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 |
|---|---|---|---|---|---|---|---|---|---|---|
TARNetPhi | # 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 ... | Shantanu48114860/PSSAM-GAN | TARNetPhi | false | 5,814 | [
"MIT"
] | 1 | c883431c1d0ebbb42691483f8ac8efaab65410b6 | https://github.com/Shantanu48114860/PSSAM-GAN/tree/c883431c1d0ebbb42691483f8ac8efaab65410b6 |
ClassBlock | # 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.... | Inch-Z/volo | ClassBlock | false | 11,520 | [
"Apache-2.0"
] | 0 | 8bbb40838f5cc889ccae26b97438ea73cb1b4e07 | https://github.com/Inch-Z/volo/tree/8bbb40838f5cc889ccae26b97438ea73cb1b4e07 |
BWCEWLoss | import torch
from torch import Tensor
from typing import Optional
from torch import nn
class LogitsInputsMixin:
@classmethod
def get_loss_inputs(cls):
"""Maps loss to the desired predicted input type."""
return LOGITS
class BWCEWLoss(nn.Module, LogitsInputsMixin):
"""Binary weighted cro... | 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 ... | jimthompson5802/ludwig | BWCEWLoss | false | 3,859 | [
"Apache-2.0"
] | 0 | 8a369328a3f839d9cdb3710be315952c7891d7c0 | https://github.com/jimthompson5802/ludwig/tree/8a369328a3f839d9cdb3710be315952c7891d7c0 |
SEConv2d | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.modules.utils import _pair
class SEConv2d(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, stride=1,
padding=0, dilation=1, groups=1, bias=False, size_splits=64,
threshold=0.005, sign_threshold=... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
from torch.nn.modules.utils import _pair
assert_size_strid... | PannenetsF/TQT | SEConv2d | false | 8,655 | [
"BSD-3-Clause"
] | 14 | 3c3125327d00efe6318b28cb1d0a199b734c2c7b | https://github.com/PannenetsF/TQT/tree/3c3125327d00efe6318b28cb1d0a199b734c2c7b |
Net | import torch
import torch.nn as nn
import torch.nn.functional as F
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(1, 32, 3)
self.conv2 = nn.Conv2d(32, 64, 3)
self.pool = nn.MaxPool2d(2, 2)
self.dropout1 = nn.Dropout2d()
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | yito0427/pytorch-basic | Net | false | 4,626 | [
"MIT"
] | 0 | 316cf460edb24da5f25dea9426c1a123912719cf | https://github.com/yito0427/pytorch-basic/tree/316cf460edb24da5f25dea9426c1a123912719cf |
dy_mixprop | import torch
import torch.utils.data
import torch.nn as nn
class linear(nn.Module):
def __init__(self, c_in, c_out, bias=True):
super(linear, self).__init__()
self.mlp = torch.nn.Conv2d(c_in, c_out, kernel_size=(1, 1), padding
=(0, 0), stride=(1, 1), bias=bias)
def forward(self, ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | kevin-xuan/Traffic-Benchmark | dy_mixprop | false | 15,865 | [
"MIT"
] | 120 | b9f8e40b4df9b58f5ad88432dc070cbbbcdc0228 | https://github.com/kevin-xuan/Traffic-Benchmark/tree/b9f8e40b4df9b58f5ad88432dc070cbbbcdc0228 |
StableBCELoss | # 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.utils.dat... | ELEKTRONN/elektronn3 | StableBCELoss | false | 13,603 | [
"MIT"
] | 124 | 19c751855dffc67b744cd43e757aa4a5bd577d9b | https://github.com/ELEKTRONN/elektronn3/tree/19c751855dffc67b744cd43e757aa4a5bd577d9b |
BasicLinearReLULinear | import torch
import torch.nn as nn
class BasicLinearReLULinear(nn.Module):
def __init__(self, in_features, out_features=5, bias=False):
super().__init__()
self.fc1 = nn.Linear(in_features, out_features, bias=bias)
self.relu1 = nn.ReLU()
self.fc2 = nn.Linear(out_features, 1, bias=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 import triton_helpers
import torch.nn as nn
assert_... | dkrako/captum | BasicLinearReLULinear | false | 10,031 | [
"BSD-3-Clause"
] | 0 | b5297bacbaec4e37f353a27de5e728bc2cbc1694 | https://github.com/dkrako/captum/tree/b5297bacbaec4e37f353a27de5e728bc2cbc1694 |
IWDiscriminator | import torch
from torch import nn
class IWConv2d(nn.Module):
def __init__(self, input_dim, output_dim, kernel_size, he_init=True,
stride=1, bias=True):
super(IWConv2d, self).__init__()
self.he_init = he_init
self.padding = int((kernel_size - 1) / 2)
self.conv = nn.Conv2d(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._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | MIC-DKFZ/mood | IWDiscriminator | false | 8,662 | [
"Apache-2.0"
] | 42 | a01303adb4256653b133e2f7cd4741d366b681f7 | https://github.com/MIC-DKFZ/mood/tree/a01303adb4256653b133e2f7cd4741d366b681f7 |
Generator | import torch
import torch.nn as nn
class Generator(nn.Module):
def __init__(self, input_length: 'int'):
super(Generator, self).__init__()
self.dense_layer = nn.Linear(int(input_length), int(input_length))
self.activation = nn.Sigmoid()
def forward(self, x):
return self.activa... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | msank00/ganTutorial | Generator | false | 12,798 | [
"MIT"
] | 0 | 7657ff8cbb0cd66c98b5fd91bf19677e467aac68 | https://github.com/msank00/ganTutorial/tree/7657ff8cbb0cd66c98b5fd91bf19677e467aac68 |
CCAMDec | from torch.nn import Module
import torch
from torch.nn import Parameter
from torch.nn import Softmax
from torch.nn.parameter import Parameter
class CCAMDec(Module):
"""
CCAM decoding module
"""
def __init__(self):
super(CCAMDec, self).__init__()
self.softmax = Softmax(dim=-1)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | bfjei2825401/siamban | CCAMDec | false | 10,403 | [
"Apache-2.0"
] | 0 | c41d58742b146dfc8960053453227c6e9fec1bac | https://github.com/bfjei2825401/siamban/tree/c41d58742b146dfc8960053453227c6e9fec1bac |
BinaryLogisticRegressionLoss | import torch
from torch import nn as nn
def binary_logistic_regression_loss(reg_score, label, threshold=0.5,
ratio_range=(1.05, 21), eps=1e-05):
"""Binary Logistic Regression Loss."""
