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 |
|---|---|---|---|---|---|---|---|---|---|---|
FeatClassifier | # 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_... | pilambdagammarho/Anomaly-Detection-Benchmarking | FeatClassifier | false | 12,886 | [
"MIT"
] | 0 | 7199b703f78fcfd66268323e594a4af135c0a7e7 | https://github.com/pilambdagammarho/Anomaly-Detection-Benchmarking/tree/7199b703f78fcfd66268323e594a4af135c0a7e7 |
TV_L2Loss | import torch
import torch.nn as nn
import torch.utils.data
class TV_L2Loss(nn.Module):
def __init__(self):
super(TV_L2Loss, self).__init__()
def forward(self, x):
batch_size = x.size()[0]
h_x = x.size()[2]
w_x = x.size()[3]
count_h = self.tensor_size(x[:, :, 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
import torch.nn as nn
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C.... | alsgkals2/SRResCGAN | TV_L2Loss | false | 14,825 | [
"MIT"
] | 81 | a71201a93e1819045f9c7711743812546d3a1f31 | https://github.com/alsgkals2/SRResCGAN/tree/a71201a93e1819045f9c7711743812546d3a1f31 |
Conv2dZeros | # 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.... | Eladhi/VI_Glow | Conv2dZeros | false | 5,124 | [
"MIT"
] | 1 | 9c48fbf8fa10c81fc2354a07fcc2837a77d06cef | https://github.com/Eladhi/VI_Glow/tree/9c48fbf8fa10c81fc2354a07fcc2837a77d06cef |
AsymLoss | # 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 numpy as np
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dyna... | DoggyLiu0116/MamboNet | AsymLoss | false | 5,073 | [
"MIT"
] | 1 | 3b708091422491f660c4bd5eb12b06ce3b8a5f79 | https://github.com/DoggyLiu0116/MamboNet/tree/3b708091422491f660c4bd5eb12b06ce3b8a5f79 |
BartClassificationHead | # 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.utils.... | awslabs/gap-text2sql | BartClassificationHead | false | 14,930 | [
"Apache-2.0"
] | 75 | 83af3f08a6c108f7cbacb8125e2a7ec9255c81b0 | https://github.com/awslabs/gap-text2sql/tree/83af3f08a6c108f7cbacb8125e2a7ec9255c81b0 |
StyleResidual | import torch
from torch import nn
import torch.utils.data
import torch.optim
class StyleResidual(nn.Module):
"""Styling."""
def __init__(self, d_channel: 'int', d_style: 'int', kernel_size: 'int'=1):
super().__init__()
self.rs = nn.Conv1d(in_channels=d_style, out_channels=d_channel,
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
import torch.utils.data
import torch.optim
assert_size_stri... | niklub/NeMo | StyleResidual | false | 7,342 | [
"Apache-2.0"
] | 1 | 4bcb2321cd16835f63afe3dfe993e6d56bcf2c0c | https://github.com/niklub/NeMo/tree/4bcb2321cd16835f63afe3dfe993e6d56bcf2c0c |
ContrastiveLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert... | htn274/siamese-triplet | ContrastiveLoss | false | 10,170 | [
"BSD-3-Clause"
] | 0 | d468fb939a7ab072a0e1cf1c507a87df1a901852 | https://github.com/htn274/siamese-triplet/tree/d468fb939a7ab072a0e1cf1c507a87df1a901852 |
ImageProcessor | # 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... | alasin/vqa_pytorch | ImageProcessor | false | 6,147 | [
"MIT"
] | 1 | 8a311226d8eea56ef79f6be3c864ec05768e2895 | https://github.com/alasin/vqa_pytorch/tree/8a311226d8eea56ef79f6be3c864ec05768e2895 |
DiceWithLogitsLoss | # 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.optim
assert_size_stride = torch._C._dynamo.guards.ass... | Bobholamovic/SimpleCV | DiceWithLogitsLoss | false | 7,803 | [
"MIT"
] | 44 | f4edacf088d0155725a469e227de847820bdfa53 | https://github.com/Bobholamovic/SimpleCV/tree/f4edacf088d0155725a469e227de847820bdfa53 |
BinaryReg | import torch
import torch.nn as nn
import torch.utils.data
class BinaryReg(nn.Module):
"""Regularization for encouraging the outputs to be binary.
"""
def __init__(self, alpha=0.1):
super().__init__()
self.alpha = alpha
def forward(self, pred):
diff = pred - 0.5
diff ... | 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
... | Shray64/pytorch_connectomics | BinaryReg | false | 1,060 | [
"MIT"
] | 0 | d6c814f11ac2f8418ede5ae220a93016f50214fc | https://github.com/Shray64/pytorch_connectomics/tree/d6c814f11ac2f8418ede5ae220a93016f50214fc |
MaskedHuberLoss | # 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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_str... | gabrieleangeletti/GndNet | MaskedHuberLoss | false | 6,720 | [
"MIT"
] | 1 | 323af65c9c16a725805f480ff799936b77b04d53 | https://github.com/gabrieleangeletti/GndNet/tree/323af65c9c16a725805f480ff799936b77b04d53 |
MultiHeadLinearAttention | import torch
import torch.nn as nn
import torch.nn.functional as F
class Linear(nn.Linear):
def __init__(self, in_features, out_features, bias=True):
super(Linear, self).__init__(in_features=in_features, out_features=
out_features, bias=bias)
self.noise = None
self.vn_std = No... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | gheyret/EfficientConformer | MultiHeadLinearAttention | false | 15,438 | [
"Apache-2.0"
] | 101 | b28a0aaa3b182f72abaccbeb12df0402adf96097 | https://github.com/gheyret/EfficientConformer/tree/b28a0aaa3b182f72abaccbeb12df0402adf96097 |
