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 |
|---|---|---|---|---|---|---|---|---|---|---|
Mean | import torch
from torch import nn
class Mean(nn.Module):
def __init__(self, *args):
super(Mean, self).__init__()
self.shape = args
def forward(self, x):
return x.mean(self.shape)
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 import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empt... | vitskvara/shape-guided-anomaly-detection | Mean | false | 4,496 | [
"MIT"
] | 0 | 6685b2e0b97968a6d0f478d2920486da107b277f | https://github.com/vitskvara/shape-guided-anomaly-detection/tree/6685b2e0b97968a6d0f478d2920486da107b277f |
ResidualBlock | import torch
import torch.nn as nn
import torch.nn.functional as F
class ResidualBlock(nn.Module):
def __init__(self, input_channel, output_channel, upsample=True):
super(ResidualBlock, self).__init__()
self.conv1 = nn.Conv2d(input_channel, output_channel, kernel_size=3,
padding=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
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Holmes-Alan/Photo2Sketch | ResidualBlock | false | 564 | [
"MIT"
] | 0 | 43a0ca6bb8a8e645b35a2ab23d11ed5efe117e09 | https://github.com/Holmes-Alan/Photo2Sketch/tree/43a0ca6bb8a8e645b35a2ab23d11ed5efe117e09 |
QModReLU | import torch
import torch.nn.functional as F
import torch.fx
class QModReLU(torch.nn.Module):
"""
Quaternion ModeReLU
"""
def __init__(self, bias=0):
super().__init__()
self.bias = torch.nn.Parameter(torch.Tensor([bias]))
def forward(self, x):
norm = x.norm()
retu... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import torch.fx
assert_size_... | eleGAN23/HI2I | QModReLU | false | 6,648 | [
"MIT"
] | 1 | 7730ee0963614290099b011c113048ef6d1b149c | https://github.com/eleGAN23/HI2I/tree/7730ee0963614290099b011c113048ef6d1b149c |
GELU2 | # 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... | DQiaole/ZITS | GELU2 | false | 7,958 | [
"Apache-2.0"
] | 40 | 5f7a060167790789d5e29a3d14d3c2ef8a34e765 | https://github.com/DQiaole/ZITS/tree/5f7a060167790789d5e29a3d14d3c2ef8a34e765 |
TransformDecoder4 | # 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.... | MingSun-Tse/pytorch-AdaIN | TransformDecoder4 | false | 2,694 | [
"MIT"
] | 0 | 02ae320345232983c754ea233613aedc21e4d348 | https://github.com/MingSun-Tse/pytorch-AdaIN/tree/02ae320345232983c754ea233613aedc21e4d348 |
GCNdecoder | # 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
i... | Roxbili/topoGAN | GCNdecoder | false | 5,772 | [
"MIT"
] | 1 | 25cc397bf8925e485d3a39837b8bce552118f5dc | https://github.com/Roxbili/topoGAN/tree/25cc397bf8925e485d3a39837b8bce552118f5dc |
SimpleClampMinModel | # 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.jit
import torch.onnx
import torch.nn
assert_size_stride = torch._C._dynamo.... | YaronBenAtar/glow | SimpleClampMinModel | false | 14,654 | [
"Apache-2.0"
] | 2,838 | a13706a4239fa7eaf059c670dc573e3eb0768f86 | https://github.com/YaronBenAtar/glow/tree/a13706a4239fa7eaf059c670dc573e3eb0768f86 |
FullyConnected | import torch
import torch.utils.data
import torch.nn as nn
def _init_weights(layer):
"""
Init weights of the layer
:param layer:
:return:
"""
nn.init.xavier_uniform_(layer.weight)
if layer.bias is not None:
nn.init.zeros_(layer.bias)
class FullyConnected(nn.Module):
def __in... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.utils.data
impor... | AntoBcc/benchmarking-gnns | FullyConnected | false | 2,024 | [
"MIT"
] | 0 | c5750054b2f4ba0822f203fa18d382f6a3b16542 | https://github.com/AntoBcc/benchmarking-gnns/tree/c5750054b2f4ba0822f203fa18d382f6a3b16542 |
BentIdentity | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_... | fmhoward/pysurvival | BentIdentity | false | 12,376 | [
"Apache-2.0"
] | 0 | 3fea55f09477e9f0844845e09d6ea60434436e2e | https://github.com/fmhoward/pysurvival/tree/3fea55f09477e9f0844845e09d6ea60434436e2e |
AsymmetricLossOptimized | # 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... | ckvic3/query2labels | AsymmetricLossOptimized | false | 1,728 | [
"MIT"
] | 0 | e9c30e1b445be773be397a093fa66aef71d54556 | https://github.com/ckvic3/query2labels/tree/e9c30e1b445be773be397a093fa66aef71d54556 |
UNetSeeInDark | import torch
from torch import nn
class UNetSeeInDark(nn.Module):
def __init__(self, in_channels=4, out_channels=3):
super(UNetSeeInDark, self).__init__()
self.conv1_1 = nn.Conv2d(in_channels, 32, kernel_size=3, stride=1,
padding=1)
self.conv1_2 = nn.Conv2d(32, 32, kernel_size... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
assert_s... | Vandermode/ELD | UNetSeeInDark | false | 14,603 | [
"MIT"
] | 258 | aa0edb44a8fc20e01f83c1f6e93ee70d3190e142 | https://github.com/Vandermode/ELD/tree/aa0edb44a8fc20e01f83c1f6e93ee70d3190e142 |
CrossEntropyLoss | # 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.nn.modules.... | ModelTC/EOD | CrossEntropyLoss | false | 14,059 | [
"Apache-2.0"
] | 196 | 164bff80486e9ae6a095a97667b365c46ceabd86 | https://github.com/ModelTC/EOD/tree/164bff80486e9ae6a095a97667b365c46ceabd86 |
UNet | # 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.functional import ... | Thomasedv/AI_Interpolation | UNet | false | 6,045 | [
"MIT"
