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 |
|---|---|---|---|---|---|---|---|---|---|---|
LOGMSELoss | import torch
import torch.nn as nn
class LOGMSELoss(nn.Module):
def __init__(self):
super().__init__()
self.mse = nn.MSELoss()
def forward(self, input, target):
return torch.log(self.mse(input, target))
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4]... | 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
... | julschoen/Latent-Space-Exploration-CT | LOGMSELoss | false | 6,990 | [
"MIT"
] | 1 | 39440c83362181efc48cad69777e5671a7bf3de9 | https://github.com/julschoen/Latent-Space-Exploration-CT/tree/39440c83362181efc48cad69777e5671a7bf3de9 |
GCN | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import math
import torch.nn a... | alecokas/swahili-text-gcn | GCN | false | 18,265 | [
"MIT"
] | 4 | 14b8196b30baac2a05c869a1f6c17a912d1adcea | https://github.com/alecokas/swahili-text-gcn/tree/14b8196b30baac2a05c869a1f6c17a912d1adcea |
TinyConvNet3d | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
assert_size_stride = torch._C... | FynnBe/tiktorch | TinyConvNet3d | false | 11,440 | [
"MIT"
] | 0 | 60c6fa9700e7ff73e44338e8755c56c6e8846f2f | https://github.com/FynnBe/tiktorch/tree/60c6fa9700e7ff73e44338e8755c56c6e8846f2f |
NestedNetInnerModule | # 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
from typing import Counter
from collections import Counter... | synthara/M-SFV-SyntharaFVcore | NestedNetInnerModule | false | 10,902 | [
"Apache-2.0"
] | 0 | b4d2167a110aaecf3df442f58793ca2cb7b028ba | https://github.com/synthara/M-SFV-SyntharaFVcore/tree/b4d2167a110aaecf3df442f58793ca2cb7b028ba |
BCEWithLogitsWithClassWeightLoss | import torch
from torch import Tensor
from typing import NoReturn
from torch import nn
class BCEWithLogitsWithClassWeightLoss(nn.BCEWithLogitsLoss):
""" finished, checked,
"""
__name__ = 'BCEWithLogitsWithClassWeightsLoss'
def __init__(self, class_weight: 'Tensor') ->NoReturn:
""" finished, 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 import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch ... | DeepPSP/torch_ecg | BCEWithLogitsWithClassWeightLoss | false | 17,203 | [
"MIT"
] | 9 | 6db5ffb063d0e8fb4ce97029a0d184a658f43a37 | https://github.com/DeepPSP/torch_ecg/tree/6db5ffb063d0e8fb4ce97029a0d184a658f43a37 |
ChannelPool | import torch
import torch.nn as nn
class ChannelPool(nn.Module):
def forward(self, x):
return torch.cat((torch.max(x, 1)[0].unsqueeze(1), torch.mean(x, 1)
.unsqueeze(1)), dim=1)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
| import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | Linus4world/mrs-gan | ChannelPool | false | 5,535 | [
"BSD-2-Clause"
] | 1 | 64669251584a7421cce3a5173983a2275dcb438a | https://github.com/Linus4world/mrs-gan/tree/64669251584a7421cce3a5173983a2275dcb438a |
Entmax15 | from torch.autograd import Function
import torch
from torch import nn
def _make_ix_like(X, dim):
d = X.size(dim)
rho = torch.arange(1, d + 1, device=X.device, dtype=X.dtype)
view = [1] * X.dim()
view[0] = -1
return rho.view(view).transpose(0, dim)
def _roll_last(X, dim):
if dim == -1:
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
from torch.autograd import F... | gitlost-murali/awesome-align | Entmax15 | false | 3,545 | [
"BSD-3-Clause"
] | 0 | 39fb45ca85a98e005447bddb52c48e65ce7d399b | https://github.com/gitlost-murali/awesome-align/tree/39fb45ca85a98e005447bddb52c48e65ce7d399b |
FocalLoss | import torch
import torch.nn as nn
def log_minus_sigmoid(x):
return torch.clamp(-x, max=0) - torch.log(1 + torch.exp(-torch.abs(x))
) + 0.5 * torch.clamp(x, min=0, max=0)
def log_sigmoid(x):
return torch.clamp(x, max=0) - torch.log(1 + torch.exp(-torch.abs(x))
) + 0.5 * torch.clamp(x, min=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
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | LIANGKE23/Siamese-FC-KF-CF | FocalLoss | false | 17,558 | [
"MIT"
] | 10 | 3d9db19c0f39f0588a5061cd182bfbfc37dca76f | https://github.com/LIANGKE23/Siamese-FC-KF-CF/tree/3d9db19c0f39f0588a5061cd182bfbfc37dca76f |
NetVLAD | # 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.... | Guido27/project_vg | NetVLAD | false | 9,132 | [
"MIT"
] | 0 | 3322fc355742929f43f3d97204398035645d968c | https://github.com/Guido27/project_vg/tree/3322fc355742929f43f3d97204398035645d968c |
EqualLinear | from torch.autograd import Function
import math
import torch
from torch import nn
import torch.nn.functional as F
def fused_leaky_relu(input, bias, negative_slope=0.2, scale=2 ** 0.5):
return FusedLeakyReLUFunction.apply(input, bias, negative_slope, scale)
class FusedLeakyReLUFunctionBackward(Function):
@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.autograd import Function
import math
from torch import nn
assert_size... | ArashVahabpour/encoder4editing | EqualLinear | false | 1,976 | [
"MIT"
] | 0 | 819b90ecd7397fbe2ab7cb30eb451dab0f3149fd | https://github.com/ArashVahabpour/encoder4editing/tree/819b90ecd7397fbe2ab7cb30eb451dab0f3149fd |
MaxMarginRankingLoss | # 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 as th
import torch.optim
import torch.utils.data
asser... | awesome-archive/Video-to-Online-Platform | MaxMarginRankingLoss | false | 6,287 | [
"Apache-2.0"
] | 1 | 4f91724133a817e79bce91e0abbd46cf38a31167 | https://github.com/awesome-archive/Video-to-Online-Platform/tree/4f91724133a817e79bce91e0abbd46cf38a31167 |
Encoding | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | CVIU-CSU/M2MRF-Lesion-Segmentation | Encoding | false | 17,066 | [
"Apache-2.0"
] | 10 | 13af87927f4cdeca70e35d570edd1aec43b387b6 | https://github.com/CVIU-CSU/M2MRF-Lesion-Segmentation/tree/13af87927f4cdeca70e35d570edd1aec43b387b6 |
PositionwiseFeedForward | # 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.... | Caiyuan-Zheng/Consistency_Regularization_STR | PositionwiseFeedForward | false | 2,089 | [
"MIT"
] | 0 | 7c7ce69390c429974cb2d1969b0d9d6707e6723f | https://github.com/Caiyuan-Zheng/Consistency_Regularization_STR/tree/7c7ce69390c429974cb2d1969b0d9d6707e6723f |
BiaffineAttention | import torch
import torch.nn as nn
class BiaffineAttention(nn.Module):
def __init__(self, in_features, out_features):
super(BiaffineAttention, self).__init__()
self.in_features = in_features
self.out_features = out_features
self.bilinear = torch.nn.Bilinear(in_features, in_feature... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | vietbt/ViTextnormASR | BiaffineAttention | false | 10,925 | [
"Apache-2.0"
] | 0 | 57444aa7247c67b2628d1802e9ed53dae4857ee4 | https://github.com/vietbt/ViTextnormASR/tree/57444aa7247c67b2628d1802e9ed53dae4857ee4 |
SimpleAbsModule | import torch
import torch.jit
import torch.onnx
import torch.nn
class SimpleAbsModule(torch.nn.Module):
def __init__(self):
super(SimpleAbsModule, self).__init__()
def forward(self, a):
return torch.abs(a + a)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.jit
import torch.onnx
import torch.nn
assert_size_stride = t... | opti-mix/glow | SimpleAbsModule | false | 7,384 | [
"Apache-2.0"
] | 1 | 4ba074df5da9822986a23a6679ab592c22660f6d | https://github.com/opti-mix/glow/tree/4ba074df5da9822986a23a6679ab592c22660f6d |
Multiply | import torch
from abc import ABC
class BaseOperator(ABC):
"""
Abstract class defining the basic structure for operator implementations in Hummingbird.
