed6808a9dad14 |
SlidingWindowCmn | import torch
from torch import Tensor
import torchaudio.functional as F
class SlidingWindowCmn(torch.nn.Module):
"""
Apply sliding-window cepstral mean (and optionally variance) normalization per utterance.
Args:
cmn_window (int, optional): Window in frames for running average CMN computation (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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret... | tbright17/audio | SlidingWindowCmn | false | 10,920 | [
"BSD-2-Clause"
] | 0 | 00d38203e401b8d9472a8f8394a10e2c309be02c | https://github.com/tbright17/audio/tree/00d38203e401b8d9472a8f8394a10e2c309be02c |
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
from torch._inductor.runtime.... | jesterhazy/sagemaker-pytorch-container | Net | false | 6,942 | [
"Apache-2.0"
] | 1 | 2eb4ba9216e5d72cd4d61eadc173764a41dea6b9 | https://github.com/jesterhazy/sagemaker-pytorch-container/tree/2eb4ba9216e5d72cd4d61eadc173764a41dea6b9 |
Aggregate | # 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 as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._emp... | AntonCh-G/schnetpack | Aggregate | false | 1,960 | [
"MIT"
] | 0 | 16f48d59b18415c18c9e324e3c3f9ebb24ce9f0d | https://github.com/AntonCh-G/schnetpack/tree/16f48d59b18415c18c9e324e3c3f9ebb24ce9f0d |
TanhDeepLiftModel | # 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_... | LMdeLiangMi/captum | TanhDeepLiftModel | false | 5,474 | [
"BSD-3-Clause"
] | 1 | 8bd9686013fe0ba8996e9b1cbeb0ea8e91512787 | https://github.com/LMdeLiangMi/captum/tree/8bd9686013fe0ba8996e9b1cbeb0ea8e91512787 |
ResidualBlock | import torch
from torch import nn
import torch.nn.functional as F
class ResidualBlock(nn.Module):
"""
Residual block from R2D3/IMPALA
Taken from [1,2]
"""
def __init__(self, num_channels, first_conv_weight_scale):
super().__init__()
self.conv1 = nn.Conv2d(num_channels, num_channe... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
assert_s... | Oliver-Tautz/minecraft-bc-2020 | ResidualBlock | false | 11,775 | [
"MIT"
] | 0 | d18ba1c1afbf92f897e4bbe70c2c71292510f928 | https://github.com/Oliver-Tautz/minecraft-bc-2020/tree/d18ba1c1afbf92f897e4bbe70c2c71292510f928 |
DepthWiseSeperableConv | # 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... | BishmoyPaul/lama | DepthWiseSeperableConv | false | 13,391 | [
"Apache-2.0"
] | 2,133 | c7f5af9c167a15e2b0b741b1419237de52c4af05 | https://github.com/BishmoyPaul/lama/tree/c7f5af9c167a15e2b0b741b1419237de52c4af05 |
FFN | # 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... | VKCOM/TopicsDataset | FFN | false | 5,924 | [
"MIT"
] | 1 | 149919321ba61a8f17b22f62f60f4aedec43d72b | https://github.com/VKCOM/TopicsDataset/tree/149919321ba61a8f17b22f62f60f4aedec43d72b |
Conv2dTime | # 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... | anway/augmented-neural-odes | Conv2dTime | false | 14,883 | [
"MIT"
] | 449 | 561cfa540ef292d117ba9cf083758281774f3f22 | https://github.com/anway/augmented-neural-odes/tree/561cfa540ef292d117ba9cf083758281774f3f22 |
Decoder | import torch
import torch.nn as nn
class Decoder(nn.Module):
def __init__(self):
super(Decoder, self).__init__()
self.pre11 = nn.ReflectionPad2d((1, 1, 1, 1))
self.conv11 = nn.Conv2d(in_channels=512, out_channels=256,
kernel_size=3, stride=1)
self.relu11 = nn.ReLU(inpl... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | ShiZhuming/StyleTransfer | Decoder | false | 17,941 | [
"MIT"
] | 10 | cba2a3ceb733a2d129d52d4a3cac07c7651bd928 | https://github.com/ShiZhuming/StyleTransfer/tree/cba2a3ceb733a2d129d52d4a3cac07c7651bd928 |
MMDLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch import nn
import torch.nn.parallel
assert_size_stride = torch.... | DA4EVENT/home | MMDLoss | false | 17,184 | [
"MIT"
] | 5 | 18cc93a795ce132e05b886aa34565a102915b1c6 | https://github.com/DA4EVENT/home/tree/18cc93a795ce132e05b886aa34565a102915b1c6 |
PolicyGradientLoss | import torch
import torch.nn.functional as F
from torch.nn.modules.loss import _Loss
class PolicyGradientLoss(_Loss):
def __init__(self, size_average=None, reduce=None, reduction='mean'):
super().__init__(size_average, reduce, reduction)
def forward(self, nn_outputs, actions, returns):
outpu... | 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.nn.modules.... | opium-sh/prl | PolicyGradientLoss | false | 16,214 | [
"MIT"
] | 51 | 3e21f8c7c87cfc7aee84d9e264c3a8b2bc549076 | https://github.com/opium-sh/prl/tree/3e21f8c7c87cfc7aee84d9e264c3a8b2bc549076 |
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