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 |
|---|---|---|---|---|---|---|---|---|---|---|
ConcatClassifierHead | from _paritybench_helpers import _mock_config
from torch.nn import Module
import torch
import torch.nn as nn
import torch.nn
class ConcatClassifierHead(Module):
def __init__(self, config: 'dict'):
super(ConcatClassifierHead, self).__init__()
self.linear_layer_1 = nn.Linear(config['max_objects_per... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch.nn import Module
i... | SpyrosMouselinos/DeltaFormers | ConcatClassifierHead | false | 5,857 | [
"Apache-2.0"
] | 1 | 38508fa9b85f2c50aa0031b67e7e8feff1a75b27 | https://github.com/SpyrosMouselinos/DeltaFormers/tree/38508fa9b85f2c50aa0031b67e7e8feff1a75b27 |
RelateModule | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn
class RelateModule(nn.Module):
def __init__(self, dim):
super().__init__()
self.conv1 = nn.Conv2d(dim, dim, kernel_size=(3, 3), padding=1,
dilation=(1, 1))
self.conv2 = nn.Conv2d(dim, dim, 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
import torch.nn as nn
import ... | SpyrosMouselinos/DeltaFormers | RelateModule | false | 5,858 | [
"Apache-2.0"
] | 1 | 38508fa9b85f2c50aa0031b67e7e8feff1a75b27 | https://github.com/SpyrosMouselinos/DeltaFormers/tree/38508fa9b85f2c50aa0031b67e7e8feff1a75b27 |
MNIST_CNN | import torch
import torch.nn.functional as F
import torch.nn as nn
import torch.utils.data
class SqueezeLastTwo(nn.Module):
"""A module which squeezes the last two dimensions, ordinary squeeze can be a problem for batch size 1"""
def __init__(self):
super(SqueezeLastTwo, self).__init__()
def for... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | SirRob1997/DomainBed | MNIST_CNN | false | 5,859 | [
"MIT"
] | 1 | 7399a2b0a63df48f4b67755a3f33901223d5c8fb | https://github.com/SirRob1997/DomainBed/tree/7399a2b0a63df48f4b67755a3f33901223d5c8fb |
LanguageModelCriterion | import torch
import torch.nn as nn
from torch.autograd import *
class LanguageModelCriterion(nn.Module):
def __init__(self):
super(LanguageModelCriterion, self).__init__()
def forward(self, input, target, mask):
if target.ndim == 3:
target = target.reshape(-1, target.shape[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
import torch.nn as nn
from torch.autograd import *
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torc... | SunZongdi/self-critical.pytorch | LanguageModelCriterion | false | 5,861 | [
"MIT"
] | 1 | 6cecbeb949e68007b72e84198cf74f9fb288aeda | https://github.com/SunZongdi/self-critical.pytorch/tree/6cecbeb949e68007b72e84198cf74f9fb288aeda |
RewardCriterion | import torch
import torch.nn as nn
from torch.autograd import *
class RewardCriterion(nn.Module):
def __init__(self):
super(RewardCriterion, self).__init__()
def forward(self, input, seq, reward):
input = input.gather(2, seq.unsqueeze(2)).squeeze(2)
input = input.reshape(-1)
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
from torch.autograd import *
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torc... | SunZongdi/self-critical.pytorch | RewardCriterion | false | 5,862 | [
"MIT"
] | 1 | 6cecbeb949e68007b72e84198cf74f9fb288aeda | https://github.com/SunZongdi/self-critical.pytorch/tree/6cecbeb949e68007b72e84198cf74f9fb288aeda |
VNet | import torch
import torch.nn as nn
class VNet(nn.Module):
def __init__(self, input_size, hidden_size, output_size=1):
super(VNet, self).__init__()
self.linear1 = nn.Linear(input_size, hidden_size)
self.relu1 = nn.ReLU(inplace=True)
self.linear2 = nn.Linear(hidden_size, output_size... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | Stranger469/wrench | VNet | false | 5,863 | [
"Apache-2.0"
] | 1 | ab717ac26a76649c8fdb946a28dffe7e682c80ba | https://github.com/Stranger469/wrench/tree/ab717ac26a76649c8fdb946a28dffe7e682c80ba |
Attention | from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import *
class Attention(nn.Module):
def __init__(self, opt):
super(Attention, self).__init__()
self.rnn_size = opt.rnn_size
self.att_hid_size = opt.att_hid... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | SunZongdi/self-critical.pytorch | Attention | false | 5,864 | [
"MIT"
] | 1 | 6cecbeb949e68007b72e84198cf74f9fb288aeda | https://github.com/SunZongdi/self-critical.pytorch/tree/6cecbeb949e68007b72e84198cf74f9fb288aeda |
StackedAttention | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn
class StackedAttention(nn.Module):
def __init__(self, input_dim, hidden_dim):
super(StackedAttention, self).__init__()
self.Wv = nn.Conv2d(input_dim, hidden_dim, kernel_size=(1, 1),
padding=(0, 0))
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | SpyrosMouselinos/DeltaFormers | StackedAttention | false | 5,865 | [
"Apache-2.0"
] | 1 | 38508fa9b85f2c50aa0031b67e7e8feff1a75b27 | https://github.com/SpyrosMouselinos/DeltaFormers/tree/38508fa9b85f2c50aa0031b67e7e8feff1a75b27 |
CNN | import torch
import torch.nn as nn
import torch.nn.functional as F
class CNN(nn.Module):
"""
Convolutional Neural Network.
"""
def __init__(self):
super().__init__()
self.conv1 = nn.Conv2d(1, 20, kernel_size=5, stride=1)
self.fc1 = nn.Linear(8 * 8 * 20, 64)
self.fc2 = ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | StanislawSwierc/Ax | CNN | false | 5,866 | [
"MIT"
] | 1 | 175dff2294af4548ae258105346eeaca22a30197 | https://github.com/StanislawSwierc/Ax/tree/175dff2294af4548ae258105346eeaca22a30197 |
BinaryLogisticRegressionLoss | import torch
import torch.nn as nn
def binary_logistic_regression_loss(reg_score, label, threshold=0.5,
ratio_range=(1.05, 21), eps=1e-05):
"""Binary Logistic Regression Loss."""
label = label.view(-1)
reg_score = reg_score.contiguous().view(-1)
pmask = (label > threshold).float()
num_positive... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | SvipRepetitionCounting/TransRAC | BinaryLogisticRegressionLoss | false | 5,867 | [
"Apache-2.0"
] | 1 | eec12553dfa1e2fde6356b0e2703c633d225feb3 | https://github.com/SvipRepetitionCounting/TransRAC/tree/eec12553dfa1e2fde6356b0e2703c633d225feb3 |
Autoencoder | import torch
class Autoencoder(torch.nn.Module):
def __init__(self):
super().__init__()
self.conv1 = torch.nn.Conv2d(1, 8, 3, padding=1)
self.conv2 = torch.nn.Conv2d(8, 8, 3, padding=1)
self.conv3 = torch.nn.Conv2d(8, 16, 3, padding=1)
self.conv4 = torch.nn.Conv2d(16, 16, ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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... | SpaceMeerkat/CAE | Autoencoder | false | 5,868 | [
"MIT"
] | 1 | 8c5e2fbe751810a87ca155d0e3d53797f52fd9ea | https://github.com/SpaceMeerkat/CAE/tree/8c5e2fbe751810a87ca155d0e3d53797f52fd9ea |
InceptionA | import torch
import torch.nn.functional as F
class InceptionA(torch.nn.Module):
def __init__(self, in_channels):
super(InceptionA, self).__init__()
self.branch1x1 = torch.nn.Conv2d(in_channels, 16, kernel_size=(1, 1))
self.branch_pool = torch.nn.Conv2d(in_channels, 24, kernel_size=(1, 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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cu... | StarsStation/DeepLearning | InceptionA | false | 5,869 | [
"MIT"
] | 1 | a4c833af93652069f19a8c6f0b1e42cde64bbb79 | https://github.com/StarsStation/DeepLearning/tree/a4c833af93652069f19a8c6f0b1e42cde64bbb79 |
DotProductAttention | import math
import torch
from torch import nn
def masked_softmax(X, valid_len):
"""Perform softmax by filtering out some elements."""
if valid_len is None:
return nn.functional.softmax(X, dim=-1)
else:
shape = X.shape
if valid_len.dim() == 1:
valid_len = torch.repeat_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.... | StevenJokess/d2l-en-read | DotProductAttention | false | 5,870 | [
"MIT"
] | 1 | 71b0f35971063b9fe5f21319b8072d61c9e5a298 | https://github.com/StevenJokess/d2l-en-read/tree/71b0f35971063b9fe5f21319b8072d61c9e5a298 |
Linear_dynamics | import torch
import torch.utils.data
from torch import nn
class Linear_dynamics(nn.Module):
def __init__(self, device='cpu'):
super(Linear_dynamics, self).__init__()
self.time = nn.Parameter(torch.ones(1) * 0.7)
self.device = device
self
def forward(self, x, v):
retur... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.utils.data
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._... | SuperXiang/GMN | Linear_dynamics | false | 5,871 | [
"MIT"
] | 1 | b74364e5b9f424b63a5ce63a207a6e4a067d7d3b | https://github.com/SuperXiang/GMN/tree/b74364e5b9f424b63a5ce63a207a6e4a067d7d3b |
ContrastiveLoss | import torch
from torchvision.transforms import functional as F
import torch.nn as nn
import torch.nn.functional as F
class ContrastiveLoss(nn.Module):
"""
Contrastive loss function.