label = label.view(-1)
reg_score = reg_score.contiguous().view(-1)
pmask = (label > threshold).float()
num_pos... | 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... | HypnosXC/mmaction2 | BinaryLogisticRegressionLoss | false | 13,821 | [
"Apache-2.0"
] | 549 | a26d5f981449445a5e22a0a60d8b285e06c3dd6e | https://github.com/HypnosXC/mmaction2/tree/a26d5f981449445a5e22a0a60d8b285e06c3dd6e |
SSIM | import torch
from torch import nn
from functools import *
class SSIM(nn.Module):
"""Layer to compute the SSIM loss between a pair of images
"""
def __init__(self):
super(SSIM, self).__init__()
self.mu_x_pool = nn.AvgPool2d(3, 1)
self.mu_y_pool = nn.AvgPool2d(3, 1)
self.sig... | 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
f... | JaviBite/TFG | SSIM | false | 2,414 | [
"MIT"
] | 0 | e406580697132f53b63a7c983daaa098af45b52c | https://github.com/JaviBite/TFG/tree/e406580697132f53b63a7c983daaa098af45b52c |
FeatureExtractor | # 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 ... | Akella17/Deep-Bayesian-Quadrature-Policy-Optimization | FeatureExtractor | false | 7,665 | [
"MIT"
] | 16 | e98fd68046486c002c33cf019db2ce66da18615b | https://github.com/Akella17/Deep-Bayesian-Quadrature-Policy-Optimization/tree/e98fd68046486c002c33cf019db2ce66da18615b |
CosineSimilarityLoss | # 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 torch.nn import Module
... | NullConvergence/torch_temp | CosineSimilarityLoss | false | 17,745 | [
"MIT"
] | 3 | 29a0d7190f0be6124f51bd85b8320cd8b3cef29a | https://github.com/NullConvergence/torch_temp/tree/29a0d7190f0be6124f51bd85b8320cd8b3cef29a |
ResidualBlock | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.... | MKFMIKU/PFFNet | ResidualBlock | false | 8,503 | [
"MIT"
] | 41 | e506010a7cf00a32e77681845bdaf78ba88b027d | https://github.com/MKFMIKU/PFFNet/tree/e506010a7cf00a32e77681845bdaf78ba88b027d |
ToRGB | # 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.autograd import Function
import math
import torch.nn as nn
import tor... | songquanpeng/BlendGAN | ToRGB | false | 16,509 | [
"MIT",
"BSD-2-Clause",
"Apache-2.0"
] | 67 | cbf7225c50c548ee955614715ae3f8fa4d68ee13 | https://github.com/songquanpeng/BlendGAN/tree/cbf7225c50c548ee955614715ae3f8fa4d68ee13 |
ReLUExp | import torch
import torch.onnx
import torch.nn as nn
import torch.nn.functional as F
class ReLUExp(nn.Module):
def __init__(self):
super().__init__()
def forward(self, x):
return torch.exp(F.relu(x))
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
retur... | 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.onnx
impo... | mil-tokyo/webdnn | ReLUExp | false | 16,084 | [
"MIT"
] | 1,967 | 38a60fd3e1a4e72bc01108189a3aa51e0752aecd | https://github.com/mil-tokyo/webdnn/tree/38a60fd3e1a4e72bc01108189a3aa51e0752aecd |
Mlp | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import numpy as np
... | au55555/classification-pytorch | Mlp | false | 6,283 | [
"MIT"
] | 1 | 1937599ae6e688ed7af7470f69964fb6f97241c4 | https://github.com/au55555/classification-pytorch/tree/1937599ae6e688ed7af7470f69964fb6f97241c4 |
Sinkhorn_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 import nn
import t... | sfox14/butterfly | Sinkhorn_Net | false | 16,387 | [
"Apache-2.0"
] | 52 | 13cc15cee5bdb7adaf376219aaf20fab0459e9ef | https://github.com/sfox14/butterfly/tree/13cc15cee5bdb7adaf376219aaf20fab0459e9ef |
rSoftMax | # 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... | DengpanFu/fast-reid-v0 | rSoftMax | false | 9,090 | [
"Apache-2.0"
] | 0 | e444c0187ccb6ef3b8348f8c5f0c5a0814b3683e | https://github.com/DengpanFu/fast-reid-v0/tree/e444c0187ccb6ef3b8348f8c5f0c5a0814b3683e |
MultiLabelSoftMarginLoss | import torch
from torch.nn.modules.loss import _WeightedLoss
import torch.nn.parallel
import torch.optim
import torch.utils.data
def binary_cross_entropy(input, target, eps=1e-10):
"""if not (target.size() == input.size()):
warnings.warn("Using a target size ({}) that is different to the input size ({}) i... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch.nn.modules.loss import _WeightedLoss
import torch.nn.parallel
... | NIRVANALAN/microscopy | MultiLabelSoftMarginLoss | false | 5,624 | [
"MIT"
] | 1 | 4e48e51ebb11d8af44b71e8b497cc5da3b097c9b | https://github.com/NIRVANALAN/microscopy/tree/4e48e51ebb11d8af44b71e8b497cc5da3b097c9b |
AttCeLoss | # 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
... | lonePatient/TorchBlocks | AttCeLoss | false | 15,954 | [
"MIT"
] | 82 | 4a65d746cc8a396cb7df73ed4644d97ddf843e29 | https://github.com/lonePatient/TorchBlocks/tree/4a65d746cc8a396cb7df73ed4644d97ddf843e29 |
Decoder | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | finloop/usad | Decoder | false | 15,350 | [
"BSD-3-Clause"
] | 65 | 5e1bf326af5f1325fa4676a2de978cae6db0481c | https://github.com/finloop/usad/tree/5e1bf326af5f1325fa4676a2de978cae6db0481c |
Pooling | import torch
from torch import nn
class Pooling(nn.Module):
"""Implementation of pooling for PoolFormer."""