MuSigmaEncoder | # 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... | deltaskelta/neural-processes | MuSigmaEncoder | false | 1,823 | [
"MIT"
] | 0 | 34a6b98b7a9142f5e5f87f7f1644217d5aa9e1bb | https://github.com/deltaskelta/neural-processes/tree/34a6b98b7a9142f5e5f87f7f1644217d5aa9e1bb |
Attention | import math
import torch
import torch.nn.functional as F
import torch.nn as nn
class Attention(nn.Module):
def __init__(self, embed_dim, hidden_dim=None, out_dim=None, n_head=1,
score_function='dot_product', dropout=0):
""" Attention Mechanism
:param embed_dim:
:param hidden_dim:
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | aquibjaved/ABSA-PyTorch | Attention | false | 9,768 | [
"MIT"
] | 0 | fd904250ceec436e49dc50694f79891c0c67d6b1 | https://github.com/aquibjaved/ABSA-PyTorch/tree/fd904250ceec436e49dc50694f79891c0c67d6b1 |
GreedyCTCDecoder | # 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... | ghdrl95/Naver-Speech-Hackathon | GreedyCTCDecoder | false | 6,737 | [
"Apache-2.0"
] | 1 | 10b4526d98ce535415cb91d24338790d9c175b63 | https://github.com/ghdrl95/Naver-Speech-Hackathon/tree/10b4526d98ce535415cb91d24338790d9c175b63 |
LayerNorm | import torch
import torch.utils.data
import torch.nn as nn
class LayerNorm(nn.Module):
def __init__(self, features, eps=1e-06, gamma=1.0, beta=0.0, learnable=
False):
super(LayerNorm, self).__init__()
if learnable:
self.gamma = nn.Parameter(torch.ones(features))
se... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.utils.data
import torch.nn as nn
assert_size_stride = torch._C._dy... | BingjieTang/DepthAwareCNN | LayerNorm | false | 164 | [
"MIT"
] | 0 | 9d72a7dc921d1dd550507018d4b51968ef89bbb7 | https://github.com/BingjieTang/DepthAwareCNN/tree/9d72a7dc921d1dd550507018d4b51968ef89bbb7 |
Bilinear | # 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... | tpimentelms/dep-parser | Bilinear | false | 10,888 | [
"MIT"
] | 0 | be622cdd9a8b0ba85a28c39129ae2cdbfef03901 | https://github.com/tpimentelms/dep-parser/tree/be622cdd9a8b0ba85a28c39129ae2cdbfef03901 |
Highway | # 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... | uwnlp/piqa | Highway | false | 16,648 | [
"Apache-2.0"
] | 89 | e18f2189c93965c94655d5cc943dcecdc2c1ea57 | https://github.com/uwnlp/piqa/tree/e18f2189c93965c94655d5cc943dcecdc2c1ea57 |
weight_quantize_fn | # 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... | MohammedHAlali/pytorch_DoReFaNet | weight_quantize_fn | false | 853 | [
"MIT"
] | 0 | d208089b9172f02c09cc6633158ed5b5d6cd7f1e | https://github.com/MohammedHAlali/pytorch_DoReFaNet/tree/d208089b9172f02c09cc6633158ed5b5d6cd7f1e |
ClassificationModel | import torch
import torch.nn as nn
class ClassificationModel(nn.Module):
def __init__(self, num_features_in, num_anchors=9, num_classes=80,
prior=0.01, feature_size=256):
super(ClassificationModel, self).__init__()
self.num_classes = num_classes
self.num_anchors = num_anchors
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | abhi1kumar/AP-loss | ClassificationModel | false | 14,750 | [
"MIT"
] | 158 | 87f51b212761ef233422dbaaf799444fb453a10e | https://github.com/abhi1kumar/AP-loss/tree/87f51b212761ef233422dbaaf799444fb453a10e |
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
import torch.nn as nn
import... | CASIA-IVA-Lab/PASS_reID | GeneralizedMeanPooling | false | 17,028 | [
"Apache-2.0"
] | 5 | 46dc6d25f4396e35ac1a766ad2dcaa580beccf15 | https://github.com/CASIA-IVA-Lab/PASS_reID/tree/46dc6d25f4396e35ac1a766ad2dcaa580beccf15 |
BertPredictionHeadTransform | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import math
from to... | Cyndi-Tokyotech/Fin_Text_Analysis_ML | BertPredictionHeadTransform | false | 11,120 | [
"MIT"
] | 0 | 7f9b6c1ea78f8e6f32c003b2de32809722df88d4 | https://github.com/Cyndi-Tokyotech/Fin_Text_Analysis_ML/tree/7f9b6c1ea78f8e6f32c003b2de32809722df88d4 |
OFLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch.nn.modules.loss import _Loss
assert_size_stride = torch._C._dy... | ezxzeng/FFB6D | OFLoss | false | 15,326 | [
"MIT"
] | 145 | fd0ea6471532ab1dc68f9a58b52d9a63f8fb76f2 | https://github.com/ezxzeng/FFB6D/tree/fd0ea6471532ab1dc68f9a58b52d9a63f8fb76f2 |
SoftGate | # 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
from torch.nn import init as init
from torchvision.models import vgg as vgg
import torch.utils.data
from torch.ut... | hyunobae/BasicSR | SoftGate | false | 12,519 | [
"Apache-2.0"
] | 0 | f2c2fc6cf28933658816c808f55c95fa20b16483 | https://github.com/hyunobae/BasicSR/tree/f2c2fc6cf28933658816c808f55c95fa20b16483 |
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... | AsianZeus/Diverse-Facial-Edit | ScaledLeakyReLU | false | 9,398 | [
"Apache-2.0"
] | 0 | 3d4b1b41546a08a1fa3cb164ade33e319806b12b | https://github.com/AsianZeus/Diverse-Facial-Edit/tree/3d4b1b41546a08a1fa3cb164ade33e319806b12b |