Based on: http://yann.lecun.com/exdb/publis/pdf/hadsell-chopra-lecun-06.pdf
"""
def __init__(self, margin: 'float'... | 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... | Swall0w/cougar | ContrastiveLoss | false | 5,872 | [
"MIT"
] | 1 | 9161b2b1d0c256f4bb952ec190351684f28ec1b7 | https://github.com/Swall0w/cougar/tree/9161b2b1d0c256f4bb952ec190351684f28ec1b7 |
SeqFC1 | import torch
import torch.nn as nn
import torch.nn.functional as F
class SeqFC1(nn.Module):
""" Neural network definition
"""
def __init__(self, size):
super(SeqFC1, self).__init__()
self.size = size
self.fc1 = nn.Linear(in_features=self.size, out_features=16)
self.fc2 = n... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | Thibaud-Ardoin/Dial-a-Ride | SeqFC1 | false | 5,873 | [
"MIT"
] | 1 | 7d9b3cd904d3194dccad31fec2533e2cf58cad0c | https://github.com/Thibaud-Ardoin/Dial-a-Ride/tree/7d9b3cd904d3194dccad31fec2533e2cf58cad0c |
Net_BP | import torch
import torch.nn.functional as F
class Net_BP(torch.nn.Module):
def __init__(self, n_features, n_hidden=50, n_output=1):
super(Net_BP, self).__init__()
self.hidden = torch.nn.Linear(n_features, n_hidden)
self.predict = torch.nn.Linear(n_hidden, n_output)
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._inductor.runtime import triton_helpers
assert_size_stride = torch._C... | Tappai/PV_prediction | Net_BP | false | 5,874 | [
"Apache-2.0"
] | 1 | 2ff1e1af183a28f07ebc2ec2979488eb8e246813 | https://github.com/Tappai/PV_prediction/tree/2ff1e1af183a28f07ebc2ec2979488eb8e246813 |
DQN | import torch
import torch.nn as nn
class DQN(nn.Module):
def __init__(self, size, upscale_factor, layer_size, channels):
super(DQN, self).__init__()
self.relu = nn.ReLU()
self.fc1 = nn.Linear(in_features=size ** 2, out_features=layer_size)
self.fc2 = nn.Linear(in_features=layer_si... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | Thibaud-Ardoin/Dial-a-Ride | DQN | false | 5,875 | [
"MIT"
] | 1 | 7d9b3cd904d3194dccad31fec2533e2cf58cad0c | https://github.com/Thibaud-Ardoin/Dial-a-Ride/tree/7d9b3cd904d3194dccad31fec2533e2cf58cad0c |
SameModule | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn
class SameModule(nn.Module):
def __init__(self, dim):
super().__init__()
self.conv = nn.Conv2d(dim + 1, 1, kernel_size=(1, 1))
torch.nn.init.kaiming_normal_(self.conv.weight)
self.dim = dim
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
from torch._inductor.runtime.... | SpyrosMouselinos/DeltaFormers | SameModule | false | 5,876 | [
"Apache-2.0"
] | 1 | 38508fa9b85f2c50aa0031b67e7e8feff1a75b27 | https://github.com/SpyrosMouselinos/DeltaFormers/tree/38508fa9b85f2c50aa0031b67e7e8feff1a75b27 |
FC1 | import torch
import torch.nn as nn
import torch.nn.functional as F
class FC1(nn.Module):
""" Neural network definition
"""
def __init__(self, size, hidden_layers):
super(FC1, self).__init__()
self.size = size
self.hidden_layers = hidden_layers
self.fc1 = nn.Linear(in_featu... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | Thibaud-Ardoin/Dial-a-Ride | FC1 | false | 5,877 | [
"MIT"
] | 1 | 7d9b3cd904d3194dccad31fec2533e2cf58cad0c | https://github.com/Thibaud-Ardoin/Dial-a-Ride/tree/7d9b3cd904d3194dccad31fec2533e2cf58cad0c |
HyperLinear | import math
import torch
import torch.nn.functional as F
import torch.nn as nn
class HyperLinear(nn.Module):
def __init__(self, in_features, out_features, num_hparams, bias=True):
super(HyperLinear, self).__init__()
self.in_features = in_features
self.out_features = out_features
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 math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.a... | ThrunGroup/implicit-hyper-opt | HyperLinear | false | 5,878 | [
"MIT"
] | 1 | fe4ac539c947ca8083049d23c5f1f67f44cd09f0 | https://github.com/ThrunGroup/implicit-hyper-opt/tree/fe4ac539c947ca8083049d23c5f1f67f44cd09f0 |
DQN | import torch
import torch.nn as nn
import torch.nn.functional as F
class DQN(nn.Module):
def __init__(self, input_size, hidden_1_size, hidden_2_size, output_size):
super().__init__()
self.fc1 = nn.Linear(input_size, hidden_1_size)
self.fc2 = nn.Linear(hidden_1_size, hidden_2_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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | TejaswiniMedi/DRL | DQN | false | 5,879 | [
"MIT"
] | 1 | d4a694c5e505822e6e8627be52afd0ccc60f80ef | https://github.com/TejaswiniMedi/DRL/tree/d4a694c5e505822e6e8627be52afd0ccc60f80ef |
PositionwiseFeedForward | import torch
import torch.nn as nn
import torch.nn.functional as F
class PositionwiseFeedForward(nn.Module):
"""A two-feed-forward-layer module.
Parameters
----------
d_model : int
embed_dim.
d_inner : int
dff.
dropout : float
dropout rate.
"""
def __init__(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
import torch.nn as nn
assert_... | TaoranJ/PC-RNN | PositionwiseFeedForward | false | 5,880 | [
"MIT"
] | 1 | f360b464cf68737fefd5e6093e55056838693b1b | https://github.com/TaoranJ/PC-RNN/tree/f360b464cf68737fefd5e6093e55056838693b1b |
Switch | import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.init import kaiming_normal
def ZeroInitializer(param):
shape = param.size()
init = np.zeros(shape).astype(np.float32)
param.data.set_(torch.from_numpy(init))
def Linear(initializer=kaiming_normal, bias_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.triton_helpers import libdevice
import numpy as np
... | TaoMiner/eesc | Switch | false | 5,881 | [
"Apache-2.0"
] | 1 | fa0ca532333cad2262d20707899f97a6c8a99cfb | https://github.com/TaoMiner/eesc/tree/fa0ca532333cad2262d20707899f97a6c8a99cfb |
PerOutputClassifierHead | from _paritybench_helpers import _mock_config
from torch.nn import Module
import torch
import torch.nn as nn
import torch.nn
class PerOutputClassifierHead(Module):
def __init__(self, config: 'dict'):
super(PerOutputClassifierHead, self).__init__()
self.linear_layer_1 = nn.Linear(config['hidden_di... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch.nn import Module
i... | SpyrosMouselinos/DeltaFormers | PerOutputClassifierHead | false | 5,882 | [
"Apache-2.0"
] | 1 | 38508fa9b85f2c50aa0031b67e7e8feff1a75b27 | https://github.com/SpyrosMouselinos/DeltaFormers/tree/38508fa9b85f2c50aa0031b67e7e8feff1a75b27 |
Net | import torch
import torch.nn as nn
import torch.nn.functional as F
class Net(nn.Module):
def __init__(self, board_width, board_height):
super(Net, self).__init__()
self.board_width = board_width
self.board_height = board_height
self.conv1 = nn.Conv2d(4, 32, kernel_size=3, padding=... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | SummitChen/ComputationalAdvertisement | Net | false | 5,883 | [
"MIT"
] | 1 | 05a9e8bd82ca834219121de4257185d63f592d78 | https://github.com/SummitChen/ComputationalAdvertisement/tree/05a9e8bd82ca834219121de4257185d63f592d78 |
VAE | import torch
from torch import nn
from torch.nn import functional as F
class VAE(nn.Module):
def __init__(self):
super(VAE, self).__init__()
self.fc1 = nn.Linear(84 * 84, 400)
self.fc21 = nn.Linear(400, 20)
self.fc22 = nn.Linear(400, 20)
self.fc3 = nn.Linear(20, 400)
... | import torch
from torch import device
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from... | TannerSorensen/speech_production_manifolds | VAE | false | 5,884 | [
"MIT"
] | 1 | 0dcc2c099ad0e1e157c7f108e28f5957d4ac2f48 | https://github.com/TannerSorensen/speech_production_manifolds/tree/0dcc2c099ad0e1e157c7f108e28f5957d4ac2f48 |
down | import torch
from torch.functional import F
import torch.nn as nn
import torch.nn.functional as F
class down(nn.Module):
"""
A class for creating neural network blocks containing layers:
Average Pooling --> Convlution + Leaky ReLU --> Convolution + Leaky ReLU
This is used in the UNet Class t... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | Thomasedv/AI_Interpolation | down | false | 5,885 | [
"MIT"
] | 1 | cee51d92185a43a60797785554ee1ae924e5da0d | https://github.com/Thomasedv/AI_Interpolation/tree/cee51d92185a43a60797785554ee1ae924e5da0d |
BMNLoss | import torch
import torch.nn.functional as F
import torch.nn as nn
def binary_logistic_regression_loss(reg_score, label, threshold=0.5,
ratio_range=(1.05, 21), eps=1e-05):
"""Binary Logistic Regression Loss."""