] | 1 | cee51d92185a43a60797785554ee1ae924e5da0d | https://github.com/Thomasedv/AI_Interpolation/tree/cee51d92185a43a60797785554ee1ae924e5da0d |
Mfm | import torch
import torch.nn as nn
class Mfm(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size=3, stride=1,
padding=1, f_type=1):
super(Mfm, self).__init__()
self.out_channels = out_channels
if f_type == 1:
self.filter = nn.Conv2d(in_channels, 2 * 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
import torch.nn as nn
assert_... | githubhjx/Deep-Learning- | Mfm | false | 12,447 | [
"Apache-2.0"
] | 0 | 5a22fb5696d930ed334aa1cbf2b213956b1c7026 | https://github.com/githubhjx/Deep-Learning-/tree/5a22fb5696d930ed334aa1cbf2b213956b1c7026 |
SIMSE | import torch
import torch.nn as nn
class SIMSE(nn.Module):
def __init__(self):
super(SIMSE, self).__init__()
def forward(self, pred, real):
diffs = torch.add(real, -pred)
n = torch.numel(diffs.data)
simse = torch.sum(diffs).pow(2) / n ** 2
return simse
def get_input... | 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... | Clement25/Multimodal-Attack | SIMSE | false | 296 | [
"MIT"
] | 0 | bd04ee099d457e87b6e6ee918c03f65a589bcb9a | https://github.com/Clement25/Multimodal-Attack/tree/bd04ee099d457e87b6e6ee918c03f65a589bcb9a |
self_attn_mini | import torch
import torch.nn as nn
class self_attn_mini(nn.Module):
def __init__(self, input_size):
super(self_attn_mini, self).__init__()
self.input_size = input_size
self.key = nn.Linear(input_size, input_size, bias=False)
self.query = nn.Linear(input_size, input_size, bias=Fals... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | aryanbdps9/aml_music_generation | self_attn_mini | false | 1,487 | [
"MIT"
] | 0 | 42c8895128327a2884dbfeb8413e0060fef6e22f | https://github.com/aryanbdps9/aml_music_generation/tree/42c8895128327a2884dbfeb8413e0060fef6e22f |
IOU | import torch
def _iou(pred, target, size_average=True):
b = pred.shape[0]
IoU = 0.0
for i in range(0, b):
Iand1 = torch.sum(target[i, :, :, :] * pred[i, :, :, :])
Ior1 = torch.sum(target[i, :, :, :]) + torch.sum(pred[i, :, :, :]
) - Iand1
IoU1 = Iand1 / Ior1
IoU... | 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... | Morales97/BASNet | IOU | false | 14,066 | [
"MIT"
] | 977 | 4c2074f769ec0a3f61b2de60b56666ebe67da858 | https://github.com/Morales97/BASNet/tree/4c2074f769ec0a3f61b2de60b56666ebe67da858 |
MixLoss | import torch
import torch.nn as nn
import torch.nn.functional as F
from itertools import filterfalse
def flatten_binary_scores(scores, labels, ignore=None):
"""
Flattens predictions in the batch (binary case)
Remove labels equal to 'ignore'
"""
scores = scores.view(-1)
labels = labels.view(-1)... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torc... | lyakaap/pytorch-template | MixLoss | false | 15,978 | [
"MIT"
] | 140 | eff9f0a4dd50fa49c3b949065247598d5eabc91e | https://github.com/lyakaap/pytorch-template/tree/eff9f0a4dd50fa49c3b949065247598d5eabc91e |
TripletSemihardLoss | # 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.... | CompVis/metric-learning-divide-and-conquer-improved | TripletSemihardLoss | false | 8,193 | [
"MIT"
] | 11 | 33fe768a54376a090e2d7139898177b06e8903d2 | https://github.com/CompVis/metric-learning-divide-and-conquer-improved/tree/33fe768a54376a090e2d7139898177b06e8903d2 |
GlobalAvgPool2d | import torch
import torch.nn as nn
class GlobalAvgPool2d(nn.Module):
def __init__(self):
"""Global average pooling over the input's spatial dimensions"""
super(GlobalAvgPool2d, self).__init__()
def forward(self, inputs):
in_size = inputs.size()
return inputs.view((in_size[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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | Anikily/CDinkNet | GlobalAvgPool2d | false | 16,908 | [
"MIT"
] | 4 | 490736855475a51bb2984412e88ac7d50d817a3c | https://github.com/Anikily/CDinkNet/tree/490736855475a51bb2984412e88ac7d50d817a3c |
SoftDetectionModule | # 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
... | JimmyYourHonor/D2-net-fast-ap | SoftDetectionModule | false | 5,398 | [
"BSD-3-Clause-Clear"
] | 1 | c4c0db23eae3aa4e3079b80b57887b4cb963b1e8 | https://github.com/JimmyYourHonor/D2-net-fast-ap/tree/c4c0db23eae3aa4e3079b80b57887b4cb963b1e8 |
StyledConv | import math
import torch
from torch import nn
from torch.nn import functional as F
def fused_leaky_relu(input, bias, negative_slope=0.2, scale=2 ** 0.5):
rest_dim = [1] * (input.ndim - bias.ndim - 1)
input = input
if input.ndim == 3:
return F.leaky_relu(input + bias.view(1, *rest_dim, bias.shape[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
from torch._inductor.runtime.triton_helpers import libdevice
import math
from to... | davidetalon/StyleCLIP | StyledConv | false | 12,254 | [
"MIT"
] | 0 | 1cbf552b322cd90c417f26a259143382e2b7af8f | https://github.com/davidetalon/StyleCLIP/tree/1cbf552b322cd90c417f26a259143382e2b7af8f |
Attention | import torch
import torch.nn as nn
import torch.utils.data
class Attention(nn.Module):
def __init__(self, in_size, hidden_size):
super(Attention, self).__init__()
self.hidden = nn.Linear(in_size, hidden_size)
nn.init.orthogonal_(self.hidden.weight.data)
self.out = nn.Linear(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 import triton_helpers