def __init__(self, pool_size=3):
super().__init__()
self.pool = nn.AvgPool2d(pool_size, stride=1, padding=pool_size //
2, count_include_pad=False)
def forward(self, x)... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_str... | Jack-Hu-2001/UniverseNet | Pooling | false | 13,863 | [
"Apache-2.0"
] | 314 | 03e7b8442286f951c65fe730ec86b9441005ac1b | https://github.com/Jack-Hu-2001/UniverseNet/tree/03e7b8442286f951c65fe730ec86b9441005ac1b |
BinaryDiceLoss | # 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... | hehaoming/RSI-ChangeDetection | BinaryDiceLoss | false | 6,792 | [
"MIT"
] | 1 | f24a1d79c03fb9fefc49bc91bc94b3c120992496 | https://github.com/hehaoming/RSI-ChangeDetection/tree/f24a1d79c03fb9fefc49bc91bc94b3c120992496 |
FocalLoss | import torch
import torch.utils.data
import torch
import torch.nn as nn
from torch.nn import functional as F
class FocalLoss(nn.Module):
def __init__(self, weight=None, gamma=1.0, num_classes=80):
super(FocalLoss, self).__init__()
assert gamma >= 0
self.gamma = gamma
self.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
import torch.utils.dat... | WdBlink/Teacher-Student-Faster-Rcnn | FocalLoss | false | 9,559 | [
"MIT"
] | 0 | df8085c61e334abb04bab5e8192de8cb4ce2b2af | https://github.com/WdBlink/Teacher-Student-Faster-Rcnn/tree/df8085c61e334abb04bab5e8192de8cb4ce2b2af |
ParityPonderGRU | from torch.nn import Module
import torch
from torch import nn
from typing import Tuple
import torch.utils.data
import torch.nn.functional
import torch.autograd
class ParityPonderGRU(Module):
"""
## PonderNet with GRU for Parity Task
This is a simple model that uses a [GRU Cell](https://pytorch.org/docs/s... | import torch
from torch import device
from torch._inductor.select_algorithm import extern_kernels
import triton
import 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
from torch import nn
import... | ppvalluri09/annotated_deep_learning_paper_implementations | ParityPonderGRU | false | 11,096 | [
"MIT"
] | 0 | 387b6dfd1ef1f6d295e9394c24b5798071d9a3e4 | https://github.com/ppvalluri09/annotated_deep_learning_paper_implementations/tree/387b6dfd1ef1f6d295e9394c24b5798071d9a3e4 |
SplitAndConcat | # 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.quantization.quantize_fx
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size... | JacobSzwejbka/d2go | SplitAndConcat | false | 618 | [
"Apache-2.0"
] | 0 | d86ecc92eb97f14fcd97d626185f61c6817351e4 | https://github.com/JacobSzwejbka/d2go/tree/d86ecc92eb97f14fcd97d626185f61c6817351e4 |
RecursiveNet | import torch
from typing import Any
import torch.nn as nn
class RecursiveNet(nn.Module):
""" Model that uses a layer recursively in computation. """
def __init__(self) ->None:
super().__init__()
self.conv1 = nn.Conv2d(64, 64, 3, 1, 1)
def forward(self, x: 'torch.Tensor', args1: 'Any'=Non... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | e-dorigatti/torchinfo | RecursiveNet | false | 12,335 | [
"MIT"
] | 0 | 9fa0e677fb7002e89afd5b1bb372fe8c1dd813d6 | https://github.com/e-dorigatti/torchinfo/tree/9fa0e677fb7002e89afd5b1bb372fe8c1dd813d6 |
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
import torch.nn as nn
assert_... | krishsethi19/dffml | ConvNet | false | 10,368 | [
"MIT"
] | 0 | 2dd0a9c4a125a9739d27228128bbd381a8e0fef4 | https://github.com/krishsethi19/dffml/tree/2dd0a9c4a125a9739d27228128bbd381a8e0fef4 |
BertLayerNorm | from torch.nn import Module
import torch
import torch.nn as nn
class BertLayerNorm(Module):
def __init__(self, hidden_size, eps=1e-12):
super(BertLayerNorm, self).__init__()
self.shape = torch.Size((hidden_size,))
self.eps = eps
self.weight = nn.Parameter(torch.ones(hidden_size))
... | 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.nn import Module
import torch.nn as nn
assert_size_stride = torch._C... | axiserr/Hetu | BertLayerNorm | false | 14,933 | [
"Apache-2.0"
] | 82 | 0052f727488db0570d6b37f63549b43b0920bc29 | https://github.com/axiserr/Hetu/tree/0052f727488db0570d6b37f63549b43b0920bc29 |
ResidualBlock | # 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.... | ImageProcessingCentraleLille2021/fast-neural-style | ResidualBlock | false | 13,846 | [
"MIT"
] | 350 | e77456c35c2a49f90227119d158828a0964c7e13 | https://github.com/ImageProcessingCentraleLille2021/fast-neural-style/tree/e77456c35c2a49f90227119d158828a0964c7e13 |
NegSamplingLoss | # 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... | Ahren09/RecBole | NegSamplingLoss | false | 1,922 | [
"MIT"
] | 0 | b3921818dfbc1b81f9eda8d5e9f05bc9d9114089 | https://github.com/Ahren09/RecBole/tree/b3921818dfbc1b81f9eda8d5e9f05bc9d9114089 |
SourceContextGate | import torch
import torch.nn as nn
import torch.cuda
class ContextGate(nn.Module):
"""Implement up to the computation of the gate"""
def __init__(self, embeddings_size, decoder_size, attention_size,
output_size):
super(ContextGate, self).__init__()
input_size = embeddings_size + decod... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | AngusGLChen/qg | SourceContextGate | false | 4,869 | [
"MIT"
] | 1 | 3ebc5b94348a4c313829a6c71705fbc9dadd8181 | https://github.com/AngusGLChen/qg/tree/3ebc5b94348a4c313829a6c71705fbc9dadd8181 |
Attention | # 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.... | Divyanshu23/model-zoo | Attention | false | 8,109 | [
"MIT"
] | 43 | 2eea6df691d302e182bb1ff8ec5af3542de562ba | https://github.com/Divyanshu23/model-zoo/tree/2eea6df691d302e182bb1ff8ec5af3542de562ba |
Policy_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.... | BLUECARVIN/RL_baseline | Policy_Net | false | 135 | [
"MIT"
] | 0 | 436538f49ee505e14672a67ba3c1f60886cbbea8 | https://github.com/BLUECARVIN/RL_baseline/tree/436538f49ee505e14672a67ba3c1f60886cbbea8 |
PaddedMaxPool2d | import torch
import torch.nn as nn
import torch.nn.functional as F
class PaddedMaxPool2d(nn.Module):
""" Maxpool layer with a replicating padding.
