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 |
|---|---|---|---|---|---|---|---|---|---|---|
Dueling_Critic | import torch
import torch.nn.functional as F
import torch.nn as nn
class Dueling_Critic(nn.Module):
def __init__(self, input_size, output_size, hidden_size):
super().__init__()
self.input_size = input_size
self.output_size = output_size
self.linear1 = nn.Linear(input_size, hidden_... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | FlickerNiko/ai_lib | Dueling_Critic | false | 13,693 | [
"MIT"
] | 99 | 7087d4569c9a827d35dd8735b55a080834d31a82 | https://github.com/FlickerNiko/ai_lib/tree/7087d4569c9a827d35dd8735b55a080834d31a82 |
EncoderImagePrecomp | # 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 numpy as np
... | ChopinSharp/SCAN | EncoderImagePrecomp | false | 4,994 | [
"Apache-2.0"
] | 1 | 4a165b2aeb3007685054d0c550540893b2006b17 | https://github.com/ChopinSharp/SCAN/tree/4a165b2aeb3007685054d0c550540893b2006b17 |
ConcatConv1d | # 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... | puneat/SS-using-NODE | ConcatConv1d | false | 4,147 | [
"MIT"
] | 0 | 29f053769420a2d1cab1ad45f59a912c2ac737da | https://github.com/puneat/SS-using-NODE/tree/29f053769420a2d1cab1ad45f59a912c2ac737da |
SimpleCumSumModule | # 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.jit
import torch.onnx
import torch.nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torc... | briancoutinho/glow | SimpleCumSumModule | false | 12,570 | [
"Apache-2.0"
] | 0 | 4c919d60b3c33296c4109aec8020a1733c98f5b5 | https://github.com/briancoutinho/glow/tree/4c919d60b3c33296c4109aec8020a1733c98f5b5 |
ResNetBlockGroupNorm | import torch
import torch.nn as nn
def conv3x3(in_planes, out_planes, stride=1):
"""3x3 convolution with padding"""
return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
padding=1, bias=False)
class ResNetBlockGroupNorm(nn.Module):
def __init__(self, inplanes, planes, num_groups... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | andrecianflone/wolf | ResNetBlockGroupNorm | false | 14,857 | [
"Apache-2.0"
] | 75 | 826bbedc58d4d29871110349356868066a3108e6 | https://github.com/andrecianflone/wolf/tree/826bbedc58d4d29871110349356868066a3108e6 |
InnerProductLayer | # 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
from sklearn.metrics import *
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = tor... | liyunrui/DeepCTR-Torch | InnerProductLayer | false | 12,951 | [
"Apache-2.0"
] | 0 | 392fd6d39d9ca0ac854022136cdb4d5c68e3a592 | https://github.com/liyunrui/DeepCTR-Torch/tree/392fd6d39d9ca0ac854022136cdb4d5c68e3a592 |
LocalNet | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.... | ZombaSY/DeepLPF | LocalNet | false | 1,345 | [
"BSD-3-Clause"
] | 0 | adce64ae01bc9e32f465a354cb1f6534f0d13597 | https://github.com/ZombaSY/DeepLPF/tree/adce64ae01bc9e32f465a354cb1f6534f0d13597 |
DilatedResidualLayer | import torch
from torch import nn
import torch.nn.functional as F
class DilatedResidualLayer(nn.Module):
def __init__(self, dilation, in_channels, out_channels):
super(DilatedResidualLayer, self).__init__()
self.conv_dilated = nn.Conv1d(in_channels, out_channels, 3, padding
=dilation,... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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... | Jaakik/hydra-ml | DilatedResidualLayer | false | 11,535 | [
"MIT"
] | 0 | eae54fc478163130c94450a2a2ddea4f204c1ea9 | https://github.com/Jaakik/hydra-ml/tree/eae54fc478163130c94450a2a2ddea4f204c1ea9 |
ResidualBlock_noBN | # 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... | LCM1999/VolumeRescaling | ResidualBlock_noBN | false | 17,575 | [
"Apache-2.0"
] | 4 | 3eeabf057e68804ed945711b440f19e419c10d7a | https://github.com/LCM1999/VolumeRescaling/tree/3eeabf057e68804ed945711b440f19e419c10d7a |
UpsamplingBilinear | import torch
import torch.nn as nn
from torch.quantization import QuantStub
from torch.quantization import DeQuantStub
class UpsamplingBilinear(nn.Module):
def __init__(self):
super().__init__()
self.quant = QuantStub()
self.dequant = DeQuantStub()
def forward(self, x):
x = 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
import torch.nn as nn
from torch.quantization import QuantStub
from torch.quantization im... | Leslie-Fang/incubator-tvm | UpsamplingBilinear | false | 9,303 | [
"Apache-2.0"
] | 0 | aa035f4650926f5e714b02cbab6d974f0a17352f | https://github.com/Leslie-Fang/incubator-tvm/tree/aa035f4650926f5e714b02cbab6d974f0a17352f |
AcM | # 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.... | MIMUW-RL/spp-rl | AcM | false | 17,644 | [
"MIT"
] | 7 | 86b96cdd220cc4eae86f7cfd26924c69b498dcc6 | https://github.com/MIMUW-RL/spp-rl/tree/86b96cdd220cc4eae86f7cfd26924c69b498dcc6 |
net2 | # 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.... | luisesanmartin/dwelling-recognition | net2 | false | 7,139 | [
"MIT"
] | 1 | b2437b64088a26746947c1c88077c96332e7b9c6 | https://github.com/luisesanmartin/dwelling-recognition/tree/b2437b64088a26746947c1c88077c96332e7b9c6 |
qy | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.... | IamWangYunKai/DG-TrajGen | qy | false | 8,303 | [
"MIT"
] | 31 | 0a8aab7e1c05111a5afe43d53801c55942e9ff56 | https://github.com/IamWangYunKai/DG-TrajGen/tree/0a8aab7e1c05111a5afe43d53801c55942e9ff56 |
LabelPredictor | import torch
from torch import nn
class LabelPredictor(nn.Module):
def __init__(self, nz_feat, classify_rot=True):
super(LabelPredictor, self).__init__()
self.pred_layer = nn.Linear(nz_feat, 1)
def forward(self, feat):
pred = self.pred_layer.forward(feat)
pred = torch.sigmoid... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import 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... | JasonQSY/Associative3D | LabelPredictor | false | 8,359 | [
"MIT"
] | 25 | c50818b593ec48c38ed7ee3e109c23531089da32 | https://github.com/JasonQSY/Associative3D/tree/c50818b593ec48c38ed7ee3e109c23531089da32 |
SequentialCNNNet | import torch
import torch.nn as nn
class SequentialCNNNet(nn.Module):
def __init__(self):
super(SequentialCNNNet, self).__init__()
self.conv1 = nn.Conv2d(3, 64, 5)
self.pool = nn.MaxPool2d(2, 2)
self.conv2 = nn.Conv2d(64, 128, 5)
self.fc1 = nn.Linear(128 * 5 * 5, 1024)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | fangkaimin/pytorch_classification_new | SequentialCNNNet | false | 10,122 | [
"MIT"
] | 0 | 21032e7ab91f0f3106ba07aa97657a023b1cc717 | https://github.com/fangkaimin/pytorch_classification_new/tree/21032e7ab91f0f3106ba07aa97657a023b1cc717 |
ConvNorm | import torch
import torch.utils.data
class ConvNorm(torch.nn.Module):
def __init__(self, in_channels, out_channels, kernel_size=1, stride=1,
padding=None, dilation=1, bias=True, w_init_gain='linear'):
super(ConvNorm, self).__init__()
if padding is None:
assert kernel_size % 2 ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size... | AeroXi/Tacotron2-Mandarin | ConvNorm | false | 13,246 | [
"MIT"
] | 67 | b7bc213d1c1a9c3e2f2e11f69f586c2582010668 | https://github.com/AeroXi/Tacotron2-Mandarin/tree/b7bc213d1c1a9c3e2f2e11f69f586c2582010668 |
NasPathBranch | import torch
import torch.nn as nn
import torch.utils.data
def conv1x1(in_channels, out_channels, stride=1, bias=False):
"""
Convolution 1x1 layer.
