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 |
|---|---|---|---|---|---|---|---|---|---|---|
InverseSigmoid | import torch
import torch.utils.data
def inverseSigmoid(y):
"""
inverse of y=torch.sigmoid(y)
:param y:
:return: x
"""
return torch.log(-y / (y - 1))
class InverseSigmoid(torch.nn.Module):
def forward(self, y):
return inverseSigmoid(y)
def get_inputs():
return [torch.rand([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
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.asse... | khoehlein/fV-SRN | InverseSigmoid | false | 3,831 | [
"MIT"
] | 0 | 601f3e952b090df92e875c233c2c9ca646523948 | https://github.com/khoehlein/fV-SRN/tree/601f3e952b090df92e875c233c2c9ca646523948 |
InverseSoftplus | import torch
import torch.utils.data
def inverseSoftplus(y, beta=1, threshold=20):
"""
inverse of y=torch.nn.functional.softplus(x, beta, threshold)
:param y: the output of the softplus
:param beta: the smoothness of the step
:param threshold: the threshold after which a linear function is used
: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.utils.data
assert_size_stride = torch._C._dynamo.guards.asse... | khoehlein/fV-SRN | InverseSoftplus | false | 3,832 | [
"MIT"
] | 0 | 601f3e952b090df92e875c233c2c9ca646523948 | https://github.com/khoehlein/fV-SRN/tree/601f3e952b090df92e875c233c2c9ca646523948 |
MultiHeadAttention | import math
import torch
from torch import nn
import torch.nn.functional as F
class MultiHeadAttention(nn.Module):
def __init__(self, emb_size, n_heads=8, mask=False):
"""
Arguments:
emb_size: Size of input Embeddings
n_heads: Number of heads for MultiHead Attention
Laye... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | kcmankar/TransformerFromScratch | MultiHeadAttention | false | 3,833 | [
"MIT"
] | 0 | 4c68d507f3b0b9713822964e3769283ca0ddc685 | https://github.com/kcmankar/TransformerFromScratch/tree/4c68d507f3b0b9713822964e3769283ca0ddc685 |
decoder3 | import torch
import torch.nn
import torch
import torch.nn as nn
class decoder3(nn.Module):
def __init__(self, W, v2):
super(decoder3, self).__init__()
self.reflecPad7 = nn.ZeroPad2d((1, 1, 1, 1))
self.conv7 = nn.Conv2d(int(256 * W), int(128 * W), 3, 1, 0)
self.relu7 = nn.ReLU(inpl... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn
import torch
... | kamieen03/style-transfer-server | decoder3 | false | 3,834 | [
"BSD-2-Clause"
] | 0 | 91727ec62080215a0b870ce043faf0657137b84b | https://github.com/kamieen03/style-transfer-server/tree/91727ec62080215a0b870ce043faf0657137b84b |
LReluCustom | import torch
from torch import nn
class LReluCustom(nn.Module):
def __init__(self, leak=0.1):
super(LReluCustom, self).__init__()
self.leak = leak
def forward(self, x):
return torch.max(x, self.leak * 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 import triton_helpers
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empt... | kkulczak/phrases_reconstruction_GAN | LReluCustom | false | 3,835 | [
"MIT"
] | 0 | 5cf273416eb714f813a8d603942a442f0933cbff | https://github.com/kkulczak/phrases_reconstruction_GAN/tree/5cf273416eb714f813a8d603942a442f0933cbff |
TripletLoss | import torch
from torch import nn
import torch.nn.functional as F
class TripletLoss(nn.Module):
"""
Triplet loss
Takes embeddings of an anchor sample, a positive sample and a negative sample
"""
def __init__(self, margin=2.0):
super(TripletLoss, self).__init__()
self.margin = marg... | 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... | kmi-robots/object_reasoner | TripletLoss | false | 3,836 | [
"Apache-2.0"
] | 0 | 2d45bdb3ee745e0d866a152e8d81cbb375fa2985 | https://github.com/kmi-robots/object_reasoner/tree/2d45bdb3ee745e0d866a152e8d81cbb375fa2985 |
Net | import torch
class Net(torch.nn.Module):
def __init__(self, n_input, n_hidden, n_output):
super(Net, self).__init__()
self.layer1 = torch.nn.Linear(n_input, n_hidden)
self.layer2 = torch.nn.Linear(n_hidden, n_hidden)
self.layer3 = torch.nn.Linear(n_hidden, n_hidden)
self.l... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | kimukook/variable_length_oscillating_pendulum | Net | false | 3,837 | [
"MIT"
] | 0 | 486aa95fe4b9cbaa6cbeb542209259484f48e191 | https://github.com/kimukook/variable_length_oscillating_pendulum/tree/486aa95fe4b9cbaa6cbeb542209259484f48e191 |
Discriminator | import torch
import numpy as np
import torch.nn as nn
from torch.nn import functional as F
class Discriminator(nn.Module):
def __init__(self, img_shape, hidden_dim=1024):
super().__init__()
in_dim = int(np.prod(img_shape))
self.fc1 = nn.Linear(in_dim, hidden_dim)
self.fc2 = nn.Lin... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import numpy as np
import torch.nn as nn
assert_size_stride = torch._C._dynamo.g... | jfrancis71/pytorch-lightning-bolts | Discriminator | false | 3,838 | [
"Apache-2.0"
] | 0 | 8a4cf8f61644c28d6df54ccffe3a52d6f5fce5a6 | https://github.com/jfrancis71/pytorch-lightning-bolts/tree/8a4cf8f61644c28d6df54ccffe3a52d6f5fce5a6 |
ResidualSineLayer | import torch
import numpy as np
import torch.nn as nn
import torch.utils.data
class ResidualSineLayer(nn.Module):
"""
From Lu & Berger 2021, Compressive Neural Representations of Volumetric Scalar Fields
https://github.com/matthewberger/neurcomp/blob/main/siren.py
"""
def __init__(self, features:... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import numpy ... | khoehlein/fV-SRN | ResidualSineLayer | false | 3,839 | [
"MIT"
] | 0 | 601f3e952b090df92e875c233c2c9ca646523948 | https://github.com/khoehlein/fV-SRN/tree/601f3e952b090df92e875c233c2c9ca646523948 |
BertPredictionHeadTransform | from _paritybench_helpers import _mock_config
import math
import torch
from torch import nn
def gelu(x):
"""Gaussian Error Linear Unitという活性化関数です。
LeLUが0でカクっと不連続なので、そこを連続になるように滑らかにした形のLeLUです。
"""
return x * 0.5 * (1.0 + torch.erf(x / math.sqrt(2.0)))
class BertLayerNorm(nn.Module):
def __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 math
from to... | kimihitosugiyama/text_analysis | BertPredictionHeadTransform | false | 3,840 | [
"Apache-2.0"
] | 0 | 8f51022957928c31e52af1e0fd407daca3addb40 | https://github.com/kimihitosugiyama/text_analysis/tree/8f51022957928c31e52af1e0fd407daca3addb40 |
BertPreTrainingHeads | from _paritybench_helpers import _mock_config
import math
import torch
from torch import nn
def gelu(x):
"""Gaussian Error Linear Unitという活性化関数です。
LeLUが0でカクっと不連続なので、そこを連続になるように滑らかにした形のLeLUです。
"""
return x * 0.5 * (1.0 + torch.erf(x / math.sqrt(2.0)))
class BertLayerNorm(nn.Module):
def __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 math
from to... | kimihitosugiyama/text_analysis | BertPreTrainingHeads | false | 3,841 | [
"Apache-2.0"
] | 0 | 8f51022957928c31e52af1e0fd407daca3addb40 | https://github.com/kimihitosugiyama/text_analysis/tree/8f51022957928c31e52af1e0fd407daca3addb40 |
MSELoss | import torch
import torch.nn as nn
def reduction_batch_based(image_loss, M):
divisor = torch.sum(M)
if divisor == 0:
return 0
else:
return torch.sum(image_loss) / divisor
def mse_loss(prediction, target, mask, reduction=reduction_batch_based):
M = torch.sum(mask, (1, 2))
res = pr... | 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... | kopetri/MIDAS_pytorch | MSELoss | false | 3,842 | [
"MIT"
] | 0 | 9e933bd241ee18b487dcd2b65c28a55d8a923292 | https://github.com/kopetri/MIDAS_pytorch/tree/9e933bd241ee18b487dcd2b65c28a55d8a923292 |
QNetwork | import torch
import torch.nn as nn
import torch.nn.functional as F
class QNetwork(nn.Module):
def __init__(self, statedim, actiondim, hiddendim, init_w=0.0003):
super().__init__()
self.linear1 = nn.Linear(statedim + actiondim, hiddendim)
self.linear2 = nn.Linear(hiddendim, hiddendim)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | kiranprasad/multiagent-particle-envs | QNetwork | false | 3,843 | [
"MIT"
] | 0 | e28e3ff6606e80f11ee16bb2c42f21c442ad29a8 | https://github.com/kiranprasad/multiagent-particle-envs/tree/e28e3ff6606e80f11ee16bb2c42f21c442ad29a8 |
FFN | import torch
import torch.nn as nn
import torch.utils.data
class Conv(nn.Module):
"""
Convolution Module
"""
def __init__(self, in_channels, out_channels, kernel_size=1, stride=1,
padding=0, dilation=1, bias=True, w_init='linear'):
"""
:param in_channels: dimension of 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
from torch._inductor.runtime.... | kidconan/fast_speech_trans | FFN | false | 3,844 | [
"MIT"
] | 0 | 4d1d8fe0a871e37165e2a6333a11751ce2a017c0 | https://github.com/kidconan/fast_speech_trans/tree/4d1d8fe0a871e37165e2a6333a11751ce2a017c0 |
ContrastiveLoss | import torch
import torch.nn.functional as F
class ContrastiveLoss(torch.nn.Module):
"""
Contrastive loss function.
