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 |
|---|---|---|---|---|---|---|---|---|---|---|
GHMC | import torch
import torch.nn as nn
import torch.nn.functional as F
def _expand_onehot_labels(labels, label_weights, label_channels):
bin_labels = labels.new_full((labels.size(0), label_channels), 0)
inds = torch.nonzero((labels >= 0) & (labels < label_channels),
as_tuple=False).squeeze()
if inds.n... | 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
... | AtticusJohnson/mmdetection | GHMC | false | 11,218 | [
"Apache-2.0"
] | 0 | d8d89bafcce13d3b32b1fb3366be3bb9830546c2 | https://github.com/AtticusJohnson/mmdetection/tree/d8d89bafcce13d3b32b1fb3366be3bb9830546c2 |
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.utils.data
import torch.nn as nn
import torch
import torch.nn.parallel
assert_size_stride = torch._C._dynamo.guards.assert_size... | adymaharana/VLCStoryGan | GLU | false | 18,246 | [
"MIT"
] | 10 | 74112404689e8144c2ed2d375e1e5a1cde09debb | https://github.com/adymaharana/VLCStoryGan/tree/74112404689e8144c2ed2d375e1e5a1cde09debb |
ActNorm | # 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 math as tl_math
import torch.nn as nn
import torch.utils.data
from torch.nn import Parame... | XinZhang525/fGAIL | ActNorm | false | 18,128 | [
"MIT"
] | 4 | 682d70286685612558e072d9a1668779b8ae325b | https://github.com/XinZhang525/fGAIL/tree/682d70286685612558e072d9a1668779b8ae325b |
MaskedTemporalPooling | import torch
from typing import Optional
import torch.utils.data
import torch.nn
class MaskedTemporalPooling(torch.nn.Module):
"""
Applies temporal pooling operations on masked inputs. For each pooling operation
all masked values are ignored.
"""
def __init__(self, method: '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._inductor.runtime import triton_helpers
import torch.utils.data
import torch.nn
assert_size_stride = torch._C._dynamo.guards.asse... | kevinmtian/pytorchvideo | MaskedTemporalPooling | false | 15,822 | [
"Apache-2.0"
] | 2,391 | 168e16859a6029ef8ebeb476f9163bebb6c6b87d | https://github.com/kevinmtian/pytorchvideo/tree/168e16859a6029ef8ebeb476f9163bebb6c6b87d |
LR | import torch
import torch.nn as nn
import torch.utils.data
class LR(nn.Module):
def __init__(self, feature_nums, output_dim=1):
super(LR, self).__init__()
self.linear = nn.Linear(feature_nums, output_dim)
self.bias = nn.Parameter(torch.zeros((output_dim,)))
def forward(self, 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
import torch.nn as nn
import torch.utils.data
assert_size_stride = torch._C._dyn... | JiaXingBinggan/LSTM_Project | LR | false | 9,130 | [
"Apache-2.0"
] | 0 | 9d84fb96951f2f6036cb58e9c839bb879a09cbcc | https://github.com/JiaXingBinggan/LSTM_Project/tree/9d84fb96951f2f6036cb58e9c839bb879a09cbcc |
FCNet | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
from typing import *
class FCNet(nn.Module):
def __init__(self, input_size, output_size):
super().__init__()
self.l1 = nn.Linear(input_size, 5)
self.relu = nn.ReLU()
self.l2 = nn... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import ... | Johnsonms/NNI_master | FCNet | false | 11,609 | [
"MIT"
] | 0 | e5e5c7aed89cf3189cffe1056464833c15eb54ff | https://github.com/Johnsonms/NNI_master/tree/e5e5c7aed89cf3189cffe1056464833c15eb54ff |
MultiLayeredConv1d | # 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... | Rexiome/NATSpeech | MultiLayeredConv1d | false | 14,293 | [
"MIT"
] | 561 | 238165e8cd430531b69c484cabb032c1313ee73b | https://github.com/Rexiome/NATSpeech/tree/238165e8cd430531b69c484cabb032c1313ee73b |
BatchLinear | # 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... | KIMGEONUNG/multi-memory-siren | BatchLinear | false | 707 | [
"MIT"
] | 0 | b372e1b9abe2b3fb502d808eb3a47c2ad287ca3b | https://github.com/KIMGEONUNG/multi-memory-siren/tree/b372e1b9abe2b3fb502d808eb3a47c2ad287ca3b |
NLKDifferenceOffset | # 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.... | HKUST-KnowComp/EFO-1-QA-benchmark | NLKDifferenceOffset | false | 17,364 | [
"MIT"
] | 9 | 600fb02c76ab631f93ee362ceb789216ec085790 | https://github.com/HKUST-KnowComp/EFO-1-QA-benchmark/tree/600fb02c76ab631f93ee362ceb789216ec085790 |
DiceLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import functools
impor... | CuttlefishXuan/mmsegmentation-1 | DiceLoss | false | 13,538 | [
"Apache-2.0"
] | 789 | 13771312da1a66d5cd642df6aa370affd3f5ceac | https://github.com/CuttlefishXuan/mmsegmentation-1/tree/13771312da1a66d5cd642df6aa370affd3f5ceac |
OutPutBlock | # 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... | HiLab-git/WSL4MIS | OutPutBlock | false | 8,269 | [
"MIT"
] | 29 | 9683e7c7409b95c0ac2169fe7964f6ca04c80d9a | https://github.com/HiLab-git/WSL4MIS/tree/9683e7c7409b95c0ac2169fe7964f6ca04c80d9a |
GeLU | import math
import torch
import torch.nn as nn
def gelu(x):
"""Implementation of the gelu activation function.
