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 |
|---|---|---|---|---|---|---|---|---|---|---|
ChannelSELayer | import torch
import torch.nn as nn
import torch.utils.data
import torch.utils
from matplotlib import cm as cm
from torch.nn.parallel import *
from torchvision.models import *
from torchvision.datasets import *
class ChannelSELayer(nn.Module):
"""
Copied from https://github.com/ai-med/squeeze_and_excitation/bl... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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 ... | CrispyHarder/ppuda | ChannelSELayer | false | 817 | [
"MIT"
] | 0 | 15950ba297188163eaadd8ab69268ee7f6ffcf2a | https://github.com/CrispyHarder/ppuda/tree/15950ba297188163eaadd8ab69268ee7f6ffcf2a |
NeuralNetMultiplePositionalArgumentsMultiOutputsWithoutDependency | import torch
import torch.nn
import torch.onnx
import torch.utils.checkpoint
class NeuralNetMultiplePositionalArgumentsMultiOutputsWithoutDependency(torch
.nn.Module):
def __init__(self, input_size, hidden_size, num_classes):
super(NeuralNetMultiplePositionalArgumentsMultiOutputsWithoutDependency
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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 | NeuralNetMultiplePositionalArgumentsMultiOutputsWithoutDependency | false | 5,906 | [
"MIT"
] | 1 | 435010ab6873974803591fa22262ed8b3e36e44d | https://github.com/TingGong1/onnxruntime/tree/435010ab6873974803591fa22262ed8b3e36e44d |
laplace | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert... | pierreglaser/MMD-gradient-flow | laplace | false | 10,692 | [
"BSD-3-Clause"
] | 0 | 43591137e1d04bed5153887a364fae72621b01ae | https://github.com/pierreglaser/MMD-gradient-flow/tree/43591137e1d04bed5153887a364fae72621b01ae |
MSELoss | import torch
import torch.nn as nn
import torch.nn.functional as F
class MSELoss(nn.Module):
"""MSE loss for coordinate regression."""
def __init__(self, use_target_weight=False, loss_weight=1.0):
super().__init__()
self.criterion = F.mse_loss
self.use_target_weight = use_target_weigh... | 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.functional as F
assert_size_stride = torch._C._dyna... | atoaiari/mmpose | MSELoss | false | 6,269 | [
"Apache-2.0"
] | 1 | 256a9117767008e8c33b4038a346aca12233e300 | https://github.com/atoaiari/mmpose/tree/256a9117767008e8c33b4038a346aca12233e300 |
SeqRNN | # 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.... | dblakely/FastSK | SeqRNN | false | 9,966 | [
"Apache-2.0"
] | 0 | bd0d4cef89c3d7d661f4c6abc094423ab6d1c7e1 | https://github.com/dblakely/FastSK/tree/bd0d4cef89c3d7d661f4c6abc094423ab6d1c7e1 |
quadexp | import torch
import torch as tr
import torch.nn as nn
class quadexp(nn.Module):
def __init__(self, sigma=2.0):
super(quadexp, self).__init__()
self.sigma = sigma
def forward(self, x: 'tr.Tensor'):
return tr.exp(-x ** 2 / self.sigma ** 2)
def get_inputs():
return [torch.rand([4,... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert... | pierreglaser/MMD-gradient-flow | quadexp | false | 10,688 | [
"BSD-3-Clause"
] | 0 | 43591137e1d04bed5153887a364fae72621b01ae | https://github.com/pierreglaser/MMD-gradient-flow/tree/43591137e1d04bed5153887a364fae72621b01ae |
Classify | # 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... | Alex-Beh/hand_tracking | Classify | false | 11,159 | [
"Apache-2.0"
] | 0 | 40ac39e10ed5815d9400d6a87149015ad6ab9686 | https://github.com/Alex-Beh/hand_tracking/tree/40ac39e10ed5815d9400d6a87149015ad6ab9686 |
CNN | import torch
import torch.nn as nn
import torch.nn.functional as F
class CNN(nn.Module):
def __init__(self, action_dim=7):
super(CNN, self).__init__()
self.action_dim = action_dim
self.conv1 = nn.Conv2d(3, 16, 5, padding=2)
self.conv2 = nn.Conv2d(16, 32, 5, padding=2)
self... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | HeegerGao/CRIL | CNN | false | 17,398 | [
"MIT"
] | 9 | c4095bca7cf5c8e376b0014447b1422c1b5b6cec | https://github.com/HeegerGao/CRIL/tree/c4095bca7cf5c8e376b0014447b1422c1b5b6cec |
UpdateCell | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch import n... | alarca94/recbole-extension | UpdateCell | false | 6,138 | [
"MIT"
] | 1 | 171d4e58c83d35838307503d85e6c006701b3003 | https://github.com/alarca94/recbole-extension/tree/171d4e58c83d35838307503d85e6c006701b3003 |
NTM | from _paritybench_helpers import _mock_config
import logging
import torch
import numpy as np
from torch.nn import functional as F
import torch.multiprocessing
from torch import nn
import torch.utils.data
class NTM(nn.Module):
def __init__(self, opt, hidden_dim=500, l1_strength=0.001):
super(NTM, 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._inductor.runtime.... | WuDiDaBinGe/TAKG | NTM | false | 1,899 | [
"MIT"
] | 0 | 83e608e677a4ee74722d18cb5ef430f4f6c6ad31 | https://github.com/WuDiDaBinGe/TAKG/tree/83e608e677a4ee74722d18cb5ef430f4f6c6ad31 |
PolicyNet | # 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_... | Jontahan/kvad | PolicyNet | false | 9,173 | [
"MIT"
] | 0 | 1b22db801048beb948b34bdd615ebe8630d13d9f | https://github.com/Jontahan/kvad/tree/1b22db801048beb948b34bdd615ebe8630d13d9f |
Upsample2d | from _paritybench_helpers import _mock_config
import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
def _setup_kernel(k):
k = np.asarray(k, dtype=np.float32)
if k.ndim == 1:
k = np.outer(k, k)
k /= np.sum(k)
assert k.ndim == 2