Args:
kernel_size (int or tuple): Kernel size for maxpooling
stride (int or tuple, optional): The stride of the window; Default ``kernel_size``
... | 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... | FenryrMKIII/objectDetection-lightnet | PaddedMaxPool2d | false | 2,241 | [
"MIT"
] | 0 | 3a1fa7b77227210060714a9e22d7d241888b36b4 | https://github.com/FenryrMKIII/objectDetection-lightnet/tree/3a1fa7b77227210060714a9e22d7d241888b36b4 |
Discrete | import torch
import torch.nn as nn
class Discrete(nn.Module):
def __init__(self):
super().__init__()
def forward(self, x):
return nn.functional.softmax(x, 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 import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | wandb/cli | Discrete | false | 10,901 | [
"MIT"
] | 0 | 4a21c2c0c9944734f4c30a8e1453aaf45609e415 | https://github.com/wandb/cli/tree/4a21c2c0c9944734f4c30a8e1453aaf45609e415 |
HuberLoss | # 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... | HamzaHz2/rlkit | HuberLoss | false | 5,261 | [
"MIT"
] | 1 | 55f30c2f1830693624bc5d4085ab9a1ac80b30c4 | https://github.com/HamzaHz2/rlkit/tree/55f30c2f1830693624bc5d4085ab9a1ac80b30c4 |
Decoder | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
im... | einbandi/samplednn | Decoder | false | 6,643 | [
"MIT"
] | 1 | 3525e46ab5096a569dde40e5a10d6ee05128ec7d | https://github.com/einbandi/samplednn/tree/3525e46ab5096a569dde40e5a10d6ee05128ec7d |
ZeroPad1d | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.utils.data
import torch.onnx.operators
import torch.optim
import torch.optim.lr_scheduler
assert_size_str... | AbhilashMathews/adahessian | ZeroPad1d | false | 4,833 | [
"MIT"
] | 1 | bacccecc7a078c3e9e72aa55b17d8e46d21dc9c9 | https://github.com/AbhilashMathews/adahessian/tree/bacccecc7a078c3e9e72aa55b17d8e46d21dc9c9 |
ConcatBlock | import torch
import torch.nn as nn
import torch.nn.functional
class ConcatBlock(nn.Module):
def __init__(self, in_channels, out_channels):
super(ConcatBlock, self).__init__()
self.in_chns = in_channels
self.out_chns = out_channels
self.conv1 = nn.Conv2d(self.in_chns, self.in_chns,... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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
assert_size_stride = torch._C._... | TheSeaOfStars123/SSL4MIS | ConcatBlock | false | 2,896 | [
"MIT"
] | 0 | a3fb6e8c996683eb79dc3f20e965064b7f5d2b3d | https://github.com/TheSeaOfStars123/SSL4MIS/tree/a3fb6e8c996683eb79dc3f20e965064b7f5d2b3d |
ShuffleCatChunk | # 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... | AbhinandanVellanki/yolact_edge | ShuffleCatChunk | false | 1,956 | [
"MIT"
] | 0 | 06d6318cf70ef511b19aa1c14f0476e4ffac2722 | https://github.com/AbhinandanVellanki/yolact_edge/tree/06d6318cf70ef511b19aa1c14f0476e4ffac2722 |
Discriminator | import torch
import torch.nn as nn
class Discriminator(nn.Module):
def __init__(self, state_dim, action_dim):
super(Discriminator, self).__init__()
self.l1 = nn.Linear(state_dim + action_dim, 500)
self.l2 = nn.Linear(500, 300)
self.l3 = nn.Linear(300, 300)
self.l4 = nn.Lin... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | rortiz9/meleeml | Discriminator | false | 4,203 | [
"MIT"
] | 0 | 9be4bf53a377dfb46dbb3b51f102f1bffc0124d2 | https://github.com/rortiz9/meleeml/tree/9be4bf53a377dfb46dbb3b51f102f1bffc0124d2 |
Value_estimate | import torch
import torch.nn as nn
import torch.nn.functional as F
class Value_estimate(nn.Module):
def __init__(self, input_shape, device, output_shape=1, hidden_shape=128):
super(Value_estimate, self).__init__()
self.device = device
self.dropout = nn.Dropout(p=0.01)
self.input_s... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | pupupue/Deep-RL-atari | Value_estimate | false | 7,492 | [
"MIT"
] | 1 | 9b97157f87826feafcf272761d7eef9693a2b2c4 | https://github.com/pupupue/Deep-RL-atari/tree/9b97157f87826feafcf272761d7eef9693a2b2c4 |
FPNSegHead | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
class FPNSegHead(nn.Module):
def __init__(self, num_in, num_mid, num_out):
super().__init__()
self.block0 = nn.Conv2d(num_in, num_mid, kernel_size=3, padding=1,
bias=False)
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 ... | hoangnguyen11291/Kuril-DeBlur | FPNSegHead | false | 12,506 | [
"BSD-3-Clause"
] | 0 | 7c36fc50780e3dda82eb42443d5623d34e6b02a6 | https://github.com/hoangnguyen11291/Kuril-DeBlur/tree/7c36fc50780e3dda82eb42443d5623d34e6b02a6 |
makeStyle | # 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_... | cellimnet/scellseg-publish | makeStyle | false | 6,403 | [
"BSD-3-Clause"
] | 1 | 03bfbae11fedcf430c40419c9afadf55cbd3034d | https://github.com/cellimnet/scellseg-publish/tree/03bfbae11fedcf430c40419c9afadf55cbd3034d |
StdConv2d | # 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 ... | kayvane1/BiT-Tobacco-800 | StdConv2d | false | 10,325 | [
"Apache-2.0"
] | 0 | fd937cc3f8fc1d5e45744defd82d112c10281433 | https://github.com/kayvane1/BiT-Tobacco-800/tree/fd937cc3f8fc1d5e45744defd82d112c10281433 |
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_... | SuzaKrish/mmdetection | SpatialAttention | false | 1,101 | [
"Apache-2.0"
] | 0 | 31c16891d7493252262e738bcbf05326dba866b2 | https://github.com/SuzaKrish/mmdetection/tree/31c16891d7493252262e738bcbf05326dba866b2 |