from torch._inductor.runtime.... | gchrupala/platalea | Attention | false | 6,761 | [
"Apache-2.0"
] | 1 | 65833307bb6c5ad6cbdd6b17ad8ca59cf51fcd81 | https://github.com/gchrupala/platalea/tree/65833307bb6c5ad6cbdd6b17ad8ca59cf51fcd81 |
ATT | # 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_... | HuangHaoyu1997/pytorch_DGN | ATT | false | 13,778 | [
"MIT"
] | 48 | f1b1a157a9b1678f9238f64458f44412b796d00e | https://github.com/HuangHaoyu1997/pytorch_DGN/tree/f1b1a157a9b1678f9238f64458f44412b796d00e |
BinaryNLLEntropy | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch.... | Jupaoqq/Jupaoqq_LaRL | BinaryNLLEntropy | false | 673 | [
"Apache-2.0"
] | 0 | ae64adda5627987d71f2948f499daa11e9f309ad | https://github.com/Jupaoqq/Jupaoqq_LaRL/tree/ae64adda5627987d71f2948f499daa11e9f309ad |
Generator | # 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
import ... | bigabig/saliency | Generator | false | 6,509 | [
"Apache-2.0"
] | 1 | 83618c90ea419ee05fbed116e8ad7bb2b331ecf5 | https://github.com/bigabig/saliency/tree/83618c90ea419ee05fbed116e8ad7bb2b331ecf5 |
SpatialAttention | import torch
from torch import nn
class SpatialAttention(nn.Module):
def __init__(self, kernel_size=7):
super().__init__()
self.conv = nn.Conv2d(2, 1, kernel_size=kernel_size, padding=
kernel_size // 2)
self.sigmoid = nn.Sigmoid()
def forward(self, x):
max_result,... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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... | lulor/project_vg | SpatialAttention | false | 7,129 | [
"MIT"
] | 1 | 27b0c3b3038c5a666dde516a0a265ae8ddf2059f | https://github.com/lulor/project_vg/tree/27b0c3b3038c5a666dde516a0a265ae8ddf2059f |
GeneralizedDiceLoss | import torch
from torch import nn
from torch.autograd import Variable
def expand_as_one_hot(input, C, ignore_index=None):
"""
Converts NxDxHxW label image to NxCxDxHxW, where each label is stored in a separate channel
:param input: 4D input image (NxDxHxW)
:param C: number of channels/labels
:para... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empt... | SohamMazumder/Federated_Segmentation | GeneralizedDiceLoss | false | 5,833 | [
"MIT"
] | 1 | d4eb681441003ba20f8b251a42a811c8c436f04e | https://github.com/SohamMazumder/Federated_Segmentation/tree/d4eb681441003ba20f8b251a42a811c8c436f04e |
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... | RoyNijhuis/FaceFormer | SeparableBlock | false | 9,435 | [
"Apache-2.0",
"BSD-2-Clause",
"MIT"
] | 0 | 197d6598b705b988a4ad275c2333bcde6a5eaf9f | https://github.com/RoyNijhuis/FaceFormer/tree/197d6598b705b988a4ad275c2333bcde6a5eaf9f |
MaxPoolStride1 | # 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.utils.data
import torch.utils.data.distributed
import ... | Sarathismg/Pose-Estimator-Old-Version | MaxPoolStride1 | false | 1,062 | [
"Apache-2.0"
] | 0 | ecaa03769323b94a4d7222e2d3606d1ce92a2fae | https://github.com/Sarathismg/Pose-Estimator-Old-Version/tree/ecaa03769323b94a4d7222e2d3606d1ce92a2fae |
TerLinear | import torch
import numpy as np
from itertools import product as product
import torch.nn.functional as F
from torch import nn
import torch.optim
import torch.utils.data
def ternary_threshold(delta: 'float'=0.7, *ws):
"""Ternary threshold find in ws."""
assert isinstance(delta, float)
num_params = sum_w = ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | ninfueng/a-PyTorch-Tutorial-to-Object-Detection | TerLinear | false | 10,639 | [
"MIT"
] | 0 | fc7544720a7e939f5a56f4f7214e4965b7775f77 | https://github.com/ninfueng/a-PyTorch-Tutorial-to-Object-Detection/tree/fc7544720a7e939f5a56f4f7214e4965b7775f77 |
GRU | # 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 as tc
... | gushu333/DA4NMT | GRU | false | 6,764 | [
"Apache-2.0"
] | 1 | dba52a3d3784cd795b6f9aaf655b63475a848798 | https://github.com/gushu333/DA4NMT/tree/dba52a3d3784cd795b6f9aaf655b63475a848798 |
EqualizedLinear | import math
import torch
import numpy as np
from torch import nn
import torch.nn.functional as F
import torch.utils.data
from typing import List
import torch.nn.functional
import torch.autograd
class EqualizedWeight(nn.Module):
"""
<a id="equalized_weight"></a>
## Learning-rate Equalized Weights Paramete... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import math
import numpy as np
from torch import nn
import torch.utils.data
from... | techthiyanes/annotated_deep_learning_paper_implementations | EqualizedLinear | false | 16,579 | [
"MIT"
] | 3,714 | 8af24da2dd39a9a87482a4d18c2dc829bbd3fd47 | https://github.com/techthiyanes/annotated_deep_learning_paper_implementations/tree/8af24da2dd39a9a87482a4d18c2dc829bbd3fd47 |
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.... | nce3xin/spam | Net | false | 7,334 | [
"MIT"
] | 1 | 908421d5cf2dd103e2a7044bf1c8586aaf5f2ada | https://github.com/nce3xin/spam/tree/908421d5cf2dd103e2a7044bf1c8586aaf5f2ada |
ResNetV2 | import torch
import torch.nn as nn
from collections import OrderedDict
import torch.nn.functional as F
def conv1x1(cin, cout, stride=1, bias=False):
return StdConv2d(cin, cout, kernel_size=1, stride=stride, padding=0,
bias=bias)
def conv3x3(cin, cout, stride=1, groups=1, bias=False):
