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 |
|---|---|---|---|---|---|---|---|---|---|---|
QNet | # 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... | Lovestarni/Reinforcement-learning-with-tensorflow | QNet | false | 9,288 | [
"MIT"
] | 0 | 822a4ae812b044687c11138ef9c9db1e1190f98c | https://github.com/Lovestarni/Reinforcement-learning-with-tensorflow/tree/822a4ae812b044687c11138ef9c9db1e1190f98c |
CQAttention | # 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.... | timgianitsos/squad | CQAttention | false | 13,197 | [
"MIT"
] | 0 | 6ab502652e3528cfeeddfb8eba05221443a35294 | https://github.com/timgianitsos/squad/tree/6ab502652e3528cfeeddfb8eba05221443a35294 |
SmoothL1Loss | import functools
import torch
import torch.nn as nn
import torch.nn.functional as F
def reduce_loss(loss, reduction):
"""Reduce loss as specified.
Args:
loss (Tensor): Elementwise loss tensor.
reduction (str): Options are "none", "mean" and "sum".
Return:
Tensor: Reduced loss ten... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import functools
impor... | AtticusJohnson/mmdetection | SmoothL1Loss | false | 11,245 | [
"Apache-2.0"
] | 0 | d8d89bafcce13d3b32b1fb3366be3bb9830546c2 | https://github.com/AtticusJohnson/mmdetection/tree/d8d89bafcce13d3b32b1fb3366be3bb9830546c2 |
MultiHeadAttentionBlock | # 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.... | EGO4D/episodic-memory | MultiHeadAttentionBlock | false | 8,121 | [
"MIT"
] | 27 | 2a3464882cd4f665c358c1b05a6397339e33c2e1 | https://github.com/EGO4D/episodic-memory/tree/2a3464882cd4f665c358c1b05a6397339e33c2e1 |
SReLU | import torch
from torch import nn
from torch.nn.parameter import Parameter
class SReLU(nn.Module):
"""
SReLU (S-shaped Rectified Linear Activation Unit): a combination of three linear functions, which perform mapping R → R with the following formulation:
.. math::
h(x_i) = \\left\\{\\begin{matri... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
from torch.nn.parameter import Parameter
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_... | THEFASHIONGEEK/Echo | SReLU | false | 11,911 | [
"MIT"
] | 0 | 8dcf279ca528f2bfd255f79de07c1a221512c6a0 | https://github.com/THEFASHIONGEEK/Echo/tree/8dcf279ca528f2bfd255f79de07c1a221512c6a0 |
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.utils.data
from tor... | WoojunePark/BasicSR | ToRGB | false | 18,121 | [
"Apache-2.0"
] | 9 | e0910b022b924bb913045fc412a5470dc2242cf0 | https://github.com/WoojunePark/BasicSR/tree/e0910b022b924bb913045fc412a5470dc2242cf0 |
Conv | # 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... | LindaCY/fastNLP | Conv | false | 17,624 | [
"Apache-2.0"
] | 4 | 3fa95b6cfc31211453bc21792e3eef87948858da | https://github.com/LindaCY/fastNLP/tree/3fa95b6cfc31211453bc21792e3eef87948858da |
BCEDiceLoss | import torch
import torch.nn as nn
import torch.nn.functional as F
def dice_loss(smooth=1):
"""Create Dice Loss.
Args:
smooth (float, optional): Smoothing value. A larger
smooth value (also known as Laplace smooth, or
Additive smooth) can be used to avoid overfitting.
... | 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... | Pandinosaurus/Depth-Estimation-Segmentation | BCEDiceLoss | false | 17,801 | [
"MIT"
] | 4 | 2eea883c96bf106774ea94464fc16c6baea86a95 | https://github.com/Pandinosaurus/Depth-Estimation-Segmentation/tree/2eea883c96bf106774ea94464fc16c6baea86a95 |
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.... | Columbine21/PyTorch-NLP | Attention | false | 8,907 | [
"BSD-3-Clause"
] | 0 | 63460d0951a0406b4b7cb99d3a290dcef0721eff | https://github.com/Columbine21/PyTorch-NLP/tree/63460d0951a0406b4b7cb99d3a290dcef0721eff |
MultiHeadAttention | import math
import torch
from torch import nn
class ScaledDotProductAttention(nn.Module):
"""
Scaled Dot-Product Attention Layer
Attributes
----------
softmax : nn.Functional
softmax function applied at the last dimension
"""
def __init__(self, dropout=0.1):
super(ScaledD... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import math
from torch import nn
assert_size_stride = torch._C._dynamo.guards.as... | dugusword/transformer | MultiHeadAttention | false | 6,612 | [
"MIT"
] | 1 | 7aa10968f0e60d545bbd17f1f8c1dfb7ee88c62b | https://github.com/dugusword/transformer/tree/7aa10968f0e60d545bbd17f1f8c1dfb7ee88c62b |
TokenClassifier | import torch
import torch.nn as nn
def transformer_weights_init(module, std_init_range=0.02, xavier=True):
"""
Initialize different weights in Transformer model.