Based on: http://yann.lecun.com/exdb/publis/pdf/hadsell-chopra-lecun-06.pdf
"""
def __init__(self, margin=2.0):
super(ContrastiveLoss, self).__init__()
self.margin =... | 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
assert_size_stride = torch._... | kornellewy/face_one_shot_learing | ContrastiveLoss | false | 3,845 | [
"MIT"
] | 0 | 4cd8c8b1807717f921853043858a6f7ad5259917 | https://github.com/kornellewy/face_one_shot_learing/tree/4cd8c8b1807717f921853043858a6f7ad5259917 |
EmbedNet | from _paritybench_helpers import _mock_config
import torch
from torchvision.transforms import functional as F
import torch.utils.data
from torch import nn
import torch.nn.functional as F
class EmbedNet(nn.Module):
def __init__(self, cfg):
super(EmbedNet, self).__init__()
self.embed_conv1 = 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
from torch._inductor.runtime import triton_helpers
import torch.utils.data
from ... | hwfan/mega.pytorch | EmbedNet | false | 3,846 | [
"BSD-2-Clause"
] | 0 | a07b2267daad73c9482233cfe754d59b8ae2f688 | https://github.com/hwfan/mega.pytorch/tree/a07b2267daad73c9482233cfe754d59b8ae2f688 |
CAM_Use | import torch
import torch.nn as nn
import torch.utils.data
class CAM_Use(nn.Module):
""" Channel attention module"""
def __init__(self, in_dim):
super(CAM_Use, self).__init__()
self.chanel_in = in_dim
self.gamma = nn.Parameter(torch.zeros(1))
def forward(self, x, attention):
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.utils.data
assert_size_stride = torch._C._dyn... | jbcnrlz/san | CAM_Use | false | 3,847 | [
"MIT"
] | 0 | 1eab20f83d3c7dba5607e22d1c70768905b62b12 | https://github.com/jbcnrlz/san/tree/1eab20f83d3c7dba5607e22d1c70768905b62b12 |
CAM_Calculate | import torch
import torch.nn as nn
import torch.utils.data
class CAM_Calculate(nn.Module):
""" Channel attention module"""
def __init__(self, in_dim):
super(CAM_Calculate, self).__init__()
self.chanel_in = in_dim
self.softmax = nn.Softmax(dim=-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._inductor.runtime.... | jbcnrlz/san | CAM_Calculate | false | 3,848 | [
"MIT"
] | 0 | 1eab20f83d3c7dba5607e22d1c70768905b62b12 | https://github.com/jbcnrlz/san/tree/1eab20f83d3c7dba5607e22d1c70768905b62b12 |
FFN | import torch
import torch.nn as nn
import torch as t
class Conv(nn.Module):
"""
Convolution Module
"""
def __init__(self, in_channels, out_channels, kernel_size=1, stride=1,
padding=0, dilation=1, bias=True, w_init='linear'):
"""
:param in_channels: dimension of 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
from torch._inductor.runtime.... | kongziyue1234/mooc | FFN | false | 3,849 | [
"MIT"
] | 0 | 3b0c822dd55c1066cbc829137e6c424dcda5067e | https://github.com/kongziyue1234/mooc/tree/3b0c822dd55c1066cbc829137e6c424dcda5067e |
PairwiseRankingLoss | import torch
import torch.nn as nn
class PairwiseRankingLoss(nn.Module):
"""
Pairwise ranking loss
"""
def __init__(self, margin):
super(PairwiseRankingLoss, self).__init__()
self.margin = margin
def forward(self, anchor1, anchor2, img_sentc, sent_imgc):
cost_sent = torch... | 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... | ktodorov/uva-semantics-19 | PairwiseRankingLoss | false | 3,850 | [
"MIT"
] | 0 | c20e4f1d00f6693a8a46dd1d5576cfd3adced896 | https://github.com/ktodorov/uva-semantics-19/tree/c20e4f1d00f6693a8a46dd1d5576cfd3adced896 |
MeanEncoder | import torch
import torch.nn as nn
class BaseEncoder(nn.Module):
def __init__(self):
super(BaseEncoder, self).__init__()
self._input_dimensions = 0
@property
def input_dimensions(self):
return self._input_dimensions
class MeanEncoder(BaseEncoder):
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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | ktodorov/uva-semantics-19 | MeanEncoder | false | 3,851 | [
"MIT"
] | 0 | c20e4f1d00f6693a8a46dd1d5576cfd3adced896 | https://github.com/ktodorov/uva-semantics-19/tree/c20e4f1d00f6693a8a46dd1d5576cfd3adced896 |
OneConv3d | import logging
import torch
class OneConv3d(torch.nn.Module):
"""OneConv3d.
"""
def __init__(self, out_channels=2):
super().__init__()
self.layer = torch.nn.Conv3d(in_channels=1, out_channels=
out_channels, kernel_size=3, padding=1)
def forward(self, x):
logging.d... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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
@triton.jit
de... | kirchhausenlab/incasem | OneConv3d | false | 3,852 | [
"BSD-3-Clause"
] | 0 | ee9e007c5c04571e547e2fb5af5e800bd2d2b435 | https://github.com/kirchhausenlab/incasem/tree/ee9e007c5c04571e547e2fb5af5e800bd2d2b435 |
encoder4 | import torch
import torch.nn
import torch
import torch.nn as nn
class encoder4(nn.Module):
def __init__(self):
super(encoder4, self).__init__()
self.conv1 = nn.Conv2d(3, 3, 1, 1, 0)
self.reflecPad1 = nn.ReflectionPad2d((1, 1, 1, 1))
self.conv2 = nn.Conv2d(3, 64, 3, 1, 0)
s... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | kamieen03/style-transfer-server | encoder4 | false | 3,853 | [
"BSD-2-Clause"
] | 0 | 91727ec62080215a0b870ce043faf0657137b84b | https://github.com/kamieen03/style-transfer-server/tree/91727ec62080215a0b870ce043faf0657137b84b |
AlexNet | import torch
import torch.nn as nn
class AlexNet(nn.Module):
"""
Our first more "succesful" network, slightly modified AlexNet
that accepts images in with 1 channel (i.e. grayscale).
