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 |
|---|---|---|---|---|---|---|---|---|---|---|
LSTM | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | askliar/deep_learning | LSTM | false | 1,569 | [
"MIT"
] | 0 | e61b2391a3258d18719bf12d9ed1404620ce6c02 | https://github.com/askliar/deep_learning/tree/e61b2391a3258d18719bf12d9ed1404620ce6c02 |
BinaryMinus | import abc
import inspect
import torch
import warnings
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
from typing import Any
from typing import *
def get_module_name(cls_or_func):
module_name = cls_or_func.__module__
if module_name == '__main__':
for frm in i... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import abc
import inspect
import warnings
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
from typ... | Johnsonms/NNI_master | BinaryMinus | false | 11,571 | [
"MIT"
] | 0 | e5e5c7aed89cf3189cffe1056464833c15eb54ff | https://github.com/Johnsonms/NNI_master/tree/e5e5c7aed89cf3189cffe1056464833c15eb54ff |
GroupLinear | import torch
import torch.optim
import torch.nn as nn
import torch.nn.functional as f
class GroupLinear(nn.Module):
def __init__(self, groups, channels, map_size, dropout=None):
super(GroupLinear, self).__init__()
self.groups = groups
self.channels = channels
self.map_size = map_s... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.optim
import torch.nn as nn
assert_size_stride = torch._C._dynamo.g... | tiruns/grad_proj | GroupLinear | false | 4,427 | [
"MIT"
] | 0 | 8882ff1e3205e346e972d963480c57dbf5aef407 | https://github.com/tiruns/grad_proj/tree/8882ff1e3205e346e972d963480c57dbf5aef407 |
MiniBatchDiscrimination | import torch
import torch.nn as nn
from torch.nn import init
class MiniBatchDiscrimination(nn.Module):
"""
source: https://gist.github.com/t-ae/732f78671643de97bbe2c46519972491
paper: Salimans et al. 2016. Improved Methods for Training GANs
"""
def __init__(self, in_features, out_features, kernel... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.... | danielnflam/GAN-Tests | MiniBatchDiscrimination | false | 3,428 | [
"BSD-3-Clause"
] | 0 | f112e27b802d717f64a8f2cfa79b9898667da14c | https://github.com/danielnflam/GAN-Tests/tree/f112e27b802d717f64a8f2cfa79b9898667da14c |
BaselineActor | # 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.... | greenstar1151/pytorch-benchmark | BaselineActor | false | 10,440 | [
"BSD-3-Clause"
] | 0 | 8b7808d3be6b7ca1d57f1812e35fd2df5e470f8b | https://github.com/greenstar1151/pytorch-benchmark/tree/8b7808d3be6b7ca1d57f1812e35fd2df5e470f8b |
MLP | import torch
import torch.nn as nn
class MLP(nn.Module):
def __init__(self, input_size, output_size, hidden_size=500,
weight_decay=0.0):
super(MLP, self).__init__()
self.i2h = nn.Linear(in_features=input_size, out_features=hidden_size)
self.Dropout = nn.Dropout(p=0.5)
self... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | gchrupala/lyz | MLP | false | 12,419 | [
"MIT"
] | 0 | e1e99899af65f6c4cb1fd77485f6fa61ba3500f5 | https://github.com/gchrupala/lyz/tree/e1e99899af65f6c4cb1fd77485f6fa61ba3500f5 |
Standardize | from torch.nn import Module
import torch
from torch.nn import init
from torch.nn.parameter import Parameter
class Standardize(Module):
"""
Applies (element-wise) standardization with trainable translation parameter μ and scale parameter σ, i.e. computes
(x - μ) / σ where '/' is applied element-wise.
... | 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
from torch.nn import init
from torch.nn.parameter import Parameter
assert_size_stride = torch._C._dynamo.guards.... | COMP6248-Reproducability-Challenge/MoveBrick_Reproducibility_DeepSAD | Standardize | false | 11,277 | [
"MIT"
] | 0 | 8985dc9cd8741010362c6ca51e72648b7bd3908f | https://github.com/COMP6248-Reproducability-Challenge/MoveBrick_Reproducibility_DeepSAD/tree/8985dc9cd8741010362c6ca51e72648b7bd3908f |
GLU | # 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 Tensor
from torch import nn as nn
import torch.nn.functional a... | gdevos010/darts | GLU | false | 3,663 | [
"Apache-2.0"
] | 0 | 96c97c1e241500ae7b91d32bbfa21d811e4a7d71 | https://github.com/gdevos010/darts/tree/96c97c1e241500ae7b91d32bbfa21d811e4a7d71 |
ResidualAttentionBlock | # 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.... | HIT-SCIR-xuanxuan/OpenKS | ResidualAttentionBlock | false | 13,756 | [
"Apache-2.0"
] | 88 | a7f2ce0890822113322aad22e98d6c961e63caef | https://github.com/HIT-SCIR-xuanxuan/OpenKS/tree/a7f2ce0890822113322aad22e98d6c961e63caef |
BridgeFeatLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
import... | neka-nat/Transfer-Learning-Library | BridgeFeatLoss | false | 16,144 | [
"MIT"
] | 1,474 | a3b27b0d7562fa90a02e914140b37ab438469e6c | https://github.com/neka-nat/Transfer-Learning-Library/tree/a3b27b0d7562fa90a02e914140b37ab438469e6c |
Hflip | import torch
import torch.nn as nn
def hflip(input: 'torch.Tensor') ->torch.Tensor:
return torch.flip(input, [-1])
class Hflip(nn.Module):
"""Horizontally flip a tensor image or a batch of tensor images.
Input must be a tensor of shape (C, H, W) or a batch of tensors :math:`(*, C, H, W)`.
