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 |
|---|---|---|---|---|---|---|---|---|---|---|
FocalLoss | import torch
import torch.nn.functional as F
class FocalLoss(torch.nn.Module):
def __init__(self, gamma=2):
super().__init__()
self.gamma = gamma
def forward(self, input, target):
if not target.size() == input.size():
raise ValueError(
'Target size ({}) mu... | 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
assert_size... | rskmoi/kaggle-imet | FocalLoss | false | 7,582 | [
"MIT"
] | 1 | 483e9e6dbae5b1d8e023e0812c4b990afca874bc | https://github.com/rskmoi/kaggle-imet/tree/483e9e6dbae5b1d8e023e0812c4b990afca874bc |
ECToCA3 | import torch
import torch.nn as nn
import torch.nn.functional as F
class ECToCA3(nn.Module):
def __init__(self, D_in, D_out):
super(ECToCA3, self).__init__()
self.fc1 = nn.Linear(D_in, 800)
self.fc2 = nn.Linear(800, D_out)
def forward(self, x):
x = F.leaky_relu(self.fc1(x), 0... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | sachio222/aha4 | ECToCA3 | false | 7,583 | [
"MIT"
] | 1 | ec378fe1bace85e325ad7cb8686b8ba321dc97d0 | https://github.com/sachio222/aha4/tree/ec378fe1bace85e325ad7cb8686b8ba321dc97d0 |
n_to_one | import torch
from torch import nn
class n_to_one(nn.Module):
def __init__(self):
super().__init__()
self.conv1 = nn.Conv2d(3, 3, 1, 1, bias=False)
self.conv2 = nn.Conv2d(3, 3, 1, 1, bias=False)
def forward(self, x1, x2):
y1 = self.conv1(x1)
y2 = self.conv2(x2)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_st... | sailfish009/torch-toolbox | n_to_one | false | 7,584 | [
"BSD-3-Clause"
] | 1 | 80dfc22c697b9f323e097de72af04f0e5435d7b4 | https://github.com/sailfish009/torch-toolbox/tree/80dfc22c697b9f323e097de72af04f0e5435d7b4 |
ActorDDPGNonConvNetwork | import torch
import numpy as np
import torch.nn as nn
from numpy import *
def fanin_init(size, fanin=None):
fanin = fanin or size[0]
v = 1.0 / np.sqrt(fanin)
return torch.Tensor(size).uniform_(-v, v)
class ActorDDPGNonConvNetwork(nn.Module):
def __init__(self, num_hidden_layers, output_action, inpu... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | ruyueshuo/MaskTrackRCNN | ActorDDPGNonConvNetwork | false | 7,585 | [
"Apache-2.0"
] | 1 | 3c6ada36be3c2b2df32176349ec5c0ee5b24e724 | https://github.com/ruyueshuo/MaskTrackRCNN/tree/3c6ada36be3c2b2df32176349ec5c0ee5b24e724 |
CA1 | import torch
import torch.nn as nn
import torch.nn.functional as F
class CA1(nn.Module):
"""Reconstructs the inputs that originated from EC network.
Consists of 2 fully connected layers, recieving inputs from CA3
and outputs to EC.
"""
def __init__(self, N, D_in, D_out, resize_dim):
sup... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | sachio222/aha4 | CA1 | false | 7,586 | [
"MIT"
] | 1 | ec378fe1bace85e325ad7cb8686b8ba321dc97d0 | https://github.com/sachio222/aha4/tree/ec378fe1bace85e325ad7cb8686b8ba321dc97d0 |
Sparsemax | from torch.autograd import Function
import torch
import torch.nn as nn
def _make_ix_like(X, dim):
d = X.size(dim)
rho = torch.arange(1, d + 1, device=X.device, dtype=X.dtype)
view = [1] * X.dim()
view[0] = -1
return rho.view(view).transpose(0, dim)
def _roll_last(X, dim):
if dim == -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.autograd import Function
import torch.nn as nn
assert_size_stride = torch._C._... | roholazandie/entmax | Sparsemax | false | 7,587 | [
"MIT"
] | 1 | 657374e6a792ec6840b6f78bc759cc1f51570aad | https://github.com/roholazandie/entmax/tree/657374e6a792ec6840b6f78bc759cc1f51570aad |
L0Loss | import torch
from torch import nn
class L0Loss(nn.Module):
"""L0loss from
"Noise2Noise: Learning Image Restoration without Clean Data"
<https://arxiv.org/pdf/1803.04189>`_ paper.
"""
def __init__(self, gamma=2, eps=1e-08):
super(L0Loss, self).__init__()
self.gamma = gamma
... | 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
a... | sailfish009/torch-toolbox | L0Loss | false | 7,588 | [
"BSD-3-Clause"
] | 1 | 80dfc22c697b9f323e097de72af04f0e5435d7b4 | https://github.com/sailfish009/torch-toolbox/tree/80dfc22c697b9f323e097de72af04f0e5435d7b4 |
BertSelfOutput | from _paritybench_helpers import _mock_config
import torch
from torch import nn
class BertLayerNorm(nn.Module):
def __init__(self, config, variance_epsilon=1e-12):
"""Construct a layernorm module in the TF style (epsilon inside the square root).
"""
super(BertLayerNorm, 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.triton_helpers import libdevice
from torch import n... | BLimmie/pytorch-pretrained-BERT | BertSelfOutput | false | 7,589 | [
"Apache-2.0"
] | 1 | 2ac4b29641e569020ed2acc28016f481f617052b | https://github.com/BLimmie/pytorch-pretrained-BERT/tree/2ac4b29641e569020ed2acc28016f481f617052b |
LossPredictionLoss | import torch
import torch.nn as nn
class LossPredictionLoss(nn.Module):
def __init__(self, margin=1.0):
super(LossPredictionLoss, self).__init__()
self.margin = margin
def forward(self, input, target):
input = (input - input.flip(0))[:len(input) // 2]
target = (target - targe... | 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... | saksman/deepfish_adaptation | LossPredictionLoss | false | 7,590 | [
"MIT"
] | 1 | 0413def87ec1d3cb67fa043a2fb60ef7e0d73539 | https://github.com/saksman/deepfish_adaptation/tree/0413def87ec1d3cb67fa043a2fb60ef7e0d73539 |
SmallMaskNet | import torch
import torch.nn as nn
import torch.nn.functional as F
class SmallMaskNet(nn.Module):
"""A three-layer network for predicting mask"""
def __init__(self, input, output):
super(SmallMaskNet, self).__init__()
self.conv1 = nn.Conv2d(input, 32, 5, padding=2)
self.conv2 = nn.Con... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | saikatdutta/NME-VFI | SmallMaskNet | false | 7,591 | [
"Apache-2.0"
] | 1 | 5915e2336ea3ed7113a9c6a91bbc7f6b5deaac17 | https://github.com/saikatdutta/NME-VFI/tree/5915e2336ea3ed7113a9c6a91bbc7f6b5deaac17 |
ContinuousEmbeddings | import math
import torch
from torch import Tensor
from torch import nn
import torch.nn.functional as F
def _get_activation_fn(activation):
if activation == 'relu':
return nn.ReLU(inplace=True)
if activation == 'leaky_relu':
return nn.LeakyReLU(inplace=True)
elif activation == 'gelu':
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import math
from torch import nn
import torch.nn.functional as F
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strid... | sallypannn/pytorch-widedeep | ContinuousEmbeddings | false | 7,592 | [
"MIT"
] | 1 | ab4a209a2a3bff539f543a66ac51306042ed6693 | https://github.com/sallypannn/pytorch-widedeep/tree/ab4a209a2a3bff539f543a66ac51306042ed6693 |
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 |
L2Softmax | import math
import torch
from torch.nn import functional as F
from torch.nn.modules.loss import _WeightedLoss
class L2Softmax(_WeightedLoss):
"""L2Softmax from
`"L2-constrained Softmax Loss for Discriminative Face Verification"
<https://arxiv.org/abs/1703.09507>`_ paper.
