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 |
|---|---|---|---|---|---|---|---|---|---|---|
SelfExpression | import torch
import torch.nn as nn
class SelfExpression(nn.Module):
def __init__(self, n):
super(SelfExpression, self).__init__()
self.Coefficient = nn.Parameter(1e-08 * torch.ones(n, n, dtype=
torch.float32), requires_grad=True)
def forward(self, x):
y = torch.matmul(sel... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | Xanadu12138/DSCN-superpixels | SelfExpression | false | 18,092 | [
"MIT"
] | 4 | babe16edde9c61699ef203effbfc9f03246765f3 | https://github.com/Xanadu12138/DSCN-superpixels/tree/babe16edde9c61699ef203effbfc9f03246765f3 |
AddTensors | # 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.hub
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo... | theoway/raster-vision | AddTensors | false | 16,577 | [
"Apache-2.0"
] | 1,577 | dab675517f904771e2ce8c052494f8a6f1ddc026 | https://github.com/theoway/raster-vision/tree/dab675517f904771e2ce8c052494f8a6f1ddc026 |
SEModule | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.utils.data
impor... | dakotahawkins/impersonator | SEModule | false | 6,526 | [
"MIT"
] | 1 | 87d59167a10fd70aaa95be4fafbf4c8a32eb1a37 | https://github.com/dakotahawkins/impersonator/tree/87d59167a10fd70aaa95be4fafbf4c8a32eb1a37 |
LayerCake | import torch
class LayerCake(torch.nn.Module):
def __init__(self, D_in, H1, H2, H3, H4, H5, D_out):
"""
In the constructor we instantiate two nn.Linear modules and assign them as
member variables.
"""
super(LayerCake, self).__init__()
self.linear1 = torch.nn.Linear... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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... | Saran-nns/delve | LayerCake | false | 1,021 | [
"MIT"
] | 0 | 3489d8aa13181b392d3c47a19f9d9a47d87f8790 | https://github.com/Saran-nns/delve/tree/3489d8aa13181b392d3c47a19f9d9a47d87f8790 |
GraphResConvolution | from torch.nn import Module
import torch
import torch.autograd
import torch.nn as nn
from torch.nn.modules.module import Module
class GraphConvolution(Module):
"""
Simple GCN layer, similar to https://arxiv.org/abs/1609.02907.
"""
def __init__(self, state_dim, name='', out_state_dim=None):
su... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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... | SowmyaAitha/Palmira | GraphResConvolution | false | 17,943 | [
"MIT"
] | 6 | c3ae884e35b8b3703a5e4ba52d7b0bdae6da1bad | https://github.com/SowmyaAitha/Palmira/tree/c3ae884e35b8b3703a5e4ba52d7b0bdae6da1bad |
FloorDivConst | # 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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_c... | Ilyabasharov/torch2trt | FloorDivConst | false | 2,520 | [
"MIT"
] | 0 | 76bf298b3da408509665e23e2494922b131afb10 | https://github.com/Ilyabasharov/torch2trt/tree/76bf298b3da408509665e23e2494922b131afb10 |
Hidden2Discrete | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data
import torch.nn.init
class Hidden2Discrete(nn.Module):
def __init__(self, input_size, y_size, k_size, is_lstm=False, has_bias=True
):
super(Hidden2Discrete, self).__init__()
self.y_size = y_size
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | ljw23/ConvLab-2 | Hidden2Discrete | false | 15,931 | [
"Apache-2.0"
] | 339 | 13d48ea0e441701bd66100689b6c25b561f15525 | https://github.com/ljw23/ConvLab-2/tree/13d48ea0e441701bd66100689b6c25b561f15525 |
CharbonnierLoss | # 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 functools
import torc... | hejm37/mmediting | CharbonnierLoss | false | 12,477 | [
"Apache-2.0"
] | 0 | d4086aaf8a36ae830f1714aad585900d24ad1156 | https://github.com/hejm37/mmediting/tree/d4086aaf8a36ae830f1714aad585900d24ad1156 |
Scale | # 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... | MickeyZeng/Data-Visualization | Scale | false | 832 | [
"MIT"
] | 0 | c7005d1096545d7a5eb96dd0c9bc13e874d42fa4 | https://github.com/MickeyZeng/Data-Visualization/tree/c7005d1096545d7a5eb96dd0c9bc13e874d42fa4 |
HardSigmoid | # 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... | Chandrima-04/gimmebio | HardSigmoid | false | 17,077 | [
"MIT"
] | 3 | cb3e66380006d5c5c00ff70bfb87317dd252c312 | https://github.com/Chandrima-04/gimmebio/tree/cb3e66380006d5c5c00ff70bfb87317dd252c312 |
AbsModel | from torch.nn import Module
import torch
from torch import Tensor
from torch.nn import Identity
from torch.nn.modules import Module
import torch.optim.lr_scheduler
class AbsLayer(Module):
def forward(self, x: 'Tensor') ->Tensor:
return torch.abs(x).reshape((-1, 1))
class AbsModel(Module):
"""Fake m... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch.nn import Module
from torch import Tensor
from torch.nn import... | Ektagavas/avalanche | AbsModel | false | 11,448 | [
"MIT"
] | 0 | 6671dc748078532709aad07b9e28ad6c903ab12b | https://github.com/Ektagavas/avalanche/tree/6671dc748078532709aad07b9e28ad6c903ab12b |
CriticNetwork | import torch
import torch.nn.functional as func
class CriticNetwork(torch.nn.Module):
def __init__(self, s_space, a_space):
super(CriticNetwork, self).__init__()
self.s_dense = torch.nn.Linear(s_space, 50)
self.a_dense = torch.nn.Linear(a_space, 50)
self.q_dense = torch.nn.Linear(... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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... | yutiansut/Personae | CriticNetwork | false | 16,778 | [
"MIT"
] | 1,046 | e5e89cbaaf2c4708952d25fdb25e99837aecdb4e | https://github.com/yutiansut/Personae/tree/e5e89cbaaf2c4708952d25fdb25e99837aecdb4e |
LearnedPositionalEncoding | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.cuda
import torch.distributed
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strid... | fangleai/encoder-agnostic-adaptation | LearnedPositionalEncoding | false | 15,340 | [
"MIT"
] | 70 | d917e654152df202dd35bba49c409c3ecd24eaf7 | https://github.com/fangleai/encoder-agnostic-adaptation/tree/d917e654152df202dd35bba49c409c3ecd24eaf7 |
Quantization | # 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.utils.data
impo... | peterhan91/Invertible-Image-Rescaling | Quantization | false | 4,109 | [
"Apache-2.0"
] | 0 | b92162f5e9be2cff2f5dba379914fcded4e04f4c | https://github.com/peterhan91/Invertible-Image-Rescaling/tree/b92162f5e9be2cff2f5dba379914fcded4e04f4c |
NormedMSE | # 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... | gongda0e/AVT | NormedMSE | false | 15,444 | [
"Apache-2.0"
] | 102 | d6a7032b86416e852c76cc04a20ccabe34f111dc | https://github.com/gongda0e/AVT/tree/d6a7032b86416e852c76cc04a20ccabe34f111dc |
GELU | # 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.nn.parallel
import torch.optim
import torch.... | EddieMG/LateTemporalModeling3DCNN | GELU | false | 2,265 | [
"MIT"
] | 0 | 94c87dc1d31d09bc310d0e735a2e55453976cb0d | https://github.com/EddieMG/LateTemporalModeling3DCNN/tree/94c87dc1d31d09bc310d0e735a2e55453976cb0d |
Attention | import torch
class Attention(torch.nn.Module):
"""
attention_size_1: Number of neurons in 1st attention layer.