"""
def __init__(self, regression=False, classification=False, transformer=
False, anomaly_detection=False, **kwargs):
"""
Arg... | 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 abc import ABC
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_stri... | kvenkman/hummingbird | Multiply | false | 3,862 | [
"MIT"
] | 0 | dac08f4ff4a4103df4a8e83329a02f2d804bf34d | https://github.com/kvenkman/hummingbird/tree/dac08f4ff4a4103df4a8e83329a02f2d804bf34d |
Accuracy | # 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.nn import *
from torch.optim import *
from torch.optim.lr_scheduler import *
a... | UNIST-LIM-Lab/NeuBoots | Accuracy | false | 2,914 | [
"MIT"
] | 0 | 196adf9e1ece2abc145f69966504bac2676e5b5e | https://github.com/UNIST-LIM-Lab/NeuBoots/tree/196adf9e1ece2abc145f69966504bac2676e5b5e |
DSCLoss | import torch
import torch.nn as nn
class DSCLoss(nn.Module):
def __init__(self):
super(DSCLoss, self).__init__()
def forward(self, input, target):
N = target.size(0)
smooth = 1
input_flat = input.view(N, -1)
target_flat = target.view(N, -1)
input_flat * target... | 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... | SeffyVon/ECG_MICResNet | DSCLoss | false | 17,911 | [
"BSD-3-Clause"
] | 5 | 8c6a319b5822ddfb130738eb1d9cdc3c21b24209 | https://github.com/SeffyVon/ECG_MICResNet/tree/8c6a319b5822ddfb130738eb1d9cdc3c21b24209 |
DiceLoss | import torch
import torch.nn as nn
class DiceLoss(nn.Module):
def __init__(self, ignore_target=-1):
super().__init__()
self.ignore_target = ignore_target
def forward(self, input, target):
"""
:param input: (N), logit
:param target: (N), {0, 1}
:return:
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | JamesWang007/PointRCNN | DiceLoss | false | 11,540 | [
"MIT"
] | 0 | ea0812c52e6767b976fc50fed61e6b72fa6cdf81 | https://github.com/JamesWang007/PointRCNN/tree/ea0812c52e6767b976fc50fed61e6b72fa6cdf81 |
CosLoss | import torch
import torch.nn as nn
import torch.nn.functional as F
class CosLoss(nn.Module):
def __init__(self):
super().__init__()
def forward(self, state_S, state_T, mask=None):
"""
This is the loss used in DistilBERT
:param state_S: Tensor of shape (batch_size, length, h... | 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... | Raiselimit/TorchBlocks | CosLoss | false | 5,739 | [
"MIT"
] | 1 | a5baecb9a2470ff175087475630f2b7db3f7ef51 | https://github.com/Raiselimit/TorchBlocks/tree/a5baecb9a2470ff175087475630f2b7db3f7ef51 |
BinaryNLLEntropy | # 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... | msft-shahins/ConvLab-2 | BinaryNLLEntropy | false | 12,795 | [
"Apache-2.0"
] | 0 | ad74c0e9e021916f9330af11e046ed72914b7740 | https://github.com/msft-shahins/ConvLab-2/tree/ad74c0e9e021916f9330af11e046ed72914b7740 |
CoordConv | # 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... | justinjohn0306/CIPS-3D | CoordConv | false | 7,001 | [
"MIT"
] | 1 | 69a910a7841846419a6b5e03182c8cf061a82584 | https://github.com/justinjohn0306/CIPS-3D/tree/69a910a7841846419a6b5e03182c8cf061a82584 |
DiffLoss | import torch
import torch.nn as nn
import torch.utils.checkpoint
class DiffLoss(nn.Module):
def __init__(self):
super(DiffLoss, self).__init__()
def forward(self, input1, input2):
batch_size = input1.size(0)
input1 = input1.view(batch_size, -1)
input2 = input2.view(batch_size... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | byamao1/MMSA | DiffLoss | false | 14,991 | [
"MIT"
] | 198 | 1a894d042144c9ac75b3465d38871ce8c2987251 | https://github.com/byamao1/MMSA/tree/1a894d042144c9ac75b3465d38871ce8c2987251 |
Prototype | # 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.... | wenqiangxie/Prototype-Net | Prototype | false | 4,528 | [
"MIT"
] | 0 | a5ddd9976b78828d87806f9451a5092de3ff5c69 | https://github.com/wenqiangxie/Prototype-Net/tree/a5ddd9976b78828d87806f9451a5092de3ff5c69 |
ValueNetwork | # 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_... | NadeemWard/pytorch_simple_policy_gradients | ValueNetwork | false | 17,733 | [
"MIT"
] | 5 | d0ae66b46860504a077fdffdac45b5077c12c480 | https://github.com/NadeemWard/pytorch_simple_policy_gradients/tree/d0ae66b46860504a077fdffdac45b5077c12c480 |
DiscShiftLoss | import torch
import torch.nn as nn
class DiscShiftLoss(nn.Module):
"""Disc shift loss.