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
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import math... | sailfish009/torch-toolbox | L2Softmax | false | 7,594 | [
"BSD-3-Clause"
] | 1 | 80dfc22c697b9f323e097de72af04f0e5435d7b4 | https://github.com/sailfish009/torch-toolbox/tree/80dfc22c697b9f323e097de72af04f0e5435d7b4 |
Conv | import torch
from torch import nn
from torch.nn.functional import interpolate
from typing import cast
class Interpolate(nn.Module):
def __init__(self, scale_factor: 'float'=1.0, mode: 'str'='nearest'
) ->None:
super().__init__()
self.scale_factor = scale_factor
self.mode = mode
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | sakshi-06/pystiche | Conv | false | 7,595 | [
"BSD-3-Clause"
] | 1 | 21a67364b332a34a2308a929f200900c76be5b73 | https://github.com/sakshi-06/pystiche/tree/21a67364b332a34a2308a929f200900c76be5b73 |
Wide | import math
import torch
from torch import Tensor
from torch import nn
class Wide(nn.Module):
"""wide (linear) component
Linear model implemented via an Embedding layer connected to the output
neuron(s).
Parameters
-----------
wide_dim: int
size of the Embedding layer. `wide_dim` is ... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import math
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guard... | sallypannn/pytorch-widedeep | Wide | false | 7,596 | [
"MIT"
] | 1 | ab4a209a2a3bff539f543a66ac51306042ed6693 | https://github.com/sallypannn/pytorch-widedeep/tree/ab4a209a2a3bff539f543a66ac51306042ed6693 |
ConvMeanPool | import torch
import torch.nn as nn
class ConvMeanPool(nn.Module):
def __init__(self, input_dim, output_dim, kernel_size=3, biases=True,
adjust_padding=False):
super().__init__()
if not adjust_padding:
conv = nn.Conv2d(input_dim, output_dim, kernel_size, stride=1,
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | samsartor/score_sde | ConvMeanPool | false | 7,597 | [
"Apache-2.0"
] | 1 | d25c8d092a68d643c796d771c55f80075aa041d1 | https://github.com/samsartor/score_sde/tree/d25c8d092a68d643c796d771c55f80075aa041d1 |
UpsampleConv | import torch
import torch.nn as nn
class UpsampleConv(nn.Module):
def __init__(self, input_dim, output_dim, kernel_size=3, biases=True):
super().__init__()
self.conv = nn.Conv2d(input_dim, output_dim, kernel_size, stride=1,
padding=kernel_size // 2, bias=biases)
self.pixelshuf... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | samsartor/score_sde | UpsampleConv | false | 7,598 | [
"Apache-2.0"
] | 1 | d25c8d092a68d643c796d771c55f80075aa041d1 | https://github.com/samsartor/score_sde/tree/d25c8d092a68d643c796d771c55f80075aa041d1 |
MeanPoolConv | import torch
import torch.nn as nn
class MeanPoolConv(nn.Module):
def __init__(self, input_dim, output_dim, kernel_size=3, biases=True):
super().__init__()
self.conv = nn.Conv2d(input_dim, output_dim, kernel_size, stride=1,
padding=kernel_size // 2, bias=biases)
def forward(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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | samsartor/score_sde | MeanPoolConv | false | 7,599 | [
"Apache-2.0"
] | 1 | d25c8d092a68d643c796d771c55f80075aa041d1 | https://github.com/samsartor/score_sde/tree/d25c8d092a68d643c796d771c55f80075aa041d1 |
MultiHeadAttentionLayer | import math
import torch
import torch.nn as nn
class MultiHeadAttentionLayer(nn.Module):
def __init__(self, d_model, n_heads, dropout):
super().__init__()
assert d_model % n_heads == 0
self.d_model = d_model
self.n_heads = n_heads
self.head_dim = d_model // n_heads
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | salvacarrion/nmt-continual-learning | MultiHeadAttentionLayer | false | 7,600 | [
"MIT"
] | 1 | 302147ac9c270f3341a68a72c803c457f05ff37b | https://github.com/salvacarrion/nmt-continual-learning/tree/302147ac9c270f3341a68a72c803c457f05ff37b |
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_... | samsartor/score_sde | VarianceNorm2d | false | 7,601 | [
"Apache-2.0"
] | 1 | d25c8d092a68d643c796d771c55f80075aa041d1 | https://github.com/samsartor/score_sde/tree/d25c8d092a68d643c796d771c55f80075aa041d1 |
INDeConv | import torch
import torch.nn as nn
class INDeConv(nn.Module):
def __init__(self, in_planes, out_planes, kernel_size, stride=1,
padding=0, out_padding=0, dilation=1, groups=1, relu=True, ins_n=
True, bias=False):
super(INDeConv, self).__init__()
self.out_channels = out_planes
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | samsgood0310/Unsupervised-Defect-Segmentation | INDeConv | false | 7,602 | [
"Apache-2.0"
] | 1 | 66af32506cd6e60c356890616e28d679622fd8e6 | https://github.com/samsgood0310/Unsupervised-Defect-Segmentation/tree/66af32506cd6e60c356890616e28d679622fd8e6 |
PlainRefiner | import torch
import torch.nn as nn
class PlainRefiner(nn.Module):
"""Simple refiner from Deep Image Matting.
Args:
conv_channels (int): Number of channels produced by the three main
convolutional layer.
loss_refine (dict): Config of the loss of the refiner. Default: None.
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | rivergold/mmediting | PlainRefiner | false | 7,603 | [
"Apache-2.0"
] | 1 | fd972635c48bb065db29d1b5090592a87c7263d2 | https://github.com/rivergold/mmediting/tree/fd972635c48bb065db29d1b5090592a87c7263d2 |
InstanceNorm2dPlus | import torch
import torch.nn as nn
class InstanceNorm2dPlus(nn.Module):
def __init__(self, num_features, bias=True):
super().__init__()
self.num_features = num_features
self.bias = bias
self.instance_norm = nn.InstanceNorm2d(num_features, affine=False,
track_running_st... | 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_... | samsartor/score_sde | InstanceNorm2dPlus | false | 7,604 | [
"Apache-2.0"
] | 1 | d25c8d092a68d643c796d771c55f80075aa041d1 | https://github.com/samsartor/score_sde/tree/d25c8d092a68d643c796d771c55f80075aa041d1 |
BiaffineAttention | import torch
import torch.utils.checkpoint
import torch.utils.data
class BiaffineAttention(torch.nn.Module):
"""Implements a biaffine attention operator for binary relation classification.
PyTorch implementation of the biaffine attention operator from "End-to-end neural relation
extraction using deep bia... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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.checkpoint
import torch.utils.data
assert_size_stride = torch... | rushabh-v/unilm | BiaffineAttention | false | 7,605 | [
"MIT"
] | 1 | a62a023bd5d3500c23ac454be0a8b0107e18a6ce | https://github.com/rushabh-v/unilm/tree/a62a023bd5d3500c23ac454be0a8b0107e18a6ce |
SSD300 | import torch
import torchvision
from torch import nn
import torch.nn.functional as F
from math import sqrt
from itertools import product as product
import torch.optim
import torch.utils.data
def decimate(tensor, m):
"""
Decimate a tensor by a factor 'm', i.e. downsample by keeping every 'm'th value.
This... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | dee-walia20/SSD-Implementation-using-Pytorch | SSD300 | false | 7,606 | [
"MIT"
] | 1 | 2a7dcdcea2787f4bffd45f335819f08af2b525dd | https://github.com/dee-walia20/SSD-Implementation-using-Pytorch/tree/2a7dcdcea2787f4bffd45f335819f08af2b525dd |
ResidualBlock | import torch
import torch.nn as nn
from functools import partial
def ncsn_conv3x3(in_planes, out_planes, stride=1, bias=True, dilation=1,
init_scale=1.0, padding=1):
"""3x3 convolution with PyTorch initialization. Same as NCSNv1/NCSNv2."""
init_scale = 1e-10 if init_scale == 0 else init_scale
conv = 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.triton_helpers import libdevice
import torch.nn as ... | samsartor/score_sde | ResidualBlock | false | 7,607 | [
"Apache-2.0"
] | 1 | d25c8d092a68d643c796d771c55f80075aa041d1 | https://github.com/samsartor/score_sde/tree/d25c8d092a68d643c796d771c55f80075aa041d1 |
LinActorCritic | import torch
class LinActorCritic(torch.nn.Module):
def __init__(self, actor_lr, epsilon, in_dim, h_dim, out_dim):
super(LinActorCritic, self).__init__()
self.in_dim = in_dim
self.out_dim = out_dim
self.h_dim = h_dim
self.epsilon = epsilon
self.define_network()
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | Gregory-Eales/mban | LinActorCritic | false | 7,608 | [
"Apache-2.0"
] | 1 | d8b35db51c7e601b1db777d9a80343600374250b | https://github.com/Gregory-Eales/mban/tree/d8b35db51c7e601b1db777d9a80343600374250b |
Net | import torch
import torch.nn as nn
import torch.nn.functional as F
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(1, 32, 5)
self.pool1 = nn.MaxPool2d(2, 2)
self.conv2 = nn.Conv2d(32, 64, 5)
self.pool2 = nn.MaxPool2d(2, 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 torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | piyushpathak03/Facial-key-point-detection | Net | false | 7,609 | [
"Apache-2.0"
] | 1 | 863eeeac50c46befb17ecf7610cd341ea0e65291 | https://github.com/piyushpathak03/Facial-key-point-detection/tree/863eeeac50c46befb17ecf7610cd341ea0e65291 |
BertImagePooler | from _paritybench_helpers import _mock_config
import torch
import torch.optim
import torch.utils.data
from torch import nn
import torch
class BertImagePooler(nn.Module):
def __init__(self, config):
super(BertImagePooler, self).__init__()
self.dense = nn.Linear(config.v_hidden_size, config.bi_hidd... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.optim
import tor... | ChoiIseungil/vilbert-multi-task | BertImagePooler | false | 7,610 | [
"MIT"
] | 1 | 37d14b9aed9c48117a820e05157c7ccd3dd20d5b | https://github.com/ChoiIseungil/vilbert-multi-task/tree/37d14b9aed9c48117a820e05157c7ccd3dd20d5b |
Net2 | import torch
import torch.nn as nn
from torch.testing._internal.common_utils import *
class MyRelu2(torch.autograd.Function):
@staticmethod
def forward(ctx, input):
ctx.save_for_backward(input)
return input.clamp(min=0)
class Net2(nn.Module):
def __init__(self):
super().__init_... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
from torch.testing._internal.common_utils import *
assert_size_stri... | LexcaliburR/notebook | Net2 | false | 7,611 | [
"MIT"
] | 1 | 84a8f3801dff20d07caa0ed2584e722656fb5726 | https://github.com/LexcaliburR/notebook/tree/84a8f3801dff20d07caa0ed2584e722656fb5726 |
Conv2d | from torch.autograd import Function
import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
def _setup_kernel(k):
k = np.asarray(k, dtype=np.float32)
if k.ndim == 1:
k = np.outer(k, k)
k /= np.sum(k)
assert k.ndim == 2
assert k.shape[0] == k.shape[1]
retur... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import 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 numpy as np
import torch.nn as nn
imp... | samsartor/score_sde | Conv2d | false | 7,612 | [
"Apache-2.0"
] | 1 | d25c8d092a68d643c796d771c55f80075aa041d1 | https://github.com/samsartor/score_sde/tree/d25c8d092a68d643c796d771c55f80075aa041d1 |
BertOutput | from _paritybench_helpers import _mock_config
import torch
import torch.nn
import torch.nn as nn
class BertOutput(nn.Module):
"""BERT output layer.
Based on: BERT (pytorch-transformer)
https://github.com/huggingface/transformers
"""
def __init__(self, config) ->None:
super(BertOutput, sel... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn
imp... | Erotemic/MONAI | BertOutput | false | 7,613 | [
"Apache-2.0"
] | 1 | a9cd2d88168107281a2abcc2f63efaed80580e79 | https://github.com/Erotemic/MONAI/tree/a9cd2d88168107281a2abcc2f63efaed80580e79 |
BERTLowRank | from _paritybench_helpers import _mock_config
import math
import torch
import torch.nn as nn
def gelu(x):
"""Implementation of the gelu activation function.
For information: OpenAI GPT's gelu is slightly different (and gives slightly different results):
0.5 * x * (1 + torch.tanh(math.sqrt(2 / math... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import math
import ... | DAQuestionAnswering/Bert-n-Pals | BERTLowRank | false | 7,614 | [
"MIT"
] | 1 | d5a288b9ac62259e70c249635108ba3906e19f00 | https://github.com/DAQuestionAnswering/Bert-n-Pals/tree/d5a288b9ac62259e70c249635108ba3906e19f00 |
Decoder | import torch
import torch.nn as nn
class INConv(nn.Module):
def __init__(self, in_planes, out_planes, kernel_size, stride=1,
padding=0, dilation=1, groups=1, relu=True, ins_n=True, bias=False):
super(INConv, self).__init__()
self.out_channels = out_planes
self.conv = nn.Conv2d(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.... | samsgood0310/Unsupervised-Defect-Segmentation | Decoder | false | 7,615 | [
"Apache-2.0"
] | 1 | 66af32506cd6e60c356890616e28d679622fd8e6 | https://github.com/samsgood0310/Unsupervised-Defect-Segmentation/tree/66af32506cd6e60c356890616e28d679622fd8e6 |
FrameAvgPool | from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
class FrameAvgPool(nn.Module):
def __init__(self, cfg):
super(FrameAvgPool, self).__init__()
input_size = cfg.INPUT_SIZE
hidden_size = cfg.HIDDEN_SIZE
kernel_size = cfg.KERNEL_SIZE
stride = cf... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | CFM-MSG/Code_LEORN | FrameAvgPool | false | 7,616 | [
"MIT"
] | 1 | fabea1e1ded973a4db692e51e2df442bde55f626 | https://github.com/CFM-MSG/Code_LEORN/tree/fabea1e1ded973a4db692e51e2df442bde55f626 |
BertOutput | from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
import torch.utils.checkpoint
class BertLayerNorm(nn.Module):
"""LayerNorm层, 见Transformer(一), 讲编码器(encoder)的第3部分"""
def __init__(self, hidden_size, eps=1e-12, conditional=False):
"""Construct a layernorm module in the TF... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | Elvisambition/bert_seq2seq | BertOutput | false | 7,617 | [
"Apache-2.0"
] | 1 | 643ac537c16872f0d13200de06001d8201a54fbb | https://github.com/Elvisambition/bert_seq2seq/tree/643ac537c16872f0d13200de06001d8201a54fbb |
EncoderLayer | import math
import torch
import torch.nn as nn
class MultiHeadAttentionLayer(nn.Module):
def __init__(self, d_model, n_heads, dropout):
super().__init__()
assert d_model % n_heads == 0
self.d_model = d_model
self.n_heads = n_heads
self.head_dim = d_model // n_heads
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | salvacarrion/nmt-continual-learning | EncoderLayer | false | 7,618 | [
"MIT"
] | 1 | 302147ac9c270f3341a68a72c803c457f05ff37b | https://github.com/salvacarrion/nmt-continual-learning/tree/302147ac9c270f3341a68a72c803c457f05ff37b |
T5DenseReluDense | from _paritybench_helpers import _mock_config
import torch
import torch.nn.functional as F
import torch.nn as nn
import torch.utils.checkpoint
class T5DenseReluDense(nn.Module):
def __init__(self, config):
super().__init__()
self.wi = nn.Linear(config.d_model, config.d_ff, bias=False)
sel... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import ... | Elvisambition/bert_seq2seq | T5DenseReluDense | false | 7,619 | [
"Apache-2.0"
] | 1 | 643ac537c16872f0d13200de06001d8201a54fbb | https://github.com/Elvisambition/bert_seq2seq/tree/643ac537c16872f0d13200de06001d8201a54fbb |
AdapterLayer | from _paritybench_helpers import _mock_config
import math
import torch
import torch.nn as nn
def gelu(x):
"""Implementation of the gelu activation function.