For information: OpenAI GPT's gelu is slightly different (and gives slightly different results):
0.5 * x * (1 + torch.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 * torch.pow(x, 3))))
... | 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 math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.... | AsmitaBhat30/lxmert | GeLU | false | 4,865 | [
"MIT"
] | 1 | 90292dc36a25c04c4f76fe9119e3141d5dc05874 | https://github.com/AsmitaBhat30/lxmert/tree/90292dc36a25c04c4f76fe9119e3141d5dc05874 |
MSELoss | # 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._C
import torch.serialization
from torch import nn
import torch.nn.functional as F
from typing import *
assert_size_stride = to... | shuaizzZ/mmsegmentation | MSELoss | false | 4,320 | [
"Apache-2.0"
] | 0 | a6c6b348dbf8c4a0a39ffbdb832a1e82309c533c | https://github.com/shuaizzZ/mmsegmentation/tree/a6c6b348dbf8c4a0a39ffbdb832a1e82309c533c |
TransposeLayer | # 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... | BUTSpeechFIT/beer | TransposeLayer | false | 16,964 | [
"MIT"
] | 6 | 43fb9027a859db28d2f2f8709260ca2ce9501e25 | https://github.com/BUTSpeechFIT/beer/tree/43fb9027a859db28d2f2f8709260ca2ce9501e25 |
SimpleReluModel | import torch
import torch.jit
import torch.nn.functional as F
import torch.onnx
import torch.nn
class SimpleReluModel(torch.nn.Module):
def __init__(self, inplace=False):
super(SimpleReluModel, self).__init__()
self.inplace = inplace
def forward(self, tensor):
other = F.relu(tensor, ... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.jit
import torch.onnx
import torch.nn
assert_size_stride = torch._C._dynamo.... | YaronBenAtar/glow | SimpleReluModel | false | 14,674 | [
"Apache-2.0"
] | 2,838 | a13706a4239fa7eaf059c670dc573e3eb0768f86 | https://github.com/YaronBenAtar/glow/tree/a13706a4239fa7eaf059c670dc573e3eb0768f86 |
SE | import torch
from itertools import chain as chain
import torch.utils.data
import torch.nn as nn
class SwishEfficient(torch.autograd.Function):
"""Swish activation function: x * sigmoid(x)."""
@staticmethod
def forward(ctx, x):
result = x * torch.sigmoid(x)
ctx.save_for_backward(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 itertools import chain a... | Drill-D/SlowFast | SE | false | 2,180 | [
"Apache-2.0"
] | 0 | d55ae1cf30a9415858a9bd5da983790a2b418653 | https://github.com/Drill-D/SlowFast/tree/d55ae1cf30a9415858a9bd5da983790a2b418653 |
VarianceNorm2d | import torch
import torch.nn as nn
class VarianceNorm2d(nn.Module):
def __init__(self, num_features, bias=False):
super().__init__()
self.num_features = num_features
self.bias = bias
self.alpha = nn.Parameter(torch.zeros(num_features))
self.alpha.data.normal_(1, 0.02)
... | 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_... | henryaddison/score_sde_pytorch | VarianceNorm2d | false | 12,488 | [
"Apache-2.0"
] | 0 | be07c3a3346bf8ceadabf6a3b436db5d5c3d0252 | https://github.com/henryaddison/score_sde_pytorch/tree/be07c3a3346bf8ceadabf6a3b436db5d5c3d0252 |
HLCriterion | import torch
import torch.nn.functional as F
from torch.nn.modules.loss import _Loss
from torch.optim.lr_scheduler import *
class Criterion(_Loss):
def __init__(self, alpha=1.0, name='criterion'):
super().__init__()
"""Alpha is used to weight each loss term
"""
self.alpha = alpha
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch.... | chunhuililili/mt_dnn | HLCriterion | false | 10,199 | [
"MIT"
] | 0 | 4c6efaf21724c7b8103a05e46b5b44d7b246225e | https://github.com/chunhuililili/mt_dnn/tree/4c6efaf21724c7b8103a05e46b5b44d7b246225e |
KGCN | from torch.nn import Module
import math
import torch
import torch.nn.functional as F
import torch.nn as nn
from torch.nn.parameter import Parameter
from torch.nn.modules.module import Module
class GraphConvolution(Module):
"""
Simple GCN layer
"""
def __init__(self, in_features, out_features, bias=Tr... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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... | gear/gfnn | KGCN | false | 15,418 | [
"MIT"
] | 46 | 36667861caacba921469d43917d002896e832c3f | https://github.com/gear/gfnn/tree/36667861caacba921469d43917d002896e832c3f |
NeuralNetMultiplePositionalArgumentsMultiOutputsWithDependency | import torch
import torch.nn
import torch.onnx
import torch.utils.checkpoint
class NeuralNetMultiplePositionalArgumentsMultiOutputsWithDependency(torch.
nn.Module):
def __init__(self, input_size, hidden_size, num_classes):
super(NeuralNetMultiplePositionalArgumentsMultiOutputsWithDependency,
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | TingGong1/onnxruntime | NeuralNetMultiplePositionalArgumentsMultiOutputsWithDependency | false | 5,904 | [
"MIT"
] | 1 | 435010ab6873974803591fa22262ed8b3e36e44d | https://github.com/TingGong1/onnxruntime/tree/435010ab6873974803591fa22262ed8b3e36e44d |
NsSymKlCriterion | import torch
import torch.nn.functional as F
from torch.nn.modules.loss import _Loss
from torch.optim.lr_scheduler import *
def stable_kl(logit, target, epsilon=1e-06, reduce=True):
logit = logit.view(-1, logit.size(-1)).float()
target = target.view(-1, target.size(-1)).float()
bs = logit.size(0)
p = ... | 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... | chunhuililili/mt_dnn | NsSymKlCriterion | false | 10,212 | [
"MIT"
] | 0 | 4c6efaf21724c7b8103a05e46b5b44d7b246225e | https://github.com/chunhuililili/mt_dnn/tree/4c6efaf21724c7b8103a05e46b5b44d7b246225e |
SelfAttentionGPT2 | # 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.... | wjeliot/former | SelfAttentionGPT2 | false | 13,105 | [
"MIT"
] | 0 | 38bd29b68b110e1e3eddae3106f7db2ffc0e5ce8 | https://github.com/wjeliot/former/tree/38bd29b68b110e1e3eddae3106f7db2ffc0e5ce8 |
TorchMod | # 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 | TorchMod | false | 2,557 | [
"MIT"
] | 0 | 76bf298b3da408509665e23e2494922b131afb10 | https://github.com/Ilyabasharov/torch2trt/tree/76bf298b3da408509665e23e2494922b131afb10 |
ConvLayer | # 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... | suryawanshishantanu6/Multi-Style-Transfer | ConvLayer | false | 10,855 | [
"MIT"
] | 0 | c5c211847de676596580a8a9afda940ac76abbb1 | https://github.com/suryawanshishantanu6/Multi-Style-Transfer/tree/c5c211847de676596580a8a9afda940ac76abbb1 |
LengthPredictor | import torch
from torch.nn import functional as F
from torch import nn
from torchvision import models as models
import torch.onnx
import torch.nn
class LengthPredictionLoss(nn.Module):
def __init__(self, max_delta=50):
super().__init__()
self.max_delta = max_delta
def forward(self, logits, 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.nn import function... | ygnn123/training_extensions | LengthPredictor | false | 4,729 | [
"Apache-2.0"
] | 0 | c3aeba9359b0d4e0ef9c054de777d3ec081a9892 | https://github.com/ygnn123/training_extensions/tree/c3aeba9359b0d4e0ef9c054de777d3ec081a9892 |
GDL | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import numpy as np
from torch import nn
import torch.jit
import torch.nn.functional
assert_size_stride = torch._C._dynamo.guards.assert_size... | CamilaGL/nnUNet | GDL | false | 207 | [
"Apache-2.0"
] | 0 | 471ab73a6e4f67fc72d476183b5344be4cccf7ca | https://github.com/CamilaGL/nnUNet/tree/471ab73a6e4f67fc72d476183b5344be4cccf7ca |
IoU | import torch
import torch.nn as nn
class IoU(nn.Module):
"""
This class implements the IoU for validation. Not gradients supported.