Args:
loss_weight (float, optional): Loss weight. Defaults to 1.0.
"""
def __init__(self, loss_weight=0.1):
super(DiscShiftLoss, self).__init__()
self.loss_weight = loss_weight
... | 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... | rivergold/mmediting | DiscShiftLoss | false | 7,561 | [
"Apache-2.0"
] | 1 | fd972635c48bb065db29d1b5090592a87c7263d2 | https://github.com/rivergold/mmediting/tree/fd972635c48bb065db29d1b5090592a87c7263d2 |
RMulInt | # 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... | ahangchen/torch2trt | RMulInt | false | 6,096 | [
"MIT"
] | 1 | 53c663f0e0570ef7ffd6771354ae3478f63bd328 | https://github.com/ahangchen/torch2trt/tree/53c663f0e0570ef7ffd6771354ae3478f63bd328 |
ModulatedToRGB | # 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
from copy import deepcopy
from functools import partial
fr... | Juggernaut93/mmediting | ModulatedToRGB | false | 13,938 | [
"Apache-2.0"
] | 1,884 | 8ef46ace29756dd2df1d92f2f73a33646e33e007 | https://github.com/Juggernaut93/mmediting/tree/8ef46ace29756dd2df1d92f2f73a33646e33e007 |
DenseCrossEntropy | # 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
... | prakhar154/Cassava-Leaf-Disease-Classification | DenseCrossEntropy | false | 4,132 | [
"MIT"
] | 0 | 04824834a6a1898c77858e8134bd3767c64789f2 | https://github.com/prakhar154/Cassava-Leaf-Disease-Classification/tree/04824834a6a1898c77858e8134bd3767c64789f2 |
LDEPooling | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn
assert... | fancyliumeng/asv-subtools | LDEPooling | false | 6,681 | [
"Apache-2.0"
] | 1 | 56a13484472e7ae6eb00d762c00d57e581e78eb4 | https://github.com/fancyliumeng/asv-subtools/tree/56a13484472e7ae6eb00d762c00d57e581e78eb4 |
MinMaxNorm | # 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... | iclementine/speedyspeech | MinMaxNorm | false | 10,402 | [
"BSD-3-Clause"
] | 0 | db527587a3699b71082d61c9e9fad7ed795d1980 | https://github.com/iclementine/speedyspeech/tree/db527587a3699b71082d61c9e9fad7ed795d1980 |
ClipLayer | import torch
import torch.nn as nn
def clip_data(data, max_norm):
norms = torch.norm(data.reshape(data.shape[0], -1), dim=-1)
scale = (max_norm / norms).clamp(max=1.0)
data *= scale.reshape(-1, 1, 1, 1)
return data
class ClipLayer(nn.Module):
def __init__(self, max_norm):
super(ClipLaye... | 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... | lxuechen/Handcrafted-DP | ClipLayer | false | 10,490 | [
"MIT"
] | 0 | 64ca4759238027e307d8e88215a0a86fc8f3b395 | https://github.com/lxuechen/Handcrafted-DP/tree/64ca4759238027e307d8e88215a0a86fc8f3b395 |
IOUloss | import torch
import torch.nn as nn
import torch.utils.data
class IOUloss(nn.Module):
def __init__(self, reduction='none', loss_type='iou'):
super(IOUloss, self).__init__()
self.reduction = reduction
self.loss_type = loss_type
def forward(self, pred, target):
assert pred.shape... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import torch.utils.data
assert_size_stride = torch._C._dynamo.guard... | hyperfraise/ByteTrack | IOUloss | false | 15,556 | [
"MIT"
] | 1,039 | d742a3321c14a7412f024f2218142c7441c1b699 | https://github.com/hyperfraise/ByteTrack/tree/d742a3321c14a7412f024f2218142c7441c1b699 |
AttMseLoss | import torch
import torch.nn as nn
import torch.nn.functional as F
class AttMseLoss(nn.Module):
def __init__(self):
super().__init__()
def forward(self, attention_S, attention_T, mask=None):
"""
Calculate the mse loss between attention_S and attention_T.
:param logits_S: Ten... | 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... | Raiselimit/TorchBlocks | AttMseLoss | false | 5,733 | [
"MIT"
] | 1 | a5baecb9a2470ff175087475630f2b7db3f7ef51 | https://github.com/Raiselimit/TorchBlocks/tree/a5baecb9a2470ff175087475630f2b7db3f7ef51 |
ELU | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert... | ashutoshml/lightning-tutorials | ELU | false | 6,250 | [
"Apache-2.0"
] | 1 | 898b8b6f9852c0b80f034a3187bc1cd34dd521ce | https://github.com/ashutoshml/lightning-tutorials/tree/898b8b6f9852c0b80f034a3187bc1cd34dd521ce |
ATLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch import Tens... | BunnyNoBugs/DeepPavlov | ATLoss | false | 11,258 | [
"Apache-2.0"
] | 0 | b2213db633a669d27d6f745dd780530574ccf8b5 | https://github.com/BunnyNoBugs/DeepPavlov/tree/b2213db633a669d27d6f745dd780530574ccf8b5 |
MultinomialKLDivergenceLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch import nn
a... | AuCson/SEDST | MultinomialKLDivergenceLoss | false | 7,714 | [
"MIT"
] | 23 | 1c1691e2abc50eb2120ed49c874090f6c4f741d3 | https://github.com/AuCson/SEDST/tree/1c1691e2abc50eb2120ed49c874090f6c4f741d3 |
CoAttention | # 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.... | GMDennis/claf | CoAttention | false | 8,240 | [
"MIT"
] | 10 | d1e064e593127e5d654f000f5506c5ae1caab5ce | https://github.com/GMDennis/claf/tree/d1e064e593127e5d654f000f5506c5ae1caab5ce |
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.... | edbltn/fairseq | MultiheadAttention | false | 12,347 | [
"BSD-3-Clause"
] | 0 | e4d25fd96f1e38190400dbbdbc77eeda71ac50a0 | https://github.com/edbltn/fairseq/tree/e4d25fd96f1e38190400dbbdbc77eeda71ac50a0 |
Corr | # 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
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | dattientran/attorch | Corr | false | 12,395 | [
"MIT"
] | 0 | 469b225846c6d8a7d833ebac19d040c7a407a0ff | https://github.com/dattientran/attorch/tree/469b225846c6d8a7d833ebac19d040c7a407a0ff |
ScaleHead | import torch
import torch.nn as nn
class ScaleHead(nn.Module):
def __init__(self):
super().__init__()
self.flatten = torch.flatten
self.dot = torch.dot
def forward(self, mag, height):
curr_mag = self.flatten(mag, start_dim=1)
curr_height = self.flatten(height, start_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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | shvedfun/geo_pos_baseline | ScaleHead | false | 12,982 | [
"Apache-2.0"
] | 0 | 024716bfdaefd23baccfb5a0d2686015385d7b9c | https://github.com/shvedfun/geo_pos_baseline/tree/024716bfdaefd23baccfb5a0d2686015385d7b9c |
ScaledLeakyReLU | # 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... | ArdWang/GFPGAN | ScaledLeakyReLU | false | 11,243 | [
"BSD-3-Clause"
] | 0 | f984ec32754190fad0b9b7a60d372aac84e57173 | https://github.com/ArdWang/GFPGAN/tree/f984ec32754190fad0b9b7a60d372aac84e57173 |
PARALoss | import torch
import torch.nn as nn
import torch.nn.functional as F
class PARALoss(nn.Module):
"""
Softmax classifier for sentence-level relation extraction.
