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 |
|---|---|---|---|---|---|---|---|---|---|---|
RKDAngleLoss | import torch
import torch.nn as nn
import torch.nn.functional as F
class RKDAngleLoss(nn.Module):
"""
Module for calculating RKD Angle Loss
"""
def forward(self, teacher, student, normalize=True):
"""
Forward function
:param teacher (torch.FloatTensor): Prediction made by the... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Het-Shah/KD_Lib | RKDAngleLoss | false | 5,340 | [
"MIT"
] | 1 | 5577250cf74e3a529033b244da9b2b9fcf7623a9 | https://github.com/Het-Shah/KD_Lib/tree/5577250cf74e3a529033b244da9b2b9fcf7623a9 |
GNN_Encoder | from torch.nn import Module
import math
import torch
from torch.nn.parameter import Parameter
from torch.nn.modules.module import Module
import torch.nn as nn
import torch.nn.functional as F
class GraphConvolution(Module):
"""
Simple GCN layer, similar to https://arxiv.org/abs/1609.02907
"""
def __in... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch.nn import Module
i... | Zhen-Tan-dmml/GFCIL | GNN_Encoder | false | 18,174 | [
"MIT"
] | 7 | 9b78210418711a795280c588f55aef63f7df5b3b | https://github.com/Zhen-Tan-dmml/GFCIL/tree/9b78210418711a795280c588f55aef63f7df5b3b |
MultiHead | # 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.... | FGDBTKD/decaNLP | MultiHead | false | 13,680 | [
"BSD-3-Clause"
] | 2,361 | ff2d7e18cc226197bb8fe5fe796c4b8bc0395e86 | https://github.com/FGDBTKD/decaNLP/tree/ff2d7e18cc226197bb8fe5fe796c4b8bc0395e86 |
GradientReversal | import torch
class GradientReversalFunction(torch.autograd.Function):
"""
Gradient Reversal Layer from:
Unsupervised Domain Adaptation by Backpropagation (Ganin & Lempitsky, 2015)
Forward pass is the identity function.
In the backward pass,
the upstream gradients are multiplied by -lambda (i.e... | 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... | ishine/CDFSE_FastSpeech2 | GradientReversal | false | 12,539 | [
"MIT"
] | 0 | f0facd077fa3e11b2704f2e8a1d1315bd1f4f493 | https://github.com/ishine/CDFSE_FastSpeech2/tree/f0facd077fa3e11b2704f2e8a1d1315bd1f4f493 |
TestMul | # 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... | AliaksandrSiarohin/pytorch2keras | TestMul | false | 8,893 | [
"MIT"
] | 0 | 9c8ee213cff43ade152b1de78fa76fd05ec8b40a | https://github.com/AliaksandrSiarohin/pytorch2keras/tree/9c8ee213cff43ade152b1de78fa76fd05ec8b40a |
FastRNNCell | import torch
import torch.nn as nn
import torch.onnx
from itertools import product as product
def gen_nonlinearity(A, nonlinearity):
"""
Returns required activation for a tensor based on the inputs
nonlinearity is either a callable or a value in
['tanh', 'sigmoid', 'relu', 'quantTanh', 'quantSigm... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | ShishirPatil/EdgeML-1 | FastRNNCell | false | 1,067 | [
"MIT"
] | 0 | cbba9f8b989e545788427c004eb8450e7e4c1a21 | https://github.com/ShishirPatil/EdgeML-1/tree/cbba9f8b989e545788427c004eb8450e7e4c1a21 |
SpatialGate | import torch
import torch.nn as nn
import torch.nn.functional as F
class BasicConv(nn.Module):
def __init__(self, in_planes, out_planes, kernel_size, stride=1,
padding=0, dilation=1, groups=1, relu=False, bn=False, bias=True):
super(BasicConv, self).__init__()
self.out_channels = out_plan... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | BJTU-MIMO/Channel_estimation_MRDN | SpatialGate | false | 139 | [
"MIT"
] | 0 | f41972998a5403c901bc3e5d68d4acd05e9a7f6c | https://github.com/BJTU-MIMO/Channel_estimation_MRDN/tree/f41972998a5403c901bc3e5d68d4acd05e9a7f6c |
StateInitZero | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
from torchvision import models as models
import torch.nn.parallel
import torch.optim
import torch.utils.data
import tor... | JinYAnGHe/openvino_training_extensions | StateInitZero | false | 3,025 | [
"Apache-2.0"
] | 0 | a0b4456a3c9fe6c1b7eabc9d5eb4e74d01453dee | https://github.com/JinYAnGHe/openvino_training_extensions/tree/a0b4456a3c9fe6c1b7eabc9d5eb4e74d01453dee |
MeanVoxelFeatureExtractor | import torch
import torch.nn as nn
class VoxelFeatureExtractor(nn.Module):
def __init__(self, **kwargs):
super().__init__()
def get_output_feature_dim(self):
raise NotImplementedError
def forward(self, **kwargs):
raise NotImplementedError
class MeanVoxelFeatureExtractor(VoxelF... | 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... | charlesyz/PCDet | MeanVoxelFeatureExtractor | false | 1,660 | [
"Apache-2.0"
] | 0 | 1eb6b1dc5a3d563d7532b1c8ee3be007cbeafc80 | https://github.com/charlesyz/PCDet/tree/1eb6b1dc5a3d563d7532b1c8ee3be007cbeafc80 |
EqualLinear | from torch.autograd import Function
import math
import torch
from torch import nn as nn
from torch.nn import functional as F
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
def fused_leaky_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.autograd import Function
import math
from torch import nn as nn
from ... | Lotayou/BasicSR | EqualLinear | false | 2,585 | [
"Apache-2.0",
"MIT"
] | 0 | 6cf9a706dd680d54f7dc26e87318ff79f76c0dbf | https://github.com/Lotayou/BasicSR/tree/6cf9a706dd680d54f7dc26e87318ff79f76c0dbf |
Accuracy | import torch
from torch import nn
def accuracy(logits, labels, ignore_index: 'int'=-100):
with torch.no_grad():
valid_mask = labels != ignore_index
predictions = logits.float().argmax(-1)
correct = (predictions == labels) * valid_mask
return correct.sum().float() / valid_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 import triton_helpers
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empt... | IC-hub/ProteinLM | Accuracy | false | 13,820 | [
"Apache-2.0"
] | 59 | 58fbf1f674569cf814becf32f71dd0d8f0c592fa | https://github.com/IC-hub/ProteinLM/tree/58fbf1f674569cf814becf32f71dd0d8f0c592fa |
NonoverlapReg | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.utils.data
import torch.nn as nn
import torch.nn.parallel
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty... | Atharva-Peshkar/pytorch_connectomics | NonoverlapReg | false | 13,314 | [
"MIT"
] | 99 | 8eccd9640a9a454d4df095a3529a030e58f882f5 | https://github.com/Atharva-Peshkar/pytorch_connectomics/tree/8eccd9640a9a454d4df095a3529a030e58f882f5 |
CosReLU | # 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
... | awlange/pysurvival | CosReLU | false | 14,926 | [
"Apache-2.0"
] | 242 | 841b9bc6ce700ba8898d2a1488aa9cd25ee7a8e6 | https://github.com/awlange/pysurvival/tree/841b9bc6ce700ba8898d2a1488aa9cd25ee7a8e6 |
NavigatorBranch | # 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_... | iofthetiger/pkuad | NavigatorBranch | false | 6,905 | [
"Apache-2.0"
] | 1 | 07496d108c614c84be028f344830becc9cac8fe5 | https://github.com/iofthetiger/pkuad/tree/07496d108c614c84be028f344830becc9cac8fe5 |
BMNLoss | import torch
import torch.nn.functional as F
import torch.nn as nn
def binary_logistic_regression_loss(reg_score, label, threshold=0.5,
ratio_range=(1.05, 21), eps=1e-05):
"""Binary Logistic Regression Loss."""