"""
def __init__(self, threshold: 'float'=0.5) ->None:
"""
Constructor method
:param threshold: (float) Threshold to be applied
"""
... | 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... | ChristophReich1996/Cell-DETR | IoU | false | 13,490 | [
"MIT"
] | 55 | 4d0c3a2d3ffd19184c8443e5b3a6dccc053c77ea | https://github.com/ChristophReich1996/Cell-DETR/tree/4d0c3a2d3ffd19184c8443e5b3a6dccc053c77ea |
GatedPooling1 | # 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_... | RicherMans/Dcase2018_pooling | GatedPooling1 | false | 8,696 | [
"Apache-2.0"
] | 13 | 10540502bba7215a1ba157614b39fedecb079d9b | https://github.com/RicherMans/Dcase2018_pooling/tree/10540502bba7215a1ba157614b39fedecb079d9b |
SimpleATanModule | # 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.jit
import torch.onnx
import torch.nn
assert_size_stride = torch._... | opti-mix/glow | SimpleATanModule | false | 7,382 | [
"Apache-2.0"
] | 1 | 4ba074df5da9822986a23a6679ab592c22660f6d | https://github.com/opti-mix/glow/tree/4ba074df5da9822986a23a6679ab592c22660f6d |
AuxMLP | # 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... | EE559DeepLearningEPFL/Project1 | AuxMLP | false | 397 | [
"MIT"
] | 0 | cbafdfee26771ae0ba3cd36375e68d92e9f108b2 | https://github.com/EE559DeepLearningEPFL/Project1/tree/cbafdfee26771ae0ba3cd36375e68d92e9f108b2 |
MultiheadedAttention | # 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.... | valterlej/CustomBMT | MultiheadedAttention | false | 16,854 | [
"MIT"
] | 157 | c9326752d1355c81f845f2caab9c047be76067de | https://github.com/valterlej/CustomBMT/tree/c9326752d1355c81f845f2caab9c047be76067de |
ISAB | # 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.... | Behrouz-Babaki/NCG4CVRP | ISAB | false | 4,910 | [
"MIT"
] | 1 | 87d63366c0b461f44ce8e982159a1e207af77b44 | https://github.com/Behrouz-Babaki/NCG4CVRP/tree/87d63366c0b461f44ce8e982159a1e207af77b44 |
LeakyReLU | import torch
import torch.nn as nn
class ActivationFunction(nn.Module):
def __init__(self):
super().__init__()
self.name = self.__class__.__name__
self.config = {'name': self.name}
class LeakyReLU(ActivationFunction):
def __init__(self, alpha=0.1):
super().__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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | ashutoshml/lightning-tutorials | LeakyReLU | false | 6,251 | [
"Apache-2.0"
] | 1 | 898b8b6f9852c0b80f034a3187bc1cd34dd521ce | https://github.com/ashutoshml/lightning-tutorials/tree/898b8b6f9852c0b80f034a3187bc1cd34dd521ce |
BasicBlock | # 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 ab... | Mattdl/RehearsalRevealed | BasicBlock | false | 8,530 | [
"MIT"
] | 12 | f9cd2548f6c6d3ff119b40fecdb0df6fcd1525f6 | https://github.com/Mattdl/RehearsalRevealed/tree/f9cd2548f6c6d3ff119b40fecdb0df6fcd1525f6 |
HGNN_embedding | # 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 math
from torch import... | young917/HGNN | HGNN_embedding | false | 4,632 | [
"MIT"
] | 0 | 41017f4315f459e1250830ca6c498b920d57e80a | https://github.com/young917/HGNN/tree/41017f4315f459e1250830ca6c498b920d57e80a |
PaddedInstanceNorm1d | import torch
import torch.nn as nn
class PaddedInstanceNorm1d(nn.Module):
def __init__(self, num_features, eps=1e-05, momentum=0.1, affine=False,
track_running_stats=False):
super().__init__()
self.num_features = num_features
self.eps = eps
self.momentum = momentum
... | 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_... | shaun95/cotatron | PaddedInstanceNorm1d | false | 16,414 | [
"BSD-3-Clause"
] | 202 | 2d0254399a3063ba1d2f77bef535cc148041236e | https://github.com/shaun95/cotatron/tree/2d0254399a3063ba1d2f77bef535cc148041236e |
Repeat_Explore_Mechanism | import torch
import torch.nn as nn
class Repeat_Explore_Mechanism(nn.Module):
def __init__(self, device, hidden_size, seq_len, dropout_prob):
super(Repeat_Explore_Mechanism, self).__init__()
self.dropout = nn.Dropout(dropout_prob)
self.hidden_size = hidden_size
self.device = devic... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | Ahren09/RecBole | Repeat_Explore_Mechanism | false | 1,932 | [
"MIT"
] | 0 | b3921818dfbc1b81f9eda8d5e9f05bc9d9114089 | https://github.com/Ahren09/RecBole/tree/b3921818dfbc1b81f9eda8d5e9f05bc9d9114089 |
Mutan | import torch
import torch.nn as nn
import torch.nn.functional as F
class Mutan(nn.Module):
def __init__(self, input_dims, output_dim, mm_dim=1600, rank=15, shared
=False, normalize=False, dropout_input=0.0, dropout_pre_lin=0.0,
dropout_output=0.0):
super(Mutan, self).__init__()
se... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | JoannaLXY/block.bootstrap.pytorch | Mutan | false | 11,759 | [
"BSD-3-Clause"
] | 0 | 42c3e7616b704e05c6ff2376ff68b5b18044fe77 | https://github.com/JoannaLXY/block.bootstrap.pytorch/tree/42c3e7616b704e05c6ff2376ff68b5b18044fe77 |
BiAttention | # 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.... | KaihuaTang/scene-graph-benchmark.pytorch | BiAttention | false | 5,443 | [
"MIT"
] | 1 | 45cd54f7465b81d3154e94fcab2b554a09637f6f | https://github.com/KaihuaTang/scene-graph-benchmark.pytorch/tree/45cd54f7465b81d3154e94fcab2b554a09637f6f |
Charbonnier | import torch
import torch.nn as nn
import torch.utils.model_zoo
class Charbonnier(nn.Module):
def __init__(self):
super(Charbonnier, self).__init__()
self.eps = 1e-06
def forward(self, X, Y):
diff = torch.add(X, -Y)
error = torch.sqrt(diff * diff + self.eps)
loss = to... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
import... | davefiorino/EDSR-PyTorch | Charbonnier | false | 1,797 | [
"MIT"
] | 0 | 97ad32a09a71816a36c45d92cdb2ea7ab42ba685 | https://github.com/davefiorino/EDSR-PyTorch/tree/97ad32a09a71816a36c45d92cdb2ea7ab42ba685 |
GAPTripletMarginLoss | # 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 import nn
import ... | ELEKTRONN/elektronn3 | GAPTripletMarginLoss | false | 13,596 | [
"MIT"
] | 124 | 19c751855dffc67b744cd43e757aa4a5bd577d9b | https://github.com/ELEKTRONN/elektronn3/tree/19c751855dffc67b744cd43e757aa4a5bd577d9b |