assert k.shape[0] == k.shape[1]... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import numpy as np
import tor... | tomguluson92/StyleGAN2_PyTorch | Upsample2d | false | 16,601 | [
"MIT"
] | 89 | 4ab7354c85cb986d2b77f5238c4a18c5efd1db1b | https://github.com/tomguluson92/StyleGAN2_PyTorch/tree/4ab7354c85cb986d2b77f5238c4a18c5efd1db1b |
TorchDiv | # 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... | ahangchen/torch2trt | TorchDiv | false | 6,121 | [
"MIT"
] | 1 | 53c663f0e0570ef7ffd6771354ae3478f63bd328 | https://github.com/ahangchen/torch2trt/tree/53c663f0e0570ef7ffd6771354ae3478f63bd328 |
Swish | import torch
import torch.nn as nn
import torch.distributed
class Swish(nn.Module):
def __init__(self):
super(Swish, self).__init__()
self.beta = nn.Parameter(torch.tensor(1.0))
def forward(self, x):
return x * torch.sigmoid(self.beta * x)
def get_inputs():
return [torch.rand([... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.distributed
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C... | shnhrtkyk/PointFlow | Swish | false | 12,975 | [
"MIT"
] | 0 | 26b8fac79bf3e71533f5c8b12f90cf5f9a385a99 | https://github.com/shnhrtkyk/PointFlow/tree/26b8fac79bf3e71533f5c8b12f90cf5f9a385a99 |
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
from torch._inductor.runtime.... | D-hash-code/ffjord-rnode-finalweek-mnist | BasicBlock | false | 2,146 | [
"MIT"
] | 0 | 4cabcbadda79c68df53ec25f1f8fe03cfeee78f9 | https://github.com/D-hash-code/ffjord-rnode-finalweek-mnist/tree/4cabcbadda79c68df53ec25f1f8fe03cfeee78f9 |
ProtoLoss | # 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... | gradjitta/Prototypical-Networks | ProtoLoss | false | 10,191 | [
"MIT"
] | 0 | 9ec344f7299353889e2087224b80a74519ca1a3c | https://github.com/gradjitta/Prototypical-Networks/tree/9ec344f7299353889e2087224b80a74519ca1a3c |
ConvSig | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.utils.data
import torch.nn as nn
import torch.nn.parallel
from torc... | dumpmemory/NonDeepNetworks | ConvSig | false | 15,254 | [
"BSD-3-Clause"
] | 307 | 5513bf588f4e64c99583440507232675c2e21e34 | https://github.com/dumpmemory/NonDeepNetworks/tree/5513bf588f4e64c99583440507232675c2e21e34 |
Highway | import torch
from torch import nn
import torch.utils.data
class Highway(nn.Module):
def __init__(self, input_dim, dropout):
super(Highway, self).__init__()
self.input_linear = nn.Linear(input_dim, input_dim)
self.relu = nn.ReLU()
self.gate_linear = nn.Linear(input_dim, input_dim)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
import t... | uwnlp/piqa | Highway | false | 16,648 | [
"Apache-2.0"
] | 89 | e18f2189c93965c94655d5cc943dcecdc2c1ea57 | https://github.com/uwnlp/piqa/tree/e18f2189c93965c94655d5cc943dcecdc2c1ea57 |
Downsample | import torch
import torch.nn as nn
class Downsample(nn.Module):
def __init__(self, in_channels, with_conv):
super().__init__()
self.with_conv = with_conv
if self.with_conv:
self.conv = torch.nn.Conv2d(in_channels, in_channels,
kernel_size=3, stride=2, 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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | CasualGANPapers/Make-A-Scene | Downsample | false | 13,449 | [
"MIT"
] | 47 | 4457ef91ccf4a345f3178cf821f12b49df616b6d | https://github.com/CasualGANPapers/Make-A-Scene/tree/4457ef91ccf4a345f3178cf821f12b49df616b6d |
NoiseInjection | import torch
from torch import nn
class NoiseInjection(nn.Module):
def __init__(self, channel):
super().__init__()
self.weight = nn.Parameter(torch.zeros(1, channel, 1, 1))
def forward(self, image, noise):
return image + self.weight * noise
def get_inputs():
return [torch.rand(... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_str... | uzielroy/StyleGan_FewShot | NoiseInjection | false | 16,650 | [
"MIT"
] | 76 | 94e4c49dbf39d1c6299f33787afb3e471ece11e3 | https://github.com/uzielroy/StyleGan_FewShot/tree/94e4c49dbf39d1c6299f33787afb3e471ece11e3 |
SpatialPyramidPooling | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from math import sqrt
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.... | addisonklinke/pytorch-architectures | SpatialPyramidPooling | false | 18,223 | [
"MIT"
] | 6 | a5739b9b90db726db29b02166a9b1a7e52eb1eba | https://github.com/addisonklinke/pytorch-architectures/tree/a5739b9b90db726db29b02166a9b1a7e52eb1eba |
PopArt | import torch
import numpy as np
import torch.nn as nn
class PopArt(nn.Module):
"""Normalize a vector of observations - across the first norm_axes dimensions"""
def __init__(self, input_shape, norm_axes=1, beta=0.99999,
per_element_update=False, epsilon=1e-05, device=torch.device('cpu')):
supe... | 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 numpy as np
import to... | rainwangphy/TRPO-in-MARL | PopArt | false | 4,170 | [
"MIT"
] | 0 | 22229abba417708922ecf6455c1c5180dbe80391 | https://github.com/rainwangphy/TRPO-in-MARL/tree/22229abba417708922ecf6455c1c5180dbe80391 |
Self_Attentive_Pooling | # 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.... | ikunsaikou/lhy_ML2021Spring | Self_Attentive_Pooling | false | 6,870 | [
"WTFPL"
] | 1 | 80d8922077e2f5abba6a440c17654a143ebc8c9c | https://github.com/ikunsaikou/lhy_ML2021Spring/tree/80d8922077e2f5abba6a440c17654a143ebc8c9c |
CombinedTargetMSELoss | import torch
import torch.nn as nn
class CombinedTargetMSELoss(nn.Module):
"""MSE loss for combined target.
CombinedTarget: The combination of classification target
(response map) and regression target (offset map).