Parameters:
----------
in_channels : int
Number of input channels.
out_channels : int
Number of output channels.
stride : 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
import torch.nn as nn
import torch.utils.data
assert_size_stride = torch._C._dyn... | earhian/imgclsmob | NasPathBranch | false | 6,628 | [
"MIT"
] | 1 | c87c0942420876941868c016211073dec4392e4d | https://github.com/earhian/imgclsmob/tree/c87c0942420876941868c016211073dec4392e4d |
ConvAE | # 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
import torch.nn a... | qilinli/DSC-Net | ConvAE | false | 4,154 | [
"MIT"
] | 0 | c0e7a3cae3e07c34b2989234f568c7007cf0fc55 | https://github.com/qilinli/DSC-Net/tree/c0e7a3cae3e07c34b2989234f568c7007cf0fc55 |
CapsuleLoss | import torch
import torch.nn as nn
import torch.nn.functional as F
class MarginLoss(nn.Module):
def __init__(self, size_average=False, loss_lambda=0.5):
"""
Margin loss for digit existence
Eq. (4): L_k = T_k * max(0, m+ - ||v_k||)^2 + lambda * (1 - T_k) * max(0, ||v_k|| - m-)^2
Args:
size_ave... | 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... | richardsun-voyager/capsule-network | CapsuleLoss | false | 7,554 | [
"MIT"
] | 1 | 349cec1caa9ab95ff4b3333c33d04b1bdb442f67 | https://github.com/richardsun-voyager/capsule-network/tree/349cec1caa9ab95ff4b3333c33d04b1bdb442f67 |
Vgg16 | import torch
from torch import nn
import torch.nn.functional as F
class Vgg16(nn.Module):
def __init__(self):
super(Vgg16, self).__init__()
self.conv1_1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1)
self.conv1_2 = nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1)
self... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
assert_s... | YueZHOU0926/MUNIT_3D | Vgg16 | false | 12,055 | [
"MIT"
] | 0 | 5cb22b5f3cb127d5b2c4eea038254a7881bab372 | https://github.com/YueZHOU0926/MUNIT_3D/tree/5cb22b5f3cb127d5b2c4eea038254a7881bab372 |
WScaleLayer | # 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... | ChandreyeeB/Blind-Image-Deconvolution-using-Deep-Generative-Priors | WScaleLayer | false | 7,848 | [
"MIT"
] | 24 | 4198bd2d325a32ffc4e714c486540e63440ab110 | https://github.com/ChandreyeeB/Blind-Image-Deconvolution-using-Deep-Generative-Priors/tree/4198bd2d325a32ffc4e714c486540e63440ab110 |
ChannelAttention_avg | # 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... | HT-hlf/mmdetection_miner-2.22.0 | ChannelAttention_avg | false | 2,318 | [
"Apache-2.0"
] | 0 | 76eb94d6547f9f95cd58f41bb5c91941e82322b9 | https://github.com/HT-hlf/mmdetection_miner-2.22.0/tree/76eb94d6547f9f95cd58f41bb5c91941e82322b9 |
LN_self | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_... | EkdeepSLubana/BeyondBatchNorm | LN_self | false | 17,240 | [
"MIT"
] | 10 | 2ab1626a1ebfdfe55f0a4bc6ac24c8bbdd4e0196 | https://github.com/EkdeepSLubana/BeyondBatchNorm/tree/2ab1626a1ebfdfe55f0a4bc6ac24c8bbdd4e0196 |
TwoMLPHead | # 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
from ... | billchenxi/vision | TwoMLPHead | false | 1,548 | [
"BSD-3-Clause"
] | 0 | 5608b06a6fbc9c84284dad9f07a16df61da10f85 | https://github.com/billchenxi/vision/tree/5608b06a6fbc9c84284dad9f07a16df61da10f85 |
ResidualBlock | # 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... | qiyuqianxai/debvc | ResidualBlock | false | 10,784 | [
"MIT"
] | 0 | 1d919019a3191d1c6a7da9b8f16e47bca6b3aef9 | https://github.com/qiyuqianxai/debvc/tree/1d919019a3191d1c6a7da9b8f16e47bca6b3aef9 |
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 math as tl_math
assert_size_s... | JEF1056/Reconstruction-Style | ConvLayer | false | 17,447 | [
"MIT"
] | 6 | 3430d9e9f05c6980ae251cf15b619148a2c899d6 | https://github.com/JEF1056/Reconstruction-Style/tree/3430d9e9f05c6980ae251cf15b619148a2c899d6 |
DenseCrossEntropy | # 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
... | i-pan/kaggle-melanoma | DenseCrossEntropy | false | 15,569 | [
"MIT"
] | 68 | caaec0d7e9cafc7b405eb86e7fdf00107d89e1d9 | https://github.com/i-pan/kaggle-melanoma/tree/caaec0d7e9cafc7b405eb86e7fdf00107d89e1d9 |
MidNet4 | import torch
import torch.nn as nn
class MidNet4(nn.Module):
def forward(self, x_in):
"""Network with dilation rate 4
:param x_in: input convolutional features
:returns: processed convolutional features
:rtype: Tensor
"""
x = self.lrelu(self.conv1(x_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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | deshwalmahesh/CURL---cpu-gpu | MidNet4 | false | 3,415 | [
"BSD-3-Clause"
] | 0 | f4e87275b6cce556b9e04a188cf7ae13d810d82a | https://github.com/deshwalmahesh/CURL---cpu-gpu/tree/f4e87275b6cce556b9e04a188cf7ae13d810d82a |
GateContextSelectionLayer | # 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... | KirkGuo/HCN | GateContextSelectionLayer | false | 5,437 | [
"MIT"
] | 1 | 7d8020c8d76413b6ca3a359fb2e9b34652949e17 | https://github.com/KirkGuo/HCN/tree/7d8020c8d76413b6ca3a359fb2e9b34652949e17 |
Critic | # 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_... | Chris0919/Deep-reinforcement-learning-with-pytorch | Critic | false | 4,999 | [
"MIT"
] | 1 | a4f458dde7659654fcae4635d25f6bd05a5d2d6c | https://github.com/Chris0919/Deep-reinforcement-learning-with-pytorch/tree/a4f458dde7659654fcae4635d25f6bd05a5d2d6c |