label = label.view(-1)
reg_score = reg_score.contiguous().view(-1)
pmask = (label > thr... | import torch
from torch import device
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_ma... | SvipRepetitionCounting/TransRAC | BMNLoss | false | 5,886 | [
"Apache-2.0"
] | 1 | eec12553dfa1e2fde6356b0e2703c633d225feb3 | https://github.com/SvipRepetitionCounting/TransRAC/tree/eec12553dfa1e2fde6356b0e2703c633d225feb3 |
L1_Charbonnier_loss | import torch
import torch.nn as nn
class L1_Charbonnier_loss(nn.Module):
"""L1 Charbonnierloss."""
def __init__(self):
super(L1_Charbonnier_loss, self).__init__()
self.eps = 1e-06
def forward(self, X, Y):
diff = torch.add(X, -Y)
error = torch.sqrt(diff * diff + self.eps)
... | 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... | Tiger1994/LapSRN | L1_Charbonnier_loss | false | 5,887 | [
"MIT"
] | 1 | 4f2222ebad97ad6730fe352f5a3c8a06f0f61e7a | https://github.com/Tiger1994/LapSRN/tree/4f2222ebad97ad6730fe352f5a3c8a06f0f61e7a |
FC2 | import torch
import torch.nn as nn
import torch.nn.functional as F
class FC2(nn.Module):
""" Neural network definition
"""
def __init__(self, size):
super(FC2, self).__init__()
self.size = size
self.fc1 = nn.Linear(in_features=self.size ** 2, out_features=128)
self.fc2 = n... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | Thibaud-Ardoin/Dial-a-Ride | FC2 | false | 5,888 | [
"MIT"
] | 1 | 7d9b3cd904d3194dccad31fec2533e2cf58cad0c | https://github.com/Thibaud-Ardoin/Dial-a-Ride/tree/7d9b3cd904d3194dccad31fec2533e2cf58cad0c |
NeuralNetMultiplePositionalArguments | import torch
import torch.nn
import torch.onnx
import torch.utils.checkpoint
class NeuralNetMultiplePositionalArguments(torch.nn.Module):
def __init__(self, input_size, hidden_size, num_classes):
super(NeuralNetMultiplePositionalArguments, self).__init__()
self.fc1 = torch.nn.Linear(input_size, 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
from torch._inductor.runtime import triton_helpers
import torch.nn
import torch.... | TingGong1/onnxruntime | NeuralNetMultiplePositionalArguments | false | 5,889 | [
"MIT"
] | 1 | 435010ab6873974803591fa22262ed8b3e36e44d | https://github.com/TingGong1/onnxruntime/tree/435010ab6873974803591fa22262ed8b3e36e44d |
HuggingfaceFastGelu | import torch
import torch.nn
import torch.onnx
import torch.utils.checkpoint
class HuggingfaceFastGelu(torch.nn.Module):
def forward(self, x):
return 0.5 * x * (1.0 + torch.tanh(x * 0.7978845608 * (1.0 +
0.044715 * x * x)))
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
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._inductor.runtime.triton_helpers import libdevice
import torch.nn
import torch.onnx
import torch.utils.checkpoint
assert_size_str... | TingGong1/onnxruntime | HuggingfaceFastGelu | false | 5,890 | [
"MIT"
] | 1 | 435010ab6873974803591fa22262ed8b3e36e44d | https://github.com/TingGong1/onnxruntime/tree/435010ab6873974803591fa22262ed8b3e36e44d |
IIDIsotropicGaussianUVLoss | import math
import torch
import torch.utils.data
from torch import nn
import torch.nn.functional as F
class IIDIsotropicGaussianUVLoss(nn.Module):
"""
Loss for the case of iid residuals with isotropic covariance:
$Sigma_i = sigma_i^2 I$
The loss (negative log likelihood) is then:
$1/2 sum_{i=1}^n ... | 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... | TinBacon/FastAutoAugmentation | IIDIsotropicGaussianUVLoss | false | 5,891 | [
"Apache-2.0"
] | 1 | 011e4e348fd9a937a29df11695dc71410f555d0a | https://github.com/TinBacon/FastAutoAugmentation/tree/011e4e348fd9a937a29df11695dc71410f555d0a |
IndepAnisotropicGaussianUVLoss | import math
import torch
import torch.utils.data
from torch import nn
import torch.nn.functional as F
class IndepAnisotropicGaussianUVLoss(nn.Module):
"""
Loss for the case of independent residuals with anisotropic covariances:
$Sigma_i = sigma_i^2 I + r_i r_i^T$
The loss (negative log likelihood) 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
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import math... | TinBacon/FastAutoAugmentation | IndepAnisotropicGaussianUVLoss | false | 5,892 | [
"Apache-2.0"
] | 1 | 011e4e348fd9a937a29df11695dc71410f555d0a | https://github.com/TinBacon/FastAutoAugmentation/tree/011e4e348fd9a937a29df11695dc71410f555d0a |
BVNet | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn
class BVNet(nn.Module):
"""
Baseline REINFORCE - Value Calculating Network
"""
def __init__(self, input_size):
super(BVNet, self).__init__()
self.input_size = input_size
self.fc1 = nn.Linear(inp... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | SpyrosMouselinos/DeltaFormers | BVNet | false | 5,893 | [
"Apache-2.0"
] | 1 | 38508fa9b85f2c50aa0031b67e7e8feff1a75b27 | https://github.com/SpyrosMouselinos/DeltaFormers/tree/38508fa9b85f2c50aa0031b67e7e8feff1a75b27 |
MegatronFastGelu | import torch
import torch.nn
import torch.onnx
import torch.utils.checkpoint
class MegatronFastGelu(torch.nn.Module):
def forward(self, x):
return 0.5 * x * (1.0 + torch.tanh(0.7978845608028654 * x * (1.0 +
0.044715 * x * x)))
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def g... | 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
import torch.onnx
import torch.utils.checkpoint
assert_size_str... | TingGong1/onnxruntime | MegatronFastGelu | false | 5,894 | [
"MIT"
] | 1 | 435010ab6873974803591fa22262ed8b3e36e44d | https://github.com/TingGong1/onnxruntime/tree/435010ab6873974803591fa22262ed8b3e36e44d |
MegatronGelu | import torch
import torch.nn
import torch.onnx
import torch.utils.checkpoint
class MegatronGelu(torch.nn.Module):
def forward(self, x):
return x * 0.5 * (torch.erf(x / 1.41421) + 1.0)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
| import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn
import torch.onnx
import torch.utils.checkpoint
assert_size_str... | TingGong1/onnxruntime | MegatronGelu | false | 5,895 | [
"MIT"
] | 1 | 435010ab6873974803591fa22262ed8b3e36e44d | https://github.com/TingGong1/onnxruntime/tree/435010ab6873974803591fa22262ed8b3e36e44d |
SelfAttention | import torch
import torch.nn as nn
class SelfAttention(nn.Module):
def __init__(self, embed_size, heads):
super(SelfAttention, self).__init__()
self.embed_size = embed_size
self.heads = heads
self.head_dim = embed_size // heads
assert self.head_dim * heads == embed_size, '... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Thibaud-Ardoin/Dial-a-Ride | SelfAttention | false | 5,896 | [
"MIT"
] | 1 | 7d9b3cd904d3194dccad31fec2533e2cf58cad0c | https://github.com/Thibaud-Ardoin/Dial-a-Ride/tree/7d9b3cd904d3194dccad31fec2533e2cf58cad0c |
NeuralNetPartialNoGradModel | import torch
import torch.nn
import torch.onnx
import torch.utils.checkpoint
class NeuralNetPartialNoGradModel(torch.nn.Module):
def __init__(self, input_size, hidden_size, num_classes):
super(NeuralNetPartialNoGradModel, self).__init__()
self.fc1 = torch.nn.Linear(input_size, hidden_size).requir... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | TingGong1/onnxruntime | NeuralNetPartialNoGradModel | false | 5,897 | [
"MIT"