Args:
module: torch.nn.Module to be initialized
std_init_range: standard deviation of normal initializer
xavier: if True... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | ngxingyu/Domain-Transfer-for-Punctuation-Retrieval | TokenClassifier | false | 7,332 | [
"Apache-2.0"
] | 1 | f5aa0ea0946c68aaf7fcf49a5085e6c823766a2f | https://github.com/ngxingyu/Domain-Transfer-for-Punctuation-Retrieval/tree/f5aa0ea0946c68aaf7fcf49a5085e6c823766a2f |
AlbertAttentionWithoutSkipConnection | # 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.... | twistedcubic/attention-rank-collapse | AlbertAttentionWithoutSkipConnection | false | 16,646 | [
"Apache-2.0"
] | 118 | 38b5df6dc2add25f6d945e48a6baf96862368c20 | https://github.com/twistedcubic/attention-rank-collapse/tree/38b5df6dc2add25f6d945e48a6baf96862368c20 |
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
assert_size_stride = torch._... | AssassionXY/HOR | ContrastiveLoss | false | 63 | [
"Apache-2.0"
] | 0 | a4c91d90a59eb2b144d827afff626b7eac907320 | https://github.com/AssassionXY/HOR/tree/a4c91d90a59eb2b144d827afff626b7eac907320 |
cSEmodule | # 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_... | HwangJohn/feature_representation | cSEmodule | false | 2,356 | [
"MIT"
] | 0 | 27389caacc9c026b65f47ab0cbb4e6d0465e6a60 | https://github.com/HwangJohn/feature_representation/tree/27389caacc9c026b65f47ab0cbb4e6d0465e6a60 |
GraphConvolution | # 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... | Kanaricc/TDRG | GraphConvolution | false | 8,391 | [
"Apache-2.0"
] | 16 | 91416976c8887877775f516ebee60469449e7e5f | https://github.com/Kanaricc/TDRG/tree/91416976c8887877775f516ebee60469449e7e5f |
softCrossEntropy | # 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
assert_size... | Benjamin-Lee/cyphercat | softCrossEntropy | false | 8,951 | [
"Apache-2.0"
] | 0 | d8df0544337d4e7e14c2463264c008b7811d35b3 | https://github.com/Benjamin-Lee/cyphercat/tree/d8df0544337d4e7e14c2463264c008b7811d35b3 |
SoftmaxLoss | import torch
import torch.nn as nn
class SoftmaxLoss(nn.Module):
def __init__(self, hidden_dim, speaker_num, **kwargs):
"""
Softmax Loss
"""
super(SoftmaxLoss, self).__init__()
self.fc = nn.Linear(hidden_dim, speaker_num)
self.loss = nn.CrossEntropyLoss()
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
from torch._inductor.runtime.... | gcambara/s3prl | SoftmaxLoss | false | 15,416 | [
"MIT"
] | 856 | 33284ebde3a903ed8604d6dae85669d0174ae1d3 | https://github.com/gcambara/s3prl/tree/33284ebde3a903ed8604d6dae85669d0174ae1d3 |
SeparableConv2d_same | import torch
import torch.nn as nn
import torch.nn.functional as F
def fixed_padding(inputs, kernel_size, rate):
kernel_size_effective = kernel_size + (kernel_size - 1) * (rate - 1)
pad_total = kernel_size_effective - 1
pad_beg = pad_total // 2
pad_end = pad_total - pad_beg
padded_inputs = F.pad(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
import torch.nn as nn
import torch.nn.functional as F
assert_size_stride = torch... | Mnsy-Syl/new_20201103 | SeparableConv2d_same | false | 14,057 | [
"MIT"
] | 46 | 9ee39f1c69a4cba896b30f007560fcbe8ac89c02 | https://github.com/Mnsy-Syl/new_20201103/tree/9ee39f1c69a4cba896b30f007560fcbe8ac89c02 |
LayerNorm | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
from torch.nn.parameter import Parameter
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided... | cwxcwx0319/Dictionary | LayerNorm | false | 15,097 | [
"Apache-2.0"
] | 82 | 55fb9a602a212f9c3a69a318fec31da1d07279df | https://github.com/cwxcwx0319/Dictionary/tree/55fb9a602a212f9c3a69a318fec31da1d07279df |
Actor | import torch
import numpy as np
import torch.nn.functional as F
import torch.nn as nn
def hidden_init(layer):
fan_in = layer.weight.data.size()[0]
lim = 1.0 / np.sqrt(fan_in)
return -lim, lim
class Actor(nn.Module):
"""Actor (Policy) Model."""
def __init__(self, state_size, action_size, seed, f... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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
... | rbak/deep-rl-udacity-project-3 | Actor | false | 12,930 | [
"MIT"
] | 0 | 4bf2aec6b0ef27636ebd11dfd4b442554208cffb | https://github.com/rbak/deep-rl-udacity-project-3/tree/4bf2aec6b0ef27636ebd11dfd4b442554208cffb |
L2Norm | import torch
import torch.nn as nn
class L2Norm(nn.Module):
def __init__(self, n_dims, scale=20.0, eps=1e-10):
"""L2 normalization layer.
Args:
n_dims (int): Number of dimensions to be normalized
scale (float, optional): Defaults to 20..
eps (float, optional):... | 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_... | ChengBo5/mask-text-detector | L2Norm | false | 250 | [
"Apache-2.0"
] | 0 | ce93e45ed1d982ec0ef6ad977c02e49326bf255a | https://github.com/ChengBo5/mask-text-detector/tree/ce93e45ed1d982ec0ef6ad977c02e49326bf255a |
NonLocal | import torch
import torch._C
import torch.serialization
from torch import nn
from typing import *
def int_size(x):
size = tuple(int(s) for s in x.size())
return size
class NonLocal(nn.Module):
def __init__(self, in_channels):
super(NonLocal, self).__init__()
self.inter_channel = in_chan... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | shuaizzZ/mmsegmentation | NonLocal | false | 4,332 | [
"Apache-2.0"
] | 0 | a6c6b348dbf8c4a0a39ffbdb832a1e82309c533c | https://github.com/shuaizzZ/mmsegmentation/tree/a6c6b348dbf8c4a0a39ffbdb832a1e82309c533c |
PixelNorm | # 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... | ArashVahabpour/encoder4editing | PixelNorm | false | 1,963 | [
"MIT"
] | 0 | 819b90ecd7397fbe2ab7cb30eb451dab0f3149fd | https://github.com/ArashVahabpour/encoder4editing/tree/819b90ecd7397fbe2ab7cb30eb451dab0f3149fd |
SqueezeAndExcitation | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
class SqueezeAndExcitation(nn.Module):
def __init__(self, planes, squeeze):
super(SqueezeAndExcitation, self).__init__()
self.squeeze = nn.Linear(planes, sque... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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 ... | FujitsuLaboratories/CAC | SqueezeAndExcitation | false | 17,303 | [
"Apache-2.0"