Args:
... | 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... | justanhduc/kornia | Hflip | false | 15,750 | [
"ECL-2.0",
"Apache-2.0"
] | 51 | c14081292dfb2491fad50ba10e27491cad8cb3e3 | https://github.com/justanhduc/kornia/tree/c14081292dfb2491fad50ba10e27491cad8cb3e3 |
CNN | import torch
import torch.nn as nn
import torch.nn.functional as F
class CNN(nn.Module):
def __init__(self):
super(CNN, self).__init__()
self.conv1 = nn.Conv2d(1, 10, kernel_size=5)
self.conv2 = nn.Conv2d(10, 20, kernel_size=5)
self.conv2_drop = nn.Dropout2d()
self.fc1 = n... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | awesome-archive/DeepLearningWithPyTorch | CNN | false | 14,931 | [
"MIT"
] | 85 | 921e3c1bc33f88e2b749dd1f9dac8a414bd4a1ee | https://github.com/awesome-archive/DeepLearningWithPyTorch/tree/921e3c1bc33f88e2b749dd1f9dac8a414bd4a1ee |
Luong_Attention | import torch
import torch.nn as nn
import torch.nn.functional as F
class Luong_Attention(nn.Module):
def __init__(self, hidden_size, score='general'):
super(Luong_Attention, self).__init__()
assert score.lower() in ['concat', 'general', 'dot']
self.score = score.lower()
def wn(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.... | placaille/nmt-comp550 | Luong_Attention | false | 7,499 | [
"MIT"
] | 1 | 5809ca68dbd7e5452361700f905740a783f9451c | https://github.com/placaille/nmt-comp550/tree/5809ca68dbd7e5452361700f905740a783f9451c |
Bicubic | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
from torch.nn import Module
... | ShivanshuPurohit/Diffusion | Bicubic | false | 1,057 | [
"MIT"
] | 0 | 9a190d9aa4ed9767cf223e4ef57d0c31690f92cc | https://github.com/ShivanshuPurohit/Diffusion/tree/9a190d9aa4ed9767cf223e4ef57d0c31690f92cc |
BetaMish | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.gu... | dcrmg/Efficient-Segmentation-Networks | BetaMish | false | 3,429 | [
"MIT"
] | 0 | e2f2d90d69e4e9af464678b0f02bc754c28f643d | https://github.com/dcrmg/Efficient-Segmentation-Networks/tree/e2f2d90d69e4e9af464678b0f02bc754c28f643d |
LReluCustom | import torch
from torch import nn
class LReluCustom(nn.Module):
def __init__(self, leak=0.1):
super(LReluCustom, self).__init__()
self.leak = leak
def forward(self, x):
return torch.max(x, self.leak * x)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs(... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empt... | kkulczak/phrases_reconstruction_GAN | LReluCustom | false | 3,835 | [
"MIT"
] | 0 | 5cf273416eb714f813a8d603942a442f0933cbff | https://github.com/kkulczak/phrases_reconstruction_GAN/tree/5cf273416eb714f813a8d603942a442f0933cbff |
styleLoss_v2 | import torch
import torch.nn as nn
def calc_mean_std(feat, eps=1e-05):
size = feat.size()
assert len(size) == 4
N, C = size[:2]
feat_var = feat.view(N, C, -1).var(dim=2) + eps
feat_std = feat_var.sqrt().view(N, C, 1, 1)
feat_mean = feat.view(N, C, -1).mean(dim=2).view(N, C, 1, 1)
return fe... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | Holmes-Alan/Photo2Sketch | styleLoss_v2 | false | 539 | [
"MIT"
] | 0 | 43a0ca6bb8a8e645b35a2ab23d11ed5efe117e09 | https://github.com/Holmes-Alan/Photo2Sketch/tree/43a0ca6bb8a8e645b35a2ab23d11ed5efe117e09 |
Mean | # 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... | WillyChen123/CDFNet | Mean | false | 1,230 | [
"MIT"
] | 0 | 12d6b288aa2a8301683395a75bd44a7be44b7f2a | https://github.com/WillyChen123/CDFNet/tree/12d6b288aa2a8301683395a75bd44a7be44b7f2a |
WorldNet | # 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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cu... | tim-ts-chu/mbpo | WorldNet | false | 10,854 | [
"MIT"
] | 0 | 0d98e6e80499a82812d3361658e0707c0b489fc5 | https://github.com/tim-ts-chu/mbpo/tree/0d98e6e80499a82812d3361658e0707c0b489fc5 |
GL | # 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... | wofmanaf/ResT | GL | false | 16,728 | [
"Apache-2.0"
] | 178 | 508e30b28036e2cb882a03d24268dc70eb0c82a3 | https://github.com/wofmanaf/ResT/tree/508e30b28036e2cb882a03d24268dc70eb0c82a3 |
PreActBlock | # 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... | cwmok/LapIRN | PreActBlock | false | 15,095 | [
"MIT"
] | 53 | d8f96770a704b1f190955cc26297c7b01a270b0a | https://github.com/cwmok/LapIRN/tree/d8f96770a704b1f190955cc26297c7b01a270b0a |
MultiheadAttention | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | ChHanXiao/mmdetection | MultiheadAttention | false | 9,169 | [
"Apache-2.0"
] | 0 | 324aa5a042857a9b57abe37385e1210709a20d02 | https://github.com/ChHanXiao/mmdetection/tree/324aa5a042857a9b57abe37385e1210709a20d02 |
ResNetBlockGroupNorm | 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)
class ResNetBlockGroupNorm(nn.Module):
def __init__(self, inplanes, planes, num_groups... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | TRUMANCFY/wolf | ResNetBlockGroupNorm | false | 2,965 | [
"Apache-2.0"
] | 0 | 1a21479256e4f51885e2d2fdd449b1faa61277a6 | https://github.com/TRUMANCFY/wolf/tree/1a21479256e4f51885e2d2fdd449b1faa61277a6 |
GLU | # 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.onnx
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strid... | Nakachi-S/AttnGAN | GLU | false | 869 | [
"MIT"
] | 0 | 2dfd1e38f78f2a58895d81131cd8c17e74dbacb2 | https://github.com/Nakachi-S/AttnGAN/tree/2dfd1e38f78f2a58895d81131cd8c17e74dbacb2 |