Attributes
----------
c: torch.nn.modules.conv.Conv2d
Applies a 2D convolution over an input signal co... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | jamessoni/x-ray-ML-classification | AlexNet | false | 3,854 | [
"MIT"
] | 0 | 6934f37631d367cdbe813fa6a2cbdc673c64c503 | https://github.com/jamessoni/x-ray-ML-classification/tree/6934f37631d367cdbe813fa6a2cbdc673c64c503 |
Attention | import math
import torch
import torch.nn as nn
import torch as t
class Linear(nn.Module):
"""
Linear Module
"""
def __init__(self, in_dim, out_dim, bias=True, w_init='linear'):
"""
:param in_dim: dimension of input
:param out_dim: dimension of output
:param bias: boole... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | kongziyue1234/mooc | Attention | false | 3,855 | [
"MIT"
] | 0 | 3b0c822dd55c1066cbc829137e6c424dcda5067e | https://github.com/kongziyue1234/mooc/tree/3b0c822dd55c1066cbc829137e6c424dcda5067e |
OrientedIOUloss | import torch
import torch.nn as nn
import torch.utils.data
class OrientedIOUloss(nn.Module):
def __init__(self, reduction='none', loss_type='iou'):
super(OrientedIOUloss, self).__init__()
self.reduction = reduction
self.loss_type = loss_type
self.loss_mse = nn.MSELoss(reduction=re... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import torch.utils.data
assert_size_stride = torch._C._dynamo.guard... | kuazhangxiaoai/YOLOX | OrientedIOUloss | false | 3,856 | [
"Apache-2.0"
] | 0 | 7aff49b25a8a80c4c33e941da416500eda72b1a2 | https://github.com/kuazhangxiaoai/YOLOX/tree/7aff49b25a8a80c4c33e941da416500eda72b1a2 |
ReduceMax | import torch
class ReduceMax(torch.nn.Module):
def forward(self, inputs, mask=None):
return torch.amax(inputs, dim=1)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
| import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torc... | jimthompson5802/ludwig | ReduceMax | false | 3,857 | [
"Apache-2.0"
] | 0 | 8a369328a3f839d9cdb3710be315952c7891d7c0 | https://github.com/jimthompson5802/ludwig/tree/8a369328a3f839d9cdb3710be315952c7891d7c0 |
IOUloss | import torch
import torch.nn as nn
import torch.utils.data
class IOUloss(nn.Module):
def __init__(self, reduction='none', loss_type='iou'):
super(IOUloss, self).__init__()
self.reduction = reduction
self.loss_type = loss_type
def forward(self, pred, target):
assert pred.shape... | 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
assert_size_stride = torch._C._dynamo.guard... | kuazhangxiaoai/YOLOX | IOUloss | false | 3,858 | [
"Apache-2.0"
] | 0 | 7aff49b25a8a80c4c33e941da416500eda72b1a2 | https://github.com/kuazhangxiaoai/YOLOX/tree/7aff49b25a8a80c4c33e941da416500eda72b1a2 |
BWCEWLoss | import torch
from torch import Tensor
from typing import Optional
from torch import nn
class LogitsInputsMixin:
@classmethod
def get_loss_inputs(cls):
"""Maps loss to the desired predicted input type."""
return LOGITS
class BWCEWLoss(nn.Module, LogitsInputsMixin):
"""Binary weighted cro... | 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 ... | jimthompson5802/ludwig | BWCEWLoss | false | 3,859 | [
"Apache-2.0"
] | 0 | 8a369328a3f839d9cdb3710be315952c7891d7c0 | https://github.com/jimthompson5802/ludwig/tree/8a369328a3f839d9cdb3710be315952c7891d7c0 |
Scaler | import torch
from abc import ABC
class BaseOperator(ABC):
"""
Abstract class defining the basic structure for operator implementations in Hummingbird.
"""
def __init__(self, regression=False, classification=False, transformer=
False, anomaly_detection=False, **kwargs):
"""
Arg... | 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 abc import ABC
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_stri... | kvenkman/hummingbird | Scaler | false | 3,860 | [
"MIT"
] | 0 | dac08f4ff4a4103df4a8e83329a02f2d804bf34d | https://github.com/kvenkman/hummingbird/tree/dac08f4ff4a4103df4a8e83329a02f2d804bf34d |
CausalConv1d | import torch
from torch import nn as nn
class CausalConv1d(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, dilation=1,
**kwargs):
super().__init__()
self.pad = (kernel_size - 1) * dilation
self.conv = nn.Conv1d(in_channels, out_channels, kernel_size,
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_s... | kschamplin/astro-classifier-neo | CausalConv1d | false | 3,861 | [
"MIT"
] | 0 | 44fcb8ba41ef549c16360df7fd470f56c42da9b3 | https://github.com/kschamplin/astro-classifier-neo/tree/44fcb8ba41ef549c16360df7fd470f56c42da9b3 |
Multiply | import torch
from abc import ABC
class BaseOperator(ABC):
"""
Abstract class defining the basic structure for operator implementations in Hummingbird.
"""
def __init__(self, regression=False, classification=False, transformer=
False, anomaly_detection=False, **kwargs):
"""
Arg... | 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 abc import ABC
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_stri... | kvenkman/hummingbird | Multiply | false | 3,862 | [
"MIT"
] | 0 | dac08f4ff4a4103df4a8e83329a02f2d804bf34d | https://github.com/kvenkman/hummingbird/tree/dac08f4ff4a4103df4a8e83329a02f2d804bf34d |
ReduceLast | import torch
def sequence_length_3D(sequence: 'torch.Tensor') ->torch.Tensor:
used = torch.sign(torch.amax(torch.abs(sequence), dim=2))
length = torch.sum(used, 1)
length = length.int()
return length
class ReduceLast(torch.nn.Module):
def forward(self, inputs, mask=None):
batch_size = i... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
assert_size_stride = t... | jimthompson5802/ludwig | ReduceLast | false | 3,863 | [
"Apache-2.0"
] | 0 | 8a369328a3f839d9cdb3710be315952c7891d7c0 | https://github.com/jimthompson5802/ludwig/tree/8a369328a3f839d9cdb3710be315952c7891d7c0 |
ContrastiveLoss | import torch
import torch.nn as nn
import torch.nn.functional as F
class ContrastiveLoss(nn.Module):
def __init__(self, margin=1.5):
super(ContrastiveLoss, self).__init__()
self.margin = margin
def forward(self, output1, output2, weight):
pairdist = F.pairwise_distance(output1, outpu... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert... | kvswim/kv_jhu_cv | ContrastiveLoss | false | 3,864 | [
"MIT"
] | 0 | 2ddf7a9d497aef116a7c043157b8631cea45000d | https://github.com/kvswim/kv_jhu_cv/tree/2ddf7a9d497aef116a7c043157b8631cea45000d |
SigmoidCrossEntropyLoss | import torch
from torch import Tensor
from typing import List
from typing import Optional
from typing import Union
from torch import nn
class LogitsInputsMixin:
@classmethod
def get_loss_inputs(cls):
"""Maps loss to the desired predicted input type."""