"""
def __init__(self):
"""
Args:
sentence_encoder: encoder for sentences
num_class: number of classes
... | 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... | igorvlnascimento/open-nre | PARALoss | false | 12,517 | [
"MIT"
] | 0 | a6e42ef074d62be4d3ceb571f412d5be8c0502d7 | https://github.com/igorvlnascimento/open-nre/tree/a6e42ef074d62be4d3ceb571f412d5be8c0502d7 |
Pooling | import torch
import torch.nn as nn
class Pooling(nn.Module):
"""
Implementation of pooling for PoolFormer
--pool_size: pooling size
"""
def __init__(self, pool_size=3):
super().__init__()
self.pool = nn.AvgPool2d(pool_size, stride=1, padding=pool_size //
2, count_incl... | 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... | hyenal/tensorflow-image-models | Pooling | false | 3,642 | [
"Apache-2.0"
] | 0 | 2012be8ecc7bc23e84dc2488d3e4fe1c80dbfb2c | https://github.com/hyenal/tensorflow-image-models/tree/2012be8ecc7bc23e84dc2488d3e4fe1c80dbfb2c |
PositionwiseFeedForward | # 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 | PositionwiseFeedForward | false | 2,775 | [
"MIT"
] | 0 | 64197e55d275e5c819bc786a9ff19849cdf2f6b9 | https://github.com/RogerTsai917/attention-is-all-you-need-pytorch/tree/64197e55d275e5c819bc786a9ff19849cdf2f6b9 |
SpatialAttention | # 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_... | lee-zq/VesselSeg-pytorch | SpatialAttention | false | 15,894 | [
"Apache-2.0"
] | 83 | b4f6571fc1fb1fbdaad60ff9282a54a1f1c455fa | https://github.com/lee-zq/VesselSeg-pytorch/tree/b4f6571fc1fb1fbdaad60ff9282a54a1f1c455fa |
hswish | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | Ecalose/dddd_trainer | hswish | false | 13,623 | [
"Apache-2.0"
] | 80 | ef0c6b271cc2898403375f53f813481ffbf6b02c | https://github.com/Ecalose/dddd_trainer/tree/ef0c6b271cc2898403375f53f813481ffbf6b02c |
AuxiliaryConvolutions | # 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
from ite... | aarashfeizi/a-PyTorch-Tutorial-to-Object-Detection | AuxiliaryConvolutions | false | 1,398 | [
"MIT"
] | 0 | a9e1f3092d4b8c094bff5cd0897e0e3c1e0bc9c2 | https://github.com/aarashfeizi/a-PyTorch-Tutorial-to-Object-Detection/tree/a9e1f3092d4b8c094bff5cd0897e0e3c1e0bc9c2 |
SpatialAttention | import torch
import torch.nn as 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.conv = 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
import torch.nn as nn
assert_... | DoubtedSteam/MPANet | SpatialAttention | false | 7,982 | [
"MIT"
] | 25 | fe4f3f1d83c45485b1498786f89ace96c634f187 | https://github.com/DoubtedSteam/MPANet/tree/fe4f3f1d83c45485b1498786f89ace96c634f187 |
Exponent | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn.functional
from torch import nn
assert_size_stride = torc... | drivendataorg/DrivenData-2021-Geopose-Solution | Exponent | false | 6,602 | [
"MIT"
] | 1 | fc1dead0aeb1ade9e9d87b55f56e631c57e966a6 | https://github.com/drivendataorg/DrivenData-2021-Geopose-Solution/tree/fc1dead0aeb1ade9e9d87b55f56e631c57e966a6 |
AmplitudeToDB | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import math
from typing impo... | Nayef211/audio | AmplitudeToDB | false | 11,743 | [
"BSD-2-Clause"
] | 0 | 241ab1e8284e589262f510ee9411baf2bc374ded | https://github.com/Nayef211/audio/tree/241ab1e8284e589262f510ee9411baf2bc374ded |
Get_gradient_nopadding | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.utils.... | BlueAmulet/BasicSR | Get_gradient_nopadding | false | 7,811 | [
"Apache-2.0"
] | 12 | 7040913d8659a05af4c2428feb71c260efbf1e9c | https://github.com/BlueAmulet/BasicSR/tree/7040913d8659a05af4c2428feb71c260efbf1e9c |
EPE3DLoss | import torch
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.nn as nn
class EPE3DLoss(nn.Module):
def __init__(self):
super(EPE3DLoss, self).__init__()
def forward(self, input, target):
return torch.norm(input - target, p=2, dim=1)
def get_inputs():
ret... | 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.parallel
import torch.optim
import torch.utils.data
import torc... | MayankSingal/TrackThisFlow | EPE3DLoss | false | 2,632 | [
"MIT"
] | 0 | 3a76d2a5f2f43ab24c14468d9c751d9f25ee6f3c | https://github.com/MayankSingal/TrackThisFlow/tree/3a76d2a5f2f43ab24c14468d9c751d9f25ee6f3c |
GenNoise | import torch
import torch.nn as nn
class GenNoise(nn.Module):
def __init__(self, dim2):
super(GenNoise, self).__init__()
self.dim2 = dim2
def forward(self, x):
a = list(x.size())
a[1] = self.dim2
b = torch.zeros(a).type_as(x.data)
b.normal_()
x = torch... | 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... | GuYuanjie/DeepFusionPrior | GenNoise | false | 5,220 | [
"MIT"
] | 1 | a7126e073ed8c49b6a9a662492b64aaeee56cc01 | https://github.com/GuYuanjie/DeepFusionPrior/tree/a7126e073ed8c49b6a9a662492b64aaeee56cc01 |
HLCriterion | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch.... | chunhuililili/mt_dnn | HLCriterion | false | 10,199 | [
"MIT"
] | 0 | 4c6efaf21724c7b8103a05e46b5b44d7b246225e | https://github.com/chunhuililili/mt_dnn/tree/4c6efaf21724c7b8103a05e46b5b44d7b246225e |
SoftTargetCrossEntropy | import torch
import torch.nn.functional as F
from torch.nn.modules.loss import _Loss
import torch.optim
import torch._utils
import torch.nn
class SoftTargetCrossEntropy(_Loss):
def __init__(self, loss_weight=1.0):
super(SoftTargetCrossEntropy, self).__init__()
self.loss_weight = loss_weight
... | 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.... | ModelTC/EOD | SoftTargetCrossEntropy | false | 14,062 | [
"Apache-2.0"
] | 196 | 164bff80486e9ae6a095a97667b365c46ceabd86 | https://github.com/ModelTC/EOD/tree/164bff80486e9ae6a095a97667b365c46ceabd86 |