BCEIoULoss | import torch
from typing import Callable
from functools import partial
from torch import nn
import torch.distributed
from torch.nn.modules.loss import *
from torch.nn.modules import *
from torch.optim import *
from torch.optim.lr_scheduler import *
import torch.backends
def get_activation_fn(activation: 'str'=None):
... | 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 typing... | arjunshibu/catalyst | BCEIoULoss | false | 6,242 | [
"Apache-2.0"
] | 1 | 7160540f09530b803e5664e57db3e951fdc4dab3 | https://github.com/arjunshibu/catalyst/tree/7160540f09530b803e5664e57db3e951fdc4dab3 |
_netD_Q | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.utils.data
class _netD_Q(nn.Module):
"""
Second part of auxiliary network Q
"""
def __init__(self, nd=10):
super(_netD_Q, self).__init__()
self.linear = nn.Linear(128, nd, bias=True)
self.softmax = nn.Log... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | nathalia-kim/nu_gan | _netD_Q | false | 10,721 | [
"MIT"
] | 0 | c1d0891945bd7ac3d95869db91f490f57f203110 | https://github.com/nathalia-kim/nu_gan/tree/c1d0891945bd7ac3d95869db91f490f57f203110 |
MFH | import torch
from torch import nn
from torch.nn import functional as F
class MFH(nn.Module):
def __init__(self, input_dims, output_dim, mm_dim=1200, factor=2,
activ_input='relu', activ_output='relu', normalize=False,
dropout_input=0.0, dropout_pre_lin=0.0, dropout_output=0.0):
super(MFH, ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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... | AndresPMD/GCN_classification | MFH | false | 7,720 | [
"MIT"
] | 39 | b005c4256d68f1f90a7f73e7fdb3d066448de28c | https://github.com/AndresPMD/GCN_classification/tree/b005c4256d68f1f90a7f73e7fdb3d066448de28c |
SelfAttention | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | maltevogl/CVDD-PyTorch | SelfAttention | false | 15,987 | [
"MIT"
] | 48 | 9299894720a8d3d0a329d92c9d2702f43112ff63 | https://github.com/maltevogl/CVDD-PyTorch/tree/9299894720a8d3d0a329d92c9d2702f43112ff63 |
TemporalEmbedding | # 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 math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guar... | LeoYoung1996/Experiment | TemporalEmbedding | false | 9,307 | [
"Apache-2.0"
] | 0 | e3e875e0fd9b0367b761c51d9862b9da5e448576 | https://github.com/LeoYoung1996/Experiment/tree/e3e875e0fd9b0367b761c51d9862b9da5e448576 |
Conv2DBlock | import torch
import torch.nn as nn
def act_layer(act):
if act == 'relu':
return nn.ReLU()
elif act == 'lrelu':
return nn.LeakyReLU(LRELU_SLOPE)
elif act == 'elu':
return nn.ELU()
elif act == 'tanh':
return nn.Tanh()
elif act == 'prelu':
return nn.PReLU()
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | rll-research/ARM | Conv2DBlock | false | 16,323 | [
"BSD-3-Clause"
] | 46 | 7a51e00fabdcdbd8ad2b235266c66115e79deeb0 | https://github.com/rll-research/ARM/tree/7a51e00fabdcdbd8ad2b235266c66115e79deeb0 |
BMNLoss | import torch
import torch.nn.functional as F
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 > thr... | import torch
from torch import device
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_ma... | giahaowjx/mmaction2 | BMNLoss | false | 10,339 | [
"Apache-2.0"
] | 0 | 4f95e9b91354acdcae768ce94e01d3821bba0154 | https://github.com/giahaowjx/mmaction2/tree/4f95e9b91354acdcae768ce94e01d3821bba0154 |
GNNLayer | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch.nn import Module
f... | drzhang3/SDCN | GNNLayer | false | 15,229 | [
"Apache-2.0"
] | 146 | 3d11365bcb4af2cbe9625362737f1224aeea3b72 | https://github.com/drzhang3/SDCN/tree/3d11365bcb4af2cbe9625362737f1224aeea3b72 |
L1CompositionLoss | import functools
import torch
from torch.nn import functional as F
import torch.nn as nn
def reduce_loss(loss, reduction):
"""Reduce loss as specified.
Args:
loss (Tensor): Elementwise loss tensor.
reduction (str): Options are "none", "mean" and "sum".
Returns:
Tensor: Reduced lo... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import functools
from ... | Jason-Khan/mmediting | L1CompositionLoss | false | 627 | [
"Apache-2.0"
] | 0 | d187f95a675dff3eb975a575bd9278d643b5b645 | https://github.com/Jason-Khan/mmediting/tree/d187f95a675dff3eb975a575bd9278d643b5b645 |
PropMaxPool | from _paritybench_helpers import _mock_config
import torch
import torch.nn.parallel
import torch.nn as nn
import torch.utils.data
import torch.backends.cudnn
class PropMaxPool(nn.Module):
def __init__(self, cfg):
super(PropMaxPool, self).__init__()
num_layers = cfg.NUM_LAYERS
self.layers ... | 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.parallel
import torch.nn as nn
import torch.utils.data
import torch.backe... | MicroTensor-ai/episodic-memory | PropMaxPool | false | 13,215 | [
"MIT"
] | 0 | 295a3752ab94c7a6f45355aa2c54bffbf84b574f | https://github.com/MicroTensor-ai/episodic-memory/tree/295a3752ab94c7a6f45355aa2c54bffbf84b574f |
IoULoss | import torch
import torch.nn as nn
import torch.nn.functional as F
class IoULoss(nn.Module):
"""
The IoU metric, or Jaccard Index, is similar to the Dice metric and is calculated as the ratio between the overlap
of the positive instances between two sets, and their mutual combined values
"""
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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | Exdenta/torchsat | IoULoss | false | 13,652 | [
"MIT"
] | 316 | 70ea3db758757104fb3ba618ddf7997f0f3a75b4 | https://github.com/Exdenta/torchsat/tree/70ea3db758757104fb3ba618ddf7997f0f3a75b4 |
PlainRefiner | # 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_... | rivergold/mmediting | PlainRefiner | false | 7,603 | [
"Apache-2.0"
] | 1 | fd972635c48bb065db29d1b5090592a87c7263d2 | https://github.com/rivergold/mmediting/tree/fd972635c48bb065db29d1b5090592a87c7263d2 |
CoxPHLossSorted | import torch
from torch import Tensor
from torch import nn as nn
def cox_ph_loss_sorted(log_h: 'Tensor', events: 'Tensor', eps: 'float'=1e-07
) ->Tensor:
"""Requires the input to be sorted by descending duration time.