return LOGITS
class SigmoidCrossEntrop... | 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 ... | jimthompson5802/ludwig | SigmoidCrossEntropyLoss | false | 3,865 | [
"Apache-2.0"
] | 0 | 8a369328a3f839d9cdb3710be315952c7891d7c0 | https://github.com/jimthompson5802/ludwig/tree/8a369328a3f839d9cdb3710be315952c7891d7c0 |
Layer | import torch
import torch.nn.functional as F
import torch.nn as nn
class Layer(nn.Module):
def __init__(self, input_dim, output_dim, p, name=None):
super(Layer, self).__init__()
self.name = name
self.register_parameter(name='w', param=nn.Parameter(torch.empty(1,
input_dim, out... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | kw-lee/VIforSDEs | Layer | false | 3,866 | [
"MIT"
] | 0 | dcba3832aaad0aebc921a3b0628c43046d651629 | https://github.com/kw-lee/VIforSDEs/tree/dcba3832aaad0aebc921a3b0628c43046d651629 |
Block | import torch
import torch.nn as nn
class Mlp(nn.Module):
def __init__(self, in_features, hidden_features=None, out_features=None,
act_layer=nn.GELU, drop=0.0):
super().__init__()
out_features = out_features or in_features
hidden_features = hidden_features or in_features
se... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | kris927b/ViLT | Block | false | 3,867 | [
"Apache-2.0"
] | 0 | db96f20ebc656f1995aa573cbcbca0fe31f55c42 | https://github.com/kris927b/ViLT/tree/db96f20ebc656f1995aa573cbcbca0fe31f55c42 |
encoder3 | import torch
import torch.nn
import torch
import torch.nn as nn
class encoder3(nn.Module):
def __init__(self, W, v2):
super(encoder3, self).__init__()
self.conv1 = nn.Conv2d(3, 3, 1, 1, 0)
self.reflecPad1 = nn.ZeroPad2d((1, 1, 1, 1))
self.conv2 = nn.Conv2d(3, 32 if v2 else int(64 ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn
import torch
... | kamieen03/style-transfer-server | encoder3 | false | 3,868 | [
"BSD-2-Clause"
] | 0 | 91727ec62080215a0b870ce043faf0657137b84b | https://github.com/kamieen03/style-transfer-server/tree/91727ec62080215a0b870ce043faf0657137b84b |
Actor | import torch
import torch.nn as nn
from collections import OrderedDict
class Actor(nn.Module):
def __init__(self, state_size, action_size, actor_fc_sizes=[256, 128, 64]):
super(Actor, self).__init__()
sequence_dict_actor = OrderedDict()
sequence_dict_actor['fc0'] = nn.Linear(state_size, a... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | kurohi/deepreinforcement-udacity | Actor | false | 3,869 | [
"MIT"
] | 0 | ea8bfcce9a36ca41aa0d7595326b915a494ed5f2 | https://github.com/kurohi/deepreinforcement-udacity/tree/ea8bfcce9a36ca41aa0d7595326b915a494ed5f2 |
Critic | import torch
import torch.nn as nn
from collections import OrderedDict
class Critic(nn.Module):
def __init__(self, state_size, action_size, critic_fc_sizes=[256, 128, 64]
):
super(Critic, self).__init__()
sequence_dict_critic = OrderedDict()
self.critic_first_layer = nn.Linear(sta... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
from collections import OrderedDict
assert_size_stride = t... | kurohi/deepreinforcement-udacity | Critic | false | 3,870 | [
"MIT"
] | 0 | ea8bfcce9a36ca41aa0d7595326b915a494ed5f2 | https://github.com/kurohi/deepreinforcement-udacity/tree/ea8bfcce9a36ca41aa0d7595326b915a494ed5f2 |
Model | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self, act_dim):
super().__init__()
self.conv1 = nn.Conv2d(3, 6, 5)
self.pool = nn.MaxPool2d(2, 2)
self.conv2 = nn.Conv2d(6, 16, 5)
self.fc1 = nn.Linear(2704, 520)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | krishanrana/robot_learning_algorithms | Model | false | 3,871 | [
"MIT"
] | 0 | 3e66c9bf44e81ff281195130c71bcc6ebdf5ccda | https://github.com/krishanrana/robot_learning_algorithms/tree/3e66c9bf44e81ff281195130c71bcc6ebdf5ccda |
SPP | import torch
import torch.nn as nn
import torch.optim
import torch.utils.data
class SPP(nn.Module):
"""
Spatial Pyramid Pooling
"""
def __init__(self):
super(SPP, self).__init__()
def forward(self, x):
x_1 = torch.nn.functional.max_pool2d(x, 5, stride=1, padding=2)
x_... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import torch.optim
import torch.utils.data
assert_size_stride = tor... | ldylab/learning_yolo_family_with_pytorch | SPP | false | 3,872 | [
"MIT"
] | 0 | 63fd8d65e5ccd55c9ec124052bbcb040e0d9c549 | https://github.com/ldylab/learning_yolo_family_with_pytorch/tree/63fd8d65e5ccd55c9ec124052bbcb040e0d9c549 |
_DynamicGates | from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
class _DynamicGates(nn.Module):
"""Internal class to wrap the dynamic gate parameters into a dedicated PyTorch Module"""
def __init__(self, cfg: 'Config', input_size: 'int'):
super(_DynamicGates, self).__init__()
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | kyleniemeyer/neuralhydrology | _DynamicGates | false | 3,873 | [
"BSD-3-Clause"
] | 0 | 440fda715c4f746a2d56b058b9af2f0e03c36aa0 | https://github.com/kyleniemeyer/neuralhydrology/tree/440fda715c4f746a2d56b058b9af2f0e03c36aa0 |
LIN | import torch
import torch.nn as nn
from torch.nn.parameter import Parameter
class LIN(nn.Module):
def __init__(self, num_features, eps=1e-05):
super(LIN, self).__init__()
self.eps = eps
self.rho = Parameter(torch.Tensor(1, num_features, 1, 1))
self.gamma = Parameter(torch.Tensor(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.triton_helpers import libdevice
import torch.nn as nn
from torch.nn.parameter import Parameter
assert_size_stri... | ldzhangyu/photo2cartoon | LIN | false | 3,874 | [
"MIT"
] | 0 | d5b371e77e61018c28109db67e8306e5e6064800 | https://github.com/ldzhangyu/photo2cartoon/tree/d5b371e77e61018c28109db67e8306e5e6064800 |
GlobalAttention | import torch
import torch.nn as nn
import torch.cuda
def aeq(*args):
"""
Assert all arguments have the same value
"""
arguments = (arg for arg in args)
first = next(arguments)
assert all(arg == first for arg in arguments
), 'Not all arguments have the same value: ' + str(args)
def se... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | kurianbenoy/QG-Net | GlobalAttention | false | 3,875 | [
"MIT"
] | 0 | 074c697530aaaa259a3e16467a020846b1085af1 | https://github.com/kurianbenoy/QG-Net/tree/074c697530aaaa259a3e16467a020846b1085af1 |
mnistmodel_C | import torch
from torch import nn
import torch.nn.functional as F
class mnistmodel_C(nn.Module):
def __init__(self):
super(mnistmodel_C, self).__init__()
self.conv1 = nn.Conv2d(in_channels=1, out_channels=128, kernel_size
=3, padding=1)
self.conv2 = nn.Conv2d(in_channels=128, ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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... | layel2/layyer-lib | mnistmodel_C | false | 3,876 | [
"MIT"
] | 0 | db48b5c38098ee93d2d34693d98e5ef4d319d919 | https://github.com/layel2/layyer-lib/tree/db48b5c38098ee93d2d34693d98e5ef4d319d919 |
mnistmodel_B | import torch
from torch import nn
import torch.nn.functional as F
class mnistmodel_B(nn.Module):
def __init__(self):
super(mnistmodel_B, self).__init__()
self.conv1 = nn.Conv2d(in_channels=1, out_channels=64, kernel_size=
8, stride=2, padding=3)
self.conv2 = nn.Conv2d(in_chann... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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... | layel2/layyer-lib | mnistmodel_B | false | 3,877 | [
"MIT"
] | 0 | db48b5c38098ee93d2d34693d98e5ef4d319d919 | https://github.com/layel2/layyer-lib/tree/db48b5c38098ee93d2d34693d98e5ef4d319d919 |
discriminator | import torch