label = label.view(-1)
reg_score = reg_score.contiguous().view(-1)
pmask = (label > thr... | import torch
from torch import device
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_ma... | SvipRepetitionCounting/TransRAC | BMNLoss | false | 5,886 | [
"Apache-2.0"
] | 1 | eec12553dfa1e2fde6356b0e2703c633d225feb3 | https://github.com/SvipRepetitionCounting/TransRAC/tree/eec12553dfa1e2fde6356b0e2703c633d225feb3 |
ResidualAttentionBlock | import torch
from torch import nn
from collections import OrderedDict
class LayerNorm(nn.LayerNorm):
"""Subclass torch's LayerNorm to handle fp16."""
def forward(self, x: 'torch.Tensor'):
orig_type = x.dtype
ret = super().forward(x.type(torch.float32))
return ret.type(orig_type)
cla... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | HIT-SCIR-xuanxuan/OpenKS | ResidualAttentionBlock | false | 13,756 | [
"Apache-2.0"
] | 88 | a7f2ce0890822113322aad22e98d6c961e63caef | https://github.com/HIT-SCIR-xuanxuan/OpenKS/tree/a7f2ce0890822113322aad22e98d6c961e63caef |
ContractiveAutoencoder | import torch
import torch.utils.data
import torch.nn as nn
class ContractiveAutoencoder(nn.Module):
"""
Simple contractive autoencoder with a single hidden layer.
Constructor parameters:
- num_inputs: Number of input features
- num_hidden_layer_inputs: Number of input features for the sin... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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... | rocklegende/DL2020_R3 | ContractiveAutoencoder | false | 10,700 | [
"MIT"
] | 0 | 467ed759a9f9935d56863c79f71040e922d72829 | https://github.com/rocklegende/DL2020_R3/tree/467ed759a9f9935d56863c79f71040e922d72829 |
OneLayerFCBodyWithAction | # 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 function... | Marianoetchart/DeepRL | OneLayerFCBodyWithAction | false | 2,637 | [
"Apache-2.0"
] | 0 | 40d4825694c0890440859166de56701fc1f61d5b | https://github.com/Marianoetchart/DeepRL/tree/40d4825694c0890440859166de56701fc1f61d5b |
expandEncoder | # 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.... | Geunwoo-Jeon/iclr_17_compression | expandEncoder | false | 13,861 | [
"MIT"
] | 56 | a28746b1f1c518d91125d8f289d9511cde488c77 | https://github.com/Geunwoo-Jeon/iclr_17_compression/tree/a28746b1f1c518d91125d8f289d9511cde488c77 |
GraphEmbedding | import math
import torch
import torch.nn as nn
class GraphEmbedding(nn.Module):
def __init__(self, input_size, ebd_size, use_cuda=True, use_sdne=True,
add_noise=False, is_training=True):
super(GraphEmbedding, self).__init__()
self.use_cuda = use_cuda
self.use_sdne = use_sdne
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.a... | Lance0226/CIS700_Convex_Hull_RL | GraphEmbedding | false | 2,504 | [
"MIT"
] | 0 | 3c87e063209d535d75fde719bf17f20dd5e68635 | https://github.com/Lance0226/CIS700_Convex_Hull_RL/tree/3c87e063209d535d75fde719bf17f20dd5e68635 |
BinLinear | import torch
from itertools import product as product
import torch.nn.functional as F
from torch import nn
import torch.optim
import torch.utils.data
class BinQuant(torch.autograd.Function):
"""BinaryConnect quantization.
Refer:
https://pytorch.org/tutorials/beginner/examples_autograd/two_layer_net_cu... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from itertools import product as product
from torch import nn
import torch.optim... | ninfueng/a-PyTorch-Tutorial-to-Object-Detection | BinLinear | false | 10,635 | [
"MIT"
] | 0 | fc7544720a7e939f5a56f4f7214e4965b7775f77 | https://github.com/ninfueng/a-PyTorch-Tutorial-to-Object-Detection/tree/fc7544720a7e939f5a56f4f7214e4965b7775f77 |
InceptionAux | # 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_... | Hiroaki-Ozaki/modelib-classification | InceptionAux | false | 17,403 | [
"WTFPL"
] | 10 | 11077704cc0bc9a42fc4b94da60b57d31ff0f65c | https://github.com/Hiroaki-Ozaki/modelib-classification/tree/11077704cc0bc9a42fc4b94da60b57d31ff0f65c |
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
from torch import nn
assert_s... | GreenCUBIC/GasBotty | TwoMLPHead | false | 9,528 | [
"MIT"
] | 0 | 158f5991201c80bf4cbbbb9deabc9954ff19bbb1 | https://github.com/GreenCUBIC/GasBotty/tree/158f5991201c80bf4cbbbb9deabc9954ff19bbb1 |
DiscShiftLoss | import torch
import torch.nn as nn
class DiscShiftLoss(nn.Module):
"""Disc shift loss.
Args:
loss_weight (float, optional): Loss weight. Defaults to 1.0.