NTimesTanh | import torch
import torch.nn as nn
class NTimesTanh(nn.Module):
def __init__(self, N):
super(NTimesTanh, self).__init__()
self.N = N
self.tanh = nn.Tanh()
def forward(self, x):
return self.tanh(x) * self.N
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_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
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_... | Prinsphield/ELEGANT | NTimesTanh | false | 14,238 | [
"MIT"
] | 276 | 26827e679cbef2074693ffb0d3f36426e481f7f5 | https://github.com/Prinsphield/ELEGANT/tree/26827e679cbef2074693ffb0d3f36426e481f7f5 |
EqualLinear | import torch
import torch.nn.functional as F
from torch import nn
class EqualLinear(nn.Module):
def __init__(self, in_dim, out_dim, lr_mul=1, bias=True):
super().__init__()
self.weight = nn.Parameter(torch.randn(out_dim, in_dim))
if bias:
self.bias = nn.Parameter(torch.zeros(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... | yoona-ai/stylegan2-pytorch | EqualLinear | false | 16,761 | [
"MIT"
] | 2,954 | eceb8aacb669f19b79cc74c7160a85252b1086d6 | https://github.com/yoona-ai/stylegan2-pytorch/tree/eceb8aacb669f19b79cc74c7160a85252b1086d6 |
Edg_Capture | import torch
import torch.nn as nn
import torch.nn.functional as F
class Edg_Capture(nn.Module):
def __init__(self):
super(Edg_Capture, self).__init__()
kernel = [[-1, -1, -1], [-1, 8, -1], [-1, -1, -1]]
kernel = torch.FloatTensor(kernel).unsqueeze(0).unsqueeze(0)
self.weight = 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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | TaoWangzj/PCFAN | Edg_Capture | false | 17,991 | [
"MIT"
] | 7 | f6ddc8fd2e72a45431891acf0b25135499c84485 | https://github.com/TaoWangzj/PCFAN/tree/f6ddc8fd2e72a45431891acf0b25135499c84485 |
AE_big | import torch
import torch.nn as nn
import torch.utils.data
class AE_big(nn.Module):
def __init__(self, n_features=4):
super(AE_big, self).__init__()
self.en1 = nn.Linear(n_features, 8)
self.en2 = nn.Linear(8, 6)
self.en3 = nn.Linear(6, 4)
self.en4 = nn.Linear(4, 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.triton_helpers import libdevice
import torch.nn as ... | gitter-badger/HEPAutoencoders | AE_big | false | 12,446 | [
"Apache-2.0"
] | 0 | 43010cd66fa4335a04b30b87926148e1c8d92de9 | https://github.com/gitter-badger/HEPAutoencoders/tree/43010cd66fa4335a04b30b87926148e1c8d92de9 |
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... | CiscoDevNet/vo-id | Normalize3D | false | 17,079 | [
"MIT"
] | 7 | 9a01f866c7539a9cd095d9627ba4f65ad540ea6b | https://github.com/CiscoDevNet/vo-id/tree/9a01f866c7539a9cd095d9627ba4f65ad540ea6b |
GraphEncoder | import torch
import numpy as np
from torch import nn
import torch.nn.functional as F
from collections import OrderedDict
from sklearn.cluster import KMeans
class GraphEncoder(nn.Module):
def __init__(self, layers, clusters):
super(GraphEncoder, self).__init__()
self.layers = nn.Sequential(Ordered... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import numpy as np
from torch import nn
import torch.nn.functional as F
from col... | SusheendharVijay/ClusterEncoder | GraphEncoder | false | 3,128 | [
"MIT"
] | 0 | 1ebdb4280027f88010cea2d3535b457cf648d311 | https://github.com/SusheendharVijay/ClusterEncoder/tree/1ebdb4280027f88010cea2d3535b457cf648d311 |
InstanceLayerNorm2d | # 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... | belphegor2211/khoa_luan | InstanceLayerNorm2d | false | 9,983 | [
"MIT"
] | 0 | c9c163ebf3aff3005639ce7e4020e510295d1c75 | https://github.com/belphegor2211/khoa_luan/tree/c9c163ebf3aff3005639ce7e4020e510295d1c75 |
Foo | import torch
import torch.jit
import torch.onnx
import torch.nn
class Foo(torch.nn.Module):
def __init__(self):
super(Foo, self).__init__()
self.conv1 = torch.nn.Conv2d(3, 6, 3)
self.relu = torch.nn.ReLU()
self.conv2 = torch.nn.Conv2d(6, 16, 3)
def forward(self, x):
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.jit
import torch... | andreas-hommel/glow | Foo | false | 3,336 | [
"Apache-2.0"
] | 0 | 2bbbf8188a2a941e85677c83f2146bbd076a262e | https://github.com/andreas-hommel/glow/tree/2bbbf8188a2a941e85677c83f2146bbd076a262e |
MaskedCrossEntropyCriterion | import torch
import torch.nn as nn
from torch.nn.modules.loss import _WeightedLoss
class MaskedCrossEntropyCriterion(_WeightedLoss):
def __init__(self, ignore_index=[-100], reduce=None):
super(MaskedCrossEntropyCriterion, self).__init__()
self.padding_idx = ignore_index
self.reduce = redu... | 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.... | ArkanDH/Team5-Inverse-Cooking-Stuff | MaskedCrossEntropyCriterion | false | 1,983 | [
"MIT"
] | 0 | ec224918b25fb7a04aa09995e4d11804448df7dd | https://github.com/ArkanDH/Team5-Inverse-Cooking-Stuff/tree/ec224918b25fb7a04aa09995e4d11804448df7dd |
Wave | import torch
import torch.nn as nn
import torch.nn.functional as F
class CausalConv1d(nn.Conv1d):
def __init__(self, input_size, hidden_size, kernel_size, stride=1,
dilation=1, groups=1, bias=True, sigmoid=None, tanh=None):
self.left_padding = (kernel_size - 1) * dilation
super(CausalConv... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | jpeg729/pytorch-bits | Wave | false | 15,746 | [
"MIT"
] | 73 | 5d107094042c27472dfb7dee77506b603f5d3e45 | https://github.com/jpeg729/pytorch-bits/tree/5d107094042c27472dfb7dee77506b603f5d3e45 |
VonmisesLossBiternion | import torch
class VonmisesLossBiternion(torch.nn.Module):
"""Von mises loss function for biternion inputs
see: Beyer et al.: Biternion Nets: Continuous Head Pose Regression from
Discrete Training Labels, GCPR 2015.