SelfAttention | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | MukundhMurthy/viral-mutation | SelfAttention | false | 9,335 | [
"MIT"
] | 0 | 371422e418e8adc1ab9e68d2f09bd2f8aa5f00f0 | https://github.com/MukundhMurthy/viral-mutation/tree/371422e418e8adc1ab9e68d2f09bd2f8aa5f00f0 |
CmapPafHeadAttention | import torch
import torch.utils.data
import torch.nn
import torch.optim
class UpsampleCBR(torch.nn.Sequential):
def __init__(self, input_channels, output_channels, count=1, num_flat=0):
layers = []
for i in range(count):
if i == 0:
inch = input_channels
els... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | intflow/trt_openpose | CmapPafHeadAttention | false | 3,805 | [
"MIT"
] | 0 | 526b1b0d463f1c86a45ca4d4cd77a41732c7654b | https://github.com/intflow/trt_openpose/tree/526b1b0d463f1c86a45ca4d4cd77a41732c7654b |
SplitChannels | # 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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_... | ClashLuke/memcnn | SplitChannels | false | 289 | [
"MIT"
] | 0 | 1d48132282c02506ca3d35540f819c4c9130eab4 | https://github.com/ClashLuke/memcnn/tree/1d48132282c02506ca3d35540f819c4c9130eab4 |
AttentionLayer | # 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.... | jonndoe/Character-Level-Language-Modeling-with-Deeper-Self-Attention-pytorch | AttentionLayer | false | 3,781 | [
"MIT"
] | 0 | d27d2d390f0831330405c16bd29c7f331ad2007a | https://github.com/jonndoe/Character-Level-Language-Modeling-with-Deeper-Self-Attention-pytorch/tree/d27d2d390f0831330405c16bd29c7f331ad2007a |
RobertaClassificationHead | from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
class RobertaClassificationHead(nn.Module):
"""Head for sentence-level classification tasks."""
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size * 2, config.hidden_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 ... | AlexShypula/CodeGen | RobertaClassificationHead | false | 14,554 | [
"MIT"
] | 241 | 2e5f8090c4369fd3f0ebec4a867503edc1362d5d | https://github.com/AlexShypula/CodeGen/tree/2e5f8090c4369fd3f0ebec4a867503edc1362d5d |
OneParam | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch import nn
import torch.utils.data
assert_size_stride = torch._C._dyn... | safwanhossain/grad_constraints | OneParam | false | 10,728 | [
"MIT"
] | 0 | fb66f5e01dff6a587e5f1e9b5316f19b4be36ca7 | https://github.com/safwanhossain/grad_constraints/tree/fb66f5e01dff6a587e5f1e9b5316f19b4be36ca7 |
make_dense_LReLU | import torch
import torch.nn as nn
import torch.nn.functional as F
class make_dense_LReLU(nn.Module):
def __init__(self, nChannels, growthRate, kernel_size=3):
super(make_dense_LReLU, self).__init__()
self.conv = nn.Conv2d(nChannels, growthRate, kernel_size=
kernel_size, padding=(kern... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | BJTU-MIMO/Channel_estimation_MRDN | make_dense_LReLU | false | 137 | [
"MIT"
] | 0 | f41972998a5403c901bc3e5d68d4acd05e9a7f6c | https://github.com/BJTU-MIMO/Channel_estimation_MRDN/tree/f41972998a5403c901bc3e5d68d4acd05e9a7f6c |
BasicBlock | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | aouedions11/SSFL-Benchmarking-Semi-supervised-Federated-Learning | BasicBlock | false | 6,231 | [
"MIT"
] | 1 | 78aec81919bf95ed4677d0e0a4ebbbe3be455742 | https://github.com/aouedions11/SSFL-Benchmarking-Semi-supervised-Federated-Learning/tree/78aec81919bf95ed4677d0e0a4ebbbe3be455742 |
LastLevelMaxPool | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.utils.data
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._... | AmanKishore/maskrcnn-benchmark | LastLevelMaxPool | false | 9,333 | [
"MIT"
] | 0 | c95a00feaeba6fb4f9c3cd9a60bf1fdab98e696d | https://github.com/AmanKishore/maskrcnn-benchmark/tree/c95a00feaeba6fb4f9c3cd9a60bf1fdab98e696d |
TransformerDecoderLayer | # 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.... | ruiguo-bio/smer | TransformerDecoderLayer | false | 12,963 | [
"MIT"
] | 0 | e50c814629d02d9e0892b705d5b6273a3537cb11 | https://github.com/ruiguo-bio/smer/tree/e50c814629d02d9e0892b705d5b6273a3537cb11 |
ConvSwishInplace | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
import torch.cuda
import torch.backends.cudnn
import torch.... | XiaobingSuper/intel-extension-for-pytorch | ConvSwishInplace | false | 9,719 | [
"Apache-2.0"
] | 0 | b61029be10e46e6d2e13b0e700c81f8e59164df0 | https://github.com/XiaobingSuper/intel-extension-for-pytorch/tree/b61029be10e46e6d2e13b0e700c81f8e59164df0 |
FeatureCorrelation | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | JiwonCocoder/-Joint-Learning-of-Feature-Extraction-and-Cost-Aggregation-for-Semantic-Matching | FeatureCorrelation | false | 5,408 | [
"MIT"
] | 1 | b79e0e20fd5a1a9ddc0ffa9d7a92e0ebd21018b9 | https://github.com/JiwonCocoder/-Joint-Learning-of-Feature-Extraction-and-Cost-Aggregation-for-Semantic-Matching/tree/b79e0e20fd5a1a9ddc0ffa9d7a92e0ebd21018b9 |
ScaledDotProductAttention | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | HolyCrap96/mmocr-1 | ScaledDotProductAttention | false | 9,190 | [
"Apache-2.0"
] | 0 | c6c4acd39b1c56fec1b87530b2d241fe8af4ceed | https://github.com/HolyCrap96/mmocr-1/tree/c6c4acd39b1c56fec1b87530b2d241fe8af4ceed |
MultimodalHead | import torch
from torch import nn
class MultimodalHead(nn.Module):
"""
Multimodal head for the conv net outputs.