Paper ref: Huang et al. The Devil is in the Details: Delving into
... | 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... | carolchenyx/mmpose | CombinedTargetMSELoss | false | 15,001 | [
"Apache-2.0"
] | 367 | cd74bf1d0b13954188cc678415fd0ef98a74b46b | https://github.com/carolchenyx/mmpose/tree/cd74bf1d0b13954188cc678415fd0ef98a74b46b |
SineODE | import math
import torch
class SineODE(torch.nn.Module):
def __init__(self, device):
super(SineODE, self).__init__()
def forward(self, t, y):
return 2 * y / t + t ** 4 * torch.sin(2 * t) - t ** 2 + 4 * t ** 3
def y_exact(self, t):
return -0.5 * t ** 4 * torch.cos(2 * t) + 0.5 * ... | 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 math
assert_size_stride = torch._C._dynamo.guards.assert_size_stri... | BoyanJIANG/4D-Compositional-Representation | SineODE | false | 7,850 | [
"Apache-2.0"
] | 12 | 64d5f4bbd6b8e6bc3bfd8f76736f6d468c71a73c | https://github.com/BoyanJIANG/4D-Compositional-Representation/tree/64d5f4bbd6b8e6bc3bfd8f76736f6d468c71a73c |
AELossPureCls | import torch
import torch.nn as nn
import torch.cuda
def _ae_loss(tag0, tag1, mask):
num = mask.sum(dim=1, keepdim=True).float()
tag0 = tag0.squeeze()
tag1 = tag1.squeeze()
tag_mean = (tag0 + tag1) / 2
tag0 = torch.pow(tag0 - tag_mean, 2) / (num + 0.0001)
tag0 = tag0[mask].sum()
tag1 = tor... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import torch.cuda
assert_size_stride = torch._C._dynamo.guards.asse... | abhithosar/chartocr_cv | AELossPureCls | false | 6,070 | [
"BSD-3-Clause"
] | 1 | 388b95710a02ded0532b021f64c58d8d3e1cc639 | https://github.com/abhithosar/chartocr_cv/tree/388b95710a02ded0532b021f64c58d8d3e1cc639 |
ConvTemporalGraphical | import torch
import torch.nn as nn
import torch._utils
import torch.utils.data
import torch.utils.data.distributed
import torch.nn.parallel
import torch.optim
class ConvTemporalGraphical(nn.Module):
"""The basic module for applying a graph convolution.
Args:
in_channels (int): Number of channels in t... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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
import torch.utils.data
import torch.u... | fsImageries/video-to-pose3D | ConvTemporalGraphical | false | 10,185 | [
"MIT"
] | 0 | 098c87ce19dc3331da03e6eac0b9744684eb66f6 | https://github.com/fsImageries/video-to-pose3D/tree/098c87ce19dc3331da03e6eac0b9744684eb66f6 |
TCN_SLID | # 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.... | ddcas/singing-language-identification | TCN_SLID | false | 1,843 | [
"MIT"
] | 0 | d104419b196d56d4de37cff47c32e88e28c58690 | https://github.com/ddcas/singing-language-identification/tree/d104419b196d56d4de37cff47c32e88e28c58690 |
BinaryFocalLoss | # 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... | uncharted-distil/d3m-primitives | BinaryFocalLoss | false | 13,054 | [
"Apache-2.0"
] | 0 | e8d37dbe302c0f2bae4e7f7fa241a46faebc9b79 | https://github.com/uncharted-distil/d3m-primitives/tree/e8d37dbe302c0f2bae4e7f7fa241a46faebc9b79 |
DQN_hot3 | # 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 ... | CoAxLab/azad | DQN_hot3 | false | 17,176 | [
"MIT"
] | 6 | d1498069dd8856e93ae077b34dd7c9f1c7ce80e6 | https://github.com/CoAxLab/azad/tree/d1498069dd8856e93ae077b34dd7c9f1c7ce80e6 |
LeakyReLU | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import numpy as np
import torch.nn as nn
from numbers import N... | THAKAORI/SalsaNext | LeakyReLU | false | 11,917 | [
"MIT"
] | 0 | 855cd7e9ebb83ee62538ba4753a011ada7bbfb6c | https://github.com/THAKAORI/SalsaNext/tree/855cd7e9ebb83ee62538ba4753a011ada7bbfb6c |
SoftmaxAffineLayer | # 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.... | fancyliumeng/asv-subtools | SoftmaxAffineLayer | false | 6,691 | [
"Apache-2.0"
] | 1 | 56a13484472e7ae6eb00d762c00d57e581e78eb4 | https://github.com/fancyliumeng/asv-subtools/tree/56a13484472e7ae6eb00d762c00d57e581e78eb4 |
AvgPool2d | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch.nn import Module
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._em... | abogaziah/PySyft | AvgPool2d | false | 1,352 | [
"Apache-2.0"
] | 0 | 812dc6f350261793c67a928786fc081158f22a76 | https://github.com/abogaziah/PySyft/tree/812dc6f350261793c67a928786fc081158f22a76 |
BCEDiceLoss | import torch
from torch import nn as nn
def flatten(tensor):
"""Flattens a given tensor such that the channel axis is first.
The shapes are transformed as follows:
(N, C, D, H, W) -> (C, N * D * H * W)
"""
C = tensor.size(1)
axis_order = (1, 0) + tuple(range(2, tensor.dim()))
transposed... | 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 ... | PerceptionComputingLab/PARSE2022 | BCEDiceLoss | false | 9,430 | [
"Apache-2.0"
] | 0 | a34886ed9d06b424bc93953f1b2f79540ad9ebf6 | https://github.com/PerceptionComputingLab/PARSE2022/tree/a34886ed9d06b424bc93953f1b2f79540ad9ebf6 |
LinearSum | import torch
from torch import nn
from torch.nn import functional as F
class LinearSum(nn.Module):
def __init__(self, input_dims, output_dim, mm_dim=1200, activ_input=
'relu', activ_output='relu', normalize=False, dropout_input=0.0,
dropout_pre_lin=0.0, dropout_output=0.0):
super(LinearSu... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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 | LinearSum | false | 7,721 | [
"MIT"
] | 39 | b005c4256d68f1f90a7f73e7fdb3d066448de28c | https://github.com/AndresPMD/GCN_classification/tree/b005c4256d68f1f90a7f73e7fdb3d066448de28c |
LogLog | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert... | fmhoward/pysurvival | LogLog | false | 12,385 | [
"Apache-2.0"
] | 0 | 3fea55f09477e9f0844845e09d6ea60434436e2e | https://github.com/fmhoward/pysurvival/tree/3fea55f09477e9f0844845e09d6ea60434436e2e |