"""
def __init__(self, kappa):
super(VonmisesLossBiternion, self)._... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
assert_size_s... | TUI-NICR/multi-task-person-perception | VonmisesLossBiternion | false | 17,966 | [
"BSD-3-Clause"
] | 4 | 81666eb42be9522fd726448e82e8bbf04138ffa3 | https://github.com/TUI-NICR/multi-task-person-perception/tree/81666eb42be9522fd726448e82e8bbf04138ffa3 |
GaussianKernel | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
from typing import Opt... | mstoelzle/Transfer-Learning-Library | GaussianKernel | false | 12,876 | [
"MIT"
] | 0 | 7d5022668cbe6d1bedbc7c386d44b9d89c272d6b | https://github.com/mstoelzle/Transfer-Learning-Library/tree/7d5022668cbe6d1bedbc7c386d44b9d89c272d6b |
Wav2Vec2ClassificationHead | # 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... | D4shka/MMEmotionRecognition | Wav2Vec2ClassificationHead | false | 8,801 | [
"MIT"
] | 11 | 37572a506f8247eb5b14d59139e1f9b52f5f694b | https://github.com/D4shka/MMEmotionRecognition/tree/37572a506f8247eb5b14d59139e1f9b52f5f694b |
DuelDQNet | import torch
import torch.nn as nn
from torch.nn import functional as F
class DuelDQNet(nn.Module):
"""
Definition: DuelDQNet(obs_size, act_size, hid_size=256)
"""
def __init__(self, obs_size, act_size, hid_size=256):
super().__init__()
self.base = nn.Linear(obs_size, hid_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_... | ayjabri/DeepRL | DuelDQNet | false | 1,507 | [
"MIT"
] | 0 | 0be095e3a3d04f60b4cdc97ed330dffc17b3024a | https://github.com/ayjabri/DeepRL/tree/0be095e3a3d04f60b4cdc97ed330dffc17b3024a |
TD3Critic | import torch
import torch.nn as nn
import torch.nn.functional as F
class TD3Critic(nn.Module):
def __init__(self, state_dim, action_dim):
super(TD3Critic, self).__init__()
self.l1 = nn.Linear(state_dim + action_dim, 256)
self.l2 = nn.Linear(256, 256)
self.l3 = nn.Linear(256, 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 ... | AkiraHero/rlll | TD3Critic | false | 11,179 | [
"MIT"
] | 0 | f86e1105600629d29b8dca7a7483e7dcb8253056 | https://github.com/AkiraHero/rlll/tree/f86e1105600629d29b8dca7a7483e7dcb8253056 |
CHI_Block | # 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.... | Vegetebird/MHFormer | CHI_Block | false | 14,594 | [
"MIT"
] | 83 | 68d793414e13c256249431a45ac49949930c8e7f | https://github.com/Vegetebird/MHFormer/tree/68d793414e13c256249431a45ac49949930c8e7f |
XTanhLoss | import torch
class XTanhLoss(torch.nn.Module):
def __init__(self):
super().__init__()
def forward(self, y_t, y_prime_t):
ey_t = y_t - y_prime_t
return torch.mean(ey_t * torch.tanh(ey_t))
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4])]
def get_ini... | 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._... | tuantle/regression-losses-pytorch | XTanhLoss | false | 16,625 | [
"MIT"
] | 82 | 2893f4439ada5df239e3afd0ec7e781dd61403e9 | https://github.com/tuantle/regression-losses-pytorch/tree/2893f4439ada5df239e3afd0ec7e781dd61403e9 |
LT | # 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... | NVIDIA-AI-IOT-private/torch2trt | LT | false | 10,517 | [
"MIT"
] | 0 | 953d60039e0c81e90eea467c3df2e6e3f7040242 | https://github.com/NVIDIA-AI-IOT-private/torch2trt/tree/953d60039e0c81e90eea467c3df2e6e3f7040242 |
NeuralNetNonDifferentiableOutput | import torch
import torch.nn
import torch.onnx
class NeuralNetNonDifferentiableOutput(torch.nn.Module):
def __init__(self, input_size, hidden_size, num_classes):
super(NeuralNetNonDifferentiableOutput, self).__init__()
self.fc1 = torch.nn.Linear(input_size, hidden_size)
self.relu = torch.... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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
import torch.... | RyanUnderhill/onnxruntime | NeuralNetNonDifferentiableOutput | false | 11,826 | [
"MIT"
] | 0 | 6df4e293ffbb47d739d2dedfbb87fa6234b8c37c | https://github.com/RyanUnderhill/onnxruntime/tree/6df4e293ffbb47d739d2dedfbb87fa6234b8c37c |
CNNCifar | from _paritybench_helpers import _mock_config
import torch
from torch import nn
import torch.nn.functional as F
class CNNCifar(nn.Module):
def __init__(self, args):
super(CNNCifar, self).__init__()
self.conv1 = nn.Conv2d(3, 6, 5)
self.pool = nn.MaxPool2d(2, 2)
self.conv2 = nn.Conv... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | EugeneYuZ/RL-FL | CNNCifar | false | 3,641 | [
"MIT"
] | 0 | cb4cc2a17eda1dbf60d696e361f31e433d8dbdea | https://github.com/EugeneYuZ/RL-FL/tree/cb4cc2a17eda1dbf60d696e361f31e433d8dbdea |
SLP | import torch