Mish | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.gu... | minhduc0711/labelImg | Mish | false | 12,782 | [
"MIT"
] | 0 | 5030721bb6a59424bfed1d7c09b56e01d08662a1 | https://github.com/minhduc0711/labelImg/tree/5030721bb6a59424bfed1d7c09b56e01d08662a1 |
NormalizeLinear | import math
import torch
from torch.nn import functional as F
import torch.nn as nn
import torch.nn.init as init
class NormalizeLinear(nn.Module):
def __init__(self, in_features, num_class):
super(NormalizeLinear, self).__init__()
self.weight = nn.Parameter(torch.Tensor(num_class, in_features))
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | DoubtedSteam/MPANet | NormalizeLinear | false | 7,980 | [
"MIT"
] | 25 | fe4f3f1d83c45485b1498786f89ace96c634f187 | https://github.com/DoubtedSteam/MPANet/tree/fe4f3f1d83c45485b1498786f89ace96c634f187 |
ResBlock | import torch
import torch.nn as nn
def conv3x3(in_planes, out_planes, stride=1):
"""3x3 convolution with padding"""
return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
padding=1, bias=False)
def norm(dim):
return nn.GroupNorm(min(32, dim), dim)
class ResBlock(nn.Module):
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | navaro1/parking_prediction | ResBlock | false | 12,897 | [
"MIT"
] | 0 | c532a2f75155abc9c0d4be9c955eabe368591932 | https://github.com/navaro1/parking_prediction/tree/c532a2f75155abc9c0d4be9c955eabe368591932 |
AutoregressiveShift | # 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... | alisiahkoohi/survae_flows | AutoregressiveShift | false | 14,785 | [
"MIT"
] | 262 | e1747b05524c7ab540a211ed360ab3e67bc3e96d | https://github.com/alisiahkoohi/survae_flows/tree/e1747b05524c7ab540a211ed360ab3e67bc3e96d |
ResidualSineLayer | # 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 numpy ... | khoehlein/fV-SRN | ResidualSineLayer | false | 3,839 | [
"MIT"
] | 0 | 601f3e952b090df92e875c233c2c9ca646523948 | https://github.com/khoehlein/fV-SRN/tree/601f3e952b090df92e875c233c2c9ca646523948 |
iMAE | import torch
import torch.nn as nn
class iMAE(nn.Module):
def __init__(self):
super(iMAE, self).__init__()
def forward(self, outputs, target, *args):
outputs = outputs / 1000.0
target = target / 1000.0
outputs[outputs == 0] = -1
target[target == 0] = -1
output... | 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... | anglixjtu/MSG_CHN_WACV20 | iMAE | false | 14,856 | [
"Apache-2.0"
] | 61 | 6910894cf3caed2ffde27586f96b132b0c1d1a98 | https://github.com/anglixjtu/MSG_CHN_WACV20/tree/6910894cf3caed2ffde27586f96b132b0c1d1a98 |
ShuffleBlock | # 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... | Geunwoo-Jeon/pytorch-cifar | ShuffleBlock | false | 2,340 | [
"MIT"
] | 0 | b06eeb65bbc0a4eccd124ed3c5367da70ab1ed20 | https://github.com/Geunwoo-Jeon/pytorch-cifar/tree/b06eeb65bbc0a4eccd124ed3c5367da70ab1ed20 |
ResConvGLU | import math
import torch
class Conv1d(torch.nn.Conv1d):
def __init__(self, *args, **kwargs):
super(Conv1d, self).__init__(*args, **kwargs)
def reset_parameters(self):
torch.nn.init.kaiming_normal_(self.weight, nonlinearity='relu')
if self.bias is not None:
torch.nn.init.z... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | CODEJIN/PWGAN_Torch | ResConvGLU | false | 4,945 | [
"MIT"
] | 1 | 9bef273a55d1fa24575633d6473b304418e93374 | https://github.com/CODEJIN/PWGAN_Torch/tree/9bef273a55d1fa24575633d6473b304418e93374 |
M | import torch
import torch.nn.parallel
import torch.utils.data
import torch.onnx
import torch.fx
import torch.optim
import torch.utils.data.distributed
class M(torch.nn.Module):
def __init__(self):
super().__init__()
def forward(self, x, y):
y = torch.cat([x, y])
return y
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
import torch.nn.parallel
import torch.utils.data
import torch.onnx
import torch.fx
import torch.optim
import torch.utils.data.distributed
as... | lenaguignard/examples | M | false | 15,889 | [
"BSD-3-Clause"
] | 19,783 | 973e77b725a6028289a90170f0b237ea2e71d4f2 | https://github.com/lenaguignard/examples/tree/973e77b725a6028289a90170f0b237ea2e71d4f2 |
DDPG | # 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 | DDPG | false | 130 | [
"MIT"
] | 0 | 436538f49ee505e14672a67ba3c1f60886cbbea8 | https://github.com/BLUECARVIN/RL_baseline/tree/436538f49ee505e14672a67ba3c1f60886cbbea8 |
MultiHeadAttention | import torch
import torch.nn.functional as F
import torch.nn as nn
class MultiHeadAttention(nn.Module):
def __init__(self, in_dim, out_dim, out_heads, relation_dim=0, residual
=False, projection=True, layer_norm=True):
super().__init__()
self.in_dim = in_dim
self.out_dim = out_dim... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | PaParaZz1/DI-engine | MultiHeadAttention | false | 11,864 | [
"Apache-2.0"
] | 0 | b38144117c1ebc6eb860d8637ec8866dfbcdf2de | https://github.com/PaParaZz1/DI-engine/tree/b38144117c1ebc6eb860d8637ec8866dfbcdf2de |
ResizeConv | import torch
from torch import nn
import torch.utils.data
def get_conv(dim=3):
"""Chooses an implementation for a convolution layer."""