"""
def __init__(self, loss_weight=0.1):
super().__init__()
self.loss_weight = loss_weight
def forward(self, ... | 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... | Juggernaut93/mmediting | DiscShiftLoss | false | 13,915 | [
"Apache-2.0"
] | 1,884 | 8ef46ace29756dd2df1d92f2f73a33646e33e007 | https://github.com/Juggernaut93/mmediting/tree/8ef46ace29756dd2df1d92f2f73a33646e33e007 |
JointsDistLoss | # 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.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
import torch.nn as nn
assert_size_st... | nuguziii/deep-high-resolution-net.pytorch | JointsDistLoss | false | 10,620 | [
"MIT"
] | 0 | 3c053e97201fbeb35ff48cbc567ffb37b5e0b436 | https://github.com/nuguziii/deep-high-resolution-net.pytorch/tree/3c053e97201fbeb35ff48cbc567ffb37b5e0b436 |
Critic | import torch
import numpy as np
import torch.nn.functional as F
from torch import nn
def hidden_init(layer):
fan_in = layer.weight.data.size()[0]
lim = 1.0 / np.sqrt(fan_in)
return -lim, lim
class Critic(nn.Module):
"""Critic (Value) Model."""
def __init__(self, state_size, action_size, seed, f... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import numpy as np
from torch... | tjkemp/tennis-example | Critic | false | 13,041 | [
"MIT"
] | 0 | 3cb0c52a93c65f88872cf44e3782bf87d9d8cef3 | https://github.com/tjkemp/tennis-example/tree/3cb0c52a93c65f88872cf44e3782bf87d9d8cef3 |
Encoder_mse | import torch
from typing import Iterable
from torch.distributions import Normal
from torch import nn as nn
def reparameterize_gaussian(mu, var):
return Normal(mu, var.sqrt()).rsample()
class Encoder_mse(nn.Module):
"""Encodes data of ``n_input`` dimensions into a latent space of ``n_output``
dimensions ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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
fr... | Famingzhao/scMVP | Encoder_mse | false | 9,523 | [
"MIT"
] | 0 | fb0d2d2523d0ae10e10725babe8da7de63c2eef4 | https://github.com/Famingzhao/scMVP/tree/fb0d2d2523d0ae10e10725babe8da7de63c2eef4 |
L2Norm | import torch
import torch.nn as nn
class L2Norm(nn.Module):
def __init__(self, n_channels, scale=1.0):
super(L2Norm, self).__init__()
self.n_channels = n_channels
self.scale = scale
self.eps = 1e-10
self.weight = nn.Parameter(torch.Tensor(self.n_channels))
self.wei... | 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_... | ashuk203/face-alignment | L2Norm | false | 6,244 | [
"BSD-3-Clause"
] | 1 | 1f6452ae05ede0db9bbc48331d67d8b239fa9994 | https://github.com/ashuk203/face-alignment/tree/1f6452ae05ede0db9bbc48331d67d8b239fa9994 |
LayerNorm | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_... | Kyumin-Park/Protein-Chemical-Releativity-BERT | LayerNorm | false | 1,897 | [
"MIT"
] | 0 | 6a339f4e640d99199f38a00769f5872c2a53ac55 | https://github.com/Kyumin-Park/Protein-Chemical-Releativity-BERT/tree/6a339f4e640d99199f38a00769f5872c2a53ac55 |
DocUnetLoss | import torch
import torch.nn as nn
import torch.nn.functional as F
class DocUnetLoss(nn.Module):
"""
只使用一个unet的loss 目前使用这个loss训练的比较好
"""
def __init__(self, r=0.1):
super(DocUnetLoss, self).__init__()
self.r = r
def forward(self, y, label):
d = y - label
lossf = 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 math as tl_math
import torch.nn as nn
... | hologerry/DewarpNet | DocUnetLoss | false | 3,611 | [
"MIT"
] | 0 | b0a11b9fbb98bd124e65d3165ce177d9ebf2e836 | https://github.com/hologerry/DewarpNet/tree/b0a11b9fbb98bd124e65d3165ce177d9ebf2e836 |
MuLawDecoding | import torch
from torch import Tensor
import torchaudio.functional as F
class MuLawDecoding(torch.nn.Module):
"""Decode mu-law encoded signal. For more info see the
`Wikipedia Entry <https://en.wikipedia.org/wiki/%CE%9C-law_algorithm>`_
This expects an input with values between 0 and quantization_channe... | 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... | tbright17/audio | MuLawDecoding | false | 10,917 | [
"BSD-2-Clause"
] | 0 | 00d38203e401b8d9472a8f8394a10e2c309be02c | https://github.com/tbright17/audio/tree/00d38203e401b8d9472a8f8394a10e2c309be02c |
PositionwiseFeedForward | import torch
import torch.nn as nn
import torch.nn.functional as F
class PositionwiseFeedForward(nn.Module):
""" A two-feed-forward-layer module """
def __init__(self, d_in, d_hid, dropout=0.1):
super().__init__()
self.w_1 = nn.Linear(d_in, d_hid)
self.w_2 = nn.Linear(d_hid, d_in)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Rajathbharadwaj/algorithmic-efficiency | PositionwiseFeedForward | false | 14,271 | [
"Apache-2.0"
] | 49 | 47d2928836e0574bc54cc3ad58860dd4daf86cce | https://github.com/Rajathbharadwaj/algorithmic-efficiency/tree/47d2928836e0574bc54cc3ad58860dd4daf86cce |
MaxFeature | # 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_... | akimotty877/mmediting | MaxFeature | false | 3,070 | [
"Apache-2.0"
] | 0 | cae872d6f3e867ba144c7c0dbc29a0ee1a29e5a6 | https://github.com/akimotty877/mmediting/tree/cae872d6f3e867ba144c7c0dbc29a0ee1a29e5a6 |