MseCriterion | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch.nn.modules.loss import _Loss
from torch.optim.lr_scheduler import *
assert_siz... | anlewy/mt-dnn | MseCriterion | false | 14,863 | [
"MIT"
] | 2,075 | eeb6f01ce0630e61a52b8c9c6f7537cd34978e45 | https://github.com/anlewy/mt-dnn/tree/eeb6f01ce0630e61a52b8c9c6f7537cd34978e45 |
TVLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.j... | Holmes-Alan/Photo2Sketch | TVLoss | false | 530 | [
"MIT"
] | 0 | 43a0ca6bb8a8e645b35a2ab23d11ed5efe117e09 | https://github.com/Holmes-Alan/Photo2Sketch/tree/43a0ca6bb8a8e645b35a2ab23d11ed5efe117e09 |
DiscriminatorLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch.nn import Module
import torch.utils.data
import torch.nn.functional
import tor... | techthiyanes/annotated_deep_learning_paper_implementations | DiscriminatorLoss | false | 16,545 | [
"MIT"
] | 3,714 | 8af24da2dd39a9a87482a4d18c2dc829bbd3fd47 | https://github.com/techthiyanes/annotated_deep_learning_paper_implementations/tree/8af24da2dd39a9a87482a4d18c2dc829bbd3fd47 |
PositionwiseFeedForward | import torch
import torch.nn as nn
class LayerNorm(nn.Module):
"""
Layer Normalization class
"""
def __init__(self, features, eps=1e-06):
super(LayerNorm, self).__init__()
self.weight = nn.Parameter(torch.ones(features))
self.bias = nn.Parameter(torch.zeros(features))
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | chengjunyan1/Graph-Sparse-Transformer | PositionwiseFeedForward | false | 6,425 | [
"Apache-2.0"
] | 1 | 2c3b77f81789ca80e0c30c32f0c702b2d3bac048 | https://github.com/chengjunyan1/Graph-Sparse-Transformer/tree/2c3b77f81789ca80e0c30c32f0c702b2d3bac048 |
AttentionPool | import torch
import torch.nn as nn
class AttentionPool(nn.Module):
"""docstring for AttentionPool"""
def __init__(self, inputdim, outputdim=10, pooldim=1, **kwargs):
super().__init__()
self.inputdim = inputdim
self.outputdim = outputdim
self.pooldim = pooldim
self.tran... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | AjianIronSide/Datadriven-GPVAD | AttentionPool | false | 8,898 | [
"MIT"
] | 0 | 8590b5f794beb9640b8fe70ac1f5add5944425b3 | https://github.com/AjianIronSide/Datadriven-GPVAD/tree/8590b5f794beb9640b8fe70ac1f5add5944425b3 |
Classifier | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import 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
reinterpret_tensor = torch._C._dynamo.guards._reinterp... | Project-Agni/Detection | Classifier | false | 947 | [
"MIT"
] | 0 | 6b2c8ec25f8bd2bd15995d67f2808352cec9e2af | https://github.com/Project-Agni/Detection/tree/6b2c8ec25f8bd2bd15995d67f2808352cec9e2af |
TokenMixer | import torch
import torch.nn as nn
class TokenMixer(nn.Module):
def __init__(self, input_size, hidden_size, dropout=None):
super(TokenMixer, self).__init__()
self.fc1 = nn.Linear(input_size, hidden_size)
self.fc2 = nn.Linear(hidden_size, input_size)
self.dropout = None
if ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | TheRealMarVin/mlp-mixer | TokenMixer | false | 1,147 | [
"MIT"
] | 0 | 2124cb5c5adfc7af473cab535095471d4943adab | https://github.com/TheRealMarVin/mlp-mixer/tree/2124cb5c5adfc7af473cab535095471d4943adab |
RegularizedLinear | # 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.... | krayyalasomayajula/inferno | RegularizedLinear | false | 3,947 | [
"Apache-2.0"
] | 0 | 1c56f34ff19c69dec3d3cb6287b659345bce3492 | https://github.com/krayyalasomayajula/inferno/tree/1c56f34ff19c69dec3d3cb6287b659345bce3492 |
TVLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert... | vlbthambawita/polyp-inpainting | TVLoss | false | 4,504 | [
"MIT"
] | 0 | f1d754f8ffb3f6d991206b2a661933ff32de0d7a | https://github.com/vlbthambawita/polyp-inpainting/tree/f1d754f8ffb3f6d991206b2a661933ff32de0d7a |
L1_Charbonnier_loss | import torch
import torch.nn as nn
class L1_Charbonnier_loss(nn.Module):
"""L1 Charbonnierloss loss function where the epsilon has been taken as 1e-3 from the paper"""
def __init__(self):
super(L1_Charbonnier_loss, self).__init__()
self.eps = 0.001
def forward(self, X, Y):
diff =... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert... | ankurbhatia24/image-super-resolution | L1_Charbonnier_loss | false | 9,752 | [
"Apache-2.0"
] | 0 | 7ebc2be70e1a940addb6ba886a663f88167e6007 | https://github.com/ankurbhatia24/image-super-resolution/tree/7ebc2be70e1a940addb6ba886a663f88167e6007 |
Router | from torch.nn import Module
import torch
from torch import nn
import torch.utils.data
import torch.nn.functional
import torch.autograd
class Squash(Module):
'\n ## Squash\n\n This is **squashing** function from paper, given by equation $(1)$.\n\n $$\\mathbf{v}_j = \x0crac{{\\lVert \\mathbf{s}_j \rVert}^2... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | techthiyanes/annotated_deep_learning_paper_implementations | Router | false | 16,649 | [
"MIT"
] | 3,714 | 8af24da2dd39a9a87482a4d18c2dc829bbd3fd47 | https://github.com/techthiyanes/annotated_deep_learning_paper_implementations/tree/8af24da2dd39a9a87482a4d18c2dc829bbd3fd47 |
D_V | from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
class D_V(nn.Module):
def __init__(self, args):
super(D_V, self).__init__()
self._relu = nn.ReLU()
self._ws1 = nn.Linear(args.video_feature_dim, args.