] | 1 | 435010ab6873974803591fa22262ed8b3e36e44d | https://github.com/TingGong1/onnxruntime/tree/435010ab6873974803591fa22262ed8b3e36e44d |
KLLoss | import torch
from torch import Tensor
class KLLoss(torch.nn.KLDivLoss):
def __init__(self, batch_wise=False):
super(KLLoss, self).__init__(reduction='batchmean')
self.batch_wise = batch_wise
def forward(self, input: 'Tensor', target: 'Tensor') ->Tensor:
if self.batch_wise:
... | 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... | Tomoya-K-0504/deepSELF | KLLoss | false | 5,898 | [
"MIT"
] | 1 | 0e5a7d0169b3e9edcb5c8d9802140a84ce5cb69a | https://github.com/Tomoya-K-0504/deepSELF/tree/0e5a7d0169b3e9edcb5c8d9802140a84ce5cb69a |
SANet | import torch
import torch.nn as nn
import torch.backends.cudnn
def calc_mean_std(feat, eps=1e-05):
size = feat.size()
assert len(size) == 4
N, C = size[:2]
feat_var = feat.view(N, C, -1).var(dim=2) + eps
feat_std = feat_var.sqrt().view(N, C, 1, 1)
feat_mean = feat.view(N, C, -1).mean(dim=2).vi... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | TimandXiyu/SANet-style-transfer- | SANet | false | 5,899 | [
"MIT"
] | 1 | 91c3dd1344d1dded61aa2e79618240a49345b40e | https://github.com/TimandXiyu/SANet-style-transfer-/tree/91c3dd1344d1dded61aa2e79618240a49345b40e |
LayerNorm | import torch
import torch.nn as nn
import torch.nn
import torch.onnx
import torch.utils.checkpoint
class LayerNorm(nn.Module):
def __init__(self, hidden_size, epsilon, cast_fp16=True, formula=0):
super().__init__()
self.layer_norm = nn.LayerNorm(hidden_size, eps=epsilon)
self.layer_norm.b... | 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.nn
import torch.onnx
import torch.utils.chec... | TingGong1/onnxruntime | LayerNorm | false | 5,900 | [
"MIT"
] | 1 | 435010ab6873974803591fa22262ed8b3e36e44d | https://github.com/TingGong1/onnxruntime/tree/435010ab6873974803591fa22262ed8b3e36e44d |
AttentionSeq2Vec | from torch.nn import Module
import torch
from torch.nn import Linear
from typing import Optional
from torch.nn import Tanh
def masked_softmax(vector: 'torch.FloatTensor', mask: 'torch.ByteTensor'):
"""
计算带有 masked 的 softmax
:param vector: shape: (B, seq_len)
:param mask: shape: (B, seq_len),
:retu... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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... | Tiffany-HONG/easytext | AttentionSeq2Vec | false | 5,901 | [
"MIT"
] | 1 | 9c717d11240d96fab98b0532084ebb5c093d55bd | https://github.com/Tiffany-HONG/easytext/tree/9c717d11240d96fab98b0532084ebb5c093d55bd |
NeuralNetNonDifferentiableOutput | import torch
import torch.nn
import torch.onnx
import torch.utils.checkpoint
class NeuralNetNonDifferentiableOutput(torch.nn.Module):
def __init__(self, input_size, hidden_size, num_classes):
super(NeuralNetNonDifferentiableOutput, self).__init__()
self.fc1 = torch.nn.Linear(input_size, hidden_si... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn
import torch.... | TingGong1/onnxruntime | NeuralNetNonDifferentiableOutput | false | 5,902 | [
"MIT"
] | 1 | 435010ab6873974803591fa22262ed8b3e36e44d | https://github.com/TingGong1/onnxruntime/tree/435010ab6873974803591fa22262ed8b3e36e44d |
Normalize | import torch
from torch import Tensor
class Normalize(torch.nn.Module):
def forward(self, x: 'Tensor'):
return (x - x.mean()) / x.std()
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
from torch._inductor.runtime.triton_helpers import libdevice
assert_size_stride = torch._... | Tomoya-K-0504/deepSELF | Normalize | false | 5,903 | [
"MIT"
] | 1 | 0e5a7d0169b3e9edcb5c8d9802140a84ce5cb69a | https://github.com/Tomoya-K-0504/deepSELF/tree/0e5a7d0169b3e9edcb5c8d9802140a84ce5cb69a |
NeuralNetMultiplePositionalArgumentsMultiOutputsWithDependency | import torch
import torch.nn
import torch.onnx
import torch.utils.checkpoint
class NeuralNetMultiplePositionalArgumentsMultiOutputsWithDependency(torch.
nn.Module):
def __init__(self, input_size, hidden_size, num_classes):
super(NeuralNetMultiplePositionalArgumentsMultiOutputsWithDependency,
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | TingGong1/onnxruntime | NeuralNetMultiplePositionalArgumentsMultiOutputsWithDependency | false | 5,904 | [
"MIT"
] | 1 | 435010ab6873974803591fa22262ed8b3e36e44d | https://github.com/TingGong1/onnxruntime/tree/435010ab6873974803591fa22262ed8b3e36e44d |
PositionalScaledDotProductAttention | import torch
import torch.utils.data
import torch
import torch.nn as nn
import torch.nn.functional as F
class PositionalScaledDotProductAttention(nn.Module):
""" Scaled Dot-Product Attention with optional positional encodings """
def __init__(self, temperature, positional_encoding=None, attn_dropout=0.1
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | TomerRonen34/MeshCNN | PositionalScaledDotProductAttention | false | 5,905 | [
"MIT"
] | 1 | 8c50f3804c48044b78572d652a42184640e904d9 | https://github.com/TomerRonen34/MeshCNN/tree/8c50f3804c48044b78572d652a42184640e904d9 |
NeuralNetMultiplePositionalArgumentsMultiOutputsWithoutDependency | import torch
import torch.nn
import torch.onnx
import torch.utils.checkpoint
class NeuralNetMultiplePositionalArgumentsMultiOutputsWithoutDependency(torch
.nn.Module):
def __init__(self, input_size, hidden_size, num_classes):
super(NeuralNetMultiplePositionalArgumentsMultiOutputsWithoutDependency
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | TingGong1/onnxruntime | NeuralNetMultiplePositionalArgumentsMultiOutputsWithoutDependency | false | 5,906 | [
"MIT"
] | 1 | 435010ab6873974803591fa22262ed8b3e36e44d | https://github.com/TingGong1/onnxruntime/tree/435010ab6873974803591fa22262ed8b3e36e44d |
ScaledDotProductAttention | import torch
import torch.utils.data
import torch
import torch.nn as nn
import torch.nn.functional as F
class ScaledDotProductAttention(nn.Module):
"""
Scaled Dot-Product Attention
from https://github.com/jadore801120/attention-is-all-you-need-pytorch
by Yu-Hsiang Huang
"""
def __init__(self,... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | TomerRonen34/MeshCNN | ScaledDotProductAttention | false | 5,907 | [
"MIT"
] | 1 | 8c50f3804c48044b78572d652a42184640e904d9 | https://github.com/TomerRonen34/MeshCNN/tree/8c50f3804c48044b78572d652a42184640e904d9 |
ConvPredictor | import torch
import torch.nn as nn
class ConvPredictor(nn.Module):
def __init__(self, input_dim, output_dim, groups):
super(ConvPredictor, self).__init__()
self.feature_maps = input_dim
self.groups = groups
self.output_dim = output_dim
self.conv = nn.Conv1d(in_channels=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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | TomScheffers/Residual-Prediction-Networks-using-Pytorch | ConvPredictor | false | 5,908 | [
"MIT"
] | 1 | c0e8b60c188414d71c389a0fd034f50017c24a93 | https://github.com/TomScheffers/Residual-Prediction-Networks-using-Pytorch/tree/c0e8b60c188414d71c389a0fd034f50017c24a93 |
L2Norm | import torch
import torch.nn as nn
import torch.nn.init as init
import torch.utils.data
from numpy.random import *
class L2Norm(nn.Module):
def __init__(self, n_channels, scale):