import torch.nn as nn
import torch.nn.functional as F
class SLP(nn.Module):
def __init__(self, l_dim, r_dim, hidden_dim, non_linear=F.tanh):
super(SLP, self).__init__()
self.u_R = nn.Linear(hidden_dim, 1, bias=False)
self.f = non_linear
self.ffn = nn.Linear(l_dim * 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
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | QingkaiZeng/GenTaxo | SLP | false | 8,717 | [
"MIT"
] | 28 | 10257a1714d14c6a4c49cbfa0b507408f718cdf0 | https://github.com/QingkaiZeng/GenTaxo/tree/10257a1714d14c6a4c49cbfa0b507408f718cdf0 |
AddReadout | # 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... | aditya-agrawal-30502/vformer | AddReadout | false | 14,738 | [
"MIT"
] | 90 | e1f4950f980238442ff1dc39a8f0791e4fbc9dac | https://github.com/aditya-agrawal-30502/vformer/tree/e1f4950f980238442ff1dc39a8f0791e4fbc9dac |
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.... | jkimbf/transformer-1 | EncoderLayer | false | 15,732 | [
"Apache-2.0"
] | 233 | 6cd29731197822d6db641cdbfad3b045b8a294e4 | https://github.com/jkimbf/transformer-1/tree/6cd29731197822d6db641cdbfad3b045b8a294e4 |
Model | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, input_dim, output_class_num, **kwargs):
super(Model, self).__init__()
self.linear = nn.Linear(input_dim, output_class_num)
def forward(self, features):
pooled = features.mean(dim=1)
predicted = 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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | AyushExel/s3prl | Model | false | 1,993 | [
"MIT"
] | 0 | 6531904e9621a778978b9cfef3ba9f582e56639a | https://github.com/AyushExel/s3prl/tree/6531904e9621a778978b9cfef3ba9f582e56639a |
DepthConv2d | import torch
import numpy as np
import torch.nn as nn
from torch.autograd import Variable
class tLN(nn.Module):
def __init__(self, dimension, eps=1e-08, trainable=True):
super(tLN, self).__init__()
self.eps = eps
if trainable:
self.gain = nn.Parameter(torch.ones(1, 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.triton_helpers import libdevice
import numpy as np
... | rbodo/pytorch-OpCounter | DepthConv2d | false | 7,549 | [
"MIT"
] | 1 | 1857cbb5f9e53343fb349af84efdfde2554a2691 | https://github.com/rbodo/pytorch-OpCounter/tree/1857cbb5f9e53343fb349af84efdfde2554a2691 |
Softmax | import torch
import torch.nn as nn
class Softmax(nn.Module):
def forward(self, x):
y = torch.exp(x)
return y / torch.sum(y, dim=0)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
| import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert... | fmhoward/pysurvival | Softmax | false | 12,377 | [
"Apache-2.0"
] | 0 | 3fea55f09477e9f0844845e09d6ea60434436e2e | https://github.com/fmhoward/pysurvival/tree/3fea55f09477e9f0844845e09d6ea60434436e2e |
TwoLayerFCBodyWithAction | # 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.optim
import tor... | DMIU-ShELL/deeprl-shell | TwoLayerFCBodyWithAction | false | 9,096 | [
"Apache-2.0"
] | 0 | a7845ab1c4967ba2af9486625086c3d0b176d293 | https://github.com/DMIU-ShELL/deeprl-shell/tree/a7845ab1c4967ba2af9486625086c3d0b176d293 |
ShiftedSoftplus | import torch
import torch.nn.functional as F
from torch import nn
class ShiftedSoftplus(nn.Module):
__constants__ = ['beta', 'threshold']
beta: 'int'
threshold: 'int'
def __init__(self, beta: 'int'=1, threshold: 'int'=20) ->None:
super(ShiftedSoftplus, self).__init__()
self.beta = bet... | 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 |
dehaze_net | import torch
import torch.nn as nn
import torch.optim
class dehaze_net(nn.Module):
def __init__(self):
super(dehaze_net, self).__init__()
self.relu = nn.ReLU(inplace=True)
self.e_conv1 = nn.Conv2d(3, 3, 1, 1, 0, bias=True)
self.e_conv2 = nn.Conv2d(3, 3, 3, 1, 1, bias=True)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import ... | NeilDG/PyTorch-Image-Dehazing | dehaze_net | false | 2,693 | [
"MIT"
] | 0 | 25aeebd4d5759efc1c7d5c2015cd381f805f99b2 | https://github.com/NeilDG/PyTorch-Image-Dehazing/tree/25aeebd4d5759efc1c7d5c2015cd381f805f99b2 |
Debayer3x3 | # 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
import torch.nn.functional
assert_size_stride = torch._C._dynamo... | tasptz/pytorch-debayer | Debayer3x3 | false | 13,026 | [
"MIT"
] | 0 | ec35f34a57c045eb2319f4ef87f371d95f7394c3 | https://github.com/tasptz/pytorch-debayer/tree/ec35f34a57c045eb2319f4ef87f371d95f7394c3 |
PixelNorm | import torch
import torch.nn as nn
import torch.utils.cpp_extension
def pixel_norm(x, eps=1e-06):
"""Pixel Normalization.