SpatialDepthWiseConvolution | from torch.nn import Module
import math
import torch
from torch import nn
import torch.utils.data
import torch.nn.functional
import torch.autograd
class SpatialDepthWiseConvolution(Module):
"""
## Spatial Depth Wise Convolution
This is actually slower
"""
def __init__(self, d_k: 'int', kernel_si... | 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
import math
from torch import nn
import torch.utils.data
import torch.nn.functional
import torch.autograd
assert... | mcx/annotated_deep_learning_paper_implementations | SpatialDepthWiseConvolution | false | 7,210 | [
"MIT"
] | 1 | f169f3a71dd2d36eb28ad31062d3475efa367b88 | https://github.com/mcx/annotated_deep_learning_paper_implementations/tree/f169f3a71dd2d36eb28ad31062d3475efa367b88 |
ConvBlock | import torch
import torch.nn as nn
class Block(nn.Module):
def __init__(self):
"""Initialisation for a lower-level DeepLPF conv block
:returns: N/A
:rtype: N/A
"""
super(Block, self).__init__()
def conv3x3(self, in_channels, out_channels, stride=1):
"""Repre... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | sjmoran/CURL | ConvBlock | false | 16,475 | [
"BSD-3-Clause"
] | 125 | 919e519717b66e14d92ac6fa404c328ee3f254a5 | https://github.com/sjmoran/CURL/tree/919e519717b66e14d92ac6fa404c328ee3f254a5 |
ZeroConv2d | import torch
from torch import nn
from torch.nn import functional as F
class ZeroConv2d(nn.Module):
def __init__(self, in_channel, out_channel, padding=1):
super(ZeroConv2d, self).__init__()
self.conv = nn.Conv2d(in_channel, out_channel, 3, padding=0)
self.conv.weight.data.zero_()
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 im... | XeniaLLL/glow-pytorch | ZeroConv2d | false | 11,967 | [
"MIT"
] | 0 | 66d434e57853de1aaafaa5a5533d21705dc92e10 | https://github.com/XeniaLLL/glow-pytorch/tree/66d434e57853de1aaafaa5a5533d21705dc92e10 |
Attention | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | avniculae/segmenter | Attention | false | 9,775 | [
"MIT"
] | 0 | ca9683399b7dae13a8ccbadc744826306b8dbf94 | https://github.com/avniculae/segmenter/tree/ca9683399b7dae13a8ccbadc744826306b8dbf94 |
AttentionPool2d | import torch
import torch.nn.functional as F
from torch import nn
class AttentionPool2d(nn.Module):
def __init__(self, spacial_dim: 'int', embed_dim: 'int', num_heads:
'int', output_dim: 'int'=None):
super().__init__()
self.positional_embedding = nn.Parameter(torch.randn(spacial_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.... | Artanic30/RentalPrediction | AttentionPool2d | false | 2,015 | [
"MIT"
] | 0 | 5804ab9b453d2a40bce2bb304c31efc98a803ed8 | https://github.com/Artanic30/RentalPrediction/tree/5804ab9b453d2a40bce2bb304c31efc98a803ed8 |
TransformerEncoderPostNormLayer | import torch
import torch.nn.functional as F
from torch import nn
from typing import Optional
from torch.nn import LayerNorm
def _get_activation_fn(activation):
if activation == 'relu':
return F.relu
elif activation == 'gelu':
return F.gelu
raise RuntimeError('activation should be relu/gel... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | JDBumgardner/stone_ground_hearth_battles | TransformerEncoderPostNormLayer | false | 8,309 | [
"Apache-2.0"
] | 20 | 9fe095651fab60e8ddbf563f0b9b7f3e723d5f4f | https://github.com/JDBumgardner/stone_ground_hearth_battles/tree/9fe095651fab60e8ddbf563f0b9b7f3e723d5f4f |
PcamPool | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_str... | iampartho/EEE426 | PcamPool | false | 3,644 | [
"Apache-2.0"
] | 0 | a706660c0efcd4adea44d54c57a34bcaa4439ec1 | https://github.com/iampartho/EEE426/tree/a706660c0efcd4adea44d54c57a34bcaa4439ec1 |
AdaIN | import math
import torch
import torch.nn as nn
from numpy import prod
def getLayerNormalizationFactor(x, gain, fromTF):
"""
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()
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | jwen307/pytorch_GAN_zoo | AdaIN | false | 10,371 | [
"BSD-3-Clause"
] | 0 | b1e538a2f03fda42bd7a12872238b770ea5e0f23 | https://github.com/jwen307/pytorch_GAN_zoo/tree/b1e538a2f03fda42bd7a12872238b770ea5e0f23 |
VGG16 | import torch
import numpy as np
import torchvision.transforms.functional as F
import torch.nn as nn
import torch.nn.functional as F
class Normalize:
def __init__(self, mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)):
self.mean = mean
self.std = std
def undo(self, imgarr):
proc... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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... | SharhadBashar/1-stage-wseg | VGG16 | false | 5,913 | [
"Apache-2.0"
] | 1 | 83bf13444f5039ffed2de1605f09b3f90b525586 | https://github.com/SharhadBashar/1-stage-wseg/tree/83bf13444f5039ffed2de1605f09b3f90b525586 |
GaussNorm2D | import torch
import torch.nn as nn
import torch.autograd
class GaussNorm2D(nn.Module):
"""
This will normalize a saliency map to range from 0 to 1 via normal cumulative distribution function.
Input and output will be a 3D tensor of size [batch size x height x width].
In... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
import torch.autograd
assert_size_stride = torch._C._dyna... | LLNL/fastcam | GaussNorm2D | false | 8,441 | [
"BSD-3-Clause"
] | 25 | 99cefe37528014247319468cf05f54fef259d3bf | https://github.com/LLNL/fastcam/tree/99cefe37528014247319468cf05f54fef259d3bf |
stage_1_block | # 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... | H-Liu1997/Pytorch_Pose_Estimation_Framework | stage_1_block | false | 5,272 | [
"MIT"
] | 1 | 06616b3459ff639f8486e6ea4f93922597788b2a | https://github.com/H-Liu1997/Pytorch_Pose_Estimation_Framework/tree/06616b3459ff639f8486e6ea4f93922597788b2a |
BipolarSigmoid | import torch
import torch.nn as nn
class BipolarSigmoid(nn.Module):
def forward(self, x):
return (1.0 - torch.exp(-x)) / (1.0 + torch.exp(-x))
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
| import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert... | fmhoward/pysurvival | BipolarSigmoid | false | 12,373 | [
"Apache-2.0"
] | 0 | 3fea55f09477e9f0844845e09d6ea60434436e2e | https://github.com/fmhoward/pysurvival/tree/3fea55f09477e9f0844845e09d6ea60434436e2e |
Model | import torch
from torch import nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self, input_dim):
super(Model, self).__init__()
self.layer1 = nn.Linear(input_dim, 50)
self.layer2 = nn.Linear(50, 20)
self.layer3 = nn.Linear(20, 1)
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
from torch._inductor.runtime import triton_helpers
from torch import nn
assert_s... | mlsquare/kitchen | Model | false | 4,023 | [
"MIT"
] | 0 | 3664fd289f7ea5c20cdd55e96ebe29b77effa062 | https://github.com/mlsquare/kitchen/tree/3664fd289f7ea5c20cdd55e96ebe29b77effa062 |
LogisticCumulativeLink | # 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... | EthanRosenthal/medallion | LogisticCumulativeLink | false | 13,671 | [
"MIT"
] | 74 | 063fe875f5122063e6f616512cffd9ffa4df1974 | https://github.com/EthanRosenthal/medallion/tree/063fe875f5122063e6f616512cffd9ffa4df1974 |
BertPooler | # 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 ... | Aksh97/VGCN-BERT | BertPooler | false | 14,809 | [
"MIT"
] | 106 | 62b5ae5a3c53f4bff555027d87a57d3a994a32bb | https://github.com/Aksh97/VGCN-BERT/tree/62b5ae5a3c53f4bff555027d87a57d3a994a32bb |
SpatialAttention | import torch
from torch import nn
class SpatialAttention(nn.Module):
def __init__(self, kernel_size=7):
super(SpatialAttention, self).__init__()
assert kernel_size in (3, 7), 'kernel size must be 3 or 7'
padding = 3 if kernel_size == 7 else 1
self.conv1 = nn.Conv2d(2, 1, kernel_si... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
assert_s... | Panpan-Chen/Attention-Block-U-net | SpatialAttention | false | 9,438 | [
"MIT"
] | 0 | 7e0cef46ea485db1bb9a9e4511eb0535e460179e | https://github.com/Panpan-Chen/Attention-Block-U-net/tree/7e0cef46ea485db1bb9a9e4511eb0535e460179e |
CriticNetwork | # 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_... | PuzeLiu/mushroom-rl | CriticNetwork | false | 14,249 | [
"MIT"
] | 344 | 99942b425e66b4ddcc26009d7105dde23841e95d | https://github.com/PuzeLiu/mushroom-rl/tree/99942b425e66b4ddcc26009d7105dde23841e95d |
InstanceNormLayer | # 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_... | AsianZeus/Diverse-Facial-Edit | InstanceNormLayer | false | 9,396 | [
"Apache-2.0"
] | 0 | 3d4b1b41546a08a1fa3cb164ade33e319806b12b | https://github.com/AsianZeus/Diverse-Facial-Edit/tree/3d4b1b41546a08a1fa3cb164ade33e319806b12b |
EuclideanMean | import torch
from torch import Tensor
import torch.utils.data.dataloader
from torch import nn
import torch.nn
class EuclideanMean(nn.Module):
"""Implement a EuclideanMean object."""
def forward(self, data: 'Tensor') ->Tensor:
"""Performs a forward pass through the network.
Parameters
... | 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.dataloader
from torch import nn
import torch.nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empt... | adriensas/flair | EuclideanMean | false | 9,743 | [
"MIT"
] | 0 | f01b0e7ff9a87d3862acae50aeaffdc8e8b8ac21 | https://github.com/adriensas/flair/tree/f01b0e7ff9a87d3862acae50aeaffdc8e8b8ac21 |
TwoLinearsModel | import torch
import torch.nn as nn
import torch.nn
import torch.utils.data
import torch.utils.tensorboard._pytorch_graph
import torch.onnx.symbolic_caffe2
class TwoLinearsModel(nn.Module):
def __init__(self, per_sample_shape: 'list', hidden_size: 'int',
output_size: 'int'):
super(TwoLinearsModel,... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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 ... | Rohan-Chaudhury/aimet | TwoLinearsModel | false | 18,014 | [
"BSD-3-Clause"
] | 3 | 1c38cac8cc0fd32dca40ce5e39940805d29f7a4a | https://github.com/Rohan-Chaudhury/aimet/tree/1c38cac8cc0fd32dca40ce5e39940805d29f7a4a |
mbr_convex_hull | # 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.... | hlesmqh/WS3D | mbr_convex_hull | false | 15,548 | [
"MIT"
] | 100 | 6816eeb135923a59de34ee5d94be2d0fd3ec83f9 | https://github.com/hlesmqh/WS3D/tree/6816eeb135923a59de34ee5d94be2d0fd3ec83f9 |
MaxPoolPad | # 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... | GoalballAnalysis/GUI | MaxPoolPad | false | 2,301 | [
"MIT"
] | 0 | c7f1cc27f4fd7f861c3ca09f5ca25d1a3f19a8a7 | https://github.com/GoalballAnalysis/GUI/tree/c7f1cc27f4fd7f861c3ca09f5ca25d1a3f19a8a7 |
Bc | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.nn.parallel
import torch.utils.data
import to... | AyufhSri/GANAccImprover | Bc | false | 83 | [
"MIT"
] | 0 | eff3a944bd6e5d9761ec815f28c0d32c87096308 | https://github.com/AyufhSri/GANAccImprover/tree/eff3a944bd6e5d9761ec815f28c0d32c87096308 |
LgRegv | import torch
import torch.nn as nn
class LgRegv(torch.nn.Module):
"""
TODO: pre-training
from power to voronoi
"""
def __init__(self, dim, nla):
super(LgRegv, self).__init__()
self.linear = nn.Linear(dim, nla, bias=False)
def forward(self, x):
ba = -torch.sum((self.li... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | horsepurve/DeepVoro | LgRegv | false | 3,626 | [
"MIT"
] | 0 | 1b67a8e0d51e1c966a2af96d4b6a495f8390f608 | https://github.com/horsepurve/DeepVoro/tree/1b67a8e0d51e1c966a2af96d4b6a495f8390f608 |
Attentive | import torch
import torch.nn as nn
class Attentive(nn.Module):
def __init__(self, isize):
super(Attentive, self).__init__()
self.w = nn.Parameter(torch.ones(isize))
def forward(self, x):
return x @ torch.diag(self.w)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | SUBLIME-GSL/SUBLIME | Attentive | false | 8,739 | [
"MIT"
] | 19 | 2c9b193abb3f15ae9bab33815e568010057a5564 | https://github.com/SUBLIME-GSL/SUBLIME/tree/2c9b193abb3f15ae9bab33815e568010057a5564 |
ActFirstResBlock | # 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.... | cplusx/SIGN | ActFirstResBlock | false | 1,754 | [
"Apache-2.0"
] | 0 | 9777fc3ddd4c6f799caeefe1e72482d5b1ecd8ae | https://github.com/cplusx/SIGN/tree/9777fc3ddd4c6f799caeefe1e72482d5b1ecd8ae |
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._... | opti-mix/glow | SimpleTanhModel | false | 7,420 | [
"Apache-2.0"
] | 1 | 4ba074df5da9822986a23a6679ab592c22660f6d | https://github.com/opti-mix/glow/tree/4ba074df5da9822986a23a6679ab592c22660f6d |
Net5 | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import numpy as np
import tor... | derangedhk417/ML-Lessons | Net5 | false | 9,993 | [
"MIT"
] | 0 | 3433e3fa6324791b74771fcfd8a6c5361ba69c53 | https://github.com/derangedhk417/ML-Lessons/tree/3433e3fa6324791b74771fcfd8a6c5361ba69c53 |
ConvNCFBPRLoss | import torch
import torch.nn as nn
class ConvNCFBPRLoss(nn.Module):
""" ConvNCFBPRLoss, based on Bayesian Personalized Ranking,
Shape:
- Pos_score: (N)
- Neg_score: (N), same shape as the Pos_score
- Output: scalar.