super(L2Norm, self).__init__()
self.n_channels = n_channels
self.gamma = scale or None
self.eps = 1e-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
import torch.nn.init as init
import torch.utils.data
from... | Tony-Khor/PyTorch-From-Zero-to-All | L2Norm | false | 5,909 | [
"MIT"
] | 1 | d8f9b6d81fe390dee93a887f342dc818553e61b3 | https://github.com/Tony-Khor/PyTorch-From-Zero-to-All/tree/d8f9b6d81fe390dee93a887f342dc818553e61b3 |
Pooling | import torch
import torch.nn as nn
class Pooling(nn.Module):
"""
Implementation of pooling for PoolFormer
--pool_size: pooling size
"""
def __init__(self, pool_size=3):
super().__init__()
self.pool = nn.AvgPool2d(pool_size, stride=1, padding=pool_size //
2, count_incl... | 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... | TranNhiem/MVAR_SSL | Pooling | false | 5,910 | [
"MIT"
] | 1 | 339964db4d40f06a92866675ff99ef67cd968cca | https://github.com/TranNhiem/MVAR_SSL/tree/339964db4d40f06a92866675ff99ef67cd968cca |
Transform | import torch
import torch.nn as nn
import torch.backends.cudnn
def calc_mean_std(feat, eps=1e-05):
size = feat.size()
assert len(size) == 4
N, C = size[:2]
feat_var = feat.view(N, C, -1).var(dim=2) + eps
feat_std = feat_var.sqrt().view(N, C, 1, 1)
feat_mean = feat.view(N, C, -1).mean(dim=2).vi... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | TimandXiyu/SANet-style-transfer- | Transform | false | 5,911 | [
"MIT"
] | 1 | 91c3dd1344d1dded61aa2e79618240a49345b40e | https://github.com/TimandXiyu/SANet-style-transfer-/tree/91c3dd1344d1dded61aa2e79618240a49345b40e |
VGG16 | import torch
import numpy as np
import torchvision.transforms.functional as F
import torch.nn as nn
import torch.nn.functional as F
class Normalize:
def __init__(self, mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)):
self.mean = mean
self.std = std
def undo(self, imgarr):
proc... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import numpy as np
import tor... | SharhadBashar/1-stage-wseg | VGG16 | false | 5,913 | [
"Apache-2.0"
] | 1 | 83bf13444f5039ffed2de1605f09b3f90b525586 | https://github.com/SharhadBashar/1-stage-wseg/tree/83bf13444f5039ffed2de1605f09b3f90b525586 |
L2Norm | import torch
import torch.nn as nn
class L2Norm(nn.Module):
"""Channel-wise L2 normalization."""
def __init__(self, in_channels):
super(L2Norm, self).__init__()
self.weight = nn.Parameter(torch.randn(in_channels))
def forward(self, x):
"""out = weight * x / sqrt(\\sum x_i^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 import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert... | TropComplique/ssd-pytorch | L2Norm | false | 5,914 | [
"MIT"
] | 1 | e91af875c65dc64a21b838a6645fc803ef690dcf | https://github.com/TropComplique/ssd-pytorch/tree/e91af875c65dc64a21b838a6645fc803ef690dcf |
UnitNorm | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
class UnitNorm(nn.Module):
def forward(self, x):
x = nn.functional.normalize(x, dim=1)
return x
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[],... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
import... | UMBCvision/CMSF | UnitNorm | false | 5,915 | [
"MIT"
] | 1 | 4aaac1833a0c8cfd67aa05762e43478983d74c08 | https://github.com/UMBCvision/CMSF/tree/4aaac1833a0c8cfd67aa05762e43478983d74c08 |
Whitening2d | import torch
import torch.nn as nn
from torch.cuda.amp import custom_fwd
from torch.nn.functional import conv2d
class Whitening2d(nn.Module):
def __init__(self, output_dim: 'int', eps: 'float'=0.0):
"""Layer that computes hard whitening for W-MSE using the Cholesky decomposition.
Args:
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | TranNhiem/MVAR_SSL | Whitening2d | false | 5,916 | [
"MIT"
] | 1 | 339964db4d40f06a92866675ff99ef67cd968cca | https://github.com/TranNhiem/MVAR_SSL/tree/339964db4d40f06a92866675ff99ef67cd968cca |
UpsampleConv2d | import torch
import torch.nn.functional as F
import torch.nn as nn
class UpsampleConv2d(nn.Module):
"""
Avoid checkerboard patterns by upsampling the image and convolving.
https://distill.pub/2016/deconv-checkerboard/
"""
def __init__(self, in_channels, out_channels, kernel_size, stride,
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | TrueMatthewKirkham/face-preserving-style-transfer | UpsampleConv2d | false | 5,917 | [
"MIT"
] | 1 | ae8a9509570227ea52776fba85658022124c886c | https://github.com/TrueMatthewKirkham/face-preserving-style-transfer/tree/ae8a9509570227ea52776fba85658022124c886c |
LayerNormChannel | import torch
import torch.nn as nn
class LayerNormChannel(nn.Module):
"""
LayerNorm only for Channel Dimension.
Input: tensor in shape [B, C, H, W]
"""
def __init__(self, num_channels, eps=1e-05):
super().__init__()
self.weight = nn.Parameter(torch.ones(num_channels))
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_... | TranNhiem/MVAR_SSL | LayerNormChannel | false | 5,918 | [
"MIT"
] | 1 | 339964db4d40f06a92866675ff99ef67cd968cca | https://github.com/TranNhiem/MVAR_SSL/tree/339964db4d40f06a92866675ff99ef67cd968cca |
MarginRankingLearningLoss | import torch
from torch import nn
import torch.nn.functional as F
class MarginRankingLearningLoss(nn.Module):
def __init__(self, margin=1.0):
super(MarginRankingLearningLoss, self).__init__()
self.margin = margin
def forward(self, inputs, targets):
random = torch.randperm(inputs.size... | import torch
from torch import device
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
assert_size_stride = torch._C._dynamo.guard... | VKCOM/TopicsDataset | MarginRankingLearningLoss | false | 5,919 | [
"MIT"
] | 1 | 149919321ba61a8f17b22f62f60f4aedec43d72b | https://github.com/VKCOM/TopicsDataset/tree/149919321ba61a8f17b22f62f60f4aedec43d72b |
GumbelQuantizer | import torch
import torch.nn as nn
from torch.nn import functional as F
class GumbelQuantizer(nn.Module):
def __init__(self, input_dim, num_latents, embedding_dim):
super().__init__()
self.embedding_dim = embedding_dim
self.num_latents = num_latents
self.proj = nn.Conv2d(input_dim... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | TobiasNorlund/vq-vae | GumbelQuantizer | false | 5,920 | [
"Apache-2.0"
] | 1 | bdfc35f35491e8d4877a13f7f84d6cbdcc69daa0 | https://github.com/TobiasNorlund/vq-vae/tree/bdfc35f35491e8d4877a13f7f84d6cbdcc69daa0 |
Conv1dSamePadding | import torch
from torch import nn
import torch.nn.functional as F
def conv1d_same_padding(input, weight, bias, stride, dilation, groups):
kernel, dilation, stride = weight.size(2), dilation[0], stride[0]
l_out = l_in = input.size(2)
padding = (l_out - 1) * stride - l_in + dilation * (kernel - 1) + 1
i... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
import torch.nn.functional as F
assert_size_stride = torch.... | UlysseCoteAllard/LongShortNetworkBipolar | Conv1dSamePadding | false | 5,921 | [
"Apache-2.0"
] | 1 | f6d146b967b4747f02d6589a0483d6c67394ee87 | https://github.com/UlysseCoteAllard/LongShortNetworkBipolar/tree/f6d146b967b4747f02d6589a0483d6c67394ee87 |
ResidualBlock | import torch
import torch.nn as nn
class ResidualBlock(nn.Module):
"""Redisual network block for style transfer."""