ScaledDotProductAttentionMemory | import torch
import numpy as np
from torch import nn
class ScaledDotProductAttentionMemory(nn.Module):
"""
Scaled dot-product attention with memory
"""
def __init__(self, d_model, d_k, d_v, h, m):
"""
:param d_model: Output dimensionality of the model
:param d_k: Dimensionalit... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | Kilichbek/artemis-m2-transformer | ScaledDotProductAttentionMemory | false | 17,550 | [
"MIT"
] | 8 | 99f7e797965710bf2565283d6b5028a6fe32664c | https://github.com/Kilichbek/artemis-m2-transformer/tree/99f7e797965710bf2565283d6b5028a6fe32664c |
DenseGraphConv | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import math
from torch.nn import Parameter
import torch.utils.data
assert_size_s... | cshjin/pytorch_geometric | DenseGraphConv | false | 1,763 | [
"MIT"
] | 0 | 8dd0e76beb72135949a275edd851f80f7b97648f | https://github.com/cshjin/pytorch_geometric/tree/8dd0e76beb72135949a275edd851f80f7b97648f |
Unet | import torch
import torch.nn as nn
def crop(image, new_shape):
plus_h, plus_w = 0, 0
if new_shape[2] % 2 != 0:
plus_h = 1
if new_shape[3] % 2 != 0:
plus_w = 1
middle_height = image.shape[2] // 2
middle_weight = image.shape[3] // 2
go_height = new_shape[2] // 2
go_weight = 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_... | furkannturkmen/pytorch-CNN-architecture | Unet | false | 10,222 | [
"MIT"
] | 0 | 6a864811f51409c1526224c288fe608010e0c888 | https://github.com/furkannturkmen/pytorch-CNN-architecture/tree/6a864811f51409c1526224c288fe608010e0c888 |
InverseDepthSmoothnessLoss | # 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... | IEM-Computer-Vision/kornia | InverseDepthSmoothnessLoss | false | 9,297 | [
"ECL-2.0",
"Apache-2.0"
] | 0 | f98bd9a2158a6e59cda076d55d476acf13f4e0af | https://github.com/IEM-Computer-Vision/kornia/tree/f98bd9a2158a6e59cda076d55d476acf13f4e0af |
PlanarFlow | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torch.utils.data
import torch.nn as nn
assert_size_stri... | BratChar/variational-item-response-theory-public | PlanarFlow | false | 13,420 | [
"MIT"
] | 52 | 12862157e99506a0ed7018f1b8a485d4e61fb5bf | https://github.com/BratChar/variational-item-response-theory-public/tree/12862157e99506a0ed7018f1b8a485d4e61fb5bf |
NaiveGate | import torch
import torch.nn as nn
import torch.nn.functional as F
class NaiveGate(nn.Module):
"""
A naive gate implementation that defines the standard behavior of the gate
which determines which experts the tokens are going to.
Both the indecies and the score, or confidence, are output to the parent... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | whn09/fastmoe | NaiveGate | false | 4,533 | [
"Apache-2.0"
] | 0 | d0ffaffc6431abcd3ea6d0287dbf09f8cd727a0a | https://github.com/whn09/fastmoe/tree/d0ffaffc6431abcd3ea6d0287dbf09f8cd727a0a |
NormLoss | # 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... | acycliq/cellpose | NormLoss | false | 12,045 | [
"BSD-3-Clause"
] | 0 | 6d7a3f692206bf791e3ea7bd9524ee6df628ed8a | https://github.com/acycliq/cellpose/tree/6d7a3f692206bf791e3ea7bd9524ee6df628ed8a |
ConvReLUNorm | import torch
import torch.cuda
import torch.distributed
import torch.utils.data
import torch.optim
class ConvReLUNorm(torch.nn.Module):
def __init__(self, in_channels, out_channels, kernel_size=1, dropout=0.0):
super(ConvReLUNorm, self).__init__()
self.conv = torch.nn.Conv1d(in_channels, out_chan... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | Oreoluwa1234/NeMo | ConvReLUNorm | false | 9,707 | [
"Apache-2.0"
] | 0 | b01e3ceed34efe31fd43866685dbdd19a6b30928 | https://github.com/Oreoluwa1234/NeMo/tree/b01e3ceed34efe31fd43866685dbdd19a6b30928 |
RNNCell | # 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... | sdhnshu/HandsOnDeepLearningWithPytorch | RNNCell | false | 16,379 | [
"MIT"
] | 87 | 2292a952a4cb112b03d5db4048c78bc503eb858d | https://github.com/sdhnshu/HandsOnDeepLearningWithPytorch/tree/2292a952a4cb112b03d5db4048c78bc503eb858d |
LogSumPenalty | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch.nn import M... | dlej/adaptive-dropout | LogSumPenalty | false | 10,067 | [
"MIT"
] | 0 | 0540b2d06f1f97eb5861c6917eec6c086d33dfa8 | https://github.com/dlej/adaptive-dropout/tree/0540b2d06f1f97eb5861c6917eec6c086d33dfa8 |
GAT | import torch
import torch.nn as nn
import torch.nn.functional as F
class GraphAttentionLayer(nn.Module):
"""
Simple GAT layer, similar to https://arxiv.org/abs/1710.10903
"""
def __init__(self, in_features, out_features, dropout, alpha, concat=True):
super(GraphAttentionLayer, 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
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | daiki-kimura/commonsense-rl | GAT | false | 12,256 | [
"Apache-2.0"
] | 0 | 5513926957b6501ce9cfa46f77f8f2c1c4892fa5 | https://github.com/daiki-kimura/commonsense-rl/tree/5513926957b6501ce9cfa46f77f8f2c1c4892fa5 |
Normalize | from torch.nn import Module
import torch
import torch.utils.data
import torch.nn.functional as F
class Normalize(Module):
"""Performs :math:`L_p` normalization of inputs over specified dimension.