from torch import nn
import torch.nn.functional as F
class discriminator(nn.Module):
def __init__(self):
super(discriminator, self).__init__()
self.d1 = nn.Conv2d(in_channels=1, out_channels=4, kernel_size=2,
stride=2, padding=0)
self.d2 = nn.Conv2d(in_channels=4,... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | layel2/layyer-lib | discriminator | false | 3,878 | [
"MIT"
] | 0 | db48b5c38098ee93d2d34693d98e5ef4d319d919 | https://github.com/layel2/layyer-lib/tree/db48b5c38098ee93d2d34693d98e5ef4d319d919 |
PartialConv | import math
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.utils.data
def weights_init(init_type='gaussian'):
def init_fun(m):
classname = m.__class__.__name__
if (classname.find('Conv') == 0 or classname.find('Linear') == 0
) and hasattr(m, '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
import math
import torch.nn as nn
import torch.nn.parallel
import torch.utils.da... | labcontext/image-inpainting-oldpaper | PartialConv | false | 3,879 | [
"Apache-2.0"
] | 0 | da4683a2c58d662e443ea24ab93fd9d8fcb96bda | https://github.com/labcontext/image-inpainting-oldpaper/tree/da4683a2c58d662e443ea24ab93fd9d8fcb96bda |
mnistmodel_A | import torch
from torch import nn
import torch.nn.functional as F
class mnistmodel_A(nn.Module):
def __init__(self):
super(mnistmodel_A, self).__init__()
self.conv1 = nn.Conv2d(in_channels=1, out_channels=64, kernel_size=
5, stride=1, padding=2)
self.conv2 = nn.Conv2d(in_chann... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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... | layel2/layyer-lib | mnistmodel_A | false | 3,880 | [
"MIT"
] | 0 | db48b5c38098ee93d2d34693d98e5ef4d319d919 | https://github.com/layel2/layyer-lib/tree/db48b5c38098ee93d2d34693d98e5ef4d319d919 |
discriminator2 | import torch
from torch import nn
import torch.nn.functional as F
class discriminator2(nn.Module):
def __init__(self):
super().__init__()
self.d1 = nn.Conv2d(in_channels=1, out_channels=4, kernel_size=3,
stride=1, padding=1)
self.d2 = nn.Conv2d(in_channels=4, out_channels=8, 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 import triton_helpers
from torch._inductor.runtime.... | layel2/layyer-lib | discriminator2 | false | 3,881 | [
"MIT"
] | 0 | db48b5c38098ee93d2d34693d98e5ef4d319d919 | https://github.com/layel2/layyer-lib/tree/db48b5c38098ee93d2d34693d98e5ef4d319d919 |
nnNorm | import torch
import torch.nn as nn
import torch.nn.functional as F
class nnNorm(nn.Module):
def __init__(self, dim=-1):
super().__init__()
self.dim = dim
def forward(self, x):
return F.normalize(x, dim=self.dim)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_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 libdevice
import torch.nn as nn
assert... | learning-group-structure/paper | nnNorm | false | 3,882 | [
"MIT"
] | 0 | 96abf7e25cb7e95f45d6eb025257c0ba9e22fc55 | https://github.com/learning-group-structure/paper/tree/96abf7e25cb7e95f45d6eb025257c0ba9e22fc55 |
HLoss | import torch
from torch import nn
import torch.nn.functional as F
class HLoss(nn.Module):
"""
Entropy loss used for entropy maximization.
"""
def __init__(self, ignore_index=-1):
super(HLoss, self).__init__()
self.ignore_index = ignore_index
def forward(self, x, labels):
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | lemon234071/oc_parlai | HLoss | false | 3,883 | [
"MIT"
] | 0 | 33a0e57c48e58903cb1666e367a7bb9ef012de0c | https://github.com/lemon234071/oc_parlai/tree/33a0e57c48e58903cb1666e367a7bb9ef012de0c |
gen_ab_cf | import torch
from torch import nn
import torch.nn.functional as F
class gen_ab_cf(nn.Module):
def __init__(self):
super().__init__()
self.d1 = nn.Conv2d(in_channels=3, out_channels=8, kernel_size=3,
stride=1, padding=1)
self.d2 = nn.Conv2d(in_channels=8, out_channels=16, kerne... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | layel2/layyer-lib | gen_ab_cf | false | 3,884 | [
"MIT"
] | 0 | db48b5c38098ee93d2d34693d98e5ef4d319d919 | https://github.com/layel2/layyer-lib/tree/db48b5c38098ee93d2d34693d98e5ef4d319d919 |
MultiLinear | import torch
import numpy as np
import torch.nn as nn
def tensor(x, dtype=torch.float32):
if torch.is_tensor(x):
return x.type(dtype)
x = torch.tensor(x, device=Config.DEVICE, dtype=dtype)
return x
def batch_linear(input, weight, bias=None):
""" input: (N, D), weight: (N, D, H), bias: (N, H)... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import numpy as np
import torch.nn as nn
assert_size_stride = torch._C._dynamo.g... | lchenat/TSA | MultiLinear | false | 3,885 | [
"Apache-2.0"
] | 0 | 661266ba16e06f63962b306a7c30d25f37920c2d | https://github.com/lchenat/TSA/tree/661266ba16e06f63962b306a7c30d25f37920c2d |
TransformerBlock | import torch
import torchvision.transforms.functional as F
import torch.nn as nn
import torch.nn.functional as F
class TransformerBlock(nn.Module):
def __init__(self, seq_len: 'int', embed_channels: 'int', mlp_dims:
'int', num_heads: 'int'):
super().__init__()
self.embed_channels = embed_... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | ketan0/ddim | TransformerBlock | false | 3,886 | [
"MIT"
] | 0 | 26f2de1107885a3f332dd8435b73a1eaedbe10a8 | https://github.com/ketan0/ddim/tree/26f2de1107885a3f332dd8435b73a1eaedbe10a8 |
EntMinLoss | import torch
import torch.nn as nn
import torch.nn.functional as F
class EntMinLoss(nn.Module):
def __init__(self):
super().__init__()
def forward(self, f_x):
soft_f_x = F.softmax(f_x, dim=-1)
log_soft_f_x = F.log_softmax(f_x, dim=-1)
ent = -torch.sum(soft_f_x * log_soft_f_x)... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | leoandeol/ldir | EntMinLoss | false | 3,887 | [
"MIT"
] | 0 | f90408c5fb16a52c6c5a76fff1c46b9062343ad5 | https://github.com/leoandeol/ldir/tree/f90408c5fb16a52c6c5a76fff1c46b9062343ad5 |
CeCriterion | 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 math as tl_math
from torch.nn.modules.... | johnson7788/mt-dnn | CeCriterion | false | 3,888 | [
"MIT"
] | 0 | 26e5c4a5bfdbf1a1dd1c903e606db1c070568237 | https://github.com/johnson7788/mt-dnn/tree/26e5c4a5bfdbf1a1dd1c903e606db1c070568237 |
gconv | import torch
import torch.nn as nn
import torch.utils.model_zoo
class gconv(nn.Module):
def __init__(self, channel):
super(gconv, self).__init__()
self.relu = nn.ReLU()
self.conv = nn.Conv2d(channel, channel, kernel_size=3, padding=1)
def forward(self, x):
y = self.conv(x)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import ... | lee-zq/MRDN | gconv | false | 3,889 | [
"Apache-2.0"
] | 0 | 976c1f8cd0d4b1943378149ef836bb86dd5fc0cd | https://github.com/lee-zq/MRDN/tree/976c1f8cd0d4b1943378149ef836bb86dd5fc0cd |
adaLIN | import torch
import torch.nn as nn
from torch.nn.parameter import Parameter
class adaLIN(nn.Module):
def __init__(self, num_features, eps=1e-05):
super(adaLIN, self).__init__()
self.eps = eps
self.rho = Parameter(torch.Tensor(1, num_features, 1, 1))
self.rho.data.fill_(0.9)
d... | 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
from torch.nn.parameter import Parameter
assert_size_stri... | ldzhangyu/photo2cartoon | adaLIN | false | 3,890 | [
"MIT"
] | 0 | d5b371e77e61018c28109db67e8306e5e6064800 | https://github.com/ldzhangyu/photo2cartoon/tree/d5b371e77e61018c28109db67e8306e5e6064800 |
LinearModel | import torch
import torch.nn.functional as F
import torch.nn as nn
class LinearModel(nn.Module):
"""Model creation.