attention_size_2: Number of neurons in 2nd attention layer.
"""
def __init__(self, attention_size_1, attention_size_2):
super(Attention, self).__init__()
self.att... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | EgemenGuray/Subgraph-Classifier | Attention | false | 2,189 | [
"MIT"
] | 0 | b85d28c521701f41dcd698aed40d4c80d454e893 | https://github.com/EgemenGuray/Subgraph-Classifier/tree/b85d28c521701f41dcd698aed40d4c80d454e893 |
L1DistanceLoss | # 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... | AnReu/structural-probes | L1DistanceLoss | false | 1,877 | [
"Apache-2.0"
] | 0 | fdc99dc124fa6df3dbdd5ba48a90f08bb6bf37b7 | https://github.com/AnReu/structural-probes/tree/fdc99dc124fa6df3dbdd5ba48a90f08bb6bf37b7 |
MultiHeadAttentionLayer | # 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.... | salvacarrion/nmt-continual-learning | MultiHeadAttentionLayer | false | 7,600 | [
"MIT"
] | 1 | 302147ac9c270f3341a68a72c803c457f05ff37b | https://github.com/salvacarrion/nmt-continual-learning/tree/302147ac9c270f3341a68a72c803c457f05ff37b |
FBANKNormalizer | # 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.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_... | lorenlugosch/autoregressive-models | FBANKNormalizer | false | 7,115 | [
"Apache-2.0"
] | 1 | 2c50bc331d3b68cc7144f7456591bbc2321cc658 | https://github.com/lorenlugosch/autoregressive-models/tree/2c50bc331d3b68cc7144f7456591bbc2321cc658 |
TransformerLayer | # 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.... | Lalihoo/yolov5-detect | TransformerLayer | false | 9,626 | [
"MIT"
] | 0 | 265c3137ea3586d913541501a1562488fbe59e9e | https://github.com/Lalihoo/yolov5-detect/tree/265c3137ea3586d913541501a1562488fbe59e9e |
BinaryLogisticRegressionLoss | import torch
import torch.nn as nn
def binary_logistic_regression_loss(reg_score, label, threshold=0.5,
ratio_range=(1.05, 21), eps=1e-05):
"""Binary Logistic Regression Loss."""
label = label.view(-1)
reg_score = reg_score.contiguous().view(-1)
pmask = (label > threshold).float()
num_positive... | 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
... | Alexis-Fab/mmaction2 | BinaryLogisticRegressionLoss | false | 11,210 | [
"Apache-2.0"
] | 0 | 6f76bb465a7164f907318cf58f77fc3d613f8f0f | https://github.com/Alexis-Fab/mmaction2/tree/6f76bb465a7164f907318cf58f77fc3d613f8f0f |
InnerProductLayer | import torch
import torch.nn as nn
from sklearn.metrics import *
class InnerProductLayer(nn.Module):
"""InnerProduct Layer used in PNN that compute the element-wise
product or inner product between feature vectors.
Input shape
- a list of 3D tensor with shape: ``(batch_size,1,embedding_size)``.
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
from sklearn.metrics import *
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = tor... | chenkkkk/DeepCTR-PyTorch | InnerProductLayer | false | 6,432 | [
"Apache-2.0"
] | 1 | a10a3ace4ad79171e7fb182407b3e4d22bf753e7 | https://github.com/chenkkkk/DeepCTR-PyTorch/tree/a10a3ace4ad79171e7fb182407b3e4d22bf753e7 |
NAC | from torch.nn import Module
import torch
from torch.nn.parameter import Parameter
from torch.nn import functional
from torch.nn import init
from torch.nn.modules import Module
import torch.utils.data
class NAC(Module):
def __init__(self, n_in, n_out):
super().__init__()
self.W_hat = Parameter(tor... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch.nn impor... | Caerisse/deep_focus | NAC | false | 188 | [
"MIT"
] | 0 | a6549e0b222a01569b224fb651666ef5dbb5072f | https://github.com/Caerisse/deep_focus/tree/a6549e0b222a01569b224fb651666ef5dbb5072f |
ModMBStddevLayer | import torch
import torch.nn as nn
class ModMBStddevLayer(nn.Module):
"""Modified MiniBatch Stddev Layer.
This layer is modified from ``MiniBatchStddevLayer`` used in PGGAN. In
StyleGAN2, the authors add a new feature, `channel_groups`, into this
layer.