This normalization is proposed in:
Progressive Growing of GANs for Improved Quality, Stability, and Variation
Args:
x (torch.Tensor): Tensor to be normalized.
eps... | 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.cpp_extension
assert_size_stride = tor... | bladesaber/mmgeneration | PixelNorm | false | 1,884 | [
"Apache-2.0"
] | 0 | 158b49f7efd8028f231f6e9ca758ae0e20dd72ae | https://github.com/bladesaber/mmgeneration/tree/158b49f7efd8028f231f6e9ca758ae0e20dd72ae |
Embedder | import math
import torch
from torch import nn
import torch.nn
import torch.optim
class Embedder(nn.Module):
def __init__(self, dim_in, dim_out):
super(Embedder, self).__init__()
self.dim_in = dim_in
self.dim_out = dim_out
self.linear = nn.Linear(self.dim_in, self.dim_out)
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
from torch import nn
import torch.nn
import torch.optim
assert_size_stride = tor... | OregonWebSells/ReAgent | Embedder | false | 5,689 | [
"BSD-3-Clause"
] | 1 | 866f91785ca86db32fb67744aa063fe77791ff21 | https://github.com/OregonWebSells/ReAgent/tree/866f91785ca86db32fb67744aa063fe77791ff21 |
Net | import torch
import torch.nn as tnn
class Net(tnn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = tnn.Conv2d(3, 6, 5)
self.pool = tnn.MaxPool2d(2, 2)
self.conv2 = tnn.Conv2d(6, 16, 5)
self.fc1 = tnn.Linear(16 * 5 * 5, 120)
self.fc2 = tnn.Linea... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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 tnn
assert... | jittor-online-first/jittor | Net | false | 12,620 | [
"Apache-2.0"
] | 0 | 4217359f86cbcf174fab27c3b723487a8d78b729 | https://github.com/jittor-online-first/jittor/tree/4217359f86cbcf174fab27c3b723487a8d78b729 |
LinearAttention | import torch
from torch import nn
class LinearAttention(nn.Module):
def __init__(self, dim, heads=4, dim_head=32):
super().__init__()
self.heads = heads
self.dim_head = dim_head
self.hidden_dim = dim_head * heads
self.to_qkv = nn.Conv2d(dim, self.hidden_dim * 3, 1, bias=Fa... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | DavidRuhe/simple-variational-diffusion-models | LinearAttention | false | 17,251 | [
"MIT"
] | 4 | a32355bf052a8f08e9c1919080588d0b22c8de4e | https://github.com/DavidRuhe/simple-variational-diffusion-models/tree/a32355bf052a8f08e9c1919080588d0b22c8de4e |
ESA | import torch
from torch import nn
import torch.nn.functional as F
class ESA(nn.Module):
def __init__(self, channel=64, reduction=4, bias=True):
super(ESA, self).__init__()
self.r_nc = channel // reduction
self.conv1 = nn.Conv2d(channel, self.r_nc, kernel_size=1)
self.conv21 = nn.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 import nn
assert_s... | samuro95/Prox-PnP | ESA | false | 10,974 | [
"MIT"
] | 0 | c05a48a586f0ef27c8ddc14e0a4c2c3d6814f8c9 | https://github.com/samuro95/Prox-PnP/tree/c05a48a586f0ef27c8ddc14e0a4c2c3d6814f8c9 |
Swish | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | a07458666/UncertaintyFlow | Swish | false | 1,328 | [
"MIT"
] | 0 | cef2512901d4e27bb22fc3997522cd47c03b569c | https://github.com/a07458666/UncertaintyFlow/tree/cef2512901d4e27bb22fc3997522cd47c03b569c |
FC_A | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.a... | sergkuzn148/stg | FC_A | false | 16,385 | [
"MIT"
] | 96 | 84d9f53ae3665c423836a4d0176dc3b22de62b19 | https://github.com/sergkuzn148/stg/tree/84d9f53ae3665c423836a4d0176dc3b22de62b19 |
FT | # 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... | Capetian/FaceX-Zoo | FT | false | 4,961 | [
"Apache-2.0"
] | 1 | 029786c40d8aba15d891d33973de25fcd7e5399a | https://github.com/Capetian/FaceX-Zoo/tree/029786c40d8aba15d891d33973de25fcd7e5399a |
Model | # 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_... | Catastropha/ignis | Model | false | 8,883 | [
"MIT"
] | 0 | 0fce3b4502666bf3257670c11e3a9c018e04baac | https://github.com/Catastropha/ignis/tree/0fce3b4502666bf3257670c11e3a9c018e04baac |
cross_entropy_prob | # 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
... | zwx8981/DBCNN-Pytorch | cross_entropy_prob | false | 16,830 | [
"MIT"
] | 150 | 16c3156054a30a3eabb45dffcf538f42452a14f3 | https://github.com/zwx8981/DBCNN-Pytorch/tree/16c3156054a30a3eabb45dffcf538f42452a14f3 |
SRCNN | # 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.... | Sardhendu/mmediting | SRCNN | false | 9,893 | [
"Apache-2.0"
] | 0 | 623b59ac758d856abc9fab7e845beeab61074d8f | https://github.com/Sardhendu/mmediting/tree/623b59ac758d856abc9fab7e845beeab61074d8f |
StyleLoss | import torch
import torch.nn as nn
class GramMatrix(nn.Module):
def forward(self, input):
n_batches, n_channels, height, width = input.size()
flattened = input.view(n_batches, n_channels, height * width)
return torch.bmm(flattened, flattened.transpose(1, 2)).div_(height *
widt... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | andreweskeclarke/style-transfer | StyleLoss | false | 1,451 | [
"MIT"
] | 0 | e4b18f4cdb3f473bf946f12cc39447b2f6bb15ca | https://github.com/andreweskeclarke/style-transfer/tree/e4b18f4cdb3f473bf946f12cc39447b2f6bb15ca |
BinaryMin | import abc
import inspect
import torch
import warnings
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
from typing import Any
from typing import *
def get_module_name(cls_or_func):
module_name = cls_or_func.__module__
if module_name == '__main__':
for frm in 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 import triton_helpers
import abc
import inspect
import warnings
import torch.nn as nn
import torch.nn.parallel
... | Johnsonms/NNI_master | BinaryMin | false | 11,572 | [
"MIT"
] | 0 | e5e5c7aed89cf3189cffe1056464833c15eb54ff | https://github.com/Johnsonms/NNI_master/tree/e5e5c7aed89cf3189cffe1056464833c15eb54ff |
Vgg16 | import torch
import torch.nn.functional as F
from torch import nn
from torch.nn import *
class Vgg16(nn.Module):
def __init__(self):
super(Vgg16, self).__init__()
self.conv1_1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1)
self.conv1_2 = nn.Conv2d(64, 64, kernel_size=3, stride=1, ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
from tor... | entc17-fyp-27/GCL | Vgg16 | false | 3,550 | [
"MIT"
] | 0 | df3964b1ea07a5b825e35720377153f3c143f79b | https://github.com/entc17-fyp-27/GCL/tree/df3964b1ea07a5b825e35720377153f3c143f79b |
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