"""
def __init__(self, input_dim, output_dim):
super(LinearModel, self).__init__()
self.layer1 = nn.Linear(input_dim, 50)
self.layer2 = nn.Linear(50, 50)
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
from torch._inductor.runtime.... | learniotai/iotai-sensor-classifications | LinearModel | false | 3,891 | [
"Apache-2.0"
] | 0 | ba2527cb317afa30a5c495d1cddc16f7dc2936ed | https://github.com/learniotai/iotai-sensor-classifications/tree/ba2527cb317afa30a5c495d1cddc16f7dc2936ed |
_MCLSTMCell | from _paritybench_helpers import _mock_config
import torch
from typing import Tuple
import torch.nn as nn
class _Gate(nn.Module):
"""Utility class to implement a standard sigmoid gate"""
def __init__(self, in_features: 'int', out_features: 'int'):
super(_Gate, self).__init__()
self.fc = 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.... | kyleniemeyer/neuralhydrology | _MCLSTMCell | false | 3,892 | [
"BSD-3-Clause"
] | 0 | 440fda715c4f746a2d56b058b9af2f0e03c36aa0 | https://github.com/kyleniemeyer/neuralhydrology/tree/440fda715c4f746a2d56b058b9af2f0e03c36aa0 |
ConvModel | import torch
import torch.nn.functional as F
import torch.nn as nn
class ConvModel(nn.Module):
"""Convolution 2D model."""
def __init__(self, input_dim, output_dim):
super(ConvModel, self).__init__()
self._input_dim = input_dim
self._output_dim = output_dim
self.conv1 = 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
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | learniotai/iotai-sensor-classifications | ConvModel | false | 3,894 | [
"Apache-2.0"
] | 0 | ba2527cb317afa30a5c495d1cddc16f7dc2936ed | https://github.com/learniotai/iotai-sensor-classifications/tree/ba2527cb317afa30a5c495d1cddc16f7dc2936ed |
MseCriterion | 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.nn.modules.loss import _Loss
from torch.optim.lr_scheduler import *
assert_siz... | johnson7788/mt-dnn | MseCriterion | false | 3,895 | [
"MIT"
] | 0 | 26e5c4a5bfdbf1a1dd1c903e606db1c070568237 | https://github.com/johnson7788/mt-dnn/tree/26e5c4a5bfdbf1a1dd1c903e606db1c070568237 |
BiLinearSim | from _paritybench_helpers import _mock_config
import torch
from torch.optim.lr_scheduler import *
class BiLinearSim(torch.nn.Module):
def __init__(self, config):
super().__init__()
self.linear = torch.nn.Linear(config.hidden_size, config.
hidden_size, bias=False)
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
from torch.optim.lr_scheduler import *
assert_size_stride = torch._C._dynamo.gua... | johnson7788/mt-dnn | BiLinearSim | false | 3,896 | [
"MIT"
] | 0 | 26e5c4a5bfdbf1a1dd1c903e606db1c070568237 | https://github.com/johnson7788/mt-dnn/tree/26e5c4a5bfdbf1a1dd1c903e606db1c070568237 |
Cosine | from _paritybench_helpers import _mock_config
import torch
from torch.optim.lr_scheduler import *
class Cosine(torch.nn.Module):
def __init__(self, config):
super().__init__()
def forward(self, src, tgt):
src = src.float()
tgt = tgt.float()
return (torch.matmul(src, tgt.trans... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.optim.lr... | johnson7788/mt-dnn | Cosine | false | 3,897 | [
"MIT"
] | 0 | 26e5c4a5bfdbf1a1dd1c903e606db1c070568237 | https://github.com/johnson7788/mt-dnn/tree/26e5c4a5bfdbf1a1dd1c903e606db1c070568237 |
HLCriterion | import torch
import torch.nn.functional as F
from torch.nn.modules.loss import _Loss
from torch.optim.lr_scheduler import *
class Criterion(_Loss):
def __init__(self, alpha=1.0, name='criterion'):
super().__init__()
"""Alpha is used to weight each loss term
"""
self.alpha = alpha
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch.... | johnson7788/mt-dnn | HLCriterion | false | 3,898 | [
"MIT"
] | 0 | 26e5c4a5bfdbf1a1dd1c903e606db1c070568237 | https://github.com/johnson7788/mt-dnn/tree/26e5c4a5bfdbf1a1dd1c903e606db1c070568237 |
NsKlCriterion | import torch
import torch.nn.functional as F
from torch.nn.modules.loss import _Loss
from torch.optim.lr_scheduler import *
def stable_kl(logit, target, epsilon=1e-06, reduce=True):
logit = logit.view(-1, logit.size(-1)).float()
target = target.view(-1, target.size(-1)).float()
bs = logit.size(0)
p = ... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn.functi... | johnson7788/mt-dnn | NsKlCriterion | false | 3,899 | [
"MIT"
] | 0 | 26e5c4a5bfdbf1a1dd1c903e606db1c070568237 | https://github.com/johnson7788/mt-dnn/tree/26e5c4a5bfdbf1a1dd1c903e606db1c070568237 |
JSCriterion | 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.... | johnson7788/mt-dnn | JSCriterion | false | 3,900 | [
"MIT"
] | 0 | 26e5c4a5bfdbf1a1dd1c903e606db1c070568237 | https://github.com/johnson7788/mt-dnn/tree/26e5c4a5bfdbf1a1dd1c903e606db1c070568237 |
MinibatchStdDev | import torch
class MinibatchStdDev(torch.nn.Module):
"""
Concatenate a constant statistic calculated across the minibatch to each pixel location (i, j) as a new channel.
Here the standard deviation averaged over channels and locations. This is to increase variation of images produced
by the generator.... | 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... | leogeier/dl-2020-prog-gan | MinibatchStdDev | false | 3,901 | [
"MIT"
] | 0 | 12f28353548188af31cc14ee18a5444ad3d95a0c | https://github.com/leogeier/dl-2020-prog-gan/tree/12f28353548188af31cc14ee18a5444ad3d95a0c |
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.... | johnson7788/mt-dnn | KlCriterion | false | 3,902 | [
"MIT"
] | 0 | 26e5c4a5bfdbf1a1dd1c903e606db1c070568237 | https://github.com/johnson7788/mt-dnn/tree/26e5c4a5bfdbf1a1dd1c903e606db1c070568237 |
gen_ba_cf | import torch
from torch import nn
import torch.nn.functional as F
class gen_ba_cf(nn.Module):
def __init__(self):
super().__init__()
self.d1 = nn.Conv2d(in_channels=3, out_channels=8, kernel_size=3,
stride=1, padding=1)
self.d2 = nn.Conv2d(in_channels=8, out_channels=16, kerne... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | layel2/layyer-lib | gen_ba_cf | false | 3,903 | [
"MIT"
] | 0 | db48b5c38098ee93d2d34693d98e5ef4d319d919 | https://github.com/layel2/layyer-lib/tree/db48b5c38098ee93d2d34693d98e5ef4d319d919 |
NsSymKlCriterion | import torch
import torch.nn.functional as F
from torch.nn.modules.loss import _Loss
from torch.optim.lr_scheduler import *
def stable_kl(logit, target, epsilon=1e-06, reduce=True):
logit = logit.view(-1, logit.size(-1)).float()
target = target.view(-1, target.size(-1)).float()
bs = logit.size(0)
p = ... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn.functi... | johnson7788/mt-dnn | NsSymKlCriterion | false | 3,904 | [
"MIT"
] | 0 | 26e5c4a5bfdbf1a1dd1c903e606db1c070568237 | https://github.com/johnson7788/mt-dnn/tree/26e5c4a5bfdbf1a1dd1c903e606db1c070568237 |
SymKlCriterion | import torch
import torch.nn.functional as F
from torch.nn.modules.loss import _Loss
from torch.optim.lr_scheduler import *
class Criterion(_Loss):
def __init__(self, alpha=1.0, name='criterion'):
super().__init__()
"""Alpha is used to weight each loss term
"""
self.alpha = alpha