def __init__(self, nchannels):
"""Create a block of a residual network."""
super(ResidualBlock, self).__init__()
self.conv1 = nn.Conv2d(nchannels, nchannels, 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._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | TrueMatthewKirkham/face-preserving-style-transfer | ResidualBlock | false | 5,922 | [
"MIT"
] | 1 | ae8a9509570227ea52776fba85658022124c886c | https://github.com/TrueMatthewKirkham/face-preserving-style-transfer/tree/ae8a9509570227ea52776fba85658022124c886c |
MultiHeadAttention | import torch
import numpy as np
import torch.utils.data
import torch
import torch.nn as nn
import torch.nn.functional as F
class PositionalEncoding(nn.Module):
def __init__(self, max_pos, d_k):
super().__init__()
self.w_rpr = nn.Linear(d_k, max_pos + 1, bias=False)
def __call__(self, q, dist... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | TomerRonen34/MeshCNN | MultiHeadAttention | false | 5,923 | [
"MIT"
] | 1 | 8c50f3804c48044b78572d652a42184640e904d9 | https://github.com/TomerRonen34/MeshCNN/tree/8c50f3804c48044b78572d652a42184640e904d9 |
FFN | import torch
from torch import nn
import torch.nn.functional as F
class FFN(nn.Module):
def __init__(self, d):
super().__init__()
self.fc_1 = nn.Linear(2 * d, 4 * d)
self.drop = nn.Dropout(0.1)
self.fc_2 = nn.Linear(4 * d, d)
def forward(self, x_1, x_2):
x = self.fc_1... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
assert_s... | VKCOM/TopicsDataset | FFN | false | 5,924 | [
"MIT"
] | 1 | 149919321ba61a8f17b22f62f60f4aedec43d72b | https://github.com/VKCOM/TopicsDataset/tree/149919321ba61a8f17b22f62f60f4aedec43d72b |
GSAHelper | import torch
from torch import nn
class GSAHelper(nn.Module):
def __init__(self, d):
super().__init__()
self.d = d
self.fc_k = nn.Linear(self.d, self.d)
self.fc_q = nn.Linear(self.d, self.d)
self.fc_kq = nn.Linear(self.d, self.d)
def forward(self, k, q):
m = 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.... | VKCOM/TopicsDataset | GSAHelper | false | 5,925 | [
"MIT"
] | 1 | 149919321ba61a8f17b22f62f60f4aedec43d72b | https://github.com/VKCOM/TopicsDataset/tree/149919321ba61a8f17b22f62f60f4aedec43d72b |
PoolFormerBlock | import math
import torch
import warnings
import torch.nn as nn
def _no_grad_trunc_normal_(tensor, mean, std, a, b):
"""Copy & paste from PyTorch official master until it's in a few official releases - RW
Method based on https://people.sc.fsu.edu/~jburkardt/presentations/truncated_normal.pdf
"""
def 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 math
import ... | TranNhiem/MVAR_SSL | PoolFormerBlock | false | 5,926 | [
"MIT"
] | 1 | 339964db4d40f06a92866675ff99ef67cd968cca | https://github.com/TranNhiem/MVAR_SSL/tree/339964db4d40f06a92866675ff99ef67cd968cca |
ActorCritic | import math
import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
def log_normal_density(x, mean, log_std, std):
"""returns guassian density given x on log scale"""
variance = std.pow(2)
log_density = -(x - mean).pow(2) / (2 * variance) - 0.5 * np.log(2 * np.pi
) - ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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... | Tzenthin/pytorch-ppo-sac-HalfCheetah-v2 | ActorCritic | false | 5,927 | [
"MIT"
] | 1 | 282a4104ec577056a141909e29dc97ed425a566c | https://github.com/Tzenthin/pytorch-ppo-sac-HalfCheetah-v2/tree/282a4104ec577056a141909e29dc97ed425a566c |
AttentionPool2d | import torch
from torch import nn
from torch.nn import functional as F
class AttentionPool2d(nn.Module):
def __init__(self, spacial_dim: 'int', embed_dim: 'int', num_heads:
'int', output_dim: 'int'=None):
super().__init__()
self.positional_embedding = nn.Parameter(torch.randn(spacial_dim ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Vaishaal/CLIP | AttentionPool2d | false | 5,928 | [
"MIT"
] | 1 | 16adcf2a5ff41d6a3f1bb45165aa348031fdbafe | https://github.com/Vaishaal/CLIP/tree/16adcf2a5ff41d6a3f1bb45165aa348031fdbafe |
AttnBahd | import torch
from torch import nn as nn
class AttnBahd(nn.Module):
def __init__(self, encoder_out_dim, decoder_hid_dim, attn_dim=None):
"""
Attention mechanism
:param encoder_out_dim: Dimension of hidden states of the encoder h_j
:param decoder_hid_dim: Dimension of the hidden 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
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | UKPLab/acl2018-msr-workshop-binlin | AttnBahd | false | 5,929 | [
"Apache-2.0"
] | 1 | 9b8021dfa14a8bc131df117fa9985699fc8cedea | https://github.com/UKPLab/acl2018-msr-workshop-binlin/tree/9b8021dfa14a8bc131df117fa9985699fc8cedea |
GSA | import torch
from torch import nn
class GSAHelper(nn.Module):
def __init__(self, d):
super().__init__()
self.d = d
self.fc_k = nn.Linear(self.d, self.d)
self.fc_q = nn.Linear(self.d, self.d)
self.fc_kq = nn.Linear(self.d, self.d)
def forward(self, k, q):
m = 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.... | VKCOM/TopicsDataset | GSA | false | 5,930 | [
"MIT"
] | 1 | 149919321ba61a8f17b22f62f60f4aedec43d72b | https://github.com/VKCOM/TopicsDataset/tree/149919321ba61a8f17b22f62f60f4aedec43d72b |
CpuSpeedModel | import torch
import torch.nn as nn
class CpuSpeedModel(nn.Module):
def __init__(self, input_size, output_size):
super(CpuSpeedModel, self).__init__()
hidden_size = 100
self.linear1 = nn.Linear(input_size, hidden_size)
self.linear2 = nn.Linear(hidden_size, hidden_size)
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... | VVKot/mlinsecond-general-cpu | CpuSpeedModel | false | 5,931 | [
"MIT"
] | 1 | d3e08027dc3152b5c88c2e5bf4b365eedbdcb0d1 | https://github.com/VVKot/mlinsecond-general-cpu/tree/d3e08027dc3152b5c88c2e5bf4b365eedbdcb0d1 |
SinkhornKnopp | import torch
import torch.distributed as dist
class SinkhornKnopp(torch.nn.Module):
def __init__(self, num_iters: 'int'=3, epsilon: 'float'=0.05,
world_size: 'int'=1):
"""Approximates optimal transport using the Sinkhorn-Knopp algorithm.
A simple iterative method to approach the double s... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_str... | TranNhiem/MVAR_SSL | SinkhornKnopp | false | 5,932 | [
"MIT"
] | 1 | 339964db4d40f06a92866675ff99ef67cd968cca | https://github.com/TranNhiem/MVAR_SSL/tree/339964db4d40f06a92866675ff99ef67cd968cca |
WeightNet | import torch
import torch.nn as nn
class WeightNet(nn.Module):
"""WeightNet in Temporal interlace module.