] | 8 | d12df8e47f61eaf7d7b0ed355e2d1aa296453f86 | https://github.com/FujitsuLaboratories/CAC/tree/d12df8e47f61eaf7d7b0ed355e2d1aa296453f86 |
LinearModel | # 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.... | learniotai/iotai-sensor-classifications | LinearModel | false | 3,891 | [
"Apache-2.0"
] | 0 | ba2527cb317afa30a5c495d1cddc16f7dc2936ed | https://github.com/learniotai/iotai-sensor-classifications/tree/ba2527cb317afa30a5c495d1cddc16f7dc2936ed |
tofp16 | import torch
import torch.nn as nn
import torch.nn.parallel
class tofp16(nn.Module):
def __init__(self):
super(tofp16, self).__init__()
def forward(self, input):
return input.half()
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
import torch.nn as nn
import torch.nn.parallel
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C... | HuaijiaLin/AGSS-VOS | tofp16 | false | 8,245 | [
"MIT"
] | 11 | e9272365aa45bf098316d7111238fe0ab8df8a17 | https://github.com/HuaijiaLin/AGSS-VOS/tree/e9272365aa45bf098316d7111238fe0ab8df8a17 |
CumMax | import torch
import torch.nn as nn
class CumMax(nn.Module):
def __init__(self):
super(CumMax, self).__init__()
def forward(self, input):
return torch.cumsum(nn.Softmax(dim=-1)(input), -1)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
| import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | Hritikbansal/RNNs_SVA_OOD | CumMax | false | 17,388 | [
"MIT"
] | 4 | a1c73955342d9d35c49da5fcb7b315e37b0f75d1 | https://github.com/Hritikbansal/RNNs_SVA_OOD/tree/a1c73955342d9d35c49da5fcb7b315e37b0f75d1 |
ReferenceWeightBinarizationModule | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch import nn
from torchvision import models as models
import torc... | JinYAnGHe/openvino_training_extensions | ReferenceWeightBinarizationModule | false | 3,024 | [
"Apache-2.0"
] | 0 | a0b4456a3c9fe6c1b7eabc9d5eb4e74d01453dee | https://github.com/JinYAnGHe/openvino_training_extensions/tree/a0b4456a3c9fe6c1b7eabc9d5eb4e74d01453dee |
AvgPool2d | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch.nn import Module
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._em... | Rahul-160/PySyft | AvgPool2d | false | 17,833 | [
"Apache-2.0"
] | 7 | 182627db2369d6f93aa0667f5ea2abee5b878d58 | https://github.com/Rahul-160/PySyft/tree/182627db2369d6f93aa0667f5ea2abee5b878d58 |
CrossEntropy | # 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... | shinoyuki222/torch-light | CrossEntropy | false | 16,418 | [
"MIT"
] | 310 | 4799805d9bcae82a9f12a574dcf9fdd838c92ee9 | https://github.com/shinoyuki222/torch-light/tree/4799805d9bcae82a9f12a574dcf9fdd838c92ee9 |
CELoss | # 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.... | Wadaboa/titanet | CELoss | false | 18,084 | [
"MIT"
] | 4 | b07e3074e79ea8c1129fb0adb8315e06bb4943ea | https://github.com/Wadaboa/titanet/tree/b07e3074e79ea8c1129fb0adb8315e06bb4943ea |
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.... | rmfan/nni | ActorCritic | false | 10,969 | [
"MIT"
] | 0 | 727ee1ce47e070061fe3dab8a2da5d3cd5e55546 | https://github.com/rmfan/nni/tree/727ee1ce47e070061fe3dab8a2da5d3cd5e55546 |
MLP | import torch
from torch import Tensor
from torch import nn
class MLP(nn.Module):
def __init__(self, dim, embed_dim):
super().__init__()
self.proj = nn.Linear(dim, embed_dim)
def forward(self, x: 'Tensor') ->Tensor:
x = x.flatten(2).transpose(1, 2)
x = self.proj(x)
ret... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import 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... | Genevievekim/segformer | MLP | false | 17,326 | [
"MIT"
] | 10 | 4a0800746ade51101ec2573c683b06eccadb9683 | https://github.com/Genevievekim/segformer/tree/4a0800746ade51101ec2573c683b06eccadb9683 |
location_network | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.distributions import Normal
class location_network(nn.Module):
"""
Uses the internal state `h_t` of the core network to
produce the location coordinates `l_t` for the next
time step.
Concretely, feeds the hidden state `... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | felixnon/foveated-visual-attention | location_network | false | 12,371 | [
"MIT"
] | 0 | 7e7d9a5ef24ec42eb76ba72f783bb2227bdb4851 | https://github.com/felixnon/foveated-visual-attention/tree/7e7d9a5ef24ec42eb76ba72f783bb2227bdb4851 |
hsigmoid | import torch
import torch.onnx
import torch
import torch.nn as nn
import torch.nn.functional as F
class hsigmoid(nn.Module):
def forward(self, x):
out = F.relu6(x + 3, inplace=True) / 6
return out
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.onnx
import torch
import torch.nn as nn
assert_size_stride = torch._C._dynam... | LukasKratochvila/pytorch-ssd | hsigmoid | false | 2,583 | [
"MIT"
] | 0 | de6ed2be6ce0b03634d4cbf41622cfe5c87d077c | https://github.com/LukasKratochvila/pytorch-ssd/tree/de6ed2be6ce0b03634d4cbf41622cfe5c87d077c |
Mnist_CNN | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.quantization
import torch.onnx
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
class Mnist_CNN(nn.Module):
def __init__(self):
super().__init__()
self.conv1 = nn... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import ... | voyageth/PyTorch-tutorials-kr | Mnist_CNN | false | 4,514 | [
"BSD-3-Clause"
] | 0 | 05d2dd5931abfca6ce1e0b297f4ceb7f4eae6239 | https://github.com/voyageth/PyTorch-tutorials-kr/tree/05d2dd5931abfca6ce1e0b297f4ceb7f4eae6239 |
GT | import torch
class GT(torch.nn.Module):
def __init__(self):
super(GT, self).__init__()
def forward(self, x, y):
return x > y
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
| import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.j... | PogChamper/torch2trt | GT | false | 14,189 | [
"MIT"
] | 3,363 | 43b12627ec0de4d212efb6d02b07570205085ccc | https://github.com/PogChamper/torch2trt/tree/43b12627ec0de4d212efb6d02b07570205085ccc |
GroupedChannelNorm | # 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.utils.data
import torch
import torch.nn as nn
assert_size_stride =... | Theomat/colorization-av-enseirb-2020 | GroupedChannelNorm | false | 14,468 | [
"Apache-2.0"
] | 1,422 | c54c2388ea39a62289fa2f1c51b4757bf55d3c4f | https://github.com/Theomat/colorization-av-enseirb-2020/tree/c54c2388ea39a62289fa2f1c51b4757bf55d3c4f |