if dim == 3:
return nn.Conv3d
elif dim == 2:
return nn.Conv2d
else:
raise ValueError('dim has to be 2 or 3')
def planar_kernel(x):
"""Re... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
import torch.utils.data
assert_size_stride = torch._C._dyna... | ELEKTRONN/elektronn3 | ResizeConv | false | 13,629 | [
"MIT"
] | 124 | 19c751855dffc67b744cd43e757aa4a5bd577d9b | https://github.com/ELEKTRONN/elektronn3/tree/19c751855dffc67b744cd43e757aa4a5bd577d9b |
MultiHeadAttention | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
class ScaledDotProductAttention(nn.Module):
def __init__(self):
super(ScaledDotProductAttention, self).__init__()
def forward(self, query, key, value, mask=None):
_1, _2, query_sequence_length, _3 = query.size()
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | SeungoneKim/Transformer_implementation | MultiHeadAttention | false | 1,043 | [
"Apache-2.0"
] | 0 | a52bf552eb645fc9bfb812cc26842fc147d6c008 | https://github.com/SeungoneKim/Transformer_implementation/tree/a52bf552eb645fc9bfb812cc26842fc147d6c008 |
CosNorm_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.triton_helpers import libdevice
import math
import ... | caisarl76/BalancedMetaSoftmax-Classification | CosNorm_Classifier | false | 3,260 | [
"BSD-3-Clause"
] | 0 | 48b9c8af19de261d95a5ef38f5780cbadf7bb64b | https://github.com/caisarl76/BalancedMetaSoftmax-Classification/tree/48b9c8af19de261d95a5ef38f5780cbadf7bb64b |
Highway | # 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... | Emotional-Text-to-Speech/tacotron_pytorch | Highway | false | 5,136 | [
"MIT"
] | 1 | e6b1a3907afb01fe31bcbd77c677667adf6733f5 | https://github.com/Emotional-Text-to-Speech/tacotron_pytorch/tree/e6b1a3907afb01fe31bcbd77c677667adf6733f5 |
IOUloss | import torch
import torch.nn as nn
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[0] == target.shape[0]
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | JJLimmm/YOLOx | IOUloss | false | 9,316 | [
"Apache-2.0"
] | 0 | 85fdb819be84dfec3a8306cb74872a1c0ef28e3e | https://github.com/JJLimmm/YOLOx/tree/85fdb819be84dfec3a8306cb74872a1c0ef28e3e |
ReQUNet | # 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.... | RoyHirsch/DeepLearningCourse | ReQUNet | false | 1,002 | [
"MIT"
] | 0 | 9036c0fdbb08b610524d7be991f8e4b490a82c6c | https://github.com/RoyHirsch/DeepLearningCourse/tree/9036c0fdbb08b610524d7be991f8e4b490a82c6c |
SimpleGeluModule | import torch
import torch.nn.functional as F
import torch.jit
import torch.onnx
import torch.nn
class SimpleGeluModule(torch.nn.Module):
def forward(self, tensor):
return F.gelu(tensor + tensor)
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 libdevice
import torch.jit
import torch.onnx
import torch.nn
assert_size_stride = torch._... | andreas-hommel/glow | SimpleGeluModule | false | 3,333 | [
"Apache-2.0"
] | 0 | 2bbbf8188a2a941e85677c83f2146bbd076a262e | https://github.com/andreas-hommel/glow/tree/2bbbf8188a2a941e85677c83f2146bbd076a262e |
CFRB | # 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 collections import Order... | hduba/KAIR | CFRB | false | 3,622 | [
"MIT"
] | 0 | dbd7596c7e4a4667b9b7baac369fc6c02571fa58 | https://github.com/hduba/KAIR/tree/dbd7596c7e4a4667b9b7baac369fc6c02571fa58 |
BinaryLoss | # 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
... | PengJingchao/DFNet | BinaryLoss | false | 941 | [
"MIT"
] | 0 | 49e83501f81515aebca211351e315896da7afc54 | https://github.com/PengJingchao/DFNet/tree/49e83501f81515aebca211351e315896da7afc54 |
RingLoss | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import warnings
import torch.nn as nn
from torchvision.transforms import *
asse... | theodorhusefest/ABD-Net | RingLoss | false | 13,035 | [
"MIT"
] | 0 | 4ad71205954726b88d081ca079c28378f74e3007 | https://github.com/theodorhusefest/ABD-Net/tree/4ad71205954726b88d081ca079c28378f74e3007 |
ActorCritic | # 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.... | Johnsonms/NNI_master | ActorCritic | false | 11,593 | [
"MIT"
] | 0 | e5e5c7aed89cf3189cffe1056464833c15eb54ff | https://github.com/Johnsonms/NNI_master/tree/e5e5c7aed89cf3189cffe1056464833c15eb54ff |
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 ... | Quallle/TransUNet | StdConv2d | false | 9,439 | [
"Apache-2.0"
] | 0 | cf62a2a021e096c105b3fc62958a1eeb231e7a8f | https://github.com/Quallle/TransUNet/tree/cf62a2a021e096c105b3fc62958a1eeb231e7a8f |
UpSampleConv | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_st... | MIC-DKFZ/mood | UpSampleConv | false | 8,507 | [
"Apache-2.0"
] | 42 | a01303adb4256653b133e2f7cd4741d366b681f7 | https://github.com/MIC-DKFZ/mood/tree/a01303adb4256653b133e2f7cd4741d366b681f7 |
injective_pad | import torch
import torch.nn as nn
class injective_pad(nn.Module):
def __init__(self, pad_size):
super(injective_pad, self).__init__()
self.pad_size = pad_size
self.pad = nn.ZeroPad2d((0, 0, 0, pad_size))
def forward(self, x):
x = x.permute(0, 2, 1, 3)
x = self.pad(x)... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | Schwartz-Zha/My-invertible-resnet | injective_pad | false | 1,034 | [
"MIT"
] | 0 | 5415975bb0d640f3bf3ef4a7b986563e84109270 | https://github.com/Schwartz-Zha/My-invertible-resnet/tree/5415975bb0d640f3bf3ef4a7b986563e84109270 |
qy | import torch
import torch.nn.functional as F
import torch.nn as nn
class qy(nn.Module):
def __init__(self, d_dim, x_dim, y_dim, z_dim):
super(qy, self).__init__()
self.fc1 = nn.Linear(z_dim, y_dim)
torch.nn.init.xavier_uniform_(self.fc1.weight)
self.fc1.bias.data.zero_()