For information: OpenAI GPT's gelu is slightly different (and gives slightly different results):
0.5 * x * (1 + torch.tanh(math.sqrt(2 / math... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import math
import ... | DAQuestionAnswering/Bert-n-Pals | AdapterLayer | false | 7,620 | [
"MIT"
] | 1 | d5a288b9ac62259e70c249635108ba3906e19f00 | https://github.com/DAQuestionAnswering/Bert-n-Pals/tree/d5a288b9ac62259e70c249635108ba3906e19f00 |
BertSelfAttention | from _paritybench_helpers import _mock_config
import math
import torch
import torch.nn as nn
class BertSelfAttention(nn.Module):
def __init__(self, config):
super(BertSelfAttention, self).__init__()
if config.hidden_size % config.num_attention_heads != 0:
raise ValueError(
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | AlanFokCo/bert-chinese-horovod-elastic | BertSelfAttention | false | 7,621 | [
"Apache-2.0"
] | 1 | 02317d0857e0e8e313dd63ead61ca9996b25548e | https://github.com/AlanFokCo/bert-chinese-horovod-elastic/tree/02317d0857e0e8e313dd63ead61ca9996b25548e |
RobertaClassificationHead | from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
import torch.utils.data
import torch.nn
class RobertaClassificationHead(nn.Module):
"""Head for sentence-level classification tasks."""
def __init__(self, config):
super(RobertaClassificationHead, 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.triton_helpers import libdevice
import torch.nn as ... | GavinGuan95/Generative-VQA | RobertaClassificationHead | false | 7,622 | [
"MIT"
] | 1 | 0912e3a2426809ef4d4eb40bae667b31c2269161 | https://github.com/GavinGuan95/Generative-VQA/tree/0912e3a2426809ef4d4eb40bae667b31c2269161 |
Actor | import torch
class Actor(torch.nn.Module):
def __init__(self, actor_lr, epsilon):
super(Actor, self).__init__()
self.epsilon = epsilon
self.define_network()
self.optimizer = torch.optim.Adam(params=self.parameters(), lr=actor_lr
)
self.device = torch.device('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 torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Gregory-Eales/Proximal-Policy-Optimization | Actor | false | 7,623 | [
"Apache-2.0"
] | 1 | 134f930bd1436c34e79af9344fe70f75e11c8a30 | https://github.com/Gregory-Eales/Proximal-Policy-Optimization/tree/134f930bd1436c34e79af9344fe70f75e11c8a30 |
Normalization | from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
class Normalization(nn.Module):
def __init__(self, cfg):
super(Normalization, self).__init__()
self.normalizer = nn.LayerNorm(cfg.embedding_dim,
elementwise_affine=True)
def forward(self, input):
... | 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_... | JustinLiam/DAN | Normalization | false | 7,624 | [
"MIT"
] | 1 | eb29cddad6c93e591854b115ef524643b1cd471c | https://github.com/JustinLiam/DAN/tree/eb29cddad6c93e591854b115ef524643b1cd471c |
SingleHeadAttention | from _paritybench_helpers import _mock_config
import math
import torch
import torch.nn as nn
class SingleHeadAttention(nn.Module):
def __init__(self, cfg):
super(SingleHeadAttention, self).__init__()
self.input_dim = cfg.embedding_dim
self.embedding_dim = cfg.embedding_dim
self.va... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | JustinLiam/DAN | SingleHeadAttention | false | 7,625 | [
"MIT"
] | 1 | eb29cddad6c93e591854b115ef524643b1cd471c | https://github.com/JustinLiam/DAN/tree/eb29cddad6c93e591854b115ef524643b1cd471c |
SparsemaxBisect | from torch.autograd import Function
import torch
import torch.nn as nn
def sparsemax_bisect(X, dim=-1, n_iter=50, ensure_sum_one=True):
"""sparsemax: normalizing sparse transform (a la softmax), via bisection.
Solves the projection:
min_p ||x - p||_2 s.t. p >= 0, sum(p) == 1.
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
from torch._inductor.runtime import triton_helpers
from torch.autograd import Function
import torch.nn as nn
assert_size_stride = torch._C._... | roholazandie/entmax | SparsemaxBisect | false | 7,626 | [
"MIT"
] | 1 | 657374e6a792ec6840b6f78bc759cc1f51570aad | https://github.com/roholazandie/entmax/tree/657374e6a792ec6840b6f78bc759cc1f51570aad |
GPT2Layer | from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
import torch.nn.functional as F
class MultiHeadSelfAttention(nn.Module):
def __init__(self, d_ipt: 'int', n_head: 'int', dropout_p: 'float'=0.1):
super(MultiHeadSelfAttention, self).__init__()
self.qkv_linear = nn.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
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | DunZhang/GPT2SourceCode | GPT2Layer | false | 7,627 | [
"MIT"
] | 1 | d598dbae278c93f88469d45ec025da4cfa7d69ee | https://github.com/DunZhang/GPT2SourceCode/tree/d598dbae278c93f88469d45ec025da4cfa7d69ee |
Gaussian | import torch
from torch import Tensor
import torch.utils.tensorboard
import torch.utils.data
class Gaussian(torch.nn.Module):
"""Gaussian activation"""
def forward(self, x: 'Tensor') ->Tensor:
return torch.exp(-x * x)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
... | 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.utils.tensorboard
import torch.utils.data
assert_size_stride... | raimis/torchani | Gaussian | false | 7,628 | [
"MIT"
] | 1 | 19882c6e18174e08423706a536366f89029a740a | https://github.com/raimis/torchani/tree/19882c6e18174e08423706a536366f89029a740a |
EntmaxBisect | from torch.autograd import Function
import torch
import torch.nn as nn
def entmax_bisect(X, alpha=1.5, dim=-1, n_iter=50, ensure_sum_one=True):
"""alpha-entmax: normalizing sparse transform (a la softmax).
Solves the optimization problem:
max_p <x, p> - H_a(p) s.t. p >= 0, sum(p) == 1.
wh... | 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.autograd import F... | roholazandie/entmax | EntmaxBisect | false | 7,629 | [
"MIT"
] | 1 | 657374e6a792ec6840b6f78bc759cc1f51570aad | https://github.com/roholazandie/entmax/tree/657374e6a792ec6840b6f78bc759cc1f51570aad |
ResNetV2 | import torch
import torch.nn as nn
import torch.nn.functional as F
from collections import OrderedDict
import torch.utils.data
def conv1x1(cin, cout, stride=1, bias=False):
return StdConv2d(cin, cout, kernel_size=1, stride=stride, padding=0,
bias=bias)
def conv3x3(in_planes, out_planes, stride=1):
r... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | matsuolab/DomainBed | ResNetV2 | false | 7,630 | [
"MIT"
] | 1 | 00e0e3d183b36fd4d0c50442012149794a6504c2 | https://github.com/matsuolab/DomainBed/tree/00e0e3d183b36fd4d0c50442012149794a6504c2 |
HSwish | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data.distributed
class HSwish(nn.Module):
def __init__(self, inplace=True):
super(HSwish, self).__init__()
self.inplace = inplace
def forward(self, x):
out = x * F.relu6(x + 3, inplace=self.inplace)... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import torch.utils.data.distributed
assert_size_stride = torch._C._... | AberHu/ImageNet-training | HSwish | false | 7,631 | [
"MIT"
] | 12 | 7201eb140176f4d7ec1ed0ff5c27deba2dfb60c2 | https://github.com/AberHu/ImageNet-training/tree/7201eb140176f4d7ec1ed0ff5c27deba2dfb60c2 |
Normalize | import torch
import torch.utils.data
class Normalize(torch.nn.Module):
def __init__(self):
super(Normalize, self).__init__()
self.normalize = torch.nn.functional.normalize
def forward(self, x):
x = self.normalize(x, dim=-1)
return x
def get_inputs():
return [torch.rand(... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import torch.utils.data
asse... | Alescontrela/AMP_for_hardware | Normalize | false | 7,632 | [
"BSD-3-Clause"
] | 11 | bfb0dbdcf32bdf83a916790bddf193fffc7e79b8 | https://github.com/Alescontrela/AMP_for_hardware/tree/bfb0dbdcf32bdf83a916790bddf193fffc7e79b8 |
ResizeTransform | import torch
import torch.nn as nn
import torch.nn.functional as nnf
import torch.utils
class ResizeTransform(nn.Module):
"""
Resize a transform, which involves resizing the vector field *and* rescaling it.