ResnetBlockConv2d | import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
def pixel_norm(x):
sigma = x.norm(dim=1, keepdim=True)
out = x / (sigma + 1e-05)
return out
class EqualizedLR(nn.Module):
def __init__(self, module):
super().__init__()
self.module = 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
import numpy as np
import tor... | DveloperY0115/texture_fields | ResnetBlockConv2d | false | 13,621 | [
"MIT"
] | 78 | 28c277696e0a658ffff3496892810d5a0ef03f65 | https://github.com/DveloperY0115/texture_fields/tree/28c277696e0a658ffff3496892810d5a0ef03f65 |
ModMBStddevLayer | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
import torch.distributed as dist
import torch.autograd as... | jiangwenj02/mmgeneration | ModMBStddevLayer | false | 12,609 | [
"Apache-2.0"
] | 0 | da9ad377ae19260467fc332ddb88f505c38a915a | https://github.com/jiangwenj02/mmgeneration/tree/da9ad377ae19260467fc332ddb88f505c38a915a |
MLP | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn
class MLP(nn.Module):
"""
This is just an MLP with 1 hidden layer
"""
def __init__(self, n_units, dropout=0.1):
super(MLP, self).__init__()
self.w_1 = nn.Linear(n_units, 2048)
self.w_2 = nn.Line... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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 ... | adijo/ift6135-rnn | MLP | false | 9,657 | [
"Apache-2.0"
] | 0 | 88ebcd621cea4042f5ada688f2452ce25d02b761 | https://github.com/adijo/ift6135-rnn/tree/88ebcd621cea4042f5ada688f2452ce25d02b761 |
PEScaling | # 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.nn import Module
f... | M4rt1nM4yr/recipient_line_detection_DAS22 | PEScaling | false | 809 | [
"MIT"
] | 0 | be5ed87940ff2c2740cf86130743538a2ba6ac4b | https://github.com/M4rt1nM4yr/recipient_line_detection_DAS22/tree/be5ed87940ff2c2740cf86130743538a2ba6ac4b |
SeparableBlock | from torch.nn import Module
import torch
from torch.nn import Linear
class SeparableBlock(Module):
def __init__(self, input_size, kernel_channels_in, kernel_channels_out,
kernel_size):
super(SeparableBlock, self).__init__()
self.input_size = input_size
self.kernel_size = kernel_si... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch.nn import Module
from torch.nn import Linear
assert_size_stride = tor... | morzh/hyperstyle | SeparableBlock | false | 16,116 | [
"MIT"
] | 692 | ed87f620143d045f374aa42712a43abd751a90e6 | https://github.com/morzh/hyperstyle/tree/ed87f620143d045f374aa42712a43abd751a90e6 |
TagLineLoss | # 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... | Benjamintdk/DSAI-Project | TagLineLoss | false | 8,839 | [
"Apache-2.0"
] | 0 | 684b74fcef43972e3f4d308f006fb3b4c8191b18 | https://github.com/Benjamintdk/DSAI-Project/tree/684b74fcef43972e3f4d308f006fb3b4c8191b18 |
SELayer_ECA | import torch
import torch.nn as nn
class SELayer_ECA(nn.Module):
"""Constructs a ECA module.
Args:
channel: Number of channels of the input feature map
k_size: Adaptive selection of kernel size
"""
def __init__(self, channel, k_size=3):
super(SELayer_ECA, 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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | rahulmangalampalli/esvit | SELayer_ECA | false | 12,922 | [
"MIT"
] | 0 | 5caf6e36b088ae2e7aaa4100b307eec991078e3e | https://github.com/rahulmangalampalli/esvit/tree/5caf6e36b088ae2e7aaa4100b307eec991078e3e |
StochasticGate | # 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... | SharhadBashar/1-stage-wseg | StochasticGate | false | 5,817 | [
"Apache-2.0"
] | 1 | 83bf13444f5039ffed2de1605f09b3f90b525586 | https://github.com/SharhadBashar/1-stage-wseg/tree/83bf13444f5039ffed2de1605f09b3f90b525586 |
Fair | # 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
... | SimoneDutto/EDSR | Fair | false | 11,882 | [
"MIT"
] | 0 | a13fd4e4950649f9a33aa2089c6db4e3920ea4d2 | https://github.com/SimoneDutto/EDSR/tree/a13fd4e4950649f9a33aa2089c6db4e3920ea4d2 |
GreedySearch | import torch
import torch.nn as nn
def cuda():
return torch.cuda.is_available()
def get_device():
return torch.device('cuda' if cuda() else 'cpu')
class Search(nn.Module):
"""Base search class."""
def __init__(self, *args, **kwargs):
super().__init__()
self.device = get_device()
... | 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... | PaccMann/paccmann_chemistry | GreedySearch | false | 18,366 | [
"MIT"
] | 9 | f7e9735aafb936f837c38b5055c654be178f385f | https://github.com/PaccMann/paccmann_chemistry/tree/f7e9735aafb936f837c38b5055c654be178f385f |
SelfAttention_naive | import math
import torch
from torch import nn
import torch.nn.functional as F
class SelfAttention_naive(nn.Module):
def __init__(self, dim_emb, dim_internal, heads=8, mask=False, dropout=
0.0, dtype=torch.float32):
"""
A single self attention block
:param dim_emb: embedding dimen... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | insop/transformer_simple | SelfAttention_naive | false | 6,884 | [
"Apache-2.0"
] | 1 | d07e6c3b9ddc9687d332ac3a980bbce22880ad46 | https://github.com/insop/transformer_simple/tree/d07e6c3b9ddc9687d332ac3a980bbce22880ad46 |
GradLoss | # 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
... | Khoronus/MonoDepth-FPN-PyTorch | GradLoss | false | 721 | [
"MIT"
] | 0 | 6e41e297723d1490c537e04afff905c61d6f0ff8 | https://github.com/Khoronus/MonoDepth-FPN-PyTorch/tree/6e41e297723d1490c537e04afff905c61d6f0ff8 |
EqualizedLinear | import torch
from torch import nn
import torch.nn.functional as F
class EqualizedLinear(nn.Module):
def __init__(self, input_size, output_size, gain=2 ** 0.5, lrmul=0.01):
super().__init__()
he_std = gain * input_size ** -0.5
init_std = 1.0 / lrmul
self.w_mul = he_std * lrmul
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import 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... | hejj16/Landscape-StyleGAN | EqualizedLinear | false | 6,793 | [
"MIT"
] | 1 | a93cd32b588ab21da9d7589e705ca6f09db18408 | https://github.com/hejj16/Landscape-StyleGAN/tree/a93cd32b588ab21da9d7589e705ca6f09db18408 |
Debayer3x3 | import torch
import torch.nn
import torch.nn.functional
class Debayer3x3(torch.nn.Module):
"""Demosaicing of Bayer images using 3x3 convolutions.