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._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | sautami26/DIVA | qy | false | 4,262 | [
"MIT"
] | 0 | 52af683db216cb6e2ac777597fd9ec744ce7c8f2 | https://github.com/sautami26/DIVA/tree/52af683db216cb6e2ac777597fd9ec744ce7c8f2 |
SimpleReciprocalModel | # 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.jit
import torch.onnx
import torch.nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torc... | opti-mix/glow | SimpleReciprocalModel | false | 7,410 | [
"Apache-2.0"
] | 1 | 4ba074df5da9822986a23a6679ab592c22660f6d | https://github.com/opti-mix/glow/tree/4ba074df5da9822986a23a6679ab592c22660f6d |
VGGNet | import torch
import torch.nn as nn
import torch.nn.functional as F
class VGGNet(nn.Module):
def __init__(self):
super(VGGNet, self).__init__()
self.conv1 = nn.Conv2d(3, 32, kernel_size=(3, 3), stride=(1, 1),
padding=(1, 1))
self.conv2 = nn.Conv2d(32, 32, kernel_size=(3, 3), st... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | miyosuda/oculomotor | VGGNet | false | 7,295 | [
"Apache-2.0"
] | 1 | 78e7ec61a808d058116c69bff1ea71ecf117c126 | https://github.com/miyosuda/oculomotor/tree/78e7ec61a808d058116c69bff1ea71ecf117c126 |
BCEDiceLoss | # 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... | MIPT-Oulu/Collagen | BCEDiceLoss | false | 17,666 | [
"MIT"
] | 4 | 0cbc4285d60e5c9fcc89f629fcf4321e80b7452c | https://github.com/MIPT-Oulu/Collagen/tree/0cbc4285d60e5c9fcc89f629fcf4321e80b7452c |
triplet_my_loss | # 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... | Xavierxhq/fruit_identification | triplet_my_loss | false | 5,989 | [
"MIT"
] | 1 | 54cdf2c3e0aad26ae98b081e44ad1655b6f0a758 | https://github.com/Xavierxhq/fruit_identification/tree/54cdf2c3e0aad26ae98b081e44ad1655b6f0a758 |
HardSwish | import torch
import torch.nn as nn
import torch.nn.functional as F
class HardSwish(nn.Module):
def __init__(self, inplace=True):
super(HardSwish, self).__init__()
self.inplace = inplace
def forward(self, x):
return x * F.relu6(x + 3.0, inplace=self.inplace) / 6.0
def get_inputs():
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | Culturenotes/Network-Slimming | HardSwish | false | 7,914 | [
"Apache-2.0"
] | 12 | 9004ab4c1f6bcbf8f317a37984ed3f8db39ecbe2 | https://github.com/Culturenotes/Network-Slimming/tree/9004ab4c1f6bcbf8f317a37984ed3f8db39ecbe2 |
PSNR | import torch
import torch.nn as nn
import torch.nn.functional as F
class PSNR(nn.Module):
def __init__(self, max_val=1.0, mode='Y'):
super(PSNR, self).__init__()
self.max_val = max_val
self.mode = mode
def forward(self, x, y):
if self.mode == 'Y' and x.shape[1] == 3 and y.sha... | 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... | S-aiueo32/srntt-pytorch | PSNR | false | 14,372 | [
"Apache-2.0"
] | 88 | 4ea0aa22a54a2d1b1f19c4a43596a693b9e7c067 | https://github.com/S-aiueo32/srntt-pytorch/tree/4ea0aa22a54a2d1b1f19c4a43596a693b9e7c067 |
CNN | import torch
import torch.nn as nn
import torch.nn.functional as F
class CNN(nn.Module):
def __init__(self, input_size=50, hidden_size=256, dropout=0,
kernel_size=3, padding=1, activation_function=F.relu):
"""
Args:
input_size: dimention of input embedding
kernel_s... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import ... | igorvlnascimento/DeepREF | CNN | false | 3,649 | [
"MIT"
] | 0 | 0fed8120571e44e12ee3d1861289bc101c0a275f | https://github.com/igorvlnascimento/DeepREF/tree/0fed8120571e44e12ee3d1861289bc101c0a275f |
ResBlk | # 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.... | UdonDa/StarGAN-v2-pytorch-nonofficial | ResBlk | false | 18,050 | [
"MIT"
] | 9 | 219df6b7fd4bd533686e2093ee914a337914ca9b | https://github.com/UdonDa/StarGAN-v2-pytorch-nonofficial/tree/219df6b7fd4bd533686e2093ee914a337914ca9b |
CNNAutoencoder | # 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_... | mariajmolina/ML-for-S2S | CNNAutoencoder | false | 10,582 | [
"MIT"
] | 0 | 3de32e72042ba7e8b37a433579fa9c5630246d8c | https://github.com/mariajmolina/ML-for-S2S/tree/3de32e72042ba7e8b37a433579fa9c5630246d8c |
DQN | from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
import torch.nn.functional as F
class DQN(nn.Module):
def __init__(self, args):
super().__init__()
self.state_space = args.state_space
self.fc1 = nn.Linear(self.state_space, args.hidden_size)
self.fc2... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | wotmd5731/pseudo_random_gen | DQN | false | 4,547 | [
"MIT"
] | 0 | f79810cd5ac79afe0a73dee73aa21bd8c01aeb9b | https://github.com/wotmd5731/pseudo_random_gen/tree/f79810cd5ac79afe0a73dee73aa21bd8c01aeb9b |
MultiHeadAttn | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | aalto-speech/FinnishXL | MultiHeadAttn | false | 6,077 | [
"Apache-2.0"
] | 1 | 42afe376162dd08d5eaa0639aed4221fa3db4cc2 | https://github.com/aalto-speech/FinnishXL/tree/42afe376162dd08d5eaa0639aed4221fa3db4cc2 |
Conv2dStaticSamePadding | import math
import torch
from torch import nn
from torch.nn import functional as F
class Conv2dStaticSamePadding(nn.Module):
"""
created by Zylo117
The real keras/tensorflow conv2d with same padding
"""
def __init__(self, in_channels, out_channels, kernel_size, stride=1,
bias=False, group... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import 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... | tujikuangmo/FishNet | Conv2dStaticSamePadding | false | 13,050 | [
"MIT"
] | 0 | 1c2f7112639416bd12a02585a9e04e1d05960520 | https://github.com/tujikuangmo/FishNet/tree/1c2f7112639416bd12a02585a9e04e1d05960520 |
LocalDiscrepancy | # 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.... | BIT-DA/RIPU | LocalDiscrepancy | false | 16,980 | [