Does:
.. math::
v = \\frac{v}{\\max(\\lVert v \\rVert_p, \\epsilon)}
for each subtensor v over ... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
from torch.nn import Module
... | RL-WWW/ISST | Normalize | false | 17,820 | [
"BSD-3-Clause"
] | 5 | 42b656686fa9660794007a0bc00a7177937410e9 | https://github.com/RL-WWW/ISST/tree/42b656686fa9660794007a0bc00a7177937410e9 |
SEModule | import torch
import torch.nn as nn
import torch.nn.functional as F
class Hswish(nn.Module):
def __init__(self, inplace=True):
super(Hswish, self).__init__()
self.inplace = inplace
def forward(self, x):
return x * F.relu6(x + 3.0, inplace=self.inplace) / 6.0
class Hsigmoid(nn.Module... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import ... | manjrekarom/PaddleOCR2Pytorch | SEModule | false | 15,996 | [
"Apache-2.0"
] | 364 | 6d98508f4c85b9dd3bf022924b0ecc5354ec8281 | https://github.com/manjrekarom/PaddleOCR2Pytorch/tree/6d98508f4c85b9dd3bf022924b0ecc5354ec8281 |
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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_... | AlansBoyHeart/vit-pytorch | GEGLU | false | 1,910 | [
"MIT"
] | 0 | 1959adae0bdd7801475bba34d7d61bdc529b4616 | https://github.com/AlansBoyHeart/vit-pytorch/tree/1959adae0bdd7801475bba34d7d61bdc529b4616 |
Accuracy | # 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
... | BShennette/Pno-ai | Accuracy | false | 11,228 | [
"MIT"
] | 0 | 486434bfb40887d06e3d12a66831b9e0e7d020c2 | https://github.com/BShennette/Pno-ai/tree/486434bfb40887d06e3d12a66831b9e0e7d020c2 |
BasicBlock1 | # 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_... | Houseqin/PytorchToCaffe | BasicBlock1 | false | 9,112 | [
"MIT"
] | 0 | e94224ba6414e76369f191e7e3d9731c12ce2bd7 | https://github.com/Houseqin/PytorchToCaffe/tree/e94224ba6414e76369f191e7e3d9731c12ce2bd7 |
TargetContextGate | import torch
import torch.nn as nn
import torch.cuda
class ContextGate(nn.Module):
"""
Context gate is a decoder module that takes as input the previous word
embedding, the current decoder state and the attention state, and
produces a gate.
The gate can be used to select the input from the target ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | AndrewM1998/MultimodalNMT | TargetContextGate | false | 7,710 | [
"MIT"
] | 40 | b66d3a40ac9bc5c11ae124f51d1a9abf7cd6a04b | https://github.com/AndrewM1998/MultimodalNMT/tree/b66d3a40ac9bc5c11ae124f51d1a9abf7cd6a04b |
TransformerEncoderLayer | import torch
from torch import Tensor
from typing import Optional
from torch import nn
def _get_activation_fn(activation: 'str'):
if activation == 'relu':
return nn.functional.relu
elif activation == 'gelu':
return nn.functional.gelu
raise RuntimeError('activation should be relu/gelu, not ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | johnjosephmorgan/snowfall | TransformerEncoderLayer | false | 12,761 | [
"Apache-2.0"
] | 0 | 604d789c0aed035626d6745e6d7a427168063cae | https://github.com/johnjosephmorgan/snowfall/tree/604d789c0aed035626d6745e6d7a427168063cae |
RMSE_log | import torch
import torch.nn as nn
import torch.nn.functional as F
class RMSE_log(nn.Module):
def __init__(self):
super(RMSE_log, self).__init__()
def forward(self, fake, real):
if not fake.shape == real.shape:
_, _, H, W = real.shape
fake = F.upsample(fake, size=(H, ... | 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... | d4l3k/crowds | RMSE_log | false | 12,237 | [
"MIT"
] | 0 | a57eee80d66498474c86cec22dd77be9d627ad97 | https://github.com/d4l3k/crowds/tree/a57eee80d66498474c86cec22dd77be9d627ad97 |
SimpleTanhModel | import torch
import torch.jit
import torch.onnx
import torch.nn
class SimpleTanhModel(torch.nn.Module):
def __init__(self, inplace=False):
super(SimpleTanhModel, self).__init__()
self.inplace = inplace
def forward(self, tensor):
tensor = tensor + tensor
return tensor.tanh_() ... | 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._... | andreas-hommel/glow | SimpleTanhModel | false | 3,357 | [
"Apache-2.0"
] | 0 | 2bbbf8188a2a941e85677c83f2146bbd076a262e | https://github.com/andreas-hommel/glow/tree/2bbbf8188a2a941e85677c83f2146bbd076a262e |
VAE | import torch
from torch import nn
import torch.utils.data
from torch.nn import functional as F
import torch.cuda
class VAE(nn.Module):
def __init__(self):
super(VAE, self).__init__()
self.fc1 = nn.Linear(784, 400)
self.fc21 = nn.Linear(400, 20)
self.fc22 = nn.Linear(400, 20)
... | import torch
from torch import device
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from... | Code-Cornelius/libraries | VAE | false | 5,032 | [
"MIT"
] | 1 | 2ebd5f78dcedfdce1416280d7d40de7691906951 | https://github.com/Code-Cornelius/libraries/tree/2ebd5f78dcedfdce1416280d7d40de7691906951 |
Network | import torch
import torch.nn as nn
import torch.nn.functional as F
class Network(nn.Module):
def __init__(self, input_size, nb_action):
super(Network, self).__init__()
self.input_size = input_size
self.nb_action = nb_action
self.fc1 = nn.Linear(input_size, 30)
self.fc2 = 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_... | ExileExodus/Deep-Reinforcement-Learning | Network | false | 9,014 | [
"MIT"
] | 0 | 0007e5c4b74e920c250a15c18762966e1b55c17d | https://github.com/ExileExodus/Deep-Reinforcement-Learning/tree/0007e5c4b74e920c250a15c18762966e1b55c17d |
BatchDHCN | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils
import torch.utils.data
import torch.optim
class BatchDHCN(nn.Module):
"""docstring for BatchDHCN"""
def __init__(self, embed_size=512, output_size=512, num_channel=2,
conv_size=3, batch_norm=True):
super(Ba... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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 ... | deeplearning2020/self | BatchDHCN | false | 1,830 | [
"MIT"
] | 0 | cf0e6f9acdcfe17906c6327042d25ac9c8894885 | https://github.com/deeplearning2020/self/tree/cf0e6f9acdcfe17906c6327042d25ac9c8894885 |
_MultipleInputNetwork | import torch
import torch.nn as _nn
class _MultipleInputNetwork(_nn.Module):
def __init__(self):
super(_MultipleInputNetwork, self).__init__()
self.conv = _nn.Conv2d(3, 16, 3)
def forward(self, inp1, inp2):
inp = inp1 * inp2
out = self.conv(inp)
return out
def get_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
import torch.nn as _nn
assert_size_stride = torch._C._dynamo.guards.assert_size_... | Yifanfanfanfan/torchutils | _MultipleInputNetwork | false | 18,129 | [
"MIT"
] | 9 | 939331d28fcee97bfb0a4b2eaab8e799877fb0dc | https://github.com/Yifanfanfanfan/torchutils/tree/939331d28fcee97bfb0a4b2eaab8e799877fb0dc |
UpsampleConv | # 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... | Sriram-Ravula/Inverse_Meta | UpsampleConv | false | 2,851 | [
"MIT"
] | 0 | c6c1e4ae0d670093156249c60d74373b22d61f01 | https://github.com/Sriram-Ravula/Inverse_Meta/tree/c6c1e4ae0d670093156249c60d74373b22d61f01 |
CoralLayer | import torch
class CoralLayer(torch.nn.Module):
""" Implements CORAL layer described in
Cao, Mirjalili, and Raschka (2020)
*Rank Consistent Ordinal Regression for Neural Networks
with Application to Age Estimation*
Pattern Recognition Letters, https://doi.org/10.1016/j.patrec.2020.11.008
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cu... | CHNxindong/corn-ordinal-neuralnet | CoralLayer | false | 4,933 | [
"MIT"
] | 1 | 7f8a45614cb6488e9c019c5e9d3a5aee0d714e94 | https://github.com/CHNxindong/corn-ordinal-neuralnet/tree/7f8a45614cb6488e9c019c5e9d3a5aee0d714e94 |
L2 | import torch
import torch.utils.data
import torch
import torch.nn as nn
class L2(nn.Module):
def __init__(self):
super(L2, self).__init__()
def forward(self, output, target):
lossvalue = torch.norm(output - target, p=2, dim=1).mean()
return lossvalue
def get_inputs():
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
from torch._inductor.runtime.triton_helpers import libdevice
import torch.utils.data
import torch
import torch.nn as nn
assert_size_stride =... | WeisiX/ITAS3D | L2 | false | 18,111 | [
"MIT"
] | 4 | fc861e0cb2d4516905bfadab5e5e880c2b021832 | https://github.com/WeisiX/ITAS3D/tree/fc861e0cb2d4516905bfadab5e5e880c2b021832 |
Net | import torch
from torch import Tensor
from torch.functional import Tensor
import torch.nn as nn
import torch.nn.functional as F
class Net(nn.Module):
def __init__(self):
super().__init__()
self.conv1 = nn.Conv2d(3, 60, 5)
self.pool = nn.MaxPool2d(2, 2)
self.conv2 = nn.Conv2d(60, 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_... | minister19/RL_pytorch_get_started | Net | false | 4,014 | [
"MIT"
] | 0 | e444f524a14d329f9a25c53f102bc96c4ea36ad8 | https://github.com/minister19/RL_pytorch_get_started/tree/e444f524a14d329f9a25c53f102bc96c4ea36ad8 |
NormScaleFeature | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | uncbiag/FeatureMapICON | NormScaleFeature | false | 10,874 | [
"Apache-2.0"
] | 0 | 04160d0ce4e8f7615e1c59a1be5c6b8340b5b6e5 | https://github.com/uncbiag/FeatureMapICON/tree/04160d0ce4e8f7615e1c59a1be5c6b8340b5b6e5 |
BertOutput | # 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.utils.... | IsmaelElsharkawi/new_pororo_repo | BertOutput | false | 8,811 | [
"MIT"
] | 19 | 4617083b420615b8a3eb0f44d02e4e91a8f407f7 | https://github.com/IsmaelElsharkawi/new_pororo_repo/tree/4617083b420615b8a3eb0f44d02e4e91a8f407f7 |
TransposeMultiheadAttention | import torch
import torch.nn as nn
from typing import Optional
import torch.utils.data
import torch.nn
class TransposeMultiheadAttention(nn.Module):
"""
Wrapper for nn.MultiheadAttention which first transposes the input tensor
from (batch_size, seq_len, feature_dim) to (seq_length, batch_size, feature_dim... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | TheShadow29/pytorchvideo | TransposeMultiheadAttention | false | 9,701 | [
"Apache-2.0"
] | 0 | 39a3e34e33fb0e1ec142288df08f6e8c3585961a | https://github.com/TheShadow29/pytorchvideo/tree/39a3e34e33fb0e1ec142288df08f6e8c3585961a |
AdjustSigmoid | from torch.nn import Module
import torch
from torch import Tensor
from typing import Optional
def KORNIA_CHECK_IS_TENSOR(x, msg: 'Optional[str]'=None):
if not isinstance(x, Tensor):
raise TypeError(f'Not a Tensor type. Got: {type(x)}.\n{msg}')
def adjust_sigmoid(image: 'Tensor', cutoff: 'float'=0.5, gai... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch.nn import Module
from torch import Tensor
from typing import O... | YanivHollander/kornia | AdjustSigmoid | false | 14,628 | [
"ECL-2.0",
"Apache-2.0"
] | 418 | ccd258d0956da89b1feca96448eff8e4969d405a | https://github.com/YanivHollander/kornia/tree/ccd258d0956da89b1feca96448eff8e4969d405a |
AdaIN | import math
import torch
import torch.nn as nn
from numpy import prod
def getLayerNormalizationFactor(x):
"""
Get He's constant for the given layer