return StdConv2... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | HodaEb/ViT-pytorch | ResNetV2 | false | 3,009 | [
"MIT"
] | 0 | 2643740b1d846ae666635bb0f5a71bceba208675 | https://github.com/HodaEb/ViT-pytorch/tree/2643740b1d846ae666635bb0f5a71bceba208675 |
MarginRankingLoss_learning_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
from torch import device
import triton
import triton.language as tl
from 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.guar... | Pepijnnn/MasterThesis | MarginRankingLoss_learning_loss | false | 936 | [
"MIT"
] | 0 | 7ec831f5e55f5f181e0196fa78284e2846ce2e26 | https://github.com/Pepijnnn/MasterThesis/tree/7ec831f5e55f5f181e0196fa78284e2846ce2e26 |
HadamardProduct | # 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
import ... | CFM-MSG/SDN | HadamardProduct | false | 193 | [
"MIT"
] | 0 | f309602dc2bb73117355003f3744f8e5450dbccc | https://github.com/CFM-MSG/SDN/tree/f309602dc2bb73117355003f3744f8e5450dbccc |
Network | # 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... | joshsia/random-maze-rl | Network | false | 10,302 | [
"MIT"
] | 0 | 016b67d23bfba63182cf06ca17bc9a75baca6ee5 | https://github.com/joshsia/random-maze-rl/tree/016b67d23bfba63182cf06ca17bc9a75baca6ee5 |
CELoss | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.parallel
import torch.optim
import torch.utils.data
class CELoss(nn.Module):
def __init__(self, ratio=1, weight=None, size_average=None,
ignore_index=-100, reduce=None, reduction='mean'):
super(CELoss, self).__init... | 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
... | ahmad4633/mmfashion | CELoss | false | 6,128 | [
"Apache-2.0"
] | 1 | ad2c911bf71bb95dce340a963e7f83c477a84824 | https://github.com/ahmad4633/mmfashion/tree/ad2c911bf71bb95dce340a963e7f83c477a84824 |
Pooling | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
from typing import *
assert_size_stride = torch._C... | rmfan/nni | Pooling | false | 10,941 | [
"MIT"
] | 0 | 727ee1ce47e070061fe3dab8a2da5d3cd5e55546 | https://github.com/rmfan/nni/tree/727ee1ce47e070061fe3dab8a2da5d3cd5e55546 |
GeM | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data
class GeM(nn.Module):
def __init__(self, p=3.0, eps=1e-06, freeze_p=True):
super(GeM, self).__init__()
self.p = p if freeze_p else Parameter(torch.ones(1) * p)
self.eps = eps
def forward(self, ... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
import... | RichardDominik/AIC21-MTMC | GeM | false | 14,311 | [
"MIT"
] | 63 | f69f63f9c40e2dc98e98c7af1cebe3d5605307ee | https://github.com/RichardDominik/AIC21-MTMC/tree/f69f63f9c40e2dc98e98c7af1cebe3d5605307ee |
AttentionLayer | import math
import torch
from torch import nn
from torch.nn import functional as F
import torch.nn.init
def Linear(in_features, out_features, dropout=0.0):
m = nn.Linear(in_features, out_features)
m.weight.data.normal_(mean=0, std=math.sqrt((1 - dropout) / in_features))
m.bias.data.zero_()
return nn.u... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | Shiyang-Yan/Discrete-continous-PG-for-Retrieval | AttentionLayer | false | 17,932 | [
"Apache-2.0"
] | 8 | 39fd7a81f732ae043c2ea20352a0c55b72834639 | https://github.com/Shiyang-Yan/Discrete-continous-PG-for-Retrieval/tree/39fd7a81f732ae043c2ea20352a0c55b72834639 |
ConvMeanPool | import torch
from torch import nn
from matplotlib import pyplot as pyplot
class MyConvo2d(nn.Module):
def __init__(self, input_dim, output_dim, kernel_size, he_init=True,
stride=1, bias=True):
super(MyConvo2d, self).__init__()
self.he_init = he_init
self.padding = int((kernel_size... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
from matplotlib import pyplot as pyplot
assert_size_stride ... | ameya005/Conn_InvNet | ConvMeanPool | false | 3,166 | [
"MIT"
] | 0 | 848a90e45808e540d3047d92b8d0a220da1bc5e7 | https://github.com/ameya005/Conn_InvNet/tree/848a90e45808e540d3047d92b8d0a220da1bc5e7 |
Downsample | # 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... | kimfunn/spatial-smoothing | Downsample | false | 15,841 | [
"Apache-2.0"
] | 438 | 4f849d57c66c2dbdfaa56fc28727e95eddfd337c | https://github.com/kimfunn/spatial-smoothing/tree/4f849d57c66c2dbdfaa56fc28727e95eddfd337c |
EmbeddingModel | import torch
class EmbeddingModel(torch.nn.Module):
@staticmethod
def forward(inputs):
return inputs.repeat(1, 10)
def get_inputs():
return [torch.rand([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... | jina-ai/finetuner | EmbeddingModel | false | 15,682 | [
"Apache-2.0"
] | 270 | 6b8701c6ca372310364e6791c1c2761700dfc150 | https://github.com/jina-ai/finetuner/tree/6b8701c6ca372310364e6791c1c2761700dfc150 |
UnusedIndices | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
import torch.onnx
assert_size_stride = torch._C._dynamo.guards.asser... | Piteryo/onnx2pytorch | UnusedIndices | false | 9,454 | [
"Apache-2.0"
] | 0 | c25b3a5289ee7073d644d280a112c15382b7f690 | https://github.com/Piteryo/onnx2pytorch/tree/c25b3a5289ee7073d644d280a112c15382b7f690 |
ColorJitterLayer | from torch.autograd import Function
import math
import numbers
import torch
import numpy as np
import torch.nn as nn
import torch.utils.cpp_extension
def hsv2rgb(hsv):
"""Convert a 4-d HSV tensor to the RGB counterpart.