gconv | # 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 ... | lee-zq/MRDN | gconv | false | 3,889 | [
"Apache-2.0"
] | 0 | 976c1f8cd0d4b1943378149ef836bb86dd5fc0cd | https://github.com/lee-zq/MRDN/tree/976c1f8cd0d4b1943378149ef836bb86dd5fc0cd |
RMSLELoss | # 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... | cerisara/weibull-knowledge-informed-ml | RMSLELoss | false | 1,647 | [
"MIT"
] | 0 | 19017817f5324fb1fffd8322d2d3567a6271948c | https://github.com/cerisara/weibull-knowledge-informed-ml/tree/19017817f5324fb1fffd8322d2d3567a6271948c |
Affine | # 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... | rushirajsherlocked/External-Attention-pytorch | Affine | false | 4,209 | [
"MIT"
] | 0 | 7d6814b2d90909adf81c62f3f8a89e30a59d6481 | https://github.com/rushirajsherlocked/External-Attention-pytorch/tree/7d6814b2d90909adf81c62f3f8a89e30a59d6481 |
CumulativeMagSpectralNorm | import torch
import torch.nn as nn
class CumulativeMagSpectralNorm(nn.Module):
def __init__(self, cumulative=False, use_mid_freq_mu=False):
"""
Args:
cumulative: 是否采用累积的方式计算 mu
use_mid_freq_mu: 仅采用中心频率的 mu 来代替全局 mu
Notes:
先算均值再累加 等同于 先累加再算均值
... | 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... | intflow/FullSubNet | CumulativeMagSpectralNorm | false | 12,538 | [
"MIT"
] | 0 | 193091acac4c747730db5ace33fd1b8870e7c735 | https://github.com/intflow/FullSubNet/tree/193091acac4c747730db5ace33fd1b8870e7c735 |
EncoderSlot | import torch
from torch import nn
class EncoderSlot(nn.Module):
def __init__(self):
super().__init__()
self.conv_1 = nn.Conv2d(in_channels=1, out_channels=64, kernel_size=5)
self.conv_2 = nn.Conv2d(in_channels=64, out_channels=64, kernel_size=5)
self.conv_3 = nn.Conv2d(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 import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_st... | CatarauCorina/representation_learning | EncoderSlot | false | 8,917 | [
"Apache-2.0"
] | 0 | bb467761b03e5d8ac20c2f705f3bfdb84a7c3842 | https://github.com/CatarauCorina/representation_learning/tree/bb467761b03e5d8ac20c2f705f3bfdb84a7c3842 |
InvConv2d | import torch
from torch import nn
from torch.nn import functional as F
class InvConv2d(nn.Module):
def __init__(self, in_channel):
super().__init__()
weight = torch.randn(in_channel, in_channel)
q, _ = torch.qr(weight)
weight = q.unsqueeze(2).unsqueeze(3)
self.weight = 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 import nn
from torch.nn import functional as F
assert_size_stride = t... | HugoSenetaire/glow-pytorch | InvConv2d | false | 577 | [
"MIT"
] | 0 | 7f11be87cac9770df63867910c34738dedee6f56 | https://github.com/HugoSenetaire/glow-pytorch/tree/7f11be87cac9770df63867910c34738dedee6f56 |
Unet | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | furkannturkmen/pytorch-CNN-architecture | Unet | false | 10,222 | [
"MIT"
] | 0 | 6a864811f51409c1526224c288fe608010e0c888 | https://github.com/furkannturkmen/pytorch-CNN-architecture/tree/6a864811f51409c1526224c288fe608010e0c888 |
PPMConcat | # 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._C
import torch.serialization
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strid... | AlexanderDokuchaev/mmsegmentation | PPMConcat | false | 11,176 | [
"Apache-2.0"
] | 0 | 0c443ee370cce6227661b802184072174c4e3f64 | https://github.com/AlexanderDokuchaev/mmsegmentation/tree/0c443ee370cce6227661b802184072174c4e3f64 |
LRN | # 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_... | txsing/dissect | LRN | false | 10,865 | [
"MIT"
] | 0 | 3564605f7be9672c2cfc2ee19ca42225398a6e01 | https://github.com/txsing/dissect/tree/3564605f7be9672c2cfc2ee19ca42225398a6e01 |
Reorg | import torch
import torch.nn as nn
class Reorg(nn.Module):
def __init__(self, stride=2):
super(Reorg, self).__init__()
self.stride = stride
def forward(self, x):
stride = self.stride
assert x.data.dim() == 4
B = x.data.size(0)
C = x.data.size(1)
H = x.... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | Zhang-Jack/adversarial_yolo2 | Reorg | false | 18,177 | [
"MIT"
] | 8 | 91c2a4793047f656482cebf0309984db823e8030 | https://github.com/Zhang-Jack/adversarial_yolo2/tree/91c2a4793047f656482cebf0309984db823e8030 |
SigmaL1SmoothLoss | # 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
... | DineshChauhan/fastai_docs | SigmaL1SmoothLoss | false | 11,370 | [
"Apache-2.0"
] | 0 | cf4d88073fb6f3ef7331b5360618b8dd95eb9345 | https://github.com/DineshChauhan/fastai_docs/tree/cf4d88073fb6f3ef7331b5360618b8dd95eb9345 |
KLD | # 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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_str... | SchubertLab/mvTCR | KLD | false | 8,742 | [
"MIT"
] | 16 | d815749e24650f69ef68054e0078d490af91b71d | https://github.com/SchubertLab/mvTCR/tree/d815749e24650f69ef68054e0078d490af91b71d |
Highway | import torch
from torch import nn
class Highway(nn.Module):
def __init__(self, in_size, out_size):
super(Highway, self).__init__()
self.H = nn.Linear(in_size, out_size)
self.H.bias.data.zero_()
self.T = nn.Linear(in_size, out_size)
self.T.bias.data.fill_(-1)
self.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
from torch._inductor.runtime import triton_helpers
from torch import nn
assert_s... | puppyapple/tacotron_pytorch | Highway | false | 16,286 | [
"MIT"
] | 278 | 800bf8b0538c91f1104e99d8e7c1b645bb6154d3 | https://github.com/puppyapple/tacotron_pytorch/tree/800bf8b0538c91f1104e99d8e7c1b645bb6154d3 |
MLM | import math
import torch
from torch import nn
import torch.nn.functional as F
def get_mask_subset_with_prob(mask, prob):
batch, seq_len, device = *mask.shape, mask.device
max_masked = math.ceil(prob * seq_len)
num_tokens = mask.sum(dim=-1, keepdim=True)
mask_excess = mask.cumsum(dim=-1) > (num_tokens ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | aced125/alphafold2 | MLM | false | 6,071 | [