See DatasetDurationSorted.
We calculate the negative log of $( rac{h_i}{\\sum_{j \\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 Tens... | abhishek1015/MT-TS-Net | CoxPHLossSorted | false | 6,060 | [
"MIT"
] | 1 | f927f64cddd790ce1ddf07cbbd93ada332f96ba3 | https://github.com/abhishek1015/MT-TS-Net/tree/f927f64cddd790ce1ddf07cbbd93ada332f96ba3 |
SelfAttentionBatch | # 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.... | shubaoyu/CRSLab | SelfAttentionBatch | false | 10,819 | [
"MIT"
] | 0 | a05730e8b2c03df278587be34923fa818945d4c4 | https://github.com/shubaoyu/CRSLab/tree/a05730e8b2c03df278587be34923fa818945d4c4 |
BertAttention | # 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.... | RoshanTanisha/TVCaption | BertAttention | false | 1,901 | [
"MIT"
] | 0 | 8b14a340134ec69ed87426ee1f0e93e53f6456e5 | https://github.com/RoshanTanisha/TVCaption/tree/8b14a340134ec69ed87426ee1f0e93e53f6456e5 |
SigmoidFocalLoss | import torch
import torch.nn.functional as F
import torch.nn as nn
class SigmoidFocalLoss(nn.Module):
def __init__(self, alpha: 'float'=-1, gamma: 'float'=2, reduction:
'str'='none'):
super(SigmoidFocalLoss, self).__init__()
self.alpha = alpha
self.gamma = gamma
self.reduc... | 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... | EryiXie/PlaneRecNet | SigmoidFocalLoss | false | 8,061 | [
"MIT"
] | 34 | 534e23e6c5db2235ab1e5a9419fb4bfec3ffa943 | https://github.com/EryiXie/PlaneRecNet/tree/534e23e6c5db2235ab1e5a9419fb4bfec3ffa943 |
MPJPELoss | import torch
import torch.nn as nn
class MPJPELoss(nn.Module):
"""MPJPE (Mean Per Joint Position Error) loss.
Args:
use_target_weight (bool): Option to use weighted MSE loss.
Different joint types may have different target weights.
loss_weight (float): Weight of the loss. Default:... | 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_... | ALISCIFP/mmpose | MPJPELoss | false | 2,057 | [
"Apache-2.0"
] | 0 | 2433e3dbcc44baa2253e2a7c748ba0216937933e | https://github.com/ALISCIFP/mmpose/tree/2433e3dbcc44baa2253e2a7c748ba0216937933e |
SpatialGather_Module | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch._utils
class SpatialGather_Module(nn.Module):
"""
Aggregate the context features according to the initial
predicted probability distribution.
Employ the soft-weighted method to aggregate the context.
O... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | garylidd/semantic-segmentation | SpatialGather_Module | false | 10,114 | [
"BSD-3-Clause"
] | 0 | 64ae675076bea12ab994e7ae88d719a413e9c484 | https://github.com/garylidd/semantic-segmentation/tree/64ae675076bea12ab994e7ae88d719a413e9c484 |
GlobalAvgPool | import torch
import torch as th
from torch import nn
class GlobalAvgPool(nn.Module):
def __init__(self):
super(GlobalAvgPool, self).__init__()
def forward(self, x):
return th.mean(x, dim=[-2, -1])
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_str... | Fork-for-Modify/VideoFeatureExtractor | GlobalAvgPool | false | 8,101 | [
"Apache-2.0"
] | 15 | a73bb5a575a318c2d71bc8dd2432c8941c35a77f | https://github.com/Fork-for-Modify/VideoFeatureExtractor/tree/a73bb5a575a318c2d71bc8dd2432c8941c35a77f |
NetDepth | # 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 ... | dustasa/senior_software_HW | NetDepth | false | 3,450 | [
"Apache-2.0"
] | 0 | 767d1d7bbd5e7d7414c17fa14b92b942e53d84ed | https://github.com/dustasa/senior_software_HW/tree/767d1d7bbd5e7d7414c17fa14b92b942e53d84ed |
ConcatAvgMaxPooling | # 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... | SamitHuang/CELNet | ConcatAvgMaxPooling | false | 5,794 | [
"MIT"
] | 1 | 51e067fdb16e723a45a0a60399d568b58cdc011d | https://github.com/SamitHuang/CELNet/tree/51e067fdb16e723a45a0a60399d568b58cdc011d |
SIMSE | import torch
import torch.nn as nn
import torch.utils.checkpoint
class SIMSE(nn.Module):
def __init__(self):
super(SIMSE, self).__init__()
def forward(self, pred, real):
diffs = torch.add(real, -pred)
n = torch.numel(diffs.data)
simse = torch.sum(diffs).pow(2) / n ** 2
... | 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.checkpoint
assert_size_stride = torch._C._dynamo... | Wang-Chuanyu/MMSA | SIMSE | false | 5,955 | [
"MIT"
] | 1 | 2a720530c369e68656102287edb651780e827135 | https://github.com/Wang-Chuanyu/MMSA/tree/2a720530c369e68656102287edb651780e827135 |
Word2Vec | # 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.... | salmanedhi/NNTI-WS2021-NLP-Project | Word2Vec | false | 4,253 | [
"MIT"
] | 0 | 5b0a8f1258ef4e835a6e647082a8286078a0bdd6 | https://github.com/salmanedhi/NNTI-WS2021-NLP-Project/tree/5b0a8f1258ef4e835a6e647082a8286078a0bdd6 |
WeightNet | import torch
from torch import nn as nn
class WeightNet(nn.Module):
"""WeightNet in Temporal interlace module.