"""
def __init__(self, group_size=4, c... | 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_... | Juggernaut93/mmediting | ModMBStddevLayer | false | 13,907 | [
"Apache-2.0"
] | 1,884 | 8ef46ace29756dd2df1d92f2f73a33646e33e007 | https://github.com/Juggernaut93/mmediting/tree/8ef46ace29756dd2df1d92f2f73a33646e33e007 |
ToRGB | import math
import torch
import torch.utils.data
import torch
import torch.nn as nn
import torch.nn.functional as F
def make_kernel(k):
k = torch.tensor(k, dtype=torch.float32)
if len(k.shape) == 1:
k = k[None, :] * k[:, None]
k /= k.sum()
return k
def upfirdn2d_native(input, kernel, up_x, 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
import math
import torch.utils.data
import torch
import torch.nn as nn
import to... | a11isonliu/contrastive-unpaired-translation | ToRGB | false | 9,858 | [
"BSD-3-Clause"
] | 0 | 67651ed9877cae121d9398f46094ce8dbc678802 | https://github.com/a11isonliu/contrastive-unpaired-translation/tree/67651ed9877cae121d9398f46094ce8dbc678802 |
FCNet | import torch
import numpy as np
from torch.autograd import Variable
import torch.nn as nn
import torch.nn.functional as F
import torch.optim
class BasicNet:
def __init__(self, optimizer_fn, gpu, LSTM=False):
self.gpu = gpu and torch.cuda.is_available()
self.LSTM = LSTM
if self.gpu:
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import numpy as np
from torch... | G-Flor/deeprl | FCNet | false | 5,548 | [
"Apache-2.0"
] | 1 | aeae2c5d585e5853dc638968b1f090eb60abd351 | https://github.com/G-Flor/deeprl/tree/aeae2c5d585e5853dc638968b1f090eb60abd351 |
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 import nn
import torch.nn.functional as F
import torch.nn.parallel
as... | Eurus-Holmes/CHABCNet | GCN | false | 8,125 | [
"BSD-2-Clause"
] | 11 | 8d3985c7680981e58751d043880b5b5a818cc1d3 | https://github.com/Eurus-Holmes/CHABCNet/tree/8d3985c7680981e58751d043880b5b5a818cc1d3 |
SubtractMedian | # 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... | CPJKU/kagglebirds2020 | SubtractMedian | false | 17,045 | [
"MIT"
] | 4 | f86b459389b1d0b0af96ebc9252ffc8496c272e8 | https://github.com/CPJKU/kagglebirds2020/tree/f86b459389b1d0b0af96ebc9252ffc8496c272e8 |
MaxLayer | # 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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_str... | MoritzWag/LPDN | MaxLayer | false | 2,818 | [
"MIT"
] | 0 | a88a5a03f18c7f87879918369b8dc7a0e3abb02b | https://github.com/MoritzWag/LPDN/tree/a88a5a03f18c7f87879918369b8dc7a0e3abb02b |
Critic | import torch
import torch.nn as nn
import torch.nn.functional as F
class Critic(nn.Module):
def __init__(self, state_dim, action_dim):
super(Critic, self).__init__()
self.l1 = nn.Linear(state_dim + action_dim, 7)
self.l2 = nn.Linear(7, 6)
self.l3 = nn.Linear(6, 1)
self.l4 ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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 ... | LampKang/CityLearn | Critic | false | 2,495 | [
"MIT"
] | 0 | d6c178054c385ca991a5384e287f18a1d6380159 | https://github.com/LampKang/CityLearn/tree/d6c178054c385ca991a5384e287f18a1d6380159 |
HighwayNetwork | import torch
from torch import nn
import torch.nn.functional as F
class HighwayNetwork(nn.Module):
def __init__(self, size):
super().__init__()
self.W1 = nn.Linear(size, size)
self.W2 = nn.Linear(size, size)
self.W1.bias.data.fill_(0.0)
def forward(self, x):
x1 = self... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
assert_s... | KonstantinPakulev/OSM-one-shot-multispeaker | HighwayNetwork | false | 2,457 | [
"MIT"
] | 0 | 5cee1b6cb7dc7a3b4b24171340855a42824925f7 | https://github.com/KonstantinPakulev/OSM-one-shot-multispeaker/tree/5cee1b6cb7dc7a3b4b24171340855a42824925f7 |
PixelNormLayer | import torch
import torch.nn as nn
class PixelNormLayer(nn.Module):
def __init__(self):
super(PixelNormLayer, self).__init__()
def forward(self, x):
return x / torch.sqrt(torch.mean(x ** 2, dim=1, keepdim=True) + 1e-08)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_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.triton_helpers import libdevice
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_... | BeningSobariah/ark-stroller | PixelNormLayer | false | 11,241 | [
"Apache-2.0"
] | 0 | af2036a1726523d5aca9b1040bfc1fad5c3420f2 | https://github.com/BeningSobariah/ark-stroller/tree/af2036a1726523d5aca9b1040bfc1fad5c3420f2 |
FusedLeakyReLU | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_str... | jchetboun/anycost-gan | FusedLeakyReLU | false | 10,386 | [
"MIT"
] | 0 | 7e0005e50b915e2dfeb90fe7a9846c5df38d7c06 | https://github.com/jchetboun/anycost-gan/tree/7e0005e50b915e2dfeb90fe7a9846c5df38d7c06 |
SigmoidFocalClassificationLoss | # 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... | AhmedMoamen62/OpenPCDet | SigmoidFocalClassificationLoss | false | 11,158 | [
"Apache-2.0"
] | 0 | 4d61d099819f40096f795def2c012990d03711cd | https://github.com/AhmedMoamen62/OpenPCDet/tree/4d61d099819f40096f795def2c012990d03711cd |
AvgPoolShortening | # 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
from torch import nn
import torch.utils.data
import torch.nn.functional
import torch.autograd
assert_size_stride... | mcx/annotated_deep_learning_paper_implementations | AvgPoolShortening | false | 7,187 | [