"MIT"
] | 1 | c85682ece37d37c608773cef3ec342b9ddc7fca0 | https://github.com/aced125/alphafold2/tree/c85682ece37d37c608773cef3ec342b9ddc7fca0 |
TranslateX | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | Hayoung93/UDA | TranslateX | false | 958 | [
"Apache-2.0"
] | 0 | a587b01c76141d64e7cead55b62e0f3ed75890bf | https://github.com/Hayoung93/UDA/tree/a587b01c76141d64e7cead55b62e0f3ed75890bf |
LogSumDualPenalty | from torch.nn import Module
import torch
class LogSumDualPenalty(Module):
def __init__(self, epsilon=1):
super(LogSumDualPenalty, self).__init__()
self.epsilon = epsilon
def forward(self, input):
eta = input
sqrt = torch.sqrt(self.epsilon ** 2 + 4 * eta)
return 2 * 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, math as tl_math
from torch.... | dlej/adaptive-dropout | LogSumDualPenalty | false | 10,075 | [
"MIT"
] | 0 | 0540b2d06f1f97eb5861c6917eec6c086d33dfa8 | https://github.com/dlej/adaptive-dropout/tree/0540b2d06f1f97eb5861c6917eec6c086d33dfa8 |
CosineDistance | import torch
import numpy as np
import torch.optim
def _acos_safe(x: 'torch.Tensor', eps: 'float'=0.0001):
slope = np.arccos(1.0 - eps) / eps
buf = torch.empty_like(x)
good = torch.abs(x) <= 1.0 - eps
bad = ~good
sign = torch.sign(x[bad])
buf[good] = torch.acos(x[good])
buf[bad] = torch.ac... | 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
import torch.optim
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.... | ai-in-motion/moai | CosineDistance | false | 18,323 | [
"Apache-2.0"
] | 10 | e38cac046c059d2e2331ef4883bbabc5a500a5cf | https://github.com/ai-in-motion/moai/tree/e38cac046c059d2e2331ef4883bbabc5a500a5cf |
GEGLU | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | Gitsamshi/DALLE-pytorch | GEGLU | false | 13,718 | [
"MIT"
] | 4,025 | 6cfc43158a4615865e97c839133290afcf289824 | https://github.com/Gitsamshi/DALLE-pytorch/tree/6cfc43158a4615865e97c839133290afcf289824 |
Reciprocal | import torch
import torch.onnx
import torch.nn as nn
class Reciprocal(nn.Module):
def forward(self, x):
return torch.reciprocal(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.onnx
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynam... | mil-tokyo/webdnn | Reciprocal | false | 16,074 | [
"MIT"
] | 1,967 | 38a60fd3e1a4e72bc01108189a3aa51e0752aecd | https://github.com/mil-tokyo/webdnn/tree/38a60fd3e1a4e72bc01108189a3aa51e0752aecd |
STLayer | import torch
class StraightThrough(torch.autograd.Function):
@staticmethod
def forward(ctx, x):
return torch.sign(x)
@staticmethod
def backward(ctx, grad):
return grad.clamp(-1.0, 1.0)
class STLayer(torch.nn.Module):
def __init__(self):
super(STLayer, self).__init__()
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.j... | alper111/affordance-learning | STLayer | false | 18,288 | [
"MIT"
] | 3 | 21b70f689a8299c6af7cd4ed763fc3133cf1681f | https://github.com/alper111/affordance-learning/tree/21b70f689a8299c6af7cd4ed763fc3133cf1681f |
Encoder_H | import torch
import torch.nn as nn
class Encoder_H(nn.Module):
def __init__(self, input_shape=(64, 64), z_dim=10, nc=3, padding=1):
super(Encoder_H, self).__init__()
self.conv2d_1 = nn.Conv2d(nc, 32, 4, 2, padding)
self.conv2d_2 = nn.Conv2d(32, 32, 4, 2, padding)
self.conv2d_3 = n... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | KinWaiCheuk/Beta-VAE | Encoder_H | false | 5,669 | [
"MIT"
] | 1 | 57f538320fed76b54e8489656b11dc83c06d1584 | https://github.com/KinWaiCheuk/Beta-VAE/tree/57f538320fed76b54e8489656b11dc83c06d1584 |
PatchEmbedding | import torch
from torch import Tensor
from torch import nn
class PatchEmbedding(nn.Module):
"""Image to Patch Embedding
"""
def __init__(self, img_size=224, patch_size=16, embed_dim=768):
super().__init__()
assert img_size % patch_size == 0, 'Image size must be divisible by patch size'
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_st... | alhamami/Object-Detection-And-Tracking | PatchEmbedding | false | 18,267 | [
"MIT"
] | 5 | a211a1dc103e812c539cd0ee16a2da4251943bed | https://github.com/alhamami/Object-Detection-And-Tracking/tree/a211a1dc103e812c539cd0ee16a2da4251943bed |
LayerNorm | import torch
import torch.nn as nn
class LayerNorm(nn.Module):
"""
Layer Normalization
(https://arxiv.org/abs/1607.06450)
"""
def __init__(self, normalized_shape, eps=1e-05):
super(LayerNorm, self).__init__()
self.gamma = nn.Parameter(torch.ones(normalized_shape))
self.bet... | 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_... | hamishivi/claf | LayerNorm | false | 3,558 | [
"MIT"
] | 0 | 8e35f30e3fc4a45a45cc0766eb6ab55a6ba3f0c2 | https://github.com/hamishivi/claf/tree/8e35f30e3fc4a45a45cc0766eb6ab55a6ba3f0c2 |
Encoder5 | import torch
import numpy as np
import torch.nn as nn
class Encoder5(nn.Module):
def __init__(self, model=None, fixed=False):
super(Encoder5, self).__init__()
self.fixed = fixed
self.conv0 = nn.Conv2d(3, 3, 1, 1, 0)
self.conv0.weight = nn.Parameter(torch.from_numpy(np.array([[[[0]... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | MingSun-Tse/pytorch-AdaIN | Encoder5 | false | 2,758 | [
"MIT"
] | 0 | 02ae320345232983c754ea233613aedc21e4d348 | https://github.com/MingSun-Tse/pytorch-AdaIN/tree/02ae320345232983c754ea233613aedc21e4d348 |
Attention | # 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.... | MINATILO/pytroch-geometric | Attention | false | 9,414 | [
"MIT"
] | 0 | 706aba3b4a6477a83a1fb73eb3cf0ee9661b70e4 | https://github.com/MINATILO/pytroch-geometric/tree/706aba3b4a6477a83a1fb73eb3cf0ee9661b70e4 |
SEModule | import torch
import torch.nn as nn
import torch.utils
import torch.nn.functional as F
import torch.utils.data
from collections import OrderedDict
def make_divisible(v, divisor, min_val=None):
"""
This function is taken from the original tf repo.