"""
def __init__(self, vel_resize, ndims):
super().__init__()
self.factor = 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
import torch.utils
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dyna... | Alison-brie/MultiPropReg | ResizeTransform | false | 7,633 | [
"MIT"
] | 14 | 526d843b161c0e2e53ec5c7c47de6964c6a44c60 | https://github.com/Alison-brie/MultiPropReg/tree/526d843b161c0e2e53ec5c7c47de6964c6a44c60 |
LinearBlock | import torch
from torch import nn
class LinearBlock(nn.Module):
def __init__(self, in_dim, out_dim, norm='none', activation='relu'):
super(LinearBlock, self).__init__()
use_bias = True
self.fc = nn.Linear(in_dim, out_dim, bias=use_bias)
norm_dim = out_dim
if norm == 'bn':
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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... | Alikfp/research-GANwriting | LinearBlock | false | 7,634 | [
"MIT"
] | 41 | 2190954218a733deac52c929f51bb85bca5d7216 | https://github.com/Alikfp/research-GANwriting/tree/2190954218a733deac52c929f51bb85bca5d7216 |
ResizeConv2d | import torch
import torch.nn.functional as F
import torch.nn as nn
import torch.cuda
import torch.optim
import torch.utils.data
class ResizeConv2d(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, scale_factor,
mode='nearest'):
super().__init__()
self.scale_factor = s... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.cuda
import torch.optim
import torch.utils.da... | AhmadQasim/MedAL | ResizeConv2d | false | 7,635 | [
"MIT"
] | 13 | 0ad6064d0d07f23722034b866ba86d93b62517f4 | https://github.com/AhmadQasim/MedAL/tree/0ad6064d0d07f23722034b866ba86d93b62517f4 |
BalancedL1Loss | import functools
import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
def reduce_loss(loss, reduction):
"""Reduce loss as specified.
Args:
loss (Tensor): Elementwise loss tensor.
reduction (str): Options are "none", "mean" and "sum".
Return:
Tenso... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import functools
impor... | AllenPeng0209/SaccadeNet | BalancedL1Loss | false | 7,636 | [
"Apache-2.0"
] | 30 | 0fce4266cbffc9a2c5f70335efa636da849ce70c | https://github.com/AllenPeng0209/SaccadeNet/tree/0fce4266cbffc9a2c5f70335efa636da849ce70c |
Conv2dBlock | import torch
import torch.nn.functional as F
from torch import nn
class AdaptiveInstanceNorm2d(nn.Module):
def __init__(self, num_features, eps=1e-05, momentum=0.1):
super(AdaptiveInstanceNorm2d, self).__init__()
self.num_features = num_features
self.eps = eps
self.momentum = mome... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.functional as... | Alikfp/research-GANwriting | Conv2dBlock | false | 7,637 | [
"MIT"
] | 41 | 2190954218a733deac52c929f51bb85bca5d7216 | https://github.com/Alikfp/research-GANwriting/tree/2190954218a733deac52c929f51bb85bca5d7216 |
WeightedCrossEntropyLoss | import torch
import torch.nn as nn
import torch.nn.functional as F
class WeightedCrossEntropyLoss(nn.Module):
"""
Transform input to fit the fomation of PyTorch offical cross entropy loss
with anchor-wise weighting.
"""
def __init__(self):
super(WeightedCrossEntropyLoss, self).__init__()
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | AbangLZU/OpenPCDet | WeightedCrossEntropyLoss | false | 7,638 | [
"Apache-2.0"
] | 29 | eeea3f24d392f692228c1ad4e28c0dc9d0e25665 | https://github.com/AbangLZU/OpenPCDet/tree/eeea3f24d392f692228c1ad4e28c0dc9d0e25665 |
GlobalAvgPool2d | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.parallel
import torch.optim
import torch.utils.data
class GlobalAvgPool2d(nn.Module):
def __init__(self):
super(GlobalAvgPool2d, self).__init__()
def forward(self, x):
N = x.data.size(0)
C = x.data.siz... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.asser... | Alin1102/Yolov3_Dartnet2Caffe | GlobalAvgPool2d | false | 7,639 | [
"MIT"
] | 21 | b4284b080f53c1ac73c1930b1b1c4e07dcd97559 | https://github.com/Alin1102/Yolov3_Dartnet2Caffe/tree/b4284b080f53c1ac73c1930b1b1c4e07dcd97559 |
Eltwise | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
class Eltwise(nn.Module):
def __init__(self, operation='+'):
super(Eltwise, self).__init__()
self.operation = operation
def forward(self, x1, x2):
if self.operation == '+' or self.o... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.asser... | Alin1102/Yolov3_Dartnet2Caffe | Eltwise | false | 7,640 | [
"MIT"
] | 21 | b4284b080f53c1ac73c1930b1b1c4e07dcd97559 | https://github.com/Alin1102/Yolov3_Dartnet2Caffe/tree/b4284b080f53c1ac73c1930b1b1c4e07dcd97559 |
MaxPoolStride1 | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.parallel
import torch.optim
import torch.utils.data
class MaxPoolStride1(nn.Module):
def __init__(self):
super(MaxPoolStride1, self).__init__()
def forward(self, x):
x = F.max_pool2d(F.pad(x, (0, 1, 0, 1), mod... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data... | Alin1102/Yolov3_Dartnet2Caffe | MaxPoolStride1 | false | 7,641 | [
"MIT"
] | 21 | b4284b080f53c1ac73c1930b1b1c4e07dcd97559 | https://github.com/Alin1102/Yolov3_Dartnet2Caffe/tree/b4284b080f53c1ac73c1930b1b1c4e07dcd97559 |
SigmoidFocalClassificationLoss | import torch
import torch.nn as nn
class SigmoidFocalClassificationLoss(nn.Module):
"""
Sigmoid focal cross entropy loss.
"""
def __init__(self, gamma: 'float'=2.0, alpha: 'float'=0.25):
"""
Args:
gamma: Weighting parameter to balance loss for hard and easy examples.