>>> %timeit hsv2rgb_lookup(hsv)
2.37 ms ± 13.4 µs per loop (mean ± std. dev. of 7 runs... | 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.... | hugobloem/PyTorch-StudioGAN | ColorJitterLayer | false | 12,654 | [
"MIT"
] | 0 | 3deab27c0774adba5a94c7f452d32d4cbc3b117c | https://github.com/hugobloem/PyTorch-StudioGAN/tree/3deab27c0774adba5a94c7f452d32d4cbc3b117c |
CorrelationAlignmentLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.utils.data
assert_size_stride = torch._C._dyn... | neka-nat/Transfer-Learning-Library | CorrelationAlignmentLoss | false | 16,152 | [
"MIT"
] | 1,474 | a3b27b0d7562fa90a02e914140b37ab438469e6c | https://github.com/neka-nat/Transfer-Learning-Library/tree/a3b27b0d7562fa90a02e914140b37ab438469e6c |
Cosine | # 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
from torch.optim.lr... | kiminh/mt-dnn | Cosine | false | 7,036 | [
"MIT"
] | 1 | 133884b380244dbe74acc4d7507e551b2c5035b3 | https://github.com/kiminh/mt-dnn/tree/133884b380244dbe74acc4d7507e551b2c5035b3 |
GlobalAttentionGeneral | # 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.... | Thesis-02F/Style-Attn | GlobalAttentionGeneral | false | 1,145 | [
"MIT"
] | 0 | 55f78de4858e395ebf9750a23923fd772600290f | https://github.com/Thesis-02F/Style-Attn/tree/55f78de4858e395ebf9750a23923fd772600290f |
BertSelfAttention | from _paritybench_helpers import _mock_config
import math
import torch
from torch import nn
class BertSelfAttention(nn.Module):
def __init__(self, config):
super(BertSelfAttention, self).__init__()
if config.hidden_size % config.num_attention_heads != 0:
raise ValueError(
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | Adoni/pytorch-pretrained-BERT | BertSelfAttention | false | 3,503 | [
"Apache-2.0"
] | 0 | 845c33f00e933626dcfc96e0923ecf034295ef75 | https://github.com/Adoni/pytorch-pretrained-BERT/tree/845c33f00e933626dcfc96e0923ecf034295ef75 |
ScaledDotProductAttention | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | daiki-kimura/commonsense-rl | ScaledDotProductAttention | false | 12,245 | [
"Apache-2.0"
] | 0 | 5513926957b6501ce9cfa46f77f8f2c1c4892fa5 | https://github.com/daiki-kimura/commonsense-rl/tree/5513926957b6501ce9cfa46f77f8f2c1c4892fa5 |
Alignment | # 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.... | Chriskuei/FedMatch | Alignment | false | 18,378 | [
"Apache-2.0"
] | 4 | 305e8c4bbb398712b00c883a986dfec17b500f76 | https://github.com/Chriskuei/FedMatch/tree/305e8c4bbb398712b00c883a986dfec17b500f76 |
CausalConv2d | import torch
from torch import nn
import torch.utils.data
class WNConv2d(nn.Module):
def __init__(self, in_channel, out_channel, kernel_size, stride=1,
padding=0, bias=True, activation=None):
super().__init__()
self.conv = nn.utils.weight_norm(nn.Conv2d(in_channel, out_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._inductor.runtime.triton_helpers import libdevice
from torch import n... | Shivanshu-Gupta/KaoKore-VQ-VAE2 | CausalConv2d | false | 1,075 | [
"MIT"
] | 0 | 38a88ba312dee3c0e2c1aaf02e1c1754ba19ac0c | https://github.com/Shivanshu-Gupta/KaoKore-VQ-VAE2/tree/38a88ba312dee3c0e2c1aaf02e1c1754ba19ac0c |
BCELoss | import torch
import torch.nn as nn
import torch.nn.functional as F
class BCELoss(nn.Module):
"""Binary Cross Entropy loss."""
def __init__(self, use_target_weight=False, loss_weight=1.0):
super().__init__()
self.criterion = F.binary_cross_entropy
self.use_target_weight = use_target_we... | 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... | ALISCIFP/mmpose | BCELoss | false | 2,053 | [
"Apache-2.0"
] | 0 | 2433e3dbcc44baa2253e2a7c748ba0216937933e | https://github.com/ALISCIFP/mmpose/tree/2433e3dbcc44baa2253e2a7c748ba0216937933e |
act_PRT | import torch
import torch.nn as nn
import torch.utils.model_zoo
class act_PRT(nn.Module):
def __init__(self, affine=True):
super(act_PRT, self).__init__()
self.relu = nn.ReLU(inplace=False)
self.prelu = nn.PReLU(num_parameters=1)
self.tanh = nn.Tanh()
def forward(self, x):
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
import... | Cheeun/FDSR | act_PRT | false | 4,984 | [
"MIT"
] | 1 | 28b1c3c102334c5336038d0a0f6e1fceb393659a | https://github.com/Cheeun/FDSR/tree/28b1c3c102334c5336038d0a0f6e1fceb393659a |
BasicBlock | import torch
def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1, padding=0):
"""3x3 convolution with padding"""
return torch.nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=
stride, padding=padding, groups=groups, bias=False, dilation=dilation)
class BasicBlock(torch.nn.Module)... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
assert_size_stride = torch._C... | leggedrobotics/DeLORA | BasicBlock | false | 15,900 | [
"BSD-3-Clause"
] | 154 | 909948d63a9517e6dd54bedcf099f6b39ded2cb4 | https://github.com/leggedrobotics/DeLORA/tree/909948d63a9517e6dd54bedcf099f6b39ded2cb4 |
TransformerEncoderLayer | # 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.... | IA3005/NLP_ens | TransformerEncoderLayer | false | 11,607 | [
"MIT"
] | 0 | 794ebbff46d5e6d5476f29b577b40bbb52991246 | https://github.com/IA3005/NLP_ens/tree/794ebbff46d5e6d5476f29b577b40bbb52991246 |
StdConv3d | import torch
from torch import nn
import torch.jit
import torch.nn.functional as F
import torch.utils.data
import torch.nn.functional
class StdConv3d(nn.Conv3d):
def forward(self, x):
w = self.weight
v, m = torch.var_mean(w, dim=[1, 2, 3], keepdim=True, unbiased=False)