It ensures that all layers have a channel number that is div... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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 ... | MAC-AutoML/XNAS | SEModule | false | 17,643 | [
"MIT"
] | 9 | 2c54ceb09b255cbcabd67f3c39fc777c4b2403f4 | https://github.com/MAC-AutoML/XNAS/tree/2c54ceb09b255cbcabd67f3c39fc777c4b2403f4 |
SpatialAttn | import torch
from torch import nn
class SpatialAttn(nn.Module):
"""Spatial Attention Layer"""
def __init__(self):
super(SpatialAttn, self).__init__()
def forward(self, x):
x = x.mean(1, keepdim=True)
h = x.size(2)
w = x.size(3)
x = x.view(x.size(0), -1)
z ... | 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... | YUE-FAN/Spatial-Attention | SpatialAttn | false | 6,011 | [
"MIT"
] | 1 | 71cf324f0fb0829355e5ca322058ebbb9d8be610 | https://github.com/YUE-FAN/Spatial-Attention/tree/71cf324f0fb0829355e5ca322058ebbb9d8be610 |
SpatialAttention | # 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_... | abandonsea/BBS-Net | SpatialAttention | false | 14,736 | [
"MIT"
] | 66 | fd4e60bf3025d0cec745c0594b7104c5746f6d0f | https://github.com/abandonsea/BBS-Net/tree/fd4e60bf3025d0cec745c0594b7104c5746f6d0f |
Fire | # 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 to... | BiggerBinBin/e3d_handpose_x-master | Fire | false | 634 | [
"Apache-2.0"
] | 0 | 20d091a8a019d85de26c81d02985868f79d5de84 | https://github.com/BiggerBinBin/e3d_handpose_x-master/tree/20d091a8a019d85de26c81d02985868f79d5de84 |
RankCrossEntropyLoss | import torch
import torch.nn as nn
import torch.nn.functional as F
class RankCrossEntropyLoss(nn.Module):
"""Creates a criterion that measures rank cross entropy loss."""
__constants__ = ['num_neg']
def __init__(self, num_neg: 'int'=1):
"""
:class:`RankCrossEntropyLoss` constructor.
... | 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
... | ThuYShao/MatchZoo-py | RankCrossEntropyLoss | false | 11,977 | [
"Apache-2.0"
] | 0 | dd8ff1328af58d3d14aacd1a7d56d79bbf847c15 | https://github.com/ThuYShao/MatchZoo-py/tree/dd8ff1328af58d3d14aacd1a7d56d79bbf847c15 |
MyElementwiseModule | import torch
import torch.nn.parallel
import torch.utils.data
import torch.onnx
import torch.fx
import torch.optim
import torch.utils.data.distributed
class MyElementwiseModule(torch.nn.Module):
def forward(self, x, y):
return x * y + y
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn.parallel
import torch.utils.data
import torch.onnx
import torch.fx
import torch.optim
import torch.utils.data.distributed
as... | siaimes/examples | MyElementwiseModule | false | 10,768 | [
"BSD-3-Clause"
] | 0 | 340d6fac5c4fce827c08b92b8f2aa7152b1a63b3 | https://github.com/siaimes/examples/tree/340d6fac5c4fce827c08b92b8f2aa7152b1a63b3 |
SENet | import torch
import torch.nn as nn
class SENet(nn.Module):
"""support estimation network"""
def __init__(self, input_size: 'int', hidden_size: 'int', output_dims:
'int') ->None:
super(SENet, self).__init__()
self.l_1 = nn.Linear(input_size, hidden_size)
self.l_2 = nn.Linear(hi... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | PaParaZz1/DI-engine | SENet | false | 11,850 | [
"Apache-2.0"
] | 0 | b38144117c1ebc6eb860d8637ec8866dfbcdf2de | https://github.com/PaParaZz1/DI-engine/tree/b38144117c1ebc6eb860d8637ec8866dfbcdf2de |
RelPositionMultiHeadedAttention | import math
import torch
from typing import Optional
from typing import Tuple
from torch import nn
class MultiHeadedAttention(nn.Module):
"""Multi-Head Attention layer.
Args:
n_head (int): The number of heads.
n_feat (int): The number of features.
dropout_rate (float): Dropout rate.
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | InfluencerNGZK/wenet | RelPositionMultiHeadedAttention | false | 2,405 | [
"Apache-2.0"
] | 0 | 9a3c7f70a78ce675f5e013b1f67a06d1d23fba3e | https://github.com/InfluencerNGZK/wenet/tree/9a3c7f70a78ce675f5e013b1f67a06d1d23fba3e |
CNN_CIFAR10 | import torch
import torch.nn as nn
import torch.nn.functional as F
class CNN_CIFAR10(nn.Module):
def __init__(self, num_channels, num_classes):
super(CNN_CIFAR10, self).__init__()
self.conv1 = nn.Conv2d(num_channels, 32, 3, stride=1, padding=0)
self.conv2 = nn.Conv2d(32, 64, 3, stride=1, ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | Billy1900/Noise-Adaption-Layer | CNN_CIFAR10 | false | 17,000 | [
"MIT"
] | 5 | 57b52dc4873f8eba7b8332db0ca3e593c2e3ffa8 | https://github.com/Billy1900/Noise-Adaption-Layer/tree/57b52dc4873f8eba7b8332db0ca3e593c2e3ffa8 |
TSAFusion | import torch
from torch import nn as nn
from torch.nn import init as init
from torchvision.models import vgg as vgg
import torch.utils.data
from torch.utils import data as data
from torch import autograd as autograd
class TSAFusion(nn.Module):
"""Temporal Spatial Attention (TSA) fusion module.