... | 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... | AbangLZU/OpenPCDet | SigmoidFocalClassificationLoss | false | 7,642 | [
"Apache-2.0"
] | 29 | eeea3f24d392f692228c1ad4e28c0dc9d0e25665 | https://github.com/AbangLZU/OpenPCDet/tree/eeea3f24d392f692228c1ad4e28c0dc9d0e25665 |
L2Norm | import torch
import torch.nn as nn
class L2Norm(nn.Module):
def __init__(self, n_dims, scale=20.0, eps=1e-10):
super(L2Norm, self).__init__()
self.n_dims = n_dims
self.weight = nn.Parameter(torch.Tensor(self.n_dims))
self.eps = eps
self.scale = scale
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.triton_helpers import libdevice
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_... | AllenPeng0209/SaccadeNet | L2Norm | false | 7,643 | [
"Apache-2.0"
] | 30 | 0fce4266cbffc9a2c5f70335efa636da849ce70c | https://github.com/AllenPeng0209/SaccadeNet/tree/0fce4266cbffc9a2c5f70335efa636da849ce70c |
Policy | import torch
import torch.nn as nn
class Policy(nn.Module):
def __init__(self, num_inputs, num_outputs):
super(Policy, self).__init__()
self.affine1 = nn.Linear(num_inputs, 64)
self.affine2 = nn.Linear(64, 64)
self.action_mean = nn.Linear(64, num_outputs)
self.action_mean.... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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... | Akella17/Deep-Bayesian-Quadrature-Policy-Optimization | Policy | false | 7,644 | [
"MIT"
] | 16 | e98fd68046486c002c33cf019db2ce66da18615b | https://github.com/Akella17/Deep-Bayesian-Quadrature-Policy-Optimization/tree/e98fd68046486c002c33cf019db2ce66da18615b |
HSigmoid | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data.distributed
class HSigmoid(nn.Module):
def __init__(self, inplace=True):
super(HSigmoid, self).__init__()
self.inplace = inplace
def forward(self, x):
out = F.relu6(x + 3, inplace=self.inplace)... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import torch.utils.data.distributed
assert_size_stride = torch._C._... | AberHu/ImageNet-training | HSigmoid | false | 7,645 | [
"MIT"
] | 12 | 7201eb140176f4d7ec1ed0ff5c27deba2dfb60c2 | https://github.com/AberHu/ImageNet-training/tree/7201eb140176f4d7ec1ed0ff5c27deba2dfb60c2 |
Scale | import torch
import torch.nn as nn
class Scale(nn.Module):
def __init__(self, scale=1.0):
super(Scale, self).__init__()
self.scale = nn.Parameter(torch.tensor(scale, dtype=torch.float))
def forward(self, x):
return x * self.scale
def get_inputs():
return [torch.rand([4, 4, 4, 4... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | AllenPeng0209/SaccadeNet | Scale | false | 7,646 | [
"Apache-2.0"
] | 30 | 0fce4266cbffc9a2c5f70335efa636da849ce70c | https://github.com/AllenPeng0209/SaccadeNet/tree/0fce4266cbffc9a2c5f70335efa636da849ce70c |
Abs | import torch
import torch.nn as nn
class ModuleWrapper(nn.Module):
"""Wrapper for nn.Module with support for arbitrary flags and a universal forward pass"""
def __init__(self):
super(ModuleWrapper, self).__init__()
def set_flag(self, flag_name, value):
setattr(self, flag_name, value)
... | 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... | AlliedToasters/elko_den | Abs | false | 7,647 | [
"Apache-2.0"
] | 38 | 4e69f7f5c0dc7ffad54c7e190a2b75aba2eab7d2 | https://github.com/AlliedToasters/elko_den/tree/4e69f7f5c0dc7ffad54c7e190a2b75aba2eab7d2 |
GHMC | import torch
import torch.nn as nn
import torch.nn.functional as F
def _expand_binary_labels(labels, label_weights, label_channels):
bin_labels = labels.new_full((labels.size(0), label_channels), 0)
inds = torch.nonzero(labels >= 1).squeeze()
if inds.numel() > 0:
bin_labels[inds, labels[inds] - 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 math as tl_math
import torch.nn as nn
... | AllenPeng0209/SaccadeNet | GHMC | false | 7,648 | [
"Apache-2.0"
] | 30 | 0fce4266cbffc9a2c5f70335efa636da849ce70c | https://github.com/AllenPeng0209/SaccadeNet/tree/0fce4266cbffc9a2c5f70335efa636da849ce70c |
ConvWS2d | import torch
import torch.nn as nn
import torch.nn.functional as F
def conv_ws_2d(input, weight, bias=None, stride=1, padding=0, dilation=1,
groups=1, eps=1e-05):
c_in = weight.size(0)
weight_flat = weight.view(c_in, -1)
mean = weight_flat.mean(dim=1, keepdim=True).view(c_in, 1, 1, 1)
std = weight... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | AllenPeng0209/SaccadeNet | ConvWS2d | false | 7,649 | [
"Apache-2.0"
] | 30 | 0fce4266cbffc9a2c5f70335efa636da849ce70c | https://github.com/AllenPeng0209/SaccadeNet/tree/0fce4266cbffc9a2c5f70335efa636da849ce70c |
Attention | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
class Attention(nn.Module):
def __init__(self, embed_dim, hidden_dim=None, n_head=1, score_function
='scaled_dot_product'):
super(Attention, self).__init__()
if hidden_dim is None:
hidden_dim = embe... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | AlbertoPaz/ABSA-PyTorch | Attention | false | 7,650 | [
"MIT"
] | 20 | 070a4b6f20cde0e2021c72b84c534659d749f36e | https://github.com/AlbertoPaz/ABSA-PyTorch/tree/070a4b6f20cde0e2021c72b84c534659d749f36e |
SmoothL1Loss | import functools
import torch
import torch.nn as nn
import torch.nn.functional as F
def reduce_loss(loss, reduction):
"""Reduce loss as specified.
Args:
loss (Tensor): Elementwise loss tensor.
reduction (str): Options are "none", "mean" and "sum".
Return:
Tensor: Reduced loss ten... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import functools
impor... | AllenPeng0209/SaccadeNet | SmoothL1Loss | false | 7,651 | [
"Apache-2.0"
] | 30 | 0fce4266cbffc9a2c5f70335efa636da849ce70c | https://github.com/AllenPeng0209/SaccadeNet/tree/0fce4266cbffc9a2c5f70335efa636da849ce70c |
KL_loss_softmax | import torch
import torch.nn as nn
import torch.nn.init
class KL_loss_softmax(nn.Module):
"""
Compute KL_divergence between all prediction score (already sum=1, omit softmax function)
"""
def __init__(self):
super(KL_loss_softmax, self).__init__()
self.KL_loss = nn.KLDivLoss(reduce=Fa... | 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... | AndresPMD/semantic_adaptive_margin | KL_loss_softmax | false | 7,652 | [
"Apache-2.0"
] | 12 | 1e8bf2f1836498c48df030cb0a967b72b52e8460 | https://github.com/AndresPMD/semantic_adaptive_margin/tree/1e8bf2f1836498c48df030cb0a967b72b52e8460 |
Centered_Grad | import torch
import torch.nn as nn
class Centered_Grad(nn.Module):
def __init__(self):
super(Centered_Grad, self).__init__()
self.x_ker_init = torch.tensor([[[[-0.5, 0, 0.5]]]], dtype=torch.
float, requires_grad=True)
self.y_ker_init = torch.tensor([[[[-0.5], [0], [0.5]]]], dt... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | AmazingAng/pytorch-tvnet | Centered_Grad | false | 7,653 | [
"MIT"
] | 12 | e880d3ce15f55e5d9a11b423cfd1e0461de4fedb | https://github.com/AmazingAng/pytorch-tvnet/tree/e880d3ce15f55e5d9a11b423cfd1e0461de4fedb |
ShuffleBlock | import torch
from torch import nn
class ShuffleBlock(nn.Module):
def __init__(self, groups=2):
super(ShuffleBlock, self).__init__()
self.groups = groups
def forward(self, x):
"""Channel shuffle: [N,C,H,W] -> [N,g,C/g,H,W] -> [N,C/g,g,H,w] -> [N,C,H,W]"""
N, C, H, W = x.size()... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_str... | Alibaba-MIIL/HeadSharingKD | ShuffleBlock | false | 7,654 | [
"BSD-2-Clause"
] | 15 | 8e2738bf069c7d12ec933f9b9107f267f7b6603a | https://github.com/Alibaba-MIIL/HeadSharingKD/tree/8e2738bf069c7d12ec933f9b9107f267f7b6603a |
GLU | import torch
import torch.nn as nn
class GLU(nn.Module):
def __init__(self, input_channel, output_channel):
super(GLU, self).__init__()
self.linear_left = nn.Linear(input_channel, output_channel)
self.linear_right = nn.Linear(input_channel, output_channel)
def forward(self, x):
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | Abdulmajid-Murad/deep_probabilistic_forecast | GLU | false | 7,655 | [
"MIT"
] | 11 | 399846381af4bb789021c9f63f121dd69fa0125d | https://github.com/Abdulmajid-Murad/deep_probabilistic_forecast/tree/399846381af4bb789021c9f63f121dd69fa0125d |
ActFirstResBlock | import torch
import torch.nn.functional as F
from torch import nn
class AdaptiveInstanceNorm2d(nn.Module):
def __init__(self, num_features, eps=1e-05, momentum=0.1):
super(AdaptiveInstanceNorm2d, self).__init__()
self.num_features = num_features
self.eps = eps
self.momentum = mome... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | Alikfp/research-GANwriting | ActFirstResBlock | false | 7,656 | [
"MIT"
] | 41 | 2190954218a733deac52c929f51bb85bca5d7216 | https://github.com/Alikfp/research-GANwriting/tree/2190954218a733deac52c929f51bb85bca5d7216 |
Multi_feature_fusing | import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.init
def l2norm(X, dim=-1, eps=1e-12):
"""L2-normalize columns of X
"""
norm = torch.pow(X, 2).sum(dim=dim, keepdim=True).sqrt() + eps
X = torch.div(X, norm)
return X
class Multi_feature_fusing(... | 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 numpy as np
import torch.nn as nn
import torch.nn.init
assert_size_strid... | AndresPMD/semantic_adaptive_margin | Multi_feature_fusing | false | 7,657 | [
"Apache-2.0"
] | 12 | 1e8bf2f1836498c48df030cb0a967b72b52e8460 | https://github.com/AndresPMD/semantic_adaptive_margin/tree/1e8bf2f1836498c48df030cb0a967b72b52e8460 |
Permute | import torch
from torch import nn
class Permute(nn.Module):
def __init__(self, permutation=[2, 1, 0]):
super().__init__()
self.permutation = permutation
def forward(self, input):
return input[:, self.permutation]
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_ini... | 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... | Alibaba-AAIG/Beyond-ImageNet-Attack | Permute | false | 7,658 | [
"MIT"
] | 23 | c14b4844b64a8035b8fe033a617c0567224a9fa4 | https://github.com/Alibaba-AAIG/Beyond-ImageNet-Attack/tree/c14b4844b64a8035b8fe033a617c0567224a9fa4 |
FactorTransfer | import torch
from torch import nn
import torch.nn.functional as F
class FactorTransfer(nn.Module):
"""Paraphrasing Complex Network: Network Compression via Factor Transfer, NeurIPS 2018"""
def __init__(self, p1=2, p2=1):
super(FactorTransfer, self).__init__()
self.p1 = p1
self.p2 = p2... | 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 ... | Alibaba-MIIL/HeadSharingKD | FactorTransfer | false | 7,659 | [
"BSD-2-Clause"
] | 15 | 8e2738bf069c7d12ec933f9b9107f267f7b6603a | https://github.com/Alibaba-MIIL/HeadSharingKD/tree/8e2738bf069c7d12ec933f9b9107f267f7b6603a |
GHMR | import torch
import torch.nn as nn
class GHMR(nn.Module):
"""GHM Regression Loss.