Examples::
>>> loss = ConvNCFBPRLoss()
>>> ... | 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
... | MIracleyin/RecBole-notebook | ConvNCFBPRLoss | false | 9,575 | [
"MIT"
] | 0 | ef32b3e57a297ff4889dec1f63c7984f8f901a23 | https://github.com/MIracleyin/RecBole-notebook/tree/ef32b3e57a297ff4889dec1f63c7984f8f901a23 |
TotalVariation | import torch
import torch.nn as nn
def total_variation(img: 'torch.Tensor') ->torch.Tensor:
"""Function that computes Total Variation.
See :class:`~kornia.losses.TotalVariation` for details.
"""
if not torch.is_tensor(img):
raise TypeError(f'Input type is not a torch.Tensor. Got {type(img)}')... | 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... | connorlee77/kornia | TotalVariation | false | 6,481 | [
"ECL-2.0",
"Apache-2.0"
] | 1 | af5b1f76bedf2a7fc0e0da2386b1be3032b6534f | https://github.com/connorlee77/kornia/tree/af5b1f76bedf2a7fc0e0da2386b1be3032b6534f |
N_TransE | # 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.functional as F
assert_size_stride = torch._C._dynamo.guards.as... | TMUITLab/EAFR | N_TransE | false | 1,115 | [
"MIT"
] | 0 | dadb6485d48711ccb8aa2f03760aeb437645f1ff | https://github.com/TMUITLab/EAFR/tree/dadb6485d48711ccb8aa2f03760aeb437645f1ff |
Policy | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.parallel
import torch.utils.data
import torch.optim
import torch.autograd
class Policy(nn.Module):
def __init__(self):
super(Policy, self).__init__()
self.affine1 = nn.Linear(4, 128)
self.affine2 = nn.Linea... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | BestSonny/examples | Policy | false | 7,768 | [
"BSD-3-Clause"
] | 13 | 4b7365c0db22133d1793e53bb3674c2d0ebaeac1 | https://github.com/BestSonny/examples/tree/4b7365c0db22133d1793e53bb3674c2d0ebaeac1 |
KD | import torch
import torch.nn as nn
import torch.nn.functional as F
class KD(nn.Module):
def __init__(self, alpha, T):
super(KD, self).__init__()
self.alpha = alpha
self.T = T
def forward(self, output_stu, output_tch, label):
loss_stu = F.cross_entropy(output_stu, label)
... | 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... | amoonfana/Knowledge_Distillation | KD | false | 6,200 | [
"Apache-2.0"
] | 1 | 1ee814a8f70ae00d17e1e1ee778d5420d96c43c4 | https://github.com/amoonfana/Knowledge_Distillation/tree/1ee814a8f70ae00d17e1e1ee778d5420d96c43c4 |
DQN | # 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_... | AndrejHafner/tetris-reinforcement-learning | DQN | false | 1,968 | [
"MIT"
] | 0 | 52db5d8ce7f9162b15575456a0effc69dd7fb2bf | https://github.com/AndrejHafner/tetris-reinforcement-learning/tree/52db5d8ce7f9162b15575456a0effc69dd7fb2bf |
TriNTN | import torch
import torch.nn as nn
import torch.nn.functional as F
class RawNTN(nn.Module):
def __init__(self, l_dim, r_dim, k=5, non_linear=torch.tanh):
super(RawNTN, self).__init__()
self.u_R = nn.Linear(k, 1, bias=False)
self.f = non_linear
self.W = nn.Bilinear(l_dim, r_dim, k,... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | QingkaiZeng/GenTaxo | TriNTN | false | 8,724 | [
"MIT"
] | 28 | 10257a1714d14c6a4c49cbfa0b507408f718cdf0 | https://github.com/QingkaiZeng/GenTaxo/tree/10257a1714d14c6a4c49cbfa0b507408f718cdf0 |
Correlation | # 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
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_... | bobo0810/RepDistiller | Correlation | false | 10,156 | [
"BSD-2-Clause"
] | 0 | 0a4cea2142221b9b31c8e995920273f5619b37f8 | https://github.com/bobo0810/RepDistiller/tree/0a4cea2142221b9b31c8e995920273f5619b37f8 |
Mlayer | import torch
import torch.nn as nn
class Mlayer(nn.Module):
def __init__(self, in_channel, out_channel, stride=1):
super(Mlayer, self).__init__()
m_s = torch.zeros([1, in_channel, 1, 1], requires_grad=True)
self.m_s = torch.nn.Parameter(m_s)
self.register_parameter('m_scale', self... | 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... | Sharingsky/resrep | Mlayer | false | 9,496 | [
"MIT"
] | 0 | a173d1bc256b75b2c902024929e406863ce48b9b | https://github.com/Sharingsky/resrep/tree/a173d1bc256b75b2c902024929e406863ce48b9b |
ConvRelu | # 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... | HugoPopo/robosat.pink | ConvRelu | false | 2,349 | [
"MIT"
] | 0 | daa6a0cd6dff68103b9bcc78a8c9a15d8912c42d | https://github.com/HugoPopo/robosat.pink/tree/daa6a0cd6dff68103b9bcc78a8c9a15d8912c42d |
Focal_loss | # 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... | BCV-Uniandes/SAMA | Focal_loss | false | 110 | [
"BSD-3-Clause"
] | 0 | 4c732c71486af17efed17480e363298cb65c851f | https://github.com/BCV-Uniandes/SAMA/tree/4c732c71486af17efed17480e363298cb65c851f |
DynamicConv2d | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.nn
assert_size_stride = torch._C._dynamo.guar... | arielclj/singa-easy | DynamicConv2d | false | 1,468 | [
"Apache-2.0"
] | 0 | fd4bc601a5501062936f874df14711a3cefa1346 | https://github.com/arielclj/singa-easy/tree/fd4bc601a5501062936f874df14711a3cefa1346 |
BERTLayerNorm | from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
class BERTLayerNorm(nn.Module):
def __init__(self, config, multi_params=None, variance_epsilon=1e-12):
"""Construct a layernorm module in the TF style (epsilon inside the square root).