The WeightNet consists of two parts: one convolution layer
and a sigmoid function. Following the convolution layer, the sigmoid
function and rescale module can scale our output to the range (0, 2... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | VisualAnalysisOfHumans/LOVEU_TRACK1_TOP3_SUBMISSION | WeightNet | false | 5,933 | [
"MIT"
] | 1 | 6f4d1c7e6883d6b0664fcd04265f437247afab54 | https://github.com/VisualAnalysisOfHumans/LOVEU_TRACK1_TOP3_SUBMISSION/tree/6f4d1c7e6883d6b0664fcd04265f437247afab54 |
ResidualSequential | import torch
import torch.optim
import torch.nn as nn
import torch.nn.init
class ResidualSequential(nn.Sequential):
def __init__(self, *args):
super(ResidualSequential, self).__init__(*args)
def forward(self, x):
out = super(ResidualSequential, self).forward(x)
x_ = None
if 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.optim
import torch.nn as nn
import torch.nn.init
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_... | Volodimirich/DL-in-denoising-MCT-rock-images | ResidualSequential | false | 5,934 | [
"MIT"
] | 1 | 0201d42a45221e4e0faaf50c59bf48c435bcdc82 | https://github.com/Volodimirich/DL-in-denoising-MCT-rock-images/tree/0201d42a45221e4e0faaf50c59bf48c435bcdc82 |
TorchModule | import torch
import torch.nn
class TorchLinearModule(torch.nn.Module):
def __init__(self, in_size, out_size):
super(TorchLinearModule, self).__init__()
self._linear = torch.nn.Linear(in_size, out_size)
def forward(self, x):
return self._linear(x)
class TorchModule(torch.nn.Module):... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn
ass... | VedPatwardhan/ivy | TorchModule | false | 5,935 | [
"Apache-2.0"
] | 1 | 7b2105fa8cf38879444a1029bfaa7f0b2f27717a | https://github.com/VedPatwardhan/ivy/tree/7b2105fa8cf38879444a1029bfaa7f0b2f27717a |
TVLoss | import torch
from torch import Tensor
import torch.utils.data
import torch.utils.data.dataset
import torch
import torch.nn as nn
import torch.utils.data.distributed
class TVLoss(nn.Module):
"""Regularization loss based on Li FeiFei."""
def __init__(self, weight: 'Tensor') ->None:
"""The weight inform... | 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 Tensor
import torch.utils.data
import torch.utils.data.dataset
import torch
import torch.nn as nn
import torch.utils.data.... | Tubbz-alt/SRGAN-PyTorch-2 | TVLoss | false | 5,936 | [
"Apache-2.0"
] | 1 | c1a01c99287a6212a3dc76ac17baafcf1c9f3013 | https://github.com/Tubbz-alt/SRGAN-PyTorch-2/tree/c1a01c99287a6212a3dc76ac17baafcf1c9f3013 |
OffsetNet | import torch
import torch.nn as nn
class OffsetNet(nn.Module):
"""OffsetNet in Temporal interlace module.
The OffsetNet consists of one convolution layer and two fc layers
with a relu activation following with a sigmoid function. Following
the convolution layer, two fc layers and relu are applied to ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | VisualAnalysisOfHumans/LOVEU_TRACK1_TOP3_SUBMISSION | OffsetNet | false | 5,937 | [
"MIT"
] | 1 | 6f4d1c7e6883d6b0664fcd04265f437247afab54 | https://github.com/VisualAnalysisOfHumans/LOVEU_TRACK1_TOP3_SUBMISSION/tree/6f4d1c7e6883d6b0664fcd04265f437247afab54 |
CombinedPooling | import torch
import torch.optim
import torch.utils.data
import torch.nn as nn
import torch.nn.parallel
import torch.utils.data.distributed
class CombinedPooling(nn.Module):
def __init__(self):
super().__init__()
self.max_pooling = nn.AdaptiveMaxPool2d(1)
self.avg_pooling = nn.AdaptiveAvgP... | 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.optim
import torch.utils.data
import torch.nn as nn
import torch.nn.parallel... | VisualComputingInstitute/CROWDBOT_perception | CombinedPooling | false | 5,938 | [
"MIT"
] | 1 | df98f3f658c39fb3fa4ac0456f1214f7918009f6 | https://github.com/VisualComputingInstitute/CROWDBOT_perception/tree/df98f3f658c39fb3fa4ac0456f1214f7918009f6 |
SEModule | import torch
import torch.nn as nn
class SEModule(nn.Module):
def __init__(self, channels, reduction=1 / 16):
super().__init__()
self.avg_pool = nn.AdaptiveAvgPool3d(1)
self.bottleneck = self._round_width(channels, reduction)
self.fc1 = nn.Conv3d(channels, self.bottleneck, kernel_... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | VisualAnalysisOfHumans/LOVEU_TRACK1_TOP3_SUBMISSION | SEModule | false | 5,939 | [
"MIT"
] | 1 | 6f4d1c7e6883d6b0664fcd04265f437247afab54 | https://github.com/VisualAnalysisOfHumans/LOVEU_TRACK1_TOP3_SUBMISSION/tree/6f4d1c7e6883d6b0664fcd04265f437247afab54 |
PointWiseFeedForward | import torch
class PointWiseFeedForward(torch.nn.Module):
def __init__(self, hidden_units, dropout_rate):
super(PointWiseFeedForward, self).__init__()
self.conv1 = torch.nn.Conv1d(hidden_units, hidden_units, kernel_size=1)
self.dropout1 = torch.nn.Dropout(p=dropout_rate)
self.relu... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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... | Vivdaddy/recsys-filterbubbles | PointWiseFeedForward | false | 5,940 | [
"MIT"
] | 1 | d21639bce515ffef5ba2db530dc2505eee1f83c0 | https://github.com/Vivdaddy/recsys-filterbubbles/tree/d21639bce515ffef5ba2db530dc2505eee1f83c0 |
SigmaL1SmoothLoss | import torch
from torch import nn
class SigmaL1SmoothLoss(nn.Module):
def forward(self, pred, targ):
reg_diff = torch.abs(targ - pred)
reg_loss = torch.where(torch.le(reg_diff, 1 / 9), 4.5 * torch.pow(
reg_diff, 2), reg_diff - 1 / 18)
return reg_loss.mean()
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 import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch import nn
a... | VrunArya/Hacktoberfest2021 | SigmaL1SmoothLoss | false | 5,941 | [
"MIT"
] | 1 | 5e739e52310dabf8b131abe5ecf906e13711b9d6 | https://github.com/VrunArya/Hacktoberfest2021/tree/5e739e52310dabf8b131abe5ecf906e13711b9d6 |
ChebConv | import torch
import torch.nn as nn
import torch.nn.init as init
class ChebConv(nn.Module):
"""
The ChebNet convolution operation.
:param in_c: int, number of input dim
:param out_c: int, number of output dim
:param K: int, the order of Chebyshev Polynomial,切比雪夫展开多少阶
"""
def __init__(self... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | V-cyberpunk-01/GNN | ChebConv | false | 5,942 | [
"MIT"
] | 1 | 25a6b24f4d8fad626af33f98e189b221c50406cd | https://github.com/V-cyberpunk-01/GNN/tree/25a6b24f4d8fad626af33f98e189b221c50406cd |
Loss_fn | import torch
import torch.nn as nn
class Loss_fn(nn.Module):
def __init__(self, eps=0.001):
super().__init__()
self.eps = eps
def forward(self, ip, target):
diff = ip - target
loss = torch.mean(torch.sqrt(diff * diff + self.eps * self.eps))
return loss
def get_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 import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert... | Vrushank264/Low-Light-Enhancement | Loss_fn | false | 5,943 | [
"MIT"
] | 1 | 3c13a10a16eab8183b8fbd0c063d9815b662259a | https://github.com/Vrushank264/Low-Light-Enhancement/tree/3c13a10a16eab8183b8fbd0c063d9815b662259a |
TemporallyBatchedAdditiveAttention | import torch
import torch.nn as nn
import torch.nn.functional as F
class AdditiveAttention(nn.Module):
def __init__(self, encoder_hidden_state_dim, decoder_hidden_state_dim,
internal_dim=None):
super(AdditiveAttention, self).__init__()
if internal_dim is None:
internal_dim = i... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Vision-CAIR/UnlikelihoodMotionForecasting | TemporallyBatchedAdditiveAttention | false | 5,944 | [
"MIT"
] | 1 | 556d6a3ed3e4e0e2d88108d7dbb48933313b58aa | https://github.com/Vision-CAIR/UnlikelihoodMotionForecasting/tree/556d6a3ed3e4e0e2d88108d7dbb48933313b58aa |
FocalLoss2d | import torch
import torch.nn as nn
class FocalLoss2d(nn.Module):
def __init__(self, alpha=0.25, gamma=2, ignore_index=None, reduction=
'mean', **kwargs):
super(FocalLoss2d, self).__init__()
self.alpha = alpha
self.gamma = gamma
self.smooth = 1e-06
self.ignore_index... | 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
... | WHU-YH-jx/bionetwork_segmentation | FocalLoss2d | false | 5,945 | [
"MIT"
] | 1 | 556c5b61a1a3784875b31eacb8c6bb418d70ee9a | https://github.com/WHU-YH-jx/bionetwork_segmentation/tree/556c5b61a1a3784875b31eacb8c6bb418d70ee9a |
SpatialAttention | import torch
import torch.nn as nn
class CompressChannels(nn.Module):
"""
Compresses the input channels to 2 by concatenating the results of
Global Average Pooling(GAP) and Global Max Pooling(GMP).