"MIT"
] | 1 | f169f3a71dd2d36eb28ad31062d3475efa367b88 | https://github.com/mcx/annotated_deep_learning_paper_implementations/tree/f169f3a71dd2d36eb28ad31062d3475efa367b88 |
EncoderLayer | # 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.... | MadanMl/PyTorch-Transformer-for-RUL-Prediction | EncoderLayer | false | 8,531 | [
"Apache-2.0"
] | 25 | 5bf0a4739abdecbbc88118ea413393997bdc1e24 | https://github.com/MadanMl/PyTorch-Transformer-for-RUL-Prediction/tree/5bf0a4739abdecbbc88118ea413393997bdc1e24 |
EncoderLayer | import torch
import torch.nn as nn
import torch.nn.functional as F
class SPA(nn.Module):
""" Selective parallel attention """
def __init__(self, n_head: 'int'=8, d_v: 'int'=64):
super().__init__()
self.gap = nn.AdaptiveAvgPool1d(1)
self.sk = nn.Linear(d_v, n_head * d_v)
self.s... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | WOMMOW/AIT | EncoderLayer | false | 1,229 | [
"MIT"
] | 0 | 305fe7962bf9c5c24b6854e3ff0b7e2e669bf5a5 | https://github.com/WOMMOW/AIT/tree/305fe7962bf9c5c24b6854e3ff0b7e2e669bf5a5 |
Bottleneck_v2 | # 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 ... | JudeDavis1/intel-extension-for-pytorch | Bottleneck_v2 | false | 2,597 | [
"Apache-2.0"
] | 0 | 364e34cb4917a709f5108c07d4005bf82f3d5067 | https://github.com/JudeDavis1/intel-extension-for-pytorch/tree/364e34cb4917a709f5108c07d4005bf82f3d5067 |
BinaryLayer | import torch
import torch.nn as nn
class BinaryLayer(nn.Module):
def forward(self, input):
"""Forward function for binary layer
:param input: data
:returns: sign of data
:rtype: Tensor
"""
return torch.sign(input)
def backward(self, grad_output):
"""... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | ZombaSY/DeepLPF | BinaryLayer | false | 1,314 | [
"BSD-3-Clause"
] | 0 | adce64ae01bc9e32f465a354cb1f6534f0d13597 | https://github.com/ZombaSY/DeepLPF/tree/adce64ae01bc9e32f465a354cb1f6534f0d13597 |
SpatialAttention | import torch
from torch import nn
class SpatialAttention(nn.Module):
def __init__(self, kernel_size=7):
super(SpatialAttention, self).__init__()
assert kernel_size in (3, 7), 'kernel size must be 3 or 7'
padding = 3 if kernel_size == 7 else 1
self.conv1 = nn.Conv2d(2, 1, 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._inductor.runtime import triton_helpers
from torch import nn
assert_s... | Vanova/argus-freesound | SpatialAttention | false | 11,953 | [
"MIT"
] | 0 | 55f6e1b5ca1fd95c985f88a3e3fb0c81f8317b9d | https://github.com/Vanova/argus-freesound/tree/55f6e1b5ca1fd95c985f88a3e3fb0c81f8317b9d |
TensorClampOptionMax | # 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... | Akababa/torch2trt | TensorClampOptionMax | false | 18,429 | [
"MIT"
] | 2 | 03063b74a7eb40f5aac88d49be6b8b5e4e4e92d7 | https://github.com/Akababa/torch2trt/tree/03063b74a7eb40f5aac88d49be6b8b5e4e4e92d7 |
FusedLeakyReLU | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.utils.data
import torch
from torch import nn
import torch.nn.functional as F
assert_size_stride = torch._C._dynamo.guards.asser... | guyii54/Contrastive-I2I | FusedLeakyReLU | false | 6,769 | [
"BSD-3-Clause"
] | 1 | e73daa0f9d3770c2280a304c39678d5b22440647 | https://github.com/guyii54/Contrastive-I2I/tree/e73daa0f9d3770c2280a304c39678d5b22440647 |
SirenModule | import math
import torch
import torch.nn
class SirenModule(torch.nn.Module):
def __init__(self, in_features, out_features, weight_multiplier=1.0):
super(SirenModule, self).__init__()
self.linear = torch.nn.Linear(in_features, out_features)
init_bounds = math.sqrt(6 / in_features) * weight... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 math
i... | ashwinpn/Computer-Vision | SirenModule | false | 6,264 | [
"MIT"
] | 1 | 9dc3abfe416385171b76e2bad6872e10f36a12b4 | https://github.com/ashwinpn/Computer-Vision/tree/9dc3abfe416385171b76e2bad6872e10f36a12b4 |
DiscretePolicyFunction | # 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.... | himanshusahni/task-biased-url | DiscretePolicyFunction | false | 10,263 | [
"MIT"
] | 0 | 28e4ec318d46d84065b6e197fa9f4100bd4a4c34 | https://github.com/himanshusahni/task-biased-url/tree/28e4ec318d46d84065b6e197fa9f4100bd4a4c34 |
My_loss_focus2 | import torch
import torch.nn as nn
class My_loss_focus2(nn.Module):
def __init__(self):
super().__init__()
def forward(self, x, y, batch_size):
return torch.sum(torch.log1p(torch.abs(x - y))) / batch_size / 4
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 import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torc... | H-Liu1997/Pytorch_Pose_Estimation_Framework | My_loss_focus2 | false | 5,252 | [
"MIT"
] | 1 | 06616b3459ff639f8486e6ea4f93922597788b2a | https://github.com/H-Liu1997/Pytorch_Pose_Estimation_Framework/tree/06616b3459ff639f8486e6ea4f93922597788b2a |
LandmarkHead | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
from itertools import product as product
assert_size_strid... | qw85639229/Car_License_SVM | LandmarkHead | false | 7,569 | [
"MIT"
] | 1 | c5b0062e84e5000c7940b1d90cc7c63e52afed21 | https://github.com/qw85639229/Car_License_SVM/tree/c5b0062e84e5000c7940b1d90cc7c63e52afed21 |
BilinearAttention | import torch
import torch.utils.data
from torch import nn
class BilinearAttention(nn.Module):
"""
:param enc_dim: Scalar.