DV_middle_feature_dim, bias=False)
se... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | HCShi/IONet | D_V | false | 18,364 | [
"MIT"
] | 4 | 42e3c0455a1ecb610f458e814d7310d685b2be7b | https://github.com/HCShi/IONet/tree/42e3c0455a1ecb610f458e814d7310d685b2be7b |
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.... | cimeister/neural-transducer | TransformerEncoderLayer | false | 1,744 | [
"MIT"
] | 0 | e4dfc718bbcf93254ce23750e5428c5131ddfb98 | https://github.com/cimeister/neural-transducer/tree/e4dfc718bbcf93254ce23750e5428c5131ddfb98 |
patch_extractor | import torch
import torch.nn as nn
class patch_extractor(nn.Module):
"""
Module for creating custom patch extractor
"""
def __init__(self, patch_size, pad=False):
super(patch_extractor, self).__init__()
self.im2pat = nn.Unfold(kernel_size=patch_size)
self.pad = pad
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... | Xmaster6y/wgenpatex | patch_extractor | false | 18,119 | [
"MIT"
] | 8 | 08079dc131cc2e9c74ee4f9e16cf9b58667f2b07 | https://github.com/Xmaster6y/wgenpatex/tree/08079dc131cc2e9c74ee4f9e16cf9b58667f2b07 |
SMOEScaleMap | # 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.autograd
assert_size_stride = torch._C._dyna... | LLNL/fastcam | SMOEScaleMap | false | 8,423 | [
"BSD-3-Clause"
] | 25 | 99cefe37528014247319468cf05f54fef259d3bf | https://github.com/LLNL/fastcam/tree/99cefe37528014247319468cf05f54fef259d3bf |
BatchSpectralPenalizationLoss | import torch
import torch.nn as nn
import torch.utils.data
class BatchSpectralPenalizationLoss(nn.Module):
"""Batch spectral penalization loss from `Transferability vs. Discriminability: Batch
Spectral Penalization for Adversarial Domain Adaptation (ICML 2019)
<http://ise.thss.tsinghua.edu.cn/~mlong/doc/b... | 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.... | XianyuanLiu/Transfer-Learning-Library | BatchSpectralPenalizationLoss | false | 10,144 | [
"MIT"
] | 0 | 25f83f32437032df88ca6101ecd1f63ec7a0aa2c | https://github.com/XianyuanLiu/Transfer-Learning-Library/tree/25f83f32437032df88ca6101ecd1f63ec7a0aa2c |
GLU | # 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... | aagnone3/dc19t2 | GLU | false | 1,348 | [
"Apache-2.0"
] | 0 | cc7baf2a8fe73d28c224f0bf68b5355efd96c24f | https://github.com/aagnone3/dc19t2/tree/cc7baf2a8fe73d28c224f0bf68b5355efd96c24f |
SpatialGatherModule | # 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.... | Atten4Vis/DemystifyLocalViT | SpatialGatherModule | false | 13,346 | [
"MIT"
] | 64 | 2e2327caec6d56ae2c8aa861b32bb62f3cdb786e | https://github.com/Atten4Vis/DemystifyLocalViT/tree/2e2327caec6d56ae2c8aa861b32bb62f3cdb786e |
PositiveLinear | import torch
import torch.nn as nn
import torch.nn.functional as F
class PositiveLinear(nn.Linear):
def forward(self, input):
return F.linear(input, self.weight ** 2, self.bias)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {'in_features': 4, 'out_feat... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | oguzserbetci/monotone-network | PositiveLinear | false | 10,593 | [
"MIT"
] | 0 | 33a317a1dde1a3d3e74dcbe3eb12d1a81e745c95 | https://github.com/oguzserbetci/monotone-network/tree/33a317a1dde1a3d3e74dcbe3eb12d1a81e745c95 |
FullAttention | # 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.... | francescodisalvo05/LoFTR | FullAttention | false | 12,401 | [
"Apache-2.0"
] | 0 | 66372ebbe1ea97d57fe6cb8b5acf5cd92a87ef8d | https://github.com/francescodisalvo05/LoFTR/tree/66372ebbe1ea97d57fe6cb8b5acf5cd92a87ef8d |
FC | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn
import torch.... | AndrejOrsula/O-CNN | FC | false | 9,038 | [
"MIT"
] | 0 | e17290a206c3fe23d80873fb21d7243f71e2e9df | https://github.com/AndrejOrsula/O-CNN/tree/e17290a206c3fe23d80873fb21d7243f71e2e9df |
CDCM | import torch
import torch.nn as nn
class CDCM(nn.Module):
"""
Compact Dilation Convolution based Module
"""
def __init__(self, in_channels, out_channels):
super(CDCM, self).__init__()
self.relu1 = nn.ReLU()
self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=1,
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | arkel23/mmgeneration | CDCM | false | 9,943 | [
"Apache-2.0"
] | 0 | 41a30e2972f2037f6aac60ed761bed3fe47bfe4d | https://github.com/arkel23/mmgeneration/tree/41a30e2972f2037f6aac60ed761bed3fe47bfe4d |
FBetaLoss | # 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... | quqixun/ECG-MLC | FBetaLoss | false | 10,729 | [
"MIT"
] | 0 | 582d68200b79e3b2ac322c1ed17630727e283605 | https://github.com/quqixun/ECG-MLC/tree/582d68200b79e3b2ac322c1ed17630727e283605 |
BaselineNN | # 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... | severilov/master-thesis | BaselineNN | false | 4,301 | [
"MIT"
] | 0 | 145382d5d551761fcdbd2b77d7b96fabcc8f78ec | https://github.com/severilov/master-thesis/tree/145382d5d551761fcdbd2b77d7b96fabcc8f78ec |
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
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_... | DeVriesMatt/PyTorch-GAN | LayerNorm | false | 11,336 | [
"MIT"
] | 0 | dc6488b1f7af06a954ae3ff5a33816e1a892046f | https://github.com/DeVriesMatt/PyTorch-GAN/tree/dc6488b1f7af06a954ae3ff5a33816e1a892046f |
ArcMarginProduct | # 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.... | Casyfill/catalyst | ArcMarginProduct | false | 9,006 | [
"Apache-2.0"
] | 0 | 7f63545dbc53902c3dd959463def28a67a16a989 | https://github.com/Casyfill/catalyst/tree/7f63545dbc53902c3dd959463def28a67a16a989 |
SimplifiedScaledDotProductAttention | import torch
import numpy as np
from torch import nn
from torch.nn import init
class SimplifiedScaledDotProductAttention(nn.Module):
"""
Scaled dot-product attention
"""
def __init__(self, d_model, h, dropout=0.1):
"""
:param d_model: Output dimensionality of the model
:param ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | Nitin-Mane/External-Attention-pytorch | SimplifiedScaledDotProductAttention | false | 14,110 | [
"MIT"
] | 4,466 | 1ceda306c41063af11c956334747763444a4d83f | https://github.com/Nitin-Mane/External-Attention-pytorch/tree/1ceda306c41063af11c956334747763444a4d83f |
F1Loss | import torch
import torch._C
import torch.serialization
from torch import nn
import torch.nn.functional as F
from typing import *
def reduce_loss(loss, reduction):
"""Reduce loss as specified.