The WeightNet consists of two parts: one convolution layer
and a sigmoid function. Following the convolution layer, the sigmoid
function and rescale module can scale our output to the range ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_s... | HypnosXC/mmaction2 | WeightNet | false | 13,819 | [
"Apache-2.0"
] | 549 | a26d5f981449445a5e22a0a60d8b285e06c3dd6e | https://github.com/HypnosXC/mmaction2/tree/a26d5f981449445a5e22a0a60d8b285e06c3dd6e |
GELU | import torch
import torch.nn as nn
class GELU(nn.Module):
def forward(self, x):
return torch.sigmoid(1.702 * x) * 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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | YuShen0118/SAAP_Auto-driving_Platform | GELU | false | 18,148 | [
"MIT"
] | 4 | 785f899fb3b3ad92075318f9fcb69b8e09597202 | https://github.com/YuShen0118/SAAP_Auto-driving_Platform/tree/785f899fb3b3ad92075318f9fcb69b8e09597202 |
HardMish | import torch
import torch.nn as nn
import torch.nn.parallel
def hard_mish(x, inplace: 'bool'=False):
""" Hard Mish
Experimental, based on notes by Mish author Diganta Misra at
https://github.com/digantamisra98/H-Mish/blob/0da20d4bc58e696b6803f2523c58d3c8a82782d0/README.md
"""
if inplace:
... | 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 |
Net | import torch
import torch.nn as nn
import torch.nn.functional as F
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.lin1 = nn.Linear(4, 50)
self.lin2 = nn.Linear(50, 50)
self.out = nn.Linear(50, 3)
def forward(self, x):
x = F.relu(self.lin1(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
import torch.nn as nn
assert_... | Elli1993/custom_net_on_movidius | Net | false | 17,242 | [
"MIT"
] | 9 | cd7ed784e6d38fe696c1ae1ff0e5a31d1b52c7dc | https://github.com/Elli1993/custom_net_on_movidius/tree/cd7ed784e6d38fe696c1ae1ff0e5a31d1b52c7dc |
PotCoSirenModule | import torch
class PotCoSirenModule(torch.nn.Module):
def __init__(self, in_features, out_features, weight_multiplier=1.0):
super(PotCoSirenModule, self).__init__()
self.linear = torch.nn.Linear(in_features, out_features // 2)
torch.nn.init.uniform_(self.linear.weight, a=-weight_multiplie... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
assert_size_s... | qway/nerfmeshes | PotCoSirenModule | false | 16,311 | [
"MIT"
] | 113 | d983dcbbcfec1337c9f2040969213c6d1ea0c39e | https://github.com/qway/nerfmeshes/tree/d983dcbbcfec1337c9f2040969213c6d1ea0c39e |
ChebConv | # 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... | TAN-OpenLab/TCSE-net | ChebConv | false | 9,546 | [
"Apache-2.0"
] | 0 | fc6ecf704a9c128a9b5b6853cffa8486ee0f54e8 | https://github.com/TAN-OpenLab/TCSE-net/tree/fc6ecf704a9c128a9b5b6853cffa8486ee0f54e8 |
InvConv2d | import torch
from torch import nn
from torch.nn import functional as F
class InvConv2d(nn.Module):
def __init__(self, in_channel):
"""
a flow contains the equivalent of a permutation that reverses the ordering of the channels
replact the fixed permutation with a (learned) invertible 1x1 c... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
from torch.nn import functional as F
assert_size_stride = t... | XeniaLLL/glow-pytorch | InvConv2d | false | 11,974 | [
"MIT"
] | 0 | 66d434e57853de1aaafaa5a5533d21705dc92e10 | https://github.com/XeniaLLL/glow-pytorch/tree/66d434e57853de1aaafaa5a5533d21705dc92e10 |
GainesMul | import torch
class GainesMul(torch.nn.Module):
"""
this module is for Gaines stochastic multiplication, supporting unipolar/bipolar
"""
def __init__(self, mode='bipolar', stype=torch.float):
super(GainesMul, self).__init__()
self.mode = mode
self.stype = stype
def UnaryMu... | 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... | libingzheren/Stochastic_Computing | GainesMul | false | 7,080 | [
"MIT"
] | 1 | c02461454618e9ce0c86ce695fad9e95d1ca5e00 | https://github.com/libingzheren/Stochastic_Computing/tree/c02461454618e9ce0c86ce695fad9e95d1ca5e00 |
Affine2D | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | alexandre-giuly/Project-Acoustic-Scene-Classification-DCASE | Affine2D | false | 9,693 | [
"Apache-2.0"
] | 0 | 13b565c20e59f204151d2dafbd221c7e1b9303c5 | https://github.com/alexandre-giuly/Project-Acoustic-Scene-Classification-DCASE/tree/13b565c20e59f204151d2dafbd221c7e1b9303c5 |
Actor | # 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.... | rortiz9/meleeml | Actor | false | 4,212 | [
"MIT"
] | 0 | 9be4bf53a377dfb46dbb3b51f102f1bffc0124d2 | https://github.com/rortiz9/meleeml/tree/9be4bf53a377dfb46dbb3b51f102f1bffc0124d2 |
Tile | import torch
import torch.nn as nn
class Tile(nn.Module):
def __init__(self, max_size, dim):
super(Tile, self).__init__()
self.max_size = max_size
self.dim = dim
def forward(self, input):
return input.repeat(*[(self.max_size if x == self.dim else 1) for x in
range... | 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... | ixaxaar/pytorch-npi | Tile | false | 6,907 | [
"MIT"