"""
super(BERTLayerNorm... | 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_... | Chriskuei/FedMatch | BERTLayerNorm | false | 18,370 | [
"Apache-2.0"
] | 4 | 305e8c4bbb398712b00c883a986dfec17b500f76 | https://github.com/Chriskuei/FedMatch/tree/305e8c4bbb398712b00c883a986dfec17b500f76 |
ClassicalConv5 | # 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.... | mit-han-lab/pytorch-quantum | ClassicalConv5 | false | 16,107 | [
"MIT"
] | 98 | 05cf000d689307f6b1fe02d12744ad455685935b | https://github.com/mit-han-lab/pytorch-quantum/tree/05cf000d689307f6b1fe02d12744ad455685935b |
TonemappedRelativeMSE | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torc... | qbhan/pathembed | TonemappedRelativeMSE | false | 7,497 | [
"MIT"
] | 1 | c21823529840593bf606e10696f5879e5adb51b2 | https://github.com/qbhan/pathembed/tree/c21823529840593bf606e10696f5879e5adb51b2 |
CriticNet | # 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_... | danthe42/drlnd_p2 | CriticNet | false | 1,789 | [
"MIT"
] | 0 | 693813feb7c99f3e01da583e5b67e4f8904639c4 | https://github.com/danthe42/drlnd_p2/tree/693813feb7c99f3e01da583e5b67e4f8904639c4 |
SpatialAttentionLayer | # 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.... | HolmesShuan/Compact-Global-Descriptor | SpatialAttentionLayer | false | 8,247 | [
"BSD-2-Clause"
] | 24 | 715601bd7fce76596db960f7dc480241d443fa66 | https://github.com/HolmesShuan/Compact-Global-Descriptor/tree/715601bd7fce76596db960f7dc480241d443fa66 |
KlCriterion | import torch
import torch.nn.functional as F
from torch.nn.modules.loss import _Loss
from torch.optim.lr_scheduler import *
class Criterion(_Loss):
def __init__(self, alpha=1.0, name='criterion'):
super().__init__()
"""Alpha is used to weight each loss term
"""
self.alpha = alpha
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch.... | anlewy/mt-dnn | KlCriterion | false | 14,868 | [
"MIT"
] | 2,075 | eeb6f01ce0630e61a52b8c9c6f7537cd34978e45 | https://github.com/anlewy/mt-dnn/tree/eeb6f01ce0630e61a52b8c9c6f7537cd34978e45 |
MixtureDensityHead | from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
from torch.autograd import Variable
from torch.distributions import Categorical
class MixtureDensityHead(nn.Module):
def __init__(self, config: 'DictConfig', **kwargs):
self.hparams = config
super().__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.triton_helpers import libdevice
import torch.nn as ... | Actis92/pytorch_tabular | MixtureDensityHead | false | 7,380 | [
"MIT"
] | 1 | 78dabf5e7b97d8ff24db4bc83d9d0a2273941bbe | https://github.com/Actis92/pytorch_tabular/tree/78dabf5e7b97d8ff24db4bc83d9d0a2273941bbe |
ChamferLoss | # 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
import torch.nn as nn
assert_... | RRemixx/DMRDenoise | ChamferLoss | false | 14,264 | [
"MIT"
] | 79 | 026d25f9eaf98fdfd85a67caeb9b49cab71148e9 | https://github.com/RRemixx/DMRDenoise/tree/026d25f9eaf98fdfd85a67caeb9b49cab71148e9 |
RobustScannerFusionLayer | import torch
import torch.nn as nn
class RobustScannerFusionLayer(nn.Module):
def __init__(self, dim_model, dim=-1):
super().__init__()
self.dim_model = dim_model
self.dim = dim
self.linear_layer = nn.Linear(dim_model * 2, dim_model * 2)
self.glu_layer = nn.GLU(dim=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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | SamDM/mmocr | RobustScannerFusionLayer | false | 9,499 | [
"Apache-2.0"
] | 0 | 4cb69141ff8d28c8b1437bf28242e368a0e6ec4f | https://github.com/SamDM/mmocr/tree/4cb69141ff8d28c8b1437bf28242e368a0e6ec4f |
Gdn | # 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
from torch.autograd... | adynmiles/DARTS-FQA | Gdn | false | 6,093 | [
"MIT"
] | 1 | a088a0efeb1160d0cdbf2b2a3e30f132c16eb53f | https://github.com/adynmiles/DARTS-FQA/tree/a088a0efeb1160d0cdbf2b2a3e30f132c16eb53f |
LossAnneal | import torch
import torch.nn as nn
import torch.nn.functional as F
class TensorGradient(nn.Module):
"""
the gradient of tensor
"""
def __init__(self, L1=True):
super(TensorGradient, self).__init__()
self.L1 = L1
def forward(self, img):
w, h = img.size(-2), img.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
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torc... | xenbaloch/efficientderain | LossAnneal | false | 16,745 | [
"MIT"
] | 109 | d5646815fd14a5a03c859102ecd2f298db7e53be | https://github.com/xenbaloch/efficientderain/tree/d5646815fd14a5a03c859102ecd2f298db7e53be |
ConvDropoutLayerNorm | # 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... | Clemens123/transformers | ConvDropoutLayerNorm | false | 11,490 | [
"Apache-2.0"
] | 0 | 22abe7bbc587c16ec30f9d1aa549dcbeba6e9e26 | https://github.com/Clemens123/transformers/tree/22abe7bbc587c16ec30f9d1aa549dcbeba6e9e26 |
ScaledDotProductAttentionMemory | # 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.... | quanha72/mesh-memory-transformer | ScaledDotProductAttentionMemory | false | 12,923 | [
"BSD-3-Clause"
] | 0 | 0eeae459efdb8e85926ce8595536409fdbfc4f99 | https://github.com/quanha72/mesh-memory-transformer/tree/0eeae459efdb8e85926ce8595536409fdbfc4f99 |
WineLoss | # 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
... | ZhongyuanW/foundation_wines | WineLoss | false | 3,015 | [
"Apache-2.0"
] | 0 | 92b9ae0ece46ecd291a9101ebef9e9421ead92d6 | https://github.com/ZhongyuanW/foundation_wines/tree/92b9ae0ece46ecd291a9101ebef9e9421ead92d6 |