Requires BG-Bayer color filter array layout. That is,
the image[1,1]='B', image[1,2]='G'. This corresponds
to OpenCV naming conventions.
Compared to D... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn
import torch.nn.functional
assert_size_stride = torch._C._dynamo... | tasptz/pytorch-debayer | Debayer3x3 | false | 13,026 | [
"MIT"
] | 0 | ec35f34a57c045eb2319f4ef87f371d95f7394c3 | https://github.com/tasptz/pytorch-debayer/tree/ec35f34a57c045eb2319f4ef87f371d95f7394c3 |
_ImpalaCNN | import torch
from typing import Tuple
from torch import nn
class _ImpalaResBlock(nn.Module):
def __init__(self, n_channels: 'int'):
super().__init__()
self.n_channels = n_channels
kernel_size = 3
padding = 1
self.relu = nn.ReLU()
self.relu_inplace = nn.ReLU()
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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 typing import Tuple
from... | IBM/vsrl-framework | _ImpalaCNN | false | 8,511 | [
"MIT"
] | 44 | 42e0853bffb5efbb66cd97178aff9e10ad18c5a9 | https://github.com/IBM/vsrl-framework/tree/42e0853bffb5efbb66cd97178aff9e10ad18c5a9 |
chroma_subsampling | # 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... | mlomnitz/DifferentiableJPEG | chroma_subsampling | false | 16,096 | [
"MIT"
] | 86 | a5767feba955a1bcb78600135a09c36a806f6249 | https://github.com/mlomnitz/DifferentiableJPEG/tree/a5767feba955a1bcb78600135a09c36a806f6249 |
LandmarkHead | # 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 itertools import product as product
import torch.nn as nn
assert_size_strid... | Jung-Jun-Uk/mixface | LandmarkHead | false | 17,538 | [
"MIT"
] | 10 | cee17f99d5e22bf962d9bccbda44a57ab8493173 | https://github.com/Jung-Jun-Uk/mixface/tree/cee17f99d5e22bf962d9bccbda44a57ab8493173 |
PCENlr | import torch
import torch.nn as nn
class PCENlr(nn.Module):
"""
A Low-rank version for per-channel energy normalization.
"""
def __init__(self, N, T):
super(PCENlr, self).__init__()
self.N = N
self.T = T
self.lr_enc = nn.Linear(self.T, 1, bias=False)
self.l... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | Js-Mim/wagner_vad | PCENlr | false | 5,410 | [
"MIT"
] | 1 | cc682bd7a8f496a26fe4be39ea2b2d68e493c5ba | https://github.com/Js-Mim/wagner_vad/tree/cc682bd7a8f496a26fe4be39ea2b2d68e493c5ba |
F_conv | import torch
import warnings
import torch.nn as nn
import torch.nn.functional as F
import torch.optim
class F_conv(nn.Module):
"""ResNet transformation, not itself reversible, just used below"""
def __init__(self, in_channels, channels, channels_hidden=None, stride=
None, kernel_size=3, leaky_slope=0... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import warnings
import torch.nn as nn
import torch.optim
assert_size_stride = to... | zimmerrol/FrEIA | F_conv | false | 4,672 | [
"MIT"
] | 0 | 73d01ab8c90e0deb5e242d66405bd168db06dc19 | https://github.com/zimmerrol/FrEIA/tree/73d01ab8c90e0deb5e242d66405bd168db06dc19 |
L1_Charbonnier_loss | import torch
from torch.nn import init as init
from torch.nn.modules.loss import _Loss
class L1_Charbonnier_loss(_Loss):
"""
L1 Charbonnierloss
"""
def __init__(self, para):
super(L1_Charbonnier_loss, self).__init__()
self.eps = 0.001
def forward(self, X, Y):
diff = torch... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
from torch.nn import init as... | RunqiuBao/Event_ESTRNN | L1_Charbonnier_loss | false | 14,329 | [
"MIT"
] | 180 | 6d156cc42a3a33bd0b4b7c4c4be98f943ff53acb | https://github.com/RunqiuBao/Event_ESTRNN/tree/6d156cc42a3a33bd0b4b7c4c4be98f943ff53acb |
LSTM | import torch
from typing import Tuple
import torch.nn as nn
class LSTM(nn.Module):
"""Implementation of the standard LSTM.
TODO: Include ref and LaTeX equations
Parameters
----------
input_size : int
Number of input features
hidden_size : int
Number of hidden/memory cells.