HxWxC => HxWx2
"""
def forward(self, x):
return torch.cat((torch.max(x, 1)... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | Vrushank264/Low-Light-Enhancement | SpatialAttention | false | 5,946 | [
"MIT"
] | 1 | 3c13a10a16eab8183b8fbd0c063d9815b662259a | https://github.com/Vrushank264/Low-Light-Enhancement/tree/3c13a10a16eab8183b8fbd0c063d9815b662259a |
DiceLoss | import torch
import torch.nn as nn
class DiceLoss(nn.Module):
def __init__(self):
super().__init__()
def forward(self, input, target):
smooth = 1e-05
num = target.size(0)
input = input.view(num, -1)
target = target.view(num, -1)
intersection = input * target
... | 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... | WHU-YH-jx/bionetwork_segmentation | DiceLoss | false | 5,947 | [
"MIT"
] | 1 | 556c5b61a1a3784875b31eacb8c6bb418d70ee9a | https://github.com/WHU-YH-jx/bionetwork_segmentation/tree/556c5b61a1a3784875b31eacb8c6bb418d70ee9a |
DiffLoss | import torch
import torch.nn as nn
import torch.utils.checkpoint
class DiffLoss(nn.Module):
def __init__(self):
super(DiffLoss, self).__init__()
def forward(self, input1, input2):
batch_size = input1.size(0)
input1 = input1.view(batch_size, -1)
input2 = input2.view(batch_size... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | Wang-Chuanyu/MMSA | DiffLoss | false | 5,948 | [
"MIT"
] | 1 | 2a720530c369e68656102287edb651780e827135 | https://github.com/Wang-Chuanyu/MMSA/tree/2a720530c369e68656102287edb651780e827135 |
BBoxTransform | import torch
from torch import nn
import torch.onnx
class BBoxTransform(nn.Module):
def forward(self, anchors, regression):
"""
decode_box_outputs adapted from https://github.com/google/automl/blob/master/efficientdet/anchors.py
Args:
anchors: [batchsize, boxes, (y1, x1, y2, ... | 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
import torch.onnx
assert_size_stride = torch._C._dyn... | Wabinab/eye_of_ml | BBoxTransform | false | 5,949 | [
"Apache-2.0"
] | 1 | 9c475ddf4e56d84bc5a23d871d59169bc6061ab0 | https://github.com/Wabinab/eye_of_ml/tree/9c475ddf4e56d84bc5a23d871d59169bc6061ab0 |
MSE | import torch
import torch.nn as nn
import torch.utils.checkpoint
class MSE(nn.Module):
def __init__(self):
super(MSE, self).__init__()
def forward(self, pred, real):
diffs = torch.add(real, -pred)
n = torch.numel(diffs.data)
mse = torch.sum(diffs.pow(2)) / n
return ms... | 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.checkpoint
assert_size_stride = torch._C._dynamo... | Wang-Chuanyu/MMSA | MSE | false | 5,950 | [
"MIT"
] | 1 | 2a720530c369e68656102287edb651780e827135 | https://github.com/Wang-Chuanyu/MMSA/tree/2a720530c369e68656102287edb651780e827135 |
AdditiveAttention | import torch
import torch.nn as nn
import torch.nn.functional as F
class AdditiveAttention(nn.Module):
def __init__(self, encoder_hidden_state_dim, decoder_hidden_state_dim,
internal_dim=None):
super(AdditiveAttention, self).__init__()
if internal_dim is None:
internal_dim = i... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Vision-CAIR/UnlikelihoodMotionForecasting | AdditiveAttention | false | 5,951 | [
"MIT"
] | 1 | 556d6a3ed3e4e0e2d88108d7dbb48933313b58aa | https://github.com/Vision-CAIR/UnlikelihoodMotionForecasting/tree/556d6a3ed3e4e0e2d88108d7dbb48933313b58aa |
CapsuleLoss | import torch
from torch import nn
import torch.nn.functional as F
class CapsuleLoss(nn.Module):
def __init__(self):
super(CapsuleLoss, self).__init__()
def forward(self, output, target):
class_loss = (target * F.relu(0.9 - output) + 0.5 * (1 - target) *
F.relu(output - 0.1)).mean... | 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... | WdBlink/AugMix-3DOCUNet-Brats2019 | CapsuleLoss | false | 5,952 | [
"MIT"
] | 1 | 125c6c8682b51a550eeac9173d13d0a211576abc | https://github.com/WdBlink/AugMix-3DOCUNet-Brats2019/tree/125c6c8682b51a550eeac9173d13d0a211576abc |
DDPGConvBody | import torch
import torch.nn as nn
import torch.nn.functional as F
def layer_init(layer, w_scale=1.0):
nn.init.orthogonal_(layer.weight.data)
layer.weight.data.mul_(w_scale)
nn.init.constant_(layer.bias.data, 0)
return layer
class DDPGConvBody(nn.Module):
def __init__(self, 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.triton_helpers import libdevice
import torch.nn as ... | Sohojoe/UdacityDeepRL-Project2 | DDPGConvBody | false | 5,953 | [
"MIT"
] | 1 | 7137eea0b606ea32d00424d23130ff213f03ecf1 | https://github.com/Sohojoe/UdacityDeepRL-Project2/tree/7137eea0b606ea32d00424d23130ff213f03ecf1 |
CustomKLLoss | import torch
from torch.nn.modules.loss import _Loss
class CustomKLLoss(_Loss):
"""
KL_Loss = (|dot(mean , mean)| + |dot(std, std)| - |log(dot(std, std))| - 1) / N
N is the total number of image voxels
"""
def __init__(self, *args, **kwargs):
super(CustomKLLoss, self).__init__()
def ... | 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.... | WdBlink/AugMix-3DOCUNet-Brats2019 | CustomKLLoss | false | 5,954 | [
"MIT"
] | 1 | 125c6c8682b51a550eeac9173d13d0a211576abc | https://github.com/WdBlink/AugMix-3DOCUNet-Brats2019/tree/125c6c8682b51a550eeac9173d13d0a211576abc |
SIMSE | import torch
import torch.nn as nn
import torch.utils.checkpoint
class SIMSE(nn.Module):
def __init__(self):
super(SIMSE, self).__init__()
def forward(self, pred, real):
diffs = torch.add(real, -pred)
n = torch.numel(diffs.data)
simse = torch.sum(diffs).pow(2) / n ** 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 import triton_helpers
import torch.nn as nn
import torch.utils.checkpoint
assert_size_stride = torch._C._dynamo... | Wang-Chuanyu/MMSA | SIMSE | false | 5,955 | [
"MIT"
] | 1 | 2a720530c369e68656102287edb651780e827135 | https://github.com/Wang-Chuanyu/MMSA/tree/2a720530c369e68656102287edb651780e827135 |
GridAttentionBlock | import torch
import torch.nn.functional as F
import torch.nn as nn
class GridAttentionBlock(nn.Module):
def __init__(self, in_channels):
super(GridAttentionBlock, self).__init__()
self.inter_channels = in_channels
self.in_channels = in_channels
self.gating_channels = in_channels
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | WHU-YH-jx/bionetwork_segmentation | GridAttentionBlock | false | 5,956 | [
"MIT"
] | 1 | 556c5b61a1a3784875b31eacb8c6bb418d70ee9a | https://github.com/WHU-YH-jx/bionetwork_segmentation/tree/556c5b61a1a3784875b31eacb8c6bb418d70ee9a |
Relu_Caps | import torch
from torch import nn
import torch.nn.functional as F
class Relu_Caps(nn.Module):
def __init__(self, num_C, num_D, theta=0.2, eps=0.0001):
super(Relu_Caps, self).__init__()
self.num_C = num_C
self.num_D = num_D
self.theta = theta
self.eps = eps
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
from torch import nn
assert_... | WdBlink/AugMix-3DOCUNet-Brats2019 | Relu_Caps | false | 5,957 | [
"MIT"
] | 1 | 125c6c8682b51a550eeac9173d13d0a211576abc | https://github.com/WdBlink/AugMix-3DOCUNet-Brats2019/tree/125c6c8682b51a550eeac9173d13d0a211576abc |
DiceLoss | import torch
from torch import nn
from torch.autograd import Variable
def compute_per_channel_dice(input, target, epsilon=1e-05, ignore_index=
None, weight=None):
assert input.size() == target.size(
), "'input' and 'target' must have the same shape"
if ignore_index is not None:
mask = targ... | 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... | WdBlink/AugMix-3DOCUNet-Brats2019 | DiceLoss | false | 5,958 | [
"MIT"
] | 1 | 125c6c8682b51a550eeac9173d13d0a211576abc | https://github.com/WdBlink/AugMix-3DOCUNet-Brats2019/tree/125c6c8682b51a550eeac9173d13d0a211576abc |
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