This layer concatenate the outputs of audio and visual convoluational nets
and performs a fully-connected projection
"""
def __init__(self, dim_in, num_classes, dropout_rate=0.0, a... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | JiwanChung/acav100m | MultimodalHead | false | 8,360 | [
"MIT"
] | 27 | 51cb948d5682da69334a8d05d2df631971b60215 | https://github.com/JiwanChung/acav100m/tree/51cb948d5682da69334a8d05d2df631971b60215 |
MultiHead | # 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.... | MichiganCOG/Video-Grounding | MultiHead | false | 8,549 | [
"MIT"
] | 41 | 3e0ec0b69578a59be583911590354fe77d357cab | https://github.com/MichiganCOG/Video-Grounding/tree/3e0ec0b69578a59be583911590354fe77d357cab |
SimpleMulModule | import torch
import torch.jit
import torch.onnx
import torch.nn
class SimpleMulModule(torch.nn.Module):
def __init__(self):
super(SimpleMulModule, self).__init__()
def forward(self, left, right):
other = left.mul(right.item() if right.size() == torch.Size([]) else
right)
... | 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.jit
import torch.onnx
import torch.nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torc... | andreas-hommel/glow | SimpleMulModule | false | 3,343 | [
"Apache-2.0"
] | 0 | 2bbbf8188a2a941e85677c83f2146bbd076a262e | https://github.com/andreas-hommel/glow/tree/2bbbf8188a2a941e85677c83f2146bbd076a262e |
WMAE | # 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... | j1a0m0e4sNTU/MachineLearning2019 | WMAE | false | 3,680 | [
"MIT"
] | 0 | 44a7a3387837e53134bcf5eb8fcf95daf4dff48d | https://github.com/j1a0m0e4sNTU/MachineLearning2019/tree/44a7a3387837e53134bcf5eb8fcf95daf4dff48d |
SkipLastTargetChannelWrapper | # 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... | PerceptionComputingLab/PARSE2022 | SkipLastTargetChannelWrapper | false | 9,427 | [
"Apache-2.0"
] | 0 | a34886ed9d06b424bc93953f1b2f79540ad9ebf6 | https://github.com/PerceptionComputingLab/PARSE2022/tree/a34886ed9d06b424bc93953f1b2f79540ad9ebf6 |
BinarizeActivations | import torch
import torch.multiprocessing
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
import torch.autograd as autograd
class BinarizeWeight(autograd.Function):
@staticmethod
def forward(ctx, scores):
out = scores.clone... | 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.multiprocessing
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.da... | adityakusupati/LLC-2.0 | BinarizeActivations | false | 18,219 | [
"MIT"
] | 10 | 38608bbaa425b15dcf5c971000b7a1b08120fb5c | https://github.com/adityakusupati/LLC-2.0/tree/38608bbaa425b15dcf5c971000b7a1b08120fb5c |
CORALLoss | # 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
assert_size_stride ... | KevinMusgrave/pytorch-adapt | CORALLoss | false | 13,947 | [
"MIT"
] | 131 | ff1491e1bfcc586afb8ee619712c8816ddf10358 | https://github.com/KevinMusgrave/pytorch-adapt/tree/ff1491e1bfcc586afb8ee619712c8816ddf10358 |
InteractiveKLLoss | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.nn.functional as F
class InteractiveKLLoss(nn.Module):
def __init__(self, temperature):
super().__init__()
self.temperature = temperature
self.kl_loss = nn.KLDivLoss()
... | 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... | savan77/nni | InteractiveKLLoss | false | 4,273 | [
"MIT"
] | 0 | 510213393d9cae58c5a8cccd21f322f7bba4e0cf | https://github.com/savan77/nni/tree/510213393d9cae58c5a8cccd21f322f7bba4e0cf |
OrthoLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | Vermeille/Torchelie | OrthoLoss | false | 14,545 | [
"MIT"
] | 117 | 43957d83238372ae6436aac90127865c2040b76c | https://github.com/Vermeille/Torchelie/tree/43957d83238372ae6436aac90127865c2040b76c |
FCDiscriminatorCriterion | import torch
import torch.nn as nn
import torch.nn.functional as F
class FCDiscriminatorCriterion(nn.Module):
def __init__(self):
super(FCDiscriminatorCriterion, self).__init__()
def forward(self, pred, gt):
loss = F.binary_cross_entropy_with_logits(pred, gt, reduction='none')
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 libdevice, math as tl_math
import torc... | charlesCXK/PixelSSL | FCDiscriminatorCriterion | false | 1,664 | [
"Apache-2.0"
] | 0 | 2e85e12c1db5b24206bfbbf2d7f6348ae82b2105 | https://github.com/charlesCXK/PixelSSL/tree/2e85e12c1db5b24206bfbbf2d7f6348ae82b2105 |
Actor | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | BenKang34/deep-reinforcement-learning-nanodegree | Actor | false | 145 | [
"MIT"
] | 0 | 17c9007f757dfb1217c869fdee51798c4a21ba92 | https://github.com/BenKang34/deep-reinforcement-learning-nanodegree/tree/17c9007f757dfb1217c869fdee51798c4a21ba92 |
L1 | # 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... | mrernst/rl_robotics_research | L1 | false | 10,613 | [
"MIT"
] | 0 | 0bc446cfb69591cb4ee3ce8d39815c463090a5f6 | https://github.com/mrernst/rl_robotics_research/tree/0bc446cfb69591cb4ee3ce8d39815c463090a5f6 |
LblLoss | # 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 import nn
from torchvision.models import *
assert_size_stride = torch._C._dyna... | amoshyc/human-pose-estimation | LblLoss | false | 9,729 | [
"Apache-2.0"
] | 0 | 8fd2962caee43b979f44637441d88d80f2ea951e | https://github.com/amoshyc/human-pose-estimation/tree/8fd2962caee43b979f44637441d88d80f2ea951e |
up | import torch
from torch import nn
import torch.nn.functional as F
class up(nn.Module):
def __init__(self, in_ch, out_ch):
super(up, self).__init__()
self.up_scale = nn.ConvTranspose2d(in_ch, out_ch, 2, stride=2)
def forward(self, x1, x2):
x2 = self.up_scale(x2)
diffY = x1.siz... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import 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... | aribryan/segmentation_revisit | up | false | 12,115 | [
"MIT"
] | 0 | a37747cfa7bfa7bfd4c0c01983421f632cd719ba | https://github.com/aribryan/segmentation_revisit/tree/a37747cfa7bfa7bfd4c0c01983421f632cd719ba |
GeneralizedMeanPooling | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
from torch.nn import Module
... | dongan-beta/deep-image-retrieval | GeneralizedMeanPooling | false | 15,200 | [
"BSD-3-Clause"
] | 253 | 3e0885f88da328aefb7abb2fa350f8860a4bd52d | https://github.com/dongan-beta/deep-image-retrieval/tree/3e0885f88da328aefb7abb2fa350f8860a4bd52d |
group | import torch
import torch.nn as nn
class mfm(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size=3, stride=1,
padding=1, type=1):
super(mfm, self).__init__()
self.out_channels = out_channels
if type == 1:
self.filter = nn.Conv2d(in_channels, 2 * out_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
import torch.nn as nn
assert_... | BradyFU/DVG-Face | group | false | 7,819 | [
"MIT"
] | 33 | 16d51fe7da6e4a52d144e938afb3072eb8e4e8de | https://github.com/BradyFU/DVG-Face/tree/16d51fe7da6e4a52d144e938afb3072eb8e4e8de |
GatedActivation | import torch
from torch import nn
class GatedActivation(nn.Module):
"""Activation function which computes actiation_fn(f) * sigmoid(g).