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | danielsuo/toy_flood | LSTM | false | 15,134 | [
"MIT"
] | 49 | 471d3c4091d86d4a00fbf910937d4e60fdaf79a1 | https://github.com/danielsuo/toy_flood/tree/471d3c4091d86d4a00fbf910937d4e60fdaf79a1 |
FPELU | # 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 random
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guard... | dawnclaude/onnx2keras | FPELU | false | 15,138 | [
"MIT"
] | 115 | 3d2a47c0a228b91fd434232274e216e491da36e3 | https://github.com/dawnclaude/onnx2keras/tree/3d2a47c0a228b91fd434232274e216e491da36e3 |
Upsample | import torch
import torch.nn as nn
import torch.nn.functional as F
class Upsample(nn.Module):
""" nn.Upsample is deprecated """
def __init__(self, scale_factor, mode='nearest'):
super(Upsample, self).__init__()
self.scale_factor = scale_factor
self.mode = mode
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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | AIplayblocks/littlecarroute | Upsample | false | 4,765 | [
"MIT"
] | 1 | e20b4a318746637dd1e2170b175201bd8ba1e7d5 | https://github.com/AIplayblocks/littlecarroute/tree/e20b4a318746637dd1e2170b175201bd8ba1e7d5 |
ChannelSELayer3D | import torch
import torch.nn as nn
class ChannelSELayer3D(nn.Module):
"""
3D extension of Squeeze-and-Excitation (SE) block described in:
*Hu et al., Squeeze-and-Excitation Networks, arXiv:1709.01507*
*Zhu et al., AnatomyNet, arXiv:arXiv:1808.05238*
"""
def __init__(self, num_channels... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | Jianrong-Lu/Head-and-Neck-Tumour-Segmentation-and-Prediction-of-Patient-Survival | ChannelSELayer3D | false | 655 | [
"MIT"
] | 0 | 257cf17ce6d405166dd8449f3b34e305cb5103b2 | https://github.com/Jianrong-Lu/Head-and-Neck-Tumour-Segmentation-and-Prediction-of-Patient-Survival/tree/257cf17ce6d405166dd8449f3b34e305cb5103b2 |
TransposeConv2dLayer | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
from torch.nn import Parameter
assert_size_stride = torch.... | kangzhiq/DeepFillv2_Pytorch | TransposeConv2dLayer | false | 10,433 | [
"MIT"
] | 0 | 9c7ed61b25bb995713f89108b712490737abe1b1 | https://github.com/kangzhiq/DeepFillv2_Pytorch/tree/9c7ed61b25bb995713f89108b712490737abe1b1 |
Nreparameterize | # 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
fr... | pimdh/lie-vae | Nreparameterize | false | 16,257 | [
"MIT"
] | 83 | 0e0cc4d533c064fcfc405e8a75449f8b2f6cf8cf | https://github.com/pimdh/lie-vae/tree/0e0cc4d533c064fcfc405e8a75449f8b2f6cf8cf |
CNNPolicy | # 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.... | anindex/deepRL-projects | CNNPolicy | false | 1,471 | [
"MIT"
] | 0 | bed03d1f985c8340fc75f715028b632bdce40641 | https://github.com/anindex/deepRL-projects/tree/bed03d1f985c8340fc75f715028b632bdce40641 |
LINEAR_LOGSOFTMAX | import torch
import torch.nn as nn
class LINEAR_LOGSOFTMAX(nn.Module):
def __init__(self, input_dim, nclass):
super(LINEAR_LOGSOFTMAX, self).__init__()
self.fc = nn.Linear(input_dim, nclass)
self.logic = nn.LogSoftmax(dim=1)
def forward(self, x):
o = self.logic(self.fc(x))
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Huihui-z/CE-GZSL | LINEAR_LOGSOFTMAX | false | 13,817 | [
"MIT"
] | 58 | 7bf5358ac4727ea1dc2dc9dec2f453b014500bd8 | https://github.com/Huihui-z/CE-GZSL/tree/7bf5358ac4727ea1dc2dc9dec2f453b014500bd8 |
WaveletConv | import torch
import torch.nn as nn
class WaveletConv(nn.Module):
def __init__(self, in_features, out_features, num_nodes, bias=False):
super().__init__()
self.in_features = in_features
self.out_features = out_features
self.w = nn.Linear(in_features, out_features, bias=bias)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | EdisonLeeeee/GraphGallery | WaveletConv | false | 13,643 | [
"MIT"
] | 300 | 4eec9c5136bda14809bd22584b26cc346cdb633b | https://github.com/EdisonLeeeee/GraphGallery/tree/4eec9c5136bda14809bd22584b26cc346cdb633b |
LanguageModelCriterion | # 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
from torch.autograd import *
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torc... | SunZongdi/self-critical.pytorch | LanguageModelCriterion | false | 5,861 | [
"MIT"
] | 1 | 6cecbeb949e68007b72e84198cf74f9fb288aeda | https://github.com/SunZongdi/self-critical.pytorch/tree/6cecbeb949e68007b72e84198cf74f9fb288aeda |
RGAN_D | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import DataLoader as DataLoader
class RGAN_D(nn.Module):
def __init__(self, in_size, hidden_size, num_outcomes):
super(RGAN_D, self).__init__()
self.L1 = nn.Linear(in_size, hidden_size)
self.L2 = nn.L... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | COMP6248-Reproducability-Challenge/Reproducible-Or-Not-Reproducible-That-Is-The-Question | RGAN_D | false | 4,954 | [
"MIT"
] | 1 | 7e2e632189a3669397f67efa99c8de4924967968 | https://github.com/COMP6248-Reproducability-Challenge/Reproducible-Or-Not-Reproducible-That-Is-The-Question/tree/7e2e632189a3669397f67efa99c8de4924967968 |
ConvMLPStage | from torch.nn import Module
import torch
import torch.nn as nn
from torch.nn import Linear
from torch.nn import LayerNorm
from torch.nn import Conv2d
from torch.nn import GELU
from torch.nn import Identity
def drop_path(x, drop_prob: 'float'=0.0, training: 'bool'=False):
"""
Obtained from: github.com:rwightma... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 impor... | dumpmemory/Convolutional-MLPs | ConvMLPStage | false | 15,267 | [
"Apache-2.0"
] | 117 | 89008c686e48803c012038f21f97e56276aa84ad | https://github.com/dumpmemory/Convolutional-MLPs/tree/89008c686e48803c012038f21f97e56276aa84ad |
DownBlock | # 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... | MatusBako/MakeFacesGreatAgain | DownBlock | false | 834 | [
"MIT"
] | 0 | e4941a8460db79dec566ed02d4b23eafb416a6db | https://github.com/MatusBako/MakeFacesGreatAgain/tree/e4941a8460db79dec566ed02d4b23eafb416a6db |
FocalLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch ... | cxz/tgs-salt-identification-challenge | FocalLoss | false | 6,516 | [
"MIT"
] | 1 | 859f3d7f2d3184532c42c34444500eec3b03b1c8 | https://github.com/cxz/tgs-salt-identification-challenge/tree/859f3d7f2d3184532c42c34444500eec3b03b1c8 |
GHMR | import torch
import torch.nn as nn
class GHMR(nn.Module):
"""GHM Regression Loss.