"MIT"
] | 9 | 125edf112c9ded1e7497aedb2a092331824df100 | https://github.com/BIT-DA/RIPU/tree/125edf112c9ded1e7497aedb2a092331824df100 |
NeuralNetNonDifferentiableOutput | # 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
import torch.... | TingGong1/onnxruntime | NeuralNetNonDifferentiableOutput | false | 5,902 | [
"MIT"
] | 1 | 435010ab6873974803591fa22262ed8b3e36e44d | https://github.com/TingGong1/onnxruntime/tree/435010ab6873974803591fa22262ed8b3e36e44d |
HLCriterion | import torch
import torch.nn.functional as F
from torch.nn.modules.loss import _Loss
from torch.optim.lr_scheduler import *
class Criterion(_Loss):
def __init__(self, alpha=1.0, name='criterion'):
super().__init__()
"""Alpha is used to weight each loss term
"""
self.alpha = alpha
... | 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.... | kiminh/mt-dnn | HLCriterion | false | 7,030 | [
"MIT"
] | 1 | 133884b380244dbe74acc4d7507e551b2c5035b3 | https://github.com/kiminh/mt-dnn/tree/133884b380244dbe74acc4d7507e551b2c5035b3 |
FBetaLoss | import torch
import torch.nn as nn
class FBetaLoss(nn.Module):
def __init__(self, beta=1):
super(FBetaLoss, self).__init__()
self.eps = 1e-08
self.beta = beta
self.beta2 = beta ** 2
return
def forward(self, inputs, target):
inputs = torch.sigmoid(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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | quqixun/ECG-MLC | FBetaLoss | false | 10,729 | [
"MIT"
] | 0 | 582d68200b79e3b2ac322c1ed17630727e283605 | https://github.com/quqixun/ECG-MLC/tree/582d68200b79e3b2ac322c1ed17630727e283605 |
ChannelWiseDivergence | # 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... | HIT-cwh/mmrazor | ChannelWiseDivergence | false | 13,784 | [
"Apache-2.0"
] | 553 | 2dad24044d7f1dad88f20221f8fc071dd40fdd4f | https://github.com/HIT-cwh/mmrazor/tree/2dad24044d7f1dad88f20221f8fc071dd40fdd4f |
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.... | tkhkaeio/PyTorch-GAN | Net | false | 10,882 | [
"MIT"
] | 0 | 565c67cae168a42c6822c787562a1f7a5b35a2ab | https://github.com/tkhkaeio/PyTorch-GAN/tree/565c67cae168a42c6822c787562a1f7a5b35a2ab |
ClassHead | import torch
import torch.nn as nn
from itertools import product as product
class ClassHead(nn.Module):
def __init__(self, inchannels=512, num_anchors=2):
super(ClassHead, self).__init__()
self.num_anchors = num_anchors
self.conv1x1 = nn.Conv2d(inchannels, self.num_anchors * 2,
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
from itertools import product as product
assert_size_strid... | chennnnnnnnn/face_detection | ClassHead | false | 3,348 | [
"MIT"
] | 0 | 77d5a9098d9e1a65ac5093a23620ed5d99dc0723 | https://github.com/chennnnnnnnn/face_detection/tree/77d5a9098d9e1a65ac5093a23620ed5d99dc0723 |
Net | import torch
import torch.nn as nn
class FcCat(nn.Module):
def __init__(self, nIn, nOut):
super(FcCat, self).__init__()
self.fc = nn.Linear(nIn, nOut, bias=False)
def forward(self, x):
out = torch.cat((x, self.fc(x)), 1)
return out
class Net(nn.Module):
def __init__(se... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | zwh930712/densenet.pytorch | Net | false | 16,836 | [
"Apache-2.0"
] | 826 | d1cd5e1957975628286e516512c6d1c14430f810 | https://github.com/zwh930712/densenet.pytorch/tree/d1cd5e1957975628286e516512c6d1c14430f810 |
CNN | # 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... | anukaal/opytimizer | CNN | false | 14,901 | [
"Apache-2.0"
] | 528 | 5f1ccc0da80e6a4cabd99578fa24cf4f6466f9b9 | https://github.com/anukaal/opytimizer/tree/5f1ccc0da80e6a4cabd99578fa24cf4f6466f9b9 |
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.... | chunglabmit/phathom | Classifier | false | 6,448 | [
"MIT"
] | 1 | 304db7a95e898e9b03d6b2640172752d21a7e3ed | https://github.com/chunglabmit/phathom/tree/304db7a95e898e9b03d6b2640172752d21a7e3ed |
MeanPooling | import torch
from torch import nn
class MeanPooling(nn.Module):
def __init__(self):
super(MeanPooling, self).__init__()
def forward(self, doc_state, entity_mapping, entity_lens):
entity_states = entity_mapping.unsqueeze(3) * doc_state.unsqueeze(1)
mean_pooled = torch.sum(entity_state... | 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... | HLTCHKUST/MulQG | MeanPooling | false | 8,185 | [
"MIT"
] | 19 | 8e257f2d6c0f03c07ea8a0bf0e8f55b0cde60605 | https://github.com/HLTCHKUST/MulQG/tree/8e257f2d6c0f03c07ea8a0bf0e8f55b0cde60605 |
myFeature | import torch
class myFeature(torch.nn.Module):
"""
Feature: sin(x)
"""
def __init__(self):
super(myFeature, self).__init__()
def forward(self, x):
return torch.sin(x[:, 0] * torch.pi) * torch.sin(x[:, 1] * torch.pi)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def ... | 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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_str... | ndem0/PINA | myFeature | false | 10,716 | [
"MIT"
] | 0 | 1812ddb8d96a9c8aeb80ce35002dbd115e7d7931 | https://github.com/ndem0/PINA/tree/1812ddb8d96a9c8aeb80ce35002dbd115e7d7931 |
SDNE_layer | import torch
import torch.utils.data
import torch.nn as nn
import torch.nn.functional as F
class SDNE_layer(nn.Module):
def __init__(self, num_node, hidden_size1, hidden_size2, droput, alpha,
beta, nu1, nu2):
super(SDNE_layer, self).__init__()
self.num_node = num_node
self.hidden_... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | ChengzhiPiao/cogdl | SDNE_layer | false | 5,017 | [
"MIT"
] | 1 | 182e0b95b3dfbe771570037c58aacd8f677b6500 | https://github.com/ChengzhiPiao/cogdl/tree/182e0b95b3dfbe771570037c58aacd8f677b6500 |