Args:
loss (Tensor): Elementwise loss tensor.
reduction (str): Options are "none", "mean" and "sum".... | 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._C
import torch.serialization
from torch import nn
import torch.nn.functional as F
from typing import *
assert_size_stride = to... | shuaizzZ/mmsegmentation | F1Loss | false | 4,331 | [
"Apache-2.0"
] | 0 | a6c6b348dbf8c4a0a39ffbdb832a1e82309c533c | https://github.com/shuaizzZ/mmsegmentation/tree/a6c6b348dbf8c4a0a39ffbdb832a1e82309c533c |
BlobDoG | import torch
from torch import Tensor
from typing import Optional
import torch.nn as nn
from typing import List
def KORNIA_CHECK_IS_TENSOR(x, msg: 'Optional[str]'=None):
if not isinstance(x, Tensor):
raise TypeError(f'Not a Tensor type. Got: {type(x)}.\n{msg}')
def KORNIA_CHECK_SHAPE(x, shape: 'List[str... | 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 Tensor
from typing import Optional
import torch.nn as nn
from typing import List
assert_size_stride = torch._C._dynamo.gua... | YanivHollander/kornia | BlobDoG | false | 14,636 | [
"ECL-2.0",
"Apache-2.0"
] | 418 | ccd258d0956da89b1feca96448eff8e4969d405a | https://github.com/YanivHollander/kornia/tree/ccd258d0956da89b1feca96448eff8e4969d405a |
CNNNet | import torch
import torch.nn.functional as F
import torch.nn as nn
class CNNNet(nn.Module):
def __init__(self):
super(CNNNet, self).__init__()
self.conv1 = nn.Conv2d(3, 64, 5)
self.pool = nn.MaxPool2d(2, 2)
self.conv2 = nn.Conv2d(64, 128, 5)
self.fc1 = nn.Linear(128 * 5 * ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | fangkaimin/pytorch_classification_new | CNNNet | false | 10,128 | [
"MIT"
] | 0 | 21032e7ab91f0f3106ba07aa97657a023b1cc717 | https://github.com/fangkaimin/pytorch_classification_new/tree/21032e7ab91f0f3106ba07aa97657a023b1cc717 |
Quantize | # 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
import torch.nn.functional as F
assert_size_stride = torch... | unilight/crank | Quantize | false | 4,482 | [
"MIT"
] | 0 | 0dc5d9df17f3186155b1c9583ab604ff218ad9a6 | https://github.com/unilight/crank/tree/0dc5d9df17f3186155b1c9583ab604ff218ad9a6 |
Encoder | import torch
import torch.nn as nn
class Encoder(nn.Module):
def __init__(self, input_dim, hidden_dim, latent_dim):
super(Encoder, self).__init__()
self.FC_input = nn.Linear(input_dim, hidden_dim)
self.FC_mean = nn.Linear(hidden_dim, latent_dim)
self.FC_var = nn.Linear(hidden_dim,... | import torch
from torch import device
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from... | RasmusJuul/dtu_mlops | Encoder | false | 979 | [
"Apache-2.0"
] | 0 | 98bca082067aa7575bb8e8193991723d474f0850 | https://github.com/RasmusJuul/dtu_mlops/tree/98bca082067aa7575bb8e8193991723d474f0850 |
InceptionA | # 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... | HuangCongQing/pytorch | InceptionA | false | 8,237 | [
"MIT"
] | 12 | 2b2b01d74b45cbe4e467da229798609e79cec97c | https://github.com/HuangCongQing/pytorch/tree/2b2b01d74b45cbe4e467da229798609e79cec97c |
DiceLoss | import torch
from torch import nn
from torch.autograd import Variable
def flatten(tensor):
"""Flattens a given tensor such that the channel axis is first.
The shapes are transformed as follows:
(N, C, D, H, W) -> (C, N * D * H * W)
"""
C = tensor.size(1)
axis_order = (1, 0) + tuple(range(2,... | 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... | joowlim/pytorch-3dunet | DiceLoss | false | 10,400 | [
"MIT"
] | 0 | d08049f60b619627521efd0fb171247e1536b262 | https://github.com/joowlim/pytorch-3dunet/tree/d08049f60b619627521efd0fb171247e1536b262 |
MHSA | import torch
import torch.utils.data
import torch.nn as nn
class MHSA(nn.Module):
def __init__(self, n_dims, width=14, height=14, heads=4):
super(MHSA, self).__init__()
self.heads = heads
self.query = nn.Conv2d(n_dims, n_dims, kernel_size=1)
self.key = nn.Conv2d(n_dims, n_dims, ke... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | binghuiwu98/discriminatory-yolov5 | MHSA | false | 12,172 | [
"Apache-2.0"
] | 0 | 831bfdb8e0df38e247a72ca029ee3301fc14a311 | https://github.com/binghuiwu98/discriminatory-yolov5/tree/831bfdb8e0df38e247a72ca029ee3301fc14a311 |
Cos | import torch
import torch.nn as nn
class Cos(nn.Module):
def __init__(self):
super().__init__()
def forward(self, X: 'torch.Tensor'):
return torch.cos(X)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
| import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert... | alartum/sngp-pytorch | Cos | false | 6,141 | [
"Apache-2.0"
] | 1 | 8d1f6c22d7ae635feeff0c0912624589e31e2e62 | https://github.com/alartum/sngp-pytorch/tree/8d1f6c22d7ae635feeff0c0912624589e31e2e62 |
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... | Kritikalcoder/PySyft | AvgPool2d | false | 5,447 | [
"Apache-2.0"
] | 1 | 4c418084607de74cac7b7795f91168992c555f50 | https://github.com/Kritikalcoder/PySyft/tree/4c418084607de74cac7b7795f91168992c555f50 |
ChannelMixer | import torch
import torch.nn as nn
class ChannelMixer(nn.Module):
def __init__(self, input_size, hidden_size, dropout=None):
super(ChannelMixer, self).__init__()
self.fc1 = nn.Linear(input_size, hidden_size)
self.fc2 = nn.Linear(hidden_size, input_size)
self.dropout = None
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | TheRealMarVin/mlp-mixer | ChannelMixer | false | 1,136 | [
"MIT"
] | 0 | 2124cb5c5adfc7af473cab535095471d4943adab | https://github.com/TheRealMarVin/mlp-mixer/tree/2124cb5c5adfc7af473cab535095471d4943adab |
RON | # 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 math import sqrt as sqrt
from itertools import produc... | KaiOtter/pytorch_DSOD_variants | RON | false | 5,422 | [