Details of the theorem can be viewed in the paper
"Gradient Harmonized Single-stage Detector"
https://arxiv.org/abs/1811.05181
Args:
mu (float): The parameter for the Authentic Smooth L1 loss.
bins ... | 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... | AllenPeng0209/SaccadeNet | GHMR | false | 7,660 | [
"Apache-2.0"
] | 30 | 0fce4266cbffc9a2c5f70335efa636da849ce70c | https://github.com/AllenPeng0209/SaccadeNet/tree/0fce4266cbffc9a2c5f70335efa636da849ce70c |
Forward_Grad | import torch
import torch.nn as nn
import torch.nn.functional as F
class Forward_Grad(nn.Module):
def __init__(self):
super(Forward_Grad, self).__init__()
self.x_ker_init = torch.tensor([[[[-1, 1]]]], dtype=torch.float,
requires_grad=True)
self.y_ker_init = torch.tensor([[[[-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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | AmazingAng/pytorch-tvnet | Forward_Grad | false | 7,661 | [
"MIT"
] | 12 | e880d3ce15f55e5d9a11b423cfd1e0461de4fedb | https://github.com/AmazingAng/pytorch-tvnet/tree/e880d3ce15f55e5d9a11b423cfd1e0461de4fedb |
EncoderImagePrecomp | import torch
import numpy as np
from collections import OrderedDict
import torch.nn as nn
import torch.nn.init
def l2norm(X, dim=-1, eps=1e-12):
"""L2-normalize columns of X
"""
norm = torch.pow(X, 2).sum(dim=dim, keepdim=True).sqrt() + eps
X = torch.div(X, norm)
return X
class EncoderImagePreco... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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
... | AndresPMD/semantic_adaptive_margin | EncoderImagePrecomp | false | 7,662 | [
"Apache-2.0"
] | 12 | 1e8bf2f1836498c48df030cb0a967b72b52e8460 | https://github.com/AndresPMD/semantic_adaptive_margin/tree/1e8bf2f1836498c48df030cb0a967b72b52e8460 |
Normalize | import torch
from torch import nn
class Normalize(nn.Module):
"""normalization layer"""
def __init__(self, power=2):
super(Normalize, self).__init__()
self.power = power
def forward(self, x):
norm = x.pow(self.power).sum(1, keepdim=True).pow(1.0 / self.power)
out = x.div(... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | Alibaba-MIIL/HeadSharingKD | Normalize | false | 7,663 | [
"BSD-2-Clause"
] | 15 | 8e2738bf069c7d12ec933f9b9107f267f7b6603a | https://github.com/Alibaba-MIIL/HeadSharingKD/tree/8e2738bf069c7d12ec933f9b9107f267f7b6603a |
LinearBlock | import torch
import torch.nn as nn
import torch.utils.data
def l2normalize(v, eps=1e-12):
return v / (v.norm() + eps)
class LayerNorm(nn.Module):
def __init__(self, num_features, eps=1e-05, affine=True):
super(LayerNorm, self).__init__()
self.num_features = num_features
self.affine ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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 ... | AllenPu/mbdg | LinearBlock | false | 7,664 | [
"MIT"
] | 27 | 243f53a57dcf4bfb6e717c0c9f64a839cff8d548 | https://github.com/AllenPu/mbdg/tree/243f53a57dcf4bfb6e717c0c9f64a839cff8d548 |
FeatureExtractor | import torch
import torch.nn as nn
class FeatureExtractor(nn.Module):
def __init__(self, num_inputs, num_outputs):
super(FeatureExtractor, self).__init__()
self.affine1 = nn.Linear(num_inputs, 64)
self.affine2 = nn.Linear(64, 48)
self.affine3 = nn.Linear(48, num_outputs)
def ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | Akella17/Deep-Bayesian-Quadrature-Policy-Optimization | FeatureExtractor | false | 7,665 | [
"MIT"
] | 16 | e98fd68046486c002c33cf019db2ce66da18615b | https://github.com/Akella17/Deep-Bayesian-Quadrature-Policy-Optimization/tree/e98fd68046486c002c33cf019db2ce66da18615b |
mlp | import torch
import torch.nn as nn
class mlp(nn.Module):
def __init__(self, seq_len):
super(mlp, self).__init__()
self.lin1 = nn.Linear(seq_len, 2048)
self.lin2 = nn.Linear(2048, 2048)
self.lin3 = nn.Linear(2048, seq_len)
self.relu = nn.ReLU()
def forward(self, 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_... | AliRoyat/MACS_IQA | mlp | false | 7,666 | [
"Apache-2.0"
] | 16 | d37ac72170dc0271065a7c54273b70ed52aee4b8 | https://github.com/AliRoyat/MACS_IQA/tree/d37ac72170dc0271065a7c54273b70ed52aee4b8 |
Normalize_one | import torch
from torch import nn
class Normalize_one(nn.Module):
def __init__(self, mean, std):
super(Normalize_one, self).__init__()
self.mean = mean
self.std = std
def forward(self, input):
x = input.clone()
x = (x - self.mean) / self.std
return x
def get... | 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... | Alibaba-AAIG/Beyond-ImageNet-Attack | Normalize_one | false | 7,667 | [
"MIT"
] | 23 | c14b4844b64a8035b8fe033a617c0567224a9fa4 | https://github.com/Alibaba-AAIG/Beyond-ImageNet-Attack/tree/c14b4844b64a8035b8fe033a617c0567224a9fa4 |
FPNHead | import torch
import torch.nn as nn
class FPNHead(nn.Module):
""""this is the FPNHead class common to all backbones"""
def __init__(self, num_in, num_mid, num_out):
super(FPNHead, self).__init__()
self.block0 = nn.Conv2d(num_in, num_mid, kernel_size=3, padding=1,
bias=False)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | AmaldevHari/Ghost-DeblurGAN | FPNHead | false | 7,668 | [
"MIT"
] | 16 | e725e5dad6a5fa5865d317e6644d96d0e800eae6 | https://github.com/AmaldevHari/Ghost-DeblurGAN/tree/e725e5dad6a5fa5865d317e6644d96d0e800eae6 |
LayerNorm | import torch
import torch.nn as nn
import torch.utils.data
class LayerNorm(nn.Module):
def __init__(self, num_features, eps=1e-05, affine=True):
super(LayerNorm, self).__init__()
self.num_features = num_features
self.affine = affine
self.eps = eps
if self.affine:
... | 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.utils.data
assert_size_stride = torch._C._dy... | AllenPu/mbdg | LayerNorm | false | 7,669 | [
"MIT"
] | 27 | 243f53a57dcf4bfb6e717c0c9f64a839cff8d548 | https://github.com/AllenPu/mbdg/tree/243f53a57dcf4bfb6e717c0c9f64a839cff8d548 |
HintLoss | import torch
from torch import nn
class HintLoss(nn.Module):
"""Fitnets: hints for thin deep nets, ICLR 2015"""
def __init__(self):
super(HintLoss, self).__init__()
self.crit = nn.MSELoss()
def forward(self, f_s, f_t):
loss = self.crit(f_s, f_t)
return loss
def get_inpu... | 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... | Alibaba-MIIL/HeadSharingKD | HintLoss | false | 7,670 | [
"BSD-2-Clause"
] | 15 | 8e2738bf069c7d12ec933f9b9107f267f7b6603a | https://github.com/Alibaba-MIIL/HeadSharingKD/tree/8e2738bf069c7d12ec933f9b9107f267f7b6603a |
DeContraster | import torch
import torch.distributions
import torch.utils.data
class AdversarialNoiseGenerator(torch.nn.Module):
def __init__(self):
super().__init__()
return
def forward(self, x):
raise NotImplementedError()