w = (w - m) / torch... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch import n... | Magnety/nnUNet | StdConv3d | false | 9,599 | [
"Apache-2.0"
] | 0 | f07e6fdf191377550c57bcdc8859798486f60443 | https://github.com/Magnety/nnUNet/tree/f07e6fdf191377550c57bcdc8859798486f60443 |
Rescale | import torch
import torch.nn as nn
class Rescale(nn.Module):
def __init__(self, sign):
super(Rescale, self).__init__()
rgb_mean = 0.4488, 0.4371, 0.404
bias = sign * torch.Tensor(rgb_mean).reshape(1, 3, 1, 1)
self.bias = nn.Parameter(bias, requires_grad=False)
def forward(sel... | 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... | zeta1999/torchSR | Rescale | false | 11,091 | [
"MIT"
] | 0 | 8f8154486f6c0f09942ccf86cdcbf496e2309d4e | https://github.com/zeta1999/torchSR/tree/8f8154486f6c0f09942ccf86cdcbf496e2309d4e |
ModelNet | # 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_... | patelrajnath/makeesy-deep-learning | ModelNet | false | 7,451 | [
"Apache-2.0"
] | 1 | 172f8a4301d6b60927824a56648d60559ba3f14e | https://github.com/patelrajnath/makeesy-deep-learning/tree/172f8a4301d6b60927824a56648d60559ba3f14e |
L2Norm | import torch
import torch.nn as nn
class Scale(nn.Module):
def __init__(self, nchannels, bias=True, init_scale=1.0):
super().__init__()
self.nchannels = nchannels
self.weight = nn.Parameter(torch.Tensor(1, nchannels, 1, 1))
if bias:
self.bias = nn.Parameter(torch.Tenso... | 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_... | CuongNguyen218/ObjectDetection-OneStageDet | L2Norm | false | 335 | [
"MIT"
] | 0 | 60efe8b0ee6782b2aea20a32264b2ce1fc21901f | https://github.com/CuongNguyen218/ObjectDetection-OneStageDet/tree/60efe8b0ee6782b2aea20a32264b2ce1fc21901f |
ReduceSumModule | # 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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torc... | MichaelZhero/nncase | ReduceSumModule | false | 11,935 | [
"Apache-2.0"
] | 0 | 0fae6ce90d7adff386e1a286cd2b42422f4b850a | https://github.com/MichaelZhero/nncase/tree/0fae6ce90d7adff386e1a286cd2b42422f4b850a |
SReLU | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | juancprzs/ISONet | SReLU | false | 6,989 | [
"MIT"
] | 1 | a0422942b53255d093197aa93c77cc3fa941bcdf | https://github.com/juancprzs/ISONet/tree/a0422942b53255d093197aa93c77cc3fa941bcdf |
SummaryNet | # 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_... | Wrede/BNN-LFI | SummaryNet | false | 2,960 | [
"MIT"
] | 0 | 8c5094f01c1eef286bdd84613c7259d534d2eb7e | https://github.com/Wrede/BNN-LFI/tree/8c5094f01c1eef286bdd84613c7259d534d2eb7e |
GatedLinearUnit | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_... | ZhuangweiKang/pytorch-ts | GatedLinearUnit | false | 2,994 | [
"Apache-2.0",
"MIT"
] | 0 | 076d456358fd1bac96becba4f1ba38ec5a5fcf4d | https://github.com/ZhuangweiKang/pytorch-ts/tree/076d456358fd1bac96becba4f1ba38ec5a5fcf4d |
HyperpriorSynthesis | # 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
import ... | sedrickkeh/high-fidelity-dual-image | HyperpriorSynthesis | false | 16,419 | [
"Apache-2.0"
] | 266 | 9cefd378467826b91596653df38666e469bb23e0 | https://github.com/sedrickkeh/high-fidelity-dual-image/tree/9cefd378467826b91596653df38666e469bb23e0 |
Triaffine | # 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... | matejklemen/morphological-dependency-parsing | Triaffine | false | 3,981 | [
"MIT"
] | 0 | 2ab24b8621debe6e3288ade01c9604a06f9bd453 | https://github.com/matejklemen/morphological-dependency-parsing/tree/2ab24b8621debe6e3288ade01c9604a06f9bd453 |
SelfGated | # 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.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size... | jamaalhay/Final_Proj | SelfGated | false | 15,666 | [
"MIT"
] | 104 | 3f524a90fee5a3cb21466ab76f630d060792045d | https://github.com/jamaalhay/Final_Proj/tree/3f524a90fee5a3cb21466ab76f630d060792045d |
ShallowCombination | # 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... | stefan-it/bertram | ShallowCombination | false | 16,497 | [
"Apache-2.0"
] | 50 | 2e449cdc677577d1ca8b9daf852f324be4074940 | https://github.com/stefan-it/bertram/tree/2e449cdc677577d1ca8b9daf852f324be4074940 |
StateAttention | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | zhangyuejoslin/selfmonitoring-agent | StateAttention | false | 4,663 | [
"MIT"
] | 0 | 9401ceb492f6c4576d62404b62e815d184136b24 | https://github.com/zhangyuejoslin/selfmonitoring-agent/tree/9401ceb492f6c4576d62404b62e815d184136b24 |
GCN | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch.nn import Module
import math
import torch.nn as nn
from torch.nn.modu... | caojiangxia/BiGI | GCN | false | 14,997 | [
"MIT"
] | 57 | ed54c20523a5b3f295b90a9c08f7c54e8258d04a | https://github.com/caojiangxia/BiGI/tree/ed54c20523a5b3f295b90a9c08f7c54e8258d04a |
ModulatedConv2d | # 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 ... | guyii54/Contrastive-I2I | ModulatedConv2d | false | 6,777 | [
"BSD-3-Clause"
] | 1 | e73daa0f9d3770c2280a304c39678d5b22440647 | https://github.com/guyii54/Contrastive-I2I/tree/e73daa0f9d3770c2280a304c39678d5b22440647 |
TemperatureScaleTrainer | import torch
import torch.nn as nn
class TemperatureScaleTrainer(nn.Module):
def __init__(self):
super(TemperatureScaleTrainer, self).__init__()
self._temperature = nn.Parameter(torch.ones(1), requires_grad=True)
if torch.cuda.is_available():