ScaleNorm | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
from torch import nn
assert_... | booydar/x-transformers | ScaleNorm | false | 3,233 | [
"MIT"
] | 0 | 97f0a854fdf4df8a3fbf6a580e2375463af3538c | https://github.com/booydar/x-transformers/tree/97f0a854fdf4df8a3fbf6a580e2375463af3538c |
DiscriminatorHingeLoss | import torch
import torch.nn as nn
class DiscriminatorHingeLoss(nn.Module):
def __init__(self, reduction='mean'):
super(DiscriminatorHingeLoss, self).__init__()
if reduction not in ['mean', 'sum']:
raise ValueError(
'Valid values for the reduction param are `mean`, `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
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | kpandey008/SAGAN | DiscriminatorHingeLoss | false | 10,432 | [
"MIT"
] | 0 | 8e673d2ccabeb0450faf30dcb347b9ff2d710ae2 | https://github.com/kpandey008/SAGAN/tree/8e673d2ccabeb0450faf30dcb347b9ff2d710ae2 |
GC3d | import torch
import torch.nn as nn
import torch.nn
class GC3d(nn.Module):
def __init__(self, inplanes, planes, kh=7, kw=7, mdim=256, which_conv=
nn.Conv3d):
super(GC3d, self).__init__()
self.conv_l1 = which_conv(inplanes, mdim, kernel_size=(1, kh, 1),
padding=(0, int(kh / 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
import torch.nn
assert_size_stride = torch._C._dynamo.guar... | Schmiddo/d2conv3d | GC3d | false | 8,756 | [
"MIT"
] | 16 | 9b330be56f0dfb9657a63e3fb3394ab36b35a67b | https://github.com/Schmiddo/d2conv3d/tree/9b330be56f0dfb9657a63e3fb3394ab36b35a67b |
FloorDiv | import torch
class FloorDiv(torch.nn.Module):
def __init__(self):
super(FloorDiv, self).__init__()
def forward(self, x, y):
return x // y
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
| import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_c... | NVIDIA-AI-IOT-private/torch2trt | FloorDiv | false | 10,509 | [
"MIT"
] | 0 | 953d60039e0c81e90eea467c3df2e6e3f7040242 | https://github.com/NVIDIA-AI-IOT-private/torch2trt/tree/953d60039e0c81e90eea467c3df2e6e3f7040242 |
BerHuLoss | # 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
... | aliyun/dro-sfm | BerHuLoss | false | 14,794 | [
"MIT"
] | 147 | 8707e2e0ef799d7d47418a018060f503ef449fe3 | https://github.com/aliyun/dro-sfm/tree/8707e2e0ef799d7d47418a018060f503ef449fe3 |
ClassHead | import torch
from itertools import product as product
import torch.nn as nn
class ClassHead(nn.Module):
def __init__(self, inchannels=512, num_anchors=3):
super(ClassHead, self).__init__()
self.num_anchors = num_anchors
self.conv1x1 = nn.Conv2d(inchannels, self.num_anchors * 2,
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from itertools import product as product
import torch.nn as nn
assert_size_strid... | Edward1900/Face-Detector-1MB-with-landmark | ClassHead | false | 13,701 | [
"MIT"
] | 907 | 16c16c4efa74b0264e0fd7fe0ddc0160f540a4bf | https://github.com/Edward1900/Face-Detector-1MB-with-landmark/tree/16c16c4efa74b0264e0fd7fe0ddc0160f540a4bf |
DotRole | from _paritybench_helpers import _mock_config
import torch
import torch as th
import torch.nn as nn
class DotRole(nn.Module):
def __init__(self, args):
super(DotRole, self).__init__()
self.args = args
self.n_actions = args.n_actions
self.q_fc = nn.Linear(args.rnn_hidden_dim, 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
import torch as th
import torch.nn as nn
assert_size_stride = torch._C._dynamo.g... | OkYongChoi/smac | DotRole | false | 18,383 | [
"Apache-2.0"
] | 8 | 5b2b59e42d17a124e97feeecf9154a3a0aa9d260 | https://github.com/OkYongChoi/smac/tree/5b2b59e42d17a124e97feeecf9154a3a0aa9d260 |
CuboidPoseHead | # 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... | chenxinfeng4/mmpose | CuboidPoseHead | false | 12,252 | [
"Apache-2.0"
] | 0 | b0aac4178c1f3d679d2a007e1d9c6c567fc2607d | https://github.com/chenxinfeng4/mmpose/tree/b0aac4178c1f3d679d2a007e1d9c6c567fc2607d |
GeLU | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_... | IamHimon/re2 | GeLU | false | 11,502 | [
"Apache-2.0"
] | 0 | d16b0ffc385f7b118a6160d035250da8d6320534 | https://github.com/IamHimon/re2/tree/d16b0ffc385f7b118a6160d035250da8d6320534 |
ReLU | # 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... | SaumilShah66/dqn_uav | ReLU | false | 9,577 | [
"MIT"
] | 0 | 2bf780369e964b870624aebcff16c0714cad03c1 | https://github.com/SaumilShah66/dqn_uav/tree/2bf780369e964b870624aebcff16c0714cad03c1 |
InnerProductDecoderMLP | # 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 ... | WanyuGroup/CVPR2022-OrphicX | InnerProductDecoderMLP | false | 1,209 | [
"MIT"
] | 0 | 98d8d8259439c45661573e575cf956331df16abc | https://github.com/WanyuGroup/CVPR2022-OrphicX/tree/98d8d8259439c45661573e575cf956331df16abc |
RegLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_... | geekinglcq/HRec | RegLoss | false | 15,414 | [
"MIT"
] | 49 | b3a67f7721e6e73a7af37d308b5b00e9df68d495 | https://github.com/geekinglcq/HRec/tree/b3a67f7721e6e73a7af37d308b5b00e9df68d495 |
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
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | tan-huaiyu/Network_science-and-Evolutionary_dynamics | Generator | false | 13,017 | [
"Apache-2.0"
] | 0 | 4bdaaed18c6f230213fd69a31144db8e97eb0c7b | https://github.com/tan-huaiyu/Network_science-and-Evolutionary_dynamics/tree/4bdaaed18c6f230213fd69a31144db8e97eb0c7b |
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