] | 1 | 50b028840c00f7807fb6490ce6bb0918832dc360 | https://github.com/ixaxaar/pytorch-npi/tree/50b028840c00f7807fb6490ce6bb0918832dc360 |
Fcn8s | # 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 numpy as np
import tor... | lxxue/cil-road-segmentation-2019 | Fcn8s | false | 11,090 | [
"MIT"
] | 0 | c6477556dc3d6d9c8ed2f2a3f185b4d986a03bb4 | https://github.com/lxxue/cil-road-segmentation-2019/tree/c6477556dc3d6d9c8ed2f2a3f185b4d986a03bb4 |
DuelingQNetwork | import torch
import torch.nn.functional as F
import torch.nn as nn
class DuelingQNetwork(nn.Module):
"""Dueling Q-network (https://arxiv.org/abs/1511.06581)"""
def __init__(self, state_size, action_size, hidsize1=128, hidsize2=128,
seed=None):
super(DuelingQNetwork, self).__init__()
i... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | HarryTanNguyen/flatland-railway-enviroment | DuelingQNetwork | false | 5,282 | [
"MIT"
] | 1 | 5306871a6dbedd8d2745be4ff0caf0515e4d88ac | https://github.com/HarryTanNguyen/flatland-railway-enviroment/tree/5306871a6dbedd8d2745be4ff0caf0515e4d88ac |
TransposedConvLayer | # 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... | DA4EVENT/home | TransposedConvLayer | false | 17,185 | [
"MIT"
] | 5 | 18cc93a795ce132e05b886aa34565a102915b1c6 | https://github.com/DA4EVENT/home/tree/18cc93a795ce132e05b886aa34565a102915b1c6 |
AugCNN | import torch
import torch.nn as nn
import torch.nn.functional as F
def apply_init_(modules):
"""
Initialize NN modules
"""
for m in modules:
if isinstance(m, nn.Conv2d):
nn.init.xavier_uniform_(m.weight)
if m.bias is not None:
nn.init.constant_(m.bias, 0... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.nn.functional as F
assert_size_stride = torch... | jajajag/auto-drac | AugCNN | false | 10,231 | [
"MIT"
] | 0 | 2241f9f5f10a4d863a8b9d198da1d39e5feb59a0 | https://github.com/jajajag/auto-drac/tree/2241f9f5f10a4d863a8b9d198da1d39e5feb59a0 |
ChannelNorm | import torch
import torch.nn as nn
class ChannelNorm(nn.Module):
def __init__(self):
super(ChannelNorm, self).__init__()
def forward(self, x):
divider = torch.max(torch.max(torch.abs(x), dim=0)[0], dim=1)[0
] + 1e-05
divider = divider.unsqueeze(0).unsqueeze(2)
div... | 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
... | Finspire13/RL-Surgical-Gesture-Segmentation | ChannelNorm | false | 8,096 | [
"MIT"
] | 40 | 0cb166208f463cd36726f91d1ccaa25093736b47 | https://github.com/Finspire13/RL-Surgical-Gesture-Segmentation/tree/0cb166208f463cd36726f91d1ccaa25093736b47 |
SoftmaxScaleController | # 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
assert_size_stride = t... | niloofar17/MetaDialog | SoftmaxScaleController | false | 16,196 | [
"Apache-2.0"
] | 204 | d75b84a02807d53d9596e72c2f698e5a4f180369 | https://github.com/niloofar17/MetaDialog/tree/d75b84a02807d53d9596e72c2f698e5a4f180369 |
FrobeniusNormLoss | import torch
import torch.nn as nn
def get_outnorm(x: 'torch.Tensor', out_norm: 'str'='') ->torch.Tensor:
""" Common function to get a loss normalization value. Can
normalize by either the batch size ('b'), the number of
channels ('c'), the image size ('i') or combinations
('bi', 'bci', et... | 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... | grofit/traiNNer | FrobeniusNormLoss | false | 15,470 | [
"Apache-2.0"
] | 78 | 12d006fd44ed304e4178839c53b1f3d95ca25dcb | https://github.com/grofit/traiNNer/tree/12d006fd44ed304e4178839c53b1f3d95ca25dcb |
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 import nn
import torch.utils.data
from torchvision.transforms import *
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | HamsterBiz/iSeeBetter | TVLoss | false | 11,633 | [
"MIT"
] | 0 | a71cee61583bdedab1f3b368e2cb7dc5ad969aed | https://github.com/HamsterBiz/iSeeBetter/tree/a71cee61583bdedab1f3b368e2cb7dc5ad969aed |
Invertible1x1Conv | # 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.functional as F
from torch.autograd import Variable
import torch... | Moon-sung-woo/VAE_Tacotron_korean | Invertible1x1Conv | false | 2,676 | [
"BSD-3-Clause"
] | 0 | dafa4ea557235350211b7a2187da1d6855eb5e9f | https://github.com/Moon-sung-woo/VAE_Tacotron_korean/tree/dafa4ea557235350211b7a2187da1d6855eb5e9f |
GCN | from torch.nn import Module
import math
import torch
import torch.nn.functional as F
import torch.nn as nn
class GraphConvolution(Module):
"""
A Graph Convolution Layer (GCN)
"""
def __init__(self, in_features, out_features, bias=True):
super(GraphConvolution, self).__init__()
self.in... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch.nn impor... | AlexMinhao/NAS_GNN | GCN | false | 10 | [
"Apache-2.0"
] | 0 | 89183988a96e1d6baed910ab3843c13282f8b077 | https://github.com/AlexMinhao/NAS_GNN/tree/89183988a96e1d6baed910ab3843c13282f8b077 |
CoxPHLoss | import torch
from torch import Tensor
from torch import nn as nn
def cox_ph_loss_sorted(log_h: 'Tensor', events: 'Tensor', eps: 'float'=1e-07
) ->Tensor:
"""Requires the input to be sorted by descending duration time.
See DatasetDurationSorted.