Temporal: Calc... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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 as nn
fr... | hyunobae/BasicSR | TSAFusion | false | 12,598 | [
"Apache-2.0"
] | 0 | f2c2fc6cf28933658816c808f55c95fa20b16483 | https://github.com/hyunobae/BasicSR/tree/f2c2fc6cf28933658816c808f55c95fa20b16483 |
Scale | # 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
import torch.nn.parallel
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C.... | XDong18/AdelaiDet | Scale | false | 12,061 | [
"BSD-2-Clause"
] | 0 | 837cd1078923892fe6e84ac29fd0963f1b2c474f | https://github.com/XDong18/AdelaiDet/tree/837cd1078923892fe6e84ac29fd0963f1b2c474f |
TanhHyperSolver | # 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 ... | Juju-botu/diffeqml-research | TanhHyperSolver | false | 13,919 | [
"Apache-2.0"
] | 49 | aa796c87447e5299ec4f25a07fc4d032afb1f63e | https://github.com/Juju-botu/diffeqml-research/tree/aa796c87447e5299ec4f25a07fc4d032afb1f63e |
ToRGB | from torch.autograd import Function
import math
import torch
import torchvision.transforms.functional as F
from torch import nn
from torch.nn import functional as F
def fused_leaky_relu(input, bias, negative_slope=0.2, scale=2 ** 0.5):
return FusedLeakyReLUFunction.apply(input, bias, negative_slope, scale)
def ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch.autograd import Function
import math
import torchvision.transforms.fu... | CTPLab/IID_representation_learning | ToRGB | false | 5,203 | [
"MIT"
] | 1 | b9dc13536963f9af332b039f7cc772e2f1090c62 | https://github.com/CTPLab/IID_representation_learning/tree/b9dc13536963f9af332b039f7cc772e2f1090c62 |
NeuralNetPartialNoGradModel | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn
import torch.... | RyanUnderhill/onnxruntime | NeuralNetPartialNoGradModel | false | 11,833 | [
"MIT"
] | 0 | 6df4e293ffbb47d739d2dedfbb87fa6234b8c37c | https://github.com/RyanUnderhill/onnxruntime/tree/6df4e293ffbb47d739d2dedfbb87fa6234b8c37c |
LayerNormLSTMCell | import torch
import torch.distributed
import torch
import torch.nn as nn
import torch.nn.functional as F
class LayerNormLSTMCell(nn.LSTMCell):
def __init__(self, input_size, hidden_size, bias=True):
super().__init__(input_size, hidden_size, bias)
self.ln_ih = nn.LayerNorm(4 * hidden_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.triton_helpers import libdevice
import torch.distri... | dimoteo333/TL-DR | LayerNormLSTMCell | false | 12,293 | [
"Apache-2.0"
] | 0 | b3bebc51e70a48294d7762fa73375cf1bf2ff068 | https://github.com/dimoteo333/TL-DR/tree/b3bebc51e70a48294d7762fa73375cf1bf2ff068 |
DeConv2dBlock | import torch
from torch import nn
class DeConv2dBlock(nn.Module):
"""
Similar to a LeNet block
4x upsampling, dimension hard-coded
"""
def __init__(self, in_dim: 'int', hidden_dim: 'int', out_dim: 'int',
stride: 'int'=2, kernel_size: 'int'=3, padding: 'int'=2,
output_padding: 'int... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_st... | Jimmy-INL/fourier-transformer | DeConv2dBlock | false | 5,393 | [
"MIT"
] | 1 | 44a6ebc68aef24a4eb9aaa2a8c518ede56ec47ce | https://github.com/Jimmy-INL/fourier-transformer/tree/44a6ebc68aef24a4eb9aaa2a8c518ede56ec47ce |
RegressionModel | # 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 ... | DerekGloudemans/3D-detector-trials | RegressionModel | false | 2,169 | [
"MIT"
] | 0 | 480274567eaa84c5c883260ef62f150c7a23ffd3 | https://github.com/DerekGloudemans/3D-detector-trials/tree/480274567eaa84c5c883260ef62f150c7a23ffd3 |
AdMSoftmaxLoss | # 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.... | AyushExel/s3prl | AdMSoftmaxLoss | false | 1,992 | [
"MIT"
] | 0 | 6531904e9621a778978b9cfef3ba9f582e56639a | https://github.com/AyushExel/s3prl/tree/6531904e9621a778978b9cfef3ba9f582e56639a |
MetaAconC | # 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... | Lalihoo/yolov5-detect | MetaAconC | false | 9,620 | [
"MIT"
] | 0 | 265c3137ea3586d913541501a1562488fbe59e9e | https://github.com/Lalihoo/yolov5-detect/tree/265c3137ea3586d913541501a1562488fbe59e9e |
FscoreMetric | # 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... | JACKYLUO1991/HybridNet | FscoreMetric | false | 17,454 | [
"Apache-2.0"
] | 6 | eb97d8a048ca4bb4087bc542360172e169a08dbf | https://github.com/JACKYLUO1991/HybridNet/tree/eb97d8a048ca4bb4087bc542360172e169a08dbf |
ToLongTensor | import torch
from torch import Tensor
from typing import List
import torch.nn as nn
class ToLongTensor(nn.Module):
"""Convert a list of integers to long tensor
"""
def __init__(self):
super(ToLongTensor, self).__init__()
def forward(self, tokens: 'List[List[int]]') ->Tensor:
return 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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | NivekT/text | ToLongTensor | false | 11,751 | [
"BSD-3-Clause"
] | 0 | 4908d3c88f92296a4c23be2f064ccde13cce50ce | https://github.com/NivekT/text/tree/4908d3c88f92296a4c23be2f064ccde13cce50ce |
JointsMSELoss | # 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.optim
import torch.utils.data
from torch.nn.init import *
assert_size_stride = torch._C._dynamo.guards.as... | EVA4-RS-Group/Phase2 | JointsMSELoss | false | 390 | [
"Apache-2.0"
] | 0 | 7c551e3894979cc425dd51baeddbfa5a51b7878d | https://github.com/EVA4-RS-Group/Phase2/tree/7c551e3894979cc425dd51baeddbfa5a51b7878d |
GRU122 | import torch
import torch.nn as nn
class GRU122(nn.Module):
def __init__(self, input_size, hidden_size):
super(GRU122, self).__init__()
self.hidden_size = hidden_size
self.wir = nn.Linear(in_features=input_size, out_features=2 *
hidden_size)
self.whr = nn.Linear(in_fea... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | smeznar/ProGED | GRU122 | false | 10,805 | [
"BSD-3-Clause"
] | 0 | 191cfd2b7b1fece819109a4b61e3f7533332fd74 | https://github.com/smeznar/ProGED/tree/191cfd2b7b1fece819109a4b61e3f7533332fd74 |
CustomGroupNorm | import torch
class CustomGroupNorm(torch.nn.Module):
"""
Custom Group Norm which adds n_groups=2 as default parameter
"""
def __init__(self, n_features, n_groups=2):
"""
Parameters
----------
n_features : int
number of input features
n_groups : int... | 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... | vuamitom/shapenet | CustomGroupNorm | false | 4,507 | [
"BSD-2-Clause"
] | 0 | 9eb3dadc91801756cb3460707c37146c8176643e | https://github.com/vuamitom/shapenet/tree/9eb3dadc91801756cb3460707c37146c8176643e |
InnerProductDecoder | import torch
import torch.nn
import torch.nn.modules.loss
import torch.nn.functional as F
import torch.nn as nn
class InnerProductDecoder(nn.Module):
"""Decoder for using inner product for prediction."""