https://www.cv-foundation.org/openaccess/content_iccv_2015/papers/He_Delving_Deep_into_ICCV_2015_paper.pdf
"""
size = x.weight.size()
fan_in = pro... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Andribi/pytorch_GAN_zoo | AdaIN | false | 4,862 | [
"BSD-3-Clause"
] | 1 | b37c7268cbd4ec7dc61ba65a3ccf11af71247597 | https://github.com/Andribi/pytorch_GAN_zoo/tree/b37c7268cbd4ec7dc61ba65a3ccf11af71247597 |
PolicyNetwork | # 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... | watermeleon/spot_mini_mini | PolicyNetwork | false | 13,101 | [
"MIT"
] | 0 | 8622d3b0e0a95f7c548cacb6722a94f61a7e2b4b | https://github.com/watermeleon/spot_mini_mini/tree/8622d3b0e0a95f7c548cacb6722a94f61a7e2b4b |
Model | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cu... | Saran-nns/gradients | Model | false | 1,018 | [
"MIT"
] | 0 | 67f9ff92589047828563dbbe30f225dca7ad47fd | https://github.com/Saran-nns/gradients/tree/67f9ff92589047828563dbbe30f225dca7ad47fd |
MSE_disc | # 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... | Sampson-Lee/SIB-Net | MSE_disc | false | 2,809 | [
"MIT"
] | 0 | 650399082e9237327fa38168ccfc7d48153a1db5 | https://github.com/Sampson-Lee/SIB-Net/tree/650399082e9237327fa38168ccfc7d48153a1db5 |
SphericalBesselBasis | import math
import torch
import numpy as np
class SphericalBesselBasis(torch.nn.Module):
"""
1D spherical Bessel basis
Parameters
----------
num_radial: int
Controls maximum frequency.
cutoff: float
Cutoff distance in Angstrom.
"""
def __init__(self, num_radial: '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 math as tl_math
import math
import numpy as np
assert_size_stride = torch._C._dynamo.guar... | RolnickLab/ocp | SphericalBesselBasis | false | 2,781 | [
"MIT"
] | 0 | e120c3b90203a46f5fc7626f0b5c8979e4944765 | https://github.com/RolnickLab/ocp/tree/e120c3b90203a46f5fc7626f0b5c8979e4944765 |
ISAB | import math
import torch
import numpy as np
import torch.nn.functional as F
import torch.nn as nn
def qkv_attention(queries, keys, values, presence=None):
"""
Transformer-like self-attention.
Args:
queries: Tensor of shape [B, N, d_k].
keys: Tensor of shape [B, M, d_k].
values: : Tensor... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | karayanni/torch-scae | ISAB | false | 10,442 | [
"Apache-2.0"
] | 0 | e044662d8942d8d1923d13d071f375144cf4a1e8 | https://github.com/karayanni/torch-scae/tree/e044662d8942d8d1923d13d071f375144cf4a1e8 |
KlCriterion | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch.... | kiminh/mt-dnn | KlCriterion | false | 7,029 | [
"MIT"
] | 1 | 133884b380244dbe74acc4d7507e551b2c5035b3 | https://github.com/kiminh/mt-dnn/tree/133884b380244dbe74acc4d7507e551b2c5035b3 |
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_... | DeepTitan/PNDM | VarianceNorm2d | false | 13,568 | [
"Apache-2.0"
] | 61 | 4037a4f40011c9a0d47b92303e64d47fcc7ed56a | https://github.com/DeepTitan/PNDM/tree/4037a4f40011c9a0d47b92303e64d47fcc7ed56a |
Downsample | import torch
import torch.nn as nn
def conv_nd(dims, *args, **kwargs):
"""
Create a 1D, 2D, or 3D convolution module.
"""
if dims == 1:
return nn.Conv1d(*args, **kwargs)
elif dims == 2:
return nn.Conv2d(*args, **kwargs)
elif dims == 3:
return nn.Conv3d(*args, **kwargs)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | francoismaze/improved-diffusion | Downsample | false | 10,076 | [
"MIT"
] | 0 | bb403ba2437d6d834bb285b7259549fb3fa40f1b | https://github.com/francoismaze/improved-diffusion/tree/bb403ba2437d6d834bb285b7259549fb3fa40f1b |
ConvBlock | # 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... | Sid1057/sid1057.github.io | ConvBlock | false | 17,936 | [
"MIT"
] | 4 | 623d1731e308b42b6f86304dcfd671a061b414bf | https://github.com/Sid1057/sid1057.github.io/tree/623d1731e308b42b6f86304dcfd671a061b414bf |
LayerScaleBlock | import torch
import torch.nn as nn
def drop_path(x, drop_prob: 'float'=0.0, training: 'bool'=False):
"""Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
This is the same as the DropConnect impl I created for EfficientNet, etc networks, however,
the original name is... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | yifanc96/yifanc-DL | LayerScaleBlock | false | 11,101 | [
"MIT"
] | 0 | 25d56cec776fb151c8f6bcbd997bca94f07f3597 | https://github.com/yifanc96/yifanc-DL/tree/25d56cec776fb151c8f6bcbd997bca94f07f3597 |
JaccardLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empt... | ktncktnc/SpaceNet_Off_Nadir_Solutions | JaccardLoss | false | 15,860 | [
"Apache-2.0"
] | 164 | 2a9ef1c3b72fb749c808ddb8593a85cb16b9f1ca | https://github.com/ktncktnc/SpaceNet_Off_Nadir_Solutions/tree/2a9ef1c3b72fb749c808ddb8593a85cb16b9f1ca |
TwoMLPHead | import torch
from torch import nn
import torch.nn.functional as F
class TwoMLPHead(nn.Module):
"""
Standard heads for FPN-based models
Arguments:
in_channels (int): number of input channels
representation_size (int): size of the intermediate representation
"""
def __init__(self, ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
assert_s... | GreenCUBIC/GasBotty | TwoMLPHead | false | 9,528 | [
"MIT"
] | 0 | 158f5991201c80bf4cbbbb9deabc9954ff19bbb1 | https://github.com/GreenCUBIC/GasBotty/tree/158f5991201c80bf4cbbbb9deabc9954ff19bbb1 |
CosineBasisLinear | import torch
import numpy as np
from torch import nn
def cosine_basis_functions(x, n_basis_functions=64):
"""Cosine basis functions used to embed quantile thresholds.