self._temperature
self... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | Kageshimasu/temperature-scaling-optimizer | TemperatureScaleTrainer | false | 1,196 | [
"MIT"
] | 0 | 3af562e6c3fefef97aec0431d08b8e8275d275c7 | https://github.com/Kageshimasu/temperature-scaling-optimizer/tree/3af562e6c3fefef97aec0431d08b8e8275d275c7 |
ResnetBlock | # 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
from torch.nn import functional as F
import torch.utils.dat... | arnabgho/GAN_stability | ResnetBlock | false | 12,116 | [
"MIT"
] | 0 | 5037d1d856be58818d1c825cd415e0d90d90aff2 | https://github.com/arnabgho/GAN_stability/tree/5037d1d856be58818d1c825cd415e0d90d90aff2 |
Gaussian_Distance | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch ... | FupingWu90/VarDA | Gaussian_Distance | false | 8,170 | [
"MIT"
] | 14 | cfea269a4f608128bb5b13a778619b17d7123bfa | https://github.com/FupingWu90/VarDA/tree/cfea269a4f608128bb5b13a778619b17d7123bfa |
AdaAttN | # 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.... | JerryLeolfl/AdaAttN | AdaAttN | false | 2,412 | [
"MIT"
] | 0 | 062c66f7818b344e3730ce9d6df7af03f9acb4f5 | https://github.com/JerryLeolfl/AdaAttN/tree/062c66f7818b344e3730ce9d6df7af03f9acb4f5 |
Block | import torch
import torch as th
from torch import nn
def drop_path(x, drop_prob: 'float'=0.0, training: 'bool'=False):
"""Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
This is the same as the DropConnect impl I created for EfficientNet, etc networks, however,
th... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | q5628077/Transformer-in-RL | Block | false | 4,206 | [
"MIT"
] | 0 | 14679656779a372d91d9fbd89bd802b5ff34c200 | https://github.com/q5628077/Transformer-in-RL/tree/14679656779a372d91d9fbd89bd802b5ff34c200 |
VGG_16 | import torch
import torch.nn.functional as F
import torch.nn as nn
class VGG_16(nn.Module):
"""
VGG-16 without pooling layer before fc layer
"""
def __init__(self):
super(VGG_16, self).__init__()
self.convolution1_1 = nn.Conv2d(3, 64, 3, padding=1)
self.convolution1_2 = nn.Con... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | qiu9yu/Lets_OCR | VGG_16 | false | 16,368 | [
"MIT"
] | 671 | 62d68b044250d02a9d5ac8c4fbd08cec83faa0d1 | https://github.com/qiu9yu/Lets_OCR/tree/62d68b044250d02a9d5ac8c4fbd08cec83faa0d1 |
DenseBlock | import torch
from torch import nn
from torch.nn import functional as F
class CausalConv1d(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size=2, dilation=2):
super(CausalConv1d, self).__init__()
self.padding = dilation
self.causal_conv = nn.Conv1d(in_channels, out_channe... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch import n... | yuwl798180/FewRel | DenseBlock | false | 4,647 | [
"MIT"
] | 0 | 8126e440b5d5d178e221cfb4a97a69cabd771fa4 | https://github.com/yuwl798180/FewRel/tree/8126e440b5d5d178e221cfb4a97a69cabd771fa4 |
SequenceCrossEntropyLoss | import torch
import torch.utils.data
from torch import nn
from torch.nn.utils.rnn import *
from sklearn.metrics import *
class SequenceCrossEntropyLoss(nn.Module):
def __init__(self):
super(SequenceCrossEntropyLoss, self).__init__()
self.criterion = nn.CrossEntropyLoss(reduce=False)
def forw... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.utils.dat... | APMplusplus/falkon | SequenceCrossEntropyLoss | false | 18,449 | [
"Apache-2.0"
] | 2 | 95708ed0b28c4ec0f611446a478e9c3445eb3508 | https://github.com/APMplusplus/falkon/tree/95708ed0b28c4ec0f611446a478e9c3445eb3508 |
AdaIN | import math
import torch
import torch.nn as nn
from numpy import prod
def getLayerNormalizationFactor(x):
"""
Get He's constant for the given layer
https://www.cv-foundation.org/openaccess/content_iccv_2015/papers/He_Delving_Deep_into_ICCV_2015_paper.pdf
"""
size = x.weight.size()
fan_in = pro... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | AnetaKaczynska/video-GAN | AdaIN | false | 44 | [
"BSD-3-Clause"
] | 0 | e30e54c18265c658a65b1b26b57b4f499b58bfc6 | https://github.com/AnetaKaczynska/video-GAN/tree/e30e54c18265c658a65b1b26b57b4f499b58bfc6 |
ConvTransposeInstanceNorm2d | import torch
import torch.nn as nn
from typing import Tuple
from typing import Union
class ConvTransposeInstanceNorm2d(nn.Module):
def __init__(self, in_channels: 'int', out_channels: 'int', kernel_size:
'Union[int, Tuple[int]]', stride: 'Union[int, Tuple[int]]'=1,
padding: 'Union[int, Tuple[int]... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | Geson-anko/ReimplementCycleGAN | ConvTransposeInstanceNorm2d | false | 2,288 | [
"MIT"
] | 0 | 3bd40c519d53a7ebb284c718e935e3832326633f | https://github.com/Geson-anko/ReimplementCycleGAN/tree/3bd40c519d53a7ebb284c718e935e3832326633f |
RelErrorLoss | import torch
from torch import nn
class RelErrorLoss(nn.Module):
def __init__(self):
super(RelErrorLoss, self).__init__()
self.eps = 1e-06
def forward(self, input, target):
return torch.mean(torch.abs(target - input) / (target + self.eps))
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
from torch import nn
a... | NREL/ml-combustion-pdf-models | RelErrorLoss | false | 17,726 | [
"Apache-2.0"
] | 6 | 0505b9c54ab4c1e2b7ef8ca9f59f76bfb2e3732d | https://github.com/NREL/ml-combustion-pdf-models/tree/0505b9c54ab4c1e2b7ef8ca9f59f76bfb2e3732d |
ResidualConvUnit | import torch
import torch.nn as nn
class ResidualConvUnit(nn.Module):
"""Residual convolution module.
"""
def __init__(self, features):
"""Init.