FuseLayer | from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
class FuseLayer(nn.Module):
def __init__(self, config):
super().__init__()
self.linear1 = nn.Linear(4 * config.hidden_size, config.hidden_size)
self.linear2 = nn.Linear(4 * config.hidden_size, config.hidden_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_... | qinyiwei/MuTual | FuseLayer | false | 4,165 | [
"MIT"
] | 0 | 3bdd13c1388d6136b8944666dfd434870760cc93 | https://github.com/qinyiwei/MuTual/tree/3bdd13c1388d6136b8944666dfd434870760cc93 |
Cartesian | import torch
from torch import nn
import torch.utils.data
import torch.utils.data.distributed
import torch.optim
class Cartesian(nn.Module):
def forward(self, x):
r, phi = x[..., 0], x[..., 1]
return torch.stack((r * torch.cos(phi), r * torch.sin(phi)), dim=-1)
def get_inputs():
return [tor... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch import nn
import torch.utils.data
import torch.utils.data.dist... | kapoor1992/fastMRI | Cartesian | false | 10,498 | [
"MIT"
] | 0 | 6b0af94663faa55a2dd901a6a5cbb7d7b5f4cf6d | https://github.com/kapoor1992/fastMRI/tree/6b0af94663faa55a2dd901a6a5cbb7d7b5f4cf6d |
context_embedding | import torch
import torch.nn.functional as F
class CausalConv1d(torch.nn.Conv1d):
def __init__(self, in_channels, out_channels, kernel_size, stride=1,
dilation=1, groups=1, bias=True):
super(CausalConv1d, self).__init__(in_channels, out_channels,
kernel_size=kernel_size, stride=stride... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.fun... | xingtaodhu/logdeep | context_embedding | false | 10,995 | [
"MIT"
] | 0 | 9626fa4b3345799940cb293c7aedb34dd33b5637 | https://github.com/xingtaodhu/logdeep/tree/9626fa4b3345799940cb293c7aedb34dd33b5637 |
SimpleSliceModel | # 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.onnx
import torch.nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guar... | mlupon/glow | SimpleSliceModel | false | 4,020 | [
"Apache-2.0"
] | 0 | aedaa7b98617f1a2db651608e7f7c916a7d2c766 | https://github.com/mlupon/glow/tree/aedaa7b98617f1a2db651608e7f7c916a7d2c766 |
Attention | import torch
from torch import nn
class Attention(nn.Module):
def __init__(self, feature_dim, max_seq_len=70):
super().__init__()
self.attention_fc = nn.Linear(feature_dim, 1)
self.bias = nn.Parameter(torch.zeros(1, max_seq_len, 1,
requires_grad=True))
def forward(self, r... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
fr... | tanreinama/jigsaw_unintendedbiasclassification_validation_model | Attention | false | 10,817 | [
"Apache-2.0"
] | 0 | af1644488e0d0f7d54ce5d8186ae38a8b079b2db | https://github.com/tanreinama/jigsaw_unintendedbiasclassification_validation_model/tree/af1644488e0d0f7d54ce5d8186ae38a8b079b2db |
SpatialPyramidPooling | # 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... | BDeMo/yolov4-pytorch | SpatialPyramidPooling | false | 2,009 | [
"MIT"
] | 0 | 2434afc88d0890bdb19c5655bb7c577d22bf18d3 | https://github.com/BDeMo/yolov4-pytorch/tree/2434afc88d0890bdb19c5655bb7c577d22bf18d3 |
GlobalAttention | # 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.... | BoonthichaSaejia/ThaiSum | GlobalAttention | false | 7,829 | [
"Apache-2.0"
] | 23 | fdb99eab23e60a933acf4e84836f53ddf05b7c8b | https://github.com/BoonthichaSaejia/ThaiSum/tree/fdb99eab23e60a933acf4e84836f53ddf05b7c8b |
VariableSelectionNetwork | # 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.... | OneToolsCollection/4paradigm-AutoX | VariableSelectionNetwork | false | 9,370 | [
"Apache-2.0"
] | 0 | f8e838021354de17f5bb9bc44e9d68d12dda6427 | https://github.com/OneToolsCollection/4paradigm-AutoX/tree/f8e838021354de17f5bb9bc44e9d68d12dda6427 |
ShuffleCatAlt | import torch
import torch.nn as nn
class ShuffleCatAlt(nn.Module):
def forward(self, a, b):
assert a.size() == b.size()
n, c, h, w = a.size()
x = torch.zeros(n, c * 2, h, w, dtype=a.dtype, device=a.device)
x[:, ::2] = a
x[:, 1::2] = b
return x
def get_inputs():
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | tony23545/yolact_edge | ShuffleCatAlt | false | 10,915 | [
"MIT"
] | 0 | 11840512ab46f22dce6aea37a7823110175adffa | https://github.com/tony23545/yolact_edge/tree/11840512ab46f22dce6aea37a7823110175adffa |
NumPredictor | import torch
import torch.nn.functional as F
import torch.nn as nn
class NumPredictor(nn.Module):
def __init__(self, latent_dim):
self.latent_dim = latent_dim
super(NumPredictor, self).__init__()
self.reg_1 = nn.Linear(self.latent_dim, 1)
def forward(self, x):
x = F.relu(self... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | alibaba/FederatedScope | NumPredictor | false | 18,261 | [
"Apache-2.0"
] | 9 | fcf6d237624769ea094cfd68803901622f14fc23 | https://github.com/alibaba/FederatedScope/tree/fcf6d237624769ea094cfd68803901622f14fc23 |
NeuralNet | import torch
import torch.nn as nn
class NeuralNet(nn.Module):
def __init__(self, input_size, hidden_size, num_classes):
super(NeuralNet, self).__init__()
self.l1 = nn.Linear(input_size, hidden_size)
self.l2 = nn.Linear(hidden_size, hidden_size)
self.l3 = nn.Linear(hidden_size, nu... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | AlejandroE25/pytorch-chatbot | NeuralNet | false | 8,836 | [
"MIT"
] | 0 | b9d7926f2f897f3a8513e8796b38f928715738af | https://github.com/AlejandroE25/pytorch-chatbot/tree/b9d7926f2f897f3a8513e8796b38f928715738af |
BertAttention | from _paritybench_helpers import _mock_config
import math
import torch
from torch import nn
class BertLayerNorm(nn.Module):
def __init__(self, hidden_size, eps=1e-12):
"""Construct a layernorm module in the TF style (epsilon inside the square root).