Args:
x (torch.Tensor): Input.
n_basis_functions (int): Number of cosine basis functions.
Returns:
ndarray: Embed... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import numpy ... | g-votte/pfrl | CosineBasisLinear | false | 15,381 | [
"MIT"
] | 824 | 4c30c1d73f0941a2b649b62937eec346bb55a95e | https://github.com/g-votte/pfrl/tree/4c30c1d73f0941a2b649b62937eec346bb55a95e |
TwoLayerCNN | import torch
import torch.nn as nn
import torch.nn.functional as F
class TwoLayerCNN(nn.Module):
def __init__(self, C, M, embedding, channel, mtc_input, *args, **kwargs):
super(TwoLayerCNN, self).__init__()
self.C = C
self.M = M
self.embedding = embedding
self.mtc_input = ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | imvladikon/string-embed | TwoLayerCNN | false | 3,669 | [
"MIT"
] | 0 | 49e5ab0ada37b497dac51974aff16eeac65627a0 | https://github.com/imvladikon/string-embed/tree/49e5ab0ada37b497dac51974aff16eeac65627a0 |
BasicNN | # AOT ID: ['1_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.... | dbczumar/clipper | BasicNN | false | 3,401 | [
"Apache-2.0"
] | 0 | 80c97d27a38d60caaebb2a1ae6a995dd7ff1c82d | https://github.com/dbczumar/clipper/tree/80c97d27a38d60caaebb2a1ae6a995dd7ff1c82d |
JointBoneLoss | # 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
assert_size_stride = torch._C._dynamo.guards.assert_size_strid... | SevenMoGod/movenet.pytorch | JointBoneLoss | false | 14,395 | [
"MIT"
] | 87 | 95ec8535245228aa4335243e68722810e50bcaf8 | https://github.com/SevenMoGod/movenet.pytorch/tree/95ec8535245228aa4335243e68722810e50bcaf8 |
Conv2d | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.utils.data
assert_size_stride = torch._C._dyn... | CookiePPP/mellotron | Conv2d | false | 9,058 | [
"BSD-3-Clause"
] | 0 | 488425981c19cd0eddddea13d1348da4bfef8d26 | https://github.com/CookiePPP/mellotron/tree/488425981c19cd0eddddea13d1348da4bfef8d26 |
Mish | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torch.nn as nn
import torch.optim.lr_scheduler
import t... | csharpshooter/DeepLearning | Mish | false | 1,747 | [
"MIT"
] | 0 | c1d20660c32076468970f7376931e1fcd0d2644e | https://github.com/csharpshooter/DeepLearning/tree/c1d20660c32076468970f7376931e1fcd0d2644e |
SphereLoss | # 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.... | ace19-dev/image-retrieval-pytorch | SphereLoss | false | 18,236 | [
"MIT"
] | 9 | 19bd4ae5efea5b6184c345f693646bcd9a0fc8cf | https://github.com/ace19-dev/image-retrieval-pytorch/tree/19bd4ae5efea5b6184c345f693646bcd9a0fc8cf |
GCN | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch.nn import Module
i... | Brain03Yao/M2TGCN | GCN | false | 17,008 | [
"MIT"
] | 6 | 72c65687fa52c618740cd6d1db7366116f68398c | https://github.com/Brain03Yao/M2TGCN/tree/72c65687fa52c618740cd6d1db7366116f68398c |
Fusion | import torch
import torch.nn as nn
import torch.utils.checkpoint
class Fusion(nn.Module):
"""
The subnetwork that is used in TFN for video and audio in the pre-fusion stage
"""
def __init__(self, in_size, hidden_size, n_class, dropout, modal_name=
'text'):
"""
Args:
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | dumpmemory/MMSA | Fusion | false | 6,613 | [
"MIT"
] | 1 | 08b3a7f4529c380356eeb1cf6bf9a89e7c9701e7 | https://github.com/dumpmemory/MMSA/tree/08b3a7f4529c380356eeb1cf6bf9a89e7c9701e7 |
Net | import torch
import torch.nn as nn
import torch.nn.functional
class Net(nn.Module):
def __init__(self, num_inputs=784, num_outputs=10, num_hiddens=256,
is_training=True):
super(Net, self).__init__()
self.num_inputs = num_inputs
self.num_outputs = num_outputs
self.num_hidde... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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 ... | samjz06/d2l-pytorch | Net | false | 7,593 | [
"Apache-2.0"
] | 1 | 80eca3f7d217eefb4f6ae08aae24c6a3c2714898 | https://github.com/samjz06/d2l-pytorch/tree/80eca3f7d217eefb4f6ae08aae24c6a3c2714898 |
BinaryLogisticRegressionLoss | import torch
import torch.nn as nn
def binary_logistic_regression_loss(reg_score, label, threshold=0.5,
ratio_range=(1.05, 21), eps=1e-05):
"""Binary Logistic Regression Loss."""