:param dec_dim: Scalar
"""
def __init__(self, enc_dim, dec_dim):
super(BilinearAttention, self).__init__()
self.W = nn.Linear(enc_dim, dec_dim)
def forwa... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | AstraliteHeart/cookietts | BilinearAttention | false | 7,759 | [
"BSD-3-Clause"
] | 25 | c871f5f7b5790656d5b57bcd9e63946a2da52f0f | https://github.com/AstraliteHeart/cookietts/tree/c871f5f7b5790656d5b57bcd9e63946a2da52f0f |
BertPreTrainingHeads | # 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.... | SivilTaram/dialogue-utterance-rewriter-pytorch | BertPreTrainingHeads | false | 2,930 | [
"MIT"
] | 0 | 92c2254958b7a1ee9199836f7f2236575270983f | https://github.com/SivilTaram/dialogue-utterance-rewriter-pytorch/tree/92c2254958b7a1ee9199836f7f2236575270983f |
Gate | import torch
import torch.nn as nn
class Gate(nn.Module):
def __init__(self, input_dim):
super(Gate, self).__init__()
self.linear = nn.Linear(input_dim * 4, 1, bias=True)
self.sigmoid = nn.Sigmoid()
def forward(self, x, y):
z = torch.cat([x, y, x * y, x - y], dim=2)
r... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | hgrhgy/NumSeq2SQL | Gate | false | 10,257 | [
"MIT"
] | 0 | 6f22fdf108736f979afa2dbd3af14aa9ad4718aa | https://github.com/hgrhgy/NumSeq2SQL/tree/6f22fdf108736f979afa2dbd3af14aa9ad4718aa |
Norm | # 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_... | AviVarma/torchASN-Transformer | Norm | false | 95 | [
"MIT"
] | 0 | 55bccf4cdb099cd8e9ac99f5f87f989ce2add983 | https://github.com/AviVarma/torchASN-Transformer/tree/55bccf4cdb099cd8e9ac99f5f87f989ce2add983 |
TransposeGatedConv2d | # 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 ... | piggy2303/DeepFillv2_Pytorch | TransposeGatedConv2d | false | 7,479 | [
"MIT"
] | 1 | dd35299f11704f878ed7a33e14ccd51a9d64baaf | https://github.com/piggy2303/DeepFillv2_Pytorch/tree/dd35299f11704f878ed7a33e14ccd51a9d64baaf |
RNN | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
from torch.autograd import Variable
assert_size_stride = t... | igorwood/practical-pytorch | RNN | false | 15,604 | [
"MIT"
] | 4,847 | c08fc28ba1f7d6838c3938076cc1b03d90dccace | https://github.com/igorwood/practical-pytorch/tree/c08fc28ba1f7d6838c3938076cc1b03d90dccace |
BboxHead | import torch
from itertools import product as product
import torch.nn as nn
class BboxHead(nn.Module):
def __init__(self, inchannels=512, num_anchors=3):
super(BboxHead, self).__init__()
self.conv1x1 = nn.Conv2d(inchannels, num_anchors * 4, kernel_size=(
1, 1), stride=1, 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 itertools import product as product
import torch.nn as nn
assert_size_strid... | Edward1900/Face-Detector-1MB-with-landmark | BboxHead | false | 13,702 | [
"MIT"
] | 907 | 16c16c4efa74b0264e0fd7fe0ddc0160f540a4bf | https://github.com/Edward1900/Face-Detector-1MB-with-landmark/tree/16c16c4efa74b0264e0fd7fe0ddc0160f540a4bf |
SplitAndConcat | import torch
import torch.nn as nn
import torch.utils.data
class SplitAndConcat(nn.Module):
"""Split the data from split_dim and concatenate in concat_dim.
@param split_dim from which axis the data will be chunk
@param concat_dim to which axis the data will be concatenated
@param chunk size of the da... | 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.... | tsubauaaa/d2go | SplitAndConcat | false | 4,461 | [
"Apache-2.0"
] | 0 | 9f746159ebf78ce79f644c405ca8695bc29d1075 | https://github.com/tsubauaaa/d2go/tree/9f746159ebf78ce79f644c405ca8695bc29d1075 |
PositionAttentionModule | # 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.... | LeftAttention/Attention-Codebase | PositionAttentionModule | false | 17,655 | [
"Apache-2.0"
] | 6 | 348ec66233a7c0f95a3cb5f0f11641e2a7a9b9c3 | https://github.com/LeftAttention/Attention-Codebase/tree/348ec66233a7c0f95a3cb5f0f11641e2a7a9b9c3 |
GEGLU | import torch
from torch import nn
import torch.nn.functional as F
class GEGLU(nn.Module):
def forward(self, x):
x, gates = x.chunk(2, dim=-1)
return x * F.gelu(gates)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
| import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | NomadicDaggy/DALLE-pytorch | GEGLU | false | 11,756 | [
"MIT"
] | 0 | ecadc12e8063763ad45de50773e5c746262cdfd3 | https://github.com/NomadicDaggy/DALLE-pytorch/tree/ecadc12e8063763ad45de50773e5c746262cdfd3 |
CNN | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import ... | stxxllbu/CS224n-winter-together | CNN | false | 16,501 | [
"Apache-2.0"
] | 468 | eae158ed8e88dc7c8638e25bac4c4fc8eeddcc8c | https://github.com/stxxllbu/CS224n-winter-together/tree/eae158ed8e88dc7c8638e25bac4c4fc8eeddcc8c |
RegLoss | # 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_... | Ahren09/RecBole | RegLoss | false | 1,950 | [
"MIT"
] | 0 | b3921818dfbc1b81f9eda8d5e9f05bc9d9114089 | https://github.com/Ahren09/RecBole/tree/b3921818dfbc1b81f9eda8d5e9f05bc9d9114089 |
BertImagePooler | from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
class BertImagePooler(nn.Module):
def __init__(self, config):
super(BertImagePooler, self).__init__()
self.dense = nn.Linear(config.v_hidden_size, config.bi_hidden_size)
self.activation = nn.ReLU()
def f... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | BigRedT/gpv-1 | BertImagePooler | false | 15,660 | [
"Apache-2.0"
] | 45 | 6a0c2173b44961cb492d00f94864c461aa77641d | https://github.com/BigRedT/gpv-1/tree/6a0c2173b44961cb492d00f94864c461aa77641d |
Img_decoder_v3 | # 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.... | Holmes-Alan/Photo2Sketch | Img_decoder_v3 | false | 638 | [
"MIT"
] | 0 | 43a0ca6bb8a8e645b35a2ab23d11ed5efe117e09 | https://github.com/Holmes-Alan/Photo2Sketch/tree/43a0ca6bb8a8e645b35a2ab23d11ed5efe117e09 |
EqualLinear | # 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 math
from torch import nn
from torch.nn import functional as F
assert_siz... | Liamkuo/SAIR | EqualLinear | false | 17,567 | [
"MIT"
] | 6 | 0fb289cd975b5a196b58e7d16bac00e31fd41d39 | https://github.com/Liamkuo/SAIR/tree/0fb289cd975b5a196b58e7d16bac00e31fd41d39 |
InnerProductLoss | import torch
import torch.nn as nn
import torch.nn.functional as F
class InnerProductLoss(nn.Module):
"""This is the inner-product loss used in CFKG for optimization.