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch.... | johnson7788/mt-dnn | SymKlCriterion | false | 3,905 | [
"MIT"
] | 0 | 26e5c4a5bfdbf1a1dd1c903e606db1c070568237 | https://github.com/johnson7788/mt-dnn/tree/26e5c4a5bfdbf1a1dd1c903e606db1c070568237 |
MultiheadAttentionWrapper | import torch
import torch.nn.functional as F
import torch.nn as nn
from torch.nn.utils import weight_norm
from torch.optim.lr_scheduler import *
def linear(x):
return x
def activation(func_a):
"""Activation function wrapper
"""
try:
f = eval(func_a)
except:
f = linear
return ... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn.functional as F
import torch.nn as nn
from torch.nn.utils import weight_norm
from torch.optim.lr_scheduler import *
assert_s... | johnson7788/mt-dnn | MultiheadAttentionWrapper | false | 3,906 | [
"MIT"
] | 0 | 26e5c4a5bfdbf1a1dd1c903e606db1c070568237 | https://github.com/johnson7788/mt-dnn/tree/26e5c4a5bfdbf1a1dd1c903e606db1c070568237 |
RNNAgent | from _paritybench_helpers import _mock_config
import torch
import torch.nn.functional as F
import torch.nn as nn
class RNNAgent(nn.Module):
def __init__(self, input_shape, args):
super(RNNAgent, self).__init__()
self.args = args
self.fc1 = nn.Linear(input_shape, args.rnn_hidden_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
import torch.nn as nn
assert_... | johnson7788/pymarl2 | RNNAgent | false | 3,907 | [
"Apache-2.0"
] | 0 | 8ec3e58fc3325ae82165cae0a5ea8a391ce42bd5 | https://github.com/johnson7788/pymarl2/tree/8ec3e58fc3325ae82165cae0a5ea8a391ce42bd5 |
TransformerEncoderLayer | import math
import torch
from torch import nn
import torch.nn.functional as F
def _normalize(tensor, norm_layer):
"""
Broadcast layer norm
"""
size = tensor.size()
return norm_layer(tensor.view(-1, size[-1])).view(size)
class MultiHeadAttention(nn.Module):
def __init__(self, n_heads, dim, d... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | lemon234071/oc_parlai | TransformerEncoderLayer | false | 3,908 | [
"MIT"
] | 0 | 33a0e57c48e58903cb1666e367a7bb9ef012de0c | https://github.com/lemon234071/oc_parlai/tree/33a0e57c48e58903cb1666e367a7bb9ef012de0c |
SelfAttention | import torch
import torch.nn.functional as F
import torch.nn as nn
class SelfAttention(nn.Module):
def __init__(self, input_size, heads, embed_size):
super().__init__()
self.input_size = input_size
self.heads = heads
self.emb_size = embed_size
self.tokeys = nn.Linear(self.... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | johnson7788/pymarl2 | SelfAttention | false | 3,909 | [
"Apache-2.0"
] | 0 | 8ec3e58fc3325ae82165cae0a5ea8a391ce42bd5 | https://github.com/johnson7788/pymarl2/tree/8ec3e58fc3325ae82165cae0a5ea8a391ce42bd5 |
HorizontalMaxPool2d | import torch
import torch.nn as nn
class HorizontalMaxPool2d(nn.Module):
def __init__(self):
super(HorizontalMaxPool2d, self).__init__()
def forward(self, x):
inp_size = x.size()
return nn.functional.max_pool2d(input=x, kernel_size=(1, inp_size[3]))
def get_inputs():
return [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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | linkserendipity/AlignedReID | HorizontalMaxPool2d | false | 3,910 | [
"MIT"
] | 0 | 142a9ebdc200ef4da001f91c1f592e4ff02b2f77 | https://github.com/linkserendipity/AlignedReID/tree/142a9ebdc200ef4da001f91c1f592e4ff02b2f77 |
TransformerDecoderLayer | import math
import torch
from torch import nn
import torch.nn.functional as F
def _normalize(tensor, norm_layer):
"""
Broadcast layer norm
"""
size = tensor.size()
return norm_layer(tensor.view(-1, size[-1])).view(size)
class MultiHeadAttention(nn.Module):
def __init__(self, n_heads, dim, d... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | lemon234071/oc_parlai | TransformerDecoderLayer | false | 3,911 | [
"MIT"
] | 0 | 33a0e57c48e58903cb1666e367a7bb9ef012de0c | https://github.com/lemon234071/oc_parlai/tree/33a0e57c48e58903cb1666e367a7bb9ef012de0c |
RingLoss | import torch
import torch.nn as nn
class RingLoss(nn.Module):
"""Ring loss.
Reference:
Zheng et al. Ring loss: Convex Feature Normalization for Face Recognition. CVPR 2018.
"""
def __init__(self, weight_ring=1.0):
super(RingLoss, self).__init__()
self.radius = nn.Parameter(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.triton_helpers import libdevice
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_... | linkserendipity/AlignedReID | RingLoss | false | 3,912 | [
"MIT"
] | 0 | 142a9ebdc200ef4da001f91c1f592e4ff02b2f77 | https://github.com/linkserendipity/AlignedReID/tree/142a9ebdc200ef4da001f91c1f592e4ff02b2f77 |
DetNet2 | import torch
import torch.nn as nn
class DetNet2(nn.Module):
def __init__(self):
super().__init__()
self.conv1 = nn.Conv2d(3, 3, 1, padding=1)
def forward(self, x):
x = self.conv1(x)
return x
def get_inputs():
return [torch.rand([4, 3, 64, 64])]
def get_init_inputs():... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | liangzhao123/topic_ws | DetNet2 | false | 3,913 | [
"Apache-2.0"
] | 0 | ef7aba11b975eab5f657101ed696b49ec94b5f86 | https://github.com/liangzhao123/topic_ws/tree/ef7aba11b975eab5f657101ed696b49ec94b5f86 |
MaskedL1Loss | import torch
import torch.utils.data
import torch.nn as nn
class MaskedL1Loss(nn.Module):
def __init__(self):
super(MaskedL1Loss, self).__init__()
self.criterion = nn.L1Loss()
def forward(self, input, target, mask):
mask = mask.expand(-1, input.size()[1], -1, -1)
loss = 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
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.utils.dat... | lichnost/head2head | MaskedL1Loss | false | 3,914 | [
"MIT"
] | 0 | b0ec8b6965c9a32f3727dee9c164a7aaff027c5f | https://github.com/lichnost/head2head/tree/b0ec8b6965c9a32f3727dee9c164a7aaff027c5f |
dis_cf | import torch
from torch import nn
import torch.nn.functional as F
class dis_cf(nn.Module):
def __init__(self):
super().__init__()
self.d1 = nn.Conv2d(in_channels=3, out_channels=8, kernel_size=3,
stride=1, padding=1)
self.d2 = nn.Conv2d(in_channels=8, out_channels=16, kernel_s... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | layel2/layyer-lib | dis_cf | false | 3,915 | [
"MIT"
] | 0 | db48b5c38098ee93d2d34693d98e5ef4d319d919 | https://github.com/layel2/layyer-lib/tree/db48b5c38098ee93d2d34693d98e5ef4d319d919 |
KLMutualLoss | import torch
import torch.nn as nn
class KLMutualLoss(nn.Module):
def __init__(self):
super(KLMutualLoss, self).__init__()
self.kl_loss = nn.KLDivLoss(size_average=False)
self.log_softmax = nn.functional.log_softmax
self.softmax = nn.functional.softmax
def forward(self, pred1... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torc... | linkserendipity/AlignedReID | KLMutualLoss | false | 3,916 | [
"MIT"
] | 0 | 142a9ebdc200ef4da001f91c1f592e4ff02b2f77 | https://github.com/linkserendipity/AlignedReID/tree/142a9ebdc200ef4da001f91c1f592e4ff02b2f77 |
LayerNorm | import torch
import torch.nn as nn
import torch.optim
class LayerNorm(nn.Module):
"""Construct a layernorm module in the OpenAI style (epsilon inside the square root)."""