Details of the theorem can be viewed in the paper
"Gradient Harmonized Single-stage Detector"
https://arxiv.org/abs/1811.05181
Args:
mu (float): The parameter for the Authentic Smooth L1 loss.
bins ... | 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... | CK-er/mmdet | GHMR | false | 2,094 | [
"Apache-2.0"
] | 0 | 9bea4068efbcf7bf739dbe41917a68d525c29868 | https://github.com/CK-er/mmdet/tree/9bea4068efbcf7bf739dbe41917a68d525c29868 |
SineLayer | # 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 ... | ccxiaotoancai/Anim-NeRF | SineLayer | false | 6,398 | [
"MIT"
] | 1 | 1342a9e2d02411a09acecac40ac325f38708b9c9 | https://github.com/ccxiaotoancai/Anim-NeRF/tree/1342a9e2d02411a09acecac40ac325f38708b9c9 |
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.... | GraphGrailAi/summ-abs-dev | GlobalAttention | false | 2,321 | [
"MIT"
] | 0 | 512f253bf72b6529589b29d06959b560b79f1cde | https://github.com/GraphGrailAi/summ-abs-dev/tree/512f253bf72b6529589b29d06959b560b79f1cde |
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.... | shahrukhx01/transformers-bisected | SelfAttention | false | 10,797 | [
"Apache-2.0"
] | 0 | a97647aca7963e6f9d4fce5a067ba68d393072d6 | https://github.com/shahrukhx01/transformers-bisected/tree/a97647aca7963e6f9d4fce5a067ba68d393072d6 |
EntropyLoss | import math
import torch
from torch import nn
import torch.utils.data
class EntropyLoss(nn.Module):
def __init__(self):
super().__init__()
def forward(self, x, eps=1e-08):
logN = math.log(float(x.shape[0]))
x = x * (x + eps).log() / logN
neg_entropy = x.sum(1)
return ... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch import nn
import torch.utils.data
assert_size_stride = torch._... | Joshua-Schroijen/deepproblog | EntropyLoss | false | 672 | [
"Apache-2.0"
] | 0 | 4ae56f1e860010b7857b29d5bd76fb1555d5e19d | https://github.com/Joshua-Schroijen/deepproblog/tree/4ae56f1e860010b7857b29d5bd76fb1555d5e19d |
RGBDiff | import torch
from torch import nn
from torchvision import models as models
import torch.onnx
import torch.nn
class RGBDiff(nn.Module):
def __init__(self, dim=1):
super().__init__()
self.dim = dim
def forward(self, image):
"""
Args:
image (torch.Tensor): (N x T 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
from torchvision import models as models
import torch.onnx
import torch.nn
assert_size_stride = torch._C._dynamo.guards... | krodyush/training_extensions | RGBDiff | false | 10,973 | [
"Apache-2.0"
] | 0 | 542f4004dfbc6fc62a622065367ba4f85a703dd3 | https://github.com/krodyush/training_extensions/tree/542f4004dfbc6fc62a622065367ba4f85a703dd3 |
AttentionSet | # 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.... | marcos0318/query2box | AttentionSet | false | 16,019 | [
"MIT"
] | 140 | cc8b47e21a5addf17ee5a3c68412b638ef3956f3 | https://github.com/marcos0318/query2box/tree/cc8b47e21a5addf17ee5a3c68412b638ef3956f3 |
AttnLayer | import torch
import torch.nn.functional as F
import torch.nn as nn
class AttnLayer(nn.Module):
"""Attention layer.
w is context vector.
Formula:
$$
v_i=tanh(Wh_i+b)\\
lpha_i = v_i^Tw\\
lpha_i = softmax(lpha_i)\\
Vec = \\sum_0^L lpha_ih_i
$$
"""
... | 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.... | WiseDoge/Text-Classification-PyTorch | AttnLayer | false | 18,070 | [
"MIT"
] | 6 | 9371eeed6bd7ecf1d529c8f2a6c997fcde67a559 | https://github.com/WiseDoge/Text-Classification-PyTorch/tree/9371eeed6bd7ecf1d529c8f2a6c997fcde67a559 |
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... | IDayday/YOLOv4_CAM | SpatialPyramidPooling | false | 8,296 | [
"Apache-2.0"
] | 34 | 8df61f1c59c197126f0385c1ec1cf65a29a80cec | https://github.com/IDayday/YOLOv4_CAM/tree/8df61f1c59c197126f0385c1ec1cf65a29a80cec |
WNConv2d | # 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... | Shivanshu-Gupta/KaoKore-VQ-VAE2 | WNConv2d | false | 1,093 | [
"MIT"
] | 0 | 38a88ba312dee3c0e2c1aaf02e1c1754ba19ac0c | https://github.com/Shivanshu-Gupta/KaoKore-VQ-VAE2/tree/38a88ba312dee3c0e2c1aaf02e1c1754ba19ac0c |
RNNAgent | # 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_... | benellis3/pymarl2 | RNNAgent | false | 14,948 | [
"Apache-2.0"
] | 401 | 0875995a0e0b9692ea64484478b369c7f6c0cf44 | https://github.com/benellis3/pymarl2/tree/0875995a0e0b9692ea64484478b369c7f6c0cf44 |
marginLoss | # 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... | DSRnD/UMLs | marginLoss | false | 2,121 | [
"MIT"
] | 0 | a524bc45bc3f2dc8b4a90f73f69e23ee36ba8be9 | https://github.com/DSRnD/UMLs/tree/a524bc45bc3f2dc8b4a90f73f69e23ee36ba8be9 |
MultiNonLinearClassifier | # 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... | qhjqhj00/NLI | MultiNonLinearClassifier | false | 12,910 | [
"Apache-2.0"
] | 0 | a5baaf1903e6a22a7bdd1d68a4aaf1680c57d265 | https://github.com/qhjqhj00/NLI/tree/a5baaf1903e6a22a7bdd1d68a4aaf1680c57d265 |
ChannelNorm | import torch
import torch.nn as nn
def channel_norm(image):
img = image.flatten(2)
avg = img.mean(-1)[:, :, None, None]
var = img.var(-1)[:, :, None, None]
return (image - avg) / torch.sqrt(var + 1e-06)
class ChannelNorm(nn.Module):
def forward(self, image):
return channel_norm(image)
... | 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_... | rlmwang/torch-tools | ChannelNorm | false | 10,795 | [
"MIT"
] | 0 | 822132534d73414f26045bad38a0a345661b057f | https://github.com/rlmwang/torch-tools/tree/822132534d73414f26045bad38a0a345661b057f |
MultiClassDiceLoss | import torch
import torch.nn as nn
class DiceLoss(nn.Module):
"""DiceLoss.