We calculate the negative log of $( rac{h_i}{\\sum_{j \\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 import Tensor
from torch import nn as nn
assert_size_stride = torch._C._dynamo... | abhishek1015/MT-TS-Net | CoxPHLoss | false | 6,069 | [
"MIT"
] | 1 | f927f64cddd790ce1ddf07cbbd93ada332f96ba3 | https://github.com/abhishek1015/MT-TS-Net/tree/f927f64cddd790ce1ddf07cbbd93ada332f96ba3 |
SymKlCriterion | import torch
import torch.nn.functional as F
from torch.nn.modules.loss import _Loss
from torch.optim.lr_scheduler import *
class Criterion(_Loss):
def __init__(self, alpha=1.0, name='criterion'):
super().__init__()
"""Alpha is used to weight each loss term
"""
self.alpha = alpha
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch.... | anlewy/mt-dnn | SymKlCriterion | false | 14,870 | [
"MIT"
] | 2,075 | eeb6f01ce0630e61a52b8c9c6f7537cd34978e45 | https://github.com/anlewy/mt-dnn/tree/eeb6f01ce0630e61a52b8c9c6f7537cd34978e45 |
MLP | import torch
import torch.autograd
import torch.nn as nn
class MLP(nn.Module):
def __init__(self, n_in, n_out, dropout=0):
super().__init__()
self.linear = nn.Linear(n_in, n_out)
self.activation = nn.GELU()
self.dropout = nn.Dropout(dropout)
def forward(self, x):
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.triton_helpers import libdevice
import torch.autogr... | dumpmemory/W2NER | MLP | false | 15,266 | [
"MIT"
] | 128 | fb1b6eb1111eb001b1c965097d995244b840bdda | https://github.com/dumpmemory/W2NER/tree/fb1b6eb1111eb001b1c965097d995244b840bdda |
GCNLayer | import torch
import torch.nn as nn
class GCNLayer(nn.Module):
def __init__(self, c_in, c_out):
super().__init__()
self.projection = nn.Linear(c_in, c_out)
def forward(self, node_feats, adj_matrix):
"""
Inputs:
node_feats - Tensor with node features of shape [batch... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | onlyrico/lightning-tutorials | GCNLayer | false | 12,855 | [
"Apache-2.0"
] | 0 | b5d5c4015422f8c70411e57734d73bb6c1472999 | https://github.com/onlyrico/lightning-tutorials/tree/b5d5c4015422f8c70411e57734d73bb6c1472999 |
MNIST_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
from torch._inductor.runtime.... | SirRob1997/DomainBed | MNIST_CNN | false | 5,859 | [
"MIT"
] | 1 | 7399a2b0a63df48f4b67755a3f33901223d5c8fb | https://github.com/SirRob1997/DomainBed/tree/7399a2b0a63df48f4b67755a3f33901223d5c8fb |
AdaptiveMaxPool2d | import torch
import torch.nn as nn
import torch.nn.functional as F
class _SpikeAdaptiveMaxPoolNd(nn.Module):
def __init__(self, output_size):
super(_SpikeAdaptiveMaxPoolNd, self).__init__()
self.output_size = output_size
self.return_indices = True
def reset_state(self):
pass
... | 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... | tomking/PySNN | AdaptiveMaxPool2d | false | 16,594 | [
"MIT"
] | 175 | c99ba6cd28a518dc07cab765acac9b69ac6fe36b | https://github.com/tomking/PySNN/tree/c99ba6cd28a518dc07cab765acac9b69ac6fe36b |
HEL | # 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
import torch.nn.functional as F
assert_size_stride ... | lartpang/HDFNet | HEL | false | 15,881 | [
"MIT"
] | 67 | e2e4136a336f171481d2a6a954e901568932b8d3 | https://github.com/lartpang/HDFNet/tree/e2e4136a336f171481d2a6a954e901568932b8d3 |
GCNModelVAE | from torch.nn import Module
import torch
import torch.nn.functional as F
from torch.nn.modules.module import Module
from torch.nn.parameter import Parameter
import torch.nn as nn
import torch.nn.modules.loss
class GraphConvolution(Module):
"""
Simple GCN layer, similar to https://arxiv.org/abs/1609.02907
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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... | peterfeifanchen/scGNN | GCNModelVAE | false | 16,265 | [
"MIT"
] | 60 | 4ef9013ad0f44f9f51708e9bb60e5138f5706593 | https://github.com/peterfeifanchen/scGNN/tree/4ef9013ad0f44f9f51708e9bb60e5138f5706593 |
SpatialAttentionGate | # 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 ... | airglow/nni | SpatialAttentionGate | false | 12,103 | [
"MIT"
] | 0 | 751065b788f66a6b53446620293095b1fe1b1c65 | https://github.com/airglow/nni/tree/751065b788f66a6b53446620293095b1fe1b1c65 |
idct_8x8 | import itertools
import torch
import numpy as np
import torch.nn as nn
class idct_8x8(nn.Module):
""" Inverse discrete Cosine Transformation
Input:
dcp(tensor): batch x height x width
Output:
image(tensor): batch x height x width
"""
def __init__(self):
super(idct_8x8, 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 itertools
import numpy as np
import torch.nn as nn
assert_size_stride = t... | DazhiZhong/DiffJPEG | idct_8x8 | false | 9,012 | [
"MIT"
] | 0 | e20de92539f31a57906ae4c32a41dc46e774c316 | https://github.com/DazhiZhong/DiffJPEG/tree/e20de92539f31a57906ae4c32a41dc46e774c316 |
Discriminator | import torch
import torch.nn as nn
def global_pooling(input, pooling='mean'):
if pooling == 'mean':
return input.mean(3).mean(2)
elif pooling == 'sum':
return input.sum(3).sum(2)
else:
raise NotImplementedError()
class CustomConv2d(nn.Module):
def __init__(self, in_channels,... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | ChiragCD/NR-GAN | Discriminator | false | 13,552 | [
"MIT"
] | 54 | fc455c6219b09bc8bf605715504b78b2bb801e48 | https://github.com/ChiragCD/NR-GAN/tree/fc455c6219b09bc8bf605715504b78b2bb801e48 |
SimpleClampMinModel | import torch
import torch.jit
import torch.onnx
import torch.nn
class SimpleClampMinModel(torch.nn.Module):
def __init__(self, min):
super(SimpleClampMinModel, self).__init__()
self.min = min
def forward(self, input):
return torch.clamp_min(input, self.min)
def get_inputs():
re... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.jit
import torch.onnx
import torch.nn
assert_size_stride = torch._C._dynamo.... | briancoutinho/glow | SimpleClampMinModel | false | 12,565 | [
"Apache-2.0"
] | 0 | 4c919d60b3c33296c4109aec8020a1733c98f5b5 | https://github.com/briancoutinho/glow/tree/4c919d60b3c33296c4109aec8020a1733c98f5b5 |
GreedyHashLoss | import torch
class GreedyHashLoss(torch.nn.Module):
def __init__(self):
super(GreedyHashLoss, self).__init__()
def forward(self, u):
b = GreedyHashLoss.Hash.apply(u)
loss = (u.abs() - 1).pow(3).abs().mean()
return b, loss
class Hash(torch.autograd.Function):
@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 import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
assert_size_stride = t... | TuBui/deep_image_comparator | GreedyHashLoss | false | 1,154 | [
"MIT"
] | 0 | 2dea7738d794b91a960ee9f41461a4e3ffcd5e44 | https://github.com/TuBui/deep_image_comparator/tree/2dea7738d794b91a960ee9f41461a4e3ffcd5e44 |
DotSelector | # 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 as th
from torch... | jk96491/SMAC | DotSelector | false | 15,713 | [
"Apache-2.0"
] | 64 | 7aaf4673b0eecafc4ab25f381eea20fc762af56a | https://github.com/jk96491/SMAC/tree/7aaf4673b0eecafc4ab25f381eea20fc762af56a |
Net | import torch
import torch.nn as nn
class Net(nn.Module):
def __init__(self, input_dim, output_dim, hidden_dim=None, barcode_dim=0):
super().__init__()
if hidden_dim is None:
hidden_dim = [250, 100]
self.fc1 = nn.Linear(input_dim, hidden_dim[0])
self.act = nn.ReLU()
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | mityanony404/TopGraph | Net | false | 4,019 | [
"MIT"
] | 0 | 23595ca5d3dfcd5bc5ebb771800e3fbe9a0d5eed | https://github.com/mityanony404/TopGraph/tree/23595ca5d3dfcd5bc5ebb771800e3fbe9a0d5eed |
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