"""
def __init__(self):
super(InnerProductLoss, self).__init__()
def forward(self, anchor, positive, negative):
pos_s... | 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... | dreaming-qin/RecBole | InnerProductLoss | false | 12,311 | [
"MIT"
] | 0 | d6de39521484ded60c387ca604abaf86310acdbe | https://github.com/dreaming-qin/RecBole/tree/d6de39521484ded60c387ca604abaf86310acdbe |
ReLUDeepLiftModel | # 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... | LMdeLiangMi/captum | ReLUDeepLiftModel | false | 5,466 | [
"BSD-3-Clause"
] | 1 | 8bd9686013fe0ba8996e9b1cbeb0ea8e91512787 | https://github.com/LMdeLiangMi/captum/tree/8bd9686013fe0ba8996e9b1cbeb0ea8e91512787 |
CoxPHLossSorted | import torch
def cox_ph_loss_sorted(log_h, event, eps=1e-07):
"""Requires the input to be sorted by descending duration time.
See DatasetDurationSorted.
We calculate the negative log of $( rac{h_i}{\\sum_{j \\in R_i} h_j})^d$,
where h = exp(log_h) are the hazards and R is the risk set, and d is event... | 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
assert_size_stride = t... | nikolase90/pycox | CoxPHLossSorted | false | 7,348 | [
"BSD-2-Clause"
] | 1 | 1c780253da7bab7eba0dc02e1436a68a9b812a66 | https://github.com/nikolase90/pycox/tree/1c780253da7bab7eba0dc02e1436a68a9b812a66 |
LogTaylorSoftmaxV1 | import torch
import torch.nn as nn
def taylor_softmax_v1(x, dim=1, n=4, use_log=False):
assert n % 2 == 0 and n > 0
fn = torch.ones_like(x)
denor = 1.0
for i in range(1, n + 1):
denor *= i
fn = fn + x.pow(i) / denor
out = fn / fn.sum(dim=dim, keepdims=True)
if use_log:
... | 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... | ishine/DeepKE | LogTaylorSoftmaxV1 | false | 15,608 | [
"MIT"
] | 676 | 75bcfb3e045bb2197ac5c0847693c2a647f76576 | https://github.com/ishine/DeepKE/tree/75bcfb3e045bb2197ac5c0847693c2a647f76576 |
C1 | # 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
from co... | xxchenxx/otdd | C1 | false | 13,126 | [
"MIT"
] | 0 | e63d1d170fed36957052b7bb0a0af1553b980381 | https://github.com/xxchenxx/otdd/tree/e63d1d170fed36957052b7bb0a0af1553b980381 |
Temporal_Attention | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.... | Herrccc/DR-TANet | Temporal_Attention | false | 8,242 | [
"MIT"
] | 12 | 37cc3929833d61451b2fa4a92ccd4286cfc4fd34 | https://github.com/Herrccc/DR-TANet/tree/37cc3929833d61451b2fa4a92ccd4286cfc4fd34 |
BasicConv | # 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 ... | agusgun/EDSR-PyTorch | BasicConv | false | 18,231 | [
"MIT"
] | 6 | 38ff657e2c4e2f148d38b8792bacdf8d81f8bf9f | https://github.com/agusgun/EDSR-PyTorch/tree/38ff657e2c4e2f148d38b8792bacdf8d81f8bf9f |
RpowFloat | import torch
class RpowFloat(torch.nn.Module):
def __init__(self):
super(RpowFloat, self).__init__()
def forward(self, x):
return 2.0 ** 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 libdevice
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_c... | Ilyabasharov/torch2trt | RpowFloat | false | 2,536 | [
"MIT"
] | 0 | 76bf298b3da408509665e23e2494922b131afb10 | https://github.com/Ilyabasharov/torch2trt/tree/76bf298b3da408509665e23e2494922b131afb10 |
ResidualDenseBlock | import torch
from torch import Tensor
import torch.nn as nn
class ResidualDenseBlock(nn.Module):
"""Achieves densely connected convolutional layers.
`Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993v5.pdf>` paper.
Args:
channels (int): The number of channels in the ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | cyun-404/PieESRGAN | ResidualDenseBlock | false | 3,381 | [
"Apache-2.0"
] | 0 | 22ffe683bf2389b646429494d1bc88e61a9d72c5 | https://github.com/cyun-404/PieESRGAN/tree/22ffe683bf2389b646429494d1bc88e61a9d72c5 |
FeatNet | import torch
import torch.nn as nn
from itertools import product as product
class FeatNet(nn.Module):
def __init__(self):
super(FeatNet, self).__init__()
self.conv1 = nn.Conv2d(in_channels=1, out_channels=16, kernel_size=
(3, 7), stride=1, padding=(1, 3), bias=False)
self.tanh... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | DongChengdongHangZhou/caffe-to-pytorch | FeatNet | false | 2,240 | [
"Apache-2.0"
] | 0 | 5e3104f3aa77d35bad5d2de235b067460c136fd5 | https://github.com/DongChengdongHangZhou/caffe-to-pytorch/tree/5e3104f3aa77d35bad5d2de235b067460c136fd5 |
MetaAconC | import torch
import torch.nn as nn
class MetaAconC(nn.Module):
""" ACON activation (activate or not).
MetaAconC: (p1*x-p2*x) * sigmoid(beta*(p1*x-p2*x)) + p2*x, beta is generated by a small network
according to "Activate or Not: Learning Customized Activation" <https://arxiv.org/pdf/2009.04759.pdf>.
"... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | PoCInnovation/Koic | MetaAconC | false | 8,663 | [
"MIT"
] | 13 | eca53b53b7242c1e83213ef9408366ca0a346358 | https://github.com/PoCInnovation/Koic/tree/eca53b53b7242c1e83213ef9408366ca0a346358 |
BCELoss | # 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
... | MaybeS/mnist | BCELoss | false | 17,708 | [
"MIT"
] | 8 | d0aeafce97d7308dc84adbb6ad8e547776db0cd5 | https://github.com/MaybeS/mnist/tree/d0aeafce97d7308dc84adbb6ad8e547776db0cd5 |
KDLoss | # 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... | angpo/VKD | KDLoss | false | 14,880 | [
"MIT"
] | 68 | 2a136e00dad4c73612d6efe087675604ac2416eb | https://github.com/angpo/VKD/tree/2a136e00dad4c73612d6efe087675604ac2416eb |
PatchEmbeddings | # 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
from torch import nn
import torch.utils.data
import ... | mcx/annotated_deep_learning_paper_implementations | PatchEmbeddings | false | 7,202 | [
"MIT"
] | 1 | f169f3a71dd2d36eb28ad31062d3475efa367b88 | https://github.com/mcx/annotated_deep_learning_paper_implementations/tree/f169f3a71dd2d36eb28ad31062d3475efa367b88 |
SpatialSoftmaxBZ | # 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 numpy as np
ass... | zwc662/SequentialAttack | SpatialSoftmaxBZ | false | 16,837 | [
"MIT"
] | 116 | 677b19c51ea76d794939ee126fccd75ffa0e6fe6 | https://github.com/zwc662/SequentialAttack/tree/677b19c51ea76d794939ee126fccd75ffa0e6fe6 |
CmapPafHeadAttention | import torch
import torch.utils.data
import torch.nn
import torch.optim
class UpsampleCBR(torch.nn.Sequential):
def __init__(self, input_channels, output_channels, count=1, num_flat=0):
layers = []
for i in range(count):
if i == 0:
inch = input_channels
els... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.utils.... | quantd2/trt_pose | CmapPafHeadAttention | false | 16,318 | [
"MIT"
] | 738 | 44c5e826977f20c8dad2d9725313a18cb2189853 | https://github.com/quantd2/trt_pose/tree/44c5e826977f20c8dad2d9725313a18cb2189853 |
Upsample | import torch
import torch.nn as nn
class Upsample(nn.Module):
def __init__(self, in_channels, out_channels):
super().__init__()
self.conv1x1 = nn.Conv2d(in_channels, in_channels, kernel_size=1,
stride=1, padding=0)
self.conv3x3 = nn.Conv2d(in_channels, out_channels, kernel_siz... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | loong8888/TextSnake.pytorch | Upsample | false | 7,124 | [
"MIT"
] | 1 | 49c24f71043c1895b91f8c7379995037fcc644f7 | https://github.com/loong8888/TextSnake.pytorch/tree/49c24f71043c1895b91f8c7379995037fcc644f7 |
HardMish | # 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.nn.parallel
assert_size_stride = torch._C._dynamo.guar... | HotaekHan/detr_pytorch | HardMish | false | 546 | [
"MIT"
] | 0 | 730e02db0ac8910ef782234a3990587771ad67f9 | https://github.com/HotaekHan/detr_pytorch/tree/730e02db0ac8910ef782234a3990587771ad67f9 |
DiceLoss | import torch
import torch.nn as nn
def IoU(logit, truth, smooth=1):
prob = torch.sigmoid(logit)
intersection = torch.sum(prob * truth)
union = torch.sum(prob + truth)
iou = (2 * intersection + smooth) / (union + smooth)
return iou
class DiceLoss(nn.Module):
def __init__(self, smooth=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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | evilidol/kaggle-Steel-Defect-Detection | DiceLoss | false | 6,666 | [
"MIT"
] | 1 | 41e3e360f49d706c8c79bcd442342c529648a736 | https://github.com/evilidol/kaggle-Steel-Defect-Detection/tree/41e3e360f49d706c8c79bcd442342c529648a736 |
n_to_one | # 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... | sailfish009/torch-toolbox | n_to_one | false | 7,584 | [
"BSD-3-Clause"
] | 1 | 80dfc22c697b9f323e097de72af04f0e5435d7b4 | https://github.com/sailfish009/torch-toolbox/tree/80dfc22c697b9f323e097de72af04f0e5435d7b4 |
ClampExp | import torch
import torch.utils.data
class ClampExp(torch.nn.Module):
"""
Nonlinearity min(exp(lam * x), 1)
"""
def __init__(self):
"""
Constructor
:param lam: Lambda parameter
"""
super(ClampExp, self).__init__()
def forward(self, x):
one = torch.... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.utils.dat... | mbaddar1/normalizing-flows | ClampExp | false | 10,770 | [
"MIT"
] | 0 | d1409464a65234354b29ed9ea0ede2d12100440c | https://github.com/mbaddar1/normalizing-flows/tree/d1409464a65234354b29ed9ea0ede2d12100440c |
HeatmapLoss | import torch
import torch.nn as nn
import torch.utils.data
import torch.nn.parallel
import torch.optim
import torch.utils.data.distributed
import torch.multiprocessing
class HeatmapLoss(nn.Module):
def __init__(self):
super().__init__()
def forward(self, pred, gt, mask):
assert pred.size() =... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.utils.data
import torch.nn.parallel
import torch.optim
import torch.utils.data.distributed
import torch.m... | ahmedelmahy/HRNet-Bottom-Up-Pose-Estimation | HeatmapLoss | false | 14,757 | [
"MIT"
] | 129 | cf5831249999f0b307d5aa948ebdcdef981ba68f | https://github.com/ahmedelmahy/HRNet-Bottom-Up-Pose-Estimation/tree/cf5831249999f0b307d5aa948ebdcdef981ba68f |
ConvBlock | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data
from torch.cuda import *
def conv3x3(in_channels, out_channels):
return nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1,
padding=1, bias=True)
class ConvBlock(nn.Module):
def __init__(self, in_ch... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | jabae/detectEM | ConvBlock | false | 6,920 | [
"MIT"
] | 1 | 2d1a5116164d0bed0a8ea767a227d05a8970a448 | https://github.com/jabae/detectEM/tree/2d1a5116164d0bed0a8ea767a227d05a8970a448 |
Deconv2d | import torch
import torch.nn as nn
class Deconv2d(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, stride=1, bn
=False, activation='leakyrelu', dropout=False):
super(Deconv2d, self).__init__()
padding = int((kernel_size - 1) / 2)