"""
super(BertLayerNorm, self).__init__... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Sy-Zhang/recurrent-transformer | BertAttention | false | 11,129 | [
"MIT"
] | 0 | f66ba49a2c9ec42759d3d00d497b49ffe39e18de | https://github.com/Sy-Zhang/recurrent-transformer/tree/f66ba49a2c9ec42759d3d00d497b49ffe39e18de |
BasicConv2d | # 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... | JinkaiZheng/TraND | BasicConv2d | false | 8,344 | [
"MIT"
] | 33 | a8babc34073ee126789969bd97e149bae4015953 | https://github.com/JinkaiZheng/TraND/tree/a8babc34073ee126789969bd97e149bae4015953 |
MAPELoss | import torch
import torch.nn as nn
class MAPELoss(nn.Module):
def __init__(self, eps=1e-08):
super(MAPELoss, self).__init__()
self.eps = eps
def forward(self, y_hat, y):
return torch.mean(torch.abs(y - y_hat) / torch.abs(y + self.eps)) * 100
def get_inputs():
return [torch.rand... | 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
... | cerisara/weibull-knowledge-informed-ml | MAPELoss | false | 1,643 | [
"MIT"
] | 0 | 19017817f5324fb1fffd8322d2d3567a6271948c | https://github.com/cerisara/weibull-knowledge-informed-ml/tree/19017817f5324fb1fffd8322d2d3567a6271948c |
TAM | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
import torch
import torch.nn.functional as F
class SEModule(nn.Module):
def __init__(self, channels, dw_conv):
super().__init__()
ks = 1
pad = (ks - 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 ... | ZijiaLewisLu/action-recognition-pytorch | TAM | false | 14,739 | [
"Apache-2.0"
] | 149 | 6ee04ed249081eb0d8e1b4a3e7a5c11fa65b8d70 | https://github.com/ZijiaLewisLu/action-recognition-pytorch/tree/6ee04ed249081eb0d8e1b4a3e7a5c11fa65b8d70 |
VAE | import torch
from torch import nn
from torch.nn import functional as F
import torch.utils.data
class Decoder(nn.Module):
""" VAE decoder """
def __init__(self, in_channels, latent_size):
super(Decoder, self).__init__()
self.latent_size = latent_size
self.in_channels = in_channels
... | 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._inductor.runtime import triton_helpers
from... | Adwaver4157/WorldModel_for_FinRL | VAE | false | 4,814 | [
"MIT"
] | 1 | 0aa0a984aadffe0f6f2e83e55678c0e9304fba05 | https://github.com/Adwaver4157/WorldModel_for_FinRL/tree/0aa0a984aadffe0f6f2e83e55678c0e9304fba05 |
AttentionCrossEntropy | import torch
import torch.nn as nn
import torch.nn.functional as F
class AttentionCrossEntropy(nn.Module):
def __init__(self):
super(AttentionCrossEntropy, self).__init__()
def forward(self, input, target):
cross_loss = torch.mul(target.float(), F.log_softmax(input, dim=1))
loss = to... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | LindgeW/sentiment-analysis-based-on-attention | AttentionCrossEntropy | false | 8,444 | [
"Apache-2.0"
] | 13 | 82ea37c8ef84eec56082d60001b1179b4c12f416 | https://github.com/LindgeW/sentiment-analysis-based-on-attention/tree/82ea37c8ef84eec56082d60001b1179b4c12f416 |
Model | import torch
import torch.nn.functional as F
from torch import nn
class Model(nn.Module):
def __init__(self, n_input: 'int', state_dict=None):
super(Model, self).__init__()
self.n_input = n_input
self.fc = nn.Linear(n_input, 20)
self.output = nn.Linear(20, 1)
nn.init.xavie... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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... | y-kamiya/devnet | Model | false | 4,602 | [
"MIT"
] | 0 | f9562c97e1025949b48d433bd9f2114e56ac67e4 | https://github.com/y-kamiya/devnet/tree/f9562c97e1025949b48d433bd9f2114e56ac67e4 |
SRNet | import torch
import torch.nn as nn
import torch.optim
class SRNet(nn.Module):
def __init__(self):
super(SRNet, self).__init__()
self.relu = nn.ReLU(inplace=True)
self.Conv1 = nn.Conv2d(3, 64, 3, 1, 1, bias=True)
self.Conv2 = nn.Conv2d(64, 64, 3, 1, 1, bias=True)
self.Conv3... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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 ... | MayankSingal/PyTorch-Zero-Shot-Super-Resolution | SRNet | false | 5,590 | [
"MIT"
] | 1 | 3521b02fd338fc90eef88c551a8bed4afc54c8c6 | https://github.com/MayankSingal/PyTorch-Zero-Shot-Super-Resolution/tree/3521b02fd338fc90eef88c551a8bed4afc54c8c6 |
PairCosineSim | import torch
import torch.utils.data
import torch.nn as nn
class PairCosineSim(nn.Module):
def __init__(self):
super(PairCosineSim, self).__init__()
def forward(self, supports, target):
"""
Calculates pairwise cosine similarity of support sets with target sample.
:param supp... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | SamujjwalSam/MatchingNetworks4XC | PairCosineSim | false | 1,026 | [
"MIT"
] | 0 | 2519cc1a527ea121c4966c1a860d890d5182f887 | https://github.com/SamujjwalSam/MatchingNetworks4XC/tree/2519cc1a527ea121c4966c1a860d890d5182f887 |
GRUCell | # 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
... | YilliaJing/PM2.5-GNN | GRUCell | false | 14,692 | [
"MIT"
] | 91 | 7aacc6b6b9562ad2a9dad6197e6c4d73607ebdf2 | https://github.com/YilliaJing/PM2.5-GNN/tree/7aacc6b6b9562ad2a9dad6197e6c4d73607ebdf2 |
Critic | # 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_... | KhalilWong/Learn-RL | Critic | false | 2,462 | [
"MIT"
] | 0 | 9f63c5adafab1413362366d28d8711096ce6648c | https://github.com/KhalilWong/Learn-RL/tree/9f63c5adafab1413362366d28d8711096ce6648c |
TinyCnn | # 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_... | ngduduong/captum | TinyCnn | false | 4,084 | [
"BSD-3-Clause"
] | 0 | 6fe5f0f23ea975e73e0c0dee79bdc01b4223d283 | https://github.com/ngduduong/captum/tree/6fe5f0f23ea975e73e0c0dee79bdc01b4223d283 |
MemoryReader | import math
import torch
import torch.nn
import torch.nn.functional as F
import torch.utils.data.dataset
class MemoryReader(torch.nn.Module):
def __init__(self):
super(MemoryReader, self).__init__()
def forward(self, m_key, m_val, q_key, q_val):
B, D_e, T, H, W = m_key.size()
_, D_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._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | hzxie/RMNet | MemoryReader | false | 15,570 | [
"MIT"
] | 66 | 32a16f9c9473463a41dd6e95f72b06dd830fc1eb | https://github.com/hzxie/RMNet/tree/32a16f9c9473463a41dd6e95f72b06dd830fc1eb |
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