def __init__(self, n_state, e=1e-05):
super(LayerNorm, self).__init__()
self.g = nn.Parameter(torch.ones(n_state))
... | 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.optim
assert_size_stride = torch._C._dynamo.... | lidayuls/comet-commonsense-v1 | LayerNorm | false | 3,917 | [
"Apache-2.0"
] | 0 | d0c8475b8432358c59c0d957c2d928521741c057 | https://github.com/lidayuls/comet-commonsense-v1/tree/d0c8475b8432358c59c0d957c2d928521741c057 |
CrossEntropyLoss | import torch
import torch.utils.data
import torch.nn as nn
class CrossEntropyLoss(nn.Module):
def __init__(self, label_nc):
super(CrossEntropyLoss, self).__init__()
self.softmax = nn.LogSoftmax(dim=1)
self.criterion = nn.NLLLoss2d()
def forward(self, output, label):
label = l... | 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.utils.dat... | lichnost/head2head | CrossEntropyLoss | false | 3,918 | [
"MIT"
] | 0 | b0ec8b6965c9a32f3727dee9c164a7aaff027c5f | https://github.com/lichnost/head2head/tree/b0ec8b6965c9a32f3727dee9c164a7aaff027c5f |
Critic | import torch
import torch.nn as nn
import torch.nn.functional as F
class Critic(nn.Module):
def __init__(self, state_dim, action_dim):
super(Critic, self).__init__()
self.l1 = nn.Linear(state_dim + action_dim, 256)
self.l2 = nn.Linear(256, 256)
self.l3 = nn.Linear(256, 1)
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 import triton_helpers
import torch.nn as nn
import ... | linsats/GRAC2.0 | Critic | false | 3,919 | [
"MIT"
] | 0 | 2fde25103b2316a3435ef0ebdbf471ec4e204fbe | https://github.com/linsats/GRAC2.0/tree/2fde25103b2316a3435ef0ebdbf471ec4e204fbe |
Policy | import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
from torch.distributions import Categorical
class Policy(nn.Module):
def __init__(self, n_features=4, n_actions=2, device=torch.device('cpu')):
super(Policy, self).__init__()
self.fc1 = nn.Linear(n_features, 128... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | linklab/minimal_rl | Policy | false | 3,920 | [
"MIT"
] | 0 | 382d99ca355ea405414c4ed1077fb4e8ed3532a9 | https://github.com/linklab/minimal_rl/tree/382d99ca355ea405414c4ed1077fb4e8ed3532a9 |
MLP | import torch
import torch.nn as nn
import torch.nn.functional as F
class MLP(nn.Module):
"""
Simple MLP to demonstrate Jacobian regularization.
"""
def __init__(self, in_channel=1, im_size=28, num_classes=10,
fc_channel1=200, fc_channel2=200):
super(MLP, self).__init__()
compr... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | linzzzzzz/jacobian_regularizer | MLP | false | 3,921 | [
"MIT"
] | 0 | c74d5b13e670f3ad1fd5a7cec225bca3853b3565 | https://github.com/linzzzzzz/jacobian_regularizer/tree/c74d5b13e670f3ad1fd5a7cec225bca3853b3565 |
RFDB | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.model_zoo
def activation(act_type, inplace=True, neg_slope=0.05, n_prelu=1):
act_type = act_type.lower()
if act_type == 'relu':
layer = nn.ReLU(inplace)
elif act_type == 'lrelu':
layer = nn.LeakyReLU(neg_... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import ... | lee-zq/MRDN | RFDB | false | 3,922 | [
"Apache-2.0"
] | 0 | 976c1f8cd0d4b1943378149ef836bb86dd5fc0cd | https://github.com/lee-zq/MRDN/tree/976c1f8cd0d4b1943378149ef836bb86dd5fc0cd |
NTimesTanh | import torch
import torch.nn as nn
class NTimesTanh(nn.Module):
def __init__(self, N):
super(NTimesTanh, self).__init__()
self.N = N
self.tanh = nn.Tanh()
def forward(self, x):
return self.tanh(x) * self.N
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_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
assert_size_stride = torch._C._dynamo.guards.assert_size_... | liuzeyuMr/ELEGANT_cvte | NTimesTanh | false | 3,923 | [
"MIT"
] | 0 | eb8039310023f91e25e37ff8d907844afd50e0a5 | https://github.com/liuzeyuMr/ELEGANT_cvte/tree/eb8039310023f91e25e37ff8d907844afd50e0a5 |
Actor | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.distributions import Normal
class Actor(nn.Module):
def __init__(self, state_dim, action_dim, max_action):
super(Actor, self).__init__()
self.l1 = nn.Linear(state_dim, 256)
self.l2 = nn.Linear(256, 256)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | linsats/GRAC2.0 | Actor | false | 3,924 | [
"MIT"
] | 0 | 2fde25103b2316a3435ef0ebdbf471ec4e204fbe | https://github.com/linsats/GRAC2.0/tree/2fde25103b2316a3435ef0ebdbf471ec4e204fbe |
BCEIoULoss | import torch
from typing import Callable
from functools import partial
from torch import nn
import torch.distributed
from torch.nn.modules.loss import *
from torch.nn.modules import *
from torch.optim import *
from torch.optim.lr_scheduler import *
import torch.backends
def get_activation_fn(activation: 'str'=None):
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from typing... | litvinich/catalyst | BCEIoULoss | false | 3,925 | [
"Apache-2.0"
] | 0 | b039bb69597d3fe48eed8c34342fa9be968b776e | https://github.com/litvinich/catalyst/tree/b039bb69597d3fe48eed8c34342fa9be968b776e |
Normalization | import torch
import torch.nn as nn
from torch.nn.parameter import Parameter
from torch.nn import Parameter
class Normalization(nn.Module):
def __init__(self):
super(Normalization, self).__init__()
self.alpha = Parameter(torch.ones(1))
self.beta = Parameter(torch.zeros(1))
def forward... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
from t... | liuzeyuMr/ELEGANT_cvte | Normalization | false | 3,926 | [
"MIT"
] | 0 | eb8039310023f91e25e37ff8d907844afd50e0a5 | https://github.com/liuzeyuMr/ELEGANT_cvte/tree/eb8039310023f91e25e37ff8d907844afd50e0a5 |
NormMLP | import torch
import torch.nn as nn
import torch.nn.functional as F
class NormMLP(nn.Module):
def __init__(self, input_size, output_size):
super(NormMLP, self).__init__()
self.linear = nn.Linear(input_size, output_size)
self.layer_norm = nn.LayerNorm(output_size)
def forward(self, act... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | lizhaoliu-Lec/apl | NormMLP | false | 3,927 | [
"MIT"
] | 0 | 3c8837f93b21353f9dd3ed7e0dd02982d0caab4c | https://github.com/lizhaoliu-Lec/apl/tree/3c8837f93b21353f9dd3ed7e0dd02982d0caab4c |
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_... | kctsiolis/RepDistiller | Correlation | false | 3,928 | [
"BSD-2-Clause"
] | 0 | ce88f6e53fcf8ef81c5bac2d20ad31628dd279ac | https://github.com/kctsiolis/RepDistiller/tree/ce88f6e53fcf8ef81c5bac2d20ad31628dd279ac |
SimBasedLoss | import torch
from torch import nn
import torch.nn.functional as F
class SimBasedLoss(nn.Module):
def __init__(self):
super(SimBasedLoss, self).__init__()
def forward(self, y_s, y_t):
y_s = F.normalize(y_s, p=2, dim=1)
y_t = F.normalize(y_t, p=2, dim=1)
student_sims = torch.ma... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | kctsiolis/RepDistiller | SimBasedLoss | false | 3,929 | [
"BSD-2-Clause"
] | 0 | ce88f6e53fcf8ef81c5bac2d20ad31628dd279ac | https://github.com/kctsiolis/RepDistiller/tree/ce88f6e53fcf8ef81c5bac2d20ad31628dd279ac |
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 ... | kctsiolis/RepDistiller | FactorTransfer | false | 3,930 | [
"BSD-2-Clause"
] | 0 | ce88f6e53fcf8ef81c5bac2d20ad31628dd279ac | https://github.com/kctsiolis/RepDistiller/tree/ce88f6e53fcf8ef81c5bac2d20ad31628dd279ac |
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 ... | kctsiolis/RepDistiller | DistillKL | false | 3,931 | [
"BSD-2-Clause"
] | 0 | ce88f6e53fcf8ef81c5bac2d20ad31628dd279ac | https://github.com/kctsiolis/RepDistiller/tree/ce88f6e53fcf8ef81c5bac2d20ad31628dd279ac |
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