"MIT"
] | 1 | f29088b13b24f24e2cf20e9a2dc800cd6dbde145 | https://github.com/KaiOtter/pytorch_DSOD_variants/tree/f29088b13b24f24e2cf20e9a2dc800cd6dbde145 |
FourLayerSemSegNetWideView | import torch
import torch.nn as nn
class FourLayerSemSegNetWideView(nn.Module):
def __init__(self, in_channel, out_channel):
super().__init__()
self.conv1 = torch.nn.Conv2d(in_channel, 6, kernel_size=3, padding=
1, stride=1)
self.conv1d100 = torch.nn.Conv2d(in_channel, 2, kern... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | benkoger/kasanka | FourLayerSemSegNetWideView | false | 12,165 | [
"Apache-2.0"
] | 0 | d5b1d32b7abf54845af0832da577137397089001 | https://github.com/benkoger/kasanka/tree/d5b1d32b7abf54845af0832da577137397089001 |
Attention | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim
class Attention(nn.Module):
def __init__(self, embed_dim, hidden_dim=None, out_dim=None, n_head=1,
score_function='dot_product', dropout=0):
""" Attention Mechanism
:param embed_dim:
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | NouamaneTazi/conv-emotion | Attention | false | 14,123 | [
"MIT"
] | 488 | 0c9dcb9cc5234a7ca8cf6af81aabe28ef3814d0e | https://github.com/NouamaneTazi/conv-emotion/tree/0c9dcb9cc5234a7ca8cf6af81aabe28ef3814d0e |
TransformerNet | import functools
import torch
def get_norm_layer(norm_type='instance', affine_state=True):
if norm_type == 'batch':
norm_layer = functools.partial(torch.nn.BatchNorm2d, affine=True)
elif norm_type == 'instance':
norm_layer = functools.partial(torch.nn.InstanceNorm2d, affine=
affine... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | JunhongH/CP-GAN | TransformerNet | false | 17,543 | [
"Apache-2.0"
] | 9 | 5ac129da8cf6d010dc0da03bb4637d20c822d50b | https://github.com/JunhongH/CP-GAN/tree/5ac129da8cf6d010dc0da03bb4637d20c822d50b |
Conv_Blocks | # 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.... | HRHLALALA/GoalGAN | Conv_Blocks | false | 9,098 | [
"MIT"
] | 0 | 01443f2a578333a0d5ab3a449bc7da69f5023190 | https://github.com/HRHLALALA/GoalGAN/tree/01443f2a578333a0d5ab3a449bc7da69f5023190 |
LayerReLU6 | import torch
import torch.nn as nn
class LayerReLU6(nn.Module):
"""
Test for nn.layers based types
"""
def __init__(self):
super(LayerReLU6, self).__init__()
self.relu = nn.ReLU6()
def forward(self, x):
x = self.relu(x)
return x
def get_inputs():
return [tor... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | dawnclaude/onnx2keras | LayerReLU6 | false | 15,150 | [
"MIT"
] | 115 | 3d2a47c0a228b91fd434232274e216e491da36e3 | https://github.com/dawnclaude/onnx2keras/tree/3d2a47c0a228b91fd434232274e216e491da36e3 |
Encoder | import torch
import torch.nn as nn
import torch.nn.functional as F
class Encoder(nn.Module):
"""Estimation of the nonnegative mixture weight by a 1-D conv layer.
"""
def __init__(self, L, N):
super(Encoder, self).__init__()
self.L, self.N = L, N
self.conv1d_U = nn.Conv1d(1, N, ker... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | zhangxinaaaa/Conv-TasNet | Encoder | false | 11,037 | [
"MIT"
] | 0 | 4622d93d0b9dbe23584addd4f4b9463255651652 | https://github.com/zhangxinaaaa/Conv-TasNet/tree/4622d93d0b9dbe23584addd4f4b9463255651652 |
CPAMDec | # 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.... | coolgrasshopper/amodal_road_segmentation | CPAMDec | false | 6,534 | [
"MIT"
] | 1 | 462209242973815055f085ada99772af32082f5c | https://github.com/coolgrasshopper/amodal_road_segmentation/tree/462209242973815055f085ada99772af32082f5c |
AnyHead | # 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
import torch.utils.data
assert_size_stride = torch._C._dyn... | Pre-release/BAKE | AnyHead | false | 14,234 | [
"MIT"
] | 67 | 2899b38d556a9151f55079c1b9888d462369aec8 | https://github.com/Pre-release/BAKE/tree/2899b38d556a9151f55079c1b9888d462369aec8 |
WeightedFeatureFusion | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C.... | CaptainEven/MCMOT-ByteTrack | WeightedFeatureFusion | false | 7,837 | [
"MIT"
] | 20 | e014275cfb25147dfa6f49cdbed24e91e5d6c41e | https://github.com/CaptainEven/MCMOT-ByteTrack/tree/e014275cfb25147dfa6f49cdbed24e91e5d6c41e |
OHEM_CrossEntroy_Loss | import torch
from torch import nn
class OHEM_CrossEntroy_Loss(nn.Module):
def __init__(self, threshold, keep_num):
super(OHEM_CrossEntroy_Loss, self).__init__()
self.threshold = threshold
self.keep_num = keep_num
self.loss_function = nn.CrossEntropyLoss(reduction='none')
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
from torch import nn
a... | HaowenWeiJohn/CV_Project | OHEM_CrossEntroy_Loss | false | 584 | [
"MIT"
] | 0 | 8e2414796f60a8c3fe452f3721e4a6ef7edfdb11 | https://github.com/HaowenWeiJohn/CV_Project/tree/8e2414796f60a8c3fe452f3721e4a6ef7edfdb11 |
ContinuousNet | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
def set_init(layers):
for layer in layers:
nn.init.normal_(layer.weight, mean=0.0, std=0.1)
nn.init.constant_(layer.bias, 0.0)
class ContinuousNet(nn.Module):
def __init__(self, s_dim, a_dim):
super(Conti... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | lws803/pytorch-A3C | ContinuousNet | false | 10,467 | [
"MIT"
] | 0 | 944e7f42a8fa54b7d6efbe169d8a3467b20a0f7f | https://github.com/lws803/pytorch-A3C/tree/944e7f42a8fa54b7d6efbe169d8a3467b20a0f7f |
T2A | # 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
import torch.cuda
import torch.distributed
assert_size_str... | InitialBug/BiSET | T2A | false | 13,833 | [
"MIT"
] | 47 | a697a3c61014281bbd83cd37ede29b1263c8832f | https://github.com/InitialBug/BiSET/tree/a697a3c61014281bbd83cd37ede29b1263c8832f |