.. seealso::
Milletari, Fausto, Nassir Navab, and Seyed-Ahmad Ahmadi. "V-net: Fully convolutional neural networks for
volumetric medical image segmentation." 2016 fourth international conference on 3D vision (3DV). IEE... | 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... | ivadomed-profile-analysis-project/ivadomed | MultiClassDiceLoss | false | 15,653 | [
"MIT"
] | 87 | 3b53e2cb2b210511943da439401e2471fd387876 | https://github.com/ivadomed-profile-analysis-project/ivadomed/tree/3b53e2cb2b210511943da439401e2471fd387876 |
OutputLayer | # 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.cuda
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo... | CoyoteLeo/QANet-pytorch | OutputLayer | false | 2,119 | [
"MIT"
] | 0 | a2d5290915c91c4bc84db142e8ce50c47a7a37d0 | https://github.com/CoyoteLeo/QANet-pytorch/tree/a2d5290915c91c4bc84db142e8ce50c47a7a37d0 |
DiceLoss | # 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... | hlesmqh/WS3D | DiceLoss | false | 15,522 | [
"MIT"
] | 100 | 6816eeb135923a59de34ee5d94be2d0fd3ec83f9 | https://github.com/hlesmqh/WS3D/tree/6816eeb135923a59de34ee5d94be2d0fd3ec83f9 |
SymKlCriterion | 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.... | johnson7788/mt-dnn | SymKlCriterion | false | 3,905 | [
"MIT"
] | 0 | 26e5c4a5bfdbf1a1dd1c903e606db1c070568237 | https://github.com/johnson7788/mt-dnn/tree/26e5c4a5bfdbf1a1dd1c903e606db1c070568237 |
TransformerNet2 | import torch
class TransformerNet2(torch.nn.Module):
def __init__(self):
super(TransformerNet2, self).__init__()
self.tanh = torch.nn.Tanh()
self.a = 10
def forward(self, r, p):
m = -0.5 * self.tanh(self.a * (p - 2 * r)) + 0.5 * self.tanh(self.a *
(p - 2 * (1 - r)... | 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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_c... | Ekko-zn/StegoAdv | TransformerNet2 | false | 5,110 | [
"MIT"
] | 1 | 2852dbc85d66f30efb7127695c0d75806bf4aa4c | https://github.com/Ekko-zn/StegoAdv/tree/2852dbc85d66f30efb7127695c0d75806bf4aa4c |
L2Norm | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
from math import sqrt as sqrt
from itertools import produ... | robtu328/TextDetCorner | L2Norm | false | 16,331 | [
"Python-2.0",
"OLDAP-2.7"
] | 331 | f37ef0e1d2068c5fbd643855acd21787a2c122c5 | https://github.com/robtu328/TextDetCorner/tree/f37ef0e1d2068c5fbd643855acd21787a2c122c5 |
FusedLeakyReLU | # 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
import torch.nn.functional as F
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size_strid... | HappyBelief/ContraD | FusedLeakyReLU | false | 13,747 | [
"MIT"
] | 168 | abb72562ddac8d8ab37fe9af6ac4c44c61e8ea0f | https://github.com/HappyBelief/ContraD/tree/abb72562ddac8d8ab37fe9af6ac4c44c61e8ea0f |
CGRU_cell | import torch
import torch.nn as nn
from torch.autograd import Variable
import torch.nn.functional as f
from math import sqrt as sqrt
from itertools import product as product
class CGRU_cell(nn.Module):
"""Initialize a basic Conv GRU cell.
Args:
filter_size: int that is the height and width of the filter... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | zhujiagang/realtime-refined-random | CGRU_cell | false | 11,041 | [
"MIT"
] | 0 | 3aa8169049ab8be8b1ea5a78bbe9b89ac6c15593 | https://github.com/zhujiagang/realtime-refined-random/tree/3aa8169049ab8be8b1ea5a78bbe9b89ac6c15593 |
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... | Chris-hughes10/YOLOX | IOUloss | false | 5,003 | [
"Apache-2.0"
] | 1 | 981df30285839469a23cb925ed0a0f3714e46514 | https://github.com/Chris-hughes10/YOLOX/tree/981df30285839469a23cb925ed0a0f3714e46514 |
MSELossWithIgnore | # 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.functional
from torch import nn
assert_size_stride = torch._C._dynamo.gua... | drivendataorg/DrivenData-2021-Geopose-Solution | MSELossWithIgnore | false | 6,600 | [
"MIT"
] | 1 | fc1dead0aeb1ade9e9d87b55f56e631c57e966a6 | https://github.com/drivendataorg/DrivenData-2021-Geopose-Solution/tree/fc1dead0aeb1ade9e9d87b55f56e631c57e966a6 |
Get_gradient_nopadding | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | mansum6/ESRGAN | Get_gradient_nopadding | false | 3,978 | [
"Apache-2.0"
] | 0 | 8a6b2ce20600840490ee0525cb105617b8e85c73 | https://github.com/mansum6/ESRGAN/tree/8a6b2ce20600840490ee0525cb105617b8e85c73 |
TripletMarginCosineLoss | from torch.nn import Module
import torch
from torch.nn.functional import cosine_similarity
def triplet_margin_cosine_loss(anchor, positive, negative, margin=1.0, eps=
1e-08, sum_loss=False):
'Creates a criterion that measures the triplet cosine loss given input\n tensors x1, x2, x3 and a margin with a valu... | 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
... | ZhangShiyue/oposum | TripletMarginCosineLoss | false | 14,724 | [
"Apache-2.0"
] | 97 | 5aefea20c5c0846b4cf09a5b4643ffb0b2ff39d8 | https://github.com/ZhangShiyue/oposum/tree/5aefea20c5c0846b4cf09a5b4643ffb0b2ff39d8 |
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... | ppomelo/Attentive-Transformation-Based-Normalization | BCEDiceLoss | false | 4,133 | [
"Apache-2.0"
] | 0 | 62ad02eb025613e90f4fe0e0a9f0f85839e53092 | https://github.com/ppomelo/Attentive-Transformation-Based-Normalization/tree/62ad02eb025613e90f4fe0e0a9f0f85839e53092 |
Accuracy | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn.functi... | HelenGuohx/cv-ferattn-code | Accuracy | false | 5,306 | [
"MIT"
] | 1 | faa9b7850fe2a0f8c08193bb129b5fec4639d616 | https://github.com/HelenGuohx/cv-ferattn-code/tree/faa9b7850fe2a0f8c08193bb129b5fec4639d616 |
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