self.conv = nn.ConvTranspose2... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | RQuispeC/pytorch-ACSCP | Deconv2d | false | 8,685 | [
"MIT"
] | 25 | c83f08632012c2245250ff9c5140814461db575c | https://github.com/RQuispeC/pytorch-ACSCP/tree/c83f08632012c2245250ff9c5140814461db575c |
MeanSquared | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn.functi... | gsaiabhishek/AUTOMATA | MeanSquared | false | 12,479 | [
"MIT"
] | 0 | e944992a7bf3a50bc8951a303294b3a798822176 | https://github.com/gsaiabhishek/AUTOMATA/tree/e944992a7bf3a50bc8951a303294b3a798822176 |
KlCriterion | import torch
import torch.nn.functional as F
from torch.nn.modules.loss import _Loss
from torch.optim.lr_scheduler import *
class Criterion(_Loss):
def __init__(self, alpha=1.0, name='criterion'):
super().__init__()
"""Alpha is used to weight each loss term
"""
self.alpha = alpha
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch.... | kiminh/mt-dnn | KlCriterion | false | 7,029 | [
"MIT"
] | 1 | 133884b380244dbe74acc4d7507e551b2c5035b3 | https://github.com/kiminh/mt-dnn/tree/133884b380244dbe74acc4d7507e551b2c5035b3 |
ResidualBlock | import torch
import torch.nn as nn
import torch.utils.data
class ResidualBlock(nn.Module):
def __init__(self, in_channels, out_channels, activation='relu'):
super().__init__()
self.in_channels, self.out_channels, self.activation = (in_channels,
out_channels, activation)
self.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
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import torch.utils.data
assert_size_stride = torch._C._dynamo.guard... | L-Net-1992/DI-engine | ResidualBlock | false | 5,490 | [
"Apache-2.0"
] | 1 | 06803b4e18fa64bbed0fd1d44952242c0c063b0f | https://github.com/L-Net-1992/DI-engine/tree/06803b4e18fa64bbed0fd1d44952242c0c063b0f |
OscBase | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
im... | aupilot/a2c | OscBase | false | 12,142 | [
"MIT"
] | 0 | cd7e8892f91ce0c8b4c221eb6be31ebbee81d663 | https://github.com/aupilot/a2c/tree/cd7e8892f91ce0c8b4c221eb6be31ebbee81d663 |
LayerNorm | import torch
import torch.nn as nn
from torch.nn.parameter import Parameter
from torch.optim.lr_scheduler import *
from torch.nn import Parameter
class LayerNorm(nn.Module):
def __init__(self, hidden_size, eps=0.0001):
super(LayerNorm, self).__init__()
self.alpha = Parameter(torch.ones(1, 1, hidd... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
from torch.nn.parameter import Parameter
from torch.optim... | aerinkim/squad_2018 | LayerNorm | false | 3,098 | [
"BSD-3-Clause"
] | 0 | 4479fa7ce92d8ab2f2eeb1823991d416924d8561 | https://github.com/aerinkim/squad_2018/tree/4479fa7ce92d8ab2f2eeb1823991d416924d8561 |
MultiLayerPerceptron | # 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.... | ShantanuNair/NeMo | MultiLayerPerceptron | false | 17,901 | [
"Apache-2.0"
] | 10 | d01b7bbc3fdb1bbf14789f71b8f368cf0aa8f86b | https://github.com/ShantanuNair/NeMo/tree/d01b7bbc3fdb1bbf14789f71b8f368cf0aa8f86b |
CircleLoss | # 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.... | JacobARose/image-utils | CircleLoss | false | 594 | [
"MIT"
] | 0 | aa0e005c0b4df5198d188b074f4e21f8d8f97962 | https://github.com/JacobARose/image-utils/tree/aa0e005c0b4df5198d188b074f4e21f8d8f97962 |
ZSSRNet | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
import t... | LiFH/MySR | ZSSRNet | false | 776 | [
"MIT"
] | 0 | f6075f8711853aba6f0aae9cef18c5da84abb78c | https://github.com/LiFH/MySR/tree/f6075f8711853aba6f0aae9cef18c5da84abb78c |
TransformerNet2 | # 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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_c... | Ekko-zn/StegoAdv | TransformerNet2 | false | 5,110 | [
"MIT"
] | 1 | 2852dbc85d66f30efb7127695c0d75806bf4aa4c | https://github.com/Ekko-zn/StegoAdv/tree/2852dbc85d66f30efb7127695c0d75806bf4aa4c |
TensorConstantLinear | import torch
class TensorConstantLinear(torch.nn.Module):
def __init__(self, weight=1, bias=0):
self.weight = weight
self.bias = bias
super().__init__()
def forward(self, input):
return self.weight * input + self.bias
def get_inputs():
return [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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.j... | Minyus/pipelinex | TensorConstantLinear | false | 14,032 | [
"Apache-2.0"
] | 188 | f35c524ec9c50751ee27d9a42d98317e16f1c544 | https://github.com/Minyus/pipelinex/tree/f35c524ec9c50751ee27d9a42d98317e16f1c544 |
merge_layer | # 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 ... | EpiSci/SoCRATES | merge_layer | false | 17,261 | [
"MIT"
] | 6 | 901a896c5a765e3cb56f290188cde71c8707192d | https://github.com/EpiSci/SoCRATES/tree/901a896c5a765e3cb56f290188cde71c8707192d |
CSAM | # 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_... | ZitongYu/pidinet | CSAM | false | 18,251 | [
"MIT"
] | 5 | 15cdf9fb056549934877675bf7571b427f86db55 | https://github.com/ZitongYu/pidinet/tree/15cdf9fb056549934877675bf7571b427f86db55 |
WL1Loss | import torch
import torch.nn as nn
class WL1Loss(nn.Module):
def __init__(self):
super(WL1Loss, self).__init__()
def forward(self, pred, target, weight):
return torch.mean(weight * torch.abs(pred - target))
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4]), t... | 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
... | tccoin/UM-545-Machine-Learning | WL1Loss | false | 4,410 | [
"MIT"
] | 0 | 0854d7ad7e546c009edeb4a4d3e507ce95b99cf8 | https://github.com/tccoin/UM-545-Machine-Learning/tree/0854d7ad7e546c009edeb4a4d3e507ce95b99cf8 |
BERTLhuc | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
from torch.nn.parameter import Parameter
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided... | DAQuestionAnswering/Bert-n-Pals | BERTLhuc | false | 7,035 | [
"MIT"
] | 1 | d5a288b9ac62259e70c249635108ba3906e19f00 | https://github.com/DAQuestionAnswering/Bert-n-Pals/tree/d5a288b9ac62259e70c249635108ba3906e19f00 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.