class DeContraster(AdversarialNoiseGenerator):
def __init__(self, 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
from torch._inductor.runtime import triton_helpers
import torch.distributions
import torch.utils.data
assert_size_stride = torch._C._dynamo.... | AlexMeinke/Provable-OOD-Detection | DeContraster | false | 7,671 | [
"MIT"
] | 21 | 9a132aec994ff718c96b81885736ab866df60d87 | https://github.com/AlexMeinke/Provable-OOD-Detection/tree/9a132aec994ff718c96b81885736ab866df60d87 |
BinaryLoss | import torch
import torch.nn as nn
import torch.distributions
import torch.utils.data
class BinaryLoss(nn.Module):
def __init__(self):
super().__init__()
def forward(self, output):
return torch.logaddexp(torch.tensor([1.0], device=output.device), -
output)
def get_inputs():
... | 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.distributions
import torch.... | AlexMeinke/Provable-OOD-Detection | BinaryLoss | false | 7,672 | [
"MIT"
] | 21 | 9a132aec994ff718c96b81885736ab866df60d87 | https://github.com/AlexMeinke/Provable-OOD-Detection/tree/9a132aec994ff718c96b81885736ab866df60d87 |
EncoderImageWeightNormPrecomp | import torch
from collections import OrderedDict
import torch.nn as nn
import torch.nn.init
from torch.nn.utils.weight_norm import weight_norm
def l2norm(X, dim=-1, eps=1e-12):
"""L2-normalize columns of X
"""
norm = torch.pow(X, 2).sum(dim=dim, keepdim=True).sqrt() + eps
X = torch.div(X, norm)
re... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 collections im... | AndresPMD/semantic_adaptive_margin | EncoderImageWeightNormPrecomp | false | 7,673 | [
"Apache-2.0"
] | 12 | 1e8bf2f1836498c48df030cb0a967b72b52e8460 | https://github.com/AndresPMD/semantic_adaptive_margin/tree/1e8bf2f1836498c48df030cb0a967b72b52e8460 |
PKT | import torch
from torch import nn
class PKT(nn.Module):
"""Probabilistic Knowledge Transfer for deep representation learning
Code from author: https://github.com/passalis/probabilistic_kt"""
def __init__(self):
super(PKT, self).__init__()
def forward(self, f_s, f_t):
return self.cosi... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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... | Alibaba-MIIL/HeadSharingKD | PKT | false | 7,674 | [
"BSD-2-Clause"
] | 15 | 8e2738bf069c7d12ec933f9b9107f267f7b6603a | https://github.com/Alibaba-MIIL/HeadSharingKD/tree/8e2738bf069c7d12ec933f9b9107f267f7b6603a |
LinearVarianceUnif | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
from torch.nn import Parameter
class ModuleWrapper(nn.Module):
"""Wrapper for nn.Module with support for arbitrary flags and a universal forward pass"""
def __init__(self):
super(ModuleW... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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
from torch.nn import Parameter
assert_size_str... | AlliedToasters/elko_den | LinearVarianceUnif | false | 7,675 | [
"Apache-2.0"
] | 38 | 4e69f7f5c0dc7ffad54c7e190a2b75aba2eab7d2 | https://github.com/AlliedToasters/elko_den/tree/4e69f7f5c0dc7ffad54c7e190a2b75aba2eab7d2 |
KDE | import torch
from torch import nn
import torch.nn.functional as F
class KDE(nn.Module):
"""KD on embeddings - KDE"""
def __init__(self):
super(KDE, self).__init__()
def forward(self, embedding_s, embedding_t):
inputs_embed = F.normalize(embedding_s, p=2.0, dim=1)
targets_embed = ... | 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_... | Alibaba-MIIL/HeadSharingKD | KDE | false | 7,676 | [
"BSD-2-Clause"
] | 15 | 8e2738bf069c7d12ec933f9b9107f267f7b6603a | https://github.com/Alibaba-MIIL/HeadSharingKD/tree/8e2738bf069c7d12ec933f9b9107f267f7b6603a |
MNIST_CNN | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data
class MNIST_CNN(nn.Module):
"""
Hand-tuned architecture for MNIST.
Weirdness I've noticed so far with this architecture:
- adding a linear layer after the mean-pool in features hurts
RotatedMNIST-100 gen... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | AllenPu/mbdg | MNIST_CNN | false | 7,677 | [
"MIT"
] | 27 | 243f53a57dcf4bfb6e717c0c9f64a839cff8d548 | https://github.com/AllenPu/mbdg/tree/243f53a57dcf4bfb6e717c0c9f64a839cff8d548 |
LinearVariance | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
from torch.nn import Parameter
class ModuleWrapper(nn.Module):
"""Wrapper for nn.Module with support for arbitrary flags and a universal forward pass"""
def __init__(self):
super(ModuleW... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import math
import ... | AlliedToasters/elko_den | LinearVariance | false | 7,678 | [
"Apache-2.0"
] | 38 | 4e69f7f5c0dc7ffad54c7e190a2b75aba2eab7d2 | https://github.com/AlliedToasters/elko_den/tree/4e69f7f5c0dc7ffad54c7e190a2b75aba2eab7d2 |
DistillKL | import torch
from torch import nn
import torch.nn.functional as F
class DistillKL(nn.Module):
"""Distilling the Knowledge in a Neural Network"""
def __init__(self, T):
super(DistillKL, self).__init__()
self.T = T
def forward(self, y_s, y_t):
p_s = F.log_softmax(y_s / self.T, dim=... | 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 ... | Alibaba-MIIL/HeadSharingKD | DistillKL | false | 7,679 | [
"BSD-2-Clause"
] | 15 | 8e2738bf069c7d12ec933f9b9107f267f7b6603a | https://github.com/Alibaba-MIIL/HeadSharingKD/tree/8e2738bf069c7d12ec933f9b9107f267f7b6603a |
Correlation | import torch
from torch import nn
class Correlation(nn.Module):
"""Correlation Congruence for Knowledge Distillation, ICCV 2019.
The authors nicely shared the code with me. I restructured their code to be
compatible with my running framework. Credits go to the original author"""
def __init__(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.triton_helpers import math as tl_math
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_... | Alibaba-MIIL/HeadSharingKD | Correlation | false | 7,680 | [
"BSD-2-Clause"
] | 15 | 8e2738bf069c7d12ec933f9b9107f267f7b6603a | https://github.com/Alibaba-MIIL/HeadSharingKD/tree/8e2738bf069c7d12ec933f9b9107f267f7b6603a |
HINet | import torch
import torch.nn as nn
class HINet(nn.Module):
def __init__(self, in_ch):
super(HINet, self).__init__()
self.instance_norm = nn.InstanceNorm2d(in_ch - in_ch // 2, affine=True)
def forward(self, x):
channels = x.shape[1]
channels_i = channels - channels // 2
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_... | AmaldevHari/Ghost-DeblurGAN | HINet | false | 7,681 | [
"MIT"
] | 16 | e725e5dad6a5fa5865d317e6644d96d0e800eae6 | https://github.com/AmaldevHari/Ghost-DeblurGAN/tree/e725e5dad6a5fa5865d317e6644d96d0e800eae6 |
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