The f and g correspond to the top 1/2 and bottom 1/2 of the input channels.
"""
def __init__(self, activation_fn=torch.tanh):
"""Initializes a new GatedActi... | 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... | cobypenso/pytorch-generative | GatedActivation | false | 10,008 | [
"MIT"
] | 0 | 72d1a3d8045179bd3a83ee3783aa070e74a1e400 | https://github.com/cobypenso/pytorch-generative/tree/72d1a3d8045179bd3a83ee3783aa070e74a1e400 |
Conv1dSamePadding | # 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... | Wadaboa/titanet | Conv1dSamePadding | false | 18,073 | [
"MIT"
] | 4 | b07e3074e79ea8c1129fb0adb8315e06bb4943ea | https://github.com/Wadaboa/titanet/tree/b07e3074e79ea8c1129fb0adb8315e06bb4943ea |
BiInteractionPooling | import torch
import torch.nn as nn
from sklearn.metrics import *
class BiInteractionPooling(nn.Module):
"""Bi-Interaction Layer used in Neural FM,compress the
pairwise element-wise product of features into one single vector.
Input shape
- A 3D tensor with shape:``(batch_size,field_size,embeddi... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
from sklearn.metrics import *
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = tor... | Sunmyunghan/Final_Project | BiInteractionPooling | false | 1,201 | [
"MIT"
] | 0 | 28cde293dc6d07521b2e1c5613b20444aea91d21 | https://github.com/Sunmyunghan/Final_Project/tree/28cde293dc6d07521b2e1c5613b20444aea91d21 |
InvertibleUpsampling2D | # 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.autograd import Function
import numpy as np
from warnings import warn... | cetmann/iunets | InvertibleUpsampling2D | false | 15,030 | [
"MIT"
] | 86 | 80ed7cce0e505a0396c42359eaf27819222d71f6 | https://github.com/cetmann/iunets/tree/80ed7cce0e505a0396c42359eaf27819222d71f6 |
DenseBlock | # 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... | gaotianyu1350/new_fewrel_bertpair | DenseBlock | false | 15,401 | [
"MIT"
] | 180 | 27184050d476fc93576948fb26680d508a2824bb | https://github.com/gaotianyu1350/new_fewrel_bertpair/tree/27184050d476fc93576948fb26680d508a2824bb |
SelfAttention | import torch
import numpy as np
import torch.nn as nn
class SelfAttention(nn.Module):
""" Scaled Dot-Product Attention """
def __init__(self, dropout=0.1):
super(SelfAttention, self).__init__()
self.dropout = nn.Dropout(dropout)
def forward(self, query, key, value, mask=None):
ke... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | TranQuocTrinh/image_captioning | SelfAttention | false | 1,148 | [
"MIT"
] | 0 | 4c2d77426ba3b9fe9151a15a958320d5298aa190 | https://github.com/TranQuocTrinh/image_captioning/tree/4c2d77426ba3b9fe9151a15a958320d5298aa190 |
ConvRelu | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.utils.data
impor... | CorentinLemaitre/robosat.pink | ConvRelu | false | 5,051 | [
"MIT"
] | 1 | 6ec29a4dd4c0cbf953e73818d7338ee68b2451d3 | https://github.com/CorentinLemaitre/robosat.pink/tree/6ec29a4dd4c0cbf953e73818d7338ee68b2451d3 |
PreActBlock | import torch
import torch.nn as nn
import torch.nn.functional as F
class PreActBlock(nn.Module):
"""Pre-activation version of the BasicBlock."""
expansion = 1
def __init__(self, in_planes, planes, stride=1):
super(PreActBlock, self).__init__()
self.conv1 = nn.Conv2d(in_planes, planes, ker... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | appa-ayephyu/RobGAN | PreActBlock | false | 1,461 | [
"MIT"
] | 0 | 1d4577edb5b858e9d0c1e76a4c323de18201190c | https://github.com/appa-ayephyu/RobGAN/tree/1d4577edb5b858e9d0c1e76a4c323de18201190c |
SVHNConvNet | # 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... | Felix-Petersen/algovision | SVHNConvNet | false | 13,740 | [
"MIT"
] | 52 | b1b9596028af62de1c1d2c4e74cbd6168fc3ae3c | https://github.com/Felix-Petersen/algovision/tree/b1b9596028af62de1c1d2c4e74cbd6168fc3ae3c |
GaussianFocalLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import functools
impor... | ChHanXiao/mmdetection | GaussianFocalLoss | false | 9,147 | [
"Apache-2.0"
] | 0 | 324aa5a042857a9b57abe37385e1210709a20d02 | https://github.com/ChHanXiao/mmdetection/tree/324aa5a042857a9b57abe37385e1210709a20d02 |
InvConv | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import numpy as np
import torch.nn as nn
assert_size_stride = torch._C._dynamo.g... | ClaraBing/flow | InvConv | false | 294 | [
"MIT"
] | 0 | 00290326a97235e7d83303f1efff2e14214d0c36 | https://github.com/ClaraBing/flow/tree/00290326a97235e7d83303f1efff2e14214d0c36 |
InstanceNormLayer | import torch
import torch.nn as nn
class InstanceNormLayer(nn.Module):
"""Implements instance normalization layer."""