SEBlock | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
from tor... | WenmuZhou/crnn.pytorch | SEBlock | false | 14,601 | [
"Apache-2.0"
] | 46 | bf7a7c62376eee93943ca7c68e88e3d563c09aa8 | https://github.com/WenmuZhou/crnn.pytorch/tree/bf7a7c62376eee93943ca7c68e88e3d563c09aa8 |
GAT | # 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.... | juaduan/babybrainguardian | GAT | false | 6,992 | [
"MIT"
] | 1 | b871e3a83fef98c2e05dd8857721a3c964a46418 | https://github.com/juaduan/babybrainguardian/tree/b871e3a83fef98c2e05dd8857721a3c964a46418 |
AR | import torch
import torch.nn as nn
class AR(nn.Module):
def __init__(self, window):
super(AR, self).__init__()
self.linear = nn.Linear(window, 1)
def forward(self, x):
x = torch.transpose(x, 1, 2)
x = self.linear(x)
x = torch.transpose(x, 1, 2)
return x
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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | chenghaoliu89/TSForecasting_FT | AR | false | 9,990 | [
"MIT"
] | 0 | e29227e67f754919672eab9002a1b37b13ed28a0 | https://github.com/chenghaoliu89/TSForecasting_FT/tree/e29227e67f754919672eab9002a1b37b13ed28a0 |
SEBlock | import torch
from torch import nn
class SEBlock(nn.Module):
def __init__(self, num_channels):
super(SEBlock, self).__init__()
self.lin1 = nn.Conv2d(num_channels, num_channels, 1)
self.lin2 = nn.Conv2d(num_channels, num_channels, 1)
def forward(self, x):
h = nn.functional.avg_... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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... | Keleas/Wafer_maps | SEBlock | false | 17,553 | [
"MIT"
] | 7 | ee555cafab213a86baf2d9e3b7fb392e1b89a832 | https://github.com/Keleas/Wafer_maps/tree/ee555cafab213a86baf2d9e3b7fb392e1b89a832 |
GCN | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | NightmareNyx/pygcn | GCN | false | 2,705 | [
"MIT"
] | 0 | 3972f167ce7fcc41cb21284d75816dfd9a15f7ef | https://github.com/NightmareNyx/pygcn/tree/3972f167ce7fcc41cb21284d75816dfd9a15f7ef |
ChannelPool | # 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... | ananyaganesh/ftmp | ChannelPool | false | 6,193 | [
"MIT"
] | 1 | 9ee23939f0c1da854846b8ce1a9abe4e9b377031 | https://github.com/ananyaganesh/ftmp/tree/9ee23939f0c1da854846b8ce1a9abe4e9b377031 |
AdjDecoder | import torch
from torch import nn
import torch.utils.data
class AdjDecoder(nn.Module):
u""" Decode an input (parent) feature into a left-child and a right-child feature """
def __init__(self, feature_size, hidden_size):
super(AdjDecoder, self).__init__()
self.mlp = nn.Linear(feature_size, hid... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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... | kevin-kaixu/grass_pytorch | AdjDecoder | false | 15,809 | [
"Apache-2.0"
] | 85 | 1d8dc6dcc0ab3ca029e449f57c37ba3910a4f90a | https://github.com/kevin-kaixu/grass_pytorch/tree/1d8dc6dcc0ab3ca029e449f57c37ba3910a4f90a |
Actor | import torch
import torch.nn as nn
import torch.nn.functional as F
class Actor(nn.Module):
def __init__(self, state_dim, action_dim, max_action, phi=0.05):
super(Actor, self).__init__()
self.l1 = nn.Linear(state_dim + action_dim, 400)
self.l2 = nn.Linear(400, 300)
self.l3 = nn.Lin... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | DanielTakeshi/DCUR | Actor | false | 355 | [
"MIT"
] | 0 | 1cdb00e7e68060ad3bba9a497106c327f6b5a663 | https://github.com/DanielTakeshi/DCUR/tree/1cdb00e7e68060ad3bba9a497106c327f6b5a663 |
PatchEmbedding | # 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... | avniculae/segmenter | PatchEmbedding | false | 9,769 | [
"MIT"
] | 0 | ca9683399b7dae13a8ccbadc744826306b8dbf94 | https://github.com/avniculae/segmenter/tree/ca9683399b7dae13a8ccbadc744826306b8dbf94 |
ShakeResNeXt | import math
import torch
from torch import nn
import torch.nn.functional as F
from torch.autograd import Variable
class ShakeShake(torch.autograd.Function):
@staticmethod
def forward(ctx, x1, x2, training=True):
if training:
alpha = torch.FloatTensor(x1.size(0)).uniform_()
alp... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import math
from torch import... | AustinCai/gmaxup-augmentation | ShakeResNeXt | false | 94 | [
"MIT"
] | 0 | a64ca0a76eb333e5ce6b217c301d27ca04d73bce | https://github.com/AustinCai/gmaxup-augmentation/tree/a64ca0a76eb333e5ce6b217c301d27ca04d73bce |
Gaussian | import torch
from torch import Tensor
import torch.utils.tensorboard
import torch.utils.data
class Gaussian(torch.nn.Module):
"""Gaussian activation"""
def forward(self, x: 'Tensor') ->Tensor:
return torch.exp(-x * x)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.utils.tensorboard
import torch.utils.data
assert_size_stride... | raimis/torchani | Gaussian | false | 7,628 | [
"MIT"
] | 1 | 19882c6e18174e08423706a536366f89029a740a | https://github.com/raimis/torchani/tree/19882c6e18174e08423706a536366f89029a740a |
MSE | import torch
import torch.nn as nn
class MSE(nn.Module):
def __init__(self):
super(MSE, self).__init__()
def forward(self, x_true, x_pred):
return torch.sqrt(torch.mean(torch.pow(x_pred - x_true, 2), dim=-1))
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4])]... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_... | Jiangtong-Li/ZHSIR | MSE | false | 17,494 | [
"Apache-2.0"
] | 8 | fd2c0a7e79f22cbf565ccd5e13342f1b317ac9b7 | https://github.com/Jiangtong-Li/ZHSIR/tree/fd2c0a7e79f22cbf565ccd5e13342f1b317ac9b7 |
layer_basic | import torch
import numpy as np
import torch.nn as nn
class layer_basic(nn.Module):
"""
:param name: name of layer
:param input_depth: D
:param output_depth: S
:param inputs: N x D x m x m tensor