def __init__(self, dropout, act=torch.sigmoid):
super(InnerProductDecoder, self).__init__()
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn
import torch.nn.modules.loss
import torch.nn as nn
assert_size_s... | spatial-Transcriptomics/DeepST | InnerProductDecoder | false | 4,363 | [
"MIT"
] | 0 | 47ce64b06b62395cd2983939d4bf2419f558a562 | https://github.com/spatial-Transcriptomics/DeepST/tree/47ce64b06b62395cd2983939d4bf2419f558a562 |
VNLinear | import torch
import torch.nn as nn
import torch.utils.data
import torch
import torch.nn.parallel
class VNLinear(nn.Module):
def __init__(self, in_channels, out_channels):
super(VNLinear, self).__init__()
self.map_to_feat = nn.Linear(in_channels, out_channels, bias=False)
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
import torch
import torch.nn.paral... | Tiamat-Tech/vnn | VNLinear | false | 14,498 | [
"MIT"
] | 280 | f3197e210022b5f0015e0da6456adf66bd0cd73e | https://github.com/Tiamat-Tech/vnn/tree/f3197e210022b5f0015e0da6456adf66bd0cd73e |
Swish | # 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.distributed
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C... | Ugness/PointFlow | Swish | false | 2,906 | [
"MIT"
] | 0 | 238489c70b0332526cb2d506ab3e076fae20685d | https://github.com/Ugness/PointFlow/tree/238489c70b0332526cb2d506ab3e076fae20685d |
SpatialGatherModule | import torch
import torch.nn as nn
import torch.nn.functional as F
class SpatialGatherModule(nn.Module):
def __init__(self, scale=1, **kwargs):
super(SpatialGatherModule, self).__init__()
self.scale = scale
"""forward"""
def forward(self, features, probs):
batch_size, num_classes... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | CharlesPikachu/mcibi | SpatialGatherModule | false | 7,889 | [
"MIT"
] | 41 | 6ce453504741c2eed1d290306055258a377a4094 | https://github.com/CharlesPikachu/mcibi/tree/6ce453504741c2eed1d290306055258a377a4094 |
MaskNorm | import torch
from torch import nn
class MaskNorm(nn.Module):
def __init__(self, norm_nc):
super(MaskNorm, self).__init__()
self.norm_layer = nn.InstanceNorm2d(norm_nc, affine=False)
def normalize_region(self, region, mask):
_b, _c, h, w = region.size()
num_pixels = mask.sum((... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | swpang/xray-align-AR | MaskNorm | false | 13,014 | [
"MIT"
] | 0 | 43cb0173ada9d1d71a6a923d605cb6fdae4d27aa | https://github.com/swpang/xray-align-AR/tree/43cb0173ada9d1d71a6a923d605cb6fdae4d27aa |
GaussianKernel | # 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 math
import torch.nn as nn
import torch.utils.data
assert_size_str... | JonasHell/torch-em | GaussianKernel | false | 8,376 | [
"MIT"
] | 13 | 2e008e0cd2f0ea6681581374fce4f9f47b986d55 | https://github.com/JonasHell/torch-em/tree/2e008e0cd2f0ea6681581374fce4f9f47b986d55 |
KLMutualLoss | # 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... | linkserendipity/AlignedReID | KLMutualLoss | false | 3,916 | [
"MIT"
] | 0 | 142a9ebdc200ef4da001f91c1f592e4ff02b2f77 | https://github.com/linkserendipity/AlignedReID/tree/142a9ebdc200ef4da001f91c1f592e4ff02b2f77 |
ToLongTensor | # 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... | ZongHR/text | ToLongTensor | false | 3,000 | [
"BSD-3-Clause"
] | 0 | 856607154be7c784505869f10ae578346868b121 | https://github.com/ZongHR/text/tree/856607154be7c784505869f10ae578346868b121 |
FiLMLayer | import torch
import torch.nn as nn
class FiLMLayer(nn.Module):
def __init__(self, input_dim, hidden_dim):
super().__init__()
self.layer = nn.Linear(input_dim, hidden_dim)
def forward(self, x, freq, phase_shift):
x = self.layer(x)
freq = freq.unsqueeze(1).expand_as(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 math as tl_math
import torch.... | HexagonPrime/pixel-nerf | FiLMLayer | false | 2,419 | [
"BSD-2-Clause"
] | 0 | 298aa7a3451c01e6f19f73f0c756672d3de54bf9 | https://github.com/HexagonPrime/pixel-nerf/tree/298aa7a3451c01e6f19f73f0c756672d3de54bf9 |
DenseModelV3 | # 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_... | chawins/adv-exp | DenseModelV3 | false | 6,447 | [
"MIT"
] | 1 | 5423e135c5599e4ec2bf90372916d8d05c89f285 | https://github.com/chawins/adv-exp/tree/5423e135c5599e4ec2bf90372916d8d05c89f285 |
Generator | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | msank00/ganTutorial | Generator | false | 12,798 | [
"MIT"
] | 0 | 7657ff8cbb0cd66c98b5fd91bf19677e467aac68 | https://github.com/msank00/ganTutorial/tree/7657ff8cbb0cd66c98b5fd91bf19677e467aac68 |
chroma_subsampling | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | DazhiZhong/DiffJPEG | chroma_subsampling | false | 9,007 | [
"MIT"
] | 0 | e20de92539f31a57906ae4c32a41dc46e774c316 | https://github.com/DazhiZhong/DiffJPEG/tree/e20de92539f31a57906ae4c32a41dc46e774c316 |
MaxMarginRankingLoss | # 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 numpy as np
import torch as th
assert_size_stride = torch._C._dynamo.guards.assert... | ErinZhang1998/howto100m-erin | MaxMarginRankingLoss | false | 2,196 | [
"Apache-2.0"
] | 0 | 1152ea0fe328d20fcf2218a1d548644881632656 | https://github.com/ErinZhang1998/howto100m-erin/tree/1152ea0fe328d20fcf2218a1d548644881632656 |
DotProductAttention | import torch
import torch.nn as nn
import torch.nn.functional as F
class DotProductAttention(nn.Module):
"""Dot product attention.
Given a set of vector values, and a vector query, attention is a technique
to compute a weighted sum of the values, dependent on the query.
NOTE: Here we use the terminolo... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | tompoek/Listen-Attend-Spell-v2 | DotProductAttention | false | 10,856 | [
"MIT"
] | 0 | aa19543c9d23256a007d6e7a98d9cbc571e89f7f | https://github.com/tompoek/Listen-Attend-Spell-v2/tree/aa19543c9d23256a007d6e7a98d9cbc571e89f7f |
ConcatConv2d | import torch
from torch import nn
class ConcatConv2d(nn.Module):
def __init__(self, dim_in, dim_out, ksize=3, stride=1, padding=0,
dilation=1, groups=1, bias=True, transpose=False):
super(ConcatConv2d, self).__init__()
module = nn.ConvTranspose2d if transpose else nn.Conv2d
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 import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_st... | BoyanJIANG/4D-Compositional-Representation | ConcatConv2d | false | 7,867 | [
"Apache-2.0"
] | 12 | 64d5f4bbd6b8e6bc3bfd8f76736f6d468c71a73c | https://github.com/BoyanJIANG/4D-Compositional-Representation/tree/64d5f4bbd6b8e6bc3bfd8f76736f6d468c71a73c |
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