def __init__(self, epsilon=1e-08):
super().__init__()
self.epsilon = epsilon
def forward(self, x):
if len(x.shape) != 4:
raise ValueError(
... | 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_... | CV-IP/interfacegan | InstanceNormLayer | false | 13,432 | [
"MIT"
] | 855 | 5a556b8e693f6e1888f769f653aaafaaccca5dc2 | https://github.com/CV-IP/interfacegan/tree/5a556b8e693f6e1888f769f653aaafaaccca5dc2 |
RMSELoss | # 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
assert_size_stride = torch._... | SAMMiCA/DL_based_E2E_Driving | RMSELoss | false | 17,872 | [
"MIT"
] | 4 | 01f7d74a0db7ed745cf27b9a1ebab0246015ecbd | https://github.com/SAMMiCA/DL_based_E2E_Driving/tree/01f7d74a0db7ed745cf27b9a1ebab0246015ecbd |
Attention_layer | import math
import torch
import torch.nn as nn
def calculate_attention(query: 'torch.Tensor', key: 'torch.Tensor', value:
'torch.Tensor', mask: 'torch.Tensor'):
"""Calclulate Attention
@param:
query: torch.Tensor (Batch_size, max_seq_len, hidden_size)
key: torch.Tensor (Batch_size, max_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.... | OpenNLPhub/SynSetMineOnBert | Attention_layer | false | 5,691 | [
"MIT"
] | 1 | 58853363557a2397fd8b04c8e68061f9df159d6a | https://github.com/OpenNLPhub/SynSetMineOnBert/tree/58853363557a2397fd8b04c8e68061f9df159d6a |
DQN | import torch
import torch.nn as nn
import torch.nn.functional as F
class DQN(nn.Module):
def __init__(self, n_in, n_out):
super(DQN, self).__init__()
self.ln1 = nn.Linear(n_in, 32)
self.ln2 = nn.Linear(32, 32)
self.ln4 = nn.Linear(32, 32)
self.out = nn.Linear(32, n_out)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | AndrejHafner/tetris-reinforcement-learning | DQN | false | 1,968 | [
"MIT"
] | 0 | 52db5d8ce7f9162b15575456a0effc69dd7fb2bf | https://github.com/AndrejHafner/tetris-reinforcement-learning/tree/52db5d8ce7f9162b15575456a0effc69dd7fb2bf |
Polynomial3 | # 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.quantization
import torch.onnx
import torch.nn.parallel
import torch.utils.data
import torch.fx
import torch.nn
import torch.op... | Lezcano/tutorials | Polynomial3 | false | 5,515 | [
"BSD-3-Clause"
] | 1 | 24946b2e6d3d825afed6b35c1c4d618a70a88be8 | https://github.com/Lezcano/tutorials/tree/24946b2e6d3d825afed6b35c1c4d618a70a88be8 |
SelfAttentionBatch | import torch
from torch import nn
import torch.nn.functional as F
class SelfAttentionBatch(nn.Module):
def __init__(self, dim, da, alpha=0.2, dropout=0.5):
super(SelfAttentionBatch, self).__init__()
self.dim = dim
self.da = da
self.alpha = alpha
self.dropout = dropout
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Xiaolong-Qi/CRSLab | SelfAttentionBatch | false | 5,999 | [
"MIT"
] | 1 | d507378c86f4996727bf062482e1f224486d4533 | https://github.com/Xiaolong-Qi/CRSLab/tree/d507378c86f4996727bf062482e1f224486d4533 |
SmallMaskNet | import torch
import torch.nn as nn
import torch.nn.functional as F
class SmallMaskNet(nn.Module):
"""A three-layer network for predicting mask"""
def __init__(self, input, output):
super(SmallMaskNet, self).__init__()
self.conv1 = nn.Conv2d(input, 32, 5, padding=2)
self.conv2 = nn.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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | saikatdutta/NME-VFI | SmallMaskNet | false | 7,591 | [
"Apache-2.0"
] | 1 | 5915e2336ea3ed7113a9c6a91bbc7f6b5deaac17 | https://github.com/saikatdutta/NME-VFI/tree/5915e2336ea3ed7113a9c6a91bbc7f6b5deaac17 |
qy | import torch
import torch.nn as nn
import torch.nn.functional as F
def weights_init(m):
if isinstance(m, nn.Conv2d):
nn.init.kaiming_uniform_(m.weight, a=0, mode='fan_in', nonlinearity
='leaky_relu')
try:
nn.init.constant_(m.bias, 0.01)
except:
pass
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | IamWangYunKai/DG-TrajGen | qy | false | 8,303 | [
"MIT"
] | 31 | 0a8aab7e1c05111a5afe43d53801c55942e9ff56 | https://github.com/IamWangYunKai/DG-TrajGen/tree/0a8aab7e1c05111a5afe43d53801c55942e9ff56 |
Classifier | # 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.... | NightmareNyx/semi-supervised-pytorch | Classifier | false | 2,702 | [
"MIT"
] | 0 | 43bb86bc6757345bd7a4eb37d6948ee62a268f7e | https://github.com/NightmareNyx/semi-supervised-pytorch/tree/43bb86bc6757345bd7a4eb37d6948ee62a268f7e |
LsqQuan | import torch
import torch as t
import torch.utils.data
class GradScale(t.nn.Module):
def forward(self, x, scale):
y = x
y_grad = x * scale
return (y - y_grad).detach() + y_grad
class RoundPass(t.nn.Module):
def forward(self, x):
y = x.round()
y_grad = x
retu... | 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 as t
import tor... | HumberMe/lsq-net | LsqQuan | false | 556 | [
"MIT"
] | 0 | 7dcd75bff4aa7ff2d9c8a7902198fe411a38eb4c | https://github.com/HumberMe/lsq-net/tree/7dcd75bff4aa7ff2d9c8a7902198fe411a38eb4c |
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