:return: output: N x S x m x m tensor
"""
def __init__(self, input_depth, output_depth, n... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import numpy as np
import torch.nn as nn
assert_size_stride = torch._C._dynamo.g... | HyTruongSon/InvariantGraphNetworks-PyTorch | layer_basic | false | 17,435 | [
"Apache-2.0"
] | 7 | da9fdaa4f858d6fcae14b08a59d4b172a2aabaf8 | https://github.com/HyTruongSon/InvariantGraphNetworks-PyTorch/tree/da9fdaa4f858d6fcae14b08a59d4b172a2aabaf8 |
LsqQuan | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import torch as t
import tor... | HumberMe/lsq-net | LsqQuan | false | 556 | [
"MIT"
] | 0 | 7dcd75bff4aa7ff2d9c8a7902198fe411a38eb4c | https://github.com/HumberMe/lsq-net/tree/7dcd75bff4aa7ff2d9c8a7902198fe411a38eb4c |
CeCriterion | # 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.... | johnson7788/mt-dnn | CeCriterion | false | 3,888 | [
"MIT"
] | 0 | 26e5c4a5bfdbf1a1dd1c903e606db1c070568237 | https://github.com/johnson7788/mt-dnn/tree/26e5c4a5bfdbf1a1dd1c903e606db1c070568237 |
EqualLinear | from torch.autograd import Function
import math
import torch
import torch.utils.data
from torch.nn import functional as F
from torch.utils import data as data
import torch.nn as nn
from torch.nn import init as init
from torchvision.models import vgg as vgg
from torch import autograd as autograd
def fused_leaky_relu(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.autograd import Function
import math
import torch.utils.data
from tor... | achrefjarray/ESRGANplus-master | EqualLinear | false | 1,381 | [
"Apache-2.0"
] | 0 | ba470ec5c565a6dc8b48575b1e185ef6b796aec6 | https://github.com/achrefjarray/ESRGANplus-master/tree/ba470ec5c565a6dc8b48575b1e185ef6b796aec6 |
ATOCAttentionUnit | import torch
from typing import Union
from typing import Dict
import torch.nn as nn
class ATOCAttentionUnit(nn.Module):
"""
Overview:
the attention unit of the atoc network. We now implement it as two-layer MLP, same as the original paper
Interface:
__init__, forward
.. note::
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | PaParaZz1/DI-engine | ATOCAttentionUnit | false | 11,855 | [
"Apache-2.0"
] | 0 | b38144117c1ebc6eb860d8637ec8866dfbcdf2de | https://github.com/PaParaZz1/DI-engine/tree/b38144117c1ebc6eb860d8637ec8866dfbcdf2de |
Downsample | import torch
from torch import nn
class Downsample(nn.Module):
def __init__(self, dim):
super().__init__()
self.conv = nn.Conv2d(dim, dim, 3, 2, 1)
def forward(self, x):
return self.conv(x)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import 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... | DavidRuhe/simple-variational-diffusion-models | Downsample | false | 17,221 | [
"MIT"
] | 4 | a32355bf052a8f08e9c1919080588d0b22c8de4e | https://github.com/DavidRuhe/simple-variational-diffusion-models/tree/a32355bf052a8f08e9c1919080588d0b22c8de4e |
ConvRelu | import torch
import torch.utils.data
import torch.nn as nn
import torch.backends.cudnn
class ConvRelu(nn.Module):
"""3x3 convolution followed by ReLU activation building block.
"""
def __init__(self, num_in, num_out):
"""Creates a `ConvReLU` building block.
Args:
num_in: number... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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... | Iceofsky/Roofpedia | ConvRelu | false | 8,268 | [
"MIT"
] | 16 | 933dd3ff6e77ace78be6d2a23ac6692281475073 | https://github.com/Iceofsky/Roofpedia/tree/933dd3ff6e77ace78be6d2a23ac6692281475073 |
RankCrossEntropyLoss | # 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
... | zfjsail/MatchZoo-py | RankCrossEntropyLoss | false | 4,695 | [
"Apache-2.0"
] | 0 | c93e52e7db7e257b46bb8bf8df8ce1ab1944e2f2 | https://github.com/zfjsail/MatchZoo-py/tree/c93e52e7db7e257b46bb8bf8df8ce1ab1944e2f2 |
GlobalAvgPool2d | # 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... | ChenyangWang1/face_parsing | GlobalAvgPool2d | false | 2,084 | [
"MIT"
] | 0 | 506e74eb8a2094920c03f2fe0774656b1043e8a6 | https://github.com/ChenyangWang1/face_parsing/tree/506e74eb8a2094920c03f2fe0774656b1043e8a6 |
Custom_dropout | # 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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C... | QMrpy/deepchem | Custom_dropout | false | 2,738 | [
"MIT"
] | 0 | f38a21c71e7bc4fd1fa59601be2b79ce7d744bd6 | https://github.com/QMrpy/deepchem/tree/f38a21c71e7bc4fd1fa59601be2b79ce7d744bd6 |
TransformerNet | # 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.... | Ali-ry/azureml-examples | TransformerNet | false | 2,058 | [
"MIT"
] | 0 | 817ae89d2766dcafd70937a22cb3a80f100a2906 | https://github.com/Ali-ry/azureml-examples/tree/817ae89d2766dcafd70937a22cb3a80f100a2906 |
OnnxSoftmaxV1V11 | import torch
from torch import nn
class OnnxToTorchModule:
"""
Marker class for onnx2torch modules.
"""
pass
class OnnxSoftmaxV1V11(nn.Module, OnnxToTorchModule):
def __init__(self, axis: 'int'=1, is_log: 'bool'=False):
super().__init__()
self.axis = axis
self.is_log = i... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch import nn
a... | ENOT-AutoDL/onnx2torch | OnnxSoftmaxV1V11 | false | 13,633 | [
"Apache-2.0"
] | 144 | 2391987b3349bed1670ac3c1bc9062a37323abe3 | https://github.com/ENOT-AutoDL/onnx2torch/tree/2391987b3349bed1670ac3c1bc9062a37323abe3 |
TanhTransform | # 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_... | knalbant/oppel | TanhTransform | false | 7,048 | [
"MIT"
] | 1 | 03f840565ef64587ddb7a8b4145d8df7fb0279a3 | https://github.com/knalbant/oppel/tree/03f840565ef64587ddb7a8b4145d8df7fb0279a3 |
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