label = label.view(-1)
reg_score = reg_score.contiguous().view(-1)
pmask = (label > threshold).float()
num_positive... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | scenarios/dev | BinaryLogisticRegressionLoss | false | 4,455 | [
"Apache-2.0"
] | 0 | 9f91ebc142cea1c31231d233571ad59460ab6fba | https://github.com/scenarios/dev/tree/9f91ebc142cea1c31231d233571ad59460ab6fba |
UpConv | # 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... | prstrive/EPCDepth | UpConv | false | 16,287 | [
"MIT"
] | 76 | 84119c806741334b652749ee953e3eab60a3718c | https://github.com/prstrive/EPCDepth/tree/84119c806741334b652749ee953e3eab60a3718c |
SSIM | import torch
import torch.nn as nn
class SSIM(nn.Module):
"""Layer to compute the SSIM loss between a pair of images
"""
def __init__(self):
super(SSIM, self).__init__()
self.mu_x_pool = nn.AvgPool2d(3, 1)
self.mu_y_pool = nn.AvgPool2d(3, 1)
self.sig_x_pool = nn.AvgPool2d(... | 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
... | Morbotu/drone-PWS | SSIM | false | 11,724 | [
"MIT"
] | 0 | face9cbf30a55783592cce8af59c1c70da982b6a | https://github.com/Morbotu/drone-PWS/tree/face9cbf30a55783592cce8af59c1c70da982b6a |
NeuralNet | # 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_... | AlejandroE25/pytorch-chatbot | NeuralNet | false | 8,836 | [
"MIT"
] | 0 | b9d7926f2f897f3a8513e8796b38f928715738af | https://github.com/AlejandroE25/pytorch-chatbot/tree/b9d7926f2f897f3a8513e8796b38f928715738af |
VarianceC | import torch
import torch.utils.data
import torch
import torch.nn as nn
class VarianceC(nn.Module):
def __init__(self):
super(VarianceC, self).__init__()
def forward(self, x):
mean_x = torch.mean(x, dim=1, keepdim=True)
sub_x = x.sub(mean_x)
x = torch.mean(torch.mul(sub_x, su... | 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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cud... | caixin1998/pl-template | VarianceC | false | 10,112 | [
"BSD-3-Clause"
] | 0 | 6918f0289ab2b32d107e5722617d25c9a683399c | https://github.com/caixin1998/pl-template/tree/6918f0289ab2b32d107e5722617d25c9a683399c |
Conv1dCompression | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch.nn import Module
from torch import nn
import torch.utils.data
import ... | Aarsh2001/annotated_deep_learning_paper_implementations | Conv1dCompression | false | 4,786 | [
"MIT"
] | 1 | ff0d5c065da1a46769f5f66fddc252c178f8fa37 | https://github.com/Aarsh2001/annotated_deep_learning_paper_implementations/tree/ff0d5c065da1a46769f5f66fddc252c178f8fa37 |
NormalSampler | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
from torch import device
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch import nn
assert_size_stride = torch.... | ChengF-Lab/scGGN | NormalSampler | false | 2,099 | [
"MIT"
] | 0 | eab585219e6d3eb06c94057f0e3b276d1846e8b6 | https://github.com/ChengF-Lab/scGGN/tree/eab585219e6d3eb06c94057f0e3b276d1846e8b6 |
Discriminator | # 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.... | archiroid003/ICCV2019-LearningToPaint | Discriminator | false | 12,134 | [
"MIT"
] | 0 | 4b5fc263e4843c159a61e5956956b3f7812693f8 | https://github.com/archiroid003/ICCV2019-LearningToPaint/tree/4b5fc263e4843c159a61e5956956b3f7812693f8 |
LanguageModelCriterion | import torch
import torch.nn as nn
from torch.autograd import *
class LanguageModelCriterion(nn.Module):
def __init__(self):
super(LanguageModelCriterion, self).__init__()
def forward(self, input, target, mask):
target = target[:, :input.size(1)]
mask = mask[:, :input.size(1)]
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
from torch.autograd import *
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torc... | Zhendong-Wang/arsm_image_captioning | LanguageModelCriterion | false | 9,661 | [
"MIT"
] | 0 | 2282b76ab03b53952269d94d6c4b19ab98636ca5 | https://github.com/Zhendong-Wang/arsm_image_captioning/tree/2282b76ab03b53952269d94d6c4b19ab98636ca5 |
MyEntropy | # 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
... | atimashov/object_detection | MyEntropy | false | 3,135 | [
"MIT"
] | 0 | 922cd88f429156fa4668c7d718b2665e4ab875fd | https://github.com/atimashov/object_detection/tree/922cd88f429156fa4668c7d718b2665e4ab875fd |
BasicCNN1 | # 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_... | fnc11/CosDefence | BasicCNN1 | false | 10,119 | [
"MIT"
] | 0 | 94f451b7d4b36cb3b9fcc85098dae242f311532b | https://github.com/fnc11/CosDefence/tree/94f451b7d4b36cb3b9fcc85098dae242f311532b |
PreNet | import torch
import torch.nn as nn
class PreNet(nn.Module):
def __init__(self):
super(PreNet, self).__init__()
self.conv1 = nn.Conv2d(4, 8, 3, padding=1)
self.act1 = nn.LeakyReLU(0.2, inplace=False)
self.conv2 = nn.Conv2d(8, 16, 3, padding=1)
self.act2 = nn.LeakyReLU(0.2, ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | karavik18/Federated_Learning_for_Missing_MRI_Sequence | PreNet | false | 7,012 | [
"Apache-2.0"
] | 1 | 42924f8475f354e6b429d05867f99530aa485b96 | https://github.com/karavik18/Federated_Learning_for_Missing_MRI_Sequence/tree/42924f8475f354e6b429d05867f99530aa485b96 |
LigthSpeechLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch import nn
i... | entn-at/LightSpeech | LigthSpeechLoss | false | 6,654 | [
"MIT"
] | 1 | 48250fbcede4b258ba13ab17e3e83afc5fe85a01 | https://github.com/entn-at/LightSpeech/tree/48250fbcede4b258ba13ab17e3e83afc5fe85a01 |
myEncoder | import torch
import torch.nn.functional as F
class myEncoder(torch.nn.Module):
def __init__(self, fomSize, romSize):
super(myEncoder, self).__init__()
self.fc1 = torch.nn.Linear(fomSize, 200)
self.fc2 = torch.nn.Linear(200, 64)
self.fc3 = torch.nn.Linear(64, romSize)
def forw... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | Pressio/pressio4py | myEncoder | false | 17,834 | [
"Unlicense",
"BSD-3-Clause"
] | 4 | 36676dbd112a7c7960ccbf302ff14d4376c819ec | https://github.com/Pressio/pressio4py/tree/36676dbd112a7c7960ccbf302ff14d4376c819ec |
ContrastiveLoss | # 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
from to... | elloworl/FRMiner2.0 | ContrastiveLoss | false | 10,020 | [
"MIT"
] | 0 | f596530d18512a1b1b8b8d56772f006f9f53f429 | https://github.com/elloworl/FRMiner2.0/tree/f596530d18512a1b1b8b8d56772f006f9f53f429 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.