Args:
features (int): number of features
"""
super().__init__()
self.conv1 = nn.Conv2d(features, 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
import torch.nn as nn
assert_... | maayan-myheritage/3d-photo-inpainting | ResidualConvUnit | false | 10,590 | [
"MIT"
] | 0 | 6293eecfeb55ceba019655723f6efe31e8ecb177 | https://github.com/maayan-myheritage/3d-photo-inpainting/tree/6293eecfeb55ceba019655723f6efe31e8ecb177 |
SeparableConv1D | # 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... | johnjosephmorgan/snowfall | SeparableConv1D | false | 12,739 | [
"Apache-2.0"
] | 0 | 604d789c0aed035626d6745e6d7a427168063cae | https://github.com/johnjosephmorgan/snowfall/tree/604d789c0aed035626d6745e6d7a427168063cae |
VAE | # 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
import ... | niujinshuchong/stochastic_processes | VAE | false | 4,099 | [
"MIT"
] | 0 | ea2538d2f09c39bec1834df5addd37e0699a88bf | https://github.com/niujinshuchong/stochastic_processes/tree/ea2538d2f09c39bec1834df5addd37e0699a88bf |
SinusoidPositionalEmbedding | # 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... | yzhangcs/parser | SinusoidPositionalEmbedding | false | 16,789 | [
"MIT"
] | 439 | 3abebde1c9fe0bf2e99adce845aaf2a04b194f8a | https://github.com/yzhangcs/parser/tree/3abebde1c9fe0bf2e99adce845aaf2a04b194f8a |
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.... | TylerChoi1224/torchdiffeq | ResBlock | false | 1,172 | [
"MIT"
] | 0 | 72f74d9651a58ab11cdadd60682f1b61e625ef53 | https://github.com/TylerChoi1224/torchdiffeq/tree/72f74d9651a58ab11cdadd60682f1b61e625ef53 |
CrossEntropyLoss | import torch
import torch.nn.functional as F
import torch.nn as nn
def _is_long(x):
return isinstance(x, torch.LongTensor) or isinstance(x, torch.LongTensor)
def onehot(indexes, N=None, ignore_index=None):
"""
Creates a one-representation of indexes with N possible entries
if N is not specified, it ... | 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... | RicJM/weighted_c2d | CrossEntropyLoss | false | 14,315 | [
"MIT"
] | 49 | 38053869b77c1544349c53ba6f3c1325254aa413 | https://github.com/RicJM/weighted_c2d/tree/38053869b77c1544349c53ba6f3c1325254aa413 |
series_decomp | import math
import torch
import torch.nn as nn
class moving_avg(nn.Module):
"""
Moving average block to highlight the trend of time series
"""
def __init__(self, kernel_size, stride):
super(moving_avg, self).__init__()
self.kernel_size = kernel_size
self.avg = nn.AvgPool1d(ker... | 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 math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guar... | MAZiqing/FEDformer | series_decomp | false | 17,648 | [
"MIT"
] | 7 | 7914d39df829494a8172afb9676982c3789d491d | https://github.com/MAZiqing/FEDformer/tree/7914d39df829494a8172afb9676982c3789d491d |
AvgLayer | import torch
import torch.nn as nn
import torch.utils.data
class AvgLayer(nn.Module):
def forward(self, input):
return input.mean(3, keepdim=True)
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.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C.... | pYtoner/EasyOCR | AvgLayer | false | 7,437 | [
"Apache-2.0"
] | 1 | cbb2df77ae789dd4c7807541e0357d9a698ba801 | https://github.com/pYtoner/EasyOCR/tree/cbb2df77ae789dd4c7807541e0357d9a698ba801 |
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.... | IAMZn1018/ccks2021-entity-linking | SelfAttention | false | 9,105 | [
"Apache-2.0"
] | 0 | 6596b0b16d8c1fc4400c736b30ff46158d1575e4 | https://github.com/IAMZn1018/ccks2021-entity-linking/tree/6596b0b16d8c1fc4400c736b30ff46158d1575e4 |
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
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empt... | Jiongqi/RectAngle | DiceLoss | false | 9,220 | [
"MIT"
] | 0 | 558fa036d1b21b5ae0a556271ab674cd8ffe88b6 | https://github.com/Jiongqi/RectAngle/tree/558fa036d1b21b5ae0a556271ab674cd8ffe88b6 |
Padding3 | # 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._utils
assert_size_stride = torch._C._dynamo.guards.assert_size_str... | ijinjay/torch2mindspore | Padding3 | false | 3,657 | [
"MIT"
] | 0 | e4c06bd5e8a3b25b72bf158393a66c5cd1b572d2 | https://github.com/ijinjay/torch2mindspore/tree/e4c06bd5e8a3b25b72bf158393a66c5cd1b572d2 |
FeedForward | # 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... | jaketae/res-mlp | FeedForward | false | 12,600 | [
"MIT"
] | 0 | 6c957e4fe67a2f13d9b4fd3fa36b7eddcf5323fd | https://github.com/jaketae/res-mlp/tree/6c957e4fe67a2f13d9b4fd3fa36b7eddcf5323fd |
BasicUNet | import torch
from torch import nn
import torch.nn.functional as F
class BasicConvBlock(nn.Module):
def __init__(self, in_channels, out_channels):
super(BasicConvBlock, self).__init__()
self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3,
padding=1)
self.conv2 = 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
from torch._inductor.runtime.... | royerloic/aydin | BasicUNet | false | 16,420 | [
"BSD-3-Clause"
] | 78 | f9c61a24030891d008c318b250da5faec69fcd7d | https://github.com/royerloic/aydin/tree/f9c61a24030891d008c318b250da5faec69fcd7d |
BCE_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 import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch.... | ModelTC/EOD | BCE_LOSS | false | 14,056 | [
"Apache-2.0"
] | 196 | 164bff80486e9ae6a095a97667b365c46ceabd86 | https://github.com/ModelTC/EOD/tree/164bff80486e9ae6a095a97667b365c46ceabd86 |
SoftDetectionModule | import torch
import torch.nn.functional as F
import torch.nn as nn
class SoftDetectionModule(nn.Module):
def __init__(self, soft_local_max_size=3):
super(SoftDetectionModule, self).__init__()
self.soft_local_max_size = soft_local_max_size
self.pad = self.soft_local_max_size // 2
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 import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | imelekhov/d2-net | SoftDetectionModule | false | 3,666 | [
"BSD-3-Clause-Clear"
] | 0 | 68a61797c40a4d6226c1774d84d97c4f493c9955 | https://github.com/imelekhov/d2-net/tree/68a61797c40a4d6226c1774d84d97c4f493c9955 |
Actor | import torch
import torch.nn as nn
import torch.nn.functional as F
class Actor(nn.Module):
def __init__(self, input_size, action_size):
super(Actor, self).__init__()
self.fc1 = nn.Linear(input_size, 200)
self.output = nn.Linear(200, action_size)
def forward(self, x):
x = F.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._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | NeuralFlux/rl-analysis | Actor | false | 5,660 | [
"MIT"
] | 1 | bb45e1f8bb9da4683cce4bd0a5e687770a4005e2 | https://github.com/NeuralFlux/rl-analysis/tree/bb45e1f8bb9da4683cce4bd0a5e687770a4005e2 |
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